Renewable Fuel Standard (RFS) Program:
Standards for 2026 and 2027, Partial
Waiver of 2025 Cellulosic Biofuel Volume
Requirement, and Other Changes
Regulatory Impact Analysis
rnA United States
Environmental Protection
Agency
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Renewable Fuel Standard (RFS) Program:
Standards for 2026 and 2027, Partial
Waiver of 2025 Cellulosic Biofuel Volume
Requirement, and Other Changes
Regulatory Impact Analysis
U.S. Environmental Protection Agency
United States
Environmental Protectio
^1 M *Agency
EPA-420-R-26-011
March 2026
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Table of Contents
List of Acronyms and Abbreviations v
Executive Summary vii
Overview 1
Chapter 1: Review of the Implementation of the Program 3
1.1 Gasoline, Diesel, Crude Oil, and Renewable Fuels 3
1.1.1 Crude Oil Prices vs. Renewable Fuel Feedstock Price Projections 3
1.1.2 Petroleum and Renewable Fuels Imports 4
1.1.3 Refinery Margins 7
1.1.4 Transportation Fuel Demand 8
1.2 Cellulosic Biofuel 10
1.3 Biodiesel and Renewable Diesel 12
1.4 Ethanol 15
1.4.1 E85 18
1.4.2 E15 20
1.5 Other Biofuels 22
1.6 Federal Tax Credits for Biofuels 23
1.7 RIN System and Prices 24
1.7.1 RIN System 24
1.7.2 RIN Prices 25
1.8 Carryover RIN Proj ections 33
1.8.1 Carryover RINs Available After Compliance With the 2024 Standards 33
1.8.2 Carryover RINs Available for 2026 and 2027 36
1.8.3 Carryover RIN History 37
1.8.4 EMTS RIN Data 38
Chapter 2: Baselines 40
2.1 No RFS Baseline 40
2.1.1 C onventi onal Ethanol 44
2.1.2 Cellulosic Biofuel 63
2.1.3 Biomass-Based Diesel 66
2.1.4 Other Advanced Biofuel 85
2.1.5 Summary of No RFS Baseline 86
2.2 2025 Baseline 88
Chapter 3: Analyzed Volumes and Volume Changes 93
3.1 Mix of Renewable Fuel Types for the Analyzed Volumes 93
3.2 Volume Changes Analyzed with Respect to the No RFS Baseline 97
3.3 Volume Changes Analyzed with Respect to the 2025 Baseline 100
Chapter 4: Environmental Impacts 103
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4.1 AirQuality 103
4.1.1 Background on Air Quality Impacts of Biofuels 103
4.1.2 Emission Impacts of Analyzed Volumes 107
4.1.3 Air Quality Impacts of Analyzed Volumes 120
4.2 Conversion of Natural Lands 121
4.2.1 Natural Land Conversion Effects 122
4.2.2 New Literature on the Conversion of Natural Lands 125
4.2.3 Potential Natural Land Conversion Impacts 126
4.3 Soil and Water Quality 129
4.3.1 Soil and Water Quality Impacts 130
4.3.2 New Literature on Soil and Water Quality Effects 133
4.3.3 Potential Soil and Water Quality Impacts 135
4.4 Water Quantity and Availability 136
4.4.1 Water and Biofuel Crop Growth 136
4.4.2 Use of Water in Production Facilities 137
4.5 Ecosystem and Wildlife Habitat 138
4.5.1 Ecosystems and Wildlife Habitat Impacts 139
4.5.2 New Literature on Ecosystem and Wildlife Habitat Impacts 141
4.5.3 Potential Ecosystem and Wildlife Habitat Impacts 142
4.6 Ecosystem Services 143
Chapter 5: Climate Change Analysis 146
5.1 Methodology 146
5.1.1 Overview 146
5.1.2 Scenarios Assessed 153
5.1.3 SecondaryProduct-basedFuels 156
5.1.4 Crop-based Fuels 167
5.2 Assessment of Analyzed Volumes 173
5.3 Rebound Sensitivities 175
Chapter 6: Energy Security Impacts 178
6.1 Review of Historical Energy Security Literature (1981-2014) 183
6.2 Review of Energy Security Literature from the Last Decade 185
6.2.1 Oil Energy Security Studies from the Last Decade 185
6.2.2 Studies on Tight/Shale Oil 189
6.3 Cost of Existing U.S. Energy Security Policies 193
6.4 Energy Security Impacts 196
6.4.1 U.S. Oil Import Reductions 196
6.4.2 Oil Import Premiums 197
6.4.3 Energy Security Benefits 203
6.5 Feedstock Diversification 204
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6.5.1 Portfolio Diversification in the Context of Renewable Fuel Feedstocks 204
6.5.2 Concluding Observations on Feedstock Diversification and Transportation Fuel
Volatility 207
Chapter 7: Rate of Production and Consumption of Renewable Fuel 208
7.1 Cellulosic Biofuel 208
7.1.1 Cellulosic Biofuel Industry Assessment 209
7.1.2 Review of EPA's Projection of Cellulosic Biofuel in Previous Years 211
7.1.3 Projection of the 2025 Cellulosic Biofuel Volumes 214
7.1.4 Projecting the Renewable CNG/LNG Market 217
7.1.5 Projected Supply of Liquid Cellulosic Biofuels 230
7.1.6 Projected Rate of Cellulosic Biofuel Production for 2026-2027 231
7.2 Biomass-Based Diesel 232
7.2.1 Production and Use of Biomass-Based Diesel in Previous Years 232
7.2.2 Biomass-Based Diesel Production Capacity and Utilization 236
7.2.3 2025 Baseline Data and Estimates of BBD Feedstock Availability 240
7.2.4 Projections of Biomass-Based Diesel Feedstock Availability to Domestic Biofuel
Producers 241
7.2.5 Imports and Exports of Biomass-Based Diesel 257
7.2.6 Projected Rate of Production and Use of Biomass-Based Diesel 260
7.3 Imported Sugarcane Ethanol 263
7.4 Other Advanced Biofuel 265
7.5 Total Ethanol Consumption 266
7.5.1 Proj ection of Motor Gasoline Consumption 267
7.5.2 Projection of Total Ethanol Consumption 271
7.6 Corn Ethanol 271
7.7 Conventional Biodiesel and Renewable Diesel 273
Chapter 8: Infrastructure 275
8.1 Renewable Compressed Natural Gas 275
8.2 Biodiesel 276
8.3 Renewable Diesel 279
8.4 Ethanol 280
8.4.1 Ethanol Di stributi on 280
8.4.2 Infrastructure for E85 282
8.4.3 Infrastructure for El5 283
8.5 Deliverability of Materials, Goods, and Products Other Than Renewable Fuel 286
Chapter 9: Other Factors 289
9.1 Employment and Rural Economic Development Impacts 289
9.1.1 Methodology and Existing Literature 294
9.1.2 Employment Impacts using the Rule-of-Thumb Approach 305
9.1.3 Employment Impacts using NLR's JEDI model for Dry Mill Corn Ethanol 308
9.1.4 Agricultural Employment 310
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9.1.5 Rural Economic Development 313
9.1.6 Summary of Employment and Economic Impacts 317
9.2 Supply of Agricultural Commodities 322
9.3 Price of Agricultural Commodities 324
9.4 Food Prices 333
Chapter 10: Estimated Fuel Costs and Fuel Price Impacts 336
10.1 Renewable Fuel Costs 336
10.1.1 Feedstock Costs 336
10.1.2 Renewable Fuels Production Costs 341
10.1.3 Blending and Fuel Economy Cost 360
10.1.4 Distribution and Retail Costs 369
10.2 Gasoline, Diesel Fuel and Natural Gas Costs 375
10.2.1 Production Costs 375
10.2.2 Gasoline, Diesel Fuel and Natural Gas Distribution and Blending Cost 377
10.3 Fuel Energy Density and Fuel Economy Cost 379
10.4 Costs 380
10.4.1 Individual Fuels Cost Summary 381
10.4.2 Costs for the Analyzed Volumes 385
10.4.3 Sensitivity Cases 394
10.5 Estimated Fuel Price Impacts 408
10.5.1 RIN Cost and RIN Value 408
10.5.2 Estimated Fuel Price Impacts (Gasoline) 409
10.5.3 Estimated Fuel Price Impacts (Diesel) 412
10.5.4 Fuel Price Impacts of the SRE Reallocation Volumes 415
10.5.5 Cost to Transport Goods 417
10.6 Comparison of Societal Benefits and Costs 417
Chapter 11: Regulatory Flexibility Act Screening Analysis 419
11.1 Summary 419
11.2 Background 420
11.2.1 Overview of the Regulatory Flexibility Act (RFA) 420
11.2.2 Need for the Rulemaking and Rulemaking Objectives 420
11.2.3 Definition and Description of Small Entities 420
11.2.4 Reporting, Recordkeeping, and Other Compliance Requirements 421
11.3 Screening Analysis Approaches 422
11.3.1 Method 1: Market Cost Recover Method 422
11.3.2 Method 2: Full RIN Price as Cost for Small Refiners Method 422
11.4 Cost-to-Sales Ratio Result 424
11.5 Conclusion 424
11.6 Small Refiner CBI Data 425
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List of Acronyms and Abbreviations
Numerous acronyms and abbreviations are included in this document. While this may not be an
exhaustive list, to ease the reading of this document and for reference purposes, the following
acronyms and abbreviations are defined here:
AEO
Annual Energy Outlook
ASTM
American Society for Testing and Materials
BBD
Biomass-Based Diesel
bbl
Barrel
BE
Biological Evaluation
BOB
Gasoline Before Oxygenate Blending
CAA
Clean Air Act
CAFE
Corporate Average Fuel Economy
CBI
Confidential Business Information
CBOB
Conventional Gasoline Before Oxygenate Blending
CFPC
Clean Fuel Production Credit
CG
Conventional Gasoline
CI
Carbon Intensity
CLCA
Consequential Life Cycle Assessment
CNG
Compressed Natural Gas
CO
Carbon Monoxide
CWC
Cellulosic Waiver Credit
DDG
Dried Distillers Grains
DDGS
Dried Distillers Grains with Solubles
DOE
U.S. Department of Energy
DRIA
Draft Regulatory Impact Analysis
DWG
Distillers Wet Grains
EIA
U.S. Energy Information Administration
EISA
Energy Independence and Security Act of 2007
EMTS
EPA-Moderated Transaction System
EPA
U.S. Environmental Protection Agency
EPAct
Energy Policy Act of 2005
EqV
Equivalence Value
EU
European Union
FFV
Flex-Fuel Vehicle
FOG
Fats, Oils, and Greases
gal
Gallon
GDP
Gross Domestic Product
GHG
Greenhouse Gas
GREET
Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation Model
IEA
International Energy Agency
IRA
Inflation Reduction Act
LCA
Lifecycle Analysis
LCFS
Low Carbon Fuel Standard
LNG
Liquified Natural Gas
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MMBD
Million Barrels per Day
MSW
Municipal Solid Waste
MTBE
Methyl Tertiary Butyl Ether
MY
Model Year
NAICS
North American Industry Classification System
NASS
National Agricultural Statistics Service
NEMS
National Energy Modeling System
NGLs
Natural Gas Liquids
nh3
Ammonia
NHTSA
National Highway Transportation Administration
NLR
National Laboratory of the Rockies
N0X
Nitrogen Oxides
NREL
National Renewable Energy Laboratory
OBBB
One Big Beautiful Bill Act of 2025
OPEC
Organization of Petroleum Exporting Countries
OPIS
Oil Price Information Service
ORNL
Oak Ridge National Laboratory
PADD
Petroleum Administration for Defense District
PM
Particulate Matter
PTD
Product Transfer Document
RBOB
Reformulated Gasoline Before Oxygenate Blending
RFA
Regulatory Flexibility Act
RFF
Resources for the Future
RFG
Reformulated Gasoline
RFS
Renewable Fuel Standard
RIA
Regulatory Impact Analysis
RIN
Renewable Identification Number
RNG
Renewable Natural Gas
RtC3
EPA's Third Triennial Report to Congress on Biofuels and the Environment
RVO
Renewable Volume Obligation
RVP
Reid Vapor Pressure
SBA
Small Business Administration
SBREFA
Small Business Regulatory Enforcement Fairness Act of 1996
S02
Sulfur Dioxide
SOx
Sulfur Oxides
SPR
Strategic Petroleum Reserve
SRE
Small Refinery Exemption
STEO
Short Term Energy Outlook
UCO
Used Cooking Oil
USD A
U.S. Department of Agriculture
VOCs
Volatile Organic Compounds
WTI
West Texas Intermediate
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Executive Summary
The Renewable Fuel Standard (RFS) program began in 2006 pursuant to the requirements
in Clean Air Act (CAA) section 21 l(o) that were added through the Energy Policy Act of 2005
(EPAct). The statutory requirements for the RFS program were subsequently amended and
extended through the Energy Independence and Security Act of 2007 (EISA). In addition to
increasing the number of renewable fuel categories from one to four, increasing the volume
targets, and extending those volume targets from 2012 to 2022, EISA also expanded the waiver
provisions in CAA section 21 l(o)(7) that authorize EPA to waive the statutory volume targets
under certain conditions.
The statute includes annual, nationally applicable volume targets through 2022 for
cellulosic biofuel, advanced biofuel, and total renewable fuel, and through 2012 for biomass-
based diesel (BBD). For years after those for which the statute specifies volume targets, the
statute directs EPA to establish volume requirements based on a review of implementation of the
program to date and an analysis of a set of specified factors. In order to effectuate those volume
requirements, the statute required EPA to translate them through 2022 into percentage standards
that obligated parties then use to determine the compliance obligations that they must meet every
year. As discussed in Preamble Section V, we are continuing to use percentage standards as the
implementing mechanism for 2026 and 2027, as we did for 2023-2025.
In this action we are establishing the applicable volume targets for all four categories of
renewable fuel for the years 2026 and 2027. We are also establishing the annual percentage
standards for all four categories that will apply to gasoline and diesel fuel produced or imported
by obligated parties in 2026 and 2027. Finally, in addition to these volumes and standards, we
are finalizing several other changes, including the removal of renewable electricity as a
qualifying renewable fuel under the RFS program.
This Regulatory Impact Analysis (RIA) supports our rule by addressing our statutory
obligations under CAA section 21 l(o)(2)(B)(ii) for determining the applicable volume
requirements for cellulosic biofuel, BBD, advanced biofuel, and total renewable fuel.
Specifically, this section of the statute directs us to establish the applicable volumes based upon a
review of the implementation of the program and an analysis of various environmental,
economic, and other factors. We provide this analysis here, in conjunction with the analysis in
the preamble and several technical support memoranda to the docket.
Table ES-1 summarizes certain potential impacts associated with the volume
requirements in this rule, including both quantified and unquantified impacts. Not all of the
quantified impacts listed in Table ES-1 represent societal benefits or costs. For example, the
projected $10.1 billion annualized impacts on rural economic development generally do not
represent societal benefits. The only monetized societal benefits and costs are the energy security
benefits and the fuel costs. The monetized societal benefits and costs of this rule are shown in
Table ES-2.
Table ES-1 is not a comprehensive listing of all the potential impacts that EPA
considered in this rulemaking. The inclusion of an impact in this table also does not indicate that
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EPA gave it greater weight than impacts not listed in this table. A full discussion of each impact,
including the uncertainties associated with estimating the impact, is contained in the RIA
Chapter identified under the "Chapter" column of Table ES-1. EPA compiled this table to
provide additional information to the public regarding this rulemaking and to comply with OMB
Circular A-4.
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Table ES-1: Potential Annualized Quantified and Unquantified Impacts Associated with the Analyzed Volumes Relative to the
No RFS Baseline"
Potential Impacts of
Analyzed Volumes
Effect
Effect Quantified
Quantified
Impact
Chapter
Impacts on air quality
from biofuel production,
transport, and use
Increases in emissions associated with biofuel
production
Quantitative
See RIA Chapter
4.1.2.1
Increases in emissions associated with biofuel
transport
Qualitative
-
4.1.2.2
Varying emission impacts from vehicles running on
ethanol blends and pre-2007 diesel vehicles
running on biodiesel blends
Qualitative
-
4.1.2.3
Changes in ambient concentrations of air pollutants
varies by location across the U.S.
Qualitative
-
4.1.3
Impacts on climate
change from biofuel
feedstock production and
displacement of
petroleum fuels
Reduced greenhouse gas (GHG) emissions
Quantitative
-31 to 1 MMT
average annual
CChe emissions
5
Impacts on conversion of
natural lands, including
wetlands, from biofuel
feedstock production
Increased conversion of wetlands, forests, pasture,
and grasslands to cropland
Qualitative
-
4.2
Impacts on soil and water
quality from biofuel
feedstock production
Impacts to soil and water quality from increased
erosion, nutrient, and pesticide runoff due to
agricultural conversion
Qualitative
-
4.3
Other impacts to water quality, including but not
limited to leaks and spills from aboveground and
underground storage as well as biogas production
Qualitative
-
4.3
Impacts on water
quantity and availability
from biofuel and
feedstock production
Use of water resources for cropland irrigation
Qualitative
-
4.4
Use of water in production facilities
Qualitative
-
4.4
IX
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Potential Impacts of
Analyzed Volumes
Effect
Effect Quantified
Quantified
Impact
Chapter
Impacts on ecosystems
and wildlife habitat
Impacts due to loss of natural lands, changes to soil
and water quality, air quality, and water quantity
Qualitative
-
4.5
Energy security
Increased energy security
Quantitative
$411 million
6
Production and use of
renewable fuels
Increased production and use of renewable fuels
Quantitative
See RIA Chapter
7
Infrastructure
Increased development of infrastructure of deliver
and use renewable fuels
Qualitative
-
8
No adverse impact on deliverability of materials,
goods, and products other than renewable fuel
Qualitative
-
8
Jobs
Increased employment
Quantitative
96,487-108,975
jobs
9.1
Rural economic
development
Increased support for rural economic development
associated with biofuel and feedstock production
Quantitative
$10.08 billion
9.1
Commodity supply and
price impacts
Increased supply of certain agricultural
commodities
Qualitative
-
9.2
Higher corn, soybean, and soybean oil prices
Quantitative
See RIA Chapter
9.3
Higher food prices
Quantitative
$2.65-3.19
billion
9.4
Costs
Increased societal cost
Quantitative
$18.2 billion in
2026, $21.2
billion in 2027
10.4
Estimated fuel price impacts
Quantitative
See RIA Chapter
10.5
Increased costs to transport goods
Quantitative
See RIA Chapter
10.5
a This table includes both societal costs and benefits (fuel costs, energy security) as well as distributional effects or transfers (jobs, rural economic development,
higher food prices, etc.). Monetized fuel costs, energy security benefits, and rural economic development benefits in Table ES-1 represent annualized monetized
impacts using a 3% discount rate. Alternative discount rates are considered in Preamble Section V.H and in the relevant chapters cited within the table.
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Table ES-2: Societal Benefits and Costs of this Rule (million 2024
\S)
Type
Category
2026
2027
Present
Value
Annualized
Value
Societal Benefits
Energy Security
Benefits
$360
$440
$790
$410
Societal Costs
Fuel Costs
$18,240
$21,240
$38,870
$20,310
Net Benefits
Total
-$17,880
-$20,810
-$38,080
-$19,900
Note: Present and annualized values are estimated using a 3% discount rate. Computing annualized costs and
benefits from present values spreads the costs and benefits equally over each period, taking account of the discount
rate. The annualized value equals the present value divided by the sum of discount factors. For a calculation of
present and annualized values from annual impact estimates, see "Set 2 Final Rule Costs and Benefits Summary,"
available in the docket for this action. All costs and benefits are rounded to the nearest $10 million.
XI
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Overview
Chapter 1: Review of the Implementation of the Program
This chapter reviews the implementation of the RFS program, focusing on renewable fuel
production and use in the transportation sector since the RFS program began.
Chapter 2: Baselines
This chapter identifies the appropriate baselines for comparison.
Chapter 3: Analyzed Volumes and Volume Changes
This chapter identifies the specific biofuel types and associated feedstocks that are projected to
be used to meet the volumes in the Analyzed Volumes. It also identifies the differences between
the Analyzed Volumes and the baselines described in Chapter 2.
Chapter 4: Environmental Impacts
This chapter discusses the environmental factors EPA analyzed in developing the Analyzed
Volumes.
Chapter 5: Climate Change Analysis
This chapter describes potential climate impacts of the Analyzed Volumes.
Chapter 6. Energy Security Impacts
This chapter reviews the literature on energy security impacts associated with petroleum
consumption and imports and summarizes EPA's estimates of the benefits that will result from
the Analyzed Volumes.
Chapter 7: Rate of Production and Consumption of Renewable Fuel
This chapter discusses the expected annual rate of future commercial production of renewable
fuels, including advanced biofuels in each category (primarily cellulosic biofuel and BBD).
Chapter 8: Infrastructure
This chapter analyzes the impact of renewable fuels on the distribution infrastructure of the U.S.
Chapter 9: Other Factors
This chapter provides greater detail on our evaluation of impacts of renewable fuels on job
creation, rural economic development, supply and price of agricultural commodities, and food
prices.
Chapter 10: Estimated Costs and Fuel Price Impacts
This chapter estimates the impact of the use of renewable fuels on the social cost, the cost to
consumers (prices) of transportation fuel, and on the cost to transport goods. This chapter also
provides a comparison of societal benefits and costs and presents analyses for alternative volume
scenarios (Sensitivity Cases).
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Chapter 11: Screening Analysis
This chapter discusses EPA's screening analysis evaluating the potential impacts of the RFS
standards on small entities.
Note: Unless otherwise stated, all documents cited in this document are available in the docket
for this action (EPA-HQ-OAR-2024-0505). We have generally not included in the docket
Federal Register notices, court cases, statutes, regulations, materials with a Digital Object
Identifier (DOI), or previously docketed materials. These materials are easily accessible to the
public via the Internet and other means.
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Chapter 1: Review of the Implementation of the Program
The statute directs EPA to establish volumes based on several factors, including "a
review of the implementation of the program during calendar years specified in the tables ..."
CAA section 21 l(o)(2)(B)(ii). The Set 1 Rule RIA contains EPA's review of the implementation
of the RFS program from the passing of the Energy Policy Act of 2005 through 2022, the last
calendar year specified in the statutory tables.1 In determining the RFS volumes in this rule, we
have once again considered the implementation of the RFS program through 2022, described in
detail in the Set 1 Rule RIA. We have also considered developments in the petroleum fuel and
renewable fuel sectors since 2022 as part of our consideration of other relevant factors and as
informed by our assessment of implementation of the RFS program for the years specified in the
statutory tables. Throughout this document, we use the term "supply" of renewable fuel to refer
to the quantity of qualifying renewable fuel that can be used as transportation fuel, heating oil, or
jet fuel in the U.S. Unless otherwise noted, all historical data on the supply of renewable fuel is
based on data from the EPA Moderated Transaction System (EMTS).
This chapter focuses on our review of the implementation of the RFS program since
2022, with references to important observations from previous years where relevant. For a more
extensive review of the implementation of the RFS program from 2005-2022, see Chapter 1 of
the Set 1 Rule RIA.
1.1 Gasoline, Diesel, Crude Oil, and Renewable Fuels
This section compares recent and projected crude oil and renewable fuels feedstock
prices, and discusses observed and projected petroleum imports, refinery margins, and
transportation fuel demand prior to and during the recent and future years of the RFS program.
1.1.1 Crude Oil Prices vs. Renewable Fuel Feedstock Price Projections
Crude oil prices have a significant impact on the economics of increased use of
renewable fuels. When crude oil prices increase, both renewable fuel feedstock prices and
gasoline and diesel prices tend to increase as well, although gasoline and diesel prices generally
increase more relative to renewable fuel feedstock prices. Thus, higher crude oil prices generally
improve the economic competitiveness and value of renewable fuels relative to gasoline and
diesel. Conversely, lower crude oil prices tend to hurt the economic competitiveness and value of
renewable fuels.
Figure 1.1.1-1 compares the recent historical crude oil and corn and soybean oil prices
and their projected future prices in nominal dollars. The figure shows that after high crude oil
and renewable fuels feedstock prices in 2022, those prices have decreased from 2023 to 2025.
The U.S. Department of Agriculture (USD A) projects corn prices to decrease somewhat through
2026 and then increase slightly after that, while soybean oil prices are expected to continue
decreasing. The U.S. Energy Information Administration (EIA) projects crude oil prices to jump
up in 2026 and increase slightly out to 2030. It is important to note that actual crude oil prices at
1 "EPA, Renewable Fuel Standard (RFS) Program: Standards for 2023-2025 and Other Changes - Regulatory
Impact Analysis," EPA-420-R-23-015, June 2023.
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the start of 2026 are closer to the lower prices in 2025.
Figure 1.1.1-1 Historical and Projected Future Crude Oil, Corn, and Soybean Oil Nominal
Prices
Note: 2024 and earlier are historical, 2025 and later are price projections in nominal dollars.
Source: EIA, "Spot Prices," Petroleum & Other Liquids. January 26, 2026.
https://www.eia.gov/dnav/pet/pet pri spt si a. htm. USD A, "Oil Crops Yearbook," January 2026.
https://www.ers.usda.gov/data-products/oil-crops-vearbook. fanndoc, "US Average Farm Price Received Database,"
February 28, 2025. https://farmdoc.illinois.edu/decision-tools/us-average-fann-price-received-database. AEO2025,
Table 12 - Petroleum and Other Liquids Prices. USD A, "USDA Agricultural Projections to 2033," OCE-2025-1,
February 2025, https://www.usda.gov/sites/default/files/documents/USDA-Agricultural-Proiections-to-2034.pdf.
2023 2024 2025
Crude Oil Prices
2026 2027 2028 2029
Year
Soy Oil Prices Corn Prices
100
8
7
6
5
4
3
2
1
0
2030
0
2022
1.1.2 Petroleum and Renewable Fuels Imports
As discussed further in Chapter 6, energy security is an important goal of the RFS
program. Importing a significant amount of crude oil and finished petroleum products from
abroad creates an energy security concern, as there could be significant costs to the U.S.
economy if foreign supplies are disrupted. A good example is the oil embargo by the
Organization of Petroleum Exporting Countries (OPEC) against the U.S. in 1973 and 1974,
which drove up prices, reduced supply, and is credited with causing the U.S. economy to slide
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into a recession.2 It also led to Congress banning the export of U.S. crude oil from 1975 to
2015.3
At the time that Congress passed EPAct and EISA and EPA promulgated the RFS1 and
RFS2 rules, the U.S. was importing a large portion of its crude oil and significant quantities of
gasoline, particularly on the East Coast. At the time, that trend was expected to continue
indefinitely. This expectation did not factor in the eventual increase in U.S. tight oil crude
production, which largely occurred after the passage and promulgation of these laws and rules.
Using EIA data, we reviewed how petroleum imports changed before, during, and after the
passage of EPAct and EISA, and implementation of the RFS program, in the Set 1 Rule. During
the time period of 2005-2020, U.S. net imports of crude oil and refined products decreased
substantially.
Since 2022, net imports of gasoline and distillate increased some and then decreased, but
overall the changes are modest.4 EIA does not specifically project future gasoline and diesel fuel
net imports in its Annual Energy Outlook (AEO) reports. However, EIA does project and report
a total refined product net import estimate which we show in the figure along with their historical
values.5 Figure 1.1.2-1 summarizes the gasoline, distillate, and total refined product volumes.
2 Verrastro, Frank A. and Guy Caruso. "The Arab Oil Embargo-40 Years Later." Center for Strategic &
International Studies, October 16, 2013. https://www.csis.org/analvsis/arab-oil-embargo-40-vears-later.
3 1975 Energy Policy and Conservation Act; Consolidated Appropriations Act of 2016.
4 EIA, "U.S. Net Imports by Country," Petroleum & Other Liquids, February 6, 2026.
https://www.eia.gov/dnav/pet/pet move neti dc NUS-Z00 mbblpd a.htm.
5 AEO2025, Table 11 - Petroleum and Other Liquids Supply and Disposition.
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Figure 1.1.2-1: Gasoline and Distillate and Total Refined Products Net Imports
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-1200
-1400
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-7000
2022
2024
•Gasoline
2026
Year
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2028
•All Products
2030
Note: 2024 and earlier are historical, 2025 and later are price projections.
The projected decrease in net imported total refined products could indicate that EIA
projects further decreases in net imported gasoline and distillate; however, there are other refined
products which can contribute significantly to net exports. For example, currently there are
substantial exports of hydrocarbon gas liquids and residual fuel, thus, some or potentially most of
the decrease in projected net refined products could be comprised of these other products instead
of gasoline and distillate.
EIA also gathers information on, reports, and projects the net imports of ethanol,
biodiesel, and renewable diesel. Figure 1.1.2-2 summarizes the historical and projected net
imports of these renewable fuels.
6
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Figure 1.1.2-2: Corn Ethanol, Biodiesel, and Renewable Diesel Net Imports
250
200
150
"O
zT ioo
-Q
50
i/i
S 0
Q.
£ -50
I -100
-150
-200 1
2022 2023 2024 2025 2026 2027 2028 2029 2030
Year
Ethanol Biodiesel Renewable Diesel
Note: 2024 and earlier are historical, 2025 and later are projections.
Source: EIA, "U.S. Net Imports by Country," Petroleum & Other Liquids. February 6, 2026.
https://www.eia.gov/dnav/pet/pet move neti dc NUS-Z00 mbblpd a.htm. AEO2025, Table 11 - Petroleum and
Other Liquids Supply and Disposition.
Figure 1.1.2-2 shows that biodiesel and renewable diesel net imports increased
marginally until 2024. After 2024, EIA projects biodiesel to essentially be flat going forward and
renewable diesel to jump up in 2025 and remain at the higher level. Corn ethanol net imports
decreased from 2022 to 2024. After 2024, EIA projects corn ethanol net imports to mostly
remain negative and flat through 2027 and decrease somewhat after 2027. This decrease may
principally be due to EIA's projected decrease in gasoline demand, which would decrease the
volume of ethanol blended into gasoline domestically at 10%. Consequently, corn ethanol
producers would export the excess corn ethanol production volume which is not blended into
gasoline as El 5 or E85. The increased consumption of renewable fuels contributes to reductions
in net petroleum imports, though by a very modest amount.
1.1.3 Refinery Margins
Refinery margins reveal the economic health of refineries. The higher the margins for a
refinery, the greater its profitability and economic viability. Over time, refinery margins vary
considerably but must average at least a certain level for refineries to be viable over the long
term.
7
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EIA reported refinery margin data for various U.S. refinery regions, as well as for Europe
and Singapore, and is shown in Figure 1.1.3-1.6 The refinery margin data are 3-2-1 cracked
spreads, which are gross margins (excludes refinery operating costs) and the data is for the years
2020-2024.
Figure 1.1.3-1: Refinery Margins in the U.S. and Two Other Regions
Regional September refining margins (2020-2024)
dollars per gallon
$1.40
$1.20
$1.00
$0.80
$0.60
$0.40
$0.20
$0.00
Note: ARA = Amsterdam-Rotterdam-Antwerp
The figure shows that the disruption of fuel consumption in 2020 caused by the COVID-
19 pandemic resulted in severely depressed refinery margins worldwide. However, as fuel
demand rebounded in 2021 and 2022, refinery margins recovered through 2023. Due to falling
U.S. refined product demand, particularly distillate, and falling demand in China and Europe,
refinery margins dropped back down in 2024. In addition to falling demand, several large
refineries began operating in the Middle East and Africa which also contributed to lower refinery
margins.7 Although refinery margins dropped in all regions in 2024, U.S. refinery margins are
still somewhat higher than those in Western Europe and Singapore. U.S. refinery margins are
typically better than overseas refinery regions due to lower prices for purchased crude oil, and
natural gas which is used as a feedstock for refinery heat and hydrogen production.
1.1.4 Transportation F uel Demand
At the time the RFS2 program was being enacted through EISA in 2007, there had been a
consistent increase in U.S. petroleum demand and crude oil prices were very high. The RFS
program was implemented to help meet U.S. refined product demand and help to lower crude oil
prices. However, transportation fuel demand slowed starting in 2008 and has remained relatively
stable since that time.
ei?
2022
2023
2024
¦
1
¦
¦ -
1
¦
ii
1
I
i
New York Gulf Coast Chicago Los Angeles ARA Singapore
6 EIA, "Global refinery margins fall to multiyear seasonal lows in September." Today in Energy, October 15, 2024.
https:/Ayww.eia.gov/todavinenergy/detail.php?id=63447.
7 Id.
8
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Figure 1.1.4-1 shows the actual volume of gasoline, distillate, and jet fuel consumed in
the U.S. from 2022-2024, as well as the projected demand of gasoline, distillate, and jet fuel
from 2025-2030.
Figure 1.1.4-1: Actual and Projected Transportation Fuel Demand
10,1
C3
1
U
"P,
P 2.
1.
000
000
000
000
000
000
000
000
000
000
0 —
2022
2029
2030
2023 2024 2025 2026 2027 2028
Year
Motor Gasoline Distillate Fuel Jet Fuel
Source: 2022 - 2024 data is from EIA, "U.S. Product Supplied for Crude Oil and Petroleum Products," Petroleum &
Other Liquids, January 26, 2026. https://www.eia.gov/dnav/pet/pet cons psup dc nus mbblpd a.htm. 2025 - 2023
data is from AEO2025, Table 11 - Petroleum and Other Liquids Supply and Disposition.
Figure 1.1.4-1 shows that gasoline demand increased from 2022 to 2023 but is projected
to remain flat through 2026 and decrease after 2026. Distillate demand decreased from 2022 to
2024, while jet fuel increased. Based on projections in AEO2025, distillate demand is expected
to increase somewhat in 2025 and then decline slightly thereafter. Jet fuel has been increasing
very modestly and is expected to increase slightly over the years 2025 to 2030.
Several factors are contributing to lowering transportation fuel demand:
• Increased crude oil prices. Periods of higher crude oil prices as far back as 2007 and
as recent as 2022, which resulted in increased transportation fuel prices during these
time periods, which affected consumer behavior by impacting the number of miles
traveled and vehicle purchase decisions.
• Increasing fuel economy of the motor vehicle fleet. EPA and the National Highway
Transportation Administration (NHTSA) finalized standards which reduced light-
duty motor vehicle GHG emissions and increased the Corporate Average Fuel
Economy (CAFE) of motor vehicles. The trend of decreasing fuel consumption
intensity has been monitored and reported by EPA for decades.8 On balance, newer
8 "The 2024 EPA Automotive Trends Report," EPA-420-R-24-022, November 2024.
9
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vehicles consume fuel more efficiently; thus, as consumers purchase new motor
vehicles, these new vehicles consume less gasoline and diesel compared to the
vehicles sold in previous years, reducing overall petroleum demand.
• Electric vehicle penetration andfuel displacement. Electric vehicles (EVs) and plug-
in hybrid electric vehicles (PHEVs) reduce consumption of petroleum fuel by either
partially displacing petroleum fuels (in the case of PHEVs) or completely displacing
petroleum demand (in the case of EVs). Based on data for electricity demand by
light-duty vehicles, EIA estimates that EVs and PHEVs consumed 11.74 million
MWh of electricity in 2024.9 If we assume that EVs travel 3 miles per kWh of
electricity consumed, and the in-use light-duty fleet travels 22.7 miles per gallon of
gasoline consumed, then electricity consumption by light-duty vehicles displaced
1.55 billion gallons of gasoline in 2024.
1.2 Cellulosic Biofuel
The RFS2 Rule projected strong growth for cellulosic biofuels, anticipating they would
become a major contributor to total biofuel volumes.10 After that rule took effect, however,
commercial-scale production of cellulosic biofuels failed to meet those high expectations, with
actual volumes falling significantly below statutory targets for the first several years. A major
shift occurred when renewable natural gas (RNG) derived from biogas and used as transportation
fuel was recognized as a qualifying cellulosic biofuel. The RFS2 Rule did initially include a
pathway11 for generating advanced (D5) Renewable Identification Numbers (RINs) from biogas
produced at landfills, wastewater treatment plants, and manure digesters,12 but this did not
extend to generating cellulosic (D3) RINs. However, in response to industry inquiries, EPA
evaluated whether biogas from these sources could also qualify as cellulosic biofuel. Through the
2014 Pathways II Rule, EPA finalized this expansion by approving cellulosic pathways for RNG
derived from biogas from landfills, wastewater treatment facility digesters, and manure digesters
when used as transportation fuel. For clarity, in this document we refer to RNG used as
compressed or liquefied natural gas in transportation as "renewable CNG/LNG." EPA also
approved biogas derived from the cellulosic components of biomass processed in other waste
digesters to generate cellulosic RINs when used as transportation fuel.13 Although this pathway
was not initially identified in the RFS2 Rule, this expansion under the Pathways II Rule has since
been the primary driver of growth in total cellulosic biofuel volumes.
Following the implementation of the Pathways II Rule in 2014, the production of
cellulosic biofuels has experienced rapid growth, increasing from approximately 33 million RINs
in 2014 to over 1 billion RINs in 2025, (see Figure 1.2-1), with around 93% of all cellulosic
RINs generated under the RFS program in 2025 attributed to renewable CNG/LNG. This trend is
9 EIA, "Electric Power Monthly," February 2025, Table D. 1 - U.S. Estimated Consumption of Electricity by Light-
Duty Electric Vehicles Types, https://www.eia.gov/electricitv/montlilv/epm table grapher.php?t=table d 1.
10 75 FR 14674 (March 26, 2010).
11 A pathway is a combination of feedstock, production process, and fuel type. EPA has evaluated a number of
different pathways to determine the category of renewable fuel that fuel produced using the various pathway
qualifies for. The list of generally applicable pathways can be found in 40 CFR 80.1426(f).
12 75 FR 14872 (March 26, 2010).
13 79 FR 42128 (July 18, 2014).
10
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expected to continue, with total volumes steadily increasing and renewable CNG/LNG remaining
the primary source of cellulosic biofuels in the RFS program through 2027 (see Chapter 7). As
discussed further in Chapter 7.1.4, EPA also projects growth in the relatively smaller volumes of
cellulosic ethanol produced from corn kernel fiber (CKF) as part of its overall cellulosic biofuel
volume projection. Taken together, EPA projects robust growth in cellulosic volumes based on
these two primary components.
Alongside this robust growth in cellulosic volumes, EPA has also modernized the
regulatory framework for biogas-derived fuels since the RFS2 Rule. Through the Biogas
Regulatory Reform Rule (BRRR)14, finalized in the 2023 RFS Set Rule, EPA updated oversight
of renewable CNG/LNG and restructured the compliance framework for RNG. This rule
decoupled cellulosic RIN generation from the demonstration that RNG is used as transportation
fuel. In short, RNG RINs are now generated prior to use as a transportation fuel, and such RINs
are not separated—and thus made available for compliance—until the RNG RIN separator
obtains documentation demonstrating that the volume of renewable CNG/LNG was used as
transportation fuel.
Looking ahead, EPA expects the distinction between RIN generation and RIN separation
to become increasingly important for setting future volumes. Specifically, EPA anticipates a shift
from a production-constrained market to one limited by consumption. Under the Pathways II
framework, projections largely tied cellulosic RIN volumes to production capacity. Now,
however, the market for renewable CNG/LNG appears to be nearing saturation, with the existing
CNG/LNG fleet already largely supplied by biofuel. Because RINs are separated and available
for compliance only when the separator obtains documentation showing that the volume of
renewable CNG/LNG was used as transportation fuel, a saturated fleet means the size of the
vehicle fleet capable of consuming that fuel becomes a crucial factor in setting future volumes.
Accordingly, EPA has updated its methodology for projecting future cellulosic biofuel volumes
to reflect these consumption constraints, as discussed in Chapter 7.1.
Evidence of this shift in market dynamics is already noticeable, as EPA adjusted the 2024
cellulosic biofuel volume obligations.15 This adjustment was necessary because 2024 cellulosic
RIN availability failed to meet the volume requirement. Similarly, EPA is finalizing an
adjustment to the 2025 cellulosic volume requirement in this action, as presented in Preamble
Section VI and discussed in detail in Chapter 7.1.3. Therefore, while EPA is still projecting
continued growth in cellulosic biofuel production, future growth is likely to be impacted by the
ability to use it as a qualifying transportation fuel.
14 88 FR 44522 - 44541 (July 12, 2023).
15 89 FR 100442 (December 12, 2024).
11
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Figure 1.2-1: Cellulosic RIN Generation (2014-2025)
1,400
1,300
1,200
1,100
1,000
in
£
900
2
800
a
700
W
600
S3
g
500
400
300
200
100
2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025
~ Renewable CNG/LNG ~ Liquid Cellulosic Bio fuels
Note: We did not yet have final renewable CNG/LNG generation data for 2025 at the time of this analysis. Based on
the information available at the time of writing, we do not expect the reported figures to differ materially from the
final data.
Source: EMTS.
1.3 Biodiesel and Renewable Diesel
The supply of biodiesel and renewable diesel has continued to significantly exceed the
BBD volume requirements since 2022, as blending of additional volumes of BBD beyond the
BBD volume requirement has been the marginal strategy used by refiners and petroleum
marketers to meet both the advanced biofuel and total renewable fuel volume requirements since
that time. These additional volumes reflect that BBD is generally the marginal gallon of
advanced biofuel supplied to the market, as well as the marginal gallon of total renewable fuel.
As discussed in Chapter 1.7.2, the status of BBD as the marginal gallon of both advanced biofuel
and total renewable fuel is also reflected in the convergence of the RIN prices for BBD (D4),
advanced biofuels (D5), and conventional renewable fuel (D6). While we project that the
supplies of other advanced biofuels and the use of ethanol in higher level ethanol blends will
continue to increase in future years, we project that the advanced and total biofuel volumes we
are establishing in this rule will continue to provide incentives for the production and use of
BBD beyond the BBD volume requirement.
The supply of BBD to the U.S. has increased rapidly since 2022, with nearly all the
increase in the supply of BBD coming from the increased domestic production of renewable
diesel. The market preference for renewable diesel over biodiesel appears to be the result of a
combination of different factors. First, renewable diesel production capacity has increased
significantly in recent years, while the operable production capacity for biodiesel has decreased
slightly (see Chapter 7.2.2 for more detail on BBD production capacity). Renewable diesel
production facilities also tend to be much larger than biodiesel production facilities, allowing
renewable diesel producers to benefit from economies of scale. Further, renewable diesel
generates more credits per gallon than biodiesel due to the higher energy content of renewable
12
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diesel, providing additional revenue for renewable diesel producers and blenders. Perhaps most
importantly, renewable diesel can generally be blended with petroleum diesel at higher rates
without violating engine manufacturer fueling recommendations or requiring additives to meet
state specifications. This has allowed greater quantities of renewable diesel to be used in states
with low carbon fuel programs, where there is a financial incentive for distillate biofuel blending
rates to exceed warrantable biodiesel blending rates. This ability to blend at higher rates than
biodiesel in these lucrative markets has in turn allowed renewable diesel producers to claim
additional financial incentives under low carbon fuel programs that are not readily available to
biodiesel producers. As shown in Figure 1.3-1, the supply of renewable diesel has grown rapidly
in recent years and exceeded the supply of biodiesel for the first time in 2023.
In 2025 the total supply of BB D to the U.S. decreased significantly from the volumes
supplied in 2023 and 2024. This drop in the supply of BBD was likely due to a combination of
factors, including an oversupply of BBD in 2023 and 2024 relative to the RFS volume
requirements and changes to the federal tax credit available to BBD suppliers (see Chapter 1.6).
Figure 1.3-1: Supply of Biodiesel and Renewable Diesel to the U.S.
6,000
5,000
4,000
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qualifying fats, oils, and greases (FOG).16 The biggest source of imported qualifying BBD
feedstock through 2022 was canola oil from Canada, which is primarily used as a food ingredient
rather than for biofuel production. In 2022 and 2023, imports of other qualifying BBD
feedstocks, particularly FOG, increased dramatically. These imports were likely driven by
several factors, including increasing used cooking oil (UCO) collection rates in other countries,
declining demand for these feedstocks and biofuels produced from them in the European Union
(EU), and strong demand for these feedstocks for biofuel production in the U.S. driven by the
combination of federal and state incentives.
Historically, FOG could be used to produce BBD with lower carbon intensity (CI) scores
than BBD produced from crop-based feedstocks such as soybean oil and canola oil. This allowed
these fuels to generate significantly more credits in state low carbon fuel programs. The Clean
Fuel Production Credit (CFPC) federal subsidy for domestically produced renewable fuels
(hereafter referred to as "45Z" or the "45Z credit"), which replaced several pre-existing fuel tax
incentives for fuel produced after December 31, 2024, also provides greater credits for fuel with
lower carbon intensity. This combination of State and Federal incentives were factors in a large
increase in imported feedstocks in 2023 and 2024, particularly imports of feedstocks such as
FOG that can be used to produce BBD with low CI scores.
In July 2025, the One Big Beautiful Bill Act of 2025 (OBBB) included significant
changes to the CFPC.17 Importantly, OBBB prohibited the consideration of land use change in
the emission rates for biofuels. This decreased the tax advantages that BBD produced from FOG
previously received relative to BBD produced from soybean oil and canola oil. OBBB also
disqualified biofuels that were produced from feedstocks that originated outside of North
America. Together these two changes are expected to significantly impact the available
incentives for different types of BBD production. While BBD produced from domestic soybean
oil and canola oil imported from Canada previously received a much lower tax credit than BBD
produced from imported FOG, these vegetable oil-based fuels are now expected to receive a
much greater tax credit than imported FOG.
The rate of future imports of feedstocks is highly uncertain, as the destination for globally
traded feedstocks can change rapidly in response to changing market conditions and/or policy
incentives. Tariffs on these feedstocks and other trade actions could also have a significant
impact on imports of BBD feedstocks. The rapid observed increase in feedstock imports into the
U.S. since 2022 illustrates how quickly feedstock suppliers can respond to changing market
conditions. Imports of qualifying BBD feedstocks are shown in Figure 1.3-2. More detail on the
projected supply of BBD feedstocks beyond 2025, including imported feedstocks, can be found
in Chapter 7.2.3.
16 FOG as a category is comprised of a diverse collection of lipid-based feedstocks, including used cooking oil,
animal fats, and various other biogenic lipid-based secondary products.
17 Public Law 119-21, H.R. 1 "One Big Beautiful Bill," July 4, 2025.
14
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Figure 1.3-2: Imports of Qualifying BBD Feedstocks (Million Gallons BBD Equivalent)
2,500
4., 2,000
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2015 20X6 2017 2018 2019 2020 2021 2022 2023 2024 2025
¦ Soybean Oil ¦ Canola Oil ~ Used Cooking Oil BTallow
Source: UN ComTrade.
1.4 Ethanol
The predominant form of biofuel used to meet the standards under the RFS program—
and the total renewable fuel standard in particular—has been fuel ethanol. Fuel ethanol
consumed in the U.S. has predominantly been produced from corn starch, but smaller volumes
are also produced from grain sorghum starch, cellulosic biomass, non-cellulosic portions of
separated food waste, and sugarcane feedstocks. In 2005, just prior to implementation of the
RFS1 program, ethanol accounted for 97% of all biofuels consumed in the U.S. transportation
sector. In the years that followed, the total volume of ethanol used in the U.S. more than tripled
from 4.1 billion gallons in 2005 to 14.6 billion gallons in 2019, even as volumes of other biofuels
grew concurrently.18 Despite significant reductions in 2020 and 2021 due to the Covid-19
pandemic, domestic fuel ethanol consumption had returned to close to pre-pandemic levels in
2023 and 2024.19 In 2024, ethanol accounted for approximately 70% of the biofuel consumed in
the U.S.20
Total ethanol consumption is the sum of ethanol blended with fossil fuel gasoline (E0) to
create motor gasoline ethanol blends (E10, E15, and E85). There is no appreciable consumption
of pure fuel ethanol (E100) in the U.S. as a transportation fuel, in contrast to fueling practices in
some other countries such as Brazil. A common way to evaluate the relative growth of each of
these different fuel blends is to measure the average ethanol concentration in the national
18 EIA, "Monthly Energy' Review," March 2025, Tables 10.3 and 10.4.
https://www.eia.gov/totalenergv/data/montlilv/arcliive/00352503.pc . Comparison is based on ethanol-equivalence.
19 Id.
20 Id.
15
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gasoline pool. National average ethanol concentration surpassed 5% for the first time in 2007
and surpassed 10% (i.e., the "blend wall") for the first time in 2016. Since exceeding 10%, the
share of ethanol in the gasoline pool has continued to increase, although at a slower pace as the
market became saturated with E10. The total ethanol volume that can be consumed in the U.S.
from all feedstocks is a function of the relative volumes of E0, E10, E15, and E85 that together
comprise total motor gasoline consumption. Average ethanol concentration can exceed 10% only
to the extent that E15 and E85 fuel volumes can exceed the ethanol content of E10 and more than
offset the dilution caused by E0 volumes. Based on our updated methodology, EPA projects an
average ethanol concentration of 10.25% in 2026, rising to 10.27% in 2027. For details regarding
how EPA has projected the consumption volumes of each of these fuel blends and thereby
average ethanol concentration, refer to Chapter 7.5.1.
Domestic consumption of ethanol in the U.S. was very close to domestic production
through 2009. Thereafter, domestic production began exceeding domestic consumption,
indicative of an increase in exports. This split is shown in Figure 1.4-1. While EPA is projecting
continued growth in the consumption of higher-level ethanol blends such as El 5 and E85, we are
projecting that total ethanol consumption decreases slightly over the years covered by this rule
due to declining gasoline (E10) consumption.
Figure 1.4-1: Domestic Production and Consumption of Ethanol by Year
18000
17000
16000
15000
to
J 14000
& 13000
J 12000
z
11000
10000
9000
8000
^ ^ ^ ^ ^ ^ ^
Production -^^"Comsumption
Source: EIA, "Monthly Energy Review," March 2025, Table 10.3.
https://www.eia.gov/totalenergv/data/montlilY/arcliive/00352503.pdf.
EIA is the primary source of reliable data on fuel ethanol exports, and they do not report
fuel ethanol export data for years prior to 2010. Since 2010, however, ethanol exports have
grown steadily with only minor variations month to month, as shown by Figure 1.4-2. This
growth is largely attributable to a combination of domestic and international market effects, with
lower prices and plateauing demand on average for fuel ethanol in the U.S. even as prices and
demand increase elsewhere. Exports of fuel ethanol reached record volumes in 2024 reflecting
changes in renewable fuel mandates in other countries. For example, in early 2024, Colombia
16
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reinstated an E10 mandate for motor gasoline sold there, which required greater volumes of
ethanol imports for the standard to be met and coincided with a sudden reduction in ethanol
exports from Brazilian sources due to an increase in demand for fuel ethanol in Brazil.21 The
result is that more ethanol was exported from the U.S. to Colombia to fulfill their demand for
fuel. For a more in-depth discussion of the history of fuel ethanol exports and their evolution
through 2024, refer to Chapter 7.6.
Figure 1.4-2: Monthly Fuel Ethanol Exports from U.S.
250
200
in
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c
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= 100
5:
50
0
Source: EIA, "U.S. Exports of Fuel Ethanol," Petroleum & Other Liquids, January 1, 2026.
https://www.eia.gov/dnav/pet/hist/LeafHandler.ashx?n=PET&s=M EPOOXE EEX NUS-Z00 MBBL&f=m.
The gasoline market was historically dominated by ethanol-free gasoline (E0) and, since
its approval for use in all vehicles in 1979, E10. Today, consumers in large swaths of the country
have a choice between E0, E10, and higher4evel ethanol blends of El 5 and E85. Today's
consumers of motor gasoline have to weigh a series of factors in their choice of what fuel to
purchase, such as (in the case of E85, which is only approved for use in flex fuel vehicles) their
vehicle's operability and longevity, relative price, and perceptions or knowledge gaps concerning
impacts of each fuel type on fuel economy, the environment, their personal financial situation,
and the economy writ large. Since approaching and exceeding the El0 blendwall between 2010
and 2016, virtually all gasoline nationwide contains at least 10% ethanol by volume, meaning
most consumers today have little choice but to use E10 gasoline at a minimum. With the growth
of retail fueling stations offering E15 and E85, the choice has now shifted between largely E10
and these higher4evel ethanol blends. For higher-level ethanol blends, consumers likely consider
all the factors from when the choice was between E0 and E10, plus whether the fuel is legally
permitted to be used in their vehicle and whether the manufacturer has warranted their vehicle
21 S&P Global, "US ethanol exports on pace for record year, fueled by low prices and increased opportunity
overseas," November 19, 2024. https://www.spglobal.com/commoditv-insights/en/news-research/latest-
news/agriculture/111924-us-ethanol-exports-on-pace-for-record-vear-fueled-bv-low-prices-and-increased-
opportunity-overseas.
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for its use. The following sections survey recent developments in El5 and E85 gasoline in the
U.S.
1.4.1 E85
The earliest form of a higher-level ethanol blend was E85. In 1996, the first flex-fuel
vehicle (FFV) was produced that could operate on fuel containing up to 85% denatured ethanol
(83% undenatured ethanol).22 Starting in 2007, ASTM International (ASTM) limited the
maximum undenatured ethanol content of E85 to 83% in specification D5798, with a minimum
ethanol content of 51%. EIA assumes for the purposes of its AEO projections that the annual,
nationwide average ethanol concentration of E85 is 74% which is the value EPA has opted to use
in this rule, consistent with previous rulemakings.23
E85 is not considered gasoline under EPA's regulations, and as such is permitted to be
used only in FFVs. However, FFVs can operate on either gasoline or E85. Under basic economic
theory, and assuming all other factors are equal, FFV owners are more likely to purchase E85 if
they believe that doing so reduces their fuel costs. E85 reduces fuel economy in comparison to
E10, so E85 must sell at a discount to E10 if it is to represent equal or greater value in terms of
energy content. For an average gallon of E85 containing 74% ethanol, its volumetric energy
content is approximately 21% less than the energy content of E10 (or 24% lower than that of
EO).24-25 In order for E85 to be priced equivalently to gasoline on an energy-equivalent basis,
then, its price must be on average 21% lower than that of E10. As shown in Figure 1.4.1-1, the
nationwide average price of E85 compared to E10 has only occasionally achieved the requisite
energy equivalent pricing needed for FFV owners who are aware of and concerned about the fuel
economy impacts of E85, though this has occurred more often since 2023 compared to the
previous years of the RFS program. Regardless, E85 purchasers generally have no way of
knowing whether their fuel contains 83% ethanol, 51% ethanol, or something in-between,
confounding accurate value calculations.
22 The Auto Channel, "Alternative Fuel Ford Taurus," January 1996
23 AEO2025, Table 2 - Energy Consumption by Sector and Source.
24 Assumes ethanol energy content is 3.554 mill Btu per barrel and gasoline energy content is 5.222 mill Btu per
barrel. EIA, "Monthly Energy Review," March 2025, Tables A1 and A3.
https://www.eia.gov/totalenergv/data/montlilY/arcliive/00352503.pdf.
25 A comparison to E0 would be more relevant prior to 2010 when there remained significant volumes of E0 for sale
at retail stations.
18
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Figure 1.4.1-1: Volumetric Price Reduction of E85 Compared to E10a
35%
a The 21% energy- equivalence level of E85 compared to E10 assumes that E85 contains 74% ethanol.
California has been an exception recently in terms of E85 consumption, as retail station
growth rates in California have surpassed the rest of the country. This is due largely to the price
differential between E10 and E85. As shown in Figure 1.4.1-2, E85 in California has remained
approximately $2 below the price of E10 on average since at least 2022. This discount routinely
exceeds 50%, far greater than the 21% discount needed for E85 to achieve price parity with E10
on energy content. This discount provides a significant incentive for consumers to utilize E85.
Additional information on E85 nationwide and in California can be found in Chapter 7.5.
Figure 1.4.1-2: Price Comparison of California E10 and E85
$6.00
$5.00
03
u
g $4.00
LU
~o
c
o $3.00
T—1
LU
(13
25%
20%
: 15%
10%
"N,
g $2.00
"5s
O
$1.00
$0.00
1
I 5%
ESS
E10
-
Energy equivalance with i
E10
L 0%
Jan-22
Mar-22
May-22
Jul-22
Sep-22
Nov-22
Jan-23
Mar-23
May-23
Jul-23
Sep-23
Nov-23
Jan-24
Mar-24
May-24
Jul-24
Sep-24
Nov-24
Jan-25
Mar-25
May-25
Jul-25
Sep-25
Nov-25
Source: e85prices.com.
19
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1.4.2 E15
In 2011, gasoline containing up to 15% ethanol was permitted to be used in model year
(MY) 2001 and newer vehicles.26 El 5 has since been offered at an increasing number of retail
service stations.27 However, there is currently no publicly available data on actual nationwide
El 5 sales volumes. Minnesota is the only source of month-to-month El 5 sales data allowing us
to see the changes in El5 sales between the summer and winter months. We present this data in
Figure 1.4.2-1.
Sales of El 5 prior to 2019 were mostly seasonal due to the fact that El 5 did not qualify
for the 1-psi Reid vapor pressure (RVP) waiver for summer gasoline in conventional gasoline
(CG) areas that has been permitted for E10 since the summer volatility standards were
implemented in 1989.28 As shown in Figure 1.4.2-1, monthly E15 sales in Minnesota from 2015-
2018 demonstrate that sales volumes of El 5 in summer months were notably lower than in non-
summer months in this time period.
Figure 1.4.2-1: Normalized Monthly E15 Sales per Station in Minnesota
2.00
0.20
0.00
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Note: Normalized values derived by dividing the monthly E15 sales volume per station by the annual average E15
sales volume per station.
Source: Minnesota Commerce Department, "Minnesota E85 + Mid-Blends Station Report."
https://mn.gov/commerce/business/weights-measures/fuel/biodiesel/ethanol.isp.
In 2019, EPA extended the 1-psi waiver to El5 by regulation.29 EPA estimated that the
annual average El 5 sales per station in Minnesota would have been 16% higher had the 1-psi
waiver been in place from 2015-2018/° On July 2, 2021, the D.C. Circuit ruled that EPA's extension
of the 1-psi waiver to El 5 was based on an impermissible reading of the statute and vacated the
26 76 FR 4662 (January 26, 2011).
27 See Chapter 6.4.3.
28 54 FR 11883 (March 22, 1989).
29 84 FR 26980 (June 10, 2019).
311 "Estimating the impacts of the lpsi waiver for E15," Docket Item No. EPA-HQ-OAR-2019-0136-2117.
https://www.regulations.gov/document/EPA-HO-OAR-2019-Q136-2117.
20
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action. EPA subsequently issued emergency fuel waivers for the summers of 2022-2025 that
allowed E15 to take advantage of the 1-psi waiver to address issues related to fuel price and
supply. The impact of the 1-psi waiver for E15 on summer sales of E15 can be seen for 2019-
2025 in Figure 1.4.2-2. For these years, data from Minnesota on per-station sales of E15
indicates that those sales were no longer seasonal as they were prior to 2019. Average El5 sales
post-waiver remain consistent year-round compared to pre-waiver, even though the overall El5
price is slightly lower. This is possibly due to impacts from the COVID-19 pandemic and
decreased fuel sales during the start of the war in Ukraine.
Figure 1.4.2-2: Normalized Monthly E15 Sales per Station in Minnesota; Pre-and Post-
Waiver for E15
1.6
1.4
1.2
1.0
0.8
0.6
0.4
Average pre-waiver
0.2
Average post-waiver
0.0
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Note: Normalized values derived by dividing the monthly E15 sales volume per station by the annual average E15
sales volume per station.
Source: Minnesota Commerce Department, "Minnesota E85 + Mid-Blends Station Report."
https://mn.gov/commerce/business/weights-measures/fuel/biodiesel/ethanol.isp.
On February 29, 2024, EPA finalized a rule to remove the 1-psi waiver for E10 in eight
Midwestern states.31 On March 19, 2025, EPA finalized a one-year extension of the removal of
the 1-psi waiver for Ohio and nine counties in South Dakota.32 However, on January 28, 2026,
EPA approved Ohio's request to reinstate the 1-psi waiver for the 2026 summer season. The
result is that E10 and E15 are treated the same in these states with regard to RVP beginning with
the summer of 2025 (or the summer of 2026 in the case of the nine counties in South Dakota).33
Consequently, there may be no reduction in summer sales of El 5 compared to other months in
these states going forward.
31 Illinois, Iowa, Minnesota, Missouri, Nebraska, Ohio, South Dakota, and Wisconsin. 89 FR 14760.
32 90 FR 13093 (March 20, 2025).
33 In summer 2025, EPA issued a series of emergency fuel waivers that achieved the same result nationwide. That is,
E15 and E10 could be blended using the same blendstock in the summer months.
21
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1.5 Other Biofuels
Corn ethanol and BBD have dominated the biofuels landscape since implementation of
the RFS program began in 2006. In more recent years CNG/LNG derived from biogas that
qualifies as cellulosic biofuel has also contributed significant volumes to the total supply of
qualifying renewable fuel. Beyond these three main types of renewable fuel other bi ofuels have
also contributed to the total renewable fuel pool, sometimes providing the marginal volumes
needed to meet the other applicable standards. As shown in Figures 1.5-1, the supply of these
"other biofuels" has ranged from 150 million to 450 million RINs from 2015-2025. The annual
supply of biofuels other than corn ethanol, BBD, and cellulosic CNG/LNG are shown in Figure
1.5-1.
1.5-1: Supply of Biofuels Other Than Corn Ethanol, BBD, and Cellulosic CNG/LNG
(million RINs)
450
400
350
300
S 250
c
o
= 200
150
100
50
~ ~ ~ U P P P
0
2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025
~ Advanced CNG/LNG ~ Heating Oil H Gasoline/Naphtha
~ Advanced Renewable Diesel ~ Advanced Ethanol
I Conventional Biodiesel/RD
Source: EMTS.
The largest sources of these "other biofuels" in the past decade have been advanced
ethanol, renewable diesel, and conventional biodiesel and renewable diesel. The supply of
advanced ethanol has varied from year to year and appears to fluctuate depending on market
conditions. In some earlier years of the program, the U.S. imported significant amounts of
sugarcane ethanol. Virtually all domestically produced advanced ethanol has historically been
made from separated food waste. In recent years the supply of advanced ethanol has been smaller
relative to volumes supplied in 2019 and 2020. Supplies of advanced renewable diesel increased
22
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through 2023 but have been declining since then. In 2015 and 2016 significant volumes of
conventional biodiesel and renewable diesel were supplied to the U.S., but since that time only
very small volumes have been supplied. This likely reflects the growing impact state fuel
programs have had on the supply of biofuels to the U.S., as conventional biodiesel and renewable
diesel generally do not generate credits, and in some cases generate deficits, in these state
programs.
1.6 Federal Tax Credits for Biofuels
For most of the history of the RFS program only biodiesel, renewable diesel, and
CNG/LNG derived from biogas were eligible for Federal tax credits. Prior to 2025, a $1
refundable credit was available to producers and blenders of biodiesel and renewable diesel for
every gallon of biodiesel or renewable diesel, including blends, ($0.10 per gallon for small agri-
biodiesel producers) that was either produced, sold, or used in the U.S. This tax credit lapsed
several times over the past decade but was consistently available (whether prospectively or
retroactively) from the beginning of the RFS program and throughout most of the program's
history. The Inflation Reduction Act (IRA) of 2022 extended the biodiesel blenders tax credit
through 2024 but phased it out thereafter. The prospective availability of the biodiesel blenders
tax credit for 2023 and 2024, in combination with the replacement of this tax credit with the 45Z
credit were likely significant factors in the rapid increase in the supply of BBD to the U.S. in
2023 and 2024.
IRA also established two new tax credits that could apply to qualifying fuels under the
RFS program, the Sustainable Aviation Fuel Credit (SAFC), often referred to as the "40B
credit", and the 45Z credit. The SAFC provided a tax credit ranging from $1.25 to $1.75 per
gallon to any renewable jet fuel that achieves at least a 50% reduction in lifecycle GHG
emissions but was only available for fuel produced during 2023 and 2024. The SAFC thus
provides a larger incentive for renewable jet fuel in 2023 and 2024 than that provided by the
biodiesel blenders tax credit in previous years.
Starting in 2025 both the biodiesel blenders tax credit and the SAFC were replaced by
45Z. 45Z is available to all transportation fuel produced in the U.S. that has an emission rate less
than 50 kilograms of CO2 equivalent per million BTU. The magnitude of 45Z varies depending
on the type of fuel produced (renewable jet fuel vs. other transportation fuel), the emissions
factor of the fuel, and whether the fuel producer meets prevailing wage and apprenticeship
requirements.
45Z differs from the biodiesel blenders tax credit it replaced in several important ways.
First, this tax credit is available to all transportation fuels with lifecycle GHG emissions under
the specified threshold. Since 2012, BBD has been the only RFS-qualifying fuel that was eligible
for a federal tax credit. This broader eligibility under 45Z relative to the biodiesel blenders tax
credit may open up greater opportunities for non-BBD advanced biofuels to compete for market
share under the RFS program as these fuels now have similar treatment under the federal tax
provisions.
23
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45Z is also only available for biofuels produced in the U.S. Historically, significant
volumes of imported biodiesel and renewable diesel have benefited from the biodiesel blenders
tax credit. In 2025, OBBB made further amendments to 45Z. Notably, OBBB extended the
CFPC two additional years, through the end of 2029, and after 2025, required that fuels must be
produced from North American feedstocks to qualify. The restriction of 45Z to biofuels
produced from North American feedstocks may have multiple impacts on the supply of biofuel
to the U.S. Imports of BBD in 2025 decreased significantly and are expected to remain low in
future years, as these fuels will no longer be eligible for any federal biofuel tax credit. The
availability of 45Z to domestic BBD producers will advantage these producers over imported
BBD, which is projected to directionally result in lower volumes of imported BBD. Lower
volumes of imported BBD may increase the market demand for BBD produced in the U.S.,
resulting in greater domestic BBD production and/or decreased BBD exports. 45Z is also
expected to shift the source of feedstocks imported for biofuel production, with increases in the
quantity of feedstocks imported from Canada and Mexico and decrease in the quantity of
feedstocks imported from other countries. 45Z provides domestic BBD producers with
significant incentives over foreign BBD producers, but only if they can produce BBD using
domestic feedstocks or feedstocks imported from North America.
45Z also provides greater incentives for biofuels with lower emission rates. In the Set 2
proposal, EPA projected that the structure of the CFPC, together with State low carbon fuel
standard (LCFS) programs and the limited availability of domestic FOG, would result in
significant demand for imported feedstocks, particularly feedstocks that could be used to produce
BBD with low emission rates. However, the changes to 45Z in OBBB have significantly
impacted the structure of the 45Z incentive. The largest supplies of imported FOG are from
countries outside of North America, and BBD produced from these feedstocks no longer
qualifies for 45Z. Further, OBBB prohibited the consideration of land use change in the emission
rates for biofuels. This change eliminates much, of not all, of the additional incentives in 45Z to
produce biofuels from FOG versus crop-based feedstocks such as soybean oil or canola oil. As a
result, we now project lower volumes of imported FOG will be used to produce BBD in 2026
and 2027 and that greater volumes of soybean oil and canola oil sourced from the U.S. and
Canada will be used.
1.7 RIN System and Prices
1.7.1 RIN System
RINs were created by EPA under CAA section 21 l(o)(5) as a flexible credit and
compliance mechanism to enable obligated parties across the country to meet their renewable
fuel blending obligations under the RFS program without having to blend the renewable fuel
themselves.34 RINs allow: (1) Obligated parties {i.e., the refining industry) to comply with the
RFS program without producing, purchasing, or blending the renewable fuel themselves; (2)
Non-obligated blenders of renewable fuel to maintain their preexisting blending operations; and
(3) The ethanol and other biofuel industries to continue to produce biofuels, now with the
support of the RIN value. Obligated parties, of course, can and do produce, purchase, and blend
34 The RIN system was created in the RFS1 Rule (72 FR 23900; May I, 2007) and modified in the RFS2 Rule (75
FR 14670; March 26, 2010).
24
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their own renewable fuel, but the RIN system allows them the option of not doing so and instead
relying on the business practices of other market participants that are already set up to do so. In
this way the RIN system allows the RFS program to function smoothly with less market
disruption and at a lower overall cost. RINs are generated by renewable fuel producers (or in
some cases renewable fuel importers) and are assigned to the renewable fuel they produce. These
RINs can be sold together with the renewable fuel to refiners or blenders, or separated from
renewable fuel by obligated parties or when renewable fuel is blended into transportation fuel.
Once separated, RINs can be used by obligated parties to demonstrate compliance with their RFS
obligations or can be traded to other parties.
Under the RFS program, EPA created five different types of RINs: cellulosic biofuel
(D3) RINs, BBD (D4) RINs, advanced biofuel (D5) RINs, conventional renewable fuel (D6)
RINs, and cellulosic diesel RINs (D7).35 The type of RIN that can be generated for each
renewable fuel depends on a variety of factors, including the feedstock used to produce the fuel,
the type of fuel produced, and the lifecycle GHG reductions relative to petroleum fuel. As shown
in Figure 1.7-1, the obligations under the RFS regulations are nested, such that some RIN types
can be used to satisfy obligations in multiple categories.
Figure 1.7-1: Nested Structure of the RFS Program
Total renewable fuel
Advanced biofuel
r
D3/7
D4
D5
D6
t
Cellulosic
biofuel
t
BSD
t
"Other" advanced
(sugarcane ethanol,
etc) .
T
Conventional
(mostly corn-ethanol)
Non-cel lulosic advanced
Since its creation the RIN system has grown and evolved along with the RFS program.
1.7.2 RIN Prices
RIN prices have varied significantly since 2010. There have also been significant and
notable differences between the prices of each of the four major RIN types. A chart of RIN
35 40 CFR 80.1425(g).
25
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prices, as reported to EPA through EMTS, is shown in Figure 1.7.2-1.36 While there are a wide
variety of factors that impact RUN prices, including both market-based and regulatory factors, a
review of RIN prices reveals several notable aspects of the RFS program .
Figure 1.7.2-1: Historical RIN Prices in Nominal Dollars
$4.00
$3.50
$3.00
$2.50
$2.00
$1.50
$1.00
$0.50
$0.00
jiiri
*S> \*v- aV \*v> v"*3 \i> ft <£>
^ ^ ^ bV'
-------
greater than 10% ethanol are currently not optimized to take advantage of the high octane value
of ethanol; (2) The lower energy content of ethanol is more noticeable as the amount of ethanol
increases; and (3) Infrastructure limitations have restricted the availability of higher-level ethanol
blends (see Chapter 6.4).
In subsequent years, D6 RIN prices have varied significantly, but they have never
returned to the low prices observed prior to 2013. It is also notable that, from 2013-2016, D6
RIN prices remained close to, but slightly less than, D4 and D5 RIN prices. During this time,
obligated parties were purchasing D4 and D5 RINs in excess of their BBD and advanced biofuel
obligations to make up for the shortfall in conventional biofuel volume and used those RINs to
meet their total renewable fuel obligations. Essentially, given the inability to successfully
introduce higher-level ethanol blends into the market in sufficiently large quantities, the market
relied upon biodiesel and renewable diesel (primarily as advanced biofuel and BBD, but also
some volume of conventional biodiesel and renewable diesel) as the marginal RFS compliance
option when other sources of conventional biofuel were not available at competitive prices. After
2018, D6 RIN prices were, for some time, significantly lower than D4 and D5 RIN prices, but
still higher than the D6 RIN prices observed prior to 2013. These lower D6 RIN prices are
largely the result of: (1) Small refinery exemptions (SREs) granted in 2018, which reduced the
total number of D6 RINs needed for compliance with the RFS obligations to a number that was
below the E10 blendwall; and (2) The large number of carryover RINs available, as discussed in
Chapter 1.8.1. Beginning in the summer of 2020, D4, D5, and D6 RIN prices rose dramatically,
reaching nearly $2 per RIN in the summer of 2021. These RIN prices remained around $1.50 per
RIN through June 2023, before falling back to approximately $0.75 per RIN in the summer of
2023. The timing of the observed changes in RIN prices in the summer of 2023 strongly suggest
that the finalization of the RFS volume requirements for 2023-2025 in June 2023 contributed to
the drop in RIN prices. The prices for D4, D5, and D6 RINs also reflect the cost of biodiesel and
renewable diesel production (the marginal supply of RINs over this period). The prices for
soybean and other vegetable oil feedstocks were unusually high from the summer of 2021
through the summer of 2023, a period with corresponds to the period of high RIN prices for D4,
D5, and D6 RINs.
While D6 RIN prices have remained relatively high in recent years, these price levels
have not translated into higher ethanol prices for ethanol producers. After examining market
data, EPA found no correlation between D6 RIN prices and ethanol prices from 2010-2024 (see
Figure 1.7.2-2). Instead, higher D6 RIN prices have resulted in lower effective prices for ethanol
after the RINs have been separated and sold.38 Higher D6 RIN prices have thus served to
subsidize fuel blends that contain higher proportions of conventional biofuel (e.g., E85) and
increased the cost of fuel blends that contain little or no conventional biofuel (e.g., E0 and BO).39
38 The effective price is the price of the ethanol after subtracting the RIN value from the price of the ethanol with the
attached RIN.
39 Burkholder, Dallas. "A Preliminary Assessment of RIN Market Dynamics, RIN Prices, and Their Effects." EPA,
May 2015.
27
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Figure 1.7.2-2: Historical Ethanol Prices and D6 RIN Prices
54.00
$3.50
$3.00
*F
o
ro $2-50
Q£>
jjy
QJ
8 $2.00
Ql
I 51-50
JZ
4-J
LU
$1.00
$0.50
$0.00
u
|Al 1
/y
V
a/Vs
fO' v-y" vN1 v*?5 0? oJ5 \f£>
N v*v- VV \V \\a vv w \*v sNN \v \nvi \v VO-
ifvS ^ ^ s[^ \V \V i&S' \V \\v \NV jK>" \\v
%A *V Vs \\ py» "y\ ISyX "y\ fiy\ "y\
Ethanol Price D6 RIN Price
Sources: Ethanol Price from USDA Weekly Ag Roundup. D6 RIN Price from EMTS data
D5 RINs were priced at a level between D4 and D6 RINs from 2010-2013. However,
since 2013, D5 RIN prices have been nearly identical to D4 RIN prices. This shift in the relative
pricing of D5 and D4 RINs also corresponds with the market reaching the E10 blendwall. This is
because there are two primary fuel types that have been used to satisfy the advanced biofuel
requirements: sugarcane ethanol and BED. From 2010-2012, obligated parties generally met
their implied requirements for "other advanced biofuel" with sugarcane ethanol.40 This is
apparent in the volumes of sugarcane ethanol (which supplied the vast majority of volume
requirement for "other advanced" biofuels) and BBD (which did not exceed the volume
requirement for BBD by an appreciable volume) used in the U.S. in these years.41 It is also
indicated by the prices for D5 RINs, which were significantly lower than the price of D4 RINs
during this time, suggesting that it was more cost effective for obligated parties to meet their
compliance obligations with D5 RINs (generated for sugarcane ethanol) than D4 RINs
(generated for biodiesel and renewable diesel). When the E10 blendwall was reached in 2013,
however, it became much more expensive to increase the volume of ethanol blended into the
gasoline pool. While obligated parties could still import sugarcane ethanol to satisfy their
advanced biofuel obligations, doing so would reduce the volume of corn ethanol that could be
used as E10. Available non-ethanol renewable fuels were almost entirely advanced biodiesel and
40 "Other advanced biofuel" is not a category for which a volume requirement is established under the RFS program,
but is the difference between the advanced biofuel requirement and the sum of the cellulosic biofuel and BBD
requirements, both of which are nested within the advanced biofuel category.
41 See Chapters 6.3 and 6.2 for volumes of sugarcane ethanol and BBD used in the U.S., respectively.
28
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renewable diesel, so obligated parties generally used these fuels (rather than sugarcane ethanol)
to meet the advanced biofuel requirements so that they could use corn ethanol to satisfy the
remaining total renewable fuel requirements. RIN prices responded, and since 2013 the prices of
D4 and D5 RINs have been nearly identical.
D4 RIN prices, much like all RIN prices, have varied significantly since 2010. The
pricing of these RINs, however, has been relatively straightforward compared to other D-codes.
D4 RINs are generally priced to account for the price difference between biodiesel and
petroleum diesel, which in turn are largely a function of the pricing of their respective oil
supplies. Other factors can also impact this relationship; most significantly are the presence or
absence of the biodiesel tax credit and the impact of other subsidies and credits (e.g., state LCFS
credits).42 For example, as shown in Figure 1.7.2-3, D4 RIN prices increased significantly in
2021 and 2022, tracking with an increase in feedstock commodity prices (e.g., soybean oil),
which comprise greater than 80% of the cost of production of BBD. By the beginning of 2024,
soybean oil prices dropped to lower levels. This decrease in the price of soybean oil generally
corresponded to a decrease in D4 RIN prices.
42 A $1 per gallon biodiesel blenders tax credit was available to biodiesel blended every year from 2010-2024.
However, at various times this credit has expired and been reinstated retroactively. The biodiesel tax credit expired
at the end of 2009 and was not reinstated until December 2010, applying to all biodiesel blended in 2010 and 2011.
The biodiesel tax credit has since been again reauthorized semi-regularly, including in January 2013 (applying to
biodiesel produced in 2012 and 2013), December 2014 (applying to biodiesel produced in 2014), December 2015
(applying to biodiesel produced in 2015 and 2016), and February 2018 (applying to biodiesel produced in 2017). In
December 2019 the tax credit was retroactively reinstated for 2018 and 2019 and put in place prospectively through
2022. In August 2022, the tax credit was extended through 2024. Beginning in 2025, biodiesel and renewable diesel
could qualify for the CFPC.
29
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Figure 1.7.2-3: Historical Soybean Oil Prices ($/lb)
QBQ
llA.iJ
i07 2009 20m 2013 2015 201T 2019 2021 2023 2025
Source: Business Insider, "Soybean Oil." Markets Insider. January 30, 2026.
https://markets.businessinsider.com/commodities/soYbean-oil-price.
Generally, D4 REN prices have increased to a level that allows BBD to be cost-effective
with petroleum-based fuels, increasing BBD production and use. A 2020 paper exploring the
relationship between the price of D4 RINs and economic fundamentals concluded that
"movements in D4 biodiesel RIN prices at frequencies of a month or longer are well explained
by two economic fundamentals: (a) the spread between the biodiesel and diesel fuel prices and
(b) whether the $1 per gallon biodiesel tax credit is in effect."43 This same paper discusses in
greater detail the strong correlation between weekly D4 REN prices and predicted D4 RIN price
values using a model based on economic fundamentals. As state LCFS programs have come
online and increased in stringency, the value of these credits is now another increasingly
important factor. The 45Z credit, which replaced the biodiesel blending credit beginning in the
2025 tax year, seems likely to fill a role for BBD somewhat similar to the blending credit for the
years in which it is available.
Data on cellulosic RIN (D3 and D7) prices were not generally available until 2015. This
is likely due to the fact that prior to 2015, the market for cellulosic RINs was too small to support
commercial reporting services; very few cellulosic RINs were generated and traded in years prior
to 2016. From 2015—when D3 RIN prices were first regularly available—through 2018, the
price of these RINs was very closely related to the sum of the D5 RIN price plus the price of the
cellulosic waiver credit (CWC).14 This is as expected, since obligated parties can satisfy their
41 Irwin, Scott H., Kristen McConnack, and James H. Stock. "The Price of Biodiesel RINs and Economic
Fundamentals "American Journal of Agricultural Economics 102, no. 3 (Febmary 3, 2020): 734-52.
https://doi.org/10.1002/aiae,12014.
44 CAA section 21 l(o)(7)(D)(ii) established a price cap mechanism for cellulosic biofuel RINs. In implementing this
provision, EPA makes CWCs available for sale to obligated parties at a price determined by a statutory formula in
30
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cellulosic biofuel obligations through the use of either cellulosic RINs or CWCs (if available)
plus D4 or D5 RINs.45 The slight discount for D3 RINs (as opposed to the combination of a
CWC and a D5 RIN) is also as expected, as CWCs can be purchased directly from EPA when
obligated parties demonstrate compliance and carry no risk of RIN invalidity.46 This discount
tends to be larger at the beginning of the year, before narrowing near the end of the year as the
RFS compliance deadline nears for obligated parties. Starting in 2019, the D3 RIN price was
significantly lower than the CWC plus D5 RIN price. This is likely due to an over-supply of D3
RINs caused by EPA granting a relatively large number of SREs for the 2017 and 2018
compliance years, lowering the effective RFS standards. The average D3 RIN price fell to near
the D5 RIN price, before slowly increasing relative to the D5 RIN price starting in the second
half of 2019 and remaining between the D5 RIN price and the D5 plus CWC price through the
end of 2022. In 2023 EPA did not use the cellulosic waiver authority to reduce the required
volume of cellulosic biofuel and therefore did not offer CWCs. The price for D3 RINs dropped
shortly after the release of the proposed RFS standards for 2023-2025 at the end of 2022. The
drop in the D3 RIN prices was likely due to the proposed rule, which included a proposed
regulatory framework for generating D3 RINs from qualifying electricity used as transportation
fuel (eRINs). Shortly after the final rule establishing RFS standards for 2023-2025 was released
in June 2023 D3 RIN prices returned to about $3 per RIN. Notably, this rule did not finalize a
regulatory framework for eRINs and included higher projections for CNG/LNG derived from
biogas than the proposed rule. D3 RINs remained around $3 per RIN until December 2024, when
EPA proposed reductions to the cellulosic biofuel volume requirement for 2024. This reduction
not only reduced demand for D3 RINs, but also triggered the availability of cellulosic waiver
credits for the 2024 compliance year. In response to this proposal, and the eventual finalization
of the proposed reduction in June 2025, D3 RIN prices dropped to just under $2.50 per RIN for
most of 2025.
any year in which EPA reduces the required volume of cellulosic biofuel using the cellulosic waiver authority. A
CWC satisfies an obligated party's cellulosic biofuel obligation. However, a CWC does not satisfy an obligated
party's advanced biofuel and total renewable fuel obligations, unlike a cellulosic RIN, which can be used to meet all
three obligations. A cellulosic RIN has similar compliance value as a CWC (which can only be used to satisfy the
cellulosic biofuel obligation) and an advanced RIN (which can be used to satisfy the advanced biofuel and total
renewable fuel obligations).
45 CWCs are available to obligated parties for any year in which EPA implements the cellulosic waiver authority to
reduce the cellulosic biofuel volume requirement. EPA implemented the cellulosic waiver authority to reduce the
cellulosic biofuel volume requirement every year from 2010-2022 and again in 2024. EPA acknowledges that it did
not waive the 2023 cellulosic biofuel requirement. EPA is also in this action reducing the 2025 cellulosic biofuel
volume under the cellulosic waiver authority.
46 During a few time periods (such as late 2016), the price for D3 RINs was higher than the price for a CWC + D5
RIN. This was likely due to the fact that up to 20% of a previous year's RINs can be used towards compliance in
any given year, while CWCs can only be used towards compliance obligations in that year. Obligated parties likely
purchased 2016 D3 RINs at a premium anticipating the sharp increase in the CWC price in 2017.
31
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Figure 1.7.2-4: D3 RIN Prices and D5 RIN Price Plus CWC Price
$5.00
1/1/15 1/1/16 1/1/17 1/1/18 1/1/19 1/1/20 1/1/21 1/1/22 1/1/23 1/1/24 1/1/25
RIN Price ^^—D5 RIN Price ^^—D5 + CWC Price
Source: EMTS. CWC prices are available at: https://www.epa.gov/renewable-fuel-standard-program/cellulosic-
waiver-credits-under-renewable-fuel-standard-program.
The fact that the price of D3 RJNs, with some few exceptions, has generally not exceeded
the CWC plus D5 RIN price has potentially significant consequences for both the cellulosic
biofuel and petroleum fuel markets. For obligated parties, the CWC price effectively sets a
maximum price for cellulosic RINs (CWC plus the D5 RIN price) when these credits are
available and protects these parties from excessively high cellulosic RIN prices. One of the few
periods during which D3 RIN prices exceeded the CWC plus D5 RIN prices was in 2024.
Because EPA did not use the cellulosic waiver authority to reduce the cellulosic biofuel volume
in 2023, CWCs were not available in 2023. EPA did not propose to reduce the cellulosic biofuel
volumes for 2024 until December 2024. Shortly after this proposal cellulosic RIN prices dropped
below the price of the CWC + D5 RIN price, consistent with the historic trends.
The CWC price is also informational to potential cellulosic biofuel producers. Potential
cellulosic biofuel producers can use the CWC price, along with the projected price of the
petroleum fuel displaced by the cellulosic biofuel they produce and any tax credits or other
incentives available for the fuel, as an approximation of the maximum price they can reasonably
expect to receive for the cellulosic biofuel they produce. Knowing this price can help potential
cellulosic biofuel producers determine whether their cellulosic biofuel production processes are
economically viable under both current and likely future market conditions.
At the same time, the relatively high value of the CWC plus D5 RIN price, in conjunction
with EPA's statutory obligation from 2010 to 2022 to set the required volume of cellulosic
32
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biofuel at the volume expected to be produced each year47 and the relatively high cellulosic
biofuel volumes in the Set 1 Rule have resulted in generally high D3 RIN prices. These RIN
prices are realized for all cellulosic RINs, even those generated for biofuels such as CNG/LNG
derived from biogas from large landfills that can in some cases be produced at a cost that is
competitive with the petroleum fuels they displace even without the RIN value (see Chapter
10.1.2.6). Some of this excess RIN value may be passed on to consumers who use CNG/LNG
derived from biogas as transportation fuel in the form of incentives to purchase CNG/LNG
vehicles and lower cost fuel and/or longer term fixed-price fuel contracts. Even after accounting
for these incentives, a significant portion of the RIN value may remain with the biofuel producer,
the parties that dispense CNG/LNG derived from biogas, and any other parties involved in the
production of this type of cellulosic biofuel.48 Based on conversations with industry participants
a portion of these funds have often been reinvested in expanded CNG/LNG fueling infrastructure
and new biogas production facilities.
Unlike other RIN costs that are generally transferred within the liquid fuel pool (e.g.,
from consumers of fuels with relatively low renewable fuel content such as E0 or BO to
consumers of fuels with relatively high renewable fuel content such as E85 or B20), much of the
RIN value for CNG/LNG derived from biogas may be transferred from consumers who purchase
gasoline and diesel to parties outside of the liquid fuel pool (e.g., landfill owners, CNG/LNG
fleet owners). For example, according to EMTS RIN price data, the average cellulosic RIN price
was $2.40 in 2025; thus, the total cost associated with the 1.21 billion cellulosic RINs required
for compliance in 2025 was approximately $2.9 billion and the cellulosic biofuel requirement
likely increased the price of gasoline and diesel sold in the U.S. in 2025 by approximately 1.50
per gallon.49 These transfers are expected to increase through 2027 as a result of the higher
cellulosic biofuel volumes we are finalizing in this rule. Further discussion of the impact on RIN
prices on retail fuel prices can be found in Chapter 10.5.
1.8 Carryover RIN Proj ections
This section details the calculations performed by EPA to project the number of available
carryover RINs in the context of developing the 2026 and 2027 RFS standards. While the actual
number of carryover RINs available for use by obligated parties to use towards these standards
will not be known until after compliance with the preceding year's standards is complete, we are
able to project these values by using 2024 compliance data and assumptions about RIN
generation relative to RIN obligations in 2025.
1.8.1 Carryover RINs Available After Compliance With the 2024 Standards
In order to calculate the number of 2024 carryover RINs available for compliance with
the 2025 standards, we began with the 2024 RFS compliance year data in Table 1.8.1-1. From
47 CAA section 21 l(o)(7)(D).
48 EPA currently does not have sufficient data to determine the proportion of the RIN value that is used to discount
the retail price of CNG/LNG derived from biogas when used as transportation fuel.
49 EIA's January 2026 Short-Term Energy Outlook (STEO) projects gasoline and diesel consumption in 2025 was
196.5 billion gallons (12.82 million barrels per day). Dividing the total cost of cellulosic RINs in 2025 ($2.9 billion)
by the total consumption of gasoline and diesel (196.5 billion gallons) results in an estimated cost of 1.50 per gallon
of gasoline and diesel as a result of the 2025 cellulosic biofuel volume requirement.
33
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this data, we calculated that approximately 23.81 billion total RINs were retired for compliance
in the 2024 compliance year.50 Of this total, approximately 21.34 billion 2024 RINs and 2.47
billion 2023 carryover RINs were used.
Table 1.8.1-1: RINs Retired by Obligated Parties and Exporters in the 2024 Compliance
Year3
RIN Type
RIN Year
Total
2023
2024
D3
69,969,240
939,117,670
1,009,086,910
D4
1,104,174,020
6,612,812,357
7,716,986,377
D5
18,005,685
226,676,380
244,682,065
D6
1,274,536,953
13,562,182,158
14,836,719,111
D7
0
0
0
Total
2,466,685,898
21,340,788,565
23,807,474,463
a Data current as of February 20, 2026, and compiled from "RFS Compliance Data as of February 20, 2026,"
available in the docket for this action. RINs include those retired by companies with a renewable volume obligation
(RVO) as a gasoline/diesel fuel importer or refiner, as well as RINs retired by companies with an RVO as renewable
fuel exporters. Renewable fuel exporters include exporters of neat renewable fuel, as well as exporters of renewable
fuel blended with other fuels (including, but not limited to, gasoline, diesel fuel, heating oil, and jet fuel). See Table
1.8.4-1 for more detailed data.
Next, we calculated the net number of RINs that were generated in 2024. To do this, we
took the total number of RINs generated in 2024 and then removed any RINs that were generated
in error, as well as any RINs that were retired for purposes other than satisfying an obligated
party or exporter RVO (e.g., for spills, remedial actions, enforcement obligations, etc.). Using
the data in Table 1.8.1-2, we calculated that a net of approximately 25.22 billion RINs were
generated in 2024.
Table 1.8.1-2: 2024 Net RINs Generated
RIN Type
Total RINs
Generated3
RIN
Errorsb
Other RIN
Retirements0
Net RINs
Generated11
D3
1,017,137,710
3,957,670
1,893,653
1,011,286,387
D4
9,182,032,867
13,479,222
56,389,438
9,112,164,207
D5
244,734,381
10,957
3,807,001
240,916,423
D6
14,906,191,638
24,716,558
23,196,569
14,858,278,511
D7
283,259
23,875
0
259,384
Total
25,350,379,855
42,188,282
85,286,661
25,222,904,912
Note: Data from January 2026 and compiled from https://www.epa.gov/svstem/files/other-files/2026-
02/availablerins ian2026.csv and https://www.epa.gov/svstem/files/other-files/2026-
02/retiretransaction ian2026.csv.
a The total number of RINs generated includes those RINs generated for exported fuel.
b See Table 1.8.4-2 for more detailed data.
0 See Table 1.8.4-3 for more detailed data.
d Net RINs Generated = Total RINs Generated - (RIN Errors + Other RIN Retirements).
511 Includes RINs retired in the 2024 compliance year to satisfy 2023 compliance deficits.
34
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To determine the total number of 2024 carryover RINs available for compliance with the
2025 standards, we then subtracted the number of 2024 RINs retired in the 2024 compliance year
from the net number of 2024 RINs generated. We calculate that there are approximately 3.88
billion 2024 carryover RINs available, as shown in Table 1.8.1-3.
Table 1.8.1-3: 2024 Carryover RINs
Net 2024 RINs
2024 RINs Retired
2024 Carryover
RIN Type
Generated
for Compliance
RINs
D3
1,011,286,387
939,117,670
72,168,717
D4
9,112,164,207
6,612,812,357
2,499,351,850
D5
240,916,423
226,676,380
14,240,043
D6
14,858,278,511
13,562,182,158
1,296,096,353
D7
259,384
0
259,384
Total
25,222,904,912
21,340,788,565
3,882,116,347
Obligated parties are also able to carryforward a compliance deficit from one year to the
next year,51 increasing their RVO for 2025 and effectively decreasing the number of 2024
carryover RINs available for compliance with the 2025 standards. In order to account for this, we
calculate the effective number of 2024 carryover RINs available for compliance with the 2025
standards by subtracting out the 2024 compliance deficits, which have to be satisfied at the time
of compliance with the 2025 standards.52 After accounting for this adjustment, we calculate that
the total number of effective 2024 carryover RINs available for compliance with the 2025
standards is approximately 3.60 billion RINs, as shown in Table 1.8.1-4.53
51 See 40 CFR 80.1427(b).
52 The compliance deadline for the 2025 standards will be the first quarterly reporting deadline after the effective
date of the action establishing the 2026 standards. 40 CFR 80.1451(f)(l)(i)(A)(2).
53 In other words, the number of available carryover RINs is effectively reduced in light of the volume of 2024
deficits carried forward to 2025. We note, moreover, that these numbers could change based on, for instance,
enforcement actions or obligated parties truing up their RVOs pursuant to the attest engagement required by 40 CFR
80.1464.
35
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Table 1.8.1-4: Effective 2024 Carryover RINs
RFS Standard
RIN Type
2024
Carryover
RINs
2024
Compliance
Deficits"
Effective 2024
Carryover
RINsb
Cellulosic biofuel
D3+D7
72,428,101
55,110,052
17,318,049
Non-cellulosic advanced biofuel0
D4+D5
2,513,591,893
0
2,513,591,893
Conventional renewable fueld
D6
1,296,096,353
228,735,586
1,067,360,767
Total renewable fuel
All D
Codes
3,882,116,347
280,333,003
3,601,783,344
a Data current as of February 10, 2025, and compiled from "RFS Compliance Data as of February 10, 2026,"
available in the docket for this action.
b Represents the effective number of 2024 carryover RINs that are available for compliance with the 2025 standards
after accounting for deficits carried forward from 2024 into 2025.
0 Non-cellulosic advanced biofuel is not an RFS standard category but is calculated by subtracting the number of
cellulosic RINs from the number of advanced RINs.
d Conventional renewable fuel is not an RFS standard category but is calculated by subtracting the number of
advanced RINs from the number of total renewable fuel RINs.
1.8.2 Carryover RINs Available for 2026 and 2027
Given the uncertainty of the impact of compliance with the 2025 standards on the number
of available carryover RINs, we are unable to provide a quantitative analysis of the number of
carryover RINs that may be available for compliance with the 2026 and 2027 standards.54
However, if we assume that the uncertainties result in neither a net gain nor net loss of excess
RINs for 2025, and that this is also the case for 2026, then the carryover RINs that we projected
to be available in Chapter 1.8.1 would represent the number of carryover RINs available for
compliance with the 2026 and 2027 standards, as shown in Table 1.8.2-1.55
54 Sources of uncertainty that could potentially increase the number of carryover RINs include lower actual gasoline
and diesel fuel use than the projection used to derive the standards. Sources of uncertainty that could potentially
decrease the number of carryover RINs include enforcement actions and higher actual gasoline and diesel fuel use
than the projection used to derive the standards.
55 The actual number of RINs that will be available for use by obligated parties to use towards the 2026 and 2027
standards will not be known until the compliance deadline for the preceding compliance year. Even after this date,
however, this number could change based on, for instance, obligated parties truing up their RVOs pursuant to the
attest engagement required by 40 CFR 80.1464 or enforcement actions.
36
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Table 1.8.2-1: Projected Carryover RINs for 2026 and 2027
RFS Standard
RIN Type
Projected Effective
Carryover RINsa
Cellulosic biofuel
D3+D7
17,318,049
Non-cellulosic advanced biofuelb
D4+D5
2,513,591,893
Conventional renewable fuel0
D6
1,067,360,767
Total renewable fuel
All D Codes
3,601,783,344
a Represents the effective number of 2024 carryover RINs that are available for compliance with the 2025 standards
after accounting for deficits carried forward from 2024 into 2025.
b Non-cellulosic advanced biofuel is not an RFS standard category but is calculated by subtracting the number of
cellulosic RINs from the number of advanced RINs.
0 Conventional renewable fuel is not an RFS standard category but is calculated by subtracting the number of
advanced RINs from the number of total renewable fuel RINs.
1.8.3 Carryover RIN History
In order to provide a historical perspective on the number of available carryover RINs,
we calculated the absolute and effective number of carryover RINs for each year since 2013
using the same methodology described in Chapter 1.8.1. The results are provided in Table 1.8.3-
1 and Figures 1.8.3-1 through 4 and represent the number of RINs of a given vintage available
for compliance with the subsequent year's standard (e.g., the number of available carryover RINs
in 2024 are those 2024 RINs that can be used to comply with the 2025 standards).
Table 1.8.3-1: Number of Availa
)le Carryover RINs
History (million RUN
fs)
Non-Cellulosic
Conventional
Total Renewable
Compliance
Cellulosic Biofuel
Advanced Biofuel
Renewable Fuel
Fuel
Year
Absolute"
Effectiveb
Absolute"
Effectiveb
Absolute"
Effectiveb
Absolute"
Effectiveb
2013
0
0
565
538
1,087
1,045
1,652
1,583
2014
12
12
465
444
1,359
1,239
1,836
1,695
2015
39
39
372
367
1,248
1,242
1,659
1,649
2016
39
34
887
825
1,945
1,621
2,871
2,480
2017
28
8
801
683
2,981
2,437
3,810
3,129
2018
52
49
633
608
2,872
2,779
3,557
3,436
2019
64
59
344
237
2,693
2,518
3,102
2,814
2020
51
44
209
85
1,971
1,990
2,231
2,118
2021
45
28
249
0
1,530
1,386
1,824
1,322
2022
90
86
247
119
568
174
904
378
2023
70
13
1,124
1,012
1,278
341
2,472
1,366
2024
72
17
2,514
2,514
1,296
1,067
3,882
3,602
a Represents the absolute number of carryover RINs that are available for compliance with the subsequent year's
standards and does not account for carryforward deficits.
b Represents the effective number of carryover RINs that are available for compliance with the subsequent year's
standards after accounting for carryforward deficits. Standards for which deficits exceed the number of available
carryover RINs are represented as zero.
37
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1.8.4 EMTS RIN Data
Table 1.8.4-1: RINs Retired by Importers, Refiners, and Exporters in the 2024 Compliance
Year3
RIN Type
Year
Importers
Refiners
Exporters
Total
D3
2023
1,926,135
68,043,105
0
69,969,240
2024
31,636,444
907,198,495
282,731
939,117,670
D4
2023
19,012,036
989,442,645
95,719,339
1,104,174,020
2024
198,019,829
5,442,776,184
972,016,344
6,612,812,357
D5
2023
1,318,520
9,045,731
7,641,434
18,005,685
2024
6,378,714
170,861,367
49,436,299
226,676,380
D6
2023
57,205,190
1,182,131,646
35,200,117
1,274,536,953
2024
409,183,531
12,619,866,880
533,131,747
13,562,182,158
D7
2023
0
0
0
0
2024
0
0
0
0
Tota
724,680,399
21,389,366,053
1,693,428,011
23,807,474,463
a Data current as of February 20, 2025, and compiled from "RFS Compliance Data as of February 20, 2026,"
available in the docket for this action.
Table 1.8.4-2: 2024
RIN Errors
RIN Type
Import Volume
Correction
Invalid RIN
Volume error
correction
Total
Retirement Code
30
50
60
—
D3
0
3,957,670
0
3,957,670
D4
0
13,464,942
14,280
13,479,222
D5
0
8,854
2,103
10,957
D6
0
23,540,704
1,175,854
24,716,558
D7
0
23,875
0
23,875
Total
0
40,996,045
1,192,237
42,188,282
Note: Data from January 2026 and compiled from https://www.epa.gov/svstem/files/other-files/2026-
02/retiretransaction ian2026.csv.
38
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Table 1.8.4-3: Other 2024 RIN Retirements
Reported
Contaminated
Renewable fuel used in
Enforcement
RIN Type
spill
or spoiled fuel
an ocean-going vessel
Obligation
Retirement Code
10
20
40
70
D3
0
0
0
911,668
D4
1
1,982,568
13,822,787
6,293,732
D5
0
417,891
0
343,466
D6
10,996
74,234
0
20,230,860
D7
0
0
0
0
Total
10,997
2,474,693
13,822,787
27,779,726
Renewable fuel used or
Remedial
designated to be used in
Delayed RIN
action -
Remedial
any application that is
Retire per
Retirement
Action -
RIN Type
not transportation fuel
heating oil or jet fuel
80.1426(g)(3)
only
pursuant to
80.1431(c)
Retire for
Compliance
Retirement Code
90
100
110
120
D3
420,102
0
561,883
0
D4
21,272,483
0
1,393,972
0
D5
2,925,918
0
119,726
0
D6
34,753
0
2,707,966
0
D7
0
0
0
0
Total
24,653,256
0
4,783,547
0
Remediation
2020 Small
Feedstock
of Invalid
RIN Use for
Refinery
Alternative
Voluntary
RIN
using
renewable fuel
RIN Type
Compliance
Compliance
Retirement
with RINs
Total
Retirement Code
130
150
160
170
—
D3
0
0
0
0
1,893,653
D4
8,611,010
0
0
3,012,885
56,389,438
D5
0
0
0
0
3,807,001
D6
137,760
0
0
0
23,196,569
D7
0
0
0
0
0
Total
8,748,770
0
0
3,012,885
85,286,661
Note: Data from January 2026 and compiled from https://www.epa.gov/svstem/files/other-files/2026-
02/retiretransaction ian2026.csv.
39
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Chapter 2: Baselines
This document contains a collection of analyses examining factors identified in the CAA,
as well as other analyses EPA conducted to evaluate the impacts of this rule. The choice of
baseline has a first-order impact on the outcome of those analyses. In Preamble Section III.D, we
discuss the fact that a "No RFS" baseline is the most appropriate among available options for
purposes of evaluating the impacts of the volumes in this action for 2026 and 2027.
For our assessment of costs and fuel price impacts we have considered the impacts of the
Analyzed Volumes relative to both the No RFS Baseline and the 2025 Baseline. We recognize
that the 2025 Baseline may be of interest to the public as it gives an indication of changes in
volume requirements over time and how costs and fuel prices may change from current levels as
a result of this action. Nevertheless, we believe that the No RFS Baseline better represents the
overall impacts of establishing volume requirements for 2026 and 2027 relative to a scenario
where there were no RFS volume requirements for 2026 or 2027. This chapter describes our
derivation of the No RFS Baseline, as well as an alternate baseline—the 2025 Baseline—
representing actual renewable fuel consumption in 2025.
2.1 No RFS Baseline
The No RFS Baseline represents our projection of biofuel consumption in the U.S. were
the RFS program to cease to exist. Conceptually, the No RFS Baseline allows EPA to directly
project the impacts of the Analyzed Volumes for 2026 and 2027 relative to a scenario without
volume requirements. When estimating the No RFS baseline, we assumed that non-RFS federal
and state programs that support renewable fuel production and use (e.g., the federal renewable
fuels production credits and state LCFS programs), would continue to exist in 2026 and 2027; in
other words, the only current policy not in place in this baseline scenario is the RFS standards.
To project the No RFS Baseline, we began by projecting renewable fuel consumption in
the U.S. in 2026 and 2027, and in the preceding years, in the absence of RFS volume
requirements for these years. We assumed that all state mandates for renewable fuel use would
continue, and that additional volumes of renewable fuel would be used if these fuels could be
provided at a lower price than petroleum-based fuels, after taking into account available federal
and state incentives.56 Because we model the impact of state mandates and incentives, we project
biofuel consumption on a state-by-state basis The differences between the Analyzed Volumes
and the No RFS Baseline represent the volume changes that we analyzed for this rule. These
volume changes, as detailed in Chapter 3, are the starting point for the analyses presented in this
document, except where noted.
In some cases, the volume difference between the No RFS Baseline and the Analyzed
Volumes were sufficient to assess the impacts of the various factors enumerated in the statute.
For example, the GHG impacts and the costs are directly dependent on the volume of renewable
fuel used in the U.S. In other cases, however, these volume differences alone were insufficient
56 Local renewable fuel production subsidies and renewable fuel plant construction subsidies were not considered.
These subsidies could help support renewable fuel production volumes and support a slightly higher baseline
volume.
40
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and potentially misleading. For example, the final total domestic ethanol consumption volume is
660-730 million gallons per year higher than under the No RFS Baseline. This projected volume
increase could imply that additional ethanol production capacity and distribution infrastructure
will be needed to supply the Analyzed Volumes. However, total domestic ethanol consumption
in the Analyzed Volumes for 2026 and 2027 is lower than total domestic ethanol consumption
achieved in previous years, with the balance being exported. Thus, no additional ethanol
production capacity or distribution infrastructure is projected to be needed to meet the ethanol
volumes in the Analyzed Volumes for 2026 and 2027, although additional capital investments
are required to enable retail stations to sell the increased higher ethanol blends. Where
appropriate, such as in our assessment of infrastructure, we have therefore considered not only
the difference in domestic renewable fuel consumption between the No RFS Baseline and the
Analyzed Volumes, but also other relevant factors as they exist in 2025.
The lower demand for BBD in the No RFS Baseline would likely result in lower feedstock prices
and lower production costs for the finished fuel. For feedstock prices for the No RFS Baseline,
we assumed the USD A projected soybean oil prices for 2026 and 2027 would apply, which are
lower than the average soybean prices in 2025, and a likely good first estimate for the lower
prices and costs for No RFS Baseline. We assume that unrefined corn oil and FOG prices are
priced lower based on their historical price difference.
The No RFS Baseline was derived based on the relative economics of biofuels and the
petroleum fuels that those biofuels are blended into. If the blending cost of a biofuel in a
particular state is less than the petroleum fuel that it is blended into, we assume that the biofuel
would be used and displace the respective petroleum fuel, provided that the fuels distribution
system can provide the fuels and vehicles can use those biofuels. The blending cost of a biofuel
includes the value that the biofuel has when blending it into petroleum fuel. There are several
components that must be considered for each fuel:
• Production cost
• Distribution cost
• Blending value to the fuel blender (i.e., octane value and RVP cost of ethanol)
• Federal and state subsidies
• Relative energy value of the fuel, which may or may not be a factor
• Cost to upgrade retail stations to enable them to offer the renewable fuel
These various cost components of each renewable fuel are added together to determine
the value of each fuel at the point that it is to be blended into petroleum fuel. For each renewable
fuel, the combination of these various cost components is represented using an equation that will
be described in each case.
There are many similarities between this No RFS Baseline analysis and that of the cost
analysis described in Chapter 10, but there are differences as well. Table 2.1-1 summarizes the
various cost components considered for this analysis and provides comments on how this
analysis differs from the cost analysis in Chapter 10.
41
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Table 2.1-1: Comparison of No RFS Baseline Analysis to Cost Analysis
Included in No RFS
and Cost Analysis
Notes
No RFS
Cost
Production Cost
Yes
Yes
For the No RFS Baseline, capital costs are
amortized using higher return on investment with
taxes, while cost analysis uses a lower pre-tax
return on investment used for social analyses
Distribution Cost
Yes
Yes
Same
Blending Cost
Yes
Yes
Same
Fuel Economy
Cost
Yes
Yes
The cost analysis always accounts for fuel economy
cost, while the No RFS Baseline only does so if it
impacts the value of the renewable fuel to fuel
blenders
Federal and State
Subsidies
Yes
No
The social cost analysis never takes subsidies into
account as they are considered transfer payments
Conducted on a
State-by-State,
Fuel Type-by-
Fuel Type Basis
Yes
No
While a national-average cost is sufficient for the
cost analysis, it was necessary to estimate the
economics of blending renewable fuel in individual
states that offer subsidies, and by fuel type, to
assess whether the renewable fuel would be
blended into each fuel in that state
For the No RFS Baseline analysis, we use the latest projected feedstock prices (e.g., corn,
soybean oil) for estimating the production costs for their associated fuels. For some renewable
fuels, the estimated volume under a No RFS scenario is projected to be smaller than under the
RFS program. Economic theory would say that this could lower the market prices for the
agricultural feedstocks, making the renewable fuels made from them more attractive.
Nevertheless, consistent with our practice in previous RFS rules, we did not attempt to evaluate
such a feedback mechanism when estimating the No RFS baseline.57 The various economic
factors shown in Table 2.1-1 are further discussed below for each renewable fuel.58
Similarly, for the gasoline and diesel fuel prices, we use the most recent wholesale price
projections in AEO2025. Since EIA models much of the RFS program in its AEO modeling,
some price impacts of the RFS program are likely already represented in these wholesale
gasoline and diesel fuel prices. Economic theory would again say that wholesale gasoline and
diesel fuel prices would probably be lower under a No RFS scenario. However, we did not
attempt to evaluate this.
As indicated in Table 2.1-1, for the No RFS Baseline analysis we assume that the 45Z
credit applies even if the RFS program does not. That is because the 45Z credit was established
57 By not estimating lower renewable fuel prices under the No RFS Baseline, it could underestimate renewable fuel
demand under the No RFS Baseline and conservatively estimate higher costs for the Analyzed Volumes.
58 The spreadsheets used to estimate the No RFS Baseline for corn ethanol ("Corn Ethanol No RFS Baseline for Set
2 Final Rule") and biodiesel and renewable diesel ("Biodiesel and Renewable Diesel No RFS Baseline for Set 2
Final Rule") are available in the docket for this action.
42
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by a separate congressional action. The 45Z credit is also used for estimating the impact of the
RFS final volumes on prices as described in Chapter 10.5. To estimate the Federal tax credit
associated with 45Z, we estimate the producer tax credits based on carbon intensity estimates
from the 45ZCF-GREET model used for implementation of the 45Z credit.59 These estimates are
for the purposes of this analysis alone—the actual production tax credit values will differ for
individual taxpayers and based on rules and methods finalized by the Department of Treasury.
Estimates produced for this analysis should not be used as a guide for determining actual tax
credit value for any individual taxpayer. For each biofuel pathway, we use default assumptions
representative of industry average practices based on data from 45ZCF-GREET and R&D
GREET 2024-Revl. We assume the midwest electricity grid region for this analysis. Consistent
with 45Z as amended by OBBB, we (1) exclude tax credits for imported fuels and fuels produced
from imported feedstocks,60 (2) exclude indirect land use change emissions, (3) reduce the SAF
maximum credit from $1.75 per gallon to $1.00 per gallon, and (4) allow negative values for
manure-based pathways. For the RNG pathways, we use the estimates for biogas upgrading via
pressure swing adsorption and we use manure-biogas CNG CI estimates from R&D GREET
2024-Revl.61 We translate the carbon intensity estimates to monetary tax credits using the values
in the 45Z statute and assume the wage, apprenticeship and other labor requirements are satisfied
to qualify for the larger tax credit value. We estimate the tax credits in 2024 dollars.62 There is
also a 40A Small Agri-Biodiesel Producers tax credit of $0.20 per gallon that applies only for
2026 for producers of soybean oil, canola oil and tallow biodiesel with a production capacity of
less than 60 million gallons per year. These preliminary estimates are strictly limited to the
purposes of our evaluation of the No RFS Baseline and carry no significance at all for claiming
tax credits under the 45Z statute. Table 2.1-2 summarizes the 45Z credits by types of feedstocks
and fuels being produced.
59 Estimates were developed by the EPA using the 45ZCF-GREET model (March 2025 version) that was developed
by Argonne National Laboratory (ANL). The model is titled "45ZCF-GREET" because it was developed in support
of the clean fuels (CF) tax credit authorized by § 45Z of the Internal Revenue Code.
60 The 45Z credit requirement that feedstocks are produced in North America applies after December 31, 2025. The
45Z credit requirement that feedstocks are sourced from North America is exclusive to 2026. As a simplifying
assumption tor purposes of EPA's analysis for this rule, we only include 45Z credits for fuels produced from North
American feedstocks in both 2026 and 2027.
61 As amended by OBBB, the 45Z credit includes differentiated emissions rates for CNG produced from animal
manure by animal type. The Department and Treasury has not finalized the methods for estimating emissions rates
for each animal type. For this analysis, we use estimates from the R&D GREET 2024-Revl model, but the actual
credit values for individual taxpayers are likely to differ from our estimates, potentially by a large amount.
62 The 45Z credits are estimated in 2024 dollars, but we did not adjust them to nominal dollars. This did not have
any effect on the volume estimates for No RFS Baseline analysis because the very small effect would not have
affected the conclusions in each case.
43
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Table 2.1-2: 45Z Carbon Intensity and Tax Credit Estimates Used in This Analysis
Emissions Rate
Pathway
(kgC02e/mmBtu)
Credit ($/gal)
Corn Ethanol
43
$0.14
Soybean Oil Biodiesel
19
$0.62
Soybean Oil Renewable Diesel
24
$0.52
Canola Oil Biodiesel
32
$0.35
Canola Oil Renewable Diesel
35
$0.29
Tallow Biodiesel
19
$0.63
Tallow Renewable Diesel
17
$0.65
Used Cooking Oil Biodiesel
10
$0.80
Used Cooking Oil Renewable Diesel
17
$0.66
Distillers Corn Oil Biodiesel
10
$0.80
Distillers Corn Oil Renewable Diesel
12
$0.75
Landfill Gas CNG
26
$0.47
Dairy Manure CNG
-155
$4.11
Beef Manure CNG
32
$0.35
Swine Manure CNG
-150
$4.00
Layer Manure CNG
67
-
Wastewater Treatment Plant CNG
41
$0.18
Note: The 45Z credit estimates in this table are for the purposes of this analysis alone—the actual credit values will
differ for individual taxpayers and based on rules and methods finalized by the Department of Treasury. Estimates
produced for this analysis should not be used as a guide for determining actual tax credit value for any individual
taxpayer.
2.1.1 Conventional Ethanol
By far the largest volume of ethanol blended into U.S. gasoline is conventional ethanol
{i.e., ethanol generating a D6 conventional renewable fuel RIN) produced from corn starch,
though smaller quantities of conventional ethanol are produced from grain sorghum starch as
well. Some even smaller quantities of ethanol produced from separated food waste, corn and
grain sorghum fiber, and sugarcane are also blended into U.S. gasoline. These non-conventional
volumes are discussed in Chapter 2.1.2 and Chapter 2.1.4. The baseline volumes estimated in
this section are assumed to be entirely from conventional ethanol, which we assume for the
purposes of this analysis to be produced entirely from corn starch.
Ethanol is mostly blended into gasoline at 10% (i.e., E10). However, some volume of
ethanol is also blended at higher blend percentages of 15% and 51-83% (i.e., E15 and E85,
respectively).63 This section discusses the blending economics of ethanol and estimates the No
RFS Baseline for all three of these ethanol fuel blends. The No RFS Baseline analyses presented
for corn ethanol is contained in a spreadsheet entitled "Corn Ethanol No RFS Baseline for SET2
FRM" located in the Docket.
63 AFDC, "E85 (Flex Fuel)." https://afdc.energy.gov/fuels/ethanol e85.html.
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2.1.1.1 E10
The cost of blending ethanol into gasoline at 10% was analyzed by EPA in a peer
reviewed technical report.64 That report and its appendix provide both a historical review and
prospective analysis of the economics of blending ethanol into gasoline. The methodology used
in that analysis and its conclusion are summarized here.
A number of key factors were considered when evaluating the relative economics of
blending ethanol into gasoline. These factors depend on the type of gasoline the ethanol is
blended into, the season or year, and tax policies. Since ethanol is blended into gasoline at the
gasoline distribution terminal, it is most straightforward to consider those economic factors that
impact the decision to blend ethanol at that point. From that vantage point, the relative
economics of blending ethanol into gasoline—or the value of replacing ethanol in gasoline with
other components—can be summarized by the following equation:
EBCeio = (ESP + EDC - ERV-FETS - SETS) - GTP
Where:
• EBCeio is ethanol blending cost for E10
• ESP is ethanol plant gate spot price
• EDC is ethanol distribution cost
• ERV is ethanol replacement value
• FETS is federal ethanol tax subsidy
• S/'J'Sis state ethanol tax subsidy
• GTP is gasoline terminal price; all are in dollars per gallon
This equation allows us to break down these factors by year, by state, and by gasoline
type, enabling a detailed assessment of the relative blending economics of ethanol to gasoline
over time and by location. If the resulting ethanol blending cost is negative, it is assumed to be
cost-effective to blend ethanol. Since gasoline is marketed based on volume, not energy content,
the lower energy density of ethanol is not part of the ethanol blending cost equation. E10
contains about 3% less energy content than E0, and the cost of the lower energy content of the
gasoline is paid by consumers through lower fuel economy and more frequent refueling. Since
this small change in energy content is largely imperceptible to consumers65 and because gasoline
without ethanol is not widely available, refiners are able to price ethanol based on its volume
(unlike E85, for example, which must be priced lower at retail due to its more perceivably lower
energy density). Thus, energy density is not a factor in this blending cost equation for E10. It is
an important part of assessing the overall social costs of ethanol use but does not factor into the
decision to blend ethanol as E10.
64 EPA, "Economics of Blending 10 Percent Corn Ethanol into Gasoline," EPA-420-R-22-034, November 2022.
65 This is the case because the 3% reduction in average fuel economy equates to a reduction of 1 mile per gallon or
less for most vehicles. This difference is difficult to perceive against the background of normal variation in vehicle
performance under different conditions (e.g., weather), even for consumers who regularly track their fuel economy.
45
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Ethanol Plant Gate Spot Price (ESP)
We estimated future ethanol plant gate prices by gathering projected ethanol plant input
information (e.g., future corn prices projected by USD A and utility prices projected by EIA) to
estimate ethanol production costs that we presume represents plant gate prices. This is essentially
the same information used for estimating ethanol production costs for the cost analysis, except
that the capital costs are handled differently. Instead of amortizing the capital costs using a 7%
before tax rate of return on investment, capital costs are amortized using a 10% after tax return
on investment. As shown in Table 2.1.1.1-1, the capital amortization factor increases to 0.16
from 0.11 used for the cost analysis.
Table 2.1.1.1-1: Capital Amortization Factor Used for Estimating Plant Gate Spot Prices
Based on Production Costs
Depreciation
Life
Economic and
Project Life
Federal and
State Tax Rate
Return on
Investment
Resulting Capital
Amortization Factor
10 Years
15 Years
39%
10%
0.16
The year-by-year ethanol plant gate price projections based on production costs are
summarized in Table 2.1.1.1-2.66
Table 2.1.1.1-2: Projected Ethanol Plant Gate Prices (nominal $/gal)
Year
Price
2026
$1.90
2027
$1.90
Ethanol Distribution Cost (EDC)
This factor represents the added cost of moving ethanol from production plants to
gasoline distribution terminals, reflecting its different modes of transport (the gasoline terminal
prices in the equation already include distribution costs). Because ethanol is primarily produced
in the Midwest and Great Plains and distributed longer distances to the rest of the country, the
terminal price of ethanol is usually lower in the Midwest than in other parts of the U.S. Ethanol
distribution costs were estimated for EPA on a regional basis, but to conduct the analysis on a
state-by-state basis, these costs were interpolated or extrapolated to estimate state-specific costs
based on ethanol spot prices.67 The estimated distribution costs for ethanol ranged from 110/gal
in the Midwest to 290/gal when moved to the furthest distances along the U.S. coasts, and over
500/gal when shipped to Alaska and Hawaii. The distribution cost to each state, adjusted to 2026
dollars, is summarized in Table 2.1.1.1-3.
66 Projected corn ethanol production costs in nominal dollars are estimated by entering the costs of various inputs
and accounting for the costs for various byproducts into a corn ethanol cost model using estimated prices for those
inputs and byproducts in nominal dollars.
67ICF, "Modeling a 'No-RFS' Case," EPA Contract No. EP-C-16-020, July 17, 2018.
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Table 2.1.1
.1-3: Ethanol Distribution Cost by State
Average Ethanol
Distribution
Cost in 2026
Region
States
(0/gal)
New York, Pennsylvania, West Virginia
25.0
District of Columbia, Connecticut, Delaware, Maryland,
28.0
PADD 1
Massachusetts, New Jersey, Rhode Island, Virginia
Georgia, South Carolina Vermont, New Hampshire,
North Carolina
30.0
Florida, Maine
38.0
Illinois, Indiana, Iowa, Kansas, Michigan, Minnesota,
15.0
PADD2
Missouri, Nebraska, Ohio, South Dakota, Wisconsin
Kentucky, North Dakota, Oklahoma, Tennessee
28.0
PADD 3
Arkansas, Louisiana, Mississippi, Texas
21.0
Alabama, New Mexico
28.0
PADD 4
Colorado, Idaho, Montana, Utah, Wyoming
23.0
Oregon, Washington
29.0
PADD 5
Arizona, California, Nevada
34.0
Alaska, Hawaii
68.0
EthanolReplacement Value (ERV)
Ethanol has properties that provide value (primarily octane) or incur cost (vapor pressure
impacts) when it is blended into gasoline. We use the term "ethanol replacement value" to refer
to the sum of the values and costs due to these properties, including properties that increase and
decrease ethanol's blending value. Depending on where and when the ethanol is used, the
ethanol blending value is an important consideration when gasoline production is modified to
take into account the subsequent addition, or potential removal, of ethanol.
Essentially all E10 blending in the U.S. now occurs by "match-blending," where the base
gasoline ("gasoline for oxygenate blending" or BOB) is modified to account for the subsequent
addition of ethanol, in which the blending value of ethanol is important. In reformulated gasoline
(RFG) areas, refiners produce a reformulated gasoline before oxygenate blending (RBOB) that
has both a lower octane value and lower RVP tailored to still meet the RFG standards after the
addition of ethanol. This has been typical for ethanol-blended RFG since the mid-1990s. As the
use of ethanol expanded into CG areas, ethanol was initially "splash blended" into finished
gasoline, meaning that the gasoline was not lower in octane. As E10 gasoline filled up the
conventional gasoline pool, a similar match-blending process began to be used there as well,
replacing splash-blending. In these areas, a suboctane conventional gasoline for oxygenate
blending (CBOB) is produced by refiners for match-blending with ethanol. CG is also adjusted
to account for the octane value of ethanol, but unlike RFG, most CG is not adjusted for RVP due
to a 1-psi RVP waiver provided for E10 in most locations. When RBOB and CBOB are
produced, the refiner makes the decision that ethanol will be blended into their gasoline since the
BOBs cannot be sold as finished gasoline without adding 10% ethanol, but the ethanol is still
47
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blended into the gasoline at the terminal.68 It is likely that refiners make their decision on
producing BOBs based on the economics of producing finished gasoline at terminals. In the case
of such match blends, the economic value of ethanol relative to gasoline includes a consideration
of not only its value on a volumetric basis as a substitute for gasoline, but also the blending value
of ethanol resulting from its higher octane, and in some cases, its impact on volatility.
The full value of ethanol is best reflected by the cost associated with meeting all of the
gasoline standards and requirements through some means other than blending ethanol, including
any capital costs to produce ethanol replacements. To assess this, ICF conducted refinery
modeling for EPA for removing ethanol from the gasoline pool that the refinery model would
need to replace with an energy equivalent amount of gasoline.69 After aggregating the refinery
cost modeling results—which account for the octane value and volatility of ethanol, as well as
replacing its volume—the replacement costs of ethanol in regular grade CG and RFG are
summarized in Table 2.1.1.1-4. The ethanol replacement costs were estimated based on a certain
set of modeling conditions—projected prices for the year 2020 with crude oil priced at $72/bbl.70
The economics for replacing ethanol, however, would be expected to vary over time based on
changing market factors, such as the market value of RVP control costs, crude oil prices, and
particularly the market value for octane. The ethanol replacement costs were adjusted for the
years analyzed under the No RFS Baseline based on increasing nominal crude oil prices, which
likely provides a reasonable estimate of how refiners would value the octane, RVP, and other
replacement costs of ethanol overtime.71
Table 2.1.1.1-4 Ethanol Replacement Value (nominal $/gal)
Gasoline Type
Gasoline Grade
2026
2027
Conventional
Gasoline
Summertime Regular
1.93
1.93
Summertime Premium
1.45
1.46
Reformulated
Gasoline
Summer Regular
1.66
1.67
Summer Premium
1.19
1.19
Conventional and
Reformulated
Winter Regular
0.78
0.78
Winter Premium
0.59
0.59
Federal and State Ethanol Tax Subsidies (FETS and SETS)
The Federal ethanol blending tax subsidy expired in 2011, so that subsidy did not figure
into the No RFS Baseline analysis. We do, however, account for the 45Z credit for domestically
produced renewable fuels, including domestically produced corn ethanol. For purposes of this
68 The exception to this is a small amount of premium grade BOB that is sold as regular or midgrade E0.
69 The results of this refinery modeling are summarized in Chapter 10.1.3.1.1. MathPro, "Analysis of the Effects of
Low-Biofuel Use on Gasoline Properties - An Addendum to the 'No-RFS' Study," EPA Contract EP-C-16-020,
June 7, 2019.
70 The ethanol replacement value is the equivalent value of petroleum blendstocks that would be needed to make up
for ethanol's properties and volume.
71 The adjustment is a simple ratio of the crude oil price in a particular year divided by the crude oil price in the
baseline year.
48
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analysis, as described above in Chapter 2.1, we estimate the 45Z subsidy for corn ethanol to be
140 per gallon on average.72
Various state tax subsidies, however, have been provided for the use of ethanol. These
tax subsidies incentivize the blending of ethanol into the gasoline pool and directly impact the
decision of whether to use ethanol. Iowa and Illinois offer an ethanol blending subsidy of 150
and 260 per gallon, respectively.73 The California LCFS program is estimated to provide corn
ethanol an average blending credit of 170 per gallon based on a modeled projected carbon price
of $100 per metric ton.74-75 The clean fuels programs in Oregon and Washington state are
estimated to incentivize corn ethanol by 190 and 50 per gallon, respectively.76 Several states,
including Minnesota and Missouri, also have ethanol use mandates that require the use of ethanol
regardless of the economics for doing so.77 These mandates cannot be factored into the ethanol
blending cost equation, but are accounted for in EPA's overall analysis by including the ethanol
volume in gasoline in these states regardless of the blending economics. Other federal and state
subsidies—such as ethanol production subsidies, loan guarantees, grants, and any other
subsidies—were not considered by this analysis, although some of these subsidies, or a portion
of them, may already be included in the price information we used to estimate ethanol's
production cost for the No RFS Baseline. To the extent that these subsidies are not represented in
our No RFS Baseline analysis will lead to slightly underestimating the volume of corn ethanol in
our No RFS Baseline.
Gasoline Terminal Price (GTP)
Refinery rack price data—which already included the distribution costs for moving
gasoline to downstream terminals— from 2021 (the last year EIA reported this data) was used to
represent the price of gasoline to blenders on a state-by-state basis.78 This gasoline price data,
summarized in Table 2.1.1.1-5, was collected for each state and is assumed to represent the
average gasoline price for all the terminals in each state.79
72 The 45Z estimates produced for this analysis should not be used as a guide for determining actual tax credit value
for any individual taxpayer.
73 The National Agricultural Law Center, States' Biofuels Statutory Citations, https://nationalaglawcenter.org/state-
compilations/biofuels.
74 California Air Resources Board (CARB), LCFS Pathway Certified Carbon Intensities.
https://ww2.arb.ca.gov/resources/documents/lcfs-pathwaY-certified-carbon-intensities.
75 CARB, "Attachment C: LCFS Fuels and Credit Market Module Modeling,"
https://ww2.arb.ca.gov/sites/default/files/barcu/regact/2024/lcfs2Q24/15daY attc.pdf.
76 The state of New Mexico is putting in place a clean fuels program similar to California's, Oregon's and
Washington, but there is not any data yet available for estimating its incentive to biofuels. New Mexico
Enviromnent Department, "Clean Transportation Fuel Program," January 29, 2026.
https://www.env.mn.gov/climate-change-bureau/clean-fuel-standard.
77 The National Agricultural Law Center, States' Biofuels Statutory Citations, https://nationalaglawcenter.org/state-
compilations/biofuels.
78 EIA, "Spot Prices," Petroleum & Other Liquids, May 14, 2025.
https://www.eia.gov/dnav/pet/pet pri spt si a.htm.
79 EIA, "Refiner Gasoline Prices by Grade and Sales Type," Petroleum & Other Liquids, June 1, 2022.
https://www.eia.gov/dnav/pet/pet pri refmg dcu nus a.htm.
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Table 2.1.1.1-5: Gasoline Terminal Prices in 2021 ($/gal)
Gasoline Grade
Gasoline Grade
State
Regular
Premium
State
Regular
Premium
Alaska
2.73
2.74
Montana
2.23
2.68
Alabama
2.04
2.48
North Carolina
2.04
2.42
Arkansas
2.05
2.37
North Dakota
2.07
2.37
Arizona
2.26
2.56
Nebraska
2.06
2.33
California
2.72
2.98
New Hampshire
2.16
2.45
Colorado
2.23
2.58
New Jersey
2.07
2.30
Connecticut
2.11
2.40
New Mexico
2.26
2.58
DC.
2.09
2.37
Nevada
2.44
2.72
Delaware
2.10
2.32
New York
2.15
2.47
Florida
2.07
2.41
Ohio
2.04
2.43
Georgia
2.05
2.48
Oklahoma
2.05
2.23
Hawaii
2.59
2.71
Oregon
2.29
2.60
Iowa
2.03
2.30
Pennsylvania
2.07
2.32
Idaho
2.44
2.74
Rhode Island
2.14
2.48
Illinois
2.03
2.33
South Carolina
2.04
2.44
Indiana
2.03
2.36
South Dakota
2.10
2.41
Kansas
2.01
2.22
Tennessee
2.04
2.38
Kentucky
2.10
2.47
Texas
2.06
2.26
Louisiana
2.04
2.30
Utah
2.39
2.69
Massachusetts
2.12
2.35
Virginia
2.07
2.38
Maryland
2.07
2.27
Vermont
2.13
2.40
Maine
2.15
2.45
Washington
2.28
2.63
Michigan
2.05
2.49
Wisconsin
2.03
2.44
Minnesota
2.03
2.25
West Virginia
2.03
2.45
Missouri
2.06
2.33
Wyoming
2.24
2.65
Mississippi
2.03
2.41
Note: No data was provided by EIA for the values highlighted in grey; they were estimated by prices in a
neighboring state or for that state in a previous year when crude oil prices were about the same as 2021.
However, these prices were not projected for future years. Instead, we used projected
refinery wholesale gasoline price data from AEO2025 to adjust the 2021 refinery rack price data
to represent gasoline rack prices in future years. AEO2025 projected national average wholesale
gasoline price information used to adjust gasoline prices in future years, and the national average
wholesale gasoline price in 2021 to which the projected wholesale gasoline prices are compared,
are summarized in Table 2.1.1.1-6. The state-by-state gasoline prices shown in Table 2.1.1.1-5
are ratioed upwards using the adjustment factor for adjusting the 2021 wholesale gasoline prices
in each state. For example, the projected national average wholesale gasoline price adjustment
factor in 2026 is 1.03; therefore, gasoline prices in 2026 are 1.03 times higher than the prices
summarized in Table 2.1.1.1-5.
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Table 2.1.1.1-6: Nationa
Average Wholesale Gasoline Prices
Year
Wholesale
Gasoline Price
(AEO2025)
CPI
Wholesale
Gasoline Price
(nominal)
2021 Price
Adjustment
Factor
Actual National
Average Gasoline Price
2021
$2.19
-
Year dollars for
AEO2025
2024
3.13
Gasoline Price
2026
$2.18 (2024$)
3.24
$2.26
1.03
2027
$2.13 (2024$)
3.30
$2.24
1.02
Source: AEO2025, Table 20 - Macroeconomic Indicators and Table 57 - Components of Selected Petroleum
Product Prices.
The No RFS Baseline analysis revealed that it is economical to blend ethanol into the
entire gasoline pool up to 10%. As shown in Figure 2.1.1.1-1, all blending costs are negative—
ethanol is over 700/gal less expensive than gasoline in the most expensive market for blending
ethanol, and about $2.50/gal less expensive than gasoline in the least expensive market for
blending ethanol (in which a state subsidy applies).
Figure 2.1.1.1-1: Economics of Blending Ethanol up to the E10 Blendwall (nominal dollars)
C
o
u
— -150
0
u
m -200
c
01
-250
-300
2026
2027
Year
Maximum Ethanol Blending Cost —¦—Minimum Ethanol Blending Cost
2.1.1.2
E85
Some aspects of the ethanol blending cost equation developed for El 0 in Chapter
2.1.1.1—such as the Ethanol Plant Gate Spot Price (ESP) and Ethanol Distribution Cost (EDC),
remain largely the same for E85 and are not discussed further here. However, the analysis for
E85 has some important differences. The Gasoline Terminal Price (GTP) was replaced by
Ethanol Breakeven Blending Value. The Ethanol Replacement Value (ERV), which is an
important cost factor for the value of E10, is not a factor for E85, although this is discussed
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below to characterize some E85 properties. Furthermore, an additional cost applies to E85 to
account for the cost to modify retail stations to carry E85, which we have termed the Retail Cost
(RC). We do not include a fuel economy effect for E10 because consumers bear this cost, both
because they lack the ability to perceive the difference in fuel economy which creates the cost
and because they generally lack reliable access to an alternative (e.g., EO gasoline) at a more
attractive price. However, in E85's case, consumers command a lower price for E85 before
purchasing E85 because they are able to perceive the difference in fuel economy associated with
it relative to E10, which affects ethanol's value to fuel blenders at these higher rates. This E85
fuel pricing effect is captured in a breakeven price for ethanol.
The economics for using ethanol in E85 are estimated in two steps. First, we estimated
the breakeven price for ethanol blended in E85 based on the price of gasoline in each state. This
calculation is made for regular and premium grades of both CG and RFG in each state. In the
second step, the estimated ethanol plant gate price, ethanol distribution cost, retail cost, and E85
subsidies are combined together in the following equation to estimate whether ethanol blended
into E85 is economical:
EBCess = (ESP + EDC - FETS - SETS + RC) - EBBV
Where:
• EBCess is ethanol breakeven price for ethanol blended as E85
• ESP is ethanol plant gate spot price (same as section 2.1.1.1)
• EDC is ethanol distribution cost (same as section 2.1.1.1)
• FETS is federal ethanol tax subsidy
• SETS is state ethanol tax subsidy
• RC is retail cost (service station revamp cost to sell E85)
• EBBV is ethanol breakeven blending value; all are in dollars per gallon
Ethanol Replacement Value (ERV)
Blending ethanol into gasoline for E85 is different than blending for E10 because refiners
do not make a separate E85 BOB; thus, the E10 RBOBs and CBOBs are blended with ethanol to
produce E85 and there is significant octane giveaway.80 Conversely, there is no risk that the E85
blend will exceed any RVP limits because E85 has a very low RVP. In fact, the resulting E85
blend is so low in vapor pressure that it causes most E85 blends to not meet the RVP minimum
standards. In those cases, E85 is blended with less ethanol—usually 70% in the winter and up to
79% in the summer—and the year-round average is 74%, which allows ethanol to comply with
the ASTM RVP minimum standards.81
80 Octane giveaway occurs when the gasoline being sold has higher octane than required in the state where the
gasoline is being sold. For example, regular grade gasoline must meet an (R+M)/2 octane standard of 86 in most
states. When ethanol is blended into finished gasoline, the octane of the finished E10 will be approximately 3 octane
numbers higher than required.
81 ASTM D5798-21, "Standard Specification for Ethanol Fuel Blends for Flexible-Fuel Automotive Spark-Ignition
Engines."
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Although refiners do not create a lower octane BOB for blending into E85, ethanol
producers nonetheless choose to blend natural gas liquids (NGLs) with ethanol to produce E85.
NGLs are a low cost, low octane, higher RVP petroleum blending material that ethanol
producers use to denature their ethanol. Since ethanol plants already have this blendstock
material on hand, some ethanol producers blend E85 on-site using NGLs and then distribute the
finished E85 from there. When blending up E85 with NGLs, the higher RVP of the NGLs allows
blending a higher ethanol content of 83% in the summer. However, the RVP of NGLs is about
the same or slightly higher than winter gasoline, so the winter blend percentage is the same.
Because the more volatile NGLs are smaller hydrocarbons, they contain lower volumetric energy
content, which is a factor in considering their value as well. Because NGLs are used as an E85
blendstock, we also evaluated the economics of blending E85 blended with NGLs. While this is
an interesting option that ethanol producers take, we believe that most E85 is blended with
gasoline.
Federal and State Ethanol Tax Subsidies (FETS and SETS)
E85 does not and has never had access to a federal ethanol blending tax subsidy.
However, the ethanol used to produce most E85 is anticipated to have access to other federal
incentives, such as the 45Z production credit.82 Various state tax subsidies have also been
provided for the use of ethanol. These tax subsidies incentivize the blending of ethanol into the
gasoline pool and directly impact the decision of whether to use ethanol. Table 2.1.1.2-1
provides the E85 subsidies offered by different states.
Table 2.1.1.2-1: Current State E85 Subsidies (0/gal)
State
E85 Subsidy
Iowa
220
Illinois
180
Pennsylvania
160
South Dakota
140
The California, Oregon and Washington state clean fuels program blending credits for
ethanol also apply when ethanol is blended into E85. Aside from the retail cost credit offered by
USDA Higher Ethanol Blends program described below, other federal and state subsidies—such
as ethanol production subsidies, loan guarantees, grants, and any other subsidies—were not
considered by this analysis.
Retail Cost (RC)
The retail costs for E85 are estimated based on the investments needed to offer E85 at
retail stations and the estimated throughput at E85 stations.83 We obtained a report provided
under the USDA Higher Blends Infrastructure Incentive Program (HBIIP) which listed the major
equipment installed to enable storing and dispensing E85. For a typical retail station revamp to
82 As described in Chapter 2.1, we estimate an average 45Z credit of 140 per gallon for E100. Thus, the implied 45Z
subsidy per gallon of E85 will depend on the ethanol blend level. Estimates produced for this analysis should not be
used as a guide for determining actual tax credit value for any individual taxpayer.
83 The methodology used and the estimated costs for these revamps are discussed in Chapter 10.1.4.1.2.
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enable storing higher ethanol blends, a retail station installs 2.8 dispensers and 0.8 underground
storage tanks. Each dispenser is estimated to cost $53,000 and each underground storage tank is
estimated to cost $168,000.84 Thus, the estimated total cost for a typical retail station revamp to
enable selling E85 is $283,000 in 2025. These stations are estimated to sell on average 82,000
gallons of E85 per year (see Chapter 7.5), although the E85 stations in California are estimated to
sell about four times more gallons per station than retail stations outside of California. The initial
Biofuel Infrastructure Partnership Program (BIP) and HBIIP program which superseded the BIP
program, cover up to 50% and 75% of the revamp costs, respectively, so in our cost assessment
we assume that these programs cover those respective portions of the retail revamp costs. When
amortizing the unsubsidized portion of the capital cost over the gallons of E85 sold under the
HBIIP program, the total cost of the revamp adds 150/gal to the cost of E85, and 220/gal per
gallon of ethanol (accounting only for the estimated 64% of ethanol in E85 above the ethanol in
E10) in E85.85
While the typical revamp as described above involved both dispenser and tank
replacement, some service stations required a greater amount of capital changes and others less.
The HBIIP revamp data showed that some revamps are lower cost involving revamps of fewer
pieces of equipment and analyzing this case will help us understand a lower hurdle cost point for
allowing some E85 availability. The data shows that about 20% of the E85 revamps did not add
an underground storage tank and added on average 1.7 dispensers per retail station. Thus, as a
second cost point, we assessed the cost under the HBIIP program to the ethanol in E85 for those
stations which only installed dispensers. When amortizing the unsubsidized portion of the capital
cost over the gallons of E85 sold under the HBIIP program, the total cost of the revamp adds
50/gal to the cost of E85, and 80/gal to the cost of ethanol consumed as E85.86
Although not directly impacting the relative cost of E85 to petroleum fuels, the number
of E85 vehicles being produced has been declining and this can affect the throughput volume of
E85 stations and increase the per-gallon cost of the retail station revamps.87 One reason for the
decline is that the federal FFV manufacturing incentives are no longer being offered. The
demand for renewable fuels could cause manufacturers to ramp up production in future years.
Ethanol Breakeven Blending Value (EBBV)
There are downstream pricing effects for E85 that require the economics of E85 to be
assessed differently when blending ethanol into E85 compared to blending ethanol into E10.
These downstream pricing effects exist because E85 contains less energy compared to E10 on a
volumetric basis—22% and 30% less when blended with gasoline and NGLs, respectively. This
lower energy density of E85 is noticeable to consumers in their fuel economy. Because of this,
most consumers demand a lower price at retail stations for E85 relative to E10, which therefore
84 Average dispenser and underground storage tank costs based on discussions with USD A.
85 The amortized the revamp cost is calculated by multiplying the capital costs by the capital cost amortization factor
in Table 2.1.1.1-1 (0.16) and dividing by the E85 throughput volume.
86 The per-gallon cost for the retail station revamps to allow dispensing E85 is calculated by multiplying the
estimated revamp cost by the capital cost amortization factor in Table 2.1.1.1-1 (0.16) and dividing by the annual
average amount of the ethanol contained in E85 sold from the retail stations.
87 LookupAPlate, "14 Key Flex-fuel Vehicles (FFV) Statistics Every American Should Know," January 9, 2026.
https://www.lookupaplate.com/blog/flex-fuel-veliicles-ffv-statistics.
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requires that the economics of E85 be assessed at retail. Price information collected for E85
shows that it is typically priced 16% lower than E10 at retail by volume.88 89 For the No RFS
analysis, we assumed that gasoline-blended E85 is priced 16% lower than E10 and that NGL-
blended E85—which has much lower volumetric energy content—is priced 21% lower than
ElO.90
Figure 2.1.1.2-1 provides an example of how the breakeven price for ethanol is estimated
for E85 when blended with gasoline. At the top of the figure, the pricing of gasoline is shown
from terminal to retail, depicting the price impacts when distribution costs and taxes are added
on. At the bottom of each figure, the pricing of E85 is shown when blended with gasoline. The
E85 prices are then estimated at the terminal after the retail, tax, and distribution costs are
subtracted from the retail prices. Finally, the ethanol breakeven price is estimated for the ethanol
blended into E85 based on the price of gasoline at the terminal and the fraction of gasoline and
ethanol in E85.
Figure 2.1.1.2-1: Example Calculations for Ethanol Breakeven Price for Gasoline-Blended
E85
E85 & Ethanol Breakeven Price example
(Based on $80/bbl Crude)
Retail
305 c/gal
Retail
Marketing,
Retail Profits,
etc.
15 c/gal
Marketing,
Retail Profits,
etc.
256 c/gal 15 c/gal
305 c/gal x 0.84 for MPG loss = 256/gal
E10/Gasoline Pricing
Transportation
Tax
a
60 c/gal 5 c/gal
Competitive E85 Pricing
Transportation
Ta*
60 c/gal
5 c/gal
Terminal
225 c/gal
Terminal
176 c/gal
176 = 0.26*225 + 0.74* 155
Conclusion: Ethanol would have to be priced 155 c/gal
or less at the terminal to be attractive to refiners
Figure 2.1.1.2-1 shows that when the E85 is blended with gasoline, the breakeven price
of ethanol in E85 is 1550/gal at an $80/bbl crude oil price. This is 700/gal lower than the
gasoline terminal price, although the breakeven price varies by state depending on the gasoline
terminal price and tax rates. A list of gasoline tax rates by state (including all federal and state
taxes) is provided in Table 2.1.1.2-2.
88 Fuels Institute, "Retailing E85: An Analysis of Market Performance, July 2014 - August 2015," March 23, 2017.
https://www.transportationenergy.org/wp-content/uploads/2022/10/E85 2017 Report FINAL.pdf.
89 AAA, "National average gas prices," December 12, 2022. https://gasprices.aaa.com.
911 It is unclear why E85's price only reflects a portion of its lower energy content. Retailers may be choosing to
balance their profit with consumer demand, or consumers may value E85's much higher octane content, which
offsets its lower energy density.
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Table 2.1.1.2-2: Current Gasoline Tax Rates by State (Includes Federal and State Taxes;
l/gal)
State
Tax Rate
State
Tax Rate
Alaska
27.4
Montana
51.4
Alabama
47.0
North Carolina
59.2
Arkansas
43.3
North Dakota
41.4
Arizona
37.4
Nebraska
48.6
California
88.8
New Hampshire
42.2
Colorado
42.1
New Jersey
59.8
Connecticut
58.6
New Mexico
37.3
DC
41.9
Nevada
65.5
Delaware
41.4
New York
66.4
Florida
60.7
Ohio
56.9
Georgia
54.1
Oklahoma
38.4
Hawaii
68.6
Oregon
58.4
Iowa
48.9
Pennsylvania
76.0
Idaho
51.4
Rhode Island
55.4
Illinois
85.0
South Carolina
47.2
Indiana
64.1
South Dakota
48.4
Kansas
43.4
Tennessee
67.8
Kentucky
44.4
Texas
38.4
Louisiana
38.4
Utah
49.5
Massachusetts
44.9
Virginia
44.2
Maryland
55.1
Vermont
49.2
Maine
48.4
Washington
67.8
Michigan
64.8
Wisconsin
51.3
Minnesota
49.9
West Virginia
54.1
Missouri
45.9
Wyoming
42.4
Mississippi
36.8
Similar to our analysis for E10, if the ethanol blending cost is negative, ethanol is
considered economical to blend as E85 in comparison to gasoline; if it is positive, it is not
economical. Figure 2.1.1.2-3 provides some key results of the No RFS Baseline analysis for E85,
showing a range in blending values for ethanol in E85, which vary from economic to blend to not
economic to blend. For the highest cost market for E85, ethanol is priced 70-800/gal higher than
its breakeven price. It is important to understand which gasoline in which states are economically
attractive to blend E85 since this determines the potential market size.
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Figure 2.1.1.2-3: Economics of Blending Ethanol in E85 (nominal dollars)
Range in Ethanol Blending Cost in E85
E85 priced 16% lower than E10
130
CTJ
110
no
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offer E85.92 Finally, the E85 throughput volumes at retail stations are much higher than the rest
of the country which lowers the per-gallon costs of the amortized retail station upgrade costs.
Considering these various factors, we estimated a significant amount of E85 consumption in
California for the No RFS baseline analysis.
2.1.1.3 E15
The analysis for estimating the E15 baseline has similarities with how both E10 and E85
were estimated. Of the variables in the ethanol blending cost equation in Chapter 2.1.1.1, Ethanol
Plant Gate Spot Price (ESP), Ethanol Distribution Cost (EDC), and Gasoline Terminal Price
(GTP) are again the same. Like for E85, an additional cost applies to El5 to account for the cost
to modify retail stations to carry El5 and we believe that Ethanol Replacement Value (ERV)
does not apply as well, although we keep as a term and explain the possibility below for how it
could apply.
The economics to determine whether ethanol blended into El5 is economical is estimated
by combining the ethanol plant gate price, ethanol distribution cost, ethanol replacement cost,
and retail cost in the following equation:
EBCeis = (ESP + EDC - ERV-FETS - SETS + RC) - GTP
Where:
• EBCeis is ethanol blending cost for El5
• ESP is ethanol plant gate spot price (same as section 2.1.1.1)
• EDC is ethanol distribution cost (same as section 2.1.1.1)
• ERV is ethanol replacement value
• FETS is federal ethanol tax subsidy
• SETS is state ethanol tax subsidy
• RC is retail cost (service station revamp to sell El5)
• GTP is gasoline terminal price; all are in dollars per gallon (same as section 2.1.1.1)
Ethanol Replacement Value (ERV)
Blending ethanol into gasoline for E15 is different than blending for E10 because we
believe that refiners do not make a separate E15 BOB; thus, E10 BOBs are blended with ethanol
to produce El5, in which case there is octane giveaway and no blending value to refiners for
ethanol. It is possible, though, that some refineries with extra gasoline storage tanks could blend
an El 5 BOB to sell off their refinery racks; however, we have no knowledge of this currently
happening. Similarly, there should be no RVP cost for blending ethanol above that of E10
because ethanol-gasoline blends reach a maximum RVP at 10%.
92 California State Water Resources Control Board, "Replacing, Removing, or Upgrading Underground Storage
Tanks (RUST) Program." https://www.waterboards.ca.gov/water issues/programs/ustcf/rust.html.
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Another issue for E15 is that it does not automatically receive a 1-psi waiver like E10
does in the summer. However, as discussed in Chapter 1.7.2, E15 did receive a regulatory 1-psi
waiver for 2019-2021 and EPA-issued emergency fuel waivers throughout the summers of
2022-2025, which further allowed E15 to take advantage of the 1-psi waiver.93 A number of
Midwestern states petitioned EPA to remove the 1-psi waiver for El094 and EPA responded by
finalizing a rulemaking to grant those states' request to remove the 1 psi waiver for El0 starting
in 2025.95 Because the E10 1-psi waiver will be removed in those states, a new lower-RVP,
higher-cost BOB would be required for E10, which would also accommodate E15 and thus
remove a hurdle for selling El5 in the summer months in those states. However, EPA extended
the deadline for the removal of the 1-psi waiver in Ohio and nine counties in South Dakota to
2026 in response to requests by the Governors of those states due to concerns about the supply of
gasoline in the summer of 2025.96 EPA subsequently issued emergency fuel waivers in the
summer of 2025 to facilitate continued El5 availability in the Midwestern states.97 Any
permanent solution that allows El5 to be blended into the same BOB as E10 during the summer
is expected to encourage investment and increase sales of E15.
Federal and State Ethanol Tax Subsidies (FETS and SETS)
There is no federal nor state ethanol blending tax subsidy for E15.98 It is important to
know that at the time of drafting California did not allow the sale of El 5, although it appears that
California is moving towards allowing E15 in the state.99 Other Federal and State subsidies—
such as ethanol production subsidies, loan guarantees, grants, and any other subsidies—were not
considered by this analysis.
Retail Cost (RC)
The retail costs for El5 are estimated based on the investments needed to offer El5 at
retail stations and the estimated throughput at El5 stations.100 Similar to our analysis for E85, we
studied a report provided under the Higher Blends Infrastructure Incentive Program (HBIIP)
which listed the major equipment installed to enable storing and dispensing E15. For a typical
E15 retail station revamp, a retail station installs 2.8 dispensers and 0.9 underground storage
tanks. Each dispenser is estimated to cost $53,000, and each underground storage tank is
93 EPA, "Fuel Waivers," May 20, 2025. https://www.epa.gov/gasoline-standards/fuel-waivers.
94 Providing E15 with a 1-psi waiver or removing the E10 1-psi waiver—either of which would allow E15 to use the
same BOB as E10—w ould simply remove a logistical barrier to the use of E15 during summer months. However,
E15 use under the No RFS Baseline would still be governed by the relative economics of blending additional
ethanol into E10 relative to continuing to use petroleum gasoline.
95 89 FR 14760 (February 29, 2024).
96 90 FR 13093 (March 20, 2025).
97 EPA, "EPA Addresses E-10 Standards, Allows for Nationwide Year-Round E15 Sales," April 28, 2025.
https://www.epa.gov/newsreleases/epa-addresses-e-10-standards-allows-nationwide-vear-round-el5-sales.
98 Based on our review of the Department of the Treasury and IRS 45Z guidance released on January 10, 2025, corn
ethanol will likely earn a 60 per gallon subsidy. See Notice 2025-10, 2025-6 I.R.B. 682 (February 3, 2025) and
Notice 2025-11, 2025-6 I.R.B. 704 (February 3, 2025).
99 Governor Gavin Newsome, "Governor Newsom signs bill expanding fuel options to cut gas prices," October 2,
2025. https://www.gov.ca.gov/2025/10/02/governor-newsom-signs-bill-expanding-fuel-options-to-cut-gas-prices.
11111 The methodology used and the estimated costs for these revamps are discussed in Chapter 10.1.4.1.2.
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estimated to cost $168,000.101 Thus, the estimated total cost for a typical retail station revamp to
enable selling E15 is $305,000 in 2025, and that these stations sell on average 218,000 gallons of
El 5 per year. The BIP and HBIIP programs cover up to 50% and 75% of the revamp costs,
respectively, so in our cost assessment we assume that these programs cover those respective
portions of the retail revamp costs. When amortizing the unsubsidized portion of the capital cost
over the gallons of El 5 sold under the HBIIP program, the total cost of the revamp adds 60/gal
to the cost of E15, and 1130/gal of ethanol (accounting only for the estimated 5% of ethanol in
E15 above the ethanol in E10) in E15.102
The HBIIP revamp data showed that about 5% of the El 5 revamps did not add an
underground storage tank; but these projects still added on average 2.8 dispensers per retail
station. Thus, as a second cost point, we assessed the cost under the HBIIP program to the
ethanol in El 5 for those stations which only installed dispensers. When amortizing the
unsubsidized portion of the capital cost over the gallons of E15 sold under the HBIIP program,
the total cost of the revamp adds 30/gal to the cost of E15, and 550/gal per gallon of ethanol.
A new El 5 marketing strategy has emerged among a small number of retailers, which is
to solely sell El 5 as the regular grade, thus discontinuing the sale of E10 as the regular grade.
Since the retailer is not adding a new grade of gasoline—it is merely exchanging E10 for E15—
this strategy will likely increase the sales of El 5 at these retail stations since consumers will not
have a choice to refuel with E10. This would reduce the per-gallon cost of the retail station E15
retrofit costs since the cost could then be amortized across a much larger number of gallons.
Selling El 5 as a feature grade also provides cost savings to retail stations since it reduces the
number of new tanks needed at the station and increases ethanol sales without incurring
marketing costs or having to offer price discounts. We will continue to monitor this trend. If it
seems to be adopted more widely, we will incorporate it in future No RFS Baseline analyses.
E15 has different properties than E10 that allow it to be priced differently than E10. E15
has higher octane than E10, so the fuels industry could set E15 prices higher on that basis.
Conversely, E15 has lower energy density than E10, which means that consumers are not able to
drive the same distance on a tankful of El 5. The website e85prices.com, which collects
information on gasoline and ethanol-gasoline blend prices, reported that El5 is priced 8.50/gal
cheaper than E10 on average. A conversation with a gasoline retail marketer explained that when
beginning to offer E15 for sale, marketers will typically price it lower than E10 to promote E15
to consumers and increase its sales. If E15 is priced 80/gal lower than E10, it adds 1600/gal
(8/0.05) to the blending cost for blending ethanol into E15. It is likely that a significant portion
of this discount is due to the value of the RIN, which normally is passed through to the refiner.
We surmise that due to the higher cost of providing El 5, the RIN value would instead be used by
the retailer in these cases to enable these price discounts.
However, if discounting El 5 prices is a marketing strategy, this practice would likely
diminish over time and would change without the RFS program in place. We do not know what
101 Average dispenser and underground storage tank costs based on discussions with USD A.
102 The per-gallon cost for the retail station revamps to allow dispensing E15 is calculated by multiplying the
estimated revamp cost by the capital cost amortization factor in Table 2.1.1.1-1 (0.16) and dividing by the annual
average amount of the ethanol contained in E15 sold from the retail stations.
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the ultimate price of E15 would be relative to E10 if the RFS program was not in place since
many retail station owners only began to offer El5 in recent years. To maximize their profit,
retail station owners will seek the optimal El5 price that balances sales volume and pricing. For
this analysis, we assumed that El5 is priced lower than E10 consistent with how E85 is priced.103
Since E15 contains less energy than E10, we assumed that E15 is priced 1.3%, or about 30/gal,
less than E10. This estimate reflects the price discounting method typically used with E85 based
on E85's energy content.
Like E10, if the ethanol blending cost is negative, then ethanol is considered economic to
blend into gasoline to produce El 5, while it would not be economic if the value is positive.
Figure 2.1.1.3-1 provides some key results of the No RFS Baseline analysis for E15, showing a
range in blending values for ethanol in El5.
Figure 2.1.1.3-1: Economics of Blending Ethanol in E15 (nominal dollars)
230
180
130 .
80
30 ¦
-20 4
-70
2026
-High Cost E15 - w HBIIP Retail Cost
-Midwest Regular Grade; No Retail Costs
Range in Ethanol Blending Costs in E15
E15 priced 1% lower than E10
Year
Low Cost w HBIIP Retail Cost
-Average Cost No Retail Costs
The solid yellow and green lines at the top of the figure show the estimated range of
lowest to highest blending cost for the incremental 5 vol% ethanol in gasoline to produce El5.
That cost range includes the HBHP-subsidized retail costs of revamping the station to enable
storing and dispensing higher ethanol blends. It is important to recognize the cost impact due to
revamping the retail station to enable it to sell El5. Assuming a typical retail station revamp cost
of about $300,000, and that the Higher Blends Infrastructure Incentive Program (HBIIP)
program subsidized 3/4s of the cost, the retail station is estimated to need to cover a cost of about
$1.20 per gallon for that 5% increment of ethanol in E15 due solely to the station revamp cost.
To illustrate the impact that the retail revamp costs have on the cost-effectiveness of blending
El5, we include in the figure two estimated El5 blending costs without those retail revamp
costs. The blue line at the bottom of the figure shows the estimated average blending cost for the
incremental 5 vol% ethanol in gasoline without any retail revamping cost. Even if retail stations
are already compatible with higher ethanol blends, the average incremental blending cost of the
5% of ethanol in El 5 is still not cost-effective even with subsidies. The purple line at the bottom
of the figure is similar to the blue line in that no retail revamping costs are included, but the cost
1113 E85, which contains 74% ethanol and 21% less energy than E10, is typically priced 16% lower than E10.
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is for summertime, regular E15 gasoline sold in a midwestern state, and the blending costs are
slightly cost-effective due to the lower distribution costs.104 While the El5 blending costs are
marginally cost-effective in the summer, our analysis shows that El5 is not cost-effective in
Midwestern states during the winter due to lower gasoline prices.
There are two cases that would help to make El5 more economic. In one case, over 500
gasoline retailers are electing to sell only El5 as regular gasoline, which increases El5 sales at
those retail stations. This lowers the per-gallon cost of retrofitting those stations to accommodate
El 5 because those costs can be amortized across a larger number of gallons.
In the second case, if refiners and terminal operators could overcome the steep logistical
hurdles of producing and moving a hypothetical separate lower-octane BOB for El 5 to terminals
and eventually to retail stations, the gained ethanol replacement value for the El5 BOB would
also help to offset the retail cost of making El 5 available, and El 5 would likely become
economical in some summertime regular gasoline markets. Refiners and terminal operators are
unlikely to create a separate E15 BOB until sales of E15 increase significantly. Prior to that
occurring, anecdotal evidence suggests that a new low-RVP BOB could be produced to meet
either El0 or El5 volatility specifications without a waiver.105
Thus, the ethanol blending cost analysis finds the gasoline market uneconomical for El5
in the absence of the RFS program and we project that without the RFS program in place, the
fuels market would not offer El5 for sale.
2.1.1.4 No RFS Baseline Summary for E10, E85 and E15
Corn ethanol receives a federal 14 cents per gallon 45Z federal production subsidy which
benefits it whether it is blended into E10, E15 or E85. When corn ethanol is blended into E10
gasoline it benefits significantly from a hefty blending value due to its high octane content that is
realized when refiners produce a conventional blendstock for oxygen blending (CBOB) for
blending with E10 ethanol. E10 ethanol also avoids the need for any revamp costs to retail
gasoline storage and dispensing equipment. For these reasons, E10 ethanol is cost effective for
blending in all US gasoline without the RFS program.
E85 and El5 ethanol have similar issues affecting their costs. Neither receive a blending
cost benefit for their high octane content. And both face revamp costs for storage and dispensing
higher ethanol blends to enable their sale at retail stations. Despite the higher El5 sales volume,
the lower ethanol content of E15 raises their per-gallon retail station revamp costs. For these
reasons, our analysis does not support any volume of El 5 without the RFS program in place.
Despite the challenging economics for E85, we expect that E85 would continue to be used in
California without the RFS program in place. California offers subsidies to retail stations for
1114 The economics of blending economics of E15 is even more favorable when referenced to premium gasoline;
however, premium gasoline demand only comprises about 10% of total gasoline demand. Due to the low sales
volume, retailers are unlikely to justify modifying their stations to offer E15 if they were solely targeting the
premium gasoline market. For this reason, our analysis only assesses the economics of blending E15 relative to
regular grade gasoline.
1115 Hoekstra Trading, "Midwest States Pose New Challenges for Gasoline Supply," April 21, 2025.
https://lioekstratrading.com/midwest-states-pose-new-challenges-for-gasoline-supplY.
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upgrading their fuel storage and dispensing equipment, and the LCFS program subsidies would
likely increase to cause E85's consumption to enable compliance with the LCFS standards.
2.1.2 Cellulosic Biofuel
The primary type of cellulosic biofuel projected to generate substantial RINs from 2026-
2027 is compressed or liquefied renewable natural gas used in transportation (renewable
CNG/LNG.) Additionally, we believe that some volume of liquid cellulosic ethanol from CKF
will be produced during this period. Generally, cellulosic biofuels are more expensive to produce
than the fossil fuels they displace. Consequently, they would likely not be utilized as
transportation fuel without the support of incentives like the RFS program. However, new
programs, such as the 45Z credit, are situated to become a valuable new incentive for cellulosic
fuel producers in the future. The credit is technology neutral and based on carbon intensity.
Because cellulosic biofuels, especially those derived from biogas, are often able to achieve very
low carbon-intensity scores, they are well positioned to capture significant value under 45Z. As
shown in Table 2.1-2, biogas pathways exhibit among the lowest carbon-intensity values and
correspondingly higher indicative credit estimates. Despite this potential, uncertainty remains
regarding the credit's precise value, specifically due to pending final Department of Treasury
guidance. While the Department of Treasury's proposed rulemaking for the tax bill was released,
it has not been finalized and, accordingly, we have not included it in our analysis.106 Therefore,
we are limiting our analysis to states that currently have State-level incentive programs. This
section outlines our projections for cellulosic biofuel use under the No RFS Baseline.
2.1.2.1 Renewable CNG/LNG
As detailed in Chapter 10, renewable CNG/LNG is generally more expensive to produce
than fossil-based CNG/LNG. Due to this higher production cost and the demand for RNG in
sectors outside of transportation, we project that, without incentives specifically supporting the
use of renewable CNG/LNG in transportation, very little or none of this fuel would be used in
the transportation sector. Currently, three states107—California, Oregon, and Washington—have
LCFS programs that offer incentives for using CNG/LNG as transportation fuel. We assume that
these state-level incentives would support some use of renewable CNG/LNG in transportation
even in the absence of the RFS program.
To project renewable CNG/LNG use in these states, we used each state's program data
and extrapolated it through 2027. Because Washington's program began in 2023 and has limited
data, we use one method to project Washington's state-supported CNG/LNG volumes and a
different method for California and Oregon. Specifically, for California and Oregon, we
examined total CNG/LNG volumes (including both fossil and renewable), as well as volumes
solely derived from renewable CNG/LNG. Using this information, we calculated both the year-
106 91 FR 5160 (February 4, 2026).
1117 New Mexico also has a state-level program to promote low-carbon fuel use (the Clean Transportation Fuel
Standard (CTFS)), which was authorized in March 2024 and finalized in January 2026. Because the regulations
were only recently finalized, there is insufficient information for EPA to incorporate potential volumes from this
program into this analysis. New Mexico Enviromnent Department, "Clean Transportation Fuel Program," January
22, 2026. https://www.env.mn.gov/climate-change-bureau/clean-fuel-standard.
63
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over-year growth for each year and the blend rate showing the percentage of total CNG/LNG
that was renewable. This data, summarized in Table 2.1.2.1-1, indicates that the CNG/LNG
markets in Oregon and California have shifted to be almost entirely renewable, with renewable
CNG/LNG volumes averaging 97% of the total market from 2022 to 2024. This suggests limited
capacity in both states for new sources of renewable CNG/LNG to replace fossil-based
CNG/LNG, meaning that the total market has been saturated with renewable CNG/LNG.
Table 2.1.2.1-1: CNG/LNG Usage in California and Oregon (million ethanol-equivalent
gallons)
2019
2020
2021
2022
2023
2024
California
Total CNG/LNG
305.0
278.0
302.6
335.4
355.8
374.4
Year-over-year growth
7%
-9%
9%
11%
6%
5%
Renewable CNG/LNG
236.1
256.9
295.9
323.3
344.2
370.6
Blend Rate
77%
92%
98%
96%
97%
99%
Oregon
Total CNG/LNG
5.6
5.6
6.5
6.7
6.7
6.7
Year-over-year growth
7%
-1%
16%
4%
0%
0%
Renewable CNG/LNG
3.8
4.9
5.9
6.3
6.6
6.7
Blend Rate
67%
89%
91%
94%
99%
100%
Note: Only the last five years of data are shown; however, data is available for California from 2011-2023, and for
Oregon from 2016-2023.
Source: CARB, Low Carbon Fuel Standard Reporting Tool Quarterly Summaries.
https://ww2.arb.ca.gov/resources/documents/low-carbon-fuel-standard-reporting-tool-quarterlv-summaries: Oregon
DEQ, Oregon Clean Fuels Program - Quarterly Data Summaries.
https://www.oregon.gov/dea/ghgp/cfp/pages/auarterlv-data-summaries.aspx.
Despite this saturation, volumes of renewable CNG/LNG can continue to rise as the
overall CNG/LNG market grows. To project future volumes of renewable CNG/LNG in Oregon
and California, EPA calculated the average year-over-year (YOY) growth rate of the total
CNG/LNG market based on the last four years of data.108 Applying this approach yields average
year-over-year growth rates of 7% for California and 1% for Oregon in the total CNG/LNG
market. We then applied each rate to the most recent full year of total CNG/LNG data (2024) for
each state to project market growth. To estimate future renewable CNG/LNG usage, we assumed
a 97% saturation factor—reflecting average market conditions as discussed above—and applied
it to the projected totals. These results are shown in Table 2.1.2.1-2 and are used to project future
volumes by compounding growth year over year.109
1118 Only the last four years (2021-2024) were chosen to potentially minimize any impacts that the Covid-19
pandemic may have had on growth.
1119 We used the year-over-year growth in the rate of the total CNG/LNG market rather than only the renewable
CNG/LNG market as the total CNG/LNG market should better reflect future growth in a vehicle consumption-
limited market.
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Table 2.1.2.1-2: Projected CNG/LNG Usage in California and Oregon (million ethanol-
equivalent gallons)
California
Oregon
CNG/LNG
CNG/LNG
Total
Renewable
(7% YOY
(1% YOY
CNG/LNG
CNG/LNG
Year
Data Type
Growth)
Growth)
Usage
Usage
2024
Actual
382.1
6.9
389.0
387.0
2025
Projected3
402.0
6.8
408.7
407.8
2026
Projected
431.6
6.8
438.5
437.1
2027
Projected
463.5
6.9
470.4
468.6
a At the time we developed the No RFS Baseline for this rule, full-year 2025 data was not yet available for
California or Oregon.
Given that Washington's CNG/LNG fuel market is much newer than those in California
and Oregon, having only started in 2023, we used a slightly different approach to estimate future
volumes. To do so, we took the total CNG/LNG usage data for Washington in 2024 and applied
a year-over-year growth rate of 4%, determined by averaging the rates of both Oregon and
California. We then projected the total CNG/LNG market size for Washington in 2025-2027.
Given the saturation in California and Oregon's markets, we assumed that significant volumes of
renewable CNG/LNG would quickly fill the Washington market, as it may be easier for
producers to find consumers in a less saturated market. Accordingly, we projected that
Washington's blend rate would reach 97% of total CNG/LNG by the end of 2025 and onward—
an assumption that we believe is reasonable given that Washington reported an RNG blend rate
of 87% in the program's most recent year of data (2024).110 This analysis is shown in Table
2.1.2.1-4.
Table 2.1.2.1-3: CNG/LN
G Usage in Was
2023
2024
Total CNG/LNG
11.7
11.2
Year-over-year growth
N/A
(5)%
Renewable CNG/LNG
6.2
9.8
Blend Rate
53%
87%
lington (million ethanol-equivalent gallons)
Table 2.1.2.1-4: Projected Renewable CNG/LNG Usage in Washington (million RINs)
Renewable CNG/LNG Usage
Year
Data Type
(4% year-over-year growth)
2023
Actual
6.2
2024
Actual
9.8
2025
Projected111
11.3
2026
Projected
11.8
2027
Projected
12.3
1111 State of Washington Department of Ecology, "Clean Fuel Standard - Quarter 4, 2024 Data Summary," July
2025. https://apps.ecologv.wa.gov/publications/documents/2514049.pdf.
111 At the time we developed the No RFS Baseline for this rule, full-year 2025 data was not yet available for
Washington.
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Totaling the projected volumes from each state, the projected volume of RNG-based
CNG/LNG used as transportation fuel under the No RFS Baseline is summarized in Table
2.1.2.1-5.
Table 2.1.2.1-5: Renewable CNG/LNG for the No RFS Baseline (million RINs)
State
2026
2027
California
418.7
449.6
Oregon
6.6
6.7
Washington
11.8
12.3
Total
437.1
469.0
2.1.2.2 Liquid Cellulosic Biofuels
In recent years, only small quantities of liquid cellulosic biofuels have been produced,
despite substantial financial incentives from programs like the RFS, federals tax credits, and state
initiatives, such as California's LCFS program. While these state and federal incentives are
expected to continue in the coming years, we do not anticipate that they will be sufficient to
support most types of liquid cellulosic biofuel production between 2026 and 2027.
One exception is ethanol produced from CKF at existing ethanol facilities. Many corn
ethanol producers have indicated that their facilities can produce ethanol from CKF, sometimes
by adding cellulose enzymes and, in other cases, by relying solely on enzymes naturally present
in the corn kernel. In either case, we project that the cost of producing ethanol from CKF would
be comparable to, or only slightly higher than, the cost of producing ethanol from corn starch.
Because CKF-based ethanol is eligible for additional incentives through programs such as
California's LCFS, we expect that it will continue to be produced without the RFS standards at
the volumes in this rule. These volumes are shown in Table 2.1.2.2-1. More information on the
methodologies used to determine the liquid cellulosic biofuel volumes can be found in Chapter
7.1.5.
Table 2.1.2.2-1: Ethanol from CKF in the No RFS Baseline (million RINs)
Year
Volume
2026
128
2027
128
2.1.3 Biomass-Based Diesel
The No RFS Baseline analysis must consider several different factors before deriving the
final estimate of biodiesel and renewable diesel volume for the No RFS Baseline. First, the
relative economics of both biodiesel and renewable diesel compared to petroleum is evaluated by
feedstock type. The volumes of lower-cost biodiesel and renewable diesel are recorded. Second,
the total demand by feedstock type for both biodiesel and renewable diesel is totaled and
compared to, and limited by, the volumes currently being consumed with the RFS program in
place. Third, the biodiesel and renewable diesel plant capacities are each evaluated and averaged
over previous years to avoid estimating sudden large swings in plant capacities, whether plant
capacities are projected to increase or decrease. The dampened change in plant capacities is
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meant to duplicate the more measured decisions renewable fuels companies and investors likely
take when considering whether to expand or contract their renewable fuels production. The No
RFS Baseline analysis for BBD is contained in a spreadsheet entitled "Biodiesel Renewable
Diesel No RFS Baseline for SET2 FRM" placed in the docket.
2.1.3.1 Biodiesel
Estimating the economics of blending biodiesel requires different methods than those
used to estimate the economics of ethanol because biodiesel plants are more geographically
diffuse than ethanol plants. Unlike ethanol plants, which are almost exclusively located in the
Midwest, biodiesel plants are scattered around the country. The more diffuse locations of
biodiesel plants affect how we estimate distribution costs for using biodiesel. Also, refiners do
not change the properties of the diesel they produce to accommodate the downstream blending of
biodiesel, and as such there is no additional blending value associated with its use like there is
for E10. However, blending biodiesel does often require the addition of additives to
accommodate some of its properties. The blending cost of biodiesel is estimated using the
following equation:
BBC = (BSP + BDC - FBTS - SBTS) - DTP
Where:
• BBC is biodiesel blending cost
• BSP is biodiesel plant gate spot price
• BDC is biodiesel distribution cost
• FBTS is federal biodiesel tax subsidy
• SBTS is state biodiesel tax subsidy
• DTP is diesel terminal price; all are in dollars per gallon
Biodiesel Plant Gate Spot Price (BSP)
USDA collects biodiesel plant gate pricing data, which is the price paid to biodiesel
producers when they sell their biodiesel; however, USDA does not project future biodiesel
prices.112 Instead, we assumed that biodiesel production costs reflected plant gate prices and then
estimated biodiesel production costs based on future vegetable oil and utility prices. This is
essentially the same information used for estimating biodiesel production costs for the cost
analysis in Chapter 10, except that the capital costs are amortized using the capital amortization
factor in Table 2.1.1.1-1. For estimating the renewable fuel production costs, the feedstock and
by product costs, capital costs, maintenance costs and utility costs are all estimated to derive a
plant gate price estimate. The resulting projected biodiesel plant gate prices are summarized in
Table 2.1.3.1-1 for three feedstocks - soybean oil, distillers corn oil (hereinafter "corn oil"), and
FOG.113
112 USDA, "U.S. Bioenergy Statistics," October 2024, Table 16 - Biodiesel and Diesel Prices.
https://www.ers.usda.gov/data-products/us-bioenergy-statistics.
113 "FOG", or "fats, oils and greases" is a term of art in the RFS program, encompassing a variety of lipid-based
feedstocks from Animal waste material and animal byproducts (e.g., tallow) and food wastes (e.g., cooking oil, trap
grease).
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Table 2.1.3.1-1: Projected Biodiesel Plant Gate Prices (nominal $/gal)
Projected Production Cost
2026
2027
Soybean Oil
3.64
3.49
Corn Oil
3.10
2.99
FOG
2.88
2.78
Biodiesel Distribution Cost (BDC)
This factor represents the added cost of moving biodiesel from production plants to
terminals where it is blended into diesel. Unlike ethanol, which is almost exclusively produced in
the Midwest and distributed elsewhere from there, biodiesel is produced at biodiesel plants
dispersed around the country. For this reason, we took a different approach to our analysis of
biodiesel than that used for ethanol. Using 2020 EIA data (the last year this data is available), we
estimated the quantity of biodiesel produced within each PADD,114 the movement of biodiesel
between PADDs, and the imports and exports of biodiesel into and out of each PADD, as
summarized in Table 2.1.3.1-2.115116117118
Table 2.1.3.1-2: Biodiesel Production, Imports, Export, and Movement Between PADDs
and Consumption in 2023 (million gallons)
From
From
Other
PADD
Production
Imports
Exports
PADD 2
PADD 3
Movement
Consumption
PADD 1
147
242
2
59
0
1
445
PADD 2
1,139
46
114
-
3
0
727
PADD 3
221
134
99
128
0
0
368
PADD 4
0
5
5
10
0
4
15
PADD 5
202
74
40
151
14
0
396
Total
1,709
501
259
349
16
5
1,951
ICF estimated the distribution costs for distributing biodiesel both within and between
PADDs, as summarized in Table 2.1.3.1-3.119 An additional cost is added to these estimates to
account for the addition of biodiesel additives, for example, to improve biodiesel cold flow
properties and reduce oxidation downstream of the production facility—the total cost of these
additives is estimated to be 70 per gallon. The costs, estimated in 2017 dollars, are adjusted to
114 Petroleum Administration for Defense District (PADD): The 50 U.S. states and the District of Columbia are
divided into five districts. EachPADD comprise a subset of U.S. states; PADD 1: Eastern states; PADD 2: Midwest
states; PADD 3: Gulf Coast; PADD 4: Rocky Mountain States; PADD 5: Pacific Coast states.
115 EIA, "Monthly Biodiesel Production Report," February 2021, Table 5 - Biodiesel (B100) production by
petroleum administration for defense district.
https://www.eia.gov/biofuels/biodiesel/production/arcliive/2020/2020 12/biodiesel.pdf.
116 EIA, "Exports," Petroleum & Other Liquids, April 30, 2025.
https://www.eia.gov/dnav/pet/pet move exp dc NUS-Z00 mbbl a.htm.
117 EIA, "Imports by Area of Entry," Petroleum & Other Liquids, April 30, 2025.
https://www.eia.gov/dnav/pet/pet move imp dc NUS-Z00 mbbl a.htm.
118 EIA, "Movements by Pipeline, Tanker, Barge, and Rail between PAD Districts," Petroleum & Other Liquids,
April 30, 2025. https://www.eia.gov/dnav/pet/pet move ptb dc R20-R10 mbbl a.htm.
119 ICF, "Modeling a 'No-RFS' Case," EPA Contract No. EP-C-16-020, July 17, 2018.
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the year dollars being analyzed. For example, in 2026, these distribution costs are increased by
34%.
Table 2.1.2
i.1-3: Biodiesel Distribution Costs (Wgal)
Original
y Estimated Costs 2017 dollars
Adjusted to 2026 dollars
Within
From Outside
From Outside
PADD
PADD
the PADD
Additives Cost
Within PADD
the PADD
PADD 1
15
35
7
29.4
56.1
PADD 2
15
15
7
29.4
29.4
PADD 3
15
18
7
29.4
33.4
PADD 4
15
25
7
29.4
42.7
PADD 5
15
32
7
29.4
52.1
As expected, distribution costs for distributing biodiesel within a PADD are less than
when the biodiesel is distributed further away from outside the PADD. Since imports come from
outside the PADD, we used outside the PADD values for imports. Comparing these biodiesel
distribution costs to ethanol, distributing biodiesel is expected to be more expensive, which
recognizes that the larger volume of ethanol provides the opportunity to optimize the efficiency
of the distribution system more than what is financially possible for biodiesel. For example, the
greater volume of ethanol allows for greater use of unit trains and more streamlined logistics
overall. Like for ethanol, distribution costs of biodiesel to the East and West Coasts are higher
compared to distribution in the Midwest where most biofuels, including a large share of
biodiesel, are produced. Although the Rocky Mountain states are located much closer to the
Midwest, it is expensive to distribute biodiesel to the rural areas there and this affects overall
regional distribution costs.
Federal and State Biodiesel Tax Subsidies (FBTS and SBTS)
Historically, a $1.00 tax subsidy for blending biodiesel and renewable diesel into diesel
fuel was originally enacted as part of the American Jobs Creation Act of 2004 and was
subsequently extended multiple times over the course nearly 20 years thereafter. However, in the
Inflation Reduction Act passed in 2022, Congress replaced the biodiesel and renewable diesel
blending subsidy with a production subsidy starting in 2025.120 Then, in 2025, Congress
modified the renewable fuel production subsidies further to favor domestic feedstocks over
imported ones. These most recent modifications take effect in 2026 and will also apply in
2027.121 The amount of this credit is based on certain employment wage criteria and the fuel's
emissions rate. At the time we established our No RFS Baseline case for this final rule, the
Department of Treasury had not yet established the credit amounts for 2026 and 2027 based on
the most recent modifications to the statute. In lieu of these official updates, we have estimated
the value of these subsidies in 2026 and 2027.122
120 HR5376; Public Law 117-169.
121 HR1; Public Law 119-21.
122 Based on our review and analysis of OBBB, which modified the 45Z biofuel production subsidies previously
enacted by Congress in IRA, we estimated the amount of the biodiesel production subsidies that will be in place in
2026 and 2027. We estimate that biodiesel produced from soybean oil and canola oil will likely receive a 620 per
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An additional small agri-biodiesel producers tax credit was approved by Congress in
OBBB which increases the incentive for small biodiesel producers. The tax subsidy only applies
to the first 15 million gallons produced by biodiesel producers with a total production capacity
under 60 million gallons per year. Such biodiesel producers are only eligible for this additional
tax credit when processing either virgin plant oils or virgin animal fats such as beef tallow. Since
distillers corn oil undergoes some refining, we concluded that it would not qualify as virgin oil
and thus would not receive this additional tax credit. The estimated value of the biodiesel and
renewable diesel production credit by feedstock type assuming that the biodiesel producer is
meeting the labor requirements is summarized in Table 2.1.3.1-4.
Table 2.1.3.1-4: Estimated Federal
Jiodiesel Subsidies (0/gal)
Feedstock Type
Biodiesel Subsidy
Small Agri-Biodiesel Producer
Soybean Oil
620
820
Canola Oil
350
550
Corn Oil
o
00
00
Domestic FOG
o
00
1000 (tallow only)
Imported Vegetable Oils and FOG
00
200 (tallow only)
To complete our analysis, it is necessary to estimate the quantity of biodiesel produced
which would be eligible for the small agri-biodiesel producers tax credit. Table 2.1.3.1-5
summarizes the list of eligible biodiesel plants by production company and their production
capacity.123 There are an additional 36 biodiesel plants not included in Table 2.1.3.1-5 because
they are not be not eligible for the Federal Agri-Biodiesel subsidy due to their individual plant
capacities equaling or exceeding 60 million gallons per year, or in the case of companies that
own and operate multiple biodiesel plants, the combined capacities of all their biodiesel plants
exceeding 60 million gallons per year. There are an additional 10 biodiesel plants not included in
Table 2.1.3.1-5 because their plant capacities are not reported.
Table 2.1.3.1-5: Biodiesel Plants Eligible for Agri-Biodiesel Producer Tax Subsidy (million
gallons per year)
Company Name
State
Capacity
Company Name
State
Capacity
Appalachian Biofuels, LLC
VA
0
Walsh BioFuels LLC
WI
5
Revolution Fuels, Inc.
MN
0
Pacific Biodiesel
HI
5.5
Omaha Biofuels Coop
NE
0.03
Integrity Biofuels, LLC
IN
6.42
Kelley Green Biofuel
KY
0.1
White Mountain
Biodiesel
NH
6.5
Loyola University Chicago
IL
0.1
Green Biofuels Miami,
LLC
FL
7
gallon subsidy, while corn oil and domestically produced fats, oils and grease (which includes used cooking oil and
beef tallow) will likely earn a 800/gallon subsidy. The subsidy estimates are based on publicly available industry
average data and anticipated changes to the 45ZCF-GREET model related to provisions in OBBB; tax credits or
subsidies for individual producers will differ from these estimates.
123 Biodiesel Magazine, "Biodiesel Plant List - Active Plants," October 2025.
https://biodieselmagazine.com/plants/list/biodiesel.
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Company Name
State
Capacity
Company Name
State
Capacity
Alaska Green Waste
Solutions
AK
0.3
Biodiesel Las Americas
FL
7.5
Eberle Biodiesel
TX
0.3
Delek Renewables-New
Albanv
MS
7.5
Green Energy Biofuel, LLC
SC
0.3
NewDort Biodiesel, Inc.
RI
8
Thumb BioEnergy LLC
MI
0.75
Genuine Bio-Fuel, Inc.
FL
9.2
Bently Biofuels Company
NV
1
Northern Development,
Biodiesel
NE
10
Simple Fuels Biodiesel
CA
1
W2Fuel-Crawfordsville
IA
10
Smisson-Mathis Energy,
LLC
GA
1
Yale Energv
WA
10
Cape Cod Biofuels
MA
1.2
Imperial Western
Products,
CA
10.5
Mason Biodiesel, LLC
RI
1.2
Delek Renewables-
Cleburne
TX
12
Maine Standard Biofuels
ME
1.5
New Leaf Biofuel, LLC
CA
12
Northeast Biodiesel, LLC
MA
1.75
Agron Bioenergv
CA
15
American Biodiesel Energy
PA
2
Delek Renewables-
Crossett
AR
15
Down to Earth Energy
GA
2
W2Fuel-Adrian
MI
15
Griffin Industries, Inc.
KY
2
Rio Vallev Biofuels,
LLC
TX
17
Sullens Biodiesel LLC
TN
2
SeOuential
OR
17
Golden Leaf Energy, LLC
LA
2.2
Michigan Biodiesel,
LLC
MI
17.5
Adkins Energy Biodiesel
IL
2.5
Scott Petroleum
Corporation-Greenville
MS
20
Alternative Fuel Solutions
IN
3
Viridis Fuels
CA
20
Ever Cat Fuels LLC
MN
3
Steoan Co.-Joliet
IL
21
GeoGreen Biofuels, Inc.
CA
3
Canarv Renewables
Corp.
CA
22.5
Oilmatic Systems, LLC
NJ
3
Fuel: Bio One LLC
NJ
25
Wholesome Energy, LLC
VA
3
Iowa Renewable Energv
IA
30
Reco Biodiesel, LLC
VA
3.6
Crimson Renewable
Energv
CA
36
Blue Ridge Biofuels LLC
NC
4
Western Dubuaue
Biodiesel
IA
36
BioVantage Fuels, LLC
IL
5
American GreenFuels,
LLC
CT
40
Buster Biofuels LLC
CA
5
Bioenergv Development
Group
TN
40
Foothills Bio-Energies,
LLC
NC
5
GEB3
SC
40
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Company Name
State
Capacity
Company Name
State
Capacity
Greenlight Biofuels
Princess Anne, LLC
MD
5
Solfuels USA. LLC
AR
40
Maple River Energy
IA
5
Minnesota Sovbean
Processors
MN
41
ME Bio Energy, LLC
MO
5
Incobrasa Industries, Ltd.
IL
44
Natural Biodiesel Plant,
LLC
MO
5
Owensboro Grain
Biodiesel
KY
45
Northeast Nebraska
Biodiesel
NE
5
Western Iowa Energv
LLC
IA
45
Patriot Fuels Biodiesel,
LLC
IL
5
Deerfield Energv, LLC -
Now ADM Plant
MO
50
SJV Biodiesel, LLC
CA
5
Duonix, LLC (Marathon)
NE
50
Southeast Biodiesel LLC
NC
5
Mid-America Bio
Energv
MO
50
Tennessee BioEnergy
TN
5
United Biodiesel, Inc.
NY
50
Triangle Biofuels Industries
NC
5
Paseo-Cargill Energv,
LLC
MO
56
Virginia Biodiesel Refinery
VA
5
Total Capacity o:
?Eligib
e Plants (million gallons per year)
1,141
Total Capacity of Eligible Plants when limited to 15MM gals/yr
648
States also provide subsidies to blend biodiesel into diesel fuel. These state subsidies
were enacted in previous years and are presumed to continue through 2027. Table 2.1.3.1-6
summarizes the states that offer such subsidies and the subsidy amounts.
Table 2.1.3.1-6: Current State Biodiesel Subsidies (0/gal)
State
Biodiesel Subsidy
Hawaii
120
Iowa
330
Illinois
620
North Dakota
1000
Rhode Island
370
Texas
200
The California and Oregon LCFS programs and Washington State's Clean Fuels program
do not offer specific subsides per se, but through the cap-and-trade nature of their programs, they
72
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can be equated to subsidies.124 For California,125 Oregon, and Washington,126 we estimated the
equivalent per-gallon biodiesel subsidy amounts from the incentives offered by each of these
LCFS programs, which vary by year. Table 2.1.3.1-7 summarizes the projected LCFS subsidies
by year.
Table 2.1.3.1-7: Projected State LCFS subsidies (0/gal)
State
Feedstock
2026
2027
California
Soybean Oil, Canola Oil
390
370
FOG
750
730
Oregon
Soybean Oil, Canola Oil
180
170
FOG
340
330
Washington
All Feedstocks
230
230
Although different than subsidies, several states have mandates that require the diesel
fuel within their state contain a minimum quantity of biodiesel. Table 2.1.3.1-8 lists the states
that have such a mandate and the percentage of biodiesel required to be blended into diesel fuel.
Table 2.1.3.1-8: State Biodiesel Mandates
Minimum %
State
of Biodiesel
Minnesota
12.5
New Mexico
5
Oregon
5
Pennsylvania
2
Washington
2
Diesel Terminal Price (DTP)
Refinery rack price data—which already include the distribution costs for moving diesel
to downstream terminals—were used to represent the price of diesel to blenders on a state-by-
state basis. However, these prices were not projected for future years.127 Instead, we used
projected refinery wholesale price data from AEO2025 to adjust 2021 refinery rack price data
124 New Mexico's CFTS program is scheduled to take effect by July 1, 2026. We will continue to follow the
implementation of the CFTS program and include its incentives for future analyses once the program has been fully
implemented.
125 The blending incentives for California are based on recent carbon credit projections made for a state action to
increase the future mandated decreases in GHG emissions. CARB, "Attachment C: LCFS Fuels and Credit Market
Module Modeling," https://ww2.arb.ca.gov/sites/default/files/barcu/regact/2024/lcfs2024/15daY attc.pdf.
126 The values for Oregon and Washington State are recent for each of the states. While it is probable that the state
incentive values would increase if the RFS program was not in place, we did not attempt to estimate what the credit
price would be if the RFS program was not in place. State of Oregon Department of Enviromnental Quality,
"Monthly CFP Credit Transfer Report for March 2025," April 1, 2025,
https://www.oregon.gov/deq/ghgp/cfp/pages/montlilv-data.aspx. State of Washington Department of Ecology,
"Monthly CFS Credit Report for March 2025,"
https://www.ezview.wa.gov/site/alias 1962/37916/clean fuel standard data reports.aspx.
127 EIA, "Spot Prices," Petroleum & Other Liquids, May 14, 2025.
https://www.eia.gov/dnav/pet/pet pri spt si a.htm.
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(2021 was the last year EIA reported this data) to represent diesel rack prices in future years.
This diesel price data, summarized in Table 2.1.3.1-9, was collected by state and is assumed to
represent the average diesel price for all the terminals in each state. The projected U.S. average
wholesale diesel fuel prices are presented in the table, after adjusting the prices to nominal year
dollars.
Table 2.1.3.1-9: Projec
ted Diesel Terminal Prices (nominal
B/gal)
State
2026
2027
State
2026
2027
Alaska
2.94
2.99
Montana
2.62
2.67
Alabama
2.29
2.33
North Carolina
2.30
2.34
Arkansas
2.35
2.40
North Dakota
2.43
2.48
Arizona
2.47
2.52
Nebraska
2.40
2.44
California
2.60
2.65
New Hampshire
2.39
2.44
Colorado
2.50
2.55
New Jersey
2.25
2.30
Connecticut
2.24
2.28
New Mexico
2.44
2.49
District of Columbia
2.35
2.40
Nevada
2.58
2.63
Delaware
2.33
2.38
New York
2.32
2.36
Florida
2.35
2.40
Ohio
2.35
2.40
Georgia
2.29
2.33
Oklahoma
2.33
2.38
Hawaii
2.61
2.66
Oregon
2.49
2.53
Iowa
2.40
2.44
Pennsylvania
2.30
2.34
Idaho
2.70
2.75
Rhode Island
2.27
2.32
Illinois
2.31
2.35
South Carolina
2.35
2.39
Indiana
2.33
2.38
South Dakota
2.31
2.35
Kansas
2.36
2.41
Tennessee
2.32
2.36
Kentucky
2.40
2.44
Texas
2.27
2.32
Louisiana
2.20
2.24
Utah
2.81
2.86
Massachusetts
2.29
2.33
Virginia
2.31
2.35
Maryland
2.30
2.34
Vermont
2.40
2.45
Maine
2.34
2.39
Washington
2.41
2.45
Michigan
2.36
2.41
Wisconsin
2.35
2.40
Minnesota
2.43
2.48
West Virginia
2.35
2.40
Missouri
2.36
2.41
Wyoming
2.65
2.70
Mississippi
2.30
2.34
U.S. Average
2.36
2.41
Estimating the Biodiesel Volume Under the No RFS Baseline
Because there are state mandates and biodiesel blending subsidies offered by individual
states, each state is represented separately in EPA's analysis. There are two steps for determining
the No RFS Baseline biodiesel volume. First, we estimate the biodiesel volumes expected to be
consumed due to existing state mandates by applying the mandate percentage to the projected
diesel fuel consumption of that state. Second, we estimate the biodiesel volume which has a
beneficial blending cost based on the equation in Chapter 2.1.3.1. If the biodiesel blending cost is
negative, biodiesel is considered economical to blend into diesel fuel and additional
nonmandated volumes are assumed to be blended. Conversely, biodiesel is initially assumed to
74
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not be blended into diesel fuel if the biodiesel blending value is positive. Because of its relative
cost, the volume of biodiesel consumption without the RFS program would be driven mostly by
the state mandates, but also by state subsidies, mainly the California and Oregon LCFS
programs.
If our analysis showed that biodiesel would be cost-effective to blend in any volume, it
was necessary to estimate the rate at which states would then blend biodiesel, (e.g., B5, BIO or
B20). Assuming that a large volume of biodiesel would be blended into any given state's diesel
fuel for the No RFS Baseline analysis would potentially result in large volumetric swings in
some years based on the changing economics of biodiesel in those states in those years. In
reality, the marketplace is unlikely to make such swings. To avoid this problem, the biodiesel
blended into diesel fuel was not allowed to exceed the demand that occurred under the RFS
program. During 2023, the total volume of biodiesel blended into diesel fuel averaged 1,847
million gallons per year. Since we are estimating biodiesel demand by state, we limit the total
volume of biodiesel in each state to the volume of biodiesel blended into diesel fuel in that state
in 2023, which was the most recent data available at the time this analysis was conducted.
Because biodiesel consumption has generally plateaued since 2016, these percentages are
assumed to be the maximum biodiesel percentages in each state in 2026 and 2027 for the No
RFS Baseline analysis. The volume of biodiesel consumed in each state is estimated by EIA and
reported in its State Energy Data System (SEDS).128 Table 2.1.3.1-10 summarizes the percentage
of biodiesel in diesel fuel for each state based on the SEDS information.
128 EIA, "State Energy Data System," Table C2 - Energy consumption estimates for selected energy sources in
physical units, 2023. https://www.eia.gov/state/seds/sep sum/html/pdf/sum use tot.pdf.
75
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Table 2.1.3.1-10: Maximum Percent of Biodiesel in Diesel Fuel by State (%)
Alaska
0.0
Montana
0.0
Alabama
2.9
North Carolina
2.7
Arkansas
2.6
North Dakota
2.0
Arizona
0.3
Nebraska
2.5
California
7.9
New Hampshire
3.1
Colorado
0.5
New Jersey
2.3
Connecticut
2.9
New Mexico
1.9
District of Columbia
9.7
Nevada
0.1
Delaware
1.8
New York
4.6
Florida
2.8
Ohio
2.6
Georgia
2.4
Oklahoma
2.5
Hawaii
3.5
Oregon
10.3
Iowa
6.5
Pennsylvania
2.2
Idaho
0.5
Rhode Island
1.4
Illinois
6.6
South Carolina
2.2
Indiana
3.0
South Dakota
1.8
Kansas
2.4
Tennessee
2.1
Kentucky
3.1
Texas
2.0
Louisiana
2.1
Utah
0.7
Massachusetts
4.0
Virginia
2.3
Maryland
2.6
Vermont
2.5
Maine
3.2
Washington
1.6
Michigan
2.7
Wisconsin
2.1
Minnesota
12.5
West Virginia
3.0
Missouri
2.2
Wyoming
0.7
Mississippi
3.2
Table 2.1.3.1-11 lists the states where it is economical to blend biodiesel into diesel fuel
under the No RFS Baseline in the years 2026 and 2027, and summarizes the volume of biodiesel
by the biogenic oil feedstock types estimated to be used to produce the biodiesel. For the states
that mandate the percentage of biodiesel to be blended into diesel fuel, we apportioned the
biogenic oil feedstock types based on the mix of these vegetable oils being used to produce
biodiesel nationwide.129 The mix of biogenic oil feedstocks for producing mandated biodiesel is
48%, 42%, and 10% of soy oil, domestically-sourced FOG, and corn oil, respectively, in 2026
and 2027. For cases where our analysis shows biodiesel is economically viable in a state, our
analysis determines if biodiesel is economically viable for only one feedstock or more than one
feedstock.
129 Although FOG is the lowest priced feedstock type and economics would normally dictate using as much of that
feedstock type as the lowest cost option, many biodiesel plants cannot use FOG because of the free fatty acid
content that causes operational problems in their plants. Biodiesel plants also tend to be located more in the
Midwest—which is the agricultural center for the production of corn and soy oil—and they may actually have a
lower cost and more reliable option to purchase these vegetable oil types that are produced close to their plants.
76
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In a common situation where both corn and waste oil are economically viable, we
estimate the proportion of each based on the portion of each used today. For the states that would
use biodiesel based on economics, the volume of biodiesel in any state is estimated by
multiplying the biodiesel percent in each state from Table 2.1.3.1-10 by the volume of diesel fuel
consumed in that state.130 The tables list the mandated volume by each state at the top and the
volume for states where it is economical to use biodiesel. Only California and Oregon are listed
separately since these states have the largest subsidies without a mandate, while the projected
volumes for the other states are aggregated together. In the next rows in the tables, the total
biodiesel volumes by vegetable oil type and year are totaled.
Table 2.1.3.1-11: Biodiesel in the 2026 and 2027 No RFS Baseline (million gal/yr)
2026
2027
Domestic
Domestic
State
Soy Oil
Corn Oil
FOG
Soy Oil
Corn Oil
FOG
Oregon
17
3
13
17
3
13
Volume in
States with
Mandates
New Mexico
15
1
10
14
1
10
Minnesota
57
12
48
57
12
48
Washington
10
1
7
9
1
7
Pennsylvania
13
2
7
13
2
7
Total
217
214
California
0
56
226
0
52
209
Economic
Oregon
0
0
37
0
15
62
Volume
Other States
83
31
535
5
42
1,030
Total
968
1,444
Total of Mandated and
195
107
883
115
129
1,225
Economic Volumes
Total Volumes by Year
1,185
1,469
This mix of vegetable oil types is used as a starting point for establishing the No RFS
Baseline for biodiesel. However, adjustments were then made to the vegetable oil feedstock
types after the initial No RFS Baseline analysis was conducted for renewable diesel. See Chapter
2.1.3.3 for details on these adjustments and the reasoning behind them.
2.1.3.2 Renewable Diesel
While renewable diesel is produced using a much different process than biodiesel, it uses
the same feedstocks and so much of the blending cost analysis is similar. The blending cost of
renewable diesel is estimated using the following equation:
1311 Historical diesel sales volumes and projected future diesel volumes were used to project the volume of diesel sold
in each state. EIA, "Prime Supplier Sales Volume," Petroleum & Other Liquids, June 1, 2022.
https://www.eia.gov/dnav/pet/pet cons prim dcu nus a.htm. AEO2025, Table 11 - Petroleum and Other Liquids
Supply and Disposition.
77
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RDBC = (RDSP + RDDC - FRDTS - SRDTS) - DTP
Where:
• RDBC is renewable diesel blending cost
• RDSP is renewable diesel plant gate spot price
• RDDC is renewable diesel distribution cost
• FRDTS is federal renewable diesel tax subsidy
• SRDTS is state renewable diesel tax subsidy
• DTP is diesel terminal price; all are in dollars per gallon (same as section 2.1.3.1)
The diesel terminal prices (DTP) are the same as those described in Chapter 2.1.3.1 for
biodiesel, so the diesel fuel terminal prices will not be discussed further here. However, each of
the other variables in the above equation are discussed further. The state blending mandates
described in Chapter 2.1.3.1 (Table 2.1.3.1-8) are assumed to force increased volumes of
biodiesel which is a less expensive biofuel compared to renewable diesel.
Renewable Diesel Plant Gate Spot Price (RDSP)
Similar to biodiesel, we estimated future renewable diesel plant gate prices by gathering
projected renewable diesel plant input information (e.g., future biogenic oil and utility prices) to
estimate renewable diesel production costs, which we assumed represent plant gate prices. This
is essentially the same information used for estimating renewable diesel production costs for the
cost analysis described in Chapter 10, except that the capital costs are amortized using the capital
amortization factor in Table 2.1.1.1-1 and all costs are in nominal dollars. The most likely
imported feedstock is FOG, and it is estimated to be priced the same as domestic FOG. The
projected renewable diesel plant gate prices are summarized in Table 2.1.3.2-1 for three
feedstocks—soybean oil, corn oil, and FOG.
Table 2.1.3.2-1: Projected Renewable Diesel Plant Gate Prices (nominal $/gal)
Feedstock
2026
2027
Soybean Oil
4.37
4.22
Corn Oil
3.80
3.68
FOG
3.56
3.46
It is important to note that the production costs must be considered along with the Federal
subsidy amounts discussed below. For example, the plant gate production cost of FOG-based
renewable diesel is estimated to be identical for both domestic and imported FOG, however,
domestic FOG renewable diesel receives a federal subsidy while imported FOG renewable diesel
does not, which affects their relative use.
Renewable Diesel Distribution Cost (RDDC)
This factor represents the added cost of moving renewable diesel from production plants
to terminals where it is blended with diesel fuel. Unlike ethanol, which is almost exclusively
produced in the Midwest and distributed elsewhere from there, and unlike biodiesel, for which
78
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production is dispersed more broadly around the continental United States, renewable diesel is
predominantly produced on the Gulf and West Coasts. Based on the SEDS data, virtually all
renewable diesel is currently being consumed in PADD 5—mostly in California, but also some
in Oregon and Washington. If the renewable diesel is produced on the Gulf Coast, the
distribution cost is assumed to be the same as inter-PADD distribution costs for biodiesel. If the
renewable diesel is produced on the West Coast, the primary vegetable oil feedstocks are likely
to be virgin oil imported from the Midwest or imported used cooking oil, both of which would be
expected to incur an inter-PADD distribution cost. Thus, in all cases we used the biodiesel inter-
PADD distribution costs.
ICF estimated the costs for distributing renewable diesel both within and between
PADDs, as summarized in Table 2.1.3.2-2.131 An additional cost is added on to account for the
addition of renewable diesel additives, for example to improve renewable diesel flow properties
downstream of the production facility—the total cost of these additives is estimated to be 30 per
gallon. The costs, estimated in 2017 dollars, are adjusted higher to the year dollars being
analyzed which is 34% higher in 2026 as shown in the table.
Table 2.1.3.2-2: Renewable Diesel Distribution Costs (Wgal)
Distribution and Additive
Origi
nally Estimated Costs 2017$
Costs Adjusted to 2026$
Within
From Outside
Additives
Within
From Outside
PADD
PADD
the PADD
Cost
PADD
the PADD
PADD 1
7
30
3
13.4
44.1
PADD 2
7
12
3
13.3
20.0
PADD 3
7
15
3
13.4
24.0
PADD 4
7
20
3
13.4
30.7
PADD 5
7
25
3
13.4
37.4
Like for biodiesel, the Inflation Reduction Act provided renewable diesel with a
production subsidy starting in 2025 based on certain employment wage criteria and the fuel's
emissions rate.132 Then in 2025, Congress modified the renewable fuel production subsidies to
favor domestic feedstocks over imported ones which takes effect in 2026 and also applies in
2027.133 At the time we established our No RFS Baseline case for this final rule, the Department
of Treasury had not yet established the credit amounts for 2026 and 2027 based on the most
recent modifications to the statute. In lieu of these official updates, we have estimated the value
of these subsidies in 2026 and 2027. The estimated value of the renewable diesel production
credit by feedstock type assuming that the renewable diesel producers are meeting the labor
requirements is summarized in Table 2.1.3.2-3.
131 ICF, "Modeling a 'No-RFS' Case," EPA Contract No. EP-C-16-020, July 17, 2018.
132 HR5376; Public Law 117-169.
133 HR1; Public Law 119-21.
79
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Table 2.1.3.2-3: Estimated Federal Renewable Diesel Subsidies (0/gal)
Feedstock Type
Renewable Diesel Subsidy
Soybean Oil
520
Canola Oil
290
Corn Oil
750
Domestic FOG
o
00
Imported Vegetable Oils and FOG
00
The California and Oregon LCFS programs and Washington State's Clean Fuels program
do not offer specific subsides per se, but through the cap-and-trade nature of their programs, they
can be equated to subsidies.134 For California, Oregon and Washington, we estimated the
equivalent per-gallon subsidy amount from the incentives offered by its LCFS program which
vary by year.135 Table 2.1.3.2-4 summarizes the projected LCFS subsidies by year.
Table 2.1.3.2-4: Projected LCFS subsidies by State and Feedstock (0/gal)
State
Feedstock
2026
2027
California
Soybean Oil, Canola Oil
390
380
FOG
720
710
Oregon
Soybean Oil, Canola Oil
180
170
FOG
330
320
Washington
All Feedstocks
250
250
Estimating the Renewable Diesel Volume Under the No RFS Baseline
The methodology for analyzing renewable diesel volumes is structured similarly to that
for biodiesel described in Chapter 2.1.3.1. The state with the lowest renewable diesel blending
cost (e.g., the state with the largest blending subsidies) would receive renewable diesel first. The
percent of renewable diesel in any state's diesel fuel while the RFS program is in place is
considered the maximum volume of renewable diesel fuel which could occur under the No RFS
Baseline. Because renewable diesel volumes are increasing under the combination of RFS and
LCFS programs in California and Oregon, we projected the future volume of renewable diesel
assuming recent volumetric growth rates and established those volumes as the maximums
without the RFS program in place. An important difference from the analysis for biodiesel,
however, is that states are able to displace a much higher percentage of their diesel fuel,
potentially up to the quantity of biodiesel in their diesel fuel pool assuming that the combination
of biodiesel and renewable diesel could displace 100% of petroleum diesel fuel.136
134 New Mexico's CFTS program is scheduled to take effect by July 1, 2026. We will continue to follow the
implementation of the CFTS program and include its incentives for future analyses once the program has been fully
implemented.
135 The blending incentives are based on recent carbon credit values reported by each of the states. While it is
probable that the state incentive values would increase if the RFS program was not in place, we did not attempt to
estimate what the credit price might be if the RFS program was not in place. California recently approved more
stringent LCFS standards that will likely increase the carbon credit value. CARB, Monthly LCFS Credit Transfer
Activity Report for March 2024. https://ww2.arb.ca.gov/resources/documents/monthlv-lcfs-credit-transfer-activitv-
reports.
136 Renewable diesel has properties similar to petroleum diesel and thus can displace petroleum diesel without
causing vehicle compatibility or drivability issues.
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Like the other biofuels analyzed for the No RFS Baseline, if the renewable diesel
blending cost is negative, renewable diesel is considered economical to blend into diesel fuel.
Conversely, renewable diesel is assumed to not be blended into diesel if the blending value is
positive. Because of its relatively high cost, renewable diesel consumption without the RFS
program would only be blended into diesel fuel if a state offers a significant subsidy, mainly the
California, Oregon and Washington state LCFS programs.
Since renewable diesel is only consumed in a few states with LCFS-type programs and
nearly all of that in California, we established the maximum renewable diesel demand based on
their projected consumption under the RFS program accordingly. The projected maximum
renewable diesel volumes are summarized in Table 2.1.3.2-5.
Table 2.1.3.2-5 Maximum Renewable Diesel Volume by State Under the RFS Program
Year
2022
2023
2024
2025
2026
2027
Total Volume
1,350
2,290
2,980
3,630
4,321
5,012
California
1,303
2,148
2,286
2,977
3,506
3,506
Oregon
47
127
659
598
741
741
Washington
0
15
35
55
74
120
Table 2.1.3.2-6 lists the volume of renewable diesel which is economically favorable for
blending into diesel fuel by state for the years 2026 and 2027. Our analysis shows that renewable
diesel is only economical to consume in California, and this is likely due to the higher carbon
credit price, and projected lower petroleum prices by the Energy Information Administration.
The volume of economical renewable diesel is shown by vegetable oil type, assuming that the
mix of vegetable oils is consistent with the average percentage of vegetable oils consumed in the
year 2023 under the RFS program. These vegetable oil quantities are just a starting point and are
adjusted when estimating the mix of vegetable oils consumed by both biodiesel and renewable
diesel plants under a No RFS Baseline to ensure that the individual vegetable oil volumes are
below the established maximum volumes. The final vegetable oil volumes are shown in Table
2.1.3.3-6.
Table 2.1.3.2-6: Potential Volume of Renewable Diesel by Feedstock Ty
)e (million gallons)
Feedstock
Domestic
Year
State
Soybean Oil
Corn Oil
FOG
Total
California
0
0
3,506
3,506
2026
Oregon
0
0
0
0
Other States
0
0
0
0
California
0
1,451
2,055
3,506
2027
Oregon
0
0
0
0
Other States
0
0
0
0
The analysis shows that only domestic FOG is economical to renewable diesel producers
in California in 2026, while both corn oil and domestic FOG are economical in 2027. The total
mandated and economic volume of biodiesel varies in 2026 and 2027.
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This mix of vegetable oil types is used as a starting point for establishing the No RFS
Baseline for renewable diesel. However, adjustments were then made to the vegetable oil
feedstock types after the initial No RFS Baseline analysis was conducted for biodiesel. See
Chapter 2.1.3.3 for details on these adjustments and the reasoning behind them.
2.1.3.3 Final No RFS Baseline Volumes for Biodiesel and Renewable Diesel
While the volumes of biodiesel and renewable diesel by feedstock type were initially
estimated in Tables 2.1.3.1-11 and 2.1.3.2-6, using these volumes, particularly the renewable
diesel volumes, would exceed the total volume of feedstocks which is available to produce fuels
under the RFS program. To estimate the maximum vegetable oil volumes which could be
available for producing biodiesel and renewable diesel in 2026 and 2027, we used the volume of
feedstocks used in 2024, which is summarized in Table 2.1.3.3-1.
Table 2.1.3.3-1: Maximum Vegetable Oil Volumes
Year
Soy
Corn Oil
FOG
Domestic
Imported
2026
2,760
636
850
1,550
2027
2,760
636
850
1,550
Table 2.1.3.2-6 shows that domestic FOG renewable diesel is cost economic to blend into
petroleum diesel fuel in both 2026 and 2027, and corn oil renewable diesel is economic in 2027.
In both cases, imported FOG is not cost-effective to use. Table 2.1.3.3-2 summarizes the
economic volumes of biodiesel and renewable diesel by feedstock type which meets the limits of
feedstock availability—the biodiesel volumes are used to back-calculate the renewable diesel
volumes by feedstock type.
Table 2.1.3.3-2: Calculated Volume of Economic Renewable Diesel which Does not Exceed
the Current Oil Volumes
Year
Feedstock
Total
Soy/
Canola Oil
Corn
Oil
Domestic
FOG
All
Feedstocks
2026
Maximum Oil Volume
2,760
636
850
Biodiesel
1,095
19
86
650
Economic Renewable Diesel Volume
0
0
3,506
Adjusted Renewable Diesel
0
0
764
502
2027
Maximum Oil Volume
2,760
636
850
Biodiesel
110
88
355
571
Economic Renewable Diesel Volume
0
1,451
2,055
Adjusted Renewable Diesel
0
548
495
1,025
The economic analysis can project that the total mandated and economic volume of
biodiesel and economic volume of renewable diesel can vary significantly in 2026 and 2027, and
compared to previous years. Such swings in the production volume of biodiesel and renewable
diesel are unlikely to occur based on typical decisions made by plant owners and investors on
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how to invest to expand their plants, continue to operate their biodiesel and renewable diesel
plants, or idle or shut down based solely on year-ever-year economic data. Thus, to smooth out
unrealistic swings in the economics for using biodiesel and renewable diesel, we calculated the
average of biodiesel and renewable diesel demand for the year of interest and the previous three
years. Consideration of plant economics over a 4-year period is in between a short-term and
medium-term time horizon. This step attempts to reflect how biofuel plant owners and potential
biodiesel and renewable diesel plant owners, investors, or banks would seek to assess the
economics for operating, investing in, expanding, idling or shutting down biodiesel and
renewable diesel plant capacity.137 Thus, to assess the volume of biodiesel and renewable diesel
which would be produced in 2026 and 2027, it was necessary to also consider the economics of
producing biodiesel and renewable diesel in 2023, 2024, and 2025. For this reason, biodiesel and
renewable diesel economics were assessed historically for 2023-2025, and projected for 2026
and 2027, to determine the volume of biodiesel and renewable diesel that would be blended into
diesel fuel absent the RFS program.138 Table 2.1.3.3-3 summarizes the mandated biodiesel
volume, the yearly economic biodiesel and renewable diesel volume, the 4-year average
economic biodiesel and renewable diesel volume and finally the total of mandated and 4-year
average biodiesel and renewable diesel volume.
Table 2.1.3.3-3: Year-by-Year Analysis of Biodiesel Volumes for the No RFS I
State
Total of State
Mandated
Economic
4-Year Average
Mandated
Biodiesel
Biodiesel
Volume of Economic
Biodiesel Volume
Year
Volume
Volume
Biodiesel Volume
and 4-Year Volume
2023
236
336
-
-
2024
227
303
-
-
2025
222
464
-
-
2026
217
983
521
738
2027
215
339
522
737
aseline
Similarly, Table 2.1.3.3-4 summarizes the economical renewable diesel volume and 4-
year average renewable diesel volume.
137 EIA, "Investment Expectations & Decision Making In the Petroleum Refining Industry," January 4, 2007.
https://www.eia.gov/outlooks/documentation/workshops/pdf/petroleum investment.pdf.
138 No RFS Baseline analysis for Set 1 Rule with some minor adjustments made for the Set 2 No RFS Baseline
analysis.
83
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Table 2.1.3.3-4: Year-by-Year Analysis of Renewable Diesel Volumes for the No RFS
Baseline
Year
Economic
Renewable
Diesel Volume
4-Year Average Volume
of Economic Renewable
Diesel Volume
2023
1,867
-
2024
2,074
-
2025
2,249
-
2026
764
1,739
2027
1,043
1,533
The projected biodiesel volumes in Table 2.1.3.3-3, and to a lesser extent the renewable
diesel volumes in Table 2.1.3.3-4, based on the 4-year volume average, would exceed the
projected volume of feedstocks consumed under the RFS program. In this case, we assume that
both biodiesel and renewable diesel producers would need to purchase other feedstocks at a loss
to maintain the projected plant capacity. These producers would likely be willing to do so over
some period of time presuming that their plants will become profitable again in the not-too-
distant future. If operating at a loss, the plant would want to at least cover their variable costs
(feedstock and utility purchases). Alternatively, they may be able to cover a portion, or maybe
all, of their increased costs if they are able to raise their prices if they are operating in a more
constrained market. In the case of California, Washington and Oregon, the cost of a carbon credit
could increase to cover the increased cost of feedstock. Alternatively, the producers may be able
to secure lower feedstock prices if they are able to establish a long-term contract at a lower
feedstock price. The most likely source of additional feedstock would be imported used cooking
oil which became less economic due to the limitation of the 45Z credit to North American
feedstocks after 2025. At the elevated cost point where used cooking oil would be used by
renewable diesel plants, corn oil would also be purchased by renewable diesel plants up to the
availability limit of corn oil and additional soybean oil would also be purchased by biodiesel
plants.
We estimate the additional 20 cents per gallon Agri-Biodiesel subsidy for virgin
vegetable oils would cause an increase in the demand for soy and canola oils in 2026, even
without the RFS program. The Agri-biodiesel subsidy for animal tallow would help the
economics for producing biodiesel from animal tallow; however, there are multiple challenges to
biodiesel producers for using animal tallow as a feedstock.139 The animal tallow triglycerides are
comprised of a higher percentage of saturated fatty acids which causes the resulting biodiesel to
have a high cloud point. The high cloud point affects the location, season and amount of this type
of biodiesel which can be blended into diesel fuel. Also, depending on the rendering process, the
animal tallow can have high free fatty acid and sulfur contents. The high free fatty acid content
can contribute to the production of soap compounds which can lead to plugging of vehicle fuel
filters and increase the metal content of the biodiesel.140 If the animal tallow contains sulfur
139 Farm Energy, "Animal Fats for Biodiesel Production," April 3, 2019. https ://farm-energy .extension,org/animal-
fats-for-biodiesel-production.
1411 Bouaid, Abderrahim, Rodrigo Vazquez, Mercedes Martinez, and Jose Aracil. "Effect of Free Fatty Acids
Contents on Biodiesel Quality. Pilot Plant Studies." Fuel 174 (January 21, 2016): 54-62.
https://doi.Org/10.1016/i.fiieL2016.01.018.
84
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exceeding 15 ppm, it would prevent the blending of the biodiesel into diesel fuel. Conversely,
hydrotreating and associated reactions of the renewable diesel process is well suited to address
these challenges, and is the reason why animal tallow is more likely a feedstock sent to
renewable diesel plants. For these reasons, we did not assume that the agri-biodiesel subsidy
would cause biodiesel plants to process a significantly greater amount of animal tallow. We did
not assume any tariffs were in place which would cause increased demand for soy and other
virgin North American sourced oils compared to imported FOG.
The final No RFS Baseline volumes for biodiesel and renewable diesel that result from
the calculations described in Chapters 2.1.3.1 and 2.1.3.2, limited by the maximum vegetable oil
volumes in Table 2.1.3.3-1, and which meet the projected four-year average volumes as shown in
Tables 2.1.3.3-3 and 4, are summarized in Table 2.1.3.3-5. While imported FOG biodiesel and
renewable diesel is not found to be economical to blend into diesel fuel, to meet the projected
volume of biodiesel and renewable diesel plant capacities while staying within the volumetric
limits of lipids, assuming that some imported FOG would be processed by renewable diesel
plants was the most logical feedstock to bring in.
Table 2.1.3.3-5: Final No RFS Baseline Volumes for Biodiesel and Renewable Diesel
Year
2026
2027
Biodiesel
Soy
633
334
Corn Oil
19
86
FOG
Domestic
86
317
Imported
0
0
Total Biodiesel
738
737
Renewable Diesel
Soy
0
0
Corn Oil
617
550
FOG
Domestic
764
533
Imported
358
450
Total Renewable Diesel
1,739
1,533
Biodiesel and Renewable Diesel
Total
2,477
2,269
2.1.4 Other Advanced Biofuel
In addition to ethanol, cellulosic biofuel, and BBD, we also estimated volumes of other
advanced biofuels for the No RFS Baseline. These biofuels include imported sugarcane ethanol,
domestically produced advanced ethanol, non-cellulosic RNG used in CNG/LNG vehicles,
heating oil, naphtha, and advanced renewable diesel that does not qualify as BBD (coded as D5
rather than as D4). In Chapters 7.3 and 7.4, we present a derivation of the projected volumes of
these other advanced biofuels for 2026 and 2027 in the context of the Analyzed Volumes that we
analyzed. Here we discuss the deviations from those projections that we believe would apply
under the No RFS Baseline.
85
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According to data from EIA, all ethanol imports entered the U.S. through the West Coast
in 2018-2021, and the majority did so in 2022 and 2023.141 We believe that these imports were
likely used to help refiners meet the requirements of the California LCFS program, which
provides significant additional incentives for the use of advanced ethanol beyond that of the RFS
program. In the absence of the RFS program, we project that these incentives would remain.
Thus, we have assumed that the volume of imported sugarcane ethanol would be the same
regardless of whether the RFS program were in place in 2026 and 2027. For similar reasons, we
believe that domestically produced advanced ethanol, virtually all of which we expect to be
derived from separated food waste based on our analyses described in Chapter 1.5 and Chapter
7.4. would also continue to find a market in California in the absence of the RFS program.
As discussed in Chapter 7.2, a similar situation exists for advanced renewable diesel. The
vast majority of the renewable diesel consumed in the U.S. has been consumed in states with
incentives for low carbon fuels such as California and Oregon. Some renewable diesel would
continue to be consumed in these states in the absence of the RFS program, particularly that
produced from FOG due to the lower CI score assigned to it under the LCFS program. We
believe that this would also be the case for advanced renewable diesel that does not qualify as
BBD since the statutory threshold of 50% GHG reduction is the same for advanced biofuel and
for BBD, and because such renewable diesel is generally produced from FOG. Thus, we have
assumed that the volume of advanced renewable diesel that does not qualify as BBD would be
the same regardless of whether the RFS program were in place in 2026 and 2027.
Remaining forms of other advanced biofuel (i.e., non-cellulosic RNG used in CNG/LNG
vehicles, heating oil, and naphtha) are much less likely to find their way to markets such as the
California LCFS program, where the incentive would likely be insufficient to continue
supporting their use in the absence of the RFS program. Therefore, we have assumed that
consumption of these biofuels would be zero under the No RFS Baseline.
2.1.5 Summary of No RFS Baseline
Following our analysis of individual biofuel types as described above, we estimated the
constituent mix of both renewable fuel types and feedstocks that could be used under a No RFS
Baseline, as shown in Table 2.1.5-1 (in million RINs) and Table 2.1.5-2 (in million gallons).
141 EIA, "Fuel Ethanol Imports by Area of Entry," Petroleum & Other Liquids, May 30, 2025.
https://www.eia.gov/dnav/pet/pet move imp a epooxe IMP mbbl a.htm.
86
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Table 2.1.5-1: No RFS Baseline for 2026 and 2027 (million RINs)
2026
2027
Cellulosic Biofuel
565
597
CNG/LNG from biogas
437
469
Ethanol from CKF
128
128
Total Biomass-Based Diesel
4,068
3,563
Biodiesel
1,107
1,106
Soybean oil
843
440
FOG
129
476
Corn oil
29
129
Canola oil
0
0
Renewable Diesel
2,956
2,453
Soybean oil
0
0
FOG
1,203
763
Corn oil
1,049
880
Canola oil
0
0
Jet fuel from FOG
5
5
Other Advanced Biofuels
129
129
Renewable diesel from FOG
90
90
Imported sugarcane ethanol
15
15
Domestic ethanol from separated food waste
24
24
Other51
0
0
Conventional Renewable Fuel
14,040
13,958
Ethanol from corn
14,040
13,958
Biodiesel and renewable diesel from palm oil
0
0
a Composed of non-cellulosic biogas, heating oil, and naphtha.
-------
Table 2.1.5-2: No RFS Baseline for 2026 and 2027 (million gallons
2026
2027
Cellulosic Biofuel
565
597
CNG/LNG from biogas
437
469
Ethanol from CKF
128
128
Total Biomass-Based Diesel
2,477
2,270
Biodiesel
738
737
Soybean oil
556
293
FOG
86
317
Corn oil
19
86
Canola oil
77
41
Renewable Diesel
1,739
1,533
Soybean oil
0
0
FOG
1,066
927
Corn oil
617
550
Canola oil
0
0
Jet fuel from FOG
0
0
Other Advanced Biofuels
95
95
Renewable diesel from FOG
56
56
Imported sugarcane ethanol
15
15
Domestic ethanol from separated food waste
24
24
Other51
0
0
Conventional Renewable Fuel
14.040
13,958
Ethanol from corn
14,040
13,958
Biodiesel and renewable diesel from palm oil
0
0
a Composed of non-cellulosic biogas, heating oil, and naphtha.
2.2 2025 Baseline
As discussed in Preamble Section III.D.3, while we believe that the No RFS Baseline is
preferable as a point of reference for analyzing the impacts of the Analyzed Volumes, we have
also estimated some of the impacts (e.g., costs) of this rule relative to the renewable fuel volumes
used in 2025 as an additional informational case. This allows for an estimate of the incremental
impacts of the renewable fuel volumes compared to those previously finalized.
For this final rule, we updated the 2025 baseline using available data on renewable fuel
production and use in 2025 from EMTS. For this baseline we combined the fuel volumes of
renewable diesel and renewable jet fuel due to the significant overlap in facilities and
technologies that produce these fuels. To estimate the feedstocks used to produce BBD in 2025
we also considered data from EIA's Monthly Biofuels Capacity and Feedstocks Update. These
2025 baseline volumes are shown in Table 2.2-1 (in million RINs) and Table 2.2-2 (in million
gallons). A further discussion of how we used the EIA data to estimate the feedstocks used to
produce BBD in 2025 follows these tables.
88
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Table 2.2-1: Mix of Biofuels Used in 2025 (million RINs)
Cellulosic Biofuel
1,247
CNG/LNG from biogas
1,170
Ethanol from CKF
77
Diesel/jet fuel from wood waste/MSW
0
Total Biomass-Based Diesel
5,739
Biodiesel
1,653
Soybean oil
1,190
FOG
231
Corn oil
66
Canola oil
165
Renewable Diesel
4,430
Soybean oil
886
FOG
2,525
Corn oil
709
Canola oil
354
Jet fuel from FOG
0
Other Advanced Biofuels
218
Renewable diesel from FOG
70
Imported sugarcane ethanol
0
Domestic ethanol from separated food waste
20
Other51
127
Conventional Renewable Fuel
14,183
Ethanol from corn
14,183
Renewable diesel from palm oil
0
a Composed of non-cellulosic biogas, heating oil, and naphtha.
-------
Table 2.2-2: Mix of Biofuels Used in 2025 (million gallons)
Cellulosic Biofuel
1,247
CNG/LNG from biogas
1,247
Ethanol from CKF
77
Diesel/jet fuel from wood waste/MSW
0
Total Biomass-Based Diesel
3,734
Biodiesel
1,102
Soybean oil
793
FOG
154
Corn oil
44
Canola oil
110
Renewable Diesel
2,632
Soybean oil
521
FOG
1,485
Corn oil
417
Canola oil
208
Jet fuel from FOG
0
Other Advanced Biofuels
153
Renewable diesel from FOG
44
Imported sugarcane ethanol
0
Domestic ethanol from separated food waste
20
Other51
89
Conventional Renewable Fuel
14,183
Ethanol from corn
14,183
Renewable diesel from palm oil
0
a Composed of non-cellulosic biogas, heating oil, and naphtha.
To estimate the feedstocks used to supply the BBD in 2025 we first considered the data
available from EMTS. This data is summarized in Table 2.2-3.
Table 2.2-3: Feedstocks used to Produce BBD in 2025 (million RINs)
Fuel
Feedstock
RINs Generated
Biodiesel
All Other Feedstocks
84.4
Biodiesel
FOG
166.0
Biodiesel
FOG, Distillers Corn Oil
37.2
Biodiesel
FOG, Distillers Corn Oil, Soybean Oil
105.2
Biodiesel
Canola Oil
149.0
Biodiesel
Soybean Oil
1,168.8
Renewable Diesel
All Other Feedstocks
2,594.4
Renewable Diesel
FOG
1.645.1
Renewable Diesel
Distillers Corn Oil
215.3
Renewable Diesel
Soybean Oil
502.4
Renewable Jet Fuel
All Other Feedstocks
460.2
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The information on the feedstocks used to produce biodiesel from EMTS contains
sufficient specificity for us to estimate total quantities of feedstock used to produce biodiesel in
2025. While two of the categories listed in the EMTS data contain multiple feedstocks these
categories indicate which feedstocks were used and the total quantity of RINs listed for these
multi-feedstock categories are relatively small.142 To estimate the proportion of each feedstock
used to supply biodiesel to the U.S. in 2025 we estimated that RIN generation was evenly split
between each feedstock listed when multiple feedstocks were listed. We then multiplied the
proportion supplied from each feedstock by the total volume of biodiesel supplied (after
accounting for exports and RINs retired for reasons other than compliance with the RFS
obligations) to estimate the volume produced from each feedstock. These calculations are shown
in Table 2.2-4.
Table 2.2-4: Estimating Biodiesel Production in 2025 By Feedstock
RINs Generated by
(million RI
Feedstock
Vs)
FOG
240.8 (14%)
Distillers Corn Oil
74.8 (04%)
Soybean Oil
1,225.0 (72%)
Canola Oil
170.2 (10%)
Biodiesel Volumes Supplied by
Feedstock (million gallons)
Total (All Feedstocks)
1,102
FOG
154
Distillers Corn Oil
44
Soybean Oil
793
Canola Oil
110
Estimating the quantities of renewable diesel and renewable jet fuel requires additional
data, as the majority of the RINs generated for these fuels were reported under the
undifferentiated "all other feedstocks" category in the EMTS data. We therefore supplemented
the ETMS data with data on feedstock use for biodiesel and renewable diesel production from
EIA's Monthly Biofuels Capacity and Feedstocks Update. At the time this analysis was
completed (January 2026) the most recent Monthly Biofuels Capacity and Feedstock Update was
published in November 2025, with data through September 2025.143
For each of the three crop-based feedstock categories (canola oil, distillers corn oil and
soybean oil) we estimated total annual total consumption of each feedstock in 2025 by projecting
the average feedstocks use from May to September for each month in 2025 for which data had
not yet been reported (October to December). For soybean oil, EIA reported specific feedstock
consumption by renewable diesel plants for each month, which allowed for renewable feedstock
projection using the methodology described in this paragraph. For canola oil and corn oil,
specific renewable diesel consumption data for many of the months was withheld to avoid
142 We did not consider the 84.4 million RINs of biodiesel produced from "all other feedstocks" in calculating the
feedstock ratios to estimate the mix of feedstocks used to produce biodiesel in 2025.
143 EIA, "Monthly Biofuels Capacity and Feedstocks Update with data for September 2025," November 28, 2025.
https ://www. eia. gov/biofuels/update.
91
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disclosure of individual company data. To estimate the feedstock consumption for these
feedstocks we first projected the annual total for each feedstock from biodiesel plants and
renewable diesel plants combined (which was available for each month through September
2025). We then used the ratio of consumption from biodiesel plants relative to consumption from
renewable diesel plants in months where these data were reported to estimate the annual
consumption for renewable diesel plants (separately from biodiesel plants) in 2025.
For FOG, the EIA reports do not separately list feedstocks consumed by biodiesel and
renewable diesel plants.144 To estimate the consumption of FOG by renewable diesel plants we
first estimated the total use of FOG by both biodiesel and renewable diesel plants. To do this we
again estimated to annual total consumption of each feedstock in 2025 by projecting the average
feedstocks use from May to September for each month in 2025 for which data had not yet been
reported (October to December).145 We then subtracted from this total quantity of FOG the
amount of FOG feedstock we projected was consumed by biodiesel producers using the EMTS
data.146 We assumed the remainder was used to produce renewable diesel.
Finally, we then calculated the proportion of the total feedstocks consumed by renewable
diesel plants from each feedstock and multiplied the proportion supplied from each feedstock by
the total volume of renewable diesel supplied (after accounting for exports and RINs retired for
reasons other than compliance with the RFS obligations) to estimate the volume produced from
each feedstock. These calculations are shown in Table 2.2-5.
Table 2.2-5: Estimating Renewable Diesel Proc
Consumption by Feedstock (million pounds)
FOG
13,733 (57%)
Distillers Corn Oil
3,865 (16%)
Soybean Oil
4,822 (20%)
Canola Oil
1,844 (08%)
Renewable Diesel Volumes Supplied by
Feedstock (million gallons)
Total (All Feedstocks)
2,632
FOG
1,485
Distillers Corn Oil
417
Soybean Oil
521
Canola Oil
208
uction in 2025 By Feedstock
144 EIA separately lists consumption of poultry grease, tallow, white grease, yellow grease, and other waste FOG.
We used the sum of all these categories to project the annual consumption of FOG by biodiesel and renewable diesel
plants.
145 Only one month listed feedstocks consumption data for poultry grease and other FOG, with the remaining months
withheld. We estimated that the consumption of these feedstocks was equal to the value listed for each month in
2025.
146 We used an estimated conversion rate of 8 pounds of FOG to produce one gallon of biodiesel.
92
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Chapter 3: Analyzed Volumes and Volume Changes
To enable more detailed analyses of the impacts of the RFS volumes for 2026 and 2027
we have identified the specific biofuel types and associated feedstocks that are projected to be
used to meet the RFS volumes in these years. While we acknowledge that there is significant
uncertainty about the types of renewable fuels that would be used to meet the RFS volumes, we
believe that the mix of biofuel types described in this chapter are reasonable projections based on
historical data and current market trends of what could be supplied for the purpose of assessing
the potential impacts. As described in Chapter 2, we also acknowledge that the choice of baseline
affects the estimated impacts of the Analyzed Volumes. This chapter identifies the mix of
biofuels that could result from the Analyzed Volumes and the change in volumes in comparison
to the No RFS and 2025 Baselines. More information on the methodologies used to determine
these volumes can be found in Chapter 7.
3.1 Mix of Renewable Fuel Types for the Analyzed Volumes
In this section, we first summarize the component categories we analyzed in this rule, and
how these component categories relate to each statutory and implied category of renewable fuel.
Following this we summarize our estimates of the mix of more specific fuel types and feedstocks
we project will be used to meet these volumes. We have based this projected mix of fuels and
feedstocks on historical data, current market trends, and our projections of the potential supply in
2026 and 2027.
The Analyzed Volumes for 2026 and 2027 are presented in Preamble Section III. A and
are repeated in Table 3.1-1 by the component categories and in Table 3.1-2 by the statutory and
implied categories.
Table 3.1-1: Analyzed Volumes by Component Category (mil
D Code
2026
2027
Cellulosic biofuel
D3+D7
1,364
1,465
Biomass-based diesel
D4
9,961
10,118
Other advanced biofuel
D5
224
224
Conventional renewable fuel
D6
14,270
14,203
ion RINs)
Table 3.1-2: Analyzed Volumes by
Statutory and Implied Category (million RINs)
D Code
2026
2027
Cellulosic biofuel
D3+D7
1,364
1,465
Non-cellulosic advanced biofueP
D4+D5
10,185
10,342
Advanced biofuel
D3+D4+D5+D7
11,550
11,777
Conventional renewable fuelb
D6
14,270
14,203
Total renewable fuel
All
25,820
25,980
a Non-cellulosic advanced biofuel is not an RFS standard category but includes all advanced biofuels that do not
qualify as cellulosic biofuel.
b Conventional renewable fuel is not an RFS standard category but includes all renewable fuels that do not qualify as
cellulosic biofuel, BBD, or advanced biofuel.
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We next estimated the constituent mix of renewable fuel types and feedstocks that could
be used to meet the Analyzed Volumes.147 The analyses supporting these projections can be
found in Chapter 7. The mix of renewable fuels we project will be supplied to meet the Analyzed
Volumes in 2026 and 2027 are presented in million RINs (Table 3.1-3) and million gallons
(Table 3.1-4).
Table 3.1-3: Projected Biofuel Supply for 2026 and 2027 (million RINs)
2026
2027
Cellulosic Biofuel
1,364
1,435
CNG/LNG from biogas
1,236
1,307
Ethanol from CKF
128
128
Total Biomass-Based DieseP
9,961
10,118
Biodiesel
2,670
2,670
Soybean oil
1,650
1,650
FOG
225
225
Corn oil
315
315
Canola oil
480
480
Renewable Diesel
7,290
7,453
Soybean oil
2,244
2,336
FOG
2,225
2,270
Corn oil
1,152
1,085
Canola oil
1,668
1,762
Other Advanced Biofuels
224
224
Renewable diesel from FOG
90
90
Sugarcane ethanol
15
15
Domestic ethanol from separated food waste
24
24
Otherb
95
95
Conventional Renewable Fuel
14,270
14,203
Ethanol from corn
14,270
14,203
a Includes BBD in excess of the advanced biofuel volume requirement. The excess would be used to help meet the
conventional renewable fuel volume requirement.
b Composed of non-cellulosic biogas, heating oil, and naphtha.
147 The analyses leading to the mix of renewable fuel types and feedstocks are presented in Chapter 7.
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Table 3.1-4: Projected Biofuel Supply for 2026 and 2027 (million gallons)
2026
2027
Cellulosic Biofuel
1,364
1,435
CNG/LNG from biogas
1,236
1,307
Ethanol from CKF
128
128
Total Biomass-Based DieseP
6,074
6,445
Biodiesel
1,780
1,780
Soybean oil
1,100
1,100
FOG
150
150
Corn oil
210
210
Canola oil
320
320
Renewable Diesel
4,290
4,660
Soybean oil
1,320
1,460
FOG
1,309
1,419
Corn oil
678
678
Canola oil
981
1,101
Other Advanced Biofuels
162
162
Renewable diesel from FOG
56
56
Sugarcane ethanol
15
15
Domestic ethanol from separated food waste
24
24
Otherb
67
67
Conventional Renewable Fuel
14,270
14,203
Ethanol from corn
14,270
14,203
a Includes BBD in excess of the advanced biofuel volume requirement. The excess would be used to help meet the
conventional renewable fuel volume requirement.
b Composed of non-cellulosic biogas, heating oil, and naphtha.
The vast majority of the fuels we project to be used in the U.S. are projected to be
produced domestically from domestic feedstocks. This is the case for all cellulosic biofuels, most
other advanced biofuels (except for sugarcane ethanol, which we project will be imported) and
all conventional renewable fuel. For BBD, however, we project that a significant portion of this
fuel will be produced from imported feedstocks. Because biofuels produced from imported
feedstocks are projected to have different impacts on some of the statutory factors than fuels
produced from domestic feedstocks, we have projected the volumes of biodiesel and renewable
diesel we expected to be produced from domestic and foreign feedstocks separately. These
projections are shown in Table 3.1-5 (in million RINs) and Table 3.1-6 (in million gallons).
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Table 3.1-5: BBD Volumes from
Jomestic and Im
Biofuel
2026
2027
BBD (Total)
9.96
10.12
Biodiesel (Total)"
2.66
2.66
Domestic FOG
0.22
0.22
Domestic Soybean Oil
1.65
1.65
Domestic Canola Oil
0.00
0.00
Domestic Distillers Corn Oil
0.31
0.31
Imported FOG
0.00
0.00
Imported Soybean Oil
0.00
0.00
Imported Canola Oil
0.48
0.48
Imported Distillers Corn Oil
0.00
0.00
Renewable Diesel (Total)b
7.30
7.43
Domestic FOG
1.36
1.36
Domestic Soybean Oil
2.24
2.34
Domestic Canola Oil
0.39
0.37
Domestic Distillers Corn Oil
1.15
1.08
Imported FOG
0.87
0.91
Imported Soybean Oil
0.00
0.00
Imported Canola Oil
1.28
1.39
Imported Distillers Corn Oil
0.01
0.01
jorted Feedstocks (billion RINs)
a Includes heating oil.
b Includes renewable jet fuel.
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Table 3.1-6: BBD Volumes from
Jomestic and Im
Biofuel
2026
2027
BBD (Total)
6.07
6.44
Biodiesel (Total)"
1.78
1.78
Domestic FOG
0.15
0.15
Domestic Soybean Oil
1.10
1.10
Domestic Canola Oil
0.00
0.00
Domestic Distillers Corn Oil
0.21
0.21
Imported FOG
0.00
0.00
Imported Soybean Oil
0.00
0.00
Imported Canola Oil
0.32
0.32
Imported Distillers Corn Oil
0.00
0.00
Renewable Diesel (Total)b
4.29
4.66
Domestic FOG
0.80
0.85
Domestic Soybean Oil
1.32
1.46
Domestic Canola Oil
0.23
0.23
Domestic Distillers Corn Oil
0.68
0.68
Imported FOG
0.51
0.57
Imported Soybean Oil
0.00
0.00
Imported Canola Oil
0.75
0.87
Imported Distillers Corn Oil
0.00
0.00
jorted Feedstocks (million gallons)
a Includes heating oil.
b Includes renewable jet fuel.
3.2 Volume Changes Analyzed with Respect to the No RFS Baseline
In this RIA we have quantified the impacts of the Analyzed Volumes for 2026 and 2027
on several factors. The impacts on these factors were based on the difference in the volumes of
specific renewable fuel types between the Analyzed Volumes and the No RFS Baseline. These
differences are shown in Tables 3.2-1 (in million RINs) and Table 3.2-2 (in million gallons). The
values in these tables reflect the difference between values of the tables containing the Analyzed
Volumes (Tables 3.1-3 and 3.1-4) and the tables containing the No RFS Baseline volumes
(Tables 2.1.5-1 and 2).
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Table 3.2-1: Volume Changes Relative to the No RFS Baseline (million RINs)
2026
2027
Cellulosic Biofuel
799
838
CNG/LNG from biogas
799
838
Ethanol from CKF
0
0
Total Biomass-Based Diesel
5,893
6,555
Biodiesel
1,563
1,565
Soybean oil
816
1,210
FOG
96
-251
Corn oil
287
186
Canola oil
480
480
Renewable Diesel
4,334
5,000
Soybean oil
2,244
2,336
FOG
1,022
1,508
Corn oil
104
205
Canola oil
1,668
1,762
Jet fuel from FOG
-5
-5
Other Advanced Biofuels
95
95
Renewable diesel from FOG
0
0
Sugarcane ethanol
0
0
Domestic ethanol from separated food waste
0
0
Other51
95
95
Conventional Renewable Fuel
231
245
Ethanol from corn
231
245
' Composed of non-cellulosic biogas, heating oil, and naphtha.
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Table 3.2-2: Volume Changes Relative to the No RFS Baseline (million gallons)
2026
2027
Cellulosic Biofuel
799
838
CNG/LNG from biogas
799
838
Ethanol from CKF
0
0
Total Biomass-Based Diesel
3,597
4,175
Biodiesel
1,042
1,043
Soybean oil
544
807
FOG
64
-167
Corn oil
191
124
Canola oil
243
279
Renewable Diesel
2,551
3,127
Soybean oil
1,320
1,460
FOG
601
942
Corn oil
61
128
Canola oil
981
1,101
Jet fuel from FOG
0
0
Other Advanced Biofuels
67
67
Renewable diesel from FOG
0
0
Sugarcane ethanol
0
0
Domestic ethanol from separated food waste
0
0
Other51
67
67
Conventional Renewable Fuel
231
245
Ethanol from corn
231
245
a Composed of non-cellulosic biogas, heating oil, and naphtha.
Note that the changes in ethanol from corn shown in Tables 3.2-1 through 3.2-2 can be
entirely attributed to ethanol used as E15 and E85, since under the No RFS Baseline we project
that E10 would be used regardless of the RFS program but E15 and/or E85 would only be used
in a very few states with state incentives and mandates.148 There is some uncertainty related to
how changes in ethanol consumption will impact ethanol production. For example, ethanol
producers could respond to increased domestic demand for ethanol by increasing production or
by decreasing ethanol exports. In this latter case, decreases in domestic ethanol demand would
have little to no impact on domestic ethanol production. For the analyses conducted in support of
this rule we generally projected that any increase in ethanol consumption would result in a
gallon-for-gallon increase in ethanol production. This would be the maximum expected impact
we would expect from any changes in ethanol consumption attributable to the RFS program.
We determined that a more robust analysis could be performed for some statutory factors
if BBD produced from FOG could be disaggregated into specific types. EMTS, which is the
source of the feedstock data used in this rule, does not differentiate between different types of
FOG. Therefore, EPA used data from EIA's Monthly Biofuels Capacity and Feedstocks Update,
to estimate that FOG consisted of about 35% UCO and 65% tallow in 2025 through
148 See Chapter 2.1.1 for more discussion on El5 andE85.
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September.149 These fractions were applied to the volumes projected to be supplied in 2026 and
2027. The projected volumes of biodiesel and renewable diesel produced from UCO and tallow
are shown in Table 3.2-3. The projected increase in biodiesel and renewable diesel produced
from UCO and tallow relative to the No RFS Baseline is shown in Table 3.2-4.
Table 3.2-3: Disaggregated Biofuels Produced from FOG (million gallons)
2026
2027
Biodiesel from FOG
150
150
UCO
53
53
Tallow
98
98
Renewable diesel from FOG
1,309
1,419
UCO
458
497
Tallow
851
922
Table 3.2-4: Volume Changes in Biodiesel and Renewable Diesel Produced from FOG and
2026
2027
Biodiesel from FOG
64
-167
UCO
22
-58
Tallow
42
-109
Renewable diesel from FOG
601
942
UCO
210
330
Tallow
391
612
3.3 Volume Changes Analyzed with Respect to the 2025 Baseline
As described in Chapter 2.2, for some of the factors (e.g., cost) we also analyzed the
impacts of volume changes with respect to the 2025 Baseline. These differences are shown in
Table 3.3-1 (in million RINs) and Tables 3.3-2 (in million gallons). The values in these tables
reflect the difference between values of the tables containing Analyzed Volumes (Tables 3.1-3
and 3.1-4) and the tables containing the 2025 Baseline volumes (Tables 2.2-1 and 2).
149 EIA, "Monthly Biofuels Capacity and Feedstocks Update with data for September 2025," November 28, 2025,
Table 2b - U.S. Feedstocks consumed for production of biofuels. https://www.eia.gov/biofuels/update. For months
where information was withheld we assumed consumption of FOG was equal to the average consumption in months
where information was listed.
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Table 3.3-1: Volume Changes Relative to 2025 Baseline (million RINs)
2026
2027
Cellulosic Biofuel
117
188
CNG/LNG from biogas
66
137
Ethanol from CKF
51
51
Total Biomass-Based Diesel
4,222
4,379
Biodiesel
1,017
1,017
Soybean oil
460
460
FOG
-6
-6
Corn oil
249
249
Canola oil
315
315
Renewable Diesel
2,860
3,023
Soybean oil
1,358
1,450
FOG
-300
-255
Corn oil
444
376
Canola oil
1,314
1,408
Jet fuel from FOG
0
0
Other Advanced Biofuels
6
6
Renewable diesel from FOG
20
20
Sugarcane ethanol
15
15
Domestic ethanol from separated food waste
4
4
Other
-32
-32
Conventional Renewable Fuel
87
20
Ethanol from corn
87
20
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Table 3.3-2: Volume Changes Relative to 2025 Baseline (million gallons)
2026
2027
Cellulosic Biofuel
117
188
CNG/LNG from biogas
66
137
Ethanol from CKF
51
51
Total Biomass-Based Diesel
2,340
2,711
Biodiesel
678
678
Soybean oil
307
307
FOG
-4
-4
Corn oil
166
166
Canola oil
210
210
Renewable Diesel
1,658
2,028
Soybean oil
799
939
FOG
-176
-66
Corn oil
261
261
Canola oil
773
893
Jet fuel from FOG
0
0
Other Advanced Biofuels
9
9
Renewable diesel from FOG
13
13
Sugarcane ethanol
15
15
Domestic ethanol from separated food waste
4
4
Other
-23
-23
Conventional Renewable Fuel
87
20
Ethanol from corn
87
20
Unlike the comparison to the No RFS Baseline, the changes in ethanol from corn shown
in Table 3.3-1 and 3.3-2 are a function of both changes in total gasoline demand as well as
changes in the consumption of E15 and E85. Table 3.3-3 shows the amount of ethanol that can
be attributed to each of these factors.
Table 3.3-3: Source of Ethanol Changes in the Analyzed Volumes Relative to the 2025
Baseline (million gallons)
2026
2027
Changes in ethanol consumption attributable to changes in gasoline demand
118
6
Changes in ethanol consumption attributable to changes in El 5 and E85
consumption
39
83
Total
158
90
Note: Totals do not add due to rounding.
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Chapter 4: Environmental Impacts
CAA section 21 l(o)(2)(B)(ii) requires EPA to analyze a number of environmental factors
in its determination of the appropriate volumes to establish under the set authority, including
factors on air quality, climate change, conversion of wetlands, ecosystems, wildlife habitat, water
quality, and water supply. This chapter discusses these environmental factors except for climate
change, which is evaluated separately in Chapter 5. Where applicable, this chapter discusses
additional factors, such as soil quality and ecosystem services, per EPA's authority to consider
"other" factors as explained in more detail in Preamble Section II.B. For example, soil quality is
evaluated due to its close association and impacts on water quality. In addition, the discussions in
this chapter reference and leverage the findings from the Third Triennial Report to Congress on
Biofuels and the Environment (RtC3), finalized in January 2025, which provides additional
information on environmental impacts from biofuels and the RFS program.150
4.1 Air Quality
Air quality, as measured by the concentration of air pollutants in the ambient atmosphere,
can be affected by increased production and use of biofuels. Some air pollutants are emitted
directly (e.g., nitrogen oxides (NOx)), while other air pollutants are formed secondarily in the
atmosphere (e.g., ozone), and some air pollutants are both emitted directly and formed
secondarily (e.g., particulate matter (PM) and aldehydes). Air quality can be affected by
emissions from: (1) Production and transport of feedstocks, (2) Emissions from conversion of
feedstocks to biofuels, (3) Emissions from transport of the finished biofuels, and (4) Emissions
from combustion of biofuels in vehicles. Emissions from increased production and use of
biofuels contribute to ambient concentrations of air pollutants, and the health and environmental
effects associated with exposure to these air pollutants, including effects on children, are
discussed further in a memorandum to this docket.151
The emissions from production and transport of biofuel feedstocks and finished biofuels,
and from combustion of biofuels in vehicles, differ depending on the type of biofuel. In addition
to the type of biofuel, other factors may affect emissions, including the blend composition of
biofuel with fossil or petroleum fuel, vehicle technology, emissions control technology, and
operating conditions.
4.1.1 Background on Air Quality Impacts of Biofuels
This section summarizes current knowledge about the air quality impacts of biofuels,
specifically biofuels whose volumes are impacted by this rule. The biofuels we focus on in this
section are conventional corn ethanol, BBD (including biodiesel and renewable diesel), and
150 EPA, "Biofuels and the Environment: Third Triennial Report to Congress," EPA/600/R-24/343F, January 2025.
151 The health and environmental effects of the pollutants discussed in this chapter are described "Health and
environmental effects of pollutants discussed in Chapter 4 of Draft Regulatory Impact Analysis (DRIA) supporting
the proposed Renewable Fuel Standard (RFS) standards for 2026-2027," available in the docket for this action.
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renewable CNG/LNG.152 Chapter 4.1.2 includes an evaluation of the emission impacts
associated with the Analyzed Volumes when compared to the No RFS Baseline, and Chapter
4.1.3 describes the likely air quality impacts associated with the Analyzed Volumes when
compared to the No RFS Baseline.
When considering background information from previous work on emissions and air
quality impacts of biofuels, it is important to understand whether the rule would be increasing or
decreasing volumes of biofuels; this requires defining the baseline volume of biofuels for
comparison. Preamble Section III.B and Chapter 2 detail the determination of the Analyzed
Volumes as compared with the No RFS and 2025 Baselines. Generally, the No RFS Baseline is
used for analytical purposes and the 2025 Baseline is an additional informational case.
EPA has previously assessed the air quality impacts of biofuels in prior RFS rules,
including the RFS2 Rule and in the "anti-backsliding study" (ABS).153-154 Air quality modeling
was done for the RFS2 Rule in order to assess the impacts of the required RFS2 volumes
compared to two different baselines or reference cases, both of which included some usage of
ethanol fuels.155 The RFS2 modeling indicated that the increased use of renewable fuels
increased emissions of hydrocarbons, NOx, acetaldehyde, and ethanol and decreased emissions
of other pollutants such as carbon monoxide (CO) and benzene when evaluating production,
transport, and end use. However, the impacts of these emissions on criteria air pollutants were
highly variable from region to region. Overall, the emission changes were projected to lead to
increases in national population-weighted annual average ambient PM2.5 and ozone
concentrations. Air quality impacts associated with changes in ethanol production and transport
are expected to be primarily in the local area where the emissions occur.156
Air quality modeling was also performed for the ABS, which examined the impacts on
air quality in 2016 that might result from changes in vehicle and engine emissions associated
with renewable fuel volumes under the RFS relative to approximately 2005 levels.157 The ABS
modeling found potential increases and decreases in ambient concentrations of pollutants. For
example, compared to the "pre-RFS" scenario, the 2016 "with-RFS" scenario had increased
ozone concentrations across the eastern U.S. and in some areas in the western U.S., with some
decreases in localized areas. In the 2016 "with-RFS" scenario, concentrations of fine particulate
152 This includes all fuel categories appearing in Tables 3.2-1 and 2 with increased volumes from the No RFS
Baseline with the exception of "Other Advanced Biofuels (D5) - Other." This fuel category represents an unknown
mix of various fuel types with smaller volumes.
153 EPA, "Renewable Fuel Standard Program (RFS2) Regulatory Impact Analysis," EPA-420-R-10-006, February
2010.
154 EPA, "Clean Air Act Section 21 l(v)(l) Anti-backsliding Study," EPA-420-R-20-008, May 2020.
155 See RFS2 Rule RIA Tables 3.2.7 and 3.2.8 for the emissions impacts associated with biodiesel and ethanol
volume changes.
156 Cook, Rich, Sharon Phillips, Marc Houyoux, Pat Dolwick, Rich Mason, Catherine Yanca, Margaret Zawacki, et
al. "Air Quality Impacts of Increased Use of Ethanol Under the United States' Energy Independence and Security
Act." Atmospheric Environment 45, no. 40 (September 16, 2010): 7714-24.
https://doi.Org/10.1016/i.atmosenv.2010.08.043.
157 The ABS focused on the impacts of statutorily required renewable fuel volumes on concentrations of criteria and
toxic pollutants due to changes in vehicle and engine emissions; this study was not an examination of the lifecycle
impacts of renewable fuels on air quality.
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matter (PM2.5) were relatively unchanged in most areas, with increases in some areas and
decreases in some localized areas.
In this rule, we rely primarily on the conclusions from the RtC3, which summarized
available information on air quality impacts associated with biofuels.158 The RtC3 notes that
there is no new evidence that contradicts the fundamental conclusions of previous reports to
Congress.159 The RtC3 concluded that emissions of NOx, sulfur oxides (SOx), CO, volatile
organic compounds (VOCs), ammonia (NH3), PM2.5, and PM10, can be impacted at each stage of
biofuel production, distribution, and usage, and emphasized that the impacts associated with
feedstock and fuel production and distribution are important to consider, along with those
associated with fuel usage.
4.1.1.1 CornEthanol
Corn can be used to produce fuel ethanol for blending with gasoline and usage in
vehicles. The RtC3 states that increased corn production results in higher agricultural dust and
NH3 emissions from fertilizer use, although improved nitrogen management practices can reduce
these increases in NH3 emissions. Increased corn ethanol production and refining also leads to
increased NOx, SOx, VOCs, PM2.5, and PM10, and dispersion modeling has shown elevated
pollutant concentrations near corn biorefineries.160 Pollutant emissions associated with corn
ethanol also result from evaporative losses of VOCs during storage and transport, as well as
combustion emissions from commercial marine vessels, rail, tanker trucks, and other equipment
used to transport the ethanol to end use. Finally, the combustion of ethanol in end use
applications causes emissions of NOx, VOCs, PM2.5, and CO as well. As increased ethanol
volumes are displacing petroleum and petroleum-related emissions in each of these areas, the
overall impact on the environment is a complex issue.
The RtC3 also included a comparison of air quality impacts from corn ethanol versus
gasoline.161 Overall the total potential air quality impacts were much lower from corn ethanol
than from gasoline because much less corn ethanol is consumed than gasoline. However, results
also show a trend of increased life cycle emissions for the corn ethanol pathways compared with
petroleum-based gasoline. The trend is stronger for some pollutants (e.g., SOx and PM2.5) and
nearly negligible for others (e.g., CO and VOCs). In addition, per megajoule potential life cycle
air quality impacts were larger for corn ethanol compared with gasoline but were decreasing
through time as the industry matured and efficiencies improved.
158 EPA, "Biofuels and the Environment: Third Triennial Report to Congress," EPA/600/R-24/343F, January 2025.
Chapter 8 "Air Quality."
159 The cutoff date for publication of literature included in the RtC3 was early- to mid-2022.
1611 Lee, Eun Kyung, Xiaobo Xue Romeiko, Wangjian Zhang, Beth J. Feingold, Haider A. Khwaja, Xuesong Zhang,
and Shao Lin. "Residential Proximity to Biorefinery Sources of Air Pollution and Respiratory Diseases in New York
State." Environmental Science & Technology 55, no. 14 (July 7, 2021): 10035-45.
https://doi.org/10.1021/acs.est. Ic00698.
161 See RtC3 Chapter 8.5 "Comparison with Petroleum" for more details on results. The models run were the
Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation (GREET) model and the Bio-based
circular carbon economy Enviromnentally-extended Input-Output Model (BEIOM).
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A study that focused on papers relevant to California and was published since the RtC3
reviewed available literature and concluded that while the use of bioethanol (ethanol produced
from plants, such as sugarcane or corn) was beneficial with respect to GHG emissions, it was
associated with an increase in criteria air pollutant emissions relative to petroleum gasoline on a
per-unit basis.162
4.1.1.2 Biomass-based diesel
For the purposes of this analysis, BBD includes biodiesel and renewable diesel. Although
BBD is sourced from a variety of feedstocks, domestic soybean oil and domestic biogenic waste
fats, oils, and greases make up greater than 80% of the BBD fuel and feedstock volumes. The
RtC3 states that emissions from production of biodiesel from soybean oil vary depending on the
oil extraction method and that mechanical extraction is associated with the highest emissions.
RtC3 also states that compared to corn ethanol, data are lacking on emission and air quality
impacts of the feedstock production (soybean), storage, and transport stages of BBD
production.163 The RtC3 concluded that for heavy-duty engines model years 2007 and forward,
the impacts of biodiesel on end use emissions of criteria pollutants and precursors are
insignificant compared to petroleum diesel.
The RtC3 also included a comparison of air quality impacts from soy biodiesel and
petroleum diesel.164 The results generally show a trend of increased life cycle emissions for the
soy oil biodiesel pathways compared with petroleum diesel. The trend is stronger for some
pollutants (e.g., SOx and VOC) and less conclusive for others (e.g., CO and PM2.5). In addition,
the per megajoule potential life cycle air quality effects were larger for biodiesel compared with
petroleum diesel. However, the report also observed that per megajoule effects were decreasing
through time as the industry matured and efficiencies improved.
The 2026 California-based study by Freer-Smith, et al. reviewed available literature and
concluded that the use of biodiesel is mostly seen as having a beneficial impact on criteria
pollutant emissions relative to petroleum diesel use.165 In addition, a recent dispersion modeling
study has shown elevated pollutant concentrations near soybean biorefineries.166
162 Freer-Smith, Peter, Jack H. Bailey-Bale, Caspar L. Donnison, and Gail Taylor. "The Good, the Bad, and the
Future: Systematic Review Identifies Best Use of Biomass to Meet Air Quality and Climate Policies in California."
GCB Bioenergv 15, no. 11 (September 23, 2023): 1312-28. https://doi.org/10.1111/gcbb. 13101.
163 We do not include information on production impacts on air quality for BBD made from FOG in this section
because FOG are considered byproducts or waste products of other processes that occur regardless of producing
BBD from FOG.
164 See RtC3 Chapter 8.5 "Comparison with Petroleum" for more details on results. The models run were the
GREET model and BEIOM.
165 Freer-Smith, Peter, Jack H. Bailey-Bale, Caspar L. Donnison, and Gail Taylor. "The Good, the Bad, and the
Future: Systematic Review Identifies Best Use of Biomass to Meet Air Quality and Climate Policies in California."
GCB Bioenergv 15, no. 11 (September 23, 2023): 1312-28. https://doi.Org/10.l 111/gcbb. 13101.
166 Lee, Eun Kyung, Xiaobo Xue Romeiko, Wangjian Zhang, Beth J. Feingold, Haider A. Khwaja, Xuesong Zhang,
and Shao Lin. "Residential Proximity to Biorefinery Sources of Air Pollution and Respiratory Diseases in New York
State." Environmental Science & Technology 55, no. 14 (July 7, 2021): 10035-45.
https://doi.org/10.1021/acs.est. Ic00698.
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4.1.1.3
Renewable CNG/LNG
Renewable CNG and LNG, categorized as cellulosic biofuel in RFS, can be derived from
biogas that is produced by the anaerobic digestion of biomass by natural organisms and collected
and upgraded for use in CNG/LNG vehicles. We assume for the purposes of the RFS program
that biogas produced at landfills, municipal wastewater treatment facilities, agricultural waste
digesters, and separated municipal solid waste digesters would otherwise have been flared were
it not productively used to produce transportation fuel.
The RtC3 notes that research on biofuel impacts on air quality has focused on corn
ethanol and soy biodiesel more than on biofuels from other feedstocks. A 2023 review of studies
on biomass use pathways determined that utilizing biogas recovered from the anaerobic
digestions of municipal solid waste, water waste, animal waste, and food waste results in an
overall reduction of criteria air pollutant emissions compared to allowing the waste to
decompose in a landfill or by natural composting or decomposition.167
4.1.2 Emission Impacts of Analyzed Volumes
We have evaluated air pollutant emissions impacts from biofuels determined to have an
increase in production due to this rule. These fuels include corn ethanol, biodiesel, renewable
diesel, and renewable CNG/LNG from biogas. Chapter 4.1.2.1 estimates emissions impacts
associated with increased biofuel production, Chapter 4.1.2.2 discusses expected emissions
impacts from the transport of additional biofuels, and Chapter 4.1.2.3 focuses on impacts on end-
use or onroad emissions due to increases in the Analyzed Volumes.
As discussed in Preamble Section III.B, there are several baselines to which we can
compare the Analyzed Volumes and determine the air quality impacts of this rule. The difference
between the Analyzed Volumes and the No RFS Baseline was used to determine the emissions
impacts presented here. Chapter 3 details the volume changes associated with this rule relative to
the No RFS Baseline (Table 3.2-1 through Table 3.2-4). While using the No RFS Baseline is
most appropriate in evaluating the total impact of this rule, the 2025 Baseline, representing the
current RFS biofuels requirements, could be used to determine the emission impacts of this rule
compared to current conditions. As shown in Tables 3.3-1 through 6, the Analyzed Volumes are
lower than the 2025 Baseline volumes for several of the fuel categories.
4.1.2.1 Emissions from the Production of Biofuels
In this section, we estimate emissions associated with producing biofuels with an increase
in production volumes, relative to a No RFS Baseline, due to this rule.168 These biofuels include
conventional corn ethanol (D6), BBD (D4), including biodiesel and renewable diesel, and
renewable CNG and LNG derived from biogas (D3). We have not addressed production
emissions from other categories of biofuels, including renewable diesel co-processed with
167 Freer-Smith, Peter, Jack H. Bailey-Bale, Caspar L. Donnison, and Gail Taylor. "The Good, the Bad, and the
Future: Systematic Review Identifies Best Use of Biomass to Meet Air Quality and Climate Policies in California."
GCB Bioenergv 15, no. 11 (September 23, 2023): 1312-28. https://doi.Org/10.l 111/gcbb. 13101.
168 Biofuel volume production impacts relative to the No RFS Baseline are presented in Tables 3.2-1 through 4.
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petroleum diesel (RFS Fuel Code D5, Other Advanced Biofuel). In this analysis, we are defining
production emissions as those produced at the biorefinery and not including emissions upstream
of the refining facility (for example, emissions associated with crop production or transport of
the feedstock to the refinery). While much of the focus on emissions from the production of
biofuels has been on criteria air pollutants, there are also emissions of hazardous air pollutants
(HAPs) at biorefineries that can impact air quality.169 We have estimated emissions of selected
HAPs, or air toxics, from the production of biofuels where possible. The air toxics selected were
those determined to be risk drivers in the 2020 AirToxScreen and that could reasonably be
emitted during the refining of biofuel feedstocks.170 This list includes 1,3-butadiene,
acetaldehyde, acrolein, benzene, formaldehyde, and naphthalene.
There are several approaches, each with varying strengths and weaknesses, that could be
used to estimate pollutant emissions from the production of biofuel. A global equilibrium model,
such as the Global Change Analysis Model (GCAM), can account for interactions between
various biofuels and petroleum fuels over a full lifecycle; however, the comprehensive, global
nature of the model does not allow for the individual determination of emissions associated with
incremental processes in the full life cycle of a biofuel.171 Another option for a quantitative
evaluation of the emissions impact from the production of biofuels is to use Argonne National
Laboratory's R&D GREET (Greenhouse gases, Regulated Emissions, and Energy use in
Technologies) model.172 The GREET model allows for the evaluation of production emissions
from all biofuels impacted by this rule. However, only a limited number of criteria pollutant
emission rates, and no HAP emission rates, are available from fuel production in GREET, and
GREET cannot project market-mediated criteria air pollutant or HAP emissions impacts of
changes to fuel pathways. Another approach is to evaluate annual biorefining facility emissions
using EPA's Air Emissions Modeling Platform (EMP) as a function of the volume of fuel
produced at each facility. The most recent version, the 2022 EMP, is based on the emissions in
the 2020 National Emissions Inventory and contains both criteria air pollutant and HAP annual
emissions reported by individual biorefining facilities to state and regional air agencies, EPA,
and Federal Land Management agencies.173 In this analysis, we chose to use the EMP as the
preferred data source to determine biofuel production emissions.174 However, as described
169 Environmental Integrity Project, "Farm to Fumes: Hazardous Air Pollution from Biofuel Production," June 12,
2024. https://enviromnentalintegritv.org/wp-content/uploads/2024/06/EIP Report FanntoFumes 06.12.2024.pdf.
17112020 AirToxScreen Risk Drivers. https://www.epa.gOv/svstem/files/documents/2024-08/2020-airtoxscreen-risk-
drivers.pdf.
171 GCIMS, "GCAM: Global Change Analysis Model." https://gcims.pnnl.gov/modeling/gcam-global-change-
analvsis-model.
172 Wang, Michael, Cai, Hao, Ou, Longwen, Elgowainy, Amgad, Alam, Md Rakibul, Benavides, Pahola T„
Benvenutti, Livia, Burnham, Andrew, Do, Thai N., Farhad, Masum, Gan, Yu, Gracida-Alvarez, Ulises R., Hawkins,
Troy R., Iyer, Rakesh K„ Kar, Saurajyoti, Kelly, Jarod C., Kim, Taemin, Kolodziej, Christopher, Kwon, Hoyoung,
Lee, Uisung, Lim, Juin Y„ Liu, Xinyu, Lu, Zifeng, Morales, Michele, Ng, Clarence, Pandey, Ishan, Shukla,
Siddharth, Siddique, Nazib, Sun, Pingping, Sykora, Tom, Vyawahare, Pradeep, and Zhou, Jo. "Greenhouse gases.
Regulated Emissions, and Energy use in Technologies Model ® (2024 Excel)." Computer software. December 20,
2024. https://doi.Org/10.11578/GREET-Excel-2024/dc.20241203.l.
173 An emissions modeling platform is the full set of emissions inventories, other data files, software tools, and
scripts that process the emissions into the form needed for air quality modeling. As discussed in Chapter 4.1.3, we
did not perform air quality modeling for this rule.
174 EPA, "2022v2 Emissions Modeling Platform." https://www.epa.gov/air-emissions-modeling/2022v2-emissions-
modeling-platform.
108
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below, facility process-level data is not available for all biofuels, namely renewable CNG/LNG
from biogas, and we used GREET to determine production emissions of these fuels.175
4.1.2.1.1 Corn Ethanol and Biomass-based Diesel
To estimate the emissions impacts of fuel production from the Analyzed Volumes for
corn ethanol and BBD, RINs generated for corn ethanol, biodiesel, and renewable diesel were
compared to reported air emissions at the facility level. The facility-level emissions rates were
then used to determine a national emission factor for each pollutant and fuel type that could then
be applied to the fuel volume differences between this rule and the No RFS Baseline. Emission
factors were determined for the year 2022 as this was the most recent year that facility-level
emissions were available at the time of this analysis.
Facilities that generated corn ethanol, biodiesel, and renewable diesel RINs in 2022 were
identified through EMTS RIN generation records specifying the fuel type, number of RINs
generated, and total volume of fuel produced.176 These facilities were then matched to their
reported 2022 emissions inventory in the 2022 Emission Modeling Platform (EMP) version 2.1
through the Emissions Information Systems (EIS).177-178-179
As shown in Table 4.1.2.1.1-1, most ethanol biorefineries, but only some biodiesel and
renewable diesel refineries, reported air emissions in 2022. For example, 175 of the 187
domestic ethanol biorefineries generating RINs in 2022 have reported air pollutant emissions
available in the EMP, and these 175 ethanol facilities with reported emissions generated 97% of
the total ethanol RINs in 2022. Facilities with reported emissions information were generally
larger, with an average 85 million RINs generated in 2022 compared to an average of 33 million
RINs for facilities that did not report emissions.
175 The methodology for determining pollutant emission rates from biofuel production is discussed in
"Determination of Air Pollutant Emissions Factors from the Production of Biofuels," available in the docket for this
action.
176 EPA, "EMTS: RFS RIN Generation Report." https://www.epa.gov/fuels-registration-reporting-and-compliance-
help/emts-rfs-rin-generation-report.
177 EPA, "2022v2 Emissions Modeling Platform." https://www.epa.gov/air-emissions-modeling/2022v2-emissions-
modeling-platform.
178 EPA, "Emissions Inventory System (EIS) Gateway." https://www.epa.gov/air-emissions-inventories/emissions-
inventorv-svstem-eis-gatewav.
179 EPA, "Technical Support Document (TSD): Preparation of Emissions Inventories for the 2022v2 North
American Emissions Modeling Platform," EPA-454/B-25-002, December 2025.
https://www.epa.gov/svstem/files/documents/2025-12/2022v2 emismod tsd base december2025.pdf.
109
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Table 4.1.2.1.1-1: Number of Domestic Biorefineries Producing Ethanol, Biodiesel, and
Renewable Diesel in 2022 and the Percentage of RINs Generated at Facilities Reporting
Pollutant Emissions to Federal, State, or Local Agencies
Ethanol
Biodiesel
Renewable
Diesel
Number of facilities generating RINs
187
57
9
Number of facilities with reported emissions
175
21
4
Percentage of RINs at facilities with reported emissions
97%
61%
78%
Using the 2022v2.1 EMP annual emissions mass and total RINs generated at each
biorefinery, an emissions rate was determined for each pollutant at each facility. National
weighted emissions factors were then calculated using each facility's emission rate and fraction
of the total volume of fuel produced by category. The resulting national emissions factors are
presented in Table 4.1.2.1.1-2. The weighting was determined separately for each pollutant based
on available data. No biodiesel refining facilities reported emissions of 1,3-butadiene; therefore,
a production emissions factor of 1,3-butadiene was unable to be determined from biodiesel
production. As the equivalence value (EqV) of renewable diesel is adjusted to 1.6 RINs per
gallon in 2027 from the previous equivalence value of 1.7 RINs per gallon, emission factors for
renewable diesel with both a 1.7 EqV and a 1.6 EqV were determined and applied to the 2026
and 2027 renewable diesel volumes.
Table 4.1.2.1.1-2: Pollutant Emission Factors From Ethanol, Biodiesel, and Renewable
Diesel Production (tons/million RI>
fs)
Renewable
Renewable
Diesel
Diesel
Pollutant
Ethanol
Biodiesel
(1.7 EqV)
(1.6 EqV)
CO
0.84
0.40
0.39
0.42
nh3
0.08
0.01
0.01
0.01
NOx
1.090
0.61
0.20
0.22
PMio
0.62
0.25
0.07
0.08
PM2.5
0.50
0.16
0.07
0.08
S02
0.92
1.94
0.06
0.06
voc
1.37
2.69
0.61
0.64
1,3-Butadiene
0.00001
-
0.00001
0.00001
Acetaldehyde
0.06141
0.00187
0.00040
0.00043
Acrolein
0.01513
0.00002
0.00003
0.00003
Benzene
0.00112
0.00081
0.00676
0.00718
Formaldehyde
0.01033
0.00056
0.00363
0.00385
Naphthalene
0.00008
0.00001
0.00433
0.00460
The emission factors were then applied to the additional fuel volumes for ethanol,
biodiesel, and renewable diesel estimated from the Analyzed Volumes as compared to the No
RFS Baseline for 2026 and 2027. The emissions impacts resulting from the production of these
additional biofuel volumes are presented in Tables 4.1.2.1.1-3 and Table 4.1.2.1.1-4.
110
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Table 4.1.2.1.1-3: Emission Impact Estimates of CO, NH3, NOx, PM10, PM2.5, SO2, and
VOCs From the Production of Ethanol, Biodiesel, and Renewable Diesel for the Analyzed
Volumes Relative to the No RFS Baseline
Volume
Production Emissions (tons)
Year
Difference to
No RFS
(million
RINs)
CO
NH3
NOx
PM10
PM2.5
SO2
voc
Conventional (D6) Ethanol
2026
231
193
19
252
143
115
212
315
2027
245
205
20
267
151
122
225
335
Biomass-Based Diesel
(D4): Biodiesel
2026
1,563
622
13
948
386
253
3037
4,209
2027
1,565
622
13
949
386
253
3041
4,214
Biomass-Based Diesel
(D4): Renewable Diesel
2026
4,507
1779
52
914
330
323
250
2,728
2027
5,000
2097
61
1077
389
380
294
3,215
Table 4.1.2.1.1-4: HAP Emissions Impact Estimates From the Production of Ethanol,
Year
Volume
Difference to
No RFS
(million RINs)
Prot
uction Emissions (tons)
4>
=
¦3
s
PQ
CO
pC
2
13
O
<
£
0
u
0
<
4>
=
O)
N
=
CO
•a
pC
4)
2
13
E
0
to
4>
=
.=
.=
a
z
Conventional (D6) Ethanol
2026
212
0
14
3
0
2
0
2027
228
0
15
4
0
3
0
Biomass-Based Diesel (
34): Biodiesel
2026
1,716
-
3
0
1
1
0
2027
1,752
-
3
0
1
1
0
Biomass-Based Diesel (
34): Renewable Diesel
2026
3,823
0
2
0
29
16
19
2027
4,132
0
2
0
36
19
23
Note: An emissions estimate of zero indicates the production emissions to be less than 0.45 tons/year.
These emissions estimates assume the full additional fuel volume relative to the No RFS
Baseline will be fulfilled by increasing biofuel production at domestic bioreftneries. However,
we note that some of this additional biofuel volume may be fulfilled both by reducing exports,
whereby no changes in domestic biofuel production will occur, or by increasing imports,
whereby emission impacts would occur abroad. As such, this analysis may overestimate
111
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domestic emissions. For example, in 2022, approximately 0.1% of corn ethanol, 13% of
biodiesel, and 21% of renewable diesel RINs were issued to importers or foreign producers.180
4.1.2.1.2 Renewable CNG/LNG from Biogas
Renewable CNG/LNG is produced from biogas generated during the decomposition of
organic waste products from several feedstock pathways under the RFS program. These
feedstocks include gas produced at landfills and wastewater treatment facilities as well as animal
waste and food waste decomposed through anaerobic digestion by natural organisms. Biogas
collected from these feedstock sources can be purified and compressed or liquefied for use as
transportation fuel.
As biogas is often produced at facilities like landfills or dairy farms that have a main
purpose other than the production of renewable fuel, using facility-wide emission inventories as
an estimate for fuel production emissions, as used with liquid renewable fuels, would be
inappropriate. Consequently, to estimate emission impacts from the production of fuels from
biogas, we have used emission factors determined in Argonne National Laboratory's R&D
GREET (Greenhouse gases, Regulated Emissions, and Energy use in Technologies) 2024revl
model for CO, NOx, PMio. PM2.5, SO2, and VOCs.181 Table 4.1.2.1.2-1 summarizes emissions
resulting from the process steps of upgrading, purifying, and compressing or liquifying biogas to
create transportation fuel as published in GREET. We have excluded emissions from process
steps that would occur regardless of if the waste product would be used to produce renewable
CNG/LNG or handled through typical disposal method (e.g., onsite transport and anaerobic
breakdown). Analogous to our analysis of emissions from the production of ethanol, biodiesel,
and renewable diesel, we have also excluded emissions occurring upstream of the CNG or LNG
production facility and those from transport and storage of the finished fuel.
1811 EPA, "RINs Generated Transactions." https://www.epa.gov/fuels-registration-reporting-and-compliance-
help/rins-generated-transactions.
181 Wang, Michael, Cai, Hao, Ou, Longwen, Elgowainy, Amgad, Alam, Md Rakibul, Benavides, Pahola T„
Benvenutti, Livia, Burnham, Andrew, Do, Thai N., Farhad, Masum, Gan, Yu, Gracida-Alvarez, Ulises R., Hawkins,
Troy R., Iyer, Rakesh K„ Kar, Saurajyoti, Kelly, Jarod C., Kim, Taemin, Kolodziej, Christopher, Kwon, Hoyoung,
Lee, Uisung, Lim, Juin Y„ Liu, Xinyu, Lu, Zifeng, Morales, Michele, Ng, Clarence, Pandey, Ishan, Shukla,
Siddharth, Siddique, Nazib, Sun, Pingping, Sykora, Tom, Vyawahare, Pradeep, and Zhou, Jo. "Greenhouse gases.
Regulated Emissions, and Energy use in Technologies Model ® (2024 Excel)." Computer software. December 20,
2024. https://doi.Org/10.11578/GREET-Excel-2024/dc.20241203.l.
112
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Table 4.1.2.1.2-1: GREET Pollutant Emission Factors From the Production of Renewable
C>
\G
LNG
Animal
Food
Animal
Landfill
Wastewater
Waste
Waste
Landfill
Wastewater
Waste
Pollutant
Gas
Treatment
Digestion
Digestion
Gas
Treatment
Digestion
voc
1.28
1.06
1.34
1.56
1.74
1.57
1.84
CO
4.51
3.75
4.73
10.11
6.13
5.54
6.52
NOx
7.18
5.97
7.53
10.43
9.75
8.81
10.37
PMio
0.88
0.73
0.92
0.99
1.20
1.08
1.27
PM2.5
0.50
0.42
0.52
0.50
0.68
0.61
0.72
S02
4.54
3.77
4.76
4.01
6.16
5.57
6.55
While biogas CNG and LNG are considered a single fuel category in this rule, pollutant
emission rates differ depending on the biogas feedstock and product. To determine emissions
factors that can be applied nationally to future years, the ratio of RINs generated from biogas
feedstock sources for the year 2024 was used to determine a weighted emissions factor to apply
to 2026 and 2027. While we do not anticipate this rule would significantly alter the ratio of
biogas feedstock sources or renewable CNG:LNG, external factors may influence the industry
and affect these ratios.
The breakdown of biogas feedstock sources was determined using the 2024 RIN
generation feedstock summary report and is presented in Tables 4.1.2.1.2-2 and 3.182 The
feedstock summary report does not distinguish RINs generated at facilities producing biogas
domestically from RINs generated for imported biogas CNG and LNG. Therefore, both imported
and domestic RINs are used in determining the production emission factors for biogas. In 2024,
less than 1% of renewable CNG generating RINs were imported. Approximately 46% of
renewable LNG RINs were generated by importers, representing about 4% of the total
CNG/LNG biogas RINs. Additionally, the feedstock summary report only specifies LNG RINs
generated from biogas from landfills, agricultural digesters, and "All Other Feedstocks." In this
analysis, we have assumed biogas from wastewater treatment facilities as the feedstock for the
entirety of the "All Other Feedstock" RINs.
Table 4.1.2.1.2-2: RINs Generated in 2024 From the Production of Renewable CNG From
Facility Type
million RINs
% of CNG RINs
Landfill
581
66%
Animal Waste Digester
274
31%
Wastewater Treatment or Food Waste Digester
25
3%
Total
881
182 EPA, "RINs Generated Transactions." https://www.epa.gov/fuels-registration-reporting-and-compliance-
help/rins-generated-transactions.
113
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Table 4.1.2.1.2-3: RINs Generated in 2024 From the Production of Renewable LNG From
Biogas
Facility Type
million RINs
% of LNG RINs
Landfill
80
96%
Animal Waste Digester
3
4%
All Other Feedstock
0.04
0%
Total
83
Applying the 2024 fractions of biogas RINs from feedstock and fuel types and, weighting
by number of RINs produced from each pathway, total emissions rates for criteria air pollutants
were determined for biogas production as shown in Table 4.1.2.1.2-4. Emission rates are
presented as mass of pollutant per million RINs using the 77,000 Btu per RIN equivalence value
for renewable CNG/LNG. Emissions impacts from the production of renewable CNG/LNG from
biogas resulting from the difference between the Analyzed Volumes and the No RFS Baseline
are presented in Table 4.1.2.1.2-5.
Table 4.1.2.1.2-4: Pollutant Emission Factors From Production of Biogas Renewable CNG
and LNG
Weighted Emission Factors (g/mmBtu)
Biogas Production
Emissions Factor
Pollutant
CNG
LNG
Total Biogas
(tons/million RIN)
CO
4.57
6.15
4.70
0.399
NOx
7.26
9.78
7.47
0.634
PMio
0.89
1.20
0.92
0.078
PM2.5
0.50
0.68
0.52
0.044
S02
4.58
6.18
4.72
0.401
voc
1.29
1.74
1.33
0.113
Table 4.1.2.1.2-5: Pollutant Emission Impact Estimates From Production of Biogas
Renewable CNG and LNG Relative to the No RFS Baseline
Year
Volume
Difference to
No RFS
(million RINs)
Biogas CNG/LNG Prod
uction Emissions (tons)
CO
NOx
PM10
PM2.5
SO2
voc
2026
799
319
507
62
35
320
90
2027
838
334
532
65
37
336
95
We also acknowledge that biogas is generated from landfill and wastewater treatment
facility waste products, and the typical treatment of these waste products also results in pollutant
emissions. Biogas generated at landfills and wastewater treatment plants is typically flared for
safety and odor purposes, and these flares also generate emissions that are avoided when using
the landfill gas or wastewater treatment gas to produce biofuel. The avoided flared emissions are
not accounted for in this quantitative analysis and are a limitation of our estimates.
114
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4.1.2.1.3 Comparison of Emissions from the Production of Renewable Fuels to
Petroleum and Fossil Fuels
We compared the criteria air pollutant emission rates from the production of renewable
fuels, as determined in Chapter 4.1.2.1.1 and 4.1.2.1.2, to fossil fuel production emission rates.
While the production and use of renewable fuels may not actually reduce one-for-one the
production and use of fossil fuels, for the purposes of this comparison, we have assumed such a
one-for-one displacement. Emission rates from the production of petroleum and fossil fuels were
determined with the GREET model using process steps analogous to the steps included in our
estimates for renewable fuels.183 As with renewable fuels, we did not estimate emissions from
the transportation and storage of finished fuels. For direct comparison, production emission rates
are presented as mass of pollutant per unit energy, as renewable fuels do not necessarily have the
same energy density as their petroleum and fossil counterparts. Pollutant emission factors from
fuel production are presented in Tables 4.1.2.1.3-1 and 2.184
Emission rates from the production of petroleum gasoline were compared to those from
production of ethanol, and emission rates from production of diesel were compared to those from
production of biomass-based biodiesel and renewable diesel. Specifically, emission rates for
gasoline blendstock (EO) production were used as a comparison to emission rates for ethanol
production as GREET models gasoline containing 10% ethanol. Gasoline blendstock and
petroleum diesel production emission rates included emissions that occur at the refinery,
including intermediate product combustion, and facility non-combustion emissions. Feedstock
emissions upstream of the petroleum refinery were not included.
To compare emission rates from the production of fossil natural gas to renewable CNG
and LNG, we used emission rates which included the compression or liquefaction of natural gas
along with pipeline transport of natural gas and upstream feedstock emissions. Emissions from
feedstocks and transport for fossil natural gas were included while upstream emissions of biogas
were not. This is consistent with our flaring baseline for biogas. For the purpose of determining
pollutant emissions associated with renewable CNG/LNG production, we assume onsite fueling
of renewable CNG/LNG at the location of production. Emissions associated with the transport
and end use of renewable CNG/LNG are discussed in Section 4.1.2.2 and Section 4.1.2.3.3,
respectively.
183 Wang, Michael, Cai, Hao, Ou, Longwen, Elgowainy, Amgad, Alam, Md Rakibul, Benavides, Pahola T„
Benvenutti, Livia, Burnham, Andrew, Do, Thai N., Farhad, Masum, Gan, Yu, Gracida-Alvarez, Ulises R., Hawkins,
Troy R., Iyer, Rakesh K„ Kar, Saurajyoti, Kelly, Jarod C., Kim, Taemin, Kolodziej, Christopher, Kwon, Hoyoung,
Lee, Uisung, Lim, Juin Y„ Liu, Xinyu, Lu, Zifeng, Morales, Michele, Ng, Clarence, Pandey, Ishan, Shukla,
Siddharth, Siddique, Nazib, Sun, Pingping, Sykora, Tom, Vyawahare, Pradeep, and Zhou, Jo. "Greenhouse gases.
Regulated Emissions, and Energy use in Technologies Model ® (2024 Excel)." Computer software. December 20,
2024. https://doi.Org/10.11578/GREET-Excel-2024/dc.20241203.l.
184 The GREET model determines emission rates for only certain pollutants, limiting our analysis to those presented
in this section.
115
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Table 4.1.2.1.3-1: Comparison of Emission Rates From the Production of Corn Ethanol,
Gasoline B
endstock, I
»iodiesel, Renewab
e Diesel, and Petroleum Diesel (g/mml
Corn
Gasoline
Renewable
Petroleum
Pollutant
Ethanol
Blendstock (E0)
Biodiesel
Diesel
Diesel
CO
9.93
2.38
4.52
4.95
1.52
NOx
12.96
3.64
6.90
2.54
2.25
PM10
7.35
0.96
2.81
0.92
0.54
PM2.5
5.92
0.84
1.84
0.90
0.47
S02
10.93
1.23
22.10
0.70
0.78
voc
16.23
2.21
30.63
7.59
1.65
Table 4.1.2.1.3-2: Comparison of Emission Rates From the Production of Renewable
CNG/LNG and Fossil CNG/LNG (g/mmBtu)
Renewable
Fossil
Renewable
Fossil
Pollutant
CNG
CNG
LNG
LNG
CO
4.57
54.08
6.15
40.68
NOx
7.26
63.18
9.78
45.29
PM10
0.89
0.69
1.20
0.76
PM2.5
0.50
0.56
0.68
0.74
SO2
4.58
1.50
6.18
1.17
VOC
1.29
17.39
1.74
14.46
As seen in Table 4.1.2.1.3-1, emission rates from the production of ethanol are higher
than gasoline, and, with the exception of SO2 emissions from the production of renewable diesel,
biodiesel and renewable diesel emissions are higher than petroleum diesel. Particulate emission
rates, both PM10 and PM2.5, are comparable from the production of renewable CNG/LNG and
fossil CNG/LNG, as shown in Table 4.1.2.1.3-2. There is a lower emission rate of SO2 from the
production of fossil LNG as compared to renewable LNG. However, emission rates of other
criteria air pollutants are higher for fossil CNG/LNG than renewable CNG/LNG, primarily as a
result of emissions from sourcing fossil natural gas.
Emission impacts presented in Table 4.1.2.1.1-3 and Table 4.1.2.1.2-5 represent
emissions from the production of biofuels resulting from the difference in the fuel volumes of
this rule and the No RFS baseline. However, a No RFS scenario could result in the increased
production of petroleum or fossil fuels to meet transportation needs. To account for the potential
reduction in fossil fuel production because of this rule, we determined a net emissions impact of
the 2026 and 2027 biofuel volumes by subtracting the emissions generated from the production
of equivalent energy volumes of fossil fuels from the emissions generated from producing the
volume difference to the No RFS baseline of biofuels. The net emissions impact from the
production of the Analyzed Volumes of biofuels are presented in Table 4.1.2.1.3-4.
116
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Table 4.1.2.1.3-4: Net Emissions Impacts From the Production of Biofuels Relative to the
No RFS Baseline for Analyzed Volumes Accounting for the Potential Reduction in
Petroleum and Fossil Fuel Production
Volume
Volume
Net Po
lutant Emissions (tons)
Difference to
Difference
No RFS
to No RFS
Year
Fuel
(million RINs)
(mmBtu)
CO
NOx
PM10
pm25
SO2
VOC
Ethanol
231
17,632,230
147
181
124
99
189
273
2026
Biodiesel
1,563
124,648,208
412
639
311
189
2,930
3,982
Renewable Diesel
4,334
313,289,564
1,184
103
130
148
-29
2,054
Biogas CNG/LNG
799
61,523,000
-3,271
-3,674
15
-4
221
-1,072
Ethanol
245
18,700,850
155
192
132
105
200
289
2027
Biodiesel
1,565
124,807,707
413
640
311
189
2,933
3,987
Renewable Diesel
5,000
384,021,875
1,452
127
159
182
-36
2,518
Biogas CNG/LNG
838
64,526,000
-3,430
-3,853
16
-4
231
-1,124
As seen in Tables 4.1.2.1.3-4, our analysis estimates the production of ethanol, biodiesel,
and renewable diesel due to the Analyzed Volumes would result in additional emissions of CO,
NOx, PMio, PM2.5, and VOCs even with accounting for the potential reduction in petroleum fuel
production emissions. The production of ethanol and biodiesel contributes to additional SO2
emissions compared to a No RFS Baseline; however, the production of renewable diesel volumes
decreases emissions of SO2 if petroleum-based diesel production is reduced by an equivalent
amount. We also estimate the Analyzed Volumes of renewable CNG/LNG would reduce
emissions of CO, NOx, PM2.5, and VOCs if the production of fossil CNG/LNG were to be
reduced by the same volume, but additional SO2 and PM10 emissions would occur.
4.1.2.2 Emissions from the Transport of Biofuels
Emissions are also associated with the transport of biofuels from the production facility
to the user. This includes emissions occurring from the storage of finished fuel, leakage during
fueling or transport, and combustion emissions from the distribution mode of transport (e.g.,
road, rail). With biodiesel, renewable diesel, and renewable CNG/LNG from biogas, transport-
related emissions are expected to be comparable to those from the transport of petroleum or
fossil fuels.
Because ethanol is blended with gasoline before it can be used as a transportation fuel,
there are emissions due to the additional transport and storage for blending that would not exist
for a single product fuel. At the blending terminal, ethanol and gasoline are combined for various
fuel combinations (e.g., E10, E15, E85) prior to distribution to retail gasoline outlets where it is
sold to the customer. Primary modes of distributing ethanol to the blending terminal and the
blended fuel to the retail outlets are rail, road, or barges. Previous modeled emissions from the
transportation and storage of ethanol found the largest emission impact was to VOCs due to
evaporation.185 However, as the volume difference to the No RFS baseline for conventional corn
185 EPA, "RFS Program: Standards for 2023-2025 and Other Changes - Regulatory Impact Analysis," EPA-420-R-
23-015. Chapter 4.1.1.
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ethanol is a small fraction (less than 2%) of the 2026-2027 volumes, emissions due to the
transport of ethanol as a result of these volumes are considered to be minimal.
4.1.2.3 Emissions from the End Use of Biofuels
End-use emissions are generated when biofuels are used in vehicles. This includes
tailpipe exhaust emissions from the combustion of the fuels, non-tailpipe exhaust emissions
(such as crankcase exhaust), and evaporative emissions from fuel dispensing, leakage,
permeation, and venting. As biofuels may differ in chemical composition from fossil fuels, end-
use emissions from these fuels may also differ.
4.1.2.3.1 Ethanol
Impacts of ethanol blends on vehicle exhaust emissions are the result of complex
interactions between fuel properties, vehicle technologies, and emission control systems.
Depending on the pollutant and blend concentration, the impacts vary both in direction and
magnitude. Several test programs in recent years have evaluated the impacts of fuel properties,
including those of ethanol blends on emissions from vehicles meeting Tier 2 and Tier 3
standards.186 187 188 189 190 191 However, as E10 gasoline is economical to blend in the absence of
the RFS program after 2020, the only volumes of ethanol expected to result from this rule are
relatively small increases in ethanol used as El5 and E85. These small increases in El5 and E85
use are not expected to have a significant impact on overall vehicle evaporative and exhaust
emissions.
4.1.2.3.2 Biodiesel
Biodiesel consists of straight-chain molecules that boil in the diesel range and typically
contain at least one double bond as well as oxygen incorporated into a methyl ester group. These
chemical features can cause differences in emissions relative to petroleum diesel, primarily when
used in older engines. EPA's MOVES model estimates criteria pollutant emission impacts for
186 EPA, "Assessing the Effect of Five Gasoline Properties on Exhaust Emissions from Light-Duty Vehicles
Certified to Tier 2 Standards: Analysis of Data from EPAct Phase 3 (EPAct/V2/E-89)," EPA-420-R-13-002.
https://nepis.epa. gov/Exe/ZvPDF.cgi/P100GA0V.PDF?Dockev=P100GA0V.PDF.
187 EPA, "EPAct/V2/E-89: Assessing the Effect of Five Gasoline Properties on Exhaust Emissions from Light-Duty
Vehicles Certified to Tier 2 Standards - Final Report on Program Design and Data Collection," EPA-420-R-13-004,
April 2013. https://nepis.epa.gov/Exe/ZvPDF.cgi/P100GA80.PDF?Dockev=P100GA80.PDF.
188 Morgan, Peter, Peter Lobato, Vinay Premnath, Svitlana Kroll, Kevin Brunner, and Robert Crawford. "Impacts of
Splash-Blending on Particulate Emissions for SIDI Engines." Coordinating Research Council Report. June 26,
2018. https://crcsite.wpengine.com/wp-content/uploads/2019/05/CRC-E-94-3 Final-Report 2018-06-26.pdf.
189 Morgan, Peter, Ian Smith, Vinay Premnath Svitlana Kroll, and Robert Crawford. "Evaluation and Investigation
of Fuel Effects on Gaseous and Particulate Emissions on SIDI in-Use Vehicles." Coordinating Research Council
Report, March 2017. https://crcsite.wpengine.com/wp-content/uploads/2019/05/CRC 2017-3-21 03-20955 E94-
2FinalReport-Rev lb .pdf.
1911 Karavalakis, Georgios, Thomas Durbin, Tianbo Tang. "Comparison of Exhaust Emissions Between E10 CaRFG
and Splash Blended El5." June 2022. https://ww2.arb.ca.gov/sites/default/files/2022-07/E15 Final Report 7-14-
22 O.pdf.
191 EPA, "Exhaust Emission Impacts of Replacing Heavy Aromatic Hydrocarbons in Gasoline with Alternate Octane
Sources," EPA-420-R-23-008, April 2023. https://nepis.epa.gov/Exe/ZvPDF.cgi?Dockev=P1017FPA.pdf.
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pre-2007 engines based on data generated for B20 (20 vol%) blends of soybean-based biodiesel
in petroleum diesel and the percent change in emissions of total hydrocarbons, CO, NOx, and
PM2.5 are shown in Table 4.1.2.3.2-1.192 The biodiesel effects implemented in MOVES were
obtained from an analysis conducted as part of the 2010 RFS2 Rule.193 Studies of engines
equipped with particulate filter and selective catalytic reduction aftertreatment systems that
became widespread in 2007 and later models had shown no effect of B20 blends on emissions.
Table 4.1.2.3.2-1: Emission Impacts on Pre-2007 Heavy-Duty Diesel Engines for All Cycles
Tested on 20 Vol% Soybean-Based Biodiesel Fuel Relative to an Average Base Petroleum
Diesel Fuel
Pollutant
Percent Change in Emissions
THC (Total Hydrocarbons)
-14.1
CO
-13.8
NOx
+2.2
PM2.5
-15.6
4.1.2.3.3 Renewable Diesel
Renewable diesel is made by hydrotreating vegetable oils or other fats or greases to
remove oxygen and unsaturated bonds leaving a primarily paraffinic fuel. As a result, renewable
diesel has a higher cetane index and lower aromatics and sulfur content in comparison to
petroleum diesel fuel. Studies indicate no impact, or in some cases reductions, of regulated
pollutant and toxic emissions from vehicles operating on renewable diesel as compared to
petroleum diesel 194-195-196-197-198 Therefore, we do not expect an increase in air pollutant
emissions from the end use of renewable diesel from this rule.
192 EPA, "Fuel Effects on Exhaust Emissions from Onroad Vehicles in MOVES3," EPA-420-R-20-016, November
2020.
193 EPA, "Renewable Fuel Standard Program (RFS2) Regulatory Impact Analysis," EPA-420-R-10-006, February
2010, Appendix A.
194 Karavalakis, Georgios, Kent Johnson, and Thomas D. Durbin. "Combustion and Engine-Out Emissions
Characteristics of a Light Duty Vehicle Operating on a Hydrogenated Vegetable Oil Renewable Diesel."
Coordinating Research Council Report. July 2022. https://crcao.org/wp-content/uploads/2022/07/CRC-E-117-2Q22-
Revised-CRC-Final-Report.pdf.
195 Coordinating Research Council, "Biodiesel and Renewable Diesel Characterization and Testing in Modern LD
Diesel Passenger Cars and Trucks," Project CRC AVFL-17b, November 2014.
196 Na, Kwangsam, Subhasis Biswas, William Robertson, Keshav Sahay, Robert Okamoto, Alexander Mitchell, and
Sharon Lemieux. "Impact of Biodiesel and Renewable Diesel on Emissions of Regulated Pollutants and Greenhouse
Gases on a 2000 Heavy Duty Diesel Truck." Atmospheric Environment 107 (February 24, 2015): 307-14.
https://doi.Org/10.1016/i.atmosenv.2015.02.054.
197 Singh, Devendra, K.A. Subramanian, and Mo Garg. "Comprehensive Review of Combustion, Performance and
Emissions Characteristics of a Compression Ignition Engine Fueled With Hydroprocessed Renewable Diesel."
Renewable and Sustainable Energy Reviews 81 (July 3, 2017): 2947-54. https://doi.Org/10.1016/i.rser.2017.06.104.
198 California EPA, "Staff Report - Multimedia Evaluation of Renewable Diesel," May 2015.
https://ww2.arb.ca.gov/sites/default/files/2018-Q8/Renewable Diesel Multimedia Evaluation 5-21-15.pdf.
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4.1.2.3.4 Renewable CNG/LNG
Renewable CNG and LNG are predominantly methane and are not distinct chemically
from fossil CNG and LNG. Therefore, end use emissions of renewable CNG/LNG fuels are
expected to be no different from vehicles using fossil CNG/LNG.
4.1.3 Air Quality Impacts of Analyzed Volumes
The geographic distribution of emissions impacts due to the Analyzed Volumes varies
depending on the feedstock and the process step, and the overall impact on air quality is a
complex issue. At each step in the production, distribution, and end use stages, there are changes
to the location, amount, and composition of emissions. Full-scale photochemical air quality
modeling would be necessary to accurately project impacts on concentrations of various criteria
and air toxic pollutants across the country. However, photochemical air quality modeling is time
and resource intensive and as such requires knowledge of the volume requirements early in the
analytical process. Additionally, the spatial resolution of the air quality modeling data (12km by
12km grid cells) is not sufficient to capture very local impacts from production or the pollution
concentration gradients near roads and other transport routes. For these reasons we use the
emission impacts discussed above, rather than conducting photochemical modeling, to draw
broad conclusions regarding the likely air quality impacts associated with the Analyzed Volumes
as compared to the No RFS Baseline.
Comparing the Analyzed Volumes to the No RFS Baseline, we would expect some
localized increases in some air pollutant concentrations, particularly at locations near production
and transport routes. Production emissions from processing biofuel feedstocks would vary by
pollutant, location, and magnitude. However, we would expect increases in emissions at biofuel
production facilities due to the Analyzed Volumes, and these increases in emissions could impact
local air quality. The location of emissions from biofuel production tends to be in more rural
areas. Simultaneously, the production of petroleum fuels could decrease due to increased
volumes of biofuels, but it could also stay the same with exports increasing or imports
decreasing.199 The location, composition, and magnitude of emissions from storage and transport
of fuel would also be impacted as additional biofuels are stored and transported; the storage and
transport of petroleum fuels could also change (e.g., transport to shipping terminals rather than
gas stations). We would also expect varying impacts on end use emissions from vehicles running
on fuels containing biofuel. We would expect emission increases for some pollutants and
emission decreases for other pollutants from vehicles running on fuel with biodiesel or corn
ethanol and negligible impacts from vehicles running on fuel with renewable diesel or renewable
CNG/LNG. Overall, we expect the emission impacts from the Analyzed Volumes to be variable
in how they affect ambient concentrations of ozone and PM2.5 in specific locations across the
U.S.
In addition to drawing conclusions based on the projected emission changes from the
Analyzed Volumes, we can look at life-cycle and air quality modeling analyses included in other
renewable fuels assessments like the RtC3, the RFS2 rule, and the ABS. The per gallon results of
199 Further discussion on the potential impacts of the Analyzed Volumes on the production of petroleum fuels can be
found in Chapter 6.4.1.
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the lifecycle analysis (LCA) modeling included in the RtC3 indicate that we would expect that
increased volumes of biofuels would lead to increased criteria air pollutant emissions and air
quality impacts.200 However, as the biofuels industry continues to mature, and if efficiencies
improve with industry maturity, those increases are likely to become smaller. We can also
compare the changes in volumes and emissions for increased volumes of corn ethanol to the
RFS2 air quality modeling analysis, and we would expect the impacts of the corn ethanol
volumes on air quality to be relatively minor compared to RFS2, with any significant impacts
likely to be localized in rural areas. RFS2 included a smaller impact on BBD than what is being
finalized in this rule. In addition to RFS2 comparisons, we can also compare the changes in
volume and emissions for increased end use emissions of vehicles running on fuel with corn
ethanol and biodiesel to the ABS. The ABS only considered impacts of the RFS program on end
use emissions and overall, found relatively little change in PM2.5 concentrations and increases
and decreases in ozone concentrations depending on the location.
4.2 Conversion of Natural Lands
Regarding the conversion of wetlands and other natural lands to agriculture to meet
demand for biofuel, EPA has explored this topic extensively in the Biofuels and the
Environment: Reports to Congress, including the First Triennial Report to Congress (RtCl),201
the Second Triennial Report to Congress (RtC2),202 and the more recent RtC3. The RtCl and
RtC2 assessed how biofuels broadly may be increasing cropland and driving conversion of
natural lands (e.g., wetlands, forests, grasslands) for feedstock production. In January 2025, EPA
finalized and published the RtC3. The RtC3 builds on the previous two reports and includes new
analyses to estimate the separable effects of the RFS program from the impacts of biofuels
generally.
In the Set 1 Rule RIA and Biological Evaluation (BE),203 EPA further assessed how the
Set 1 Rule for years 2023-2025 may increase cropland. The Set 1 Rule BE was completed in
accordance with the Endangered Species Act (ESA) Section 7 consultation process to ensure that
the rule is not likely to jeopardize the continued existence of any endangered or threatened
species or result in the destruction or adverse modification of designated critical habitat for such
species. EPA has developed another BE to assess the same from potential effects of this Set 2
Rule.204
In this chapter, we first summarize the historical data and information contributing to our
current understanding of natural land conversion effects from agriculture and biofuels, as well as
our understanding of potential effects from past RFS volumes specifically (Chapter 4.2.1).
Because the Set 1 Rule RIA, finalized in 2023, included a literature review of articles examining
conversion of wetlands and other lands, we also reviewed and discuss new literature that has
200 EPA, "Biofuels and the Environment: Third Triennial Report to Congress," EPA/600/R-24/343F, January 2025.
Chapter 8.5
201 EPA, "Biofuels and the Environment: First Triennial Report to Congress," EPA/600/R-10/183F, December 2011.
202 EPA, "Biofuels and the Environment: Second Triennial Report to Congress," EPA/600/R-18/195F, June 2018.
203 EPA, "Biological Evaluation of the Renewable Fuel Standard Set Rule and Addendum," EPA-420-R-23-029,
May 2023 ("Set 1 Rule BE").
204 See "Biological Evaluation of the Renewable Fuel Standard (RFS) Set 2 Rule and EPA's Effects Determination"
("Set 2 Rule BE"), available in the docket for this action.
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come out in recent years related to this topic (Chapter 4.2.2). The last subsection explores the
potential natural land conversion impacts from this rule (Chapter 4.2.3). Related findings from
the Set 2 Rule BE, since it applies to the Set 2 Rule, are discussed in Chapter 4.2.3.
4.2.1 Natural Land Conversion Effects
The aforementioned reports and documents (e.g., Biofuels and the Environment: Reports
to Congress) have greatly contributed to EPA's understanding of how agriculture, biofuel
production and consumption, and past RFS renewable volume obligations influenced the
conversion of natural lands. The findings and conclusions from these documents relied heavily
on studies from the peer reviewed literature as well as additional analyses completed by EPA. A
summary of findings and EPA's understanding in these areas are explained in this subsection.
The conversion of natural lands (e.g., wetlands, grasslands, forests) is associated with
biofuel production and consumption through the growth of crop-based feedstocks, rather than
through the production of waste fats, oils and greases, or biogas. Corn and soybeans are the
dominant crops used for biofuel feedstock used for biofuel production, followed by canola. As
such, the production of these three feedstock crops is the main concern when it comes to
conversion of natural lands.205
The RtC3 discusses historical trends from several federal land cover datasets. Data from
the USDA National Resource Inventory (NRI), Cropland Data Layer (CDL), and Census of
Agriculture support a finding that from 2007 to 2017 there has been a 10 million-acre increase in
cultivated cropland.206 The report found that more than half of the corn and soybean increase in
this time period came from other cultivated cropland (56%). Additionally, the 10 million-acre
increase in cultivated cropland from 2007 to 2017 coincided with a 15 million-acre decline in
perennially managed land, including Conservation Reserve Program (CRP) lands, pasture, and
noncultivated cropland.
Findings from a study by Lark et al. (2015)207 showed that, from 2008 to 2012,
grasslands were the source for 77% of all new croplands. The category of "grasslands" in this
study included both native and planted grasslands, as well as those that may have been cultivated
for pasture or hay. The study authors found that just over a quarter of these grasslands, or 22% of
all lands converted, qualified as long-term grasslands. Further, they found that shrubland and
long-term idle lands each accounted for 8% of all new croplands. In contrast, 3% of lands
converted to agriculture came from forested areas and 2% came from wetlands.
2115 Though it should be highlighted that to the extent the use of FOG for biofuel production comes from shifting the
uses of those feedstocks from other uses, they may then be backfilled with crop-based feedstocks, resulting in the
very same concerns with respect to conversion of natural lands.
2116 Despite the observed increase in cropland from 2007-2017, cultivated cropland for this period was still below
historic levels. Further, the latest Census of Agriculture data suggests that harvested cropland has declined since
2017, from about 320 million acres in 2017 to 301 million acres in 2022. Though, it is important to note that this
was likely affected by a drought in the Midwestern U.S. in 2022, and, since that drought, planted and harvested
acres have recovered.
2117 Lark, Tyler J, J Meghan Salmon, and Holly K Gibbs. "Cropland Expansion Outpaces Agricultural and Biofuel
Policies in the United States." Environmental Research Letters 10, no. 4 (April 1, 2015): 044003.
https://doi.Org/10.1088/1748-9326/10/4/044003.
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Similarly, Lark et al. (2020)208 found that 88% of new croplands were sourced from
grasslands when looking at a longer timeframe, from 2008-2016. A total of 2.8 million acres, or
28%, of new cropland cultivated over this period originated from longstanding habitat sites, of
which a majority (2.3 million acres) were long-term grasslands. They found that, relative to all
land converted to new cropland over this period, 26% of converted grasslands, 29% of converted
wetlands, 44% of converted forest, and 52% of converted shrublands were previously
categorized as long-term sites.
The studies referenced above examined historical land use changes and natural land
conversion patterns that can be attributed to various causes. Demand for renewable fuel and
production of crop-based feedstocks are only two of the many potential causes for these observed
land use changes. Regarding the potential natural land conversion effects from the RFS program
alone, it is also important to note that there are many factors, including economic and policy
drivers at local, state, nation, and global scales, that influence renewable fuel production and
consumption in the U.S. For example, biodiesel tax policy in the U.S. has had a significant
impact on the volume of biodiesel and renewable diesel used in the U.S. historically, as
discussed in more detail in Chapter 7. The RFS program is only one factor that influences
renewable fuel use and consumption, and it is challenging to separate out the effects of the RFS
program from other factors. Despite the challenges, EPA has worked in recent years to evaluate
the potential effects of the RFS program alone. We summarize what EPA has previously
evaluated for past RFS volumes in the text immediately below. A discussion on the potential
effects of this rule is included in Chapter 4.2.3.
EPA's analyses conducted in past years, separate from this rule, demonstrate that the RFS
program has played a larger role in production and consumption of biodiesel and renewable
diesel compared to corn ethanol. For example, the RtC3 completed an attribution analysis for
corn ethanol and estimated that 0-9% of corn ethanol production and consumption is likely
attributable to the RFS program historically from 2006-2019. In contrast, 36% of biodiesel
production was found to be attributable to the RFS program from 2002-2020 based on a study
that used the Bioenergy Scenario Model.209 Another study which used a multi-period, partial
equilibrium economic model (BEPAM) found that land use change intensity of biodiesel ranged
from 0.78-1.5 million acres per billion gallons in 2018; in comparison, the values for corn
ethanol ranged from 0.57-0.75 million acres per billion gallons over the same period.210 Given
these findings, potential land use changes from the RFS program in past years would likely have
been greater for soybean production for biodiesel and renewable diesel, relative to corn
production for corn ethanol.
2118 Lark, Tyler J., Seth A. Spaw n. Matthew Bougie, and Holly K. Gibbs. "Cropland Expansion in the United States
Produces Marginal Yields at High Costs to Wildlife." Nature Communications 11, no. 1 (September 9, 2020).
https://doi.org/10.1038/s41467-020-18Q45-z.
2119 Miller, Jesse, Christopher Clark, Steve Peterson, and Emily Newes. "Estimated Attribution of the RFS Program
on Soybean Biodiesel in the U.S. Using the Bioenergy Scenario Model." Energy Policy 192 (July 3, 2024): 114250.
https://doi.Org/10.1016/i.enpol.2024.114250.
2111 Wang, Weiwei, and Madhu Khanna. "Land Use Effects of Biofuel Production in the US." Environmental
Research Communications 5, no. 5 (May 1, 2023): 055007. https://doi.org/10.1088/2515-7620/acdld7.
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Because grasslands, pasturelands, CRP lands, idle lands, and noncultivated cropland have
been most impacted by agricultural expansion historically, any land conversion due to the RFS
program likely affected these land types to a greater extent {i.e., more acres of conversion)
relative to wetlands and forests. Still, some effects of past RFS volumes on wetland and forest
conversion may have occurred. An analysis in the RtC3, for example, estimated that nearly
275,000 acres of wetlands concentrated in the Prairie Pothole Region were lost from 2008-2016.
However, the report recognized that only a percentage of this (0-20%) may be attributable to the
RFS program.
In addition, the Set 1 Rule RIA and BE discussed the potential for an associated increase
in crop production from the 2023-2025 Set 1 Rule alone. In the Set 1 Rule BE, EPA's analyses
estimated that the Set 1 Rule could potentially lead to an increase of as much as 2.65 million
acres of cropland by 2025, approximately 1% of the projected U.S. acreage for major field crops
in 2025. Related to this finding, it is important to note the following:
• The estimated 2.65 million acres of cropland increase from the Set 1 Rule represents the
maximum potential impact based on a number of assumptions, many of which were very
conservative in nature, that EPA made in the BE.
• Additional analyses supporting the BE suggested that the demand for biodiesel and
renewable diesel from the Set 1 Rule could be met fully by changes to imports/exports or
by projected increases in feedstock yields on existing soybean lands, highlighting the
uncertainty in knowing the exact impacts from the Set 1 Rule.
Out of the estimated 2.65 million acres, a maximum potential acreage impact of 1.93
million acres by 2025 (or 1.57 and 1.78 million acres by 2023 and 2024, respectively) was
estimated to come from soybean biodiesel volume increases in the Set 1 Rule. In addition, a
maximum potential acreage impact of 0.26 million acres by 2025 was estimated to come from
canola biodiesel. Since 2023-2025 have come to pass, we can look at BBD supply data from
those years to infer what may have actually happened. For example, as explained in Chapter 7,
through 2021 biodiesel imports from the EU had never exceeded 100 million gallons in any
single year. Biodiesel imports from the EU increased to approximately 114 million gallons in
2022 and then quite dramatically to approximately 320 million gallons in 2023. In these same
years, imports of feedstocks used by domestic biodiesel and renewable diesel producers, such as
tallow from Brazil and used cooking oil from China, increased significantly. This dynamic
shifted again in 2025 due to trade policy in the US and other countries, leading to a significant
decrease in the availability of imported Chinese used cooking oil, though this was partially offset
by an increase in tallow imports from Latin America. Given these changes in BBD imports, it is
possible that minimal to no land use impacts occurred, especially for those years that saw greater
imports of FOG and biodiesel.
Moreover, EPA acknowledges that, for any effects that may have occurred from the RFS
program, it is currently not possible to project the precise locations of agricultural expansion
with confidence due to the vast quantity of potential cropland in the U.S. and the multitude of
factors that contribute to an individual farmer's decision whether to bring additional land into
crop production. For natural lands that were converted to agriculture in past years, it is also not
possible to say which parcels of land were converted due to the RFS program alone. To date,
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EPA has advanced our knowledge of the land use impacts from the RFS program at a national
scale but understanding the impacts at the local level remains a challenge. In fact, with the
currently available science, the finest level possible for understanding the effect of biofuel
production on cropland is at the county scale, though such analyses for the Set 1 Rule rendered
limited information.211 We are unaware of any published literature or efforts that have estimated
impacts at smaller scales, such as the field or 30-meter scale.
4.2.2 New Literature on the Conversion of Natural Lands
The above subsection summarizes information known by EPA from previous work
completed, including the Biofuels and the Environment: Reports to Congress and Set 1 Rule BE.
To keep abreast of the latest science, EPA also completed a literature review of research of
articles and other federal agency assessments published in recent years.
In conducting this literature review, EPA did not find any articles or publications
examining the potential impacts of the RFS program alone on conversion of natural lands.
Nonetheless, other publications, such as the 2024 Status and Trends of Wetlands in the
Conterminous United States Report to Congress by the U.S. Fish and Wildlife Service (FWS),212
provide insights into how agriculture impacted wetland ecosystems from 2009-2019. The report
shows that agricultural activities have been a significant cause of wetland loss. From 2009-2019,
the U.S. experienced a net loss of 221,000 acres of wetlands. Of this total, the report states that
"[conversion to upland categories (agriculture, urban, forested plantation, rural development,
other upland) was the dominant driver of net wetland loss," contributing to a total loss of
194,000 wetland acres.
The report also explains that vegetated wetlands, and freshwater vegetated wetlands in
particular, were especially impacted. These wetlands are important for controlling floods,
improving water quality, and storing carbon. According to the report, the largest driver of all
freshwater wetland net loss was an increase in upland forested plantations, followed by increases
in upland agriculture.
In addition, the Status and Trends of Wetlands in the Conterminous United States report
from FWS highlights that net wetland loss has accelerated by more than 50% compared to the
previous study period (2004-2009), continuing a long-term pattern of wetland degradation. This
ongoing loss has reduced wetlands' ability to provide critical ecosystem services such as flood
control. The report also emphasizes that agriculture not only replaces wetlands, but also "reduces
wetland pollutant removal services, and increases pollutant inputs in the form of fertilizer, waste,
sediment, and toxins."
211 In the Set 1 Rule, county-level estimates would have been possible by leveraging an econometric analysis for
corn ethanol effects due to proximity to ethanol facilities, specifically, as opposed to corn ethanol crop price effects.
However, the proximity to ethanol facility effects were estimated to be zero for total cropland in the Set 1 Rule BE,
so EPA was not able to accomplish county-level estimates for this. See Li et al. (2019) and updated analyses by
Madhu Khanna as described in the Set 1 Rule BE for more information.
212 U.S. Fish & Wildlife Services, "Status and Trends of Wetlands in the Conterminous United States 2009 to 2019,"
2024. https://www.fws.gov/sites/default/files/documents/2024-04/wetlands-status-and-trends-report-2009-to-
2019 O.pdf.
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Another study by Guptaa (2024)213 highlights the concerning rate of green cover loss
worldwide, with a specific focus on wetlands, forests, and grasslands impacted by agricultural
expansion and biofuel production. The study details that agricultural expansion remains a
primary driver of green cover depletion, particularly for crops like soybeans and oil palm, which
replace diverse ecosystems with monocultures, "severely affecting biodiversity and carbon
storage." The report adds that wetland areas in the Mississippi River Delta have been
significantly impacted, as human activities such as "levee construction and oil extraction"
compound climate-driven stressors like sea-level rise, leading to further degradation of these
ecosystems.
Findings from other studies uphold our understanding that cropland expansion in the U.S.
has historically come from conversion of forest, shrubland, and grassland and that agriculture
continues to be an ongoing threat to grasslands.214 215 Bedrosian et al. (2024)216 further highlight
the risk of conversion of the sagebrush biome (e.g., in the Northern Great Plains), and the
importance of land conservation efforts to protect these vulnerable ecosystems. As stated above,
EPA found no studies or publications linking the effects of the RFS specifically to conversion of
natural lands such as grasslands and wetlands.
4.2.3 Potential Natural Land Conversion Impacts
A first step to understanding the potential natural land conversion impacts from this rule
is looking at the volume changes expected from this rule relative to the No RFS and 2025
Baselines. The projected BBD and conventional renewable fuel volume changes for the
Analyzed Volumes are shown in Tables 4.2-1 and 2. More detailed information can be found in
Chapter 3.
Table 4.2-1: Total BBD Renewable Fuel Volume Changes Relative to the No RFS Baseline
2026
2027
Relative to the No RFS Baseline
3,597
4,175
Relative to the 2025 Baseline
2,340
2,711
213 Guptaa, Rakshan. "Green Cover Depletion and Its Projection Over the Upcoming Years." Darpan International
Research Analysis 12, no. 2 (May 23, 2024): 76-87. https://doi.org/10.36676/dira.vl2.i2.06.
214 Li, Xiaoyong, Hanqin Tian, Chaoqun Lu, and Shufen Pan. "Four-century History of Land Transformation by
Humans in the United States (1630-2020): Annual and 1 Km Grid Data for the HIStory of LAND Changes
(HI SL AND-US)." Earth System Science Data 15, no. 2 (March 3, 2023): 1005-35. https://doi.org/10.5194/essd-15-
1005-2023.
215 Douglas, David J. T„ Jessica Waldinger, Zoya Buckmire, Kathryn Gibb, Juan P. Medina, Lee Sutcliffe, Christa
Beckmann, et al. "A Global Review Identifies Agriculture as the Main Threat to Declining Grassland Birds." Ibis
165, no. 4 (May 9, 2023): 1107-28. https://doi.org/10.1111/ibi. 13223.
216 Bedrosian, Geoffrey, Kevin E. Doherty, Brian H. Martin, David M. Theobald, Scott L. Morford, Joseph T. Smith,
Alexander V. Kumar, et al. "Modeling Cropland Conversion Risk to Scale-Up Averted Loss of Core Sagebrush
Rangelands." RangelandEcology & Management 97 (October 15, 2024): 73-83.
https://doi.Org/10.1016/i.rama.2024.08.011.
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Table 4.2-2: Conventional Renewable Fuel Volume Changes Relative to the No RFS
2026
2027
Relative to the No RFS Baseline
231
245
Relative to the 2025 Baseline
87
20
Based on the values in Table 4.2-1, we would expect increases in BBD volumes
attributable to this rule. BBD volumes could be met through a variety of ways, for example by
increased imports of FOG or diversions from other feedstock uses. But it could also potentially
be met, at least in part, through an increase in land conversion for agricultural lands to produce
more feedstock (specifically soybeans and, to a lesser extent, canola) to meet extra BBD volume
demand generated by this rule. Such an increase in land conversion for agricultural lands, were it
to occur, could contribute to further loss of natural lands such as grasslands, wetlands, and
forests.
The conventional renewable fuel projected volumes relative to the No RFS and 2025
Baselines tell a slightly different story (Table 4.2-2). The numbers suggest we would also see an
increase in conventional volumes from this rule, albeit to a much smaller degree compared to
BBD volumes.
For any conclusions drawn regarding the potential natural land conversion effects from
this rule (e.g., from increased BBD volumes), it is important to note the significant assumptions
and high uncertainty inherent in estimating acreage impact numbers at every step in the
underlying causal relationship between the RFS standards and the land use effects that could
result from increased production of crop-based feedstocks (Figure 4.2-1). For example,
projecting the impact of increased biofuel demand on crop-based feedstock production is
complicated by the fact that the majority of feedstocks are used in non-biofuel markets as well.
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Figure 4.2-1. Causal chain between RFS standards and impacts on land used to grow crops
RFS standards
Refiner access to renewable fuel
Avaiabi Sty, type, and price of RlNs
Relative costs between d ifferent renew able f nets
Refiner decisons about the m« of biofueltypes and/or RINs
needed tomeet the RFS standards
Size of carryover RIN bank
Relative retail price of fossi-based gasoline
anddieset versusrenewablefuel
Infrastructures support distribution, blending,
dispensing, and consumption of renewabiefuel
Total consumption of renewabiefuel in the U.&
Other state and federal programs
that require renew ab le fuels
Statutory and regulatory constraints on renewable
fuels blended nto transportation fuels
Federal and state tax incentives and grants
Consumer attitudes and preferences
Imports and exportsof renewabiefuel
Total production of renewabiefuel in the U.S.
Domestic renewabiefuel production capacity
Product on of non-crop-basedfeed stocks
for renewabiefuel production
Production of crop-based feedstocks
for renewabiefuel production
Importsand exportsof crops
Total production of crops
Crop production for human
consumption andammalfeed
E xtens* icat on vs intensification
Su ttab illy of land for grow ng crops
Alternative uses for land
t Land used to grow crops
Conservation Research Program
Of note is the "imports and exports of crops" factor in Figure 4.2-1, especially due to
trends in recent years. As explained further in Chapter 7, increasing U.S. soybean oil production
in future years will require investment to increase the domestic soybean crushing capacity.
Domestic soybean crushers that have made these investments in the past are able to do so in the
future but are unlikely to do so unless they have a reasonable expectation of increasing demand
for soybean oil to provide a return on their investments. In recent years, a significant increase in
imported FOG to domestic BBD producers is the result of increased global collection of these
feedstocks and changes to biofuel policies in both the U.S. and other countries, such that the U.S
became a preferred destination for these feedstocks. The marked shift back to lower imports of
Asian UCO and imported feedstocks and fuels in 2025 was again marked by significant shifts to
biofuel and trade policy in the U.S. and other countries. Any number of factors, such as other
countries adopting more stringent biofuel mandates, providing higher incentives for biofuels
produced from FOG, or restricting FOG exports, could quickly change these market dynamics.
Potential changes in these and other export and import dynamics complicate our understanding
of the actual land use change and natural land conversion effects from this rule.
Assuming some natural land loss effects could occur, and that land use change patterns
mirror past trends, any future expansion of agriculture attributable to this rule would most likely
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impact grasslands, pasturelands, CRP lands, idle lands, and noncultivated cropland, as
demonstrated by findings from studies discussed in Section 4.2.1. Increased cropland may
contribute to additional declines in wetlands and forests, but likely to a much lesser extent. As
such, EPA expects that any potential extensification of agriculture from this rule would likely
occur on these lands that have historically been impacted the most by agricultural expansion.
Any impacts to wetlands and forests would likely occur at a much smaller scale since historically
they have been impacted by agricultural conversion to a lesser degree than other land types. Still,
additional losses of wetlands and forests could occur in ecologically sensitive areas or in places
that are already experiencing cumulative environmental effects.
Regarding potential conversion of grasslands, pasture, idle lands, shrubland, and CRP
lands, it is also important to note that only a portion of these lands would qualify as loss of long-
term grasslands that likely support greater wildlife biodiversity, soil carbon storage, and
ecosystem services. As stated previously, Lark et al. (2015) found that, from 2008-2012,
grasslands were the source of 77% of new cropland and just over a quarter of these grasslands
that were converted qualified as long-term grasslands. Pasture, idle croplands, and CRP lands
that could fall under the category of grasslands or natural lands may also be converted due to
land use changes from this rule, but ecosystem impacts such as soil carbon and species impacts,
would likely occur to a lesser extent on these lands compared to scenarios in which conversion of
long-term grasslands occurs.
EPA further explored the potential land use change effects from this rule in a BE
document in support of ESA Section 7 consultation with the FWS and National Marine Fisheries
Service (NMFS). EPA largely used the same analytical approaches that were used in the Set 1
Rule BE. We leveraged econometric analyses available in published literature (Li et al. 2019)217
combined with updated data from Dr. Madhu Khanna218 to estimate the change in corn acres and
total cropland per billion gallons of ethanol production. New in the Set 2 Rule BE, EPA applied
the same approach to assess potential change in soybean acres and total cropland in response to
soybean oil production from this rule. Including an additional and separate analysis looking at
impacts from canola biodiesel and renewable diesel, the BE estimated that the Analyzed
Volumes could contribute up to approximately 3.4 million new acres of cropland. As discussed
previously, other factors outside of the RFS standards (e.g., import/export trends, other state and
federal policies) can also influence land use change for biofuel production. The 3.4 million acres
would also include potential conversion of pasture, idle croplands, and CRP lands that are not
considered long-term, pristine natural landscapes.
4.3 Soil and Water Quality
As was done in the Set 1 Rule RIA, soil and water quality are addressed together in one
section because, in many ways, they are intertwined. Soil health, organic matter content, erosion,
and nutrient leaching from agricultural soils affects the water quality of nearby and downstream
217 Li, Y., Miao, R., & Khanna, M. "Effects of Ethanol Plant Proximity and Crop Prices on Land-Use Change in the
United States." American Journal of Agricultural Economics 101, no. 2 (March 2019) 467-491.
https://doi.org/10.1093/aiae/aaY080.
218 Khanna, M. "Effects of Ethanol Plant Proximity and Crop Prices on Land-Use Change in the United States:
Updated Analysis for 2003-2023." September 15, 2025.
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water bodies. Potential impacts of biofuel production and use on soil and water quality, like the
potential impacts on conversion of natural lands, have been discussed in detail in the Biofuels
and the Environment: Reports to Congress. The past effects of the RFS program alone have also
been assessed in more recent years and are discussed in detail specifically in the RtC3 and Set 1
Rule BE.
In this section, we first explore the historical data and information contributing to our
understanding of soil and water quality effects from agriculture and biofuels broadly, as well as
our understanding of potential effects from past RFS volumes (Chapter 4.3.1). Because the Set 1
Rule RIA, finalized in 2023, included a literature review of articles examining soil and water
quality effects, we also reviewed and discuss new literature that has come out in recent years
related to this topic (Chapter 4.3.2). The last subsection explores the potential soil and water
quality impacts from this rule (Chapter 4.3.3).
4.3.1 Soil and Water Quality Impacts
In this subsection, we summarize findings and EPA's understanding of how agriculture,
biofuel production, and the RFS program have historically impacted soil and water quality. For
more detailed information including references to scientific articles that support such findings,
please see Chapter 9 (Soil Quality) and Chapter 10 (Water Quality) in the RtC3. Furthermore, for
the purposes of this rulemaking, we are most interested in the potential effects from domestic
production of crop-based feedstocks. We acknowledge that impacts outside the U.S. may also
occur. But for statutory and program evaluation purposes, we limit our scope to impacts within
the U.S.
It is well understood as a general scientific matter that soil quality effects from biofuels
are largely associated with production of crop-based feedstocks (corn, soybean, canola) rather
than FOG or biogas feedstocks, as the latter categories do not require land for production. The
conversion of grasslands or other lands to production of agriculture affects soil quality, with
increases in erosion and the loss of soil nutrients, organic matter, and soil carbon.
With regard to water quality, extensification of cropland typically corresponds with an
increase in nutrient (nitrogen and phosphorus) and sediment pollution from agricultural runoff,
which impairs local water quality and contributes to algal blooms and hypoxia in the Gulf of
America and other water bodies. An increase in cropland also typically corresponds to an
increase in pesticide use which detrimentally affects nearby and downstream water quality. It is
also well understood that the soil and water quality effects of converting to corn or soybeans
from other crops, such as wheat, are generally less than those of the conversion of natural lands
such as grasslands.
The unique physical, biological, and geological characteristics of the land affected are
important to understanding the magnitude of soil and water quality effects. For example, as
referenced in the Set 1 Rule RIA, LeDuc et al. (2017)219 simulated greater erosion and loss of
219 LeDuc, Stephen D.. Xuesong Zhang, Christopher M. Clark, and R. Cesar Izaurralde. "Cellulosic Feedstock
Production on Conservation Reserve Program Land: Potential Yields and Environmental Effects." GCB Bioenergv
9, no. 2 (February 26, 2016): 460-68. https://doi.org/10.1111/gcbb. 12352.
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soil carbon and nitrogen from converting low productivity, highly sloped Conservation Reserve
Program grasslands compared to those with higher productivity soils and lower slopes.
The type of feedstock crop cultivated also matters. Soybean, a nitrogen fixer, generally
requires less fertilizer application compared to corn and other crops. As such, nitrogen runoff
from soybean cropland may be lower relative to other crops. Soil and water quality impacts
further depend on whether best management practices, if any, are being applied on the
agricultural land. The adoption of conservation tillage, cover crops, and soil amendments among
other practices can help counterbalance the detrimental effects to soil and water quality from
agriculture. That said, there is nuance in the scientific literature that suggests there is still much
to be learned about these practices, as for example a recent meta-analysis suggests that some
conservation tillage (e.g., no till) actually increases nitrate leaching.220
The weather conditions on a given day or year matter as well. The amount of
precipitation will affect runoff of nutrients and sediment from agricultural lands, affecting both
edge of stream environments and the size of dead zones such as in the Gulf of America.221222
It is also important to recognize other potential effects from biofuel production and
consumption that may affect soil and water quality. For example, although perennial grasses and
other types of feedstocks are not grown at the commercial scale, the scientific literature shows
that perennial grasses or woody biomass grown on marginal lands can help restore soil
quality,223 depending on the plant species being grown and the type of land being converted.224
Chemical releases, biofuel leaks, and spills from above-ground and underground storage
tanks as well as transportation tanks can contaminate soil and groundwater. As such, increased
consumption of biofuels could increase leaks that affect soil and water quality. This is discussed
in more detail in the Set 1 Rule RIA.
In addition, consumed biogas that is upgraded to RNG may have localized soil or water
impacts, depending on the source of the biogas. Biogas sourced from anaerobic digesters
processing agricultural manure, and the manure collection associated with them, may decrease
2211 Li, Jinbo, Wei Hu, Henry Wai Chau, Mike Beare, Rogerio Cichota, Edmar Teixeira, Tom Moore, et al.
"Response of Nitrate Leaching to No-tillage Is Dependent on Soil, Climate, and Management Factors: A Global
Meta-analysis." Global Change Biology 29, no. 8 (January 26, 2023): 2172-87. https://doi.org/10. Ill 1/gcb. 16618.
221 Chang, Di, Shuo Li, and Zhengqing Lai. "Effects of Extreme Precipitation Intensity and Duration on the Runoff
and Nutrient Yields." Journal of Hydrology 626 (October 6, 2023): 130281.
https://doi.Org/10.1016/i.ihYdrol.2023.130281.
222 Lu, Chaoqun, Jien Zhang, Hanqin Tian, William G. Crumpton, Mathew J. Helmers, Wei-Jun Cai, Charles S.
Hopkinson, and Steven E. Lohrenz. "Increased Extreme Precipitation Challenges Nitrogen Load Management to the
Gulf of Mexico." Communications Earth & Environment 1, no. 1 (September 18, 2020).
https://doi.org/10.1038/s43247-020-0002Q-7.
223 Blanco-Canqui, Humberto. "Growing Dedicated Energy Crops on Marginal Lands and Ecosystem Services." Soil
Science Society of America Journal 80, no. 4 (July 1, 2016): 845-58. https://doi.org/10.2136/sssai2016.03.008Q.
224 Robertson, G. Philip, Stephen K. Hamilton, Bradford L. Barham, Bruce E. Dale, R. Cesar Izaurralde, Randall D.
Jackson, Douglas A. Landis, Scott M. Swinton, Kurt D. Thelen, and James M. Tiedje. "Cellulosic Biofuel
Contributions to a Sustainable Energy Future: Choices and Outcomes." Science 356, no. 6345 (June 30, 2017).
https://doi.org/10.1126/science.aal2324.
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pathogen risk in water. But without proper treatment, excess nutrient pollution can also be a
concern from these facilities.
An increase in cropland acreage for renewable fuel production and consumption in the
U.S. would generally be expected to lead to negative soil and water quality impacts on balance.
The RFS is only one of many factors that influence cropland acreage in the U.S. However, EPA
has worked in recent years to evaluate the potential effects of the RFS program specifically on
domestic soil and water quality, and in particular from past RFS volumes.
As described in the RtC3, EPA conducted an analysis using the Environmental Policy
Integrated Climate (EPIC) model and found that the RFS program increased erosion, nitrogen
loss, and soil organic carbon loss from 0-1.6%, 0-0.7%, and 0-1.1%, respectively, across a 12-
state region between 2008-2016. As the report notes, these modeled estimates represent RFS
effects for corn ethanol only. At the time of the analysis EPA was not able to evaluate additional
quantitative effect from the RFS Program on soybean oil-based biodiesel or renewable diesel,
nor the effect on soybean acreage, nor any effect from crop switching on existing cropland. The
report also notes that this finding for corn ethanol only comparatively represents up to 3.7% of
the nitrogen retention benefits of the Conservation Reserve Program for the entire U.S.
The RtC3 also evaluated the potential water quality impacts from agriculture and the RFS
program historically. Using the Soil and Water Assessment Tool (SWAT) model, EPA
completed an analysis of estimated historical cropland expansion on water quality in the
Missouri River Basin from 2008-2016. Grassland conversion to continuous corn resulted in the
greatest increase in total nitrogen and total phosphorus loads (6.4% and 8.7% increase,
respectively); followed by conversion to corn/soybean rotation (6.0% and 6.5%); and then
conversion to corn/wheat rotation (2.5% and 3.9%). These results represent estimated water
quality effects from general agricultural expansion in the Missouri River Basin from 2008-2016
and not the effects from the RFS program alone. Based on other analyses, the report suggests that
approximately 0-20% of the observed changes may have been due to the RFS program.
Additionally, the Set 1 Rule BE leveraged the Missouri River Basin SWAT analysis from
the RtC3 to assess the potential water quality effects from the Set 1 Rule. Results indicated that,
even if the maximum projected acreage impacts from the Set 1 Rule (2.65 million acres total)
were to occur, the water quality impacts would be small relative to total nutrient, sediment, and
pesticide effects already happening at the mouth of the Mississippi and other larger water bodies
within the action area. Moreover, based on additional qualitative analyses, EPA found in the Set
1 Rule BE that localized water quality impacts from the Set 1 Rule were likely to be discountable
as defined under the ESA.
As discussed in Chapter 4.2.1, based on what we know happened from 2023 to 2025,
those estimates from the Set 1 Rule BE likely overestimated the actual effects from the Set 1
Rule. For example, in 2023 alone there was very little increase in domestic feedstock production
and additional BBD supply came from a significant increase in imports. These observed trends
from recent years are discussed in more detail in Chapter 7.
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Beyond EPA's work in these areas, a study by Lark et al (2022)225 examined the specific
impacts from the RFS in past years. The authors found that, from 2008-2016, the RFS expanded
corn cultivation in the U.S. by 2.8 million acres and total cropland by 2.1 million acres. These
changes corresponded with an estimated increase in annual nationwide fertilizer use by 3-8%
and an increase in water quality degradants by 3-5%. EPA explained in the Set 1 Rule BE how
the coefficient (i.e., value for cropland expansion per billion gallons of ethanol) that Lark et al.
used for estimating these effects compares to the coefficient that EPA used in the RtC3 and BE.
We found that the coefficients used in both efforts were similar in magnitude. The difference in
results, however, was due to how many billion gallons of corn ethanol was assumed to come
from the RFS. In the RtC3, EPA estimated a smaller effect from the RFS based on an additional
attribution analysis that separated the effects of the RFS program from the impacts of biofuels
generally.
4.3.2 New Literature on Soil and Water Quality Effects
To assess the current state of the science, EPA also completed a review of more recent
literature related to agriculture and biofuel production soil and water quality effects. EPA looked
for articles published in recent years. EPA found no studies that directly linked potential soil and
water quality effects to the RFS program.
One study by Byers et al. (2024)226 shows how intensified agriculture disrupts soil health,
particularly through soil carbon loss and microbiome degradation. As they note, "human-driven
land use change, such as agricultural intensification, is a major driver of soil [carbon] loss
globally," making sustainable land use challenging. The study emphasizes the importance of soil
microbes in "regulating soil biogeochemical cycling processes, including soil [carbon] cycling."
By analyzing microbial DNA, the researchers discovered that intense farming areas had more
microbial genes that break down soil carbon, potentially leading to "greater loss of soil C as
respired CO2 into the atmosphere". This increase in carbon loss suggests that intensive farming
may boost GHG emissions.
Byers et al. also shows that areas with more intense land use, such as pastures, have
lower soil carbon and less microbial diversity, following what the authors call a "disturbance
gradient." This pattern of soil degradation due to intensive farming hopes to discover a balance
between high productivity and healthy ecosystems.
Byers et al. recommends strategies like "protection of remnant native forest fragments
and greater incorporation of regenerating native vegetation" to preserve soil carbon levels. These
types of practices focus on sustainable land use that can help maintain long-term soil health and
climate adaptability by diminishing some of the negative impacts of intensive agriculture.
225 Lark, Tyler J., Nathan P. Hendricks, Aaron Smith, Nicholas Pates, Seth A. Spawn-Lee, Matthew Bougie, Eric G.
Booth, Christopher J. Kucharik, and Holly K. Gibbs. "Environmental outcomes of the US Renewable Fuel
Standard." Proceedings of the National Academy of Sciences 119, no. 9 (February 14, 2022).
https://doi.org/10.1073/pnas.2101Q84119.
226 Byers, Alexa K„ Leo Condron, Steve A. Wakelin, and Amanda Black. "Land Use Intensity Is a Major Driver of
Soil Microbial and Carbon Cycling Across an Agricultural Landscape." Soil Biology and Biochemistry 196 (June 26,
2024): 109508. https://doi.Org/10.1016/i.soilbio.2024.109508.
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Other studies focused on the impacts of pesticide use on soil and water quality.
Traditionally, pesticides have been employed in the agricultural sector to minimize the yield
losses due to insects, disease, and weeds, however, the chemicals in pesticides have significant
consequences, especially when overused. When pesticides are over-applied, the excess cannot be
absorbed by the plants, and is susceptible to being washed off by precipitation into the soil; as
Zhou et al. (2025)227 explain, once these chemicals enter the soil, they can react to form new
compounds, and depending on how deep into the soil they penetrate, can either leach into
groundwater or get carried to a body of water downstream. These chemicals then contaminate
water bodies and impact both the health of aquatic ecosystems and human health. In the case of
aquatic ecosystems, these chemicals can enter the food chain and bioaccumulate at higher trophic
levels. Human health implications of pesticides include linkages to increased risk of cancer,
diabetes, respiratory disease, neurological disorders, organ damage, and reproductive syndromes.
Using a partial equilibrium model of corn-soy production and trade, Johnson et al.
(2023 )228 estimated that a reduction of 24% of U.S. demand for corn as a renewable fuel
feedstock would keep agricultural land use and nitrogen leaching rates below 2020 levels
through the year 2025. Further, they found that a 41% reduction in demand for corn as a
renewable fuel feedstock would do the same through 2030. The authors discuss how such
demand reductions have potential to mitigate short-term impacts of population and income
growth over the next decade.
In a review of consequential life cycle assessments (CLCAs), Bamber et al. (2023)229
compared a series of 23 papers from several countries comparing the environmental impact of
grain and oilseed crops used in biofuel production to traditional fossil fuels. Among other
metrics, the team compared the CLCA results on differences in eutrophication, acidification, and
toxicity, which had conflicting results. Compared to conventional fuels, the studies ranged from
a 100-fold decrease in eutrophi cation to a 45-fold increase in eutrophication; for acidification,
this ranged from a 248% decrease to a 500% increase; for toxicity, this ranged up to a 20,000-
fold increase, with some decreases being reported but no calculable percentage changes.
Of the five studies focusing on water quality impacts of corn, soybean, and canola, which
came out of the United States, Sweden, Switzerland, Spain, and Argentina, there was still
disagreement among results: three of the five studies determined that biofuel production had
greater acidification and eutrophication impacts than conventional fuel; only four of the studies
evaluated ecotoxicity, three of which determined that biofuel production was associated with
worsened ecotoxicity and/or downstream carcinogenic effects compared to conventional fuels.
The study was largely inconclusive, with the researchers suggesting a more unified approach and
227 Zhou, Wei, Mengmeng Li, and Varenyam Aclial. "A Comprehensive Review on Environmental and Human
Health Impacts of Chemical Pesticide Usage." Emerging Contaminants 11, no. 1 (August 26, 2024): 100410.
https://doi.Org/10.1016/i.emcon.2024.100410.
228 Johnson, David R., Nathan B. Geldner, Jing Liu, Uris Lantz Baldos, and Thomas Hertel. "Reducing US Biofuels
Requirements Mitigates Short-term Impacts of Global Population and Income Growth on Agricultural
Enviromnental Outcomes." Energy Policy 175 (February 24, 2023): 113497.
https://doi.Org/10.1016/i.enpol.2023.113497.
229 Bamber, Nicole, Ian Turner, Baishali Dutta, Mohammed Davoud Heidari, and Nathan Pelletier. "Consequential
Life Cycle Assessment of Grain and Oilseed Crops: Review and Recommendations." Sustainability 15, no. 7 (April
4, 2023): 6201. https://doi.org/10.3390/sul5076201.
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more specific focus on a select group of crops to ensure similar production methods are
implemented to make comparison across studies more useful (Bamber et al. 2023).
4.3.3 Potential Soil and Water Quality Impacts
As was done in Section 4.2.3 for potential natural land conversion effects, we examine
the volume changes expected from this rule relative to the No RFS and 2025 Baselines, for both
BBD and conventional renewable fuels, to understand potential soil and water quality effects.
The projected BBD volume changes (Table 4.2-1) relative to both baselines suggest that this rule
would increase demand for BBD. If BBD supply were to come from crop-based feedstocks such
as soybean and canola, then this rule could contribute to further declines in soil and water
quality.
Similarly, we would see higher conventional renewable fuel volumes attributable to this
rule relative to the No RFS Baseline, but to a smaller degree compared to BBD volumes as
indicated by smaller values in Table 4.2-2 compared to Table 4.2-1. This could contribute to
further domestic land use changes that impact soil and water quality.
Based on results from the EPIC modeling work done in the RtC3 and summarized
previously, if future impacts follow the same pattern as historical trends, the volume increases
from this rule could contribute to small percentage increases in erosion, nutrient loss, and soil
organic carbon loss. Of course, the true impacts on soil and water quality would depend on many
factors, including imports and exports of BBD supplies in the coming years. Decreasing
exportation of whole soybeans and crushing more soybeans domestically could provide more
BBD supply and thus decrease any demand for additional soybean production from agriculture.
Additionally, in the event of any land conversion to agriculture due to this rule, other
factors that are highly variable at the local scale would affect the true impacts on soil and water
quality. For example, one factor to consider is the baseline nutrient, sediment, and pesticide
loadings from pre-converted land uses. Even forests, which provide the highest water quality
among all land cover types,230 contribute to nutrient loadings in watersheds. As another example,
livestock grazing on pastureland can affect sediment runoff. Pastureland that is converted to
cropland would still contribute to soil and water quality degradation, but likely to a lesser extent
compared to natural grassland that is converted to cropland. As such, it is important to consider
the loadings from any pre-extensification or pre-intensification scenarios to understand the
potential net effect in pesticide, nutrient, and sediment loadings. In some cases, the net effect
may actually be a decrease in pollutants, as may be the case in crop conversion from corn to soy,
a nitrogen fixer, leading to a decrease in nitrogen runoff assuming nitrogen fertilizer applications
to the field decrease as well.
2311 Caldwell, Peter V., Katherine L. Martin, James M. Vose, Justin S. Baker, Travis W. Warziniack, Jennifer K.
Costanza, Gregory E. Frey, Arpita Nelira, and Christopher M. Miliiar. "Forested Watersheds Provide the Highest
Water Quality Among All Land Cover Types, but the Benefit of This Ecosystem Service Depends on Landscape
Context." The Science of the Total Environment (April 19, 2023): 163550.
https://doi.Org/10.1016/i.scitotenv.2023.163550.
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The magnitude of effects also depends on feedstocks planted, the biogeophysical traits of
the land being farmed on, the management practices in place, and many other factors that are not
determined by the RFS standards. While past analyses can provide insight, the likely future
effects of this rulemaking are not fully understood because it is not possible to understand the
true effects at the local level due to such complexities. Further, for any potential effects,
additional conservation measures—such as further adoption of conservation tillage and cover
crops—would help reduce the impacts of biofuels and the RFS program. In the Set 2 Rule BE,
EPA further explores how the Set 2 Rule may impact water quality due to land use conversion to
agriculture.
Soil and water quality effects from other issues beyond agriculture could occur in
connection to this rulemaking. These include chemical leaks and spills from storage and
transportation tanks. It is not possible at this time to attribute such leaks and spills to the RFS
program. However, if any potential effects occur, EPA expects them to be minimal, and EPA is
involved in a separate process outside of the Clean Air Act for taking corrective actions and
completing remediation for any chemical releases.
There are also concerns regarding potential soil and water quality impacts from biogas
production through manure collection and animal feeding operations on farms. However, the
majority of biogas for cellulosic biofuel is sourced from landfills and not agricultural digesters.
As such, we expect any potential impacts from agricultural digesters to be very minimal.
4.4 Water Quantity and Availability
We have previously explored this topic in the Biofuels and the Environment Reports to
Congress and Set 1 Rule RIA. We summarize major findings below.
4.4.1 Water and Biofuel Crop Growth
Growth of biofuel feedstock crops such as corn and soybeans are the primary uses of
water in the process of creating renewable fuels. Although there are several other "fuel crops"
used in the RFS program, corn and soybeans are the focus of our evaluation on water quantity as
they collectively represent a significant majority of the crop-based feedstocks consumed to
produce fuel under the RFS program.
4.4.1.1 Corn
Historically, corn has been grown in mostly rain-fed locations such as in Iowa and
Minnesota where irrigation is often not needed. Because of this, corn has historically been
considered to have a low to modest water footprint. However, with changes in cropland needs
such as meat production and the RFS program, cropland usage has shifted. With increased
production of corn for ethanol production, corn growth has expanded further into locations where
irrigation is needed more frequently.
As discussed in the Set 1 Rule BE, several studies have evaluated land use change
attributable to the volumes from the Set 1 Rule. Data from this evaluation concluded that corn
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acreage growth did not necessarily result in total crop land acreage growth. This work suggested
it was more likely that other crops were being displaced in order to plant additional corn. This
could indicate the planting of corn in locations previously not thought to be ideal for the crop's
growth (e.g., the Northern Great Plains region) and requiring the need for additional irrigation.
4.4.1.2 Soybeans
Soybeans in general require less irrigation than corn. Corn and soybeans are typically
grown in rotation and therefor in the same growing regions, which historically in the U.S. have
been primarily those regions which receive higher rainfall and require less additional irrigation.
Projections for biodiesel and renewable diesel volumes from the Analyzed Volumes
suggest an increase in soybean production in the years analyzed. Between 2022 and 2024 a large
influx of UCO was being imported and utilized in biodiesel market. Due to changes in tax
incentives further described in Chapter 1.3 UCO has become less incentivized in the biofuel
world. These tax incentive changes also increased the incentive for domestically produced
feedstocks such as canola and soybean oil. Although soybean oil demand will continue to
increase with the anticipated fuel volumes, the impact to land use change could be minimal with
implementation of other crop sustainability practices as well as optimization of the soybean oil
market.
As stated above, the irrigation of corn, soybeans, and other biofuel crops is the
predominant driver of water quantity impacts attributable to the RFS program. Some studies
show land use change over time coincided with areas experiencing groundwater depletion231. But
this correlation does not mean there is a direct, causal relationship between biofuel production
and groundwater depletion. USD A data suggests that total irrigated acres have increased in the
U.S. over time (2013-2018), however irrigation rates have declined on a per acre level over the
same time period for both corn and soybeans.232
4.4.2 Use of Water in Production Facilities
Production of biofuels also requires water for the actual production of fuels at biofuel
facilities. With increases in potential volumes in biofuel production, we can assume an increased
need for water in the fuel production process.
Similar to petroleum-based fuels, biofuel production requires the use of water to produce
fuel. At many biofuel facilities, consumption of water has declined over time through more
efficient water use, water recycling and recovery processes and reuse of wastewater, for
example. The biofuel production process itself requires less water consumption than the growth
of the biofuel feedstock crops. That said, biofuel facility water use, even with the implementation
231 Yao, Yi, Wim Thiery, Agnes Ducharne, Benjamin I. Cook, Anxin Ding, Steven J. De Hertog, Petra Sieber, et al.
"Irrigation-induced Land Water Depletion Aggravated by Climate Change." Nature Water 3, no. 12 (November 5,
2025): 1424-35. https://doi.org/10.1038/s44221-025-0Q529-l.
232 USD A, "Irrigation and Water Use," January 8, 2025. https://www.ers.usda.gov/topics/farm-practices-
management/irrigation-water-use.
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of water saving techniques, may still be locally consequential in areas that are already
experiencing stress on water availability.
Overall, while values will vary across states and counties, ethanol, biodiesel and
renewable diesel made from vegetable oils are substantially more water intensive than the
petroleum fuels they would displace.233
In summary, based on the approaches above, there will likely be some increased
irrigation pressure on water resources due to the Analyzed Volumes. Specifically, the fuel
volume increases for 2026-2027 compared to the No RFS Baseline which we estimate in
Chapter 3 will be produced from agricultural feedstocks (especially corn and soybeans) would
suggest the potential for some associated increase in crop production. This in turn would likely
increase irrigation pressure on water resources. The increased volume requirements, especially
that of biodiesel and renewable diesel, could incent greater production of soybeans. There is
uncertainty in projecting changes in acreage and irrigation rates associated with corn, soybeans,
and other crops. Additional information and modeling are needed to fully assess changes in
water demands and effects on water stressed regions, both for crop irrigation as well as impacts
of biofuel facility water use.
4.5 Ecosystem and Wildlife Habitat
The previous sections in this chapter discussed this rulemaking's potential impacts on air
quality, wetland and other natural land loss, soil quality, water quality, and water quantity.
Changes to any of these environmental end points could subsequently impact ecosystems. This
may include impacts on habitat and threatened and endangered species that are in danger of
becoming extinct in the future.
EPA has previously assessed the impacts of biofuel consumption and production on
ecosystems and wildlife in the Biofuels and the Environment: Reports to Congress. The RtC3,
the Set 1 Rule RIA, and Set 1 Rule BE further evaluate impacts from the RFS program, of which
the latter two examine potential impacts from the Set 1 Rule specifically. The BE examined
impacts of the Set 1 Rule's 2023-2025 volumes on endangered and threatened (referred to as
"listed" species), and found that the rule may affect, but is not likely to adversely affect (NLAA),
any of the 810 populations or critical habitats found in a large action area comprising most of the
U.S. where corn, soy, and canola can be grown. EPA has completed another BE for this Set 2
Rule, in accordance with ESA Section 7 consultation with the Services.
In this section, we first explore the historical data and information contributing to our
understanding of ecosystem and wildlife habitat effects from agriculture and biofuels broadly, as
well as our understanding of potential effects from past RFS volumes specifically (Chapter
4.5.1). Because the Set 1 Rule RIA and BE, finalized in 2023, included a literature review and
information examining wildlife impacts, we also reviewed and discuss new literature from more
233 Institute of Medicine of the National Academies. "The Nexus of Biofuels, Climate Change, and Human Health:
Workshop Summary." Roundtable on Enviromnental Health Sciences, Research, and Medicine; Board on
Population Health and Public Health Practice. April 2, 2014. Chapter 5, Water Use, Water Pollution, and Biofuels.
https://www.ncbi.nlm.nih.gov/sites/books/NBK196445.
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recent years related to this topic (Chapter 4.5.2). The last subsection explores the potential
ecosystem and wildlife habitat impacts from this rule (Chapter 4.5.3).
4.5.1 Ecosystems and Wildlife Habitat Impacts
In this section, we summarize previous findings and EPA's current understanding of how
agriculture, biofuel production, and the RFS program historically impacted ecosystems and
wildlife habitat. For more detailed information including references to scientific articles that
support such findings, please see Chapter 12 (Terrestrial Ecosystem Health and Biodiversity) in
the RtC3 as well as the Set 1 Rule BE.
Land conversion to cropland is generally associated with negative impacts to ecosystem
health and biodiversity. Demand for crop-based feedstocks used for biofuel production (corn,
soy, canola) can lead to further agricultural conversion which may affect species by contributing
to overall habitat loss associated with cropland extensification more generally. Because native
grasslands have seen higher conversion rates to agriculture compared to wetlands and forests, it
is likely that terrestrial wildlife species with the largest potential risk are grassland species,
including bird species and various insect species that rely on those ecosystems. However, some
impacts to species in wetland and forest ecosystems may still occur due to direct land conversion
to agriculture.
Pesticide drift, or the movement of pesticide dust or droplets through the air, can affect
nearby ecosystems and species after application to farm fields. In addition, nutrients, sediment,
and pesticides carried by agriculture runoff affect the health of aquatic ecosystems and species
that live or rely on such ecosystems. This pollution can impact water quality at nearby edge-of-
field streams and rivers as well as at a significant distance from the location of the land use
change as contaminants associated with crop production travel downstream and into major
waterways. This is particularly true for contaminants with greater mobility and contaminants that
persist for longer time periods in soil and aquatic environments.
Many species of fish, for example, rely on creeks and streams with low turbidity, well
oxygenated and moderately clean water, and riffles, pools, and runs with differing substrates of
gravel, pebble, and sand. They may also need riparian cover and cooler temperature of waters, an
abundant source of food, geo-morphically stable river channels and banks, and sufficient water
depth. Some or all of these features in creeks and streams could be affected by agricultural land
conversion and runoff. For instance, increased sediment can alter the geomorphology of streams,
and increased turbidity and nutrients could affect macroinvertebrate communities that provide
sources of food for fish.
Furthermore, excess nutrients (eutrophication) and sediment in places like the Gulf of
America and Chesapeake Bay contribute to hypoxia and dead zone conditions in the
summertime. Species that live in or rely on these estuarine and coastal ecosystems may therefore
be impacted as well.
Potential air quality and water quantity effects could also occur due to production and
consumption of biofuels, as discussed in greater detail in Sections 4.1 and 4.4, respectively. Such
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effects could subsequently impact the health of ecosystems and species that rely on clean air or
adequate water supply.
To better understand potential impacts of land use change and biofuels on listed species,
an analysis in the RtC3 found that shifts from perennial cover to corn and soybeans from 2008-
2016 occurred in areas adjacent to or within critical habitat of 27 terrestrial threatened and
endangered species across the contiguous U.S. The RtC3 found that past RFS volume obligations
during those years were just one factor out of many that could have played a role in these land
use changes. The Report states that the range of possible impacts from the RFS program likely
spanned from no impact to a negative impact on terrestrial biodiversity historically.
As stated previously, EPA also assessed the potential impacts to listed species from the
Set 1 Rule volumes in the Set 1 Rule BE. EPA identified 810 populations or critical habitats
found within a large action area that may be affected by the rulemaking. Ultimately, however,
EPA found the Set 1 Rule is NLAA listed species and their designated critical habitats. The Set 1
Rule BE document details the specific analyses and findings that led to this conclusion. In
accordance with the ESA, EPA submitted this BE and received letters of concurrence with this
NLAA determination from NMFS on July 27, 2023, and from FWS on August 3, 2023, thereby
concluding informal consultation on the Set 1 Rule.
To date, EPA's work to understand the impacts of past RFS volume obligations on
habitats and listed species has fully relied on EPA's understanding of how the RFS, separate
from other influencing factors, impacts land use change and intensification and extensification to
agriculture. EPA has advanced this understanding in recent years by including an RFS attribution
analysis in the RtC3, for example. Still, it has not been possible historically, and is still not
possible at this time to the best of our knowledge, to say which parcels of lands were converted
in the past due to the RFS program alone, nor to project with confidence where land use change
will occur in the future due to the RFS. With the currently available science, the finest grain
possible for understanding the effect of biofuel production on cropland that we are aware of is at
the county scale. However, such analyses for the Set 1 Rule rendered limited information234 and,
further, are conservative estimates as they do not directly account for trends in imports and
exports of crop-based feedstocks. These limitations make it even more challenging to fully
understand how the RFS may affect unique habitats and wildlife that live and rely on location-
specific ecosystems across the contiguous U.S.
Beyond our understanding of impacts from biofuels, the RFS, and land use change
broadly, it is well known both in the scientific literature and environmental management field at
large that conservation practices may help to mitigate any potential effects. These practices
include protecting environmentally sensitive lands and increasing habitat heterogeneity to
mitigate impacts from land conversion of habitat. Furthermore, the adoption and expansion of
sustainable conservation practices on farmland can reduce impacts on aquatic ecosystems by
234 In the Set 1 Rule, county-level estimates would have been possible by leveraging an econometric analysis for
corn ethanol effects due to proximity to ethanol facilities, specifically, as opposed to corn ethanol crop price effects.
However, the proximity to ethanol facility effects were estimated to be zero for total cropland in the Set 1 Rule BE,
so EPA was not able to accomplish county-level estimates for this. See Li et al. (2019) and updated analyses by
Madhu Khanna as described in the Set 1 Rule BE for more information.
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restoring flow and decreasing loads of nutrients, sediment, and pesticides to levels that are less
harmful to aquatic organisms.
4.5.2 New Literature on Ecosystem and Wildlife Habitat Impacts
Like in previous sections, EPA completed a literature review of research articles from
recent years to assess the current state of the science related to agriculture and biofuel production
effects on habitat and species. EPA found only one study that directly linked potential effects to
the RFS program.
The one article that related species and habitat impacts to the RFS program is from Lark
(2023).235 In this article, Lark explores how the RFS program may have affected land use
changes and critical habitat, illustrates example pathways of interaction between biofuels and
endangered species, provides examples of potentially impacted species, and proposes solutions
to mitigate harm. Lark further acknowledged in the article that the "extent, duration, and
magnitude of influence from the RFS specifically is unknown and remains a topic ripe for further
research." Lark also encouraged EPA to complete consultation with the FWS and NMFS in
accordance with the ESA, which EPA accomplished for the Set 1 Rule a few months after the
article's publication.
Other studies did not look at the potential impacts from RFS program specifically but
instead examined impacts from agriculture and biofuel crop-based feedstocks more broadly. The
findings from these studies support much of our current understanding, for example that species
that live and rely on grasslands are especially affected by agricultural conversion, and that there
is a link between agricultural activity and declining fish populations.
In one study, van der Burg et al. (2023)236 examined how biofuel crop production and oil
and gas development impact grassland bird species in North Dakota. The researchers looked at
four types of birds—Bobolink, Grasshopper Sparrow, Savannah Sparrow, and Western
Meadowlark—between 1998 and 2021. They found that biofuel feedstocks, like corn and
soybeans, had a more negative effect on grassland bird populations than oil and gas
development. The authors observed that "all four species responded positively to the proportion
of grasslands surrounding a point on the landscape. Likewise, [they] found that all four species
responded negatively to the proportion of corn and soybeans on the landscape," meaning that
birds were less likely to live in or use areas where biofuel crops dominated. They also found that
small grain crops, like wheat and barley, had less of a negative effect, and in some cases, even a
slight positive effect on the birds likely due to features on small grain fields that mimic the
vegetation structure and phenology of grasslands.
235 Lark, Tyler J. "Interactions Between U.S. Biofuels Policy and the Endangered Species Act." Biological
Conser\>ation 279 (January 16, 2023): 109869. https://doi.Org/10.1016/i.biocon.2022.109869.
236 Van Der Burg, Max Post, Clint Otto, and Garrett MacDonald. "Trending Against the Grain: Bird Population
Responses to Expanding Energy Portfolios in the US Northern Great Plains." Ecological Applications 33, no. 7 (July
7, 2023). https://doi.org/10.1002/eap.2904.
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Another study, Crawford and Alexander (2024)237 investigated the relationship between
historic fish kills and insecticide use, comparing data across 10 watersheds in Prince Edward
Island, Canada, with varying degrees of agricultural activity (including corn and soybeans).
Severely impacted watersheds—identified as those with greater than 50% of land being used for
agriculture—generally exhibited nitrate levels in excess of the guidance levels, as well as
elevated levels of other nutrients (e.g., total phosphorus) and insecticide concentrations. Though
the researchers suggested a more targeted study be performed in the future, the results achieved
point toward a link between industrial-scale pesticide use and detrimental impacts to downstream
water quality.
4.5.3 Potential Ecosystem and Wildlife Habitat Impacts
As was done in previous sections, as a first step we can look at the volume changes
expected from this rule relative to the No RFS and 2025 Baselines, for both BBD and
conventional renewable fuels, to assess potential impacts to ecosystems and wildlife. The
projected BBD volume changes from the Analyzed Volumes (Table 4.2-1) suggest that this rule
would increase demand for BBD. If BBD supply were to come from crop-based feedstocks such
as soybean oil and canola oil, then this rule could contribute to additional land use change,
declines in soil and water quality, and, subsequently, impacts to wildlife and habitat.
Similarly, EPA estimates higher conventional renewable fuel volumes attributable to this
rule relative to the No RFS Baseline, but to a smaller degree compared to BBD volumes, as
indicated by smaller values in Table 4.2-2 compared to Table 4.2-1. As such, this rule could lead
to additional demands for conventional fuel and contribute to further domestic land use changes
that impact wildlife and habitat, though to a lesser extent compared to BBD volume impacts.
Imports and exports of BBD supplies in the coming years will also play an important
role. For example, decreasing soybean exportation and crushing more soybeans domestically
would allow for greater U.S. soybean oil production without the need for increasing cropland for
crop-based feedstocks. As described in more detail in Chapter 7, BBD has been imported at
significantly greater quantities in recent years, especially starting in 2021 and through 2024.
Should similar trends continue, this could provide greater BBD supplies in the domestic market.
If BBD is largely supplied by changing import and export dynamics, then it could mean fewer
land use impacts may be expected, and minimal wildlife and ecosystem effects from this rule.
EPA completed a BE for this rule that assessed the rule's potential impacts on listed
species. As was done for the Set 1 Rule BE, EPA applied probabilistic analyses to select
available lands for conversion and estimate the overlap between potential cropland changes and
critical habitats or listed species' ranges. Supported by the analysis in the Set 2 Rule BE, we
determined that formal consultation is not required for the Set 2 Rule.
In addition, in the event that some land conversion does occur due to this rule, there are
many other factors at the local level that would affect soil and water quality, and thus ecosystems
237 Crawford, Miranda, and Alexa C. Alexander. "Fish Kills and Insecticides: Historical Water Quality Patterns in 10
Agricultural Watersheds in Prince Edward Island, Canada (2002-2022)." Frontiers in Sustainable Food Systems 8
(July 26, 2024). https://doi.org/10.3389/fsufs.2024.1356579.
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at large. These include, but are not limited to, the type of land that was converted (e.g.,
pastureland versus long-term, pristine grasslands), the feedstocks that are planted, and
management practices put in place. Further, the weather conditions on a given day or year matter
as well; the amount of precipitation will affect runoff of nutrients and sediment from agricultural
lands, affecting both edge of stream environments and the size of dead zones such as in the Gulf
of America. The true effects on ecosystems from this rulemaking are challenging to understand
due to the unique factors, conditions, as well as decisions at the local level that are not
determined by the RFS.
Impacts on air quality or water quantity from this rulemaking could also affect wildlife
and ecosystems. However, any effects on water quantity and air quality impacts would likely be
highly variable and dependent on the local context. For example, as explained in Section 4.1 of
this chapter, we would expect some localized increases in some air pollutant concentrations,
particularly at locations near production and transport routes and in more rural areas. Overall, we
expect the emission impacts from the Analyzed Volumes to be variable in how they affect
ambient concentrations of ozone and PM2.5 in specific locations across the U.S. With regard to
water quantity, there remains great uncertainty in projecting changes at the local level as well;
for example, with irrigation rates as decided by farmers for growth of corn, soybeans, and other
crops.
4.6 Ecosystem Services
Ecosystem services broadly consist of the many life-sustaining benefits humans receive
from nature, such as clean air and water, fertile soil for crop production, pollination, and flood
control.238 The United Nations Millennium Ecosystem Assessment239 categorized four different
types of ecosystem services, including:
• Provisioning Services; the provision of food, fresh water, fuel, fiber, and other goods
• Regulating Services; climate, water, and disease regulation as well as pollination
• Supporting Services; soil fermentation and nutrient cycling
• Cultural services; education, aesthetic, and cultural heritage values as well as recreation
and tourism
Several of the drivers of ecosystem loss identified in the Millennium Ecosystem
Assessment, such as climate change, poor air quality, poor water quality, water quantity, and
land use change, are expected to be impacted by the production of renewable fuels generally and
may be impacted by the Analyzed Volumes in this rule specifically.
The previous sections in this chapter discussed the projected impacts associated with this
rule on a variety of different environmental end points such as air quality, climate change, land-
use change, soil and water quality, and water quantity as required by the statute. Each of the
impacts discussed in these sections would be expected to have an impact on one or more
ecosystems services. These impacts could be positive (e.g., result in ecosystem services benefits)
238 EPA, "Ecosystem Services." https://www.epa.gov/eco-researcli/ecosYStem-services.
239 Millennium Ecosystem Assessment, "Ecosystems and Human Well-being: Synthesis," 2005.
https://www.millenniumassessment.org/documents/document.356.aspx.pdf.
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or negative. We have focused our analyses on the specific factors identified in the statute and we
have not quantified all of the human well-being changes or monetized these effects. We have,
however, provided a potential framework for how the impacts on ecosystem services might be
considered (see Figure 4.6-1). Note that there are multiple frameworks for categorizing
ecosystem services in the literature. Future analyses, such as those presented in the Triennial
Biofuels and the Environment reports to Congress, may refine this approach to better capture
incremental ecosystem service benefits and costs.
In recent years, humans have become more reliant upon these ecosystem services to the
point where ecosystems have begun to rapidly change. These changes have been made to meet
growing needs for food, water, and in the case of the RFS program, fuel. These changes,
although beneficial to human well-being, have often been at the cost of environmental well-
being. Water scarcity and land conversion are two of the most prominent potential consequences
of a robust RFS biofuels program. As stated in previous sections, there are several ways to
attempt to mitigate the effects of these volumes. Cropland is often able to have an increased
harvest which allows for significantly less potential land expansion to meet the volume needs.
Additionally, the processing of biofuels has become increasingly more water efficient than
previous methods.
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Figure 4.6-1: Framework for Considering the Impact of the RFS Volumes on Ecosystem
Services
Biophysical Changes
GHG Emissions
• Displacing Petroleum
Based Fuels
• Domestic Land Use
Change
• International Land Use
Change
Air Quality
• Potential changes in PM,
NOx, S02, VOC, CO, MH3
Water Quality and Aquatic
Habitats
Fertilizer and Pesticide
Runoff
• Sediment Runoff
• Habitat and Associated
Filtration
• Leakage from
U ndergrou nd Storage
Tanks
• Atmospheric
Deposition
Hydrology, Water Quantity,
and Flood Risk
• Tilling
• Land Use/Habitat Change
• Irrigation
Wildlife and Habitat
• Pollinating Insects
• Commercial Species
• Spectes of Public Interest
• Pest Control Species
Human Well-Being
Changes
r i
Monetary Value
Changes
Social Effects from Climate
Change
Social Cost of GHGs
Property Effects
Property Values
HP
Morbidity and Mortality Effects
Health Values
Energy, Transportation, and
Drinking Water Production
Effects
Agricultural Product Value
Wildlife Product Value
Recreation Effects
~ j
Wildlife Existence Value
Recreation Value
Soil Quality
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Chapter 5: Climate Change Analysis
CAA section 21 l(o)(2)(B)(ii) states that the basis for setting applicable renewable fuel
volumes after 2022 must include, among other things, "an analysis of.. .the impact of the
production and use of renewable fuels on the environment, including on.. .climate change." This
chapter describes our analysis of the potential climate change impacts of this rule. While the
statute requires EPA to base its determinations on, among other things, an analysis of the climate
change impact of renewable fuels, it does not require a specific type of analysis. Additionally,
while we consider the impacts on climate change as required by the statute, the range of potential
GHG emission reductions, when coupled with additional uncertainties involved in commonly
used climate change end points, makes it difficult to quantify potential climate change impacts
such as changes in global temperature.
This chapter is organized as follows: Chapter 5.1 details the methodologies, models,
scenarios, and assumptions used to assess the potential climate change impacts of the volumes
assessed for purposes of this final rule. This section describes the methods for evaluating the
GHG emissions associated with two different categories of biofuels: fuels produced from
residues, byproducts, and other non-crop feedstocks; and fuels produced from crops.240 Chapter
5.2 presents the results of an analysis of the Analyzed Volumes relative to the No RFS Baseline
Scenario. Results are presented in tons of GHG emissions changes. Chapter 5.3 presents several
sensitivity scenarios under a range of assumptions about market mediated effects in the energy
sector.
5.1 Methodology
Our assessment of the climate impacts of the Analyzed Volumes includes: (1) economic
modeling of the estimated impact of combined changes in volumes of fuels produced from crop-
based feedstocks; and (2) supply chain GHG emissions modeling for estimates for all fuels.
This section is organized as follows: Chapter 5.1.1 provides an overview of the
methodology, including comparisons with past analyses of climate change impacts under the
RFS program and the two categories of fuels and methods noted above. Chapter 5.1.2 describes
the scenarios modeled in our analysis. Chapter 5.1.3 focuses on the methodology of assessing
emissions impacts of volumes of biofuels produced from secondary products. Chapter 5.1.4
focuses on the methodology of assessing emissions impacts of volumes of fuels produced from
crops.
5.1.1 Overview
The CAA prescribes multiple assessments of the GHGs associated with biofuels,
including biofuel lifecycle assessments for the purpose of determining the qualification of a fuel
240 Additional details of this methodology and model results are available in "Economic Modeling of Climate
Change Impacts for Crop-based Fuels," available in the docket for this action.
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under the RFS program,241 and, separately, as required by CAA section 21 l(o)(2)(B)(ii),
assessments of climate change impacts of setting volume standards. These two prescribed sets of
analyses serve different purposes under the statute. While there are many methodological
similarities between the two, there are also important differences that are informed by their
statutory bases and programmatic purposes.242 This section describes our methods of assessing
the potential climate change impacts of setting volume standards under various scenarios, as
required by CAA section 21 l(o)(2)(B)(ii).243
EISA required substantial changes to the RFS program as it existed at the time. Among
these changes was the establishment of statutorily defined volumes of different categories of
renewable fuels. For three categories—renewable fuel, advanced biofuel, and cellulosic
biofuel—statutorily defined volumes extended through 2022. For the BBD category, statutory
volumes extended through 2012.244 When setting volume requirements beyond the statutorily
defined volumes, and in limited other circumstances, CAA section 21 l(o)(2)(B)(ii) states that the
basis for setting RVOs must include, among other things, "an analysis of.. .the impact of the
production and use of renewable fuels on the environment, including on.. .climate change."
The substantial changes to the RFS program prescribed by EISA were implemented in
the 2010 RFS2 Rule. At that time the volumes were still defined by the statute and EPA was not
required to analyze climate change impacts under CAA section 21 l(o)(2)(B)(ii) in order to
promulgate RVOs. Regardless, EPA developed and applied a methodology for assessing the
climate impacts of volumes established under EISA in the 2010 RFS2 Rule245 for the purpose of
inclusion in a cost benefit analysis, following guidelines established Executive Order 12866,
which provides guidance on conducting cost benefit analysis for significant regulatory actions.246
In the RFS rules setting BBD volume standards in years between and including 2013 and
2020, the climate change analysis required by CAA section 21 l(o)(2)(B)(ii) was limited to BBD
standards because volumes for the other three categories of renewable fuels were defined by
statute. In those actions, EPA concluded that volumes of BBD supplied to the U.S. market were
driven by the advanced standard, not the BBD standard, so the impact of the BBD standard on
climate change and other statutorily defined factors was negligible.247 Thus for these rules, EPA
241 "Lifecycle greenhouse gas emissions" is defined under the RFS program in CAA section 21 l(o)(l)(H) and is
applicable to the determinations of GHG reduction thresholds for different categories of fuels defined in CAA
section 21 l(o)(l)(B)(i), (D), (E), and (o)(2)(A)(i).
242 For this reason, it may be misleading or analytically inappropriate to interpret results from a climate change
analysis conducted under CAA section 21 l(o)(2)(B)(ii) as determinative evidence in a threshold determination
under CAA section 21 l(o)(l), or vice versa. See additional discussion in RTC Section 9.2.1.
243 This action does not address EPA's methodology for assessing "lifecycle greenhouse gas emissions" as defined
under CAA section 21 l(o)(l)(H) for the purpose of determining qualification of fuels under the RFS program.
244 CAA section 21 l(o)(2)(B)(i)
245 RFS2 Rule RIA, Chapter 2.7.
246 Executive Order 12866: Regulatory Planning and Review, https://www.federalregister.gov/executive-
order/12866.
247 See, e.g., discussion in the 2019 Rule: ".. .EPA's primary assessment of the statutory factors for the 2020 BBD
applicable volume is that because the BBD requirement is nested within the advanced biofuel volume requirement,
we expect that the 2020 advanced volume requirement, when set next year, will determine the level of BBD use,
production and imports that occur in 2020. Therefore, EPA continues to believe that approximately the same overall
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determined it was not required to and did not conduct quantitative analyses of the impacts of the
RVO standards on climate change.
In the 2020-2022 Rule,248 EPA utilized the volume waiver defined in CAA section
21 l(o)(7)(F) to set standards deviating from statutorily defined volumes. This waiver authority
invokes the analytical criteria set forth in 21 l(o)(2)(B)(ii). Thus, the 2020-2022 Rule was the
first time EPA conducted a quantitative assessment of climate change impacts to satisfy
211 (°)(2)(B)(ii).249
The analytical criteria set forth in 21 l(o)(2)(B)(ii) apply for all volume standards set
under the RFS program after 2022. Thus, for the Set 1 Rule, which established volume standards
for 2023, 2024 and 2025, EPA assessed the potential climate change impacts of those standards.
We again assess the potential climate change impacts of the 2026 and 2027 standards under this
rule, as required by the CAA.
The climate change assessment presented in the 2010 RFS2 rule relied on a combination
of models and additional data sources to estimate potential global GHG emissions impacts of the
RFS program from 2010-2022. This methodology was based on the best models and science
available at the time. While our 2010 analysis represented a best-in-class approach at the time of
publication, evidence gathered over the course of the 2010s and early 2020s from expert
discussions, input from public stakeholders, and EPA's review of the available literature
subsequently led us to conclude that an updated approach would be appropriate for any new
evaluations going forward.
First and most critically, some of the tools which comprised our 2010 methodology were
no longer maintained and ceased to be operational by the end of 2022. In addition, our 2010
methodology required the use of one model to represent impacts in the U.S. and another to
represent the rest of the world. This was necessary in 2010 when no suitable modeling tool was
available which integrated the global agricultural economy into a single framework. However, by
2022, several potentially suitable global models which integrated key economic sectors and
global trade were available. In 2010, we estimated land use change emissions by stitching
together individual sets of economic modeling results with satellite imagery and soil carbon
datasets using elaborate and largely manual post-processing routines, which had numerous
opportunities for user errors. By 2022, several models could integrate all these factors more
accurately and consistently. Finally, in 2010 we were reliant on historical data and forward-
looking projections from 2008 or earlier to estimate future impacts in 2022 and onward. These
factors led to the conclusion that relative to our 2010 methodology for assessing climate change
volume of BBD would likely be supplied in 2020 even if we were to mandate a somewhat lower or higher BBD
volume for 2020 in this final rule. Thus, we do not expect our 2020 BBD volume requirement to result in a
significant difference in the factors we consider pursuant to CAA section 21 l(o)(2)(B)(ii)(I)-(VI) in 2020." (83 FR
63738-39; December 11, 2018)
248 87 FR 39600 (July 1, 2022).
249 For the 2020-2022 Rule, EPA developed illustrative scenarios of the potential GHG impacts of the volume
standards by adapting results from the analysis conducted for the 2010 RFS2 Rule.
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impacts of volume standards, better tools were available to meet our statutory obligations under
the RFS program.250
Recognizing that public input on models and methods available would be integral to
incorporating the latest scientific advancements, EPA undertook multiple steps to engage with
the scientific community and develop updated approaches suitable for assessing climate change
impacts of volume standards under the RFS program. These steps included co-hosting a public
workshop on biofuel GHG modeling,251 publishing a model comparison exercise (MCE)
technical report,252 and considering the findings of a report published by the National Academy
of Sciences, Engineering and Medicine (NASEM) on lifecycle analysis methods for low-carbon
transportation fuel policies (hereafter the "the NASEM LCA Report").253
However, when technical work commenced on the Set 1 Rule, the models used to
conduct analysis of the costs and benefits of the RFS2 Rule were no longer operational and work
on the MCE and NASEM LCA Report was still ongoing. For this reason, EPA was unable to
conduct new modeling to assess climate change impacts of the volume scenarios, one of the
statutory factors listed in the CAA that EPA was obligated to assess in the Set 1 Rule. EPA
instead relied on a literature review approach for the Set 1 Rule. In that analysis, we identified
ranges of potential lifecycle GHG emissions associated with each individual fuel pathway (i.e.,
each unique combination of a feedstock, production process and fuel) from the available
scientific literature. Lifecycle emissions estimates for each individual fuel pathway were then
scaled to the projected change in the volume of that fuel to estimate a range of potential GHG
impacts of the 2023-2025 standards.
While the literature review-based approach was necessary to assess GHG impacts under
the Set 1 Rule for the reasons outlined above, it does have several deficiencies. First, combining
assessments of individual pathways of fuels, as EPA did in the Set 1 Rule literature review-based
approach, fails to represent key interactions that are present when a policy is expected to
simultaneously affect volumes of multiple fuels. The lack of representation of interactions
between fuels is particularly notable when assessing volumes of multiple fuels produced from
feedstocks with significant land use requirements. For example, in simulations representing only
an increase in corn ethanol, corn production may expand at the expense of soybean production
via crop switching, while simulations representing increases in soybean biodiesel production
may show the opposite: crop switching from corn to soybeans. Combining per-fuel effects to
assess the overall impacts of simultaneous changes in fuel volumes can introduce such
2511 Note that the 2020-2022 Rule was published in June 2022, at which point EPA had not developed an alternative
modeling framework for conducting the climate impacts analysis of an RFS standards rule. Thus, the illustrative
climate impacts analysis in the 2020-2022 Rule relied on straightforward adjustments to existing modeling
conducted in the 2010 RFS2 Rule.
251 EPA opened a public docket for the workshop (86 FR 73757; December 28, 2021) and received numerous public
comments on appropriate data, models, and interpretive methods suitable for biofuel GHG emissions assessments.
252 EPA, "Model Comparison Exercise Technical Document," EPA-420-R-23-017, June 2023.
https ://nepis. epa. gov/Exe/ZvPDF. cgi?Dockev=P 1017P9B .pdf.
253 National Academies of Sciences, Engineering, and Medicine ("NAS"). Current Methods for Life Cycle Analyses
of Low-Carbon Transportation Fuels in the United States. National Academies Press eBooks, 2022.
https://doi.org/10.17226/26402.
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inconsistencies between simulations, and can substantially affect the overall emissions estimates,
as was recognized in the 2010 RFS2 Rule.254
Second, for fuels produced from feedstocks with land use requirements (i.e., crop-based
fuels), only a handful of the studies identified in the literature review produced emissions
estimates that represented the temporal dynamics of land use change emissions; among these,
only the pathway specific modeling from EPA's own 2010 RFS2 Rule analysis reported annual
emissions impacts.255 This limitation restricted the illustrative monetized scenario, for volumes
of crop-based biofuels, to represent only the results of EPA's 2010 modeling, not the full breadth
of impacts reported across more recent studies identified in the literature.
Finally, the literature review-based analysis did not attempt to weigh or rank the validity
or robustness of the many different methods employed in studies it considered. Instead, the
literature review-based approach presented a summary of the state of recent literature on biofuel
lifecycle analysis and the wide breadth of potential impacts estimated therein.256 For the above
reasons, the climate impacts assessment in the Set 1 Rule was presented as "illustrative" of the
range of potential GHG impacts of the 2023-2025 volume standards.
While a literature review remains a reasonable methodological approach for meeting
EPA's obligation to analyze climate change impacts, the Agency has now had sufficient time to
develop an updated modeling-based approach. For the GHG impacts assessment in the Set 2 rule,
we have developed a methodology based on information gathered through the biofuel modeling
workshop, the MCE Technical Report, the NASEM LCA Report, and the literature review
conducted for the Set 1 Rule. This methodology utilizes new modeling with separate approaches
for two categories of fuels which differ in their scope of expected significant market-mediated
impacts: fuels produced from secondary products, and fuels produced from crops.
Secondary product feedstocks are, by definition, not the primary driver of an economic
activity; they are produced as secondary or tertiary outputs of a primary activity which is
responsive to market pressures. Our review of the best available science has led us to conclude
that it is appropriate to evaluate fuels produced from these feedstocks with supply chain models
254 In the RFS2 Rule EPA said: "... simply adding up the individual lifecycle results... multiplied by their respective
volumes would yield a different assessment of the overall impacts. The two analyses [individual fuel scenarios and
combined fuel scenarios] are separate in that the overall impacts capture interactions between the different fuels that
cannot be broken out into per fuels impacts, while the threshold values represent impacts of specific fuels but do not
account for all the interactions... [W]hen looking at individual 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." 75 FR 14797-14798 (March 26, 2010).
255 Most studies of GHG emissions impacts of individual biofuels present per-megajoule CI metrics that represent
average emissions over an assumed period of analysis (30 years in EPA's methodology).
256 Chapter 4.2.2 of the Set 1 Rule RIA states: "Given that all LCA studies and models have particular strengths and
weaknesses, as well as uncertainties and limitations, our goal for this compilation of literatures estimates is to
consider the ranges of published estimates, not to adjudicate which particular studies, estimates or assumptions are
most appropriate. Reflecting the many approaches to LCA and associated assumptions and uncertainties, our review
is intentionally broad and inclusive of a wide range of estimates based on a variety of study types and assumptions."
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rather than economic models.257 Although there may be market-mediated effects associated with
increased demand for these fuels and feedstocks, we have concluded that the use of currently
available economic modeling tools representing global commodity markets is not necessary or
useful in this context. Global economic models as a class currently lack detail on the supply
chains for key non-crop-based fuels, such as FOG and biogas. This makes use of dedicated
supply chain models the most appropriate choice, given that they can represent supply chains for
these fuels in significant detail. NASEM recommendations, available studies examined through
the Set 1 Rule literature review, and stakeholder input received through the 2023 LC A workshop
all support the conclusion that assessment of the GHG impacts of these fuels can be adequately
addressed using supply chain modeling. In the climate change analysis undertaken for the
purpose of assessing the costs and benefits of the RFS2 Rule, we used aspects of the GREET
supply chain model to assess this category of fuels. In our climate change analysis for this rule,
we rely more fully on a recent release of the R&D GREET model, specifically the R&D GREET
2024 Revision 1 version.258 For the purposes of this rule, we hereafter use the terms "the R&D
GREET model" to specifically mean R&D GREET 2024 Revision 1, unless otherwise noted.
Implementation and additional assumptions for our assessment of pathways for fuels produced
from secondary products are detailed in Chapter 5.1.3.
By contrast, feedstock crops used in the production of biofuels are primarily global
commodities with values influenced by multiple end-uses, including human consumption, animal
feed, biofuel production, and other industrial uses. The amount of additional production of these
crops that is needed to meet increasing U.S. demand for biofuels is directly influenced by market
reactions specific to each of these end-use sectors, including the elasticities of demand to
changes in price, and availability and affordability of substitutes. On the supply side, the source
of additional feedstock crops is also governed by market factors. Production of feedstock crops
can be expanded through changing planting patterns on existing cropland (i.e., "crop switching"),
increasing yields through more intensive use of inputs, or expanding overall cropland into other
land use types. Choices between these options are governed by availability and costs of
technologies and land which are specific to each region. Accounting for these market dynamics
requires use of a model that represents economic relationships between these key sectors for each
region. Additionally, these market dynamics have been repeatedly demonstrated to have
significant impacts on overall GHG emissions estimates. In the literature review of biofuel
lifecycle analyses conducted in the Set 1 Rule, a majority of studies of crop-based fuels that were
considered showed emission from land use change to be a significant portion of overall estimated
emissions impacts. Furthermore, EPA's MCE Technical Report found that assessment of
257 Supply chain models are detailed accounting tools which assess direct inputs, processes, and associated emissions
throughout the supply chain of a product, from feedstock production to final distribution and use. Supply chain
models typically do not represent price effects induced by changes in demand for process inputs or changes in
supply of outputs, nor the market impacts of those changes across other economic sectors. Economic models, by
contrast, are designed to represent relevant economic relationships and assess market-mediated impacts associated
with changes in supply or demand.
258 Wang, Michael, Cai, Hao, Ou, Longwen, Elgowainy, Amgad, Alam, Md Rakibul, Benavides, Pahola T„
Benvenutti, Livia, Burnham, Andrew, Do, Thai N., Farhad, Masum, Gan, Yu, Gracida-Alvarez, Ulises R., Hawkins,
Troy R., Iyer, Rakesh K„ Kar, Saurajyoti, Kelly, Jarod C., Kim, Taemin, Kolodziej, Christopher, Kwon, Hoyoung,
Lee, Uisung, Lim, Juin Y„ Liu, Xinyu, Lu, Zifeng, Morales, Michele, Ng, Clarence, Pandey, Ishan, Shukla,
Siddharth, Siddique, Nazib, Sun, Pingping, Sykora, Tom, Vyawahare, Pradeep, and Zhou, Jo. "Greenhouse gases.
Regulated Emissions, and Energy use in Technologies Model ® (2024 Excel)." Computer software. December 20,
2024. https://doi.Org/10.11578/GREET-Excel-2024/dc.20241203.l.
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emissions from land use change is critical for assessment of biofuel GHG impacts. For these
reasons, and based on our review of the available science referenced above, we continue to
conclude that production and use of crop-based fuels have the potential for substantial market-
mediated effects with significant implications for GHG emissions.259
As such, the GHG impacts of changes in volumes of these fuels are most appropriately
assessed through simulation within global economic models with detailed representations of the
key markets and biophysical processes associated with a change in biofuel production and use.260
For this analysis, we rely on modeling of changes in crop-based fuels using three of the models
considered in the 2023 MCE Technical Report: the Global Trade Analysis Project-Biofuels
model (GTAP-BIO), the Global Change Analysis Model (GCAM), and the Global Biosphere
Management Model (GLOBIOM). This modeling incorporates several important advancements
over past climate change impact assessments under the RFS program. First, whereas the
modeling for crop-based fuels conducted under the RFS2 Rule analysis imperfectly combined
results from different U.S. and international economic models and post-hoc land use change
estimation methods, the models used in the analysis for this rule have globally integrated
representations of relevant agricultural commodities, including trade, and endogenously
represent land use change. Second, these models all have benefitted from significant ongoing
development over the last decade, incorporating the latest science and agricultural and energy
system data into their simulations. Finally, for two of the three models used we were able to
conduct scenarios that represent important interactions under simultaneous changes in volumes
of fuels produced from different agricultural feedstocks—a key deficiency, noted above, of the
Set 1 Rule approach. The GTAP-BIO, GCAM, and GLOBIOM models, their relative strengths
and reasons for selection, and implementation for the Analyzed Volumes are discussed in 5.1.4
and in a memo to the docket.261
Finally, while the methodology used in this rule represents significant progress in GHG
emission impacts assessment modeling since 2010, the conclusions of the NASEM report, the
MCE Technical Report, and stakeholder input all make clear that estimating the climate change
impacts of biofuels is inherently complex and that significant uncertainties will continue to be a
feature of this type of analysis. Over the last decade, numerous studies have been published
examining how uncertainty associated with structural choices within, and parameterization of,
259 EPA first established a multi-model approach to assessing market-mediated impacts of crop-based fuels in the
RFS2 Rule. See discussion in the 2009 NPRM at 74 FR 25025 (May 26, 2009).
260 The use of economic modeling for this assessment is also well aligned with the recommendations of the 2022
NASEM LCA Report, which concluded that regulatory impact analyses should use consequential methods to
evaluate market-mediated impacts to assess the extent to which a given policy design will result in changes in GHG
emissions (Conclusion 3-1, Recommendations 2-2, 3-2).
261 See "Economic Modeling of Climate Change Impacts for Crop-based Fuels," available in the docket for this
action.
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economic models influence assessments of biofuel GHG emissions impacts.262 263 264 While this
work has yielded important insights into the most critical assumptions in these models, other
aspects of forward-looking impacts analysis are inherently uncertain. For example, changes in
future policies, both in the U.S. and in other countries, can affect international trade and land use
decisions globally, with subsequent effects on emissions outcomes. Such uncertainty cannot be
eliminated in this, or any, forward-looking impacts analysis, but further characterizing that
uncertainty and outcomes under different potential scenarios, across and within individual
models, remains an important area for continued research.
5.1.2 Scenarios Assessed
Scenarios described in Preamble Section III include estimates of volumes of different
qualifying biofuels that would be expected to be consumed in the United States under Analyzed
Volumes. The differences between the estimated future effects of these projected fuel volumes
and the estimated future effects of the parallel volumes developed for the No RFS Baseline form
the basis of our analysis of the GHG impacts of Analyzed Volumes.
5.1.2.1 Analyzed Volumes
Chapter 3 presents the renewable fuel volumes represented by each of the scenarios
assessed for this rule. Analyzed Volumes by RIN category are presented in Table 3.1-1. These
RIN volumes are translated into projected volumes by fuel and feedstock and compared against
projections of volumes by fuel and feedstock under the No RFS Baseline. The resulting
differences between the projected volumes and No RFS Baseline are presented in Table 3.2-2
and form the basis of our analysis of the potential climate change impacts of this rule.
For the climate change analysis, we assess only volumes of the qualifying fuels which are
estimated to have significant volume differences compared to the No RFS Baseline. This
includes all fuel categories appearing in Table 3.2-1 with the following exceptions: "Jet Fuel
from FOG" is assumed to be renewable diesel for the purposes of this assessment, and "Other"
shows a relatively small volume (67 million gallons delta compared to the No RFS Baseline) and
represents an unknown mix of various fuel types with smaller volumes. We have excluded this
volume from our analysis because this mix is unknown and unpredictable. However, we would
expect it to have only minor additional emissions impacts as this is a relatively small amount of
fuel compared to the overall changes in volumes; we do not believe this exclusion meaningfully
changes the results of our analysis, or the conclusions stakeholders may draw from them, in any
way.
262 Escobar, Neus, Hugo Valin, Stefan Frank, Diana Galperin, Christopher M. Wade, Leopold Ringwald, Daniel
Tanner, et al. "Understanding Uncertainty in Market-Mediated Responses to US Oilseed Biodiesel Demand:
Sensitivity of ILUC Emission Estimates to GLOBIOM Parametric Uncertainty." Environmental Science &
Technology 59, no. 1 (December 18, 2024): 302-14. https://doi.org/10.1021/acs.est.3cQ9944.
263 Golub, Alia A., and Thomas W. Hertel. "Modeling Land-Use Change Impacts of Biofuels in the Gtap-Bio
Framework" Climate Change Economics 03, no. 03 (August 1, 2012): 1250015.
https://doi.org/10.1142/s201000781250Q157.
264 Plevin, Richard J., Jason Jones, Page Kyle, Aaron W. Levy, Michael J. Shell, and Daniel J. Tanner. "Choices in
Land Representation Materially Affect Modeled Biofuel Carbon Intensity Estimates." Journal of Cleaner
Production 349 (March 22, 2022): 131477. https://doi.Org/10.1016/i.iclepro.2022.131477.
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For the purposes of our climate change analysis, we disaggregate estimated volumes of
biofuels produced from FOG into fuels produced from animal tallow and fuels produced from
used cooking oil. To do this, we assume 35% of fuels produced from FOG are produced from
used cooking oil, and 65% are produced from tallow. This assumption is based on EIA data and
is described in additional detail in Chapter 3.2. Additionally, FOG-based fuels are disaggregated
into fuels produced from domestic vs. imported feedstocks, which is discussed in Chapter 3.1.
Finally, we note that biofuels produced from distillers corn oil are treated as byproduct-based
fuels, following the convention established in the RFS2 Rule. In that rule, EPA determined that
distillers corn oil should be treated as a byproduct of the dry mill process of producing ethanol
from corn starch for the purpose of estimating the lifecycle GHG emissions of distillers corn oil-
based fuels and corn starch based fuels. Consequently, these analyses attributed the indirect land
use change emissions associated with using corn for ethanol production entirely to the corn
starch ethanol; we continue to follow that established convention for the purposes of this
analysis.
The volumes used in our climate change assessment of the Analyzed Volumes are
presented in Table 5.1.2.1-1. Our assessment of the climate impacts under these scenarios is
presented in Chapter 5.2.
Table 5.1.2.1-1: Difference in Consumption of Renewable Fuels (Trillion BTUs) in the
Analyzed Volumes Relative to the No RFS Baseline
Assessed market-
mediated GHG
Fuel
emissions?
2026
2027
CNG/LNG from biogas
62
65
Biodiesel from Corn oil
23
15
Biodiesel from Used Cooking Oil (Domestic)
3
-7
Biodiesel from Used Cooking Oil (Imported)
0
0
Biodiesel from Tallow (Domestic)
5
-13
Biodiesel from Tallow (Imported)
No
0
0
Renewable Diesel from Corn Oil
7
16
Renewable Diesel from Used Cooking Oil (Domestic)
4
16
Renewable Diesel from Used Cooking Oil (Imported)
7
5
Renewable Diesel from Tallow (Domestic)
7
30
Renewable Diesel from Tallow (Imported)
12
10
Biodiesel from Soybean oil
65
97
Biodiesel from Canola oil
29
33
Renewable Diesel from Soybean oil
Yes
162
179
Renewable Diesel from Canola oil
121
135
Ethanol from Corn starch
18
19
5.1.2.2 Period of Analysis
Any analysis of the GHG emissions impacts of biofuel policies must specify the time
period considered in the analysis {i.e., the time period over which those emissions impacts will
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be assessed). This decision can have a substantial impact on the result of the analysis,
particularly for renewable fuels produced from feedstocks with land use requirements (i.e.,
crops). To the extent that increased demand for crop-based biofuels results in cropland expansion
onto previously natural lands (e.g., new soybean hectares planted in the Brazilian Cerrado), then
an initial pulse of emissions from carbon sequestered in biomass and soils would likely take
place when the land is converted. Over time, if production and use of biofuels continue, the GHG
benefits of displacing fossil fuels may eventually "pay back" the initial increase in GHG
emissions from the initial expansion of cropland. Alternatively, to the extent that cropland is
expected to revert to grassland or forest under a baseline (no policy) scenario, then continued use
of that cropland under a policy scenario can result in foregone sequestration, with gradual but
non-linear carbon uptake, relative to the baseline. It is therefore important that an analysis of the
GHG emissions impacts of biofuels formulate assumptions about the period of analysis
intentionally and then describe these choices transparently, as we do in this subsection.
In the specific context of this rule and EPA's recurring responsibility to set RVOs under
the RFS program, EPA must determine the appropriate timeframe over which to assess the GHG
emissions impacts of setting only one or several years of RVO standards. Were EPA to use a
scenario covering only the years for which volumes are set (e.g., only 2026 and 2027 in this
rule,) our analyses of the climate impacts of setting RFS volumes would account for all of the
near term emissions increases associated with expanding use of renewable fuels produced from
crops, but would never account for the longer term emissions decreases associated with
continued displacement of fossil fuel over time through continued production and use of those
fuels. We believe it is not reasonable to limit our climate assessment to the impacts in the years
in which we are establishing volume standards. Nor would such a choice be the norm for
assessing the impacts of a policy with effects that continue over time. When impacts of a
regulatory action are anticipated to occur over a longer period than the time horizon of the
regulatory action itself, it is both appropriate and a well-established best practice to consider that
longer period in the regulatory impact analysis. For example, the cost analysis for this rule
assumes a 15-year amortization period for capital expenses associated with increasing U.S.
production capacity of affected fuels—also well beyond the time horizon covered by the
standards in this rule.
The example of EPA's LCA methodology under CAA section 21 l(o)(l)(H) provides an
instructive illustration of the necessity of and the established scientific basis for considering this
longer time horizon when estimating GHG impacts of renewable fuels. While this methodology
serves a separate purpose in implementation of the RFS program, with statutory and analytical
requirements that are distinct from our assessment of the climate impacts of setting RVO
standards discussed in this section, the lifecycle analysis methodology similarly considered the
question of the appropriate temporal scope of analysis. After considering public comments and
the input of an expert peer review panel, in the RFS2 Rule, EPA determined that our lifecycle
analysis for renewable fuels would quantify the GHG impacts over a 30-year period.265 In 2010,
EPA listed the following reasons supporting the 30-year temporal scope: 1) it aligns with the
average life of a typical biofuel production facility; 2) extending the analysis further than 30
years would add uncertainty; 3) this relatively short temporal scope (e.g., relative to 100 years,
which was supported by a number of stakeholders as an alternative to 30 years) is consistent with
265 See discussion of the selection of the 30-year period of analysis in the RFS2 Rule DRIA Chapter 2.4.5.
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science indicating the benefits of reducing emissions in the near term.266 Since our lifecycle
analysis methodology is the approach through which we determine whether individual biofuels
meet the statutory GHG reduction thresholds necessary to be included in the program,267 and
setting volumes standards is a key mechanism through which the RFS program promotes the use
of those fuels, we believe it is appropriate for our accounting for the climate benefits of
increasing volumes of those fuels to be consistent in temporal scope with the 30-year period of
analysis.
Thus, the climate change analysis for this rule considers a time horizon of 30 years of
impacts of renewable fuel consumption. For secondary product-based fuels, this represents 30
years of continuous production and displacement of fossil fuels at the volumes set in the 2027
standards. For crop-based fuels, the 30-year time horizon is represented in different ways,
depending on the model structure. For GCAM and GLOBIOM, scenarios represent 2026
volumes developed for the No RFS Baseline scenario and the Analyzed Volumes and hold
constant the 2027 volumes from 2027 through 2055, thus covering a 30-year analytical time
frame from 2026 through 2055. For GTAP-BIO, which is a static-comparative modeling
framework, scenarios represent different biofuel consumption volumes in a single timestep
(2017) and thus do not explicitly model impacts over an extended time horizon. Instead, land
area differences and associated carbon emissions are computed for the single timestep, and those
emissions are then amortized over the analytical time horizon (i.e., emissions are divided by 30).
Additional discussion of the scenarios assessed in GCAM, GLOBIOM, and GTAP-BIO can be
found in a memo to the docket.268
For the reasons outlined above, we believe a 30-year period of analysis is both reasonable
and has the benefit of being consistent with other analysis used in RFS program implementation.
However, we recognize that scenarios representing alternative renewable fuel consumption
trajectories over the portion of the 30-year timeframe extending past the modeled standards years
could be developed. Although analyses of other time frames could provide potentially useful
sensitivities for the analysis of the potential climate impacts of volume standards, EPA did not
have sufficient time to conduct such analyses at this time.
5.1.3 Secondary Product-based Fuels
For the purposes of defining categories of renewable fuel feedstocks in this climate
change analysis, secondary products, including wastes, residues, and other byproduct materials,
are considered to not be the primary driver of an economic activity; they are produced as
secondary or tertiary outputs of a primary activity which is responsive to market pressures.269
For the purposes of this analysis, we assume that the market-mediated emissions impacts for
these materials and the fuels produced from them are negligible. That is, we assume that there are
no significant emissions associated with diverting materials in this category for use as feedstock
266 Id.
267 GHG reduction thresholds for different categories of fuels under the RFS program are defined in CAA section
211(o)(l)(B)(i), (D), (E), and (o)(2)(A)(i).
268 See "Economic Modeling of Climate Change Impacts for Crop-based Fuels," available in the docket for this
action.
269 See discussion of wastes and byproducts in the RFS2 Rule at 75 FR 14794 (March 26, 2010).
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to produce renewable fuels. We recognize that some feedstocks considered to be secondary
products in this analysis are globally traded commodities with prices tracked on major price
reporting platforms. However, limitations on global production data make it challenging to
model and assess potential market-mediated effects of changes in use of these fuels. So, while
we do not estimate the market-mediated effects of fuels produced from secondary products here,
this remains an ongoing topic of interest.270
Provided that using these feedstocks for biofuel production does not cause significant
market-mediated impacts, it is then appropriate to estimate the emissions associated with these
fuels with a supply chain analysis. A supply chain analysis focuses on the direct emissions that
result from procuring and processing the feedstock into fuel, as well as the emissions associated
with transporting and using that fuel. For fuels produced from secondary products, this
represents all the relevant categories of emissions that should be considered as this type of
analysis estimates the emissions associated with all the stages of the supply chain from the
collection and transport of the feedstocks through to the production, transport and use of the
finished fuel. Listed below are the fuels that are produced from feedstocks considered to be
secondary products and which are assessed in our climate change analysis (i.e., fuels with
significant volume differences between the Analyzed Volumes and the No RFS Baseline).
• CNG/LNG produced from biogas from landfills
• Biodiesel and renewable diesel produced from distillers corn oil
• Biodiesel and renewable diesel produced from animal tallow
• Biodiesel and renewable diesel produced from used cooking oil
For each of these fuel pathways, we estimate the emissions impacts associated with the
use of these fuels based on an analysis of the supply chain lifecycle GHG emissions for each
pathway, including all relevant stages of fuel and feedstock production and distribution. Our
estimates are based on modeling with the R&D GREET model developed and maintained by
ANL. The DRIA used the 2023-Revl version of the R&D GREET model. The DRIA said,
"Time permitting, we intend to update our estimates for the final rule based on the most recent
version of the R&D GREET model available at that time." We have undertaken the work to
update these estimates using the 2024-Revl version of the R&D GREET model.271 While our
estimates rely on the R&D GREET model for data and emissions factors, we have made several
adjustments and used a particular set of co-product accounting methods as appropriate based on
2711 For example, see the following analysis of the relationship between increasing demand for fuels produced from
used cooking oil and prices of virgin vegetable oils: Swanson, Andrew, Shawn Arita, Joseph Cooper, and Seth
Meyer. "Secondary Impacts From Rising Used Cooking Oil Demand on Crop-Oil Prices r farm doc daily 14, no. 230
(December 19, 2024). https://farmdocdailv.illinois.edu/2024/12/secondarv-impacts-from-rising-used-cooking-oil-
demand-on-crop-oil-prices.html.
271 Wang, Michael, Cai, Hao, Ou, Longwen, Elgowainy, Amgad, Alam, Md Rakibul, Benavides, Pahola T„
Benvenutti, Livia, Burnham, Andrew, Do, Thai N., Farhad, Masum, Gan, Yu, Gracida-Alvarez, Ulises R., Hawkins,
Troy R., Iyer, Rakesh K„ Kar, Saurajyoti, Kelly, Jarod C., Kim, Taemin, Kolodziej, Christopher, Kwon, Hoyoung,
Lee, Uisung, Lim, Juin Y„ Liu, Xinyu, Lu, Zifeng, Morales, Michele, Ng, Clarence, Pandey, Ishan, Shukla,
Siddharth, Siddique, Nazib, Sun, Pingping, Sykora, Tom, Vyawahare, Pradeep, and Zhou, Jo. "Greenhouse gases.
Regulated Emissions, and Energy use in Technologies Model ® (2024 Excel)." Computer software. December 20,
2024. https://doi.Org/10.11578/GREET-Excel-2024/dc.20241203.l.
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the purpose of our analysis. This section describes our analysis with the R&D GREET model and
the resulting estimates.
For each fuel in this category, we multiply the volume changes, converted to megajoules,
with the lifecycle GHG emissions factor we estimate for that fuel pathway. We then compare
these emissions with the lifecycle GHG emissions associated with the fossil-based fuels they
displace; emissions from fossil-based fuels are also estimated using the R&D GREET model and
then multiplied by the assumed number of megajoules being displaced by renewable fuels.272
Emissions factors estimated using the R&D GREET model for each of the renewable and fossil-
based fuel pathways included in this part of the analysis are presented in Table 5.1.3-1.
Additional information about the assumptions used for each of these estimates is presented in the
subsections below.
Table 5.1.3-1: Emissions Factors Used for Climate Impacts Analysis of Secondary Product-
based Fuels
Fuel Pathway
gCChe/MJ
Secondary Product-based
Fuels
CNG: Landfill Biogas
29.1
Biodiesel
Distillers Corn Oil
27.7
Biodiesel
UCO (Domestic)
14.7
Biodiesel
UCO (Imported)
20.8
Biodiesel
Tallow (Domestic)
15.1
Biodiesel
Tallow (Imported)
21.1
Renewable Diesel
Distillers Corn Oil
31.3
Renewable Diesel
UCO (Domestic)
16.9
Renewable Diesel
UCO (Imported)
23.1
Renewable Diesel
Tallow (Domestic)
17.2
Renewable Diesel
Tallow (Imported)
23.4
Fossil
Fuels
Gasoline (EO)
92.1
Diesel (BO)
90.6
Natural Gas (CNG Vehicle)
70.1
Note that in our analysis of biodiesel for the final rule, volumes of fuel assessed in the
scenarios in this section are calculated using energy densities of end-use biofuels, which includes
the non-biogenic methanol portion of biodiesel. For this reason, and in contrast with the analysis
conducted for the proposed rule, we include the CO2 emissions from combustion of the non-
biogenic methanol portion of biodiesel.273
We assume that the methanol used for biodiesel production is produced from
conventional natural gas (21,652 gCChe/mmBtu), and that hydrogen for renewable diesel
272 We assume use of renewable fuels displaces use of an equal amount of the relevant fossil fuel on an energy
equivalent basis. We assume CNG derived from biogas displaces use of fossil natural gas and biodiesel and
renewable diesel of all sources displaces use of diesel produced from petroleum. A sensitivity analysis for different
levels of oil rebound (i.e.. with less than one-for-one energy equivalent displacement of fossil fuels) is presented in
Chapter 5.3.
273 In the proposed rule, volumes of fuel assessed in the analytical scenarios were calculated using RIN equivalence
values, which account only for the renewable content of the fuel.
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production is produced by steam methane reforming of conventional natural gas (8,921
kgCChe/kg). The emissions associated with fuel produced at specific facilities that use other
types of inputs (e.g., renewable electricity, renewable natural gas, electrolytic hydrogen) would
be associated with lower supply chain emissions.
5.1.3.1 CNG from Biogas
To determine the lifecycle GHG intensity of CNG from biogas, we used the R&D
GREET model for background and process data. We modeled the offsite CNG fueling scenario
where landfill gas is captured, upgraded to RNG, transported via pipeline and used to fuel a CNG
vehicle at another location. We primarily used the default R&D GREET parameters for this
analysis; the only notable exception is that we adjusted the pipeline transportation distance from
50 miles to 600 miles to match the default assumption for natural gas pipeline distribution. Given
the prevalence of book-and-claim accounting for RNG we do not see a strong reason to assume
RNG is transported a shorter distance than natural gas in the pipeline distribution network. This
adjustment increased the estimated lifecycle emissions by approximately 3 grams of CO2-
equivalent per megajoule (gCChe/MJ).
For this analysis we assume that biogas is a byproduct of landfilling and collected by the
landfills to prevent the emission of methane gas, as required by regulation,274 and flared. This
assumption is consistent with the approach that EPA adopted for Pathways II Rule.275 See in
particular the technical memo to the docket for the 2014 rule explaining EPA's rationale for
using a flaring baseline to evaluate biogas lifecycle emissions.276
Finally, the tailpipe emissions associated with CNG use as a transportation fuel were
modeled using a passenger vehicle from the 2019 model year that utilizes a spark ignition system
in its internal combustion engine. Using the above methodology, we calculate that CNG from the
assessed sources of biogas have supply chain GHG emissions and total lifecycle emissions
intensities as summarized in Table 5.1.3.1-1.
Table 5.1.3.1-1: Supply Chain Emissions Associated with CNG Fuel from Biogas
(gCChe/MJ)
Supply Chain Stage
Landfill Biogas CNG
Fuel Production
22.4
Fuel Transport and Distribution
5.6
Fuel Use
1.1
Total
29.1
274 61 FR 9905 (March 12, 1996).
275 7 9 FR 42128 (July 18, 2014).
276 "Support for Classification of Biofuel Produced from Waste Derived Biogas as Cellulosic Biofuel and Summary
of Lifecycle Analysis Assumptions and Calculations for Biofuels Produced from Waste Derived Biogas," Docket
Item No. EPA-HQ-OAR-2012-0401-0243. https://www.regulations.gov/document/EPA-HO-QAR-2012-0401-0243.
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5.1.3.2
Distillers Corn Oil-based Fuels
As part of the climate change analysis, we estimate the emissions associated with
biodiesel and renewable diesel produced from distillers corn oil. For this analysis, we assume the
feedstocks and fuels are produced and used in the U.S. using industry average production
practices. Our analysis assumes the biodiesel is produced through a standard transesterification
process and that the renewable diesel is produced through a standard hydrotreating process.
Distillers corn oil is a byproduct of dry mill corn ethanol production. At dry mill ethanol
plants, corn grain is ground and fermented to produce ethanol with co-product distillers grains
and solubles (hereafter "distillers grains"), a protein-rich livestock feed. The large majority of
dry mill ethanol plants extract distillers corn oil from the distillers grains. The distillers corn oil
is used as feedstock to produce BBD or added back to livestock feed as a source of fat and
calories. Based on the data from the R&D GREET model, on an energy content basis (lower
heating value), the outputs from an average U.S. dry mill ethanol plant with corn oil extraction
are approximately 63% ethanol, 33% distillers grains, and 4% distillers corn oil.
Given that distillers corn oil is one of three outputs from a dry mill ethanol plant, co-
product accounting methods are required to estimate what quantity of the gross emissions
associated with the ethanol supply chain are attributable to the ethanol production and what
quantity of emissions are attributable to the distillers corn oil production. To make these
estimates, we use a unit-process level energy allocation approach.277 That is, we evaluate the
emissions and outputs associated with each individual process in the supply chain and allocate
the emissions among the outputs from each of these processes based on the energy content of
each output and an assessment of the primary purpose of each process.
We allocate the supply chain emissions associated with corn farming and transport to all
three co-products on an energy basis. Thus, we allocate approximately 4% of the supply chain
corn farming and transport emissions to the distillers corn oil.278
For all but one of the unit processes within the dry mill ethanol process, we allocate all
the emissions associated with the process energy and chemical inputs to the ethanol output, as
these processes are carried out for the specific purpose of ethanol production. The one exception
is that we allocate emissions associated with distillers corn oil extraction to the corn oil. We
make this choice because extracting corn oil does not contribute to the production of ethanol or
distillers grains. The reason corn oil extraction is undertaken is not because of the decision to
produce ethanol; instead, corn oil extraction is an additional step for the purpose of producing
corn oil as a product distinct from distillers grains. Even if the corn had not been used to produce
ethanol, a separate oil extraction process would still be needed to produce corn oil. That is, any
extraction process to produce corn oil from corn would occur regardless of whether that corn was
277 ISO 14044 defines unit process as the "smallest element considered in the life cycle inventory analysis for which
input and output data are quantified."
278 This ensures that the supply chain emissions associated with distillers corn oil production are not double counted
when we sum the market-mediated emissions estimates associated with corn ethanol with the supply chain emissions
estimates associated with distillers corn oil-based biodiesel and renewable diesel.
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involved in ethanol production. For example, wet mill corn processing facilities which make
high fructose corn syrup may engage in similar unit processes to also produce corn oil.
We recognize that that there are multiple methods for co-product accounting. The
NASEM LCA Report discusses the various co-product accounting methods used in policy and
scientific literature. This report observes that "it is important to pair allocation methods with the
policy objective," but it does not make any conclusions or recommendations about which
methods are most appropriate. Overall, we observe that existing low-carbon fuel policies and
models have taken various approaches to co-product accounting, reflecting a lack of consensus
on the most appropriate approach.
While there are many viable options for co-product accounting, we believe that the unit
process energy allocation approach is the most appropriate method for the supply chain
emissions component of our analysis of biodiesel and renewable diesel produced from distillers
corn oil. Energy allocation is an appropriate approach because the primary purpose of the
biodiesel and renewable diesel production we are evaluating is to produce transportation fuel as a
source of energy. The energy allocation method is based on the physical properties of the co-
products, which are stable characteristics in the sense that they do not depend on context or
market value fluctuations. In contrast, both the system expansion approach and the market-based
allocation approaches are subject to fluctuations due to changing markets, policies, technologies,
and other factors. Using these other allocation methods therefore requires a substantial number of
additional assumptions to account for these considerations, many of which are uncertain and
difficult to parameterize, and which can also significantly influence the results of the analysis.
This makes LCA using these approaches more complex, more difficult to adequately document,
explain, and understand, and more uncertain. Thus, relative to other options, energy allocation is
a more transparent and stable methodology based on physical properties rather than fluctuating
market dynamics.
To estimate the supply chain emissions associated with growing and harvesting corn, we
use data from the R&D GREET model on the average inputs and yields associated with U.S.
corn production. The R&D GREET model sources these data from USDA's major survey
programs, the National Agricultural Statistics Service (NASS), the Economic Research Service
(ERS), and the Office of the Chief Economist (OCE) reports.279 For our analysis of the supply
chain lifecycle emissions, we assume average corn yield of 177 bushels per harvested acre. Our
analysis considers average inputs to corn farming, such as fertilizer, pesticide, diesel fuel to run
tractors, and electricity to run irrigation pumps. Based on the R&D GREET model background
data, we assume that the harvested corn is transported 10 miles by medium-duty truck to a
collection point and then 40 miles by heavy-duty truck to an ethanol plant.
We estimate the emissions associated with extracting corn oil from the distillers grains
based on data from the R&D GREET model representing an average U.S. dry mill ethanol plant
with corn oil extraction. Based on the R&D GREET model data, we assume that the oil
279 For further information, see Lee, Uisung, Hoyoung Kwon, May Wu, and Michael Wang. "Retrospective Analysis
of the U.S. Corn Ethanol Industry for 2005-2019: Implications for Greenhouse Gas Emission Reductions." Biofuels
Bioproducts andBiorefining 15, no. 5 (May 4, 2021): 1318-31. https://doi.org/10.10Q2/bbb.2225.
161
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extraction equipment consumes 183 Btu of electricity per pound of distillers corn oil output (we
assume the extraction equipment does not consume natural gas or other thermal process energy).
Our analysis includes the emissions associated with transporting the distillers corn oil
feedstock to biodiesel or renewable diesel production facilities. Based on data from the R&D
GREET model, we assume that 20% of distillers corn oil used as biofuel feedstock is transported
by rail 400 miles, and 80% of this oil is transported by heavy-duty truck 100 miles. We assume
that these feedstock transport modes and distances are the same for biodiesel and renewable
diesel production.
Our estimates include the emissions associated with biodiesel and renewable diesel
production. We use operational data from the R&D GREET model and the energy allocation
approach discussed above to account for co-products. For biodiesel production, we assume the
process inputs per pound of biodiesel output are 1.003 pounds of distillers corn oil, 1,137 Btu of
natural gas, 146 Btu of electricity and 896 Btu of methanol. The other biodiesel inputs
considered in our analysis are nitrogen gas, sodium methoxide, hydrochloric acid, and
phosphoric acid. We assume that 0.097 dry pounds of byproduct glycerin is produced per pound
of biodiesel output. For renewable diesel production, we assume the process inputs per pound of
renewable diesel output are 1.26 pounds of distillers corn oil, 352 Btu of natural gas, 185 Btu of
electricity and 2,071 Btu of hydrogen. We assume that 0.099 pounds of propane fuel mix is co-
produced per pound of renewable output.
We estimate the emissions associated with biodiesel and renewable diesel transportation
and distribution based on data and emissions factors from the R&D GREET model. We assume
that biodiesel is transported from the production facility to a bulk terminal with the following
modes and distances: 49% by barge for 200 miles, 46% by pipeline for 110 miles, and 5% by rail
for 490 miles. We assume that renewable diesel is transported from the production facility to a
bulk terminal with the following modes and distances: 8% by barge for 520 miles, 29% by rail
for 800 miles, and 63% by truck for 50 miles. We assume that both biodiesel and renewable
diesel are distributed from bulk terminals to refueling stations 30 miles via heavy-duty tanker
truck. Consistent with the R&D GREET model, we assume 0.004% of biodiesel and renewable
diesel is lost during transportation, distribution and refueling.
Finally, based on emissions factors from the R&D GREET model, we include the
emissions associated with using the biodiesel and renewable diesel in a diesel engine car. We
include the non-C02 emissions associated with biodiesel combustion. Consistent with the
methodology developed for the RFS2 Rule, we do not include the biodiesel combustion CO2
emissions because we treat the carbon in the finished fuel derived from renewable biomass as
biologically derived carbon recently originating from the atmosphere. In the context of a full
lifecycle analysis, the uptake of this carbon from the atmosphere by the renewable biomass and
the carbon dioxide emissions from combusting it cancel each other out. Therefore, instead of
evaluating both the carbon uptake and tailpipe carbon dioxide emissions, we leave both values
out of our estimates. Note that, when applicable, our analysis also accounts for all significant
supply chain and market-mediated emissions, such as from land use changes, meaning that we do
not simply assume that the ethanol or other biofuels are "carbon neutral."
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Our estimates of the supply chain GHG emissions associated with U.S. average biodiesel
and renewable diesel produced from distillers corn oil are summarized in Table 5.1.3.2-1.
Table 5.1.3.2-1: Supply Chain Emissions Associated with Distillers Corn Oil-based Fuels
(gCOie/MJ)
Supply Chain Stage
Biodiesel
Renewable Diesel
Corn Production and Transport
17.6
19.1
Corn Oil Extraction
1.2
1.2
Corn Oil Transport
0.3
0.3
Fuel Production
3.6
9.5
Fuel Transport
0.3
0.4
Fuel Use
4.7
0.7
Total
27.7
31.3
5.1.3.3 Tallow and Used Cooking Oil-based Fuels
As part of the climate change analysis, we estimate the emissions associated with
biodiesel and renewable diesel produced from animal tallow and UCO. For the analysis in this
final rule, we differentiate fuels produced from domestically produced versus imported fats, oils,
and greases, where imported feedstocks have higher lifecycle emissions estimates due to higher
emissions associated with transportation. Our analysis assumes that the biodiesel is produced
through a standard transesterification process and that the renewable diesel is produced through a
standard hydrotreating process.
Tallow is produced through rendering animal by-products from cattle slaughtering.280
While edible tallow is used as shortening for baked goods, we assume that only inedible tallow is
used as a feedstock for biofuel production. Inedible tallow is a byproduct that, when not used as
biofuel feedstock, can be used in animal feed, soap production, and lubricants.
UCO is collected from commercial kitchens and restaurants. The collected UCO is
brought to rendering facilities where excess water is removed. The result of UCO rendering is
sometimes referred to as yellow grease, but for simplicity here we use the term UCO to refer to
the UCO before and after rendering. When not used as a biofuel feedstock, UCO can be used an
additive in pet food and animal feed.
To estimate the supply chain emissions associated with collecting rendering and transport
tallow and UCO, we use data and emissions factors from the R&D GREET model representing
average U.S. practices. The R&D GREET model data are based on industry surveys and data
from the literature.
For domestic UCO collection, we assume UCO is collected at restaurants and
commercial kitchens and trucked 150 miles by heavy-duty truck to rendering facilities. We
assume that 23% of the collected UCO is transported to a transfer station and then trucked an
2811 Seber, Gonca, Robert Malina, Matthew N. Pearlson, Hakan Olcay, James I. Hileman, and Steven R.H. Barrett.
"Environmental and economic assessment of producing hydroprocessed jet and diesel fuel from waste oils and
tallow." Biomass andBioenergy 67 (May 20, 2014): 108-18. https://doi.Org/10.1016/i.biombioe.2014.04.024.
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additional 200 miles by heavy-duty truck to the rendering facilities. We assume that on average
908 Btu of natural gas and 107 Btu of electricity are used to render each pound of resulting
dewatered UCO. The rendered UCO is then transported to biofuel plants using the following
modes and distances: 95% by truck for 130 miles, and 5% by rail for 500 miles. Based on the
R&D GREET default assumptions, we assume that on average imported UCO is transported
8,975 miles by ocean tanker.
For tallow rendering, we assume 1,052 Btu of natural gas and 307 Btu of electricity are
used per pound of rendered tallow. R&D GREET assumes the following average transportation
modes and distances for transporting domestically produced tallow from rendering facilities to
biofuel plants: 20% by rail for 400 miles, and 80% by truck for 100 miles. Because imported
tallow is not explicitly represented in R&D GREET, we use the additional emissions associated
with importing UCO as a proxy for the additional transportation emissions associated with
importing tallow.
Our estimates include the emissions associated with biodiesel and renewable diesel
production. We use operational data from the R&D GREET model and the energy allocation
approach discussed above to account for co-products. For UCO and tallow biodiesel production,
we assume the process inputs per pound of biodiesel output are 1.05 pounds of UCO, 1,142 Btu
of natural gas, 147 Btu of electricity and 900 Btu of methanol. We assume that 0.071 dry pounds
of glycerin are coproduced with each pound of biodiesel output.
For renewable diesel production, we assume the process inputs and outputs are the same
when either tallow or UCO are used as feedstock. Per pound of renewable diesel output, the
process inputs are 1.26 pounds of feedstock, 352 Btu of natural gas, 185 Btu of electricity, and
2,071 Btu of hydrogen. We assume that 0.099 pounds of propone fuel mix is coproduced per
pound of renewable output.
To evaluate the emissions associated with biodiesel and renewable diesel transportation,
distribution, and use, we use the same methods and data for corn oil-based fuels as described in
Chapter 5.1.3.2. For this analysis we use emissions factors from the R&D GREET model
representing current average U.S. industry operations (see Chapter 5.1.3.2 for further description
of these emissions factors).
Our estimates of the supply chain GHG emissions associated with U.S. average biodiesel
and renewable diesel produced from UCO and tallow are summarized in Tables 5.1.3.3-1 and
5.1.3.3-2 respectively. The calculations upon which these estimates are based are contained in
spreadsheets that are available in the public docket for this rule.
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Table 5.1.3.3-1: Supply Chain Emissions Associated with UCO-based Fuels (gCChe/MJ)
Fuel Type:
Biot
iesel
Renewable Diesel
UCO Source:
Domestic
Imported
Domestic
Imported
Collection, Rendering and Transport
6.2
12.2
6.3
12.4
Fuel Production
3.6
3.6
9.5
9.5
Fuel Transport
0.3
0.3
0.4
0.4
Fuel Use
4.7
4.7
0.7
0.7
Total
14.7
20.8
16.9
23.1
Table 5.1.3.3-2: Supply Chain Emissions Associated with Tallow-based Fuels (gCChe/MJ)
Fuel Type:
Biot
iesel
Renewable Diesel
Tallow Source:
Domestic
Imported
Domestic
Imported
Collection, Rendering and Transport
6.5
12.5
6.6
12.8
Fuel Production
3.6
3.6
9.5
9.5
Fuel Transport
0.3
0.3
0.4
0.4
Fuel Use
4.7
4.7
0.7
0.7
Total
15.1
21.1
17.2
23.4
5.1.3.4 Fossil Fuel Baselines
For this climate change analysis, we assume that secondary product-based fuels displace
conventional fuels one-for-one on an energy-equivalent basis. We recognize this is likely an
oversimplification as market prices can affect the overall level of transportation fuel
consumption. However, given the lack of a robust model or methodology for estimating the
global market-mediated energy sector effects of these particular biofuels, we believe the energy-
equivalent displacement assumption is appropriate for the purposes of this analysis. See Chapter
5.3 for additional discussion and sensitivity analyses on potential alternative displacement
assumptions.
For this analysis, we assume that biodiesel and renewable diesel replace conventional
diesel fuel and that renewable CNG displaces conventional CNG. For the GTAP- and
GLOBIOM-based analysis described in Chapters 5.1.4.1 and 5.1.4.2 respectively, we assume
that ethanol displaces conventional gasoline. In this section we describe our estimates of the
GHG emissions associated with these conventional fuels.
We use the R&D GREET model to evaluate the emissions associated with these
conventional fossil fuels. The GREET model has been used for many years to estimate the
emissions associated with conventional transportation fuels. This model includes all the stages in
conventional fuel production and use, from raw material extraction through refining, fuel
transport and use in vehicles. It is widely used for this purpose, including for peer reviewed
publications and regulatory programs. The R&D GREET model analysis of petroleum leverages
site specific data for crude oil extraction from the Oil Production Greenhouse Gas Emissions
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Estimator (OPGEE) model,281282 283 and a detailed assessment of the energy intensities of 27 oil
sands projects.284 Furthermore, the R&D GREET model uses unit-process level analysis with
linear programming models with data from 43 refineries that process approximately 70% of total
crude input to U.S. refineries.285 286 For these reasons, we believe the R&D GREET model is an
appropriate method for evaluating the emissions associated with fossil fuels for the purpose of
this analysis.
Given that we are estimating the emissions associated with the conventional fuels that
would be displaced by additional renewable fuel blending, we estimate the emissions associated
with conventional fuels containing 0% biofuel blends. Thus, we evaluate gasoline with 0%
ethanol (E0), diesel with 0% biodiesel (BO) and 100% conventional CNG. For gasoline, we use
the lifecycle emissions estimates for fuel used in a spark ignition passenger car. For conventional
diesel, we use the lifecycle emissions estimates for B0 used in a compression ignition direct
injection passenger car. For CNG we evaluate fuel used in a medium-duty spark ignition CNG
vehicle. Given that we do not include the vehicle cycle emissions in our estimates (e.g.,
emissions associated with vehicle manufacturing), our estimates on a per MJ of fuel basis would
not be significantly different if we evaluated fuel used in a different type of typical vehicle, such
as spark ignition direct injection passenger car or a sport utility vehicle.
Other than selecting these fuel parameters and vehicle types, we make no other
adjustments to the R&D GREET model to produce the emissions estimates summarized in Table
5.1.2.4-1.
281 Brandt, Adam R.. Tim Yeskoo, Michael S. McNally, Kourosh Vafi, Sonia Yeh, Hao Cai, and Michael Q. Wang.
"Energy Intensity and Greenhouse Gas Emissions From Tight Oil Production in the Bakken Formation." Energy &
Fuels 30, no. 11 (October 20, 2016): 9613-21. https://doi.org/10.1021/acs.energyfuels.6b01907.
282 Yeh, Sonia, Abbas Ghandi, Bridget R. Scanlon, Adam R. Brandt, Hao Cai, Michael Q. Wang, Kourosh Vafi, and
Robert C. Reedy. "Energy Intensity and Greenhouse Gas Emissions From Oil Production in the Eagle Ford Shale."
Energy & Fuels 31, no. 2 (January 8, 2017): 1440-49. https://doi.org/10.1021/acs.energyfuels.6b02916.
283 Eker, Ilkay, Basak Kurtoglu, and Hossein Kazemi. "Multiphase Rate Transient Analysis in Unconventional
Reservoirs: Theory and Applications." SPE/CSUR Unconventional Resources Conference, September 25, 2014.
https://doi.org/10.2118/171657-ms.
284 Cai, Hao, Adam R. Brandt, Sonia Yeh, Jacob G. Englander, Jeongwoo Han, Amgad Elgowainy, and Michael Q.
Wang. "Well-to-Wheels Greenhouse Gas Emissions of Canadian Oil Sands Products: Implications for U.S.
Petroleum Fuels." Environmental Science & Technology 49, no. 13 (June 9, 2015): 8219-27.
https://doi.org/10.1021/acs.est.5b01255.
285 Elgowainy, Amgad, Jeongwoo Han, Hao Cai, Michael Wang, Grant S. Fonnan, and Vincent B. DiVita. "Energy
Efficiency and Greenhouse Gas Emission Intensity of Petroleum Products at U.S. Refineries." Environmental
Science & Technology 48, no. 13 (May 28, 2014): 7612-24. https://doi.org/10.1021/es5010347.
286 Fonnan, Grant S., Vincent B. Divita, Jeongwoo Han, Hao Cai, Amgad Elgowainy, and Michael Wang. "U.S.
Refinery Efficiency: Impacts Analysis And Implications For Fuel Carbon Policy Implementation." Environmental
Science & Technology 48, no. 13 (May 28, 2014): 7625-33. https://doi.org/10.1021/es501035a.
166
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Table 5.1.3.4-1: Supply Chain Emissions Associated with Conventional Fuels (gCChe/MJ)
Supply Chain Stage
Gasoline
Diesel
CNG
Feedstock
6.3
7.3
11.3
Fuel
12.6
7.6
2.7
Vehicle Operation
73.1
75.7
56.1
Total
92.1
90.6
70.1
5.1.4 Crop-based Fuels
For the reasons discussed in Chapter 5.1.1, our analysis of the climate change impacts of
crop-based fuels is based on economic modeling that represents the fuel volumes under
consideration in this rule. EPA's review of available models capable of biofuel GHG emissions
modeling in the 2023 MCE Technical Document included five models: ADAGE, GCAM,
GLOBIOM, GREET, and GTAP-BIO. Of these models, GREET is a supply chain model that
does not endogenously represent market-mediated impacts of biofuel consumption. Thus, relying
solely on estimates from GREET's core biofuel supply chain representation would fail to
account for the market-mediated GHG emissions effects of changing volumes of biofuels that are
produced from feedstocks with land use requirements. In the proposed rule, EPA used two
economic models to estimate potential climate change impacts: GCAM and GLOBIOM. Both
models were found in the 2023 MCE Technical Document to be capable tools for modeling
market-mediated impacts of biofuels over time. Results from both models were presented in the
draft preamble and regulatory impact analysis.
EPA received numerous detailed comments on the GCAM and GLOBIOM modeling
presented in the RIA to the proposed rule. Many comments highlighted the uncertainty in the
modeling results arising from the two models' differing parameter assumptions, model
structures, and system boundaries. Detailed responses are provided in the Response to Comments
document in the docket for this final rule. In general, we agree with comments that parametric
and model uncertainty and differences between models and resulting differences in modeled
impacts of renewable fuels should be investigated and clearly communicated; this principle is
well aligned with EPA's findings in the MCE and with NASEM recommendations.287 To this
end, in addition to the parametric sensitivity analysis conducted on our GCAM modeling in the
proposal, we have included additional sensitivity results in Chapter 5.3 that illustrate the role and
importance of potential market-mediated responses in the energy sector with regard to overall
emissions estimates. While several comments on GCAM and GLOBIOM modeling raise issues
that could benefit from additional research and/or model development, we continue to find that
each of the available analytical tools for conducting consequential analyses has relative strengths
and weaknesses, and that both models are appropriate and provide useful analysis for illustrating
a potential range of outcomes under the assessed scenarios.
In the proposed rule, we noted that while our assessment of the potential climate change
impacts of the proposed volumes considered emissions over a 30-year analytical timeframe,
GTAP-BIO is a static comparative model which cannot estimate emissions impacts over time.
For this reason, GTAP-BIO was not used in the analysis in the proposed rule. Comments on the
287 See NASEM LCA Report, Recommendation 4-2, p. 57.
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proposal argued that GTAP-BIO modeling represents reasonable alternative potential land use
outcomes that differ from the GCAM and GLOBIOM modeling, and that expanding the multi-
model approach employed in the proposal to include a computable general equilibrium model
would improve robustness and align more closely with NASEM recommendations. We agree
with commenters on the benefits of including additional economic modeling approaches in the
climate change analysis for this final rule. While EPA was unable to conduct new modeling
using GTAP-BIO for this final rule, pathway-specific results from GTAP-BIO are readily
available through the latest distribution of the R&D GREET model and provide estimated
emissions factors for three different categories of market-mediated emissions impacts.
The climate impacts estimates based on the emissions factors in GTAP-BIO do not
account for some of the interactions and effects represented in scenarios available in the other
economic models presented in this chapter. For example, relying on GREET supply chain
modeling combined with the available per-MJ emissions factors by feedstock does not represent
market-mediated energy sector impacts, nor does it represent agricultural sector interactions from
simultaneous shocks on different feedstock crops.288 However, we nevertheless believe including
estimates based on the GTAP-BIO modeling available with this final rule provides a useful
extension of our climate analysis by representing a broader range of methodologies and
modeling frameworks for assessing emissions impacts associated with crop-based biofuel use.
For this reason, our analysis in the final rule includes additional cumulative emissions impacts
estimates based on a combination of GREET supply chain modeling and other emissions factors
developed using GTAP-BIO to represent a set of market-mediated emissions impacts. More
information on the use of GREET and GTAP-BIO is presented in Chapter 5.1.4.1.
We considered the inclusion of estimates from other models but ultimately determined
not to include them in our quantitative assessment of the climate impacts for this rule. For
example, the analytical work undertaken during the MCE identified several model updates that
we believe would be necessary before results from the ADAGE model could be included in this
analysis. These included numerous updates to historical data which parameterize the model,
including a major overhaul to represent a more recent model base year, significant updates to the
model's representation of non-commercial forests and grasslands, and significant updates to the
model's representation of soil carbon pools. We were not able to complete those model updates
for the analyses undertaken for this rule.
Finally, undertaking analyses with each model discussed in this section requires
significant effort and resources. We believe that utilizing three models appropriately balances the
objective of considering framework uncertainty by comparing results using multiple models of
different types, with practical considerations of using a flexible and responsive approach that
allows updating analyses as appropriate for each volume rule within given timing and resource
constraints. For these reasons, we use the estimates derived from GTAP-BIO, GLOBIOM, and
GCAM modeling in our assessment of the climate change impacts of volumes of crop-based
biofuels under this rule. Considerations for scenario implementation specific to each of the
288 Sensitivities and additional discussion on potential energy sector impacts are presented in Chapter 5.3.
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models follow in Chapters 5.1.4.1 through 5.1.4.3. Additional details on these models, scenarios,
and necessary adjustments are provided in a memo to the docket.289
5.1.4.1 GTAP-BIO Scenario Implementation
As discussed above, we are including estimates from the GTAP-BIO model as part of the
quantitative assessment for the final rule. However, due to timing considerations, we were unable
to conduct new GTAP-BIO modeling specific to the Analyzed Volumes. Instead, we utilize
emissions factors for three categories of market-mediated effects specific to individual
feedstock-fuel pathways estimated by GTAP-BIO and developed for and distributed with the
latest release of the R&D GREET model. These emissions factors then are combined with
estimated supply chain emissions factors using the R&D GREET model for the other stages of
the fuel lifecycle, such as feedstock and biofuel production and use. Emissions factors are
expressed on a per-megajoule of finished fuel basis. These factors are then applied to the
analyzed difference in volumes of crop-based fuels between the Analyzed Volumes and No RFS
Baseline.
While the emissions factors distributed with GREET for market-mediated impacts of
crop-based fuels represent the only readily available GTAP-BIO modeling that could be used to
expand the climate change analysis in the final rule, we recognize that the scalar emissions
factors relied on were developed for integration with an attributional LCA tool for the purpose of
policy implementation, and have some limitations when used in a consequential analysis.
Our use of the GTAP-BIO modeling results distributed with GREET is limited to the
sectoral impacts in the three categories of emissions factors provided. However, GTAP-BIO is a
general equilibrium model with endogenous representation of impacts in other sectors of key
importance for consequential analysis of renewable fuel policy analysis. Notably GTAP-BIO
represents energy sector impacts, but these impacts are not included in the GREET distribution.
For example, although the GTAP-BIO model estimates the amount of petroleum fuel
displacement per unit of renewable fuel production, and marginal changes in electricity supplies,
these effects and associated emissions impacts are not included with the GREET model. Without
the scenario-specific energy sector outputs from GTAP-BIO, we are unable to account for these
dynamics. In the absence of this portion of the GTAP-BIO output, we assume a one-to-one
displacement effect with fossil fuels (i.e., we assume each MJ of biofuel production displaces an
equivalent amount of fossil transportation fuel). Given that this assumption may play a
significant role in the overall net emissions estimates for the Analyzed Volumes, alternative
displacement assumptions are considered in the sensitivity results in Chapter 5.3.
Additionally, combining per-fuel emissions factors to assess the overall impacts of
simultaneous changes in fuel volumes introduces inconsistencies between simulations and can
substantially affect the overall emissions estimates, particularly when assessing changes in
289 See "Economic Modeling of Climate Change Impacts for Crop-based Fuels," available in the docket for this
action.
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multiple fuels produced from crops which compete for farmland.290 For this reason, GCAM and
GLOBIOM modeling developed for the NPRM assessed scenarios with combined volume
shocks across all of the major crop-based fuels likely to be affected by setting RFS volumes
standards. While we were unable to conduct new modeling with GTAP-BIO for this final rule, in
future assessments, new modeling using GTAP-BIO could be conducted that better represents
the impacts on demands of multiple fuels of setting RFS volume standards.
Finally, we note that GTAP-BIO is a comparative static modeling framework and does
not dynamically represent the chosen 30-year period of analysis. Instead, for market-mediated
emissions impacts from livestock and non-feedstock crops, differences in production (and the
resulting differences in emissions) in the base year are assumed to persist for all future years
within the period of analysis. For land use change, emissions from land conversion are treated as
though they occurred instantaneously, much as GTAP-BIO does when computing a new
economic equilibrium. The emissions factors distributed with GREET amortize those land use
change emissions over a 30-year period of analysis by dividing changes in carbon stocks by 30.
This amortization calculation extends static results to represent a longer period of analysis and
implicitly assumes that future (i.e., after the GTAP reference year) land use decisions are
unaffected by the expanded need for cropland in the reference year of analysis.
While such emissions impact estimates based on static modeling approaches may
demonstrate less variance under investigations of parametric uncertainty, it is important to
recognize that this is a direct result of a lack of representation of the inherent uncertainty of
potential future impacts of current policy decisions.291 That is, the lesser variance in these
estimates due to parametric uncertainty is a consequence of the reduced scope of modeling,
which only assesses impacts within the context of data representing the base year—2017 in the
case of the GTAP-BIO results used in this analysis. For the reasons discussed in Chapter 5.1.2.2,
we believe that 30 years is an appropriate temporal scope for assessing the potential GHG
impacts of biofuel policies. Thus, analytical tools for this climate change analysis would ideally
represent the uncertainty in potential impacts of setting volumes over that timeframe. While
GTAP-BIO is unable to perform such an assessment in its current form, we continue to believe
that the benefits of including additional modeling methods and tools—in this case a computable
general equilibrium model—in our GHG impacts assessment for this volume rule provides
valuable breadth of representation of structural uncertainty across modeling frameworks.
2911 EPA explained these issues in the 2010 RFS2 Rule, stating: ".. .simply adding up the individual lifecycle
results... multiplied by their respective volumes would yield a different assessment of the overall impacts. The two
analyses [individual fuel scenarios and combined fuel scenarios] are separate in that the overall impacts capture
interactions between the different fuels that cannot be broken out into per fuels impacts, while the threshold values
represent impacts of specific fuels but do not account for all the interactions... [W]hen looking at individual 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." 75 FR 14797-98 (March 26, 2010).
291 Escobar, Neus, Hugo Valin, Stefan Frank, Diana Galperin, Christopher M. Wade, Leopold Ringwald, Daniel
Tanner, et al. "Understanding Uncertainty in Market-Mediated Responses to US Oilseed Biodiesel Demand:
Sensitivity of ILUC Emission Estimates to GLOBIOM Parametric Uncertainty." Environmental Science &
Technology 59, no. 1 (December 18, 2024): 302-14. https://doi.org/10.1021/acs.est.3c09944.
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5.1.4.2
GLOBIOM Scenario Implementation
For the proposed rule, the analytical scenario implemented in GLOBIOM represented
volume requirements that differed somewhat from the proposed volumes. This discrepancy was
necessary because of the lead time necessary to implement scenarios in economic models and
assess model outputs. Thus, for the proposal, some scaling of the modeled GLOBIOM results
was necessary to better represent the proposed volumes.292 For this final rule, we were also
unable to conduct new GLOBIOM modeling of the specific RVOs being finalized given the time
required to review public feedback on the proposal, determine the volume standards to be
finalized, and implement those within economic models. Any new modeled scenarios developed
prior to determining the Analyzed Volumes would need to be scaled, similar to the scaling
performed on the modeling conducted for the proposed rule. For this reason, we continue to rely
on GLOBIOM modeling conducted for the proposed rule, with scaling and adjustments
consistent with the method of assessing the proposed. In summary, the scaling for our analysis of
the Analyzed Volumes involves multiplying all modeled GLOBIOM impacts by 1.09 to account
for the total difference in crop-based fuel volumes between the Analyzed Volumes and the
scenarios actually simulated in GLOBIOM. Additional details on methods and data used for this
scaling are presented in a memo to the docket.293
We use GLOBIOM model outputs to estimate five different categories of emissions,
either directly or using post hoc adjustments or assumptions to supplement components which
are not represented endogenously in GLOBIOM. These categories of emissions are
1. Land Use Change
2. Livestock Production
3. Crop Production
4. Fuel Production, Transport, Distribution, and Use
5. Fossil Fuel Use
For categories of emissions that are estimated within GLOBIOM, model outputs are
translated and interpolated to provide emissions estimates for each year in the period of analysis
(2026-2055). Emissions categories represented in GLOBIOM include Land Use Change,
Livestock Production, and a subset of the components categorized as Crop Production. For other
components of Crop Production emissions not represented in GLOBIOM (e.g., emissions from
on-farm energy use) we relied on emissions factors specific to region, crop, and activity which
were developed externally to the GLOBIOM modeling. These adjustments are all described in
detail in the above referenced docket memo. For emissions associated with fuel production,
transport, distribution, and use we rely on emissions factors calculated using the R&D GREET
model.
Finally, GLOBIOM does not represent energy sector impacts or emissions associated
with biofuels displacing use of fossil fuels. We assume a straightforward one-for-one energy
equivalent displacement of fossil gasoline (for ethanol volumes) and fossil diesel (for BBD
292 See DRIA Chapter 5.
293 See "Economic Modeling of Climate Change Impacts for Crop-based Fuels," available in the docket for this
action.
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volumes) in the United States and apply emissions factors representing process energy inputs for
renewable fuel production as estimated in R&D GREET. These emissions factors are similarly
used in our assessment of fuels produced from secondary products. Alternative displacement
effects are considered in the sensitivity analyses presented in Chapter 5.3.
5.1.4.3 GCAM Scenario Implementation
As discussed above in Chapter 5.1.4.2, we were unable to conduct new economic
modeling of the Analyzed Volumes given the time required to review public feedback on the
proposal, determine the volume standards to be finalized, and implement those within economic
models. Thus, similar to the GLOBIOM analysis of the Analyzed Volumes, we rely on GCAM
modeling conducted for the proposed rule in our assessment of Analyzed Volumes, and using a
similar calculation to scale GCAM outputs to the Analyzed Volumes.
We use GCAM outputs to estimate five different categories of emissions. These
categories, however, differ from the categories discussed above for the GLOBIOM modeling.
They are:
1. Land Use Change
2. Crop Production
3. Livestock Production
4. Other Industrial
5. Fossil Fuel Use
For Land Use Change, Crop Production, Livestock Production, and Other Industrial294
emissions, GCAM endogenously represents all major emissions components. Outputs in these
categories are directly translated and interpolated to provide emissions estimates for each year in
the period of analysis (2026-2055).
Note that, because detailed energy demands are endogenously represented in GCAM, the
emissions estimates under the category of Fossil Fuel Use in GCAM represent displacement of
use of fossil-based fuels with use of biofuels, market-mediated impacts on global energy use
(e.g., "oil rebound"), and the additional energy and other inputs necessary for biofuel production.
Thus, while in the GLOBIOM modeling we include additional biofuel production and
downstream emissions estimates, these categories are represented within the fossil fuel use
category in GCAM results, with one exception. GCAM does not explicitly represent renewable
diesel—only biodiesel—which necessitated the representation of all volumes of biodiesel and
renewable diesel in the modeled scenarios as biodiesel on an energy equivalent basis. In order to
account for the different input and energy requirements between biodiesel and renewable diesel
production, we apply factors to the volume of renewable diesel in the assessed scenarios
294 Other Industrial emissions represent market mediated changes in emissions from other industrial categories
represented in GCAM. While these sectors are not represented in GLOBIOM, which therefore has no comparable
emissions category estimate, the effects are extremely minor relative to the other components estimated, and
exclusion of this category of emissions would not affect our overall assessment of the climate impacts of the
Analyzed Volumes.
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representing the marginal additional emissions associated with producing renewable diesel rather
than biodiesel. These emissions factors were developed using the R&D GREET model.
5.2 Assessment of Analyzed Volumes
Table 5.1.2.1-1 presents the volume differences between the Analyzed Volumes for 2026
and 2027 and the volumes in the No RFS Baseline. For fuels produced from secondary products,
we apply emissions factors developed using the R&D GREET model to the difference in
volumes of renewable fuels in each year and compare those emissions with similar estimates of
displaced fossil fuels. Descriptions of these factors can be found in Chapter 5.1.3. Estimated
impacts for crop-based fuels are based on economic modeling, as described in Chapter 5.1.4.
Table 5.2-1 presents cumulative emissions estimates using the model frameworks
discussed in Chapter 5.1.3 (fuels produced from secondary products) and Chapter 5.1.4 (fuels
produced from crops). Table 5.2-2 presents the resulting estimates for potential emissions
impacts of the Analyzed Volumes across all categories of fuels. All calculations are provided in
an Excel workbook in the docket for this rule.295
Table 5.2-1: Cumulative (30-year) GHG Emissions Estimates (Million Metric Tons CChe)
by Feedstock Type
Feedstock Type
Model Framework
Cumulative Emissions"
Crop
GTAP-BIO + GREET
-704
Crop
GLOBIOM + GREET
-264
Crop
GCAM
264
Secondary Product
GREET
-231
a Annual emissions estimates from each model are available in "Set 2 FRM Climate Change Analyses.xlsx,"
available in the docket for this action. These annual emissions illustrate the impact of the assumed 30-year time
horizon.
Table 5.2-2: Combined Cumulative (30-year) and Average Annual GHG Emissions
Estimates (Million Metric Tons CChe) for the Analyzed Volumes (2026-2027 Standards)
Estimate 1
Estimate 2
Estimate 3
Modeling for secondary product-based fuels
GREET
GREET
GREET
Modeling for crop-based fuels
GTAP-BIO +
GREET
GLOBIOM +
GREET
GCAM
Cumulative Emissions Impacts
-936
-496
33
Average Annual Emissions Impacts
-31
-17
1
Our analysis of the effects of the Analyzed Volumes on climate change shows a range of
potential GHG emissions impacts, from 1 million metric tons of average annual CChe emissions
increase through 2055 to 31 million metric tons of average annual CChe emissions reductions
through 2055. As noted elsewhere in this chapter, modeling of market reactions and subsequent
market-mediated emissions impacts of these volume standards is inherently uncertain, especially
when projecting impacts over multiple decades into the future. Given the differences in model
295 See "Set 2 FRM Climate Change Analyses.xlsx" available in the docket for this action.
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scopes, assumptions, and mathematical forms, and the uncertainty inherent in this type of
forward-looking modeling, differences among the estimates presented in this section should not
be interpreted as any models or modeled results being more or less "accurate" than others.
Rather, each of these models and modeled estimates presents a plausible alternative possible
long-term outcome from this action. Two of these estimates show substantial reductions in GHG
emissions relative to the assessed No RFS Baseline, while one estimate shows a comparatively
much smaller increase in GHG emissions. As a whole, these estimates demonstrate the potential
for this action to result in substantial GHG emissions reductions.
Here, we briefly summarize each set of model simulation results at a high level. GCAM
simulation results include relatively large energy sector responses to changes in U.S. biofuel
consumption, including relatively substantial incremental rebound in global oil consumption, and
notable diversion of biofuels and associated feedstocks to the United States from other regions.
Additional demand for feedstock crops is met in significant part through greater areas of
cropland relative to the No RFS Baseline, both within and outside of the United States.
GLOBIOM simulations do not internally represent energy market responses; the one-for-one
energy equivalent displacement assumption represents a simple displacement effect within the
United States only. Additional demand for feedstock crops is met in GLOBIOM simulation
results primarily through diversion from other uses, crop switching in the U.S., and cropland
expansion outside the U.S. While full simulation outputs for the GTAP-BIO modeling used in
this assessment were not available, we believe, based on similar past assessments using GTAP-
BIO, that additional demand for feedstock crops was met substantially through diversion of crops
from other uses, reduced overall demand for livestock feed, and increasing crop yields within the
U.S.296 Given the lack of simulation data from GTAP-BIO on energy market responses, our
estimates based on GTAP-BIO use a one-for-one energy equivalent displacement assumption
which represents a simple displacement effect within the United States only. Additional detail for
all of these sets of modeled results is available in the docket for this action.297
EPA is not monetizing the estimated GHG emissions for this rule. The Agency
acknowledges that this is a change from past RFS rules in which it has applied the social cost of
carbon to GHG emissions estimates to monetize the impacts of such emissions.298 However,
EPA recognizes that there are significant uncertainties related to monetization of GHGs that
include, but are not limited to:
• The magnitude of the change in climate;
• The relationship between changes in climate and the impacts and resulting economic
impacts;
• Climate-economic interactions;
• Future economic growth across all countries of the world;
296 EPA, "Model Comparison Exercise Technical Document," EPA-420-R-23-017, June 2023.
https ://nepis. epa. gov/Exe/ZvPDF. cgi?Dockev=P 1017P9B .pdf.
297 See "Economic Modeling of Climate Change Impacts for Crop-based Fuels" and "Set 2 FRM Climate Change
Analyses.xlsx," available in the docket for this action.
298 See, e.g.. Set 1 Rule RIA Chapter 4.2.4. Note that, in providing the monetized climate change impacts in the Set
1 Rule, EPA also explained that there are a number of limitations and uncertainties associated with the social cost of
carbon estimates, including uncertainties in key model parameters such as the equilibrium climate sensitivity and
questions about an appropriate discount rate. Id. at 188-89.
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• Future population growth;
• Future technological advancements;
• Impact from emissions on the regulated entities in the United States; and
• Appropriate discount rates.
Due to the orders of magnitude of uncertainties related to monetization of emissions from
GHGs, it would result in the following if EPA continued to monetize GHG impacts:
• Misleading the public that the federal government has a better understanding of
monetized climate impacts from these actions.
• Reduce confidence in the federal government and confuse the public on why the federal
government is taking actions.
• Potentially result in flawed decision making due to overreliance on highly uncertain
values.
EPA notes that, even in its past rules in which it has monetized the GHG impacts
associated with volumes, these estimates have been provided for "illustrative" purposes.299
Given that EPA continues, consistent with past practice,300 to consider climate change impacts in
terms of GHG emissions, not monetizing those emissions does not impact EPA's evaluation of
the statutory factors to determine volumes and does not implicate any reliance interests.
5.3 Rebound Sensitivities
Among the analytical methods used in this chapter to assess GHG emissions associated
with the Analyzed Volumes, only estimates derived from GCAM modeling represent
endogenous market mediated energy market impacts of increased use of biofuels.301 That is, only
the estimates derived from GCAM modeling incorporate the potential reaction of domestic and
global energy markets to the domestic displacement of use of fossil fuels that would result from
the Analyzed Volumes. In order to use the GLOBIOM and GTAP-BIO modeling of crop-based
fuels and the GREET modeling of secondary product-based fuels to assess GHG emissions
associated with the Analyzed Volumes, post-hoc assumptions about the displacement of use of
fossil fuels are required. In the central assessment of the Analyzed Volumes in Chapter 5.2, we
assumed a one-for-one displacement of fossil fuels on an energy equivalent basis.
Comments submitted on the proposal argued that the one-for-one displacement
assumption (also used in the analysis of the proposed volumes) overestimates the displacement
effect, and therefore, the emissions reductions, of increasing biofuel volumes. Central to this
argument is the concept of oil rebound, a theoretical market response defined by the following
logic: an increase in biofuel use for transportation displaces the use of some amount of fossil-
based fuels. Decreased demand for oil results in downward pressure on the price of oil. While
299 See, e.g., "RFS Program: RFS Annual Rules - Regulatory Impact Analysis," EPA-420-R-22-008, June 2022
("2020-2022 Rule RIA"), Chapter 3.2.2.3.2; Set 1 Rule RIA Table ES-1.
300 See, e.g., 88 FR 44468, 44500 (July 12, 2023).
301 While the GTAP-BIO model does endogenously represent energy sector responses to increased biofuel
production, the GTAP-BIO-based estimates used in this chapter rely on emissions factors distributed with the R&D
GREET model, which do not include GTAP-BIO modeled energy sector responses.
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costs of end-use blended fuels may go up in the U.S., downward pressure on the price of oil
globally can result in increased global demand for oil when markets equilibrate. That is, global
oil use "rebounds" from the first order displacement effect.
While rebound effects associated with biofuel use have been examined in the
literature,302 they depend on numerous interrelated market interactions that are difficult to project
with certainty and can be substantially affected by actors with significant market power (e.g.,
OPEC). While we agree with commenters that there would likely be some rebound effect, we are
unable to assess the magnitude of the effect with enough confidence to apply a single rebound
adjustment to our one-for-one displacement assumption. Thus, the estimates in Chapter 5.2 are
presented without any post-hoc adjustments to represent oil rebound that is not already
represented in the estimates (i.e., in GCAM).
However, we agree with commenters that illustrating the sensitivity of emissions
estimates to different assumptions about rebound provides valuable information about the
uncertainty in emissions estimates associated with the Analyzed Volumes. For this reason, we
present in this section several sensitivities with alternative oil rebound assumptions. As
mentioned above, GCAM modeling used our analysis endogenously represents energy market
dynamics. GCAM results from the scenario used in this assessment show roughly a 19% rebound
effect. In other words, in this scenario, the reduction in global oil consumption relative to the
baseline is roughly 81% of the increase in global biofuel consumption resulting from the
modeled U.S. volumes.303
In the table below, we provide results for the non-GCAM modeling under assumed 10%, 20%,
and 30% oil rebound. These cases correspond with 90%, 80%, and 70% displacement of
petroleum-based fuels respectively. These illustrative sensitivities are not intended to reflect the
range of possible or most likely rebound effects that EPA expects as a result of this rule; rather,
they are intended to bracket the level of oil rebound exhibited in GCAM scenarios by roughly
±10 percentage points and illustrate what emissions could look like under comparable energy
market responses. These illustrative sensitivities demonstrate how uncertainty in energy market
responses to volume standards is associated with additional uncertainty in emissions estimates.
Furthermore, these sensitivities help to explain how differences in the scopes of the modeling
tools used in EPA's climate change analysis contribute to differences in the emissions estimates
presented in Chapter 5.2. Future research into rebound effects under different market conditions
would help refine this sensitivity analysis and EPA's ability to characterize the results as more or
less likely.
3112 Rajagopal, D., G. Hochman, and D. Zilberman. "Indirect Fuel Use Change (IFUC) and the Lifecycle
Environmental Impact of Biofuel Policies." Energy Policy 39, no. 1 (October 15, 2010): 228-33.
https://doi.Org/10.1016/i.enpol.2010.09.035.
3113 Additional illustration of the energy market responses to biofuel shocks in GCAM and how the 19% rebound
effect was estimated is available in "Economic Modeling of Climate Change Impacts for Crop-based Fuels,"
available in the docket for this action.
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Table 5.3-1: 30-year Cumulative Emissions Differences Under Alternative Oil
Displacement Assumptions (Million Metric Tonnes CChe)
Modeling for Secondary
Product-based Fuels
Modeling for
Crop-based Fuels
90%
Displacement
80%
Displacement
70%
Displacement
GREET
GTAP-BIO +
GREET
-783
-631
-478
GREET
GLOBIOM +
GREET
-343
-191
-38
Note: Sensitivities presented in Table 5.3-1 are comparable to estimated cumulative emissions values presented in
Estimate 1 and Estimate 2 in Table 5.2-2, which assume one-for-one energy equivalent displacement (i.e., 100%
displacement) of fossil fuels.
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Chapter 6: Energy Security Impacts
The CAA at section 21 l(o)(2)(B)(ii) directs EPA to analyze "the impact of renewable
fuels on the energy security of the United States" in using the set authority to establish volumes.
This chapter describes our analysis of the energy security impacts of the Analyzed Volumes
relative to the No RFS Baseline.
Energy security is broadly defined as the uninterrupted availability of energy sources at
an acceptable price.304 Most discussions of U.S. energy security have historically revolved
around the topic of the economic costs of U.S. dependence on oil imports.305 However, all
exposures to global energy supply disruptions and price spikes—including those related to
renewable fuels and renewable fuel feedstocks—create risks to energy security. A related but
separate consideration is U.S. energy independence, which is achieved by reducing the exposure
of the U.S. economy to energy imports and foreign energy markets to the point where the costs
of depending on foreign energy (fossil fuels, biofuels, electricity, etc.) are so small that they have
minimal effects on the U.S.'s economic, military, or foreign policies.306 In this definition of U.S.
energy independence, it is necessary to reduce, but not eliminate, all energy imports to the U.S.
to achieve independence.
Reducing oil imports and, thus, becoming more independent from foreign suppliers of oil
has been a central goal of U.S. energy security policy for decades. In addition to evaluating
impacts on energy security, we have also considered the impacts of the Analyzed Volumes on
U.S. energy independence in this analysis. While energy independence is not a statutory factor in
the CAA, one goal of the RFS program is to improve the U.S.'s energy independence.307 As
stated above, energy independence and energy security are distinct but related concepts. Analysis
of energy independence also helps to inform our analysis of energy security.
Given the historical focus in the U.S. on reducing oil imports, the discussion and analysis
in this chapter first addresses the role of oil imports in energy security and energy independence
and the impact of the Analyzed Volumes on oil imports. Note that this analysis is generally
focused on imports of crude oil (as opposed to, for example, imports of refined petroleum
products), as crude import volume tends to be the more critical vector of U.S. exposure to global
energy market shocks. However, to the extent it is meaningful to do so, we also address other
exposures to global energy markets potentially impacted by the Analyzed Volumes.
3114IEA, "Energy Security." https://www.iea.org/topics/energy-securitv.
3115 The issue of cyberattacks is another energy security issue that could grow in significance over time. For example,
one of the U.S.'s largest pipeline operators. Colonial Pipeline, was forced to shut down after being hit by a
ransomware attack. The pipeline carries refined gasoline and jet fuel from Texas to New York. Sanger, David E„
Clifford Krauss, and Nicole Perlroth. "Cyberattack Forces a Shutdown of a Top U.S. Pipeline," New York Times,
May 8, 2021. https://www.nvtimes.com/2021/05/08/us/politics/cvberattack-colonial-pipeline.html.
3116 Greene, David L. "Measuring Energy Security: Can the United States Achieve Oil Independence?" Energy
Policy 38, no. 4 (March 7, 2009): 1614-21. https://doi.Org/10.1016/i.enpol.2009.01.041.
3117 See Americans for Clean Energy v. Env't Prot. Agency, 864 F.3d 691, 696 (D.C. Cir. 2017) ("By mandating the
replacement—at least to a certain degree—of fossil fuel with renewable fuel. Congress intended the Renewable Fuel
Program to move the United States toward greater energy independence and to reduce greenhouse gas emissions.");
id. 697 (citing 121 Stat, at 1492).
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One other such potential vector of energy security risk exposure is the role of imported
renewable fuels and renewable fuel feedstocks. The interplay between reductions in imports of
oil with market trends in imports of renewable fuels and renewable fuel feedstocks on U.S.
energy security and independence is also a significant factor to consider. We currently lack the
tools to assess these impacts with the same robustness as we address impacts from changes in
crude oil market exposure. However, we do address these factors to a limited extent in this
chapter.
The U.S. has witnessed a significant change in its exposure to the world oil market since
initial implementation of the RFS2 Rule in 2010. This shift in exposure has implications for U.S.
energy security. Between 2010 and 2025 U.S. production of crude oil and petroleum products
grew at an average annual rate of approximately 7%, as shown in Figure 6-1. The growth rate in
U.S. oil production was largely due to significant increases in U.S. tight (i.e., shale) oil
production.308 As of 2025, as a result of growing oil production, the U.S. was the largest
producer of oil in the world, producing 13.7 million barrels per day (MMBD), followed by Saudi
Arabia producing 9.2 MMBD and Russia producing 9.1 MMBD.309
During this same timeframe, U.S. consumption of crude oil and petroleum products has
grown much more slowly, with an average annual growth rate of 0.7%, as shown in Figure 6.1.
The significant increase in U.S. oil production and relatively slow growth in U.S. oil
consumption has resulted in a significant reversal in the U.S.'s petroleum trade balance position.
Two milestones from this period are notable. First, in 2011, U.S. oil production exceeded net
imports for the first time since 1996.310 Second, in 2020, the U.S. became a net exporter of crude
oil and petroleum products (i.e., the U.S. was no longer a net petroleum importer) for the first
time since the early 1950s.311 Thus, we can observe the U.S. has achieved a greater degree of
energy independence with respect to petroleum by reducing dependence on imports since 2010.
3118 EIA, "Tight oil production estimates by play," Petroleum & Other Liquids, May 2025.
https://www.eia.gov/petroleum/data.php.
3119 EIA, "Petroleum and other liquids (production)," International, October 2025.
https://www.eia.gov/international/data/world/petroleum-and-other-liauids/montlilY-petroleum-and-other-liauids-
production. Oil production estimates include crude oil and lease condensate.
3111 EIA, "Oil imports and exports". Oil and petroleum products explained, January 19, 2024.
https://www.eia.gov/energvexplained/oil-and-petroleum-products/imports-and-exports.php.
311 Id.
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Figure 6-1: U.S. Consumption, Production, and Net Imports of Crude Oil and Petroleum
Products
9000
8000
-2000
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Though less well studied than the impacts from exposure to global petroleum markets,
exposure to global renewable fuel and renewable fuel feedstock markets also has potential
implications for U.S. energy security. In recent years, a substantial quantity of imports of
renewable fuels and renewable fuel feedstocks have been used to meet the RFS volume
obligations. Trade balances for some renewable fuels and renewable fuel feedstocks have been
moving in different directions than the historical oil import trends discussed above. In particular,
there has been a recent expansion of imports of BBD feedstocks since 2021, which can be seen
in Preamble Figure III.B.2.d-2. This shift, which has been driven by a confluence of factors
discussed elsewhere (e.g., Chapter 7), has implications for energy security and energy
independence. One of these factors is the Clean Fuel Production Credit federal subsidy for
domestically-produced renewable fuels (hereafter referred to as "45Z" or the "45Z credit"),
which was established under the 45Z provisions of the Inflation Reduction Act and then was
revised under OBBB. The 45Z credit is likely to impact the competitiveness of biofuels produced
from different feedstocks in the U.S and hence imports of feedstocks into the U.S., with
implications for energy security. Prior to 2025, all qualifying biodiesel and renewable diesel was
eligible for the $1 per gallon 40A tax credit and this was available for biodiesel and renewable
diesel produced in the U.S. as well as biodiesel or renewable diesel produced in foreign countries
and used in the U.S. The 45Z credit, which replaces this $1 per gallon credit under 40A, is
limited to transportation fuels produced in the U.S., and, starting in 2026, transportation fuels
produced exclusively from North American feedstock. EPA anticipates that the change in the tax
credit will continue to negatively impact imports of biodiesel and renewable diesel in future
years. Section 7.2. has a more detailed discussion on the implications of 45Z for imports of
feedstocks.
Despite recent changes in the U.S. trade balances of oil and expansion of renewable fuel
consumption in the U.S., important energy security risks to the U.S. transportation fuel sector
still remain. These remaining risks stem from three primary sources.
First, oil, renewable fuels, and renewable fuel feedstocks are globally traded commodities
and, as a result, a price shock to any of these commodities is transmitted globally even if a
country is a net exporter of a commodity. For example, were U.S. oil producers to attempt to keep
their prices low in the face of a global oil price shock, foreign consumers would attempt to buy up
that cheaper oil, bidding up the price. In this way, U.S. consumers of oil would also be exposed to oil
price shocks, even when purchasing oil produced in the U.S. As a result, an oil price shock would
raise the price of oil and oil products that U.S. households and businesses pay for petroleum
products, which could adversely affect the U.S. economy as a whole. Additional use of
renewable fuels and renewable fuel feedstocks can dampen price impacts from oil price shocks,
if these prices are largely uncorrected, but will result in new exposure in the renewable fuel
markets.
Second, certain U.S. refineries rely on significant imports of heavy crude oil which could
be subject to direct supply disruptions. These refiners are unable to consume the lighter crude oil
produced by U.S. tight oil operations without expensive and time-consuming changes to their
refinery configurations and supply chain logistics. They therefore lack the agility to make such
changes in the face of acute short- or medium-term oil supply disruptions. While U.S. petroleum
exports now exceed imports, the volume of gross imports remains quite significant. In 2024,
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gross petroleum imports totaled roughly 8.4 MMBD.314 Likewise, there has been an expansion in
imported BBD feedstocks in recent years.
Third, oil exporters with a large share of global production have the ability to raise or
lower the price of oil by exerting their market power through OPEC to alter oil supply relative to
demand. It is somewhat uncertain how much market power OPEC will have in 2026-2027. But
in the face of such uncertainty, OPEC's market power must be considered a significant risk to
U.S. energy security. All three of the factors listed above contribute to the vulnerability of the
U.S. economy to episodic energy supply shocks and price spikes, even though the U.S. is
projected to be a net petroleum exporter for the foreseeable future.
To summarize, the increase in U.S. tight oil production and in production and
consumption of renewable fuels have contributed to a shift in the U.S. net energy trade position
in recent years. We expect these trends to continue and for the U.S. to remain a net petroleum
exporter in 2026-2027.315 However, because the U.S. is a participant in the world market for oil,
renewable fuels, and renewable fuel feedstocks, its economy cannot be shielded from worldwide
price shocks from these commodities.316 Even if the recent trend of increasing imports of
renewable fuel feedstocks changes course, renewable fuel feedstocks will still exist within the
context of global commodity markets and may be exposed to global price and supply pressures.
The potential for petroleum supply disruptions has also not been eliminated, due to the continued
need to import petroleum to satisfy the demands of the U.S. petroleum.317 In the next section, we
consider how the Analyzed Volumes may alter these exposures and reduce the economic impact
of shocks to the global energy system on U.S. energy security.
While exposure to economic shocks to global petroleum, renewable fuel, and renewable
fuel feedstock markets (e.g., global price spikes, global supply shortages) each carries some
attendant risk, diversifying the portfolio of U.S. energy supplies is a means of reducing the
potential impact of any one of these risks on U.S. energy security. As described in Chapter 1,
despite growth in renewable fuel consumption under the RFS program since 2010, petroleum
products still account for most of the U.S. transportation fuel supply. To the extent that the
Analyzed Volumes further diversify the fuel supply, there may be benefits to energy security. In
Section 6.5 below we describe an example analysis to demonstrate this point by illustrating the
potential impact of transportation fuel feedstock diversification on U.S. transportation fuel
prices.
For example, the supply of renewable fuels and renewable fuel feedstocks could be
subject to market supply disruptions such as weather-related events (e.g., droughts) in the U.S. or
abroad. To the extent that renewable fuel and renewable fuel feedstock prices are subject to only
modest price shocks, and the price shocks are not strongly correlated with oil price shocks,
blending renewable fuels with petroleum fuels will likely provide energy security benefits. The
314 EIA, "U.S. Imports from All Countries," Petroleum & Other Liquids, May 2025.
https://www.eia.gov/dnav/pet/pet move impcus a2 nus epOO imO mbblpd a.htm.
315 AEO2025, Table 11 - Petroleum and Other Liquids Supply and Disposition.
316 Bordoff, Jason. "The Myth of U.S. Energy Independence Has Gone up in Smoke." Foreign Policy, September
18, 2019. https://foreignpolicv.com/2019/09/18/the-mvth-of-u-s-energv-independence-has-gone-up-in-smoke.
317 Foreman, Dean. "Why the US must Import and Export Oil," American Petroleum Institute, June 14, 2018.
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use of renewable fuels, therefore, may dampen the impacts of oil price shocks but expose fuel
markets to new shocks. From the standpoint of U.S. energy independence, reducing imported
renewable fuels and renewable fuel feedstocks moves the U.S. towards the goal of energy
independence. This chapter focuses primarily on the literature on energy security impacts
associated with U.S. petroleum consumption and U.S. net oil imports and summarizes EPA's
estimates of the benefits of reduced U.S. net imports of petroleum that would result from this
rule. The energy security risks of using renewable fuels/feedstocks are not well understood, nor
well studied. Chapter 6.5 discusses available research and potential future research on that topic.
6.1 Review of Historical Energy Security Literature (1981-2014)
Energy security discussions are typically based around the concept of the "oil import
premium", sometimes also labeled the "oil security premium." The oil import premium is the
extra cost or impacts of importing oil beyond the price of the oil itself as a result of: (1) potential
macroeconomic disruption and increased oil import costs to the economy from oil price spikes or
"shocks"; and (2) monopsony impacts. Monopsony impacts stem from changes in the demand
for imported oil, which changes the price of all imported oil.
The so-called oil import premium gained attention as a guiding concept for energy policy
in the aftermath of the oil shocks of the 1970s (Bohi and Montgomery (1982),318 EMF (1982),319
and Plummer (1982)320). Bohi and Montgomery detailed the theoretical foundations of the oil
import premium and established many of the critical analytic relationships that can be used to
estimate the magnitude of the oil import premium. Hogan (1981)321 and Broadman and Hogan
(1986,322 1988323) revised and extended the established analytical framework to estimate optimal
oil import premia with a more detailed accounting of macroeconomic effects. Since the original
work on energy security was undertaken in the 1980s, there have been several reviews on this
topic by Leiby, Jones, Curlee and Lee (1997), and Parry and Darmstadter (20 04).324-325
The economics literature on whether oil shocks have continued to present the same level
of threat to economic stability as they were when this literature emerged in the 1980s has been
mixed over time. Hamilton (2012) reviewed the empirical literature on oil shocks and suggested
that the results were mixed, noting that some work (e.g., Rasmussen and Roitman (2011)) found
less evidence for economic effects of oil shocks or declining effects of shocks (Blanchard and
318 Bohi, D.R. and W.D. Montgomery. "Social Cost of Imported Oil and US Import Policy.". Innual Review of
Energy 7, no. 1 (November 1, 1982): 37-60. https://doi.org/10.1146/annurev.eg.07.110182.000345.
319 "World Oil: Summary Report." Energy Policy 10, no. 4 (December 1, 1982): 367. https://doi.org/10.1016/Q301-
4215(82)90059-3.
3211 Plummer, James L. Energy Vulnerability. Ballinger Publishing Company, 1982.
321 Hogan, W. "Import Management and Oil Emergencies," Chapter 9 inD. Deese and J. Nye, eds. Energy and
Security, Cambridge: Ballinger Press, 1981.
322 Broadman, Harry G. "The Social Cost of Imported Oil." Energy Policy 14, no. 3 (June 1, 1986): 242-52.
https://doi.org/10.1016/0301-4215(86)90146-1.
323 Broadman, Harry G., and William W. Hogan. "The numbers say yes." The Energy Journal 9, no. 3 (July 1,
1988): 7-30. https://doi.org/10.1177/01956574198809031.
324 Leiby, Paul N., Donald W. Jones, T. Randall Curlee, and Russell Lee. "Oil Imports: An Assessment of Benefits
and Costs." Oak Ridge National Laboratory. ORNL-6851. November 1, 1997.
325 Parry, Ian W.H., Joel Darmstadter. "The Costs of U.S. Oil Dependency." Resources for the Future, December
2003. https://media.rff.org/documents/RFF-DP-03-59.pdf.
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Gali (2010)), while other work found more evidence regarding the economic importance of oil
shocks.326 For example, Baumeister and Peersman (2012) found that an "oil price increase of a
given size seems to have a decreasing effect over time, but noted that the declining price-
elasticity of demand meant that a given physical disruption had a bigger effect on price and
turned out to have a similar effect on output as in the earlier data."327 Ramey and Vine (2010)
found "remarkable stability in the response of aggregate real variables to oil shocks once we
account for the extra costs imposed on the economy in the 1970s by price controls and a complex
system of entitlements that led to some rationing and shortages."328
Some of the literature on oil price shocks has emphasized that economic impacts depend
on the nature of the oil shock, with differences between price increases caused by a sudden
supply loss and those caused by rapidly growing demand. Analyses of oil price shocks have
confirmed that "demand-driven" oil price shocks have greater effects on oil prices and tend to
have positive effects on the economy while "supply-driven" oil shocks still have negative
economic impacts (Baumeister, Peersman, and Robays (20 1 0)).329 A paper by Kilian and
Vigfusson (2014), for example, assigned a more prominent role to the effects of price increases
that are unusual, in the sense of being beyond the range of recent experience.330 Kilian and
Vigfusson also concluded that the difference in response to oil shocks may well stem from the
different effects of demand- and supply-based price increases: "One explanation is that oil price
shocks are associated with a range of oil demand and oil supply shocks, some of which stimulate
the U.S. economy in the short-run and some of which slow down U.S. growth (see Kilian
2009) ,"331
The general conclusion that oil supply-driven shocks reduce economic output is also
reached in a paper by Cashin et al. (2014), which focused on 38 countries from 1979-2011.332
They state: "The results indicate that the economic consequences of a supply-driven oil-price
shock are very different from those of an oil-demand shock driven by global economic activity,
and vary for oil-importing countries compared to energy exporters." Cashin et al. continues
".. .oil importers (including the U.S.) typically face a long-lived fall in economic activity in
326 Rasmussen, Tobias N., and Agustin Roitman. "Oil Shocks in a Global Perspective: Are They Really That Bad?"
IMF Working Paper 11, no. 194 (January 1, 2011): 1. https://doi.org/10.5089/9781462305254.b01.
327 Baumeister, Christiane, and Gert Peersman. "The Role of Time-Varying Price Elasticities in Accounting for
Volatility Changes in the Crude Oil Market." Journal of Applied Econometrics 28, no. 7 (June 26, 2012): 1087-
1109. https://doi.org/10.1002/iae.2283.
328 Ramey, Valerie A., and Daniel J. Vine. "Oil, Automobiles, and the U.S. Economy: How Much Have Tilings
Really Changed?" NBER Macroeconomics Annual 25, no. 1 (January 1, 2011): 333-68.
https://doi.org/10.1086/657541.
329 Baumeister Christiane, Gert Peersman and Ine Van Robays. "The Economic Consequences of Oil Shocks:
Differences across Countries and Time," Reserve Bank of Australia Annual Conference - 2009.
https://www.rba.gov.au/publications/confs/2009/baumeister-peersman-vanrobavs.html.
3311 Kilian, Lutz, and Robert J. Vigfusson. "The Role of Oil Price Shocks in Causing U.S. Recessions." Journal of
Money Credit and Banking 49, no. 8 (November 16, 2017): 1747-76. https://doi.org/10. Ill 1/imcb. 12430.
331 Kilian, Lutz. "Not All Oil Price Shocks Are Alike: Disentangling Demand and Supply Shocks in the Crude Oil
Market." American Economic Review 99, no. 3 (May 1, 2009): 1053-69. https://doi.Org/10.1257/aer.99.3.1053.
332 Cashin, Paul, Kamiar Mohaddes, Maziar Raissi, and Mehdi Raissi. "The Differential Effects of Oil Demand and
Supply Shocks on the Global Economy." Energy Economics 44 (April 6, 2014): 113-34.
https://doi.Org/10.1016/i.eneco.2014.03.014.
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response to a supply-driven surge in oil prices." But almost all countries see an increase in real
output caused by an oil-demand disturbance.
Considering all of the energy security literature from the 1981-2014 timeframe, EPA's
assessment concludes that there are benefits to the U.S. from reductions of its net oil imports.
There is some debate as to the magnitude of energy security benefits from U.S. oil import
reductions. However, differences in economic impacts from oil demand and oil supply shocks
have been distinguished, with oil supply shocks resulting in economic losses in oil importing
countries. The oil import premium calculations in this analysis (described in Chapter 6.4) are
based on price shocks from potential future supply events. Oil supply shocks have been the
predominant focus of oil security issues since the oil price shocks/oil embargoes of the 1970s.
While we project an increase in imported feedstocks to make renewable fuels due to this rule, the
rule would result in an overall reduction in the combined total of imported fossil and renewable
fuel feedstocks used to make transportation fuels, moving the U.S. modestly towards the goal of
energy independence while enhancing the U.S.'s energy security.
6.2 Review of Energy Security Literature from the Last Decade
There have also been a handful of studies from the last decade (i.e., since 2015) that are
relevant for the issue of energy security. We provide a brief review and high-level summary of
each of these studies below.
6.2.1 Oil Energy Security Studies from the Last Decade
The first studies on the energy security impacts of oil we reviewed are by Resources for
the Future (RFF), a study by Brown, and two studies by Oak Ridge National Laboratory
(ORNL). The RFF study (2017) attempted to develop updated estimates of the relationship
among gross domestic product (GDP), oil supply and oil price shocks, and world oil demand and
supply elasticities.333 In a follow-on study, Brown summarized the RFF study results as well.334
The RFF argued that there have been major changes that have occurred over the last two decades
which have reduced the impacts of oil shocks on the U.S. economy. First, the U.S. became less
dependent on imported oil in the 2010s due in part to the "fracking revolution" (i.e., tight/shale
oil), and to a lesser extent, increased production of renewable fuels. In addition, RFF argued that
the U.S. economy became more resilient to oil shocks in the 2010s compared to an early 2000s.
Some of the factors that made the U.S. more resilient to oil shocks include increased global
financial integration and greater flexibility of the U.S. economy (especially labor and financial
markets).
In the RFF effort, a number of comparative modeling scenario exercises were conducted
by several economic modeling teams using three different types of energy-economic models to
examine the impacts of oil shocks on U.S. GDP. The first framework involved was a dynamic
333 Krupnick, Alan, Richard Morgenstern, Nathan Balke, Stephen P. A. Brown, Ana Maria Herrera, and Shashank
Mohan. "Oil Supply Shocks, US Gross Domestic Product, and the Oil Security Premium." Resources for the Future.
November 2017. https://media.rff.org/documents/RFF-Rpt-OilSecuritv.pdf.
334 Brown, Stephen P. A. "New Estimates of the Security Costs of U.S. Oil Consumption." Energy Policy 113
(November 22, 2017): 171-92. https://doi.Org/10.1016/i.enpol.2017.ll.003.
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stochastic general equilibrium model developed by Balke and Brown.335 The second set of
modeling frameworks used alternative structural vector autoregressive models of the global
crude oil market.336 The last of the models utilized was the National Energy Modeling System
(NEMS).337
Two key parameters were focused upon to estimate the impacts of oil shock simulations
on U.S. GDP: oil price responsiveness (i.e., the short-run price elasticity of demand for oil) and
GDP sensitivity (i.e., the elasticity of GDP to an oil price shock). The more inelastic (i.e., the less
responsive) short-run oil demand is to changes in the price of oil, the higher the price impacts of
a future oil shock. Higher price impacts from an oil shock result in higher GDP losses. The more
inelastic (i.e., less sensitive) GDP is to an oil price change, the less the loss of U.S. GDP with
future oil price shocks.
For oil price responsiveness, RFF reported three different values: a short-run price
elasticity of oil demand from their assessment of the "new literature," -0.17; a "blended"
elasticity estimate; -0.05, and short-run oil price elasticities from the "new models" RFF uses,
ranging from -0.20 to -0.35. The "blended" elasticity is characterized by RFF in the following
way: "Recognizing that these two sets of literature [old and new] represent an evolution in
thinking and modeling, but that the older literature has not been wholly overtaken by the new,
Benchmark-E [the blended elasticity] allows for a range of estimates to better capture the
uncertainty involved in calculating the oil security premiums."
The second parameter that RFF examined is the GDP sensitivity. For this parameter,
RFF's assessment of the "new literature" finds a value of-0.018, a "blended elasticity" estimate
of-0.028, and a range of GDP elasticities from the "new models" that RFF uses that range from
-0.007 to -0.027. One of the limitations of the RFF study was that the large variations in oil
price over the last 15 years are believed to be predominantly "demand shocks" (e.g., for
example, a rapid growth in global oil demand followed by the Great Recession and then the post-
recession recovery).
There have only been three situations where events have led to a potentially significant
supply-side oil shock in the last decade. The first event was the attack on the Saudi Aramco
Abqaiq oil processing facility and the Khurais oil field. On September 14, 2019, a drone and
cruise missile attack damaged the Saudi Aramco Abqaiq oil processing facility and the Khurais
oil field in eastern Saudi Arabia. The Abqaiq oil processing facility was the largest crude oil
335 Balke, Nathan S., and Stephen P.A. Brown. "Oil Supply Shocks and the U.S. Economy: An Estimated DSGE
Model." Energy Policy 116 (February 28, 2018): 357-72. https://doi.Org/10.1016/i.enpol.2018.02.027.
336 These models include:
Kilian, Lutz. "Not All Oil Price Shocks Are Alike: Disentangling Demand and Supply Shocks in the Crude Oil
Market." American Economic Review 99, no. 3 (May 1, 2009): 1053-69. https://doi.Org/10.1257/aer.99.3.1053.
Kilian, Lutz, and Daniel P. Murphy. "The Role of Inventories and Speculative Trading in the Global Market for
Crude Oil." Journal of Applied Econometrics 29, no. 3 (April 10, 2013): 454-78. https://doi.org/10.1002/iae.2322.
Baumeister, Christiane, and James D. Hamilton. "Structural Interpretation of Vector Autoregressions With
Incomplete Identification: Revisiting the Role of Oil Supply and Demand Shocks." American Economic Review 109,
no. 5 (May 1, 2019): 1873-1910. https://doi.org/10.1257/aer.20151569.
337 Mohan Shashank. "Oil Price Shocks and the US Economy: An Application of the National Energy Modeling
System." Resources for the Future. November 2017. https://media.rff.org/documents/RFF-Rpt-OilSecuritv-
Appendix.pdf.
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processing and stabilization plant in the world, with a capacity of roughly 7 million barrels of oil
a day (MMBD) or about 7% of global crude oil production capacity.338 On September 16, the
first full day of commodity trading after the attack, both Brent and West Texas Intermediate
(WTI) crude oil prices surged by $7.17/barrel and $8.34/barrel, respectively, in response to the
attack, the largest price increase in roughly a decade.
However, by September 17, Saudi Aramco reported that the Abqaiq plant was producing
2 MMBD, and they expected its entire output capacity to be fully restored by the end of
September.339 Tanker loading estimates from third-party data sources indicated that loadings at
two Saudi Arabian export facilities were restored to the pre-attack levels.340 As a result, both
Brent and WTI crude oil prices fell on September 17, but not back to their original levels. The oil
price spike from the attack on the Abqaiq plant and Khurais oil field was prominent and unusual,
as Kilian and Vigfusson (2014) describe. While pointing to possible risks to world oil supply, the
oil shock was short-lived, and generally viewed by market participants as being transitory, so it
did not influence oil markets over a sustained time period.
The second situation was the set of events leading to the recent world oil price spike
experienced in 2022. World oil prices rose fairly rapidly in the first half of 2022. For example,
on January 3, 2022, the WTI crude oil price was roughly $76/barrel.341 The WTI oil price
increased to roughly $124/barrel on March 8, 2022, a 63% increase.342 Crude oil prices increased
in the first half of 2022 because of oil supply concerns. Russia's invasion of Ukraine came
during eight consecutive quarters (from the third quarter of 2020 to the second quarter of 2022)
of global crude oil inventory decreases.343 The lower inventory of crude oil was the result of
withdrawals from storage to meet the demand that resulted from rising economic activity after
pandemic-related restrictions eased.344 More recently, as of July 28, 2025, the WTI crude oil
price was $68/barrel, a somewhat lower price than before the Russian invasion of Ukraine.345 Oil
prices at present are relatively low mainly because of a projected slowdown in world oil demand
growth, particularly in China.346 According to AEO2025, crude oil prices (i.e., the WTI crude oil
price) are expected to be flat in 2026-2027, in the range of $78-79 per barrel (2024$).347
Geopolitical disruptions that occurred in 2022 are likely to continue to affect global trade
of crude oil and refined petroleum products. In response to Russia's invasion of Ukraine in late
February 2022, the U.S. and many of its allies, particularly in Europe, announced various
338 EIA, "Saudi Arabia crude oil production outage affects global crude oil and gasoline prices," Today in Energy,
September 23, 2019. https://www.eia.gov/todavinenergy/detail.php?id=41413.
339 Id.
340 Id.
341 EIA, "Spot Prices," Petroleum & Other Liquids, May 14, 2025.
https://www.eia.gov/dnav/pet/pet pri spt si d.htm.
342 Id.
343 EIA, "Crude oil prices increased in the first half of 2022 and declined in the second half of 2022," Today in
Energy, January 4, 2023. https://www.eia.gov/todavinenergy/detail.php?id=55079.
344 Id.'
345 EIA, "Spot Prices," Petroleum & Other Liquids, July 28 2025.
https://www.eia.gov/dnav/pet/pet pri spt si d.htm.
346 EIA, "Short-Term Energy Outlook," September 2024. https://www.eia.gov/outlooks/steo/arcliives/sep24.pdf.
347 AEO2025, Table 12 - Petroleum and Other Liquids Prices.
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sanctions against Russia's petroleum industry.348 For the EU, petroleum from Russia had
accounted for a large share of all energy imports, but the EU banned imports of crude oil from
Russia starting in December 2022 and imports of refined petroleum products starting in February
2023.349 In light of this geopolitical environment and other market factors, the U.S. has seen its
refined petroleum product exports grow steadily since 2021. It is anticipated that the U.S. will
continue to witness an increase in refined petroleum product exports in 2026-2027.350
Since both significant demand and supply factors are influencing world oil prices in 2022
and the early part of 2023, it is not clear how to evaluate unfolding oil market price trends from
an energy security standpoint. Thus, the attack of the Abqaiq oil processing facility in Saudi
Arabia and the events in the world oil market in 2022 and 2023 in response to the Russian
invasion of Ukraine do not currently provide enough empirical evidence to provide an updated
estimate of the response of the U.S. economy to an oil supply shock of a significant
magnitude.351
The third event that could have led to a significant oil supply shock was the 12 day "Iran-
Israel War" in June 2025. As a result of the War, there was the potential for the closure of the
Strait of Hormuz which could have restricted oil supply from the Persian Gulf. This threat led to
a more significant geopolitical risk premium for the price of oil stemming from the conflict. In
EIA's July 2025 STEO, EIA increased its estimate of the crude oil price (i.e., the Brent crude oil
price) for the year 2025 from $66/barrel to $69/barrel (in nominal dollars).352
The August 2025 STEO significantly revised the July 2025 STEO oil market outlook.
EIA now sees world oil prices falling significantly in their August 2025 STEO.353 The price
forecast is driven largely by more oil inventory builds following OPEC+ members' decision to
accelerate the pace of oil production increases. EIA now expects global oil inventory builds will
average more than 2 million barrels per day (MMBD) in the fourth quarter of 2025 and the first
quarter of 2026, which is 0.8 million MMBD more than in the July 2025 STEO. EIA forecasts
that the Brent crude oil price will average $51/barrel (in nominal dollars) in 2026, down
significantly from its forecast of $58/barrel (in nominal dollars) in July STEO, and significantly
lower than the AEO2025 oil price forecast for 2026.
A second set of studies related to energy security were from ORNL. In the first study,
ORNL (2018) undertook a quantitative meta-analysis of world oil demand elasticities based upon
348 EIA, "U.S. petroleum product exports set a record high in 2022," Today in Energy, March 20, 2023.
https ://www. eia. gov/todavinenergy/detail.php?id=55880.
349 Id.
3511AEO2025, Table 11 - Petroleum and Other Liquids Supply and Disposition.
351 Hurricanes Katrina and Rita in 2005 primarily caused a disruption in U.S. oil refinery production, with a more
limited disruption of some crude supply in the U.S. Gulf Coast area. Thus, the loss of refined petroleum products
exceeded the loss of crude oil, and the regional impact varied even within the U.S. Hurricanes Katrina and Rita were
a different type of oil disruption event than is quantified in the Stanford Energy Modeling Forum (EMF) risk
analysis framework, which provides the oil disruption probabilities than ORNL is using.
352 EIA, "Short-Term Energy Outlook," July 2025. https://www.eia.gov/outlooks/steo/arcliives/iul25.pdf.
353 EIA. "Short-Term Energy Outlook," August 2025. https://www.eia.gov/outlooks/steo/arcliives/aug25.pdf.
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the recent economics literature.354 The ORNL study estimates oil demand elasticities for two
sectors (transportation and non-transportation) and by world regions (OECD and Non-OECD) by
meta-regression. To establish the data set for the meta-analysis, ORNL undertook a literature
search of peer reviewed journal articles and working papers between 2000-2015 that contain
estimates of oil demand elasticities. The data set consisted of 1,983 observations from 75
published studies. The study found a short-run price elasticity of world oil demand of-0.07 and
a long-run price elasticity of world oil demand of-0.26.
The second relevant ORNL (2018) study from the standpoint of energy security was a
meta-analysis that examines the impacts of oil price shocks on the U.S. economy as well as many
other net oil-importing economies.355 Nineteen studies after 2000 were identified that contain
quantitative/accessible estimates of the economic impacts of oil price shocks. Almost all studies
included in the review were published since 2008. The key result that the study found is a short-
run oil price elasticity of U.S. GDP, roughly one year after an oil shock, of-0.021, with a 68%
confidence interval of-0.006 to -0.036.
6.2.2 Studies on Tight/Shale Oil
The discovery and development of U.S. tight oil (i.e., shale oil) reserves that started in the
mid-2000s affected U.S. energy security; two of the ways this might occur are discussed here.356
First, the increased availability of domestic supplies has resulted in a reduction of U.S. oil
imports and an increasing role of the U.S. as exporter of crude oil and petroleum-based products.
In December 2015, the 40-year ban on the export of domestically produced crude oil was lifted
as part of the Consolidated Appropriations Act, 20 1 6.357 According to the GAO, the ban was
lifted in part due to increases in tight (i.e., shale) oil.358 Second, due to differences in
development cycle characteristics and average well productivity, tight oil producers could be
more price responsive than most other oil producers. However, the oil price level that triggers a
substantial increase in tight oil production appears to be higher in 2021-2023 relative to the
354 Uria-Martinez, Rocio, Paul Leiby, Gbadebo Oladosu, David Bowman, and Megan Johnson. "Using Meta-
Analysis to Estimate World Oil Demand Elasticity," Oak Ridge National Laboratory ORNL/TM-2018/1070,
December 10, 2018. https://doi.org/10.2172/1491306.
355 Oladosu, Gbadebo A., Paul N. Leiby, David C. Bowman, Rocio Uria-Martinez, and Megan M. Johnson. "Impacts
of Oil Price Shocks on the United States Economy: A Meta-analysis of the Oil Price Elasticity of GDP for Net Oil-
importing Economies." Energy Policy 115 (February 3, 2018): 523-44. https://doi.Org/10.1016/i.enpol.2018.01.032.
356 "Tight oil is a type of oil found in impermeable shale and limestone rock deposits. Also known as 'shale oil,'
tight oil is processed into gasoline, diesel, and jet fuels—just like conventional oil—but is extracted using hydraulic
fracturing, or 'fracking.'" Union of Concerned Scientists, "What Is Tight Oil?" March 3, 2015.
https://www.ucs.org/resources/what-tight-oil.
357 Pub. L. 114-113 (December 18, 2015).
358 GAO, "Crude Oil Markets: Effects of the Repeal of the Crude Oil Export Ban," GAO-21-118, October 2020.
https://www.gao.gov/assets/gao-21-118.pdf. According to the GAO, "Between 1975 and the end of 2015, the
Energy Policy and Conservation Act directed a ban on nearly all exports of U.S. crude oil. This ban was not
considered a significant policy issue when U.S. oil production was declining and import volumes were increasing.
However, U.S. crude oil production roughly doubled from 2009 to 2015, due in part to a boom in shale oil
production made possible by advancements in drilling technologies. In December 2015, Congress effectively
repealed the ban, allowing the free export of U.S. crude oil worldwide."
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2010s as tight oil producers seek higher profit margins per barrel in order to reduce the debt
burden accumulated in previous cycles of production growth.359
U.S. crude oil production increased from 5.0 MMBD in 2008 to 13.2 MMBD in 2024 and
tight oil wells have been responsible for most of the increase.360 Figure 6.2.2-1 shows tight oil
production changes from various tight oil producing regions (e.g., Eagle Ford, Bakken, etc.) in
the U.S. and the WTI crude oil spot price. Viewing Figure 6.2.2-1, one can see that the annual
average U.S. tight oil production grew from 0.5 MMBD in 2008 to 8.9 MMBD in 2024.361
Growth in U.S. tight oil production during this period was only interrupted in 2015-2016
following the world oil price downturn that began in mid-2014. The second growth phase started
in late 2016 and continued until 2020. The sharp decrease in demand that followed the onset of
the COVID-19 pandemic resulted in a 26% decrease in tight oil production in the period from
December 2019 to May 2020. U.S. tight oil production averaged 7.2 MMBD in 2020-2021 and
resumed growth in 2022-2024. The 2024 average production (8.9 MMBD) is the new all-time
peak for U.S. tight oil production. It represents a relatively modest share (10.9% in 2024) of
global crude oil supply.362
Importantly, U.S. tight oil is considered the most price-elastic component of non-OPEC
supply due to differences between its development and production cycle and that of conventional
oil wells. Unlike conventional wells where oil starts flowing naturally after drilling, shale oil
wells require the additional step of fracking to complete the well and release the oil.363 Shale oil
producers keep a stock of drilled but uncompleted wells and can optimize the timing of the
completion operation depending on price expectations. Combining this decoupling between
drilling and production with the front-loaded production profile of tight oil—the fraction of total
output from a well that is extracted in the first year of production is higher for tight oil wells than
conventional oil wells—tight oil producers have a clear incentive to be responsive to prices in
order to maximize their revenues.364
359 Kemp, John. "U.S. shale restraint pushes oil prices to multi-year high," Reuters, June 4, 2021.
https://www.reuters.com/business/energv/us-shale-restraint-pushes-oil-prices-multi-Year-liigh-kemp-2021-06-04.
3611EIA, "Crude Oil Production," Petroleum & Other Liquids, April 30, 2025.
https://www.eia.gov/dnav/pet/pet crd crpdn adc mbblpd a.htm.
361 EIA, "Short Term Energy Outlook," May 2025, Table 10b - Crude Oil and Natural Gas Production from Shale
and Tight Formations, https://www.eia.gov/outlooks/steo/tables/pdf/10btab.pdf.
362 The 2024 global crude oil production value used to compute the U.S. tight oil share (81.7 MMBD). EIA,
"Petroleum and other liquids (production)," International, May 15, 2025.
https://www.eia.gov/international/data/world/petroleum-and-other-liquids/annual-petroleum-and-other-liquids-
production.
363 Hydraulic fracturing ("fracking") involves injecting water, chemicals, and sand at high pressure to open fractures
in low-permeability rock formations and release the oil that is trapped in them.
364 Bjornland, Hilde C., Frode Martin Nordvik, and Maximilian Rolirer. "Supply Flexibility in the Shale Patch:
Evidence From North Dakota." Journal of Applied Econometrics 36, no. 3 (February 5, 2021): 273-92.
https://doi.org/10.1002/iae.2808.
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Figure 6.2.2-1: U.S. Tight Oil Production (by Producing Regions) and WTI Crude Oil Spot
Price
2008 2010 2012 2014 2016 2018 2020 2022 2024
Producing Regions Price
I Bakkeri Niobrara-Codell Eagle Ford w_.
(ND&MT) (C0&WY) ¦ (IX)
¦ Permian . , ,Me
(TX & NM Permian) Rest 0 U
Source: EIA, "Tight oil production estimates by play," Petroleum & Other Liquid, July 2025.
https://www.eia.gov/petrolevuri/data.php. EIA, "Spot Prices," Petroleum & Other Liquids, July 23, 2025.
https://www.eia.gov/dnav/pet/pet pri spt si d.htm.
Only in recent years have the implications of the "tight/shale oil revolution" been felt in
the international market where U.S. production of oil has risen to be larger than the production of
oil from either Russia or Saudi Arabia. Economic literature of the tight oil expansion in the U.S.
has a bearing on the issue of energy security as well. It could be that the large expansion in tight
oil has eroded the ability of OPEC to set world oil prices, since OPEC cannot directly influence
tight oil production deci sions. Also, by effecting the percentage of global oil supply controlled
by OPEC, the growth in U.S. oil production may be influencing OPEC's degree of market
power. But given that the tight oil expansion is a relatively recent trend, it is difficult to know
how much of an impact the increase in tight oil is having, or will have, on OPEC behavior.
Three recent studies have examined the characteristics of tight oil supply that have
relevance for the topic of energy security. In the context of energy security, recent literature has
considered the question of whether tight oil might be able to respond to an oil price shock more
quickly and substantially than conventional oil.36"' If so, then tight oil could potentially lessen the
impacts of future oil shocks on the U.S. economy by moderating the price increases from a future
oil supply shock.
365 "Tight oil is a type of oil found in impermeable shale and limestone rock deposits. Also known as 'shale oil,'
tight oil is processed into gasoline, diesel, and jet fuels—just like conventional oil—but is extracted using hydraulic
fracturing, or Tracking."' Union of Concerned Scientists, "What Is Tight Oil?" March 3, 2015.
https://www.ucs.org/resources/what-tight-oil.
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Newell and Prest (2019) looked at differences in the price responsiveness of conventional
versus shale oil wells, using a detailed data set of 150,000 oil wells, during 2005-2017 in five
major oil-producing states: Texas, North Dakota, California, Oklahoma, and Colorado.366 For
both conventional oil wells and shale oil wells (i.e., unconventional oil wells), Newell and Prest
estimated the elasticities of drilling operations and well completion operations with respect to
expected revenues and the elasticity of supply from wells already in operation with respect to
spot prices. Combining the three elasticities and accounting for the increased share of tight oil in
total U.S. oil production during the period of analysis, they concluded that U.S. oil supply
responsiveness to prices increased more than tenfold from 2006 to 2017. They found that
tight/shale oil wells were more price responsive than conventional oil wells, mostly due to their
much higher productivity, but the estimated oil supply elasticity was still small. Newell and Prest
noted that the tight oil supply response still takes more time to arise than is typically considered
for a "swing producer," referring to a supplier able to increase production quickly, within 30-90
days. In the past, only Saudi Arabia and possibly one or two other oil producers in the Middle
East have been able to ramp up oil production in such a short period of time.
Another study, by Bjornland et al. (2021), used a well-level monthly production data set
covering more than 16,000 crude oil wells in North Dakota to examine differences in supply
responses between conventional and tight/shale oil.367 They found a short-run (i.e., one-month)
supply elasticity with respect to oil price for tight oil wells of 0.71, whereas the one-month
response of conventional oil supply was not statistically different from zero. It should be noted
that the elasticity value estimated by Bjornland et al. combined the supply response to changes in
the spot price of oil as well as changes in the spread between the spot price and the 3-month
futures price.
Walls and Zheng (2022) explored the change in U.S. oil supply elasticity that resulted
from the tight oil revolution using monthly, state-level data on oil production and crude oil prices
from January 1986 to February 2019 for North Dakota, Texas, New Mexico, and Colorado.368
They conducted statistical tests that reveal an increase in the supply price elasticities starting
between 2008-2011, coinciding with the times in which tight oil production increased sharply in
each of these states. Walls and Zheng also found that supply responsiveness in the tight oil era is
greater with respect to price increases than price decreases. The short-run (one-month) supply
elasticity with respect to price increases during the tight oil area ranged from zero in Colorado to
0.076 in New Mexico; pre-tight oil, it ranged from zero to 0.021.
The results from Newell and Prest, Bjornland et al., and Walls and Zheng all suggest that
tight oil may have a larger supply response to oil prices in the short-run than conventional oil,
although the estimated short-run elasticity is still small. The three studies used data sets that end
in 2019 or earlier. The responsiveness of U.S. tight oil production to recent price increases in the
2020s does not appear to be consistent with that observed during the episodes of crude oil price
366 Newell, Richard, and Brian Prest. "The Unconventional Oil Supply Boom: Aggregate Price Response From
Microdata," October 1, 2017. https://doi.org/10.3386/w23973.
367 Bjornland, Hilde C., Frode Martin Nordvik, and Maximilian Rolirer. "Supply Flexibility in the Shale Patch:
Evidence From North Dakota." Journal of Applied Econometrics 36, no. 3 (February 5, 2021): 273-92.
https://doi.org/10.1002/iae.2808.
368 Walls, W.D., and Xiaoli Zheng. 'Tracking and Structural Shifts in Oil Supply." The Energy Journal 43, no. 3
(April 21, 2021): 1-32. https://doi.Org/10.5547/01956574.43.3.wwal.
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increases in the 2010s captured in these three studies. Despite an 80% increase in the WTI crude
oil spot price from October 2020 to the end of 2021, Figure 6.2.2-1 shows that U.S. tight oil
production has increased by only 8% in the same period. It is a somewhat challenging period in
which to examine the supply response of tight oil to its price to some degree, given that the
2020-2021 time period coincided with the COVID-19 pandemic. However, previous shale oil
production growth cycles were financed predominantly with debt, at very low interest rates.369
Most U.S. tight oil producers did not generate positive cashflow.370 As of 2021, U.S. shale oil
producers have pledged to repay their debt and reward shareholders through dividends and stock
buybacks.371 These pledges translate into higher prices that need to be reached (or sustained for a
longer period) than in the past decade to trigger large increases in drilling activity.
More recently, the Dallas Fed (i.e., the Federal Reserve Bank of Dallas) found that
activity in the U.S. oil and gas sector contracted slightly in the second quarter of 2025, according
to oil and gas executives responding to the Dallas Fed's survey. Also, oil and gas production
decreased slightly in the second quarter of 2025, according to executives at oil exploration and
production firms. On average, respondents expected a WTI crude oil price of $68/bbl at year-end
2025. When asked about longer-term expectations, oil and gas executives expect a WTI oil price
of $72/bbl in 2027.372
Finally, despite continuing uncertainty about oil market behavior and outcomes and the
sensitivity of the U.S. economy to oil shocks, it is generally agreed that it is beneficial to reduce
petroleum fuel consumption from an energy security standpoint. The relative significance of
petroleum consumption and import levels for the macroeconomic disturbances that follow from
oil price shocks is not fully understood. Recognizing that changing petroleum consumption will
change U.S. imports, our quantitative assessment of oil costs of this rule in Chapter 6.4 focuses
on those incremental social costs that follow from the resulting changes in net imports,
employing the usual oil import premium measure.
6.3 Cost of Existing U.S. Energy Security Policies
An additional often-identified component of the full economic costs of U.S. oil imports is
the costs to the U.S. taxpayers of existing U.S. energy security policies. The two primary
examples are maintaining the Strategic Petroleum Reserve (SPR) and maintaining a military
presence to help secure a stable oil supply from potentially vulnerable regions of the world.
The SPR is the largest stockpile of government-owned emergency crude oil in the world.
Established in the aftermath of the 1973/1974 oil embargo, the SPR provides the U.S. with a
response option should a disruption in commercial oil supplies threaten the U.S. economy.373
369 McLean, Bethany. "The Next Financial Crisis Lurks Underground," New York Times, September 1, 2018.
https://www.nvtimes.com/2018/09/01/opinion/the-next-financial-crisis-lurks-underground.html.
370 Id.
371 Crowley, Kevin and David Wethe. "Shale Bets on Dividends to Match Supennajors, Revive Sector," Bloomberg,
August 2, 2021. https://www.bloomberg.eom/news/articles/2021-08-02/shale-heawweights-shower-investors-with-
dividends-on-oil-rallv.
372 Federal Reserve Bank of Dallas, "Oil and gas activity contracts slightly as uncertainty remains elevated," July 2,
2025. https://www.dallasfed.org/researcli/survevs/des/2025/2502.
373 Energy Policy and Conservation Act, 42 U.S.C. § 6241(d) (1975).
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Emergency SPR drawdowns have taken place in 1991 (Operation Desert Storm), 2005
(Hurricane Katrina), 2011 (Libyan Civil War), and 2022 (War in Ukraine). All of these releases
have been in coordination with releases of strategic stocks from other International Energy
Agency (IEA) member countries. In the first four months of 2022, using the statutory authority
under Section 161 of the Energy Policy and Conservation Act, DOE conducted two emergency
SPR drawdowns in response to ongoing oil supply disruptions.374 The first drawdown resulted in
a sale of 30 million barrels in March 2022. The second drawdown, announced in April,
authorized a total release of approximately one MMBD from May to October 2022.375 In 2023,
the DOE sold 26 million barrels of oil between April and June.376 A total of 246.6 million barrels
were released from the SPR from January 2022 to July 2023. By the end of July 2023, the SPR
stock level was 346.8 million barrels (the lowest level since August 1983). To start replenishing
the stock, the SPR office purchased 60.5 million barrels through competitive solicitations
conducted between May 2023 and November 2024, for deliveries from August 2023 to May
2025. While the costs for building and maintaining the SPR are more clearly related to U.S. oil
use and imports, historically these costs have not varied in response to changes in U.S. oil import
levels. Thus, while the effect of the SPR in moderating price shocks is factored into the analysis
that EPA is using to estimate the macroeconomic oil security premiums, the cost of maintaining
the SPR is excluded.
We have also considered the possibility of quantifying the military benefits components
of energy security but have not done so here for several reasons. The literature on the military
components of energy security has described four broad categories of oil-related military and
national security costs, all of which are difficult to quantify. These include possible costs of U.S.
military programs to secure oil supplies from unstable regions of the world, the energy security
costs associated with the U.S. military's reliance on petroleum to fuel its operations, possible
national security costs associated with expanded oil revenues to "rogue states", and relatedly the
foreign policy costs of oil insecurity.
Of these categories listed above, the one that is most clearly connected to petroleum use
and is, in principle, quantifiable is the first: the cost of military programs to secure oil supplies
and stabilize oil supplying regions. There is ongoing literature on the measurement of this
component of energy security, but methodological and measurement issues—attribution and
incremental analysis—pose two significant challenges to providing a robust estimate of this
component of energy security. The attribution challenge is to determine which military programs
and expenditures can properly be attributed to oil supply protection, rather than some other
objective. The incremental analysis challenge is to estimate how much the petroleum supply
protection costs might vary if U.S. oil use were to be reduced or eliminated. Methods to address
374 DOE, "DOE Announces Emergency Notice of Sale of Crude Oil from the Strategic Petroleum Reserve to
Address Oil Supply Disruptions," March 2, 2022. https://www.energv.gov/ceser/articles/doe-announces-emergencv-
notice-sale-crude-oil-strategic-petroleum-reserve-address.
375 DOE, "DOE Announces Second Emergency Notice of Sale of Crude Oil From The Strategic Petroleum Reserve
to Address Putin's Energy Price Hike," April 4, 2022. https://www.energy.gov/articles/doe-announces-second-
emergencv-notice-sale-crude-oil-strategic-petroleum-reserve-address.
376 DOE, "DOE Issues Notice of Congressionally Mandated Sale to Purchase Crude Oil from the Strategic
Petroleum Reserve," February 13, 2023. https://www.energy.gov/ceser/articles/doe-issues-notice-congressionallv-
mandated-sale-purchase-crude-oil-strategic.
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both of these challenges are necessary for estimating the effect on military costs arising from a
modest reduction (not elimination) in oil use attributable to this action.
Since "military forces are, to a great extent, multipurpose and fungible" across theaters
and missions (Crane et al. 2009), and because the military budget is presented along regional
accounts rather than by mission, the allocation to particular missions is not always clear.377
Approaches taken usually either allocate "partial" military costs directly associated with
operations in a particular region, or allocate a share of total military costs (including some that
are indirect in the sense of supporting military activities overall) (Koplow and Martin 1998).378
The challenges of attribution and incremental analysis have led some to conclude that the
mission of oil supply protection cannot be clearly separated from others, and the military cost
component of oil security should be taken as near zero (Moore et al. 1997).379 Stern (2010), on
the other hand, argued that many of the other policy concerns in the Persian Gulf follow from oil,
and the reaction to U.S. policies taken to protect oil.380 Stern presented an estimate of military
cost for Persian Gulf force projection, addressing the challenge of cost allocation with an
activity-based cost method. He used information on actual naval force deployments rather than
budgets, focusing on the costs of carrier deployment. As a result of this different data set and
assumptions regarding allocation, the estimated costs are much higher, roughly 4-10 times, than
other estimates. Stern also provides some insight on the analysis of incremental effects, by
estimating that Persian Gulf force projection costs are relatively strongly correlated to Persian
Gulf petroleum export values and volumes. Still, the issue remains of the marginality of these
costs with respect to Persian Gulf oil supply levels, the level of U.S. oil imports, or U.S. oil
consumption levels.
Delucchi and Murphy (2008) sought to deduct from the cost of Persian Gulf military
programs the costs associated with defending U.S. interests other than the objective of providing
more stable oil supply and price to the U.S. economy.381 Excluding an estimate of cost for
missions unrelated to oil, and for the protection of oil in the interest of other countries, Delucci
and Murphy estimated military costs for all U.S. domestic oil interests of between $24-74 billion
per year. Delucchi and Murphy assumed that military costs from oil import reductions can be
scaled proportionally, attempting to address the incremental issue.
Crane et al. considered force reductions and cost savings that could be achieved if oil
security were no longer a consideration. Taking two approaches and guided by post-Cold War
force draw downs and by a top-down look at the current U.S. allocation of defense resources,
377 Crane, Keith, Andreas Goldthau, Michael Toman, Thomas Light, Stuart Johnson, Alireza Nader, Angel Rabasa,
and Harun Dogo. "Imported Oil and U.S. National Security." R. IX/) Corporation, 2009.
https://doi.org/10.7249/mg838.
378 Koplow, Douglas, and Aaron Martin. "Fueling Global Wanning: Federal Subsidies to Oil in the United States."
Greenpeace, 1998. https://www.earthtrack.net/sites/default/files/fdsuboil.pdf.
379 Moore, John L„ Carl E. Behrens, and John E. Blodgett. "Oil Imports: An Overview and Update of Economic and
Security Effects," CRS Environment and Natural Resources Policy Division 98, no. 1 (December 12, 1997): 1-14.
3811 Stern, Roger J. "United States Cost of Military Force Projection in the Persian Gulf, 1976-2007." Energy Policy
38, no. 6 (February 25, 2010): 2816-25. https://doi.Org/10.1016/i.enpol.2010.01.013.
381 Delucchi, Mark A., and James J. Murphy. "US Military Expenditures to Protect the Use of Persian Gulf Oil for
Motor Vehicles." Energy Policy 36, no. 6 (April 23, 2008): 2253-64. https://doi.Org/10.1016/i.enpol.2008.03.006.
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they concluded that $75-91 billion, or 12-15% of the current U.S. defense budget, could be
reduced.
Finally, an Issue Brief by Securing America's Future Energy (SAFE) (2018) found a
conservative estimate of approximately $81 billion per year spent by the U.S. military protecting
global oil supplies.382 This is approximately 16% of the recent U.S. Department of Defense's
budget. Spread out over the 19.8 million barrels of oil consumed daily in the U.S. in 2017, SAFE
concluded that the implicit subsidy for all petroleum consumers is approximately $11.25/barrel
of crude oil, or $0.28/gallon. According to SAFE, a more comprehensive estimate suggests the
costs could be greater than $30/barrel, or over $0.70/gallon.383
As in the examples above, an incremental analysis can estimate how military costs would
vary if the oil security mission were no longer needed, and many studies stop at this point. It is
substantially more difficult to estimate how military costs would vary if U.S. oil use or imports
were partially reduced, as is projected to be a consequence of this rule. Partial reduction of U.S.
oil use likely diminishes the magnitude of the energy security problem, but there is uncertainty
that supply protection forces and their costs could be scaled down in proportion, and there
remains the associated goal of protecting supply and transit for U.S. allies and other importing
countries, if they do not decrease their petroleum use as well.384 We are unaware of a robust
methodology for assessing the effect on military costs of a partial reduction in U.S. oil use.
Therefore, we are unable to quantify this effect resulting from the projected reduction in U.S. oil
use attributable to this rule.
6.4 Energy Security Impacts
6.4.1 U.S. Oil Import Reductions
From 2026 to 2027, the AEO2025 Reference Case projects that the U.S. will be both an
exporter and an importer of crude oil and petroleum products and, on balance, a net exporter of
petroleum.385 The U.S. produces more light crude oil than its refineries can refine.386 Thus, the
U.S. exports lighter crude oil and imports heavier crude oil to satisfy the needs of U.S. refineries
which are configured to efficiently refine heavy crude oil. Projections from AEO2025 of U.S.
consumption and trade of petroleum are presented in Table 6.4.1-1 below. These projections
confirm that the U.S. will continue to face significant exposure to global petroleum markets in
2026 and 2027. As discussed earlier in this chapter, both imports and consumption of petroleum
expose the U.S. to energy security risks from shocks in the world oil price and supplies. Because
the U.S. is projected to continue to consume significant quantities of oil and to rely on significant
382 Securing America's Future Energy, "Issue Brief - The Military Cost of Defending the Global Oil Supply,"
September 21, 2018. https://secureenergy.org/wp-content/uploads/2020/03/Militarv-Cost-of-Defending-the-Global-
Qil-Supplv.-Sep.-18.-2018.pdf.
383 Id.
384 Crane, Keith, Andreas Goldthau, Michael Toman, Thomas Light, Stuart Johnson, Alireza Nader, Angel Rabasa,
and Harun Dogo. "Imported Oil and U.S. National Security." R. IX/) Corporation, 2009.
https://doi.org/10.7249/mg838.
385 AEO2025, Table 11 - Petroleum and Other Liquids Supply and Disposition.
386 Washington Post, "Why importing and exporting oil makes sense." https://www.wasliingtonpost.com/sf/brand-
connect/wp/enterprise/whv-importing-and-exporting-oil-makes-sense.
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quantities of crude oil imports in 2026 and 2027, U.S. oil markets are expected to remain tightly
linked to trends in the world crude oil market.
Table 6.4.1-1: Projected Petroleum Consumption and Net Imports from AEO2025
(MMBD)
2026
2027
U.S. Crude Oil Exports
4.2
4.2
U.S. Crude Oil Imports
6.4
6.2
U.S. Net Petroleum Refined Product Exports3
5.4
5.6
U.S. Net Crude Oil Exports + Net Refined Petroleum Product Exports'3
3.3
3.7
U.S. Oil Consumption0
19.2
19.2
a Calculated from AEO2025, Table 11 as Net Product Exports minus Ethanol, Biodiesel, Renewable Diesel, and
Other Biomass-derived Liquid Net Exports.
b Calculated from AEO2025, Table 11 as Total Net Exports minus Ethanol, Biodiesel, Renewable Diesel, and Other
Biomass-derived Liquid Net Exports.
0 Calculated from AEO2025, Table 11 as Total Primary Supply minus Biofuels.
To estimate how the Analyzed Volumes may impact this exposure, we must first estimate
their effect on consumption and net imports of oil. Estimated differences in refined oil
consumption relative to the No RFS Baseline are presented in Table 10.4.2.1.1 -lb. As described
in Chapter 10.4.2.1.1, changes in net imports of crude oil and refined petroleum are then
estimated based on the changes in refined oil consumption using an "oil import reduction factor"
of 97.5%. That is, we estimate that 97.5% of the reduction in U.S. petroleum consumption will
come from reduced net imports, with the remaining 2.5% coming from reduced domestic
production. See Chapter 10.4.2.1.1 for more details on the calculations summarized here. The
calculated changes in net imports of crude oil are presented in Table 10.4.2.1.1-2, which amount
to reductions of roughly 0.27 MMBD in 2026 and 0.33 MMBD in 2027.
6.4.2 Oil Import Premiums
In order to understand the energy security implications of reducing U.S. net oil imports,
EPA has worked with ORNL, which has developed approaches for evaluating the social costs
and energy security implications of U.S. oil imports. The energy security estimates provided
below are based upon a methodology first developed in a peer-reviewed 2008 ORNL study.387
This 2008 ORNL study was an updated version of the approach used for estimating the energy
security benefits of U.S. oil import reductions developed in an earlier 1997 ORNL Report.388
Since 2008, ORNL has updated this methodology periodically for EPA to account for updated
projections of future energy market and economic trends reported in the EIA's AEO. For this
final rule, EPA has updated the ORNL methodology using AEO2025.
387 Leiby, Paul. "Estimating the Energy Security Benefits of Reduced U.S. Oil Imports." Oak Ridge National
Laboratory, ORNL/TM-2007/028. March 2008.
https://cfpub.epa.gov/si/si public file download.cfm?p download id=504469.
388 Leiby, Paul N., Donald W. Jones, T. Randall Curlee, and Russell Lee. "Oil Imports: An Assessment of Benefits
and Costs." Oak Ridge National Laboratory, ORNL-6851. November 1, 1997.
https://www.esd.ornl.gov/eess/energy analvsis/files/ORNL6851 .pdf.
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The ORNL methodology is used to compute the oil import security premium per barrel of
imported oil.389 The values of U.S. oil import security premium components (macroeconomic
disruption/adjustment costs and monopsony components) are numerically estimated with a
compact model of the oil market by performing simulations of market outcomes using
probabilistic distributions for the occurrence of oil supply shocks. Each of these simulations
inform the estimates of the marginal changes in economic welfare with respect to changes in
U.S. oil import levels. ORNL then summarizes the results from the individual simulations into a
mean and 90% confidence interval for the import premium.
EPA only considers the avoided macroeconomic disruption/adjustment oil import
premiums (i.e., labeled macroeconomic oil security premiums below) as costs, since the
monopsony impacts stemming from changes in oil imports are considered transfer payments. In
previous EPA rules when the U.S. was projected by EIA to be a net petroleum importer,
monopsony impacts represented reduced payments by U.S. consumers to oil producers outside of
the U.S. There was some debate among economists as to whether the U.S. exercise of its
monopsony power in oil markets (e.g., from the implementation of EPA's rules) was a "transfer
payment" or a "benefit." Given the redistributive nature of this monopsony impact from a global
perspective, and since there are no changes in resource costs when the U.S. exercises its
monopsony power, some economists argued that it is a transfer payment. Other economists
argued that monopsony impacts were a benefit since they partially address, and partially offset,
the market power of OPEC. In previous EPA rules, after weighing both countervailing
arguments, EPA concluded that the U.S.'s exercise of its monopsony power was a transfer
payment, and not a benefit.390
In the context of this rule, the U.S.'s oil trade balance position has shifted notably from
the position it held when we assessed energy security impacts for most previous RFS rules. As
discussed above (see Figure 6-1), the U.S. became a net petroleum exporter for the first time in
several decades in 2020, and these net exports have continued to grow since that time. As also
observed above, the EIA projects the U.S. will continue to be a net petroleum exporter in 2026-
2027. As a result, reductions in U.S. oil consumption and, in turn, U.S. net oil imports, lower the
world oil price, albeit modestly. But the net effect of the lower world oil price is now a decrease
in revenue for U.S. exporters of crude oil and petroleum-based products, instead of a decrease in
payments to foreign oil producers. The argument that monopsony impacts address the market
power of OPEC is therefore no longer appropriate. Thus, we continue to consider the U.S.
exercise of monopsony power to be transfer payments. We also do not consider the effect of this
rule on the costs associated with existing energy security policies (e.g., maintaining the Strategic
Petroleum Reserve or strategic military deployments), which are discussed in Chapter 6.3.
The macroeconomic oil security premiums arise from the effect of U.S. oil imports on the
expected cost of supply disruptions and accompanying price increases. A sudden increase in oil
prices triggered by a disruption in world oil supplies has two main effects: (1) it increases the
costs of oil imports in the short-run, and (2) it leads to macroeconomic contraction, dislocation,
389 The oil import premium concept is defined in Chapter 6.1.
3911 We also discuss monopsony oil import premiums in previous EPA GHG vehicle rules. See, e.g., EPA, "Revised
2023 and Later Model Year Light Duty Vehicle GHG Emissions Standards: Regulatory Impact Analysis," EPA-
420-R-21-028, December 2021, Section 3.2.5. https://nepis.epa.gov/Exe/ZvPDF.cgi?Dockev=P1013QRN.pdf.
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and GDP losses. Since future disruptions in foreign oil supplies are an uncertain prospect, each
of the disruption cost components must be weighted by the probability that 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 sharply higher petroleum prices—is
the relevant measure of their magnitude.
In addition, EPA and ORNL have worked together to revise the oil import premiums
based upon the on-going, updated energy security literature. Based on EPA and ORNL's review
of the energy security literature, EPA is using updated macroeconomic oil security premiums for
this rule. The recent economics literature (discussed in Chapter 6.2) focuses on three factors that
can influence the macroeconomic oil security premiums: (1) price elasticity of oil demand, (2)
GDP elasticity in response to oil price shocks, and (3) the impacts of the tight (i.e., shale) oil
boom. We discuss each factor below and provide a rationale for how we are developing new
estimates of the macroeconomic oil security premiums.
First, we assess the price elasticity of demand for oil. In RFS rules prior to the 2020-2022
RFS Rule, EPA used a short-run elasticity of demand for oil of-0.045.391 From the RFF study
(2017) discussed previously in Chapter 6.2.1, the "blended" price elasticity of demand for oil is -
0.05. The ORNL meta-analysis estimate of this parameter is -0.07. We find the elasticity
estimates from what RFF characterizes as the "new literature," -0.175, and from the "new
models" that RFF uses, -0.20 to -0.33, somewhat high. Most of the world's oil demand is
concentrated in the transportation sector and there are limited alternatives to oil use in this sector
in 2026-2030. According to IEA, the share of global oil consumption attributed to the
transportation sector grew from 60% in 2000 to 66% in 2019.392 The next largest sector by oil
consumption, and an area of recent growth, is petrochemicals. There are limited alternatives to
oil use in this sector also, particularly in 2026-2030. Thus, we believe it would be surprising if
short-run oil demand responsiveness has changed in the dramatic fashion implied by the RFF
"new literature" estimates.
The ORNL meta-analysis estimate encompasses the full range of the economics literature
on this topic and develops a meta-analysis estimate from the results of many different studies in a
structured way, while the RFF study's "new models" results represent only a small subset of the
economics literature's estimates. Thus, for the analysis of this rule, and consistent with the 2020-
2022 RFS Rule and the Set 1 Rule, we have increased the short-run price elasticity of demand for
oil from -0.045 to -0.07, a 56% increase.393 This increase has the effect of lowering the
macroeconomic oil security premium estimates undertaken by ORNL for EPA.
Second, we consider the elasticity of GDP to an oil price shock. In RFS rules prior to the
2020-2022 RFS Rule, a GDP elasticity to an oil price shock of-0.032 was used.394 The RFF
391 See, e.g., 75 FR 26049, May 10, 2010.
392 IEA, "World Energy Statistics and Balances." https://www.iea.org/data-and-statistics/data-product/world-energy-
statistics-and-balances.
393 EPA and ORNL worked together to develop an updated estimate of the short-run elasticity of demand for oil for
use in the ORNL model.
394 See, e.g., 75 FR 26049 (May 10, 2010).
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"blended" GDP elasticity is -0.028, the RFF's "new literature" GDP elasticity is -0.018, while
the RFF "new models" GDP elasticities range from -0.007 to -0.027. The ORNL meta-analysis
GDP elasticity is -0.021. We believe that the ORNL meta-analysis value is representative of the
economics literature on this topic since it considers a wide range of recent studies and does so in
a structured way. Also, the ORNL meta-analysis estimate is within the range of GDP elasticities
of RFF's "blended" and "new literature" elasticities. For this rule and consistent with the 2020-
2022 RFS Rule and Set 1 Rule, EPA is using a GDP elasticity of-0.021, a 34% reduction from
the GDP elasticity used previously {i.e., the -0.032 value).395 This GDP elasticity is within the
range of RFF's "new literature" elasticity, -0.018, and the elasticity EPA has used in previous
rules, -0.032, but lower than RFF's "blended" GDP elasticity, -0.028. This decrease in the GDP
elasticity has the effect of lowering the macroeconomic oil security premium estimates. For U.S.
tight oil, EPA has not made any adjustments to the ORNL model, given the limited tight oil
production response to rising world oil prices since 2020.396 Increased tight oil production still
results in energy security benefits through its impact of reducing U.S. oil imports in the ORNL
model.
Figure 6.4.2-1 shows the evolution of oil security premiums for this rule in comparison
with oil security premiums for previous EPA final rules from 2007-2024. For each rulemaking,
the estimated oil security premium value is computed using the ORNL oil security premium
model, which is based upon oil market and economic conditions projected by each relevant
AEO. The premiums are all computed following the same methodology, but under changing oil
market balances and conditions, with some parameters evolving to reflect changing
understanding of oil market flexibility and declining macroeconomic sensitivity to oil price
shocks. Each bar corresponds to the first year for which the premium was estimated in each
specific rule. Oil security premiums are estimated to be approximately $7/barrel in 2007 and
increased to $9.47 in 2011. Then, the oil security premiums decreased markedly through the
2010s, landing at $3.39/barrel in 2021. Values estimated for 2022 through 2026 have all been
approximately $3.70/barrel.
395 EPA and ORNL worked together to develop an updated estimate of the GDP elasticity to an oil shock for use in
the ORNL model. This slightly different value also was produced by an earlier draft of the ORNL meta-analysis.
396 The short-run oil supply elasticity assumed in the ORNL model is 0.06 and is applied to production from both
conventional and shale oil wells.
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Figure 6.4.2-1: Comparison of Oil Security Premiums of this Final Rule and Previous Rules
(2024$)
a. RFS1: Final Rule. (2007). Based on AE02006.
b. Final Rule for Phase 1 Greenhouse Gas Emissions Standards and Fuel Efficiency Standards for Medium- and
Heavy-Duty Engines and Vehicles (2011). Based on AEO2011.
c. Final Rule for Phase 2 Greenhouse Gas Emissions Standards and Fuel Efficiency Standards for Medium- and
Heavy-Duty Engines and Vehicles (2016). Based on AEO2015.
d. 2020-2022 RFS Rule (2022). Based on AEO2021.
e. Final Rule to Revise Existing National GHG Emissions Standards for Passenger Cars and Light Trucks
Through Model Year 2026 (2023). Based on AEO2021.
f. Set 1 Rule (2023). Based on AEO2023.
g. Greenhouse Gas Emissions Standards for Heavy-Duty Vehicles - Phase 3 (2024). Based on AEO2023.
h. Final Rule: Multi-Pollutant Emissions Standards for Model Years 2027 and Later Light-Duty and Medium-
Duty Vehicles (2024). Based on AEO2023.
i. RFS Set 2 Rule 2026-2027: NPRM. Based on AEO2023.
j. RFS Set 2 Rule 2026-2027: Final Rule. Based on AEO2025.
Multiple factors drive the change in magnitude of the U.S. oil security premiums over
time. First, the U.S. oil trade balance is a key component in the calculation of premiums. The
marginal change in expected net oil import costs during disruption events depends directly on the
magnitude of net oil imports. The U.S. went from being a net importer of crude oil and
petroleum products of roughly 12 million barrels per day in 2007 to becoming a next exporter in
2020. This trend reversal is mirrored in the evolution of the oil import premium from 2011 to
2021 in Figure 6.4.2-1. Second, in the calculation of premiums, OPEC's share of global oil
production is modelled to influence the size of potential supply disruptions. OPEC is the main
production region that is assumed to have an insecure supply and is subject to the disruption
events considered in the premium calculation. A larger share of OPEC production implies more
oil supply is at risk and potentially higher price shocks from oil market disruptions. Third, all
else equal, the oil import premium varies with oil price levels, with higher price levels presenting
a risk of even larger price shocks during a disruption, with a greater effect on GDP and the net
trade balance in oil.
Fourth, two important parameters going into the oil security premium calculation (the
elasticity of demand for oil with respect to oil prices and the U.S. elasticity of U.S. GDP with
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respect to oil prices) have been updated over time to reflect the U.S. economy's evolving market
structure. See the discussion above for more details about how the short-run price elasticity of
demand for oil increased, indicating greater short-run flexibility. In addition, the central value
used for elasticity of GDP in response to oil price shocks has been updated based upon the most
recent estimations in the economics literature.
The combined effect of all these factors aligns directionally with the evolution of oil
security premium values shown in Figure 6.4.2-1. From 2007 to 2011, despite U.S. net oil
imports trending downward, the oil security premium value increased due to higher oil prices
and higher OPEC market share. The decreasing trend from 2011 to 2021 resulted from a
combination of decreases in U.S. net oil imports and oil prices. Additionally, the premiums for
year 2021 and later are based on calculations with more price elastic oil demand and less elastic
U.S. GDP to price shocks. Small increases in the premium estimates for 2023 and 2024 relative
to 2021 can be mostly explained by modest changes in expected market conditions, including
higher oil prices projections.
The premia estimates in the NPRM were computed based on AEO2023. For the final
rule, the estimates were updated based on AEO2025. Relative to the AEO2023 values, in
AEO2025, the disruption premia start with lower values, and then the rise to somewhat higher
values after approximately 2035. Premia estimates computed for 2026-2030, computed with
AEO2025 inputs are 5%-7% lower than the ones computed with AEO2023 inputs. This modest
decline results from offsetting effects between improving oil market conditions and
macroeconomic growth. The combined effect of differences in the projections of several
macroeconomic and oil market variables between the two AEOs explain the lower estimated
premia, which can be further understood by decomposing the optimal disruption premium into its
two components: the disruption import-cost component and the larger disruption macroeconomic
component. First, in AEO2025 projections, the U.S. is a larger net exporter of all petroleum
liquids than in AEO2023. In AEO2023, the average projected net exports in 2026-2030 are 2.41
million barrels per day vs 3.8 million barrels per day in AEO2025. Somewhat larger net exports
result in improved terms of trade during disruption events. Because the U.S. is a net exporter
over this time period, the "disruption import-cost component" of the premium is negative {i.e., a
benefit that partially offsets macroeconomic disruption costs). Second, AEO2025 projects the
Brent spot price, used in the oil security premium calculations as representative of the world oil
price, to be 10% lower in 2026-2030 than AEO2023. All else equal, a lower baseline price will
also lead to lower oil prices during the simulated supply disruptions and, therefore, lower
expected GDP disruption costs. Third, the projected OPEC share of global oil supply is 7
percentage points lower in AEO2025 than AEO2023 (30% vs. 37%). This contributes to a lower
share of global oil supply at risk for oil supply shocks and lower expected price impacts from the
set of disruptions simulated. Finally, real U.S. GDP projected for 2026-2030 is 9% higher in
AEO2025 than AEO2023. For a given set of disruption risks, price shocks, and GDP sensitivity
to price changes, the expected GDP disruption component is proportional to economic size.
However, the other three factors mentioned above more than offset the role of the larger GDP in
the oil security premium calculations resulting in about 6% lower premia values using AEO2025
inputs than using AEO2023 inputs.
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Table 6.4.2-1 provides EPA's estimates of the macroeconomic oil security premiums for
2026-2027, showing that they are gradually increasing over this time period. The
macroeconomic oil security premiums range from $3.69/barrel to $3.67 /barrel in 2026 and 2027,
respectively. In terms of cents per gallon, the macroeconomic oil security premiums range from
0.088 cents per gallon in 2026 and 0.087 cents per gallon in 2027. These estimates of the
macroeconomic oil security premiums are actual values as opposed to discounted values,
implying that they do not reflect the time value of money.
Table 6.4.2-1: Macroeconomic Oil Security Premiums (2024$/barrel)
Year
Macroeconomic Oil Security Premiums
(2024$/Barrel of Reduced Oil Imports)
2026
$3.69
($0.25-$7.25)
2027
$3.67
($0.16-$7.31)
Note: Top values in each cell are mean values. Values in parentheses are 90% confidence intervals.
6.4.3 Energy Security Benefits
Estimates of the total annual energy security benefits of the Analyzed Volumes are based
on the ORNL oil import premium methodology with updated oil import premium estimates
reflecting the energy security literature and using AEO2025. Section 6.4.2 above discusses how
and why the premia estimates have changed based on the AEO (2023 vs. 2025). To calculate
total energy security benefits, annual macroeconomic oil security premiums (Table 6.4.2-1) are
multiplied by the annual reduction in U.S. net oil imports (Table 6.4.1-1). The total annual
energy security benefits are presented in Tables 6.4.3-1. We do not consider military cost
impacts or the monopsony effect of U.S. crude oil and refined petroleum product import
changes. These benefit estimates are actual values as opposed to discounted values, implying that
they do not reflect the time value of money. The present value and annualized value of estimated
energy security impacts of the Analyzed Volumes using 3% and 7% discount rates are presented
in Preamble Section III.H.2 and in RIA Chapter 10.6.
Table 6.4.3-1: Total Annual Energy Security Benefits for the Analyzed Volumes (millions
2024$, undiscounted)
Year
Total Energy Security Benefits
Final Renewable Fuel Volumes
2026
$361
($24-$710)
2027
$438
($19-$873)
Note: Top values in each cell are the mean values, while the values in parentheses define 90% confidence intervals.
U.S. net oil import reductions used for the energy security analysis in this section are a combination of reduced U.S.
imports of gasoline, diesel fuel, and crude oil from Chapter 10.4.2.1.1 converted to crude oil-equivalent barrels.
The impact on crude oil and refined petroleum product exports alone does not fully
capture the broader implications of this rule. This rule will likely result in substantial imports of
feedstocks that are used to produce renewable fuels to meet the RFS renewable fuel volume
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requirements. As discussed in Chapter 10.4.2.1, if, for example, we conservatively assume that
all the volume of canola oil and used cooking oil demanded to meet the increased volumes are
imported, as much as 26.3% (in 2026) and 27.5% (in 2027) of the increased vegetable oil
demanded under the Analyzed Volumes could be imported. Additionally, this would mean that
26.4 % in 2026 and 27.6 % in 2027 of the renewable fuels used to meet the renewable fuel
volumes will be derived from imported feedstocks.
6.5 Feedstock Diversification
This section is a preliminary attempt to analyse the impact of biofuel and biofuel
feedstock imports on U.S. energy security. Biofuels and biofuel feedstocks are traded on global
markets and as such are susceptible to the same risk factors that affect the supply and price of
energy and agricultural commodities globally. As discussed across Chapter 3, Chapter 7, and
Chapter 9, the Analyzed Volumes are estimated to intensify the competition for limited
feedstocks, most notably vegetable oils and FOG, relative to the previous RFS volume standards.
The same is not necessarily true for imported biofuels specifically, as our analyses generally
project that these imports will decline substantially in 2026 and 2027 compared to 2024 and
previous years, due to changes in federal tax incentives (see Chapter 3 and Chapter 7).
Nevertheless, exposure of the U.S. renewable fuel and biofuel feedstock supply to global supply
and price volatility risks, and the associated potential risks to energy security benefits from this
exposure, are understandably a concern.
EPA has not previously analyzed the supply and price volatility risk factors associated
with renewable fuels and renewable fuel feedstocks as part of our energy security analysis. This
is a relatively unexplored space in economic literature, and we could not identify methods of
sufficient robustness to merit inclusion of estimated social benefits associated with the reduction
of these risks. However, some relevant literature does exist, allowing us to begin consideration of
these factors, and perhaps build on this understanding in future volume-setting efforts.
In this subsection, we consider approaches that may begin to shed light on this issue and
suggest areas of future work. We examine the concept of portfolio diversification in the context
of renewable fuel feedstocks. We then consider how diversifying this portfolio of energy
products might affect energy security and suggest areas for future work.
6.5.1 Portfolio Diversification in the Context of Renewable Fuel Feedstocks
Volatility in renewable fuel feedstock prices has the potential to be transmitted to
finished transportation fuel prices that consumers face. Price volatility can arise from numerous
vectors, including from global supply shocks and other commodity market disruptions and from
fluctuations in competition among fuels and feedstocks to meet the RFS standards. Price
volatility and supply volatility are intertwined but separate concepts and an in-depth analysis
might explore those nuances. However, for the purposes of this discussion, we will largely treat
them as an interconnected nexus of risks to energy security.
Across economic sectors, portfolio diversification is a common strategy to reduce
exposure to price and supply volatility risks. The concept of portfolio diversification itself is the
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main tenet of Markowitz's Modern Portfolio Theory (1952)397 (hereafter "portfolio theory"),
which explains how and why this strategy is effective. Specifically, portfolio theory explains that
diversification can mitigate unsystematic risk (i.e., the risk specific to an individual financial
holding or economic sector) and lead to more predictable returns overall. In Markowitz's original
formulation, portfolio theory observes that low stock price correlations among a group of stocks
indicate that prices for that group of stocks are relatively less likely to move in the same
direction in response to an economic shock compared to a group of stocks whose prices are
highly correlated. This observation illustrates the potential for diversification to reduce risk
within the portfolio; by combining uncorrected assets, the poor performance of one asset during
a market downturn or some other type of economic shock (e.g., in the context of biofuel
feedstocks, an unexpectedly low feedstock crop yield in a particular growing region) can be
offset by the better performance of another, leading to more stable, risk-adjusted returns for the
overall portfolio over time. This phenomenon applies not only to stocks and stock portfolios, but
also to commodities such as transportation fuels and feedstocks.
What this means in the context of transportation fuels is that diversifying feedstock types
and geographic sources could result in reduced overall volatility of transportation fuel prices,
since adverse shocks affecting agricultural feedstock prices (e.g., a drought causing low crop
yields) and shocks affecting petroleum prices (e.g., geopolitical instability) are likely unrelated.
Demonstrating this effect would, however, require an assessment of the extent to which changes
in prices of feedstocks are passed through to prices of finished fuels. Empirically, there is
evidence in the literature on the volatility passthrough of crude oil prices to transportation fuel
prices.398 While literature on the volatility passthrough of biofuel feedstock prices to
transportation fuel prices is a bit dated, it exists nonetheless, and points to the potential merits of
feedstock diversification to reduce volatility in feedstock prices and hence finished fuel prices.
As explained earlier in this chapter, we define energy security the "uninterrupted availability of
energy sources at an acceptable price."399 Recognizing this, high volatility in energy markets has
the potential to make the "acceptable price" component unpredictable and at times unaffordable,
with the potential to lower the energy security benefits accruing from this rule.
With these motivations in mind, we analyzed some simple statistical relationships to
understand at a first principles level how feedstock diversification may potentially impact
volatility of transportation fuel prices and hence improve energy security. As an illustrative
example, we chose to focus on the case of diesel and biodiesel (with soybean oil and canola oil
as the assumed feedstocks used in biodiesel production). Data on canola oil and soybean oil were
sourced from USDA's Oil Crops Yearbook.400 Data on Brent and WTI prices were sourced from
the FRED database maintained by the St Louis Federal Reserve Bank.401-402
397 Markowitz, Harry M. "Portfolio Selection: Efficient Diversification of Investments." Yale University Press,
1959. http://www.istor.Org/stable/i.cttlbh4c8h.
398 See "Volatility Passthrough," available in the docket for this action.
399 IEA, "Energy Security." https://www.iea.org/topics/energy-securitv.
41111 USD A, "Oil Crops Yearbook," March 2025. https://www.ers.usda.gov/data-products/oil-crops-Yearbook.
4111 Federal Reserve Economic Data, "Global price of Brent Crude," January 22, 2026.
https://fred.stlouisfed.org/series/POILBREUSDA.
4112 Federal Reserve Economic Data, "Global price of WTI Crude," January 22, 2026.
https://fred.stlouisfed.org/series/POILWTIUSDA.
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Correlations in monthly returns of canola oil and soybean oil with Brent and WTI were
computed. Volatility metrics for each of these series were also computed over the same time
period. All prices were expressed in nominal 2024$ per metric ton.403 Table 6.5.1-1 below shows
the pairwise correlation in returns (i.e., the strength of the correlation between each feedstock
price and each other feedstock price) across the various feedstocks for the time period of our
analysis (1991-2024). Table 6.5.1-2 below shows the volatility404 in feedstock prices between
1991 and 2024.
Table 6.5.
-1: Pairwise Correlation in Feedstock Prices (1991-2024)
Canola
Soybean
WTI
Brent
Canola
1.00
0.44
0.20
0.21
Soybean
0.44
1.00
0.20
0.21
WTI
0.20
0.20
1.00
0.95
Brent
0.21
0.21
0.95
1.00
Table 6.5.1-2: Volatility in Feedstock Prices (1991-2024)
Feedstock
Volatility
Canola Oil
6.27%
Soybean Oil
5.75%
Brent Crude
9.39%
WTI Crude
9.24%
From Table 6.5.1-1 we see that the correlations in monthly returns of crude oil with both
canola and soybean oil are low. Conversely, soybean oil and canola oil prices are fairly
correlated with one another, and Brent and WTI crude oil prices are strongly correlated with each
other, which aligns with our general understanding of the global markets for vegetable oil and
crude oil. As noted above, low stock price correlations among a group of stocks (feedstocks in
this case) are indicative of the potential for diversification to reduce risk/volatility. Furthermore,
from Table 2 we note that both canola and soybean oil have been less volatile than crude oil
during that same period. The fact that both canola oil and soybean oil are less volatile than crude
oil and that the correlation of returns of crude oil prices with canola oil and soybean oil prices are
low or negative are indicative of the potential for soybean oil- and canola oil-based biodiesel to
reduce fuel price volatility.
Table 6.5.1-3 below shows the volatility in monthly returns (computed from average
monthly retail fuel prices) in B99/B100 and B20 biodiesel blends and onroad diesel pre-RFS2
(i.e., before 2010) and after initial implementation of RFS2 (i.e., 2010 and onward). B99/B100,
B20, and on-road diesel data were obtained from DOE's Alternative Fuels Data Center.405
4113 Monthly prices were adjusted by the Index of personal consumption expenditure. Federal Reserve Economic
Data, "Personal Consumption Expenditures: Chain-type Price Index," February 20, 2026.
https://fred.stlouisfed.org/series/PCEPI.
4114 We calculate volatility as the standard deviation of monthly price returns relative to the long-run average price
return across the entire time series. As an example, if returns on the price of a commodity increase by 5% every
year, then over a 5-year period the mean increase would be about 0.05/5. The standard deviation over this 5-year
period would then be computed as the square root of the deviation from this mean and when expressed as a
percentage would be the volatility.
4115 AFDC, "Fuel Prices," https ://afdc. energy. gov/fuels/prices. html.
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Table 6.5.1-3: Volatility in Diesel, B20, and B99/B100
Fuel
2003-2025
2003-2009
2010-2025
Diesel
11.1%
14.7%
9.4%
B20
10.2%
13.3%
8.8%
B99/B100
7.5%
11.0%
6.3%
Note: Data onB99/B100 was available starting 2005.
A comparison of the price volatility estimates across this time series shows that the
volatility in returns for diesel, B20 and B99/B100 all appear to have decreased after the
implementation of the RFS2 program in 2010. These estimates further show that B99/B100 has
the lowest price volatility of the three fuel types across each time segment.
6.5.2 Concluding Observations on Feedstock Diversification and
Transportation Fuel Volatility
Feedstock diversification (feedstock mix as well as the source of the mix itself) may have
contributed to the lower volatility in transportation fuel prices. Based on this limited analysis,
however, we can at best infer correlation and not causation. To understand both directional
causation and the strength of this causal relationship, we would need to carry out more detailed
analysis, starting with the potential for risk mitigation (lower volatility) through diversifying
origin or source of the feedstocks. Diversification in terms of the feedstock point of origin may
also affect price correlations and hence their impact on feedstock portfolio volatility and the
volatility of finished transportation fuels. Additionally, we would need to use more robust
techniques to quantify the extent to which there is a volatility passthrough from feedstock prices
to finished transportation fuel prices. A more comprehensive understanding and analysis of these
impacts would allow us to better assess the implications of feedstock diversification for energy
security.
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Chapter 7: Rate of Production and Consumption of Renewable Fuel
This chapter outlines the anticipated annual rate of future commercial production for
renewable fuels, including advanced biofuels in categories such as cellulosic biofuel and BBD in
2026 and 2027. Our projections for this period are based on historical data and other relevant
factors, considering both domestically produced biofuels and imported biofuels available for use
in the United States.406
We also project the use (i.e., consumption) of qualifying renewable fuels in the U.S.
While not an explicit factor that we must consider under the statute, domestic consumption of
qualifying renewable fuels as transportation fuel is the primary basis for compliance with our
RFS standards. It is also inherent in the requisite consideration of infrastructure which is
addressed in Chapter 8, and in the cost to consumers of transportation fuel which is addressed in
Chapter 10. For 2026 and 2027 the projection of consumption is based on our assessment of
production, exports and imports, infrastructure constraints on distributing and using biofuels,
costs, and other factors explained below and throughout this document. Sometimes, we term this
overall resulting use of biofuels as the "supply" of biofuels. In general, we expect that all
cellulosic biofuels produced in the U.S. will be used here as they have been historically. By
contrast, some quantities of domestically produced advanced and conventional renewable fuels
have historically been exported, and we expect exports of such fuels to continue through 2027.
We discuss the production and use of each major type of biofuel in turn: cellulosic
biofuel (Chapter 7.1), BBD (biodiesel and renewable diesel) (Chapter 7.2), imported sugarcane
ethanol (Chapter 7.3), other advanced biofuels (besides ethanol, biodiesel, and renewable diesel)
(Chapter 7.4), total ethanol (Chapter 7.5), corn ethanol (Chapter 7.6), and conventional biodiesel
and renewable diesel (Chapter 7.7).
7.1 Cellulosic Biofuel
The RFS2 Rule projected strong growth for cellulosic biofuels, anticipating they would
become a major contributor to total biofuel volumes.407 After that rule took effect, however,
commercial-scale production of cellulosic biofuels failed to meet those high expectations, with
actual volumes falling significantly below statutory targets through 2022. More recently,
however, production has increased steadily, reaching record levels in 2025, driven mainly by
RNG, which is consumed as CNG/LNG.408 However, small volumes of liquid cellulosic
biofuels, particularly ethanol produced from CKF, have also played a contributing role (see
Figure 7.1-1). This section describes our assessment of the expected production rate and
consumption of qualifying cellulosic biofuel for 2025-2027, along with some of the uncertainties
associated with the projected volume for these years.
406 This is what we generally mean when we use the term biofuel "production" in this chapter and do not specify
whether we are discussing domestic production or imports.
407 75 FR 14674 (March 26, 2010).
408 The majority of the cellulosic RINs generated for renewable CNG/LNG are sourced from biogas from landfills;
however, the biogas may come from a variety of sources including municipal wastewater treatment facility
digesters, agricultural digesters, separated municipal solid waste (MSW) digesters, and the cellulosic components of
biomass processed in other waste digesters.
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Figure 7.1-1: Cellulosic RIN Generation (2014-2025)
1,400
1,300
1,200
1,100
1,000
£
900
2
800
a
700
600
7*,
500
400
300
200
100
2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025
~ Renewable CNG/LNG ~ Liquid Cellulosic Biofuels
Note: We did not yet have final renewable CNG/LNG generation data for 2025 at the time of this analysis. Based on
the information available at the time of writing, we do not expect the reported figures to differ materially from the
final data.
Source: EMTS.
To project the volume of cellulosic biofuel production in 2025-2027, we evaluated
several key factors. These include assessing the accuracy of previous methodologies used to
estimate cellulosic biofuel production, data reported to EPA through EMTS, the projected use of
renewable CNG/LNG as transportation fuel, and insights gathered from meetings with
representatives of facilities that have recently produced or have the potential to produce
qualifying volumes of cellulosic biofuel by 2027.
This section of Chapter 7 is organized as follows: Chapter 7.1.1 provides an industry-
wide assessment of the cellulosic biofuel sector to understand its current state. Chapter 7.1.2
reviews and analyzes EPA's previous cellulosic biofuel projections. Chapter 7.1.3 addresses the
projected volume of cellulosic biofuel for 2025. Chapter 7.1.4 addresses the projected volume of
renewable CNG/LNG from 2026-2027. Chapter 7.1.5 focuses on the projected production of
liquid cellulosic biofuels from 2026-2027. Finally, Chapter 7.1.6 summarizes the overall
projected rate of cellulosic biofuel production for 2026-2027.
7.1.1 Cellulosic Biofuel Industry Assessment
This section evaluates the cellulosic biofuel producers expected to generate qualifying
cellulosic biofuel through 2027. This includes producers of both D3 RIN-generating cellulosic
biofuels and D7 RIN-generating cellulosic diesel. Analysis of existing RIN data shows two
primary contributors: renewable CNG/LNG as well as ethanol produced from CKF. Beyond
these main sources, we have also looked at other potential contributors that could impact the
future of cellulosic biofuel production.
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7.1.1.1 Renewable Compressed Natural Gas and Liquefied Natural Gas
In July 2014, EPA approved cellulosic biofuel pathways under the "Pathways II" Rule,
allowing RNG to generate cellulosic (D3) RINs when used as transportation fuel. Eligible biogas
sources include landfills, separated municipal solid waste digesters, municipal wastewater
treatment facilities, agricultural digesters, and the cellulosic components of biomass processed in
other waste digesters. Since the implementation of the Pathways II Rule, cellulosic biofuel
production has grown significantly, increasing from approximately 33 million RINs in 2014 to
over 1 billion RINs in 2025. In 2025, approximately 93% of cellulosic RINs came from
renewable CNG/LNG (see Figure 7.1-1). We expect renewable CNG/LNG to remain the primary
source through 2027.
7.1.1.2 Ethanol from Corn Kernel Fiber
Outside of renewable CNG/LNG, few other sources of commercial-scale cellulosic
biofuel exist. A notable exception is ethanol made from CKF. During corn ethanol production,
part of the cellulosic fraction of the kernel fiber can be co-processed with corn starch to produce
cellulosic ethanol. With limited additional processing or equipment changes, facilities can co-
produce meaningful volumes of cellulosic ethanol alongside starch ethanol.
To qualify for D3 RINs, facilities must quantify the ethanol derived from the cellulosic
portion, using reliable methods that distinguish it from starch-derived ethanol. In September
2022, EPA issued updated guidance on analytical methods for quantifying ethanol when co-
processing CKF and corn starch and has engaged with registered cellulosic producers.409 As a
result, EPA anticipates that most facilities currently producing corn starch ethanol will generate
D3 RINs for cellulosic ethanol during the years analyzed in this rule. Given the large scale of
corn starch ethanol production, ethanol from CKF is expected to make a meaningful contribution
to future cellulosic biofuel volumes.
7.1.1.3 Other Cellulosic Biofuels
Between 2026 and 2027, EPA expects that commercial-scale production of cellulosic
biofuel, beyond renewable CNG/LNG and ethanol produced from CKF, to remain very limited.
While cellulosic ethanol from grain sorghum fiber could be produced, we do not expect it to
have any impact during the years analyzed under this rulemaking. Because of this, we assume
that any volumes attributable to grain sorghum cellulosic ethanol are captured within our
analysis of CKF ethanol. In the past, small volumes of D7 RINs have been generated from
foreign facilities producing cellulosic heating oil/diesel. While this production is worth noting,
the total volume has remained consistently low,410 making it almost indistinguishable from the
background uncertainty of any future projections. Outside of these sources, there are several
cellulosic biofuel production facilities in various stages of development, construction, and
commissioning that may be capable of producing small volumes of cellulosic biofuel by 2027.
These facilities primarily focus on producing cellulosic hydrocarbons from feedstocks such as
409 EPA, "Guidance on Qualifying an Analytical Method for Determining the Cellulosic Converted Fraction of Corn
Kernel Fiber Co-Processed with Starch," EPA-420-B-22-041, September 2022.
410 EMTS data reports that from 2020 to 2025, annual D7 RIN generation varied from 55,892 to 236,790 RINs.
210
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separated MSW, precommercial thinnings, and tree residues, which can be blended into gasoline,
diesel, and jet fuel. Several of these facilities are currently registered with EPA and have the
potential to generate RINs for qualifying cellulosic biofuel by 2027. However, according to data
from EMTS, none of these facilities have generated cellulosic RINs for liquid cellulosic biofuel
since March 2019. As a result, while these facilities have been considered potential sources of
cellulosic biofuel, we do not project any volume of other cellulosic biofuel through 2027.
7.1.2 Review of EPA's Projection of Cellulosic Biofuel in Previous Years
Before estimating future cellulosic biofuel volumes, we first review and evaluate the
accuracy of EPA's past projections to identify potential improvements to past methodologies.
Table 7.1.2-1 provides a comparison of actual cellulosic biofuel volumes—including cellulosic
biofuel (which generate D3 RINs) and cellulosic diesel (which generate D7 RINs)—against
EPA's projections from 2015 to 2025. This data shows that EPA projections underestimated total
cellulosic RIN availability in 2015, 2018, and 2022, while overestimating it in 2016, 2017, 2019,
2020,411 2023, 2024, and 2025.412 This variability highlights the inherent challenges in
forecasting cellulosic biofuel production and emphasizes the need to continue refining our
projection methods to improve accuracy in the future.
411 Cellulosic biofuel production in 2020 was affected by the COVID-19 pandemic. Since the projections were made
before the pandemic, the resulting overestimates are attributed to the pandemic's impact, rather than to any issues in
EPA's projection methodology.
412 See Chapter 7.1.3 for more information on the 2025 cellulosic biofuel projection.
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Table 7.1.2-1: Projected and Actual Cellulosic Biofuel RIN Availability (million RINs)
Projected Volume
Actual Volume3
Liquid
Cellulosic
Renewable
Total
Cellulosic
Liquid
Cellulosic
Renewable
Total
Cellulosic
Year
Source
Biofuel
CNG/LNG
Biofuelb
Biofuel
CNG/LNG
Biofuelb
2015
c,d
2
33
35
<1
53
53
2016
d
23
207
230
4
186
190
2017
e
13
298
311
12
239
251
2018
f
14
274
288
11
304
315
2019
g
20
399
418
11
403
414
2020
h,i
16
577
593
2
503
505
2021
J
N/A
N/A
N/A
1
562
563
2022
k
0
632
632
1
662
663
2023
1
7
831
840
1
772
773
2024
l,m
51
1,039
1,090
43
971
1,014
2025
l,n
77
1,299
1,380
85
1,126
1,210
a Actual production volumes are the total number of RINs generated minus the number of RINs retired for reasons
other than compliance with the annual standards, based on EMTS data.
b Total cellulosic biofuel may not be precisely equal to the sum of liquid cellulosic biofuel and renewable
CNG/LNG due to rounding.
0 Projected and actual volumes for 2015 represent only the final 3 months of 2015 (October-December) as EPA used
actual RIN generation data for the first 9 months of the year.
d 2014-2016 RFS Rule (80 FR 77506; December 14, 2015).
e 2017 RFS Rule (81 FR 89760; December 12, 2016).
f 2018 RFS Rule (82 FR 58486; December 12, 2017).
« 2019 RFS Rule (83 FR 63704; December 11, 2018).
h 2020 RFS Rule (85 FR 7016; February 6, 2020).
1 Cellulosic biofuel production in 2020 was affected by the COVID-19 pandemic, likely causing the actual volumes
to fall short of the projections.
J The 2021 cellulosic volume requirement was retroactively established at the actual volume of cellulosic biofuel
produced in 2021. 2020-2022 RFS Rule (87 FR 39600; July 1, 2022).
k 2020-2022 RFS Rule (87 FR 39600; July 1, 2022).
1 Set 1 Rule (88 FR 44468; July 12, 2023).
m EPA waived the 2024 cellulosic volumes down from the projections made in the Set 1 Rule given that a RIN
deficit was carried from 2023 into 2024, compounded by another shortfall in 2024 shortfall and the limited
availability of 2023 carryover RINs. (90 FR 29751; July 7, 2025).
n EPA is revising the 2025 cellulosic biofuel volume requirement in this rule, as discussed in Preamble Section VI
and Chapter 7.1.3. We do not yet have final RNG RIN generation data at the time of this action. This data can lag
because producers must submit pipeline statements verifying production and delivery, which takes additional time.
Based on the information available at the time of writing, we do not expect the reported figures to differ materially
from the final data.
Reviewing the data in this table, EPA's projections for liquid cellulosic biofuel were
consistently higher than actual production from 2015 through 2017. In response, the 2018 final
rule revised our approach to incorporate more recent data and improve accuracy. The updated
method first grouped potential producers into (1) "consistent producers" with a demonstrated
record of commercial-scale output and (2) "new producers" still ramping up. For each group, we
defined a likely production range and then applied a percentile value, calibrated to historical
performance, to generate a single projected volume per group.
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Despite this refinement, EPA continued to over-project from 2018 to 2020, with 2020
particularly affected by COVID-19 disruptions that could not be anticipated. In 2022, by
contrast, the method under-projected liquid cellulosic volumes. For the 2023-2025 rule, we
again updated our analysis, with a particular focus on the growth of ethanol from CKF, including
more granular assumptions about facility ramp-up and feedstock availability. Based on observed
outcomes, we slightly under-projected liquid cellulosic volumes in 2023 and 2024 and slightly
over-projected in 2025.
Next, we turn to the projection of renewable CNG/LNG. From 2015 to 2017, EPA
applied a facility-by-facility approach to project renewable CNG/LNG production, estimating
volumes for individual companies or facilities. However, this methodology also significantly
overestimated renewable CNG/LNG production in 2016 and 2017, prompting EPA to develop a
broader industry-wide projection method, first implemented in 2018.
This broader approach estimates future production by applying an industry-wide, year-
over-year growth rate to current renewable CNG/LNG production rate. Specifically, EPA
analyzes RIN generation data from the most recent 24 months available at the time of each
rulemaking and calculates a growth rate from that period. This growth rate is then applied to the
latest full calendar year of data and compounded for each subsequent year to project future
production. This updated approach reflects the maturity of the renewable CNG/LNG industry,
which has a greater number of potential producers than the liquid cellulosic biofuel industry. In
such mature markets, industry-wide projections tend to be more accurate than a facility-by-
facility method, as broader economic trends generally outweigh the performance of individual
facilities.
The industry-wide approach slightly under-projected renewable CNG/LNG in 2018,
2019, and 2022. This approach overestimated production in 2020, likely due to the impacts of
COVID-19. For the rulemaking that established volumes for 2023-2025, EPA again applied this
methodology. However, unlike in the 2018-2022 rules, the growth rate for projections was
calculated based on data from 2015-2022, rather than the previous 24 months. This adjustment
was made to counteract the anticipated negative impacts of the COVID-19 pandemic on the 2020
and 2021 data, with pre-pandemic growth rates believed to reflect future renewable CNG/LNG
production potential more accurately. The available information shows that EPA overestimated
renewable CNG/LNG production for all years projected in the Set 1 Rule, including 2023, 2024,
and 2025. For more details, refer to Chapter 7.1.3.
Reflecting on these past projections highlights two key points. First, estimating these
volumes is inherently challenging, underscoring the need to continually refine our methods for
greater accuracy. Second, the production of renewable CNG/LNG has consistently exceeded that
of liquid cellulosic biofuel. This difference likely results from several factors, including the
maturity of renewable CNG/LNG production technology relative to liquid cellulosic biofuel
technologies, the lower production costs for RNG (see Chapter 10), and the relatively high value
of the cellulosic RIN. While we project liquid cellulosic biofuel and renewable CNG/LNG
volumes separately, the overall accuracy of the combined cellulosic biofuel volume projection is
ultimately what matters for obligated parties.
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7.1.2.1 Cellulosic RIN Generation Versus Separation
Historically, EPA's efforts to project cellulosic biofuel volumes have been complicated
by the industry's novelty and the volatility of emerging technologies. More recently, changes to
the RFS regulatory framework have also added a new temporal rhythm for RIN generation and
separation and affected our methods for projecting industry-wide growth rates. Specifically, EPA
updated the regulatory framework for biogas-derived fuels to enhance oversight. Through the
Biogas Regulatory Reform Rule (BRRR),413 finalized in the 2023 RFS Set 1 Rule, EPA
restructured the oversight and compliance framework for biogas-derived RNG. A key change in
the BRRR, effective in early 2025, requires RINs to remain attached to the RNG after generation
and move with the fuel through the supply chain rather than be separated immediately by the
producer. Separation of the RIN can now only occur once a registered "RIN separator"
demonstrates that the RNG has been physically withdrawn from the grid and used as
transportation fuel, thereby closing the loop between production and usage. In short, RNG RINs
are now generated prior to use as a transportation fuel, and such RINs are not separated—and
thus made available for compliance—until the RNG RIN separator obtains documentation
demonstrating that the volume of renewable CNG/LNG was used as transportation fuel.
Consequently, relying solely on cellulosic RIN generation data would overstate the actual
number of cellulosic RINs available for compliance, as it assumes every unit of RNG for which a
RIN is generated is successfully dispensed to a vehicle, ignoring distinct downstream constraints
on consumption. Essentially, conflating the two would ignore the possibility of produced fuel
never finding a compliant end-use as transportation fuel. Thus, looking forward, EPA expects
this distinction between RINs generated and RINs separated and made available for compliance
to become increasingly critical when setting cellulosic volumes. By focusing on RIN separation
and availability data when considering future volumes, we can more accurately account for real-
word usage and ensure that mandated volumes reflect the fuel that can actually reach the
transportation market
7.1.3 Projection of the 2025 Cellulosic Biofuel Volumes
As discussed in Chapter 7.1.2, EPA overestimated cellulosic biofuel volumes for 2023
and 2024, largely due to over-projecting D3 RIN availability from renewable CNG/LNG, which
constitutes most of the cellulosic volume. In both years, the supply of available D3 RINs from
renewable CNG/LNG was insufficient to meet the Set 1 cellulosic biofuel requirement. In
response, EPA adjusted the 2024 cellulosic biofuel volume requirement.414
While the exact causes of these past shortfalls are unclear and may involve multiple
factors, EPA has long recognized that the renewable CNG/LNG market could reach a saturation
point, where nearly all eligible CNG/LNG vehicles are already fueled by renewable CNG/LNG.
Under the RFS regulations for biogas-derived renewable fuel (see Chapter 7.1.2.1), RINs are
generated when the RNG is produced;415 however, RINs are not separated—and thus made
available for compliance—until the RNG RIN separator obtains documentation demonstrating
413 88 FR 44522 - 44541 (July 12, 2023)
414 90 FR 29751 (July 7, 2025).
415 40 CFR 80.125(b).
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that the volume of renewable CNG/LNG was used as transportation fuel.416 Because of this, the
growth of the CNG/LNG vehicle market could act as a ceiling when determining appropriate
volumes.
While EPA had anticipated this eventual limitation, we did not believe the market had
reached this point when establishing the 2023-2025 volume requirements. At the time of that
rulemaking, EPA projected future volumes based on the assumption that RNG production
capacity, not renewable CNG/LNG market consumption, would be the primary constraint on
cellulosic RIN availability. Accordingly, as discussed in Chapter 7.1.2, EPA used the historical
growth rate of cellulosic RIN generation to set appropriate future volumes for 2023-2025.
However, in that rulemaking EPA acknowledged that this methodology might become less
appropriate as the CNG/LNG vehicle market becomes increasingly saturated with renewable
CNG/LNG.417 Given the shortfalls in projecting the 2023 and 2024 volumes, EPA has reason to
believe that the CNG/LNG vehicle market is approaching this saturation point. Because of this,
the methods originally used to set the 2025 volumes may no longer be dependable.
Further evidence of consumption constraints appears in cellulosic RIN retirement data,
which show a substantial increase in retirements due to RNG being used in non-transportation
applications. Specifically, comparing 2020-2024 and 2025 retirement data (see Table 7.1.3-1),
we observe a pronounced rise, providing additional evidence of market consumption limits.418
Table 7.1.3-1: Cellulosic RINs Retired Due to Use in Non-Transportation Applications
(million RINs)
Year
Cellulosic RINs Retired
2020
0.0
2021
0.0
2022
0.0
2023
0.0
2024
0.4
2025
74.5
With these considerations in mind, we reevaluated the expected availability of cellulosic
RINs for 2025. To estimate the total number available for compliance, we assessed both how
much cellulosic fuel is likely to be produced in 2025 and how much of that fuel can realistically
be used in transportation. We then compared these estimates against the cellulosic RINs
generated and available for compliance as of this analysis. Given the timing, we already have
most of the actual 2025 cellulosic production data in the form of RIN generation data.419 Based
416 40 CFR 80.125(d).
417 Set 1 Rule RIA Chapter 6.1.3.
418 See "RIN retirement data from January 2026" RIN data file available at: https://www.epa.gov/fuels-registration-
reporting-and-compliance-help/spreadsheet-rin-retirement-data-renewable-fuel.
419 We do not yet have final RNG RIN generation data at the time of this analysis. This data can lag because
producers must submit pipeline statements verilying production and delivery, which takes additional time. Based on
the information available at the time of writing, we do not expect the reported figures to differ materially from the
final data.
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on this data, and as shown in Table 7.1.3-2, we estimate 2025 cellulosic RIN generation, and by
extension cellulosic biofuel production, to be 1.29 billion RINs.420
Table 7.1.3-2: 2025 Cellulosic RES
Month
RINs Generated
January
84
February
83
March
99
April
106
May
116
June
109
July
116
August
119
September
114
October
114
November
113
December
115
Total
1,289
Generations (millions of RINs)
As noted earlier, RIN generation and production capacity estimates are only half of the
challenge of projecting future renewable CNG/LNG volumes. Relying solely on production
estimates would overstate compliance-eligible RINs because this method assumes every RNG
unit tied to a RIN is used in a vehicle and ignores downstream consumption constraints. To
address this issue, we estimated how much RNG can reasonably be used as transportation fuel by
evaluating the size of the CNG/LNG vehicle fleet and its fuel consumption rates. The resulting
estimate is shown in Table 7.1.3-3, and the calculation methodology is provided in Chapter
7.1.4.1.
Ethanol from CKF differs from renewable CNG/LNG in this context because production
is a reasonable proxy for consumption. For fuels other than renewable CNG/LNG—including
CKF ethanol—producers must report to EPA within five business days of the production or sale
of the fuel,421 so CKF ethanol does not exhibit the same reporting lag seen with renewable
CNG/LNG. Accordingly, we assume the 2025 CKF ethanol data available as of this analysis
reflects the final 2025 generation volumes. Additionally, because CKF ethanol is chemically
indistinguishable from starch ethanol, it can be used in the same applications. Moreover, given
the higher value of cellulosic fuels relative to conventional fuels, obligated parties have an
incentive to prioritize cellulosic ethanol and separate those RINs before conventional ethanol.
Combined with the large size of the ethanol market and the prevalence of ethanol-capable
vehicles, it is reasonable to assume CKF ethanol will be fully consumed. Because of this, we can
use actual 2025 RIN generation data for our estimate of both CKF production and consumption.
4211 See "Available RINs to date from January 2026" RIN data file available at: https://www.epa.gov/fuels-
registration-reporting-and-compliance-help/spreadsheet-available-rins-date-renewable-fuel.
421 40 CFR 80.1452
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Combining this renewable CNG/LNG consumption estimate with the actual cellulosic
ethanol volume produced and consumed in 2025, we project 2025 total cellulosic biofuel
consumption volumes as shown below in Table 7.1.3-3.
Table 7.1.3-3: Projected 2025 Cellulosic Fuel Consumption (million RINs)
Estimated
Renewable
CNG/LNG
Actual
Ethanol
from CKF
Projected
Total
Cellulosic
Cellulosic Fuel Consumption
1,170
84
1,254
A comparison of the 1.29 billion RIN upper limit based on in-year production data with
our bottom-up estimate of 1.25 billion RINs (renewable CNG/LNG consumption plus CKF
production; see Table 7.1.3-3) indicates that production capacity is not expected to be the main
constraint on 2025 cellulosic RINs; the limiting factor is expected to be the use of renewable
CNG/LNG as transportation fuel. This is even clearer when accounting for RINs already retired
for reasons other than annual compliance. Taking the total number of cellulosic RINs generated
in 2025 through the date of this analysis (1.29 billion cellulosic RINs),422 and subtracting the
number of RINs retired for reasons other than annual compliance by refiners (0.08 billion
RINs)423 we estimate that approximately 1.21 billion cellulosic RINs will be both generated and
available for compliance in 2025. Because the estimate of cellulosic RINs generated and
available for compliance is lower than both the production- and consumption-based estimates,
we use it to reevaluate expected 2025 availability. As a result, we project 1.21 billion RINs as the
final 2025 availability and adjust the 2025 cellulosic fuel volume accordingly.424
7.1.4 Proj ecting the Renewable CNG/LNG Market
As discussed in the previous chapters, RINs from renewable CNG/LNG can only be used
by obligated parties to demonstrate compliance with the RFS obligations when the renewable
CNG/LNG is used as transportation fuel. To do so, raw biogas from eligible sources must first be
collected and upgraded. This upgrading process involves removing contaminants and other
undesirable components from the biogas. Biogas that has been upgraded and distributed through
a closed, private distribution system is defined as "treated biogas," whereas biogas that has been
upgraded to be suitable for injection into the commercial natural gas pipeline system is defined
as RNG.425 While treated biogas is typically used at the site of production, RNG is injected into
the commercial natural gas pipeline system. Because RNG is upgraded to meet pipeline
specifications, it is functionally identical to fossil-based natural gas. Once injected into pipelines,
RNG can be used just like fossil-based natural gas—for fueling CNG/LNG vehicles, generating
electricity, residential heating, and various industrial and commercial applications. Currently,
large volumes of biogas are produced at landfills and wastewater treatment plants across the
422 See "Available RINs to date from January 2026" RIN data file available at: https://www.epa.gov/fuels-
registration-reporting-and-compliance-help/spreadsheet-available-rins-date-renewable-fuel.
423 See "RIN retirement data from January 2026" RIN data file available at: https://www.epa.gov/fuels-registration-
reporting-and-compliance-help/spreadsheet-rin-retirement-data-renewable-fuel.
424 See Preamble Section VII.
425 40 CFR 80.2.
217
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U.S., with further potential for biogas generation from manure and other agricultural residues.426
Although the quantity of biogas from qualifying sources potentially far exceeds current
CNG/LNG usage as transportation fuel,427 much of this biogas is not being upgraded to
RNG428—a necessary step for its use in CNG/LNG vehicles. Instead, due to the significant
capital investment required for collection and treatment, much of this biogas is currently either
flared or used for onsite electricity generation.429
Despite these challenges, the incentive created by the cellulosic biofuel RIN has led to
rapid growth in renewable CNG/LNG430 use since 2014 (see Table 7.1.4-1). Considering this
incentive, we believe that the volume of renewable CNG/LNG can continue to grow under the
influence of the RFS through 2027. At the same time, however, there are several market factors
that we expect could limit the rate of growth of this biofuel in future years. As initially discussed
in Chapter 7.1.3, we believe the market for renewable CNG/LNG used as transportation fuel is
becoming increasingly demand4imited, a factor that must be considered when projecting future
volumes. The following subsections further explore the supply and demand dynamics of RFS-
qualifying renewable CNG/LNG.
Table 7.1.4-1: RIN Generation (million RINs) and Annual Growth Rate for Renewable
CNG/LNG
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025a
Renewable
CNG/LNG RIN
Generation
140
189
241
304
404
504
568
667
773
973
1,206
Annual Growth
Rate
-
35%
28%
26%
33%
25%
13%
18%
16%
26%
24%
a Final 2025 generation data are not yet available at the time of this analysis. The figures presented are estimates
based on the best available information, and given the timing of this action we do not expect them to differ
materially from the final data.
7.1.4.1 Projected Demand of Renewable CNG/LNG
To estimate the future demand for renewable CNG/LNG, we first looked to identify an
appropriate estimate for all CNG/LNG usage in transportation, including both fossil and
renewable sources. Because RINs can only be used to demonstrate compliance with the RFS
obligations when the fuel is used in transportation, the maximum potential transportation use of
CNG/LNG defines the theoretical and practical upper limit for compliance-eligible renewable
426 American Biogas Council, "Biogas Market Snapshot," April 2025. https://americanbiogascouncil.org/biogas-
market-snapshot.
427 A discussion of EPA's estimates for current and future CNG/LNG usage as transportation fuel is in Chapter
7.1.4.1.
428 EPA, "LFG Energy Project Development Handbook," January 2024.
https://www.epa.gov/svstem/files/documents/2024-01/pdh full.pdf.
429 EPA, "LMOP Landfill and Project Database." https://www.epa.gov/lmop/lmop-landfill-and-proiect-database.
4311 RNG is biogas upgraded to commercial pipeline quality and injected into the commercial natural gas pipeline
system; "treated biogas" is biogas conditioned for transportation use but not injected and delivered via closed or
private distribution systems. In this document, we use "RNG" to refer to both RNG and treated biogas. We likewise
use "renewable CNG/LNG" to refer to both when used as transportation fuel in CNG/LNG vehicles, and we use this
term where such use is eligible for, and results in, RIN generation and separation under the RFS program.
218
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CNG/LNG volumes. Several projections exist for total CNG/LNG usage in the 2026-2027
period. One key source is AEO2025, which projects nationwide CNG/LNG use as transportation
fuel will be equivalent to 1,343 and 1,403 million ethanol-equivalent gallons for the years 2026
and 2027 respectively (see Table 7.1.4.1-1). However, these AEO projections include all
transportation-related energy usage, including sectors like international shipping, which are
outside the RFS scope.431 After adjusting the AEO estimates to exclude non-relevant CNG/LNG
volumes,432 the revised projections indicate lower estimates equivalent to 1,256; and 1,316
million ethanol-equivalent gallons for the years 2026 and 2027 respectively, as shown in Table
7.1.4.1-1.
Table 7.1.4.1-1: Projected CNG/LNG Transportation Usage from EIA's 2025 AEO433
(million ethanol-equivalent gallons)
2026
2027
CNG/LNG Transportation Usage
1,342
1,403
Adjusted3 CNG/LNG Transportation Usage
1,256
1,316
a Usage adjusted to exclude volumes attributed to international shipping.
Additionally, given the high likelihood of nationwide consumption limitations emerging
by the mid-to-late 2020s, we believe it is valuable to develop an additional estimate of future
CNG/LNG demand to compare with the AEO estimate and more comprehensively assess
potential saturation points. To that end, EPA created a separate estimate of future CNG/LNG
demand, independent of the AEO estimate. Referred to in this section as the "EPA Estimate," it
was developed using a combination of data sources and modeling techniques tailored to different
vehicle categories, consistent with the methods used in the Set 2 proposal but updated with the
most recent data. Commenters generally agreed with our methodologies for estimating
consumption, though some urged more aggressive assumptions for fuel use and anticipated
market growth. We address these points in detail in RTC Section 3; however, based on the
available data, we believe the EPA Estimate strikes an appropriate balance that reflects potential
growth in total CNG/LNG consumption while remaining grounded in the available data and
observed market trends. We provide the specifics of the EPA Estimate, including data sources,
key assumptions, and category-specific modeling, in the paragraphs that follow.
The vehicle categories in the EPA Estimate are based primarily on the U.S. Department
of Transportation (DOT) Highway Performance Monitoring System (HPMS) vehicle
classifications, as outlined in Table VM-1 of the Federal Highway Administration's (FHWA)
annual Highway Statistics report.434 The HPMS classifications include light-duty vehicles with a
431 Under the definition of transportation fuel in 40 CFR 80.2, fuel for use in ocean-going vessels is excluded as a
transportation fuel.
432 Volumes attributed to: Light-Duty Vehicle, Commercial Light Trucks, Domestic Shipping, Freight Trucks,
Freight Rail, Transit Buses, and School Buses were included. Volumes attributed to: International Shipping were
excluded due to these being ocean-going vessels.
433 AEO2025, Table 36 - Transportation Sector Energy Use by Fuel Type Within a Mode.
434 FHWA, Highway Statistics Series, Table VM-1: Annual Vehicle Distance Traveled in Miles and Related Data -
2022. https://www.fliwa.dot.gov/policYinfonnation/statistics/2022/vml.cfm.
219
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short wheelbase, light-duty vehicles with a long wheelbase, motorcycles, buses, single unit
trucks,435 and combination trucks.
For this analysis, EPA consolidated short- and long- wheelbase light-duty vehicles into a
single "light-duty vehicle" category. Motorcycles were excluded from EPA's estimates of
CNG/LNG consumption, as historically, no motorcycles have been powered by these fuels. EPA
further refined the bus category by distinguishing between "school buses" and "transit buses"
based on the availability of data that allowed for a more detailed analysis of their fuel
consumption. Additionally, refuse haulers were separated from other single-unit trucks because
of the historically high usage rate of CNG/LNG for these vehicles, resulting in the refuse hauler
category being a key sector in EPA's fuel consumption estimates. As a result of this additional
refinement, EPA chose to estimate fuel consumption for the following vehicle categories: light-
duty vehicles, public transit, school buses, refuse trucks, single unit trucks (excluding refuse
haulers), and combination trucks.
To estimate fuel consumption from the light-duty vehicle category, EPA relied on data
from the U.S. Department of Energy (DOE) as the primary source for vehicle count
information.436 This vehicle count data for natural gas vehicles was integrated with assumptions
for average vehicle miles traveled (VMT)437 and average fuel efficiency438 in the light-duty
vehicle sector. Using this methodology, EPA calculated a total estimate for CNG/LNG
consumption among light-duty vehicles, which is shown in Table 7.1.4.1-2. Based on current
trends, EPA does not anticipate significant growth in CNG/LNG volumes within the light-duty
sector, given the limited introduction of new light-duty natural gas vehicles models. Notably, no
new CNG light-duty vehicle models have been introduced since MY2022.439 Despite the lack of
growth and the likely decrease in vehicle numbers due to future scrappage, EPA has opted to
keep the vehicle count steady in this analysis for simplicity, given that consumption for the light-
duty category is already minimal.
Table 7.1.4.1-2: CNG/LNG Usage from the Light-duty Vehicle Sector (million ethanol-
equivalent ga
Ions)
Year
Data Type
Vehicle Count
CNG/LNG Usage
2024
Actual
23,700
22
2025-2027
Projected
23,700
22
Note: Calculated using an average efficiency of 17.8 miles per gasoline-equivalent gallon and an average VMT of
11,318 miles per vehicle.
435 Single-Unit: single frame trucks that have 2-axles and at least 6 tires or a gross vehicle weight rating exceeding
10,000 lbs.
436 AFDC, "Vehicle Registration Counts by State," 2024. https ://afdc .energy.gov/vehicle-registration?vear=2024.
437 AFDC, "Average Annual Vehicle Miles Traveled by Major Vehicle Category," September 2024.
https://afdc.energy.gov/data/10309.
438 AFDC, "Average Fuel Economy by Major Vehicle Category," January 2024. https://afdc.energy.gov/data/10310.
439 AFDC, "Fuel and Advanced Technology Vehicles," Model Year 2022
(https://afdc.energv.gov/veliicles/searcli/download.pdf7veaF2022). Model Year 2023
(https://afdc.energv.gov/veliicles/searcli/download.pdf7veaF2023). Model Year 2024
(https://afdc.energy.gov/veliicles/searcli/download.pdf7veaF2024) and Model Year 2025,
(https://afdc.energy.gov/veliicles/searcli/download.pdf7veaF2025).
220
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To estimate consumption from public transit, EPA utilized data from the American
Public Transportation Association's 2025 Public Transportation Fact Book, which provides
energy consumption data separated by fuel type.440 Data from this source indicate significant
variability in annual fuel use, with no clear trend beyond a marked decline during the COVID-19
pandemic. The most recent years of actual data, 2022 and 2023, show higher transit CNG/LNG
usage, potentially signaling a return toward pre-pandemic ridership levels, although it may still
reflect residual pandemic effects. Given this volatility and the lack of a stable trend, EPA
calculated an average annual growth rate using data from 2014 onward, which aligns with the
period when renewable CNG/LNG was classified as a cellulosic biofuel under the RFS program.
We applied the resulting average growth rate of 2.2% per year to project CNG/LNG
consumption in public transportation beyond 2023. The projected usage is presented in Table
7.1.4.1-3.
Table 7.1.4.1-3: CNG/LNG Usage from the Public Transportation Sector (million ethanol-
equivalent gallons)
CNG/LNG
Year-over Year
Year
Data Type
Usage
Growth
2014
Actual
288
N/A
2015
Actual
302
4.8%
2016
Actual
321
6.4%
2017
Actual
317
-1.3%
2018
Actual
328
3.4%
2019
Actual
347
5.8%
2020
Actual
314
-9.4%
2021
Actual
303
-3.5%
2022
Actual
320
5.5%
2023
Actual
345
7.9%
2024
Projected
353
2.2%
2025
Projected
360
2.2%
2026
Projected
368
2.2%
2027
Projected
376
2.2%
For school buses, EPA is using data from the World Resources Institute's Dataset of U.S.
School Bus Fleets,441 which provides information on the composition of school bus fleets across
the U.S. This dataset includes data from 46 states and the District of Columbia; however, it does
have some limitations. Specifically, data are unavailable for four states: Colorado, Hawaii,
Louisiana, and New Hampshire. In addition, several states (Illinois, Indiana, Michigan,
Mississippi, Nevada, Oklahoma, Oregon, South Carolina, Texas, Wisconsin, and Wyoming)
have high rates of records with unknown fuel type. To address these gaps, EPA estimated CNG
school bus counts for the affected states by combining state population figures with state-level
4411 American Public Transportation Association, "2025 Public Transportation Fact Book," Appendix A: Historical
Tables, Table 58 - Non-Diesel Fossil Fuel Consumption by Fuel Type, https://www.apta.com/research-technical-
resources/transit-statistics/public-transportation-fact-book.
441 Lazer, Leah, Lydia Freehafer, and Jessica Wang. "Dataset of U.S. School Bus Fleets (State Summary) Version
3," World Resources Institute, May 10, 2024. https://datasets.wri.org/datasets/usa-school-bus-fleets.
221
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CNGbus counts from states with well-characterized fleets.442 The estimated vehicle count data
for CNG buses was then combined with average VMT443 and average fuel efficiency specific to
school buses.444 This approach resulted in an estimate of total CNG/LNG consumption for the
school bus sector. Because the dataset covers only March-November 2022 and does not track
trends over time, EPA adopted a conservative projection approach: no growth or decline factors
are applied, and the number of CNG school buses is held constant over the analysis period. This
decision reflects recent funding trends,445 which largely favor electric buses over CNG.446 Given
the limited evidence for future growth in the CNG school bus fleet, applying a growth rate could
overstate CNG use in this sector. Accordingly, EPA assumes no change in CNG school bus
counts over time for this analysis. Based on this assumption, the estimated consumption data for
the school bus sector are presented in Table 7.1.4.1-4. We are aware of recent developments that
may influence future CNG school bus adoption, including EPA's announcement to revamp the
Clean School Bus Program.447 EPA will continue to analyze this vehicle market in future
rulemakings. However, given the recent nature of these changes and the limited number of years
that we are establishing volumes, we do not expect them to affect our current assumption of no
growth or decline.
Table 7.1.4.1-4: CNG/LNG Usage from the School Bus Sector (million ethanol-equivalent
Year-over-Year
CNG/LNG
Year
Data Type
Vehicle Count
Growth
Usage
2022
Actual
7,085
N/A
23
2023
Projected
7,085
0%
23
2024
Projected
7,085
0%
23
2025
Projected
7,085
0%
23
2026
Projected
7,085
0%
23
2027
Projected
7,085
0%
23
Note: Calculated using an average efficiency of 6.46 miles per gasoline-equivalent gallon and an average VMT of
14,084 miles per vehicle.
For refuse trucks, EPA derived a vehicle count estimate from fleet information reported
in the sustainability reports of the largest waste management companies in the U.S. In addition,
our review found no reliable centralized data regarding the specific number or growth rates of
municipality-owned refuse trucks. Because of this lack of data, municipal fleets were not
explicitly isolated in this analysis. Instead, our estimates rely on the robust data available from
the major private providers that dominate the market. For many companies, especially smaller
442 The spreadsheet used to estimate the CNG bus counts can be found in "Data and Methods for Estimating School
Bus and Refuse Hauler CNG Populations (Set 2 Final Rulemaking)," available in the docket for this action.
443 AFDC, "Average Annual Vehicle Miles Traveled by Major Vehicle Category," September 2024.
https://afdc.energy.gov/data/10309.
444 AFDC, "Average Fuel Economy by Major Vehicle Category," January 2024. https://afdc.energy.gov/data/10310.
445 EPA, "Clean School Bus Program Awards." https://www.epa.gov/cleanschoolbus/clean-school-bus-program-
awards.
446 EPA, "Clean School Bus Program Rebates." https://www.epa.gov/cleanschoolbus/clean-school-bus-program-
rebates.
447 EPA, "EPA Announces Path Forward to Revamp the Clean School Bus Program to Provide Safe, Affordable,
Efficient Transportation for America's Youth," February 19, 2026. https://www.epa.gov/newsreleases/epa-
announces-path-forward-revamp-clean-school-bus-program-provide-safe-affordable.
222
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ones, data were more limited, and historical data were unavailable for several of the years
reviewed. In cases with missing information, EPA applied average growth rates from companies
with available data to estimate vehicle counts for those periods. Following this approach, total
vehicle counts from the analyzed companies were aggregated448 and are shown in Table 7.1.4.1-
5. Using this aggregated dataset, EPA calculated an average annual growth rate, which was then
applied to the most recent vehicle totals to project future vehicle counts. These projected vehicle
counts, in conjunction with average VMT449 and average fuel efficiency for refuse haulers,450
were used to estimate total CNG/LNG consumption within the refuse hauler sector. Comparing
this vehicle count to data from The Transportation Project, we note that The Transportation
Project reports "Over 17,000 natural gas refuse trucks operate across the country and about 60%
of new trucks on order are NGVs [Natural Gas Vehicles]."451 This figure is lower than EPA's
estimate of approximately 23,000 vehicles in 2024. However, given the rapid growth and
adoption of CNG/LNG usage in this sector, EPA believes that its higher estimate may better
represent future vehicle counts. The resulting data for refuse haulers are presented in Table
7.1.4.1-6.
Table 7.1.4.1-5: Estimated Refuse Hauler Vehicle Counts
Company
2018
2019
2020
2021
2022
2023
2024
Waste Management
7,944
8,924
10,388
10,832
11,307
12,119
13,170
Republic Services
3,200
3,200
3,423
3,444
3,380
3,440
3,646
Waste Connections
1,070
1,119
1,166
1,090
1,070
1,069
1,069
Clean Harbors
-
-
-
-
13
100
211
GFL Environmental
-
776
983
1,179
1,238
1,515
1,637
Recology
-
1,950
2,080
2,158
2,210
2,184
2,236
Waste Pro USA
800
800
800
800
800
847
900
Casella Waste Systems
-
-
-
30
44
40
43
Total
13,014
16,769
18,840
19,533
20,062
21,315
22,913
Note: Vehicle counts are estimated based on limited data, including information available only from earlier years.
Source: WM Sustainability Reports (https://sustainabilitv.wm.com/esg-data-center): Republic Services SASB
Reports (https://investor.republicservices.com/financials/reports): Waste Connections Sustainability Reports
(https://sustainabilitv.wasteconnections.com/sustainabilitv-data-hub.html): Clean Harbors Sustainability Reports
(https://www.cleanharbors.com/sites/g/files/bdczcs356/files/2023-
1 l/CLH%20Sustainabilitv%20Supplement%20110323.pdf): GFL Enviromnental SASB Reports
(https://investors.gflenv.com/Englisli/esg/sustainabilitv/default.aspx): Recology Sustainability Reports
(https://www.recologv.com/sustainabilitv-at-recologv): Waste Pro USA (https://www.wasteprousa.com/blog/waste-
pro-recognized-for-eco-friendlv-operations): Casella Waste Systems SASB Reports.
448 The spreadsheet used to estimate the CNG refuse hauler counts can be found in "Data and Methods for
Estimating School Bus and Refuse Hauler CNG Populations (Set 2 Final Rulemaking)," available in the docket for
this action.
449 AFDC, "Average Annual Vehicle Miles Traveled by Major Vehicle Category," September 2024.
https://afdc.energy.gov/data/10309.
4511 AFDC, "Average Fuel Economy by Major Vehicle Category," January 2024. https://afdc.energy.gov/data/10310.
451 The Transport Project, "Vehicles for every route", https://transportproiect.org/vehicles.
223
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Table 7.1.4.1-6: CNG/LNG Usage from the Refuse Hauler Sector (million ethanol-
Year-over Year
CNG/LNG
Year
Data Type3
Vehicle Count
Growth
Usage
2019
Actual
16,769
N/A
253
2020
Actual
18,840
12.3%
284
2021
Actual
19,533
3.7%
294
2022
Actual
20,062
2.7%
302
2023
Actual
21,315
6.2%
321
2024
Actualb
22,913
7.5%
345
2025
Projected
24,402
6.5%
367
2026
Projected
25,987
6.5%
391
2027
Projected
27,675
6.5%
417
Note: Calculated using an average efficiency of 2.48 miles per gasoline-equivalent gallon and an average VMT of
25,000 miles per vehicle.
a In this context, what we call actual data remains an estimate.
b Calculated using both projections and actual data.
For single-unit trucks (excluding refuse haulers) and combination trucks, EPA used
national CNG/LNG vehicle counts processed by the Motor Vehicle Emission Simulator
(MOVES5) for each calendar year, derived from vehicle registration data for 2014, 2020, and
2023.452 The MOVES5 team interpolated between those registration years to produce annual
counts for the intervening years (2015-2019 and 2021-2022), and we relied on that fully
interpolated time series in our analysis. Although MOVES5 includes forward-looking vehicle
population trajectories that reflect anticipated market responses to the regulatory landscape at the
time the model was developed, we did not use those projections. Instead, our future-year counts
(shown in Table 7.1.4.1-9 and 7.1.4.1-10) are based on a simple extrapolation of historical
growth observed in the processed registration-based data.
In addition to registration data, EPA incorporated average VMT and fuel efficiency data
from the Bureau of Transportation Statistics' National Transportation Statistics publication.453
Using historic vehicle counts, along with average VMT and fuel efficiency for both single-unit
and combination trucks, EPA estimated total CNG/LNG consumption in these sectors for each
year from 2014 to 2022, shown in Tables 7.1.4.1-7 and 8. Based on this aggregated dataset, EPA
calculated the average annual growth rate of total CNG/LNG usage and applied it to the most
recent totals to project future fuel volumes, shown in Tables 7.1.4.1-9 and 10.
452 Vehicle count data shown in Tables 7.1.4.1-7 and 8 are from the Motor Vehicle Emission Simulator (MOVES5),
(https://www.epa.gov/moves/latest-version-motor-veliicle-emission-simulator-moves). The years for which EPA has
national vehicle registration data from correspond to the update schedule of the MOVES model. For information on
how this data was derived, see EPA, "Population and Activity of Onroad Vehicles in MOVES5," EPA-420-R-24-
019, November 2024, Chapters 4 and 5. https://nepis.epa.gov/Exe/ZvPDF.cgi?Dockev=P101CUN7.pdf.
453 Bureau of Transportation Statistics, National Transportation Statistics, Table 4-13 - Single-Unit 2-Axle 6-Tire or
More Truck Fuel Consumption and Travel (https://www.bts.gov/content/single-unit-2-axle-6-tire-or-more-truck-
fuel-consumption-and-travel) and Table 4-14 - Combination Truck Fuel Consumption and Travel.
(https://www.bts.gov/content/combination-truck-fuel-consumption-and-travel).
224
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Table 7.1.4.1-7: CNG/LNG Usage from the Single Unit Truck Sector (miles; miles per
diesel-eo
uivalent gallon; mil
ion ethanol-equivalent gallons)
Year
Single Unit Truck
Vehicle Count
Single Unit Truck
VMT
Single Unit Truck
Fuel Economy
Single Unit Truck
Fuel Consumption
2014
11,710
13,123
7.34
35.5
2015
14,286
12,960
7.38
42.5
2016
17,160
12,958
7.39
51.0
2017
19,025
12,435
7.44
53.9
2018
22,832
11,687
7.51
60.3
2019
25,335
12,278
7.49
70.4
2020
27,250
11,892
7.56
72.6
2021
29,899
12,287
7.67
81.2
2022
32,020
12,290
7.93
84.1
Note: Diesel-equivalent gallons (DGE) converted to ethanol-equivalent gallons (EGE) using 1 EGE = 0.59 DGE.
Table 7.1.4.1-8: CNG/LNG Usage from the Combination Truck Sector (miles; miles per
diesel-equivalent gallon; million ethanol-equivalent gallons)
Combination
Combination
Combination
Combination
Truck
Truck
Truck
Truck
Year
Vehicle Count
VMT
Fuel Economy
Fuel Consumption
2014
4,539
65,897
5.83
86.9
2015
6,908
61,978
5.89
123.1
2016
8,527
63,428
5.91
155.2
2017
9,667
62,751
5.98
172.0
2018
10,832
63,374
6.07
191.6
2019
11,420
59,929
6.05
191.8
2020
11,967
60,120
6.16
197.9
2021
14,048
62,169
6.42
230.6
2022
17,256
60,018
6.91
254.0
Note: Diesel-equivalent gallons (DGE) converted to ethanol-equivalent gallons (EGE) using 1 EGE = 0.59 DGE.
Table 7.1.4.1-9: CNG/LNG Usage from the Single Unit Truck Sector (million ethanol-
Year
Data Type
CNG/LNG Usage
Year-over Year Growth
2017
Actual
53.9
N/A
2018
Actual
60.3
11.8%
2019
Actual
70.4
16.8%
2020
Actual
72.6
3.1%
2021
Actual
81.2
11.8%
2022
Actual
84.1
3.6%
2023
Projected
92.0
9.4%
2024
Projected
100.7
9.4%
2025
Projected
110.1
9.4%
2026
Projected
120.5
9.4%
2027
Projected
131.8
9.4%
225
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Table 7.1.4.1-10: CNG/LNG Usage from the Combination Truck Sector (million ethanol-
Year
Data Type
CNG/LNG Usage
Year-over Year Growth
2017
Actual
172.0
N/A
2018
Actual
191.6
11.4%
2019
Actual
191.8
0.1%
2020
Actual
197.9
3.2%
2021
Actual
230.6
16.5%
2022
Actual
254.0
10.1%
2023
Projected
274.8
8.2%
2024
Projected
297.3
8.2%
2025
Projected
321.7
8.2%
2026
Projected
348.1
8.2%
2027
Projected
376.6
8.2%
In the proposal, EPA included an alternative scenario that projected accelerated
deployment of CNG/LNG vehicles, driven largely by the market entry of the Cummins XI5N
engine.454 Under this scenario, we modeled rapid expansion in which natural gas engines
captured up to 10% of the new freight truck market by 20 3 0.455 Although many commenters
supported higher market share projections, EPA does not yet see sufficient evidence of a
structural shift in purchasing behavior to support a 10% market share. Accordingly, we are
retaining a more conservative growth rate in this final rulemaking. We expect to continue
discussions with stakeholders on this issue, as it will be critical for future rulemakings. Further
details on this decision are provided in the RTC Section 3.
After estimating volumes for each vehicle category, we aggregated these individual totals
to produce an overall "EPA Estimate" of future CNG/LNG consumption, shown in Table
7.1.4.1-11.
Table 7.1.4.1-11: Total CNG/LNG Usage for the "EPA Estimate" (million ethanol-
equivalent gallons)
2025
2026
2027
Light-duty Vehicles
22
22
22
Public Transportation
360
368
376
School Buses
23
23
23
Refuse Trucks
367
391
417
Single Unit Trucks
110
121
132
Combination Trucks
322
348
377
Total
1,205
1,273
1,347
Note: Totals may not be precisely equal to the sum of each vehicle sector due to rounding.
454 Cummins, "Engines - X15N (2024)." https://www.cummins.com/engines/xl5n-2024.
455 Cummins stated that their goal is for this engine to reach 10% of market sales by 2030. See: Patrick Campbell,
Cummins Alternative Power Technologies - Regional Sales Manager, Fleets and Fuel Conference Presentation from
BIOGAS AMERICAS 2024 (May 13-16, 2024). https://YOutu.be/fQH6i lcclkl (19:15 in video).
226
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This aggregated CNG/LNG consumption is broadly consistent with the AEO estimate
shown in Table 7.1.4.1-1, though it is slightly higher. Given this alignment and our stronger
understanding of the inputs to EPA's demand-side estimate, we rely on EPA's consumption
estimate for subsequent analyses. With respect to that estimate, commenters generally agreed
with the overall methodology but urged more aggressive assumptions for per-vehicle fuel use
and anticipated market growth. EPA assumed per-vehicle consumption comparable to
diesel-equivalent vehicles, while many commenters recommended higher per-vehicle usage.
Because market-wide data on per-vehicle CNG/LNG consumption remain limited and duty
cycles vary widely, we are maintaining our current assumptions. We will continue discussions
with stakeholders on these issues, which will be important for future rulemakings. Additional
detail on these decisions is provided in RTC Section 3.
With the consumption estimate selected, we next looked to determine how much of the
total CNG/LNG market could be met with renewable CNG/LNG. In a model scenario where all
fossil-based CNG/LNG could be fully replaced by renewable CNG/LNG, this total CNG/LNG
estimates would serve as the maximum potential renewable CNG/LNG volumes, with no further
adjustment needed. In practice, however, facility-level constraints, including infrastructure
limitations, costs, and other operational considerations, make 100% replacement unlikely, so
some fossil-based CNG/LNG would remain in use. To better reflect realistic renewable
CNG/LNG consumption in a saturated market, we calibrated our projections to observed market
conditions, drawing on data from California's LCFS program to benchmark RNG uptake under
saturation. Since its inception in 2011, California's LCFS program has awarded credits for both
renewable and fossil-based natural gas used as transportation fuel within the state. In 2014, when
RNG used as CNG/LNG was classified as a cellulosic biofuel under the RFS program, the
utilization of renewable CNG/LNG surged significantly due to the ability to generate lucrative
credits under both programs for displacing existing fossil CNG/LNG demand. This aggressive
growth under both the LCFS and RFS has resulted in renewable CNG/LNG dominating the
market in California. As seen in Table 7.1.4.1-14, the California CNG/LNG market has shifted to
be almost entirely renewable, with volumes accounting for an average of 97% of the total market
from 2022 through 2024. Thus, we assumed that California represents a mature, fully saturated
market.
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Table 7.1.4.1-12: California Low Carbon Fuel Standard Program Data (million ethanol-
equivalent gallons)456 i
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
Renewable
CNG/LNG
49
113
151
181
203
236
257
296
323
344
370
Fossil-based
CNG/LNG
164
122
95
87
82
69
21
7
12
12
4
Total
CNG/LNG
213
234
247
268
285
305
278
303
335
356
374
Year-over-
year Growth
of Total
-
10%
5%
8%
6%
7%
-9%
9%
11%
6%
5%
Renewable
CNG/LNG
Blend Rate
23%
48%
61%
68%
71%
77%
92%
98%
96%
97%
99%
Subsequently, we assume that any fully saturated CNG/LNG market would consist of
approximately 97% renewable CNG/LNG. Using this approach, we applied a 97% utilization
factor to the EPA projections for future CNG/LNG volumes to estimate the potential renewable
CNG/LNG consumption under saturated market conditions. These consumption estimates for
renewable CNG/LNG are detailed in Table 7.1.4.1-13.
Table 7.1.4.1-13: Projected Maximum Amount of Renewable CNG/LNG That Could Be
Used (million RINs)
2025
2026
2027
EPA CNG/LNG Consumption Estimate
1,205
1,273
1,347
Renewable CNG/LNG Usage Assuming 97% Replacement
1,169
1,235
1,306
7.1.4.2 Projected Supply of RNG
In addition to projecting future demand for renewable CNG/LNG, EPA also analyzed the
potential production capacity of RNG under unrestricted market conditions, assuming no
consumption limitations. This analysis was conducted to assess whether the market is genuinely
constrained by consumption rather than production capacity. To do so, we utilized the same
industry-wide production projection methodology that has been employed in RFS standard-
setting rules since 2018. This methodology is based on applying an industry-wide year-over-year
growth rate to the current production rate of RNG (see Chapter 7.1.2 for more information on
this methodology).
To determine the growth rate, EPA analyzed D3 RNG RIN generation data over the two
most recent full calendar years.457 While EPA has historically relied on the most recent rolling
24-month period, we determined that aligning the data to full calendar years (January through
December) was necessary to minimize the distortions of seasonality. Historically, RIN
456 CARB, "Low Carbon Fuel Standard Reporting Tool Quarterly Summaries."
https://ww2.arb.ca.gov/resources/documents/low-carbon-fuel-standard-reporting-tool-auarterlY-summaries.
457 See "Available RINs to date from January 2026" RIN data file available at: https://www.epa.gov/fuels-
registration-reporting-and-compliance-help/spreadsheet-available-rins-date-renewable-fuel.
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generation follows a cyclical trend: a slowdown at the start of the calendar year followed by a
surge in generation at year-end (see 2024 data in Table 7.1.4.2-1). However, early 2025 deviated
from this historical pattern, likely due to new biogas oversight regulations taking effect (see
Chapter 7.1.2.1). By utilizing full calendar years, we capture the complete seasonal cycle and the
impacts of the regulatory changes. Accordingly, based on this analysis, we determined a year-
over-year growth rate of 24% for 2025 over 2024, as shown in Table 7.1.4.2-2.
Table 7.1.4.2-1: D3 RNG RIN Generation Data for Last Two Calendar Years (million
RINs)
Month
2024
2025
January
7
76
February
69
77
March
66
92
April
74
97
May
75
109
June
81
104
July
79
109
August
85
112
September
89
106
October
87
108
November
91
107
December
170
107
Total
973
1,206
Table 7.1.4.2-2: Actual D3 RN<
j RIN Generation Data (million RINs)
Volume Generated Between
Jan. 2024 - Dec. 2024
Volume Generated Between
Jan. 2025 - Dec. 2025a
Y ear-Over-Y ear
Increase
973
1,206
24%
a This was the most recent 12 months for which data was available at the time of this analysis.
EPA applied this 24% year-over-year growth rate to the estimated 2025 total of cellulosic
RNG RINs. Specifically, we multiplied the calculated growth rate by the volume of RNG
supplied in 2025, based on actual RIN generation. The RNG volume potential projected using
this methodology are shown in Table 7.1.4.2-3.
Table 7.1.4.2-3: Projected Production Potential of RNG (million RINs)
Year
Date Type
Growth Rate
Volume
2025
Actual
24%
1,206
2026
Projected
24%
1,495
2027
Projected
24%
1,853
The projected production shown in Table 7.1.4.2-3 serves as the estimated volume of
RNG that could be produced without any constraint on demand for use as transportation fuel.
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7.1.4.3 Projected Volume of Renewable CNG/LNG
In Table 7.1.4.3-1 we combine the consumption estimate selected from Chapter 7.1.4.1
with the production estimates from Chapter 7.1.4.2.
Table 7.1.4.3-1: Estimated Production of RNG and Estimated Consumption of Renewable
CNG/LNG (million RINs)
2026
2027
RNG Production
1,495
1,853
Renewable CNG/LNG Consumption
1,235
1,306
Based on our analysis of renewable CNG/LNG consumption and RNG production, we
reach the same conclusion as in the proposal: in 2026 and 2027, cellulosic volumes from
renewable CNG/LNG are constrained by total CNG/LNG use in transportation, not by
production. Commenters were divided on this point; some agreed that consumption, rather than
production, will limit volumes in the near term, while others argued that we should base our
analyzed volumes solely on projected production. We address these comments in detail in RTC
Section 3. However, we have chosen not to adopt a production-only approach; we continue to
consider both production and consumption. Accordingly, EPA projects future volumes based on
projected consumption of renewable CNG/LNG. These estimated volumes are shown in Table
7.1.4.3-2.
Table 7.1.4.3-2: Projected Volume Renewable CNG/LNG (million RINs)
2026
2027
Volume of Renewable CNG/LNG
1,235
1,306
7.1.5 Projected Supply of Liquid Cellulosic Biofuels
Several technologies are currently being developed to produce liquid fuels from
cellulosic biomass. However, most of these technologies are unlikely to yield significant
volumes by 2027. One notable exception is the production of ethanol from CKF, which is
currently in use at numerous commercial-scale production facilities using one of several
processes.
Many of these processes involve simultaneously co-processing of both the starch and
cellulosic components of the corn kernel. However, to be eligible for generating cellulosic RINs,
facilities must accurately determine the amount of ethanol produced specifically from the
cellulosic portion. This requires the ability to reliably and precisely calculate the ethanol derived
from the cellulosic component, distinct from the starch portion of the corn kernel. In September
2022, EPA issued updated guidance on analytical methods that could be used to quantify the
amount of ethanol produced when co-processing CKF and corn starch.458 One potential
limitation on CKF ethanol production would be if production through compliant methods were
not feasible for some facilities.
458 EPA, "Guidance on Qualifying an Analytical Method for Determining the Cellulosic Converted Fraction of Corn
Kernel Fiber Co-Processed with Starch," EPA-420-B-22-041, September 2022.
230
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To evaluate the practical extent of this potential limitation, EPA has had substantive
discussions with technology providers intending to use analytical methods consistent with this
guidance, as well as with owners of facilities registered as cellulosic biofuel producers using
these methods. Based on information from these technology providers, EPA believes that
cellulosic ethanol production from CKF is technically feasible at all existing corn ethanol
facilities with minimal additional processing units or modification. However, for the purposes of
this analysis, we have applied a 90% adoption ceiling. This 10% adjustment accounts for site-
specific idiosyncratic factors that may preclude 100% implementation. By assuming 90%
adoption, EPA's analysis reflects a robust yet pragmatic expectation of the industry's capacity
from 2026-2027.
Based on data submitted to EPA by renewable fuel producers generating cellulosic RINs
for CKF ethanol, the current industry-wide average conversion among registered facilities is
approximately 1%. Accordingly, for this analysis we use a 1% conversion rate. We recognize
that some parties have claimed they can demonstrate up to 1.5% conversion using analytical
methods consistent with EPA guidance, but we do not yet have sufficient data to support
adopting that higher rate.
Commenters generally supported our inclusion of robust volumes of CKF ethanol. Some,
however, urged more aggressive assumptions for facility participation and conversion efficiency.
We address these comments in detail in RTC Section 3; however, based on the available data, we
do not find sufficient support to increase these rates at this time.
The projected production of cellulosic ethanol form CKF, as shown in Table 7.1.4-1, is
based on projections of total corn ethanol production (see Chapter 7.6 for more information on
our total corn ethanol projections), with a 90% facility participation rate and a 1% conversion
efficiency applied.
Table 7.1.4-1: Pro jected Production of Ethanol from CKF (million RINs)
Year
Volume
2026
128
2027
128
7.1.6 Projected Rate of Cellulosic Biofuel Production for 2026-2027
After projecting production of ethanol from CKF and renewable CNG/LNG, EPA
combined these estimates to project total cellulosic biofuel production for 2026-2027. These
projections are shown in Table 7.1.6-1.
Table 7.1.6-1: Projected Production of Cellulosic Biofuel in 2026-2027 (million RINs)
2026
2027
Renewable CNG/LNG
1,235
1,306
Ethanol from CKF
128
128
Total Cellulosic Biofuel
1,364
1,434
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7.2 Biomass-Based Diesel
Since 2010 when the BBD volume requirement was added to the RFS program,
production of BBD has generally increased. The volume of BBD supplied in any given year is
influenced by a number of factors including production capacity, feedstock availability and cost,
available incentives, the availability of imported BBD, the demand for BBD in foreign markets,
and other economic factors. From 2010 through 2015 the vast majority of BBD supplied to the
U.S. was biodiesel. Since 2015, increasing volumes of renewable diesel have also been supplied.
In 2023, the quantity of renewable diesel supplied to the U.S. surpassed the supply of biodiesel
for the first time. Production of renewable diesel is expected to continue to increase in future
years. Along with biodiesel and renewable diesel, there are also small volumes of renewable jet
fuel and heating oil that qualify as BBD. However, as the vast majority of BBD is biodiesel and
renewable diesel, we have focused on these fuels in this section.
This section presents information on the factors we consider in projecting the domestic
production and net imports of BBD in 2026 and 2027. First, we present the available data on
biodiesel and renewable diesel production, import, and use in previous years (Chapter 7.2.1).
Next, we assess the current and projected future production capacity for biodiesel and renewable
diesel (Chapter 7.2.2). The availability of qualifying feedstocks for biodiesel and renewable
diesel production (Chapter 7.2.3) and potential imports and exports of BBD (Chapter 7.2.4) are
in the following sections. Finally, we describe our assessment of the rate of production and use
of qualifying BBD in 2026 and 2027 based on this information (Chapter 7.2.5) and discuss some
of the uncertainties associated with those volumes. This section addresses the projected rate of
production and consumption of all BBD projected to be produced and used in the U.S. in 2026
and 2027, regardless of whether the production and use of the BBD is driven by the BBD,
advanced, or total renewable fuel volume requirements. An analysis of the projected rate of
production and consumption of advanced (D5) biodiesel and renewable diesel and conventional
(D6) biodiesel and renewable diesel can be found in Chapters 7.4 and 7.7, respectively.
7.2.1 Production and Use of Biomass-Based Diesel in Previous Years
As a first step in considering the rates of production and use of BBD in future years we
review the volumes of BBD produced domestically, imported, and exported in previous years.
Reviewing the historical volumes is useful since there are many complex and inter-related
factors beyond simple total production capacity that could affect the supply of BBD. These
factors include, but are not limited to, the RFS volume requirements (including the BBD,
advanced biofuel, and total renewable fuel requirements), the availability of BBD feedstocks,459
demand for those feedstocks in other markets and internationally, the federal tax credits available
459 Throughout this chapter we refer to BBD as well as BBD feedstocks. In this context, BBD refers to any biodiesel
or renewable diesel for which RINs can be generated that satisfy an obligated party's BBD biofuel obligation (i.e.,
D4 RINs). While cellulosic diesel (D7) can also contribute towards an obligated party's advanced biofuel obligation,
these fuels are included instead in the projection of cellulosic biofuel presented in Chapter 6.1. An advanced
biodiesel or renewable feedstock refers to any of the biodiesel, renewable diesel, jet fuel, and heating oil feedstocks
listed in Table 1 to 40 CFR 80.1426 or in petition approvals issued pursuant to 40 CFR 80.1416 that can be used to
produce fuel that qualifies for D4 or D5 RINs. These feedstocks include but are not limited to: soybean oil; oil from
annual cover crops; oil from algae grown photosynthetically; biogenic waste oils/fats/greases; non-food grade corn
oil; camelina sativa oil; and canola/rapeseed oil (see Rows F, G, and H of Table 1 to 40 CFR 80.1426).
232
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to biodiesel and renewable diesel producers, tariffs on imported biodiesel and renewable diesel
(and the feedstocks used to produce these fuels), biofuel policies in other countries, import and
distribution infrastructure, and other market-based factors. Thus, while historical data and trends
alone are insufficient to project the volumes of biodiesel and renewable diesel that could be
provided in future years, historical data can serve as a useful reference point in considering
future volumes. Production, import, export, and total volumes of BBD are shown in Table 7.2.1-
1.
Table 7.2.1-1: BBD (D4) Production, Imports, and Exports from 2016 to 2025 (million
gallons) i
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025a
Domestic Biodiesel
1,581
1,552
1,841
1,706
1,802
1,701
1,614
1,665
1,656
1,100
(Annual Change)
(+336)
(-29)
(+289)
(-135)
(+96)
(-101)
(-87)
(+51)
(-9)
(-556)
Imported Biodiesel
562
462
175
185
209
208
240
501
398
34
(Annual Change)
(+301)
(-100)
(-287)
(+10)
(+24)
(-1)
(+32)
(+261)
(-103)
(-364)
Exported Biodiesel
89
129
74
76
88
91
117
98
82
32
(Annual Change)
(+16)
(+40)
(-55)
(+2)
(+12)
(+3)
(+26)
(-19)
(-16)
(-50)
Total Biodiesel
2,054
1,885
1,942
1,815
1,924
1,817
1,738
2,067
1,972
1,102
(Annual Change)b
(+621)
(-169)
(+57)
(-127)
(+109)
(-107)
(-79)
(+329)
(-95)
(-870)
Domestic
Renewable Diesel
(Annual Change)
231
252
282
454
472
777
1,369
2,344
3,064
2,720
(+62)
(+21)
(+30)
(+172)
(+18)
(+305)
(+592)
(+975)
(+720)
(-344)
Imported
Renewable Diesel
(Annual Change)
165
191
176
267
280
362
311
361
428
105
(+45)
(+26)
(-15)
(+91)
(+13)
(+82)
(-51)
(+50)
(+67)
(-323)
Exported
Renewable Diesel
(Annual Change)
40
37
80
145
223
241
326
414
581
458
(+19)
(-3)
(+43)
(+65)
(+78)
(+18)
(+85)
(+88)
(+167)
(-123)
Total Renewable
Diesel
(Annual Change)b
356
406
378
576
529
897
1,354
2,291
2,912
2,367
(+88)
(+50)
(-28)
(+198)
(-47)
(+368)
(+457)
(+937)
(+621)
(-545)
Total BBD
2,412
2,293
2,322
2,393
2,457
2,717
3,106
4,376
4,884
3,734
(Annual Change)0
(+711)
(-119)
(+29)
(+71)
(+64)
(+260)
(+389)
(+1,270)
(+508)
(-1,150)
Note: All data from EMTS. EPA reviewed all BBD RINs retired for reasons other than demonstrating compliance
with the RFS standards and subtracted these RINs from the RIN generation totals for each category to calculate the
volume in each year. Similar tables of biodiesel and renewable diesel production, imports, and exports presented in
previous annual rules included advanced (D5) biodiesel and renewable diesel. This table does not include D5 or D6
biodiesel and renewable diesel. These fuels are discussed in Chapters 7.4 and 7.7, respectively.
a Data for 2025 are preliminary.
b Total is equal to domestic production plus imports minus exports.
0 Total BBD includes D4 jet fuel. These volumes were small (<20 million gallons per year) through 2023, but
increased to 112 million gallons in 2024 and 264 million gallons in 2025.
Since 2016, the year-over-year changes in the volume of BBD used in the U.S. have
varied greatly, from a low of 1.15 billion fewer gallons from 2024 to 2025 to a high of 1.27
billion additional gallons from 2022 to 2023. As discussed previously, these changes were likely
influenced by multiple factors. This historical information does not by itself demonstrate that the
maximum previously observed annual increase of 1.27 billion gallons of BBD would be
reasonable to expect in a future year, nor does it indicate that greater increases are not possible.
Significant changes have occurred in both the fuel and feedstock markets (discussed further
below) that will impact the rates of growth of biodiesel and renewable diesel production and use
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in future years. Rather, this data illustrates both the magnitude of the changes in BBD in previous
years and the significant variability in these changes.
This data also shows the increasing importance of renewable diesel in the BBD pool. In
2016 approximately 15% of all BBD was renewable diesel, and the remaining 85% was
biodiesel. However, since 2016 nearly all the net growth in the BBD category has been in
renewable diesel volume. By 2024 production and net imports of renewable diesel had increased
not only in absolute terms (from 365 million gallons in 2016 to 2.95 billion gallons in 2024), but
also as a percentage of the BBD pool. In 2025 approximately 63% of all BBD was renewable
diesel, while the remaining 37% was biodiesel. As discussed further in the following sections,
we expect that renewable diesel will represent an even greater percentage of total BBD in future
years.
The historic data indicate that the biodiesel tax policy in the U.S. can have a significant
impact on the volume of biodiesel and renewable diesel used in the U.S. in any given year. The
availability of federal tax credits has also historically provided biodiesel and renewable diesel
with a competitive advantage relative to other biofuels that have not historically qualified for
most of the federal biofuel tax credits (e.g., the 40A biodiesel and renewable diesel blending
credit). This is likely one of the factors that has contributed to the high growth of BBD relative to
other advanced biofuels over the years.
While the biodiesel blenders tax credit (known to many as the "40A" credit) applied in
each tax year from 2010 through 2024, it was historically only prospectively in effect during the
calendar year in 2011, 2013, 2016, and 2020-2024, while other years it has been applied
retroactively. Years in which the biodiesel blenders tax credit was in effect during the calendar
year (2011, 2013, 2016, 2020-2024) generally resulted in significant increases in the volume of
BBD used in the U.S. over the previous year (629 million gallons, 711 million gallons, 64
million gallons,460 260 million gallons, 389 million gallons, 1,272 million gallons, and 508
gallons, respectively). However, following the large increases in 2013 and 2016, there was little
to no growth in the use of BBD in the following years. Data from 2018 and 2019 suggests that
while the availability of the tax credit certainly incentivizes an increasing supply of biodiesel and
renewable diesel, supply increases can also occur in the absence of the tax credit, likely as the
result of the incentives provided by the RFS program, state LCFS programs, trade dynamics, and
other economic factors.
Beginning in 2025, the structure of the federal tax credit available to biodiesel and
renewable diesel changed significantly. Prior to 2025 all qualifying biodiesel and renewable
diesel (including biodiesel and renewable diesel co-processed with petroleum) was eligible for
the $1 per gallon 40A tax credit. This tax credit was available for biodiesel and renewable diesel
produced in the U.S. as well as biodiesel or renewable diesel produced in foreign countries and
used in the U.S. In 2025, the 45Z credit for domestically produced renewable fuels came into
effect. This credit consolidates and replaces the previous $1 per gallon credit for blending
biodiesel and renewable diesel into diesel fuel under 40A and also provides a production credit
for other alternative fuels and sustainable aviation fuel. This credit differs from the biodiesel
blenders tax credit in several significant ways. First, it is available to other forms of
460 This is the volume increase in 2020, which was impacted by the COVID-19 pandemic.
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transportation fuel, potentially including ethanol. Second, beginning January 1, 2026, the tax
credit is available only for transportation fuel produced in the United States with feedstocks from
the US, Canada, or Mexico. Finally, and perhaps more importantly, the magnitude of the tax
credit is a function of the CI of the transportation fuel. This means that fuels must have lifecycle
GHG emissions lower than 50 kilograms CO2 equivalent per mmBTU to qualify for the tax
credit, and fuels with lower GHG emissions are eligible for a higher tax credit than fuels with
higher GHG emissions. The tax credit amount rises based on the CI of the transportation fuel—
up to $1.00 per gallon (as of January 1, 2026) for all liquid fuels—provided certain wage and
labor requirements are met. Fuels derived from animal manure may exceed a $1.00 per gallon
credit, as they can potentially have a negative emissions rate. The structure of the 45Z credit
therefore has a significant impact on the relative competitiveness of biofuels produced from
different feedstocks in the U.S. market.
Another important factor highlighted by the historic data is the impact of changing
renewable fuel and trade policies in other countries on the supply of biodiesel and renewable
diesel to the U.S. In December 2017, the U.S. International Trade Commission adopted tariffs on
biodiesel imported from Argentina and Indonesia.461 According to data from EIA, no biodiesel
has been imported from Argentina or Indonesia since September 2017, after a preliminary
decision to impose tariffs on biodiesel imported from these countries was announced in August
2017.462 As a result of these tariffs, total imports of biodiesel into the U.S. were significantly
lower in 2018 than they had been in 2016 and 2017. The decrease in imported biodiesel did not,
however, result in a decrease in the volume of BBD supplied to the U.S. in 2018. Instead, higher
domestic production of BBD, in combination with lower exported volumes of domestically
produced biodiesel, resulted in an overall increase in the volume of BBD supplied in 2018 and
subsequent years.
More recently changes in demand for biodiesel in the EU resulted in significant increased
imports to the U.S. from the EU. Through 2021 biodiesel imports from the EU had never
exceeded 100 million gallons in any single year.463 Biodiesel imports from the EU increased to
approximately 114 million gallons in 2022 and then quite dramatically to approximately 320
million gallons in 2023.464 In these same years, imports of feedstocks used by domestic biodiesel
and renewable diesel producers, such as tallow from Brazil and used cooking oil from China,
increased significantly. These countries had historically exported biofuel feedstocks to the EU,
and increased exports to the U.S. were likely impacted by declining demand for these feedstocks
by biofuel producers in the EU. This dynamic shifted again in 2025, when the removal of a
Chinese government-issued export rebate was shortly followed by a high-tariff environment,
leading to a significant decrease in the availability of imported Chinese used cooking oil. This
was partially offset by an increase in tallow imports from Latin America. These impacts are
discussed in greater detail in Chapters 7.2.3 and 7.2.4.
461 USITC, "Biodiesel from Argentina and Indonesia Injures U.S. Industry, says USITC," December 5, 2017.
https://www.usitc.eov/press room/news release/2017/erl20511876.htm.
462 EIA, "U.S. Imports by Country of Origin - Biodiesel," Petroleum & Other Liquids, April 30, 2025.
https://www.eia.gov/dnav/pet/pet move impcus a2 nus epoordb imO mbbl a.htm.
463 Id. Total reported biodiesel imports from the EU include imports from Belgium, Finland, France, Germany, Italy,
the Netherlands, Norway, Portugal, and Spain.
464 Id.
235
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7.2.2 Biomass-Based Diesel Production Capacity and Utilization
One of the factors considered when projecting the rate of production of BBD in future
years is the production capacity. This section focuses on current and projected future BBD
production capacity. While many of the biodiesel and renewable diesel production facilities
considered in this section are also capable of producing conventional biodiesel and renewable
diesel, very low volumes of conventional biodiesel and renewable diesel have been supplied to
the U.S. in recent years.465 Domestic biodiesel production capacity, domestic biodiesel
production, and the utilization rate of the existing biodiesel production capacity each year is
shown in Figure 7.2.2-1. Active biodiesel production capacity in the U.S. has experienced
modest growth in recent years, from approximately 2.1 billion gallons in 2012 to just over 2.5
billion gallons in 2019.466 As of November 2025, active biodiesel production capacity has
decreased to approximately 2 billion gallons.467 While production of biodiesel has generally
increased during this time period (with the notable exception of 2025), excess production
capacity remains. Facility utilization was below 75% for each year through 2022, but increased
to 82% in 2023 due in part to decreases in the operating biodiesel capacity since 2019. EPA data
on total registered biodiesel production capacity in the U.S., which includes both facilities that
are producing biodiesel and idled facilities, is much higher, approximately 3.9 billion gallons.
Active biodiesel capacity as reported by EIA is the aggregate production capacity of biodiesel
facilities that produced biodiesel in any given month, while the total registered capacity based on
EPA data includes all registered facilities, regardless of whether they are currently producing
biodiesel or not. This data suggest thats domestic biodiesel production capacity is unlikely to
limit biodiesel production in future years. In 2025, utilization of biodiesel capacity was only
56%, according to EIA data.
465 EMTS data indicates that from 2018-2022, no conventional biodiesel or renewable diesel was supplied to the
U.S. In 2023, 10 million gallons of conventional biodiesel and renewable diesel were supplied. As there are
currently no approved pathway for generating RINs for conventional biodiesel and renewable diesel, conventional
biodiesel and renewable diesel can only generate RINs if produced at grandfathered facilities that are exempt from
the 20% GHG emission reduction requirements per 40 CFR 80.1403.
466 EIA, "Monthly Biodiesel Production Report With data for December 2020," February 2021,
https://www.eia.gov/biofuels/biodiesel/production/arcliive/2020/2020 12/biodiesel.pdf.
467 EIA, "U.S. Total Biofuels Operable Production Capacity," Petroleum & Other Liquids. February 6, 2026.
https://www.eia.gov/dnav/pet/pet pnp capbio dcu nus m.htm.
236
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Figure 7.2.2-1: U.S. Biodiesel Production Capacity, Production, and Capacity Utilization
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Unlike domestic biodiesel production capacity, domestic renewable diesel production
capacity has increased significantly in recent years, from approximately 280 million gallons in
2017 to approximately 5 billion gallons in November 2025 (Figure 7.2.2-2).468 Domestic
renewable diesel production has increased along with production capacity in recent years, and
capacity utilization at domestic renewable diesel production facilities has typically been high,
with a precipitous decline in 2025. Further, much of the unused capacity was likely the result of
facilities ramping up new capacity to full production rates. Unlike the biodiesel industry, in
which unused production capacity has persisted for many years, renewable diesel production
neared or exceeded the production capacity from the previous year in every year from 2017
through 2024. As renewable diesel production capacity continues to expand aggressively, it is
unclear if this trend will continue in future years (production did not exceed prior year capacity
in 2025), particularly as affordable feedstocks may become more scarce with increasing
renewable diesel production (see Chapter 7.2.3 for further discussion of available feedstocks).
468 RFS facility registration data and EIA, "U.S. Total Biofuels Operable Production Capacity," Petroleum & Other
Liquids, February 6, 2026. https://www.eia.gov/dnav/pet/pet pnp capbio dcu nus m.htm.
237
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Figure 7.2.2-2: U.S. Renewable Diesel Production Capacity, Production, and Capacity
Utilization
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Source: Renewable diesel production volumes are from EIA, "Monthly Energy Review," March 2025, Table 10.4b.
https://www.eia.gov/totalenergv/data/montlilY/arcliive/00352503.pdf, as well as EMTS. Renewable diesel
production capacity for 2012-2020 is from EMTS. Renewable diesel production capacity for 2021-2025 is from
EIA, "U.S. Total Biofuels Operable Production Capacity," Petroleum & Other Liquids. February 6, 2026.
https://www.eia.gov/dnav/pet/pet pnp capbio dcu nus m.htm. EIA first reported renewable diesel production
capacity in 2021. Production capacity shown for 2021-2023 is the average of the monthly reported production
capacities. Capacity utilization is calculated by dividing actual production by the total production capacity.
A number of parties have announced their intentions to build new renewable diesel
production capacity with the potential to begin production of renewable diesel and/or jet fuel
through 2030. These new facilities include new renewable diesel production facilities,
expansions of existing renewable diesel production facilities, and the conversion of units at
petroleum refineries to produce renewable diesel. In 2023, EIA projected that renewable diesel
production capacity would continue to expand and could reach nearly 6 billion gallons by
2025.469 However, as of November 2025, that number had reached just over 5 billion gallons.470
A recent report published by the National Renewable Energy Laboratory (NREL) estimated that
by 2028 the domestic production capacity for renewable diesel and jet fuel could increase to 9.6
billion gallons per year.471 A map of the facilities expected to begin producing renewable diesel
and/or jet fuel by 2028 from the NREL study is shown in Figure 7.2.2-3.
469 EIA, "Domestic renewable diesel capacity could more than double through 2025,". Today in Energy, February 2,
2023. https://www.eia.gov/todavinenergv/detail.php?id=55399.
4711 EIA, "U.S. Total Biofuels Operable Production Capacity," Petroleum & Other Liquids. February 6, 2026.
https://www.eia.gov/dnav/pet/pet pnp capbio dcu nus m.htm
471 Calderon, Oscar Rosales, Ling Tao, Zia Abdullah, Michael Talmadge, Anelia Milbrandt, Sharon Smolinski,
Kristi Moriarty, et al. "Sustainable Aviation Fuel State-of-Industry Report: Hydroprocessed Esters and Fatty Acids
Pathway," National Renewable Energy Laboratory, NREL/TP-5100-87803, July 30, 2024.
https://doi.org/10.2172/2426563.
120%
100%
2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025
238
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We note, however, that despite the potential for rapidly increasing production capacity
over the next several years, feedstock limitations (discussed in Chapter 7.2.3) are not expected to
support utilization of all of this planned capacity. It is also possible that some of these projects
may be delayed or cancelled. Thus, it is likely that the domestic renewable diesel production will
fall short of the 9.6 billion gallons implied by the sum of current production capacity and
announced new and expanded facilities. Nevertheless, it appears that domestic production
capacity of renewable diesel and SAF is poised to experience continued growth during the
timeframe of this rule.
Figure 7.2.3-3: New or Expanded Renewable Diesel and Jet Fuel Production Capacity in
the U.S. Through 2028
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Kristi Moriartv. et al. ''Sustainable Aviation Fuel State-of-Industry Report: Hydroprocessed Esters and Fatty Acids
Pathway," National Renewable Energy Laboratory, NREL/TP-5100-87803, July 30, 2024.
htlns://doi.org/10.2172/2426563.
While renewable diesel and biodiesel capacity utilization has averaged 78% and 70%,
respectively, over the past decade, the renewable diesel industry has demonstrated the ability to
exceed a 90% capacity utilization rate472, in line with typical utilization rates seen in the
petroleum industry. As the renewable fuels industry matures and demand for biofuels grows, it is
reasonable to project greater capacity utilization of domestic renewable fuel production, as this is
more cost effective than a new build. However, sustained demand for SAF may supplement the
growth rate of RD/SAF facilities, which are often co-processed. If the industry, which thus far
has undershot the expected 6 billion gallons detailed in this study by 2025, is capable of
sustained growth in addition to greater capacity utilization, one could expect an annual capacity
472 The renewable diesel industry exceeded 90% capacity utilization in 2017 and 2021. See Figure 7.2.3-2.
239
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increase of up to 1.2 billion gallons. This considers the projected capacity of this study less the
deficit the industry has undershot by thus far.
7.2.3 2025 Baseline Data and Estimates of BBD Feedstock Availability
Given oscillating trends and frequent upheavals in virgin oilseed and FOG markets, it is
difficult to predict the exact nature of some feedstock growth in the future. While some growth
rates are assured, historical trends from the past 25 years were disrupted by events such as
COVID-19, the Russian invasion of Ukraine, abrupt changes in tariff policy, and complicated
supply chains have made feedstock acquisition writ large difficult to predict. While it is clear
there is an abundance of feedstocks available to U.S. BBD producers, it is less with which exact
feedstock they will fulfill the volume mandates of this rule.
In the hierarchy of feedstocks, it is apparent that domestic and North American
feedstocks are heavily advantaged, in large part due to the 45Z credit and the ability of shorter
and local supply chains to withstand rapidly shifting trade dynamics. In this environment,
soybean oil, Canadian canola oil, North American UCO, and DCO will likely be economically
advantageous, with gaps between their supply and the analyzed volumes made up by varying
amounts of other eligible feedstocks, most likely dependent on individual contracts and
relationships. EPA considers these feedstocks most likely to maintain or surpass their historical
growth rates, as there are similar or greater incentives for them to do so between a lack of tariffs,
high demand, and tax advantages. EPA first assessed feedstock availability in 2025 before
beginning to project growth rates.
2025 saw a decline in BBD production, as evidenced in Chapter 7.2.1. However,
feedstock availability remained high. In the United States, distiller's corn oil has been a stable
and increasing source of domestic BBD production. Distiller's corn oil output in the U.S. in 2025
was 627 million gallons, of which 560 million gallons is estimated to have been used for
BBD.473 Soybean oil also remained a steadfast source of feedstocks in 2025, and total
availability was estimated to remain beyond 3.85 billion gallons in the 2025/2026 marketing year
after reaching 3.79 billion gallons in the 2024/2025 marketing year.474-475-476 Used cooking oil
and other fats, oils, and greases continued to mirror population growth, with availability reaching
between 2.8 billion and 6 billion gallons in 2025.477 Canola oil, a burgeoning feedstock
especially in renewable diesel production, reached an estimated domestic production of 1.3
billion gallons in the 2024/2025 marketing year after reaching 1.2 billion gallons in the
473 USD A, "Grain Crushings and Co-Products Production Monthly," February 2, 2026.
https://esinis.nal.usda.gov/sites/default/release-files/795755/cagc0226.pdf.
474 Latest data available at the time of writing due to the publication timelines of agricultural marketing year data by
USD A.
475 USD A, "Oilseed Crop Yearbook," Soybean Oil," March 20, 2025. https://www.ers.usda.gov/data-products/oil-
crops-vearbook.
476 USD A, "Grains and Oilseeds Outlook for 2026," February 19, 2026.
https://www.usda.gov/sites/default/files/documents/2026AOF-grains-oilseeds-outlook.pdf.
477 2.8 billion gallons represents million gallons of BBD equivalent using total global exports. 6 billion gallons
represents the global potential collection. Global Data, "UCO Supply Outlook," August 2023.
https://cleanfuels.org/wp-content/uploads/GlobalData UCO-Supplv-Outlook Sep2023.pdf.
240
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2023/2024 marketing year, with an additional 1.3 billion gallons of Canadian canola oil available
in the 2023/2024 marketing year.478
Beyond these now-favored sources, there are large quantities of importable feedstocks
available globally. While imported soybean oil has rarely made inroads into the U.S., there are
large quantities of (mostly Latin American) soybean oil available to domestic biodiesel
producers. EPA does not project any importation of Latin American soybean oil, but it is an
available and eligible feedstock, though heavily disadvantaged.
FOG, on the other hand, is a plentiful and economical feedstock, especially for coastal
refineries. While total production data encompassing all sources of FOG publicly, import/export
databases do offer an insight into total FOG availability to U.S. domestic BBD producers. Using
partial year data, EPA estimates that total global UCO exports numbered 17.9 billion gallons
(2.23 billion gallons BBD equivalent) and global tallow exports reached 5.1 billion gallons (636
million gallons BBD equivalent) in 2025.479 Of this amount, in 2025, an estimated 7.1 billion
gallons (886 million gallons BBD equivalent) was imported into the United States.480
Total production data and import/export data show the theoretical limit of BBD feedstock
acquisition. Some of this supply is committed to local foreign industries or other domestic
industries, such as food or other industrial uses, but given the incentives from tax, trade, and
biofuel policy, it is likely that some of that supply is fungible.
7.2.4 Projections of Biomass-Based Diesel Feedstock Availability to Domestic
Biofuel Producers
As EPA considered the historical rate of production of BBD through 2027, an important
factor influencing the Analyzed Volumes was our assessment of the availability of qualifying
feedstocks. To assess the availability of feedstocks for producing BBD through 2027 we first
reviewed the feedstocks used by domestic BBD producers (including both domestically
produced and imported feedstocks) in previous years. This review of feedstocks used by
domestic BBD producers in previous years can provide information about the feedstocks most
likely to be used by domestic BBD producers in future years, as well as the likely increase in the
availability of such feedstocks in future years. A summary of the feedstocks used to produce
BBD from 2012 through 2025 is shown in Figure 7.2.3-1.
478 USD A, "Oilseed Crop Yearbook," Canola Oil, March 20, 2025. https://www.ers.usda.gov/data-products/oil-
crops-vearbook.
479 United Nations, "UN Comtrade Database," https://comtradeplus.un.org.
480 Id.
241
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Figure 7.2.3-1: Feedstocks Used to Produce BBD in the U.S.
11111111
2018 2019 2020 2021 2022 2023 2024 2025
Year
¦ FOG BSOY ¦CANOLA BCORN
Source: EMTS.
Historically, the largest sources of feedstock used by domestic BBD producers have been
FOG (which includes both used cooking oil and animal fats) and soybean oil, with smaller
volumes of distillers corn oil and canola oil. Through 2021, FOG was primarily sourced
domestically and the total supply to BBD producers was relatively stable. Beginning in 2021, the
quantity of FOG used for domestic BBD production increased significantly, primarily as the
result of increasing imports of these feedstocks. The soybean oil and distillers corn oil used by
domestic BBD producers have also historically been primarily sourced domestically. Use of
soybean oil and distillers corn oil by BBD producers has generally increased with the increased
domestic production of these feedstocks. Finally, relatively small quantities of canola oil have
been used by domestic BBD producers historically; however, the use of canola oil increased
notably from 2023-2024. This increase was likely the result of EPA's approval of a RIN
generating pathway for renewable diesel produced from canola oil. Most canola oil used in the
U.S. is imported from Canada, but smaller volumes of canola oil are also produced domestically.
Projecting the availability of feedstocks to domestic BBD producers requires a
consideration of a wide range of factors including the total production and/or collection of these
feedstocks (both in the U.S. and foreign countries) and competition for these feedstocks from
both non-biofuel markets and biofuel producers in other countries. Each of these factors are in
turn impacted by a variety of technical and political issues that are very difficult to project with
certainty in future years. To illustrate these complex dynamics, consider the potential growth in
soybean oil and FOG to U.S. biofuel producers. Increasing U.S. soybean oil production in future
years will require investment to increase the domestic soybean crushing capacity. Domestic
soybean crushers that have made these investments in the past are able to do so in the future but
are unlikely to do so unless they have a reasonable expectation of increasing demand for soybean
oil to provide a return on their investments. The recent observed increase of imported FOG to
domestic BBD producers is the result of increased global collection of these feedstocks and
changes to biofuel policies in both the U.S. and other countries, such that the U.S. became a
preferred destination for these feedstocks. The marked shift back to lower imports of Asian UCO
242
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and imported feedstocks and fuels in 2025 was again marked by significant shifts to biofuel and
trade policy in the U.S. and other countries. Any number of factors, such as other countries
adopting more stringent biofuel mandates, providing higher incentives for biofuels produced
from FOG, or restricting FOG exports, could quickly change these market dynamics.
The remainder of Chapter 7.2.3 provides more detail on the historic and projected future
supply of these feedstocks to domestic BBD producers. In general, these sections focus on
projecting the total quantity of feedstocks that could be provided to domestic producers if there
are sufficient economic incentives to increase the production and/or collection of these
feedstocks and if the U.S. remains a preferred destination for these feedstocks. A further
discussion of the uncertainties related to these projections, and how these uncertainties impact
the Analyzed Volumes, can be found in Chapter 7.2.5 and Preamble Section III.A.2,
respectively. In our discussion of available feedstocks we have differentiated between
domestically sourced feedstocks and imported feedstocks, as both the historic trends and factors
that are expected to impact future supplies to BBD producers often differ depending on the
source of the feedstock. While this section considers the availability of imported feedstocks to
domestic BBD producers, it does not consider BBD imported from foreign producers, which is
covered in Chapter 7.2.4.
7.2.4.1 Domestic BBD Feedstocks
Domestic feedstocks used for BBD production have historically come from three primary
sources: FOG (including UCO and animal fats), distillers corn oil, and soybean oil. Domestic
BBD producers generally do not report whether the feedstock they use to produce biofuel is
sourced domestically or imported. In many cases EPA had to infer the quantity of BBD
feedstock from domestic sources based on total reported feedstock use records of the quantity of
BBD feedstocks imported to the U.S. from UN Comtrade.481 While this data has its own
limitations (for example, it only reports total import quantities of various products and does not
identify the importers or the industries using the imported feedstock), we have been able to
reasonably estimate the quantities of domestic and imported feedstocks used by BBD producers
using a combination of EMTS data on domestic biofuel production by feedstock, domestic
feedstock production from USDA and other sources, and import data from UN Comtrade.
Domestic BBD production from FOG in the U.S. was mostly from domestically sourced
feedstocks and was relatively stable from 2014 through 2020. However, from 2022 through 2024
FOG imports and total domestic usage increased. These feedstocks are generally byproducts of
other industries. Their historical growth prior to 2014 domestically was driven by the greater
economic incentive provided by the RFS and LCFS programs, increasing collection rates,
reducing disposal, and shifting them from other uses. Once the majority of FOG that could
economically be collected in the U.S. had been used productively, the subsequent growth in the
collection of these feedstocks has tended to follow population growth. We expect this trend to
continue in future years and that any significant increases in the availability of FOG to domestic
biofuel producers will primarily be the result of increased imports of these feedstocks (see
Chapter 7.2.3.2 for a discussion of the availability of imported FOG to domestic BBD
producers).
481 United Nations, "UN Comtrade Database," https://comtradeplus.un.org.
243
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To project increases in the supply of FOG from the U.S. to domestic BBD producers we
relied primarily on historical data. Table 7.2.3.1-1 shows the total quantity of FOG used by
domestic BBD producers each year from 2014 through 2025. Prior to the significant increase in
imported FOG in 2021, the general trend in the production of BBD from FOG was relatively
small but predictable growth. From 2014 through 2021 the average annual increase in the
domestic production of BBD from FOG was approximately 25 million gallons per year. A study
conducted by Global Data similarly projected that the domestic supply of UCO would increase
by approximately 30 million gallons per year from 2022 through 2030.482 Based on this data we
project that the domestic supply of FOG to BBD producers will increase at a rate of
approximately 25 million gallons per year through 2027.
Table 7.2.3.1-1: Domesl
ic BBD product
tion from FOG
million gallons
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025*
391
406
477
469
518
516
455
587
869
1,395
1970
1890
*Estimated from partial year 2025 EMTS data
Production of BBD from distillers corn oil has also generally increased through 2025.
The most significant increases in the volume of BBD produced from distillers corn occurred
through 2018, as more corn ethanol plants installed equipment to produce distillers corn oil and
corn ethanol production expanded (see Table 7.2.3.1-2). However, production of BBD from this
feedstock has been fairly consistent at about 250-350 million gallons per year since 2017. Total
production of distillers corn oil in the U.S. in 2025 was approximately enough to produce 630
million gallons of BBD. This suggests that distillers corn oil could be used to produce over 60
million gallons of additional BBD, but that would require shifting distillers corn oil from other
existing uses, such as animal feed, which would then have to be backfilled with other new
sources.483 It is also possible that domestic production of distillers corn oil could increase or
decrease in future years for a variety of reasons, including new varieties of corn with higher oil
content, greater extraction rates, or changes in U.S. ethanol production for domestic or
international markets. While it is possible that the use of distillers corn oil by domestic BBD
producers will increase in future years through the diversion of this feedstock from other markets
or increased production, we project that there will not be any increase (or decrease) in the supply
of distillers corn oil to domestic BBD producers through 2027.
Table 7.2.3.1-2: Domesl
tic BBD product
tion from Distillers Corn Oil (million gallons)
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025*
187
183
224
245
308
278
226
299
325
332
520
560
*Estimated from partial year 2025 EMTS data available at the time of writing.
The remaining volume of domestic BBD has been produced from soybean oil and canola
oil. The largest source of BBD production in the U.S. historically has been soybean oil. While
there have been small quantities of soybean oil imported into the U.S. in previous years, the vast
482 Global Data, "UCO Supply Outlook," August 2023. https://cleanfuels.org/wp-content/uploads/GlobalData UCO-
Supplv-Outlook Sep2023.pdf. Annual growth in UCO collection based on estimated growth in per capita UCO
collection rates from 2022-2030.
483 For a discussion of backfilling when oil is removed from dried distillers grains, see 83 FR 37735 (August 2,
2018).
244
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majority of soybean oil available in the U.S. is from domestic sources due to the large domestic
soybean oil industry and significant tariffs on imported soybean oil.484 Conversely, the domestic
canola oil industry is relatively small, and most of the canola oil used in the U.S. is imported
from Canada. Domestic production of canola oil has been relatively stable since 2013/2014,485
and we are therefore only projecting small increases in the availability of domestic canola oil to
U.S. biofuel producers through 2027. Our projections of potential increases in imported canola
oil from Canada are covered in Chapter 7.2.3.2. However, there does hypothetically exist the
potential for greater quantities of canola oil to be shifted away from other end uses to biofuel
production. For the 2023/24 harvest year, domestic disappearance of canola oil was about 8.9
billion pounds across all industries and including exports.486 Less than half of that volume was
used as biofuel feedstock. In a hypothetical scenario where all domestic canola oil was shifted to
biofuel production, there would be sufficient supply to produce about 662 million gallons of
BBD from this domestically produced canola oil.
Use of soybean oil to produce biodiesel increased from approximately 5.1 billion pounds
in the 2013/2014 agricultural marketing year to approximately 13 billion pounds in the
2023/2024 agricultural marketing year, the latest data available at the time the analyses for this
rule were completed.487 This time period saw significant increases in total soybean oil production
(through increased domestic soybean crushing) and the use of soybean oil for biofuel production,
both in absolute terms and relative to other markets. Domestic soybean crushing increased by
39% from 2013/2014 (1,734 million bushels) to 2024/2025 (2,410 million bushels). At the same
time that domestic soybean oil production was increasing, the percentage of all soybean oil
produced in the U.S. for biodiesel also increased, from approximately 25% in 2013/2014 to
approximately 48% in 2023/2024.
As a point of reference, if all soybeans grown in the U.S. in 2023/2024 were crushed
domestically (rather than exported) we project that domestic soybean oil production would be
approximately 53.1 billion pounds, enough feedstock to produce approximately 6.6 billion
gallons of BBD. In the near term it is not possible to crush all the soybeans produced in the U.S.
domestically due to crushing capacity limitations, nor is it likely possible to divert all soybean oil
to biofuel production due to strong demand in other non-biofuel industries such as food. These
numbers illustrate, however, the theoretical maximum level of BBD production from the current
U.S. soybean crop if recent trends toward increasing domestic soybean crushing and greater use
of soybean oil for biofuel production relative to other markets were to continue indefinitely. We
note, however, that shifting greater quantities of soybean oil from current markets for increased
biofuel production could result in these markets turning to other sources of vegetable oil such as
palm oil.
Additional soybean oil availability to biofuel producers in future years is primarily
expected to come from increased domestic crushing of soybeans. Some quantities of soybean oil
used in non-biofuel markets are also likely to be shifted into the biofuels market. While the total
484 USD A, "Oil Crops Yearbook," March 2025. https://www.ers.usda.gov/data-products/oil-crops-Yearbook. The
agricultural marketing year for soybeans runs from September to August.
485 Id.
486 Id.
487 Id.
245
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use of soybean oil for non-biofuel markets has remained relatively stable in the past five years,
strong incentives for biofuel use from 45Z, the RFS, and state clean fuels programs are stronger
than they have historically been.488 U.S. soybean oil production could continue to increase in
future with investments in expanding domestic soybean crush capacity, with increases in
soybean crush likely resulting in reduced soybean exports. Since 2000/2001 the percentage of
U.S. soybean production that has been crushed domestically has varied from a low of 44% in the
2016/2017 agricultural year to a high of 67% in the 2007/2008 marketing year.489 Most of the
rest of the whole soybeans are exported to foreign countries, where the beans are then crushed to
produce soybean meal and soy oil for their own markets.
Strong demand for vegetable oil has already resulted in increasing domestic crushing of
soybeans. Recent data from USDA indicates that soybean crushing reached record levels of
approximately 3.8 billion gallons in the 2024/2025 agricultural marketing year and is expected to
increase to 4 billion gallons in 2026/2027.490 There have also been numerous investment
announcements to increase domestic soybean crush capacity through the construction of new
facilities as well as the expansion of existing facilities. Crush capacity expansion that is planned
and/or currently under construction is expected to continue to add to domestic soybean crush
capacity through 2027. Accounting for only facilities that have broken ground and are currently
under construction the domestic soy crush industry is projected to add the equivalent capacity of
360 million gallons in 2026. Including proposed and announced facilities (that will be completed
2027 or beyond), the domestic soy crush industry was expected to add up to the equivalent of
757 million gallons of crush capacity as soon as 20 2 7491-492-493 Future crush expansion in 2028
and beyond is dependent on the expected demand for soybean oil in these years from the biofuel
sector and other markets. If the increased domestic crushing capacity results in reduced exports
of whole soybeans (rather than increased soybean production), this increased soybean oil
production could be achieved with little impact on overall U.S. soybean production. However,
shifting soybean crushing to the U.S. and using the oil domestically would decrease soy oil
supplies abroad. Foreign countries could respond to this reduction of soybean oil supply by
increasing their consumption of other vegetable oils.
The USDA Agricultural Projections to 2033 project increasing domestic soybean oil
production through 2030 as a result of an increased soybean crushing. USDA projects that
domestic soybean oil production will increase by approximately 2 billion pounds from 2025 (28
billion pounds) to 2030 (30 billion pounds).494 If this entire increase in soybean oil production
488 Id.
489 Id.
4911 USDA, "Grains and Oilseeds Outlook," https://www.usda.gov/sites/default/files/documents/2026AQF-grains-
oilseeds-outlook.pdf. February 2026
491 American Soybean Association, "Soybean Crush Expansion, 2025 Update," April 10, 2025.
https://sovgrowers.com/news-releases/sovbean-crush-expansion-2025-update.
492 USDA, "Oil Crops Outlook: February 2026," February 12, 2026.
https://ers.usda.gov/sites/default/files/ laserfiche/outlooks/113803/OCS-26b.pdf?v=57362.
493 Projection using early 2025 data, the latest available at the time of writing.
494 USDA, "USDA Agricultural Projections to 2033," OCE-2024-1, February 2024.
https://www.usda.gov/sites/default/files/documents/USDA-Agricultural-Proiections-to-2033.pdf. For each year, we
converted soybean oil production projections to calendar year prices by weighting production in the first agricultural
marketing year (e.g., 2024/2025 for the 2025 price) by 0.75 and production in the second agricultural marketing year
(e.g., 2025/2026 for the 2025 price) by 0.25.
246
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were used to produce biodiesel or renewable diesel, it would result in an increase of
approximately 250 million gallons of biofuel from 2025 to 2030, or an increase of approximately
50 million gallons per year.495 These projections are based on macroeconomic forecasts and do
not appear to account for the number of facilities that have recently begun construction or
announced plans to build or expand soybean crushing facilities, which could significantly
increase domestic soybean oil production through 2027. As such, they are better projections of
domestic soybean oil production with static RFS volume requirements at 2025 levels rather than
projections of potential domestic soybean oil production supported by strong and growing RFS
volume requirements.
EMTS data on domestic BBD production from soybean oil (see Table 7.2.3.1-3) show
that the use of soybean oil for BBD production has increased significantly since 2014. The
average annual increase in domestic BBD produced from soybean oil from 2014-2023 was
approximately 90 million gallons per year. More recent data showed an accelerated growth rate
before a sudden decline. From 2021 to 2023 the average annual growth rate increased to
approximately 170 million gallons per year, and the increase from 2022 to 2023 was
approximately 260 million gallons. 2023 to 2024 saw an increase of about 260 million gallons,
and 2024 to 2025 saw overall BBD production decrease, resulting in a decrease in overall use of
soy oil in BBD of 310 million gallons. However, the percentage of total BBD produced from soy
oil in 2025 remained similar to 2023 and 2024 (32%, 26%, and 26%, respectively).
Table 7.2.3.1-3: Domesl
tic BBD product
tion from Soybean Oil (million gallons)
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
641
616
774
745
984
987
1,123
1,072
1,159
1,418
1680
1370
Recent announcements of plans to invest in increasing the domestic soybean crush
capacity suggest that the higher observed rates of growth in the supply of soybean oil to BBD
producers may continue in future years. The American Soybean Association projected that the
increase in domestic soybean oil production from 2023-2025 would be sufficient to produce
approximately 700 million gallons of biodiesel and renewable diesel.496 The Clean Fuels
Alliance America submitted a study conducted by LMC international that found that the
projected growth in soybean oil production in the U.S. from 2021-2025 would be sufficient to
produce approximately 750-800 million gallons of biodiesel and renewable diesel.497 More
recently a study conducted by S&P Global projected that U.S. soybean crushing expansion from
2024 through 2027 would increase crush capacity by 700 million bushels, producing enough
soybean oil to produce approximately 1 billion gallons of renewable diesel.498 These estimates
are summarized in Table 7.2.3.1-4.
495 These projections are based on the existing RFS volume requirements through 2025 and not any assumed
increase in RFS volumes for 2026 and beyond. Future growth projections are therefore based on increases in future
demand from non-biofuel sectors.
496 Comment submitted by American Soybean Association (ASA), Docket Item No. EPA-HQ-OAR-2021-0427-
0579. https://www.regulations.gov/comment/EPA-HO-OAR-2021-0427-0579.
497 Comment submitted by Clean Fuels Alliance America, Docket Item No. EPA-HQ-OAR-2021-0427-0805.
https://www.regulations.gOv/comment/EPA-HO-OAR-2021-0427-0805.
498 S&P Global, "Availability of Feedstocks for Biofuel Use - Key Highlights," July 2024.
https://www.nopa.org/wp-content/uploads/2024/07/NOPA-SPGCI-Availabilitv-of-Feedstocks-Kev-Higlilights.pdf.
247
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Table 7.2.3.1-4: Summary of Projections of Soybean Oil Supply to BBD Producers (million
gallons BBD)
Estimated
Annual Average
Data Source
Increase
Timeframe
Increase
USDA Agricultural Projections to 2033
250
2025-2030
50
EMTS Data
780
2014-2023
90
EMTS Data
350
2021-2023
170
American Soybean Association
700
2023-2025
350
LMC
750-800
2021-2025
200
S&P Global
1,000
2024-2027
250
The higher observed and projected increases in domestic soybean oil production occurred
following a period when soybean oil prices were historically high. From 2013/2014 through
2018/2019 the average price of soybean oil was approximately $0.31 per pound.499 Starting in
2019/2020 soybean oil prices increased dramatically and remained high for several years,
averaging $0.61 per pound from 2019/2020 through 2023/2024.500 Industry data suggest that the
construction timeline for a soybean crushing facility is approximately 2 years, aligning the
observed and projected periods of significant growth in domestic soybean oil production with a
two year lag of the observed price increase. Thus, the available data suggest that future increases
in domestic soybean crushing are possible, but that future increases are dependent on increased
demand for soybean oil, whether from BBD producers or other markets.
Figure 7.2.3.1-1: Historical Domestic Soy Oil Price
80.00
70.00
1 60.00
ZJ
CL 50.00
E 40.00
-------
soybean oil prices lead to increased investment in soybean oil production. These estimates are
therefore likely indicative of the level of increases in domestic soybean oil production that could
be achieved with continued high demand for domestic soybean oil.
In addition to increasing U.S. soybean crushing, additional quantities of soybean oil
could be made available for biofuel production from decreased exports of soybean oil itself.
Prior to the ramp-up in biodiesel and renewable diesel use the U.S. exported significant
quantities of soybean oil, with soybean oil exports peaking in 2009/2010 at approximately 3.4
billion pounds.501 Since that time soybean oil exports have generally decreased as the quantity of
soybean oil used for domestic biofuel production has increased. USD A estimates that in the
2022/2023 agricultural marketing year soybean oil exports decreased by approximately 90% to
0.38 billion pounds before slightly increasing to 0.62 billion pounds in 2023/2024, still well
below recent historical export levels.502 While it is possible that these soybean oil exports could
be diverted to domestic biofuel production in future years, diverting all of the soybean oil exports
from 2022/2023 to biofuel production would only increase BBD production by approximately 50
million gallons per year.
Based on our review of historical data on the use of soybean oil for domestic BBD
production and the available projections of increases in soybean crushing capacity in future
years, we project that the production of BBD from domestic soybean oil could increase at a rate
of approximately 250 million gallons per year through 2027 if supported by increases in demand.
This does not account for any potential diversion from non-biofuel markets. Non-biofuel markets
for soybean oil have shrunk as the RFS has grown in scope, and there are many alternatives for
domestic soybean oil in the food industry, such as imported soybean oil from Argentia or Brazil
or imported canola oil.
7.2.4.2 Imported BBD F eedstocks
In recent years domestic BBD producers have used increasing quantities of imported
feedstocks to produce BBD. The primary imported feedstocks used for BBD production are FOG
(UCO and animal fats) and canola oil. While we do not have reliable information on the quantity
of imported soybean oil used for BBD production, USD A estimates total soybean oil imports of
approximately 621 million pounds in the agricultural marketing year 2023/2024.503 Even if this
entire volume were used for BBD production it would result in approximately 80 million gallons
of renewable diesel per year. Similarly, we are not aware of any available data on imported
distillers corn oil used for BBD production. While we do not project significant imports of
distillers corn ethanol through 2027 it is possible that imported distillers corn ethanol may
become a source of feedstock to domestic BBD producers as corn ethanol production in foreign
countries such as Brazil increases.504 This chapter focuses on projected imports of FOG and
501 Id.
502 Id.
503 Id.
5114 Colussi, Joana, Nick Paulson, Gary Schnitkey, and Jim Baltz. "Brazil Emerges as Corn-Ethanol Producer with
Expansion of Second Crop Corn." farmdoc daily (13):120, June 30, 2023.
https://fanndocdailY.illinois.edu/2023/06/brazil-emerges-as-corn-ethanol-producer-with-expansion-of-second-crop-
corn.html.
249
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canola oil, which are projected to be the dominant sources of imported BBD feedstocks through
2027.
Imports of FOG to the U.S. were relatively small through 2021. From 2014-2021 imports
of FOG increased gradually, reaching a total of about 0.5 million metric tons in 2021.505 Imports
of these feedstocks increased significantly in 2022 and 2023 (see Table 7.2.4.2-1). This rapid rise
in the imports of FOG is likely due to a number of factors, including the rapid increase in
renewable diesel production capacity,506 greater incentives from California's LCFS program and
other state clean fuels programs (and further disincentives for seed oil use in LCFS) for BBD
produced from FOG, and biofuel policies internationally, especially in the EU and now China.
Table 7.2.4.2-1: U.S.]
mports of FC
>G (million metric tons)
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
UCO
0.02
0.02
0.02
0.04
0.06
0.09
0.08
0.13
0.40
1.41
2.45
2.11
Animal Fats
0.06
0.06
0.08
0.08
0.14
0.19
0.24
0.33
0.55
0.79
0.88
1.11
Total
0.08
0.09
0.10
0.12
0.20
0.28
0.33
0.46
0.95
2.20
3.33
3.22
Projecting the future supply of imported FOG to U.S. BBD producers is an inherently
difficult task, as it requires projecting not only the global supply of these feedstocks but also
making assumptions about future worldwide market conditions for these feedstocks. For
example, the recent increase in UCO imports to the U.S. is largely a function of the U.S.
surpassing the EU as the preferred destination for UCO exports from foreign countries. The
subsequent decline was largely a function of U.S. imposed tariffs, declining U.S. biofuel
production, and removal of Chinese export rebates on UCO. These types of market changes can
dramatically impact the supply of imported feedstocks to the U.S., and it is not possible to
project these changes with any degree of certainty. In this Chapter we have projected the
potential available supply of imported FOG to the U.S. assuming that the U.S. maintains some
levels of UCO imports, especially pertaining to Asia, while also beginning to see imports of
UCO from Mexico as a result of the altered 45Z credit that requires feedstock use from North
America. In Chapter 7.2.6 we provide further discussion on some of the key uncertainties related
to market conditions for imported feedstocks, and potential future scenarios if other countries
increase their market share of imported FOG in future years.
To project potential future supplies of imported FOG, we first examined several data sets,
including historical data on FOG imports and projections of future FOG imports from several
sources. EPA considered both historical data on total FOG imports sourced from UN Comtrade
and data on domestic BBD production from FOG from EMTS. The data considered are shown in
Table 7.2.4.2-2. In 2022, UCO imports began to increase dramatically until levelling off in 2025,
while animal fat imports have continued to grow by approximately 64 million gallons per year.
The fact that the increase in the quantity of BBD produced from FOG from 2021 to 2025 is
505 Data on imports of FOG from UN Comtrade. Data for imports of UCO and animal fats are based on HS Codes
1518 and 1502.10, respectively.
506 In general, renewable diesel production facilities are able to process FOG feedstocks, while only a subset of
biodiesel production facilities can process these feedstocks. Additionally, many renewable diesel production
facilities are located near ports and have easier access to imported feedstocks.
250
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larger than the total quantity of FOG imported suggests that very little, if any, FOG was used in
markets other than biofuel production.
Table 7.2.4.2-2: FOG Imports and Domestic BBD Production from FOG (mi lion gallons)
2019
2020
2021
2022
2023
2024
2025
UCO Imports3
24
23
36
109
387
673
581
Animal Fat Imports3
53
67
91
152
218
242
305
Total FOG Imports3
77
90
127
261
605
924
886
BBD Produced from FOGb
516
455
587
869
1,395
1920
1,870
a Import data from UN Comtrade (Data on FOG represents HS code 1518 and data on animal fats represents HS
code 1502.10). Data from Comtrade was converted from kilograms to million gallons BBD assuming 8 lbs FOG per
gallon of BBD.
b Data from EMTS.
EPA also considered available estimates of potential FOG imports. A study conducted by
Global Data on behalf of the Clean Fuels Alliance America in August 2023 projected the
potential increase in the supply of UCO to domestic BBD producers of approximately 0.9 billion
gallons from 2025 to 2030 in their base case, an increase of approximately 180 million gallons
per year.507 This same study also estimated additional global potential beyond the base case.
Global Data estimated that the additional global potential could increase the global supply of
UCO available to biofuel producers to support an additional 4.7 billion gallons of BBD
production from 2025 to 2030. Another study conducted by S&P Global projected that UCO
imports to the U.S. could increase by about 5 billion pounds per year from 2023 - 2030 (enough
to produce over 600 million gallons of BBD) and tallow imports could increase by about 3.25
billion pounds from 2023-2030 (enough to produce over 400 million gallons of BBD).508 The
estimates of the potential growth rate in the production of BBD from FOG from both the historic
data and these studies are summarized in Table 7.2.4.2-3.
5117 Global Data, "UCO Supply Outlook," August 2023. https://cleanfuels.org/wp-content/uploads/GlobalData UCO-
Supplv-Outlook Sep2023.pdf.
5118 S&P Global, "Availability of Feedstocks for Biofuel Use," July 2024. https://www.nopa.org/wp-
content/uploads/2024/07/N6p A-SPGCI-Availabilitv-of-Feedstocks-Kev-Highlights.pdf.
251
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Table 7.2.4.2-3: Summary of Projections of FOG Supply to BBD Producers (million gallons
BBD)
Estimated
Annual Average
Data Source
Increase
Timeframe
Increase
All FOG (undifferentiated)
EMTS Data
808
2021-2023
404
UCO
UN Comtrade Imports
351
2021-2023
176
Global Data (Base Case)
900
2025-2030
180
Global Data (Base + Potential)
5,600
2025-2030
1,120
S&P Global
600
2023-2030
86
Tallow
UN Comtrade Imports
127
2021-2023
64
S&P Global
400
2023-2030
58
Based on our review of the historical data on the use of imported UCO and tallow for
domestic BBD production and the available projections of increases in the imports of these
feedstocks in future years, we project that the production of BBD from imported UCO could
increase at a rate of 200 million gallons per year through 2030 and imported tallow could
increase at a rate of 50 million gallons per year through 2030. These projections are slightly
lower than the observed rate of increase in domestic BBD production from FOG from 2021-
2023 when the U.S. became a more significant importer of these feedstocks. We note, however,
that these numbers also include increases from domestic sources of FOG. It should be noted that,
while general supply dynamics will generally remain unchanged, these feedstocks going forward
are more likely to be second choices as the effects of 45Z and trade policy take hold over the
broader FOG market.
To assess the feasibility of increasing imports of FOG for biodiesel production we
considered the global volumes of these feedstocks exported over the past five years for which
data were available and the total quantity of these feedstocks imported by the U.S. This
information is shown in Figure 7.2.4.2-1. During this time period, global exports of tallow have
increased gradually, while UCO exports have increased more quickly. However, policy actions
by the U.S. to discourage imports and China to discourage exports, including high tariffs on
Asian used cooking oil and removal of export rebates, respectively, have dampened growth in
imports, with 2025 seeing a large decline in overall UCO imports to the U.S. from China. While
previous growth rates of 250 million gallons per year are likely to continue in an environment
similar to those of 2019-2023, it is far more likely that UCO imports will continue to see a
decline given the policy uncertainties and robust growth in domestic and North American crush
of virgin seed oils.
One exception to this trend is Canada and Mexico. The Canadian FOG market is
relatively developed, with rates of UCO collection rivalling the US. US imports of Canadian
FOG reached an estimated 70 million gallons in 2023.509 Mexican used cooking oil is now
highly advantaged, thanks to a lower tariff environment compared to Asian competitors,
509 Global Data, "UCO Supply Outlook," August 2023.
252
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eligibility for the 45Z credit, low operating costs, and proximity to US refineries and logistics
networks. Given the untapped potential of this market, high expected domestic biodiesel and
renewable diesel capacity utilization, and faltering Asian UCO imports to the US, we believe that
used cooking oil collection in Mexico will begin to reach its full potential. In 2010, Sheinbaum et
al. estimated that UCO potential in Mexico was concentrated in high density areas and could
reach collection rates of 45%, or 245 million gallons.510 More recently, Global Data estimated
that Mexico consumes about 5.4 gallons per capita of cooking oil. At similar collection rates, this
would be a potential of 322 million gallons of Mexican UCO in 2023.511 Given increasing
population, high incentives for collection, and an 80% urban population, we project growth in
U.S. imports of Mexican UCO of 50 million gallons per year, mirroring domestic U.S. UCO
growth. Collection is projected to ramp up by 2027 in response to the RFS, tax incentives, and
trade dynamics.512
Gallons)
In addition to imported FOG, we also project significant quantities of imported canola oil
could be made available to domestic BBD producers in future years. As with other potential
sources of feedstock for domestic BBD producers, we used the historic BBD production and
canola oil import data as a starting point for our future projections. Annual domestic BBD
production and canola oil imports are shown in Table 7.2.4.2-4. From 2014 through 2022
production of BBD from canola oil fluctuated between 100 and 200 million gallons per year.
M" Sheinbaum-Pardo. Claudia. Andrea Calderon-Irazoque, and Mariana Ramirez-Suarez. "Potential of Biodiesel
From Waste Cooking Oil in Mexico.'* Biomass andBioenergv 56 (June 7, 2013): 230-38.
https://doi.org/10.1016/i.biombioe.2013.05 008.
511 Global Data, "UCO Supply Outlook," August 2023. https://cleanFuels.org/wp-content/uploads/GlobalData UCO-
Supplv-Outlook Sep2023.pdf.
512 UN Habitat "Country Ov erview - Mexico." https://unliabitat.org/mexico.
Figure 7.2.4.2-1: Global FOG Exports and U.S. vs. Non-U.S. Imports (Million
3,500
3,000
2,500
2,000
ra
U
c 1,500
o
1,000
2019 2020 2021
Global UCO Exports
US UCO and Tallow Imports
2022 2023 2024
¦ Global Tallow Exports
^Non-U,S. Imports
2025
253
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Production of BBD from canola oil increased significantly in 2025, to approximately 600 million
gallons likely as the result of increased canola crushing in Canada and the approval of the
pathway for renewable diesel to generate BBD RINs for fuel produced from canola oil in
2023.513 Since the 2010/2011 agricultural marketing year approximately 70% of all canola oil
supplied to the U.S. has been imported,514 and greater than 95% of the imported canola oil is
imported from Canada.515 We anticipate that through 2027 Canada will be the predominant
source of any increase in imports of canola oil to domestic BBD producers and have therefore
primarily focused on potential imports from Canada in projecting the availability of canola oil
for biofuel production.
Table 7.2.4.2-4: Domestic BBD Production from Canola Oil and Total Canola Oil Imports
2014
2015
2106
2017
2018
2019
2020
2021
2022
2023
2024
2025
Domestic BBD
Production from
Canola Oil
141
115
207
177
159
157
159
166
174
344
542
600
Canola Oil Imports
for All Uses
433
470
509
541
505
493
506
524
610
809
906
974
We also considered projections of increased canola oil production in Canada in future
years and how much of that increased production would be made available to U.S. biofuel
producers. In the Set 1 rule EPA used publicly available data to project increasing canola oil
production in Canada from 2022-2025. We projected that total canola oil production in Canada
would increase by approximately 1.1 million metric tons from 2022-2025, and that half of this
increase in production would be available to U.S. BBD producers (enough canola oil to produce
approximately 280 million gallons of BBD). Given more recent data, we have adjusted our
estimates of Canadian canola crush capacity. The Canadian Canola Council reports average
annual growth in domestic oilseed production of 33.2 million gallons. The industry in Canada
crushed 11.4 MMT, or approximately 1330 million gallons of canola in marketing year
2024/2025, and capacity is expected to reach 15 MMT by the end of 2026, in part thanks to the
soon opening Cargill Regina Canola Crush Facility, which will bring online 1 MMT of
capacity.516-517-518 The USDA Foreign Agricultural Service reports a 28% year over year growth
in Canadian canola crush capacity since 2021. As seen in Table 7.2.4.2-5, capacity continues to
grow, and capacity utilization has reached 90%+ after the industry recovered from a Sino-
Canadian trade war in 2023.
513 87 FR 73956 (December 2, 2022).
514 USDA, "Oil Crops Yearbook," March 2025. https://www.ers.usda.gov/data-products/oil-crops-Yearbook.
515 Data from UN Comtrade.
516 Canadian Canola Council, "Domestic Processing," https://www.canolacouncil.org/markets-stats/processing.
517 USDA, "Oilseeds and Products Annual - Canada," March 31, 2025.
https://apps.fas.usda.gov/newgainapi/api/Report/DownloadReportBvFileName?fileName=Qilseeds%20and%20Prod
ucts%20Annual Ottawa Canada CA2025-0017.pdf.
518 Cargill, "Regina Canola Crush Facility." https://www.cargillag.ca/regina.
254
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Table 7.2.4.2-5: Canadian Crush Capacity, Oilseed Production, and Capacity Utilization
Marketing Year
20/21
21/22
22/23
23/24
24/25
25/26b
Capacity3 (MMT)
11.10
11.10
11.20
13.46
14.46
16.86
Production, Canola Oil (MMT)
4.62
3.69
4.25
4.81
4.95
5.17
Capacity Utilization
94%
77%
89%
82%
79%
71%
a As reported by the Foreign Agricultural Service
b All 2025/2026 data projected based on available 2025/2026 Marketing Year data from Canadian Canola Council
and Ag Canada.
The data laid out in Table 7.2.4.2-5 suggests that, while capacity has continued to grow at
a high rate, actual crush has lagged capacity growth. However, the industry has demonstrated
historically that it is able to run at 90%+ capacity utilization, including from 2015-2021. Given
historic run rates, new capacity expansions, and full recovery from the COVID-19 pandemic, it
is reasonable to expect a maximum of 1,933 million gallons in 2026. Canola oil usage in biofuel
in the U.S. reached 50% of all available canola oil in 2024, meaning 966 million gallons
available for use as BBD in 2025. This assumption is conservative, especially considering the
proportion of canola oil used for biofuels in the U.S. has increased every year since 2015.519-520
S&P Global estimated that canola oil production in Canada would increase by about 5.4 billion
pounds from 2024-2027. This quantity of canola could be used to produce approximately 700
million gallons of BBD if it were all used for this purpose.
To estimate how much of this biofuel could be available to U.S. BBD producers we
considered projected BBD demand in Canada. Canada passed the Clean Fuels Regulations in
2022. These regulations require decreasing carbon intensities from transportation fuel used in
Canada each year from 2023-2030. USDA FAS estimated BBD use in Canada in 2023 at
approximately 370 million gallons.521 A document published by Environment Canada in
February 2024 projected that under a current measures scenario BBD consumption in Canada
could increase to approximately 1.1 billion gallons in 20 3 5.522 Reaching 1.1 billion gallons of
BBD consumption in 2035 would require an average annual increase of approximately 60
million gallons per year. If Canadian canola oil production continues to increase at the rate
projected by S&P Global from 2024-2027 (175 million gallons per year) the availability of
canola oil to U.S. BBD producers could increase by over 100 million gallons per year after
accounting for the projected increase in BBD demand in Canada. The estimates of the potential
growth rate in the production of BBD from canola oil from both the historic data and these future
projections are summarized in Table 7.2.4.2-5.
519 USDA, "Oilseed Crop Yearbook," Canola Oil, March 20, 2025. https://www.ers.usda.gov/data-products/oil-
crops-vearbook.
5211 Canola Council of Canada, "Market and Statistics," https://www.canolacouncil.org/markets-stats.
521 USDA, "Biofuels Annual - Canada," December 8, 2024.
https://apps.fas.usda.gov/newgainapi/api/Report/DownloadReportBvFileName?fileName=Biofuels%20Annual Otta
wa Canada CA2024-0057.pdf.
522 Canada Energy Regulator, "Market Snapshot: Bioenergy Use Could Double in Canada's Net-Zero Future,"
February 21, 2024. https://www.cer-rec.gc.ca/en/data-analvsis/energv-markets/market-snapshots/2024/market-
snapshot-bio-energy-use-could-double-canadas-net-zero-future.html.
255
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Table 7.2.4.2-5: Summary of Projections of Canola Oil Supply to BBD Producers (million
gallons BBD)
Estimated
Annual Average
Data Source
Increase
Timeframe
Increase
EMTS BBD Production from Canola Oil
178
2021-2023
89
USD A Canola Oil Imports
285
2021-2023
143
EPA Projection in Set 1
283
2022-2025
94
S&P Global (Total)a
700
2024-2027
175
S&P Global (50%)a
350
2024-2027
88
EPA Projection13
366
2025-2026
366
a Estimate includes expansion in 2024.
b One year estimate based on certain capacity expansion. Planned expansion through 2028 exceeds 18.7 MMT, but
the industry is prone to longer timelines as a result of trade policy induced demand uncertainty
At the time of these estimates, the 45Z credit did not advantage Canadian canola oil the
way it does in 2026, after passage of the OBBB. Canadian canola oil is now advantaged thanks
to the removal of land use change impact considerations and the newly limited geographic scope
(North America only). Based on our review of the historical data on the use of imported canola
oil used for domestic BBD production and the available projections of increases in the imports of
canola oil in future years we project that the production of BBD from imported canola oil could
increase at a rate of between 100 and 150 million gallons per year through 2030. This projection
is similar to the observed rate of increase in BBD produced from canola oil since 2021, as well
as the average annual increase of canola oil we projected would be available to domestic BBD
producers in the Set 1 Rule, taking into account a greater demand for canola thanks to changes to
the RFS, US trade policy, and the 45Z credit.
7.2.4.3 Emerging Oil seed F eedstocks
In addition to the feedstocks that have historically been used for BBD production, a
number of BBD producers have expressed interest in using emerging feedstocks, such as oil
from camelina, carinata, or pennycress, for BBD production in future years. These emerging
oilseed crops are most often intended to be grown as second crops on existing cropland and/or as
an alternative to fallow years when no commercial crops would otherwise be grown. As such,
they have the potential to increase vegetable oil production with little or no increase in total
cropland or displacement of other crops. While there are currently approved RFS pathways for
several of these emerging oilseed feedstocks, very little BBD has been produced from them to
date. We anticipate that there will be increasing interest in developing and adopting these crops
in future years. The process of introducing new crops at commercial scale, however, generally
takes many years. At this time we are not projecting significant volumes of vegetable oil will be
made available to domestic BBD producers from emerging oilseeds through 2027.
7.2.4.4 Summary of the Availability of BBD Feedstocks
As described in the preceding Chapters, EPA has projected the average annual increase in
the feedstocks available to domestic BBD producers. We have considered the primary feedstocks
used to produce BBD since the beginning of the RFS program (FOG, distillers corn oil, soybean
256
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oil, and canola oil) from both domestic and imported feedstocks. Our projections assume that
soybean oil will remain a preferred choice for biodiesel and become more popular in renewable
diesel production as a result of trade policy and the 45Z credit. They also project a modest
increase in imported FOG, representing acknowledgement of potential growth due to high
volumes and greater supply dampened by tariffs and changes to the 45Z credit excluding non-
North American feedstocks. They also assume a high growth rate of Canadian canola crush that
led to increased imports to replace imported FOG feedstocks, especially in West Coast
renewable diesel facilities. Outside of these three main categories (domestic soy, imported FOG,
and imported canola), we project few changes to feedstock availability or historical growth rates
in other feedstocks.
These projections represent the projected annual volume increases that could be achieved
under favorable market conditions. Because these feedstocks are reasonable substitutes for each
other in many markets we have greater confidence in the total projected annual increase than in
any of the individual feedstock estimates. The actual supply of any single feedstock will likely
depend on a number of factors, including the incentive available for biofuel produced from each
feedstock in the U.S. and other countries. For example, some mixed feedstock facilities could
just as easily accept FOG over canola, or vice versa. Their choices are likely to ultimately come
down to price at time of contract. Our projected annual increases of the potential BBD feedstocks
are summarized in Table 7.2.4.4-1. These increases represent an acknowledgement of continued
preference for continuation of pre-2025 feedstock usage while also evaluating how facilities with
diminished production might respond to increased volumes well above 2025 production. The gap
between production in 2025 and the volumes in the out years of this rule may be filled by a
variety of feedstocks, but most likely will be fulfilled by continued FOG imports, canola oil
imports from Canada, or domestic soy oil. Further discussion of the likely supply of these
feedstocks and domestic BBD production in alternative circumstances can be found in Chapter
7.2.6.
Table 7.2.4.4-1: Projected Annual Increase in BBD Feedstocks Through 2027 (million
gallons BBD equivalent)
Feedstock
Projected Annual
Average Increase
Domestic FOG
50
Domestic Distillers Corn Oil
0
Domestic Soybean Oil
140
Imported UCO
40
Imported Tallow/Animal Fats
20
Imported Canola Oil
120
Emerging Vegetable Oils
0
Total
625
7.2.5 Imports and Exports of Biomass-Based Diesel
In evaluating the potential consumption of BBD through 2027 we also examined BBD
imports and exports in previous years. Since 2014 gross biodiesel imports have generally
averaged about 200 million gallons per year, with the exception of 2015-2017 and 2023-2024.
257
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During this time (2015-2017) biodiesel imports from Argentina surged, with biodiesel imported
from Argentina responsible for 64% of all biodiesel imports in these three years. In August 2017,
the U.S. announced preliminary tariffs on biodiesel imported from Argentina and Indonesia.523
These tariffs were subsequently confirmed in April 20 1 8.524 Since the time the preliminary tariffs
were announced, EIA has not reported any biodiesel imported from these countries.525 After the
imposition of these tariffs, imports of biodiesel from other countries increased marginally;
however, the biggest effect of these tariffs has been a decrease in the total volume of imported
biodiesel back to approximately 200 million gallons during 2018-2022. However, in 2023
biodiesel imports increased significantly once again and again in 2024 Most of the increase in
biodiesel imports were supplied by countries in the EU, including Germany, Italy, and Spain.526
The increase in imports from the EU observed in 2023 reflect both strong biodiesel demand in
the U.S. (supported by incentives such as the RFS program, the federal tax credit, and state
incentives) and weakening demand for these fuels in the EU.
Similarly, renewable diesel imports generally increased from 2014-2021. Since 2021,
renewable diesel imports were relatively stable, ranging between 300 and 400 million gallons per
year, before a steep decrease in 2025 to less than 200 million gallons. A significant factor in the
increasing imports of renewable diesel appears to be the California LCFS program, as most of
the renewable diesel consumed in the U.S. (including both domestically produced and imported
renewable diesel) has been consumed in California where it could benefit from both RFS and
LCFS incentives.527
In 2025, these dynamics abruptly changed after tax credit changes and lower domestic
consumption of these fuels. The $1 40A credit was no longer in effect and its replacement, the
45Z clean fuel production credit, only applies to domestic production. Imports of both fuels
reached their lowest levels since 2012, with biodiesel imports slumping to 2,000 barrels per day
(approximately 30 million gallons per year) and renewable diesel down to 5,000 barrels per day
(approximately 75 million gallons per year)528
Exports of RIN generating biodiesel, based on EMTS data, have been fairly consistent
since 2014, generally ranging between 70 and 130 million gallons per year. According to EMTS
data, renewable diesel exports increased with domestic renewable diesel production, reaching
over 400 million gallons in 2023. Increasing exports of renewable diesel reflect the existence of
biofuel mandates and significant financial incentives creating high demand in other countries that
the U.S. must compete with. As one example, Canada recently finalized new Clean Fuel
523 82 FR 40748 (August 28, 2017).
524 8 3 FR 18278 (April 26, 2018).
525 EIA, "U.S. Imports by Country of Origin - Biodiesel," Petroleum & Other Liquids, April 30, 2025.
https://www.eia.gov/dnav/pet/pet move impcus a2 nus EPOORDB imO mbbl a.htm.
526 Id.
527 Data from California's LCFS program indicates that approximately 1.97 billion gallons of renewable diesel were
consumed in California in 2023, the most recent year for which data are available (CARB, LCFS Data Dashboard.
https://ww3.arb.ca.gov/fuels/lcfs/dashboard/dashboard.htm). Data from EMTS indicates that 2.36 billion gallons of
renewable diesel were consumed in the U.S. in 2023, including both renewable diesel that generated BBD RINs and
advanced RINs.
528 EIA, "U.S. biodiesel and renewable diesel imports fall sharply in 2025 after tax credit change," September 4,
2025. https://www.eia.gov/todavinenergy/detail.php?id=66045.
258
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Regulations that require increasing volumes of low-carbon fuels in future years.529 Biodiesel and
renewable diesel imports, exports, and net imports are shown in Figure 7.2.5-1.
Figure 7.2.5-1: Biodiesel and Renewable Diesel Imports, Exports, and Net Imports
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2015
2016
2017
2018
2019
2020
2021
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¦ Biodiesel ¦ Renewable Diesel
The fact that there are both imports and exports of BBD simultaneously suggests that
there are efficiencies associated with importing into and exporting from certain parts of the
country as well as economic advantages associated with the use of BB D from different
feedstocks in different foreign and domestic markets. Historically, this is because the biodiesel
blenders tax credit was able to be claimed on fuel both used and produced in the US. Thus, it was
possible to claim it on imported fuels and exported fuels produced domestically as well. This
dynamic has changed with the passage of IRA and subsequent amendments to the 45Z credit in
OBBB. The 45Z credit is limited to transportation fuels produced in the U.S. from North
American feedstocks. We expect the change in the tax credit will continue to negatively impact
imports of biodiesel and renewable diesel in future years. While feedstock imports from North
American countries will continue, finished fuel imports are likely to decline. This, in turn, may
lead to a decrease in exports as U.S. markets seek alternatives to imported biodiesel and
renewable diesel that are no longer eligible for the tax credit.
In projecting net imports of BBD through 2027 we considered both the historical trends
and the impact of the 45Z credit. Net BBD imports have been relatively stable at around 200
million gallons per year since 2018. As imported BBD is not eligible for the 45Z credit, we
529 Tuttle, Robert. "Canada Releases California-Style Fuel Rules to Cut Emissions," Bloomberg, June 29, 2022.
https://www.bloomberg.com/news/articles/2022-06-29/canada-releases-califoniia-stTle-fuel-rules-to-cut-emissions.
259
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expect imports of BBD will likely decline in future years. We do not expect imports of BBD to
cease altogether, as some BBD producers with established markets in the U.S. may not find it
economically viable to find alternative markets for their fuels. We acknowledge that there is
significant uncertainty in projecting the available supply of imported BBD in future years. In
addition to the normal market uncertainty, the potential market reactions to the new tax policy
make it even more difficult to project BBD net imports going forward. Ultimately, we project
that any decrease in BBD imports will likely be offset, in whole or in large part, by decreases in
BBD exports, such that we do not project significant changes to net BBD imports through 2027.
7.2.6 Projected Rate of Production and Use of Biomass-Based Diesel
The preceding sections describe the factors EPA considered when projecting the rate of
production and use of BBD through 2027. These factors include the supply of these fuels to the
U.S. in previous years, the current and projected BBD production capacity, the availability for
the market to consume these fuels, the available supply of feedstocks to domestic BBD
producers, and the projected imports and exports of BBD. After reviewing this data we have
determined that the factor most likely to limit the production and use of BBD through 2027 is the
production capacity of domestic BBD facilities.
The availability of qualifying feedstocks is not a hard limit. The global oil market, writ
large, generally moves in tandem and there are options for swapping out different feedstocks at
many facilities. Our projections have generally assumed somewhat consistent demand for
potential BBD feedstocks from food markets and other industries in the past. However, given
strong support for domestic feedstocks due to 45Z credit changes, high biofuel volume mandates
from state programs alongside the RFS, and co-location of many rail terminals, biofuel facilities,
and food manufacturers, some oils previously destined for food use may instead be bid away to
the biofuel market. We have focused our assessment of BBD feedstock availability on
projections of increasing feedstock production rather than immediate diversion from existing
uses, but believe that up to 20% of existing soy oil may be diverted from food markets as a result
of the 45Z credit, geographic constraints, and high volume mandates that induce 90% capacity
utilization of existing biofuel capacity. For example, producers in the Midwestern and Southern
U.S. have access to large quantities of domestic soybean oil feedstocks, with ample rail and
barge capacity. These facilities are more likely than West Coast facilities to bid away soy oil
from food markets, as West Coast facilities are rail capacity limited and are more likely to
continue to source FOG or switch to canola oil. The large increases in canola oil we project in
2026 and 2027, as well as a relatively untapped Latin American soy oil market, make it likely
that the market can absorb increases to biofuel oil supply and backfilling food oil markets.530 We
recognize, however, that it is possible that feedstocks supplied to domestic BBD producers can
be sourced from both new production/collection of feedstocks as well as diversions of these
feedstocks from existing uses.
To project the available quantity of BBD in the U.S. through 2027 we started with
production capacity utilization. As discussed in Chapter 7.2.2, renewable diesel capacity
utilization has in past individual years surpassed 90% capacity utilization, mirroring capacity
530 Latin American soybean oil is not incented to be used as a biofuel feedstock in the United States, but it can serve
as a replacement for domestic soybean oil crushed in the U.S. destined for food use.
260
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utilization of more mature corollaries, such as the oil refining sector.531 Biodiesel production has
also neared 90%, in 2023. Given the incentivized environment for biofuels at the time of
analysis, historical precedent, and capabilities of the BBD production industry and its analogs,
EPA projects volumes corresponding to 90% production capacity. Table 7.2.6-1 details this.
Table 7.2.6-1: Production Capacity and LJti
2026
2027
Production Capacity3
6,705
7,205
BBD Volume
6,074
6,445
Production Capacity Utilization13
90.6%
89.5%
ization for Analyzed Volumes (million gallons)
a 2026 production capacity reflects November 2025 production capacity data. 2027 production capacity is a
conservative estimate of the total potential capacity increase. Estimates of potential renewable diesel and biodiesel
capacity growth are discussed further in Chapter 7.2.2. Given the potentially unused or otherwise mothballed
biodiesel capacity alongside the aggressive growth potential of renewable diesel, this is a capacity increase likely to
be achieved.
b We recognize these do not precisely correspond to 90% capacity numbers. To enable a thorough analysis of the
impacts of the required volumes the volumes must be set some time in advance of the final rule. Our estimate of the
capacity utilization was updated using more recent data after the volumes for analysis had been set, resulting in a
temporal disconnect between various data products, including capacities.
From this point, we then applied existing feedstock baselines and projected growth rates,
discussed in greater detail in Chapter 7.2.3 and 7.2.4. Feedstocks with greater incentives and
certainty were prioritized in terms of fulfilling the volume obligations, and then the remaining
gap between the volumes and the "certain" feedstocks was then apportioned between three major
feedstocks—Canadian canola, which stands poised to benefit from greater trading dynamics and
the 45Z credit, domestic soybean oil, which will likely be diverted from the non-biofuel market,
and imported FOG, which, despite exclusion from 45Z (if imported from outside North America)
and higher tariffs, benefits from a lower base cost and greater incentives in many U.S. state
programs. Table 7.2.6-2 contains volumes prior to apportionment of this gap, which is again
projected to be filled with a greater supply of canola oil, diverted soybean oil from non-biofuel
uses, and imported FOG. In reality, individual circumstances and supplier and trade relationships
may alter the exact mix.
531 EIA, "Refinery Utilization and Capacity," February 6, 2026.
https://www.eia.gov/dnav/pet/pet pnp unc dcu nus m.htm.
261
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Table 7.2.6-2: Feedstock Availability from North America Less Imports and Diversion
2026
2027
Feedstock
Biodiesel
Renewable Diesel
Biodiesel
Renewable Diesel
Domestic FOG
0.15
0.8
0.15
0.85
Domestic Soy
1
1.06
1
1.13
Domestic Canola
0
0.23
0
0.23
Domestic Corn Oil
0.21
0.68
0.21
0.68
Imported FOG
0
0.148
0
0.148
Imported Soy
0
0
0
0
Imported Canola
0.32
0.39
0.32
0.44
Imported Corn Oil
0
0
0
0
Total
4.99
5.16
Note: Table does not include feedstocks that could be imported from countries outside of North America or
feedstocks that could be diverted from non-biofuel markets
This left an artificial shortfall of 1.07 billion gallons in 2026 and 1.28 billion gallons in
2027. Given that feedstock acquisition will largely be based on geography, supply chain
dynamics, and individual decision-making, it is difficult to predict with certainty what feedstocks
might fill the remaining volume requirements. However, as discussed in section 7.2.4, there is
broad availability of BBD feedstocks globally, well beyond the required volumes. EPA thus
chose to project equal apportionment of the remaining volumes to three major feedstock
categories, as previously mentioned. Soybean oil diverted from non-biofuel markets,
representing a 20% diversion of soybean oil in the food market, is easily backfilled with other
virgin seed oils, including Latin American soybean oil. Canola oil crush is growing rapidly in
Canada, and diversions from exports and food markets are possible. While not as incentivized at
a national level, imported FOG remains a useful stopgap due to price and lingering incentives
from state clean fuels programs. Based on supply trends, recently announced crush capacity
expansions, and stronger incentives for North American feedstocks, we project that much of the
growth in the BBD category will come from renewable diesel and jet fuel, and that domestic
biodiesel production will remain relatively constant in the years to come. The annual supply of
BBD through 2027 by fuel type and feedstock that result from these projections are shown in
Tables 7.2.6-4 in million gallons.
262
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Table 7.2.6-4: Projected Supply of BBD Through 2027 (million gallons)
Category
2026
2027
Biomass-Based Diesel
6,074
6,445
Biodiesel
1,780
1,780
Soybean Oil
1,100
1,100
FOG
150
150
Corn Oil
210
210
Canola Oil
320
320
Renewable Diesel
4,290
4,660
Soybean Oil
1,320
1,460
FOG
1,309
1,419
Corn Oil
678
678
Canola Oil
981
1,101
These projected volumes reflect two significant assumptions, both of which are subject to
significant uncertainty. The first assumption is that policies in the U.S. (including the RFS, 45Z
credit, and other State-level incentives and volume mandates) continue to provide a strong
incentive for BBD produced from virgin vegetable oils such as soybean oil and canola oil. The
projected increases in the supply of BBD produced from soybean oil and canola oil are primarily
driven by projected investment in and expansion of soybean and canola crushing facilities in the
U.S. and Canada in addition to those already underway. They also acknowledge high potential
for FOG usage in tandem with adverse trade policies that will likely sunset some portion of the
U.S. FOG import market. The second assumption is that strong volumes (and strong state
programs), in tandem with various tax credits, continue to send a strong signal to the virgin
oilseed market and biofuel producers. These volumes reflect our assessment that the BBD
industry is capable of achieving 90%+ capacity utilization, as canola and soy crush continue to
grow in North America and state programs continue to incentivize FOG despite changes to 45Z
and trade policy.
The changing import patterns for these feedstocks also demonstrate that the primary
destination for these feedstocks can change quickly. Even if incentives for BBD produced from
these feedstocks remain strong through 2027, the rate of increase in the imported volumes of
FOG could decline if, for example, other countries adopt mandates or even greater incentives for
biofuels produced from these feedstocks in future years. It is also possible that countries
currently exporting these feedstocks, primarily in Asia and South America, may seek to establish
their own biofuel programs through a combination of incentives, biofuel use mandates, or taxes
or prohibitions on the export of these feedstocks.
7.3 Imported Sugarcane Ethanol
The predominant available source of advanced biofuel other than cellulosic biofuel and
BBD has historically been imported ethanol. Most of the ethanol imported to the U.S. has been
produced in Brazil from sugarcane and imported either directly from Brazil or through the
Caribbean Basin Initiative and Central America Free Trade Agreement (CAFTA). However, data
through 2025 demonstrates considerable variability in imports of ethanol, with no ethanol
imported since 2023.
263
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Figure 7.3-1: Historical Ethanol Imports
500
450
400
350
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300
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2
150
100
50
0
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Source: EIA, "U.S. Fuel Ethanol Imports," Petroleum & Other Liquids, February' 4, 2026.
https://www.eia. gov/dnav/pet/hist/LeafHandl6r.ashx?n=PET&s=MFEIMUSl&f= A.
Moreover, data from EIA indicates that all 2018-2021 ethanol imports entered the U.S.
through the West Coast, as did the majority of ethanol imports in 2022 and 2023. We believe that
these imports were likely used to help refiners meet the requirements of the California LCFS
program, which provide significant additional incentives for the use of advanced ethanol beyond
the RFS program.
As noted in previous RFS rulemakings, the high variability in historical ethanol import
volumes makes any projection of future imports uncertain.332 However, import volumes for more
recent years are likely to provide a better basis for making future projections than import
volumes for earlier years. To address these issues, we developed a methodology used in the final
rulemaking which established the volume requirements for 2022 as well as the Set 1 Rule that
established volumes for 2023-2025. Specifically, we used a weighted average of import volumes
for all years where the weighting was higher for more recent years and lower for earlier years.
The weighting factor for any given year's volume was twice as large as the weighting factor for
the previous year's volume. This approach provided a better predictor of future imports of
sugarcane ethanol than either simple averages of historical volumes or a trendline based on
historical volumes.
We have again used this methodology in this action to estimate the volumes of imported
ethanol that could be expected in the future. The volumes and weighting factors we are using are
shown in Table 7.3-1. The resulting weighted average is 15 million gallons. As we are projecting
volumes for 2026 and 2027 in this action, and this is the latest data available, the same projection
applies for each year.
m See. e.g., 85 FR 7032-33 (February 6. 2020) and 87 FR 39600 (July 1, 2022).
264
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Table 7.3-1: Annual Advanced Ethanol Imports and Weighting Factors
Year
Imported Advanced
Ethanol9 (million gallons)
Weighting Factor
2015
89
0.00098
2016
34
0.00195
2017
74
0.00391
2018
78
0.00781
2019
196
0.0156
2020
185
0.03125
2021
60
0.0625
2022
81
0.125
2023
21
0.25
2024
0
0.5
2025
0
1
1 Based on RINs generated for imported ethanol and assigned a D code of 5 according to EMTS.
As noted above, the future projection of imports of ethanol is inherently imprecise, and
actual imports in years 2026 and 2027 could be lower or higher than 15 million gallons. Factors
that could affect import volumes include uncertainty in world ethanol demand and prices,
constraints associated with the El0 blendwall in the U.S., world demand for and prices of sugar,
the cost of sugarcane ethanol relative to that of corn ethanol, and transportation fuel prices and
demand, potential tariffs or other trade barriers.
7.4 Other Advanced Biofuel
In addition to cellulosic biofuel, imported ethanol, and BBD, there are other advanced
biofuels that can be supplied in 2026 and 2027. These other advanced biofuels include non-
cellulosic CNG, naphtha, heating oil, renewable diesel co-processed with petroleum, and
domestically produced advanced ethanol. However, the supply of these fuels has been relatively
low in the last several years.
265
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Table 7.4-1: Historical Supply of Other Advanced Biofuels (million ethanol-equivalent
gallons)
Domestic
Heating
Renewable
Year
CNG/LNG
Ethanol
Oil
Naphtha
Diesel (D5)
Total
2015
0
25
1
24
8
58
2016
0
27
2
27
8
64
2017
2
25
2
32
9
70
2018
2
25
3
31
40
101
2019
5
24
3
37
58
127
2020
5
23
3
33
86
150
2021
7
26
2
33
105
173
2022
6
29
3
71
118
227
2023
7
30
4
33
120
194
2024
2
28
4
67
109
210
2025
4
20
4
119
70
217
We have used the same weighted averaging approach (see Table 7.3-1) for other
advanced biofuels as we have used for sugarcane ethanol to project the supply of these other
advanced biofuels. Based on this approach, the weighted average of other advanced biofuels is
209 million RINs. This volume of other advanced biofuel is composed of 24 million RINs of
domestic advanced ethanol (primarily derived from separated food waste), 90 million RINs of
co-processed renewable diesel, 87 million RINs of naphtha, and 8 million RINs of other advance
biofuels (non-cellulosic RNG and heating oil). We used the results of this weighted averge
approach to project the supply of these other advanced biofuels in 2026 and 2027.
We do not believe the available data and the methodology we employed can reasonably
be used to project new developments in the production of other advanced biofuels. We recognize
that the potential exists for additional volumes of advanced biofuel from sources such as D5
renewable jet fuel, liquefied petroleum gas (LPG), butanol, and non-cellulosic CNG and LNG
from biogas produced in digesters. However, since these types of fuels have been produced in
very small amounts in the past, if at all, we do not believe the market will make available
substantial volumes from these sources in 2026 and 2027.
7.5 Total Ethanol Consumption
Total ethanol consumption is the sum of ethanol blended with petroleum-based gasoline
(E0) to create E10, E15, and E85 transportation fuel blends. In the Set 1 Rule, EPA projected
ethanol concentration in the national gasoline pool for future years using a least-squares
regression model using El 5 and E85 fueling station population data.533 With the addition of new
data, EPA was able to produce a projection of total ethanol consumption that we believe better
represents current and future ethanol consumption. Thus, EPA opted to take a different approach.
For this rule, volume data from the BIP/HBIIP program and volume data acquired directly from
six states with high volumes of higher-level ethanol blends (CA, KS, IA, MN, NY, and ND) has
533 For more details on our prior methods, see Set 1 Rule RIA Chapter 6.5.1.
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enabled EPA to employ this more robust data-driven, bottom-up approach to projecting total
ethanol consumption for the years 2026-2027.
7.5.1 Projection of Motor Gasoline Consumption
The national average ethanol concentration in the nationwide gasoline pool surpassed 5%
in 2007 and experienced a period of rapid growth, finally surpassing 10% (i.e., the "blend wall")
for the first time in 2016. The share of ethanol in the gasoline pool has continued to increase
since then, albeit at a slower pace after the market became saturated with E10. The total volume
of ethanol that can be consumed, including that produced from corn and sorghum, cellulosic
biomass, sugarcane, and the non-cellulosic portions of separated food waste, is a function of the
relative volumes of E0, E10, E15, and E85 that comprise pool-wide motor gasoline consumption.
Average ethanol concentration can exceed 10% only to the extent ethanol in El 5 and E85 fuels
can exceed the ethanol content of E10 and more than offset the dilution effect of E0 volumes.
As mentioned, EPA has adopted a new, simplified, bottom-up methodology for
projecting ethanol consumption into the future for the years covered by this rule. Volume
projections were generated using data on fueling station populations and the average volume sold
per station (i.e., station throughput) using the following relation:
Total volume = Number of stations * Avg gallons sold per station
Volumes were projected for E0, E10, E15, and E85 independently for economic analysis
because distribution practices and costs vary between different proofs of ethanol-gasoline
blends.534 A tabular presentation of the main variables and their data sources (or derivations of
their values, where data was not available) is shown in Table 7.5.1-1.
In producing projections of station counts across the years covered by this rule, EPA
projected E85 in California separate from the remainder of the country. This is due to the unique
policy environment in California, where E85 sales are disproportionately high due to California's
high gasoline prices and the added incentive provided by the California Low Carbon Fuel
Standard allowing E85 pricing to be much more favorable than in other states. Approximately
10%) of E85 stations are in California and these stations move much greater volumes of fuel on a
per-station basis. To reflect the most recent trends in E85 sales, we used only data from years
2021-2024 to extrapolate projections of E85 stations nationally and E85 volumes for California.
We are unaware that a similar E85 sales trend has occurred to date in Oregon or Washington,
despite the presence of similar state-level policy incentives. For this reason, we did not model
these states separately from the remainder of the country.
534 See Chapter 10 for more detailed analyses of renewable fuel costs.
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Table 7.5.1-1: Main Variables and Data Sources Used for the Projection of Poolwide
Ethanol Consumption (2026-2(
)27)
Variable
Data Source or Derivation
EO Stations
Pure-Gas.org535
El5 Stations
AFDC536/Prime the Pump537
E85 Stations (Non-CA)
E85 Stations (CA)
EO Throughput
Iowa Department of Revenue538
El5 Throughput
Derived from BTP539/HBTTP540
E85 Throughput (Non-CA)
State Reports (IA, MN, NY, KS, and ND)
E85 Throughput (CA)
Calculated quotient of:
E85 Total Volume (CA) / E85 Station Counts (CA)
EO Total Volume
Calculated product of
EO Stations * EO Throughput
E10 Total Volume
Remainder of projected motor gasoline consumption published
by EIA in AEO2025541 that is not EO, El5, or E85
El5 Total Volume
Calculated product of
El5 Stations * El5 Throughput
E85 Total Volume (Non-CA)
Calculated product of
E85 Stations (Non-CA) * E85 Throughput (Non-CA)
E85 Total Volume (CA)
CARB542
The annual number of stations offering EO, E15 and E85 in the U.S. are shown in Table
7.5.1-2. Historical annual averages of El 5 station populations based on data provided by Prime
the Pump and corroborated by DOE's Alternative Fuel Data Center (AFDC). The AFDC does
not publish E15 station population data but does report just over 3,000 stations offering E15
blends for sale in 2023. Based on stakeholder data and recently updated numbers from Prime the
Pump, EPA expects about 4,500 E15 fueling stations in operation at the end of 2025.543 As stated
above, EPA separated E85 projections from California from the remainder of the nation. The
total and broken out stations are shown in the table below.
535 Pure-Gas.org, "Stations." https://www.pure-gas.org.
536 AFDC, "Historical Alternative Fueling Station Counts." https://afdc.energy.gov/stations/states.
537 Prime the Pump is anbiofuels industry-led program seeking to encourage and expand retail adoption of E15 by
building out infrastructure.
538 Iowa Department of Revenue, "2023 Retailers Fuel Gallons Annual Report," March 2024.
https ://revenue .iowa. gov/media/3 846/download?inline.
539 USD A, "Biofuel Infrastructure Partnership." https://sandbox.fsa.usda.gov/programs-and-services/energy-
pro grams/bip/index.
5411 USD A, "Higher Blends Infrastructure Incentive Program." https://www.rd.usda. gov/hbiip.
541 AEO2025, Table 2 - Energy Consumption by Sector and Source.
542 CARB, "Annual E85 Volumes," April 11, 2025. https://ww2.arb.ca.gov/resources/documents/alternative-fuels-
annual-e85 -volumes.
543 Projection based on historical growth of E15 Stations. Growth Energy, "Historical Growth of E15 Stations,"
https://growthenergy.org/data-set-category/higher-blends-retail-footprint. This projected number was reached by
early 2026. Growth Energy, "Retailer & Supplier Hub," https://growthenergy.org/retailer-supplier-hub.
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Table 7.5.1-2: Average Annual Number of Stations by Gasoline Blend
Year
E0 Stations
E15 Stations
E85 Stations
Total Stations
California
Non-California
2014
8,160
105
2,713
94
2,619
2015
9,591
184
2,932
107
2,825
2016
11,044
431
3,091
119
2,972
2017
12,356
1,214
3,251
146
3,105
2018
13,509
1,700
3,567
161
3,406
2019
14,362
2,081
3,717
195
3,522
2020
15,479
2,302
3,841
198
3,643
2021
15,855
2,605
4,063
271
3,792
2022
16,993
2,758
4,420
331
4,089
2023
17,012
3,414
4,495
369
4,126
2024
17,206
3,724
4,586
513
4,073
2025
17,541
4,503*
4,859
555*
4,304
* Value projected for this rule.
Source: AFDC, "Historical Alternative Fueling Station Counts," https://afdc.energy.gov/stations/states: Growth
Energy, "Historical Growth of E15 Stations," https://growthenergy.org/data-set-categorv/higher-blends-retail-
footprint: Pure-gas.org, EO station counts.
Table 7.5.1-3 shows the projection of retail fueling station growth broken down by
gasoline blend. EPA is projecting slight or moderate growth in station counts for stations
offering all blend types, with the fastest growing blends between 2026-2027 being E85 in
California and E15. EO stations were extrapolated based on historical data from Pure-Gas.org.
Table 7.5.1-3: Projections of Retail Fueling Station Population by Gasoline Blend
Blend
2026
2027
E0
17,786
18,052
E15
4,682
5,052
E85 (non-CA)
4,427
4,520
E85 (CA)
664
745
Table 7.5.1-4 lists projected average throughput for stations selling each gasoline blend.
To calculate station throughput for California E85 stations, we simply took the quotient of total
E85 volume sold in California as reported by CARB and the total number of E85 stations in
California from AFDC. E85 throughput for non-California stations was projected using data
reported by five other states that produce or consume elevated volumes of E85 (IA, MN, NY,
KS, and ND).544 Throughput data for E85 stations reported by these five states are the best
available E85 data that EPA is aware of outside of California and we therefore believe it is
reasonable to accept this data as a proxy for all non-California states because they comprise a
significant share of national E85 stations (representing nearly 25% of all E85 stations located
outside of California). For these states combined, historical station counts, and total gallons sold
for years 2015-2024 were used to calculate an average E85 sales per station figure for each year.
We then extrapolated those average throughputs using regression analysis to calculate the
average E85 gallons sold per station in all states besides California for years 2026-2027. The
544 See "E85 Consumption Based on State Data for RFS Set 2 FRM," available in the docket for this action.
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result shows very little growth in E85 throughput outside of California across the years covered
in this rule. El5 throughput was projected based on BIP/HBIIP data provided to EPA. Retail
sales data for EO stations is sparse, but Iowa's Retailer Fuel Gallons annual report provides the
basis for calculating EO throughput in Iowa, which we treated as representative of national
average EO throughput mirroring the methods used in the Set 1 Rule. Annual EO throughput
declines from 115,862 gallons per station in 2026 to 114,944 gallons per station in 2027, as more
volumes of ethanol-free gasoline are replaced by increased sales of higher4evel ethanol blends
as availability of these fuels continues to grow.
Table 7.5.1-4: Projected Average Annual Throughput Volume by Gasoline Blend (gallons
Blend
2026
2027
EO
115,862
114,944
E15
213,285
222,638
E85 (non-CA)
61,971
63,563
E85 (CA)
255,733
255,359
The total volumes of each gasoline blend that EPA projects to be consumed during the
time covered by this rule are shown in Table 7.5.1-5. Projected volumes of E10 are the
calculated difference between projected overall national motor gasoline consumption as
published by EIA and the sum of EPA's projected volumes of EO, E15, and E85.545 Increasing
station counts and increasing throughputs of El 5 and E85 gasoline, coupled with decreasing
throughputs of EO gasoline and increased penetration of electric vehicles, causes overall gasoline
consumption to decrease across these years while E15 and E85 grow as a share of the total.
Table 7.5.1-5: Projection of Total Motor Gasoline Consumption by Blend Level (million
Blend
2026
2027
E0
2,061
2,075
E10
137,404
136,301
E15
998
1,124
E85 (non-CA)
274
287
E85 (CA)
169
190
There is inherent uncertainty in any projection of future conditions. Market dynamics can
shift rapidly with new policy signals and political pressure. For example, E15 has historically not
been approved for sale in the state of California and as such no El 5 gallons are sold in the state
in these projections. However, on October 25, 2024, the Governor of California directed CARB
to expedite the approval of E15 gasoline to be sold in California.546 If this were to come to pass,
it would open a large, currently untapped market for El5 which would see much higher volumes
than projected in this rule. Similarly, a recent Iowa state law has set new retail E15 access
requirements and will require every retail gasoline fueling station in the state to advertise and sell
545 AEO2025, Table 11 - Petroleum and Other Liquids Supply and Disposition.
546 Letter to CARB from California Governor Gavin Newsom, October 25, 2024, available at
https://d35tlsYewk4d42.cloudfront.net/file/2894/10.25.24-letter-to-CARB.pdf.
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E15 from at least one dispenser beginning in 2026.547 This would likewise increase the volume
of E15 we would expect to see consumed for these years.
7.5.2 Projection of Total Ethanol Consumption
Total gasoline volumes from Table 7.5.1-5 were used to calculate total ethanol
consumption. To do this, EPA assumes E10 contains 10.16% ethanol by volume (based on data
retrieved by RFG Survey Association and assuming 2% denaturant), El5 contains 15% ethanol
by volume, and E85 contains 74% ethanol by volume (consistent with EIA assumptions). Our
projection results in over 14.4 billion gallons of ethanol consumed in 2026 and a slight decline in
2027. Table 7.5.2-1 depicts EPA's projections of total ethanol consumption aggregated by blend
level, rounded to the nearest million gallons.
Table 7.5.2-1: Projeci
tion of Tol
Blend
2026
2027
E10
13,960
13,848
E15
149
168
E85
328
353
Total
14,438
14,370
al Ethanol Consumption by Blend Level (million gallons)
Table 7.5.2-2 shows EPA's projection of total ethanol consumption (equating to the
ethanol volumes) and the difference between these volumes and the No RFS and 2025 Baselines.
Based on our projections, we expect to see a pool-wide ethanol concentration that rises in the
projected years.
Table 7.5.2-2: Total Ethanol Consumption Pro.
ection (million ga
Ions)
Year
Ethanol
Consumption
Difference from
No RFS Baseline
Difference from
2025 Baseline
Ethanol
Concentration
2026
14,438
231
158
10.25%
2027
14,370
245
90
10.27%
7.6 Corn Ethanol
As described in more detail in Chapter 1.4, total domestic ethanol production capacity
increased dramatically between 2005 and 2010 and increased at a slower rate thereafter. By the
beginning of 2025, production capacity exceeded 18 billion gallons.548 Actual production of
ethanol in the U.S. reached about 16.3 billion gallons in 2025.549 Thus, while production of corn
ethanol may be limited by production capacity in the abstract, it does not appear that production
capacity will be a limiting factor in 2026-2027 for determining potential volumes.
547 AFDC, "Iowa Retail E15 Access Requirements." https://afdc.energy.gov/laws/12998.
548 EIA, "U.S. Fuel Ethanol Plant Production Capacity," Petroleum & Other Liquids, September 26, 2025.
https://www.eia.gov/petroleum/ethanolcapacitv.
549 EIA, "Monthly Energy Review," January 2026, Table 10.3.
https://www.eia.gov/totalenergv/data/montlilv/pdf/mer.pdf.
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The expected annual rate of future commercial production of corn ethanol will be driven
primarily by gasoline demand in the U.S. as most gasoline is expected to continue to contain at
least 10% ethanol. However, commercial production of corn ethanol is also a function of exports
of ethanol and to a much smaller degree the demand for El 5, and E85 fuels. As shown in
Chapters 7.5.1 and 7.5.2, the contribution of El 5 and E85 to domestic ethanol consumption is
projected to remain below 1 billion gallons into 2027. In 2024, ethanol exported from the U.S. to
foreign markets were 1.9 billion gallons.550 In the first 10 months of 2025, U.S. fuel ethanol
exports were approximately 10% higher than the first 10 months of 2024.551
Much of the growth in export volume is attributable to a combination of domestic and
international market effects, with lower prices and plateauing demand on average for fuel ethanol
in U.S. markets even as prices and demand are increasing elsewhere. For example, Brazil
(traditionally the second-largest exporter of fuel ethanol to global markets after the U.S.) has
seen their own domestic demand for fuel ethanol increase significantly in recent years due to
growth in hydrous El00 demand at retail fuel pumps. This shift has meant that as more Brazilian
ethanol is being sold domestically, there has been a corresponding reduction in Brazilian
outflows of fuel ethanol, which have been largely replaced by rising exports of American fuel
ethanol. Similarly, other countries have updated their own renewable fuel mandates which has
led to increasing demand for imported ethanol that has been met by increasing supplies from the
American ethanol industry.552
As described in Chapter 7.5.1, we estimated total ethanol consumption for 2026-2027 by
extrapolating from historical retail fueling station population and station-level throughput data
coupled with reported volumes where available. This total volume is a combination of
conventional ethanol, cellulosic ethanol, and advanced ethanol. We assume that the advanced
and cellulosic ethanol will be used preferentially under the RFS program due to their added RIN
value, federal tax credit value, and LCFS credit value (where available) and that conventional
ethanol will comprise the remainder. Our estimate of corn ethanol consumption for 2026-2027
for the purposes of estimating the mix of biofuels that could be made available is shown in Table
7.6.1-1.
Table 7.6.1-1: Calculation of
2026
2027
Total ethanol
14,438
14,370
Imported sugarcane ethanol
15
15
Domestic advanced ethanol
24
24
Ethanol from CKF
128
128
Conventional ethanol
14,270
14,203
rojected Corn Ethanol Consumption (million gallons)
5511EIA, "Exports by Destination," Petroleum & Other Liquids, April 30, 2025.
https://www.eia.gov/dnav/pet/pet move expc dc NUS-Z00 mbbl a.htm.
551 EIA, "U.S. Exports of Fuel Ethanol," Petroleum & Other Liquids, April 30, 2025.
https://www.eia.gov/dnav/pet/hist/LeafHandler.ashx?n=PET&s=M EPOOXE EEX NUS-Z00 MBBL&f=a.
552 S&P Global, "US ethanol exports on pace for record year, fueled by low prices and increased opportunity
overseas," November 19, 2024. https://www.spglobal.com/commoditv-insights/en/news-research/latest-
news/agriculture/111924-us-ethanol-exports-on-pace-for-record-vear-fueled-bv-low-prices-and-increased-
opportunity-overseas.
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7.7 Conventional Biodiesel and Renewable Diesel
While the vast majority of conventional renewable fuel supplied in the RFS program has
been corn ethanol, there have been smaller volumes of conventional biodiesel and renewable
diesel used in the U.S. in some years. Conventional biodiesel and renewable diesel can only be
produced at facilities grandfathered under the provisions of 40 CFR 80.1403 as there are
currently no valid RIN-generating pathways for the production of conventional (D6) biodiesel or
renewable diesel. These biofuels are not required to meet the 50% GHG reduction threshold to
qualify as BBD under the statutory definition, but the feedstocks used to produce grandfathered
biodiesel or renewable diesel must still meet the regulatory definition of renewable biomass, and
the biofuel produced must meet all other statutory and regulatory requirements. The quantity of
conventional biodiesel and renewable diesel consumed each year from 2015-2025 is shown in
Table 7.7-1.
Table 7.7-1: Conventional Biodiesel and
Renewable Diesel Used in the U.S. (mi
lion gallons)
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
Domestic D6 Biodiesel
0
0
0
0
0
0
0
0
3
9
0
Domestic D6 Renewable
Diesel
0
0
0
0
0
0
0
0
1
0
0
Imported D6 Biodiesel
74
113
0
0
0
0
0
0
6
6
0
Imported D6 Renewable
Diesel
86
45
2
0
0
0
0
0
0
0
0
All D6 Biodiesel and
Renewable Diesel
160
158
2
0
0
0
0
0
10
15
0
In 2015-2016 the volume of conventional biodiesel and renewable diesel used in the U.S.
was relatively small, but still significant. Use of these fuels in the U.S. dropped to very low
levels in 2017 and was less than 1 million gallons per years from 2018-2022. The supply of
conventional biodiesel and renewable diesel increased slightly in 2023 and 2024, though the
overall supply remained small (less than 0.1% of the total biofuel supply to the U.S.). In 2025 the
supply of conventional biodiesel and renewable diesel was again near zero. Most of the
conventional biodiesel and renewable diesel used in the U.S. has been imported, with the only
exceptions being small volumes in 2023 and 2024. However, conventional (D6) RINs have
continued to be generated for biodiesel and renewable diesel in recent years. From 2018 through
2024 the volumes of renewable diesel for which conventional biofuel RINs were generated each
year (in million gallons) were 107, 116, 76, 135, 75, 69, and 15 respectively. Most of these RINs
were retired for reasons other than compliance with the annual volume obligations, suggesting
that they were used outside of the U.S. or for purposes other than transportation fuel.
The potential for conventional biodiesel and renewable diesel production and use in the
U.S. from feedstocks such as palm oil is far greater than the quantity of these fuels actually
supplied in previous years. The total production capacity of registered grandfathered biodiesel
and renewable diesel producers is over 1.6 billion gallons in the U.S., with an additional 0.9
billion gallons internationally. While domestic feedstock availability may be limited, worldwide
feedstock availability does also not appear to be a limiting factor, as USDA estimates that
approximately 223 million metric tons of vegetable oil was produced globally in the 2023/2024
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agricultural marketing year.553 If all of it were to be used to produce biodiesel and renewable
diesel, this quantity of vegetable oil could be used to produce over 60 billion gallons.554 While
the majority of this vegetable oil is used for food and other non-biofuel purposes, any of this
vegetable oil that meets the regulatory definition of renewable biomass could be used to produce
conventional biodiesel or renewable diesel at a grandfathered facility so long as it meets all other
RFS program requirements. The quantity of conventional biodiesel and renewable diesel that
could be supplied to the U.S. through 2027 is not without limit, but this data suggests that large
quantities of this fuel are being or could be produced,555 and that the use of these fuels in the
U.S. is largely a function of demand for this fuel in the U.S. versus other markets.
553 USD A, "World Agricultural Supply and Demand Estimates," November 8, 2024.
https://downloads.usda.librarv.cornell.edu/usda-esmis/files/3t945q76s/s4657804b/z029qx92b/latest.pdf.
554 This calculation assumes one gallon of renewable diesel can be produced from 8 pounds of vegetable oil.
555 The OECD-FAO Agricultural Outlook 2024-2033 projects global biodiesel consumption to grow from an
average volume of about 60 billion liters (15.8 billion gallons) in 2021-2023 to approximately 79 billion liters (20.9
billion gallons) in 2033.
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Chapter 8: Infrastructure
This chapter describes the analysis of the impact of renewable fuels on the distribution
infrastructure of the U.S. The CAA indicates that this assessment must address two aspects of
infrastructure:
• Deliverability of materials, goods, and products other than renewable fuel.
• Sufficiency of infrastructure to deliver and use renewable fuel.
This chapter begins by addressing the sufficiency of infrastructure to deliver and use
different types of renewable fuels. We then address how the use of renewable fuels affects the
deliverability of materials, goods, and products other than renewable fuel.
Note that while we are projecting higher volumes of renewable fuel consumption relative
to the No RFS Baseline, in analyzing the impacts of the projected volumes on infrastructure we
have considered whether the projected volumes would require additional infrastructure relative
to that which currently exists. We believe that the existing infrastructure is the relevant point of
reference for the No RFS Baseline since it is unlikely that the infrastructure enabling and
supporting consumption of renewable fuel in 2026 would change even if we did not establish
volume requirements for future years, at least not in 2026-2027. The number of vehicles that can
consume particular renewable fuels, pipelines, storage tanks, fuel delivery vehicles, and retail
service stations generally change only on longer timescales, and only insofar as the outlook for
renewable fuel demand changes. Therefore, this chapter discusses infrastructure impacts
primarily in terms of the changes that might be needed or expected to occur in 2026-2027 in
comparison to their recent or current status.
8.1 Renewable Compressed Natural Gas
Renewable compressed natural gas and liquefied natural gas (renewable CNG/LNG)
infrastructure considerations differ from those for most other biofuels not only because it is
typically a gas rather than a liquid,556 but also because renewable CNG/LNG can be processed to
be chemically identical to fossil-based CNG/LNG, which is used for many purposes including
transportation.557 Fossil-based natural gas was used in CNG/LNG vehicles for many years prior
to the introduction of renewable CNG/LNG. The RFS program allows RINs to be generated and
separated for renewable CNG/LNG that is fungible with the wider natural gas pool, provided that
a contract is in place to demonstrate that the same volume of natural gas is used for
transportation purposes and all other regulatory requirements are met.558 As the cost of running
spur pipelines for anything beyond short distances becomes prohibitively expensive, only those
renewable CNG/LNG sources that are in relatively close proximity to the existing natural gas
pipeline infrastructure are likely to be developed. Once connected to the natural gas pipeline
556 Although renewable LNG is in use, its volumes are minimal compared with renewable CNG. For more
information, see Chapter 7.1.1. Accordingly, this analysis focuses on renewable CNG.
557 Growth in biogas may require investment in additional gas cleanup operations prior to pipeline injections,
particularly in California where pipeline standards currently preclude the injection of most biogas. The potential for
such biogas cleanup costs is discussed in Chapter 10.1.2.5.1.
558 See 40 CFR 80.1426(f).
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network, renewable CNG/LNG uses the existing natural gas distribution system and CNG/LNG
vehicle refueling infrastructure, and it is used in the same CNG/LNG vehicle fleet as fossil
natural gas. According to data from the AFDC, there are currently approximately 1,359 public
and private CNG fueling stations and approximately 67 public and private LNG refueling
stations in the U.S.559
Once injected into the pipeline, processed renewable CNG/LNG is virtually
indistinguishable from fossil natural gas, so increasing transportation use depends on dedicated
vehicle fueling infrastructure. However, expanding CNG/LNG vehicle infrastructure to support
renewable CNG/LNG growth beyond the transportation sector's projected use for 2026 and
2027—estimated at 1.27-1.35 million ethanol-equivalent gallons of CNG/LNG per year—would
represent a substantial challenge.560 The incentives for increasing the use of CNG/LNG in the
transportation sector, including incentives from the RFS program and state programs such as the
California LCFS program, may be insufficient to cause a substantial increase in the CNG/LNG
vehicle fleet and refueling infrastructure. CNG/LNG vehicles are predominately used in fleet
applications where there is a unique situational advantage (e.g., a natural gas supplier's utility
fleet or landfill's waste hauler fleet). In addition, it would be more challenging to establish the
necessary contracts to demonstrate that natural gas was used in CNG/LNG vehicles outside of
fleet operations. The cost of removing impurities from raw biogas and upgrading it to renewable
CNG/LNG may be a barrier to broader use. These processing steps are needed to meet vehicle
fuel specifications and to enable injection into and transport through the natural gas distribution
system. Nevertheless, we do not expect infrastructure to constrain the use of renewable
CNG/LNG to levels below those projected to be available in Chapter 7.1.3.
8.2 Biodiesel
The RFS2 Rule projected that 1.5 billion gallons of biodiesel would be used in 2017 and
1.82 billion gallons would be used in 2022 to meet the statutory biofuel volume requirements.561
We noted that biodiesel plants tended to be more dispersed than ethanol plants, thereby
facilitating delivery to local markets by tank truck and lessening the need to distribute biodiesel
over long distances. Biodiesel imports also helped to serve coastal markets. We projected that as
biodiesel volumes grew, there would be more need for long-distance transport of domestically-
produced biodiesel. We estimated that such long-distance transport would be accomplished by
manifest rail and, to a lesser extent, by barge, since the economy of scale would not justify the
use of unit trains. We estimated that biodiesel and biodiesel blends would not be shipped by
pipeline to a significant extent due to concerns over potential contamination of jet fuel that is
also shipped by pipeline.
In 2010, much of the biodiesel blending was taking place at facilities downstream of
terminals, such as storage facilities operated by individual fuel marketers. We projected that this
559 AFDC, "Alternative Fueling Station Locator," February 25, 2026.
https://afdc.energv.gov/stations#/analvze?fuel=LNG&fuel=CNG&access=public&access=private&countrv=US&tab
=fuel
5611 See Chapter 7.1.4 for further discussion of the estimated total use of CNG/LNG as transportation fuel in 2026-
2027 and Chapter 10.1.4 for discussion of the costs associated with refueling stations.
561 See RFS2 RIA Chapter 1.2.2.
276
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would take place to a lesser extent as volumes grew with most biodiesel being blended at
terminals to the 5% (B5) blend level that is approved for use in diesel engines by all
manufacturers for distribution to retail and fleet fueling facilities. We acknowledged that the
expansion of biodiesel volumes could pose issues for petroleum terminals, but that these issues
could be resolved.562 Since vehicle refueling infrastructure is compatible with biodiesel blends
up to 20% (B20), we estimated that there would be no changes needed at retail and fleet facilities
to accommodate the projected increase in biodiesel use.
There are significant instances where actual biodiesel production and use have developed
differently than we projected in the RFS2 Rule. Most importantly, biodiesel consumption
reached over 2 billion gallons in 2016 and has remained between 1.7-2 billion gallons per year
from 2017-2022, often exceeding the 1.82 billion gallons that we projected would be used in
2022.563 Another significant difference is that much of the biodiesel blending is taking place
downstream of terminals at fuel marketer storage facilities and even at fuel retail facilities.
One factor that could somewhat ease biodiesel transportation to terminals is the fact that
in some limited cases, shipment of low-level biodiesel blends up to 5% is currently taking place
on some petroleum product pipelines that do not also carry jet fuel.564 If the transportation of
biodiesel blends via pipeline were expanded more broadly, this change could significantly reduce
the cost of biodiesel distribution. However, jet fuel is a significant product on much of the
petroleum pipeline system and concern over biodiesel contamination of jet fuel remains a
significant limitation on the ability to expand the shipment of biodiesel blends by pipeline.565
Finally, there appear to be substantial volumes of B10-B20 being used despite the fact
that a significant number of vehicle manufacturers only warranty their engines for up to B5.566
This has resulted in an uneven distribution of biodiesel use across the nation, with some parts
using more than 5% while other locales use little or no biodiesel.567
We are projecting that the Analyzed Volumes for 2026 and 2027 would require a
substantial increase in biodiesel volumes relative to the No RFS Baseline and a small increase in
volumes relative to the volume of biodiesel projected to be used in 2025 in the Set 1 Rule. The
primary expansion of BBD from the Analyzed Volumes is projected to occur through renewable
diesel, not biodiesel, as discussed in Chapter 7.4. As such, we do not anticipate any challenges
associated with the infrastructure to distribute and use biodiesel through 2027.
With these volume projections, domestic biodiesel production and/or biodiesel imports
may increase in 2026 and 2027. As discussed in Chapter 7.2, domestic biodiesel production
562 There is additional difficulty in storing and blending biodiesel because of the need for insulated and/or heated
equipment to prevent cold flow problems in the winter. This issue is typically not present for B5.
563 Biodiesel consumption numbers based on EMTS data.
564 Association of Oil Pipelines and American Petroleum Institute, "Ethanol, Biofuels, and Pipeline Transportation."
https://www.api.0rg/~/1nedia/files/oil-and-natural-gas/pipeline/aopl api ethanol transportationpdf.
5(35 ASTM specifications currently limit biodiesel contamination in jet fuel to 50 mg/kg (ASTM D1655-24b).
566 "pii0t Flying J Fuel Offerings," Docket Item No. EPA-HQ-OAR-2021-0427-0065.
https://www.regulations.gov/document/EPA-HO-OAR-2021-0427-0Q65.
567 "Average Biodiesel Blend Level By State Based on EIA Data," Docket Item No. EPA-HQ-OAR-2021-0427-
0119. https://www.regulations.gov/document/EPA-HO-OAR-2021-0427-Q119.
277
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capacity is significantly higher than current production levels. A review of monthly biodiesel
imports suggests that import infrastructure can support significantly higher volumes of
imports.568 For example, over 700 million gallons of biodiesel was imported in 2016.569 Monthly
import data suggests that 1.3 billion gallons per year of imports could be supported using the
existing infrastructure if we were to assume that the 112 million gallons of biodiesel imports that
took place in December 2016 could be maintained year-round. With increases in biodiesel
production, some additional expansion in import infrastructure may have occurred in 2025.
Therefore, we do not believe that domestic production capacity or import infrastructure
constraints would be a substantial impediment to an expansion in biodiesel volumes at current
levels.570
We anticipate that if biodiesel production and imports increase significantly, investment
in the infrastructure to transport biodiesel from the points of production to locations where it can
be consumed would be needed. These investments would primarily be associated with securing
sufficient downstream biodiesel storage and the requisite number of rail cars and tank trucks
suitable for biodiesel transport.571
Expanding biodiesel blending infrastructure to accommodate significantly higher
biodiesel volumes may also pose challenges. Many terminals that have yet to distribute biodiesel
would likely need to install the infrastructure. All vehicle refueling infrastructure is compatible
with B20 blends, thereby easing the expansion to retail of biodiesel blends made at terminals.
However, significant infrastructure changes would be needed to biodiesel storage and blending
facilities downstream of terminals and at retail facilities if substantial additional volumes of
biodiesel blends were to be made downstream of terminals.
Further, the cold flow of petroleum-based diesel dispensed to vehicles must often be
improved in the winter through the addition of #1 diesel fuel and/or cold-flow improver
additives. Biodiesel blends tend to have poorer cold flow performance than straight petroleum-
based diesel fuel. This requires the use of additional cold-flow improvers and sometimes limits
the biodiesel blend ratio that can be used under the coldest conditions.572 Biodiesel cold flow
properties are dependent on the source of the feedstock, with biodiesel produced from palm oil
being subject to wax formation at higher temperatures than soy-based biodiesel.573 Thus,
additional actions are necessary to ensure adequate cold-flow performance of palm-based
biodiesel blends compared to soy-based biodiesel. Such additional actions may be uneconomical
5® £[a "U.S. Imports of Biodiesel," Petroleum & Other Liquids, April 30, 2025.
https://www.eia.gov/dnav/pet/hist/LeafHandler.ashx?n=pet&s=m epoordb imO nus-zOO mbbl&f=a.
569 Id.
5711 The expansion of biodiesel imports to the extent discussed above is for purposes of the infrastructure analyses
only. There would be significant challenges in obtaining foreign-produced biodiesel volumes to approach such a
substantial increase in imported biodiesel. See Chapter 1.3.
571 Biodiesel rail cars and to a lesser extent tank trucks must often be insulated and or heated during the winter to
prevent cold flow problems. The use of such insulated/heated vessels is sometimes avoided by shipping pre-heated
biodiesel.
572 B5 blend levels can typically be maintained.
573 Hazrat, M. A., M. G. Rasul, M. Mofijur, M. M. K. Khan F. Djavanroodi, A. K. Azad, M. M. K. Bhuiya, and A.S.
Silitonga. "A Mini Review on the Cold Flow Properties of Biodiesel and Its Blends." Frontiers in Energy Research
8 (December 18, 2020). https://doi.org/10.3389/fenrg.202Q.598651.
278
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in some cases.574 Therefore, a substantial increase in the use of biodiesel, especially biodiesel
produced from palm oil, during the winter may be a challenge.
8.3 Renewable Diesel
The RFS2 Rule projected that the volume of "drop-in" cellulosic and renewable diesel
fuel would range from 0.15-3.4 billion gallons in 2017 and 0.15-9.5 billion gallons in 2022.575
Such fuels are referred to as drop-in fuels because their physical properties are sufficiently
similar to petroleum-based diesel to be fungible in the common diesel fuel distribution system.576
Thus, little change is needed to the fuels infrastructure system to support the use of drop-in
biofuels. The RFS2 Rule projected that the distribution infrastructure could expand in a timely
fashion to accommodate that projected range of growth in drop-in cellulosic and renewable
diesel fuel.577
In practice, much of the renewable diesel produced in the U.S. has been transported by
truck, rail, and ship, rather than by pipelines. This is in part due to the location of the renewable
diesel production and demand and the lack of available pipelines to transport renewable diesel
from production sites to demand centers. Renewable diesel can generate credits under state
LCFS programs, and the magnitude of this incentive, especially in California, has caused most
renewable diesel production in the U.S. to be shipped in segregated batches to California rather
than being blended into diesel where it is produced. Regulatory challenges have also limited the
transportation of renewable diesel via pipeline. Product transfer document (PTD) requirements
for fuel shipped by pipeline and fuel pump labeling requirements often require that the blend
level be indicated, but the concentration would often be uncertain in a fungible distribution
system. Transportation of renewable diesel via common carrier pipelines can make documenting
the use and blend levels of renewable diesel difficult, if not impossible.
The projected increase in domestic renewable diesel production through 2027 is
significant relative to both the No RFS Baseline and the 2025 Baseline, as discussed in Chapter
7.2. We expect that much of this new renewable diesel will also be used in California and other
states with state incentive programs (e.g., Oregon). Renewable diesel produced in California will
likely be distributed locally, and much of the renewable diesel produced on or near the Gulf
Coast is likely to be transported via ship. The remaining renewable diesel production facilities
are not located near the coast, and we therefore project that the fuel they produce will likely be
transported via truck and/or rail to markets where the fuel is used. This may require some
expansion to the existing infrastructure, such as additional rail cars to transport renewable diesel.
The fact that the new or expanding renewable diesel production facilities are generally located in
the western U.S., relatively close to California and Oregon, likely reduces the impact of
distributing these fuels on the transportation infrastructure, though this may be somewhat offset
by the need to transport feedstocks to the renewable diesel production facilities. While some
adjustments will likely be needed to accommodate the expected increase in renewable diesel
574 Verma, Puneet, M.P. Sharma, and Gaurav Dwivedi. "Evaluation and Enhancement of Cold Flow Properties of
Palm Oil and Its Biodiesel." Energy Reports 2 (January 9, 2016): 8-13. https://doi.Org/10.1016/i.egvr.2015.12.001
575 See RFS2 RIA Chapter 1.2.2.
576 Such drop-in fuels are typically blended with petroleum-based diesel prior to use.
577 See RFS2 RIA Chapter 1.6.
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production, we do not expect that these adjustments will inhibit the growth of renewable diesel
production or appreciably impact transportation networks in the U.S. more broadly.
8.4 Ethanol
We are anticipating that the projected volumes for 2026 and 2027 would result in
increased use of higher-level ethanol blends such as E15 and E85; E10 is economical to be
blended in the absence of the RFS program.
The infrastructure needed to deliver ethanol includes that required for distribution of
denatured ethanol from production facilities to terminals, storage and blending equipment, and
distribution of gasoline-ethanol blends to retail service stations. With regard to infrastructure
needed to use ethanol, essentially all retail service stations are certified to offer E10 and all
vehicles and equipment are designed to use E10. As a result, any infrastructure-related impacts
on the use of ethanol in 2026 and 2027 are associated with service station storage and dispensing
equipment for higher-level ethanol blends such as El5 and E85, and the vehicles capable of
using those blends. The majority of the El 5 and E85 volume projected to be used in 2026 and
2027 was already being used in 2025; consequently, the infrastructure is already in place.
However, the expanded volume in 2026 and 2027 would require additional infrastructure,
primarily the expansion of retail stations as discussed below.
Based on our analysis below of the sufficiency of infrastructure to deliver and use
ethanol, we have determined that there are constraints associated with El5 and E85 that limit the
rate of future growth in their consumption. These constraints are appropriately reflected in our
projections of total ethanol consumption in Chapter 7.5 since those projections represent only
moderate changes in the nationwide average ethanol concentration in comparison to earlier
C70
years.
8.4.1 Ethanol Distribution
To support the RFS2 Rule, ORNL conducted an analysis of potential distribution
constraints that might be associated with attaining the statutory volume targets through 2022.579
The ORNL analysis examined ethanol transport pathways from production to blending facilities
at terminals by rail, waterways, and roads, and projected that most ethanol would require long-
distance shipment to demand centers. The primary mode of long-distance transport in 2010 was
via manifest rail and, to a lesser extent, by barge, although transport by unit train was beginning
to spread. ORNL projected that rail would continue to be the predominate means of long-
distance ethanol transport through 2022, with a substantial increase in the use of unit trains and
continued supplemental transport by barge. ORNL concluded that there would be minimal
additional stress on most U.S. transportation networks overall to distribute the increased biofuel
volumes.
578 A nationwide average ethanol concentration above 10.00% can only occur insofar as there is consumption of E15
and/or E85.
579 Das, Sujit, Bruce Peterson, and Shih-Miao Chin. "Analysis of Fuel Ethanol Transportation Activity and Potential
Distribution Constraints." Transportation Research Record Journal of the Transportation Research Board 2168, no.
1 (January 1, 2010): 136-45. https://doi.org/10.3141/2168-16.
280
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However, ORNL stated that there would be considerable increased traffic along certain
rail corridors due to the shipment of biofuels that would require significant investment to
overcome the resulting congestion. We concluded that these investments could be made to
increase the capacity of the affected rail corridors without undue difficulty, and that therefore the
infrastructure system to the blending terminal could accommodate the projected increased
volume of ethanol in a timely fashion.580
To update and expand upon the analysis of distribution infrastructure upstream of retail
that was conducted for the 2010 RFS2 Rule, EPA contracted with ICF International Inc. ("ICF")
to conduct a literature review, background research, and stakeholder interviews to characterize
the impacts of distributing ethanol and other biofuels.581 The 2018 ICF report determined that the
conclusions from the 2009 ORNL analysis have largely turned out to be accurate based on an
absence of indicators of distribution constraints up to and including the blending terminal. ICF
noted that there were instances when the ethanol industry went through rapid expansion, such
that the rail industry was not able to fully accommodate the expansion of inter-regional trade in
ethanol for a time. However, ICF found no evidence to suggest that rail congestion from
shipment of biofuel was a persistent or common problem at the time that the study was
completed. Likewise, ICF found no evidence that marine networks, including those used for
import and export, were experiencing significant issues in accommodating increased volumes of
biofuels. Consistent with the 2010 analysis, ICF stated that the expansion of ethanol and
biodiesel volumes could pose issues for petroleum terminals, but that these issues could be
resolved. While ICF indicated that there likely had been negative impacts on rural and highway
transportation networks surrounding ethanol production facilities, ICF also determined that these
impacts could be mitigated with network infrastructure planning and increased funding for road
maintenance. ICF noted these increased costs would be small in comparison to broader
maintenance costs for roads and that the road network could accommodate substantial growth in
the movement of biofuels.
Based on the ICF study and our own assessment of the implementation of the RFS
program, we conclude that the response of the ethanol distribution infrastructure system
upstream of retail has largely unfolded as we projected in the 2010 RFS2 Rule. Ethanol imports
to coastal demand centers have helped to satisfy local demand. Ethanol transport over long
distances is primarily being accomplished by unit train and, to a lesser extent, by manifest rail
and barge. Materials compatibility issues continue to prevent ethanol and ethanol blends from
being shipped in petroleum product pipelines. Tank trucks are used to distribute ethanol to
markets close to the ethanol production facility and from rail receipt facilities to more distant
markets. Petroleum terminals have installed the necessary ethanol receipt, storage, and blending
infrastructure. Intermodal facilities, such as those that transfer ethanol directly from rail cars to
tank trucks, are also being used to ease the burden on terminals.
580 See RFS2 RIA Chapter 1.6.
581 ICF, "Task 5: Impact of Biofuels on Infrastructure," January 2018.
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8.4.2
Infrastructure for E85
E85 is permitted to be used only in designated FFVs. As of 2023, there were about 20
million registered light-duty FFVs in the U.S., representing about 7% of all spark-ignition
vehicles.582 583 The number of registered FFVs has been declining over the past several years. As
of 2025, only six FFV models were in production for consumer use.584 However, California is
seeing a resurgence in use of E85 which may push a small increase in FFVs in that region.585
Figure 8.4.2-1: Light-Duty FFV Model Offerings
100
90
80
70
60
50
40
30
20
10
0
1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017 2019 2021 2023
Source: AFDC, "Light-Duty AFV HEV and Diesel Model Offerings by Technology-Fuel," May 2024.
https://afdc.energy. gov/data/10303.
E85 is sold at retail stations where the pumps, underground storage tanks, and associated
equipment have all been certified to operate safely with the high ethanol concentrations.586 As
shown in Figure 8.4.2-2, the number of stations offering E85 has increased steadily since about
2005. By September 2025, the total number of stations offering E85 had reached 4,975.
582 AFDC, "Light-Duty AFV Registrations," June 2024. https://afdc.energv.gov/data/10861.
583 Bureau of Transportation Statistics, "Table 1-11: Number of U.S. Aircraft, Vehicles, Vessels, and Other
Conveyances," April 24, 2025. https://www.bts.gov/content/number-us-aircraft-vehicles-vessels-and-other-
convevances.
584 AFDC, "Alternative Fuel and Advanced Vehicle Search Ethanol (E85)," 2025.
https://afdc.energy.gov/veliicles/searcli/results7view mode=grid&search field=vehicle&search dir=desc&per page
=12¤t=true&displav length=25&model vear=2025&fuel id=ll.-
l&all categories=v&manufacturer id=365.377.211.235.215.223.225.379.219.213.209.351.385.275.361.387.243.22
7.239.425.263.217.391.349.383.237.221.347.395.-1.
585 Renewable Fuels Association, "RFA Calls on California to Expand Flex Fuel Vehicles for Lower Costs, Cleaner
Air," January 16, 2024. https://ethanolrfa.org/media-and-news/categorv/news-releases/article/2024/01/rfa-calls-on-
california-to-expand-flex-fuel-veliicles-for-lower-costs-cleaner-air.
586 EPA, "UST System Compatibility with Biofuels," EPA-510-K-20-001, July 2020.
282
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Figure 8.4.2-2: Number of Public and Private Retail Service Stations Offering E85
Note: Data through 2007 is annual, whereas data for 2008 and later is monthly.
Source: AFDC, "Historical Alternative Fueling Station Counts." https://afdc.energy.gov/stations/states.
Grant programs such as the USDA Biofuels Infrastructure Partnership (BIP), USDA
Higher Blends Infrastructure Incentive (HBIIP) and the ethanol industry's Prime the Pump
program, in addition to individual company efforts, have helped to fund the expansion of E85
offerings at retail stations. The combined effects of these efforts have ensured ongoing growth in
the number of stations offering E85.
Although the total number of retail stations in the U.S. has varied, the fraction of those
stations offering E85 has steadily increased. A large number of these E85 stations have
materialized in California, which has seen a large private incentivization for retail stations to
supply this blend.587 Using available data, EPA estimates retail stations offering E85 using a
linear projection. With this growth rate, we estimated the total for the projected years of 2026
and 2027 as shown in Table 8.4.2-2.
Table 8.4.2-2: Projected Annual Average Number of Stations Offering E85
Year
Stations
(Non-CA)
Stations
(CA)
2026
4,427
664
2027
4,520
745
8.4.3 Infrastructure for E15
El5 is permitted to be used only in MY2001 and newer light-duty motor vehicles.588 The
infrastructure needed to support the use of El 5 includes blending and storage equipment at
terminals, certified storage and dispensing equipment at retail service stations, and the vehicles
that are permitted to use El5. While the majority of service stations currently offering El5 do so
587 AFDC, "Historical Alternative Fueling Station Counts." https ://afdc .energy.gov/stations/states.
588 76 FR 4662 (January 26, 2011).
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through blender pumps—which can produce El 5 on demand for consumers through the
combination of E10 (or EO) and E85—the number of terminals offering preblended E15 directly
to service stations has been increasing.589
As shown in Figure 8.4.3-1, the fraction of the in-use fleet that is MY2001 and newer has
increased steadily since El 5 was approved in 2011, and with it the fraction of all gasoline
consumed by highway vehicles that is consumed by MY2001 and newer vehicles.
Figure 8.4.3-1: Fraction of In-Use Fleet and In-Use Gasoline Consumption for MY2001 and
Newer
Source: Davis, Stacy, and Robert Boundy. "Transportation Energy Data Book (Edition 40)," Oak Ridge National
Laboratory, ORNL/TM-2022/2376. May 1, 2022. Tables 3.15, 4.6, 4.7, 4.12, and 9.11.
https://doi.org/10.2172/1878695.
Based on the two modes of E15 production (terminals and blender pumps at retail
stations), and the fact that the majority of in-use vehicles are legally permitted to use E15, it
appears that the primary constraint on the consumption of El 5 in the near term is likely the
number of retail stations that offer it. Since El 5 was not approved for use until 2011, there were
no retail stations offering it before 2011. Most of the existing retail infrastructure (including the
entire system of tanks, pipes, pumps, dispensers, vent lines, and pipe dope) is not confirmed to
be entirely compatible with E15, so growth in the number of retail stations offering E15 is
dependent on investments in retail outlets to convert them to El5 compatibility or make them
compatible when newly constructed. In cases wherein a retail station already offers E85 through
a blender pump, there may be few or no investments needed for new equipment, and the decision
to offer El 5 may depend largely on the perceived economic benefit of doing so. For other station
owners, the costs can be substantial. Growth in the number of stations offering El 5 was slow
until the BIP, HBIIP, and Prime the Pump programs began providing funding for station
conversions in 2016.
589 Renewable Fuels Association, "Terminal Availability of E15 Grows as EPA Prepares to Remove RVP Barrier,"
March 12, 2019. https://ethanolrfa.org/media-and-news/categorv/blog/article/2019/03/terminal-availabilitv-of-el5-
grows-as-epa-prepares-to-remove-rvp-barrier.
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Table 8.4.3-1: Number of Retail Stations Offering E15
Year
E15 Stations
2014
105
2015
184
2016
431
2017
1,214
2018
1,700
2019
2,081
2020
2,302
2021
2,605
2022
2,758
2023
3,414
2024
3,751
2025
4,503*
* Value projected for this rule.
Source: Growth Energy, "Higher Blends Retail Footprint," October 1, 2024. https://growthenergy .org/data-set-
categorv/higher-blends-retail-footprint.
USDA followed up its BIP program with the HBIIP program, which also provides funds
to help retail service station owners to upgrade or replace their equipment to offer biofuels.
HBIIP is composed of several rounds of funding often referred to as HBIIP 1.0 and HBIIP 2.0.
This program effectively began in 2021 and had been accepting applications as recently as 2024.
With regard to equipment compatibility, even if much of the existing equipment at retail
is compatible with El5 as argued in studies from NREL590 and Stillwater Associates,591
compatibility with El5 is not the same as being approved for El5 use. Under EPA regulations,
parties storing ethanol in underground tanks in concentrations greater than 10% are required to
demonstrate compatibility of their tanks with the fuel through one of the following methods:592
• A certification or listing of underground storage tank system equipment or
components by a nationally recognized, independent testing laboratory such as
Underwriter's Laboratory.
• Written approval by the equipment or component manufacturer.
• Some other method that is determined by the agency implementing the new
requirements to be no less protective of human health and the environment.
The use of any equipment to offer El 5 that does not satisfy these requirements, even if
that equipment is technically compatible with El5, would pose potential liability for the retailer.
This issue is of particular concern for underground storage tanks and associated hardware, as the
5911 Moriarty, K„ and J. Yanowitz. "E15 And Infrastructure," National Renewable Energy Laboratory, NREL/TP-
5400-64156, Mav 21. 2015. https://doi.org/10.2172/1215238.
591 Stillwater Associates, "Infrastructure Changes and Cost to Increase RFS Ethanol Volumes through Increased E15
and E85 Sales in 2016," July 27, 2015. https ://ethanolrfa org.cvbertest.link/file/2006/Infrastructure-Changes-Cost-
to-Increase-RFS Stillwater 2016.pdf.
592 EPA, "UST System Compatibility with Biofuels," EPA-510-K-20-001, July 2020.
285
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documentation for their design and the types of materials used, and even their installation dates,
is often unavailable. As existing underground storage tank systems reach the end of their
warranties or are otherwise in need of repair or upgrade, there is an opportunity for retail station
owners to install new systems that are compatible with El5. For instance, tanks installed earlier
than 1990 have reached the end of their warranties and should be replaced to safely store fuel.
With regard to retailer concerns about litigation liability for El5 misfueling related to
vehicles not designed and/or approved for use with El5, we note that EPA regulations are
designed to address potential misfueling. These regulations require pump labeling, a misfueling
mitigation plan, surveys, PTDs, and approval of equipment configurations, providing consumers
with the information needed to avoid misfueling.593 In addition, the portion of vehicles not
designed and/or approved for E15 use continues to decline. MY2000 and earlier light-duty
vehicles represent less than 10% of the in-use fleet, and just slightly over 5% of miles traveled.
Vehicles designed and warranted by manufacturers to be fueled on El5 are likewise representing
an ever-increasing portion of the in-use fleet.
In sum, the relatively small, albeit growing, number of stations offering El5 represents a
significant constraint on the expansion of El 5 through 2027. While the applicable standards
under the RFS program could theoretically provide some incentive for retail station owners to
upgrade their equipment to offer El5, there is little direct evidence that the RFS program has
operated in this capacity in the past.
Using the E15 station information in Table 8.4.3-1, we projected the total number of E15
stations for 2026 and 2027, as shown in Table 8.4.3-2.
Table 8.4.3-2: Projected
Year
E15 Stations
2026
4,507
2027
4,872
Number of Retail Stations Offering E15
8.5 Deliverability of Materials, Goods, and Products Other Than Renewable
Fuel
The distribution of renewable fuels relies on the same rail, marine, and road infrastructure
networks that are used to deliver materials, goods, and products other than renewable fuels.
Therefore, we evaluated whether the use of renewable fuels would impact the deliverability of
other items that rely on these infrastructure networks.
The 2009 ORNL study of biofuel distribution for the 2010 RFS2 Rule concluded that
there would be minimal additional stress on most U.S. transportation networks overall due to
increased biofuel volumes.594 This indicates that the shipment of the statutory biofuel volumes
could be accommodated without impacting the deliverability of other items. However, as
593 See, e.g., 40 CFR 1090.1420 and 1090.1510.
594 Das, Sujit, Bruce Peterson, and Shih-Miao Chin. "Analysis of Fuel Ethanol Transportation Activity and Potential
Distribution Constraints." Transportation Research Record Journal of the Transportation Research Board 2168, no.
1 (January 1, 2010): 136-15. https://doi.org/10.3141/2168-16
286
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discussed in Chapter 8.5.1, ORNL noted that significant investment would be needed to
overcome congestion on certain rail corridors. The 2018 ICF study of impacts of distributing
ethanol and other biofuels determined that the conclusions from the 2009 ORNL analysis have
largely turned out to be accurate based on an absence of indicators of distribution constraints.595
However, ICF noted that there were instances when the ethanol industry went through rapid
expansion where the rail industry was not able to fully accommodate the expansion in inter-
regional trade in ethanol. During these periods, the volume of ethanol permitted to be shipped
along the sensitive rail corridors was limited to mitigate the congestion. However, ICF found no
evidence to suggest that rail congestion from shipment of biofuel was a persistent or common
problem at the time of the study's completion in 2018.
Likewise, ICF found no evidence that the shipment of biofuels has had a negative impact
on marine networks. While ICF indicated that there likely have been negative impacts on rural
and highway transportation networks surrounding ethanol production facilities, it also
determined that these impacts can be mitigated with network infrastructure planning and
increased funding for road maintenance. ICF noted these increased costs are small in relation to
broader maintenance costs for roads and that the road network can accommodate substantial
growth in the movement of biofuels.
Based on both the ORNL study and the more recent ICF study, there appears to be
minimal overall impact on transportation infrastructure from the distribution of biofuels, and the
system appears to have been able to resolve localized instances of increased stress on the system
in a timely fashion. As a result, we believe that the candidate volumes would not impact the
deliverability of materials and products other than renewable fuel.
As part of considering impacts of biofuels on the deliverability of other items, we also
considered constraints on the deliverability of feedstocks used to produce renewable fuel. We do
not anticipate constraints that would make the candidate volumes difficult to achieve. For
instance, biogas for CNG/LNG vehicles will be delivered through the same pipeline network
used to distribute natural gas (see Chapter 7.1). Since that biogas will be displacing natural gas
used in CNG/LNG vehicles, we do not expect a net increase in total volume of biogas and
natural gas delivered.
As shown in Table 3.1-7, corn ethanol consumption volumes are expected to decrease
slightly in 2026-2027, with projected volumes around 14.2 billion gallons. However, ethanol
production levels are not expected to decrease as export volumes have remained high. Corn
collection and distribution networks have been functioning without difficulty since 2018. It is
therefore anticipated that there should be no issues with the infrastructure for 2026-2027 and
beyond.
In the proposal, we estimated that the use of FOG for the production of biofuel would
increase. This projected increase in the use of FOG for biofuel production was consistent with
the observed trend in the domestic supply of FOG for biofuel production from 2014-2021,
before the rapid increase in FOG imports. In 2025, these dynamics abruptly changed after tax
credit changes, trade dynamics, and lower domestic consumption of these fuels, thus lowering
595 ICF, "Impact of Biofuels on Infrastructure," January 2018.
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our projections for FOG in the Set 2 Final Rule (see Chapter 7.2). FOG is collected and
distributed through a diverse network of trucking companies, and this increase would represent a
very small portion of their activities. As a result, we do not anticipate any hindrances to the
deliverability of FOG for the production of renewable diesel in 2026-2027.
Total soybean oil use for the production of BBD is projected to increase from
approximately 1.37 billion gallons in 2025 to approximately 3.98 billion gallons in 2027. This
projected increase is based on the expected expansion of soybean crushing over this time period
in the U.S (see Chapter 7.2). We expect that the existing infrastructure is sufficient to distribute
biodiesel and renewable diesel to the markets where these fuels are used.
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Chapter 9: Other Factors
CAA section 21 l(o)(2)(B)(ii) directs EPA to consider the impact of the use of renewable
fuels on "other factors" that have a more indirect relationship to volume standards, including job
creation, the price and supply of agricultural commodities, rural economic development, and
food prices.596'597 Broadly speaking, these factors can be thought of as various key economic
impacts of the RFS program, each of which Congress believed was important to consider when
determining the levels of renewable volume obligations. This chapter describes the ways in
which we have assessed the impact of the Analyzed Volumes on these factors through qualitative
and/or quantitative economic impact analysis. Chapter 9.1 discusses the projected employment
and rural economic development impacts of increased renewable fuel production. Chapter 9.2
discusses the projected impact on the supply of agricultural commodities. Chapters 9.3 and 9.4
discuss the impact of the Analyzed Volumes on the prices of agricultural commodities and food,
respectively.
9.1 Employment and Rural Economic Development Impacts
In this section, we discuss the potential impacts of the Analyzed Volumes on employment
and rural economic development. As noted in the Executive Summary and consistent with DRIA
Chapter 9.1, the impacts presented in this section likely constitute transfers, rather than societal
costs or benefits. For the proposed rule598, we reviewed the relevant historical data and recent
trends and the economic literature and methods available for the quantification of employment
and rural economic development impacts related to renewable fuel production and consumption.
We then presented multiple quantitative analyses of employment impacts, one using a "Rule-of-
Thumb" approach and another using the National Laboratory of the Rockies (NLR)599 Jobs and
Economic Development Impacts (JEDI) model. Based on these two analyses, we presented
estimates of agricultural employment impacts and rural economic development impacts
associated with the volume scenarios discussed in the proposal.
For the final rule, we have made use of the same methodologies presented in the
proposal. We have updated our analysis to reflect the Analyzed Volumes. However, the
modeling inputs and intermediate outputs are the same as those discussed in the proposal. In the
proposal, we estimated the employment and rural economic development impacts attributable to
representative biofuel production facilities producing RNG, BBD (i.e., biodiesel and renewable
diesel) produced from soybean oil, FOG, corn oil, and canola oil, and ethanol (which for the
purposes of this analysis we assumed would be produced from corn starch). These fuel types
were selected because they represented the vast majority of additional fuel projected to be
produced under the proposed volumes. We then applied these per-facility estimates to the
expected volumes of each fuel projected to be produced under the proposed volumes. In the final
rule, we make use of these same per-facility estimates and apply them to the projected amounts
596 As explained in Preamble Section II, we also consider several other factors besides those enumerated in the
statute.
597 The impacts evaluated in this chapter are for volume increases for 2026-2027 compared to the No RFS Baseline.
598 See DRIA Chapter 9.
599 This laboratory was formerly known as the National Renewable Energy Laboratory (NREL).
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of RNG, BBD, and ethanol associated with the Analyzed Volumes (see Chapter 3 for more
details on these volumes).
Economic models show that renewable fuel use can result in higher crop prices, though
the range of estimates in the literature is wide. A 2013 study carried out to estimate the impacts
of biofuels on corn prices projected (for 2015) price increases in the range of 5-53%.600 A report
by the National Research Council (NRC)601 on the RFS program included several studies finding
a 20-40% increase in corn prices from biofuels during 2007-2009. A working paper from the
National Center for Environmental Economics (NCEE) found that, on average, corn prices rise
by 2-3% in the long term for every billion-gallon increase in corn ethanol production, based on
an analysis of 19 studies.602 The NRC report also found that higher crop prices lead to higher
food prices, though impacts on retail food in the U.S. were estimated to be small. Studies have
found that, from a global perspective, higher crop prices might lead to higher rates of
malnutrition in developing countries.603 604 605
Some research has also suggested the growth of biofuels may also contribute to the
ongoing trend of U.S. farmland consolidation.606 These studies suggest that increasing
production of corn and soy have increasingly pushed small farms out of business because crops
like corn and soy are often cultivated in large monocropping operations.607 Historically, midsize
farms have been vital to the economies of many local communities, and their decline has
intensified economic and social difficulties in areas such as the rural Midwest.608 609 According
to an analysis by the Union of Concerned Scientists covering data from 1978-2017, it was
observed that large crop farms are expanding, small crop farms are shrinking, and midsize crop
farms are vanishing.610 During the nearly four decades examined, the overall number of farms
has decreased while farm sizes have tripled.611 This consolidation of farmland also impacts
61111 Wei Zhang et al.. "The impact of biofuel growth on agriculture: Why is the range of estimates so wide?," Food
Policy 38 (January 11, 2013): 227-39. https://doi.Org/10.1016/i.foodpoi.2012.12.002.
6111 National Research Council. Renewable fuel Standard. National Academies Press eBooks, 2011.
https://doi.org/10.17226/13105.
6112 Condon, Nicole, Heather Klemick, and Ann Wolverton. "Impacts of Ethanol Policy on Corn Prices: A Review
and Meta-analysis of Recent Evidence." Food Policy 51 (January 13, 2015): 63-73.
https://doi.org/10.1016/i.foodpol.2014.12.007.
6113IIASA, "Biofuels and Food Security - Implications of an accelerated biofuels production," March 2009.
https://pure.iiasa.ac.at/id/eprint/8984/l/XO-09-062.pdf.
6114 EPA, "Economics of Biofuels." https://! 9ianuarv2021 snapshot.epa. gov/environmental-economics/economics-
biofuels .html.
6115 Rosegrant, Mark W., Tingju Zhu, Siwa Msangi, and Timothy Sulser. "Global Scenarios for Biofuels: Impacts
and Implications*." Review of Agricultural Economics 30, no. 3 (September 1, 2008): 495-505.
https://doi.org/10.1111/i. 1467-9353.2008.00424.x.
6116 Scafidi, Angela. "Increased Biofuel Production in the US Midwest May Harm Fanners and the Climate." World
Resources Institute, February 27, 2024. https://www.wri.org/insights/increased-biofuel-production-impacts-climate-
change-farmers.
6117 Union of Concerned Scientists. "Losing Ground," April 14, 2021. https://www.ucs.org/resources/losing-ground.
608 Id.
6119 Scafidi, Angela. "Increased Biofuel Production in the US Midwest May Harm Fanners and the Climate." World
Resources Institute, February 27, 2024. https://www.wri.org/insights/increased-biofuel-production-impacts-climate-
change-fanners.
6111 Union of Concerned Scientists. "Losing Ground," April 14, 2021. https://www.ucs.org/resources/losing-ground.
611 Id.
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agricultural communities by driving up real estate prices and making it difficult for small-scale
farmers to buy or lease land.612 Our analysis in this section does not attempt to quantify or model
these phenomena or any potential impacts of demand for biofuel feedstock on farm size.
However, we acknowledge that, to the extent these ongoing trends in farm size are linked to
demand for biofuel feedstocks and do continue into the future, this may affect the magnitude of
employment and rural economic development impacts associated with our RFS program
standards.
Economic advantages of renewable fuels compared to fossil fuels stem from value added
to the renewable fuel feedstock, increased numbers of rural manufacturing jobs, and support for
the agricultural sector by providing more employment opportunities and market opportunities for
domestic crops. The impacts to the local economy from investment in new renewable fuel
production facilities, including increases in employment, output and income, and the subsequent
increases in demand for local goods and services all create additional beneficial ripple effects.613
Increased biofuel production is expected to offset employment in certain sectors. While
our analysis presented below suggests expanding biofuel production will generate new positions
in biofuel processing plants and associated industries, this expansion could also result in job
reductions or transitions in sectors such as fossil fuels. While such shifts may benefit certain
individuals and communities, in societal economic impact terms these jobs would be considered
transfers rather than net benefits to the U.S. economy.
Increased U.S. biofuel production will also necessitate the development and expansion of
production systems and networks to effectively cultivate, harvest, and transport substantial
amounts of feedstock. Additionally, industry requires technologies that can convert biomass
more efficiently and cost-effectively for various applications.614 These trends will result in
shifting employment across sectors with subsequent impacts on local and regional spending in
these impacted areas.
This section focuses on the gross employment impacts, not net impacts, and the income
impacts that follow from increased investment in renewable fuels. We evaluate how renewable
fuels affect employment and rural economic development. Subsequent sections (Chapters 9.2,
9.3, and 9.4) address the impacts on agricultural commodity supply and prices, as well as food
prices.
While these two categories of economic impacts (employment and rural economic
development) are distinct, there is significant overlap between them in the context of renewable
fuels, given the reliance of these supply chains on rural economic output. Most feedstocks used
to produce biofuels in the U.S. are produced and processed (e.g., oilseeds are crushed) in rural
areas. Biofuel production facilities themselves are also often located in rural areas. There is also
612 Scafidi, Angela. "Increased Biofuel Production in the US Midwest May Harm Fanners and the Climate." World
Resources Institute, February 27, 2024. https://www.wri.org/insights/increased-biofuel-production-impacts-climate-
change-farmers.
613 Demirbas, Ayhan. "Political, economic and enviromnental impacts of biofuels: A review." Applied Energy 86
(May 23, 2009): S108-17. https://doi.Org/10.1016/i.apenergy.2009.04.036.
614 DOE, "Jobs & Economic Impact of a Billion-Ton Bioeconomy," June 2017.
https://www.energv.gov/eere/bioenergv/articles/iobs-economic-impact-billion-ton-bioeconomY.
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overlap in the methods available to assess the impacts of renewable fuels on employment and on
rural economic development. The following subsection details these methodological options.
Due to these substantial overlaps in both impacts and methodologies, we have chosen to
analyze employment and rural economic development impacts together using a cohesive set of
methods. We first introduce the concepts of employment and rural economic development
impact analysis, followed by a discussion of available methods and existing literature.
Employment impact assessment can be conceived of as a subset of economic impact
analysis that focuses specifically on the employment-related effects of a project, policy, or event,
while the superset (economic impact analysis) examines broader economic changes. This method
is often used to assess the employment potential and impact of sectoral policies and investments.
Typically, an employment impact assessment assesses both the quantitative (number of jobs
created and associated monetary impacts) as well qualitative (type of jobs created) impacts
following a change in policy.615 Such analyses are often used to support the development of
evidence-based pro-employment policies and strategies that are appropriate to the context of the
local or national economy.
An employment impact assessment is often carried out within the framework of an input-
output model and generally characterizes three types of job impacts: direct (on-site or immediate
effects created by an increase in expenditure, i.e., a policy shock), indirect (economic activity
that occurs when a contractor/vendor or manufacturer receives payment for goods and services
and is in turn able to pay others who support the business, i.e., business to business purchases in
the supply chain taking place in the region), and induced (economic values stemming from
household spending of labor income after removal of taxes, savings and commuter income).616 In
the context of developing a biofuel plant, these impacts are further divided into two temporal
phases: Construction Phase (temporary jobs and other impacts) and Operations and Maintenance
Phase (permanent jobs and impacts).
Figures 9.1-1 and 9.1-2 illustrate the various categories of these employment impacts,
other economic impacts, and financial flows in the construction and operations phase
respectively of a biofuel facility. For both figures, the light purple boxes measure the economic
impacts in dollar terms while the dark purple ones measure the economic impacts in terms of job
numbers. The solid arrows capture the flow of financial services.617 In this section, we compute
the direct, indirect, and induced impacts from the production of biofuels to the U.S. economy.
The total indirect impacts are broken out into impacts to the agricultural sector and impacts to the
industry. Additionally, the job estimates have been computed based on changes from the No RFS
Baseline and as such should be interpreted as additive gross jobs relative to that baseline.
However, were the analysis to be carried out relative to the 2025 Baseline, some of these
615 International Labour Organization, "Employment Impact Assessments (EmpIA): Analysing the Employment
Impacts of Investments in Infrastructure," 2021.
https://www.ilo.Org/sites/default/files/wcmsp5/groups/public/@ed emp/documents/publication/wcms 774061 .pdf.
616 Demski, Joe. "Understanding IMPLAN: Direct, Indirect, and Induced Effects," IMPLAN, April 18, 2025.
https://blog.implan.com/understanding-implan-effects.
617 International Labour Organization, "Guide for Monitoring Employment and Conducting Employment Impact
Assessments (EmpIA) of Infrastructure Investments," 2020.
https://www.ilo.org/sites/default/files/wcmsp5/groups/public/%40ed emp/documents/publication/wcms 741553.pdf
292
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computed estimates would then be interpreted as jobs at risk were the RFS program
discontinued.
Figure 9.1-1: Direct, Indirect, and Induced Economic Impacts in the Construction Phase of
a Biofuel Facility
Investment in new
biofuel facility
Increased output of
construction sector
Increased Demand
for Construction
Sector Input
Increased
Employment in the
Construction Sector
Direct Effect
Indirect Effect
Increased
Employment in
Sectors producing
Construction Sector
inputs
1
r
Increase in
Consumption
'
Increased
Employment in
Sectors Producing
Goods and Services
for Consumption
Induced Effect
Figure 9.1-2: Direct, Indirect, and Induced Economic Impacts in the Operations Phase of a
Biofuel Facility
Investment in new
biofuel facility
Increased output of
Biofuel Sector
Increased Demand
for Biofuel Sector
Input
Increased
Employment in the
Biofuel Sector
Direct Effect
Indirect Effect
Increased
Employment in
Sectors producing
Biofuel Sector inputs
Agriculture
Allied Industries
r
Increase in
Consumption
t
Increased
Employment in
Sectors Producing
Goods and Services
for Consumption
Induced Effect
Rural economic development encompasses a wide range of strategies and activities, all of
which have the common goal of enhancing living standards and financial security of the rural
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community.618 Through these strategies and activities, actors seek to enhance infrastructure,
stimulate economic growth, and otherwise economically empower rural residents and
communities. This involves building rural wealth and incomes through job creation and other
channels; for example, by improving agricultural production to increase revenue from the sale of
agricultural commodities. One can assess rural economic development through an array of
metrics; the choice of metric largely depends on the dimension of development under analysis.
Metrics like income, employment, and agricultural productivity—variables that are crucial to the
financial growth and stability of the agricultural sector—are often used to assess aspects of rural
economic development. Since income and employment are also necessary metrics of our analysis
of the employment impacts of renewable fuels, and since the renewable fuel supply chain relies
substantively on rural economic output, the same methodologies that we applied in the context of
employment impact analysis can also be used to generate useful estimates of rural economic
development impacts.
9.1.1 Methodology and Existing Literature
Economic impact analysis allows policymakers to evaluate the potential consequences of
different policy options on communities and economic sectors of interest to the program.
Historically, the range of models and methods available to assess economic, environmental, and
social impacts of policies varied based on a wide variety of considerations, including
methodological discipline (e.g., machine learning based models,619 cross disciplinary models,620
statistical/econometric models), methodological scope (e.g., sector specific/local/global and or
static/dynamic), the nature of the policy question being analyzed (e.g., prescriptive vs.
prescriptive621), and data availability. In the DRIA, we applied two such methods to estimate the
impacts of the proposal. We apply these same two methods in the same manner in this analysis
of the Analyzed Volumes. Table 9.1.1-1 describes a non-exhaustive list of these approaches
based on ease of use.
618 Social For Action, "How to Measure Rural Development: Key Indicators and Metrics," November 17, 2024.
https://www.socialforaction.com/blog/how-to-measure-rural-development.
619 Peet, Evan D., Brian G. Vegetabile, Matthew Cefalu, Joseph D. Pane, and Cheryl L. Damberg. "Machine
Learning in Public Policy: The Perils and the Promise of Interpretability." R. IX/) Corporation, 2022.
https://doi.org/10.7249/pea828-l.
6211 Game, Edward T., Heather Tallis, Lydia Olander, Steven M. Alexander, Jonah Busch, Nancy Cartwright,
Elizabeth L. Kalies, et al. "Cross-discipline Evidence Principles for Sustainability Policy." Nature Sustainabilitv 1,
no. 9 (September 6, 2018): 452-54. https://doi.org/10.1038/s41893-018-0141-x.'
621 Home, Christine. "Norms." Data set. Oxford Bibliographies Online Datasets, November 27, 2013.
https://doi.org/10.1093/obo/9780199756384-0Q91.
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Table 9.1.1-1: Select Methot
s for Jobs and Economic Impact Analysis
Basic Methods
Moderate Methods
Complex Methods
Approach
- Rule of Thumb
- Meta-analysis
- Input-Output or based on
input-output
- Computable General
Equilibrium (CGE)
- Partial Equilibrium (PE)
Econometric
- System Dynamics - Linear
& Non-Linear Programming
Examples
- Rule-of-Thumb
estimates (i.e., "5
jobs/MW")
- Screening models
- Impact Analysis for
Planning (IMPLAN)a
- Regional Input-Output
Modeling System (RIMS II)b
- Jobs and Economic
Development Impacts (JEDI)
- National Energy Modeling
System (NEMS)°
- Berkeley Energy &
Resource (BEAR) Modeld
- U.S. Regional Energy Policy
(USREP) Model6
- Regional Economic Models
Inc Policy Insight (REMI PI)f
- RAND Econometric Modelg
Benefits
- Easy to use
- Minimal time
requirement
- Transparent
Inexpensive
- Easy to moderately easy to
use
- Time requirement can be
minimal but varies
- Can be inexpensive
- Widely used, accepted
- More comprehensive than
input-output
- Can model more scenarios
- Retrieve more information
Limitations
- Results can be
limited
- Often overly
simplistic
assumptions
- Inflexible
- Not very transparent
- Many restrictive
assumptions (i.e., constant
prices)
- Scenarios limited to changes
in demand
- Difficult or moderately
difficult to develop
- Can be expensive
- Not very transparent
- Assumptions vary
- Often difficult to operate or
modify
- Most require expensive
software licenses
- Difficult, expensive to build
- Data intensive
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Basic Methods
Moderate Methods
Complex Methods
- When time and
- When policy options are
- When policy options are
resources are short
well defined
well defined
0)
Xfl
- For high level
- When a high degree of
- When a high degree of
3
o
preliminary analysis
precision and analytic rigor is
precision and analytic rigor is
S3
- To get quick
desired
desired
-C
estimates of
- When sufficient data, time,
- When sufficient data, time,
£
employment, output
and financial resources are
and financial resources are
and price changes
available
available
- As a screening tool
a IMPLAN is a commercially available model that uses an input output analysis technique along with social
accounting matrices and publicly available data to carry out economic impact assessment.
b RIMS II is an input-output-based model that uses regional multipliers to help users estimate gross jobs, developed
by the Department of Commerce/Bureau of Economic Analysis.
0 The National Energy Modeling System (NEMS) is a computer-based model developed and maintained by EIA. It
is used to forecast energy supply, demand, and prices, and to analyze the impacts of various energy policies.
d BEAR is a state-level computable general equilibrium model developed by the Lawrence Berkeley National
Laboratory, which can account for many different factors affecting jobs, producing net jobs estimates.
e USREP is a computable general equilibrium model developed and maintained by the Massachusetts Institute of
Technology (MIT). The model is national but splits the United States into multiple regions.
f REMI PI is a commercial model that uses hybrid techniques, combining aspects of input-output, econometric, and
computable general equilibrium techniques, and produces net jobs estimates.
g The RAND econometric model is a commercial tool that uses sets of related equations, and mathematical and
statistical techniques to analyze economic conditions over time, generally producing net jobs estimates.
Source: NLR, "Assessment of the Value, Impact, and Validity of the Jobs and Economic Development Impacts
(JEDI) Suite of Models," August 2013. https://docs.nlr.gov/docs/IV13osti/56390.pdf. EPA, "Assessing the Multiple
Benefits of Clean Energy—A Resource for States," February 2010.
https://nepis.epa.gov/Exe/ZvPDF.cgi/P100FL09.PDF?Dockev=P100FL09.PDF.
When selecting an analytical method and interpreting the output from any of these
frameworks, however, there is a need to be mindful about the strengths and limitations of each.
There are times when a combination of these approaches may be necessary to capture the
multifaceted impacts of policies, ensuring robust and comprehensive analysis to inform effective
policymaking. In this chapter, we have relied upon multiple analytical approaches to quantify the
impacts of renewable fuels on "other factors".
9.1.1.1 Overview of Methodologies Applied
We have focused our analysis on the biofuels that are projected to have the largest
changes in volumes relative to the No RFS Baseline: corn ethanol, BBD, and RNG.622 For each
of these fuels, we have made use of methods that we were able to identify as available off-the-
shelf. We acknowledge that complex methods such as Computable General Equilibrium (CGE)
models may be helpful when a high degree of precision and analytic rigor is desired given
sufficient data, time, and financial resources.
For all biofuels, to estimate the impact of volume changes compared with the No RFS
Baseline, we have relied on a basic method (as laid out in Table 9.1.1-1)—a Rule-of-Thumb
622 The impacts evaluated in this chapter are for volume increases for 2026-2027 compared to the No RFS Baseline.
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approach—to draw conclusions about economic impacts. We utilize the results of existing
studies for estimates of multipliers or the impact per unit of biofuel and then apply these
estimates to the projected Analyzed Volumes to derive employment and other economic impacts.
For the corn ethanol case alone, we have additionally applied NLR's JEDI model. The
results using NLR's JEDI model that are presented here are linearly scaled estimates of the
Proposed Volumes. We also performed a sensitivity analysis on the results of the latter method to
capture the range of impacts to the agricultural sector and the rural economy. This approach
illustrates both how results from a simple Rule-of-Thumb type approach compared with a more
robust approach like an input-output model, and how changes in some key modeling parameters
will alter the extent of economic impacts on the agricultural sector and the rural economy. Since
these industries bear similarities in their backward and forward linkages with other sectors of the
economy (supply chain623 and logistic networks624), one can expect similar type of sectoral level
impacts; the size of the impacts, however, will be a function of the initial policy shock. It is
important to note that the impact estimates in our analysis (for all the biofuels) correspond to the
volume projections relative to the No RFS Baseline.
9.1.1.2 Rule-of-Thumb Analytical Method
Of the available methods discussed above, only input-output models appear to have been
applied recently to estimate the impact of renewable fuels on jobs and economic output. We have
identified four relevant studies with outputs which can inform our Rule-of-Thumb approach to
estimating the impacts of renewable fuels on job creation and rural economic development.
Three of these studies focused on a specific subset of the fuels we have targeted for analysis: a
2024 study on the contribution of the ethanol industry to the U.S. economy by Agriculture and
Biofuels Consulting (ABF), LLP (hereafter the ABF study),625 a 2022 study on the economic
impact analysis of biodiesel and renewable diesel on the U.S. economy by LMC International
(hereafter the LMC study),626 and a 2024 study on renewable natural gas economic impact
analysis by Guidehouse (hereafter the Guidehouse study).627 A fourth study by PWC compares
the impacts of renewable fuels to those of oil and gas, employing a similar 10 approach to the
three fuel-specific studies (hereafter the PWC study).628 The analysis in this chapter uses the
results of these studies to parameterize our Rule-of-Thumb analysis.
623 Babazadeh, Reza, Jafar Razmi, and Mir Saman Pishvaee. "Sustainable Cultivation Location Optimization of the
Jatropha Curcas L. Under Uncertainty: A Unified Fuzzy Data Envelopment Analysis Approach." Measurement 89
(April 10, 2016): 252-60. https ://doi.org/10.1016/i.measurement.2016.03.063.
624 Hong, Jae-Dong, and Judith L. Mwakalonge. "Biofuel Logistics Network Scheme Design with Combined Data
Envelopment Analysis Approach." Energy 209 (July 26, 2020): 118342.
https://doi.Org/10.1016/i.energy.2020.118342.
625 ABF Economics, "Contribution of the Ethanol Industry to the Economy of the United States in 2023," February
1, 2024. https ://d3 5t 1 svewk4d42. cloudfront. net/file/2659/RFA%202023%20Economic%20Impact%20Final.pdf.
626 LMC International. "Economic Impact of Biodiesel on the United States Economy 2022: Main Report." Clean
Fuels Alliance America. 2022. https://cleanfuels.org/wp-content/uploads/LMC Economic-Impact-of-Biodiesel-on-
the-US-Economv-2022 Main-Report November-2022.pdf.
627 Guidehouse, "Renewable Natural Gas Economic Impact Analysis," December 2024.
https://staticl.sauarespace.cOm/static/53a09c47e4b050b5ad5bf4f5/t/67577elc8695832cc7125f86/1733787172143/2
024+RNG+Economic+Impact+Report FINAL.pdf.
628 PwC, "Impacts of the Oil and Natural Gas Industry on the US Economy in 2021," April 2023.
https://www.api.Org/-/media/files/policY/american-energv/pwc/2023/api-pwc-economic-impact-report-2023.pdf.
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While other methods have been applied to estimate impacts on job creation and rural
economic development, we choose to focus on the results of input-output models in our analysis,
for several reasons. First, these have been the most widely used in recent literature. Second,
studies using these methods can provide usable impact estimates for ethanol, biodiesel,
renewable diesel, and RNG production. Third and finally, focusing on input-output model results
allows for direct comparisons across studies to characterize the range of potential impacts.
Before describing our analysis, we review the studies listed above and summarize their
main results, which are the foundation of our Rule-of-Thumb analytical approach.
9.1.1.2.1 The ABF Study of Ethanol Impacts
The ABF study uses the IMPLAN (Impact Analysis for Planning) multiplier database to
develop a model of the national economy, including sectors that support ethanol industry, the
links between them, and the level of national economic activity. The data inputs are based on the
recent benchmark input-output data and 2021 regional data published by the U.S. Bureau of
Economic Analysis. The report assesses direct effects, indirect effects, and induced effects as
well as the additional value of output of ethanol co-products (DDGS, distillers corn oil, corn
gluten meal, and corn gluten feed). The report also incorporates the explicit impact of ethanol
and DDGS exports in the economic impact analysis by applying USDA Agricultural Trade
multipliers for output and employment to the estimated value of exports for 2023 reported by
EIA and U.S. Census Bureau trade databases. The ABF study assesses the impact of ethanol
production on job creation and GDP across several sectors of the economy, including:
• Ongoing ethanol production operations, including total production effect and the impact
on farm incomes
• Research and development
• Ethanol co-product value streams
• Exports
• Construction
The ABF study estimates that the ethanol industry supported 394,464 jobs across all
sectors of the economy in 2023. Excluding the impact associated with construction, the impact
was 392,371 full-time equivalent [FTE] jobs in 2023. See Table 9.1.1.2.1-1.
Table 9.1.1.2.1-1: The ABF Study—Jobs Supported by Ethanol Production in 2023 (FTE)
All
Construction
All, Excluding
Construction
Direct
72,463
1,015
71,448
Indirect
203,597
359
203,238
Induced
118,405
718
117,687
Total
394,464
2,093
392,371
The ABF study does not assess rural economic development explicitly. However, we can
infer the impact on rural economies based on reported impacts for certain stages in the ethanol
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value chain compared to the total estimated impact of ethanol economy wide. For instance, we
could assume that corn production and ethanol production both take place predominantly within
the bounds of the rural economy. Production of ethanol feedstock (mostly corn) contributed
$27,916 million to the U.S. economy, while the manufacturing activity of ethanol production
accounted for another $14,602 million. The ABF study also estimates an economy-wide impact
of $54,226 million from the ethanol industry in 2023. With the assumptions stated above, we can
roughly estimate that the impact on rural economies is about 78% (= (27,916+14,602)/54,226) of
the total GDP impact of ethanol. The job impact is even larger in relative terms, about 91% (=
(264,464+95,166)/394,464) of the total economy-wide employment impact. See Table 9.1.1.2.1-
2.
Table 9.1.1.2.1-2: The ABF Study—Job Creation and GDP Impacts of Ethanol Production
by Sector
Job Creat
ion (FTE)
GDP (million 2023$)
Agriculture
Ethanol
Production
Agriculture
Ethanol
Production
Direct
58,324
11,781
3,137
2,602
Indirect
127,638
46,014
14,299
7,394
Induced
78,533
37,372
10,480
4,606
Total
264,464
95,166
27,916
14,602
Our estimates here could be viewed as an upper limit since we assume that both corn and
ethanol production are within the bounds of the rural economy. To get the lower limit, we
assume that only corn production (excluding ethanol production) is within the bounds of the rural
economy. The estimate of the impact on rural economy is about 51% (=27,916/54,226) of the
total GDP impact of ethanol. The job impact is about 67% (=264,464/394,464) of the total
economy-wide employment impact. We rely on the lower limit to conduct our impact analyses in
Chapters 9.1.4.1 and 9.1.5.1.
9.1.1.2.2 The LMC Study of Biodiesel and Renewable Diesel Impacts
The LMC study assesses the economic impact of BBD, including both renewable diesel
and biodiesel. The study divides its findings into several categories of effects. These include
several annual (i.e., ongoing) effects, including direct effects directly attributed to the BBD value
chain, such as fuel production facilities and oilseed crops grown and crushed at least in part for
fuel use, indirect effects associated with industries that supply the BBD value chain, and induced
effects stemming from expenditure of households from those affected industries. In addition, the
study also considers "one-off effects" that are not estimated to be sustained over time, such as
those associated with construction of new BBD production facilities.
LMC (2022) uses multipliers developed from the input-output tables from the U.S.
Department of Commerce's Bureau of Economic Analysis across 406 industries and by state.
These multipliers are applied to the direct effects that LMC estimated under the following
scenarios.
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• Baseline: 3.1 billion gallons of biodiesel supply {i.e., production plus net imports) in
2021 (the authors' estimate of actual 2021 U.S. supply)
• 3.5 billion gallons of supply in 2021
• 4.0 billion gallons
• 6.0 billion gallons
The effects are calculated for the actual 2021 U.S. production/import split of 80%
domestic production and 20% import and for an assumed production/import split of 100%
domestic production and 0% import, respectively. The analysis also assumes the 2021 market
conditions {e.g., prices), an 80% utilization rate of fuel production capital {e.g., the study
assumes 7.5 billion gallons of capacity is required to produce 6 billion gallons), and the
renewable diesel representing half of U.S. domestic production in all except the baseline scenario
(which assumes 67% biodiesel and 33% renewable diesel).
Table 9.1.1.2.2-1 summarizes the annual impacts in terms of job creation by scenario.
Based on 2021 conditions, the LMC study estimates the BBD sector's job impact was about
75,000 in that year. The study also estimates that doubling the production to 6.0 billion gallons
would have also doubled the job creation impact of the BBD sector in 2021.
Table 9.1.1.2.2-1: The LMC Study—The Annual Job Creation Impacts by Scenario (FTE)
Scenario (billion gallons of BBE
»)
3.1 (Baseline)
3.5
4.0
6.0
80%/20% U.S./import split
75,196
86,204
99,078
150,572
100%/0% U.S./import split
93,755
107,373
123,299
187,003
Table 9.1.1.2.2-2 summarizes the one-off effects associated with construction. The LMC
study estimates that doubling the production of BBD in 2021 would have created 144,500
temporary job-years {e.g., with two years to build an average plant, this implies 72,250 FTE jobs
lasting two years).
Table 9.1.1.2.2-2: The LMC Study—The One-Off Job Creation Effects from Construction
(FTE)
Scenario (billion gallons of BBD)
4.0
6.0
Total temporary construction job-years created
61,400
144,500
The LMC study does not explicitly assess the impact on rural economic development. We
can infer the impact on rural economies based on reported impacts for certain stages in the BBD
value chain compared to the total estimated impact of BBD economywide. For the purposes of
our analysis, we assume that oilseed production, crushing, and processing occur predominantly
in rural areas. Table 9.1.1.2.2-3 summarizes the estimated effects on rural development in the
LMC study based on these assumptions. The estimated impact on rural economic development
represents at least 30% of the total economy-wide impact across job, GDP, and wage estimates.
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Table 9.1.1.2.2-3: The LMC Study—Estimates of the Impacts of BBD Production in Rural
Areas
Jobs (FTE)
GDP ($2021)
Wages ($2021)
Oilseed production
28,236 jobs
38% of the total
$7.41 billion
30% of the total
$1.36 billion
38%o of the total
Oilseed processing
6,000 jobs
8% of the total
$4.97 billion
21%) of the total
$380 million
1 l%o of the total
9.1.1.2.3 The Guidehouse Study of Renewable Natural Gas Impacts
The Guidehouse study estimates the economic impact of 2024 RNG industry spending in
the U.S. in terms of job creation and GDP. The RNG industry generates direct economic effects
through annual capital expenditures and operational spending incurred by RNG facilities (e.g.,
spending on new construction and RNG production and distribution). The Guidehouse study
estimates these direct effects of RNG on the economy (e.g., the annual capital expenditures and
operational spending incurred by RNG facilities) based on a dataset on RNG facility capacity
and costs compiled and maintained by RNG Coalition. The dataset used in the report was up to
date as of October 2024. In turn, these expenditures spur business-to-business transactions within
the RNG supply chain (indirect effects) and increase household spending among RNG industry
and supply chain employees (induced effects). The Guidehouse study employs an IMPLAN
model to estimate these indirect and induced effects.
As of October 2024, the RNG Coalition database estimates there were 411 operational
RNG facilities and 130 projects under construction. The Guidehouse study estimates that the
RNG industry supported over 55,000 jobs and generated $7.2 billion in GDP in that year. Table
9.1.1.2.3-1 summarizes the three components of the job creation impacts.
Table 9.1.1.2.3-1: The <
juidehouse Study—Job Creation Impacts
Direct
Indirect
Induced
Total
Job Creation (FTE)
23,359
13,395
18,910
55,664
GDP (billion 2024$)
3.0
1.9
2.2
7.2
The Guidehouse study estimates that there are currently about 230 RNG projects in
planning phases in the U.S. Table 9.1.1.2.3-2 summarizes the planned and existing RNG
projects' job creation impacts by facility status. While the impact associated with construction is
one-off, that associated with operations is annual. The planned projects have larger one-off
impacts as well as annual impacts in terms of total job creation.
Table 9.1.1.2.3-2: The Guidehouse Study—The Planned and Existing RNG Projects' Job
Creation Impacts by Faci
ity Status (FTE
Operations
Construction
Total
Existing
23,917
31,747
55,664
Planned
19,586
74,237
93,823
The Guidehouse study does not assess rural economic development explicitly. However,
we can infer this impact by disaggregating these impacts by feedstock. The Guidehouse study
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identifies four categories of biogas feedstocks for RNG: municipal solid waste, food waste,
agricultural digesters (termed "agricultural waste" in the study), and wastewater.629 Assuming
RNG projects using agricultural digesters are those pertinent to rural areas and thus rural
economic development, we can use the impact associated with agricultural digesters as an
estimate for rural economic development. In 2024, agricultural digester projects generated the
second largest economic impacts, accounting for 19,751 jobs (35% of the total job impact) and
$2.6 billion in GDP (36% of the total GDP impact). Table 9.1.1.2.3-3 summarizes these impacts.
Table 9.1.1.2.3-3: The Guidehouse Study—Agricultural Job Creation and Rural Economic
Direct
Indirect
Induced
Total
Job creation (FTE)
8,297
4,612
6,843
19,752
GDP (billion 2024$)
1.2
0.7
0.8
2.6
9.1.1.2.4 The PWC Study of Fossil Fuel Impacts and Comparison with Three
Renewable Fuels Studies
We compare the impacts of renewable fuels in the ABF, LMC, and Guidehouse studies to
those of oil and natural gas based on PWC (2023), which also employed a similar 10 approach.
The PWC study quantifies the economic impacts of the U.S. oil and natural gas industry in terms
of employment, labor income, and value added at the national, state, and congressional district
level for 2021. They consider all three separate channels—the direct impact, the indirect impact,
and the induced impact, and in aggregate provide a measure of the total economic impact of the
oil and natural gas industry.
Industries vary in size; therefore, to assess job creation, we divide the total FTE by GDP
(in billions), measuring the number of jobs generated per $1 billion GDP. Applying this metric to
all four reports discussed in this section indicates that almost all renewable fuels (with the
exception of BBD) create more jobs per $1 billion GDP than fossil fuels. Construction impacts,
which are one-time effects, are excluded from this comparison. Table 9.1.1.2.4-1 shows
employment per billion dollars GDP for the oil and gas, ethanol, BBD, and RNG industries.
Table 9.1.1.2.4-1: The PWC Study—Employment per GDP for Fossil Fuel and Renewable
Fuel Production
Oil & Gas
Ethanol
BBD
RNG
Employment (FTE)
9,400,000
392,371
75,196
23,917
GDP (billion 2021$)
1,618
54
23
4
Employment/GDP
5,809
7,265
3,241
6,294
Since these renewable fuels rely significantly on agricultural feedstocks and are often
produced in rural areas, they contribute to rural economic development. Fossil fuels, by contrast,
do not use agricultural feedstocks, and there is no direct farm income boost from crops. In
addition, their indirect and induced effects on rural economic development may be mixed or
629 These categories differ somewhat from the categories established by EPA in Table 1 to 40 CFR 80.1426 in both
wording and substance but are a reasonable general guide for this purpose.
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negative, particularly in the long run. For instance, unlike agriculture that provides relatively
stable demand for rural labor and services, fossil fuel markets are prone to price fluctuations. The
resulting economic instability makes long-term rural development difficult. Fossil fuel
operations may displace farmland, contaminate water sources, and pollute the air, negatively
affecting crops and livestock. Table 9.1.1.2.4-2 summarizes the results based on only estimated
agricultural production.
Table 9.1.1.2.4-2: The PWC Study—Employment and GDP Impacts of Agricultural
RNG
BED
Ethanol
Employment (FTE)
19,752
28,236
264,464
GDP (billion 2021$)
2.6
7.4
27.9
In summary, the PWC study finds renewable fuels generally create more jobs per unit of
GDP than fossil fuels. This finding is supported by other studies comparing the job impacts of
fossil fuel and renewable fuel production. IEA's World Energy Employment Report630 finds that
clean energy has "surpassed the 50% mark for its share of total energy employment" and has the
biggest potential for job creation.631 Peltier finds "on average, 2.65 FTE jobs are created from $1
million spending in fossil fuels, while that same amount of spending would create 7.49 or 7.72
FTE jobs in renewables or energy efficiency.632 Thus each $1 million shifted from brown to
green energy will create a net increase of 5 jobs." Though biofuels accounted for only a small
fraction of the overall addition to clean jobs (Clean Jobs America reports that biofuels added
over 1200 jobs in 2023633), the International Renewable Energy Agency (IRENA) estimated that
liquid biofuels supported 2.42 million jobs globally in 2021 and most of these were in planting
and harvesting feedstock,634 implying that expansion of the biofuel industry will likely have the
biggest job impacts to the agricultural and rural community.
Note that the jobs created by increased biofuel production are unlikely to be completely
offset by job declines in the fossil fuel sector. Our impact analyses on employment and rural
economic development in Chapters 9.1.2 through 9.1.5 focus on the gross impacts, not net
impacts.
In Chapters 9.1.2, 9.1.4, and 9.1.5, we combine the estimates derived above from each of
these three studies with the projected production increases associated with the Analyzed
Volumes relative to the No RFS Baseline to estimate the potential impacts of this rule on jobs
and rural economic development.
6311IEA, "World Energy Employment," August 2022. https://doi.org/10.1787/5d44ff7f-en.
631 E2. "Clean Jobs America 2024," September 2024. https://cleaniobsamerica.e2.org/wp-
content/uploads/2024/09/E2-2024-Clean-Jobs-America-Report September-17-2024.pdf.
632 Garrett-Peltier, Heidi. "Green Versus Brown: Comparing the Employment Impacts of Energy Efficiency,
Renewable Energy, and Fossil Fuels Using an Input-output Model." Economic Modelling 61 (November 28, 2016):
439—47. https://doi.Org/10.1016/i.ecomnod.2016.ll.012.
633 E2. "Clean Jobs America 2024," September 2024. https://cleaniobsamerica.e2.org/wp-
content/uploads/2024/09/E2-2024-Clean-Jobs-America-Report September-17-2024.pdf.
634 IRENA. "Renewable Energy and Jobs Annual Review 2022," 2022. https://www.irena.org/-
/media/Files/IRENA/Agencv/Publication/2022/Sep/IRENA Renewable energy and jobs 2022.pdf.
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9.1.1.3 Input-Output Modeling Analytical Method for Corn Ethanol
In addition to our Rule-of-Thumb analysis, which largely relies on estimates derived
from pre-existing input-output modeling studies, we also present original input-output modeling
estimates for just the corn ethanol case using a lobs and Economic Development Impacts (JEDI)
model. The JEDI modeling suite was developed by NLR for the DOE Office of Energy
Efficiency and Renewable Energy (EERE). At a very high level, the JEDI suite relies on the use
of an input-output based methodology to estimate gross jobs and economic impacts of building
and operating selected types of renewable electricity generation and fuel plants.
Between 2004 and 2012, JEDI has been used and cited in more than 70 public studies
including 12 studies in five different peer-reviewed journals. The validity of JEDI estimates was
assessed through comparison to both published modeled estimates and data on empirical
observations of jobs associated with renewable energy projects. For the former, compared to
modeled job results for O&M of several corn ethanol plants, JEDI results ranged from 20%
lower to 28% higher. For the latter, comparison of several empirical estimates for O&M jobs at
corn ethanol plants showed that JEDI results ranged from 9% higher to 21% lower than the
empirical estimates.
According to expert evaluations, references, and various user metrics, the JEDI suite of
models is recognized as a reliable and widely utilized tool for estimating or screening gross job
numbers associated with the construction and operation of renewable energy power and fuel
facilities in the U.S. Considering the aforementioned comparisons involving both modeled and
empirical estimates, the outcomes produced by the JEDI model are fairly similar with other
modeled results and empirical observations.
The default assumptions in the JEDI model are based on interviews with industry experts
and project developers. While these input assumptions are reasonable, the user does have the
option to override some of these project specific data for some categories of inputs. Economic
multipliers contained within the model are derived from Minnesota IMPLAN Group's IMPLAN
accounting software and state data files. Construction jobs are defined as full-time equivalents
(FTE), or 2,080-hour units of labor (one construction period job equates to one full-time job for
one year). A part-time or temporary job may be considered one job by other models but would
constitute only a fraction of a job according to the JEDI models. For example, if an engineer
worked only 3 months on a wind farm project (assuming no overtime), that would be considered
one-quarter of a job by the JEDI models. Operations-period results are long term, for the life of
the project, and are reported as annual full-time-equivalent jobs and annual economic activity,
which continue to occur throughout the operating life of the facility. Like all models, the JEDI
model too has its own set of limitations, and precisely because of these the model results are
meant to be estimates and not precise forecasts.
Input-output modeling is a data-intensive effort and requires access to sector specific
multipliers635 that permit us to compute rates of change for several different variables—output,
635 Multipliers are rates of change that describe how a given change in a particular industry generates impacts in the
overall economy.
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employment, labor income, and value added. In the case of corn ethanol, IMPLAN636 maintains
a database of multipliers that is available for purchase and NLR has developed an input-output
model for Dry Mill Corn Ethanol637 using these multipliers,638 results of which have been
validated with both modeled job results as well as empirical employment data.639 However, to
the extent that such tools can be developed for BBD and RNG going forward, we may choose to
make use of them. Other tools to assess economic and environmental impacts, such as WIRED,
BEIOM,640 EMPLOY, and others641 for some of the other biofuel categories and technologies
are either in the R&D phase or employ slightly different modeling capabilities compared with
JED I, and could be used as well in future analyses if appropriate.642
9.1.2 Employment Impacts using the Rule-of-Thumb Approach
Our estimates of the employment impacts relying on the use of a basic Rule-of-Thumb
approach and existing studies are summarized by fuel type (ethanol, BBD, and RNG) in this
section. In the next section, to provide a complementary estimate of the local economic impacts
associated with constructing and operating a corn ethanol facility, we relied on NLR's JEDI
module for dry mill corn ethanol.
Changes in ethanol volumes evaluated in this rule result from increased consumption of
higher-level ethanol motor gasoline blends (e.g., E15 and E85) and a corresponding decrease in
E10 gasoline consumption that they replace. The connection between these estimated changes in
domestic consumption and domestic production of ethanol is unlikely to be a perfect correlation
as ethanol is produced not only for domestic consumption but also for export. Significant
quantities of ethanol have been exported to foreign markets in recent years (see Chapter 7.5 and
Chapter 7.6 for more details). The volume of ethanol that EPA projects to be consumed in 2026-
2030 under the No RFS Baseline is significantly less than the domestic ethanol production
capacity, and less than projected domestic ethanol production in 2025. For this reason, the exact
strength of the correlation between ethanol production and the ethanol consumption estimates
636 https ://implan. com.
637 NLR, "JEDI Corn Ethanol Model rel. CE 12.23.16." https://www.nlr.gov/docs/libraries/analvsis/Old-iedi-corn-
ethanol-model-rel-ce 12-23 - 16.xlsm.
638 NLR, "Jobs and Economic Development Impact Models," April 21, 2025. https://www.nlr.gov/analYsis/iedi.
639 Billman, L„ and D. Keyser. "Assessment of the Value, Impact, and Validity of the Jobs and Economic
Development Impacts (JEDI) Suite of Models," National Renewable Energy Laboratory. August 1, 2013.
https://doi.org/10.2172/109Q964.
6411 Avelino, Andre F.T., Patrick Lamers, Yimin Zhang, and Helena Chum. "Creating a Harmonized Time Series of
Enviromnentally-extended Input-output Tables to Assess the Evolution of the US Bioeconomy - a Retrospective
Analysis of Corn Ethanol and Soybean Biodiesel." Journal of Cleaner Production 321 (September 2, 2021):
128890. https://doi.Org/10.1016/i.iclepro.2021.128890.
641 Oke, Doris, Lauren Sittler, Hao Cai, Andre Avelino, Emily Newes, George G. Zaimes, Yimin Zhang, et al.
"Energy, Economic, and Enviromnental Impacts Assessment of Co-optimized On-road Heavy-duty Engines and
Bio-blendstocks." Sustainable Energy & Fuels 7, no. 18 (January 1, 2023): 4580-4601.
https://doi.org/10.1039/d3se00381g.
642 WIRED is an updated regional IO tool much like the JEDI suite of models that is under development and will
likely have a public release by the end of the year (2025). BEIOM is both a retrospective and prospective dynamic
environmentally extended input-output model that is not publicly available but can be used internally by NLR and
DOE. EMPLOY has the capability to model several fuel pathways (conventional petroleum products, corn starch
ethanol, cellulosic ethanol, biodiesel, renewable diesel, sustainable aviation fuel, etc.) and is used to estimate the net
impacts (economic, jobs, workforce and enviromnental) of large-scale industry deployment up to 2050.
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presented in our Analyzed Volumes is not completely clear. Thus, it is possible that a decrease in
ethanol consumption in the absence of the RFS program, such as that estimated in our forward-
looking No RFS Baseline, could result in a decrease in domestic ethanol production or
alternatively could result in increased ethanol exports.
The following is the employment impact analysis for all types of renewable fuels using
the basic method based on the three specific 10 studies of renewable fuels discussed in DRIA
Chapter 9.1.1.2. These 10 studies we rely upon are the ABF study, the LMC study, and the
Guidehouse study.
The impact estimates from these three studies are based on total production of these
renewable fuels. Table 9.1.2-1 summarizes the volume increases of RNG, BBD, and ethanol
attributable to the RFS volume requirements relative to the No RFS Baseline under the Analyzed
Volumes.
Table 9.1.2-1: Projected Production Increases under the Analyzed Volumes (million
2026
2027
RNG
799
838
BBD
3,597
4,175
Ethanol
231
245
To generate impact estimations based on projected production increases in million
ethanol equivalent gallons (in Table 9.1.2-1), we calculate the impact per million ethanol
equivalent gallons for each renewable fuel. To do so, we make two important assumptions. First,
although the impact of renewable fuels may be nonlinear (e.g., a 5% increase in production may
not require any increases in labor), we assume these impacts will scale in a linear manner. Prior
research in this space does not provide a sufficient basis to accurately estimate potential
nonlinear impacts. Absent this basis, we assume the simpler, more straightforward functional
form. Second, we estimate impacts from facility operations only. Since the projections under the
Analyzed Volumes are generally below production capacity (see Table 9.1.2-2), there may not be
a need to construct new facilities. This is also conservative and may help mitigate potential
overestimation (e.g., due to the linear assumption we make). These assumptions mirror the
analytical choices made in the DRIA as well.
Table 9.1.2-2: Production Capacity and Annual Production from the ABF Study, the LMC
Study, and the Guidehouse Study and Production Projections by Fuel
2026 Production
Production
Projections under the
Capacity
Annual Production
Analyzed Volumes
Production
Original
Original
Million Gallons
Million Gallons
Year
Unit
Unit
(ethanol equivalent)
(ethanol equivalent)
RNG
2024
133 tril Btu
878 mil gal
878
1,364
BBD
2021
4.6 bil gal
2.5 bil gal
3,925
6,074
Ethanol
2023
17.8 bil gal
15.6 bil gal
15,600
14,438
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We estimate the impacts on employment on a full-time equivalent (FTE) basis. The FTE
estimated impacts associated with the Analyzed Volumes are summarized in Table 9.1.2-3. For
BBD, the LMC study did not provide separate estimates for direct, indirect, and induced impacts
associated with operations. We use the average effective multiplier reported in the LMC study to
decompose the total impact into direct and non-direct (i.e., combined indirect and induced)
effects.
Table 9.1.2-3: Job Creation Impacts of Production Based on the ABF Study, the LMC
Study, ant
the Guidehouse Study (FTE)
Indirect +
Direct
Indirect
Induced
Induced
Total
RNG
7,948
7,505
8,464
23,917
BBD
18,799
56,397
75,196
Ethanol
71,448
203,238
117,687
392,373
Because thejob creation impacts summarized in Table 9.1.2-3 representee impacts of
differing quantities of biofuel production, we next normalized these estimates to calculate thejob
impacts per million ethanol-equivalent gallons of biofuel production. To do this we divide the
total impact estimates in Table 9.1.2-3 by the total production in million ethanol equivalent
gallons to estimate the impact per million ethanol equivalent gallons for each renewable fuel,
which are reported in Table 9.1.2-4. Compared with BBD and ethanol, RNG has the highest
direct and total impacts per million ethanol equivalent gallons (9.1 and 27.2, respectively). BBD
and ethanol have higher indirect and induced impacts relative to their direct impacts because
their multipliers are higher than RNG's.
Table 9.1.2-4: Job Creation Impacts per Million Ethanol Equivalent Gallons Based on the
ABF Study, the LMC Study, and the Guidehouse Study (FTE)
Direct
Indirect
Induced
Indirect +
Induced
Total
Multiplier
RNG
9.1
8.5
9.6
27.2
3.0
BBD
4.8
14.4
19.2
4.0
Ethanol
4.6
13.0
7.5
25.2
5.5
Using the same methodology as applied in the DRIA, we then estimate the impacts of the
projected fuel production increases by multiplying the projected production increases with the
impact per million ethanol equivalent gallons estimates. We report two sets of the projections,
one based on the direct effects only and the other based on all effects (i.e., direct, indirect, and
induced effects). Table 9.1.2-5 shows the projected job impacts relative to the No RFS Baseline
accounting for only the direct effects while Table 9.1.2-6 shows the projected job impacts
considering the direct, indirect, and induced effects. Relative to the baseline and accounting for
direct, indirect, and induced effects, BBD is projected to have the highest job creation impact,
primarily due to substantially higher production increases relative to the baseline.
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Table 9.1.2-5: Job Creation Impacts of the Projected Production Increases Based on Direct
Direct
2026
2027
RNG
7,233
7,586
BBD
17,228
19,996
Ethanol
1,058
1,122
All Fuels
25,519
28,704
Table 9.1.2-6: Job Creation Impacts of the Projected Production Increases Based on All
Direct + Indirect + Induced
2026
2027
RNG
21,765
22,827
BBD
68,912
79,986
Ethanol
5,810
6,162
All Fuels
96,487
108,975
9.1.3 Employment Impacts using NLR's JEDI model for Dry Mill Corn
Ethanol
In the case of ethanol, we were able to assess employment impacts (in both the
construction and the operations phase) using NLR's JEDI model to produce a second estimate.
We proceed under the assumption that the Analyzed Volumes for this rule relative to the No RFS
Baseline comes entirely from higher domestic production, either from continuing operations in
existing facilities which now produce a higher volume or from addition of new capacity. Using
the JEDI model, we were able to compute the direct, indirect, and induced jobs resulting from
the Analyzed Volumes under these assumptions. In this subsection, we report the cumulative
impact to direct gross jobs that result from the Analyzed Volumes. We present results for the
number of indirect (agriculture and industry) and induced operations jobs, along with the
commensurate increase in incomes and the sensitivity analysis, in subsequent sections on
Agricultural Employment (Chapter 9.1.3) and Rural Economic Development (Chapter 9.1.4).
We demonstrate results for two cases. In the first case, we assume there is no new
construction of ethanol facilities and the increased ethanol volume associated with the Analyzed
Volumes relative to the No RFS Baseline is met by increasing production levels at existing
facilities (or in the alternative the avoidance of reduced corn ethanol production that would occur
in the No RFS Baseline). Since the No RFS Baseline is forward-looking and represents a
potential future where the RFS program ceases to exist after 2025, this may be the more realistic
representation of the job impacts of the ethanol volumes in our scenarios. However, for
completeness we also present a second case, in which we assume the increased ethanol volumes
come from new construction. To the extent that retiring ethanol production capital is replaced
with new and more efficient facilities in 2026 and 2027, this analysis would be relevant to those
circumstances.
308
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For both temporary and permanent direct job impacts, the assumed size of the model
ethanol production facility in our analysis is an important assumption. In 2018, Ethanol Producer
Magazine made available data on the capacity and number of employees at 65 corn ethanol
facilities.643 These plant capacities generally compare well with those reported by EIA and
estimated in the ABF study, deviating by less than 3% from the EIA report when averaged on a
state-by-state basis. For these 65 facilities, we examined employee concentration as a function of
production capacity. The results show a nonlinear decreasing trend in employee concentration
with production capacity, suggesting economies of scale are associated with labor in ethanol
plants and larger facilities are associated with fewer jobs per unit of output (Figure 9.1.3-1).
Figure 9.1.3-1: Correlation Between Employee Concentration and Facility Size for Corn
Ethanol Facilities
2.0
1.8
c
_o
TO 1.5
J 1.3
1 1.0
Ot
$ 0.8
OJ
J? 0.5
Q.
E
^ 0.3
0.0
0 25 50 75 100 125 150
Plant Capacity (Million Gallons per Year)
•
Employees per million gal/yr capacity
= 8.30(million gal/yr capacity) 0-6
R2 = 0.72
*
*
"v.; i
i
•
*• •«
•
*. -jV •
¦ > ¦
~
i t
Urbanchuk-based estimate of
0.65 plotted here at 90 million
gals/yr average plant capacity
•
•
For the purposes of this analysis, it means that depending on the size of the ethanol plant
where increases (or avoided decreases) in ethanol production occur, the impacts are likely to be
very different. For context, of the 187 ethanol production facilities that were currently
operational in the U.S. as of January 1, 2024, approximately 57% produce less than or equal to
90 MG annually, (62.5% produce less than 100 MG or less annually), while 3.7% produce over
200 MG annually.644 Uncertainty regarding the size of the facility or facilities which will provide
the incremental volume of ethanol projected in the Analyzed Volumes. Recognizing this, for the
DRIA we developed two analytical perspectives, to bound uncertainty associated with facility
construction. In these two perspectives, we show the cumulative job impacts assuming the
construction and/or operation of both a single large facility and also assuming multiple 90 MGY
facilities that add up to the total volume projected in our scenarios for that year.
While we have analyzed a full range of scenarios based on the permutations described
above, here we present only the two scenarios which bound the lower and upper ends of the
643 Ethanol plant employment data obtained via Ethanol Producer Magazine. See "Employment information sources
for corn-ethanol facilities," available in the docket for this action.
644 EIA, "U.S. Fuel Ethanol Plant Production Capacity," Petroleum & Other Liquids, August 15, 2024.
https://www.eia.gov/petroleum/ethanolcapacitv.
309
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range of estimated employment impacts. Table 9.1.3-1 shows the cumulative number of direct
jobs (permanent annual operations jobs), construction jobs (temporary annual jobs) and total jobs
that would result under both scenarios (a single large "existing" facility that continues operations
and multiple smaller facilities that are newly constructed). The last column in table 9.1.3-1
shows the range (maximum and minimum) of expected new total (direct) jobs that would be
added to the economy during 2026 and 2027.
Table 9.1.3-1: Cumulative Direct Permanent Annual Operations Jobs, Temporary
Construction Jobs & Total Direct Jobs for Analyzed Volumes (FTE)
Year
Aggregate Volume
in mil gal
(Multi-plant
Volumes in mil gal)
Single Facility
Mult
ti-plant Facility
Range
(min
max)
Cumulative
Operations
Jobs
(Aggregate)
Cumulative
Operations
Jobs
(Aggregate)
Construction
Jobs
Total
(Direct)
Jobs
2026
231
(90,90,51)
4
154
549
703
4-703
2027
245
(90,90,65)
5
316
578
894
5-894
9.1.4 Agricultural Employment
Job creation in the agricultural sector, beyond jobs associated with the fuel production
activities discussed above, is expected primarily in the areas of production and transportation of
crops serving as renewable fuel feedstocks.
Because RNG used as CNG/LNG is produced from waste or byproduct materials (e.g.,
separated MSW, wastewater, and agricultural residue), we expect the projected increases in the
production of RNG used as CNG/LNG to have very little impact on employment related to
feedstock production. As noted above, EPA is projecting higher volumes of ethanol and BBD
production for 2026-2027 relative to the No RFS Baseline. Substantial volumes of these fuels
are expected to be produced from domestic corn, soybean oil and canola oil.
9.1.4.1
Approach
Agricultural Employment Impacts Using the Rule-of-Thumb
From these studies, we have estimated the impacts of the projected crop-based renewable
fuel volumes on agricultural employment. These estimates are summarized in Table 9.1.4.1-1.
Table 9.1.4.1-1: Agricultural Employment Impacts of Production (FTE)
Indirect +
Feedstock
Direct
Indirect
Induced
Induced
Total
RNG
Agricultural waste
2,823
2,584
3,063
8,470
BBD
Oilseed production
7,059
21,177
28,236
Ethanol
Feedstock (mostly corn)
58,324
127,638
78,533
264,464
310
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Because these estimated agricultural employment impacts represent different quantities
of biofuel production, we then divide the total impact estimates in Table 9.1.4.1-1 by the total
production estimated by each of the studies from which the estimates were derived in million
ethanol equivalent gallons. These calculations produced estimates of the impact in terms of jobs
per million ethanol equivalent gallons for each renewable fuel. These estimates are reported in
Table 9.1.4.1-2. Ethanol has the highest direct and total effects on rural employment per million
gallons of ethanol equivalent.
Table 9.1.4.1-2: Agricultural Employment Impacts per Million Ethanol Equivalent Gallons
(FTE)
Indirect +
Feedstock
Direct
Indirect
Induced
Induced
Total
RNG
Agricultural waste
3.2
2.9
3.5
9.6
BBD
Oilseed production
1.8
5.4
7.2
Ethanol
Feedstock (mostly corn)
3.7
8.2
5.0
17.0
We next estimate the agricultural employment impacts associated with the Analyzed
Volumes by multiplying the applicable volumes in our projections by the jobs per million gallons
of ethanol equivalent. We report two sets of the projections, one based on the direct effects only
and the other based on all effects (i.e., direct, indirect, and induced effects). These estimates are
presented in Table 9.1.4.1-3 and 9.1.4.1-4 respectively. Relative to the No RFS Baseline and
accounting for direct, indirect, and induced effects, BBD is projected to have the highest impact
on agricultural employment, mainly due to substantially higher projected production increases
relative to the baseline (see Chapter 3 for more details).
Table 9.1.4.1-3: Agricultural Employment Impacts of the Projected Production Increases
Direct
2026
2027
RNG
2,569
2,694
Biodiesel
6,469
7,509
Ethanol
864
916
All Fuels
9,902
11,119
Table 9.1.4.1-4: Agricultural Employment Impacts of the Projected Production Increases
Based on A
1 Effects Under the Analyzed Volumes (FTE)
Direct + Indirect + Induced
2026
2027
RNG
7,708
8,084
Biodiesel
25,876
30,034
Ethanol
3,916
4,153
All Fuels
37,500
42,272
311
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9.1.4.2 Agricultural Employment Impacts Using NLR's JEDI model for Dry
Mill Corn Ethanol
In this subsection, we apply the estimates discussed in Chapter 9.1.2 to the incremental
amount of corn ethanol projected in the Analyzed Volumes, compared with the No RFS
Baseline. From this, we were able to obtain estimates of the number of gross jobs (indirect and
induced effects) that were added to the agricultural sector and allied industries.645 As stated in
Chapter 9.1, some of these biofuels bear similarities in terms of their backward and forward
linkages to other sectors of the economy and as such the nature of the impacts (type of impacted
sectors) are going to be similar. The magnitude of the impacts will be guided by the size of the
initial policy shock.
We also carried out a sensitivity analysis on these projected estimates using research
from a previously published Model Comparison Exercise.646 Different modeling approaches
yield different answers to the question of the source of corn to support production of a higher
volume of corn ethanol. In 2022, EPA carried out a "Model Comparison Exercise" (MCE) where
the performance of five different models was compared in terms of their ability to account for the
impact of the RFS program on indirect emissions (a requirement of the CAA). One of the
scenarios that was modeled to facilitate model comparison was a corn ethanol shock, and the
results of that analysis revealed (among other things) the source of the higher corn to fuel this
shock. As expected, the results were vastly different across the competing models. Two of these
models (GCAM and GLOBIOM) were used in this rule to support the consequential components
of the Life Cycle Analysis of biofuels (see Chapter 5). We used the sourcing estimates from
those same two models in the previously published MCE to carry out the sensitivity analysis.
This will allow us to compare how changing the JEDI model's default assumption (25% of this
corn to support the higher production is to be sourced from new cropland) with the values
obtained from GCAM (47.4%) and GLOBIOM (1%) will impact the different sectors. Table
9.1.4.2-1 shows the number of gross cumulative indirect operations jobs that are added to
agriculture and allied industries and the number of gross induced operations jobs for the
Analyzed Volumes, under the assumption that: (1) incremental volumes come from additional
production at existing facilities, and (2) incremental volumes come from multiple facilities where
the largest facility corresponds to the average size of an ethanol plant in the U.S.
645 Standard limitations associated with the limitations of the JEDI model are also applicable in this case. As with the
estimates of direct jobs, please refer to the limitations of the JEDI model when it comes to interpreting these results:
NLR, "Limitations of JEDI Models." https://www.nlr.gov/analYsis/iedi/limitations.html
646 EPA, "Model Comparison Exercise Technical Document," EPA-420-R-23-017, June 2023.
https ://nepis. epa. gov/Exe/ZvPDF. cgi?Dockev=P 1017P9B .pdf.
312
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Table 9.1.4.2-1 Annual Cumulative Indirect and Induced (Gross) Jobs in Agriculture,
Year
Indirect
Operations Jobs
(Agriculture)
Indirect
Operations Jobs
(Industry)
Induced
Operations Jobs
Single
Large
Facility
Multiple
Smaller
Facilities
Single
Large
Facility
Multiple
Smaller
Facilities
Single
Large
Facility
Multiple
Smaller
Facilities
2026
706
706
467
624
520
632
2027
1,445
1,445
935
1,279
1,051
1,293
As we did for the DRIA, we carried out a sensitivity analysis on these projected
estimates. By default, the JEDI model assumes 25% of the additional corn sourced to support the
incremental corn ethanol projected to be produced under the Analyzed Volumes is to be sourced
from new cropland. We supplemented this central estimate with two bookend cases to bound
uncertainty. These cases assume approximately 47% and 1% respectively of the corn ethanol is
sourced from new production on new cropland, which will impact job creation in the affected
sectors. We applied this sensitivity to the case of continuing production at existing facilities.
Since the job impacts to the economy from the addition of multiple smaller facilities is
significantly higher, the results from the sensitivity analysis when applied to those numbers will
generate a wider range for the estimated jobs. Table 9.1.4.2-2 shows the number of gross indirect
operations jobs that emerge out of this sensitivity analysis for agriculture, industry, and gross
induced operations jobs sectors.
Table 9.1.4.2-2: Results of Sensitivity Analysis on Cumulative (Indirect & Induced) Jobs
for the Analyzed Volumes (FTE)
Indirect Operations Jobs (Agriculture) Cumulative FTE
Year
High Sourcing Value
(47%)
JEDI Default Value"
(25%)
Low Sourcing Value
(1%)
2026
1,339
706
28
2027
2,740
1,445
58
Indirect Operations Jobs (Industry) Cumulative FTE
2026
467
467
467
2027
935
935
935
Induced Operations Jobs Cumulative Fr
rE
2026
717
520
308
2027
1,455
1,051
809
a The results in this column correspond to the incremental output coming entirely from one single facility using
JEDI's default sourcing assumption estimates.
9.1.5 Rural Economic Development
Changes in biofuel production can have economic development impacts on rural
communities and financial impacts on farmers. In this final rule, we project that our Analyzed
Volumes will lead to greater consumption of ethanol, BBD (including biodiesel and renewable
diesel), and RNG used as CNG/LNG in 2026-2027 relative to the No RFS Baseline. When
313
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considered relative to 2025 production and consumption of renewable fuels, most of the
production growth (and therefore consumption growth) is projected to come from renewable
diesel. As discussed in Chapter 3, the impacts of the RFS volumes for 2026 and 2027 on
domestic ethanol production are uncertain. In their absence, domestic ethanol production could
continue at a level at or near current production volumes with increasing ethanol exports, or
alternatively, domestic ethanol production could decrease. However, even if only the renewable
diesel projections are associated with incremental additional jobs created relative to the state of
the industry in 2025, all the renewable fuel production under the 2026 and 2027 Analyzed
Volumes continues to sustain economic output in rural communities. In the face of uncertainty
regarding the continued production of many of these fuels in the absence of the RFS program, it
is worthwhile to quantify the entirety of this economic impact on rural communities.
9.1.5.1 Rural Economic Development Impacts using the Rule-of-Thumb
Approach
The Rule-of-Thumb approach and results discussed in Chapters 9.1.1 and 9.1.2,
respectively, provide a basis for estimating the impact of renewable fuels on rural economic
development. From these results, we have estimated impacts of the projected crop-based
renewable fuel volumes on rural economic development measured by GDP. These estimated
impacts on rural GDP are summarized in Table 9.1.5.1-1.
Table 9.1.5.1-1: Rural GDP Impacts of Production
Feedstock
Direct
Indirect
Induced
Indirect +
Induced
Total
RNG
(million 2024$)
Agricultural waste
657
380
358
1,395
BBD
(million 2021$)
Oilseed production
1,853
5,558
7,410
Ethanol
(million 2023$)
Feedstock (mostly corn)
3,137
14,299
10,488
27,916
We divide the total impact estimates in Table 9.1.5.1-1 by the total production of each
category of fuel in million ethanol equivalent gallons in each of the relevant studies to estimate
the impact per million ethanol equivalent gallons for each renewable fuel category. These results
are reported in Table 9.1.5.1-2.
Table 9.1.5.1-2: Rural GDP Impacts (million dollars per million ethanol-equivalent gallons)
Indirect +
Feedstock
Direct
Indirect
Induced
Induced
Total
RNG (2024$)
Agricultural waste
0.75
0.43
0.41
1.59
BBD (2021$)
Oilseed production
0.47
1.42
1.89
Ethanol (2023$)
Feedstock (mostly corn)
0.20
0.92
0.67
1.79
314
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The GDP impacts in Table 9.1.5.1-2 are based on data from different years. We use the
GDP price index from the Federal Reserve Economic Data (FRED)647 to compute the ratio of the
GDP price index in base year (2024) to the GDP price index in year t (2021-2024), as shown in
Table 9.1.5.1-3.
Table 9.1.5.1-3: GDP Price Index Ratios (Base Year is 2024)
Year
GDP Price Index
GDP Price Index Ratio
2021
110
1.137
2022
118
1.061
2023
122
1.024
2024
125
1.000
We then use the ratios to compute the GDP impacts in 2024 dollars as shown in Table
9.1.5.1-4. For example, to derive 0.54 (the real value measured in 2024 dollars in Table 9.1.5.1-
4), we multiply 0.47 (the nominal value in Table 9.1.5.1-2) by 1.137 (the ratio for 2021 in Table
9.1.5.1-3). We note that, compared with RNG, BBD and ethanol have higher impacts per million
ethanol equivalent gallons on rural economic development.
Table 9.1.5.1-4: Rural GDP Impacts (million 2024$ per million ethanol-equivalent gallons)
Feedstock
Direct
Indirect
Induced
Indirect +
Induced
Total
RNG
Agricultural waste
0.75
0.43
0.41
1.59
BBD
Oilseed production
0.54
1.61
2.15
Ethanol
Feedstock (mostly corn)
0.21
0.94
0.69
1.84
We next estimate the impacts of the Analyzed Volumes by multiplying the projected
production increases under these volumes with the impact per million ethanol equivalent gallons
estimates shown in Table 9.1.5-2. In Table 9.1.5.1-5, we report two sets of projections, one based
on the direct effects only and the other based on all effects (i.e., direct, indirect, and induced
effects). Relative to the No RFS Baseline and accounting for direct, indirect, and induced effects,
BBD is projected to have the highest impact on rural economic development, largely due to
substantially higher production increases relative to the baseline. In addition, we have discounted
these impacts using discount rates of 3% and 7%. We report the results of our discounting
calculations in Table 9.1.5.1-7. Without discounting and based only on the direct impacts, the
Analyzed Volumes are projected to create $2.58 billion and $2.92 billion in 2026 and 2027,
respectively. If we discount these direct impacts at 3%, the total impact is $5.26 billion over the
two-year horizon, or $2.63 billion per year. If we also account for indirect and induced effects,
the total impact without discounting is $9.43 billion and $10.76 billion in 2026 and 2027; with
discounting, it is $19.29 billion over the two-year horizon, or $9.65 billion per year. In addition,
if we amortize $19.29 billion over the two-year horizon, the annualized value is $10.08
billion.648
647 Federal Reserve Economic Data, "Gross domestic product (implicit price deflator)," February 20, 2026.
https://fred.stlouisfed.org/series/A191RD3A086NBEA.
648 An annualized value is the amount one would have to pay (or receive) at the end of each time period so that the
sum of all payments in present value terms equals the original stream of values. Computing annualized costs and
315
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Table 9.1.5.1-5: Rural GDP Impacts of the Projected Production Increases (million 2024$)
Direct
All Effects (Di
+ Int
rect + Indirect
uced)
2026
2027
2026
2027
RNG
599
628
1,272
1,334
BBD
1,933
2,243
7,731
8,974
Ethanol
48
51
424
450
All Fuels
2,580
2,922
9,427
10,757
Present Value at 3%
2,504
2,755
9,152
10,139
All Fuels for Two Years
5,259
19,292
All Fuels per Year
2,629
9,646
Present Value at 7%
2,411
2,552
8,810
9,396
All Fuels for Two Years
4,963
18,206
All Fuels per Year
2,482
9,103
9.1.5.2 Rural Economic Development Impacts using NLR's JEDI Model for
Dry Mill Corn Ethanol
We also estimated rural economic development impacts associated with corn ethanol
using the NLR JEDI model. These estimates assume the ethanol volumes are associated with
new domestic production and are presented as annual cumulative earnings impacts. Relying on
the JEDI model to compute the employment impacts stemming from such an increase in
production, we were able to assess the economic impacts to the rural economy under the same
scenario specifications as in Chapter 9.1.2.1.2, firstly in a situation where this higher production
comes from existing facilities and, secondly, in another situation where this higher production
comes from multiple new smaller facilities. Based on our analysis, Table 9.1.5.2-1 shows the
economic impacts of continued operations at higher volumes from existing facilities and from
multiple new average sized facilities for the Analyzed Volumes.
Table 9.1.5.2-1: Annual Cumulative Earnings from All Indirect and Induced Jobs for the
Analyzed Volumes (million 2024$)
Year
Indirect Operations
Earnings
(Agriculture)
Indirect C
Earnings
~perations
Industry)
Induced
Operations
Earnings
Single
Large
Facility
Multiple
Smaller
Facilities
Single
Large
Facility
Multiple
Smaller
Facilities
Single
Large
Facility
Multiple
Smaller
Facilities
2026
38
37
45
57
39
46
2027
78
75
89
116
79
96
benefits from present values spreads the costs and benefits equally over each period, taking account of the discount
rate.
316
-------
As in Chapter 9.1.5, we were able to conduct a sensitivity analysis where the percentage
of new cropland that was sourced to produce the additional ethanol was the parameter that was
given alternate values (see Chapter 9.1.4.2 and the DRIA for more details on this analysis). Table
9.1.5.2-1 shows the impact on earnings that emerge out of this sensitivity analysis for agriculture,
industry and other sectors (induced jobs) where the second column in Table 9.1.5.2-1 shows the
outcome when 47% of this corn (for ethanol) is sourced from new agricultural production/new
plantings, the third column shows the results if 25% is from new production, and the last column
shows the impacts when 1% is from production.
Table 9.1.5.2-1: Results of Sensitivity Analysis on Cumulative (Indirect & Induced)
Indirect
Earnings (Agriculture'
Year
High Sourcing Value
(47%)
JEDI Default Value
(25%)
Low Sourcing Value
(1%)
2026
70
39
2
2027
114
77
3
Indirecl
Earnings (Industry)
2026
44
45
44
2027
99
89
88
Induced Earnings
2026
53
39
22
2027
108
79
45
9.1.6 Summary of Employment and Economic Impacts
In this section, we summarize our main results for the employment, agricultural
employment, and rural economic development for the Analyzed Volumes. As discussed above,
the "Rule-of-Thumb" approach produces estimates for the impacts of the quantities of all three
categories of renewable fuel we analyzed—ethanol, BBD, and RNG—under the Analyzed
Volumes relative to the No RFS Baseline. The JEDI model approach provides estimates for dry
mill corn ethanol only, which we apply to the volumes of that fuel for the Analyzed Volumes
relative to the No RFS Baseline. The summary below includes the estimated potential
employment impacts, agricultural employment impacts, and rural GDP impacts associated with
the volumes of ethanol, BBD, and RNG attributable to the Analyzed Volumes.
With the "Rule-of-Thumb" approach, we estimate that all three categories of renewable
fuel we analyzed—ethanol, BBD, and RNG—are associated with increases in employment to
varying degrees. We observe that (1) RNG appears to be associated with the highest number of
direct and total jobs created per unit of biofuel (9.1 and 27.2, respectively) and (2) BBD and
ethanol have higher indirect and induced impacts relative to their direct impacts. See Table 9.1.6-
1.
317
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Table 9.1.6-1: Job Creation Impacts per Million Ethanol-Equivalent Gallons (FTE)
Direct
Indirect + Int
uced
Total
Multiplier
Indirect
Induced
Combined
RNG
9.1
8.5
9.6
27.2
3.0
BBD
4.8
14.4
19.2
4.0
Ethanol
4.6
13.0
7.5
25.2
5.5
However, BBD is projected to have the highest job creation impact overall, primarily due
to substantially higher production increases relative to the baseline, for the Analyzed Volumes
(Table 9.1.6-2a).
Using the JEDI model for corn ethanol, we created the following two scenarios to
estimate the impacts of the Analyzed Volumes on the economy: (1) we assume there is no new
construction of ethanol facilities and the increased ethanol volume associated with the Analyzed
Volumes (relative to the No RFS Baseline) is met by increasing production levels at existing
facilities (or in the alternative the avoidance of reduced corn ethanol production that would occur
in the No RFS Baseline) and (2) a second case, in which we assume the increased ethanol
volumes (relative to the No RFS Baseline) come from new construction. To the extent that
retiring ethanol production capital is replaced with new and more efficient facilities in 2026 and
2027, this analysis would be relevant to those circumstances. Tables 9.1,6-2b reports the
cumulative number of total jobs (in FTE) that would result under the Analyzed Volumes.
Table 9.1.6-2a: Employment Impacts of the Analyzed Volumes using the Rule-of-Thumb
Approach (FTE)
2026
2027
Ethanol
5,810
6,162
BBD
68,912
79,986
RNG
21,765
22,827
Total
96,487
108,975
Table 9.1.6-2b: Employment Impacts of the Analyzed Volumes using NLR's JEDI Model
for Dry Mill Corn Ethanol (FTE)
2026
2027
Ethanol
1,698-2,665
3,436-4,911
Note: The estimates are presented as ranges corresponding to the single facility outcome as the lower bound of the
ranee and the multi facility outcome as the upper bound of the range.
In terms of agricultural employment specifically, with the "Rule-of-Thumb" approach,
we use the job creation impacts associated with agricultural feedstocks to infer the effects on
agricultural employment. Ethanol has the highest direct and total effects per million gallons of
ethanol equivalent, as shown in Table 9.1.6-3.
318
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Table 9.1.6-3: Agricultural Employment Impacts per Million Ethanol Equivalent Gallons
(FTE) *
Indirect +
Feedstock
Direct
Indirect
Induced
Induced
Total
RNG
Agricultural waste
3.2
2.9
3.5
9.6
BBD
Oilseed production
1.8
5.4
7.2
Ethanol
Feedstock (mostly corn)
3.7
8.2
5.0
17.0
Relative to the No RFS Baseline and accounting for direct, indirect, and induced effects,
BBD is projected to have the highest impact on agricultural employment, mainly due to
substantially higher production increases relative to the baseline, for the Analyzed Volumes. See
Table 9.1.6-4a.
Once again, using the JEDI model for corn ethanol, we created the following two
scenarios to estimate the impacts of the Analyzed Volumes on the economy: (1) we assume there
is no new construction of ethanol facilities and the increased ethanol volume associated with the
Analyzed Volumes (relative to the No RFS Baseline) is met by increasing production levels at
existing facilities (or in the alternative the avoidance of reduced corn ethanol production that
would occur in the No RFS Baseline) and (2) a second case, in which we assume the increased
ethanol volumes (relative to the No RFS Baseline) come from new construction. To the extent
that retiring ethanol production capital is replaced with new and more efficient facilities in 2026
and 2027, this analysis would be relevant to those circumstances. Table 9.1,6-4b reports the
cumulative number of total indirect operations (in agriculture and industry) jobs and the total
induced operations jobs that would result under the Analyzed Volumes. For the results of the
sensitivity analysis, please refer to Tables 9.1.4.2-1 and 9.1.4.2-2.
Table 9.1.6-4a: Agricultural Employment Impacts of the Analyzed Volumes Using the
2026
2027
Ethanol
3,916
4,153
BBD
25,876
30,034
RNG
7,708
8,084
Total
37,500
42,272
Table 9.1.6-4b: Agricultural Employment Impacts of the Analyzed Volumes Using NLR's
JEDI Model for Dry Mill Corn Ethanol (FTE)
2026
2027
Ethanol
1,693-1,962
3,431-4,017
Note: The estimates are presented as ranges corresponding to the single facility outcome as the lower bound of the
range and the multi facility outcome as the upper bound of the range.
With the "Rule-of-Thumb" approach, we also estimate that ethanol, BBD, and RNG are
all associated with increased rural economic development, again to varying degrees. Since
renewable fuels rely on agricultural feedstocks, we use the GDP impacts associated with
agricultural feedstocks to infer the effects on rural economic development. We estimate that
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BBD and ethanol have higher total impacts per million ethanol equivalent gallons on rural
economic development than does RNG. See Table 9.1.6-5.
Table 9.1.6-5: Rural GDP Impacts per Million Ethanol
Feedstock
Direct
Indirect
Induced
Indirect +
Induced
Total
RNG
Agricultural waste
0.75
0.43
0.41
1.59
BBD
Oilseed production
0.54
1.61
2.15
Ethanol
Feedstock (mostly corn)
0.21
0.94
0.69
1.84
quivalent Gallons (million 2024$)
Relative to the No RFS Baseline and accounting for direct, indirect, and induced effects,
BBD is projected to have the highest impact on rural economic development, largely due to
substantially higher production increases relative to the baseline. See Table 9.1.6-6a. The total
impact without discounting is $9.43 billion and $10.76 billion in 2026 and 2027; with
discounting at a 3% discount rate, it is $19.29 billion over the two-year horizon, or $9.65 billion
per year. In addition, if we amortize $19.29 billion over the two-year horizon, the annualized
value is $10.08 billion.
Once again, using the JEDI model for corn ethanol, we created the following two
scenarios to estimate the impacts of the Analyzed Volumes on the economy: (1) we assume there
is no new construction of ethanol facilities and the increased ethanol volume associated with the
Analyzed Volumes (relative to the No RFS Baseline) is met by increasing production levels at
existing facilities (or in the alternative the avoidance of reduced corn ethanol production that
would occur in the No RFS Baseline) and (2) a second case, in which we assume the increased
ethanol volumes (relative to the No RFS Baseline) come from new construction. To the extent
that retiring ethanol production capital is replaced with new and more efficient facilities in 2026
and 2027, this analysis would be relevant to those circumstances. Table 9.1,6-6b reports the
cumulative earnings from the number of total indirect operations (in agriculture and industry)
jobs and the total induced operations jobs (in FTE) that would result under the Analyzed
Volumes. For the results of the sensitivity analysis, please refer to Tables 9.1.5.2-1 and 9.1.5.2-2.
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Table 9.1.6-6a: Rural Economic Development Impacts of the Analyzed Volumes (million
2024$)
All Effects (Direct + Indirect + Induced)
2026
2027
RNG
1,272
1,334
BBD
7,731
8,974
Ethanol
424
450
All Fuels
9,427
10,757
Present Value at 3%
9,152
10,139
All fuels for two years
19,292
All fuels per year
9,646
Present Value at 7%
8,810
9,396
All fuels for two years
18,206
All fuels per year
9,103
Table 9.1.6-6b: Rural Economic Development Impacts of the Analyzed Volumes Using
NLR's JE]
)I Model for Dry Mill
2026
2027
Ethanol
123-140
246-288
Note: The estimates are presented as ranges corresponding to the single facility outcome as the lower bound of the
range and the multi facility outcome as the upper bound of the range.
These estimates in Chapter 9.1 for the various categories of biofuels are subject to the
limitations and assumptions of the methods employed. They are not meant to be exact estimates,
but rather to provide an estimate of general magnitude. In addition, while we estimate that
production and consumption of these biofuels will lead to higher jobs and rural GDP in some
sectors of the economy, this will likely involve some migration in jobs and rural GDP from other
sectors. As such, we anticipate that there would be job and rural GDP losses as well in some
sectors. Likewise, investments in rural development may involve some shifting of capital from
one sector to another. We do not account for any such losses in our analysis. In other words, our
estimates for jobs and rural development impacts are gross and not net estimates. While we have
also not been able to quantify the impacts of this rule on small entities in rural areas, we note that
we do anticipate that small entities (such as farms and supporting industries) will experience
benefits from this rule.
The existing literature also shows, in the long run, environmental regulation such as the
RFS program typically affects the distribution of employment among industries rather than the
general employment level.649 650 The expectation is that there will be a movement of labor
649 Arrow, Kenneth J., Maureen L. Cropper, George C. Eads, Robert W. Halin, Lester B. Lave, Roger G. Noll, Paul
R. Portney, et al. "Benefit-Cost Analysis in Enviromnental, Health, and Safety Regulation," American Enterprise
Institute, The Annapolis Center, and Resources for the Future, 1996. https://www.aei. org/wp-
content/uploads/2014/04/-benefitcost-analvsis-in-environmental-health-and-safetv-regulation 161535983778.pdf.
6511 Hafstead, Marc a. C., and Roberton C. Williams. "Jobs and Enviromnental Regulation." Environmental and
Energy Policy and the Economy 1 (January 1, 2020): 192-240. https://doi.org/10.1086/706799.
321
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towards jobs that are associated with greater environmental protection, and away from those that
are not. Even if impacts are small after long-run market adjustments to full employment, many
regulatory actions move workers in and out of jobs and industries, which are potentially
important distributional impacts of environmental regulations.651
9.2 Supply of Agricultural Commodities
Changes in biofuel production can have an impact on the supply of agricultural
commodities. The Analyzed Volumes in this rule suggest the potential for associated increases in
underlying crop production; however, the magnitude of any potential impact cannot be estimated
with any certainty. EPA notes that biogas is not produced from agricultural commodities and
therefore is not expected to affect their supply or price.
For historical context, Figure 9.2-1 shows trends in corn production and uses from
1999/2000-2024/2005.652 653 This data suggests domestic corn production has grown steadily at
a 25-year average rate of around 2% year over year, or 220 million bushels added annually.
651 Walker, W. Reed. "The Transitional Costs of Sectoral Reallocation: Evidence From the Clean Air Act and the
Workforce*." The Quarterly Journal of Economics 128, no. 4 (August 15, 2013): 1787-1835.
https://doi.org/10.1093/qie/qit022.
652 USD A, "Feed Grains Yearbook Tables," February 11, 2026, Table 4. https://www.ers.usda.gov/data-
products/feed-grains-database/feed-grains-vearbook-tables.
653 USD A, "U.S. Bioenergy Statistics," January 21, 2026, Table 5. https://www.ers.usda.gov/data-products/us-
bioenergy-statistics.
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Figure 9.2-1: Corn Production and Use
18,000
16,000
14,000
12,000
V)
"55
W 10,000
3
CO
c
° 8,000
6,000
4,000
2,000
-'rod ja on ^—Ethanol Food and Industrial Seed Feed Exports
Between 2005-2010, additional corn required to satisfy increasing ethanol production
was sourced primarily by diversion from animal feed until overall production caught up. Supply
of corn to food uses showed modest but consistent growth at historical rates during this period,
despite increased consumption as ethanol feedstock. Exports also remained relatively steady,
except for a drop corresponding to weather-related supply disruptions and elevated prices in
2011-2012. Animal feed use began to rebound after 2014 when growth in ethanol use slowed
and prices stabilized. Another factor contributing to the longer-term shift of animal feed away
from whole corn was the increasing substitution with DDGS, a byproduct of ethanol producti on.
Considering historical trends over the past two decades indicating the ability of production to rise
to meet demand, the relatively modest changes in ethanol volumes associated with this aile
relative to 2025 are likely to have minimal impact on the supply of corn to food, exports, or other
uses.
Soybean production has risen steadily over time, similar to the trend for corn production,
according to data from USD A.654 Roughly 80% of growth in soybean production since 2005 has
been associated with rising exports of soybeans, which have nearly doubled over that period.
Domestic crushing of soybeans has grown by about 25% since 2005, which is mirrored in growth
of crush products, soybean meal, and soybean oil. These data also show that exports of soybean
meal nearly doubled during this time, which together with the growth in whole soybean exports,
presents a picture consistent with expansion of meat production internationally. For context, over
95% of soybeans worldwide are eventually crushed for meal and oil.
654 USD A, "Oil Crops Yearbook," March 2025. itps://www.ers.usda.gov/data-products/oil-crops-yeaibook.
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Figure 9.2-2 shows the evolution of soybean oil production and disappearance in the U.S.
since 2001. Growth in soybean oil production over the past decade has been enabled by both
increasing crush capacity and increasing yields of oil per bushel of soybean input. The use of
soybean oil for biofuel production has also increased steeply since about 2014, with a further
uptick since 2020. In this final rule we are projecting that even greater quantities of soybean oil
will be used for biofuel production in 2026 and 2027. With the strong incentives provided by the
RFS program and the structure of the CFPC which favors the domestic production of biofuel
from North American feedstocks we may see a shift of in the quantity of soybean oil used in
non-biofuel markets to enable greater production of biofuel from domestic soybean oil. We do
not expect that this shift will result in vegetable oil shortages in the U.S. due to the wide range of
vegetable oils used in non-biofuel markets and available to U.S. consumers. However, as
discussed further in Chapter 9.3 an increase in demand for soybean oil for biofuel production is
projected to result in higher soybean oil prices and could shift the relative value relationship
between the oil and meal crush products.
Figure 9.2-2: Soybean Oil Production and Use
35,000
30,000
O
c 25,000
ro
cV
tQ'
nV
¦Soybean Oi! Production
¦Soybean Oil Exports
•Soybean Oil for Biodiesel
Other Domestic Uses
9.3 Price of Agricultural Commodities
Agricultural commodities are bought and sold on the international market, where prices
are determined by trends and upsets in worldwide production and consumption. Renewable fuels
are only one factor among many (e.g., droughts and storm damage) in determining commodity
prices. Thus, models that attempt to project prices at specific times in the future, or in reaction to
specific demand perturbations, necessarily contain high levels of uncertainty. This section
reviews historical trends and presents key observations from the relevant literature.
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In the U.S., corn and soybeans are generally only harvested once per year, and therefore
storage is a critical factor in the supply chain. After harvest, grain stores are replenished and then
drawn down throughout the year. In recent years, 10-15% of the previous year's overall corn
production is typically still in storage at the time of the new harvest.65' If demand rises after
harvest, stocks may be drawn down faster than expected. Conversely, if demand decreases,
stocks accumulate into the next season.
Storage also has the effect of dampening price shocks in years when harvests are smaller
than expected. In 2012, a drought year, corn stocks fell to the lowest levels since 2000, putting
upward pressure on futures prices, which in turn served as a market signal to induce more corn
planting in the upcoming season. Work done by Informa Economics for the Renewable Fuels
Association in 2016 examined the historical relationship between corn usage, stocks, and futures
prices.656 Figure 9.3-1 shows the strong correlation between futures prices and the stock-to-usage
ratio, illustrating that the latter is a key driver of market signals. More generally, crop prices are
influenced by an array of factors from worldwide weather patterns to biofuel policies to
international tariffs and trade wars.
Figure 9.3-1: Corn Ending Stocks / Use Ratio Versus Futures Price
$8.00
_ :b/ uu • 2011
^ • 2010
| $6.00 Ff = 0.8255 \
iZ df.
325
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gallons of ethanol.657 Focusing only on scenarios where a supply response is included gives a
result of 3%. A supply response refers to scenarios where farmers can respond to price signals in
subsequent year(s) and plant additional crops to meet a larger demand. This is appropriate, as the
scope of the analysis is biofuel policy (rather than something unforeseen like weather shocks). A
similar meta-analysis was done in 2016 by FAPRI-Missouri that considered other studies.658
This paper found an increase of $0.19 per bushel per billion gallons, or $0.15 if a supply
response is included, a figure that is generally consistent with the 3% impact above if applied to
the corn price in 2016.659
EPA is projecting a marginal increase in ethanol volumes for years 2026-2027 relative to
the No RFS Baseline. We note, however, that in recent years domestic ethanol production has
exceeded consumption, with significant volumes being exported. This trend appears very likely
to continue during 2026-2027, as our projected consumption volumes remain below USDA's
projected domestic production volumes for these years.660 A history of significant export
volumes complicates predicting the impact of the projected volumes on agricultural commodity
prices. It is possible that an increase in domestic corn ethanol consumption may result in
decreased exports and minimal change in overall domestic production volumes. Were this to
occur, we would expect little to no net change in domestic corn demand, and thus corn prices,
which we would expect to maintain their current levels. Alternatively, lower U.S. net corn
exports could reduce supply to international markets and put upward pressure on global corn
prices. It is also possible (though less likely) that an increase in consumption would result in an
increase in domestic corn ethanol production. In this case we would expect a correlated change
in corn demand and corn prices.
To illustrate the potential impact of the volumes on corn prices, we have calculated the
projected impact in 2026-2027 if the Analyzed Volumes result in increased corn ethanol
production relative to the No RFS Baseline. The projected price impacts are calculated using a
value from the literature of 3% increase in the price of a bushel of corn per billion gallons of
corn ethanol produced, as described above. The projected impact of the Analyzed Volumes on
corn prices relative to the No RFS Baseline are shown in Table 9.3-1.
657 Condon, Nicole, Heather Klemick, and Ann Wolverton. "Impacts of Ethanol Policy on Corn Prices: A Review
and Meta-analysis of Recent Evidence." Food Policy 51 (January 13, 2015): 63-73.
https://doi.org/10.1016/i.foodpol.2014.12.007.
658 FAPRI, "Literature Review of Estimated Market Effects of U.S. Corn Starch Ethanol." FAPRI-MU Report #01-
16, February 2016. https://ethanolrfa.org/file/2007/FAPRI-Report-01-16.pdf.
659 These two studies are both based on older market conditions that may no longer hold. They also examine a one-
billion-gallon shock size, which is larger than the volume of corn ethanol attributable to this final rule.
6611 USD A, "USDA Agricultural Projections to 2034," OCE-2025-1, February 2025.
https://doi.org/10.32747/2025.9015815.ers.
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Table 9.3-1: Projected Impact on Corn Prices Relative to No RFS Baseline
Units
2026
2027
Corn Price3
$ per bushel
$3.97
$4.07
Corn Price Response
(%) per billion
gallons of ethanol
3%
3%
Corn Ethanol Volume Increase
Relative to No RFS Baseline
billion gallons
0.231
0.245
Corn Price Increase Relative to
No RFS Baseline
$ per bushel
$0.03
$0.03
a Corn prices are from: USD A, "USDA Agricultural Projections to 2034," OCE-2025-1, February 2025.
https://doi.org/10.32747/2025.9Q15815.ers. Prices represent the average price for a calendar year. For corn, the price
is calculated using 1/3 of the price for the first agricultural marketing year (e.g., 2025/2026 for 2026) and 2/3 of the
price for the second agricultural marketing year (e.g., 2026/2027 for 2026).
For biodiesel and renewable diesel production, the primary commodity input of interest is
soybean oil, which has an indirect link to soybean production. Soybean oil is produced by
crushing soybeans, a process which coproduces soybean meal. The supply and prices of soybean
oil and soybean meal can move independently from each other. The crush quantities vary from
year to year, depending on the crush margin, which is defined as the sum of soybean oil and meal
prices minus the soybean price. Oversupplying either oil or meal markets can cause prices to fall,
decreasing the crush margin. Figure 9.3-2 shows historical trends in soybean oil prices alongside
allocation to biofuel and other uses, based on data taken from the USDA Oil Crops Yearbook.661
Use of soybean oil in domestic biofuel rose from 0.8 million tons in 2005 to 7 million tons in
2024. Other domestic uses besides biofuel increased steadily through 2005, decreased slightly
from 2005-2010, and have remained relatively consistent since 2010. Exports of soybean oil are
a minor demand and remained consistent for many years until falling following steep price
increases since 2020.
While additional research examining the correlation between soybean oil price and its use
in biofuel production historically would be beneficial, soybean oil prices have been reactive in
recent years to anticipated and announced biofuel policy developments. These price fluctuations
have contributed to a rising share of soybean value derived from soybean oil over time.
However, the price of soybean oil is influenced by many factors occurring in the broader
economy, including petroleum prices, supply chain disruptions on a range of inputs (e.g.,
fertilizer), prices of other vegetable oils, weather-related shortages of vegetable oils
internationally, as well as general price inflation. While increased soybean oil demand for
biofuel production was likely a contributing factor to the sharp price increase in soybean oil
prices in 2020 and 2021, poor weather conditions in South America and Malaysia were also
likely a significant factor.662
661 USDA, "Oil Crops Yearbook," March 2025. https://www.ers.usda.gov/data-products/oil-crops-Yearbook.
662 Wilson, Nick. "Oil prices surge — vegetable oil, that is," Marketplace, February 17, 2022.
https://www.marketplace.org/storv/2022/02/17/oil-prices-surge-vegetable-oil-that-is.
327
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Figure 9.3-2: Soybean Oil Price and Allocation to Biofuel and Exports
80
70
60
50 o-
TO
C
40 E
o
c
30 3
L.
Q_
20
10
0
There are relatively few quantitative studies on the impacts of BBD production on
soybean oil and soybean prices, and they show a range of results.663 664 665 This is in part because
these studies have included a variety of different policy combinations, none of which isolated the
impact of the RFS program on BBD demand. Ethanol demand could impact the soybean markets
even in the absence of increased demand for BBD from the RFS program due to increased
competition for cropland and other inputs. The largest impacts are estimated when the BBD
obligations are modeled jointly with the conventional and cellulosic ethanol obligations.
To illustrate the potential impact of the Analyzed Volumes on soybean prices, we
calculate the projected price effects for the 2026 to 2027 period relative to the No RFS Baseline.
As in the Set 1 Rule, our projections are primarily based on a linear equilibrium displacement
model by Lusk, which estimates the price impact of a 20% shock to current biofuel volumes.666
This model links such a shock to changes in prices of soybean oil and related commodities.
Several aspects of this study are important to note to properly understand and apply its
findings. First, the model of soybean oil price changes in response to shocks described in the
Lusk paper (hereafter "the Lusk model") expresses results in terms of percentage changes. For
example, as presented in the paper, a 20% exogenous increase in the quantity of soybean oil
demanded for biofuel use, combined with relatively inelastic supply and downward-sloping
663 Kim, Hyunseok, and GianCarlo Moschini. "The Dynamics of Supply: U.S. Corn and Soybeans in the Biofuel
Era." Land Economics 94, no. 4 (October 3, 2018): 593-613. https://doi.Org/10.3368/le.94.4.593.
664 Santeramo, Fabio Gaetano. "Cross-Price Elasticities for Oils and Fats in the US and the EU." International
Council on Clean Transportation, 2017. https://theicct.org/sites/default/files/publications/Cross-price-elasticities-for-
oils-fats-US-EU ICCT consultant-report 06032017.pdf.
665 Williams, Brian R., and Gayle Pounds-Barnett. "Producer Supply Response for Area Planted of Seven Major
U.S. Crops." U.S. Department of Agriculture, Economic Research Sen'ice, 2023.
https://doi.org/10.32747/2023.8134361.ers.
666 Lusk, Jayson L. "Food and Fuel: Modeling Food System Wide Impacts of Increase in Demand for Soybean Oil,"
November 10, 2022. https://ag.purdue.edu/cfdas/wp-content/uploads/2022/12/report sovmodel revisedl3.pdf.
328
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demand, requires a higher equilibrium price to clear the market; solving the system yields an
8.17% increase in the soybean oil price. Second, the Lusk model is linear. If one shock is a scalar
multiple of another, the resulting price and quantity changes scale accordingly. For example,
doubling the shock from 20% to 40% doubles the price response. Thus, a 40% shock implies a
soybean oil price increase of 16.34% (2 x 8.17%> = 16.34%). Third, the Lusk model calculates
percentage changes as deviations from a baseline (reference) year. Absolute shocks should be
converted back to percentage deviations using the same baseline quantities. If the baseline
changes materially, or elasticities vary with price levels, linearity may not hold. Consequently,
results should be interpreted as local comparative-static approximations around the reference
equilibrium rather than large structural changes. Our calculations use 2025 as the baseline
(reference) year.
The following illustrates how the soybean oil price impacts in Table 9.3-2 are derived:
(1) The soybean oil prices for 2026 and 2027 in the No RFS Baseline are not observed
and are inferred using Lusk's linear percentage-change model. In the Lusk model, for soybean
oil, a 20% demand increase yields an 8.17% price increase, so price changes scale at 8.17/20. For
2026, the estimated demand change is -58%. This is computed as the percentage difference
between the quantity of soybean oil projected to be used in 2026 without the RFS volume
requirements (573 million gallons) and the quantity of soybean oil used for biofuel production in
2025 (1,355 million gallons), i.e., (573 - 1,355) / 1,355 ~ -58%. Taking this -58% estimate as a
given assumption, the implied price change is approximately -24% (-58%) x [8.17/ 20] ~ -24%).
Using the 2025 base price of $0.49/lb, this gives a No RFS price of approximately $0.38/lb (0.49
x [l-24%>] ~ 0.38) in 2026. For 2027, the estimated demand change is -78%. This is computed as
the percentage difference between the quantity of soybean oil projected to be used in 2027
without the RFS volume requirements (302 million gallons) and the quantity of soybean oil used
for biofuel production in 2025 (1,355 million gallons), i.e., (302 - 1,355) / 1,355 ~ -78%. Taking
this -78%) estimate as a given assumption, the implied price change is -32% (-78%) x [8.17/ 20) ~
-32%). Using the 2025 base price of $0.49/lb, this gives a No RFS price of $0.33/lb (0.49 x [1-
32%] ~ 0.33) in 2027.
(2) The soybean oil prices in 2026 and 2027 with the RFS volume requirements in place
must similarly be projected using Lusk's linear percentage-change model. In the Lusk model, for
soybean oil, a 20% demand increase yields an 8.17%> price increase, so price changes scale at
8.17/20. For 2026, the estimated demand change is +84%. This is computed as the percentage
difference between the quantity of soybean oil projected to be used in 2026 with the RFS volume
requirements (2,495 million gallons) and the quantity of soybean oil used for biofuel production
in 2025 (1,355 million gallons), i.e., (2,495 - 1,355) / 1,355 ~ +84%. Taking this +84% estimate
as a given assumption, the implied price change is +34% (+84%) x [8.17/ 20] ~ +34%). Using the
2025 base price of $0.49/lb, this gives a projected soybean oil price of $0.66/lb (0.49 x [1+34%]
~ 0.66) in 2026. For 2027, the estimated demand change is +95%). This is computed as the
percentage difference between the quantity of soybean oil projected to be used in 2027 with the
RFS volume requirements (2,639 million gallons) and the quantity of soybean oil used for
biofuel production in 2025 (1,355 million gallons), i.e., (2,639 - 1,355) / 1,355 ~ +95%). Taking
this +95%) estimate as a given assumption, the implied price change is +39% (+95%) x [8.17/ 20]
329
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~ +39%). Using the 2025 base price of $0.49/lb, this gives a projected soybean oil price of
$0.68/lb (0.49 x [1+39%] ~ 0.68) in 2027.
Comparing (1) and (2), the soybean price differences between the No RFS Baseline and
the scenario with the RFS volume requirements are $0.28/lb for 2026 (0.66 - 0.38 = 0.28) and
$0.35/lb for 2027 (0.68 - 0.33 = 0.35). See Table 9.3-2.
Furthermore, our soybean meal price impact calculations are based on the Lusk model's
cross-price relationship that an 8.17% increase in soybean oil price is associated with a 1.93%
decrease in soybean meal price. Using 2025 as the common reference year, we first convert the
absolute soybean oil price shocks to percentage deviations from the 2025 oil price of $0.49/lb: a
$0.28/lb increase in 2026 corresponds to 58% (~ 0.28/0.49) and a $0.35/lb increase in 2027
corresponds to 70% (~ 0.35/0.49). We then map soybean oil price changes to soybean meal price
changes using the Lusk ratio, -1.93%/8.17%, which implies a linear relationship over the range
considered. We conclude that a 58% increase in oil price is associated with a 14% (~ 58%*[-
1.93%]/8.17%) decrease in soybean meal price, and a 70% increase in soybean oil price is
associated with a 17% (~ 70%*[-1.93%]/8.17%) decrease in soybean meal price. Given a 2025
soybean meal market price of $291.34/ton, a 14% decrease corresponds to a reduction of
approximately $40/ton, and a 17% decrease corresponds to a reduction of approximately $49/ton,
as shown in Table 9.3-2.
The projected impacts of the Analyzed Volumes on soybean oil and soybean meal prices
at the time of this rule based on the Lusk model are shown in Tables 9.3-2. Note that, a positive
soybean oil shock yields a negative soybean meal response under this model; results reflect a
proportional (linear) application of the Lusk relationship; and minor differences may arise from
rounding.
Table 9.3-2: Projected Impact of the Analyzed Volumes on Soybean Oil and Meal Prices
Relative to the No RFS Baseline
Units
2026
2027
Soybean Oil Increase Relative to
No RFS Baseline3
million gallons
1,922
2,337
Projected Soybean Oil Price
(under RFS)b
$ per pound
$0.66
$0.68
Soybean Oil Price Increase
Relative to No RFS Baselineb
$ per pound
$0.28
$0.35
Soybean Meal Price Change
Relative to No RFS Baselineb
$ per ton
-$40
-$49
a Soybean oil volumes in this table are 3.1% higher than the volume of biofuels produced from soybean oil
attributable to this rule from Table 3.2.2. This 3.1% increase accounts for the fact that it takes slightly more than one
gallon of soybean oil to produce one gallon of renewable diesel.
b Prices are calculated using estimates from the Lusk paper, with 2025 as the baseline (reference) year when the
soybean oil market price was $0.49/lb and soybean meal market price was $291.34/ton. Business Insider, "Soybean
Oil Spot Price Chart," https://markets.businessinsider.com/commodities/soYbean-oil-price. and "Soybean Meal Spot
Price Chart," https://markets.businessinsider.com/commodities/sovbean-meal-price.
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The changes in the prices of soybean oil and soybean meal presented in Table 9.3-2 are
calculated considering only the increase in the quantity of soybean oil used for biofuel
production. This is consistent with the Lusk model, which modeled the impacts of increased
soybean oil demand for biofuel production assuming demand for other commodities used for
biofuel production remained constant. However, we project that to meet the volumes we are
finalizing for 2026 and 2027 in this rule we will see significant increases in the use of both
soybean oil and canola oil for biofuel production. This is an important consideration, as canola
oil is a relatively common substitute for soybean oil. Simultaneously increasing the demand for
both soybean oil and canola oil for biofuel demand could have an even greater impact on the
prices of soybean oil and soybean meal than if we were only increasing demand for soybean oil.
We were unable to find any estimates of the price impact of simultaneous increasing
demand for soybean oil and canola oil for biofuel production in the published literature. To
estimate the potential price impacts of this scenario we once again used the price responses from
the Lusk model. However, rather than considering only the increase in the use of soybean oil for
biofuel production for this scenario we also considered the increase in canola oil relative to the
2025 baseline. These larger quantities of vegetable oils used for biofuel production (3,720
million gallons in 2026 and 3,980 million gallons in 2027) result in larger projected increases in
the soybean oil prices and larger projected decreases in the soybean meal prices. These results
are summarized in Table 9.3-3.
Table 9.3-3: Projected Impact of the Analyzed Volumes on Soybean Oil and Meal Prices
Relative to the No RFS Baseline (High Price Sensitivit
ty)
Units
2026
2027
Soybean Oil Biofuel Increase
Relative to No RFS Baseline
million gallons
3,088
3,647
Projected Soybean Oil Price (High
Price Sensitivity)
$ per pound
$0.86
$0.90
Soybean Oil Price Increase Relative
to No RFS Baseline
$ per pound
$0.48
$0.57
Soybean Meal Price Change
Relative to No RFS Baseline
$ per ton
-$68
-$79
The results of the Lusk modeling, on which our price impacts for soybean oil and
soybean meal are based, are supported by empirical data. Analysis published by Irwin at the
University of Illinois indicates that soybean oil prices often move separately from meal and bean
prices, and that the latter two are closely correlated.667 In recent years soybean oil prices appear
to have increased significantly relative to soybean meal prices, as shown in Figure 9.3-3. From
2016 through the end of 2020, the value of soybean oil relative to soybean meal was relatively
stable, with soybean oil representing around 33% of the value of a soybean on average.668
Starting in 2021, the relative value of the soybean oil has increased significantly, averaging 47%
in the 21/22 crop year. Examining the $/bushel value contribution of the components, we see that
667 Irwin, Scott, "The Value of Soybean Oil in the Soybean Crush: Further Evidence on the Impact of the U.S.
Biodiesel Boom." farm doc daily (7): 169, September 14, 2017. https://farmdocdailv.illinois.edu/2017/09/the-value-
of-sovbean-oil-in-the-sovbean-crushhtml.
668 USD A, "Oil Crops Yearbook," March 2025. https://www.ers.usda.gov/data-products/oil-crops-vearbook.
331
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the oil value has more than doubled while the meal has increased by around 30%. These trends
suggest the value of the soybean is shifting more toward the oil than the meal in recent years.
The supply of soybean oil may be tightening relative to soybean meal, with rising soybean oil
prices exerting some downward pressure on soybean meal prices.
Figure 9.3-3: Relative Values of Soybean Oil and Soybean Meal
(HI
0% 0
2016 2017 2018 2019 2020 2021 2022
Oil share Meal share Oil value Meal value
In addition to the price impacts on corn, soybean oil, and soybean meal, we also
estimated price changes for other feed grains (grain sorghum, barley, and oats) and dried
distillers grains. We adjusted the prices of these commodities, as they historically compete with
corn in the feed market, and to a lesser extent for acreage. The price adjustments for grain
sorghum, barley, oats, and DDG are based on historical price relationships of these commodities
with corn. As with corn and soybean oil, we assumed that the prices in the USD A Agricultural
Projections to 2034 represent projected prices of the Analyzed Volumes and adjusted the
projected prices for these commodities lower in our price projections for the No RFS Baseline.
The projected impact of the Analyzed Volumes on sorghum, barley, oat, and DDG prices are
shown in Table 9.3-3.
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Table 9.3-3: Projected Impact of the Analyzed Volumes on Prices of Other Commodities
Relative to No RFS Baseline
2026
2027
Price Change Factor Relative to Corn Price Change3
Corn; $/bushel
$1.00
$1.00
Sorghum; $/bushel
$0.93
$0.93
Barley; $/bushel
$0.88
$0.88
Oats; $/bushel
$0.72
$0.72
Distillers Grains; $/Pound
$0.02
$0.02
Projected Price Impact Relative to No RFS Baseline
Corn; $/bushel
$0.03
$0.03
Sorghum; $/bushel
$0.02
$0.03
Barley; $/bushel
$0.02
$0.02
Oats; $/bushel
$0.02
$0.02
Distillers Grains; $/ton
$0.90
$0.99
a These factors were developed in conjunction with USDA in the 2012 evaluation of the use of the general waiver
authority. See "Methodology for Estimating Impacts on Food Expenditures, CPI for Food and CPI for All Items,"
Docket Item No. EPA-HQ-OAR-2012-0632-2546. https://www.regulations.gov/document/EPA-HO-OAR-2Q12-
0632-2546.
9.4 Food Prices
The above impact on commodity prices may in turn have a ripple impact on food prices
and the many other products produced from these commodities. Because the Analyzed Volumes
are projected to have a relatively small impact on the overall world commodity markets, and
since the cost of these commodities tends to be a relatively small component in the cost of food,
the projected impact of this rule on food prices is relatively modest. Further, we note that the
projected impact of the Analyzed Volumes on food prices does not represent a cost, but rather a
transfer, since higher food prices that result from higher commodity prices represent increased
income for feedstock producers (e.g., corn and soybean farmers).669
To project the impact of the Analyzed Volumes on food prices, we used a methodology
developed in conjunction with USDA in assessing requests from the governors of several states
to reduce the 2012 RFS Rule volumes using the general waiver authority.670 This methodology
generally uses estimates of the impact of biofuel volumes on commodity prices (e.g., corn,
soybean oil, etc.) to calculate the estimated impacts on total food expenditures. This estimated
change in food expenditures is then compared to total food expenditures. Finally, the ratio of the
estimated change in food expenditures to the total food expenditures is used to estimate the
change in food expenditures for the average consumer unit.
669 In other words, food price impacts represent the movement of money within society (from consumers of foods to
the producers of foods) as opposed to additional costs that society as a whole incurs. We note that while the CAA
specifically directs EPA to calculate the impacts on "food prices," as opposed to calculating the impact on the cost
to consumers of food. We acknowledge that these market interactions are affected by deadweight losses, but we
have not estimated the proportion of deadweight losses to transfers in this rule.
670 7 7 FR 70752 (November 27, 2012).
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In Chapter 9.3, we presented estimates of the impact of the Analyzed Volumes on
commodity prices relative to the No RFS Baseline. These estimates are the starting point for our
estimate of the impact of the RFS volumes on food prices. EPA used those price impacts in
combination with the projected use of commodities for food to project the impact of commodity
prices on total food expenditures, which are shown in Table 9.4-1. This analysis assumes
changes in commodity prices are fully passed on to consumers at the retail level and therefore
changes in total food expenditures may be estimated by multiplying the quantity of these
commodities used for food and feed. Feed use is included to capture the effects of the change in
the price of the commodity on livestock agriculture production costs, and ultimately the effects
on retail prices of foods produced from livestock.671
EPA recognizes that projecting that the price of distillers grains (DDG) increases
proportionally to the price of corn may overstate the impact of this rule on these commodities
and ultimately on food prices. It is possible increasing demand for biofuels may result in an over-
supply of DDG as a co-product of biofuel production. Thus, while biofuel production may
increase the prices of corn and food produced from corn, it may not increase the price of DDG.
This could mitigate the overall impact of this rule on food prices. There is not sufficient data to
project how increasing demand for corn for biofuel production would impact the price of DDG.
If the price for distillers grains increases less than the price of corn (or if it decreases) in response
to increased demand for biofuels, a smaller impact on food prices than what we have estimated
for the volumes could be expected.
This methodology assumes no response from consumers to changes in commodity prices
{i.e., perfect inelasticity of demand to changes in price) and therefore may overstate the change
in food expenditures. However, previous research suggests that demand for food is very inelastic
and therefore this methodology should provide a close approximation of the change in food
expenditures.672 Our estimates of the increase of food expenditures only reflect expenditures in
the U.S. Due to the integrated nature of agricultural commodity markets, the projected increases
in agricultural commodity prices may also impact food prices and expenditures globally. EPA
has not attempted to quantify these global impacts.
671 This methodology includes the expected price impact on all crops used as animal feed and does not account for
the livestock produced for the export market or imported meat or animal products.
672 USD A, "The Demand for Disaggregated Food-Away-From-Home and Food-at-Home Products in the United
States." Economic Research Report 139, August 2012.
https://ers.usda.gov/sites/default/files/ laserfiche/publications/45003/30438 errl39.pdf?v=69115.
334
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Table 9.4-1: Changes in Food Expenditures of the Analyzed Volumes Relative to the No
RFS Baseline
Year
Commodity
Commodity Price
Change
Quantity Used for Food and
Feed3
Change in
Expenditures
2026
Corn
$0.03 per Bushel
7,415 million Bushels
$187 million
Grain Sorghum
$0.02 per Bushel
110 million Bushels
$3 million
Barley
$0.02 per Bushel
157 million Bushels
$3 million
Oats
$0.02 per Bushel
125 million Bushels
$2 million
Soybean Oil
$0.28 per Pound
14,438 million Pounds
$4,100 million
Soybean Meal
-$39.88 per Short Ton
42,344 thousand Short Tons
-$1,689 million
DDG
$0.90 per Short Ton
47 million Short Tons
$42 million
Total
$2,649 million
2027
Corn
$0.03 per Bushel
7,463 million Bushels
$207 million
Grain Sorghum
$0.03 per Bushel
110 million Bushels
$3 million
Barley
$0.02 per Bushel
158 million Bushels
$4 million
Oats
$0.02 per Bushel
128 million Bushels
$3 million
Soybean Oil
$0.35 per Pound
14,488 million Pounds
$5,013 million
Soybean Meal
-$48.51 per Short Ton
42,950 thousand Short Tons
-$2,083 million
DDG
$0.99 per Short Ton
47 million Short Tons
$47 million
Total
$3,193 million
a Quantity used for food and feed was calculated from: USD A, "USD A Agricultural Projections to 2034," OCE-
2025-1, February 2025. https://doi.org/10.32747/2025.9Q15815.ers. In general, this quantity is the sum of Feed &
Residual & Food, and Seed & Industrial. For corn, we subtracted the quantity used for Ethanol & By-products from
this total. DDG was calculated based on the production of 17 pounds of DDG for every bushel of corn used to
produce ethanol. Finally, soybean oil is equal to the amount listed for food, feed & other industrial and soybean
meal is the total quantity of domestic disappearance.
Finally, we compared the estimated change in food expenditures to total food
expenditures as reported by the Bureau of Labor and Statistics in their 2024 survey.673
Specifically, we used 2024 values for the number of consumer units and average food
expenditures per consumer unit as the baseline, assuming no material change from 2024-2027;
accordingly, we held these inputs constant at their 2024 levels. We then applied the ratio of the
estimated change in total food expenditures to total food expenditures to estimate the
corresponding change in average food spending per consumer unit, as shown in Tables 9.4-2.
Table 9.4-2: Change in Food Expenditures per Consumer Unit of the Analyzed Volumes
Relative to No RFS Baseline
2026
2027
Number of Consumer Units (thousands)
135,760
135,760
Food Expenditures per Consumer Unit
$10,169
$10,169
Total Food Expenditures (millions)
$1,380,543
$1,380,543
Change in Food Expenditures (millions)
$2,649
$3,193
Percent Change in Food Expenditures
0.19%
0.23%
Projected Food Expenditure Increase
$19.51
$23.52
673 Bureau of Labor and Statistics, "Consumer Expenditures - 2024," USDL-25-1586, December 19, 2025.
https://www.bls. gov/news.release/pdf/cesan.r)df.
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Chapter 10: Estimated Fuel Costs and Fuel Price Impacts
The statute directs EPA to assess the impact of the use of renewable fuels on the cost to
consumers of transportation fuel and on the cost to transport goods in using the set authority.674
In this chapter, we assess the social costs of renewable fuels, the social costs of the petroleum
fuels which the biofuels replace, the fuel economy effect based on each fuel's energy density,
and the impacts of this rule on social costs, the costs to consumers of transportation fuel, and the
costs to transport goods. Other nonfuel costs and price impacts attributed to the RFS program are
estimated elsewhere.
We are establishing RFS volume requirements for 2026 and 2027 after we have evaluated
different possible volume requirements for the proposed rule. In this chapter, we present our
assessment of costs for the Analyzed Volumes relative to the No RFS Baseline, as well as
incremental to the 2025 Baseline. In projecting the costs and fuel price impacts in this chapter,
we rely on AEO2025, which was the most recently published version at the time the analyses
were conducted, and the most recent agricultural projections.
The Analyzed Volumes are projected to cause a large increase in the consumption of
domestic vegetable oil and animal fats used by producers of BBD to generate credits under the
RFS program. This will likely pose a challenge for the agricultural sector to provide the required
volumes of these domestically sourced feedstocks. Thus, we expect to experience price increases
for these feedstocks. We project higher prices for the BBD and its feedstocks in response to this
likely increase in demand and have conducted sensitivity analyses at higher and lower vegetable
oil prices to bound these projections.
The estimated costs for the Analyzed Volumes at the different price sensitivities are all
summarized in this chapter. Chapter 10.1 provides estimates of the renewable fuel costs,
including feedstock costs, renewable fuel production, blending, fuel economy and distribution
costs. Chapter 10.2 contains estimates of petroleum production and distribution costs. Chapter
10.4 contains subsections that summarize the changes in renewable fuel and petroleum volumes
relative to the No RFS and 2025 Baselines, as well as the estimated total and per-gallon costs
associated with the change in fuel volumes.675 In all cases, costs are reported in 2024 dollars.
10.1 Renewable Fuel Costs
10.1.1 Feedstock Costs
For most renewable fuels, the feedstock costs are a primary contributing factor to the cost
to produce and use the renewable fuels. We first describe our estimates of changes in the
production cost for these feedstocks and then provide information describing our estimates for
the production, distribution and blending costs for the various renewable fuels.
674 CAA section 21 l(o)(2)(B)(ii)(V).
675 The calculation of the cost estimates for the Analyzed Volumes relative to the No RFS and 2025 Baselines can be
found in "Estimated Fuel Costs for Set 2 Final Rule," available in the docket for this action.
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For estimating feedstock costs, we used projections of feedstock prices for 2026 and 2027
from multiple sources, including EIA and USDA.676 We also adjusted our estimates to account
for differences between these projections. Crude oil prices affect the cost of growing renewable
fuel feedstocks, the cost of transporting them to the renewable fuel production plants, the cost of
transporting the produced renewable fuel from the plant to market, and may also impact the cost
of producing the renewable fuel. Because USDA agricultural price projections were based on
lower crude oil price projections than those by EIA, the USDA agricultural price projections may
have underestimated the agricultural prices that would be consistent with the EIA petroleum
price projections. Therefore, we adjusted the USDA price projections for both corn and soybean
oil to align these inputs to our cost analysis to common crude oil price assumptions. We also
adjusted the projected nominal prices to constant year 2024 dollars.
10.1.1.1 Corn and Corn Ethanol Plant Byproducts
The price of corn is the most important input to estimating the cost of corn ethanol. Table
10.1.1.1-1 shows the derivation of the corn prices used in this cost analysis, which adjusts the
projected crop prices for crude oil price differences and for inflation. To help to explain the
derivation in the discussion below, we refer to the relevant row number in Table 10.1.1.1-1.
As a starting point we used future corn price projections from USDA. We started with the
2026 to 2027 USDA projected corn prices (row #1).677 However, the USDA corn prices are
reported in nominal dollars, reflecting the inflated value of the dollars in those years. The first
adjustment we made was to convert those USDA corn prices reported in nominal dollars into the
2024 dollars used across this cost analysis (row #2).678
Next, we made an adjustment to account for the different crude oil price projections that
USDA used (row #3) compared to those projected by EIA (row #5) because crude oil prices
affect agricultural commodity prices.679 680 Because EIA is the U.S. reference organization for
projecting petroleum prices, we adjusted the USDA inflation-adjusted corn prices to put them on
the same basis with the petroleum costs which are based on EIA crude oil prices. To do so, we
first adjusted the crude oil prices used by USDA (row #3) to 2024 dollars (row #4). Then we
used a regression of corn prices and crude oil prices to estimate the corn prices at USDA crude
oil prices adjusted to 2024 dollars (row #6) and the corn prices at the EIA crude oil prices (row
#6), to enable an adjustment of USDA corn prices to be consistent with the EIA crude oil prices.
676 USDA, "USDA Agricultural Projections to 2034," OCE-2025-1, February 2025.
https://ers.usda.gov/sites/default/files/ laserfiche/outlooks/110966/QCE-2025-1.pdf.
677 Id.
678 USDA reports estimated future inflation rates that are used for adjusting nominal dollar values to 2024$.
679 There seems to be an association between the renewable fuel feedstock costs and crude oil prices (regression
analysis reveals an R-squared of 0.55 for corn and crude oil). Since USDA estimated renewable fuel feedstock
prices based on lower crude oil prices, adjusting their renewable fuel feedstock prices higher to be consistent with
EIA crude oil prices better syncs the two price projections and leads to a better estimate of costs.
6811 Camp, Kevin M„ "The relationship between crude oil prices and export prices of major agricultural
commodities," Global Economy, Bureau of Labor Statistics, April 2019. https://www.bls.gov/opub/btn/volume-
8/pdf/the-relationship-between-crude-oil-and-export-prices-of-maior-agricultural-commodities.pdf.
337
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The regression of corn prices and crude oil prices is based on monthly corn prices between
January 2012 and November 2024, which yielded the following equation:681-682-683
Corn Price ($/bushel) = Crude Oil Price ($/bbl) x 0.0445 + 1.54
The corn prices estimated by this regression were not used directly for the cost analysis.
We made this choice because we observe that farmers are more efficient at producing corn today
than in the past, as evidenced by improving corn yields over time.684 As corn productivity
continues to increase in 2026 and 2027, production of corn is likely to be on a different
supply/demand point on the corn price curve relative to 2024 and prior years. Instead of using
the regressed corn prices directly, the difference in regressed corn prices (row #8) was added to
the USDA projected corn prices adjusted to 2024 dollars (row #2) to derive the final adjusted
corn prices (row #9) subsequently used as an input value for estimating corn ethanol costs as
shown in Table 10.1.1.1-1.
Table 10.1.1.1-1: Derivation of Corn Feedstock Production Costs ($/bushel for corn, $/bbl
for Crude Oil) i
Row #
2026
2027
Corn Prices
USDA Nominal $
1
3.90
4.00
USDA 2024$
2
3.75
3.76
Crude Oil
Prices
USDA Nominal $
3
71.6
70.2
USDA 2024$
4
68.8
66.1
EIA 2024$
5
77.6
76.6
Regressed
Corn Prices
Based on USDA 2024$
6
4.60
4.48
Based on EIA 2024$
7
4.99
4.95
Corn Prices
Difference in Regressed Corn Prices EIA - USDA
8
+0.39
+0.47
Corn Prices
Adjusted USDA 2024$
9
4.14
4.23
Both the inflation and crude oil price adjustment are modest, and their effects cause
offsetting effects. Regardless, we believe these corrections improve the rigor of our estimates.
Since corn ethanol plants also produce byproducts which can be sold for additional value,
we also estimated the prices for those byproducts, specifically DDGS and corn oil, which is
estimated in Chapter 10.1.1.2. Since USDA does not estimate future prices for DDGS, these
were obtained by agricultural price projections made by the University of Missouri, Food and
Agricultural Policy Research Institute (FAPRI).685 The FAPRI DDGS projected prices are
681 We chose the years from 2012-2024 because of the wide range in crude oil and corn prices that existed over this
time period.
682 USDA, "Corn Prices Received by Fanners," Quick Stats, 2024.
https://quickstats.nass.usda.gov/results/3538FFA4-F207-383E-A9CF-09AlF14Q8C77.
683 EIA, "U.S. Crude Oil Composite Acquisition Cost by Refiners," Petroleum & Other Liquids, May 1, 2025.
https://www.eia. gov/dnav/pet/hist/LeafHandler.ashx?n=PET&s=R0000 3&f=a.
684 USDA, "Corn: Yield by Year, US," January 12, 2026.
https://www.nass.usda.gov/Charts and Maps/Field Crops/cornvld.php.
685 FAPRI, "2025 U.S. Agricultural Market Outlook," FAPRI-MU Report #01-25, April 2025.
https://fapri.missouri.edu/wp-content/uploads/2025/04/2025-Baseline-Outlook.pdf.
338
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reported in nominal dollars, so we adjusted the price projections to 2024 dollars. Table 10.1.1.1-
2 summarizes DDGS prices used in the cost analysis.
Table 10.1.]
1.1-2: DD<
jS Prices
2026
2027
Nominal
$156.2
$156.7
2024$
$150.0
$147.5
10.1.1.2 Soybean Oil, Corn Oil, and Fats, Oils, and Greases Prices
Soybean oil, FOG, corn oil, and canola oil were identified in Chapter 2 as the feedstocks
for producing virtually all biodiesel and renewable diesel fuels expected to contribute to the
Analyzed Volumes for 2026 and 2027. For the cost analysis, canola oil volumes are combined
with soybean oil volumes to estimate a single volume of virgin vegetable oil feedstock price
estimate; we refer to this solely as soybean oil in the remainder of this analysis.
Different sets of prices were estimated and used depending on the cost scenario being
modeled. Table 10.1.1.2-1 summarizes the categories of prices that we modelled.
Table 10.1.1.2-1: Categories of Prices used in Modeling Costs
Case
Baseline
Control Cases
No RFS
2025
Principal Case
USDA B AU
2025 Average
High Prices
High Price Case
USDA B AU
2025 Average
Very High Prices
Low Price Case
USDA B AU
2025 Average
Low Prices - Same as Baseline
Because both soybean and canola have similar levels of hydrogen unsaturation, it is
reasonable to assume that canola and soybean oils would have similar production costs.686
Soybean oil price projections made by USD A are used as a starting point for our baseline cost
analysis.687 We followed the same methodology we used for soybean oil prices as described
above for corn prices; this process is summarized in Table 11.1.1.2-2 and the description that
follows references the rows in that Table to aid in understanding. The first step required
converting USD A projected soybean oil prices in nominal dollars (row #1), converting to 2024
dollars (row #2), and adjusting for the differences in crude oil prices (row #4 for USD A in 2024
dollars) and EIA (row #5). When adjusting for the differences in crude oil prices, a regression of
686 Kim, Juyoung, Deok Nyun Kim, Sung Ho Lee, Sang-Ho Yoo, and Suyong Lee. "Correlation of Fatty Acid
Composition of Vegetable Oils With Rheological Behaviour and Oil Uptake." Food Chemistry 118, no. 2 (May 14,
2009): 398-402. https://doi.Org/10.1016/i.foodchem.2009.05.011.
687 USD A, "USDA Agricultural Projections to 2034," OCE-2025-1, February 2025.
https://www.usda.gov/sites/default/files/documents/USDA-Agricultural-Proiections-to-2033.pdf.
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monthly soybean oil and crude oil prices between January 2012 and November 2024 yielded the
following equation:688-689-690
Soybean Oil Price ($/lb) = Crude Oil Price ($/bbl) x 0.259 + 19.06
The soybean oil prices (row #6) based on USD A crude oil prices and the soybean oil
prices (row #7) based on EIA crude oil prices were not used in the cost analysis directly. Rather
the difference in regressed soybean oil prices (row #8) was added to the adjusted USD A soybean
prices (row #2) to derive the adjusted soybean oil prices (row #9).
Table 10.1.1.2-2: Derivation of Soybean Oil Feedstock Production Costs (cents/pound for
soybean oil, $/bbl for crude oil)
Row #
2026
2027
Soybean Oil Prices
USDA Nominal $
1
0.40
0.38
USDA 2024$
2
0.38
0.36
Crude Oil Prices
USDA Nominal $
3
71.6
70.2
USDA 2024$
4
68.8
66.1
EIA 2024$
5
77.6
76.6
Regressed
Soybean Oil Prices
Based on USDA 2024$
6
0.421
0.410
Based on EIA 2024$
7
0.454
0.451
Soybean Oil Prices
Difference in Regressed
Soybean Oil Prices EIA - USDA
8
+0.034
+0.041
Soybean Oil Prices
Adjusted USDA 2024$
9
0.42
0.40
Neither USDA nor FAPRI project future corn oil or FOG prices. Instead, future prices for
these oils were estimated based on the historical differences between them and soybean oil's spot
prices.691 Corn oil and FOG spot prices were compared to soybean oil spot prices between
January 2016 and December 2022. Over that time period, soybean oil averaged about 400 per
pound, ranging from 29-680 per pound. Corn oil and FOG prices were compared to soybean oil
prices, and these were found on average to be priced at 82.7% and 75.4% of soybean oil price
respectively. A good reason why corn oil and FOG tend to be priced lower is because they are
waste oils requiring some processing before they can be feedstocks for biodiesel and renewable
diesel production facilities. These percentages were then applied to the soybean oil prices used in
this cost analysis to derive the prices for FOG and corn oil used in this cost analysis.
688 There seems to be an association between the renewable fuel feedstock costs and crude oil prices (regression
analysis reveals an R-squared of 0.73 for soybean oil and crude oil). Since USDA estimated renewable fuel
feedstock prices based on lower crude oil prices, adjusting their renewable fuel feedstock prices higher to be
consistent with EIA crude oil prices better synchronizes the two price projections and leads to a better estimate of
costs.
689 Federal Reserve Economic Data, "Global price of Soybeans Oil," May 13, 2025.
https://fred.stlouisfed.org/series/PSOILUSDM.
6911 EIA, "U.S. Crude Oil Composite Acquisition Cost by Refiners," Petroleum & Other Liquids, May 1, 2025.
https://www.eia. gov/dnav/pet/hist/LeafHandler.ashx?n=PET&s=R00Q0 3&f=a.
691 USDA, "Oil Crops Yearbook," March 2025. https://www.ers.usda.gov/data-products/oil-crops-Yearbook.
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It was necessary to derive the vegetable prices for the 2025 baseline and control cases
which are based on high and very high vegetable oil prices. Chapter 9.3 describes the
methodology used and lists the estimated prices for soybean oil prices.
The projected soybean oil, FOG, and corn oil prices used for the baseline cost analysis
are summarized in Table 10.1.1.2-3. Similar to the estimate of USD A baseline estimate for
vegetable oil prices, the relative lower prices for corn oil and FOG are maintained for all the
price estimates.
Table 1C
1.1.1.2-3: Projected Vegetable Oil Prices (2024$/lb)
Year
Case
Feedstock
Baseline
Control Case
No RFS
2025
2026
Principal Case
Soybean Oil
0.42
0.49
0.66
Corn Oil
0.35
0.41
0.55
FOG
0.32
0.37
0.50
High Price Case
Soybean Oil
Same as
Principal
Baseline Case
Same as
Principal
Baseline Case
0.86
Corn Oil
0.71
FOG
0.65
Low Price Case
Soybean Oil
Same as
Principal
Baseline Case
Same as
Principal
Baseline Case
Baseline price
Corn Oil
Baseline price
FOG
Baseline price
2027
Principal Case
Soybean Oil
0.40
0.49
0.68
Corn Oil
0.33
0.41
0.56
FOG
0.30
0.37
0.51
High Price Case
Soybean Oil
Same as
Principal
Baseline Case
Same as
Principal
Baseline Case
0.90
Corn Oil
0.74
FOG
0.68
Low Price Case
Soybean Oil
Same as
Principal
Baseline Case
Same as
Principal
Baseline Case
Baseline Price
Corn Oil
Baseline Price
FOG
Baseline Price
10.1.1.3 Biogas
For this analysis we assume that biogas is produced at landfills and collected to prevent
the release of methane gas as required by regulation, and then flared, burned to produce
electricity, or upgraded for use as natural gas. Since biogas is a waste gas from existing landfills,
we assumed no feedstock cost for biogas. The cost of the necessary steps to collect, purify, and
distribute biogas are all discussed under the sections discussing fuel production and distribution
costs.
10.1.2 Renewable Fuels Production Costs
This section assesses the production costs of renewable fuels, including the feedstock
costs described above as well as fixed capital costs, fixed operating costs, and variable utility and
fuel operating costs. We generally express the production costs on a per-gallon basis for the
renewable fuels being produced. The one exception is biogas which is reported on a per-million
341
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British thermal units (Btu) basis and also on a per ethanol-equivalent volume basis. The detailed
cost summaries presented for each renewable fuel in this section are based on projected cost
inputs for the year 2026.692
10.1.2.1 Cost Factors
10.1.2.1.1 Fixed Capital and Operating Costs
For this production cost analysis, we developed estimates of the capital costs and other
fixed costs of biofuel production, which were then applied to the production volume of
renewable fuels. We estimated these fixed costs in two parts. First, we used specific economic
assumptions to develop capital cost amortization factors. These assumptions are summarized in
Table 10.1.2.1.1-1. These capital cost amortization factors are used in the following section for
converting the one-time, total capital cost of a biofuel-related facility to an equivalent per-gallon
cost.693 The resulting 0.11 capital cost amortization factor is the same factor used by EPA in the
cost estimation calculations made for other rulemakings and technical papers 694-695-696-697-698
Table 10.1.2.1.1-1: Economic Cost Factors Used in Calculating Capital Amortization
Factors
Resulting
Economic
Federal and
Return on
Capital
Amortization
Depreciation
and Project
State Tax
Investment
Amortization
Scheme
Life
Life
Rate
(ROI)
Factor
Societal Cost
10 Years
15 Years
0%
7%
0.11
Capital costs were adjusted to 2024 dollars for this analysis. The Chemical Engineering
Plant Index (CEPI) capital cost index was used to adjust capital costs to 2024 dollars.699
Consistent with the increased inflation observed over recent years, the CEPI capital cost index
for 2024 represents a large increase in capital costs when adjusting capital costs to the year 2024.
Second, we estimated the fixed operating costs of biofuel production; these include
692 All the costs summarized in this chapter are calculated in "Estimated Fuel Costs for Set 2 Final Rule," available
in the docket for this action.
693 The capital amortization factor is applied to the aggregate capital cost to create an amortized annual capital cost
that occurs each year for the 15 years of the economic and project life of the unit. The depreciation rate of 10% and
economic and project life of 15 years are typical for these types of calculations. The 7% return on investment and
the zeroing out of Federal and State taxes is specified by OMB Circular A-4 for these calculations.
694 EPA, "Regulatory Impact Analysis - Control of Air Pollution from New Motor Vehicles: Tier 2 Motor Vehicle
Emissions Standards and Gasoline Sulfur Control Requirements," EPA-420-R-99-023, December 1999;.
695 EPA, "Technical Support Document for the Heavy-Duty Engine and Vehicle Standards and Highway Diesel Fuel
Sulfur Control Requirements: Air Quality Modeling Analyses," EPA-420-R-00-028, December 2000.
696 Wyborny, Lester. "Cost Estimates of Long-Term Options for Addressing Boutique Fuels," EPA, October 22,
2001."
697 EPA, "Final Regulatory Analysis - Control of Emissions from Nonroad Diesel Engines," EPA-420-R-04-007,
May 2004.
698 RFS2 Rule RIA.
699 Chemical Engineering, "The Chemical Engineering Plant Cost Index," https://www.chemengonline.com/pci-
home.
342
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maintenance costs, insurance costs, rent, laboratory charges and miscellaneous chemical
supplies.700 Maintenance costs can range from 1% to 8% for industrial processes.701 We
estimated the aggregated annual fixed operating costs to be 5.5% of the capital costs for all
renewable fuels production facilities.
10.1.2.1.2 Variable Utility and Fuel Operating Costs
Utility and fuel inputs are variable operating costs incurred to run the renewable fuel
production plants on a day-to-day basis and are based on the unit throughput. The most obvious
of the variable costs are utilities (electricity, natural gas, and water) which are required to operate
the renewable fuels plants. Natural gas is consumed for heating process streams, including
feedstocks which must be heated prior to being sent to reactors and distillation columns for
separating coproducts. Electricity is necessary to run pumps, compressors, plant controls and
other plant operations. Water can be necessary as part of the process (reaction medium) or used
in heat exchangers and cooling towers.
Projected electricity and natural gas prices are based on national average values from
AEO2025. The cost of process water is generally quite minimal, but a cost is estimated for it
nonetheless since renewable fuel technologies can use fairly large quantities.702 703 The utility
costs used for the cost analysis are summarized in Table 10.1.2.1.2-1.
Table 10.1.2.1.2-1: Summary of Utility Cost Factors (2024$)
Year
Natural Gas
($/MMBtu)
Electricity
(fif/kWh)
Water
(S/1000 gals)
2026
4.29
7.98
3.0
2027
4.45
8.08
3.0
10.1.2.2 Corn Ethanol Production Costs
Most corn ethanol plant input and output information used in our analysis were based on
a 2019 survey of corn ethanol plants.704 However, some plant information was sourced from an
older analysis.705 Coproduct output levels were based on newer analysis of 2022 data. Corn
ethanol plants have become more efficient over time; however, these improvements require
71111 Peters, Klaus D., Max S. Timmerhaus, and Ronald E. West. Plant Design and Economics for Chemical
Engineers. 5th ed. McGraw Hill, 2003.
7111 McNair, Sam. "Budgeting for Maintenance: A Behavior-Based Approach," Life Cycle Engineering, 2011.
https://www.wethegoverned.eom/wp-content/uploads/2019/07/110912-Life-CYcle-Engineering-budgeting-
maintenance.pdf.
7112 Haas, Michael J, Andrew J McAloon, Winnie C Yee, and Thomas A Foglia. "A Process Model to Estimate
Biodiesel Production Costs." Bioresource Technology 97, no. 4 (June 3, 2005): 671-78.
https://doi.Org/10.1016/i.biortech.2005.03.039.
7113 DOE, "Water and Wastewater Annual Escalation Rates for Selected Cities across the United States," September
2017. https://doi.org/10.2172/1413878.
7114 Lee, Uisung, Hoyoung Kwon, May Wu, and Michael Wang. "Retrospective Analysis of the U.S. Corn Ethanol
Industry for 2005-2019: Implications for Greenhouse Gas Emission Reductions." Biofuels Bioproducts and
Biorefining 15, no. 5 (May 4, 2021): 1318-31. https://doi.org/10.1002/bbb.2225.
7115 Mueller, Steffen. "2012 Corn Ethanol: Emerging Plant Energy and Enviromnental Technologies." April 29,
2013.
343
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increased capital and maintenance costs which largely offset the efficiency cost savings. Thus,
the estimated corn ethanol costs are fairly stable over time.
Capital costs were based on a review of corn ethanol construction costs for a 100 million
gallon per year dry mill corn ethanol plant in 2016. For this analysis the capital costs were scaled
to the U.S. average sized corn ethanol plant with a nameplate capacity of 85 million gallons per
year and assumed to operate at 90% of nameplate capacity, therefore producing 76 million
gallons of ethanol per year.706 Since the capital cost is based on the total construction cost of
already constructed corn ethanol plants, no contingency cost factors are applied to the capital
costs. Corn prices are farm gate prices and a transportation spreadsheet was used to estimate an
average cost of 60 per bushel to transport the corn to corn ethanol plants.707 Of the dry mill corn
ethanol plants in the 2012 survey, 74% were separating and selling corn oil; however, we believe
that by now all corn ethanol plants are separating and selling corn oil due to the increased
demand for vegetable oil for producing biofuel.
The quantity of dried distillers grain with solubles (DDGS) produced by corn ethanol
plants was estimated from USDA DDGS production data from February to October 2022. USDA
reports DDGS production for four different categories of DDGS: dried distillers grain (DDG),
dried distillers grain with solubles (DDGS), distillers wet grain (DWG) with 65% or more
moisture, and distillers wet grain (DWG) with 40-64% moisture.708 The production quantity of
DWG is adjusted to an equivalent of dried DDG. The DWG with 65% or more moisture is
assumed to have 75% moisture, while the DWG with 40-65% moisture is assumed to have 52%
moisture. Both wet distiller grain categories are adjusted to dry distiller grain quantities assuming
that dried distiller grains contain 11% moisture.709 Table 10.1.2.2-1 summarizes and averages the
quantity of distiller grains by category, reporting the quantity of wet distiller grains both before
and after adjusting them to equivalent dry grains amounts.
7116 Irwin, Scott. "Weekly Output: Ethanol Plants Remain Barely Profitable," Successful Farming, March 16, 2018.
https://www.agriculture.com/news/business/weeklv-outlook-ethanol-plants-remain-barelv-profitable.
7117 Edwards, William. "Estimating Grain Transportation Costs." .1" Decision Maker File A3-41, August 2017.
https://www.extension.iastate.edu/agdm/crops/litml/a3-41.html.
7118 USDA, "Grain Crushings and Co-Products Production Monthly," February 2, 2026.
https://esmis.nal.usda.gov/sites/default/release-files/795755/cagc0226.pdf.
7119 Shurson, Jerry. "DDGS present handling and storage considerations," National Hog Farmer. May 29, 2019.
https://www.nationalhogfanner.com/liog-nutrition/ddgs-present-handling-and-storage-considerations.
344
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Table 10.1.2.2-1: USDA-Reported DDG (tons) and Corn Ethanol (million gallons)
Production for a Portion of 2022
February
March
April
May
June
July
August
September
October
Average
DWG 65%+
Wet
1,293,312
1,382,790
1,321,275
1,328,402
1,283,359
1,279,210
1,322,744
1,249,996
1,397,867
-
DWG 40-65
Wet
492,839
562,599
517,270
468,772
494,792
495,386
544,168
498,142
490,060
-
DWG 65%+
Dry
363,290
388,424
371,145
373,147
360,494
359,329
371,557
351,122
392,659
370,130
DWG 40-65
Dry
287,951
328,710
302,225
273,889
289,092
289,439
317,941
291,049
286,327
296,291
DDG
303,788
372,813
328,691
322,855
346,591
334,122
335,885
281,984
388,993
335,080
DDGS
1,693,253
1,877,338
1,704,698
1,896,665
1,918,611
1,934, 355
1,867,735
1,613,088
1,745,419
1,805,685
Total
DDG/DDGS
2,626,132
2,626,132
2,626,132
2,626,132
2,626,132
2,626,132
2,626,132
2,626,132
2,626,132
2,626,132
Ethanol
Volume
1,189
1,327
1,217
1,315
1,314
1,322
1,280
1,139
1,321
1,269
Pounds
DDGS/gal
ethanol
4.42
4.43
4.41
4.33
4.40
4.38
4.48
4.42
4.23
4.39
After averaging the production volume of each grain type over the 9 months, they are
totaled and divided by the average ethanol production volume. This analysis estimates DDGS
production to be 4.4 pounds per gallon of ethanol produced (12.5 pounds per bushel of corn).
Corn oil production from ethanol plants was estimated using a similar analysis as that
conducted for DDGS. The corn oil production by month is summarized and averaged in Table
10.1.2.2.-2.
Table 10.1.2.2-2: USDA-Reported Corn Oil Production for a Portion of 2022 (tons)
February
March
April
May
June
July
August
September
October
Average
Corn Oil
154,933
174,657
163,024
177,158
184,350
187,853
180,062
159,873
186,770
174,298
To estimate the corn oil production from corn ethanol plants, the average corn oil
production is divided by the average corn ethanol production volume summarized in Table
10.1.2.2-1. Based on this analysis, corn oil production from corn ethanol plants is estimated to be
0.27 lbs per gallon of ethanol (0.79 pounds per bushel of corn).
Table 10.1.2.2-3 contains the plant demand and outputs and capital costs for corn ethanol
plants and provides an estimate of the estimated corn ethanol production cost for year 2023.
345
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Table 10.1.2.2-3: Corn Ethanol Plant Demands, Production Levels, and Capital Costs for
2026 (2024$)
Category of Plant
Plant
Cost
Cost
Input/Output
Inputs/Outputs
Cost per Input
(MM$)
($/gal)
Ethanol Yield
2.86 gal/bushel
$4.20/bushel
112
1.47
DDG Yield
4.4 lb/gal
$150/ton
-25.1
-0.33
Corn Oil Yield
0.27 lb/gal
34.6/lb
-7.1
-0.09
C02 Yield
1 lb/gal
$12/ton
Thermal Demand
22,480 Btu/gal
$4.21/MMBtu
7.0
0.09
Electricity Demand
0.63 kWh/gal
8.370/kwh
4.0
0.05
Water Use
2.7 gal/gal
$3/1000 gals
0.6
0.01
Labor Cost
$0.07/gal
-
5.3
0.07
Capital Cost (2024$,
76 million Gals/Yr)
$3.27/gal Plant
Capital Cost
31.9
0.42
Annual Fixed Cost
5.5% of Total
Capital Cost
16.0
0.21
Denaturant
2 vol%
0.5
0.01
Total Cost
143
1.88
The projected corn ethanol production cost for an 85-million-gallon capacity ethanol
plant producing 76 million gallons per year of ethanol is $1.88 per gallon of denatured ethanol
for 2026 and $1.92 per gallon for 2027. For a basis of comparison, these corn ethanol wholesale
cost estimates are slightly higher than the AEO2025 projections of $1.81/gallon and $1.84/gallon
for 2026 and 2027, respectively.
10.1.2.3 Biodiesel Production Costs
Biodiesel production costs for this rule were estimated using an ASPEN cost model
developed by USDA for a 38 million gallon-per-year (MGY) transesterification biodiesel plant
processing degummed soybean oil as feedstock. Details on the model are given in a 2006
technical publication by Haas.710'711 Although dated, this model likely still provides
representative cost estimates because the process is fairly simple and unlikely to have changed
over time, and consequently its cost are likely to be fairly stable over time as well. Furthermore,
the biodiesel costs are primarily (>80%) determined by the feedstock prices.
The biodiesel process comprises three separate subprocesses:
1. Transesterification to produce fatty acid methyl esters (biodiesel) and coproduct
glycerol (glycerine);
2. Biodiesel purification to meet biodiesel purity specifications; and
3. Glycerol recovery.712
710 Haas, M.J, A process model to estimate biodiesel production costs, Bioresource Technology 97 (2006) 671-678.
711 Since 2006 when the HAAS biodiesel plant survey was conducted, biodiesel plants may have achieved improved
energy efficiency, but also experienced increased costs to improve product quality and expand the quality of
feedstocks they can process.
712 Haas, M.J, A process model to estimate biodiesel production costs, Bioresource Technology 97 (2006) 671-678.
346
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For the transesterification process modeled by Haas, soybean oil is continuously fed
along with methanol and a catalyst sodium methoxide to a stirred tank reactor heated to 60 °C.
After a residence time of 1 hour, the contents exit the reactor and the glycerol is separated using
a centrifuge and sent to a glycerol recovery unit. The methyl ester stream, which contains
unreacted methanol and catalyst, is sent to a second reactor along with additional methanol and
catalyst. Again, the reactants reside in the second stirred tank reactor for 1 hour heated to 60 °C.
The products from the second reactor are fed to a centrifuge which again separates the glycerol
from the other reactants. The reaction efficiency is assumed to be 90% in each reactor, consistent
with published reports, resulting in 99% combined conversion across both reactors.
The methyl ester is purified by washing with mildly acidic (4.5 pH) water to neutralize
the catalyst and convert any soaps (sodium or potassium carboxylic acids) to free fatty acids. The
solution is then centrifuged to separate the biodiesel from the aqueous phase. The remaining
water in the biodiesel is removed by a vacuum dryer to a maximum 0.05% of water by volume.
The glycerol can have a high value if it can be purified to U.S. Pharmacopia (USP) grade
to enable using this material for food or medicine. However, this purification process is
expensive. Most biodiesel plants create a crude glycerol (glycerine) grade, which is 80%
glycerol, and sell the crude glycerol for further refining by others. To create the crude glycerol,
the various glycerol streams are combined and treated with hydrochloric acid to convert the
soaps to free acids, allowing removal by centrifugation and sending to waste. The glycerol
stream is then neutralized (pH brought back up to neutral) with caustic soda. Methanol is
recovered from this stream by distillation and the methanol is recycled back into the process. The
glycerol stream is distilled to remove it from the remaining water, which is recycled back into
the process. The glycerol is now at least 80% pure, adequate to sell as crude glycerol.
We made a series of adjustments to the Haas model output. The capital cost is adjusted
from 2006 dollars to 2024 dollars using a ratio of the capital cost index from the Chemical
Engineering Cost Index. This adjustment increased installed capital cost from $11.4 million per
38 MGY plant to $18.2 million per plant. Fixed operating costs are estimated to comprise 5.5%
of the plant cost. Prices were found for methanol,713 sodium methoxide,714 hydrochloric acid,715
sodium hydroxide,716 and glycerine.717 718 The value of methanol is from a Methanex report, plus
150 added on for distribution costs.719
713 Methanex, "Methanex Methanol Price Sheet," January 31, 2023. https://www.methanex.com/about-
methanol/pricing.
714 Global Sources, "Sodium Methoxide Bulk Price," June 2025. https://www.globalsources.com/cliina-
suppliers/methoxy.htm.
715 Business AnalytiQ, "Hydrochloric Acid price index," June 2025.
https://businessanalvtiq.com/procurementanalvtics/index/hvdrocliloric-acid-price-index.
716 Federal Reserve Economic Data, "Producer Price Index by Commodity: Chemicals and Allied Products:
Chlorine, Sodium Hydroxide, and Other Alkalies," May 2025. https://fred.stlouisfed.org/series/WPU06130302.
717 Alibaba, "Competitive Price 99.7% Refined Food/USP/Industry Grade Glycerol Glycerine," February 2023.
https://www.alibaba.com/product-detail/Competitive-Price-80-99-7-Refined 1600713799582.html.
718 Irwin, Scott. "2021 Was a Devastating Year for Biodiesel Production Profits." farmdoc daily (12):21, February
16, 2022. https://fanndocdailv.illinois.edu/2022/02/2021-was-a-devastating-vear-for-biodiesel-production-
profits.html.
719 Methanex, "Current Posted Prices," January 31, 2023. https://www.methanex.com/about-methanol/pricing.
347
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The value of the glycerin co-product has been volatile due to a large increase in
production in biodiesel facilities that has been balanced at times by new uses. Glycerine has
traditionally been used for petrochemical-based products, but there is increased demand in
personal care and other consumer products as the standard of living increases in many parts of
the world. Some facilities are even experimenting with using it as a supplemental fuel.720 We can
expect that new uses for glycerin will continue to be found as long as it is plentiful and
sufficiently cheap. We use recent cost information of about 320 per pound for glycerin.721
Table 10.1.2.3-1 also shows the production cost allocation for the soybean oil-to-
biodiesel facility. Production cost for biodiesel is primarily a function of feedstock price, with
other process inputs, facility, labor, and energy comprising much smaller fractions.
Table 10.1.2.3-1: Soybean-Biodiesel Production Cost for 2026 (2024$)
Cost
Unit Demands
Cost per Unit
(thousands)
Cost ($/gal)
Soybean Oil Feed
76,875 (1000 lb)
660/lb722
50,738
5.07
Methanol
7422 (1000 lb)
$2.54/gal
2932
0.29
Sodium Methoxide
927 (1000 lb)
$250/ton
116
0.012
Hydrochloric Acid
529 (1000 lb)
$250/MT
60.1
0.006
Sodium Hydroxide
369 (1000 lb)
$200/ton
36.9
0.004
Water
2478 (1000 lb)
$3/1000 gals
1.2
0.00
Glycerine
9000 (1000 lb)
320/lb
(2880)
(0.29)
Natural Gas
66.9 million SCF
4.212 $/MMBtu
282
0.028
Electricity
1008 kW
8.37 0/kWh
739
0.074
Labor
0.05
Capital Cost 2006$
11.35 ($million)
-
-
-
Capital Cost 2024$
18.54 ($million)
1,999
0.20
Fixed Cost
5.5%
1.000
0.10
Total Cost
55,022
5.55
As shown in Table 10.1.2.3-1, biodiesel produced from soybean oil is estimated to cost
$5.55 per gallon in 2026. The estimated biodiesel production cost for all vegetable oil types and
for both years is summarized in Table 10.1.2.3-2.
7211 Yang, Fangxia, Milford A Hanna, and Runcang Sun. "Value-added Uses for Crude Glycerol~a Byproduct of
Biodiesel Production." Biotechnology for Biofuels 5, no. 1 (March 14, 2012). https://doi.org/10.1186/1754-6834-5-
13.
721 Alibaba, "Factory Supply Food Grade Tech Grade Cas56-81-5 Glycerol Pure Glycerin," 2026.
https://www.alibaba.com/product-detail/Factorv-Supplv-Food-Grade-Tech-
Grade 1601160081092.html?spm=a2700.7724857.0.0.58067c75KA7rvn.
722 See Chapter 10.1.1.2.
348
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Table 10.1.2.3-2: Summary of Estimated Biodiesel Production Costs (2024$/gal)
Year
Soybean Oil
Corn Oil
FOG
Control Case
2026
5.55
4.67
4.30
2027
5.70
4.80
4.42
High Prices
2026
6.71
5.63
5.17
2027
7.01
5.88
5.40
Low Prices
NoRFS
2026
3.69
3.14
2.90
2027
3.54
3.01
2.78
Low Prices
2025
2026
4.25
3.59
3.32
2027
4.24
3.59
3,31
10.1.2.4 Renewable Diesel Production Costs
The renewable diesel process converts plant oils or rendered fats into diesel or jet fuel
using hydrotreating. The process reacts hydrogen over a catalyst to remove oxygen from the
triglyceride molecules in the feedstock oils via a decarboxylation (removal of a carbon molecule
double-bonded to an oxygen molecule, producing carbon dioxide) and hydro-oxygenation
reaction, yielding some light petroleum products, carbon dioxide, and water as byproducts. The
byproducts created from the hydrotreating reactor are separated from the renewable diesel in a
separation unit. The reactions also saturate the olefin bonds in the feedstock oils, converting
them to paraffins, and may also isomerize some paraffins. Depending on process operating
conditions, 90-95% of the product yield by volume can be blended into diesel fuel or jet fuel,
with the rest being naphtha and light fuel gases (primarily propane). In total, the volumetric yield
is greater than 100% of the feed due to the addition of hydrogen and subsequent cracking that
occurs over the hydrotreating catalyst.
For this cost analysis we chose to focus on stand-alone renewable diesel production. We
found a project cost estimate by Diamond Green, which was $1.1 billion for a standalone 400
million gallon per year facility.723 This large plant size and its associated capital costs were
scaled down to a 220 million gallon per year plant size which is more typical of the renewable
diesel fuel plants being built for start-up through 2024.724 The capital cost for this smaller
renewable diesel fuel plant is estimated to be $768 million.
In addition to feedstock and facility costs, another significant cost input is hydrogen. We
used an estimate provided by Duke Biofuels for our hydrogen consumption estimate for
producing renewable diesel. On average, vegetable oil and FOG feedstocks require 2,000
standard cubic feet (SCF) of hydrogen per barrel of feedstock processed.725 Hydrogen costs are
estimated based on a 50 million SCF (MSCF)/day steam methane reforming hydrogen plant,
723 Advanced Biofuels USA, "Honeywell Ecofining Technology Helps Diamond Green Diesel Become One of The
World's Largest Renewable Diesel Plants," October 2, 2019. https://advancedbiofuelsusa.info/honevwell-ecofining-
technologv-hefos-diamond-green-diesel-become-one-of-the-worlds-largest-renewable-diesel-plants.
724 The typical renewable diesel plant size is based on volume-weighting renewable diesel capacity. The cost for the
smaller sized renewable diesel plant is scaled using a six-tenths factor which captures the higher per gallon cost of a
smaller sized plant. The cost scaling is calculated using the following equation: (capital cost of the original plant
size) * (new plant size/original plant size)"6. Perry, Robert H„ Don W. Green, and James O. Maloney. Perry's
Chemical Engineers' Handbook. 5th ed. McGraw-Hill Professional Publishing, 1997.
725 Conversation with Mike Ackerson, Duke Biofuels, May 2020.
349
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adjusted to represent a 32 MSCF/day plant, which would be the quantity of hydrogen required
for a typical sized 220 million gallon per year renewable diesel plant.726
Table 10.1.2.4-1: Hydrogen Plant Costs for 2026 (2024$)
Unit Demands for a 50
MM SCF/day plant
Cost per Unit
Cost for a 32
MMSCF/day plant
MM$
S/MSCF
Feed Natural Gas
730 MMBtu/hr
$4.21/MMBtu
17.2
1.47
Fuel Gas for Heat
150 MMBtu/hr
$4.21/MMBtu
3.5
0.301
Power
1200 KW
8.37/kWh
0.6
0.05
Boiler feed water
160,000 lb/hr
$3/1000 gal
0.3
0.03
Cooling water
900 gal/min
$3/1000 gal
0.9
0.08
Export Steam
120,000 lb/hr 600 psi
-3.7
-0.31
Capital Cost
$70 MM in 2016$
For a 50 MMSCF/
day plant
$81 MM in 2024$
For a 32 MMSCF/
day plant
8.7
0.74
Fixed Cost
6.7%
5.3
0.45
Total Cost
32.9
2.82
Based on our cost analysis, hydrogen is estimated to cost $2.82/MSCF in 2026. If
renewable fuel producers elect to produce and use renewable hydrogen derived from RNG as a
feedstock to their renewable diesel plant instead of hydrogen derived from fossil natural gas, we
would expect the cost to produce renewable diesel would increase. As summarized in Chapter
10.1.2.6, the cost to produce, clean up and distribute biogas as pipeline-quality RNG is higher
than similar costs to produce pipeline-quality fossil-derived natural gas. Thus, the hydrogen
produced from the biogas-derived RNG would also be more expensive. And while hydrogen can
also be produced from electrolysis, the cost of doing so is greater than steam methane reforming
of either fossil or renewable natural gas.727 For these reasons, we anticipate any non-fossil-
derived natural gas would be supplied at a cost premium in 2026 or 2027.
Our yield estimates as summarized in Table 10.1.2.4-2 were derived from material
presented by UOP and Eni at a 2007 industry conference, which describes producing renewable
diesel in a grass roots standalone production process inside a refinery.728 Despite the age of the
reference, the underlying chemistry is unlikely to have changed appreciably.
726 Meyers, Robert A. Handbook of Petroleum Refining Processes, Fourth Edition. McGraw-Hill Education, 2016.
727 Congressional Research Service, "Hydrogen Production: Overview and Issues for Congress," R48196, October 3,
2024. https://www.congress.gov/crs-product/R48196.
728 Holmgren, Jennifer, Chris Gosling, Rich Mariangeli, Terry Marker, Giovanni Faraci, and Carlo Perego. "A New
Development in Renewable Fuels: Green Diesel," National Petrochemical & Refiners Association AM-07-10, 2007.
350
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Table 10.1.2.4-2: Input and Output Streams from Renewable Diesel Plant
Vegetable Oil input
100 gal
Renewable diesel output (main product)
93.5 gal
Naphtha output (co-product)
5 gal
Light fuel gas output (co-product)
9 gal
Notably, these yield estimates assume no separation of jet fuel from renewable diesel. As
we note at the beginning of this section, many, if not most, renewable diesel plants also
coproduce jet fuel as a separate product. The separation and sale of jet fuel range molecules
would be expected to have some impact on the cost to produce renewable diesel relative to the
plant described in Table 10.1.2.4-2, but we do not estimate that difference here. We will address
costs for renewable jet fuel produced from a unit process inside of a refinery in Chapter 10.1.2.5.
We derived a cost of 6.90 per gallon of renewable diesel product to cover other costs:
utilities, labor, and other operating costs.729 Finally, the total cost per gallon was estimated at
$4.64. Table 10.1.2.4-3 provides more details for the process assumed in this analysis and
summarizes the total and per-gallon costs for the year 2026.
Table 10.1.2.4-3: Renewable Diesel Production Cost Estimate for a Greenfield 220 Million
Gallons/Yr Plant Processing Soybean Oil in 2
026(2024$)
Stream
Estimated value
MM$/yr
$/gal
Soybean Oil input
198 MMgals/yr
660/lb
1003
5.07
Naphtha output
11.8 MMgals/yr
1.610/gal
(19.0)
(0.10)
Light fuel gas output
21.2 MMgals/yr
80.30/gal
(17.0)
(0.09)
Hydrogen input
4,760 SCF/100 gals
$2.82/MSCF
26.6
0.13
Other Operating Costs
15.2
0.07
Capital Costs (2024$)
$1,031 million
113.4
0.57
Fixed Costs
5.5%
56.7
0.29
Renewable Diesel
Total Costs
921
5.95
The estimated renewable diesel production cost for all vegetable oil types and for the
years analyzed is summarized in Table 10.1.2.4-5.
729 Estimated based on the utility cost for an FCC naphtha hydrotreater. EPA, "Control of Air Pollution from Motor
Vehicles: Tier 3 Motor Vehicle Emission and Fuel Standards Final Rule - Regulatory Impact Analysis," EPA-420-
R-14-005, March 2014. https://nepis.epa.gov/Exe/ZvPDF.cgi/P100ISWM.PDF?Dockev=P 100ISWM.PDF.
351
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Table 10.1.2.4-5 Summary of Estimated Renewable Diesel Production Costs ($/gal; 2024$)
Year
Soybean Oil
Corn Oil
FOG
Control Case
2026
5.95
5.07
4.70
2027
5.97
5.07
4.69
High Prices
2026
7.10
6.03
5.57
2027
7.28
6.15
5,67
Low Prices
NoRFS
2026
4.09
3.54
3.30
2027
3.81
3.28
3.06
Low Price
2025
2026
4.64
3.99
3.72
2027
4.51
3.86
3.59
10.1.2.5 Renewable Jet Fuel
As explained in Chapter 10.1.2.4, the production process for renewable diesel can also be
used to produce jet fuel. There are two primary ways of doing so using lipids as feedstocks. The
first and lowest cost way is to simply install a distillation column and distill off the lighter
hydrocarbons, which boil in the jet fuel distillation range, that are produced by the renewable
diesel production process. The second, more expensive way, which can produce a larger amount
of jet fuel, is to hydrocrack the renewable diesel hydrocarbons which fall in the diesel fuel range
so that the hydrocarbon chains are shortened and then distill within the jet fuel distillation range.
Another emerging pathway for producing jet fuel is to use a recently developed chemical
reaction pathway called alcohol-to-jet (ATJ). In this case, alcohol compounds are reacted
together to form hydrocarbon chains which fall in the jet fuel boiling range. Ethanol and
isobutanol alcohols are possible reactants for this process. Since corn ethanol is widely available
domestically available renewable fuel in the U.S., we will estimate the cost of this technology
using corn ethanol as the feedstock.
10.1.2.5.1 Distillation
A simple distillation column can be installed to separate the jet fuel boiling range
hydrocarbons from the diesel boiling range hydrocarbons in renewable diesel. This simple
distillation column, often called a stabilizer column, is designed to separate the lighter
hydrocarbons from the heavier hydrocabons and make only a rough cut between the two
hydrocarbon ranges. This type of distillation column would not be designed to boil off many of
the heavier hydrocarbon compounds which would allow for a smaller diameter column. Also,
because of the need for only a rough cut between the light and heavy hydrocarbons, the column
would require fewer trays and therefore not be very tall. EPA obtained cost information from
Mobil Oil for such a column for the 1999 Tier 2 gasoline sulfur regulation which was designed
for a similar case of separating the light and heavy gasoline naphtha compounds to minimize the
cost to desulfurize gasoline.730
7311 EPA, "Regulatory Impact Analysis - Control of Air Pollution from New Motor Vehicles: Tier 2 Motor Vehicle
Emissions Standards and Gasoline Sulfur Control Requirements;" EPA-420-R-99-023, December 1999.
https://nepis.epa.gov/Exe/ZvPDF.cgi/P100FlUV.PDF?Dockev=P100FlUV.PDF.
352
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The capital and operating cost information, and cost to produce jet fuel, for a stabilizer
column to separate the jet fuel hydrocarbons from the rest of the renewable diesel compounds is
summarized in Table 10.1.2.5.1-1. The capital cost estimates from Mobil Oil are for a 50
thousand bbl/day unit from the year 2000. We have adjusted the costs to 2024 dollars and base it
on 14.4 thousand barrels per day (220 million gallon per year). A 20% factor is added to account
for contingency costs, and a 40% factor is added to account for offsite costs. Jet fuel comprises
hydrocarbons which range from 8 to 16 carbons in length. Analysis of the hydrocarbon chain
lengths of renewable diesel produced soybean and corn vegetable oils and animal fat
triglycerides reveals that the renewable diesel from vegetable oils typically contain about 20% 8
to 16 carbon hydrocarbons, while renewable diesel and animal fats typically contain 30% 8 to 16
carbon hydrocarbons. Since most renewable diesel is produced from vegetable oil, we assume
that 20% of the renewable diesel produced would be separated as jet fuel by this distillation
column, and we amortized the costs over only the jet fuel volume.
Table 10.1.2.5.1-1: Distillation Cost oi
' Separating Jet Fuel from Renewable Diesel (2024$
Annual Cost
(MM$)
Cost to Jet Fuel
(^/gal)
Capacity Basis
50,000 bbl/day
Capital Cost 2000$
$4.1 million
Capital Cost for a
14.5 bbl/day unit
$2.2 million
0.25
0.56
Fixed Cost
5%> of Capital
0.11
0.24
Electricity
0.17 kWh/bbl
6.690/kWh
0.06
0.14
HP Steam
36 lb/bbl
$4.48/MMBtu
1.27
2.8
Cooling Water
3 gal/bbl
$3/1000 gal
0.20
0.46
Total cost to jet
(2900 bbl/day)
1.88
4.3
Table 10.1.2.5.1-1 summarizes our estimated cost to produce renewable jet fuel from
renewable diesel oil in 2026 as 4.300 per gallon. Thus, adding a distillation column at a
renewable diesel production facility would raise the $5.95 renewable diesel production cost to
$5.99 per gallon when using distillation to produce renewable jet fuel.
10.1.2.5.2 Hydrocracking
The hydrocracking process utilizing a cracking catalyst can be used to convert some of
the renewable diesel to jet fuel. Since this is a mild hydrocracking operation, it is estimated to
only require a single stage hydrocracking reactor added after the renewable diesel hydrotreating
reactor. The hydrocracking facility includes a distillation column which separates the
hydrocracking reactor products into various streams, including jet fuel, renewable diesel,
renewable naphtha and other light products. Table 10.1.2.5.2-1 summarizes the capital and
operating cost information, input and output product information, and the estimated cost for the
added hydrocracker unit.731 A hydrocracker can produce a range of products depending on the
operating conditions and catalyst used. For this analysis, a hydrocracker processing used cooking
731 Meyers, Robert A. Handbook of Petroleum Refining Processes, Fourth Edition. McGraw-Hill Education, 2016.
353
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oil, which the study termed a waste oil, is estimated to produce 64% jet fuel, 20% naphtha, 6%
light hydrocarbons, and 10% unreacted renewable diesel. However, hydrocrackers are quite
flexible in their operations due to operating conditions and the catalyst used, so the product mix
can be quite different from that used in this study.732 The mild hydrocracking reaction is
estimated to cause the product to swell 3 vol% relative to the feed volume.
The cost of the process is reflected in final cost of the renewable jet fuel being produced.
The byproducts of the hydrocracking reaction were assigned monetary values to allow estimating
the cost of the jet fuel produced. The unreacted renewable diesel is assigned the same price as the
renewable diesel feedstock. The naphtha and light naphtha are assigned a discounted price
relative to gasoline to reflect their expected low octane value. However, these streams could have
higher value based on their renewability. As a sensitivity on the hydrocracking cost of producing
jet fuel, naphtha is also valued the same as the renewable diesel feedstock, which could reflect its
value as a renewable fuel.
Table 10.1.2.5.2-1 Estimated Renewable Jet Production Cost from Hydrocracked
Renewable Diesel (2024$)
Annual Cost
(MM$)
Cost to Jet
Fuel (^/gal)
Capacity Basis
14.4 kbbl/day
Capital Cost 2016$
$5000/bbl/day
Capital Cost 2024$
$120 million
13.2
10.1
Fixed Cost
5% of capital
6.01
4.6
Feedstock
12,916 bbl/day
$4.40/gal
871.2
667.4
Hydrogen
250 SCF/bbl
$3.32/MSCF
3.32
2.5
Natural Gas
90,000 Btu/bbl
$4.37/MMBtu
1.85
1.4
Electricity
8.4 kWh/bbl
8.37 0/kWh
3.32
2.5
Cooling Water
2 gal/bbl
$3/1000 ft3
0.03
0.02
Steam Export
10 lb/bbl
$4.37/MMBtu
-0.16
-0.12
Byproducts
Other
798
$1.61/gallon
-19.7
-15.1
Naphtha - Low
Naphtha - High
2,661
$1.61/gallon
$4.64/gallon
-65.8
-189.445
-50.4
-145.1
Renewable Diesel
1,330
$4.44/gallon
-94.7
-72.5
Total Cost - Naphtha Low
Price
718.5
550.5
Total Cost - Naphtha High
Price
594.9
455.7
The analysis shows that if the hydrocracked naphtha price is significantly reduced to a
value less than gasoline based on an assumption that its octane value is significantly lower than
gasoline, the estimated production cost of renewable jet fuel is estimated to be $1.1 per gallon
732 El-Araby, R., E. Abdelkader, G. El Diwani, and S. I. Hawash. "Bio-aviation Fuel via Catalytic Hydrocracking of
Waste Cooking Oils." Bulletin of the National Research Centre/Bulletin of the National Research Center 44, no. 1
(October 12, 2020). https://doi.org/10.1186/s42269-020-0Q425-6.
354
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more expensive than renewable diesel. If the hydrocracked naphtha is assumed to be valued at
the same price as renewable diesel, perhaps associated with a higher octane value, the estimated
production cost of the hydrocracked jet fuel falls to 150 per gallon above the assumed renewable
diesel price. Hydrocracked naphtha is typically low in octane; however, it is possible for the
hydrocracking reactor to include catalyst which could raise the octane of the hydrocracked
naphtha.
10.1.2.5.3 Alcohol-to-Jet
Three different reaction steps are involved in converting ethanol into jet fuel. First the
alcohol is dehydrated by removing the hydroxyl (-OH) group, creating an olefin with the base
hydrocarbon molecule. Thus, dehydrating ethanol produces ethylene as the intermediate product.
Second, the hydrocarbons are oligomerized, which essentially daisy-chains the individual
hydrocarbons molecules together. An obvious challenge of this second step is reacting enough of
the short hydrocarbons together such that they boil in the jet fuel range, without combining too
many together and producing diesel or an even heavier hydrocarbon. For the third step, the
double carbon-carbon bonds of the oligomerized hydrocarbons are hydrogenated to saturate the
double bonds.
We estimated the cost for converting ethanol to jet fuel based on a study which developed
an Aspen Plus technical model for the cost analysis which likely includes both onsite and offsite
costs.733 The model assumed a smaller-sized ATJ plant of 185 tons per day (20 million gallons
per year) of alcohol feedstock, which seems appropriate for an emerging technology. The model
estimates that the process produces mostly jet fuel but also produces renewable diesel and
naphtha. We credit the renewable diesel exiting the ATJ process at the feedstock price since the
renewable diesel is unreacted by the process. The model estimates a total installed capital cost,
but a 20% contingency factor is added to the reported capital cost. Table 10.1.2.5.3-1
summarizes the cost information and resulting estimated costs for the ATJ process.
733 Geleynse, Scott, Kristin Brandt, Manuel Garcia-Perez, Michael Wolcott, and Xiao Zhang. "The Alcohol-to-Jet
Conversion Pathway for Drop-In Biofuels: Techno-Economic Evaluation." ChemSusChem 11, no. 21 (September
13, 2018): 3728-41. https://doi.org/10.1002/cssc.20180169Q.
355
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Table 10.1.2.5.3-1 Estimated Cost to Convert
Corn Ethanol to Renewable Jet Fuel (2024$)
Cost to Jet and
Annual Cost
Diesel Fuels
(MM$)
(jif/gal)
Capacity Basis
Capital Cost 2024$
$20.3 million
3.0
0.30
Fixed Cost
6% of capital
1.6
0.16
Corn Ethanol
185 tons/day
(20.5 million gals/yr)
$1,83/gal
38.5
3.92
Hydrogen
1.1 tons/day
$2.82/MSCF
0.41
0.04
Utilities
$2.7 million/yr
-
2.7
0.28
Catalyst
$0.5 million/yr
-
0.5
0.05
Naphtha
1.29 million gal/yr
$1.61/gal
-2.1
-0.21
Diesel
2.26 million gal/yr
-
Jet Fuel
7.55 million gal/yr
-
Total cost to jet and
diesel (2900 bbl/day)
44.6
4.54
The cost analysis estimates that producing jet fuel from corn ethanol using the ATJ
process costs $4.54/gallon. This is less expensive than producing jet fuel from soybean oil at the
estimated high soybean oil prices, although more expensive than producing jet fuel from used
cooking oil or corn oil at low prices.
10.1.2.6 Renewable Natural Gas
RNG is generally understood to describe biogas which has been upgraded for use in place
of fossil natural gas.734 The RFS regulations at 40 CFR 80.2 provides a more detailed definition,
stipulating that RNG "is produced from biogas.. .does not require removal of additional
components to be suitable for injection into the natural gas commercial pipeline system... [and] is
used to produce renewable fuel." Biogas, in turn, is a methane and carbon-dioxide-rich gas
resulting from the decomposition of organic matter under anaerobic conditions; significant
sources of biogas include municipal solid waste, animal manure, agricultural waste, and food
waste.735 The RFS program provides a more detailed definition of "Biogas" at 40 CFR 80.2,
which includes additional program-specific stipulations. The primary product of this anaerobic
digestion of these materials is methane, which is the primary component of natural gas. Thus,
once biogas is cleaned up by removing various contaminants, it can be used by processes that
normally use natural gas.736
The largest source of biogas, which is already being collected to avoid releasing methane
into the environment, is from landfills.737 Since landfill gas is the largest source of biogas
734 EPA, "Renewable Natural Gas," https://www.epa.gov/lmop/renewable-natural-gas.
735 Id.
736 LeFevers, Daniel. "Landfill Gas to Renewable Energy," Waste Management. April 26, 2013.
https://www.eesi.org/files/042613 Daniel LeFevers.pdf.
737 EIA, "Biomass explained - Landfill gas and biogas," November 19, 2024.
https://www.eia.gov/energvexplained/biomass/landfill-gas-and-biogas.php.
356
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available for the motor vehicle fleet, this cost analysis makes the simplifying assumption that the
biogas will solely be provided by landfills.738
While in some cases RNG can be used in local fleet vehicles which are operated at the
landfill site, in most cases the fuel must be transported to the final consumer by pipeline. It is our
understanding that most new RNG production facilities require the construction of a new
pipeline to transport the RNG to a nearby common carrier pipeline. RNG is then pulled off the
pipeline at downstream locations and compressed into CNG or liquified into LNG for use in
motor vehicles. Tracking the use of RNG in motor vehicles occurs by proxy through contracts
and/or affidavits rather than through a system designed to ensure that the same methane
molecules produced at the landfill are used in CNG/LNG vehicles.
One of the costliest aspects of using biogas to produce RNG is its cleanup. Biogas
contains large amounts of carbon dioxide, nitrogen, and other contaminants such as siloxanes
which cannot be tolerated if it is to be put into a natural gas pipeline or used by fleet vehicles at
the landfill site. We estimated a cost for cleaning up landfill biogas to pipeline quality using
Version 3.5 of the Landfill Gas Energy Cost Model (LFGcost-Web).739740 The throughput
volume of landfill gas was estimated to be 1,500 SCF/min, which is about an average production
volume from biogas production facilities.741 The equations from the LFGcost-Web model for
biogas clean-up and interconnection to a natural gas pipeline are summarized in Table
10.1.2.6.1-1. We included a cost for biogas collection at a typical sized landfill which amounts to
$0.09/MSCF.742 The estimated production and clean-up costs for landfills are summarized in
Table 10.1.6.2-2. Distribution and retail costs are estimated for RNG in Chapter 10.1.4.3.
Table 10.1.2.6-1: Biogas Cleanup Cost Information
Cost Factors (2019$)
Pipeline Interconnection
$400,000
Capital Costs
6,000000*e(0 00°3*SCF/min)
Operating and Maintenance
250 x SCF/min +148,000
Electricity Costs
0.009 kWh/SCF
Note: Excludes any new offsite pipeline costs and retailing costs.
738 Landfill biogas is expected to be the supplier in the near term due to its lower cost because of its greater economy
scale and closer access to markets.
739 The current version of this model and user's manual are available at: https://www.epa.gov/lmop/lfgcost-web-
landfill-eas-energy-cost-model.
7411 This cost estimate does not include the cost for complying with California's more stringent natural gas pipeline
specifications designed to address harmful contaminants in some sources of biogas.
741 The Coalition for Renewable Natural Gas, "Economic Analysis of the U.S. Renewable Natural Gas Industry,"
December 2021. https://guidehouse.com/-/media/www/site/insights/energy/2022/guidehouse-esirng-coalition-final-
reportl22022.pdf.
742 EPA, "LFG Energy Project Development Handbook," January 2024, Chapter 4.
https://www.epa.gov/svstem/files/documents/2024-01/pdh full.pdf.
357
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Table 10.1.2.6-2 Biogas Collection and Cleanup costs (2024$)
Average (1500 scf/min)
Large (8000
scf/min)
Small (250 scf/min)
Cost
Cost
Cost
Cost
Cost
Cost
MM$/yr
$/MMBtu
MM$
$/MMBtu
MM$)
($/MMBtu
Capital Cost
1.36
3.34
3.72
1.71
0.47
6.83
Operating and
Maintenance
0.52
1.28
2.15
0.99
0.21
3.09
Electricity Costs
0.85
2.08
4.54
2.08
0.14
2.08
Total Clean-up Cost
2.74
6.70
10.41
4.78
0.82
12.00
Interconnection
0.06
0.14
0.06
0.03
0.06
0.85
Collection Cost
0.20
0.49
0.20
0.09
0.20
2.95
Collection and
Clean-up Cost
3.00
7.33
10.67
4.89
1.08
15.81
The combined biogas collection and cleanup costs for an average sized landfill amount to
$7.33 per million Btu of RNG. For larger and smaller landfills, the costs are estimated to be
$4.89/mmBTU of RNG and $15.8/mmBTU of RNG, respectively.
10.1.2.7 Sugarcane Ethanol
Unlike the starch in corn kernels which first must be depolymerized using enzymes,
sugarcane contains free sugar which, after extraction from the sugarcane, can be directly
fermented into ethanol. The fibrous portions of the sugarcane plant, known as sugarcane bagasse,
are typically combusted to produce the energy needed for the process.
We estimated the cost to produce sugarcane ethanol in two different ways. The first
method we used is based on recent data on sugarcane ethanol prices which we receive in EMTS.
These are as-received prices, so they include the cost to ship the ethanol from Brazil to the U.S.
Generally, ethanol from sugarcane produced in tropical areas is cheaper to produce than ethanol
from cellulose and is similar in cost to corn starch ethanol. This is due to favorable growing
conditions resulting in relatively low-cost feedstock, low energy input costs due to the abundance
of combustible bagasse residues, and other cost reductions gained from years of experience. The
average of sugarcane ethanol prices from EMTS was $2.73 per gallon. Other price data which
EPA receives from OPIS have shown a similar average price over the same period, which helps
to corroborate the price data from EMTS.
The second method we used is taken from a study by OECD (2008) entitled "Biofuels:
Linking Support to Performance," which provides a set of assumptions and an estimate of
production costs. Our estimate of sugarcane production costs shown in Table 10.1.2.6-1
primarily relies on the analysis described in that study.743 Our original cost estimate reported in
the RFS2 Rule assumed 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. We adjusted the capital and operating costs taken from OECD (2008) for the effects
743 EPA, "RFS2 Regulatory Impact Analysis," EPA-420-R-10-006, February 2010.
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of inflation from 2006 to 2024, as shown in Table 10.1.2.7-1. As we did for most of the other
fuel costs estimated in Chapter 10.1.2, when estimating the amortized per-gallon capital costs we
also added a 20% capital cost contingency factor to account for other costs not accounted for in
the cost analysis. We then applied our capital cost amortization parameters described in Chapter
10.1.2.1.1 above to complete the estimates. Table 10.1.2.7-1 provides the updated production
cost estimate for sugarcane ethanol.
Table 10.1.2.7-1: Sugarcane Ethanol Production Cost
Cost Basis
Sugarcane Productivity
71.5 tons/hectare
Sugarcane Consumption
2 million tons/year
Harvesting days
167
Ethanol productivity
85 L/ton feedstock (22.5 gal/ton feedstock)
Ethanol Production
170 million L/yr (45 million gals/yr)
Surplus power produced
40 kWh/ton sugarcane
RFS2 Reported
Cost ($2006)
Revised Costs
($2024)
Capital Costs
($ million)
Investment cost in million$
97
155
Investment cost for sugarcane
production
36
58
Per Gallon
Costs ($/gal)
Operating & maintenance costs
0.26
0.42
Variable sugarcane production costs
0.64
1.02
Capital costs
0.49
0.62
Total production costs
1.39
2.07
Shipping Costs to U.S.
0.15
Delivered Cost
2.22
The average FOB ethanol price of $2.73/gallon in Brazil is somewhat higher than the
estimated sugarcane ethanol production cost of $2.22/gallon. This cost/price difference can
mostly be attributed to the low (0.11) before-tax capital amortization factor that we use which
reflects the social cost of capital, and the shipping costs incorporated in the price data. As noted
in Chapter 10.1.2.1.1, EPA has a standard factor which we have used across numerous
rulemakings. However, if we were to use a more typical 0.16 after-tax capital amortization factor
used by industry, the per-gallon costs increase to $2.50 per gallon. Normally we would use the
bottom-up cost estimate described in Table 10.1.2.7-1; however, the EMTS price data may
capture some inflation effects which the bottom-up cost estimate may not capture regardless of
the applied inflation adjustment. For this reason, we used the $2.73 per gallon price data from
EMTS to represent the production and distribution costs for sugarcane ethanol.
10.1.2.8 Corn Kernel Fiber Ethanol
In addition to converting corn starch to ethanol, some of the fiber contained in the corn
kernel can also be converted to ethanol. This additional ethanol from CKF is considered
cellulosic ethanol and earns D3 RINs. Historically, this step of converting the cellulosic fibers to
ethanol was thought of as a separate unit process from the starch to ethanol conversion process,
in which DDGS would be reacted a second time in a separate reactor vessel. This process would
have additional associated fixed and variable costs. However, one or more companies have found
359
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that a small portion of the cellulosic fiber is converted to ethanol along with the starch in the
existing starch to ethanol facilities. We project that most producers will use this single reactor
design to produce the cellulosic ethanol volumes in 2026 and 2027.744-745 Anticipating that this
cellulosic ethanol would be produced in an existing starch to ethanol reactor provides cost
efficiency that would lower the overall production cost. But this also presents a challenge for
how to identify the quantity of ethanol produced from cellulose versus that produced from the
starch. To remedy this EPA published guidance on how to identify the portion of ethanol being
produced from cellulose.746
Anticipating that the cellulosic ethanol will be produced along with corn starch in an
existing reactor allows us to estimate the cost of producing this cellulosic ethanol in a
straightforward manner. Since we already estimate the capital, fixed, and variable operating cost
of producing ethanol from corn starch on a dollars-per-gallon basis, we begin by applying those
same cost estimates to the corn fiber ethanol. There are other additional cost factors to consider
on top of these initial costs, specifically the potential cost for the additional enzyme added to
convert corn fiber to ethanol and potential cost savings due to increased corn oil production.747
We estimate that the cost of the additional enzyme is approximately equally offset by the cost
savings of additional corn oil production. Therefore, for the purposes of this chapter, we simply
use the cost for producing ethanol from corn starch for the cost of producing ethanol from
cellulosic ethanol. As described in Table 10.1.2.2-3 of this chapter, this cost is estimated to be
$1.88 per gallon of denatured ethanol for 2026 and $1.92 per gallon for 2027.
10.1.3 Blending and Fuel Economy Cost
Certain renewable fuels, namely ethanol, biodiesel, and renewable diesel, are typically
blended into petroleum fuels. There are costs, and in some cases, cost savings associated with
such blending. In addition, these renewable fuels have relatively lower energy per gallon leading
to lower fuel economy (miles driven per gallon). In this section, we consider blending and fuel
economy costs for ethanol blended as E10, E15, and E85, as well as for biodiesel and renewable
diesel.
10.1.3.1 Ethanol
10.1.3.1.1 E10
Ethanol has physical properties when blended into gasoline which affect its value as a
fuel or fuel additive. Ethanol has a very high octane content, a high blending RVP when blended
into gasoline at low concentrations, and is low in energy content relative to the gasoline pool that
744 Kacmar, Jim. "Intellulose: An Innovative Approach to Your Plant's Profitability," Edeniq 2019 Distillers Grains
Symposium, May 15, 2019. https://distillersgrains.org/wp-content/uploads/2019/05/7-Kacmar-Intellulose.pdf.
745 National Corn to Ethanol Research Center, "Conversion of Corn-Kernel Fiber in Conventional Fuel-Ethanol
Plants," Project No. 0340-19-03, November 11, 2018.
746 EPA, "Guidance on Qualifying an Analytical Method for Determining the Cellulosic Converted Fraction of Corn
Kernel Fiber Co-Processed with Starch," EPA-420-B-22-041, September 2022.
https://nepis.epa.gov/Exe/ZvPDF.cgi?Dockev=P1015Q8V.pdf.
747 Kacmar, Jim. "Intellulose: An Innovative Approach to your Plant's Profitability," Edeniq 2019 Distillers Grains
Symposium, May 15, 2019. https://distillersgrains.org/wp-content/uploads/2019/05/7-Kacmar-Intellulose.pdf.
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it is blended into. Ethanol has essentially zero sulfur or benzene, adding to ethanol's value
because refineries must meet sulfur and benzene fuel standards. Each of these properties can
have a different cost impact depending on the gasoline it is being blended into (e.g., RFG versus
CG), winter versus summer gasoline, premium versus regular, blended at 10% versus El5 or
E85). These physical properties are also valued differently from a refiner's perspective compared
to that of the consumer. Refiners value ethanol's octane because they can lower the octane of the
gasoline the ethanol is being blended into, reducing their refining costs. Refiners dislike
ethanol's high blending RVP when blending ethanol in gasoline (usually RFG) at 10% because
they must remove some low-cost gasoline blendstock material (usually butane) to accommodate
the ethanol if the gasoline they are producing does not receive a 1 psi RVP waiver.
Refiners are generally not concerned about ethanol's low energy content when blending it
into gasoline since they sell gasoline on volume, not energy content, and consumers do not
appear to demand a discount for El0. Rather, this is usually only an issue for the consumers who
do not travel as far on a gallon of fuel with lower energy content. Depending on the fuel they are
purchasing, the lower energy content will be either obvious to consumers (i.e., E85), impacting
their purchase decisions, or not (i.e., E10; most consumers do not notice its lower energy content
in comparison to E0, particularly now that almost all gasoline is E10). Since the ultimate finding
of this chapter is a social cost analysis which incorporates all the costs to society, the fuel
economy effect is included in the overall cost estimates in Chapter 10.4. For the reasons
described above, this fuel economy effect is not included with the blending value estimated in
this section.
Ethanol's total blending value is estimated based on the output from refinery modeling
cases conducted by ICF/Mathpro for a projected 2020 year case assuming that crude oil would
be priced at $72/bbl.748 By averaging the costs separately for conventional and reformulated
gasolines, the refinery modeling output from the first case allowed us to estimate ethanol's
volatility cost for blending ethanol into E10 reformulated gasoline.749 Due to the options
available to refiners to replace ethanol's octane, ICF/Mathpro ran two ethanol replacement cases.
In the first cost case, the refinery model principally relied on increased alkylate
production. But to be able to replace all of ethanol's octane, the refinery model estimates that
refiners would also increase the octane of reformate (through increased reformer severity) and
increase production of isomerate, even if the primary octane replacement is alkylate. The
refinery model estimates that for this alkylate-centric case over 7.6 million barrels per day of
new refinery unit capacity would need to be added by refiners.
In the second case, instead of representing refiner reliance on large butane purchases for
producing alkylate, ICF/Mathpro modeled increased throughput to, and increased severity of,
existing reforming units to increase the octane of reformate, the product stream of the reformer.
This case still relied on other octane producing unit additions, including alkylate and isomerate,
748 The crude oil price has a first order effect on the blending value and volatility cost for blending ethanol into
gasoline. Since the crude oil price used in the refinery modeling cost analysis is about the same as the projected
crude oil price for 2021 and 2022, it was not necessary to adjust ethanol's estimated blending cost to any other dollar
value.
749 EPA's contract was with ICF Incorporated, LLC, which in turn retained Mathpro for some aspects of the work.
361
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but increased reformate volume was the principal method to increase octane. This second
reformate-centric refinery modeling case was less capital-intensive, but still added 3.7 million
barrels per day of additional refinery unit capacity and was more costly on a per-gallon basis
relative to the first cost case. Increasing the severity of reformers is relatively more expensive
because of the cost associated with the production of two by-products of the reforming process
which increase as the severity of the reformer is increased. Hydrogen is a by-product of
reforming, but reformer-produced hydrogen is much more expensive than hydrogen produced
from natural gas because natural gas has been priced much lower than crude oil. Fuel gas is
another reformer by-product which is usually used for refinery process heat but displaces much
cheaper purchased natural gas. For short-term octane needs, refiners would likely need to rely on
increasing reformate severity to avoid or minimize the amount of new refining unit capacity
additions, but given the higher overall cost, this would not be a preferable long-term solution.
Table 10.1.3.1.1-1 summarizes gasoline's marginal costs for the reference case (all
gasoline with ethanol) and ethanol's marginal costs for these two ethanol removal cases across
different gasoline types and refinery regions. In the table, Low Biofuel #1 is the reformate-
centric case while Low Biofuel #2 is the alkylate-centric case. The lower marginal values for
PADD 1 (low Biofuel #1) can be explained because Mathpro forced PADD 3 refineries to satisfy
PADD l's need for replacing ethanol's volume and octane through PADD's 3 exports into the
PADD 1 after initial refinery model runs showed PADD l's marginal costs for replacing ethanol
were exceedingly high.
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Table 10.1.3.1.1-1: Gasoline Marginal Values for Reference Case and Ethanol Marginal
Values for the Low Biofuel Cases ($/bbl)
PADD of
Gasoline
Ethanol Marg Val
Ethanol Marg Val
Gasoline
Margina
Values
Low Biofuel #1
Low Biofuel #2
Origin
Type
Grade
Summer
Winter
Summer
Winter
Summer
Winter
RFG
Prem
95.74
83.94
108.37
100.88
0
0
PADD 1
Reg
91.45
81.35
115.98
105.97
0
0
CG
Prem
92.68
83.89
123.02
100.87
0
0
Reg
88.93
81.35
136.43
105.88
0
0
RFG
Prem
88.09
81.68
132.42
110.28
113.45
96.62
PADD 2
Reg
84.80
79.77
145.38
116.02
122.86
101.61
CG
Prem
85.55
81.25
149.08
110.41
126.74
96.25
Reg
82.46
79.45
161.21
115.79
135.55
100.93
RFG
Prem
85.42
78.31
121.69
94.72
118.51
89.77
PADD 3
Reg
81.86
76.39
134.67
98.45
131.29
94.48
CG
Prem
83.64
78.78
133.95
95.13
129.37
89.91
Reg
79.97
76.76
146.78
98.46
142.00
94.55
CG
Prem
79.8
77.0
135.5
115.2
150.1
103.1
PADD 4
Reg
77.4
75.1
149.0
124.0
168.1
110.0
Low
Prem
94.5
-
136.5
-
151.2
RVP
Reg
98.3
-
150.1
-
169.2
RFG
Prem
96.89
83.68
37.68
96.05
0
0
PADD 5
Reg
91.61
82.01
62.46
97.37
0
0
CG
Prem
77.63
83.00
118.14
98.01
0
0
Reg
73.38
81.12
126.14
97.68
0
0
Note: The option for additional alkylation was not available for PADDs 1 and 3, which is why there are no marginal
costs reported for the Low Biofuel #2 case.
The gasoline-ethanol difference in marginal values is calculated and summarized in Table
10.1.3.1.1-2. Note that the values in Table 10.1.3.1.1-1 are in dollars per barrel, while the values
in Table 10.1.3.1.1-2 are in cents per gallon.750
750 The ethanol replacement cost is calculated by subtracting the gasoline marginal value from the ethanol marginal
value, and then converting the cost difference from dollars per barrel to cents per gallon. For example, the PADD 2
premium summertime RFG ethanol replacement value for the Low Biofuel Case #2 is calculated as $113.45 -
$88.09 = $25.36 per barrel = 60.390 per gallon.
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Table 10.1.3.1.1-2: Marginal Ethanol Replacement Cost by Gasoline Type and Season
(g/gal)
PADD of
Low Biofuel #1
Low Biofuel #2
Gasoline Volume
Gasoline
Reformate-centric
Alkylate
-centric
thousand
bbl/day
Origin
Type
Grade
Summer
Winter
Summer
Winter
Summer
Winter
RFG
Prem
30.07
40.35
0
0
56
56
PADD 1
Reg
58.41
58.62
0
0
330
332
CG
Prem
72.23
40.43
0
0
19
18
Reg
113.10
58.42
0
0
200
189
RFG
Prem
105.56
68.08
60.39
35.55
32
33
PADD 2
Reg
144.23
86.31
90.61
52.00
269
275
CG
Prem
151.27
69.44
98.08
35.73
146
156
Reg
187.51
86.52
126.41
51.15
1,663
1,786
RFG
Prem
86.35
39.08
78.77
27.29
131
114
PADD 3
Reg
125.74
52.52
117.69
43.07
785
638
CG
Prem
119.78
38.93
108.86
26.50
369
366
Reg
159.07
51.68
147.69
42.38
2,685
2,649
CG
Prem
132.70
90.86
167.45
62.16
58
60
PADD 4
Reg
170.67
116.41
216.07
83.19
283
289
Low
Prem
100.19
0.00
135.02
0.00
3
3
RVP
Reg
123.27
0.00
168.77
0.00
0
0
RFG
Prem
-140.97
29.46
0
0
218
214
PADD 5
Reg
-69.39
36.56
0
0
823
810
CG
Prem
96.44
35.73
0
0
57
52
Reg
125.61
39.43
0
0
309
285
The regional ethanol replacement costs are volume-weighted together to develop
national-average ethanol replacement costs by gasoline grade and season. These costs are only
presented for the conventional gasoline pool. Table 10.1.3.1.1-3 summarizes these estimated
ethanol-replacement costs.
Table 10.1.3.1.1-3: National Average Ethanol Replacement Cost by Gasoline Grade and
Season (l/gal)
Gasoline
Grade
Reformate-centric
Alkylate-centric
Summer
Winter
Summer
Winter
Conventional
Premium
124.6
50.8
112.0
32.7
Regular
165.1
66.8
144.2
48.2
To estimate the volatility cost, ethanol's marginal values in Table 10.1.3.1.1-1 forRFG
are subtracted from those for CG, although the values are calculated separately for premium and
regular grade gasolines. These calculated values are summarized in Table 10.1.3.1.1-4. Although
this analysis could have separately analyzed RVP-controlled conventional gasoline without a
waiver, it did not since its gasoline volume was less than 2% of the total gasoline pool.
364
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Table 10.1.3.1.1-4 Ethanol's RVP Blending Cost in Reformulated Gasoline in 2020 by
PADD ($/gal)
Gasoline PADD
Gasoline Grade
RFG-CG Marginal
Values ($/bbl)
PADD 1
Premium
9.74
Regular
10.53
PADD 2
Premium
9.59
Regular
9.32
PADD 3
Premium
9.31
Regular
9.25
PADD 5 (CA)
Premium
58.79
Regular
62.59
The ethanol RVP blending cost estimated by the refinery model are volume-weighted
together to develop national-average values, and ethanol's RVP blending costs are calculated
separately for premium and regular grades of summertime RFG, and summarized in Table
10.1.3.1.1-5. The PADD 5 RFG, which is California RFG, is modeled to have a volatility cost
which is five times higher than other RFG areas. The cost of complying with California RFG
standards may be higher than that for other RFG areas, but a factor of five seemed much too high
and was considered an outlier.751 Therefore, the modeled California RFG ethanol marginal costs,
which should reflect ethanol's volatility cost, were omitted from this analysis and the PADD 1 -
3 costs were volume-weighted together and used for all RFG areas, including California.
Table 10.1.3.1.1-5: Calculated RVP Blending Costs by Fuel Grade
Gasoline Grade
Cost (^/gal)
Nationwide
Aggregated Cost
Premium
22.5
Regular
22.8
Although the ethanol replacement cost was based on a refinery modeling case when
ethanol was solely removed from conventional gasoline, it would likely be about the same for
RFG as well, so we assumed that they were the same for RFG.752 However, it is necessary to add
in ethanol's volatility cost for RFG, which for ethanol's removal would be a cost savings. The
230 per gallon volatility cost for regular and premium gasoline, respectively, is subtracted from
ethanol's replacement cost to estimate the ethanol replacement cost for RFG. The ethanol
replacement costs for both CG and RFG are shown in Table 10.1.3.1.1-6. The ethanol
replacement costs are then further aggregated to national, year-round averages for each octane
replacement scenario and summarized at the bottom of the table.
751 The volatility cost of complying with California RFG standards would be based on the lower price it could get for
pentane less than the price of gasoline. Pentane is the likely light hydrocarbon that California refiners remove to
comply with the California RFG volatility standards with ethanol blended into RFG. The lowest price California
could get for the removed pentane is if it had to rail it back to the Gulf Coast. That transportation cost will not be
more than twice the price difference of Gulf Coast pentane to the price of California RFG. Thus, we would not
expect the volatility cost to exceed twice that outside of California.
752 Both RFG and CG must meet many of the same gasoline property specifications, including sulfur and benzene, as
well as ASTM D4814, and octane costs are based on the same gasoline blendstocks in either case. PADD 4 is not
included in the cost tables because there is no RFG sold there.
365
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Table 10.1.3.1.1-6: Aggregated Ethanol Marginal Replacement Cost (^/gallon)
Low Biofuel #1
Low Biofuel #2
Reformate-centric
Alkylate
-centric
Gasoline
Grade
Summer
Winter
Summer
Winter
Conv.
Prem
124.58
50.79
112.04
32.65
Reg
165.11
66.83
144.23
48.19
RFG
Prem
105.58
50.79
93.04
32.65
Reg
144.11
66.83
123.23
48.19
Annual Average
82.23
68.65
Refiners would pursue the lowest cost means to produce their fuels, which in this case
would be the alkylate-centric method. Therefore, for evaluating the cost of using ethanol in
gasoline at 10%, we used the cost of 68.650 per gallon estimated for that method to represent the
cost of blending ethanol as E10. This 68.650 per gallon cost represents ethanol's average
nationwide blending replacement cost in U.S. gasoline when crude oil is priced at $72/bbl, since
that is the crude price ICF/Mathpro used in their original analysis. However, for AEO2025, EIA
projects crude oil prices to average about $81/bbl across 2026 and 2027. We used this higher
projected crude oil price to ratio ethanol's blending value to $77.4 per gallon in 2026 and 2027.
This estimate can be thought of as the additional value or cost savings to gasoline refiners per
gallon of ethanol that results from blending 10% ethanol into gasoline today.
10.1.3.1.2 Higher Level Ethanol Blends
While there is a considerable blending cost savings associated with blending ethanol as
E10, there currently is not a blending cost savings for blending ethanol as E15 or E85. The
blending costs for higher level ethanol blends are considerably different from E10 in large part
due to the inability in most instances to take advantage of the octane benefit associated with the
additional ethanol.753 Furthermore, the congressional 1 psi RVP waiver which applies for
blending E10 gasoline in summer conventional gasoline does not apply to blending El 5,
requiring a lower RVP and therefore higher cost gasoline blendstock. However, this is only an
added cost in the summer and only in conventional gasoline areas.
There have been, and there continue to be, steps taken to facilitate the blending of El 5
into summertime conventional gasoline. EPA granted El 5 a 1 psi waiver that took effect in the
summer of 2019; however, this waiver was struck down by a federal court in 2021. For the
summers of 2022-2025, EPA granted numerous emergency waivers to allow El5 to continue to
be sold with a 1 psi RVP waiver. In addition, eight states petitioned EPA to allow them to
remove the 1 psi waiver for blending E10 gasoline. EPA issued a final rulemaking granting those
petitions effective in 2025, although through a regulatory action requested by the 8 states, the
753 See RIA Chapter 2.1.1 for further details.
366
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removal of the E10 1 psi waiver for those 8 states was delayed until 20 26.754-755-756 Additionally,
EPA granted El5 a nationwide 1 psi waiver for the summer of 2025.757
El5 could potentially realize a blending cost benefit based on the increased octane for the
additional ethanol if refiners could create and distribute a low RVP, low octane El 5 blendstock
for oxygenate blending (BOB). However, this would require a widespread shift by refineries,
pipelines, and terminals in an entire geographical region to produce and distribute another even
lower octane BOB specially designed for producing El 5 instead of El 0.758 This would most
likely only occur if El 5 becomes the predominant gasoline used in that region because of the
limitations of the distribution system and experience with the historic conversion to E10. Since
this could not feasibly happen during the time period of this rulemaking, we have not included
any octane blending benefit for the additional ethanol blended into El 5 in excess of the ethanol
blended in E10 (the additional 5%).759 Thus, the gasoline BOB used to produce El5 in the winter
months is the same as that used for producing E10, resulting in a higher octane fuel which cannot
generally be priced to represent the full value of that octane. In the summer months, El 5 would
also incur the additional RVP control costs, except in those states which have rescinded the E10
1 psi waiver.
There also is not a blending cost benefit for ethanol blended as E85 resulting from its
high octane beyond that which is already being realized when blending E10. When producing
E85, ethanol's high octane results in significant overcompliance with the minimum octane
standard. Refiners do not produce a low octane BOB for producing E85 to realize a cost savings.
Conversely, ethanol plants produce E85 by adding a denaturant to ethanol, which typically is a
low cost, low octane, high RVP hydrocarbon commonly known as NGLs. The corn ethanol
plants add an additional quantity of the NGLs, above the quantity needed to denature the ethanol,
to produce the E85. Thus, E85 produced from NGLs does realize a cost savings. But NGLs are
also lower in energy density, offsetting the potential cost savings to consumers. Regardless, there
is no RVP blending cost for E85 because the high portion of ethanol results in lower RVP
instead of higher RVP; therefore, a lower RVP blendstock is not needed for producing E85. In
fact, to adjust for the lower RVP of E85 blends, E85 is actually blended at roughly 74% ethanol
754 8 9 FR 14760 (February 29, 2024).
755 90 FR 13093 (March 20, 2025).
756 EPA, "EPA Addresses E-10 Standards, Allows for Nationwide Year-Round E15 Sales," April 28, 2025.
https://www.epa.gov/newsreleases/epa-addresses-e-10-standards-allows-nationwide-Year-round-el5-sales.
757 EPA, "Fuel Waivers." https://www.epa.gov/gasoline-standards/fuel-waivers.
758 Some refiners may have extra tankage available to allow producing and storing a lower octane E15 blendstock to
enable selling E15 over its own terminal rack to local retail stations. Refinery rack gasoline sales, however, are
usually a small portion of a refinery's gasoline sales.
759 The RFG pool always took advantage of ethanol's high octane as it was needed to cause a reduction in aromatics
to reduce the emissions of air toxics under the Complex Model-the former compliance tool of the RFG program.
When ethanol replaced methyl tertiary butyl ether (MTBE) as the oxygenate in 2005 when the RFG oxygen
requirement was rescinded, refiners took advantage of ethanol's high octane content. The CG pool, however, could
not take advantage of ethanol's high octane until an entire U.S. gasoline market (i.e.. Midwest) was blended with
ethanol, and then that gasoline market shifted over all at once to a sub-octane blendstock for oxygenate blending
(CBOB). Reviewing CG aromatics levels (high octane aromatics decrease when refiners produce sub-octane
CBOB), refiners switched the CG pool over to low octane CBOB from 2008-2013, which is around the time when
the U.S. reached the E10 blendwall.
367
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on average over both the summer and winter, instead of 85%, to have sufficiently high RVP to
avoid RVP minimum limits.760
The societal cost of using ethanol must include ethanol's lower energy density (fuel
economy effect). Ethanol has about 33% lower energy density than gasoline blendstock (CBOB
and RBOB).761 Accounting for ethanol's lower energy density adds about 80 cents per gallon of
ethanol for all the ethanol blends to account for the additional cost to consumers for having to
purchase a greater volume of less energy dense fuel to travel the same distance.762
10.1.3.2 Biodiesel and Renewable Diesel
Biodiesel and renewable diesel fuel have properties that could cause a cost savings or
incur a cost. Both fuels have higher cetane value relative to petroleum diesel.763 764 Although
ICF/Mathpro considered the possibility of the petroleum refining industry taking advantage of
that property, they concluded that most markets are not cetane-limited and that as a result
refiners likely would not take advantage of this property of biodiesel and renewable diesel.765 At
this time, we do not have any evidence that refiners are capitalizing on biodiesel and renewable
diesel's higher cetane value.
Conversely, a blending cost could be incurred for biodiesel due to the addition of
additives to prevent oxidation and lower pour or cloud point. The need to add pour point
additives is primarily a cold weather issue and likely contributes to the lower observed blending
rates of biodiesel into diesel fuel in the winter compared to the summer, particularly in northern
areas. However, for our analysis, no additive costs were included for biodiesel because we do not
have a good estimate for them.
As with ethanol, the societal cost of using biodiesel and renewable diesel must include
their lower energy density in comparison to petroleum-based diesel fuel, which impacts fuel
economy. Accounting for this fuel economy effect adds about 270 and 170 per gallon to the
societal cost of biodiesel and renewable diesel, respectively.766
7611E85 can have RVP levels that are too low, which makes starting a parked car difficult. When blended at about
70% ethanol, the RVP of the ethanol-gasoline blend is a little higher than E85 blends, improving cold starts.
761 EIA, "Frequently Asked Questions - How much ethanol is in gasoline, and how does it affect fuel economy?"
April 1, 2024. https://www.eia. gov/tools/faas/faa.php?id=27&t=10.
762 The fuel economy cost is calculated by subtracting the density of ethanol from the density of gasoline, all divided
by the density of gasoline and multiplied by the price of gasoline.
763 Farm Energy, "Animal Fats for Biodiesel Production," January 31, 2014. https://farm-
energv.extension.org/animal-fats-for-biodiesel-production.
764 McConnick, Robert, and Teresa Alleman. "Renewable Diesel Fuel," NREL, July 18, 2016.
https://cleancities.energy.gOv/files/u/news events/document/document url/182/McConnick Alleman RD Overv
iew 2016 07 18.pdf.
765 "Not cetane-limited" means that refiners are normally producing diesel that meets the cetane standard. Thus,
refiners will not achieve any diesel fuel production savings by blending in high cetane biodiesel and renewable
diesel. ICF, "Modeling a 'No-RFS' Case," EPA Contract No'. EP-C-16-020, July 17, 2018.
766 The fuel economy cost for biodiesel is calculated by subtracting the density of biodiesel from the density of
diesel fuel, all divided by the density of diesel fuel and multiplied by the price of diesel fuel; for renewable diesel,
substitute renewable diesel for biodiesel.
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10.1.4 Distribution and Retail Costs
In this section, we evaluate the costs of distributing biofuels from the places where they
are produced to retail stations as well as the costs of dispensing these fuels at those retail stations.
10.1.4.1 Ethanol
10.1.4.1.1 Distribution Costs
Distribution costs are the freight costs to distribute the ethanol, although the total
distribution costs could also include the amortized capital costs of newly or recently installed
distribution infrastructure. A significant amount of capital has already been invested to enable
ethanol to be blended nationwide as E10, and a small amount of ethanol as E85 and El 5.
Virtually all terminals, including those co-located with refineries, standalone product distribution
terminals, and port terminals, have made investments over the last 15-plus years to enable the
distribution and blending of ethanol. Thus, these capital costs may be sunk. However, in the part
of the analysis where we estimate ethanol's distribution costs using spot ethanol prices, as
described below, we may inherently be including some distribution capital costs which are still
being recovered.
As part of the effort by ICF/Mathpro to estimate use of renewable fuels in the absence of
the RFS program, ICF estimated distribution costs for ethanol and biodiesel. We used these cost
estimates for this rulemaking.767 ICF estimated ethanol's distribution costs based on ethanol spot
prices that are available from the marketplace. The spot prices likely represent the operating and
maintenance costs, and any capital costs which are being recovered. Certain publications,
including OPIS and Argus, publish ethanol spot prices for certain cities, and these spot prices
were consulted for estimating ethanol's distribution costs. These spot prices are tracked because
they represent unit train origination and receiving locations where the custody of the ethanol
changes hands in the distribution system. For the ethanol consumed in the Midwest, the ethanol
is likely to be moved by trucks directly to the terminals in the Midwest. For the areas adjacent to
the Midwest, the ethanol is assumed to be moved by truck for the areas nearest to the Midwest
(eg., to Colorado and Wyoming), and by manifest train for the adjacent areas further out (eg., to
Utah and Idaho). These various means for distributing ethanol and their associated costs were
accounted for when estimating the ethanol's distribution cost to and within each region. For the
ethanol being shipped by unit train out of the Midwest, the ICF distribution cost analysis
assumed that the ethanol is collected in Chicago by truck or manifest rail at an average cost of 70
per gallon and then moved out of the Midwest to other areas mostly using unit trains. Since ICF
completed its analysis, we discovered that most corn ethanol plants are capable of sourcing unit
trains from their plants.768 Thus, the 70 per gallon transportation cost from corn ethanol plants to
Chicago is not necessary and this cost was removed from the estimated cost to each destination.
Once the ethanol is moved to a unit train or manifest train receiving terminal, there are
many other terminals in these areas which must also receive the ethanol. Ethanol must then be
767 Id.
768 EIA, "Rail congestion, cold weather raise ethanol spot prices," Today in Energy, April 3, 2014.
https://www.eia. gov/todavinenergy/detail.php?id= 15691.
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moved either by truck or, if further away, by manifest train from the unit train receiving
terminals to the other terminals. Since many of these other terminals do not have sidings for rail
car offloading, the manifest train ethanol must be offloaded to trucks at tank car-truck transfer
locations before it can be received by these other terminals. ICF estimated that, on average, the
further distribution of ethanol from these unit train receiving terminals to the rest of the terminals
would cost an additional 90 or 110 per gallon, depending on the PADD. Table 10.1.4.1.1-1
provides the estimate of ethanol distribution costs for the various parts of the country estimated
by ICF, both the original estimates and our revised estimate, which removes ICF's original
assumed 70 per gallon transportation cost to Chicago.
Table 10.1.4.1.1-1: Ethanol Distribution Costs for Certain Cities or Areas (2017$)
Distribution Cost (0/gal) to:
Location
Hub/Terminal
Total (0/gal)
To
From
Blending
Revised
PADD
Area
Chicago
Chicago
Terminal
ICF Estimate
Estimate
Florida/Tampa
17.8
11.0
35.8
28.8
S outheast/ Atl anta
11.7
11.0
29.7
22.7
PADD 1
VA/DC/MD
9.7
11.0
27.7
20.7
Pittsburgh
6.2
11.0
24.2
17.2
New York
7.7
11.0
25.7
18.7
PADD 2
Chicago
0.0
11.0
18.0
11.0
Tennessee
7.0
9.7
11.0
27.7
20.7
PADD 3
Dallas
4.5
11.0
22.5
15.5
PADD 4
6.2
11.0
24.2
17.2
Los Angeles
16.4
9.0
32.4
25.4
PADD 5
Arizona
16.4
9.0
32.4
25.4
Nevada
12.4
9.0
28.4
21.4
Northwest
12.4
9.0
28.4
21.4
We volume-weighted the various revised regional distribution cost estimates for PADDs
1 through 5 to derive a PADD-average ethanol distribution cost for all PADDs. Table 10.1.4.1.1-
2 summarizes the estimated average ethanol distribution cost by PADD, and the average for the
U.S. adjusted to 2024 dollars.
Table 10.1.4.1.1-2: Average Ethanol Distribution Cost by PADD and the U.S. (2024$)
Gasoline Volume
Average Ethanol
Distribution Cost
Region
(kgal/day)
(0/gal)
PADD 1
123,700
22.2
PADD 2
102,400
11.0
PADD 3
68,500
15.5
PADD 4
15,100
17.2
PADD 5
63,400
24.4
U.S. Average (2017$)
373,100
18.1
U.S. Average 2024$
20.1
370
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10.1.4.1.2 Retail Costs
This analysis assigns no infrastructure costs to E10 but does assign infrastructure costs to
El5 and E85. However, infrastructure investments and improvements are needed on an ongoing
basis to maintain the retail availability of all fuels (i.e., E10, E15, and E85). The preponderance
of HBIIP investments, for instance, support new dispensers for all fuel types and E10 sales at
these locations far outweigh those of higher-level ethanol blends. However, we believe that the
primary reason for the revamps, and the reason why retailers are receiving an HBIIP subsidy, is
to facilitate the sales of higher-level ethanol blends. By apportioning all infrastructure costs to
higher-level ethanol blends and none to the E10 that would be sold from the same dispenser, this
approach is the best estimate of the marginal costs of higher-level ethanol blends consistent with
a social cost estimate. The El5 and E85 volumes that we are using in this cost analysis are
summarized in Chapter 6.5.2.
The retail costs for El5 and E85 are estimated based on the investments that must be
made to offer such ethanol blends. For this final rule, a more objective means was used to
estimate the El5 and E85 retail upgrading costs compared with the method used at the proposal
stage. The HBIIP program provides reports on the number of retail outlets for which the station
owners have requested and received grants to upgrade their retail stations enabling them to offer
E15 and E85, in addition to E10. We reviewed one of the reports which provided grants for a
total of 58 projects, which upgraded the refueling equipment of 78 retail stations. Of the 78 retail
stations, 39 retail stations were installing El5 compatible refueling equipment, while 42 retail
stations were installing E85 compatible refueling equipment.
To this end, we reviewed literature and conferred with EPA's Office of Underground
Storage Tanks on what might be considered "typical" for El5 and E85 equipment installations
for a typically sized retail station selling these blends.769-770-771-772
For the typical retail station being revamped to sell El5, the station was installing 0.9
underground storage tanks and installing 2.8 new dispensers. While in reality a station cannot
install fractions of a storage tank or dispenser, the use of these fractional values allows us to
apply the resulting cost estimates across all retail outlets as a simple average cost. The manager
of HBIIP estimated that each E15- and E85-compatible dispenser costs $53,000, and each E15-
compatible underground storage is estimated to cost $168,000, for a total cost of $305,000 per
revamped retail station wanting to sell El5. We assume that HBIIP recipients are installing
equipment to sell El5 or E85 but not both.
The typical retail station being revamped to sell E85 add 0.8 underground storage tanks
and 2.8 E85-compatible dispensers. Using the same costs for USTs and for El5- and E85-
769 Moriarty, K„ and J. Yanowitz. "E15 And Infrastructure," National Renewable Energy Laboratory, NREL/TP-
5400-64156. Mav 21. 2015. https://doi.org/10.2172/1215238.
7711 EPA, "E15's Compatibility withUST Systems," January 2020. https://www.epa.gov/sites/default/files/202Q-
01/documents/e 15-ust-compatibilitv-statement-1 -23-20.pdf.
771 EPA, "UST System Compatibility with Biofuels," EPA-510-K-20-001, July 2020.
https://www.epa.gov/sites/default/files/2020-07/documents/ust compatibility booklet formatted final 7-13-
2020 508.pdf.
772 Conversations with Ryan Haerer, Office of Underground Storage Tanks; Spring 2022.
371
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compatible dispensers, the total revamp cost to revamp a typical retail station to provide E85 is
estimated to cost $283,000 per retail station.
Retail stations can incur costs which are higher or lower than the retail revamp costs we
estimate for offering E15 and E85. If the retail station already has dispensers, tanks and other
equipment that can offer El5 or E85 fuel, then perhaps only a few thousand dollars would need
to be spent to make some dispenser parts compatible with the higher concentration ethanol. On
the other hand, if a large, high throughput retail station needs new dispensers and also needs to
install one or more separate storage tanks and other equipment to store and dispense El5 or E85,
then the installation costs would be much higher—potentially over a million dollars. A small
percentage of retail stations each year must undergo a significant overhaul once their retail
station equipment (tank piping, dispensers, and other associated equipment) has significantly
deteriorated, and when they do so the newly installed equipment is compatible with the higher
ethanol blends. In this case, the station renovation cost for higher ethanol blends is solely the
incremental cost of the ethanol-compatible equipment over non-ethanol-compatible equipment
because the replacement cost for the equipment can be attributed to normal maintenance. The
retail revamp costs to offer higher ethanol blends estimated here attempts to find a middle
ground for representative costs of stations that need to replace dispensers and add tanks to offer
E15 and E85.
To estimate the per-gallon cost, it is necessary to estimate the volume of E85 and E15
sold at each station which offers these blends. These per-station volume estimates were based on
data collected by USD A through their BIP program and made available to EPA.773 The total
volumes of El 5 and E85 sold were divided by the estimated number of El 5 and E85 retail
stations to estimate the volume per retail station. As a result, retail stations offering El5 are
estimated to sell 217 thousand gallons of El 5 per year while retail stations offering E85 are
estimated to sell 85 thousand gallons of E85 per year. Using the amortization factor shown in
Table 10.1.2.1.1-1 and amortizing these retail costs over the volume of ethanol in El 5 and E85
(15% for E15 and 74% for E85), covering the cost of capital for the retail equipment adds $1.02
and $50 per gallon to the ethanol portion of E15 and E85, respectively.774 When solely
amortizing this retail cost solely over the 5% and 64% of ethanol that is incremental to E10, the
cost is $3.08 and 570 per gallon of ethanol in E15 and E85 in excess of E10, respectively.
Another potential retail cost that could apply for E85 is the increased time spent
refueling. Since a motor vehicle travels fewer miles on a tankful of E85 compared to refueling
with E10, the driver will need to refuel more often when running their vehicle on E85. This
additional time is a cost to the driver. Such a refueling cost was not estimated for E85 for this
rule.
773 "Communication with USD A on the BIP program 1-19-22," Docket Item No. EPA-HQ-OAR-2021-0324-0734.
https://www.regulations.gov/document/EPA-HO-OAR-2021-0324-Q734.
774 The amortized cost is calculated by multiplying the retail station revamp cost by the capital amortization factor in
Table 10.1.2.1.1-1 (0.11) and then dividing by the annual throughput volume of ethanol contained inE15 orE85 at a
typical retail station.
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10.1.4.2 Biodiesel and Renewable Diesel Distribution Costs
Biodiesel distribution costs were determined by ICF under contract to EPA based on an
estimate of biodiesel being moved by rail and by truck within each PADD and between
PADDs.775 While biodiesel production is more spread out across the country than ethanol, a
significant amount must still be moved long distances to match production to demand. The
internal PADD rail costs were estimated to be 150 per gallon and truck movements for shorter
fuel movements were estimated based on distance moved. Movement of these fuels between
PADDs was assumed to be made by rail for most areas and by ship from the Gulf Coast to the
West Coast. ICF relied on EIA reports for biofuel movements between PADDs. Based on these
analyses, the inter-PADD movements were estimated to cost 15-320 per gallon, depending on
the distance that the biodiesel must travel.
Renewable diesel fuel distribution costs are assumed to be the same as biodiesel. Because
renewable diesel is very similar in quality as diesel fuel, it can more readily be blended in more
places in the diesel fuel distribution system, including at refineries, where the renewable diesel
fuel would be moved by the same distribution system as diesel fuel. Thus, if renewable diesel is
used locally its distribution costs would likely be lower than biodiesel. However, much of the
renewable diesel is expected to be distributed to the West Coast to help meet the Low Carbon
Fuel Standard programs there.
Table 10.1.4.2-1 summarizes the biodiesel and renewable diesel distribution costs for
each PADD, taking into account the amount of fuel that is distributed within PADDs and
between PADDs, and shows the national average distribution cost and that average cost adjusted
to 2024 dollars.
Table 10.1.4.2-1: Estimated Biodiesel and Renewable Diesel Fuel Distribution Cost by
PADD (2024$)
Destination
Location
PADD Total
Transportation Cost (Wgal)
PADD 1
21.6
PADD 2
15.0
PADD 3
16.0
PADD 4
25.0
PADD 5
23.8
U.S. Avg.
17.7
U.S. Average 2024$
20.5
10.1.4.3 Renewable Natural Gas
10.1.4.3.1 Distribution Costs
We assume for the purposes of this cost analysis that all RNG is gathered from landfill
off-gassing and cleaned up before being transported to where it can be used. Typically, in such
775 ICF, "Modeling a 'No-RFS' Case," EPA Contract No. EP-C-16-020, July 17, 2018.
373
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cases, this RNG will end up in a nearby natural gas pipeline, but in some rare cases it also could
be compressed or liquified for dispensing into the onboard CNG or LNG tanks of a local truck
fleet at or near the landfill site.
Information on the length of pipeline needed to bring landfill gas to a nearby natural gas
pipeline is not readily available, but we made some assumptions to estimate this distance.
Landfills are generally located near to, although generally not in, urban areas to keep the
transportation costs lower for hauling the municipal waste to the landfill. The landfill gas is
estimated to be moved 5 miles to access a commercial natural gas pipeline. Each mile of pipeline
is estimated to cost $1 million to install, adding up to $6.6 million for the entire 5 mile pipeline
after adjusting from 2019 to 2024 dollars.776 We modeled a typical volume example case of 750
SCF of biogas being captured per minute and then cleaned up to pipeline-grade RNG to
represent the cost for a typically sized landfill.777 When the pipeline capital costs are amortized
over that typical volume of landfill biogas-based RNG, the pipeline capital cost is estimated to be
$1.84 per million Btu.778 While we focus on the case of landfill biogas-based RNG in this
instance, if the biogas generation and/or RNG upgrading facility is located far from an existing
natural gas pipeline, such as a farm generating biogas from animal waste, the pipeline cost from
distributing the RNG can be very expensive and maybe prohibitive.
Once the RNG is transported through the new pipeline to the natural gas pipeline, it
incurs a cost for distribution through the existing natural gas pipeline. Landfills are generally
located near urban areas, which are destinations for natural gas pipelines. This means that the
distribution costs for RNG into the natural gas pipeline would likely be less than that for fossil
natural gas, which is generally distributed longer distances from natural gas production areas.
Fossil natural gas will incur both variable and fixed operating costs in the upstream pipelines,
which RNG will avoid by being injected downstream. Furthermore, the addition of RNG
downstream in the natural gas pipeline system can help the natural gas distribution system avoid
capital investments that would otherwise be necessary to debottleneck the upstream natural gas
pipeline system to meet commercial and industrial sector demand increases. If we assume that
RNG would be injected into a natural gas pipeline at least large enough to serve commercial
consumers, the RNG distribution cost can be based on commercial natural gas distribution costs
which are represented by the natural gas prices to commercial consumers. As summarized in
Table 10.2.2-2, distribution of natural gas to commercial consumers is estimated to cost
$6.07/MSCF in 2026. We could not find detailed cost information for the distribution of
commercial natural gas through different parts of the distribution system that would allow us to
scale the commercial natural gas distribution costs to the portion of the natural gas pipeline used
by RNG. For this reason, half of the commercial natural gas distribution cost, or about
776 EPA, "LFGcost-Web - Landfill Gas Energy Cost Model." https://www.epa.gov/lmop/lfgcost-web-landfill-gas-
energy-cost-model.
777 The Coalition for Renewable Natural Gas, "Economic Analysis of the U.S. Renewable Natural Gas Industry,"
December 2021. https://guidehouse.com/-/media/www/site/insights/energy/2022/guidehouse-esirng-coalition-final-
reportl22022.pdf.
778 The 5.3 million capital cost is amortized over the biogas volume by first multiplying it by the capital cost
amortization factor (0.11) to derive an annual average cost, and then dividing this volume by the annual volume of
biogas which is estimated to be flowing at 600 SCF/min.
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$2.53/MSCF, is assumed to apply to biogas for distribution to the natural gas pipeline.779
While this cost analysis assumes the biogas is being produced entirely at landfills, it is
worthwhile to consider the situation other RNG producers are likely to face to distribute their
biogas. Like landfills, RNG production at wastewater treatment plants and municipal waste
digesters are located near cities and thus would likely have distribution costs similar to landfills.
Conversely, agricultural waste digesters are much more likely to be located in rural areas further
away from both natural gas pipelines and urban areas. The distribution costs for RNG producers
using agricultural waste digesters would likely be higher. Many of these rural locations may be
so remote that the RNG could be considered stranded and not readily available for use as
transportation fuel, although such stranded locations could perhaps still provide RNG to local
truck fleets which distribute agricultural products.780
10.1.4.3.2 Retail Costs
Retail facilities to dispense RNG are more expensive compared to other transportation
fuel retail costs. One information source provided an estimate that a larger sized CNG retail
facility would cost about $4.61 per million Btu, so this was used for the RNG retail cost.781
When adjusted to 2024 dollars, the estimated retail cost to dispense RNG is estimated to be $6.40
per million Btu.
10.2 Gasoline, Diesel Fuel and Natural Gas Costs
10.2.1 Production Costs
As renewable fuel use increases or decreases, the volume of petroleum-based products,
such as gasoline and diesel fuel, would decrease or increase, respectively. This change in
finished refinery petroleum products results in a change in refinery industry costs. The change in
costs would essentially be the volume of fuel displaced multiplied by the cost for producing the
fuel.
In addition, there could be a situation where we may need to account for capital
investments made by the refining industry. For example, increasing renewable fuel standards
could reduce capital investments refiners would otherwise make to increase refined product
production above previous levels. In this case increased renewable fuel capital investments
would offset decreased refining industry investments. However, we have not assumed for this
analysis that there would be any reduction in refining industry investments considering the
current situation. After the economic impact of the Covid-19 pandemic, EIA data shows that the
779 Biogas producers tell us that they are being charged an equivalent distribution price that natural gas producers are
being charged which essentially assumes that they are using the entire natural gas pipeline. This pricing scheme,
though, does not represent the true social cost for distributing biogas, and a separate distribution cost is estimated for
biogas.
780 The term "stranded" means the cost to recover and use the biogas is too high to justify installing the equipment
collect upgrade and distribute it for commercial use. The facility may more easily be able to use the biogas onsite to
generate electricity.
781 Clean Fuel Connection, Inc. "Permitting CNG and LNG Stations, Best Practices Guide for Host Sites and Local
Permitting Authorities."
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demand for gasoline and diesel fuel is stable or somewhat in decline.782 Thus, we would not
anticipate there to be refined product investment regardless of the renewable fuel volumes and
thus no savings that would offset renewable fuel investments.
10.2.1.1 Gasoline and Diesel Fuel Production Costs
The production cost of gasoline and diesel fuel are based on the projected wholesale price
for gasoline and diesel fuel provided in AEO2025.783 The projected Brent crude oil prices and
gasoline and diesel fuel wholesale prices in 2026 and 2027 are summarized in Table 10.2.1.1-1.
10.2.1.1-1: Estimated Gasoline Production Costs
2026
2027
Brent Crude Oil Prices ($/bbl)
81.5
80.4
Wholesale Prices (Assumed to
be Production Costs) ($/gal)
Gasoline
2.18
2.14
Diesel Fuel
2.28
2.29
Since the EIA modeling for its AEO includes a representation of the RFS program, some
price impact of the RFS program is likely represented in these wholesale gasoline and diesel fuel
prices. The AEO modeling includes the most recent RFS standards promulgated at the time of
analysis, so the wholesale price estimates in AEO2025 would be optimal for modeling the final
rule RFS standards incremental to the 2025 Baseline. The RFS impact on the AEO gasoline and
diesel fuel prices will slightly bias the cost analysis conducted for the No RFS Baseline.
However, the impact on the estimated costs is expected to be minimal and within the accuracy of
the cost analysis.
10.2.1.2 Natural Gas Production Cost
For the most part, the increased consumption of RNG has been displacing fossil natural
gas, although that is starting to change as new CNG trucks are displacing diesel fuel fueled
trucks. Because our primary analysis is relative to the No RFS Baseline, the majority of trucks
converting to using RNG have displaced fossil natural gas, thus, our cost analysis will assume
that RNG is displacing fossil natural gas. For estimating the cost of RNG relative to natural gas,
it is necessary to estimate the production cost of fossil natural gas. The natural gas production
cost can be estimated using natural gas spot prices. In AEO2025, EIA projects the natural gas
spot price for Henry Hub to average $2.78/MSCF in 2026 and decrease slightly in 2027.784 The
Henry Hub spot price most closely represents the natural gas field price, and thus is a proxy for
its production cost.
782 AEO2025, Table 12 - Petroleum and Other Liquid Prices, and Table 57 - Component of Selected Petroleum
Product Prices.
783 AEO2025, Table 57 - Components of Selected Petroleum Product Prices.
784 AEO2025, Table 13 - Natural Gas Supply, Disposition, and Prices.
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10.2.2 Gasoline, Diesel Fuel and Natural Gas Distribution and Blending Cost
10.2.2.1 Gasoline and Diesel Fuel
Gasoline and diesel fuel distribution costs from refineries to terminals are estimated as
the difference between wholesale prices and terminal prices (which we estimated based on
historical sales-for-resale prices). This results in estimated gasoline and diesel fuel distribution
costs to the terminal of 50 and 80 per gallon, respectively.
We also estimated the distribution costs from terminals to retail stations, which also
contains the retailing costs. To do so, we first calculated the retail costs of gasoline, less taxes.
We calculated this by subtracting average federal and state taxes, which are 550 per gallon for
gasoline and 640 gallon for diesel fuel, from historical gasoline and diesel fuel retail prices.785
Then, we calculated the difference between historical retail prices (less taxes) and historical
terminal prices (estimated as sales for resales prices) to estimate the distribution costs from the
terminal to retail and retail costs. Price data for 2017 to 2019 was used to estimate the
distribution and retail costs—these years were chosen because they avoided the price distortions
associated with the Covid-19 pandemic and war in Ukraine.786 The resulting distribution costs
for gasoline and diesel fuel are 50 and 80 per gallon, respectively. The retail costs for gasoline
and diesel fuel are estimated to be 200 and 400 per gallon, respectively. These various prices and
estimated costs are summarized in Table 10.2.2.1-1.787
Table 10.2.2.1-1: Estimated Gasoline and Diesel Fuel Distribution and Retail Costs ($/gal)
Gasoline
Diesel Fuel
2017
2018
2019
Average
2017
2018
2019
Average
Bulk Price
1.64
1.94
1.74
1.77
1.62
2.05
1.86
1.85
Sales for Resale
1.69
1.98
1.81
1.83
1.69
2.13
1.96
1.93
Retail Price
2.42
2.72
2.60
2.58
2.65
3.18
3.06
2.96
Taxes
0.55
0.55
0.55
0.55
0.64
0.64
0.64
0.64
Distribution Costs
0.05
0.04
0.07
0.05
0.07
0.08
0.08
0.08
Retail Costs
0.18
0.19
0.24
0.20
0.32
0.41
0.46
0.40
We then apply the estimated gasoline and diesel fuel distribution and retail costs, adjusted
to 2024 dollars, to the projected wholesale gasoline and diesel fuel prices in Table 10.2.1.1-1 for
each year to estimate the gasoline and diesel fuel prices from refinery to retail. These gasoline
and diesel fuel prices are summarized in Table 10.2.2.1-2.
785 American Petroleum Institute, "Gasoline and Diesel Taxes," January 1, 2022. https://www.api.org/-
/media/files/statistics/state-motor-fuel-notes-summarv-ianuarv-2022.pdf.
786 EIA, "Refiner Gasoline Prices by Grade and Sales Type," Petroleum & Other Liquids, June 1, 2022.
https://www.eia.gov/dnav/pet/pet pri refine dcu nus a.htm.
787 After completing our cost analysis, we learned that AEO2025 also projects what they refer to as "distribution
costs," which in Table 10.2.2.1-2 corresponds to the sum of (1) Terminal and Retail Costs (which represents the
difference between wholesale and retail prices) and (2) Distribution Cost. As a basis for comparison, the gasoline
and wholesale prices reported in Table 10.2.2.1-2 are lower than those reported in AEO2025. In 2026, Table
10.2.2.1-2 reports AEO2025-defined "distribution costs" as $0.26/gallon and $0.54/gallon for gasoline and diesel,
respectively. In AEO2025, these corresponding projections are $0.33/gallon and $0.56/gallon, respectively. We may
use these AEO estimates for future rulemakings.
377
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Table 10.2.2.1-2: Projected Gasoline ant
2026
2027
Brent Crude Oil Prices
$81.5
$80.4
Retail Cost minus taxes
$2.46
$2.41
Terminal and Retail Costs
$0.23
$0.23
Gasoline
Terminal Costs
$2.24
$2.19
Distribution Cost
$0.06
$0.06
Production Cost
(from Table 10.2.1.1-1)
$2.18
$2.13
Retail Cost minus taxes
$2.79
$2.80
Terminal and Retail Costs
$0.45
$0.45
Diesel Fuel
Terminal Costs
$2.36
$2.37
Distribution Cost
$0.09
$0.09
Production Cost
$2.28
$2.29
(from Table 10.2.1.1-1)
Diesel Production Costs (2024$; $/gal)
10.2.2.2 Natural Gas
EIA projects natural gas prices downstream of natural gas production fields which can be
used to estimate natural gas distribution costs.788 The three principal natural gas consumers are
industrial, commercial, and residential. Industrial consumers consume the largest natural gas
volumes per facility, and due to the very large consumption, the distribution costs are lowest.
Commercial entities are medium sized consumers, and their distribution costs are higher than
industrial consumers. Residential consumers, because of their very low consumption, must pay a
much larger distribution cost to maintain the distribution system for much lower consumption to
each home. EIA also provides a price for natural gas sold into the transportation sector, although
this price includes road taxes which would need to be omitted for the purposes of this cost
analysis, so we did not use EIA's natural gas to transportation sector cost.789
The varying costs for these different natural gas categories permit estimating natural gas
distribution costs for natural gas consumed by motor vehicles. Natural gas produced and
distributed to retail outlets to refuel natural gas trucks and cars most likely falls in the category of
midsized consumers, or commercial users. The distribution costs of natural gas can therefore be
estimated by subtracting the projected Henry Hub prices from the projected commercial prices.
Thus, Henry Hub prices projected in AEO2025 were subtracted from the commercial prices for
2026 and 2027. Table 10.2.2.2-1 summarizes the calculation of natural gas distribution costs. To
put the natural gas costs on the same footing as the RNG, we also add $6.40 per million Btu for
retail costs.790
788 AEO2025, Table 13 - Natural Gas Supply, Disposition, and Prices.
789 Taxes are not included in social cost estimates because they are not true costs, only transfer payments.
790 Clean Fuel Connection, Inc. "Permitting CNG and LNG Stations, Best Practices Guide for Host Sites and Local
Permitting Authorities.
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Table 10.2.2.2-1: Natural Gas Distribution Cost (2024$; S/MSCF)
2026
2027
Commercial Prices
$8.85
$8.90
Henry Hub Prices $/MMBtu
$/MSCF
$2.88
$2.78
$2.74
$2.64
Pipeline Distribution Costs
$6.07
$6.26
Retail Station Costs
$6.40
$6.40
Total Average Distribution &
Retail Station Costs
$12.47
$12.66
10.3 Fuel Energy Density and Fuel Economy Cost
To estimate the change in fossil fuel volume that would occur with these changes in
renewable fuel volumes and to estimate the fuel economy cost summarized in Chapter 10.4.1, it
was necessary to estimate the energy density of each fuel. Table 10.3-1 contains the estimated
energy densities for the various renewable fuels and petroleum fuels analyzed for this cost
analysis; all values are taken from the GREET 2024 model unless otherwise noted.791 The table
is organized to show the relative energy density of a fuel type relative to the baseline fuel, which
we assume is E10 gasoline and B5 diesel fuel.
Table 10.3-1: Lower Heating Value (LHV) Energy Densities (GREET 2024)
LHV Energy
Density (Btu/gal)
Percent of
Baseline Fuel
Energy Density
Relative to E10
Gasoline
E10 Gasoline
110,428
-
Gasoline (E0)a
114,200
103.1
El5 Gasoline
108,542
98.3
E85b
86,285
78.1
Denatured Ethanol
76,477
69.2
Energy Density
Relative to B5
Diesel Fuel
B5 Diesel Fuel
128.009
-
Diesel Fuel
128,450
100.3
Renewable Diesel0
123,800
0.967
Biodiesel
119,550
0.934
Other Products
Crude Oil
129,670
Pure Ethanol
76,330
Natural Gas Liquids
83,686
a From Diesel Fuels Technical Review; Chevron Global Marketing; 2007.
b Assumed to contain 74% ethanol.
0 Energy content of renewable diesel reported to EMTS.
791 Wang, Michael, Cai, Hao, Ou, Longwen, Elgowainy, Amgad, Alam, Md Rakibul, Benavides, Pahola T„
Benvenutti, Livia, Burnham, Andrew, Do, Thai N., Farhad, Masum, Gan, Yu, Gracida-Alvarez, Ulises R., Hawkins,
Troy R., Iyer, Rakesh K„ Kar, Saurajyoti, Kelly, Jarod C., Kim, Taemin, Kolodziej, Christopher, Kwon, Hoyoung,
Lee, Uisung, Lim, Juin Y„ Liu, Xinyu, Lu, Zifeng, Morales, Michele, Ng, Clarence, Pandey, Ishan, Shukla,
Siddharth, Siddique, Nazib, Sun, Pingping, Sykora, Tom, Vyawahare, Pradeep, and Zhou, Jo. "Greenhouse gases.
Regulated Emissions, and Energy use in Technologies Model ® (2024 Excel)." Computer software. December 20,
2024. https://doi.Org/10.11578/GREET-Excel-2024/dc.20241203.l.
379
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As discussed in Chapter 10.1, when a lower density fuel replaces a higher density fuel
one-for-one on a volumetric basis, there is a loss of energy value to society in the form of lower
fuel economy. To account for this fuel economy effect in the cost analysis, the change in fossil
fuel volume displaced by a change in renewable fuel volume is estimated by the relative energy
content of the renewable and fossil fuels. If the energy density is not the same between the fossil
fuel and renewable fuel displacing it, the energy equivalent replacement is not one-for-one on a
volume basis. For example, ethanol contains about 33% lower energy per volume than the
gasoline it is displacing, such that 100 gallons of ethanol would displace 67 gallons of gasoline.
By calculating these impacts on an energy basis rather than a volume basis, the loss of energy to
society associated with the fuel economy effect is incorporated into the cost analysis. We do not
report this estimated fuel economy cost separately.
For the individual fuel cost summary in Chapter 10.4.1, it is desirable to report out a
specific fuel economy effect. To do so, the difference in energy density between the renewable
fuel and fossil fuel is divided by the fossil fuel energy density and then multiplied times the
fossil fuel cost at retail, before taxes, to estimate the fuel economy effect.
10.4 Costs
In this section, we describe the overall societal costs of the final renewable Fuel Volumes
in this rule. Costs are estimated for six of the categories of renewable fuel discussed in Chapter
10.1: corn starch ethanol, biodiesel, renewable diesel, other advanced ethanol, RNG, and CKF
ethanol. For corn starch ethanol, we have separately estimated costs for this fuel blended as E10,
E15, and E85. For biodiesel and renewable diesel, we have calculated production costs for the
quantities of fuel we project in Chapter 3 would be produced from soybean oil, corn oil, and
FOG, and then totaled them to comprise total production costs for biodiesel and renewable diesel
respectively; all other biodiesel and/or renewable diesel costs downstream of production are
assumed to be identical for fuels produced from each of these three feedstock types. Other
advanced ethanol is assumed to be sugarcane ethanol. RNG is assumed to be produced from
landfill biogas and be sold as CNG. While sugarcane ethanol and CKF ethanol could be blended
as E10, E15 or E85, we do not estimate separate costs for these blended products; instead, we
assume all sugarcane ethanol and CKF ethanol are blended as E10. For our cost analysis we use
renewable diesel cost for renewable jet fuel costs for the very small volume change which we
analyze. However, we do estimate a cost for separating renewable jet fuel from renewable diesel
fuel, and a cost for using cracking to produce a larger volume of renewable jet fuel.
Costs are presented in several different ways. First, a per-gallon, individual renewable
fuels cost summary presents our analysis of the societal cost of each renewable fuel analyzed for
this rulemaking relative to the fossil fuel being displaced.
Second, aggregate societal costs are presented for the Analyzed Volumes relative to the
No RFS and 2025 Baselines. These estimates combine the costs for all types of renewable fuel
analyzed for this rulemaking. For each case comparison, we first present the change in volume
for each renewable fuel and the quantity of fossil fuel that volume is estimated to displace. Then
we present the costs for those volume changes by cost category (production, distribution,
380
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blending) for each renewable fuel and the fossil fuel displaced by the renewable fuel. Finally, we
present total annual cost and total per-gallon costs.
Third and finally, we estimate the per-gallon cost on the total gasoline, diesel, and natural
gas pools. To do so, we used the projected total U.S. consumption volumes for each of these
fuels estimated in AEO2025 for the years 2026 and 2027. These estimates are summarized in
Table 10.4-1.
Table 10.4-1: Total Gasoline, Diesel Fuel, and Natural Gas Volumes for 2026 and 2027
2026
2027
Units
Gasoline Volume
137.05
136.13
Billion gallons
Diesel Volume
58.71
57.95
Billion gallons
Natural Gas Volume
32.38
32.84
Trillion cubic feet
Source: AEO2025, Table 11 - Petroleum and Other Liquids Supply and Disposition and Table 13 - Natural Gas
Supply, Disposition, and Prices.
10.4.1 Individual Fuels Cost Summary
Table 10.4.1-1 summarizes the estimated overall societal costs (including production,
distribution, blending, and fuel economy) individually for each of the renewable fuels analyzed
for this rulemaking for the years 2026 and 2027. These costs do not account for the federal
section 45Z Clean Fuel Production Credit (hereafter the "45Z credit" or "45Z") tax subsidies, nor
do they consider taxes or tax subsidies more generally, as these are transfer payments which are
not relevant in the estimation of societal costs. Nor do these costs consider state or local
infrastructure support funding or the funding from USDA's Blends Infrastructure Incentive
Program (HBIIP) which offsets half of the investment costs for revamping retail stations to be
compatible with E85 and E15.792 A separate illustrative line item is added for E15 and E85,
equal to V2 of their retail cost, to help demonstrate the impact that the HBIIP program would have
on the price for these fuels to consumers. The costs of renewable fuels other than RNG are
primarily influenced by the feedstock costs, which can vary significantly depending on a wide
range of factors domestically and internationally, especially since many of them are also globally
traded agricultural commodities. On the other hand, the costs of RNG are primarily associated
with upgrading, transporting, and compressing or liquefying the RNG for use as transportation
fuel.
To put the different fuels on an equivalent basis for the miles driven, the societal cost
analysis also needs to account for each fuel's energy content and impact on fuel economy, which
is first discussed in Chapter 10.3. While these costs may not always be reflected in the sales
prices among the market participants (e.g., if refiners sell, and consumers buy, gasoline based on
volume, not energy content), the varying impacts on fuel economy among the fuels nevertheless
still result in different costs to consumers in operating their vehicles, and to society as a whole,
and therefore must be accounted for in a social cost analysis. The costs associated with the
impact of renewable fuels on fuel economy are determined relative to the fuels they are assumed
to displace; ethanol displaces gasoline, biodiesel and renewable diesel displace diesel fuel, and
792 USD A, Higher Blends Infrastructure Incentive Program, https://www.rd.usda. gov/hbiip.
381
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RNG displaces natural gas.793 To the extent that RINs representing RNG incentivize some
incremental growth in sales of CNG/LNG trucks at the expense of diesel fueled trucks, then
some RNG could also displace diesel fuel. However, this is expected to be a relatively minor
occurrence in 2026 and 2027 when analyzed relative to the No RFS Baseline and so is not
included in this cost analysis. It is important to note that in estimating the social cost for this
rulemaking the fuel economy cost for ethanol blended into E10 is included since this is a cost
that consumers will bear. However, when refiners are considering whether to blend ethanol, such
as for estimating volumes for the No RFS Baseline, they do not consider the fuel economy effect.
This distinction is important for understanding ethanol's relative economic viability in the
marketplace.
The cost shown for renewable compressed natural gas (RCNG) is shown in two different
units. RCNG is shown as dollars per million Btu and dollars per ethanol-equivalent gallon. Table
10.4.1-1 is divided into three subparts, "a," "b" and "c", which present production costs,
blending, retail, and fuel economy costs, and total costs, respectively.
Table 10.4.1-la: Renewable Fuels Production Costs Estimated for 2026 and 2027 (2024$;
$/gal unless otherwise noted)
2026
2027
Corn Starch EthanoP
E10
$1.88
$1.92
El 5 w/ '/2 Retail Costs
$1.88
$1.92
El 5 w/ Retail Costs
$1.88
$1.92
E85 w/ '/2 Retail Costs
$1.88
$1.92
E85 w/ Retail Costs
$1.88
$1.92
Biodiesel
Soybean Oil
$5.55
$5.70
Corn Oil
$4.67
$4.80
FOG
$4.30
$4.42
Renewable Diesel
Soybean Oil
$5.95
$5.97
Corn Oil
$5.07
$5.07
FOG
$4.70
$4.69
Other Advanced
Sugarcane E10 Ethanol
$2.73
$2.73
Cellulosic Biofuel
RCNG ($/Gal Ethanol)
$0.37
$0.37
RCNG ($/MMBtu)
$7.33
$7.33
Corn Kernel Fiber E10 Ethanol
$1.88
$1.88
793 Fuel economy costs are calculated by multiplying the total of petroleum fuel production, distribution and retail
costs by the difference in energy density (Btu per gallon) between the petroleum fuel being displaced and the
renewable fuel, and the result of that operation is divided by the energy density of the petroleum fuel. For ethanol
blended as E10 as an example: (denatured ethanol production + distribution + blending cost) * (E10 gasoline energy
density - denatured ethanol energy density)/denatured ethanol energy density.
382
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Table 10.4.1-lb: Renewable Fuels Blending, Distribution, Retail and Fuel Economy Costs
Estimated for 2026 and 2027 (2024$; $/gal unless otherwise noted)
Blending
Cost
Distribution
Cost
Retail
Cost
Fuel Economy
Cost
Corn
Starch
Ethanol
E10
-$0.77
$0.44
$0.72
El 5 w/ '/2 Retail Costs
$0.44
$1.54
$0.72
El 5 w/ Retail Costs
$0.44
$3.08
$0.72
E85 w/ '/2 Retail Costs
$0.44
$0.29
$0.72
E85 w/ Retail Costs
$0.44
$0.57
$0.72
Biodiesel
Soybean Oil
$0.76
$0.21
Corn Oil
$0.76
$0.21
FOG
$0.76
$0.21
Renewable
Diesel
Soybean Oil
$0.76
$0.09
Corn Oil
$0.76
$0.09
FOG
$0.76
$0.09
Other
Advanced
Sugarcane E10 Ethanol
-$0.77
$0.44
$0.72
Cellulosic
Biofuel
RCNG ($/Gal Ethanol)
$0.46
$0.49
-
RCNG ($/MMBtu)
$6.02
$6.40
-
Corn Kernel Fiber E10 Ethanol
-$0.77
$0.42
$0.72
Note: Fuel economy cost is per fuel being displaced; ethanol displaces gasoline, renewable diesel and biodiesel
displaces diesel fuel, and renewable compressed natural gas (RCNG) displaces natural gas.
For modeling the societal costs of E15 and E85 shown in Chapters 10.4.2 and 10.4.3, the
cost analysis is conducted for the entire volume of El 5 and E85 and includes the blending cost
savings for the E10 BOB used to blend with E15 and E85. For the cost analysis shown here, the
cost for El 5 and E85 is solely for the ethanol volume above that blended at 10% and therefore
does not include any blending value for E10 BOBs to represent the marginal cost for the ethanol
volume above E10.
383
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Table 10.4.1-lc: Renewable Fuels Total Costs Estimated for 2026 and 2027 (2024$/gal
unless otherwise noted)
2026
2027
Corn Starch Ethanol
E10
$2.26
$2.30
El5 w/ '/2 Retail Costs
$4.58
$4.62
El5 w/ Retail Costs
$6.12
$6.16
E85 w/ '/2 Retail Costs
$3.32
$3.36
E85 w/ Retail Costs
$3.61
$3.65
Biodiesel
Soybean Oil
$6.52
$6.67
Corn Oil
$5.64
$5.77
FOG
$5.27
$5.39
Renewable Diesel
Soybean Oil
$6.80
$6.83
Corn Oil
$5.93
$5.92
FOG
$5.56
$5.54
Other Advanced
Sugarcane E10 Ethanol
$3.11
$3.11
Cellulosic Biofuel
RNG ($/Gal Ethanol)
$1.32
$1.32
RNG ($/MMBtu)
$19.75
$19.75
Corn Kernel Fiber El0 Ethanol
$2.26
$2.30
The distribution costs for the biofuels are nationwide averages, which do not capture the
substantial difference depending on the destination. For example, ethanol distribution costs from
the ethanol plants to terminals can vary from under 100 per gallon for local distribution in the
Midwest, to over 300 per gallon for moving the ethanol to the coasts. Thus, total ethanol cost
blended as E10 can vary from around $2.23-2.45 per gallon. RNG distribution includes both the
amortized capital cost of transporting the RNG to a nearby pipeline as well as the amortized
retail distribution capital costs, since the retail facilities for natural gas trucks are relatively
expensive.
Tables 10.4. l-2a and Table 10.4.l-2b summarize the production, distribution, retail and
total costs for each category of fossil transportation fuel—gasoline, diesel fuel, and natural gas.
For gasoline and diesel, production costs are based on wholesale prices in AEO2025.794
Projected natural gas spot prices from AEO2025 are used to represent both feedstock and
production costs of fossil natural gas.
The distribution costs for gasoline and diesel fuel are typical for these fuels. While they
can vary depending on the transportation distance, the differences between high and low
distribution costs for gasoline and diesel fuel are likely lower than that for renewable fuels due to
the well-established pipeline distribution system for petroleum fuels. The natural gas distribution
costs are based on the difference between the projected price for natural gas sold to commercial
entities and the projected natural gas spot price, which reflects the price at the point of
production.
794 AEO2025, Table 57 - Components of Selected Petroleum Product Prices.
384
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Table 10.4.1-2a: Gasoline, Diesel Fuel, and Natural Gas Production, Distribution and
Retail Costs for 2026 and 2027 (2024$)
2026
2027
Distribution Cost
Retail Cost
Gasoline ($/gal)
$1.92
$1.86
$0.29
Diesel Fuel ($/gal)
$2.03
$2.02
$0.53
Natural Gas $/gal ethanol
$0.32
$0.31
$0.46
$0.49
Natural Gas ($/MMBtu)
$4.21
$4.14
$6.07
$6.40
Table 10.4.1-2b: Gasoline, Diesel Fuel, and Natural Gas Total Costs for 2026 and 2027
(2024$)
2026
2027
Gasoline ($/gal)
2.21
2.14
Diesel Fuel ($/gal)
2.57
2.55
Natural Gas $/gal ethanol
1.27
1.26
Natural Gas ($/MMBtu)
16.68
16.61
Table 10.4.1-3 compares the data from Tables 10.4.1-lcand Table 10.4. l-2b to show the
relative cost of the renewable fuels with the fossil fuels they are assumed to displace.
Table 10.4.1-3: Renewable Fuel Costs Relative to Fossil Fuel Costs for 2026 and 2027
2026
2027
Corn Starch Ethanol
E10
-$0.14
-$0.05
El5 w/ '/2 Retail Costs
$2.10
$2.19
El5 w/ Retail Costs
$3.57
$3.66
E85 w/ '/2 Retail Costs
$0.93
$1.02
E85 w/ Retail Costs
$1.22
$1.31
Biodiesel
Soybean Oil
$1.95
$1.79
Corn Oil
$1.40
$1.26
FOG
$1.16
$1.03
Renewable Diesel
Soybean Oil
$2.15
$1.99
Corn Oil
$1.59
$1.46
FOG
$1.36
$1.23
Other Advanced
Sugarcane E10 Ethanol
$0.71
$0.76
Cellulosic Biofuel
RNG ($/gal ethanol)
$0.05
$0.06
RNG ($/MMBtu)
$3.07
$3.14
Corn Kernel Fiber El0 Ethanol
-$0.14
-$0.05
10.4.2 Costs for the Analyzed Volumes
In this section, we estimate the costs for the Analyzed Volumes relative to both the No
RFS Baseline as well as the 2025 Baseline. The costs are primarily driven by projected higher
vegetable oil feedstock prices due to the very high increase in vegetable oil demand to meet the
large increase in BBD volumes. Later in this chapter we also estimate costs at both much higher
385
-------
and lower vegetable oil prices. See Chapter 10.1.1.3 for a description of the vegetable oil prices
used in the cost analysis.
10.4.2.1 Analyzed Volumes Relative to the No RFS Baseline
In this section, we summarize the estimated costs for the changes in renewable fuel
volumes described in Chapter 3.2 (changes relative to the No RFS Baseline volumes are
described in Chapter 2). For this analysis we considered all societal costs, including production,
blending, and distribution costs, and differences in energy density.
10.4.2.1.1 Volumes
The renewable fuel and fossil fuel volume changes under the Analyzed Volumes relative
to the No RFS Baseline are summarized in Tables 10.4.2.1.1-la and lb, respectively. For
estimating the volume of petroleum fuel being displaced by the biofuel which is being blended
into the petroleum fuel, we rely on the energy densities from Table 10.3-1 for determining the
relative volumes.
Table 10.4.2.1.1-la: Analyzed Volumes - Renewable Fuel Volume Changes Relative to the
No RFS Baseline (million gallons, except where noted)
Change in
Renewable Fuel
Volumes
Fuel Type
2026
2027
Cellulosic Biofuel
RNG as CNG - Landfill Biogas (MMSCF)
58,925
61,801
Corn Kernel Fiber Ethanol
0
0
Non-cellulosic Advanced
Biodiesel - Soybean
627
850
Biodiesel - FOG
-79
-116
Biodiesel - Corn Oil
191
124
Biodiesel - Canola
160
236
Renewable Diesel - Soybean
1,320
1,460
Renewable Diesel - FOG
604
945
Renewable Diesel - Corn
61
128
Renewable Diesel - Canola
981
1,101
Sugarcane Ethanol
0
0
Conventional
Ethanol - E10
336
384
Ethanol - El5
150
169
Ethanol - E85
172
174
Change in RNG Volume
58,925
61,801
Change in Ethanol Volume
657
727
Change in Biodiesel Volume
899
1,094
Change in Renewable Diesel Volume
2,963
3,631
386
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Table 10.4.2.1.1-lb: Analyzed Volumes - Fossil Fuel Volume Changes Relative to the No
RFS Baseline
million gallons, except where noted)
Change in Fossil
Fuel Volumes
Fuel Type
Fuel Displaced
2026
2027
Natural Gas
Cellulosic Biofuel
RNG as CNG - Landfill Biogas (MMSCF)
-58,925
-61,801
Gasoline
Corn Kernel Fiber Ethanol
0
0
Non-cellulosic Advanced
Diesel Fuel
Biodiesel - Soybean
-576
-781
Diesel Fuel
Biodiesel - FOG
73
107
Diesel Fuel
Biodiesel - Corn Oil
-175
-114
Diesel Fuel
Biodiesel - Canola
-147
-217
Diesel Fuel
Renewable Diesel - Soybean
-1,272
-1,407
Diesel Fuel
Renewable Diesel - FOG
-579
-908
Diesel Fuel
Renewable Diesel - Corn
-59
-123
Diesel Fuel
Renewable Diesel - Canola
-945
-1,061
Gasoline
Sugarcane Ethanol
0.0
0.0
Conventional
Gasoline
Ethanol - E10
-225
-257
Gasoline
Ethanol - El 5
-100
-113
Gasoline
Ethanol - E85
-115
-116
-
Change in Gasoline Volume
-440
-487
-
Change in Diesel Fuel Volume
-3,681
-4,504
-
Change in Natural Gas Volume
-58,925
-61,801
The change in gasoline and diesel volume for each year is used to estimate the change in
imported crude oil, based on its relative energy content, and imported gasoline and diesel fuel.
The change in petroleum demanded, and its effect on both imported crude oil and imported
petroleum products, is mainly projected based on a comparison of two separate economic cases:
the Low Economic Growth Case and the Reference Case, modeled by EIA in AEO2025.795 The
AEO Low Economic Growth Case for the years 2026-2027 estimates lower refined product
demand than that of the Reference case, and due to the reduced refined product demand, AEO
estimates changes in reduced imports of crude oil refined products. The two AEO cases project
that for a volume of reduced U.S. consumption of gasoline or diesel fuel, 97.5% of that gasoline
or diesel reduction would be attributed to reduced crude oil imports and reduced imports of
refined products, while only 2.5% of the reduced U.S. product demand would be due to reduced
consumption of domestically produced crude oil and refined products.
Based on these correlations, Table 10.4.2.1.1-2 summarizes the projected change in
petroleum imports expected from the increased consumption of renewable biofuels relative to the
No RFS Baseline. The change in crude oil volume and imported petroleum products is used for
the energy security analysis contained in Chapter 6.
795 See "Change in Product Demand on Imports AEO2025 for Set 2 Final Rule," available in the docket for this
action.
387
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Table 10.4.2.1.1-2: Analyzed Volumes - Projected Change in Imported and Domestic
Crude Oil Due to Increases in Renewable Fuel Consumption - Relative to the No RFS
2026
2027
Change in Domestic Crude Oil
-106
-83
Change in Imported Crude Oil
-4,111
-5,017
We are particularly concerned with petroleum imports due to historical incidents of
imported petroleum supply shortfalls, which have since guided U.S. foreign policies to prevent
such shortfalls. However, some of the renewable fuels or commodities used as feedstocks to
produce renewable fuels (e.g., vegetable oils, oilseeds) may also be imported. Because renewable
fuel demand continues to comprise a much smaller portion of the U.S. fuels market relative to
petroleum products, and any imports and changes of imports of these fuels and feedstocks has
not attracted as much concern as petroleum imports, we would not expect the same level of
concern for increases in imports of renewable fuels and their feedstocks. However, we will
nevertheless discuss possible changes in imports of renewable fuels or their feedstocks.
The 45Z credit was revised in 2025 to incentivize the production of domestic renewable
fuels in the U.S. from North American feedstocks. Due to this change in Federal incentives,
imported feedstocks will be financially disadvantaged in 2026 and 2027. We anticipate any
renewable fuel-related imports in these years would most likely be vegetable oil and FOG
feedstocks, not finished fuels. As a result, we might expect reduced imports of canola oil from
Canada, since it is disadvantaged relative to domestically produced soybean oil. We would
expect significantly reduced imports of used cooking oil from China for the same reasons. For
this reason, we project a smaller amount, just over one quarter, of the vegetable oil consumed to
produce biodiesel and renewable diesel will be imported in 2026 and 2027. These factors are
discussed in more detail in Chapter 7.
10.4.2.1.2 Cost Impacts
The component cost (production, distribution, retail and blending; retail costos were
combined with the rest of the distribution costs) of each biofuel type compared to the fossil fuel
it is displacing under the Analyzed Volumes relative to the No RFS Baseline is summarized in
Tables 10.4.2.1.2-1. These cost estimates based on the Analyzed Volumes relative to the No RFS
baseline, are the primary costs associated with this rulemaking. Since we are projecting increased
vegetable oil prices due to significant increases in vegetable oil-based fuel volumes over the
baseline volumes, there is an additional cost due to increased vegetable oil prices for the baseline
volumes as well. For fuels that we do not project will be supplied in the No RFS Baseline (e.g.,
renewable diesel from soybean oil and canola oil), there is no cost increase for the baseline
volumes. This additional cost for BBD is a separately estimated line item (Increase in Baseline
Price) and included in the total costs.
388
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Table 10.4.2.1.2-1: Analyzed Volumes - Renewable and Petroleum Fuel Costs Relative to
the No RFS Baseline (million 2024$)
Renewable Fuel
Increase in
Petroleum Fuel
Prod.
Distrib.
Blending
Baseline Price
Prod.
Distrib.
Total
Cellulosic Biofuel
RNG as CNG - Landfill
Biogas
448
742
0
-248
-898
44
Corn Kernel Fiber Ethanol
0
0
0
0
0
0
Non-cellulosic Advanced
Biodiesel - Soybean
3481
408
0
879
-1313
-307
3149
Biodiesel - FOG
-340
-51
0
321
165
39
134
Biodiesel - Corn Oil
893
124
0
29
-400
-94
553
Biodiesel - Canola
888
104
0
297
-335
-78
877
2026
Renewable Diesel -
Soybean
7853
860
0
0
-2901
-678
5134
Renewable Diesel - FOG
2826
393
0
1488
-1327
-310
3085
Renewable Diesel - Corn
309
40
0
948
-134
-31
1131
Renewable Diesel - Canola
5836
639
0
0
-2156
-504
3816
Sugarcane Ethanol
0
0
0
0
0
0
Conventional
Ethanol - E10
631
147
-260
-490
-65
-37
Ethanol - E15
281
219
-77
-219
-29
176
Ethanol - E85
323
160
-18
-251
-33
181
Cellulosic Biofuel
RNG as CNG - Landfill
Biogas
470
778
0
-256
-942
50
Corn Kernel Fiber Ethanol
0
0
0
0
0
0
Non-cellulosic Advanced
Biodiesel - Soybean
4847
554
0
541
-1788
-416
3737
Biodiesel - FOG
-512
-76
0
434
244
57
147
Biodiesel - Corn Oil
595
81
0
154
-261
-61
508
Biodiesel - Canola
1346
154
0
182
-496
-116
1069
2027
Renewable Diesel -
Soybean
8718
951
0
0
-3222
-750
5697
Renewable Diesel - FOG
4414
615
0
1507
-2086
-485
3980
Renewable Diesel - Corn
649
83
0
984
-283
-66
1367
Renewable Diesel - Canola
6575
717
0
0
-2430
-566
4296
Sugarcane Ethanol
0
0
0
0
0
0
Conventional
Ethanol - E10
737
168
-297
-548
-74
-15
Ethanol - E15
323
247
-87
-240
-33
210
Ethanol - E85
333
162
-18
-248
-34
196
The costs are aggregated for each fossil fuel type and shown as annual totals and per-
gallon and per MSCF costs in Table 10.4.2.1.2-2.
389
-------
Table 10.4.2.1.2-2: Total Annual and Per Unit Costs Relative to the No RFS Baseline
(2024$)
Total Cost
Per-Unit
Year
Fuel Type
(million $)
Cost
Units
Gasoline
320
0.23
0/gal gasoline
2026
Diesel Fuel
17,877
30.45
0/gal diesel
Natural Gas
44
0.13
$/MSCF natural gas
Total
18,241
9.32
0/gal gasoline and diesel
Gasoline
392
0.29
0/gal gasoline
2027
Diesel Fuel
20,802
35.90
0/gal diesel
Natural Gas
50
0.15
$/MSCF natural gas
Total
21,244
10.95
0/gal gasoline and diesel
10.4.2.2 Analyzed Volumes Relative to 2025 Baseline
In this section, we summarize the estimated costs for the changes in renewable fuel
volumes described in Chapter 3.2 (changes relative to the 2025 Baseline volumes described in
Chapter 2). For this analysis we considered all societal costs (production, blending,
distribution...), and differences in energy density.
10.4.2.2.1 Volumes
The renewable fuel and fossil fuel volume changes under the Analyzed Volumes relative
to the 2025 Baseline are summarized in Tables 10.4.2.2.1-la and lb, respectively. For estimating
the volume of petroleum fuel being displaced by the biofuel which is being blended into the
petroleum fuel, we rely on the energy densities from Table 10.3-1 for determining the relative
volumes.
390
-------
Table 10.4.2.2.1-la: Analyzed Volumes - Renewable Fuel Volume Changes Relative to the
Change in Renewable
Fuel Volumes
Fuel Type
2026
2027
Cellulosic Biofuel
RNG as CNG - Landfill Biogas
(MMSCF)
4,867
10,104
Corn Kernel Fiber Ethanol
66
137
Non-cellulosic Advanced
Biodiesel - Soybean
307
307
Biodiesel - FOG
-4
-4
Biodiesel - Corn Oil
166
166
Biodiesel - Canola
210
210
Renewable Diesel - Soybean
766
906
Renewable Diesel - FOG
-269
-159
Renewable Diesel - Corn
235
235
Renewable Diesel - Canola
759
879
Sugarcane Ethanol
15
15
Conventional
Ethanol - E10
118
6
Ethanol - El 5
15
34
Ethanol - E85
24
50
Change in RNG Volume
4,867
10,104
Change in Ethanol Volume
114
46
Change in Biodiesel Volume
679
679
Change in Renewable Diesel Volume
1,491
1,861
391
-------
Table 10.4.2.2.1-lb: Analyzed Volumes - Fossil Fuel Volume Changes Relative to the 2025
Change in Fossil
Fuel Volumes
Fuel Type
Fuel Displaced
2026
2027
Natural Gas
Gasoline
Cellulosic Biofuel
RNG as CNG - Landfill Biogas
(MMSCF)
Corn Kernel Fiber Ethanol
-4,867
34
-10,104
34
Non-cellulosic Advanced
Diesel Fuel
Biodiesel - Soybean
282
282
Diesel Fuel
Biodiesel - FOG
-4
-4
Diesel Fuel
Biodiesel - Corn Oil
152
152
Diesel Fuel
Biodiesel - Canola
193
193
Diesel Fuel
Renewable Diesel - Soybean
-738
-873
Diesel Fuel
Renewable Diesel - FOG
259
153
Diesel Fuel
Renewable Diesel - Corn
-226
-226
Diesel Fuel
Renewable Diesel - Canola
-732
-847
Gasoline
Sugarcane Ethanol
10
10
Conventional
Gasoline
Ethanol - E10
-16
59
Gasoline
Ethanol - El 5
-10
-23
Gasoline
Ethanol - E85
-16
-33
-
Change in Gasoline Volume
2
48
-
Change in Diesel Fuel Volume
-813
-1,170
-
Change in Natural Gas Volume
-4,867
-10,104
Similar to the analysis conducted in Chapter 10.4.2.1.1, the change in gasoline and diesel
volume for each year is used to estimate the change in petroleum demand and its effect on both
imported crude oil, domestic crude oil, and imported petroleum products. Table 10.4.2.2.1-2
summarizes the projected change in petroleum imports expected from the increased consumption
of renewable biofuels relative to the 2025 Baseline.
Table 10.4.2.2.1-2: Analyzed Volumes - Projected Change in Petroleum Imports Due to
2026
2027
Change in Domestic Crude Oil
-21
-18
Change in Imported Crude Oil
-798
-1,109
10.4.2.2.2 Cost Impacts
The component cost (production, distribution, blending) of each biofuel type compared to
the fossil fuel it is displacing under the Analyzed Volumes relative to the 2025 Baseline is
summarized in Table 10.4.2.2.2-1. Similar to the No RFS baseline case, we are projecting
increased vegetable oil prices due to significant increases in vegetable oil-based fuel volumes
over the baseline volumes. Thus, there is an additional cost due to increased BBD costs for the
392
-------
baseline volumes. This additional cost is a separately estimated line item (Increase in Baseline
Price) and included in the total costs.
Table 10.4.2.2.2-1: Analyzed Volumes - Renewable and Petroleum Fuel Costs Relative to
the 2025 Baseline (million 2024$)
Renewable Fuel
Increase in
Petroleum Fuel
Prod.
Distrib.
Blending
Baseline Price
Prod.
Distrib.
Total
Cellulosic Biofuel
RNG as CNG - Landfill
Biogas
37
61
0
-20
-74
4
Corn Kernel Fiber Ethanol
96
15
39
-74
-10
66
Non-cellulosic Advanced
Biodiesel - Soybean
1705
200
0
1036
-643
-150
2147
Biodiesel - FOG
-17
-3
0
152
-348
-81
142
Biodiesel - Corn Oil
776
108
0
48
-440
-103
503
Biodiesel - Canola
1166
137
0
144
0
0
904
2026
Renewable Diesel -
Soybean
4557
499
0
723
-1683
-394
3703
Renewable Diesel - FOG
-1265
-175
0
3107
-516
-121
2396
Renewable Diesel - Corn
1192
153
0
478
-1668
-390
1186
Renewable Diesel - Canola
4516
494
0
290
0
0
3242
Sugarcane Ethanol
41
7
-12
-22
-3
11
Conventional
Ethanol - E10
44
10
-18
-34
-5
-3
Ethanol - E15
28
22
-8
-22
-3
18
Ethanol - E85
46
23
-3
-36
-5
26
Cellulosic Biofuel
RNG as CNG - Landfill
Biogas
77
127
0
-42
-154
8
Corn Kernel Fiber Ethanol
98
15
39
-73
-10
69
Non-cellulosic Advanced
Biodiesel - Soybean
1751
200
0
1158
-646
-150
2313
Biodiesel - FOG
-18
-3
0
170
8
2
160
Biodiesel - Corn Oil
796
108
0
53
-349
-81
527
Biodiesel - Canola
1197
137
0
161
-442
-103
950
2027
Renewable Diesel -
Soybean
5410
590
0
808
-2000
-465
4344
Renewable Diesel - FOG
-745
-104
0
1736
351
82
1320
Renewable Diesel - Corn
1191
153
0
535
-519
-121
1239
Renewable Diesel - Canola
5249
572
0
324
-1940
-452
3754
Sugarcane Ethanol
41
7
-12
-21
-3
12
Conventional
Ethanol - E10
-170
-39
69
127
17
3
Ethanol - E15
65
50
-17
-48
-7
42
Ethanol - E85
95
46
-5
-71
-10
56
The costs are aggregated for each fossil fuel type and shown as annual totals and per-
gallon and per MSCF costs in Table 10.4.2.2.2-2.
393
-------
Table 10.4.2.2.2-2: Analyzed Volumes - Total Annual and Per-Unit Costs Relative to the
2025 Baseline (2024$)
Total Cost
Per-Unit
Year
Fuel Type
(million $)
Cost
Units
Gasoline
118
0.09
0/gal gasoline
2026
Diesel Fuel
14,223
24.22
0/gal diesel
Natural Gas
4
0.01
$/MSCF natural gas
Total
14,344
7.33
0/gal gasoline and diesel
Gasoline
183
0.13
0/gal gasoline
2027
Diesel Fuel
14,606
25.21
0/gal diesel
Natural Gas
8
0.02
$/MSCF natural gas
Total
14,797
7.62
0/gal gasoline and diesel
10.4.3 Sensitivity Cases
This chapter provides alternative cost estimates as sensitivity analyses to the primary cost
analysis described in Chapter 10.4.2. The two primary sensitivity analyses are high and low
vegetable oil price estimates to bound the projected vegetable prices estimated for our primary
cost analysis. We also present a sensitivity cost analysis for alternate petroleum pricing scenarios
examining the possibility of lower crude oil prices compared to those projected in AEO2025.
10.4.3.1 High Vegetable Oil Prices Sensitivity Analyses
As summarized in Table 10.4.2.2.1-la, this rule is estimated to cause more than 2.1
billion-gallon increase in biodiesel and renewable diesel consumption in 2026 relative to 2025,
which we estimate will largely be supplied by domestic feedstock sources. For our primary cost
case, we estimate higher vegetable oil prices as a result. However, if the agricultural market
struggles more than expected to respond to these volume requirements, prices could increase
even more than what we estimate in our projections. For this reason, we conducted sensitivity
cost analyses at much higher vegetable oil prices for those feedstocks. For 2026 we assumed a
soybean oil price of 810 per pound, and for 2027 we assumed a soybean oil price of 850 per
pound, respectively, as summarized in Chapter 10.1.1.3. These prices were estimated based on a
methodology developed by Lusk et al. (2022).796 See Chapter 9.3 for more details about these
estimates. We estimated that inedible corn oil and FOG, both of which are less refined than
virgin soybean and canola oil, are estimated to be priced somewhat lower than virgin soybean oil
and canola oil.
10.4.3.1.1 High Vegetable Oil Price Scenario Relative to the No RFS Baseline
The component cost (production, distribution, blending, retail) of each biofuel type
compared to the fossil fuel it is displacing under the Analyzed Volumes at High Prices relative to
the No RFS Baseline is summarized in Table 10.4.3.1.1-1. Due to the high price increase caused
by the large increase in vegetable oil demand, we also estimate the same price increase of the
796 Lusk, Jayson L. "Food and Fuel: Modeling Food System Wide Impacts of Increase in Demand for Soybean Oil,"
November 10, 2022. https://ag.purdue.edu/cfdas/wp-content/uploads/2022/12/report sovmodel revisedl3.pdf.
394
-------
baseline volume of BBD and report that in the table, and include that additional cost in the total
costs.
Table 10.4.3.1.1-1: High Vegetable Oil Prices Scenario - Renewable and Petroleum Fuel
Costs Relative to the No RFS Baseline (million 2024$)
Renewable Fuel
Increase in
Petroleum Fuel
Prod.
Distrib.
Blending
Baseline Price
Prod.
Distrib.
Total
Cellulosic Biofuel
RNG as CNG - Landfill
Biogas
448
742
0
-248
-898
44
Corn Kernel Fiber
Ethanol
0
0
0
0
0
0
Non-cellulosic Advanced
Biodiesel - Soybean
4445
408
0
1606
-1313
-307
4840
Biodiesel - FOG
-432
-51
0
586
165
39
308
Biodiesel - Corn Oil
1136
124
0
53
-400
-94
820
Biodiesel - Canola
1134
104
0
543
-335
-78
1369
2026
Renewable Diesel -
Soybean
9881
860
0
0
-2901
-678
7162
Renewable Diesel - FOG
3540
393
0
2720
-1327
-310
5015
Renewable Diesel - Corn
387
40
0
1731
-134
-31
1993
Renewable Diesel -
Canola
7343
639
0
0
-2156
-504
5322
Sugarcane Ethanol
0
0
0
0
0
0
Conventional
Ethanol - E10
631
147
-260
-432
-65
21
Ethanol - E15
281
219
-77
-193
-29
202
Ethanol - E85
323
160
-18
-221
-33
211
Cellulosic Biofuel
RNG as CNG - Landfill
Biogas
470
778
0
-256
-942
50
Corn Kernel Fiber
Ethanol
0
0
0
0
0
0
Non-cellulosic Advanced
Biodiesel - Soybean
6284
554
0
0
-1788
-416
5598
Biodiesel - FOG
-660
-76
0
964
244
57
338
Biodiesel - Corn Oil
768
81
0
274
-261
-61
802
Biodiesel - Canola
1745
154
0
324
-496
-116
1610
2027
Renewable Diesel -
Soybean
11185
951
0
0
-3222
-750
8164
Renewable Diesel - FOG
5633
615
0
2684
-2086
-485
6361
Renewable Diesel - Corn
828
83
0
1752
-283
-66
2315
Renewable Diesel -
Canola
8435
717
0
0
-2430
-566
6156
Sugarcane Ethanol
0
0
0
0
0
0
Conventional
Ethanol - E10
737
168
-297
-548
-74
-15
Ethanol - E15
323
247
-87
-240
-33
210
Ethanol - E85
333
162
-18
-248
-34
196
The costs are aggregated for each fossil fuel type and shown as annual totals and per-
gallon and perMSCF costs in Table 10.4.3.1.1-2.
395
-------
Table 10.4.3.1.1-2: Total Annual and Per-Unit Costs Relative to the No RFS Baseline
(2024$)
Total Cost
Per-Unit
Year
Fuel Type
(million $)
Cost
Units
Gasoline
320
0.23
0/gal gasoline
2026
Diesel Fuel
26,828
45.69
0/gal diesel
Natural Gas
44
0.13
$/MSCF natural gas
Total
27,192
13.89
0/gal gasoline and diesel
Gasoline
392
0.29
0/gal gasoline
2027
Diesel Fuel
31,344
54.09
0/gal diesel
Natural Gas
50
0.15
$/MSCF natural gas
Total
31,786
16.38
0/gal gasoline and diesel
10.4.3.1.2 High Vegetable Oil Prices Scenario Relative to the 2025 Baseline
The component cost (production, distribution, blending, retail) of each biofuel type
compared to the fossil fuel it is displacing under the Analyzed Volumes at High Prices relative to
the 2025 Baseline is summarized in Table 10.4.2.3.2-1. The high price increase caused by the
large increase in vegetable oil demand is estimated to cause the same price increase for the
baseline volume of BBD, which is reported in the table, and included in the total costs.
396
-------
Table 10.4.3.1.2-1: Analyzed Volumes at High Vegetable Oil Prices - Renewable and
Petroleum Fuel Costs Relative to the 2025 Baseline (million 2024$)
Renewable Fuel
Increase in
Petroleum Fuel
Total
Prod.
Distrib.
Blending
Baseline Price
Prod.
Distrib.
Cellulosic Biofuel
RNG as CNG - Landfill
Biogas
37
61
0
-20
-74
4
Corn Kernel Fiber Ethanol
96
15
39
-74
-10
66
Non-cellulosic Advanced
Biodiesel - Soybean
2177
200
0
2256
-643
-150
3839
Biodiesel - FOG
-22
-3
0
330
-348
-81
316
Biodiesel - Corn Oil
987
108
0
104
-440
-103
770
Biodiesel - Canola
1489
137
0
313
0
0
1396
2026
Renewable Diesel -
Soybean
5734
499
0
1574
-1683
-394
5730
Renewable Diesel - FOG
-1577
-175
0
6762
-516
-121
5740
Renewable Diesel - Corn
1491
153
0
1041
-1668
-390
2048
Renewable Diesel - Canola
5681
494
0
631
0
0
4749
Sugarcane Ethanol
41
7
-12
-22
-3
11
Conventional
Ethanol - E10
44
10
-18
-34
-5
-3
Ethanol - E15
28
22
-8
-22
-3
18
Ethanol - E85
46
23
-3
-36
-5
26
Cellulosic Biofuel
RNG as CNG - Landfill
Biogas
77
127
0
-42
-154
8
Corn Kernel Fiber Ethanol
98
15
39
-73
-10
69
Non-cellulosic Advanced
2270
Biodiesel - Soybean
200
0
2499
-646
-150
4173
Biodiesel - FOG
-23
-3
0
3746
8
2
351
Biodiesel - Corn Oil
1029
108
0
115
-349
-81
821
2027
Biodiesel - Canola
1553
137
0
347
-442
-103
1491
Renewable Diesel -
Soybean
6941
590
0
1744
-2000
-465
6810
Renewable Diesel - FOG
-948
-104
0
3746
351
82
3128
Renewable Diesel - Corn
1519
153
0
1154
-519
-121
2187
Renewable Diesel - Canola
6734
572
0
699
-1940
-452
5614
Sugarcane Ethanol
41
7
-12
-21
-3
12
Conventional
Ethanol - E10
-170
-39
69
127
17
3
Ethanol - E15
65
50
-17
-48
-7
42
Ethanol - E85
95
46
-5
-71
-10
56
The costs are aggregated for each fossil fuel type and shown as annual totals and per-
gallon and perMSCF costs in Table 10.4.3.1.2-2.
397
-------
Table 10.4.3.1.2-2: Analyzed Volumes at High Prices - Total Annual and Per-Unit Costs
Relative to the 2025 Baseline (2024$)
Total Cost
Per-Unit
Year
Fuel Type
(million $)
Cost
Units
Gasoline
118
0.09
0/gal gasoline
2026
Diesel Fuel
24,586
41.88
0/gal diesel
Natural Gas
4
0.01
$/MSCF natural gas
Total
24,708
12.62
0/gal gasoline and diesel
Gasoline
183
0.13
0/gal gasoline
2027
Diesel Fuel
24,575
42.41
0/gal diesel
Natural Gas
8
0.02
$/MSCF natural gas
Total
24,766
12.76
0/gal gasoline and diesel
10.4.3.2 Low Vegetable Oil Prices Sensitivity Analyses
We analyzed the costs for the final fuel volumes relative to the No RFS Baseline, as well
as incremental to the 2025 Baseline, assuming a lower bound estimate for vegetable oil prices.
10.4.3.2.1 Low Vegetable Oil Prices Relative to the No RFS Baseline
In this section, we summarize the estimated costs for the changes in renewable fuel
volumes described in Chapter 10.4.2.1.1 (changes relative to the No RFS Baseline volumes
described in Chapter 2) assuming lower vegetable oil prices. The vegetable oil prices are based
on soybean oil prices projected by USDA (~40c/pound range) adjusted from nominal to 2024
dollars as discussed in Chapter 10.1.1.2. The costs presented here could represent the costs for
these higher BBD volumes once the vegetable oil market rebalances itself at these higher
vegetable oil volumes which likely would occur after 2026 and 2027. For this analysis we
considered all societal costs, including production, blending, and distribution costs, and
differences in energy density.
The component cost (production, distribution, blending, retail) of each biofuel type
compared to the fossil fuel it is displacing under the low vegetable oil prices scenario relative to
the No RFS Baseline is summarized in Tables 10.4.2.1.2-1. Since the vegetable oil prices are the
same as baseline prices, there is no price increase associated with the baseline BBD volumes.
398
-------
Table 10.4.3.2.1-1: Low Vegetable Oil Prices Scenario - Renewable and Petroleum Fuel
Renewable Fuel
Petroleum Fuel
Production
Distribution
Blending
Production
Distribution
Total
Cellulosic Biofuel
RNG as CNG - Landfill
Biogas
448
742
0
-248
-898
44
Corn Kernel Fiber Ethanol
0
0
0
0
0
0
Non-cellulosic Advanced
Biodiesel - Soybean
2316
408
0
-1313
-307
1104
Biodiesel - FOG
-229
-51
0
165
39
-77
Biodiesel - Corn Oil
599
124
0
-400
-94
230
Biodiesel - Canola
591
104
0
-335
-78
282
2026
Renewable Diesel -
Soybean
5402
860
0
-2901
-678
2683
Renewable Diesel - FOG
1984
393
0
-1327
-310
750
Renewable Diesel - Corn
216
40
0
-134
-31
90
Renewable Diesel - Canola
4015
639
0
-2156
-504
1994
Sugarcane Ethanol
0
0
0
0
0
0
Conventional
Ethanol - E10
631
147
-260
-432
-65
21
Ethanol - E15
281
219
-77
-193
-29
202
Ethanol - E85
323
160
-18
-221
-33
211
Cellulosic Biofuel
RNG as CNG - Landfill
Biogas
470
778
0
-256
-942
50
Corn Kernel Fiber Ethanol
0
0
0
0
0
0
Non-cellulosic Advanced
Biodiesel - Soybean
3007
554
0
-1788
-416
1356
Biodiesel - FOG
-323
-76
0
244
57
-98
Biodiesel - Corn Oil
373
81
0
-261
-61
132
Biodiesel - Canola
835
154
0
-496
-116
377
2027
Renewable Diesel -
Soybean
5561
951
0
-3222
-750
2540
Renewable Diesel - FOG
2888
615
0
-2086
-485
932
Renewable Diesel - Corn
420
83
0
-283
-66
155
Renewable Diesel - Canola
4194
717
0
-2430
-566
1915
Sugarcane Ethanol
0
0
0
0
0
0
Conventional
Ethanol - E10
737
168
-297
-478
-74
56
Ethanol - E15
323
247
-87
-210
-33
241
Ethanol - E85
333
162
-18
-216
-34
228
The costs are aggregated for each fossil fuel type and shown as annual totals and per-
gallon and perMSCF costs in Table 10.4.3.2.1-2.
399
-------
Table 10.4.3.2.1-2: Total Annual and Per-Unit Costs Relative to the No RFS Baseline
(2024$)
Total Cost
Per-Unit
Year
Fuel Type
(million $)
Cost
Units
Gasoline
320
0.23
0/gal gasoline
2026
Diesel Fuel
7,056
12.02
0/gal diesel
Natural Gas
44
0.13
$/MSCF natural gas
Total
7,420
3.79
0/gal gasoline and diesel
Gasoline
392
0.29
0/gal gasoline
2027.
Diesel Fuel
7,309
12.61
0/gal diesel
Natural Gas
50
0.15
$/MSCF natural gas
Total
7,751
3.99
0/gal gasoline and diesel
10.4.3.2.2 Low Vegetable Oil Prices Relative to the 2025 Baseline
In this section, we summarize the estimated costs for the changes in renewable fuel
volumes described in Chapter 10.4.2.2.1 (changes relative to the 2025 Baseline volumes
described in Chapter 2) assuming lower vegetable oil prices. The vegetable oil prices are based
on the average soybean oil price in 2025 (~50c/pound range) as discussed in Chapter 10.1.1.2.
The costs presented here could represent the costs for these higher BBD volumes once the
vegetable oil market rebalances itself at these higher vegetable oil volumes which likely would
occur after 2026 and 2027. For this analysis we considered all societal costs, including
production, blending, and distribution costs, and differences in energy density.
The component cost (production, distribution, blending, retail) of each biofuel type
compared to the fossil fuel it is displacing under the final renewable fuel volumes relative to the
2025 Baseline is summarized in Tables 10.4.2.2.2-1. Since the vegetable oil prices are the same
as baseline prices, there is no price increase associated with the baseline BBD volumes.
400
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Table 10.4.3.2.2-1: Low Vegetable Oil Prices Scenario - Renewable and Petroleum Fuel
Costs Relative to the 2025 Baseline (million 2024$)
Renewable Fuel
Petroleum Fuel
Production
Distribution
Blending
Production
Distribution
Total
Cellulosic Biofuel
RNG as CNG - Landfill
Biogas
37
61
0
-20
-74
4
Corn Kernel Fiber Ethanol
96
15
39
-74
-10
66
Non-cellulosic Advanced
Biodiesel - Soybean
1303
200
0
-643
-150
710
Biodiesel - FOG
-13
-3
0
-348
-81
-6
Biodiesel - Corn Oil
597
108
0
-440
-103
276
Biodiesel - Canola
892
137
0
0
0
486
2026
Renewable Diesel -
Soybean
3557
499
0
-1683
-394
1979
Renewable Diesel - FOG
-1000
-175
0
-516
-121
-446
Renewable Diesel - Corn
938
153
0
-1668
-390
454
Renewable Diesel - Canola
3525
494
0
0
0
1961
Sugarcane Ethanol
41
7
-12
-22
-3
11
Conventional
Ethanol - E10
44
10
-18
-34
-5
-3
Ethanol - E15
28
22
-8
-22
-3
18
Ethanol - E85
46
23
-3
-36
-5
26
Cellulosic Biofuel
RNG as CNG - Landfill
Biogas
77
127
0
-42
-154
8
Corn Kernel Fiber Ethanol
98
15
39
-73
-10
69
Non-cellulosic Advanced
Biodiesel - Soybean
1302
200
0
-646
-150
706
Biodiesel - FOG
-13
-3
0
8
2
-5
Biodiesel - Corn Oil
596
108
0
-349
-81
274
Biodiesel - Canola
891
137
0
-442
-103
483
2027
Renewable Diesel -
Soybean
4088
590
0
-2000
-465
2213
Renewable Diesel - FOG
-570
-104
0
351
82
-241
Renewable Diesel - Corn
907
153
0
-519
-121
421
Renewable Diesel - Canola
3966
572
0
-1940
-452
2147
Sugarcane Ethanol
41
7
-12
-21
-3
12
Conventional
Ethanol - E10
-170
-39
69
127
17
3
Ethanol - E15
65
50
-17
-48
-7
42
Ethanol - E85
95
46
-5
-71
-10
56
The costs are aggregated for each fossil fuel type and shown as annual totals and per-
gallon and per MSCF costs in Table 10.4.3.2.2-2.
401
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Table 10.4.3.2.2-2: Low Vegetable Oil Prices Scenario - Total Annual and Per-Unit Costs
Relative to the 2025 Baseline (2024$)
Total Cost
Per-Unit
Year
Fuel Type
(million $)
Cost
Units
Gasoline
118
0.09
0/gal gasoline
2026
Diesel Fuel
5,414
9.22
0/gal diesel
Natural Gas
4
0.01
$/MSCF natural gas
Total
5,536
2.83
0/gal gasoline and diesel
Gasoline
183
0.13
0/gal gasoline
2027.
Diesel Fuel
5,997
10.35
0/gal diesel
Natural Gas
8
0.02
$/MSCF natural gas
Total
6,188
3.16
0/gal gasoline and diesel
10.4.3.3 Crude Oil Sensitivity Analyses
10.4.3.3.1 Cost Impacts of Lower Crude Oil Prices
It is common for crude oil prices to deviate from the projected crude oil prices which we
use for our economic analysis, and these deviations impact the cost of the RFS program. As an
example, crude oil prices finished lower in 2025 compared to the beginning of the year and, as of
this writing, crude prices were lower than the 2026 crude oil prices forecasted in AEO2025. This
decline is associated with lower demand caused by lower economic activity combined with
higher crude oil output and these lower crude oil prices could persist in 2026.797 Compared to
AEO2025's forecast of $81.5/bbl crude oil prices in 2026, the end-of-year Brent crude oil spot
price averaged about $65/bbl. We based a crude oil sensitivity case on this $65/bbl crude oil
price in lieu of EIA's forecasted crude oil price to show how lower crude oil prices impact the
estimated costs. Because the wholesale price of gasoline and diesel fuel are used for estimating
their production costs, we needed to estimate the wholesale gasoline and diesel fuel wholesale
prices when crude oil is priced at $65/bbl. The AEO2025 low crude oil sensitivity case estimates
gasoline and diesel fuel wholesale prices at an even lower crude oil price of $38/bbl and we
interpolated the gasoline and diesel fuel wholesale prices between the two AEO cases to estimate
the gasoline and diesel fuel wholesale prices at $65/bbl. Table 10.4.3.3.1-1 summarizes the
reference case, low crude oil case and sensitivity case crude oil and wholesale prices.
797 EIA, "Crude Oil Prices Fell in 2025 Amid Oversupply, Today in Energy, January 5, 2026.
https://www.eia. gov/todavinenergy/detail.php?id=66944.
402
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Table 10.4.3.3.1-1 Crude Oil and Wholesale Prices at Different AEO2025 Crude Oil Prices
(2024$)
Case
Price
Units
2026
2027
Reference Case
Brent Crude Price
$/bbl
$81.5
$80.4
Gasoline Wholesale Price
$/gal
$2.18
$2.13
Diesel Wholesale Price
$/gal
$2.28
$2.29
Low Crude Oil
Price Case
Brent Crude Price
$/bbl
$38.5
$39.3
Gasoline Wholesale Price
$/gal
$1.51
$1.40
Diesel Wholesale Price
$/gal
$1.64
$1.57
Sensitivity Case
Crude Oil Prices
Brent Crude Price
$/bbl
$65.0
$65.0
Gasoline Wholesale Price
$/gal
$1.92
$1.86
Diesel Wholesale Price
$/gal
$2.03
$2.02
Based on the lower petroleum prices, the component cost (production, distribution,
blending retail) of each biofuel type compared to the fossil fuel it is displacing under the Low
Crude Oil Price Scenario relative to the No RFS Baseline is summarized in Tables 10.4.3.3.1-2.
403
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Table 10.4.3.3.1-2: Low Crude Oil Price Scenario - Renewable and Petroleum Fuel Costs
Relative to the No RFS Baseline (million 2024$)
Renewable Fuel
Increase in
Petroleum Fuel
Prod.
Distrib.
Blending
Baseline Price
Prod.
Distrib.
Total
Cellulosic Biofuel
RNG as CNG - Landfill
Biogas
448
742
0
0
-248
-898
44
Corn Kernel Fiber Ethanol
0
0
0
0
0
0
0
Non-cellulosic Advanced
Biodiesel - Soybean
3481
408
0
879
-1172
-307
3290
Biodiesel - FOG
-340
-51
0
321
148
39
116
Biodiesel - Corn Oil
893
124
0
29
-357
-94
596
Biodiesel - Canola
888
104
0
297
-299
-78
913
2026
Renewable Diesel -
Soybean
7853
860
0
0
-2588
-678
5447
Renewable Diesel - FOG
2826
393
0
1488
-1184
-310
3228
Renewable Diesel - Corn
309
40
0
948
-120
-31
1146
Renewable Diesel - Canola
5836
639
0
0
-1924
-504
4048
Sugarcane Ethanol
0
0
0
0
0
0
Conventional
Ethanol - E10
631
147
-260
0
-432
-65
21
Ethanol - E15
281
219
-77
0
-193
-29
202
Ethanol - E85
323
160
-18
0
-221
-33
211
Cellulosic Biofuel
RNG as CNG - Landfill
Biogas
470
778
0
0
-256
-942
50
Corn Kernel Fiber Ethanol
0
0
0
0
0
0
0
Non-cellulosic Advanced
Biodiesel - Soybean
4847
554
0
541
-1577
-416
3948
Biodiesel - FOG
-512
-76
0
434
215
57
118
Biodiesel - Corn Oil
595
81
0
154
-230
-61
539
Biodiesel - Canola
1346
154
0
182
-438
-116
1128
2027
Renewable Diesel -
Soybean
8718
951
0
0
-2843
-750
6076
Renewable Diesel - FOG
4414
615
0
1507
-1840
-485
4226
Renewable Diesel - Corn
649
83
0
984
-249
-66
1401
Renewable Diesel - Canola
6575
717
0
0
-2144
-566
4582
Sugarcane Ethanol
0
0
0
0
0
0
Conventional
Ethanol - E10
737
168
-297
-478
-74
56
Ethanol - E15
323
247
-87
-210
-33
241
Ethanol - E85
333
162
-18
-216
-34
228
The costs are aggregated for each fossil fuel type and shown as annual totals and per-
gallon and perMSCF costs in Table 10.4.3.3.1-3.
404
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Table 10.4.3.3.1-3: Low Crude Oil Price Scenario - Total Annual and Per-Unit Costs
Relative to No RFS Baseline (2024$)
Total Cost
Per-Unit
Year
Fuel Type
(million $)
Cost
Units
Gasoline
433
0.32
0/gal gasoline
2026
Diesel Fuel
18,782
31.99
0/gal diesel
Natural Gas
44
0.13
$/MSCF natural gas
Total
19,259
9.84
0/gal gasoline and diesel
Gasoline
525
0.39
0/gal gasoline
2027.
Diesel Fuel
22,018
38.00
0/gal diesel
Natural Gas
50
0.15
$/MSCF natural gas
Total
22,593
11.64
0/gal gasoline and diesel
10.4.3.3.2 Potential Cost Impact of Lower Crude Oil Demand
As estimated previously in this chapter, the increased consumption of renewable fuels
will decrease petroleum product fuel demand. Thus, a case could be made that this reduced
petroleum demand could lower crude oil prices and therefore lower costs to consumers.
AEO2025 provides a way to estimate how a decrease in petroleum demand affects crude
oil prices. When modeling two different demand cases for AEO2025, a low economic growth
case relative to the reference case, EIA models a reduction in refined product demand between
the two cases, and also estimates a lower crude oil price for the lower demand case relative to the
reference case. The low economic growth AEO2025 case shows a 425 thousand barrel per day
reduction in refined product demanded relative to the reference case for the years 2027-2030,
and also estimates an average of $0.438/bbl lower crude oil price for the low economic growth
case for those same years. Averaged over 2026 to 2027, the Set rule is estimated to cause a 300
thousand barrel per day reduction in gasoline and diesel fuel relative to the No RFS baseline
case, and 110 thousand barrel per day in gasoline and diesel fuel relative to the 2025 baseline
case. Assuming the EIA correlation is realistic, the Set rule volumes would be estimated to cause
a $0.32/bbl decrease in crude oil prices relative to the No RFS Baseline, and a $0.12/bbl decrease
in crude oil prices relative to the 2025 baseline, when averaged over both 2026 and 2027.
If we adopted this method for estimating the RFS program on crude oil prices, it would
increase the cost of the RFS program slightly (if petroleum products become less expensive, the
renewable fuels become relatively more expensive). As shown by the crude oil price sensitivity
case in Chapter 10.4.3.3.1, a fairly large change in crude oil price had a modest impact on the
RFS program cost, thus this potentially much smaller price change would have a de minimis
impact on the program cost.
Another possible cost impact to consider is how lower petroleum products prices would
affect the U.S. economy as a whole. U.S. crude oil consumption is 16.1 million barrels per day
so a cost impact could be estimated by the product of the estimated lower crude oil price and
crude oil demand. Relative to the No RFS baseline, there is an estimated cost savings of $1.87
billion dollars. Relative to the 2025 baseline, there is an estimated cost savings of $700 million.
405
-------
For several reasons, we elected not to use this method to adjust crude oil prices for the
RFS program cost analysis and maintain it as a sensitivity analysis. One reason is that there is
great uncertainty in estimating price changs due to lower demand, particularly since the elasticity
is based on EIA's AEO low economic growth case, which could be biased by being based on a
lower functioning U.S. economy. Thus, it is not clear if the change in crude oil prices is due to
the lower demand or due to the impact of a lower functioning economy being modeled by EIA
on crude oil prices (i.e., lower labor rates). Either way, the much larger petroleum market is
likely to experience a very, very small price impact due to the relatively much smaller increased
volumes of renewable fuels.
A case could also be made that there may not be any price impact at all. Multiple crude
oil producers alter their crude oil production volumes to maintain the price of crude oil within a
price band. EIA evaluated how Saudi Arabia crude changed its crude oil production to
apparently affect crude oil prices.798
798 EIA, "What drives crude oil prices: Supply OPEC," Energy and Financial Markets. April 12, 2022.
https://www.eia.gov/finance/markets/crudeoil/supplY-opec.php.
406
-------
Figure 10.4.3.3.2-1: Effect of Changing Saudi Arabian Crude Production on Crude Oil
Prices
Changes in Saudi Arabia crude oil production can affect oil prices
Changes in Saudi Arabia crude oil production and WTI crude oil prices download
million barrels per cfay (year-on-year) % change (year-on-year)
2002 2004 2006 200S 2010 2012 2014 2016 2018 2020 2022
¦ Saudi Arabia crude oil production — WTI percent change
eia" Source: U.S. Energy Information Administration. RefinHhrAn LSEG Business
Updated: Monthly | Last Updated: 04/12/2022
Oil markets often respond to changing expectations of future supply and demand- This chart shows how projections of
changes in Saudi Arabia crude oil production results in changes in WTI crude oil prices
As shown and described by the figure and its accompanying text, Saudi Arabia is able to
"swing" their crude oil production/exports by as much as 2.7 million barrels per day to affect
crude oil prices. The 2.7 million barrels per day swing in Saudi Arabian crude oil production
change correlates to about 40 billion gallons per year of BBD volume, thus dwarfing the changes
in renewable fuels volume changes in the U.S. In addition, Saudi Arabian crude supply is just
one of many crude oil supply and demand factors around the world that impact crude prices.
Some analysts have concluded that U.S. unconventional crude oil producers are also swing
producers which could impact crude oil prices.799 Thus, there is a question whether the RFS
program would affect crude oil prices at all, and if it did, what the magnitude would be. For this
reason, we consider this a speculative analysis.
799 Newell, Richard G., and Brian C. Prest. "Is The US the New Swing Producer? The Price-Responsiveness of
Tight Oil." Resources for the Future, June 19, 2017. ,iips://mcdia.rff.org/documents/RFF-WP-17-15.pdf.
407
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10.5 Estimated Fuel Price Impacts
In this section, we estimate the impact of the use of renewable fuels on the cost to
consumers of transportation fuel and the cost to transport goods. We have estimated cost to
consumers of transportation fuel by assessing the fuel price impacts associated with this
rulemaking. We do so based on the cost of renewable fuels (less available federal tax credits) and
accounting for the cross-subsidy implemented through the RIN system. We have also used
estimates of the fuel price impacts of this rule to estimate the cost to transport goods discussed in
Chapter 10.5.5.
Unlike the costs presented in Chapter 10.4, the impacts estimated in this set of analyses
may not be costs from a societal perspective. For example, revenue from transporting goods
benefits the shipping company completing that work. Likewise, each purchase of fuel benefits
the seller of that fuel to some extent. Transactions which benefit certain economic participants
while costing others are transfers from a societal perspective, not costs. For this reason, the
impacts estimated in this section are not used in Chapter 10.6, where we estimate the societal
benefits and costs of this rule.
10.5.1 RIN Cost and RIN Value
Before estimating fuel price impacts, we first estimated the RIN cost (i.e., the cost added
to the wholesale and/or retail price of each gallon of petroleum fuel to account for the RIN
obligation on the fuel) and RIN value (i.e., the value of the RINs associated with the renewable
fuel in the fuel blend) associated with producing petroleum and renewable fuels, respectively.
Because RIN prices can be impacted by a wide variety of different factors (including the prices
of renewable fuels and petroleum-based fuels, crude oil prices, biofuel feedstock commodity
prices, etc.), we are not able to project what RIN prices will be in the future. We can, however,
use the average RIN prices over the last 12 months (through December 2025) as an estimate of
future RIN prices, as shown in Table 10.5.1-1. These estimates are shown alongside the
percentage standards for 2025, as well as the percentage standards for 2026 and 2027 finalized in
this rule.
Table 10.5.1-1: Average RIN Prices (January 2025 - December 2025)
RIN
Average
RIN
2025
2026
2027
RFS Standard
Type
Price
Standard
Standard
Standard
Cellulosic Biofuel
D3/D7
$2.40
0.71%°
0.79%
0.84%
Biomass-Based Diesel
D4
$0.95
3.15%
5.24%
5.37%
Other Advanced BiofueP
D5
$0.99
0.46%
0.39%
0.40%
Conventional Renewable Fuelb
D6
$0.91
8.82%
9.08%
9.17%
a Other advanced biofuel is not an RFS standard category but is calculated by subtracting the cellulosic biofuel and
BBD standards from the advanced biofuel standard.
b Conventional renewable fuel is not an RFS standard category but is calculated by subtracting the advanced biofuel
standard from the total renewable fuel standard.
0 Reflects the partial waiver of the 2025 cellulosic biofuel standard.
408
-------
We then calculated the RIN cost for petroleum fuel by weighting the RIN price for each
D code by their respective RFS standard and summing the total. The results are shown in Table
10.5.1-2.
Table 10.5.1-2: Estimated RIN Costs for Petroleum Fuel for 2025-2027
Year
RIN Cost
($/Gallon)
2025
$0.13
2026
$0.16
2027
$0.16
Finally, we calculated RIN values for fuels. For gasoline-ethanol blends, we multiplied
the average D6 RIN price by the ethanol content of each blend {i.e., 10% for E10, 15% for E15,
and an average ethanol content of 74% for E85). For biodiesel and renewable diesel, we
multiplied the average D4 RIN price by the equivalence value of each fuel {i.e., 1.5 for biodiesel
and 1.5 for renewable diesel). The results are shown in Table 10.5.1-3.
Table 10.5.1-3: Estimated RIN Va
RIN Value
Fuel
($/Gallon)
E10
$0.09
E15
$0.14
E85
$0.67
Biodiesel
$1.43
Renewable Diesel
$1.43
ues for Fuels
10.5.2 Estimated Fuel Price Impacts (Gasoline)
In this section, we estimate the fuel price impacts of the Analyzed Volumes on gasoline
relative to the No RFS and 2025 Baselines. First, we estimated the total impact of RIN prices on
the price of gasoline-ethanol blends for the Analyzed Volumes. We began with the production
cost for each fuel (as specified in Tables 10.4.1-1 c (ethanol) and 10.4. l-2b (gasoline)),800 to
which we added the RIN price impact associated with the gasoline portion of the fuel (as
described in Table 10.5.1-2), and then subtracted the RIN value associated with the ethanol
portion of each fuel (as described in Table 10.5.1-3), which gave us each fuel's net price impact
per gallon. We then multiplied each fuel's net price impact by its volume from Table 7.5.1-5 to
derive the total price impact for each fuel pool. Finally, we calculated the average gasoline price
impact by dividing the total price impact on all fuels by the total volume of all fuels. As shown in
Tables 10.5.2-1 and 2, we estimate that the average gasoline price impact ranges from $2.14 to
$2.20 per gallon.
800 Note that for purposes of this fuel price impacts assessment, we only looked at the cost to produce and distribute
fuel to retail stations for sale to consumers (i.e., we subtracted out of the fuel economy cost for each fuel).
409
-------
Note that when estimating the cost to produce ethanol, we make the simplifying
assumption that all this ethanol is derived from corn starch801 and we use only the production
cost estimates for corn starch ethanol specified in Table 10.4.1-lc. In reality, some amount of
ethanol blended into E10, El 5, and E85 is derived from CKF, grain sorghum, separated food
waste, and other feedstocks, all of which have different costs to produce relative to ethanol
derived from corn starch. However, we believe this simplifying assumption is appropriate
because ethanol derived from corn starch represents nearly all the total ethanol blended with
gasoline. As described in Chapter 3, we estimate the total ethanol pool shares of corn starch
ethanol to be about 99% in both 2026 and 2027 under the Analyzed Volumes. For this reason,
we do not believe the gasoline price estimates would vary substantially if we did explicitly
include the volume-weighted costs to produce other types of ethanol currently blended into these
pools. Therefore, for simplicity and ease of understanding, the following estimates assume all
ethanol to be corn starch ethanol.
Table 10.5.2-1: Total Gasoline Costs - 2026
E0
E10
E15
E85
Cost to Produce ($/gal)
$2.21
$2.14
$2.30
$2.58
RIN Cost ($/gal)
$0.16
$0.14
$0.14
$0.04
RIN Value ($/gal)
$0.00
-$0.09
-$0.14
-$0.67
Tax Credit ($/gal)
$0.00
$0.01
$0.02
$0.12
Net Cost ($/gal)
$2.37
$2.20
$2.32
$2.07
Volume (mil gal)
2,061
137,404
998
443
Total Fuel Cost ($bil)
$4.9
$302.3
$2.3
$0.9
Average Cost ($/gal)
$2.20
Table 10.5.2-2: Total Gasoline Costs - 2027
E0
E10
E15
E85
Cost to Produce ($/gal)
$2.14
$2.08
$2.25
$2.59
RIN Cost ($/gal)
$0.16
$0.14
$0.14
$0.04
RIN Value ($/gal)
$0.00
-$0.09
-$0.14
-$0.67
Tax Credit ($/gal)
$0.00
$0.01
$0.02
$0.12
Net Cost ($/gal)
$2.30
$2.14
$2.27
$2.08
Volume (mil gal)
2,075
136,301
1,124
477
Total Fuel Cost ($bil)
$4.8
$291.7
$2.6
$1.0
Average Cost ($/gal)
$2.14
Next, we estimated the cost of gasoline-ethanol blends under the No RFS and 2025
Baselines. For the No RFS Baseline, we began with the production cost for each gasoline-ethanol
blend, using the relevant estimates described in Chapter 10.1, and multiplied by the volume of
each blend under the respective baseline to get the total cost for each fuel.802 We then calculated
the average gasoline cost by dividing the total cost of all fuels by the total volume of all fuels. As
801 And also from grain sorghum starch, which we assume throughout this regulatory impact analysis to be identical
in cost and other characteristics to corn starch.
802 For purposes of the No RFS Baseline analysis, we assumed that E0 volumes were held constant relative to the
Analyzed Volumes and that there would not be any volumes of E15 or E85. E10 volumes were calculated by
totaling ethanol production for each year from Table 2.1.5-2 and dividing by 0.1.
410
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shown in Tables 10.5.2-3 and 4, we estimate that the average gasoline cost under the No RFS
Baseline ranges from $2.09 to $2.15 per gallon.
Table 10.5.2-3: Total Gasoline Costs - 2026 (
Vo RFS Baseline)
E0
E10
E15
E85
Cost to Produce ($/gal)
$2.21
$2.14
$2.30
$2.58
Tax Credit ($/gal)
$0.00
$0.01
$0.02
$0.12
Net Fuel Price Impact ($/gal)
$2.21
$2.15
$2.32
$2.70
Volume (mil gal)
2,061
138,610
0
169
Total Fuel Cost ($bil)
$4.6
$298.0
$0.0
$0.5
Average Cost ($/gal)
$2.15
Table 10.5.2-4: Total Gasoline Costs - 2027 (
Vo RFS Baseline)
E0
E10
E15
E85
Cost to Produce ($/gal)
$2.14
$2.08
$2.25
$2.59
Tax Credit ($/gal)
$0.00
$0.01
$0.02
$0.12
Net Fuel Price Impact ($/gal)
$2.14
$2.09
$2.27
$2.71
Volume (mil gal)
2,075
137,642
0
190
Total Fuel Cost ($bil)
$4.4
$287.7
$0.0
$0.5
Average Cost ($/gal)
$2.09
For the 2025 Baseline, we used the same approach described above for No RFS
Baseline.803 As shown in Tables 10.5.2-5 and 6, we estimate that the average gasoline cost under
the 2025 Baseline ranges from $2.14 to $2.20 per gallon.
Table 10.5.2-5: Total Gasoline Costs - 2026 (2C
125 Baseline)
E0
E10
E15
E85
Cost to Produce ($/gal)
$2.21
$2.14
$2.30
$2.58
RIN Cost ($/gal)
$0.16
$0.14
$0.14
$0.04
RIN Value ($/gal)
$0.00
-$0.09
-$0.14
-$0.67
Tax Credit ($/gal)
$0.00
$0.01
$0.02
$0.12
Net Cost ($/gal)
$2.37
$2.20
$2.32
$2.07
Volume (mil gal)
2,049
136,239
898
410
Total Fuel Cost ($bil)
$4.9
$299.7
$2.1
$0.8
Average Cost ($/gal)
$2.20
803 2025 Baseline gasoline-ethanol blend volumes from Set 1 Rule RIA Table 6.5.2-3.
411
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Table 10.5.2-6: Total Gasoline Costs - 2027 (2C
125 Baseline)
E0
E10
E15
E85
Cost to Produce ($/gal)
$2.14
$2.08
$2.25
$2.59
RIN Cost ($/gal)
$0.16
$0.14
$0.14
$0.04
RIN Value ($/gal)
$0.00
-$0.09
-$0.14
-$0.67
Tax Credit ($/gal)
$0.00
$0.01
$0.02
$0.12
Net Cost ($/gal)
$2.30
$2.14
$2.27
$2.08
Volume (mil gal)
2,049
136,239
898
410
Total Fuel Cost ($bil)
$4.7
$291.6
$2.0
$0.9
Average Cost ($/gal)
$2.14
Finally, we calculated the fuel price impacts of this rule on gasoline for each year by
subtracting the average gasoline cost in each baseline scenario from the average gasoline cost for
the Analyzed Volumes. As shown in Table 10.5.2-7, we estimate that the fuel price impact on
gasoline under the No RFS Baseline ranges from 5.10 to 5.20 per gallon. As shown in Table
10.5.2-8, we estimate that the fuel price impact on gasoline under the 2025 Baseline is 0.00 per
gallon.
2026
2027
Average Cost (No RFS Baseline) ($/gal)
$2.15
$2.09
Average Cost (Analyzed Volumes) ($/gal)
$2.20
$2.14
Fuel Price Impact (0/gal)
5.10
5.20
Table 10.5.2-8: Total Gasoline Fuel Price Impacts (2025 Baseline)
2026
2027
Average Cost (2025 Baseline) ($/gal)
$2.20
$2.14
Average Cost (Analyzed Volumes) ($/gal)
$2.20
$2.14
Fuel Price Impact (0/gal)
o
o
o
o
10.5.3 Estimated Fuel Price Impacts (Diesel)
In this section, we estimate the fuel price impacts of the Analyzed Volumes on diesel
relative to the No RFS and 2025 Baselines. First, we estimated the total cost of diesel, biodiesel,
and renewable diesel for the Analyzed Volumes. We began with the production cost for each fuel
(as specified in Tables 10.4.1-lc (biodiesel and renewable diesel) and 10.4.l-2b (diesel)),804 and
then either added the RIN cost (for diesel) or subtracted the RIN value and tax credit (for
biodiesel and renewable diesel) associated with each fuel, which gave us each fuel's net cost per
gallon. We then multiplied each fuel's net cost by its volume from Table 3.1-4 (biodiesel and
renewable diesel) or Preamble Table VII.C-1 (diesel) to get the total cost for each fuel. Finally,
we calculated the average diesel cost by dividing the total cost of all fuels by the total volume of
all fuels. As shown in Tables 10.5.3-1 and 2, we estimate that the average diesel cost is $2.86 per
gallon.
804 Note that for purposes of this fuel price impacts assessment, we only looked at the cost to produce and distribute
fuel to retail stations for sale to consumers (i.e., we subtracted out of the fuel economy cost for each fuel).
412
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Table 10.5.3-1: Total Diesel Costs - 2026
Biodiesel
Renewable Diesel
Corn
Soybean/
Canola
Corn
Soybean/
Canola
Diesel
Oil
FOG
Oil
Oil
FOG
Oil
Cost to Produce ($/gal)
$2.57
$5.43
$5.06
$6.31
$5.84
$5.47
$6.71
RIN Cost ($/gal)
$0.16
$0.00
$0.00
$0.00
$0.00
$0.00
$0.00
RIN Value ($/gal)
$0.00
-$1.43
-$1.43
-$1.43
-$1.43
-$1.43
-$1.43
Tax Credit ($/gal)
$0.00
-$0.80
-$0.80
-$0.62
-$0.75
-$0.66
-$0.52
Net Cost ($/gal)
$2.73
$3.20
$2.83
$4.26
$3.66
$3.38
$4.76
Volume (mil gal)
58,273
210
150
1,420
678
1,365
2,301
Total Fuel Cost ($bil)
$159.1
$0.7
$0.4
$6.0
$2.5
$4.6
$11.0
Total Cost ($/gal)
$2.86
Table 10.5.3-2: Total Diesel Costs - 2027
Biodiesel
Renewable Diesel
Corn
Soybean/
Canola
Corn
Soybean/
Canola
Diesel
Oil
FOG
Oil
Oil
FOG
Oil
Cost to Produce ($/gal)
$2.55
$5.56
$5.18
$6.46
$5.83
$5.45
$6.74
RIN Cost ($/gal)
$0.16
$0.00
$0.00
$0.00
$0.00
$0.00
$0.00
RIN Value ($/gal)
$0.00
-$1.43
-$1.43
-$1.43
-$1.43
-$1.43
-$1.43
Tax Credit ($/gal)
$0.00
-$0.80
-$0.80
-$0.62
-$0.75
-$0.66
-$0.52
Net Cost ($/gal)
$2.71
$3.33
$2.95
$4.41
$3.65
$3.36
$4.79
Volume (mil gal)
57,085
210
150
1,420
678
1,475
2,561
Total Fuel Cost ($bil)
$154.7
$0.7
$0.4
$6.3
$2.5
$5.0
$12.3
Total Cost ($/gal)
$2.86
Next, we estimated the total cost of diesel under the No RFS and 2025 Baselines. For the
No RFS Baseline, we began with the production cost for each fuel and subtracted the tax credit
(for biodiesel and renewable diesel) associated with each fuel, which gave us each fuel's net cost
per gallon. We then multiplied each fuel's net cost by its volume under the respective baseline to
get the total cost for each fuel.805 We then calculated the average diesel cost by dividing the total
cost of all fuels by the total volume of all fuels. As shown in Tables 10.5.3-3 and 4, we estimate
that the average diesel cost under the No RFS Baseline ranges from $2.64 to $2.67 per gallon.
805 Biodiesel and renewable diesel volumes from Table 2.1.5-2. For purposes of the No RFS Baseline analysis, we
assumed that total diesel energy demand was held constant relative to the Analyzed Volumes to calculate petroleum
diesel fuel volumes.
413
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Table 10.5.3-3: Total Diesel Costs - 2026 (No RFS Baseline)
Diesel
Biodiesel
Renewable
Jiesel
Corn
Oil
FOG
Soybean/
Canola
Oil
Corn
Oil
FOG
Soybean/
Canola
Oil
Cost to Produce ($/gal)
$2.57
$5.43
$5.06
$6.31
$5.84
$5.47
$6.71
Tax Credit ($/gal)
$0.00
-$0.80
-$0.80
-$0.62
-$0.75
-$0.66
-$0.52
Net Cost ($/gal)
$2.57
$4.63
$4.26
$5.69
$5.09
$4.81
$6.19
Volume (mil gal)
61,722
19
86
633
617
1,122
0
Total Fuel Cost ($bil)
$158.6
$0.1
$0.4
$3.6
$3.1
$5.4
$0.0
Total Cost ($/gal)
$2.67
Table 10.5.3-4: Total Diesel Costs - 2027 (No RFS Baseline)
Biodiesel
Renewable Diesel
Corn
Soybean/
Canola
Corn
Soybean/
Canola
Diesel
Oil
FOG
Oil
Oil
FOG
Oil
Cost to Produce ($/gal)
$2.55
$5.56
$5.18
$6.46
$5.83
$5.45
$6.74
Tax Credit ($/gal)
$0.00
-$0.80
-$0.80
-$0.62
-$0.75
-$0.66
-$0.52
Net Cost ($/gal)
$2.55
$4.76
$4.38
$5.84
$5.08
$4.79
$6.22
Volume (mil gal)
61,083
86
317
334
550
983
0
Total Fuel Cost ($bil)
$155.8
$0.4
$1.4
$2.0
$2.8
$4.7
$0.0
Total Cost ($/gal)
$2.64
For the 2025 Baseline, we used the same approach described above for the No RFS
Baseline.806 As shown in Tables 10.5.3-5 and 6, we estimate that the average diesel cost under
the 2025 Baseline ranges from $2.78 to $2.80 per gallon.
Table 10.5.3-5: Total Diesel Costs - 2026 (2025 Baseline)
Biodiesel
Renewable Diesel
Corn
Soybean/
Canola
Corn
Soybean/
Canola
Diesel
Oil
FOG
Oil
Oil
FOG
Oil
Cost to Produce ($/gal)
$2.57
$5.43
$5.06
$6.31
$5.84
$5.47
$6.71
RIN Cost ($/gal)
$0.16
$0.00
$0.00
$0.00
$0.00
$0.00
$0.00
RIN Value ($/gal)
$0.00
-$1.43
-$1.43
-$1.43
-$1.43
-$1.43
-$1.43
Tax Credit ($/gal)
$0.00
-$0.80
-$0.80
-$0.62
-$0.75
-$0.66
-$0.52
Net Cost ($/gal)
$2.73
$3.20
$2.83
$4.26
$3.66
$3.38
$4.76
Volume (mil gal)
57,695
44
154
904
417
1,529
730
Total Blend Cost ($bil)
$157.5
$0.1
$0.4
$3.8
$1.5
$5.2
$3.5
Average Cost ($/gal)
$2.80
806 2025 Baseline biodiesel and renewable diesel volumes from Table 2.2-2. For purposes of the 2025 Baseline
analysis, we assumed that total diesel energy demand was held constant relative to the Analyzed Volumes to
calculate petroleum diesel fuel volumes.
414
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Table 10.5.3-6: Total Diesel Costs - 2027 (2025 Baseline)
Biodiesel
Renewable Diesel
Corn
Soybean/
Canola
Corn
Soybean/
Canola
Diesel
Oil
FOG
Oil
Oil
FOG
Oil
Cost to Produce ($/gal)
$2.55
$5.56
$5.18
$6.46
$5.83
$5.45
$6.74
RIN Cost ($/gal)
$0.16
$0.00
$0.00
$0.00
$0.00
$0.00
$0.00
RIN Value ($/gal)
$0.00
-$1.43
-$1.43
-$1.43
-$1.43
-$1.43
-$1.43
Tax Credit ($/gal)
$0.00
-$0.80
-$0.80
-$0.62
-$0.75
-$0.66
-$0.52
Net Cost ($/gal)
$2.71
$3.33
$2.95
$4.41
$3.65
$3.36
$4.79
Volume (mil gal)
57,695
44
154
904
417
1,529
730
Total Blend Cost ($bil)
$156.4
$0.1
$0.5
$4.0
$1.5
$5.1
$3.5
Average Cost ($/gal)
$2.78
Finally, we calculated the fuel price impacts on diesel for each year by subtracting the
average diesel cost for each baseline from the average diesel cost for the Analyzed Volumes. As
shown in Table 10.5.3-7, we estimate that the fuel price impact on diesel under the No RFS
Baseline ranges from 19.50 to 22.30 per gallon. As shown in Table 10.5.3-8, we estimate that the
fuel price impact on diesel under the 2025 Baseline ranges from 6.20 to 7.60 per gallon.
10.5.4 Fuel Price Impacts of the SRE Reallocation Volumes
As discussed in Preamble Section IV.B, we have concluded that the SRE reallocation
volumes will generally have no impact on the statutory factors because we project that the SRE
reallocation volumes will be met with carryover RINs. The one statutory factor that we do
project will be impacted by the SRE reallocation volumes is the impact on the cost to consumers
of transportation fuel and the cost to transport goods. The SRE reallocation volumes will result in
higher percentage standards for obligated parties than would otherwise be the case, which in turn
require obligated parties to acquire greater quantities of RINs to retire for compliance. We
project that, in aggregate, obligated parties will acquire these additional RINs by purchasing
carryover RINs from other parties (or alternatively using RINs that would have been retired for
compliance but for the 2023-2025 SREs) rather than blending additional quantities of renewable
fuel. We further project that obligated parties will pass on the costs of purchasing additional
415
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RINs to consumers, and that the SRE reallocation volumes could therefore increase the cost of
transportation fuel to consumers.
In Chapters 10.5.2 and 10.5.3 we present estimates of the price impacts on gasoline and
diesel fuel from this rule. These estimates include the price impacts of the SRE reallocation
volumes. This is because the estimates of the fuel price impacts use the total percentage
standards for 2026 and 2027, which are a function of both the renewable fuel volume
requirements and the SRE reallocation volumes. To estimate the impact of the SRE reallocation
volumes on fuel price impacts, we must first estimate the impact of the SRE reallocation
volumes on the percentage standards for each category of renewable fuel. We did this by
calculating what the percentage standards would have been absent SRE reallocation volumes and
then taking the difference between the percentage standards with and without the SRE
reallocation volumes.807 We then multiplied this difference (i.e., the portion of the percentage
standards due to SRE reallocation volumes) by the average RIN prices in Table 10.5.1-1. These
numbers are summarized in Table 10.5.4-1.
Table 10.5.4-1: Estimated Fuel
rice Impacts of the SRE Reallocation Volumes
SRE
Reallocation
RIN
RIN
Percentage
Price Impact
RFS Standard
Type
Price
2026
2027
2026
2027
Cellulosic Biofuel
D3/D7
$2.40
0.00%
0.00%
«...
o
o
«...
o
o
BBD
D4
$0.95
0.12%
0.14%
0.10
0.10
Other Advanced Biofuel
D5
$0.99
0.05%
0.06%
«...
o
o
0.10
Conventional Renewable Fuel
D6
$0.91
0.41%
0.41%
0.40
0.40
Total Renewable Fuel
N/A
N/A
N/A
0.50
0.60
Note: There is no SRE reallocation volume for cellulosic biofuel as discussed in Preamble Section IV.B.
We recognize that there is a fair amount of uncertainty regarding our estimates of the fuel
price impacts of the SRE reallocation volume. In calculating these impacts, we have used the
average RIN prices over the past 12 months for which data were available to project RIN prices
in 2026 and 2027. Higher or lower RIN prices in these years would directly result in higher or
lower estimates of the fuel price impacts of the SRE reallocation volumes respectively. Further,
as discussed in Preamble Section IV.B, we project that the entire SRE reallocation volume will
be met with carryover RINs attributable to the 2023-2025 exemptions. The cost of these RINs to
obligated parties is arguably zero in 2026 and 2027 as the cost of acquiring these RINs was
realized in 2023-2025. In the Set 1 Rule, we projected that no SREs would be granted in 2023-
2025, and therefore the cost and fuel price estimates for the Set 1 Rule included the cost of
acquiring the RINs that we project will be used to meet the compliance obligations associated
with the SRE reallocation volumes in this rule. The fuel price impacts we have projected in this
chapter are thus something of a high-end estimate where we project that the full cost of acquiring
the RINs needed for compliance with the SRE reallocation volume is passed through to
consumers despite this cost being realized in previous years.
807 See "Calculation of 2026 and 2027 RFS Percentage Standards Without SRE Reallocation Volumes," available in
the docket for this action.
416
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10.5.5 Cost to Transport Goods
In this chapter, we consider the impact of the use of renewable fuels on the cost to
transport goods. Since most goods being transported utilize diesel fuel powered trucks (as
opposed to gasoline or natural gas vehicles), we focus on the impacts on diesel fuel prices.
Reviewing the price estimates in Table 10.5.3-7, the projected price increase for diesel fuel
relative to the No RFS Baseline ranged from 19.50 per gallon in 2026 to 22.30 per gallon in
2027. As a worst-case scenario, we will use the projected diesel fuel price increase of 220 per
gallon for estimating the impact on the cost to transport goods.
The impact of fuel price increases on the price of goods is based upon a study conducted
by USD A. USD A analyzed the impact of fuel prices on the wholesale price of produce from
2000 to 2009 when fuel prices ramped up from an average of $30/bbl to over $90/bbl.808 Their
study found that a 100% increase in fuel prices resulted in a 25% increase in produce prices.
Assuming a baseline diesel fuel retail price of $2.79/gal in 2026 as summarized in Table
10.2.2.1-2 and adding 600 per gallon state and federal taxes to it, the projected 220 per gallon
increase in diesel fuel price in 2027 amounts to a 6% increase in diesel fuel prices. Applying the
25% ratio from the USDA study would indicate that the Analyzed Volumes for 2026 incremental
to the No RFS Baseline would then increase the wholesale price of produce by about 1.5%. If
produce being transported by a diesel truck costs $3 per pound, the increase in that products'
price due to the projected impact of the candidate volumes would be $0,045 per pound.809
Transport of food by other means such as rail or barge would be expected to impact food prices
less than transport by truck since rail and barge transport are both more efficient and fuel costs
would likely have a lower impact those modes of transportation costs. This estimate of the
impact on food prices is only an order of magnitude type estimate since impacts on food prices
vary greatly depending on the distance that the particular food travels by truck.
Relative to the 2025 Baseline, the impact of the Analyzed Volumes is expected to cause a
smaller increase in diesel fuel prices, thus causing a smaller increase in the cost to transport
goods. Also, our food transport cost analysis assumes that all the BBD costs are amortized over
the diesel fuel pool. If refiners amortize the BBD costs over both gasoline and diesel fuel, the
cost impact of transporting goods would be lower.
10.6 Comparison of Societal Benefits and Costs
In this section, we summarize the projected societal benefits and costs of the Analyzed
Volumes (see Table 10.6-1). This section only considers impacts of the rule that are
characterized as societal benefits or costs. Thus, for example, the projected rural economic
development impacts are not considered in this section, as many of these impacts represent
transfers (e.g., higher food prices paid by consumers to agricultural producers). The economic
impact methodologies used in Chapter 9 do not identify incremental societal benefits and costs,
so the results are not suitable for a societal benefit-cost comparison. Certain incremental benefits
and costs are discussed qualitatively in other chapters but not monetized, so they are not
8118 USDA, "How Transportation Costs Affect Fresh Fruit and Vegetable Prices," Economic Research Report 160,
November 2013. https://ers.usda. gov/sites/default/files/ laserfiche/publications/45165/41077 err!60.pdf.
8119 Coupons.com, "Comparing Prices on Groceries," May 4, 2021.
417
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presented here. For a full discussion of the impacts of each of these scenarios, including impacts
that are not considered societal benefits or costs, see the relevant chapters of this document. In
this comparison we have only considered the primary cost projections. Other cost scenarios can
be found in Chapter 10.4. The present value of the benefits, costs, and net benefits represent the
values in 2026.
Table 10.6-1: Net!
ienefits of the Analyzed Volumes in 2C
)26 and 2027 (million 2024$)
Type
Category
2026
2027
Present
Value
Annualized
Value
Societal Benefits
Energy Security Benefits
$361
$438
$786
$411
Societal Costs
Fuel Costs
$18,241
$21,244
$38,866
$20,312
Net Benefits
Total
-$17,880
-$20,806
-$38,080
-$19,901
Note: Present and annualized values are estimated using a 3% discount rate. Computing annualized costs and
benefits from present values spreads the costs and benefits equally over each period, taking account of the discount
rate. The annualized value equals the present value divided by the sum of discount factors. For a calculation of
present and annualized values from annual impact estimates, see "Set 2 FRM Costs and Benefits Summary,"
available in the docket for this action.
418
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Chapter 11: Regulatory Flexibility Act Screening Analysis
This chapter discusses EPA's screening analysis evaluating the potential impacts of the
2026 and 2027 RFS standards on small entities. The Regulatory Flexibility Act (RFA), as
amended by the Small Business Regulatory Enforcement Fairness Act of 1996 (SBREFA),
generally requires an agency to prepare a regulatory flexibility analysis of any rule subject to
notice and comment rulemaking requirements under the Administrative Procedure Act or any
other statute, unless the agency certifies that the rule will not have a significant economic impact
on a substantial number of small entities (referred to as a "No SISNOSE finding"). Pursuant to
this requirement, EPA has prepared a screening analysis for this rule.
11.1 Summary
We conducted the screening analyses by looking at the potential impacts on small entities
using two different methods and compared the cost-to-sales ratio for each method to a threshold
of 1%.810 For our first method, we compared obligated parties' cost of compliance (whether they
acquire RINs by purchasing renewable fuels with attached RINs and blending these fuels into
transportation fuel or by purchasing separated RINs) with the ability for the obligated parties to
recover these compliance costs through higher prices for the gasoline and diesel they sell with
what would be expected in the absence of the RFS program. Based on our analysis of the data,
we have determined that all obligated parties—including small refiners—fully recover the costs
of RFS compliance through higher sales prices on gasoline and diesel.811
For our second method, we estimated the cost-to-sales ratios for each small refiner that is
an obligated party under the RFS program using refinery-specific data under the worst-case
assumption that they could not recover RIN costs. While as noted above we have determined that
small refiners fully recover their RIN costs, we have nevertheless included this hypothetical
scenario in this analysis to respond to prior concerns from small refiners that they could not
recover their RIN costs. Moreover, this method seems more relevant to addressing small refiner-
specific concerns. This method emphasizes that even erroneously assuming no RIN cost
recovery by small refiners as suggested by some parties, a No SISNOSE finding would still be
appropriate.
As shown in Table 11.1-1, both methods result in a cost-to-sales ratio of less than 1%.
Therefore, EPA finds that these standards would not have a significant economic impact on a
substantial number of small entities.
8111A cost-to-sales ratio of 1% represents a typical agency threshold for determining the significance of the economic
impact on small entities. EPA, "Final Guidance for EPA Rulewriters: Regulatory Flexibility Act as amended by the
Small Business Regulatory Enforcement Fairness Act," November 2006.
https://www.epa.gov/sites/default/files/2015-06/documents/guidance-regflexact.pdf.
811 For a further discussion of the ability of obligated parties to recover the cost of RINs, see EPA, "Denial of
Petitions for Rulemaking to Change the RFS Point of Obligation," EPA-420-R-17-008, November 2017.
https ://nepis. epa. gov/Exe/ZvPDF. cgi?Dockev=P 100TB GV.pdf.
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Table 11.1-1: Estimated Cost-to-Sales Ratios of the 2026 and 2027 RFS Standards
Method
Screening Analysis
Cost-to-Sales Ratio
2026
2027
1
Cost as Part of RFS2 Rule
N/A
2
Market Cost Recovery
0.00%
3
Full RIN Price as Cost for Small Refiners
0.11-0.86%
0.12-0.98%
11.2 Background
11.2.1 Overview of the Regulatory Flexibility Act (RFA)
The RFA was amended by SBREFA to ensure that concerns regarding small entities are
adequately considered during the development of new regulations that affect those entities. The
RFA requires EPA to carefully consider the economic impacts that its rules may have on small
entities. The elements of the initial regulatory flexibility analysis accompanying a proposed rule
are set forth in 5 U.S.C. § 603, while those of the final regulatory flexibility analysis
accompanying a final rule are set forth in section 604. However, section 605(b) of the statute
provides that EPA need not conduct the section 603 or 604 analyses if we certify that the rule
will not have a significant economic impact on a substantial number of small entities.
11.2.2 Need for the Rulemaking and Rulemaking Objectives
A discussion on the need for and objectives of this action is located in Preamble Section
I. CAA section 21 l(o) requires EPA to promulgate regulations implementing the RFS program,
and to establish annual renewable fuel standards that are used by obligated parties to determine
their individual RVOs.
11.2.3 Definition and Description of Small Entities
Small entities include small businesses, small organizations, and small governmental
jurisdictions. For the purposes of assessing the impacts of a rule on small entities, a small entity
is defined as: (1) a small business according to the Small Business Administration's (SBA) size
standards; (2) a small governmental jurisdiction that is a government of a city, county, town,
school district or special district with a population of less than 50,000; or (3) a small organization
that is any not-for-profit enterprise that is independently owned and operated and is not dominant
in its field.
Small businesses (as well as large businesses) would be regulated by this rule, but not
small governmental jurisdictions or small organizations as described above. As set by SBA, the
categories of small entities that would potentially be directly affected by this rulemaking are
described in Table 11.2.3-1. Other entities may be indirectly affected by this rulemaking but are
not considered in this screening analysis.812
812 For example, small farms might benefit (see Chapter 9.1.6) whereas small entities in other industries might be
adversely affected by commodity and food price increases (see Chapters 9.3 and 9.4) or the increased price to
transport goods (see Chapter 10.5.5).
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Table 11.2.3-1: Small Business Definitions
Industry
Defined as small entity by
SBA if less than or equal to:
NAICS3 code
Gasoline and diesel refiners
1,500 employees'3
324110
a North American Industrial Classification System.
b Under the RFS program, EPA has included a provision that, in order to qualify for small refiner flexibilities, a
refiner must also produce no greater than 155,000 barrels per calendar day (bpcd) crude capacity. See 40 CFR
80.1442(a).
EPA used the criteria for small entities developed by SBA under the North American
Industry Classification System (NAICS) as a guide. Information about the characteristics of
refiners comes from sources including EIA, oil industry literature, and previous rules that have
affected the refining industry. In addition, EPA found employment information for companies
meeting the SBA definition of "small entity" using publicly available information and the
business information database Hoover's Inc. (a subsidiary of Dun & Bradstreet). These refiners
fall under the Petroleum Refineries category, 324110, as defined by NAICS.
Small entities that would be subject to this rulemaking include domestic refiners that
produce gasoline and/or diesel. Based on 2025 EIA refinery data,813 EPA believes that there are
approximately 30 refiners of gasoline and diesel subject to the RFS regulations. Of these, EPA
believes that there are currently 6 refiners (owning 7 refineries) producing gasoline and/or diesel
that meet the small entity definition of having 1,500 employees or fewer.
Table 11.2.3-2: Small Refiners
Refiner
Refinery Locations
Parent Company
Employees
American Refining Group
Bradford, PA
American Refining Group, Inc.
310
Country Mark
Mount Vernon, IN
CountryMark Cooperative
Holding Corp
425
Kern Energy
Bakersfield, CA
Casey Co.
129
Placid Oil
Port Allen, LA
The William Herbert Hunt Trust
Estate
251
San Joaquin Refining
Bakersfield, CA
San Joaquin Refining Co., LLC
130
Silver Eagle Refining
Evanston, WY
Woods Cross, UT
The International Group, Inc.
100
11.2.4 Reporting, Recordkeeping, and Other Compliance Requirements
Registration, reporting, and recordkeeping are necessary to track compliance with the
RFS standards and transactions involving RINs. However, these requirements are already in
place under the existing RFS regulations.814 While EPA is making revisions to the RFS
813 EIA, "Refinery Capacity Report 2025," Petroleum & Other Liquids, January 1, 2025.
https://www.eia.gov/petroleum/refinervcapacitv/arcliive/2025/refcap2Q25.php.
814 Prior to issuing our 2009 proposal for the general RFS regulatory program regulations required to implement the
amendments enacted pursuant to EISA, we analyzed the potential impacts on small entities of implementing the full
RFS program through 2022 and convened a Small Business Advocacy Review Panel (SBAR Panel) to assist us in
this evaluation. This information is located in the RFS2 Rule docket (Docket ID No. EPA-HQ-OAR-2005-0161).
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requirements in this action, we do not anticipate that there will be any significant cost on directly
regulated small entities.
11.3 Screening Analysis Approaches
This section concerns EPA's screening analyses performed for the 2026 and 2027 RFS
standards. For the purposes of this screening analysis, we estimated the costs of the 2026 and
2027 RFS standards relative to a "baseline" of the 2025 RFS standards (i.e., the percentage
standards established in the Set 1 Rule for 2025).
We considered two different methods for estimating the cost of the 2026 and 2027 RFS
standards to obligated parties using the baseline of the 2025 RFS standards. If, as has been
demonstrated, obligated parties recover the costs of RFS compliance through higher prices in the
marketplace for the petroleum products they sell, there is no net cost to obligated parties.
However, because various parties, including several small refiners, have continued to claim that
they are not able to recover the cost of RFS compliance in the marketplace, we also estimated the
cost-to-sales ratios for each of the 6 small refiners that are obligated parties under the RFS
program using refinery-specific data under the assumption that they could not recover RIN costs.
11.3.1 Method 1: Market Cost Recover Method
One way, and we believe the most appropriate way, to consider the impacts of the 2026
and 2027 RFS standards on obligated parties is to compare their cost of compliance with the
ability of the obligated parties to recover these compliance costs through the higher prices for the
gasoline and diesel they sell that result from the market-wide impact of the RFS program. EPA
has determined that while there is a cost to all obligated parties to acquire RINs (including small
refiners), obligated parties recover that cost through the higher sales prices they receive for the
gasoline and diesel they sell due to the market-wide impact of the RFS standards on these
products.815 EPA has examined available market data and concluded that the costs of compliance
with the RFS program are being passed downstream, as current wholesale gasoline and diesel
prices enable obligated parties to recover the cost of the RINs.816 When viewed in light of this
data, there is no net cost of compliance with the RFS standards (cost of compliance with the RFS
standards minus the increased revenue due to higher gasoline and diesel prices that result from
implementing the RFS program) to obligated parties, including small refiners. This is true
whether obligated parties acquire RINs by purchasing renewable fuels with attached RINs or by
purchasing separated RINs.
11.3.2 Method 2: Full RIN Price as Cost for Small Refiners Method
For our extreme hypothetical case, we estimated the actual change in the total cost of the
2026 and 2027 RFS standards to the 6 obligated parties that are small refiners if they acquired
the RINs necessary for compliance by purchasing separated RINs. We note, however, that in
815 For a further discussion of the ability of obligated parties to recover the cost of RINs, see EPA, "Denial of
Petitions for Rulemaking to Change the RFS Point of Obligation," EPA-420-R-17-008, November 2017.
https ://nepis. epa. gov/Exe/ZvPDF. cgi?Dockev=P 100TB GV.pdf.
816 Id.
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doing so we ignored the fact that these parties recover the cost of the RINs they purchase through
the higher market-wide prices they receive for the petroleum-based gasoline and diesel that they
sell, as discussed in Method 1. This approach would then reflect the cost they would have to pay
for compliance at the end of the year if they had spent the added revenue received from the
higher gasoline and diesel prices for other purposes.
Furthermore, we have also assumed that these parties would actually have to comply with
the 2026 and 2027 RFS standards. As described in Preamble Section V.C, the 2026 and 2027
percentage standards project that some small refineries—including those owned by the 6
obligated parties that are small refiners—receive an SRE and would not have to comply with the
2026 and 2027 RFS standards. Thus, this estimate represents a worst-case scenario for these
parties in which EPA establishes higher percentage standards based on a projection that some
small refineries will receive an exemption, but they do not receive an exemption and have to
comply with their 2026 and 2027 RFS obligations.
Because RIN prices can be impacted by a wide variety of different factors (including the
prices of renewable fuels and petroleum-based fuels, oil prices, commodity prices, etc.), EPA is
not able to project what RIN prices will be in the future. We can, however, use the average RIN
prices over the last 12 months (through December 2025) as an estimate of future RIN prices, as
shown in Table 11.3.2-1.
Table 11.3.3-1: Average RIN Prices and RFS Stanc
ards for 2026 and 2C
)27
RFS Standard
RIN
Type
Avg RIN Price
(Jan. 2025 -
Dec. 2025)
2025 RFS
Standard
2026
2027
Standard
Aa
Standard
Aa
Cellulosic
Biofuel
D3
$2.40
0.7 l%d
0.79%
0.08%
0.84%
0.13%
Biomass-Based
Diesel
D4
$0.95
3.15%
5.24%
2.09%
5.37%
2.22%
Other Advanced
Biofuelb
D5
$0.99
0.42%
0.39%
-0.03%
0.40%
-0.02%
Conventional
Renewable Fuel0
D6
$0.91
8.82%
9.07%
0.25%
9.17%
0.35%
a A represents the change relative to the baseline of the 2025 RFS standards.
b Other advanced biofuel is not an RFS standard category but is calculated by subtracting the cellulosic biofuel and
biomass-based diesel standards from the advanced biofuel standard.
0 Conventional renewable fuel is not an RFS standard category but is calculated by subtracting the advanced biofuel
standard from the total renewable fuel standard.
d Reflects the partial waiver of the 2025 cellulosic biofuel standard.
Using 2024 compliance data and SRE petition materials where available, and assuming
that the total gasoline and diesel production for each of these small refiners remains unchanged,
we estimated their RVOs for 2026 and 2027. The difference between the estimated RVOs for
each year multiplied by the estimated RIN price for each standard then gives us the estimated
cost of the 2026 and 2027 RFS standards for each small refiner that chooses to meet their
obligations by purchasing separated RINs. The actual calculations for each small refiner are
provided in Chapter 11.6; a non-CBI example of these calculations is shown in Tables 11.3.3-2
and 3.
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Table 11.3.3-2: Exam
pie Small Refiner Costs Calculation for 2026
Refiner
2024
Gas/Diesel
Prod
(mil gal)
Cellulosic
(D3)
BBD
(D4)
Other Advanced
Biofuel
(D5)
Conventional
Renewable Fuel
(D6)
Total
Cost
($mil)
A (mil
RINs)
Cost
($mil)
A (mil
RINs)
Cost
($mil)
A (mil
RINs)
Cost
($mil)
A (mil
RINs)
Cost
($mil)
Example
250
0.20
$0.48
5.23
$4.96
-0.15
-$0.15
0.65
$0.59
$5.89
Table 11.3
>.3-3: Exam
pie Small Refiner Costs Calculation for 2027
Refiner
2024
Gas/Diesel
Prod
(mil gal)
Cellulosic
(D3)
BBD
(D4)
Other Advanced
Biofuel
(D5)
Conventional
Renewable Fuel
(D6)
Total
Cost
($mil)
A (mil
RINs)
Cost
($mil)
A (mil
RINs)
Cost
($mil)
A (mil
RINs)
Cost
($mil)
A (mil
RINs)
Cost
($mil)
Example
250
0.33
$0.78
5.55
$5.27
-0.12
-$0.12
0.87
$0.80
$6.73
11.4 Cost-to-Sales Ratio Result
The final step in our methodology is to compare the total estimated costs from each of the
methods above to relevant total estimated revenue from the sales of gasoline and diesel in the
U.S. in 2026 and 2027. Since the RFS standards are proportional to the volume of gasoline and
diesel produced by each obligated party, all obligated parties (including small refiners) are
expected to experience costs (and recover those costs) to comply with the RFS standards that are
proportional to their sales volumes.
For Method 1, all obligated parties—including small refiners—recover their RFS
compliance costs and thus they have no net cost of compliance. For Method 2, we divided the
estimated costs for each small refiner by its total estimated annual sales.817 The resulting cost-to-
sales ratios for each method are shown in Table 11.4-1, along with a non-CBI example of these
calculations for Method 2 using the data from Tables 11.3.3-2 through 4.
Table 11.4-1: Estimated Cost
t-to-Sales Ratios of the 2026 and 2027 R
7S Standards
Method
Screening Analysis
Total Cost ($mil)
Total Sale ($mil)
Cost-to-Sales Ratio
2026
2027
2026
2027
2026
2027
1
Market Cost Recovery
$0
n/a
0.0%
2
Full RIN Price as Cost
for Small Refiners
(Actual)3
--
--
0.11-0.86%
0.12-0.98%
Full RIN Price as Cost
for Small Refiners
(Example)
$5.89
$6.73
$1,000
$1,000
0.59%
0.67%
a The actual calculations for Method 2 for each small refiner are provided in Chapter 11.6.
11.5 Conclusion
Based on our outreach, fact-finding, and analysis of the potential impacts of this rule on
small businesses, we have concluded that this rule would not have a significant economic impact
817 Estimated 2024 sales data gathered from SRE petitions and Hoovers, Inc. (available at
https://app.dnbhoovers.com).
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on a substantial number of small entities. As described in Method 1, since obligated parties have
been shown to recover their RFS compliance costs through the resulting higher market prices for
their petroleum products, there is no net cost to small refiners resulting from the RFS program.
However, as described in Method 2, we also conducted a worst-case sensitivity analysis
that ignored the fact that obligated parties recover their costs. Under this extreme assumption, we
were able to estimate the costs of this rule on small refiners and then use a cost-to-sales ratio test
(a ratio of the estimated annualized compliance costs to the value of sales per company) to assess
whether the costs were significant. Under this method, the cost-to-sales analyses indicated that
the 6 small refiners would be affected at less than 1% of their sales (i.e., the estimated costs of
compliance with the rule would be less than 1% of their sales). The cost-to-sales percentages
estimated using Method 2 ranged from 0.10% to 0.98%.
11.6 Small Refiner CBI Data
[Information Redacted - Claimed as CBI]
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