Renewable Fuel Standard (RFS) Program
- Standards for 2026 and 2027:
Draft Regulatory Impact Analysis
SEPA
United States
Environmental Protection
Agency
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Renewable Fuel Standard (RFS) Program -
Standards for 2026 and 2027:
Draft Regulatory Impact Analysis
This technical report does not necessarily represent final EPA decisions
or positions. It is intended to present technical analysis of issues using
data that are currently available. The purpose in the release of such
reports is to facilitate the exchange of technical information and to
inform the public of technical developments.
Assessment and Standards Division
Office of Transportation and Air Quality
U.S. Environmental Protection Agency
NOTICE
United States
Environmental Protection
Agency
EPA-420-D-25-001
June 2025
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Table of Contents
List of Acronyms and Abbreviations v
Executive Summary vii
Overview xii
Chapter 1: Review of the Implementation of the Program 1
1.1 Gasoline, Diesel, Crude Oil, and Renewable Fuels 1
1.1.1 Crude Oil Prices vs. Renewable Fuel Feedstock Price Projections 1
1.1.2 Petroleum and Renewable Fuels Imports 2
1.1.3 Refinery Margins 4
1.1.4 Transportation Fuel Demand 5
1.2 Cellulosic Biofuel 7
1.3 Biodiesel and Renewable Diesel 8
1.4 Ethanol 11
1.4.1 E85 13
1.4.2 E15 15
1.5 Other Biofuels 17
1.6 Federal Tax Credits for Biofuels 18
1.7 RIN System and Prices 20
1.7.1 RIN System 20
1.7.2 RIN Prices 21
1.8 Carryover RIN Projections 28
1.8.1 Carryover RINs Available After Compliance With the 2023 Standards 29
1.8.2 Carryover RINs Available for 2026 and 2027 31
1.8.3 Carryover RIN History 32
1.8.4 EMTS RIN Data 35
Chapter 2: Baselines 38
2.1 No RFS Baseline 38
2.1.1 Ethanol 41
2.1.2 Cellulosic Biofuel 59
2.1.3 Biomass-Based Diesel 62
2.1.4 Other Advanced Biofuel 77
2.1.5 Summary of No RFS Baseline 78
2.2 2025 Baseline 79
Chapter 3: Volume Scenarios, Proposed Volumes, and Volume Changes 84
3.1 Mix of Renewable Fuel Types for Volume Scenarios 84
3.2 Mix of Renewable Fuel Types for the Proposed Volumes 89
3.3 Volume Changes Analyzed with Respect to the No RFS Baseline 99
3.4 Volume Changes Analyzed with Respect to the 2025 Baseline 106
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Chapter 4: Environmental Impacts 119
4.1 Air Quality 119
4.1.1 Background on Air Quality Impacts of Biofuels 119
4.1.2 Emission Impacts of Proposed Volumes 123
4.1.3 Air Quality Impacts of Proposed Volumes 141
4.2 Conversion of Natural Lands 142
4.2.1 Natural Land Conversion Effects 142
4.2.2 New Literature on the Conversion of Natural Lands 146
4.2.3 Potential Natural Land Conversion Impacts From This Rule 147
4.3 Soil and Water Quality 150
4.3.1 Soil and Water Quality Impacts 151
4.3.2 New Literature on Soil and Water Quality Effects 154
4.3.3 Potential Soil and Water Quality Impacts From This Rule 156
4.4 Water Quantity and Availability 157
4.4.1 Water and Biofuel Crop Growth 158
4.4.2 Use of Water in Production Facilities 159
4.5 Ecosystem and Wildlife Habitat 159
4.5.1 Ecosystems and Wildlife Habitat Impacts 160
4.5.2 New Literature on Ecosystem and Wildlife Habitat Impacts 162
4.5.3 Potential Ecosystem and Wildlife Habitat Impacts From This Rule 163
4.6 Ecosystem Services 164
Chapter 5: Climate Change Analysis 167
5.1 Methodology 167
5.1.1 Overview 167
5.1.2 Waste- and Byproduct-based Fuels 179
5.1.3 Crop-based Fuels 189
5.2 Assessment of Analytical Volume Scenarios 201
5.2.1 Waste- and Byproduct-based Fuels 201
5.2.2 Crop-based Fuels 202
5.2.3 Summary of GHG Emission Impacts Estimates 221
5.3 Assessment of Proposed Volumes 224
Appendix 5-A: Sensitivity Analysis for Economic Modeling 227
Chapter 6: Energy Security Impact 232
6.1 Review of Historical Energy Security Literature (1981 to 2014) 236
6.2 Review of Energy Security Literature from the Last Decade 238
6.2.1 Oil Energy Security Studies from the Last Decade 238
6.2.2 Studies on Tight/Shale Oil 242
6.3 Cost of Existing U.S. Energy Security Policies 247
6.4 Energy Security Impacts 250
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6.4.1 U. S. Oil Import Reductions 250
6.4.2 Oil Import Premiums Used for This Proposed Rule 252
6.4.3 Energy Security Benefits 258
Chapter 7: Rate of Production and Consumption of Renewable Fuel 260
7.1 Cellulosic Biofuel 260
7.1.1 Cellulosic Biofuel Industry Assessment 261
7.1.2 Review of EPA's Projection of Cellulosic Biofuel in Previous Years 263
7.1.3 Projection of the 2025 Cellulosic Biofuel Volumes 266
7.1.4 Projecting the Biogas-derived CNG and LNG Market 267
7.1.5 Projected Supply of Liquid Cellulosic Biofuels 280
7.1.6 Projected Rate of Cellulosic Biofuel Production for 2026-2030 281
7.2 Biomass-Based Diesel 282
7.2.1 Production and Use of Biomass-Based Diesel in Previous Years 282
7.2.2 Biomass-Based Diesel Supply in 2024 and 2025 286
7.2.3 Biomass-Based Diesel Production Capacity and Utilization 287
7.2.4 Biomass-Based Diesel Feedstock Availability to Domestic Biofuel Producers 290
7.2.5 Imports and Exports of Biomass-Based Diesel 304
7.2.6 Projected Rate of Production and Use of Biomass-Based Diesel 307
7.3 Imported Sugarcane Ethanol 310
7.4 Other Advanced Biofuel 311
7.5 Total Ethanol Consumption 312
7.5.1 Projection of Motor Gasoline Consumption 313
7.5.2 Projection of Total Ethanol Consumption 317
7.6 Corn Ethanol 318
7.7 Conventional Biodiesel and Renewable Diesel 319
Chapter 8: Infrastructure 322
8.1 Biogas 322
8.2 Biodiesel 323
8.3 Renewable Diesel 326
8.4 Ethanol 327
8.4.1 Ethanol Distribution 327
8.4.2 Infrastructure for E85 328
8.4.3 Infrastructure for El5 330
8.5 Deliverability of Materials, Goods, and Products Other Than Renewable Fuel 333
Chapter 9: Other Factors 336
9.1 Employment and Rural Economic Development Impacts 336
9.1.1 Methodology and Existing Literature 341
9.1.2 Employment Impacts using the Rule-of-thumb Approach 352
9.1.3 Employment Impacts using NREL's JEDI model for Dry Mill Corn Ethanol 356
9.1.4 Agricultural Employment 358
9.1.5 Rural Economic Development 363
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9.1.6 Summary of Employment and Economic Impacts 368
9.2 Supply of Agricultural Commodities 375
9.3 Price of Agricultural Commodities 376
9.4 Food Prices 385
Chapter 10: Estimated Costs and Fuel Price Impacts 391
10.1 Renewable Fuel Costs 391
10.1.1 Feedstock Costs 391
10.1.2 Renewable Fuels Production Costs 395
10.1.3 Blending and Fuel Economy Cost 412
10.1.4 Distribution and Retail Costs 419
10.2 Gasoline, Diesel Fuel and Natural Gas Costs 426
10.2.1 Production Costs 426
10.2.2 Gasoline, Diesel Fuel and Natural Gas Distribution and Blending Cost 427
10.3 Fuel Energy Density and Fuel Economy Cost 429
10.4 Costs 430
10.4.1 Individual Fuels Cost Summary 430
10.4.2 Costs for the Proposed Volumes 435
10.4.3 Costs for the Low Volume Scenario 449
10.4.4 Costs for the High Volume Scenario 459
10.5 Estimated Fuel Price Impacts 469
10.5.1 RIN Cost and RIN Value 469
10.5.2 Estimated Fuel Price Impacts (Gasoline) 470
10.5.3 Estimated Fuel Price Impacts (Diesel) 473
10.5.4 Cost to Transport Goods 476
10.6 Comparison of Societal Benefits and Costs 477
Chapter 11: Regulatory Flexibility Act Screening Analysis 479
11.1 Summary 479
11.2 Background 480
11.2.1 Overview of the Regulatory Flexibility Act (RFA) 480
11.2.2 Need for the Rulemaking and Rulemaking Objectives 480
11.2.3 Definition and Description of Small Entities 480
11.2.4 Reporting, Recordkeeping, and Other Compliance Requirements 481
11.3 Screening Analysis Approaches 481
11.3.1 Method 1: Market Cost Recover Method 482
11.3.2 Method 2: Full RIN Price as Cost for Small Refiners Method 482
11.4 Cost-to-Sales Ratio Result 484
11.5 Conclusion 484
11.6 Small Refiner CBI Data 485
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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
BOB
Gasoline Before Oxygenate Blending
bpd
Barrels Per Day
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
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
EU
European Union
EV
Electric Vehicle
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
GWP
Global Warming Potential
LCA
Lifecycle Analysis
IEA
International Energy Agency
IPCC
Intergovernmental Panel on Climate Change
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
N0X
Nitrogen Oxides
NREL
National Renewable Energy Laboratory
OPEC
Organization of Petroleum Exporting Countries
OPIS
Oil Price Information Service
ORNL
Oak Ridge National Laboratory
PADD
Petroleum Administration for Defense District
PHEV
Plug-in Hybrid Electric Vehicle
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
RVO
Renewable Volume Obligation
RVP
Reid Vapor Pressure
SBA
Small Business Administration
SBREFA
Small Business Regulatory Enforcement Fairness Act of 1996
S02
Sulfur Dioxide
SPR
Strategic Petroleum Reserve
SRE
Small Refinery Exemption
STEO
Short Term Energy Outlook
UCO
Used Cooking Oil
ULSD
Ultra-Low-Sulfur Diesel
USD A
U.S. Department of Agriculture
VOC
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 in prior years 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 VI, we are continuing to use percentage
standards as the implementing mechanism for 2026 and 2027.
In this action we are proposing to establish 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 proposing in this action a number of other changes, including that imported
renewable fuel and renewable fuel produced from foreign feedstocks would generate fewer
RINs, the removal of renewable electricity as a qualifying renewable fuel under the RFS
program, and several other changes.
This Draft Regulatory Impact Analysis (DRIA) supports our proposal 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 proposed volume
requirements in this rule, including both quantified and unquantified impacts. Not all of the
monetized impacts listed in Table ES-1 represent societal benefits or costs. Specifically, the only
monetized societal benefits and costs are the energy security benefits and the fuel costs. The
projected $11.4 billion annualized impacts in rural economic development generally do not
represent societal benefits. The monetized societal benefits and costs of this proposed rule are
shown in Table ES-2.
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The table 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 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 DRIA Chapter
identified under the "More Information" column. 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 Proposed Volumes in this Rule
Relative to the No RFS Baseline"
Potential Impacts of
Proposed Volumes
Effect
Effect Quantified
Quantified
Impact
Chapter
Impacts on air quality
from biofuel production,
transport, and use
Increases in emissions associated with biofuel
production
Emission inventory
impacts
-
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
varied by location across the U.S.
Qualitative
-
4.1.3
Impacts on climate
change from biofuel
feedstock production and
displacement of
petroleum fuels
Reduced GHG emissions
Quantitative
1-16 MMT
average annual
CChe reductions
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
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Potential Impacts of
Proposed 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
Energy security
benefits
$200 million
6
Production and use of
renewable fuels
Increased
Increased production and use of renewable fuels
production and use
of renewable fuels
-
6
Infrastructure
Increased development of infrastructure of deliver
and use renewable fuels
Qualitative
7
No adverse impact on deliverability of materials,
goods, and products other than renewable fuel
Qualitative
7
Jobs
Increased employment
Quantitative
120,000 jobs
9.2
Rural economic
development
Increased support for rural economic development
associated with biofuel and feedstock production
Quantitative
$11.4 billion
9.3
Commodity supply and
price impacts
Increased supply of certain agricultural
commodities
Qualitative
9.4
Higher corn, soybean, and soybean oil prices
Commodity price
increases
-
9.5
Higher food prices
Food price increases
-
9.6
Increased societal cost
Fuel costs
$6.7 billion
10.4
Costs
Estimated Fuel Price Impacts
Cost changes
-
10.5
Increased costs to transport goods
Cost increases
-
10.5
a This table includes both societal costs and benefits (fuel costs, energy security) as well as distributional effects or transfers (jobs, rural economic development,
etc.). Monetized fuel costs, energy security benefits, and rural economic development benefits in Table ES-1 represent annualized monetized impacts using a 3%
discount rate. Alternative discount rates are considered in Preamble Section V.H and in the relevant chapters cited within the table.
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Table ES-2: Societal Benefits and Costs of this Proposed Rule (million 2022$)a
Present
Annualized
Type
Category
2026
2027
Value
Value
Societal Benefits
Energy Security Benefits
$196
$210
$387
$202
Societal Costs
Fuel Costs
$7,494
$5,936
$12,871
$6,726
Net Benefits
Total
-$7,297
-$5,726
-$12,484
-$6,524
a 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 NPRM Costs and Benefits Summary," available in the
docket for this action.
The technical analyses supporting this proposed rule, summarized in Tables ES-1 and 2
and presented in greater detail in this document, are based on the information available at the
time the analyses were completed. Since these analyses were completed, more recent data and
projections have become available. One important projection that we used extensively in our
analyses for this proposed rule was EIA's Annual Energy Outlook 2023 (AEO2023), which was
the most current version of this report at the time the analyses were conducted.1 Since that time,
EIA released Annual Energy Outlook 2025 (AEO2025) on April 15, 2025.2 Among other
changes, AEO2025 projects lower crude oil prices than AEO2023 ($78-81 per barrel in
AEO2025 vs $85-90 per barrel in AEO2023) and greater consumption of transportation fuel. All
else equal, lower petroleum fuel prices will increase the cost of renewable fuels. For example,
the projected wholesale diesel prices for 2026 and 2027 in AEO2025 are $0.52 and $0.39 per
gallon lower, respectively, than in AE02023.3 If we consider this change in isolation, it will
increase the projected per gallon costs for renewable diesel produced from soybean oil by 26% in
2026 and 18% in 2027. Other fuel types will be similarly impacted, though the magnitude of the
impact will vary be fuel type. While these per gallon cost increases provide some indication of
the impact updating to AEO2025 will have on the projected costs of this proposed rule, we note
that our consideration of new information and projections will impact the cost projections in a
variety of ways (including our projection of the No RFS baseline) and that these impacts are not
all simple to anticipate or project. For the final rule, we intend to update our analyses using the
most recent available data and projections from EIA and other sources.
1 EIA, "Annual Energy Outlook 2023" (AEO2023). https://www.eia.gov/outlooks/archive/aeo23.
2 EIA, "Annual Energy Outlook 2025" (AEO2025). https://www.eia.gov/outlooks/aeo.
3 Estimates calculated assuming that updating to AEO2025 will increase the cost of renewable diesel produced from
soybean oil relative to petroleum diesel (projected to be $2.00 per gallon in 2026 and $2.12 per gallon in 2027) by
$0.52 per gallon and $0.39 per gallon, respectively. See DRIA Chapter 10.4.1 for more detail on the cost projections
of individual renewable fuels. Wholesale diesel prices from Table 57 - Components of Selected Petroleum Price
Products.
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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: Volumes Scenarios. Proposed 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 Volume Scenarios and the Proposed Volumes. It also
identifies the differences between the Volume Scenarios and Proposed Volumes and the
baselines described in Chapter 2.
Chapter 4: Environmental Impacts
This chapter discusses the environmental factors EPA analyzed in developing the Proposed
Volumes.
Chapter 5: Climate Change Analysis
This chapter describes potential climate impacts of the Proposed 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 would result from
the Proposed 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 (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.
Chapter 11: Screening Analysis
This chapter discusses EPA's screening analysis evaluating the potential impacts of the proposed
RFS standards on small entities.
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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.4 In determining the proposed RFS volumes in this
rule we have once again considered the implementation of the RFS program since 2005,
described in detail in the Set 1 Rule RIA. We have also considered developments in the
petroleum fuel and renewable fuel sectors since 2022. 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 soy 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 in 2023 and 2024. USDA
projects corn and soy oil prices to increase somewhat in 2025 and then decrease very slightly,
while EIA projects crude oil prices to increase in 2025 out to 2030. It is important to note that the
crude oil price projections are from AEO2023, which is over two years old. However, a
4 "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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2023
2026 2027 2028 2029
Year
Soy Oil Prices Corn Prices
preliminary review of AEO2025 indicates lower projections for crude oil prices from 2026-2030
(ranging from $78-81 per barrel) than AEO2023 ($85-90 per barrel).
Figure 1.1.1-1 Historical and Projected Future Crude Oil, Corn, and Soybean Oil Prices3
a 2024 and earlier are historical, 2025 and later are price projections in nominal dollars.
Source: EIA, "Spot Prices," Petroleum & Other Liquids, May 14, 2025.
https://www.eia.gov/dnav/pet/pet pri spt si a.htm. USD A, "Oil Crops Yearbook," March 2025.
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. AEO2023,
Table 12 - Petroleum and Other Liquids Prices. USD A, "USDA Agricultural Projections to 2033," OCE-2024-1,
February 2024, https ://www.usda. gov/sites/default/files/documents/USDA-Agricultural-Proiections-to-203 3 .pdf.
100
80
60
40
20
0
2022
Crude Oil Prices
6
5
4
3
2
1
0
2030
2024 2025
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
into a recession.5 It also led to Congress banning the export of U.S. crude oil from 1975 to 2015.6
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
5 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.
6 1975 Energy Policy and Conservation Act; Consolidated Appropriations Act of 2016.
2
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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 oil
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.7 EIA does not specifically project future gasoline and diesel fuel
net imports in its 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.8 Figure 1.1.2-1
summarizes the gasoline, distillate, and total refined product volumes.
Figure 1.1.2-1: Gasoline and Distillate and Total Refined Products Net Imports"
00
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¦Gasoline
2026
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2028
-All Products
2030
a 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.
7 EIA, "U.S. Net Imports by Country," Petroleum & Other Liquids. April 30, 2025.
https://www.eia.gov/dnav/pet/pet move neti dc NUS-Z00 mbblpd a.htm.
8 AEO2023, Table 11 - Petroleum and Other Liquids Supply and Disposition.
3
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Figure 1.1.2-2: Corn Ethanol, Biodiesel and Renewable Diesel Net Imports3
60 ,
40
-160 1
2022 2023 2024 2025 2026 2027 2028 2029 2030
Year
Ethanol Biodiesel Renewable Diesel
a 2024 and earlier are historical, 2025 and later are projections.
Source: EIA, "U.S. Net Imports by Country," Petroleum & Other Liquids, April 30, 2025.
https://www.eia.gov/dnav/pet/pet move neti dc NUS-Z00 mbblpd a.htm. AEO2023, Table 11 - Petroleum and
Other Liquids Supply and Disposition.
Figure 1.1.2-2 shows that biodiesel and renewable diesel net imports increased after
2022. After 2024, EIA projects biodiesel and renewable diesel net imports to essentially be flat
going forward. Corn ethanol net imports decreased from 2022 to 2024. After 2024, EIA projects
corn ethanol net imports to decrease further. 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 volume percent. Consequently, corn ethanol producers would
export the excess corn ethanol production volume which is not blended into gasoline as El5 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.
EIA reported refinery margin data for various U.S. refinery regions, as well as for Europe
and Singapore, and is shown in Figure 1.1.3-1.9 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.
9 EIA, "Global refinery margins fall to multiyear seasonal lows in September," Today in Energy, October 15, 2024.
https://www.eia. gov/todavinenergy/detail.php?id=63447.
4
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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
eia'
$1.20
$1.00
$0.80
$0.60
$0.40
$0.20
$0.00
2022
2023
2024
ll
i
It
New York Gulf Coast Chicago
Note: ARA = Amsterdam-Rotterdam-Antwerp
Los Angeles
ARA
Singapore
The figure shows that the disruption of fuel consumption in 2020 caused by the Covid-19
pandemic caused 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.10 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 Fuel 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.
Figure 1.1.4-] 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.
10 Id.
5
-------
Figure 1.1.4-1: Actual and Projected Transportation Fuel Demand
10,000
9,000
1" 8,000
"O
2? 7,000
_q
— 6,000
"O
QJ
- 5,000
Q_
4,000
to '
E 3,000
o 2,000
>
1,000
0
2022 2023 2024 2025 2026 2027 2028 2029 2030
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, April 30, 2025. https://www.eia.gov/dnav/pet/pet cons psup dc nus mbblpd a.htm. 2025 - 2023
data is from AEO2023, Table 11 - Petroleum and Other Liquids Supply and Disposition.
Figure 1.4.4-1 shows that gasoline demand increased from 2022 to 2023, but then
decreased from 2023 to 2024. Distillate demand decreased from 2022 to 2024, while jet fuel
increased. Based on projections in AEO2023, distillate demand is expected to decline slightly
and jet fuel is expected to increase slightly over the years 2025 to 2030. AEO2023 projects that
gasoline demand will begin to decline and continue to do so through 2030.
Several factors have contributed 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 greenhouse gas (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.11 On
balance, newer 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.
11 "The 2024 EPA Automotive Trends Report," EPA-420-R-24-022, November 2024.
6
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• 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 2024.12 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 Bio fuel
The RFS2 Rule projected a favorable outlook for cellulosic biofuels, anticipating them
becoming a major contributor to the total biofuel volumes.13 Since the implementation of that
rule however, commercial-scale production of cellulosic biofuels has fallen short of these
expectations. For the first several years of the RFS2 Rule, actual production volumes were
significantly below the targets set in the rule. A major shift occurred with the inclusion of
compressed natural gas and liquified natural gas (CNG/LNG) derived from biogas as qualifying
cellulosic biofuels. Although not originally identified as a potential cellulosic biofuel pathway in
the RFS2 Rule, CNG/LNG derived from biogas has since become the primary source of
cellulosic biofuel production. The RFS2 Rule initially included a pathway14 for generating
advanced (D5) RINs from biogas produced at landfills, wastewater treatment plants, and manure
digesters.15 However, in response to industry inquiries, EPA evaluated whether biogas from
additional sources could also qualify as cellulosic biofuel. This led to the Pathways II Rule in
2014, which expanded the approved pathways to include CNG/LNG derived from biogas
sourced from landfills, wastewater treatment facility digesters, and manure digesters.
Additionally, biogas derived from the cellulosic components of biomass processed in other waste
digesters was also approved to generate cellulosic (D3) RINs16 when used as transportation
fuel.17 This expansion was a critical driver of growth in 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 920 million RINs in 2024, (see Figure 1.2-1), with around 95% of all cellulosic
RINs generated under the RFS program in 2024 attributed to CNG/LNG derived from biogas.
This trend is expected to continue, with total volumes steadily increasing and CNG/LNG
remaining the primary source of cellulosic biofuels in the RFS program through 2030 (see
12 EIA, "Electric Power Monthly," February 2025, Table D.l - U.S. Estimated Consumption of Electricity by Light-
Duty Electric Vehicles Types. https://www.eia.gov/electricitv/montlilY/epm table grapher.php?t=table d 1.
13 75 FR 14674 (March 26, 2010).
14 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).
15 75 FR 14872 (March 26, 2010).
16 One RIN can be generated for each ethanol-equivalent gallon of renewable fuel. One gallon of ethanol is eligible
to generate one RIN; other types of fuel generate RINs based on their energy content per gallon relative to ethanol.
For CNG/LNG derived from biogas, every 77,000 BTU of qualifying biogas generates one RIN.
17 79 FR 42128 (July 18, 2014).
7
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Chapter 7). Though, as discussed further in Chapter 7.1.4, EPA is also projecting smaller
volumes of ethanol produced from corn kernel fiber (CKF) as part of its overall cellulosic biofuel
volume projection.
The most significant change anticipated by EPA to impact the future of cellulosic biofuel
revolves around a shift in market constraints. Since the Pathways II Rule, cellulosic volumes
have been constrained solely by production capacity. However, for this proposal EPA expects
the market to transition from being production-limited to consumption-limited. As discussed
further in Chapter 7.1, EPA projects that the current capacity for using biogas-derived
CNG/LNG as a transportation fuel may be approaching saturation, with the RFS-eligible fleet of
active CNG/LNG vehicles being almost entirely fueled by biogas-derived CNG/LNG. Evidence
of this shift is already noticeable, as EPA retroactively adjusted the 2024 cellulosic biofuel
volume obligations.18 This adjustment was necessary because CNG/LNG production failed to
meet the volume requirement. Similarly, EPA anticipates that 2025 volumes will also fall short
of the obligations in the Set 1 Rule and is therefore proposing adjustments in this action, as
presented in Preamble Section VII 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 constrained by the ability to use it as a qualifying transportation fuel.
Figure 1.2-1: Cellulosic RINs Generated
1100
1000
900
800
C/3
£
700
a
600
o
500
§
400
300
200
100
0
20]3 2oj4 20]5 20j6 20j7 20]8 2oj9 202q 2o?j 2o22 2023 2o24
~ CNG/LNG Derived from Biogas ~ Liquid Cellulosic Biofuels
1.3 Biodiesel and Renewable Diesel
The actual supply of biodiesel and renewable diesel has continued to significantly exceed
the BBD volume requirements since 2022, as volumes of BBD beyond the BBD volume
requirement have been used to meet both the advanced biofuel and total renewable fuel volume
requirements. 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
18 89 FR 100442 (December 12, 2024).
8
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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 proposing 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 di esel 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. Renewable diesel also currently
generates more credits per gallon than biodiesel, providing additional revenue for renewable
diesel producers and blenders. Perhaps most importantly, renewable diesel can generally be
blended at higher blend 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 and claim additional financial
incentives 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.
Figure 1.3-1: Supply of Biodiesel and Renewable Diesel to the U.S.
100%
90%
80% ai
Ofl
ro
70% §
•
CL
Q-
50% ^
s
c
Ol
20% cc
10%
0%
f i Net Biodiesel Imports r I Net Renewable Diesel Imports
Renewable Diesel Supply Percentage
Source: EMTS.
6,000
5,000
4,000
^ 3,000
2,000
1,000
2015 2016 2017 2018 2019 2020 2021 2022 2023 2024
I Domestic Biodiesel Domestic Renewable Diesel
9
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Another significant development in the BBD industry is the recent increase in the
quantities of BBD feedstocks imported into the U.S. Historically, the U.S. has imported very
small quantities of qualifying BBD feedstocks such as soybean oil, canola oil, waste fats, oils,
and greases (FOG), and animal fats. 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 animal fats and FOG, increased dramatically. These imports were likely driven by
several factors, including increasing 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. Notably, FOG and animal fats can be used to produce BBD with low
carbon intensity (CI) scores. These fuels generate significantly more credits in state low carbon
fuel programs and are expected to similarly be eligible for significantly greater tax credits under
the Clean Fuel Production Credit (45Z). This combination of state and federal incentives is
projected to continue to drive increasing volumes of feedstock imports in future years,
particularly imports of feedstocks such as FOG and animal fats that can be used to produce BBD
with low CI scores. The rate of future imports of feedstocks is highly uncertain, however, 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.
Figure 1.3-2: Imports of Qualifying BBD Feedstocks (Million Gallons BBD Equivalent)
1,800
1,600
cD 1,400
3 1,200
O"
LU
§ 1,000
u
C
O
d n
800
600
400
200
2015 2016 2017 2018 2019 2020 2021 2022 2023 2024
¦ Soybean Oil ¦ Canola Oil ~ Used Cooking Oil ¦ Tallow
Source: EMTS.
10
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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 has
predominantly been produced from corn-derived biomass feedstocks, but smaller volumes are
also produced from 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.19
Despite significant reductions in 2020 and 2021 due to the Covid-19 pandemic, domestic fuel
ethanol production had returned to close to pre-pandemic levels in 2023 and 2024.20 In 2024,
ethanol accounted for approximately 70% of the biofuel consumed in the U.S.21
Total ethanol consumption is the sum of ethanol blended with fossil fuel gasoline (E0) to
create motor gasoline ethanol blends (E10, E15, and E85). 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 gasoline pool. In 2007, national average ethanol concentration
surpassed 5% for the first time 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 El5 and E85 fuel volumes can exceed the
ethanol content of E10 and more than offset the dilution caused by E0 volumes. Based on
updated methodology, EPA projects in this proposed rulemaking an average ethanol
concentration of 10.27% in 2026, rising to 10.38% in 2030. For a detailed look at 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
proposed rule due to declining gasoline (E10) consumption.
19 EIA, "Monthly Energy Review," March 2025, Tables 10.3 and 10.4.
https://www.eia.gov/totalenergv/data/montlilY/arcliive/00352503.pdf. Comparison is based on ethanol-equivalence.
20 Id.
21 Id.
11
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Figure 1.4-1: Domestic Production and Consumption of Ethanol by Year
18,000
16,000
14,000
12,000
10,000
8,000
6,000
4,000
2,000
0
2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023
Domestic Production Domestic Consumption
Source: EIA, "Monthly Energy Review," March 2025, Table 10.3.
https://www.eia.gov/totalenergv/data/montlilY/arcliive/00352503.pdf.
EIA does not report fuel ethanol export data for years prior to 2010. Since 2010, 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 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.22
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.
22 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-researcli/latest-
news/agriculture/111924-us-ethanol-exports-on-pace-for-record-vear-fueled-bv-low-prices-and-increased-
opportunity-overseas.
12
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Figure 1.4-2: Monthly Fuel Ethanol Exports from U.S.
250
200
-------
nationwide average ethanol concentration of E85 is 74% which is the value EPA has opted to use
in this proposal, consistent with previous rulemakings.24
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 E10 (or 24% lower than that of EO).25-26 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 rarely achieved the requisite energy equivalent pricing needed for
FFV owners who are aware of and concerned about the fuel economy impacts of E85.
Furthermore, E85 purchasers generally have no way of knowing whether their fuel contains 83%
ethanol, 51% ethanol, or something in-between.
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, which provides an incentive for consumers to utilize
E85. Additional information on E85 nationwide and in California can be found in Chapter 7.5.
24 AEO2023, Table 2 - Energy Consumption by Sector and Source.
25 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.
26 A comparison to EO would be more relevant prior to 2010 when there remained significant volumes of EO for sale
at retail stations.
14
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Figure 1.4.1-2: Price Comparison of California E10 and E85
$7.00
.<=> $6.00
1—
Q_
2 $5.00
"D
O $4.00
T—I
LU
¦| $3.00
1—
£
ro $2.00
u
$1.00
Feb-22 Sep-22 Mar-23 Oct-23 Apr-24 Nov-24 Jun-25
Source: e85prices.com.
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.27 El 5 has since been offered at an increasing number of retail
service stations.28 However, there is currently no publicly available data on actual nationwide
El5 sales volumes.
Sales of El 5 prior to 2019 were mostly seasonal due to the fact that El 5 did not qualify
for the 1-psi RVP waiver for summer gasoline in CG areas that has been permitted for El0 since
the summer volatility standards were implemented in 1989.29 As shown in Figure 1.4.2-1,
monthly El 5 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.30
27 76 FR 4662 (January 26, 2011).
28 See Chapter 6.4.3.
29 54 FR 11883 (March 22, 1989).
311 The only source of data on E15 sales by month that we are aware of is from Minnesota.
15
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Figure 1.4.2-1: Normalized Monthly E15 Sales per Station in Minnesota3
2.00
0.20
0.00
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
a 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 E15 by regulation.31 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 2015-2018.12 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
action. EPA subsequently issued emergency fuel waivers for the summers of 2022-2024 that
allowed El 5 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 El 5 on summer sales of El 5 can be seen for 2019-
2024 in Figure 1.4.2-2. For these years, data from Minnesota on per-station sales of El 5
indicates that those sales were no longer seasonal as they were prior to 2019. Average E15 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.
31 84 FR 26980 (June 10, 2019).
32 "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.
16
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Figure 1.4.2-2: Normalized Monthly E15 Sales per Station in Minnesota; Pre-and Post-
Waiver for E15a
1.600
1.400
1.200
1.000
0.800
0.600
0.400
0.200
0.000
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
a 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.33 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.34 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 Ohio and the nine counties in South Dakota). Consequently, there
may be no reduction in summer sales of E15 compared to other months in these states going
forward.
1.5 Other Biofuels
Although corn ethanol and BBD have dominated the biofuels landscape since
implementation of the RFS program began in 2006, other biofuels 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" reached
nearly 1 billion RINs in 2023. The annual supply of biofuels other than corn ethanol and BBD
are shown in Figure 1.5-1.
33 Illinois, Iowa, Minnesota, Missouri, Nebraska, Ohio, South Dakota, and Wisconsin. 89 FR 14760.
34 90 FR 13093.
-Average pre-waiver
¦Average post-waiver
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1.5-1: Supply of Biofuels Other Than Corn Ethanol and BBD (million RINs)
1400
1200
1000
Z 800
i n -
g 600
2015 2016 2017 2018 2019 2020 2021 2022 2023 2024
¦ CNG/LNG ~ Heating Oil El Gasoline/Naphtha
~ Advanced Renewable Diesel ¦ Advanced Ethanol ¦ Conventional Biodiesel/RD
Source: EMTS.
The largest supply of biofuel after corn ethanol and BBD has been CNG/LNG derived
from biogas. As discussed in Chapter 1.2, and in greater detail in Chapter 7.1, we expect the
supply of CNG/LNG derived from biogas to continue to increase in future years. However, we
note that increases in future years may be smaller than in recent years if the supply of CNG/LNG
is limited by the use of these fuels as qualifying transportation fuel. Advanced ethanol has been
another significant source of biofuel in recent years. The supply of advanced ethanol has varied
from year to year and appears to fluctuate depending on market conditions. 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. The supply of other types of renewable fuel such as renewable
gasoline/naphtha and advanced renewable diesel have increased since the early years of the RFS
program but have remained relatively stable since 2020.
1.6 Federal Tax Credits for Biofuels
For most of the history of the RFS program the only federal tax credit that was available
to RFS qualifying fuels was the biodiesel blenders tax credit. This tax credit provided blenders
with a $1 refundable credit for every gallon of biodiesel or renewable diesel that was either
produced or used in the U.S. This tax credit lapsed several times over the past decade but has
always been available (whether prospectively or retroactively) since the beginning of the RFS
program. The Inflation Reduction Act (IRA) of 2022 extended the biodiesel blenders tax credit
through 2024. The prospective availability of the biodiesel blenders tax credit for 2023 and 2024,
in combination with the replacement of this tax credit with the Clean Fuel Production Credit
18
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(discussed below) were likely significant factors in the rapid increase in the supply of BBD to the
U.S. in 2023 and 2024.
The IRA also established two new tax credits that could apply to qualifying fuels under
the RFS program, the Sustainable Aviation Fuel Credit and the Clean Fuel Production Credit
(CFPC). The Sustainable Aviation Fuel Credit provides 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. The Sustainable Aviation Fuel Credit thus provides a larger incentive for renewable
jet fuel in 2024 than the provided by the biodiesel blenders tax credit in the same year.
Starting in 2025 both the biodiesel blenders tax credit and the Sustainable Aviation Fuel
Credit are replaced by the CFPC. The CFPC is available to all transportation fuel produced in the
U.S. that has an emission factor less than 50 kilograms of CO2 equivalent per million BTU. The
magnitude of the CFPC 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.
The CFPC differs from the biodiesel blenders tax credit it replaces 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 the CFPC relative to the biodiesel
blenders tax credit may open up opportunities for non-BBD advanced biofuels to better compete
for market share under the RFS program as these fuels now have similar treatment under the
federal tax provisions.
The CFPC 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. The restriction of the CFPC to biofuels produced in the U.S. may have multiple
impacts on the supply of biofuel to the U.S. Imports of BBD are expected to decrease in future
years, as these fuels will no longer be eligible for the $1 per gallon federal tax credit. The
availability of the CFPC 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. The CFPC
could also indirectly result in increased imports of BBD feedstocks. With the advantage of the
CFPC, domestic BBD producers may be able to out-bid foreign BBD producers for foreign
feedstocks. Relatedly, foreign parties with access to qualifying BBD feedstocks may find it more
profitable to export the feedstock to the U.S. where it can be used to produce BBD that qualifies
for the CFPC than to use the feedstock to produce BBD and export it to the U.S. or another
country.
The CFPC also provides greater incentives for biofuels with lower emission rates. There
are significant differences in the emission rates, and thus the magnitude of the incentive available
through the CFPC, for fuels produced from wastes or by-products such as FOG or animal fats
than there are for fuels produced from agricultural commodities such as virgin vegetable oils or
corn starch. The structure of this tax credit, especially in combination with state low carbon fuel
19
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programs with similar structures, could have a significant impact on the types of biofuel supplied
to the U.S. within each broad category. For example, all BBD is eligible to generate the same
number of RINs under the RFS program whether it is produced from soybean or canola oil,
FOG, or animal fats. But domestically produced renewable diesel from FOG at a facility that
meets the prevailing wage and apprenticeship requirements would be eligible to claim a greater
CFPC credit than renewable diesel produced from soybean oil at the same facility. If these fuels
were sold in a state with a low carbon fuel program, the renewable diesel produced from FOG
could receive even greater incentives relative to renewable produced from soybean oil. The
combination of the CFPC and the state programs are projected to create a strong preference
among biofuel producers for feedstocks that enable them to produce biofuel with low emission
rates. As the supplies of these feedstocks available in the U.S. are limited and generally are
already being used for biofuel production, we project that the structure of the CFPC and state
programs will create a large incentive for imports of feedstocks such as FOG and animal fats that
can be used to produce biofuels with low emission rates.
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.35 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
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 for 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 are generally sold together with the renewable fuel to refiners or blenders. RINs can be
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).36 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
35 The RIN system was created in the RFS1 Rule (72 FR 23900; May 1, 2007) and modified in the RFS2 Rule (75
FR 14670; March 26, 2010).
36 40 CFR 80.1425(g).
20
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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
r
Advanced biofuel
r
D3/7
D4
D5
D6
t t t
Cellulosic bbd "Other" advanced
biofuel (sugarcane ethanol.
t
Conventional
(mostly corn-ethanol)
etc)
V
J
Non-cellulosic 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
prices, as reported to EPA through EMTS, is shown in Figure 1.7.2-1 ?1 While there are a wide
variety of factors that impact REN prices, including both market-based and regulatory factors, a
review of RIN prices reveals several notable aspects of the RFS program.
37 RIN prices are reported publicly on EPA's website (https://www.epa.gov/fuels-registration-reporting-and-
compliance-help/rin-trades-and-price-information). These prices are reported to EPA by the parties that trade RINs
and are inclusive of all RIN trades (with the exception of RIN prices that appear to be outliers or data entry errors).
Several other sendees also report daily RIN prices; however, these reports are generally not publicly available.
Further, the prices reported by these services generally represent only spot trades and do not include RINs traded
through long-term contracts.
21
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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
-------
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 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). The prices for soybean and other vegetable
oil feedstocks were unusually high from the summer of 2021 through the summer of 2023, a time
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.
Instead, higher D6 RIN prices have resulted in lower effective prices for ethanol after the RINs
have been separated and sold.39 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).40
39 The effective price is the price of the ethanol after subtracting the RIN value from the price of the ethanol with the
attached RIN.
40 Burkholder, Dallas. "A Preliminary Assessment of RIN Market Dynamics, RIN Prices, and Their Effects." EPA,
May 2015.
23
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Figure 1.7.2-2: Historical Ethanol Prices and D6 RIN Prices
$4.00
$3.50
$3.o° I n
ji Am1
I"" 1 W^v^im\/vv
:: V
$0.00 I I ¦
v*S> & & \*v= v-0 -tP o> <& <& ^
\V \V \V \V \V \V \V \V \V \V \V \V \V \V \V \V
*y\ ^y\ y y y Y **y\ ty\ \\ \\ *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 BBD. From 2010-2012, obligated parties generally met
their implied requirements for "other advanced biofuel" with sugarcane ethanol.41 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.42 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 El0 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
41 "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.
42 See Chapters 6.3 and 6.2 for volumes of sugarcane ethanol and BBD used in the U.S., respectively.
24
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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 fairly straightforward. 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., the $1.00 per gallon federal tax subsidy and state
LCFS credits).43 Recently, in 2021 and 2022, D4 RIN prices increased significantly, tracking
with an increase in feedstock commodity prices (e.g., soybean oil), which comprise greater than
80% of the cost of production of BBD. By the beginning of 2024, soybean oil prices dropped to
lower levels. This decrease in the price of soybean oil generally corresponded to a decrease in
D4 RIN prices.
Figure 1.7.2-3: Historical Soybean Oil Prices ($/lb)
107 2009 2011
Source: Business Insider, "Soybean Oil," Markets Insider. May 19.2025.
https://markets.businessinsider.com/commodities/sovbean-oil-price.
43 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.
25
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Generally, D4 RIN 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 ULSD prices and (b)
whether the $1 per gallon biodiesel tax credit is in effect."44This same paper discusses in greater
detail the strong correlation between weekly D4 RIN 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.
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).45 This is as expected, since obligated parties can satisfy their
cellulosic biofuel obligations through the use of either cellulosic RINs or CWCs (if available)
plus D4 or D5 RINs.46 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.47 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
44 Irwin, Scott H.. Kristen McCormack, and James H. Stock. "The Price of Biodiesel RINs and Economic
Fundamentals." American Journal of Agricultural Economics 102, no. 3 (February 3, 2020): 734-52.
https://doi.org/10.1002/aiae.12014.
45 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
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).
46 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 proposing to reduce the 2025 cellulosic
biofuel volume under the cellulosic waiver authority.
47 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.
26
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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.
Figure 1.7.2-4: D3 RIN Prices and D5 RIN Price Plus CWC Price
$5.00
$450
$4.00
$3.50
$3.00
$2.50
$2.00
$150
$1.00
$050
$-
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
^^—D3 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 RINs, with very few exceptions, has 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) and protects these parties from excessively
high cellulosic RIN prices. The CWC price is also informational to potential cellulosic biofuel
producers. Potential cellulosic biofuel producers can use the CWC price, along with the 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
27
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biofuel at the volume expected to be produced each year48 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 often be produced at a cost that is competitive
with the petroleum fuels they displace even without the RIN value. 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.49 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 EO 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 $2.65 in 2023; thus, the total cost associated with the 868 million cellulosic RINs required
for compliance in 2023 was approximately $2.3 billion and the cellulosic biofuel requirement
likely increased the price of gasoline and diesel sold in the U.S. in 2023 by approximately $0,013
per gallon.50 These transfers are expected to increase through 2025 as a result of the cellulosic
biofuel volumes finalizing in the Set 1 Rule. For example, using the average cellulosic RIN price
for January 2024 - December 2024 of $3.11 and the revised cellulosic biofuel volume we are
proposing for 2025 in this action of 1.19 billion RINs, we estimate that the cost associated with
cellulosic RIN purchases would be $3.70 billion, and would be expected to increase the price of
gasoline and diesel in 2025 by approximately $0,019 per gallon.51
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 proposed 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
48 CAA section 21 l(o)(7)(D).
49 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.
50 For the 2023 compliance year obligated parties reported an obligated volume of gasoline and diesel of 180.8
billion gallons. Dividing the total cost of cellulosic RINs in 2023 ($2.3 billion) by the total consumption of gasoline
and diesel (180.8 billion gallons) results in an estimated cost of $0,013 per gallon of gasoline and diesel as a result
of the cellulosic biofuel requirement.
51 In the 2023 AEO, EIA forecasted gasoline and diesel consumption in 2025 at 138.4 billion gallons and 52.4
billion gallons respectively. Dividing the total cost of cellulosic RINs in 2025 ($3.70 billion) by the total
consumption of gasoline and diesel (190.8 billion gallons) results in an estimated cost of $0,019 per gallon of
gasoline and diesel as a result of the cellulosic biofuel requirement.
28
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complete, we are able to project these values by using 2023 compliance data and assumptions
about RIN generation relative to RIN obligations in 2024 and 2025.
1.8.1 Carryover RINs Available After Compliance With the 2023 Standards
In order to calculate the number of 2023 carryover RINs available for compliance with
the 2024 standards, we began with the 2023 RFS compliance year data in Table 1.8.1-1. From
this data, we calculated that approximately 22.53 billion total RINs were retired for compliance
in the 2023 compliance year.52 Of this total, approximately 20.19 billion 2023 RINs and 0.34
billion 2022 carryover RINs were used.
Table 1.8.1-1: RINs Retired by Obligated Parties and Exporters in the 2023 Compliance
Year3
RIN Type
RIN Year
Total
2022
2023
D3
72,174,414
736,071,158
808,245,572
D4
76,167,987
7,026,064,533
7,102,232,520
D5
15,141,338
241,707,644
256,848,982
D6
178,935,665
14,186,802,096
14,365,737,761
D7
236,352
208,643
444,995
Total
342,655,756
22,190,854,074
22,533,509,830
a Data current as of December 10, 2024, and compiled from Table 4 at https://www. epa. gov/fuels-registration-
reporting-and-compliance-help/annual-compliance-data-obligated-parties-and. RINs include those retired by
companies with an 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 2023. To do this, we
took the total number of RINs generated in 2023 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 23.37 billion RINs were
generated in 2023.
52 Includes RINs retired in the 2023 compliance year to satisfy 2022 compliance deficits.
29
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Table 1.8.1-2: 2023 Net RINs Generated3
RIN Type
Total RINs
Generatedb
RIN
Errors0
Other RIN
Retirements'1
Net RINs
Generated6
D3
774,735,743
1,587,010
6,538,749
766,609,984
D4
7,970,109,655
8,579,454
209,386,480
7,752,143,721
D5
263,070,174
2,433,356
1,082,356
259,554,462
D6
14,838,755,529
8,301,447
241,505,732
14,588,948,350
D7
208,643
0
0
208,643
Total
23,846,879,744
20,901,267
458,513,317
23,367,465,160
a Data from December 2024 and compiled https://www.epa.gov/svstem/files/other-files/2025-
01/availablerins dec2024.csv and https://www.epa.gov/svstem/files/other-files/2025-
01/retiretransaction dec2024.csv.
b The total number of RINs generated includes those RINs generated for exported fuel.
0 See Table 1.8.4-2 for more detailed data.
d See Table 1.8.4-3 for more detailed data.
e Net RINs Generated = Total RINs Generated - (RIN Errors + Other RIN Retirements).
To determine the total number of 2023 carryover RINs available for compliance with the
2024 standards, we then subtracted the number of 2023 RINs retired in the 2023 compliance year
from the net number of 2023 RINs generated. We calculate that there are approximately 1.18
billion 2023 carryover RINs available, as shown in Table 1.8.1-3.
Table 1.8.1-3: 2023 Carryover RINs
Net 2023 RINs
2023 RINs Retired
2023 Carryover
RIN Type
Generated
for Compliance
RINs
D3
766,609,984
736,071,158
30,538,826
D4
7,752,143,721
7,026,064,533
726,079,188
D5
259,554,462
241,707,644
17,846,818
D6
14,588,948,350
14,186,802,096
402,146,254
D7
208,643
208,643
0
Total
23,367,465,160
22,190,854,074
1,176,611,086
Obligated parties are also able to carryforward a compliance deficit from one year to the
next year,53 increasing their RVO for 2024 and effectively decreasing the number of 2023
carryover RINs available for compliance with the 2024 standards. In order to account for this, we
calculate the effective number of 2023 carryover RINs available for compliance with the 2024
standards by subtracting out the 2023 compliance deficits, which have to be satisfied at the time
of compliance with the 2024 standards.54 We note, however, that 2023 compliance deficits
exceeded the number of available 2023 carryover RINs for several standards, which means that
there was a shortfall in the number of RINs available to comply with these standards in 2023 and
that some obligated parties had to carry forward a deficit into 2024. After accounting for this
53 See 40 CFR 80.1427(b).
54 The compliance deadline for the 2024 standards will be the first quarterly reporting deadline after the effective
date of the action revising the 2024 cellulosic biofuel standard. 90 FR 12109 (March 14, 2025).
30
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adjustment, the effective number of 2023 carryover RINs available for compliance with the 2024
standards are shown in Table 1.8.1-4.55
Table 1.8.1-4: Effective 2023
Carryover RI>
s
RFS Standard
RIN
Type
2023
Carryover
RINs
2023
Compliance
Deficits3
Net Surplus/
Deficitb
Effective 2023
Carryover
RINsc
Cellulosic
Biofuel
D3+D7
30,538,826
87,789,686
-57,250,860
0
Non-Cellulosic
Advanced
Biofueld
D4+D5
743,926,006
329,874,322
414,051,684
414,051,684
Conventional
Renewable Fuel®
D6
402,146,254
1,598,690,401
-1,196,544,147
0
Total Renewable
Fuel
All D
Codes
1,176,611,086
2,016,354,409
-839,743,323
0
a Data current as of December 10, 2024, and compiled from Table 6 at https://www. epa. gov/fuels-registration-
reporting-and-compliance-help/annual-compliance-data-obligated-parties-and.
b Net Surplus/Deficit = Carryover RINs - Compliance Deficits. Negative values represent a shortfall in the number
of RINs available to comply with the applicable standard and are counted as zero for purposes of determining the
effective number of available carryover RINs.
0 Represents the effective number of 2023 carryover RINs that are available for compliance with the 2024 standards
after accounting for deficits carried forward from 2023 into 2024. Standards for which deficits exceed the number of
available carryover RINs are represented as zero.
d 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.
e 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 2024 and 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.56 However, if we assume that the uncertainties result in neither a net gain nor net loss
of excess RINs for 2024 and 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 1.8.2-1.57
55 In other words, the number of available carryover RINs is effectively reduced in light of the volume of 2023
deficits carried forward to 2024. 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.
56 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.
57 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.
31
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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
0
Non-Cellulosic Advanced Biofuelb
D4+D5
414,051,684
Conventional Renewable Fuel0
D6
0
Total Renewable Fuel
All D Codes
0
a Represents the effective number of 2023 carryover RINs that are available for compliance with the 2024 standards
after accounting for deficits carried forward from 2023 into 2024. Standards for which deficits exceed the number of
available carryover RINs are represented as zero.
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.
We note that while we project that there will effectively be no carryover RINs available
for compliance with the 2026 and 2027 standards, this does not mean that actual carryover RINs
will not be available in these years. As discussed in Chapter 1.8.1, the actual number of
carryover RINs available relative to the "effective" number is a function of the volume of RIN
deficits that obligated parties carry forward from one year into the next. For example, if
obligated parties carry forward a significant volume of RIN deficits, then the absolute number of
carryover RINs available for compliance with the following year's standards will be larger than
were obligated parties to carry forward a smaller volume of RIN deficits.
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 2023 are those 2023 RINs that can be used to comply with the 2024 standards).
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.
32
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Table 1.8.3-1: Number of Availa
)le Carryover RINs
History (million RUN
Is)
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
607
2,870
2,774
3,554
3,429
2019
46
34
173
0
2,095
1,652
2,315
1,661
2020
41
16
116
0
1,654
1,202
1,811
1,058
2021
25
0
59
0
1,048
502
1,132
95
2022
73
44
98
0
192
0
362
0
2023
31
0
744
414
402
0
1,177
0
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.
Figure 1.8.3-1: Number of Available Cellulosic Biofuel Carryover RINs
V1
80
70
OL
J 60
J 50
a)
-= 40
TO
TO
>
<
30
20
10
a>
>
o
0
ro 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023
RVO Year
¦Absolute
¦ Effective
33
-------
Figure 1.8.3-2: Number of Available Non-Cellulosic Advanced Biofuel Carryover RINs
_OJ
-Q
03
>
<
O
£¦
i—
03
U
1,000
900
800
700
600
500
400
300
200
100
0
2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023
RVO Year
¦Absolute
¦ Effective
Figure 1.8.3-3: Number of Available Conventional Renewable Fuel Carryover RINs
3,500
E 3,000
C
^ 2,500
£
V 2,000
= 1,500
03
>
^ 1,000
f 500
OJ
I 0
i_
03
U
2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023
RVO Year
¦Absolute
¦ Effective
34
-------
Figure 1.8.3-4: Number of Available Total Renewable Fuel Carryover RINs
__ 4,500
Z 4,000
cc
o 3'500
= 3,000
T 2,500
2 2,000
TO
< 1,500
00
s 1,000
cd
S 500
>
03
U
Absolute Effective
1.8.4 EMTS RIN Data
Table 1.8.4-1: RINs Retired by Importers, Refiners, and Exporters in the 2023 Compliance
Year3
RIN Type
Year
Importers
Refiners
Exporters
Total
D3
2022
8,374,444
63,799,970
0
72,174,414
2023
52,025,191
684,045,967
0
736,071,158
D4
2022
10,433,480
65,189,093
545,414
76,167,987
2023
265,471,820
5,935,234,828
825,357,885
7,026,064,533
D5
2022
21
15,121,907
19,410
15,141,338
2023
9,358,170
174,238,256
58,111,218
241,707,644
D6
2022
28,194,281
144,174,697
6,566,687
178,935,665
2023
340,053,222
13,416,038,371
430,710,503
14,186,802,096
D7
2022
0
236,352
0
236,352
2023
0
208,643
0
208,643
Tota
713,910,629
20,498,288,084
1,321,311,117
22,533,509,830
a Data current as of December 10, 2024, and compiled from Table 4 at https://www. epa. gov/fuels-registration-
reporting-and-compliance-help/annual-compliance-data-obligated-parties-and.
RVO Year
35
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Table 1.8.4-2: 202;
i RIN Errors3
RIN Type
Import Volume
Correction
Invalid RIN
Volume error
correction
Total
Retirement Code
30
50
60
—
D3
0
1,587,010
0
1,587,010
D4
5,840,918
2,708,205
30,331
8,579,454
D5
0
2,408,108
25,248
2,433,356
D6
0
6,459,246
1,842,201
8,301,447
D7
0
0
0
0
Total
5,840,918
13,162,569
1,897,780
20,901,267
a Data from December 2024 and compiled from https://www.epa.gov/svstem/files/other-files/2025-
01/retiretransaction dec2024.csv.
-------
Table 1.8.4-3: Other 2023 RIN Retirements3
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
219,156
D4
286
2,330,849
7,134,963
0
D5
0
187,804
0
0
D6
109,459
497,103
0
0
D7
0
0
0
0
Total
109,745
3,015,756
7,134,963
219,156
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
0
0
118,471
1,100
D4
67,740,162
0
1,189,847
0
D5
532,728
0
317,646
0
D6
101,241,955
0
3,666,099
1,018
D7
0
0
0
0
Total
169,514,845
0
5,292,063
2,118
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
6,200,022
0
0
6,538,749
D4
12,000,000
81,321,123
0
37,669,250
209,386,480
D5
0
44,178
0
0
1,082,356
D6
24,895
135,965,203
0
0
241,505,732
D7
0
0
0
0
0
Total
12,024,895
223,530,526
0
37,669,250
458,513,317
a Data from December 2024 and compiled from https://www.epa.gov/svstem/files/other-files/2025-
01/retiretransaction dec2024.csv.
37
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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 proposed in this action for 2026 and 2027.
Although we are proposing RFS volume standards for only 2026 and 2027, we projected the No
RFS volumes for 2026-2030. This chapter describes our derivation of the No RFS Baseline, as
well as an alternate 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 in 2026-2030. Conceptually, the No RFS Baseline allows
EPA to directly project the impacts of the Low and High Volume Scenarios for 2026-2030
relative to a scenario without volume requirements. We also 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 2026-2030; 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 use in the U.S. in
2026-2030 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.58 The differences between the Volume
Scenarios 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 changes between the No RFS Baseline and the Volume
Scenarios 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 changes alone were insufficient and
potentially misleading. For example, the proposed volume for total domestic ethanol
consumption is 212-266 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 would be needed to supply the proposed volumes. However, total
domestic ethanol consumption in the Volume Scenarios for 2026-2030 is lower than total
domestic ethanol consumption achieved in previous years. Thus, no additional ethanol
production capacity or distribution infrastructure is projected to be needed to meet the ethanol
volumes in the Volume Scenarios for 2026-2030. Furthermore, we are already producing
considerably greater volumes of corn ethanol than we are able to use domestically and exporting
58 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.
38
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the excess. Therefore, again, no additional production capacity needed. Where appropriate, such
as in our assessment of infrastructure, we have therefore considered not only the change in
domestic renewable fuel consumption from the No RFS Baseline to the Volume Scenarios, but
also other relevant factors as they exist in 2025.
There are some effects of a No RFS Baseline, such as U.S. crop production, that we lack
information, sufficient time, resources, or the necessary modeling tools to estimate. U.S. crop
production has an impact on a number of the statutory factors, such as the projected conversion
of wetlands, ecosystems, and wildlife habitat, water quality, and water availability. At this time,
we have insufficient information to determine what U.S. crop acreage and production would be
under a No RFS Baseline. One potential scenario is that total U.S. crop acreage and production
would decrease in 2026-2030 if there was lower demand for crops for biofuel production from
the RFS standards. But other scenarios are also possible and may be more likely. If demand for
biofuel in the U.S. were lower in 2026-2030 in the absence of the RFS program, it is possible
that biofuel exports would increase, and the market would see little to no change in domestic
biofuel production or biofuel feedstock crop production. For instance, there have been significant
exports of ethanol in recent years,59 and both imports and exports of biodiesel and renewable
diesel.60 Foreign markets may be able to absorb additional renewable fuel exports from the U.S.
Alternatively, domestic biofuel production could decrease with little change in U.S. crop acreage
and production if there is sufficient demand for these crops in other markets, or production of
crops used for biofuel production could decrease and farmers could plant other crops on land
previously used for production of biofuel feedstocks. In cases where we have insufficient
information to determine what would happen under the No RFS Baseline, we have used the most
recent data available (generally from 2023 or 2024) as a proxy for the No RFS Baseline.
Finally, for our assessment of costs and fuel price impacts we have considered the
impacts of the Volume Scenarios 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 taking an action to establish volume requirements for 2026-
2030 versus not taking that action.
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 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 the 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)
59 See Chapter 6.6.
60 See Chapter 6.2.4.
39
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• 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 how this analysis
differs from the cost analysis.
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 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 significantly 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, due to the complexity and uncertainty for undertaking such a market analysis, we
40
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did not attempt to evaluate such a feedback mechanism.61 The various economic factors shown
in Table 2.1-1 are further discussed below for each renewable fuel.62
Similarly, for the gasoline and diesel fuel prices, we use the most recent wholesale price
projections in AEO2023. 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 and believe the impact would be minimal and within the accuracy of the
No RFS Baseline analysis.
2.1.1 Ethanol
By far the largest volume of ethanol blended into U.S. gasoline is produced from corn
and 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., El5 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.
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 for 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:
61 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 proposed volume requirements.
62 The spreadsheets used to estimate the No RFS Baseline for corn ethanol ("Corn Ethanol No RFS Baseline for Set
2 Proposed Rule") and biodiesel and renewable diesel ("Biodiesel and Renewable Diesel No RFS Baseline for Set 2
Proposed Rule") are available in the docket for this action.
63 AFDC, "E85 (Flex Fuel)." https://afdc.energy.gov/fuels/ethanol e85.html.
64 EPA, "Economics of Blending 10 Percent Corn Ethanol into Gasoline," EPA-420-R-22-034, November 2022.
41
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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
• SETS is, 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 EO, 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.
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 USDA 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.
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.
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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
1.88
2027
1.89
2028
1.91
2029
1.92
2030
1.94
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 includes distribution costs). Because ethanol is primarily produced
in the Midwest 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 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
Region
States
Average Ethanol
Distribution
Cost (0/gal)
PADD 1
New York, Pennsylvania, West Virginia
18.7
District of Columbia, Connecticut, Delaware, Maryland,
Massachusetts, New Jersey, Rhode Island, Virginia
20.7
Georgia, South Carolina Vermont, New Hampshire,
North Carolina
22.7
Florida, Maine
28.8
PADD2
Illinois, Indiana, Iowa, Kansas, Michigan, Minnesota,
Missouri, Nebraska, Ohio, South Dakota, Wisconsin
11.0
Kentucky, North Dakota, Oklahoma, Tennessee
20.7
PADD 3
Arkansas, Louisiana, Mississippi, Texas
15.5
Alabama, New Mexico
20.7
PADD 4
Colorado, Idaho, Montana, Utah, Wyoming
17.2
PADD 5
Oregon, Washington
21.4
Arizona, California, Nevada
25.4
Alaska, Hawaii
51.0
EthanolReplacement Value (ERV)
Ethanol has properties that provide value (primarily octane) or cost (vapor pressure
impacts) when it is blended into gasoline. We use the term "ethanol replacement value" to refer
to the sum of the 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 before oxygenate blending" or BOB) is modified to account for the
subsequent addition of ethanol, in which the blending value of ethanol is important. In 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 conventional gasoline (CG) areas, a similar match-blending process began
to be used there as well, replacing splash-blending. In these areas, a conventional gasoline before
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
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
68 The exception to this is a small amount of premium grade BOB that is sold as regular or midgrade E0.
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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.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.
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 over time.
Table 2.1.1.1-4 Ethanol Replacement Value (nominal $/gal)
Gasoline Type
Gasoline Grade
Year
2026
2027
2028
2029
2030
Conventional
Gasoline
Summertime Regular
2.23
2.28
2.35
2.42
2.48
Summertime Premium
1.69
1.72
1.77
1.82
1.87
Reformulated
Gasoline
Summer Regular
1.93
1.96
2.02
2.08
2.14
Summer Premium
1.38
1.41
1.45
1.49
1.54
Conventional and
Reformulated
Winter Regular
0.90
0.92
0.95
0.98
1.01
Winter Premium
0.69
0.70
0.72
0.74
0.76
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. A potentially new federal subsidy for corn ethanol is
established under the 45Z provisions of the Inflation Reduction Act; however, when this analysis
was conducted, the guidance related to this tax credit had not yet been released and so we did not
assume any 45Z subsidy for corn ethanol. For this reason, we did not assume any federal tax
subsidy for corn ethanol.70
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 Based on our review of the U.S. Department of the Treasury and Internal Revenue Service (IRS) 45Z guidance
released on January 10, 2025, corn ethanol will likely earn a subsidy of 10 or 60 per gallon, depending on whether
the production facility meets prevailing wage and apprenticeship requirements. 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). We intend to include this
subsidy in the analysis for the final rule.
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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/gal
and 260/gal, respectively.71 The California LCFS program is estimated to provide corn ethanol
an average blending credit of 120/gal.72-73 Several states, including Minnesota and Missouri, also
have ethanol use mandates that require the use of ethanol regardless of the economics for doing
so.74 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 from 2019—which already included the distribution costs for
moving gasoline to downstream terminals—were used to represent the price of gasoline to
blenders on a state-by-state basis.75 However, these prices were not projected for future years.
Instead, we used projected refinery wholesale price data from AEO2023 to adjust the 2019
refinery rack price data to represent gasoline rack prices in future years. We used 2019 data
instead of the most recent data to avoid abnormal pricing effects caused by the Covid-19
pandemic or the subsequent supply issues that emerged when the pandemic was subsiding.
Further price effects after the pandemic were caused by the geopolitical conflict between Russia
and Ukraine which are avoided by using the earlier price data. The 2018 gasoline price data was
used over that of 2019 because crude oil prices in 2018 are closer to the crude oil prices
projected by AEO2023 and there likely would be less error involved with a smaller adjustment.
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.76
71 The National Agricultural Law Center, States' Biofuels Statutory Citations, https://nationalaglawcenter.org/state-
compilations/biofuels.
72 California Air Resources Board (CARB), LCFS Pathway Certified Carbon Intensities.
https://ww2.arb.ca.gov/resources/documents/lcfs-pathwaY-certified-carbon-intensities.
73 CARB, "Weekly LCFS Credit Transfer Activity Reports," May 11, 2025.
https://ww3.arb.ca.gov/fuels/lcfs/credit/lrtweeklvcreditreports.htm.
74 The National Agricultural Law Center, States' Biofuels Statutory Citations, https://nationalaglawcenter.org/state-
compilations/biofuels.
75 EIA, "Spot Prices," Petroleum & Other Liquids, May 14, 2025.
https://www.eia.gov/dnav/pet/pet pri spt si a.htm.
76 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 2019 ($/gal)a
Gasoline Grade
Gasoline Grade
State
Regular
Premium
State
Regular
Premium
Alaska
2.37
2.44
Montana
1.84
2.30
Alabama
1.68
2.11
North Carolina
1.69
2.07
Arkansas
1.70
2.03
North Dakota
1.77
2.18
Arizona
2.00
2.29
Nebraska
1.74
2.55
California
2.37
2.61
New Hampshire
1.80
2.09
Colorado
1.85
2.26
New Jersey
1.72
2.91
Connecticut
1.77
2.09
New Mexico
1.82
2.18
DC.
1.79
2.01
Nevada
2.11
2.36
Delaware
1.74
2.02
New York
1.78
2.14
Florida
1.72
2.07
Ohio
1.73
2.21
Georgia
1.69
2.10
Oklahoma
1.72
1.94
Hawaii
2.23
2.35
Oregon
1.95
2.26
Iowa
1.73
2.06
Pennsylvania
1.72
2.04
Idaho
1.92
2.21
Rhode Island
1.78
2.01
Illinois
1.75
2.17
South Carolina
1.69
2.09
Indiana
1.72
2.16
South Dakota
1.75
2.10
Kansas
1.71
1.97
Tennessee
1.68
2.03
Kentucky
1.75
2.16
Texas
1.72
1.98
Louisiana
1.66
1.92
Utah
1.86
2.13
Massachusetts
1.75
2.00
Virginia
1.73
2.06
Maryland
1.74
2.00
Vermont
1.76
2.13
Maine
1.83
2.17
Washington
1.97
2.30
Michigan
1.74
2.26
Wisconsin
1.75
2.24
Minnesota
1.73
2.01
West Virginia
1.75
2.13
Missouri
1.74
2.08
Wyoming
1.78
2.18
Mississippi
1.69
2.09
a 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 2018.
The AEO2023 projected national average wholesale gasoline price information used to
adjust gasoline prices in future years, and the national average wholesale gasoline price in 2018
that the projected wholesale gasoline prices are compared to, are summarized in Table 2.1.1.1-6.
The differences in prices are additive to the state-by-state gasoline prices shown in Table 2.1.1.1-
5. For example, the projected national average wholesale gasoline price in 2026 is $2.50 per
gallon, which is 520 per gallon more than the national average gasoline price in 2018; therefore,
gasoline prices in 2026 are 520 per gallon higher than the prices summarized in Table 2.1.1.1-5.
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Table 2.1.1.1-6: National Average Wholesale Gasoline Prices
Wholesale
Wholesale
2018 Price
Gasoline Price
Gasoline Price
Adjustment
Year
(AEO2023)
CPI
(nominal)
Factor
Actual National
2018
$1.98
Average Gasoline Price
2022
2.93
2026
$2.24
3.06
$2.50
1.26
2027
$2.22
3.14
$2.52
1.27
Gasoline Price
2028
$2.23
3.20
$2.59
1.31
2029
$2.24
3.27
$2.65
1.34
2030
$2.25
3.33
$2.72
1.37
Source: AEO2023, 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, ethanol is over 400/gal less
expensive than gasoline in the most expensive market for blending ethanol, and about $2/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)
2.1.1.2 E85
Some aspects of the ethanol blending cost equation developed for E10 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 is estimated in two steps. First, we estimated the
breakeven price for ethanol blended in E85 based on the price of gasoline price 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:
EBCe85 = (ESP + EDC - FETS - SETS + RC) - EBBV
Where:
• EBCess is ethanol breakeven price for ethanol blended as E85
• ESP is ethanol plant gate spot price
• EDC is ethanol distribution cost
• FETS is federal ethanol tax subsidy
• SETS is, state ethanol tax subsidy
• RC is retail cost (service station revamp 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.77 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.78
77 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.
78 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 see the opportunity 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.
Federal and State Ethanol Tax Subsidies (FETS and SETS)
There is no federal ethanol blending tax subsidy and there never has been one, for E85.79
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. Table 2.1.1.2-1 provides the E85 subsidies offered by
different states.
Table 2.1.1.2-1: Current State E85 Subsidies (^/gal)
State
E85 Subsidy
Iowa
16
Kansas
12.5
Michigan
11
New York
53
Pennsylvania
25
South Dakota
14
The California and Oregon LCFS blending credits for ethanol apply when ethanol is
blended into E85 as well (Oregon's blending credit is assumed to be the same as California's).
The blending credit applies to E85, so its credit is amortized over the ethanol portion of E85 to
assess the blending value of ethanol. Aside from the retail cost credit offered by USD A 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.80 We estimated the total cost for a
typical retail station revamp to enable selling E85 to be $50,300 and that these stations sell on
79 Based on our review of the U.S. 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). We intend to include this subsidy in the analysis for the final
rule.
80 The methodology used and the estimated costs for these revamps are discussed in Chapter 10.1.4.1.2.
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average 84,000 gallons of E85 per year. When amortizing this capital cost over the gallons of
E85 sold, the total cost of the revamp adds 150/gal to the cost of blending ethanol into E85
(accounting only for the estimated 64% of ethanol in E85 above the ethanol in E10).
Ethanol Breakeven Blending Value (EBBV)
There are downstream pricing effects for E85 that require the economics of E85 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, so they demand a
lower price at retail stations relative to E10, which therefore 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.8182 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 E10.83
Figure 2.1.1.2-1 provides an example for 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.
81 Fuels Institute, "Retailing E85: An Analysis of Market Performance, July 2014 - August 2015," March 23, 2017.
https://www.transportationenergv.org/wp-content/uploads/2022/10/E85 2017 Report FINAL.pdf.
82 AAA, "National average gas prices," December 12, 2022. https://gasprices.aaa.com.
83 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.
51
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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)
Marketing,
Retail Profits,
etc.
E10/Gasoline Pricing
Tax Transportation
fa
305 c/gal ISc/gal 60 c/gal 5c/gal
Competitive E85 Pricing
Marketing,
0 Retail Profits,
etc.
(256l/gaT) 15 c/gal
305 c/gal x 0.84 for MPG loss <256/ga?)
Transportation
225 c/gal
Terminal
60 c/gal
5 c/gal 176 c/gal
176 = 0.26*225 + 0.74* 155
Conclusion: Ethanol would have to be pricec(l55 c/gaj)
or less at the terminal to be attractive torefiners
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, which 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.
52
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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
Montana
51
Alabama
48
North Carolina
55
Arkansas
43
North Dakota
41
Arizona
37
Nebraska
49
California
79
New Hampshire
42
Colorado
40
New Jersey
60
Connecticut
66
New Mexico
37
DC
42
Nevada
52
Delaware
41
New York
63
Florida
61
Ohio
57
Georgia
55
Oklahoma
38
Hawaii
70
Oregon
54
Iowa
49
Pennsylvania
76
Idaho
51
Rhode Island
55
Illinois
58
South Carolina
47
Indiana
69
South Dakota
48
Kansas
49
Tennessee
68
Kentucky
44
Texas
38
Louisiana
38
Utah
50
Massachusetts
45
Virginia
40
Maryland
55
Vermont
49
Maine
48
Washington
68
Michigan
51
Wisconsin
51
Minnesota
47
West Virginia
54
Missouri
36
Wyoming
42
Mississippi
37
As 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. But for
lowest cost market for E85, ethanol is 60-700/gal lower 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.
53
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Figure 2.1.1.2-3: Economics of Blending Ethanol in E85 (nominal dollars)
ro
CO)
u 70
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New York is the lowest cost market for E85, there are very few E85 stations there, even with the
RFS program in place, so we did not assume any E85 sales in New York under the No RFS
Baseline.
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 El 5 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 El 5 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
• EDC is ethanol distribution cost
• 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 El 5)
• GTP is gasoline terminal price; all are in dollars per gallon
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 El 5, 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%.
Another issue for E15 is that it does not receive a 1-psi waiver like E10 does in the
summer. However, as discussed in Chapter 1.7.2, El 5 did receive a regulatory 1-psi waiver for
2019-2021 and EPA-issued emergency fuel waivers throughout the summers of 2022-2024,
55
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which further allowed El 5 to take advantage of the 1-psi waiver.85 A number of Midwestern
states petitioned EPA to remove the 1-psi waiver for El086 and EPA responded by finalizing a
rulemaking to grant those states' request to remove the 1 psi waiver for El0 starting in 2025.87
Because the E10 1-psi waiver was 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 2025.88 EPA subsequently issued emergency fuel waivers in the summer of 2025 to
facilitate continued El5 availability in the Midwestern states.89 Any permanent solution that
allows E15 to be blended into the same BOB as E10 during the summer is expected to encourage
investment and increase sales of El 5.
Federal and State Ethanol Tax Subsidies (FETS and SETS)
There is no federal nor state ethanol blending tax subsidy for El5.90 It is important to
know that California does not allow the sale of E15, although California could allow E15 in the
future. 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.91 We estimated the total cost for a
typical retail station revamp to enable selling E15 to be $133,000 (although there is a large range
from zero costs up to many hundreds of thousand dollars), and that these stations sell on average
229,000 gallons of E15 per year. When amortizing this capital cost over the gallons of E15 sold,
the total cost of the revamp adds over $2/gal to the cost of blending ethanol into El 5 (accounting
only for the 5% of ethanol in E15 above the ethanol in E10).
A new El 5 marketing strategy has emerged by a small number of retailers, which is to
solely sell E15 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 increase the sales of El 5 at these retail stations since consumers will not have a
85 EPA, "Fuel Waivers," May 20, 2025. https://www.epa.gov/gasoline-standards/fuel-waivers.
86 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.
87 89 FR 14760 (February 29, 2024).
88 90 FR 13093 (March 20, 2025).
89 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.
911 Based on our review of the U.S. 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). We intend to include this subsidy in the analysis for the final
rule.
91 The methodology used and the estimated costs for these revamps are discussed in Chapter 10.1.4.1.2.
56
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choice to refuel with E10, and this would reduce the per-gallon cost of the retail station El 5
retrofit costs. Selling El5 as a feature grade provides costs savings to retail stations since it
reduces the number of tanks at the station and increases ethanol sales without marketing or price
discounts. We will continue to monitor this trend and 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, El5 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 E15 is priced 8.50/gal
cheaper than E10. A conversation with a gasoline retail marketer explained that when beginning
to offer E15 for sale, marketers will typically price it lower than E10 as a means to promote E15
to consumers and increase its sales. If El 5 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,
but due to the higher cost of providing El 5, the RIN value would be used by the retailer.
However, if this 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 the ultimate price of El 5
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
E15 is priced lower than E10 consistent with how E85 is priced.92 Since E15 contains less energy
than E10, we assumed that El 5 is priced 1.3%, or about 30/gal, less than E10 which is reflective
of the price discount that typically is used with E85 based on E85's energy content.
Similar to 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 El 5,
showing a range in blending values for ethanol in El5, which vary from economic to blend to not
economic to blend.
92 E85, which contains 74% ethanol and 21% less energy than E10, is typically priced 16% lower than E10.
57
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Figure 2.1.1.3-1: Economics of Blending Ethanol in E15 (nominal dollars)
Range in Ethanol Blending Costs in E15
230
E15 priced 1% lower than E10
ClO
180
u
130
c
ju
80
~
30
o
-20
o
-70
-C
LU
LO
2026 2027 2028 2029 2030
T~\
LU
Year
—*—High Cost E15 - w 1/2 Retail Cost
—¦— Avg Cost E15 w/1/2 Retail Cost
--~—Washington State Regular Grade no Retail Costs
—¦— Washington State Regular Grade w 1/2 Retail
The three solid lines at the top of the figure show the estimated low, average, and high
cost of blending the incremental 5 volume percent ethanol in regular grade E10 to produce El 5.
The lowest cost estimate represented by the solid red line is for producing an El5 blend in the
State of Washington, a lowest cost state for blending El 5. 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 $132,000, and that the Higher Blends Infrastructure Incentive Program
(HBIIP) program subsidized half the cost, the retail station is estimated to need to cover a cost of
about 800 per gallon for that 5% increment of ethanol in El 5. This is shown in Figure 2.1.1.3-1
as the difference between the dashed red line and the solid red line, which represents the average
El5 cost without any retail cost included. While it would not be economic anywhere to blend
El 5 if the retail outlet would need to cover half of the estimated retail cost, if the retail cost is
fully covered by subsidies, or if the retail station is already El 5 compatible and the fuel
dispensers are already capable of dispensing E15, the E15 would be economic in many cases.
For example, if excluding the estimated retail cost for blending E15 in Washington State, there
would be a blending advantage of about 200 per gallon.93
There are two cases that would help to make El5 economic. In one case, over 500
gasoline retailers are electing to only sell El5, which increases El5 sales at those retail stations.
This lowers the per-gallon cost of retrofitting those stations to accommodate El 5.
In the second case, if refiners and terminal operators could overcome the steep logistical
hurdles of producing and moving a 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 be economical in
some summertime regular gasoline markets. Refiners and terminal operators are unlikely to
create a separate El 5 BOB until sales of El 5 increase significantly. Prior to that occurring,
93 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.
58
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anecdotal evidence suggests that a new low-RVP BOB could be produced to meet either E10 or
El5 volatility specifications without a waiver.94 Thus, the ethanol blending cost analysis finds
the gasoline market uneconomical for E15 in the absence of the RFS program.
After reviewing the El5 blending economics, we project that without the RFS program in
place, the fuels market would not offer El5 for sale.
2.1.2 Cellulosic Biofuel
The primary type of cellulosic biofuel projected to generate substantial RINs from 2026-
2030 is CNG/LNG derived from biogas. Additionally, we believe that some volume of liquid
cellulosic ethanol from corn kernel fiber (CKF) will be produced during this period. Cellulosic
biofuels generally cost more to produce than the fossil fuels they displace and, as a result, would
generally not be used without the incentives provided by the RFS program. There are, however,
certain state incentive programs that we project would sufficiently support the use of some
cellulosic biofuels, even without the added incentives from the RFS program. This section
outlines our projections for cellulosic biofuel use under the No RFS Baseline.
2.1.2.1 CNG/LNG Derived from Biogas
As detailed in Chapter 10, CNG/LNG derived from biogas is generally more expensive to
produce than fossil-based natural gas. 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 states95—California, Oregon, and
Washington—have LCFS programs that offer incentives for using CNG/LNG as a transportation
fuel. We assume that these state-level incentives would support some use of CNG/LNG in
transportation even in the absence of the RFS program.
To project the amount of CNG/LNG used as transportation fuel in these states, we relied
on data from each state's programs and extrapolated it through 2030. Specifically, for California
and Oregon, we examined total CNG/LNG volumes (including both fossil and biogas-derived),
as well as volumes solely derived from biogas. Using this information, we calculated both the
year-over-year growth for each year and the blend rate showing the percentage of total
CNG/LNG that was biogas-derived. 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 biogas-based,
with biogas-derived volumes averaging 97% of the total market from 2022 to 2023. This
suggests limited capacity in both states for new sources of biogas-derived CNG/LNG to replace
fossil-based CNG/LNG, meaning that the total market has been saturated with biogas-derived
CNG/LNG.
94 Hoekstra Trading, "Midwest States Pose New Challenges for Gasoline Supply," April 21, 2025.
https://hoekstratrading.com/midwest-states-pose-new-challenges-for-gasoline-supplY.
95 New Mexico also has a state-level program to promote low-carbon fuel use (the Clean Transportation Fuel
Standard (CTFS)). However, since this program was only authorized in March 2024, there is currently insufficient
information for EPA to incorporate potential volumes from this program into this analysis. New Mexico
Enviromnent Department, "Clean Transportation Fuel Program," March 19, 2025. https://www.env.mn. gov/climate-
change-bureau/clean-fuel-standard.
59
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Table 2.1.2.1-1: CNG/LNG Usage in California and Oregon (million ethanol-equivalent
gallons)3 i
2019
2020
2021
2022
2023
California13
Total CNG/LNG
305.0
278.0
302.6
335.4
355.8
Year-over-year growth
7%
-9%
9%
11%
6%
Biogas-derived CNG/LNG
236.1
256.9
295.9
323.3
344.2
Blend Rate
77%
92%
98%
96%
97%
Oregon0
Total CNG/LNG
5.6
5.6
6.5
6.7
6.7
Year-over-year growth
7%
-1%
16%
4%
0%
Biogas-derived CNG/LNG
3.8
4.9
5.9
6.3
6.6
Blend Rate
67%
89%
91%
94%
99%
" Only the last five years of data are shown; however, data is available for California from 2011-2023, and for
Oregon from 2016-2023.
b CARB, Low Carbon Fuel Standard Reporting Tool Quarterly Summaries.
https://ww2.arb.ca.gov/resources/documents/low-carbon-fuel-standard-reporting-tool-auarterlY-summaries.
0 Oregon DEQ, Oregon Clean Fuels Program - Quarterly Data Summaries.
https://www.oregon.gov/dea/ghgp/cfp/pages/auarterlY-data-summaries.aspx.
Despite this saturation, volumes of biogas-derived CNG/LNG can continue to rise as the
overall CNG/LNG market grows. To project future volumes of biogas-derived CNG/LNG in
Oregon and California, EPA calculated the average year-over-year growth rate of the total
CNG/LNG market based on the last three years of data.96 Doing so results in average year-over-
year growth rates for the total CNG/LNG market of 8% and 2% for California and Oregon,
respectively. This rate was then applied to the most recent full year of available biogas-derived
CNG/LNG data (2023), as shown in Table 2.1.2.1-2, and used to project future production by
compounding each successive year.97
Table 2.1.2.1-2: Projected Biogas-derived CNG/LNG Usage in California and Oregon
California
Oregon
(8% Year-over-
(2% Year-over-
Year
Data Type
year Growth)
year Growth)
2023
Actual
344.2
6.6
2024
Projected3
373.3
6.8
2025
Projected
404.9
6.9
2026
Projected
439.1
7.0
2027
Projected
476.2
7.2
2028
Projected
516.4
7.3
2029
Projected
560.1
7.5
2030
Projected
607.4
7.6
a At the time we developed the No RFS Baseline for this proposal, full-year 2024 data was not yet available for
California or Oregon.
96 Only the last three years (2021-2023) were chosen to potentially minimize any impacts that the Covid-19
pandemic may have had on growth.
97 We used the year-over-year growth in the rate of the total CNG/LNG market rather than only the biogas-derived
market as the total CNG/LNG market should better reflect future growth in a vehicle consumption-limited market.
60
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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. First, we calculated the blend rate of biogas-derived CNG/LNG as a percentage of the
total CNG/LNG market, as shown in Table 2.1.2.1-3. Then, using a year-over-year growth rate
determined by averaging the rates of both Oregon and California, we projected the total
CNG/LNG market size for Washington in 2024. Given the saturation in California and Oregon's
markets, we assumed that significant volumes of biogas-derived 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 2024—an assumption that we believe is reasonable given that
Washington reported an RNG blend rate of 53% in their program's first year (2023), which had
already increased to around 75% by the first quarter of 2024.98 After 2024, however, we applied
only the average year-over-year growth rate averaged from California and Oregon to future
Washington projections, as shown in Table 2.1.2.1-4.
Table 2.1.2.1-3: CNG/LNG Usage in Washington (million ethanol-equivalent gallons)
2023
Total CNG/LNG
10.7
Year-over-year growth
N/A
Biogas-derived CNG/LNG
5.72
Blend Rate
53%
Table 2.1.2.1-4: Projected Biogas-derived CNG/LNG Usage in Washington (million
ethanol-equivalent ga
Ions)
Year
Data Type
Biogas-derived
CNG/LNG Usage
(5% year-over-
year Growth)
2023
Actual
5.7
2024
Projected"
10.9a
2025
Projected
11.5
2026
Projected
12.1
2027
Projected
12.7
2028
Projected
13.4
2029
Projected
14.1
2030
Projected
14.8
a Projected using both 5% year-over-year growth rate and the assumption that biogas-derived CNG/LNG would
reach a 97% blend rate in 2024.
98 State of Washington Department of Ecology, "Clean Fuel Standard - Quarter 1, 2024 Data Summary," September
2024. https://apps.ecology. wa. gov/publications/documents/2414075 .pdf.
99 At the time we developed the No RFS Baseline for this proposal, full-year 2024 data was not yet available for
Washington.
61
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Totaling the projected volumes from each state, the projected volume of renewable
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: Biogas-derived CNG/LNG for the No RFS Baseline (million ethanol-
equivalent gallons)
State
2026
2027
2028
2029
2030
California
439.1
476.2
516.4
560.1
607.4
Oregon
7.0
7.2
7.3
7.5
7.6
Washington
12.1
12.7
13.4
14.1
14.8
Total
458.2
496.1
537.2
581.6
629.8
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 2030.
One likely 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 would continue to be produced without
the RFS standards at the volumes proposed in this rule. These volumes are shown in Table
2.1.2.2-1. More information on the methodologies used to determine the proposed 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 ethanol-equivalent
Year
Volume
2026
124
2027
123
2028
122
2029
120
2030
119
2.1.3 Biomass-Based Diesel
2.1.3.1 Biodiesel
Estimating the economics of blending biodiesel is different than ethanol because, unlike
corn ethanol plants that are almost exclusively located in the Midwest, biodiesel plants are more
62
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scattered around the country. The more diffuse location of biodiesel plants affects 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.100 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. The resulting projected biodiesel plant gate prices are summarized in
Table 2.1.3.1-1.
Table 2.1.3.1-1: Projected Biodiesel Plant Gate Prices (nominal $/gal)
Projected Production Cost
2026
2027
2028
2029
2030
Soybean Oil
4.45
4.15
4.10
4.02
3.95
Corn Oil
3.78
3.54
3.50
3.44
3.37
Waste Oil
3.50
3.28
3.24
3.19
3.13
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 predominantly produced in the
Midwest, but there are also biodiesel plants dispersed around the country. For this reason, we
took a very different approach for this analysis. Using 2020 EIA data, we estimated the quantity
11111 USDA, "U.S. Bioenergy Statistics," October 2024, Table 16 - Biodiesel and Diesel Prices.
https://www.ers.usda.gov/data-products/us-bioenergy-statistics.
63
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of biodiesel produced within each PADD,101 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-
2 102.103.104.105
Table 2.1.3.1-2: Biodiesel Production, Imports, Export, and Movement Between PADDs
and Consumption in 2020 (million gallons)
From
From
Other
PADD
Production
Imports
Exports
PADD 2
PADD 3
Movement
Consumption
PADD 1
74
91
8
120
2
-4
275
PADD 2
1,304
47
84
-
0
1
1,268
PADD 3
315
11
21
168
0
0
473
PADD 4
0
20
3
15
0
0
32
PADD 5
125
27
26
157
39
1
323
Total
1,818
197
142
460
41
-2
2,372
ICF estimated the distribution costs for distributing biodiesel both within and between
PADDs, as summarized in Table 2.1.3.1-3.106 An additional cost is added on 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 the year
dollars being analyzed. For example, in 2026, these distribution costs are increased by 34% and
increased to 45% in 2030.
Table 2.1.2
1.1-3: Biodiesel Distribution Costs (^/gal)
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
1111 Petroleum Administration for Defense District (PADD): The 50 U.S. states and the District of Columbia are
divided into five districts. Each PADD 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.
1112 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/2020/202Q 12/biodiesel.pdf.
1113 EIA, "Exports," Petroleum & Other Liquids, April 30, 2025.
https://www.eia.gov/dnav/pet/pet move exp dc NUS-Z00 mbbl a.htm.
1114 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.
1115 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.
106 ICF, "Modeling a 'No-RFS' Case," EPA Contract No. EP-C-16-020, July 17, 2018.
64
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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 distribution
system more so than 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 of the biofuels 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.
Federal and State Biodiesel Tax Subsidies (FBTS and SBTS)
Historically, there has been a $1.00 tax subsidy for blending biodiesel and renewable
diesel into diesel as part of the American Jobs Creation Act of 2004, which has been extended
multiple times over the past 20 years. 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. The amount of the production credit is based on certain employment wage
criteria, and estimated impact on GHG emissions. When we were conducting our No RFS
Baseline case for this proposed rulemaking, the Department of Treasury had not yet established
the credit amounts, so we projected the value of biodiesel subsidies based on information and
conversations with Treasury at the time.107 The estimated value of the biodiesel and renewable
diesel production credit by feedstock type is summarized in Table 2.1.3.1-4.
Table 2.1.3.1-4: Estimated Federal Biodiesel Subsidies (^/gal)
Feedstock Type
Biodiesel Subsidy
Soy Oil
20
Corn Oil
70
Waste Oil and Fats
59
States also provide subsidies to blend biodiesel into diesel. These state subsidies were
enacted in previous years and are presumed to continue through 2030. Table 2.1.3.1-5
summarizes the states that offer such subsidies and their amounts.
107 Based on our review of the U.S. Department of the Treasury and IRS 45Z guidance released on January 10, 2025,
biodiesel produced from soybean oil, corn oil, and UCO will likely earn a 390, 800, and 800 per gallon subsidy,
respectively, which are slightly higher than what we estimated and used in this analysis. 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). Thus, the No RFS
Baseline may be slightly higher due to the larger biodiesel production subsidies. We intend to include these updated
biodiesel production subsidies in the analysis for the final rule.
65
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Table 2.1.3.1-5: Current State Biodiesel Subsidies (^/gal)
State
Biodiesel Subsidy
Hawaii
12
Iowa
3.5
Illinois
19
North Dakota
100
Rhode Island
30
Texas
20
The California and Oregon Low Carbon Fuel Standards (LCFS) 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.108 Oregon also has a biodiesel blending
mandate, which requires that their diesel contain 5% biodiesel. 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.109 Table 2.1.3.1-6 summarizes the projected LCFS
subsidies by year.
Table 2.1.3.1-6: Projected California and Oregon LCFS subsidies ($/ga )
State
Feedstock
2026
2027
2028
2029
2030
California
and Oregon
Soy, Canola
0.45
0.43
0.42
0.40
0.38
Waste Fats and Grease
0.87
0.85
0.83
0.81
0.80
Washington
All Feedstocks
0.24
0.24
0.24
0.24
0.24
Although different than subsidies, several states have mandates that require the diesel
within their state contain a minimum quantity of biodiesel. Table 2.1.3.1-7 lists the states that
have such a mandate and the percentage of biodiesel required to be blended into diesel.
Table 2.1.3.1-7: State Biodiesel Mandates
Minimum %
State
of Biodiesel
Minnesota
12.5
New Mexico
5
Oregon
5
Pennsylvania
2
Washington
2
1118 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.
1119 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 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/montlilY-lcfs-credit-transfer-activitv-reports.
66
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Diesel Terminal Price (DTP)
Refinery rack price data—which already includes 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.110 Instead, we used
projected refinery wholesale price data from AEO2023 to adjust the 2019 refinery rack price data
to represent diesel rack prices in future years. We used 2019 data instead of more recent data to
avoid abnormal pricing effects caused by the emergence and recovery from the Covid-19
pandemic and the emergence of geopolitical conflicts.111 This diesel price data, summarized in
Table 2.1.3.1-6, 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 prices are presented in
the table, after adjusting the prices to nominal year dollars.
Table 2.1.3.1-6: Projected Diesel Terminal Prices (nominal $/gal)
State
2026
2027
2028
2029
2030
State
2026
2027
2028
2029
2030
Alaska
3.88
3.78
3.72
3.81
3.90
Montana
3.19
3.11
3.06
3.13
3.21
Alabama
3.07
2.99
2.94
3.02
3.09
North Carolina
3.11
3.03
2.98
3.05
3.12
Arkansas
3.09
3.02
2.96
3.04
3.11
North Dakota
3.16
3.08
3.02
3.10
3.17
Arizona
3.31
3.22
3.17
3.25
3.33
Nebraska
3.16
3.08
3.03
3.11
3.18
California
3.51
3.42
3.37
3.45
3.53
New Hampshire
3.16
3.08
3.03
3.10
3.18
Colorado
3.22
3.13
3.08
3.16
3.23
New Jersey
3.08
3.00
2.95
3.02
3.09
Connecticut
3.13
3.05
3.00
3.08
3.15
New Mexico
3.26
3.18
3.13
3.20
3.28
District of Columbia
3.11
3.03
2.98
3.05
3.13
Nevada
3.33
3.24
3.19
3.26
3.34
Delaware
3.11
3.03
2.98
3.05
3.13
New York
3.19
3.11
3.05
3.13
3.20
Florida
3.16
3.08
3.02
3.10
3.17
Ohio
3.05
2.97
2.92
3.00
3.07
Georgia
3.10
3.02
2.97
3.04
3.11
Oklahoma
3.05
2.97
2.92
2.99
3.06
Hawaii
3.46
3.37
3.31
3.39
3.47
Oregon
3.26
3.18
3.12
3.20
3.28
Iowa
3.15
3.07
3.02
3.09
3.17
Pennsylvania
3.09
3.01
2.96
3.03
3.11
Idaho
3.20
3.12
3.07
3.14
3.22
Rhode Island
3.11
3.03
2.98
3.06
3.13
Illinois
2.99
2.92
2.87
2.94
3.01
South Carolina
3.10
3.02
2.97
3.04
3.12
Indiana
3.03
2.95
2.90
2.97
3.04
South Dakota
3.19
3.11
3.06
3.13
3.21
Kansas
3.09
3.01
2.96
3.04
3.11
Tennessee
3.10
3.02
2.97
3.04
3.12
Kentucky
3.15
3.07
3.01
3.09
3.16
Texas
3.05
2.97
2.92
2.99
3.06
Louisiana
3.00
2.93
2.88
2.95
3.02
Utah
3.28
3.20
3.14
3.22
3.30
Massachusetts
3.16
3.08
3.02
3.10
3.17
Virginia
3.11
3.03
2.98
3.05
3.13
Maryland
3.11
3.03
2.98
3.05
3.13
Vermont
3.18
3.10
3.04
3.12
3.19
Maine
3.17
3.09
3.04
3.11
3.18
Washington
3.16
3.08
3.03
3.10
3.18
Michigan
3.05
2.98
2.93
3.00
3.07
Wisconsin
3.09
3.01
2.96
3.03
3.10
Minnesota
3.18
3.10
3.05
3.12
3.20
West Virginia
3.14
3.06
3.00
3.08
3.15
Missouri
3.12
3.04
2.99
3.06
3.13
Wyoming
3.39
3.30
3.24
3.32
3.40
Mississippi
3.05
2.97
2.92
2.99
3.06
U.S. Average
3.12
3.05
2.99
3.07
3.14
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 in EPA's analysis. There are two different steps for determining
1111EIA, "Spot Prices," Petroleum & Other Liquids, May 14, 2025.
https://www.eia.gov/dnav/pet/pet pri spt si a.htm.
111 We intend to update the base refinery rack price data to include year 2024 price data for the final rule analysis.
67
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the No RFS Baseline. First, the biodiesel volume due to the state mandates are estimated by
applying the mandate percentage to the projected diesel fuel consumption of that state.
The second step for estimating the No RFS Baseline involves estimating 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 and
additional nonmandated volumes are assumed to be blended. Conversely, biodiesel is assumed to
not be blended into diesel if the biodiesel blending value is positive. Because of its relative cost,
biodiesel consumption without the RFS program would be driven mostly by the state mandates
but would also occur absent the RFS program due to state subsidies, mainly the California and
Oregon LCFS programs.
Using the estimated year-by-year biodiesel volumes estimated or projected by the No
RFS Baseline analysis would potentially result in large volumetric swings in some years based
on the changing economics of biodiesel in certain states in those years. In reality, the
marketplace is unlikely to make such swings. To avoid this problem, the following steps were
taken to normalize the growth and use of biodiesel.
Biodiesel demand in any one historical or future year was not allowed to exceed the
demand that occurred under the RFS program. During 2021-2023, 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 2021, which was the most recent data available
at the time this analysis was conducted. Because biodiesel consumption has generally been on a
plateau, these percentages are assumed to be the maximum biodiesel percentages in any year
through 2030. The volume of biodiesel consumed in each state is estimated by EIA and reported
in its State Energy Data System (SEDS).112 Table 2.1.3.1-6 summarizes the percent of biodiesel
in diesel fuel for each state based on the SEDS information.
112 EIA, "State Energy Data System," 2022, Table C2 - Energy consumption estimates for selected energy sources
in physical units, https://www.eia.gov/state/seds/sep sum/html/pdf/sum use tot.pdf.
68
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Table 2.1.3.1-6: Maximum Percent of Biodiesel in I
>iesel
Alaska
7.2
Montana
0.0
Alabama
2.8
North Carolina
1.0
Arkansas
13.3
North Dakota
2.9
Arizona
1.4
Nebraska
2.4
California
8.6
New Hampshire
2.4
Colorado
0.8
New Jersey
1.0
Connecticut
2.5
New Mexico
2.0
District of Columbia
3.5
Nevada
1.4
Delaware
0.8
New York
6.4
Florida
1.0
Ohio
2.4
Georgia
0.9
Oklahoma
2.2
Hawaii
0.9
Oregon
9.9
Iowa
6.0
Pennsylvania
2.7
Idaho
0.7
Rhode Island
1.4
Illinois
8.2
South Carolina
1.0
Indiana
2.6
South Dakota
2.3
Kansas
2.0
Tennessee
2.1
Kentucky
2.6
Texas
2.4
Louisiana
2.0
Utah
0.6
Massachusetts
3.0
Virginia
1.1
Maryland
1.2
Vermont
2.8
Maine
2.3
Washington
2.1
Michigan
2.4
Wisconsin
2.5
Minnesota
12.8
West Virginia
1.5
Missouri
2.1
Wyoming
0.8
Mississippi
2.9
uel by State (percent)
Tables 2.1.3.1-7a and 2.1.3. l-7b list the states expected to consume biodiesel under the
No RFS Baseline in the years 2026 to 2030 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, we apportioned the biogenic oil
feedstock types based on the mix of these vegetable oils being used to produce biodiesel.113 The
mix of biooil feedstocks for producing mandated biodiesel is 50%, 42%, and 8% of soy oil,
waste oil, and corn oil, respectively. For cases where our analysis shows biodiesel is
economically viable in a state, our analysis determines if biodiesel is economically viable for
only one, or more than one feedstock. 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 fraction in each state from Table 2.1.3.1-6 by the
113 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—w hich 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.
69
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volume of diesel fuel consumed in that state.114 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 2.1.3.1-7a: 2026-2028 Biodiesel in No RFS Baseline (million gal/yr)
2026
2027
2028
Soy
Corn
Soy
Corn
Soy
Corn
State
Oil
Oil
FOG
Oil
Oil
FOG
Oil
Oil
FOG
Oregon
19
4
15
18
3
15
18
3
15
Volume in
States with
Mandates
New Mexico
18
2
15
17
2
15
17
1
15
Minnesota
58
12
50
58
12
50
57
11
49
Washington
11
2
9
11
2
9
11
2
9
Pennsylvania
17
2
10
16
2
10
17
2
10
Total
244
242
240
California
0
56
240
0
56
240
0
56
240
Economic
Oregon
0
7
31
0
7
31
0
7
31
Volume
Other States
0
7
49
0
6
640
0
36
546
Total
390
979
916
Total of Mandated and
Economic Volumes
123
92
420
120
90
1,010
120
121
915
Total Volumes by Year
634
1,221
1,156
Table 2.1.3.1-7b: 2029-2030 Biodiesel in No RFS Baseline (million gal/yr)
2029
2030
State
Soy
Oil
Corn
Oil
FOG
Soy
Oil
Corn
Oil
FOG
Oregon
18
3
15
18
3
15
Volume in
States with
Mandates
New Mexico
17
2
15
17
1
15
Minnesota
57
11
49
56
11
48
Washington
11
2
9
11
2
9
Pennsylvania
16
2
10
16
2
10
Total
238
235
California
0
56
240
0
56
240
Economic
Oregon
0
3
14
8
2
7
Volume
Other States
0
115
926
0
110
937
Total
1,354
1,360
Total of Mandated and
Economic Volumes
119
196
1,277
126
188
1280
Total Volumes by Year
1,592
1,594
The total mandated and economic volume of biodiesel varies by a significant amount
over 2026-2030. Such swings in the economic attractiveness of biodiesel would confound efforts
on the part of investors to project future returns on their investments to determine whether to
invest to expand their plants, continue to operate their plants, or shut down. Thus, to smooth out
114 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. AEO2023, Table 11 - Petroleum and Other Liquids
Supply and Disposition.
70
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the swings in the economics for using biodiesel and look at it the way plant operators and their
investors would have in the absence of the RFS program, we calculated the average of biodiesel
demand for the year of interest and the previous three years. This step attempts to reflect how
potential biodiesel investors or banks would seek to assess the economics for operating or
investing in expanding biodiesel plant capacity. Thus, to assess the volume of biodiesel which
would be economical in 2026, it was necessary to also assess the economics of producing
biodiesel in 2023, 2024, and 2025. For this reason, biodiesel economics were assessed
historically for 2023-2025, and projected for 2026-2030, to determine the volume of biodiesel
which would be economical to blend absent the RFS program. Table 2.1.3.1-8 summarizes the
mandated biodiesel volume, the yearly economics biodiesel volume, the 4-year average
economic biodiesel volume and finally the total of mandated and 4-year average biodiesel
volume.
Table 2.1.3.1-8 Year-by-Year Analysis of Biodiesel Volumes for the No RFS Baseline
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
254
870
552
806
2024
249
929
641
891
2025
246
359
632
878
2026
245
390
539
784
2027
242
979
567
809
2028
240
916
563
803
2029
238
1354
812
1050
2030
235
1360
812
1048
For the most part, this mix of vegetable oil types is used for biodiesel for estimating costs
for the No RFS Baseline, however, a few minor adjustments were made to the vegetable oil
feedstock types after the No RFS Baseline analysis was conducted for renewable diesel (see
Chapter 2.1.3.3).
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:
71
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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
The diesel terminal prices (DTP) are the same as that described in Chapter 2.1.3.1 for
biodiesel, so the diesel terminal prices will not be discussed further here. However, each of the
other variables in the above equation are discussed further. The state mandates described in
Chapter 2.1.3.1 are assumed to not apply 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. Imports are assumed to be produced from soybean oil and
have the same production costs as that produced domestically.115 The resulting projected
renewable diesel plant gate prices are summarized in Table 2.1.3.2-1.
Table 2.1.3.2-1: Project
ted Renewable I
>iesel Plant Gate
Feedstock
2026
2027
2028
2029
2030
Soybean Oil
5.20
4.89
4.83
4.75
4.67
Corn Oil
4.50
4.24
4.19
4.12
4.06
Waste Oil
4.20
3.96
3.92
3.86
3.80
Renewable Diesel Distribution Cost (RDDC)
This factor represents the added cost of moving renewable diesel 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, renewable diesel is predominantly
produced on the Gulf and West Coasts. Based on the SEDS data, all the renewable diesel is
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 for biodiesel. If the renewable diesel is produced on the West
Coast, the vegetable oil feedstock is likely to be virgin oil imported from the Midwest or
115 EIA, "U.S. biodiesel imports have doubled since 2022 due to low prices in Europe," Today in Energy, May 28,
2024. https://www.eia.gov/todavinenergy/detail.php?id=62123.
72
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imported use 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 distribution costs for distributing renewable diesel both within and
between PADDs, as summarized in Table 2.1.3.2-3.116 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. Although not shown in the
table, the adjustment for 2030 increases the distribution and additive costs by 45%.
Table 2.1.3.2-3: Renewable Diesel Distribution Costs (l/gal)
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 provides renewable diesel a production
subsidy starting in 2025 based on certain employment wage criteria and the fuel's emissions rate.
Since the U.S. Department of Treasury and IRS had not yet published the emissions rate table as
we were analyzing the No RFS Baseline, we projected the value of biodiesel subsidies based on
conversations with the Department of Treasury and IRS at the time.117 The estimated value of the
renewable diesel production credit by feedstock type is summarized in Table 2.1.3.2-4.
Table 2.1.3.2-4: Estimated Federal Renewable Diesel Subsidies (^/gal)
Feedstock Type
Renewable Diesel Subsidy
Soy Oil
15
Corn Oil
73
Waste Oil and Fats
62
The California and Oregon Low Carbon Fuel Standards (LCFS) and Washington State's
Clean Fuels program do not offer specific subsides per se, but through the cap-and-trade nature
116 ICF, "Modeling a 'No-RFS' Case," EPA Contract No. EP-C-16-020, July 17, 2018.
117 Based on our review of the U.S. Department of the Treasury and IRS 45Z guidance released on January 10, 2025,
renewable diesel produced from soybean oil, corn oil, and UCO will likely earn a 270, 750, and 660 per gallon
subsidy, respectively, which are slightly higher than what we estimated and used in this analysis. 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). Thus, the No
RFS Baseline may be slightly higher than what we estimated for this proposed rule due to the larger renewable
diesel production subsidies. We intend to include these updated renewable diesel production subsidies in the
analysis for the final rule.
73
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of their programs, they can be equated to subsidies.118 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.119 Table 2.1.3.2-6 summarizes the projected LCFS subsidies by
year.
Table 2.1.3.2-6: Projected California and Oregon LCFS subsit
State
Feedstock
2026
2027
2028
2029
2030
California
and Oregon
Soy, Canola
0.45
0.43
0.42
0.40
0.38
Waste Fats and Grease
0.87
0.85
0.83
0.81
0.80
Washington
All Feedstocks
0.24
0.24
0.24
0.24
0.24
ies ($/gal)
Estimating the Renewable Diesel Volume Under the No RFS Baseline
The methodology for analyzing renewable diesel volumes is structured similar to that for
biodiesel described in Chapter 2.1.3.1. The state with the lowest renewable diesel blending cost
(e.g., states with blending subsidies) would receive renewable diesel first. The percent of
renewable diesel in any state's diesel fuel 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
pool.120
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.
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 if a state offers a significant subsidy, mainly the
California and Oregon LCFS programs.
Since renewable diesel is only consumed in several states and nearly all of that in
California, we partitioned the maximum renewable diesel demand primarily to California and
some to Oregon. While some renewable diesel is currently being sold in Washington State under
the federal RFS and State Clean Renewable Fuels programs there, due to the low amount of the
state renewable diesel subsidy there as shown in Table 2.1.3.2-6, we did not allocate any of the
118 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.
119 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 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/montlilY-lcfs-credit-transfer-activitv-reports.
1211 Renewable diesel has properties similar to petroleum diesel and thus can displace petroleum diesel without
causing vehicle compatibility or drivability issues.
74
-------
maximum renewable diesel volume to Washington because the state subsidy was insufficient to
cause any demand under the No RFS program. However, we show what the estimated
extrapolated renewable diesel volume is for Washington State.121 The projected maximum
renewable diesel volumes are summarized in Table 2.1.3.2-2.
Table 2.1.3.2-2 Maximum E
'enewa
)le Diesel Volume by State
Jnder t
ie RFS Program
Year
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
Total Volume
582
991
1,360
1,476
1,703
1,931
2,159
2,387
2,615
2,843
3,071
California
2,136
2,362
2,587
2,813
2,992
Oregon
55
61
67
73
79
Washington
74
94
114
134
154
Table 2.1.3.2-3 lists the volume of renewable diesel which is economically favorable for
blending into diesel fuel by state for the years 2026-2030. Although several states are
economical for renewable diesel, the renewable diesel is essentially only being consumed in
California, with small amounts in Oregon and Washington State. For this reason, we show the
potential maximum volume of renewable diesel which can be consumed in California and
Oregon, and we aggregated the potential consumption volume in other states. 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 vegetable oil volume is below the established
maximum volumes. The final vegetable oil volumes are shown in Table 2.1.3.3-3.
Table 2.1.3.2-3: Potential Volume of Renewable Diesel by Feedstock Ty
)e (million gallons)
Year
State
Feedstock
Total
Soybean Oil
Corn Oil
FOG
2026
California
0
403
1733
2136
Oregon
0
0
50
50
Other States
0
7
38
45
2027
California
0
446
1916
2362
Oregon
0
10
45
55
Other States
0
8
75
83
2028
California
0
488
2099
2587
Oregon
0
11
49
60
Other States
0
9
47
56
2029
California
0
531
2282
2813
Oregon
0
12
54
66
Other States
0
10
42
52
2030
California
0
531
2282
2813
Oregon
0
14
59
72
Other States
0
13
106
119
121 The estimated maximum renewable diesel demand is somewhat academic since the total volume of renewable
diesel is determined by the volume of available feedstock, as described in Chapter 2.1.3.3.
75
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2.1.3.3 Final No RFS Baseline Volumes for Biodiesel and Renewable Diesel
While the volume of biodiesel and renewable diesel by feedstock type were initially
estimated in Tables 2.1.3.1-8 and 2.1.3.2-3, using these volumes, particularly the renewable
diesel volumes, would exceed a total volume by feedstock type that reflects a reasonable growth
increase from current trends, and exceed the maximum expected volume of renewable diesel
estimated in Table 2.1.3.2-2. To estimate the maximum vegetable oil volumes which could be
available for producing biodiesel and renewable diesel in 2023-2025, we reviewed the trend in
vegetable oil consumption for previous years and projected their future volumes, 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
2026
2,003
303
1,700
2027
2,117
321
1,797
2028
2,231
338
1,894
2029
2,345
355
1,990
2030
2,458
373
2,087
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, and limited by the maximum vegetable
oil volumes in Table 2.1.3.3-1, are shown in Table 2.1.3.3-2. Based on economics, we used the
following hierarchy for estimating the total volume of biomass-based diesel by feedstock type
and availability:
1) The state mandates are satisfied first which is met using biodiesel. The mix of
vegetable oil types is the same as that consumed under the RFS program in 2023.
2) The biodiesel demand in California and Oregon are the second most economical
biomass-based diesel type, but only FOG and corn oil are estimated to be cost-
effective.
3) Renewable diesel demand in California and Oregon is the third most cost-effective
biomass-based diesel, and once again only FOG and corn oil are estimated to be cost
effective.
4) The last cost-effective biomass-based diesel is biodiesel sold outside of California
and Oregon.
Since FOG and corn oil vegetable oils are the lowest in cost, the biodiesel and renewable
diesel plants are assumed to use this vegetable oil feedstock up to the maximum projected
amount.
76
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Table 2.1.3.3-2 Final No RFS Baseline Volumes for Biodiesel and Renewable Diesel (million
gallons)
Biodiesel
Renewable Diesel
Total
Corn
Corn
Biodiesel and
Year
Soy
Oil
FOG
Total
Soy
Oil
FOG
Total
Renewable Diesel
2026
114
87
379
580
0
201
1,225
1,426
2,006
2027
116
66
387
569
0
235
1,299
1,533
2,102
2028
110
85
371
566
0
230
1,381
1,611
2,177
2029
104
88
382
573
0
244
1,475
1,719
2,292
2030
109
86
376
571
0
256
1,543
1,799
2,369
The amount of renewable diesel in the No RFS Baseline is estimated to be higher for this
action than the Set 1 Rule due to lower projected vegetable oil feedstocks prices.
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 RNGused 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 2026-2030 in the context of the Volume Scenarios that we
analyzed. Here we discuss the deviations from those projections that we believe would apply
under a No RFS Baseline.
According to data from EI A, all ethanol imports entered the U.S. through the West Coast
in 2018-2021, and the majority did so in 2022 and 2023.122 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 believe 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 2026-2030. For similar reasons, we
believe that domestically produced advanced ethanol 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
122 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.
77
-------
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 2026-2030.
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 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).
Table 2.1.5-1: No RFS Baseline for 2026-2030 (million R
[Ns)
2026
2027
2028
2029
2030
Cellulosic Biofuel
582
619
659
702
749
CNG/LNG from biogas
458
496
537
582
630
Ethanol from CKF
124
123
122
120
119
Total Biomass-Based Diesel
3,156
3,310
3,429
3,614
3,753
Biodiesel
884
868
878
889
885
Soybean oil
122
132
119
120
126
FOG
587
591
584
592
583
Corn oil
135
101
134
136
134
Canola oil
0
0
0
0
0
Renewable Diesel
2,267
2,438
2,547
2,719
2,862
Soybean oil
0
0
0
0
0
FOG
1,950
2,065
2,186
2,333
2,455
Corn oil
317
373
360
387
407
Canola oil
0
0
0
0
0
Jet fuel from FOG
5
5
5
5
5
Other Advanced Biofuels
197
197
197
197
197
Renewable diesel from FOG
111
111
111
111
111
Imported sugarcane ethanol
58
58
58
58
58
Domestic ethanol from waste ethanol
28
28
28
28
28
Other51
0
0
0
0
0
Conventional Renewable Fuel
Ethanol from corn
13,571
13,434
13,278
13,099
12,906
Biodiesel and renewable diesel from palm oil
0
0
0
0
0
a Composed of non-cellulosic biogas, heating oil, and naphtha.
78
-------
Table 2.1.5-2: No RFS Baseline for 2026-2030 (million gallons)
2026
2027
2028
2029
2030
Cellulosic Biofuel
582
619
659
702
749
CNG/LNG from biogas
458
496
537
582
630
Ethanol from CKF
124
123
122
120
119
Diesel/jet fuel from wood waste/MSW
0
0
0
0
0
Total Biomass-Based Diesel
2,009
2,105
2,180
2,296
2,382
Biodiesel
589
579
585
593
590
Soybean oil
81
88
80
80
84
FOG
391
394
389
395
388
Corn oil
90
67
89
91
89
Canola oil
27
27
27
27
29
Renewable Diesel
1,417
1,524
1,592
1,700
1,789
Soybean oil
0
0
0
0
0
FOG
1,218
1,290
1,366
1,458
1,535
Corn oil
198
233
225
242
254
Canola oil
0
0
0
0
0
Jet fuel from FOG
3
3
3
3
3
Other Advanced Biofuels
155
155
155
155
155
Renewable diesel from FOG
69
69
69
69
69
Imported sugarcane ethanol
58
58
58
58
58
Domestic ethanol from waste ethanol
Other51
Conventional Renewable Fuel
13,571
13,434
13,278
13,099
12,906
Ethanol from corn
13,571
13,434
13,278
13,099
12,906
Biodiesel and renewable diesel from palm oil
0
0
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 Volume Scenarios, we have
also estimated some of the impacts (e.g., costs) of this rule relative to the renewable fuel volumes
we projected would be used to meet the 2025 volume requirements finalized in the Setl Rule as
an additional informational case. This allows for an estimate of the incremental impacts of the
proposed renewable fuel volumes compared to those previously finalized.123
For this proposal, we used the projected the mix from the Set 1 Rule of the biofuels that
would be used to meet the 2025 volume requirements.124 These volumes are shown in Table 2.2-
1 (in million RINs) and Table 2.2-2 (in million gallons).
123 These are not necessarily the volumes that we might expect to occur in 2025 based on information available
today. Such a baseline may also be relevant when assessing some of the impacts of the proposed volumes for 2026
and 2027.
124 Set 1 Rule RIA, Table 3.1-3.
79
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Table 2.2-1: Set 1 Rule Projected Mix of Biofuels in 2025 (million RINs)
Cellulosic Biofuel
1,376
CNG/LNG from biogas
1,299
Ethanol from CKF
77
Diesel/jet fuel from wood waste/MSW
0
Total Biomass-Based Diesel
6,881
Biodiesel
2,436
Soybean oil
1,430
FOG
427
Corn oil
95
Canola oil
484
Renewable Diesel
4,421
Soybean oil
1,501
FOG
1,962
Corn oil
463
Canola oil
495
Jet fuel from FOG
24
Other Advanced Biofuels
290
Renewable diesel from FOG
104
Imported sugarcane ethanol
95
Domestic ethanol from waste ethanol
27
Other51
64
Conventional Renewable Fuel
13,779
Ethanol from corn
13,779
Renewable diesel from palm oil
0
Composed of non-cellulosic biogas, heating oil, and naphtha.
80
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Table 2.2-2: Set 1 Rule Projected Mix of Biofuels in 2025 (million gallons)
Cellulosic Biofuel
1,376
CNG/LNG from biogas
1,299
Ethanol from CKF
77
Diesel/jet fuel from wood waste/MSW
0
Total Biomass-Based Diesel
4,239
Biodiesel
1,624
Soybean oil
953
FOG
285
Corn oil
62
Canola oil
323
Renewable Diesel
2,601
Soybean oil
883
FOG
1,154
Corn oil
272
Canola oil
291
Jet fuel from FOG
14
Other Advanced Biofuels
232
Renewable diesel from FOG
61
Imported sugarcane ethanol
95
Domestic ethanol from waste ethanol
27
Other51
49
Conventional Renewable Fuel
13,779
Ethanol from corn
13,779
Renewable diesel from palm oil
0
a Composed of non-cellulosic biogas, heating oil, and naphtha.
The renewable fuel volumes in Tables 2.2-1 and 2.2-2 represent the volumes of
renewable fuel EPA projected would be supplied to meet the volume requirements for 2025 in
the Set 1 Rule. Since publishing the Set 1 Rule, EPA has continued to monitor available data on
renewable fuel production and use in the U.S. While many of the projections made in the Set 1
Rule appear to be reasonably accurate, more recent data suggests other projected volumes are
likely to over-project or under-project the quantity of renewable fuel supplied in 2025.
Specifically, recent data suggests that greater quantities of biodiesel and renewable diesel will be
supplied and lower volumes of CNG/LNG derived from biogas will be supplied in 2025 relative
to the projections in the 2025 rule. In some cases, it may be informative to consider the impacts
of this proposed rule relative to our updated renewable fuel supply projections for 2025. These
updated projections are shown in Tables 2.2-3 and 2.2-4. Note that the only volumes that have
been updated in these tables (relative to Tables 2.2-1 and 2.2-2) are the projected volumes of
biomass-based diesel (including the volumes of biodiesel and renewable diesel from all
feedstocks) and the volume of CNG/LNG from biogas. For more detail on how these updated
projections were calculated see Chapter 7.2.2 (for BBD) and 7.1.3 (for CNG/LNG from biogas).
81
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Table 2.2-3: Updated Projection of Biofuels
Cellulosic Biofuel
1,190
CNG/LNG from biogas
1,113
Ethanol from CKF
77
Diesel/jet fuel from wood waste/MSW
0
Total Biomass-Based Diesel
8,181
Biodiesel
3,150
Soybean oil
1,915
FOG
514
Corn oil
186
Canola oil
535
Renewable Diesel
5,008
Soybean oil
1,120
FOG
3,203
Corn oil
466
Canola oil
219
Jet fuel from FOG
24
Other Advanced Biofuels
290
Renewable diesel from FOG
104
Imported sugarcane ethanol
95
Domestic ethanol from waste ethanol
27
Other51
64
Conventional Renewable Fuel
13,939
Ethanol from corn
13,939
Renewable diesel from palm oil
0
Supply for 2025 (million RINs)
Composed of non-cellulosic biogas, heating oil, and naphtha.
82
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Table 2.2-4: Updated Projection of Biofuels
Cellulosic Biofuel
1,190
CNG/LNG from biogas
1,113
Ethanol from CKF
77
Diesel/jet fuel from wood waste/MSW
0
Total Biomass-Based Diesel
5,060
Biodiesel
2,100
Soybean oil
1,277
FOG
343
Corn oil
124
Canola oil
357
Renewable Diesel
2,946
Soybean oil
659
FOG
1,884
Corn oil
274
Canola oil
129
Jet fuel from FOG
14
Other Advanced Biofuels
232
Renewable diesel from FOG
65
Imported sugarcane ethanol
95
Domestic ethanol from waste ethanol
27
Other51
45
Conventional Renewable Fuel
13,939
Ethanol from corn
13,939
Renewable diesel from palm oil
0
Supply for 2025 (million gallons)
Composed of non-cellulosic biogas, heating oil, and naphtha.
83
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Chapter 3: Volume Scenarios, Proposed Volumes, and Volume
Changes
For analyses in which we have quantified the impacts of the volume scenarios for 2026-
2030 we have identified the specific biofuel types and associated feedstocks that are projected to
be used to meet those volumes. While we acknowledge that there is significant uncertainty about
the types of renewable fuels that would be used to meet the volume scenarios, 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 volume scenarios. This chapter identifies the mix of biofuels that could
result from the volume scenarios 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 Volume Scenarios
The volume scenarios that we developed for 2026-2030 are presented in Preamble
Section III.C.5 and are repeated in Tables 3.1-1 and 2 by the component fuel types and in Tables
3.1-3 and 4 by the statutory and implied categories.
Table 3.1-1: Low Volume Scenarios Components (million RINs)a
D Code"
2026
2027
2028
2029
2030
Cellulosic biofuel
D3 +D7
1,298
1,362
1,431
1,504
1,583
Biomass-based diesel
D4
8,410
8,910
9,410
9,910
10,410
Other advanced biofuel
D5
249
249
249
249
249
Conventional renewable fuel
D6
13,783
13,662
13,516
13,352
13,172
a The D codes given for each component category are defined in 40 CFR 80.1425(g). D codes are used to identify
the statutory categories that can be fulfilled with each component category according to 40 CFR 80.1427(a)(2).
Table 3.1-2: High Volume Scenarios Com
ponents (million RI
Vs)a
D Code"
2026
2027
2028
2029
2030
Cellulosic biofuel
D3 +D7
1,298
1,362
1,431
1,504
1,583
Biomass-based diesel
D4
8,910
9,910
10,910
11,910
12,910
Other advanced biofuel
D5
249
249
249
249
249
Conventional renewable fuel
D6
13,783
13,662
13,516
13,352
13,172
a The D codes given for each component category are defined in 40 CFR 80.1425(g). D codes are used to identify
the statutory categories that can be fulfilled with each component category according to 40 CFR 80.1427(a)(2).
84
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Table 3.1-3: Low Volume Scenario in Statutory and Implied Categories (million RINs)a
D Code
2026
2027
2028
2029
2030
Cellulosic biofuel
D3 +D7
1,298
1,362
1,431
1,504
1,583
Non-cellulosic
advanced biofueP
D4 + D5
8,659
9,159
9,659
10,159
10,659
Advanced biofuel
D3 + D4 +
D5 + D7
9,957
10,521
11,090
11,664
12,242
Conventional
renewable fuela
D6
13,783
13,662
13,516
13,352
13,172
Total renewable fuel
All
23,740
24,183
24,606
25,015
25,414
a These are implied volume requirements, not regulatory volume requirements.
Table 3.1-4: High Volume Scenario in Statutory and Implied Categories (million RINs)a
D Code
2026
2027
2028
2029
2030
Cellulosic biofuel
D3 +D7
1,298
1,362
1,431
1,504
1,583
Non-cellulosic
advanced biofueP
D4 + D5
9,159
10,159
11,159
12,159
13,159
Advanced biofuel
D3 + D4 +
D5 + D7
10,457
11,521
12,590
13,664
14,742
Conventional
renewable fuela
D6
13,783
13,662
13,516
13,352
13,172
Total renewable fuel
All
24,240
25,183
26,106
27,015
27,914
a These are implied volume requirements, not regulatory volume requirements.
We estimated the constituent mix of renewable fuel types and feedstocks that could be
used to meet the volume scenarios as shown in Tables 3.1-5 (in million RINs) and 3.1-6 (in
million gallons).125
125 The analyses leading to the mix of renewable fuel types and feedstocks are presented in Chapter 7.
85
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Table 3.1-5: Low Volume Scenario Biofuel Supply for 2026-2030 (million RINs)
2026
2027
2028
2029
2030
Cellulosic Biofuel
1,298
1,362
1,431
1,504
1,583
CNG/LNG from biogas
1,174
1,239
1,309
1,384
1,464
Ethanol from CKF
124
123
122
120
119
Total Biomass-Based DieseP
8,410
8,910
9,410
9,910
10,410
Biodiesel
3,150
3,150
3,150
3,150
3,150
Soybean oil
1,915
1,915
1,915
1,915
1,915
FOG
514
514
514
514
514
Corn oil
186
186
186
186
186
Canola oil
535
535
535
535
535
Renewable Diesel
5,261
5,761
6,261
6,761
7,261
Soybean oil
1,124
1,184
1,244
1,304
1,364
FOG
3,485
3,925
4,365
4,805
5,245
Corn oil
443
443
443
443
443
Canola oil
208
208
208
208
208
Other Advanced Biofuels
249
249
249
249
249
Renewable diesel from FOG
111
111
111
111
111
Sugarcane ethanol
58
58
58
58
58
Domestic ethanol from waste ethanol
28
28
28
28
28
Otherb
52
52
52
52
52
Conventional Renewable Fuel
13,783
13,662
13,516
13,352
13,172
Ethanol from corn
13,783
13,662
13,516
13,352
13,172
a Includes BBD in excess of the proposed volume requirement for advanced biofuel. The excess would be used to
help meet the proposed volume requirement for conventional renewable fuel.
b Composed of non-cellulosic biogas, heating oil, and naphtha.
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Table 3.1-6: High Volume Scenario Biofuel Supply for 2026-2030 (million RINs)
2026
2027
2028
2029
2030
Cellulosic Biofuel
1,298
1,362
1,431
1,504
1,583
CNG/LNG from biogas
1,174
1,239
1,309
1,384
1,464
Ethanol from CKF
124
123
122
120
119
Total Biomass-Based DieseP
8,910
9,910
10,910
11,910
12,910
Biodiesel
3,150
3,150
3,150
3,150
3,150
Soybean oil
1,915
1,915
1,915
1,915
1,915
FOG
514
514
514
514
514
Corn oil
186
186
186
186
186
Canola oil
535
535
535
535
535
Renewable Diesel
5,761
6,761
7,761
8,761
9,761
Soybean oil
1,464
1,864
2,264
2,664
3,064
FOG
3,485
3,925
4,365
4,805
5,245
Corn oil
443
443
443
443
443
Canola oil
368
528
688
848
1,008
Other Advanced Biofuels
249
249
249
249
249
Renewable diesel from FOG
111
111
111
111
111
Sugarcane ethanol
58
58
58
58
58
Domestic ethanol from waste ethanol
28
28
28
28
28
Otherb
52
52
52
52
52
Conventional Renewable Fuel
13,783
13,662
13,516
13,352
13,172
Ethanol from corn
13,783
13,662
13,516
13,352
13,172
a Includes BBD in excess of the proposed volume requirement for advanced biofuel. The excess would be used to
help meet the proposed volume requirement for conventional renewable fuel.
b Composed of non-cellulosic biogas, heating oil, and naphtha.
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Table 3.1-7: Low Volume Scenario Biofuel Supply for 2026-2(
)30 (million gallons)
2026
2027
2028
2029
2030
Cellulosic Biofuel
1,298
1,362
1,431
1,504
1,583
CNG/LNG from biogas
1,174
1,239
1,309
1,384
1,464
Ethanol from CKF
124
123
122
120
119
Total Biomass-Based DieseP
5,388
5,700
6,013
6,325
6,638
Biodiesel
2,100
2,100
2,100
2,100
2,100
Soybean oil
1,277
1,277
1,277
1,277
1,277
FOG
342
342
342
342
342
Corn oil
124
124
124
124
124
Canola oil
357
357
357
357
357
Renewable Diesel
3,288
3,600
3,913
4,255
4,538
Soybean oil
703
740
778
815
853
FOG
2,178
2,453
2,728
3,003
3,278
Corn oil
277
277
277
277
277
Canola oil
130
130
130
130
130
Other Advanced Biofuels
192
192
192
192
192
Renewable diesel from FOG
69
69
69
69
69
Sugarcane ethanol
58
58
58
58
58
Domestic ethanol from waste ethanol
28
28
28
28
28
Otherb
37
37
37
37
37
Conventional Renewable Fuel
13,783
13,662
13,516
13,352
13,172
Ethanol from corn
13,783
13,662
13,516
13,352
13,172
a Includes BBD in excess of the proposed volume requirement for advanced biofuel. The excess would be used to
help meet the proposed volume requirement for conventional renewable fuel.
b Composed of non-cellulosic biogas, heating oil, and naphtha.
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Table 3.1-8: High Volume Scenario Biofuel Supply for 2026-2030 (million gallons
2026
2027
2028
2029
2030
Cellulosic Biofuel
1,298
1,362
1,431
1,504
1,583
CNG/LNG from biogas
1,174
1,239
1,309
1,384
1,464
Ethanol from CKF
124
123
122
120
119
Total Biomass-Based DieseP
5,700
6,325
6,950
7,575
8,200
Biodiesel
2,100
2,100
2,100
2,100
2,100
Soybean oil
1,277
1,277
1,277
1,277
1,277
FOG
342
342
342
342
342
Corn oil
124
124
124
124
124
Canola oil
357
357
357
357
357
Renewable Diesel
3,600
4,255
4,850
5,475
6,100
Soybean oil
915
1,165
1,415
1,665
1,915
FOG
2,178
2,453
2,728
3,003
3,278
Corn oil
277
277
277
277
277
Canola oil
230
330
430
530
630
Other Advanced Biofuels
192
192
192
192
192
Renewable diesel from FOG
69
69
69
69
69
Sugarcane ethanol
58
58
58
58
58
Domestic ethanol from waste ethanol
28
28
28
28
28
Otherb
37
37
37
37
37
Conventional Renewable Fuel
13,783
13,662
13,516
13,352
13,172
Ethanol from corn
13,783
13,662
13,516
13,352
13,172
a Includes BBD in excess of the proposed volume requirement for advanced biofuel. The excess would be used to
help meet the proposed volume requirement for conventional renewable fuel.
b Composed of non-cellulosic biogas, heating oil, and naphtha.
3.2 Mix of Renewable Fuel Types for the Proposed Volumes
To assess the projected impacts of this proposed rule we also identified the specific
biofuel types and associated feedstocks that are projected to be used to meet the proposed
volume requirements for 2026 and 2027 (the "Proposed Volumes"). As with the Volume
Scenarios, we acknowledge that there is significant uncertainty about the types of renewable
fuels that would be used to meet the Proposed Volumes. We believe that the mix of biofuel types
described in this chapter are reasonable projections based on historical data, current market
trends, and our projections of the potential supply in 2026 and 2027.
For three of the component volume categories (cellulosic biofuel, other advanced biofuel,
and conventional renewable fuel), the Proposed Volumes are identical those in both the Low and
High Volume Scenarios discussed in Chapter 3.1. The volumes of cellulosic biofuel and
conventional renewable fuel are expected to be limited by the quantity of these fuels (RNG and
ethanol) that will be used as transportation fuel in 2026 and 2027. Our projection of the volume
of other advanced biofuel is based on the supply of these fuels observed in throughout the history
of the RFS program.
Unlike the other three component categories of renewable fuel, the number of BBD RINs
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we expected to be supplied to meet the Proposed Volumes differs from both the Low and High
Volume Scenarios. The projected supply of BBD to meet the Proposed Volumes is based on an
updated projection of the supply of BBD in 2025126 and a projected annual growth rate of 500
million RINs per year. Tables 3.2-1 and 3.2-2 show the supply of renewable fuels projected to be
used to meet the Proposed Volumes, listed by the component volume categories and statutory
and implied categories respectively.
Table 3.2-1: Proposed Volumes Components (million RINs)a
D Code"
2026
2027
Cellulosic biofuel
D3 +D7
1,298
1,362
Biomass-based diesel
D4
8,690
9,190
Other advanced biofuel
D5
249
249
Conventional renewable fuel
D6
13,783
13,662
a The D codes given for each component category are defined in 40 CFR 80.1425(g). D codes are used to identify
the statutory categories that can be fulfilled with each component category according to 40 CFR 80.1427(a)(2).
Table 3.2-2: Proposed Volumes in
Statutory and Implied
Categories (million RINs)
D Code
2026
2027
Cellulosic biofuel
D3 +D7
1,298
1,362
Non-cellulosic advanced biofueP
D4 + D5
8,939
9,439
Advanced biofuel
D3 + D4 + D5 + D7
10,237
10,801
Conventional renewable fuela
D6
13,783
13,662
Total renewable fuel
All
24,020
24,463
a These are implied volume requirements, not regulatory volume requirements.
As with the volume scenarios, we next estimated the constituent mix of renewable fuel
types and feedstocks that could be used to meet the volume scenarios.127 Consistent with the
previous tables, the projected volumes for cellulosic biofuel, other advanced biofuel, and
conventional renewable fuel are identical to the projected volumes for both the Low and High
Volume Scenarios.
The BBD volumes projected to be used to meet the Proposed Volumes, however, are
significantly different than either the Low or High Volume Scenario. There are two reasons for
these differences. The first reason is that while both the Low and High Volume Scenarios and the
Proposed Volumes increase volumes in future years from the projected supply in 2025, we used
different data set to project the supply of BBD in 2025. For the Low and High Volume Scenarios
we projected the BBD supply in 2024 and 2025 using data through May 2024, the most recent
data available when the volume scenarios were developed. For the Proposed Volumes, we used
data through the end of 2024 to project the BBD supply for 2025. While the total volume of
BBD projected to be supplied in 2025 is very similar in both cases (8.16 billion RINs using data
126 We based our updated projection of the BBD supply in 2025 on the actual volume of BBD supplied in 2024. Due
to the significant uncertainty in the BBD market for 2025, we believe the actual supply of BBD in 2024 is the best
projection available for the supply of BBD in 2025. We anticipate updating our projection of the BBD supply in
2025 based on the most recent available data for the final rule.
127 The analyses leading to the mix of renewable fuel types and feedstocks are presented in Chapter 7.
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through May 2024 vs. 8.19 billion RINs using data through the end of 2024), the mix of biofuels
and feedstocks used to produce these biofuels differ between the two projections.
The more significant difference between the Volume Scenarios and the Proposed
Volumes is that unlike the Volume Scenarios, the Proposed Volumes include our proposal to
reduce the number of RINs generated for imported biofuels and biofuels produced from imported
feedstocks starting in 2026. As discussed further in this section, we project that even with the
increased incentive to use renewable fuel produced domestically from domestic feedstocks
provided by the reduction of RINs for imported renewable fuel and renewable fuel produced
from foreign feedstocks, some quantity of imported renewable fuel and renewable fuel produced
from foreign feedstocks will still be used to meet the Proposed Volumes for 2026 and 2027.
Because these imported renewable fuels and renewable fuels produced from imported feedstocks
would generate fewer RINs per gallon, we project that the total volume of renewable fuel needed
to meet the Proposed Volumes would be higher than the volumes estimated in both the Low and
High Volume Scenarios.
To project the supply of BBD that could be used to meet the Proposed Volumes, we
started by estimating the portion of the BBD supplied in 2024 that was imported versus produced
in the U.S. This data is summarized in Table 3.2-3.
Table 3.2-3: Supply of Domestic vs. Imported BBD in 2024 (million RINs)
Biofuel
Supply
Domestic BBD (Total)
7,723
Domestic Biodiesel
2,494
Domestic Renewable DieseP
5,229
Imported BBD (Total)
1,445
Imported Biodiesel
597
Imported Renewable DieseP
848
Domestic and Imported BBD
9,168
Exported BBD (Total)
980
Net BBD Supply
8,188
a Includes renewable jet fuel.
Source: EMTS.
Next, we estimated what proportion of the domestic BBD was produced from domestic
feedstocks compared to imported feedstocks. EPA currently does not collect data on the point of
origin of feedstocks used to produce renewable fuels in the RFS program. In the absence of data
directly from the BBD producers we used alternative data sources to estimate the origin used for
BBD production. In 2024, there were four primary feedstocks used by domestic BBD producers:
soybean oil, canola oil, distillers corn oil, and waste fats, oils, and greases (FOG).128 According
to data from USD A, imports of soybean oil and corn oil represent a very small portion of the
U.S. supply of these feedstocks. For the 2023/24 agricultural marketing year, USDA forecasted
that less than 2% of the U.S. supply of soybean oil would be imported and less than 4% of the
128 In addition to these feedstocks, there were smaller volumes of BBD produced from comingled distillers corn oil
and sorghum oil (which we have included in the total for distillers corn oil) and camelina (which we have included
in the total for soybean oil).
91
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U.S. supply of corn oil would be imported.129 We therefore projected that all of the domestic
BBD produced from soybean oil and corn oil was sourced from domestic feedstocks in 2024. We
note, however, that while we do not project that any of the domestic biodiesel and renewable
diesel was produced from imported soybean oil, EMTS data indicates that some of the imported
biodiesel and renewable diesel produced in other countries was produced from soybean oil.
Conversely, the majority of the canola oil supplied to the U.S. (about 70%) in the 2023/24
agricultural marketing year was projected to be imported. Based on this information, we project
that in 2024, all the canola oil used to produce BBD in the U.S. was imported.
Projecting the total of FOG that is used by domestic BBD producers is more complex, as
domestic BBD producers rely on significant quantities of domestic and imported FOG. To
project the quantity of FOG used for BBD production in 2024 sourced domestically (as well as
the potential for growth in the domestic supply of FOG) we considered the historic data on the
use of FOG for domestic biofuel production (see Figure 3.2-1). From 2014-2020 the number of
RINs generated for BBD produced from FOG increased steadily, at a rate of approximately 80
million RINs per year. The number of RINs generated for BBD produced from FOG increased
dramatically in 2022-2024, when the U.S. began importing significant quantities of used
cooking oil and animal fats. Based on the observed trend in the increase of RINs generated for
BBD produced from FOG from 2014-2020, which we estimate contained little to no imported
FOG, we project that approximately 1.42 billion RINs were generated for BBD produced from
domestically sourced FOG in 2024, or about 43% of the total number of RINs generated for
BBD produced from FOG. Absent any other data sources, we estimated that 43% of the domestic
biodiesel and renewable diesel produced from FOG used domestic feedstocks, while the
remaining 57% was produced from imported feedstocks. Our total estimates of the production of
BBD by fuel type and feedstock in 2024 are shown in Table 3.2-4.
129 USD A, "Oil Crops Yearbook," March 2025. https://www.ers.usda.gov/data-products/oil-crops-Yearbook.
92
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Figure 3.2-1: RINs Generated for BBD Produced from FOG
3,500
<3
O 3,000
E
o
o
CD
cq
2,500
3 2,000
~o
-------
Table 3.2-4: BBD Production by
7uel and Feedstock in
Biofuel
Volume Produced
BBD (Total)
5.76
Biodiesel (Total)3
2.15
Domestic FOG
0.15
Domestic Soybean Oil0
1.00
Domestic Canola Oil
0.00
Domestic Distillers Corn Oil
0.20
Imported FOG
0.22
Imported Soybean Oil
0.26
Imported Canola Oil
0.32
Imported Distillers Corn Oil
0.00
Renewable Diesel (Total)b
3.61
Domestic FOG
0.70
Domestic Soybean Oil0
0.69
Domestic Canola Oil
0.00
Domestic Distillers Corn Oil
0.40
Imported FOG
1.33
Imported Soybean Oil
0.10
Imported Canola Oil
0.39
Imported Distillers Corn Oil
0.00
2024 (billion gallons)
a Includes heating oil.
b Includes renewable jet fuel.
0 Includes camelina oil.
Source: EMTS. Imported categories include both imported biofuels and biofuels produced in the U.S. from imported
feedstocks.
After estimating the supply of BBD in 2024 by fuel type and feedstock, we next projected
which feedstocks would likely increase through 2027 for the Proposed Volumes. In making these
projections we first considered potential growth in the supply of domestic feedstocks, as
domestic biofuels produced from these feedstocks would generate twice the number of RINs as
those from imported feedstocks under our proposal to reduce the number of RINs generated for
imported biofuels and biofuels produced from imported feedstocks.
Our assessment of the potential for growth in the supply of BBD feedstocks is presented
in Chapter 7.2.4. In this Chapter we projected annual increases in the supply of domestic soybean
oil at 250 million gallons per year and domestic FOG of 25 million gallons per year, with no
projected growth in domestic distillers corn oil or canola oil. In all cases, we acknowledged the
uncertainty in these projections and generally presented a range of estimates of future growth
from public sources. For the Proposed Volumes, we are generally projecting annual growth rates
that are consistent with those presented in Chapter 7.2.4 (250 million gallons per year of
domestic soybean oil and no growth for domestic distillers corn oil and canola oil). The one
exception is slightly higher projected growth rate for domestic FOG (50 million gallons per year
in the Proposed Volumes based on the historic data from 2014-2020 presented in Figure 3.2-1 vs.
25 million gallons per year in the High and Low Volume Scenarios based on the data presented
in Chapter 7.2.4).
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The other significant difference in the Proposed Volumes are shifts in the use of domestic
canola oil and domestic corn oil from non-biofuel markets to biofuel markets in 2026. These
shifts are based on our projection that the potential to generate a higher number of RINs per
gallon for domestic feedstocks will incentivize BBD producers to pay higher prices for these
domestic vegetable oils than their current markets. As we are not projecting that the domestic
production of these feedstocks grows significantly in future years these feedstock increases
represent a one-time shift, which we project will occur in 2026, rather than ongoing annual
increases. We are not projecting that the use of these feedstocks in non-biofuel markets will
cease, but rather that the domestic feedstocks will be preferentially used by BBD producers and
that other markets will turn to imported canola oil and/or corn oil to satisfy their market demand,
or alternatively will switch to other vegetable oils in greater supply or reduce their use of
vegetable oils. We are not projecting a similar shift in soybean oil from non-biofuel uses to BBD
production. This is both because the use of soybean oil in non-biofuel markets has been very
stable over the past decade, suggesting that shifting soybean oil from non-biofuel markets may
prove difficult, and also because there is currently a tariff on imported soybean oil which
increases the cost of replacing domestic soybean oil with imported soybean oil in all markets.
Over time, we may see a shift of domestic soybean oil from non-biofuel markets to biofuel
production and a simultaneous increase in the imports of other vegetable oils for non-biofuel
markets, but we expect these market shifts will take time and will not significantly impact the
availability of domestic soybean oil to renewable fuel producers through 2027. We acknowledge
that there is significant fungibility between different types of vegetable oils and that in reality we
may see slightly lower shifts in the quantity of canola oil and corn oil used for BBD production
and slightly higher shifts in the quantity of soybean oil used for BBD production. Nevertheless,
we believe the total volume of domestic vegetable oils projected to shift from non-biofuel
markets to BBD production in the Proposed Volumes is reasonable.
After accounting for these changes in the supply of BBD produced from domestic
feedstocks, along with the other changes we are proposing in this rule such as the reduction of
RINs generated for imported renewable fuels and renewable fuels produced from foreign
feedstocks, the total supply of BBD is very slightly higher than needed in 2026. To balance the
projected supply of BBD and the Proposed Volumes, we reduced the projected volume of
imported biodiesel produced from soybean oil slightly to balance the projected supply and
demand of BBD. We selected imported biodiesel produced from soybean oil for this reduction as
we project this biofuel would generally be eligible for the lowest quantity of incentives under the
various state and federal incentive programs (California's LCFS program, the RFS program,
etc.).
The projected supply of BBD in the Proposed Volumes, broken out by domestic versus
imported sources, fuel type, and feedstock, are presented in Tables 3.2-5 (in million RINs) and
3.2-6 (in million gallons). Note that Table 3.2-5 takes into account the proposal to reduce the
number of RINs generated for imported renewable fuels and renewable fuels produced from
imported feedstocks and the proposed reduction in the number of RINs generated for renewable
diesel to 1.6 RINs per gallon. Tables 3.2-7 and 3.2-8 show the supply of all the renewable fuels,
broken out by fuel type and feedstock, that we project would be supplied to meet the Proposed
Volumes.
95
-------
Table 3.2-5: Proposed Volumes BSD Supply (million
Biofuel
2026
2027
BBD (Total)
8,690
9,190
Biodiesel (Total)3
2,599
2,621
Domestic FOG
220
220
Domestic Soybean Oil0
1,494
1,494
Domestic Canola Oil
0
0
Domestic Distillers Corn Oil
310
310
Imported FOG
165
165
Imported Soybean Oil
168
190
Imported Canola Oil
241
241
Imported Distillers Corn Oil
1
1
Renewable Diesel (Total)b
6,090
6,570
Domestic FOG
1,278
1,358
Domestic Soybean Oil0
1,909
2,309
Domestic Canola Oil
370
370
Domestic Distillers Corn Oil
1,085
1,085
Imported FOG
1,056
1,056
Imported Soybean Oil
81
81
Imported Canola Oil
308
308
Imported Distillers Corn Oil
3
3
a Includes heating oil.
b Includes renewable jet fuel.
0 Includes camelina oil.
-------
Table 3.2-6: Proposed Volumes BSD Supply (Million Gallons)
Biofuel
2026
2027
BBD (Total)
6,826
7,155
Biodiesel (Total)3
2,116
2,145
Domestic FOG
146
146
Domestic Soybean Oil0
996
996
Domestic Canola Oil
0
0
Domestic Distillers Corn Oil
207
207
Imported FOG
220
220
Imported Soybean Oil
224
253
Imported Canola Oil
322
322
Imported Distillers Corn Oil
1
1
Renewable Diesel (Total)b
4,711
5,011
Domestic FOG
799
849
Domestic Soybean Oil0
1,193
1,443
Domestic Canola Oil
231
231
Domestic Distillers Corn Oil
678
678
Imported FOG
1,320
1,320
Imported Soybean Oil
101
101
Imported Canola Oil
385
385
Imported Distillers Corn Oil
3
3
a Includes heating oil.
b Includes renewable jet fuel.
0 Includes camelina oil.
97
-------
Table 3.2-7: Proposed Volumes Biofuel Supply (mil
ion RINs
Biofuel
2026
2027
Cellulosic Biofuel
1,298
1,362
CNG/LNG from biogas
1,174
1,239
Ethanol from CKF
124
123
Total Biomass-Based DieseP
8,690
9,190
Biodiesel
2,600
2,620
Soybean oil
1,664
1,684
FOG
384
384
Corn oil
311
311
Canola oil
241
241
Renewable Diesel
6,090
6,570
Soybean oil
1,990
2,390
FOG
2,335
2,415
Corn oil
1,087
1,087
Canola oil
678
678
Other Advanced Biofuels
249
249
Renewable diesel from FOG
111
111
Sugarcane ethanol
58
58
Domestic ethanol from waste ethanol
28
28
Otherb
52
52
Conventional Renewable Fuel
13,783
13,662
Ethanol from corn
13,783
13,662
a Includes BBD in excess of the proposed volume requirement for advanced biofuel. The excess would be used to
help meet the proposed volume requirement for conventional renewable fuel.
b Composed of non-cellulosic biogas, heating oil, and naphtha.
98
-------
Table 3.2-8: Proposed Volumes Biofuel Supply (mil
Biofuel
2026
2027
Cellulosic Biofuel
1,298
1,362
CNG/LNG from biogas
1,174
1,239
Ethanol from CKF
124
123
Total Biomass-Based DieseP
6,826
7,155
Biodiesel
2,116
2,145
Soybean oil
1,220
1,249
FOG
366
366
Corn oil
208
208
Canola oil
322
322
Renewable Diesel
4,710
5,010
Soybean oil
1,294
1,544
FOG
2,119
2,169
Corn oil
681
681
Canola oil
616
616
Other Advanced Biofuels
192
192
Renewable diesel from FOG
69
69
Sugarcane ethanol
58
58
Domestic ethanol from waste ethanol
28
28
Otherb
37
37
Conventional Renewable Fuel
13,783
13,662
Ethanol from corn
13,783
13,662
ion gallons)
a Includes BBD in excess of the proposed volume requirement for advanced biofuel. The excess would be used to
help meet the proposed volume requirement for conventional renewable fuel.
b Composed of non-cellulosic biogas, heating oil, and naphtha.
3.3 Volume Changes Analyzed with Respect to the No RFS Baseline
For those factors for which we quantified the impacts of the volume scenarios for 2026-
2030, the impacts were based on the difference in the volumes of specific renewable fuel types
between the Volume Scenarios and the No RFS Baseline. These differences are shown in Tables
3.3-1, 2, and 5 in terms of RINs and in Tables 3.3-3, 4, and 6 in physical volumes. The values in
these tables reflect the difference between values of: (1) The tables containing the Low and High
Volume Scenarios (Tables 3.1-5 through 8) and Proposed Volumes (Tables 3.2-7 and 8), and (2)
The tables containing the No RFS Baseline volumes (Tables 2.1.5-1 and 2).
99
-------
Table 3.3-1: Volume Changes for the Low Volume Scenario Relative to the No RFS
Baseline (million RINs)
2026
2027
2028
2029
2030
Cellulosic Biofuel
716
743
772
802
834
CNG/LNG from biogas
716
743
772
802
834
Ethanol from CKF
0
0
0
0
0
Total Biomass-Based Diesel
5,255
5,600
5,981
6,297
6,658
Biodiesel
2,266
2,282
2,272
2,260
2,264
Soybean oil
1,793
1,783
1,796
1,795
1,789
FOG
-73
-77
-70
-79
-69
Corn oil
52
85
52
50
52
Canola oil
535
535
535
535
535
Renewable Diesel
2,994
3,323
3,714
4,041
4,399
Soybean oil
1,124
1,184
1,244
1,304
1,364
FOG
1,536
1,861
2,179
2,473
2,790
Corn oil
126
70
83
56
36
Canola oil
208
208
208
208
208
Jet fuel from FOG
-5
-5
-5
-5
-5
Other Advanced Biofuels
52
52
52
52
52
Renewable diesel from FOG
0
0
0
0
0
Sugarcane ethanol
0
0
0
0
0
Domestic ethanol from waste ethanol
0
0
0
0
0
Other51
52
52
52
52
52
Conventional Renewable Fuel
212
228
238
252
266
Ethanol from corn
212
228
238
252
266
a Composed of non-cellulosic biogas, heating oil, and naphtha.
-------
Table 3.3-2: Volume Changes for the High Volume Scenario Relative to the No RFS
Baseline (million RINs)
2026
2027
2028
2029
2030
Cellulosic Biofuel
716
743
772
802
834
CNG/LNG from biogas
716
743
772
802
834
Ethanol from CKF
0
0
0
0
0
Total Biomass-Based Diesel
5,755
6,600
7,481
8,297
9,158
Biodiesel
2,266
2,282
2,272
2,260
2,264
Soybean oil
1,793
1,783
1,796
1,795
1,789
FOG
-73
-77
-70
-79
-69
Corn oil
52
85
52
50
52
Canola oil
535
535
535
535
535
Renewable Diesel
3,494
4,323
5,214
6,041
6,899
Soybean oil
1,464
1,864
2,264
2,664
3,064
FOG
1,536
1,861
2,179
2,473
2,790
Corn oil
126
70
83
56
36
Canola oil
368
528
688
848
1,008
Jet fuel from FOG
-5
-5
-5
-5
-5
Other Advanced Biofuels
52
52
52
52
52
Renewable diesel from FOG
0
0
0
0
0
Sugarcane ethanol
0
0
0
0
0
Domestic ethanol from waste ethanol
0
0
0
0
0
Other51
52
52
52
52
52
Conventional Renewable Fuel
212
228
238
252
266
Ethanol from corn
212
228
238
252
266
a Composed of non-cellulosic biogas, heating oil, and naphtha.
-------
Table 3.3-3: Volume Changes for the Low Volume Scenario Relative to the No RFS
Baseline (million gallons)
2026
2027
2028
2029
2030
Cellulosic Biofuel
716
743
772
802
834
CNG/LNG from biogas
716
743
772
802
834
Ethanol from CKF
0
0
0
0
0
Total Biomass-Based Diesel
3,379
3,595
3,833
4,030
4,255
Biodiesel
1,511
1,521
1,515
1,507
1,509
Soybean oil
1,196
1,189
1,197
1,196
1,192
FOG
-49
-51
-47
-53
-46
Corn oil
34
57
35
33
35
Canola oil
319
327
330
329
328
Renewable Diesel
1,871
2,077
2,321
2,526
2,749
Soybean oil
703
740
778
815
853
FOG
960
1,163
1,362
1,545
1,744
Corn oil
79
44
52
35
22
Canola oil
130
130
130
130
130
Jet fuel from FOG
-3
-3
-3
-3
-3
Other Advanced Biofuels
37
37
37
37
37
Renewable diesel from FOG
0
0
0
0
0
Sugarcane ethanol
0
0
0
0
0
Domestic ethanol from waste ethanol
0
0
0
0
0
Other51
37
37
37
37
37
Conventional Renewable Fuel
212
228
238
252
266
Ethanol from corn
212
228
238
252
266
a Composed of non-cellulosic biogas, heating oil, and naphtha.
-------
Table 3.3-4: Volume Changes for the High Volume Scenario Relative to the No RFS
Baseline (million gallons)
2026
2027
2028
2029
2030
Cellulosic Biofuel
716
743
772
802
834
CNG/LNG from biogas
716
743
772
802
834
Ethanol from CKF
0
0
0
0
0
Total Biomass-Based Diesel
3,691
4,220
4,770
5,280
5,818
Biodiesel
1,511
1,521
1,515
1,507
1,509
Soybean oil
1,196
1,189
1,197
1,196
1,192
FOG
-49
-51
-47
-53
-46
Corn oil
34
57
35
33
35
Canola oil
319
327
330
329
328
Renewable Diesel
2,184
2,702
3,259
3,766
4,312
Soybean oil
915
1,165
1,415
1,665
1,915
FOG
960
1,163
1,362
1,545
1,744
Corn oil
79
44
52
35
22
Canola oil
230
330
430
530
630
Jet fuel from FOG
-3
-3
-3
-3
-3
Other Advanced Biofuels
37
37
37
37
37
Renewable diesel from FOG
0
0
0
0
0
Sugarcane ethanol
0
0
0
0
0
Domestic ethanol from waste ethanol
0
0
0
0
0
Other51
37
37
37
37
37
Conventional Renewable Fuel
212
228
238
252
266
Ethanol from corn
212
228
238
252
266
a Composed of non-cellulosic biogas, heating oil, and naphtha.
-------
Table 3.3-5: Volume Changes for the Proposed Volumes Relative to the No RFS Baseline
(million RINs)
2026
2027
Cellulosic Biofuel
716
743
CNG/LNG from biogas
716
743
Ethanol from CKF
0
0
Total Biomass-Based Diesel
5,534
5,880
Biodiesel
1,716
1,752
Soybean oil
1,542
1,552
FOG
-203
-207
Corn oil
176
210
Canola oil
241
241
Renewable Diesel
3,823
4,132
Soybean oil
1,990
2,390
FOG
385
350
Corn oil
770
714
Canola oil
678
678
Jet fuel from FOG
-5
-5
Other Advanced Biofuels
52
52
Renewable diesel from FOG
0
0
Sugarcane ethanol
0
0
Domestic ethanol from waste ethanol
0
0
Other51
52
52
Conventional Renewable Fuel
212
228
Ethanol from corn
212
228
a Composed of non-cellulosic biogas, heating oil, and naphtha.
104
-------
Table 3.3-6: Volume Changes for the Proposed Volumes Relative to the No RFS Baseline
(million gallons)
2026
2027
Cellulosic Biofuel
716
743
CNG/LNG from biogas
716
743
Ethanol from CKF
0
0
Total Biomass-Based Diesel
4,817
5,050
Biodiesel
1,527
1,566
Soybean oil
1,139
1,161
FOG
-25
-28
Corn oil
118
141
Canola oil
295
292
Renewable Diesel
3,293
3,486
Soybean oil
1,294
1,544
FOG
901
879
Corn oil
483
448
Canola oil
616
616
Jet fuel from FOG
-3
-3
Other Advanced Biofuels
37
37
Renewable diesel from FOG
0
0
Sugarcane ethanol
0
0
Domestic ethanol from waste ethanol
0
0
Other51
37
37
Conventional Renewable Fuel
212
228
Ethanol from corn
212
228
a Composed of non-cellulosic biogas, heating oil, and naphtha.
Note that the changes in ethanol from corn shown in Tables 3.3-1 through 3.3-4 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.130 There is some uncertainty related to
how changes in ethanol consumption will impact ethanol production. For example, ethanol
producers could respond to decreased domestic demand by decreasing production or by
increasing 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.
Tables 3.3-1 through 3.3-4 represent the change in biofuel use in the transportation sector
that could occur if the Low or High Volume Scenarios were to become the basis for the
applicable percentage standards. Tables 3.3-5 and 3.3-6 represent the change in biofuel use in the
transportation sector that could occur if the Proposed Volumes were to become the basis for the
applicable percentage standards.
130 See Chapter 2.1.1 for more discussion onE15 andE85.
105
-------
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 determine that FOG consisted of about 52% used cooking oil (UCO) and 48% tallow in 2023,
the last full year for which information was available at the time this analysis was completed.131
These fractions were applied to the volumes projected to be supplied in 2025. EPA then
projected the increases in biodiesel and renewable diesel produced from UCO and tallow for
2026-2030 (these projections are described in Chapter 7.2). The projected volumes of biodiesel
and renewable diesel produced from UCO and tallow are shown in Table 3.3-7. Note that the
volume of biodiesel and renewable diesel produced from FOG are the same in both the Low
Volume Scenario and the High Volume Scenario. The projected increase in biodiesel and
renewable diesel produced from UCO and tallow relative to the No RFS Baseline is shown in
Table 3.2-8.
Table 3.3-7: Disaggregated Biofuels Produced from FOG (million gallons)
2026
2027
2028
2029
2030
Biodiesel from FOG
342
342
342
342
342
UCO
179
179
179
179
179
Tallow
164
164
164
164
164
Renewable diesel from FOG
2,178
2,453
2,728
3,003
3,278
UCO
1,217
1,442
1,667
1,892
2,117
Tallow
961
1,011
1,061
1,111
1,161
Table 3.3-8: Volume Changes in Biodiesel and Renewable Diesel Produced from FOG and
Tallow Relative to the No RFS Baseline (million gallons)
2026
2027
2028
2029
2030
Biodiesel from FOG
-49
-51
-47
-53
-46
UCO
-25
-27
-24
-28
-24
Tallow
-25
-25
-23
-25
-22
Renewable diesel from FOG
960
1,163
1,362
1,545
1,744
UCO
499
605
708
803
907
Tallow
461
558
654
742
837
3.4 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
Tables 3.4-1, 2, and 5 in terms of RINs and in Tables 3.4-3, 4, and 6 in physical volumes. The
values in these tables reflect the difference between values of: (1) The tables containing the Low
and High Volume Scenarios (Tables 3.1-5 through 8) and Proposed Volumes (Tables 3.2-7 and
8), and (2) The tables containing the 2025 Baseline volumes (Tables 2.2-1 and 2).
131 EIA, "Monthly Biofuels Capacity and Feedstocks Update," August 2024, Table 2b - U.S. Feedstocks consumed
for production of biofuels. https://www.eia.gov/biofuels/update/arcliive/2024/2024 08Ztable2.pdf.
106
-------
Table 3.4-1: Volume Changes for the Low Volume Scenario Relative to 2025 Baseline
(million RINs)
2026
2027
2028
2029
2030
Cellulosic Biofuel
-78
-14
-55
128
207
CNG/LNG from biogas
-125
-60
10
85
165
Ethanol from CKF
47
46
45
43
42
Total Biomass-Based Diesel
1,529
2,029
2,529
3,029
3,529
Biodiesel
714
714
714
714
714
Soybean oil
485
485
485
485
485
FOG
87
87
87
87
87
Corn oil
91
91
91
91
91
Canola oil
51
51
51
51
51
Renewable Diesel
840
1,340
1,840
2,340
2,840
Soybean oil
-377
-317
-257
-197
-137
FOG
1,523
1,963
2,403
2,843
3,283
Corn oil
-20
-20
-20
-20
-20
Canola oil
-287
-287
-287
-287
-287
Jet fuel from FOG
-24
-24
-24
-24
-24
Other Advanced Biofuels
-41
-41
-41
-41
-41
Renewable diesel from FOG
7
7
7
7
7
Sugarcane ethanol
-37
-37
-37
-37
-37
Domestic ethanol from waste ethanol
1
1
1
1
1
Other
-12
-12
-12
-12
-12
Conventional Renewable Fuel
-156
-277
-423
-587
-767
Ethanol from corn
-156
-277
-423
-587
-767
107
-------
Table 3.4-2: Volume Changes for the High Volume Scenario Relative to 2025 Baseline
(million RINs)
2026
2027
2028
2029
2030
Cellulosic Biofuel
-78
-14
-55
128
207
CNG/LNG from biogas
-125
-60
10
85
165
Ethanol from CKF
47
46
45
43
42
Total Biomass-Based Diesel
2,029
3,029
4,049
5,029
6,029
Biodiesel
714
714
714
714
714
Soybean oil
485
485
485
485
485
FOG
87
87
87
87
87
Corn oil
91
91
91
91
91
Canola oil
51
51
51
51
51
Renewable Diesel
1,340
2,340
3,340
4,340
5,340
Soybean oil
-37
363
763
1,163
1,563
FOG
1,523
1,963
2,403
2,843
3,283
Corn oil
-20
-20
-20
-20
-20
Canola oil
-127
33
193
353
513
Jet fuel from FOG
-24
-24
-24
-24
-24
Other Advanced Biofuels
-41
-41
-41
-41
-41
Renewable diesel from FOG
7
7
7
7
7
Sugarcane ethanol
-37
-37
-37
-37
-37
Domestic ethanol from waste ethanol
1
1
1
1
1
Other
-12
-12
-12
-12
-12
Conventional Renewable Fuel
-156
-277
-423
-587
-767
Ethanol from corn
-156
-277
-423
-587
-767
108
-------
Table 3.4-3: Volume Changes for the Low Volume Scenario Relative to 2025 Baseline
(million gallons)
2026
2027
2028
2029
2030
Cellulosic Biofuel
-78
-14
-55
128
207
CNG/LNG from biogas
-125
-60
10
85
165
Ethanol from CKF
47
46
45
43
42
Total Biomass-Based Diesel
986
1,298
1,611
1,923
2,236
Biodiesel
476
476
476
476
476
Soybean oil
323
323
323
323
323
FOG
58
58
58
58
58
Corn oil
61
61
61
61
61
Canola oil
34
34
34
34
34
Renewable Diesel
525
837
1,150
1,462
1,775
Soybean oil
-235
-198
-160
-123
-85
FOG
952
1.227
1,502
1,777
2,052
Corn oil
-13
-13
-13
-13
-13
Canola oil
-179
-179
-179
-179
-179
Jet fuel from FOG
-15
-15
-15
-15
-15
Other Advanced Biofuels
-40
-40
-40
-40
-40
Renewable diesel from FOG
4
4
4
4
4
Sugarcane ethanol
-37
-37
-37
-37
-37
Domestic ethanol from waste ethanol
1
1
1
1
1
Other
-8
-8
-8
-8
-8
Conventional Renewable Fuel
-156
-277
-423
-587
-767
Ethanol from corn
-156
-277
-423
-587
-767
109
-------
Table 3.4-4: Volume Changes for the High Volume Scenario Relative to 2025 Baseline
(million gallons)
2026
2027
2028
2029
2030
Cellulosic Biofuel
-78
-14
-55
128
207
CNG/LNG from biogas
-125
-60
10
85
165
Ethanol from CKF
47
46
45
43
42
Total Biomass-Based Diesel
1,298
1,923
2,548
3,173
3,798
Biodiesel
476
476
476
476
476
Soybean oil
323
323
323
323
323
FOG
58
58
58
58
58
Corn oil
61
61
61
61
61
Canola oil
34
34
34
34
34
Renewable Diesel
837
1,462
2,087
2,712
3,337
Soybean oil
-23
227
477
727
977
FOG
952
1,227
1,502
1,777
2,052
Corn oil
-13
-13
-13
-13
-13
Canola oil
-79
21
121
221
321
Jet fuel from FOG
-15
-15
-15
-15
-15
Other Advanced Biofuels
-40
-40
-40
-40
-40
Renewable diesel from FOG
4
4
4
4
4
Sugarcane ethanol
-37
-37
-37
-37
-37
Domestic ethanol from waste ethanol
1
1
1
1
1
Other
-8
-8
-8
-8
-8
Conventional Renewable Fuel
-156
-277
-423
-587
-767
Ethanol from corn
-156
-277
-423
-587
-767
-------
Table 3.4-5: Volume Changes for the Proposed Volumes Relative to 2025 Baseline (million
RINs)
2026
2027
Cellulosic Biofuel
-78
-14
CNG/LNG from biogas
-125
-60
Ethanol from CKF
47
46
Total Biomass-Based Diesel
1,809
2,309
Biodiesel
164
184
Soybean oil
234
254
FOG
-43
-43
Corn oil
216
216
Canola oil
-243
-243
Renewable Diesel
1,669
2,149
Soybean oil
489
889
FOG
373
453
Corn oil
624
624
Canola oil
183
183
Jet fuel from FOG
-24
-24
Other Advanced Biofuels
-41
-41
Renewable diesel from FOG
7
7
Sugarcane ethanol
-37
-37
Domestic ethanol from waste ethanol
1
1
Other
-12
-12
Conventional Renewable Fuel
-156
-277
Ethanol from corn
-156
-277
111
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Table 3.4-6: Volume Changes for the Proposed Volumes Relative to 2025 Baseline (million
gallons)
2026
2027
Cellulosic Biofuel
-78
-14
CNG/LNG from biogas
-125
-60
Ethanol from CKF
47
46
Total Biomass-Based Diesel
2,424
2,753
Biodiesel
492
521
Soybean oil
267
296
FOG
81
81
Corn oil
145
145
Canola oil
-1
-1
Renewable Diesel
1,947
2,247
Soybean oil
356
606
FOG
893
943
Corn oil
392
392
Canola oil
307
307
Jet fuel from FOG
-15
-15
Other Advanced Biofuels
-40
-40
Renewable diesel from FOG
4
4
Sugarcane ethanol
-37
-37
Domestic ethanol from waste ethanol
1
1
Other
-8
-8
Conventional Renewable Fuel
-156
-277
Ethanol from corn
-156
-277
Unlike for the comparison to the No RFS Baseline, the changes in ethanol from corn
shown in Table 3.4-1 through 6 are a function of both changes in total gasoline demand as well
as changes in the consumption of E15 and E85. Table 3.4-7 shows the amount of ethanol that can
be attributed to each. Note that because the only differences between the Volume Scenarios and
the Proposed Volumes are the quantities of biodiesel and renewable diesel supplied, the total
ethanol consumption and the consumption of the various ethanol blends are identical under all
scenarios.
Table 3.4-7: Source of Ethanol Changes in the Volume Scenarios and Proposed Volumes
Relative to the 2025 Baseline (million gallons)
2026
2027
2028
2029
2030
Changes in ethanol consumption
attributable to changes in gasoline demand
-193
-373
-561
-777
-1,011
Changes in ethanol consumption
attributable to changes in El5 and E85
consumption
48
106
143
197
250
Total
-145
-267
-414
-580
-761
112
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Finally, as noted in Chapter 2.2, for some of the factors it may be informative to consider
the impacts of this proposed rule relative to our updated renewable fuel supply projections for
2025. This is particularly of interest for cellulosic biofuel (for which we currently project a
shortfall relative to our projections for 2025 in the Set 1 Rule) and BBD (for which we currently
project a significant over-supply relative to our projections for 2025 in the Set 1 Rule). These
differences are shown in Tables 3.4-8, 9, and 12 in terms of RINs and in Tables 3.4-10, 11, and
13 in physical volumes. The values in these tables reflect the difference between values of: (1)
The tables containing the Low and High Volume Scenarios (Tables 3.1-5 through 8) and the
Proposed Volumes (Tables 3.2-7 and 8), and (2) The tables containing the updated projection of
biofuel supply for 2025 (Tables 2.2-3 and 4).
Table 3.4-8: Volume Changes in the Low Volume Scenario Relative to Updated Projection
of Biofuel Supply for 2025 Baseline (million RINs
2026
2027
2028
2029
2030
Cellulosic Biofuel
108
172
241
314
393
CNG/LNG from biogas
61
126
196
271
351
Ethanol from CKF
47
46
45
43
42
Total Biomass-Based Diesel
229
729
1,229
1,729
2,229
Biodiesel
0
0
0
0
0
Soybean oil
0
0
0
0
0
FOG
0
0
0
0
0
Corn oil
0
0
0
0
0
Canola oil
0
0
0
0
0
Renewable Diesel
253
753
1,253
1,753
2,253
Soybean oil
4
64
124
184
244
FOG
282
722
1,162
1,602
2,042
Corn oil
-23
-23
-23
-23
-23
Canola oil
-11
-11
-11
-11
-11
Jet fuel from FOG
-24
-24
-24
-24
-24
Other Advanced Biofuels
-41
-41
-41
-41
-41
Renewable diesel from FOG
7
7
7
7
7
Sugarcane ethanol
-37
-37
-37
-37
-37
Domestic ethanol from waste ethanol
1
1
1
1
1
Other
-12
-12
-12
-12
-12
Conventional Renewable Fuel
-156
-277
-423
-587
-767
Ethanol from corn
-156
-277
-423
-587
-767
113
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Table 3.4-9: Volume Changes in the High Volume Scenario Relative to Updated Projection
of Biofuel Supply for 2025 Baseline (million RINs
2026
2027
2028
2029
2030
Cellulosic Biofuel
108
172
241
314
393
CNG/LNG from biogas
61
126
196
271
351
Ethanol from CKF
47
46
45
43
42
Total Biomass-Based Diesel
729
1,729
2,729
3,729
4,729
Biodiesel
0
0
0
0
0
Soybean oil
0
0
0
0
0
FOG
0
0
0
0
0
Corn oil
0
0
0
0
0
Canola oil
0
0
0
0
0
Renewable Diesel
753
1,753
2,753
3,753
4,753
Soybean oil
344
744
1,144
1,544
1,944
FOG
282
722
1,162
1,602
2.042
Corn oil
-23
-23
-23
-23
-23
Canola oil
149
309
469
629
789
Jet fuel from FOG
-24
-24
-24
-24
-24
Other Advanced Biofuels
-40
-40
-40
-40
-40
Renewable diesel from FOG
4
4
4
4
4
Sugarcane ethanol
-37
-37
-37
-37
-37
Domestic ethanol from waste ethanol
1
1
1
1
1
Other
-8
-8
-8
-8
-8
Conventional Renewable Fuel
-156
-277
-423
-587
-767
Ethanol from corn
-156
-277
-423
-587
-767
114
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Table 3.4-10: Volume Changes in the Low Volume Scenario Relative to Updated Projection
of Biofuel Supply for 2025 Baseline (million gallons)
2026
2027
2028
2029
2030
Cellulosic Biofuel
108
172
241
314
393
CNG/LNG from biogas
61
126
196
271
351
Ethanol from CKF
47
46
45
43
42
Total Biomass-Based Diesel
328
640
953
1,265
1,578
Biodiesel
0
0
0
0
0
Soybean oil
0
0
0
0
0
FOG
0
0
0
0
0
Corn oil
0
0
0
0
0
Canola oil
0
0
0
0
0
Renewable Diesel
342
654
967
1,309
1,592
Soybean oil
44
81
119
156
194
FOG
294
569
844
1,119
1,394
Corn oil
3
3
3
3
3
Canola oil
1
1
1
1
1
Jet fuel from FOG
-14
-14
-14
-14
-14
Other Advanced Biofuels
-41
-41
-41
-41
-41
Renewable diesel from FOG
7
7
7
7
7
Sugarcane ethanol
-37
-37
-37
-37
-37
Domestic ethanol from waste ethanol
1
1
1
1
1
Other
-12
-12
-12
-12
-12
Conventional Renewable Fuel
-156
-277
-423
-587
-767
Ethanol from corn
-156
-277
-423
-587
-767
115
-------
Table 3.4-11: Volume Changes in the High Volume Scenario Relative to Updated
Projection of Biofuel Supply for 2025 Baseline (million gal
ons)
2026
2027
2028
2029
2030
Cellulosic Biofuel
108
172
241
314
393
CNG/LNG from biogas
61
126
196
271
351
Ethanol from CKF
47
46
45
43
42
Total Biomass-Based Diesel
640
1,265
1,890
2,515
3,140
Biodiesel
0
0
0
0
0
Soybean oil
0
0
0
0
0
FOG
0
0
0
0
0
Corn oil
0
0
0
0
0
Canola oil
0
0
0
0
0
Renewable Diesel
654
1,309
1,904
2,529
3,154
Soybean oil
256
506
756
1,006
1,256
FOG
294
569
844
1,119
1,394
Corn oil
3
3
3
3
3
Canola oil
101
201
301
401
501
Jet fuel from FOG
-14
-14
-14
-14
-14
Other Advanced Biofuels
-40
-40
-40
-40
-40
Renewable diesel from FOG
4
4
4
4
4
Sugarcane ethanol
-37
-37
-37
-37
-37
Domestic ethanol from waste ethanol
1
1
1
1
1
Other
-8
-8
-8
-8
-8
Conventional Renewable Fuel
-156
-277
-423
-587
-767
Ethanol from corn
-156
-277
-423
-587
-767
-------
Table 3.4-12: Volume Changes in the Proposed Volumes Relative to Updated Projection of
Biofuel Supply for 2025 Baseline (million
RINs)
2026
2027
Cellulosic Biofuel
108
172
CNG/LNG from biogas
61
126
Ethanol from CKF
47
46
Total Biomass-Based Diesel
509
1,009
Biodiesel
-550
-530
Soybean oil
-251
-231
FOG
-130
-130
Corn oil
125
125
Canola oil
-294
-294
Renewable Diesel
1,082
1,562
Soybean oil
870
1,270
FOG
-868
-788
Corn oil
621
621
Canola oil
459
459
Jet fuel from FOG
-24
-24
Other Advanced Biofuels
-41
-41
Renewable diesel from FOG
7
7
Sugarcane ethanol
-37
-37
Domestic ethanol from waste ethanol
1
1
Other
-12
-12
Conventional Renewable Fuel
-156
-277
Ethanol from corn
-156
-277
117
-------
Table 3.4-13: Volume Changes in the Proposed Volumes Relative to Updated Projection of
2026
2027
Cellulosic Biofuel
108
172
CNG/LNG from biogas
61
126
Ethanol from CKF
47
46
Total Biomass-Based Diesel
1,766
2,095
Biodiesel
16
45
Soybean oil
-57
-28
FOG
23
23
Corn oil
84
84
Canola oil
-35
-35
Renewable Diesel
1,764
2,064
Soybean oil
635
885
FOG
235
285
Corn oil
407
407
Canola oil
487
487
Jet fuel from FOG
-14
-14
Other Advanced Biofuels
-40
-40
Renewable diesel from FOG
4
4
Sugarcane ethanol
-37
-37
Domestic ethanol from waste ethanol
1
1
Other
-8
-8
Conventional Renewable Fuel
-156
-277
Ethanol from corn
-156
-277
118
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Chapter 4: Environmental Impacts
The statute 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 to 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.132
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.133
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 but not limited to whether
biofuel is blended with petroleum fuel and the blend fractions, 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 BBD, including biodiesel and renewable diesel, ethanol, and compressed natural
132 EPA, "Biofuels and the Environment: Third Triennial Report to Congress," EPA/600/R-24/343F, January 2025.
133 See "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.
119
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gas/liquified natural gas (CNG/LNG).134 Chapter 4.1.2 includes an evaluation of the emission
impacts associated with the Proposed Volumes when compared to the No RFS Baseline and
Chapter 4.1.3 describes the likely air quality impacts associated with the Proposed 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.D and Chapter 2 detail the determination of the Proposed
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).135 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.136
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.137
The ABS 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.138 The ABS 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 matter (PM2.5) were relatively unchanged in most areas, with
increases in some areas and decreases in some localized areas.
134 This includes all fuel categories appearing in Tables 3.2-1 and 2 with one exception: "Other Advanced Biofuels -
Other" shows a relatively small volume (52 million RINs delta compared to the No RFS Baseline) and represents an
unknown mix of various fuel types with smaller volumes.
135 EPA, "Clean Air Act Section 21 l(v)(l) Anti-backsliding Study," EPA-420-R-20-008, May 2020.
136 See RFS2 Rule RIA Tables 3.2.7 and 3.2.8 for the emissions impacts associated withbiodiesel and ethanol
volume changes.
137 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.
138 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.
120
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In this rule we rely primarily on the conclusions from the Third Triennial Report to
Congress on Biofuels (RtC3), which summarized available information on air quality impacts
associated with biofuels.139 The RtC3 notes that there is no new evidence that contradicts the
fundamental conclusions of previous reports to Congress.140 The RtC3 concluded that emissions
of NOx, sulfur oxides (SOx), carbon monoxide (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 Corn Ethanol
Corn can be used to produce fuel ethanol, and 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 combustion also leads to increased NOx, SOx, VOCs, PM2.5, and
PM10, and dispersion modeling has shown elevated pollutant concentrations near corn
biorefineries.141 Additional pollutant emissions result from evaporative losses of VOCs during
storage and transport, as well as combustion emissions from commercial marine vessels, rail,
tanker trucks, and pipeline pumps 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 its 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 and
gasoline.142 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.
A study published since the RtC3, focused on papers relevant to California, reviewed
available literature and concluded that while the use of bioethanol (ethanol produced from plants,
139 RtC3 Chapter 8 "Air Quality."
1411 The cutoff date for publication of literature included in the RtC3 was early- to mid-2022.
141 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.lc00698.
142 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 Environmentally-extended Input-Output Model (BEIOM).
121
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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.143
4.1.1.2 Biomass-based diesel
For the purposes of this analysis, biomass-based diesel (BBD) includes biodiesel and
renewable diesel. Although BBD is sourced from a variety of feedstocks, domestic soybean oil
and domestic biogenic waste fats, oils, grease (FOG) make up greater than 80% of the proposed
BBD proposed 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 biomass-based diesel production.144 The RtC3
concluded that impacts of biodiesel on end use emissions of criteria pollutants and precursors are
insignificant compared to petroleum diesel for heavy-duty diesel engines from model years 2007
and forward.
The RtC3 also included a comparison of air quality impacts from soy biodiesel and
petroleum diesel.145 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 aforementioned post-RtC3, California-based study 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.146 Recent dispersion modeling has shown
elevated pollutant concentrations near soybean biorefineries.147
143 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.Ill 1/gcbb. 13101.
144 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.
145 See RtC3 Chapter 8.5 "Comparison with Petroleum" for more details on results. The models run were the
GREET model and BEIOM.
146 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. Ill 1/gcbb. 13101.
147 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.lc00698.
122
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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. Similar to BBD made from FOG, biogas produced
at landfills, municipal wastewater treatment facilities, agricultural waste digesters, and separated
municipal solid waste digesters, we currently assume for the purposes of the RFS program that
biogas 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.148
4.1.2 Emission Impacts of Proposed 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 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 Proposed Volumes.
As discussed in Preamble Section III.D, there are several baselines to which we can
compare the Proposed Volumes and determine the air quality impacts of this rule. The difference
between the Proposed Volumes and the No RFS Baseline, representing the use of biofuels in a
scenario where the RFS program did not continue to exist, 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 Volume Scenarios and
Proposed 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 a proposed
increase in production volumes, relative to a No RFS Baseline, due to this rule.149 These biofuels
include conventional corn ethanol (D6), biomass-based diesel (D4), including biodiesel and
148 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.Ill 1/gcbb. 13101.
149 Biofuel volume production impacts relative to the No RFS Baseline are presented in Tables 3.3-1 through 4.
123
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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 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 at biorefineries that can impact air quality.150 We have estimated
emissions of selected HAPs, or air toxics, from the production of biofuels where possible. The
air toxics chosen were those determined to be risk drivers in the 2020 AirToxScreen and could
reasonably be emitted during the refining of biofuel feedstocks.151 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.152 Another option for a quantitative
evaluation of the production 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.153 The GREET model allows for the evaluation of production emissions
from all biofuels impacted by this rule; however, only a limited number of CAP emission rates,
and no HAP emission rates, are available from fuel production in GREET, and GREET cannot
project market-mediated CAP 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 each facility produced. The most
recent version, the 2022 EMP, is based on the emissions in the 2020 National Emissions
Inventory and contains both CAP and HAP annual emissions reported to state and regional air
agencies, EPA, and Federal Land Management agencies by individual biorefining facilities.154 In
this analysis, we chose to use the EMP as the preferred data source to determine biofuel
1511 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.
151 2020 AirToxScreen Risk Drivers. https://www.epa.gov/svstem/files/documents/2024-08/202Q-airtoxscreen-risk-
drivers.pdf
152 GCIMS, "GCAM: Global Change Analysis Model." https://gcims.pnnl.gov/modeling/gcam-global-change-
analvsis-model
153 Wang, Michael, Elgowainy, Amgad, Lee, Uisung, Baek, Kwang H., Balchandani, Sweta, Benavides, Pahola T„
Burnham, Andrew, Cai, Hao, Chen, Peter, Gan, Yu, Gracida-Alvarez, Ulises R., Hawkins, Troy R., Huang, Tai-
Yuan, Iyer, RakeshK., Kar, Saurajyoti, Kelly, Jarod C., Kim, Taemin, Kolodziej, Christopher, Lee, Kyuha, Liu,
Xinyu, Lu, Zifeng, Masum, Farhad, Morales, Michele, Ng, Clarence, Ou, Longwen, Poddar, Tuliin, Reddi, Krishna,
Shukla, Siddharth, Singh, Udayan, Sun, Lili, Sun, Pingping, Sykora, Tom, Vyawahare, Pradeep, and Zhang, Jingyi.
"Greenhouse gases. Regulated Emissions, and Energy use in Technologies Model ® (2023 Excel)." Computer
software. October 09, 2023. https://doi.Org/10.11578/GREET-Excel-2023/dc.20230907.l.
154 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.
124
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production emissions.155 However, as described 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.156
4.1.2.1.1 Corn Ethanol and Biomass-based Diesel
To estimate the emissions impacts of fuel production from the Proposed Volumes for
corn ethanol and biomass-based diesel, 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 the EPA Moderated Transaction System (EMTS) RFS RIN generation records
specifying the fuel type, number of RINs generated, and total volume of fuel produced.157 These
facilities were then matched to their reported 2022 emissions inventory in the 2022 Emission
Modeling Platform (EMP) version 1.1 through the Emissions Information Systems
(EIS) 158-159-160
As shown in Table 4.1.2.1.1-1, most ethanol biorefineries, but only some biodiesel and
renewable diesel refineries, reported 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.
155 EPA, "Emissions Modeling Platforms." https://www.epa.gov/air-emissions-modeling/emissions-modeling-
platforms.
156 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.
157 EPA, "EMTS: RFS RIN Generation Report." https://www.epa.gov/fuels-registration-reporting-and-compliance-
help/emts-rfs-rin-generation-report.
158 EPA, "2022vl Emissions Modeling Platform." https://www.epa.gov/air-emissions-modeling/2022vl-emissions-
modeling-platform.
159 EPA, "Emissions Inventory System (EIS) Gateway." https://www.epa.gov/air-emissions-inventories/emissions-
inventorv-svstem-eis-gatewav.
1611 EPA, "Technical Support Document (TSD): Preparation of Emissions Inventories for the 2022vl North
American Emissions Modeling Platform," EPA-454/B-25-001, May 2025.
https://www.epa.gov/svstem/files/documents/2024-10/2021 emismod tsd october2024.pdf.
125
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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 2022 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.
Table 4.1.2.1.1-2: Pollutant Emission Factors From Ethanol, Biodiesel, and Renewable
Diesel Production (tons/million RI
Ns)
Pollutant
Ethanol
Biodiesel
Renewable Diesel
CO
0.835
0.398
0.395
nh3
0.082
0.008
0.012
NOx
1.090
0.606
0.203
PMio
0.618
0.247
0.073
PM2.5
0.498
0.162
0.072
S02
0.919
1.943
0.055
voc
1.366
2.693
0.605
1,3-Butadiene
9.99 x 10"6
-
1.17 x 10"6
Acetaldehyde
0.07143
0.00187
0.00040
Acrolein
0.01512
0.00002
0.00003
Benzene
0.00112
0.00081
0.00676
Formaldehyde
0.01026
0.00056
0.00363
Naphthalene
7.79 x 10"5
5.25 x 10"6
0.00433
The emission factors were then applied to the additional fuel volumes for ethanol,
biodiesel, and renewable diesel estimated from the Low and High Volume Scenarios as well as
the Proposed Volumes as compared to the No RFS Baseline for the years 2026-2030. The
emissions impacts resulting from the production of these additional biofuel volumes are
presented in Tables 4.1.2.1.1-3 through 8.
126
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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 Biofuels for the 2026-2030 Low Volume Scenario Relative
to the No RFS Baseline
Volume
Difference to
No RFS
Year
(million RINs)
CO
NH3
NOx
PM10
PM2.5
SO2
voc
Ethanol Production Emissions (tons)
2026
212
177
17
231
131
106
195
290
2027
228
190
19
249
141
113
210
311
2028
238
199
20
260
147
118
219
325
2029
252
210
21
275
156
125
232
344
2030
266
222
22
290
164
132
245
363
Biodiesel Production Emissions
(tons)
2026
2,266
901
19
1,374
559
367
4,402
6,101
2027
2,282
907
19
1,384
563
369
4,434
6,144
2028
2,272
903
19
1,378
561
368
4,414
6,118
2029
2,260
899
19
1,371
558
366
4,391
6,085
2030
2,264
900
19
1,373
559
366
4,399
6,096
Renewable Diesel
Production Emissions (tons)
2026
2,994
1,182
35
607
219
214
166
1,812
2027
3,323
1,312
38
674
243
238
184
2,011
2028
3,714
1,466
43
753
272
266
206
2,248
2029
4,041
1,595
47
819
296
289
224
2,446
2030
4,399
1,736
51
892
322
315
244
2,662
127
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Table 4.1.2.1.1-4: HAP Emissions Impact Estimates for the Production of Biofuels for the
2026-2030 Low Volume Scenario Relative to the No RFS Baseline"
Year
Volume
Difference to
No RFS
(million RINs)
1,3-Butadiene
Acetaldehyde
Acrolein
Benzene
4>
•a
J3
2
S
©
to
Naphthalene
Ethanol Production Emissions (tons)
2026
212
0
15
3
0
2
0
2027
228
0
16
3
0
2
0
2028
238
0
17
4
0
2
0
2029
252
0
18
4
0
3
0
2030
266
0
19
4
0
3
0
Biodiesel Production Emissions (tons)
2026
2,266
-
4
0
2
1
0
2027
2,282
-
4
0
2
1
0
2028
2,272
-
4
0
2
1
0
2029
2,260
-
4
0
2
1
0
2030
2,264
-
4
0
2
1
0
Renewable Diesel Proc
uction Emissions (tons)
2026
2,994
0
1
0
20
11
13
2027
3,323
0
1
0
22
12
14
2028
3,714
0
1
0
25
13
16
2029
4,041
0
2
0
27
15
17
2030
4,399
0
2
0
30
16
19
' An emissions estimate of zero indicates the production emissions to be less than 0.45 tons/year
128
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Table 4.1.2.1.1-5: Emission Impact Estimates of CO, NH3, NOx, PM10, PM2.5, SO2, and
VOCs From the Production of Biofuels for the 2026-2030 High Volume Scenario Relative
to the No RFS Baseline
Volume
Difference to
No RFS
Year
(million RINs)
CO
NH3
NOx
PM10
PM2.5
SO2
voc
Ethanol Production Emissions (tons)
2026
212
177
17
231
131
106
195
290
2027
228
190
19
249
141
113
210
311
2028
238
199
20
260
147
118
219
325
2029
252
210
21
275
156
125
232
344
2030
266
222
22
290
164
132
245
363
Biodiesel Production Emissions
[tons)
2026
2,266
901
19
1,374
559
367
4,402
6,101
2027
2,282
907
19
1,384
563
369
4,434
6,144
2028
2,272
903
19
1,378
561
368
4,414
6,118
2029
2,260
899
19
1,371
558
366
4,391
6,085
2030
2,264
900
19
1,373
559
366
4,399
6,096
Renewable Diesel
Production Emissions (tons)
2026
3,494
1,379
40
708
256
250
194
2,114
2027
4,323
1,706
50
876
316
309
240
2,616
2028
5,214
2,058
60
1,057
381
373
289
3,155
2029
6,041
2,384
70
1,225
442
432
335
3,656
2030
6,899
2,723
80
1,399
505
494
382
4,175
129
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Table 4.1.2.1.1-6: HAP Emissions Impact Estimates for the Production of Biofuels for the
Year
Volume
Difference to
No RFS
(million RINs)
1,3-Butadiene
Acetaldehyde
Acrolein
Benzene
4>
•a
J3
2
S
0
to
Naphthalene
Ethanol Production Emissions (tons)
2026
212
0
15
3
0
2
0
2027
228
0
16
3
0
2
0
2028
238
0
17
4
0
2
0
2029
252
0
18
4
0
3
0
2030
266
0
19
4
0
3
0
Biodiesel Production Emissions (tons)
2026
2,266
-
4
0
2
1
0
2027
2,282
-
4
0
2
1
0
2028
2,272
-
4
0
2
1
0
2029
2,260
-
4
0
2
1
0
2030
2,264
-
4
0
2
1
0
Renewable Diesel Proc
uction Emissions (tons)
2026
3,494
0
1
0
24
13
15
2027
4,323
0
2
0
29
16
19
2028
5,214
0
2
0
35
19
23
2029
6,041
0
2
0
41
22
26
2030
6,899
0
3
0
47
25
30
' An emissions estimate of zero indicates the production emissions to be less than 0.45 tons/year.
Table 4.1.2.1.1-7: Emission Impact Estimates of CO, NH3, NOx, PM10, PM2.5, SO2, and
VOCs From the Production of Biofuels for the Proposed Volumes Relative to the No RFS
Baseline
Year
Volume
Difference to
No RFS
(million RINs)
CO
NH3
NOx
PM10
PM2.5
SO2
voc
Ethanol Production Emissions (tons)
2026
212
177
17
231
131
106
195
290
2027
228
190
19
249
141
113
210
311
Biodiesel Production Emissions
(tons)
2026
1,716
682
15
1,041
423
278
3,334
4,620
2027
1,752
697
15
1,062
432
284
3,404
4,717
Renewable Diesel
Production Emissions (tons)
2026
3,823
1,509
44
775
280
274
212
2,314
2027
4,132
1,631
48
838
302
296
229
2,501
130
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Table 4.1.2.1.1-8: HAP Emissions Impact Estimates From the Production of Biofuels for
the Proposed Volumes Relative to the No RFS Baseline3
Year
Volume
Difference to
No RFS
(million RINs)
1,3-Butadiene
Acetaldehyde
Acrolein
Benzene
4>
•a
J3
2
E
©
to
Naphthalene
Ethanol Production Emissions (tons)
2026
212
0
15
3
0
2
0
2027
228
0
16
3
0
2
0
Biodiesel Production Emissions (tons)
2026
1,716
-
3
0
1
1
0
2027
1,752
-
3
0
1
1
0
Renewable Diesel Proc
uction Emissions (tons)
2026
3,823
0
2
0
26
14
17
2027
4,132
0
2
0
28
15
18
a 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
domestic emissions. For example, in 2022, approximately 0.098% of corn ethanol, 13% of
biodiesel, and 21% of renewable diesel RINs were issued to importers or foreign producers.161
4.1.2.1.2 Renewable CNG/LNG from Biogas
Renewable compressed natural gas (CNG) and liquefied natural gas (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 a 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) 2023revl
161 EPA, "RINs Generated Transactions." https://www.epa.gov/fuels-registration-reporting-and-compliance-
help/rins-generated-transactions.
131
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model for CO, NOx, PMio. PM2.5, SO2, and VOCs.162 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.
Table 4.1.2.1.2-1: GREET Pollutant Emission Factors From the Production of Renewable
CNG and
C1N
fG
LNG
Animal
Food
Animal
Landfill
Wastewater
Waste
Waste
Landfill
Wastewater
Waste
Pollutant
Gas
Treatment
Digestion
Digestion
Gas
Treatment
Digestion
VOC
1.0934
0.6654
0.6654
1.1278
1.4576
1.0674
1.0674
CO
3.8889
2.3666
2.3666
8.8899
5.1842
3.7964
3.7964
NOx
6.8882
4.1917
4.1917
8.6340
9.1824
6.7243
6.7243
PM10
0.9967
0.6065
0.6065
0.8545
1.3287
0.9730
0.9730
PM2.5
0.5630
0.3426
0.3426
0.4132
0.7505
0.5496
0.5496
S02
5.7654
3.5084
3.5084
3.6803
7.6856
5.6282
5.6282
While biogas CNG and LNG are considered a single fuel category in this rule, pollutant
emission rates differ dependent 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 2023 was used to determine a weighted emissions factor to apply
to 2026-2030. 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. Comparing the 2026-2030 CNG/LNG from biogas proposed fuel volumes to
the 2025 Baseline volumes, there is projected to be a reduction in renewable CNG/LNG
production from biogas in future years as compared to current production (see Preamble Section
3). We assume in this analysis that this reduction will equally impact current biogas feedstocks
and fuel products.
The breakdown of biogas feedstock sources was determined using the 2023 RIN
generation feedstock summary report and presented in Tables 4.1.2.1.2-2 and 3.163 The number
of domestic facilities for each feedstock type was obtained through EPA EMTS records and
162 Wang, Michael, Elgowainy, Amgad, Lee, Uisung, Baek, Kwang H., Balchandani, Sweta, Benavides, Pahola T„
Burnham, Andrew, Cai, Hao, Chen, Peter, Gan, Yu, Gracida-Alvarez, Ulises R., Hawkins, Troy R., Huang, Tai-
Yuan, Iyer, RakeshK., Kar, Saurajyoti, Kelly, Jarod C., Kim, Taemin, Kolodziej, Christopher, Lee, Kyuha, Liu,
Xinyu, Lu, Zifeng, Masum, Farhad, Morales, Michele, Ng, Clarence, Ou, Longwen, Poddar, Tuliin, Reddi, Krishna,
Shukla, Siddharth, Singh Udayan, Sun, Lili, Sun, Pingping, Sykora, Tom, Vyawahare, Pradeep, and Zhang, Jingyi.
"Greenhouse gases. Regulated Emissions, and Energy use in Technologies Model ® (2023 Excel)." Computer
software. October 09, 2023. https://doi.Org/10.11578/GREET-Excel-2023/dc.20230907.l.
163 EPA, "RINs Generated Transactions." https://www.epa.gov/fuels-registration-reporting-and-compliance-
help/rins-generated-transactions.
132
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excludes facilities that produced imported renewable CNG/LNG.164 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 2023, less than 1% of
renewable CNG generating RINs was imported. Approximately 43% of renewable LNG RINs
were generated by importers representing about 5% of the total CNG/LNG biogas RINs.
Table 4.1.2.1.2-2: RINs Generated in 2023 From the Production of Renewable CNG From
Biogas
Number of
Domestic
million
% of CNG
Facility Type
Facilities
RINs
RINs
Landfill
96
484
70%
Animal Waste Digester
118
182
26%
Wastewater Treatment or Food Waste Digester
21
21
3%
Total
235
688
Table 4.1.2.1.2-3: RINs Generated in 2023 From the Production of Renewable LNG From
Number of
Domestic
million
% of LNG
Facility Type
Facilities
RINs
RINs
Landfill
-
85
99.4%
Wastewater Treatment
-
0.5
0.6%
Animal Waste Digester
-
Total
20
85.5
Applying the 2023 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 Proposed Volumes and Volume Scenarios and
the No RFS Baseline are presented in Table 4.1.2.1.2-5.165
164 The EMTS reports contain CBI regarding RINs generated at individual biogas facilities. These data were used in
this analysis; however, we have aggregated some facility types in Tables 4.1.2.1.2-2 and 3 to protect this
information.
165 The renewable CNG/LNG from biogas volumes are identical in the both the Low and High Volume Scenarios as
well as the Proposed Volumes. Therefore, the emission impacts are also identical and presented here as a single
result.
133
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Table 4.1.2.1.2-4: Pollutant Emissions Factors From Production of Biogas Renewable CNG
and LNG
Weighted Emission Factors
(g/mmBtu)
Biogas Production
Total
Emissions Factor
Pollutant
CNG
LNG
Biogas
(tons/million RIN)
CO
3.45
5.18
3.64
0.309
NOx
6.10
9.17
6.44
0.546
PMio
0.88
1.33
0.93
0.079
PM2.5
0.50
0.75
0.53
0.045
S02
5.10
7.67
5.38
0.457
voc
0.97
1.46
1.02
0.087
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
715
221
391
56
32
327
62
2027
682
211
373
54
30
312
59
2028
646
200
353
51
29
295
56
2029
609
188
333
48
27
278
53
2030
570
176
311
45
25
260
49
We also acknowledge that biogas is generated from landfill and wastewater treatment
facility waste products, and the typical treatment of these waste products also result 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.
4.1.2.1.3 Comparison of Emissions from the Production of Renewable Fuels to
Petroleum and Fossil Fuels
We compared the emission rates of criteria air pollutants from the production of
renewable fuels, as determined in this analysis, to fossil fuels. 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.166 As
166 Wang, Michael, Elgowainy, Amgad, Lee, Uisung, Baek, Kwang H., Balchandani, Sweta, Benavides, Pahola T.,
Burnham, Andrew, Cai, Hao, Chen, Peter, Gan, Yu, Gracida-Alvarez, Ulises R., Hawkins, Troy R., Huang, Tai-
Yuan, Iyer, RakeshK., Kar, Saurajyoti, Kelly, Jarod C., Kim, Taemin, Kolodziej, Christopher, Lee, Kyuha, Liu,
134
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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 2.167
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, as biogas is the waste product of other industrial processes and onsite fueling of
renewable CNG/LNG was assumed.
Table 4.1.2.1.3-1: Comparison of Emission Rates From the Production of Corn Ethanol,
Gasoline Blendstock, I
»iodiesel, Renewab
e Diesel, and Petroleum Diesel (g/mml
Pollutant
Corn
Ethanol
Gasoline
Blendstock (E0)
Biodiesel
Renewable
Diesel
Petroleum
Diesel
CO
9.93
2.38
4.52
4.95
1.52
NOx
12.96
3.64
6.90
2.54
2.25
PMio
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
Xinyu, Lu, Zifeng, Masum, Farhad, Morales, Michele, Ng, Clarence, Ou, Longwen, Poddar, Tuliin, Reddi, Krishna,
Shukla, Siddharth, Singh, Udayan, Sun, Lili, Sun, Pingping, Sykora, Tom, Vyawahare, Pradeep, and Zhang, Jingyi.
"Greenhouse gases. Regulated Emissions, and Energy use in Technologies Model ® (2023 Excel)." Computer
software. October 09, 2023. https://doi.Org/10.11578/GREET-Excel-2023/dc.20230907.l.
167 The GREET model determines emission rates for only certain pollutants, limiting our analysis to those presented
in this section.
135
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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
3.45
39.63
5.18
43.84
NOx
6.10
47.31
9.17
50.14
PM10
0.88
0.70
1.33
0.80
PM2.5
0.50
0.55
0.75
0.76
S02
5.10
12.92
7.67
12.40
voc
0.97
12.09
1.46
12.58
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 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. However, emission rates of other CAPs 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 Tables 4.1.2.1.1-3 and 4 are 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, and the production of those fuels would also produce emissions. As
discussed at the beginning of this section, we have compared the emissions resulting from the
potential additional production of petroleum and fossil fuels in a No RFS Baseline scenario to
the production of biofuels from this rule in Table 4.1.2.1.3-3 assuming the production of those
fuels will be reduced by the equivalent energy volume. We determined the equivalent energy
volume of gasoline, diesel, and fossil CNG/LNG to the proposed renewable fuel volume
differences and, using the emission rates in Tables 4.1.2.1.3-1 and 2, the emissions resulting
from the production of those volumes of petroleum and fossil fuel. The net emissions presented
are the difference between emissions from the production of the biofuel and the corresponding
petroleum or fossil-based fuel.
136
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Table 4.1.2.1.3-3: Net Emissions Impacts From the Production of Biofuels Relative to the
No RFS Baseline for Low and High Volume Scenarios Accounting for the Potential
Reduction in Petroleum and Fossil Fuel Production
Year
Fuel
Volume
Difference to
No RFS
(million RINs)
Net Pollutant Emissions (tons)
CO
NOx
PMio
pm25
so2
VOC
2026
Ethanol
212
135
166
114
91
173
250
Biodiesel
2,266
598
927
451
273
4,247
5,773
Renewable Diesel (Low
Volume Scenario)
2,994
818
71
90
102
-20
1,419
Renewable Diesel (High
Volume Scenario)
3,494
955
83
105
120
-23
1,656
Biogas CNG/LNG
715
-2,212
-2,500
13
-3
-454
-675
2027
Ethanol
228
145
179
122
97
186
269
Biodiesel
2,282
602
933
454
275
4277
5,814
Renewable Diesel (Low
Volume Scenario)
3,323
908
79
99
114
-22
1,575
Renewable Diesel (High
Volume Scenario)
4,323
1,181
103
129
148
-29
2,049
Biogas CNG/LNG
682
-2,110
-2,384
13
-3
-433
-644
2028
Ethanol
238
151
187
128
102
194
281
Biodiesel
2,272
599
929
452
274
4,259
5,789
Renewable Diesel (Low
Volume Scenario)
3,714
1,015
88
111
127
-25
1,760
Renewable Diesel (High
Volume Scenario)
5,214
1,425
124
156
178
-35
2,471
Biogas CNG/LNG
646
-1,999
-2,258
12
-3
-410
-610
2029
Ethanol
252
160
198
135
108
206
297
Biodiesel
2,260
596
924
450
273
4,236
5,758
Renewable Diesel (Low
Volume Scenario)
4,041
1,104
96
121
138
-27
1,915
Renewable Diesel (High
Volume Scenario)
6,041
1,651
144
181
207
-40
2,863
Biogas CNG/LNG
609
-1,884
-2,129
11
-3
-387
-575
2030
Ethanol
266
169
209
143
114
217
314
Biodiesel
2,264
597
926
451
273
4,244
5,768
Renewable Diesel (Low
Volume Scenario)
4,399
1,202
105
132
151
-29
2,085
Renewable Diesel (High
Volume Scenario)
6,899
1,885
164
206
236
-46
3,270
Biogas CNG/LNG
570
-1,764
-1,993
10
-3
-362
-538
137
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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 Proposed Volumes Accounting for the Potential Reduction in
Petroleum and Fossil Fuel Production
Volume
Net Pollutant Emissions (tons)
Difference to
No RFS
Year
Fuel
(million RINs)
CO
NOx
PM10
pm25
SO2
VOC
Ethanol
212
135
166
114
91
173
250
2026
Biodiesel
1,716
453
702
341
207
3216
4372
Renewable Diesel
3,823
1,045
91
114
131
-26
1,812
Biogas CNG/LNG
715
-2,212
-2,500
13
-3
-454
-675
Ethanol
228
145
179
122
97
186
269
2027
Biodiesel
1,752
462
717
349
211
3284
4464
Renewable Diesel
4,132
1,129
98
124
141
-28
1,959
Biogas CNG/LNG
682
-2,110
-2,384
13
-3
-433
-644
As seen in Tables 4.1.2.1.3-4 and 4, our analysis estimates the production of ethanol,
biodiesel, and renewable diesel due to both the Low and High Volume Scenarios as well as the
Proposed Volumes would result in additional emissions of CO, NOx, PMio, PM2.5, and VOCs
even after 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 the proposed volumes of renewable diesel reduces
SO2 emissions if petroleum-based diesel production is reduced by an equivalent amount. We also
estimate the proposed volumes of renewable CNG/LNG would reduce emissions of CO, NOx,
PM2.5, SO2, and VOCs if the production of fossil CNG/LNG is reduced by the same volume, but
additional PM10 emissions would occur. In total across all biofuels, this results in a reduction in
CO and NOx from the large reductions from renewable CNG/LNG, and an increase in PM2.5,
PM10, SO2, and VOCs, mostly from the increases from biodiesel.
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.
As ethanol is blended with gasoline before use 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), and then sent 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 contribution was to VOCs due to
evaporation.168
168 Set 1 Rule RIA, Chapter 4.1.1.
138
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4.1.2.3
Emissions from the End Use of Biofuels
End-use emissions are generated when biofuels are used in vehicles and include tailpipe
exhaust emissions from the combustion of the fuels as well as non-tailpipe emissions generated
by evaporation from dispensing, leakage, permeation, and venting. As ethanol differs in chemical
composition from gasoline, and biodiesel and renewable diesel differ from petroleum-based
diesel, tailpipe and non-tailpipe emissions from these fuels may also differ.
Renewable CNG and LNG are predominantly methane and not distinct chemically from
fossil CNG and LNG. Therefore, end-use emissions of renewable CNG/LNG fuels are expected
to be similar to vehicles using fossil CNG/LNG.
4.1.2.3.1 Ethanol
After distribution of ethanol-gasoline fuel blends to the retail outlet stations, end use at
the vehicle occurs. Emissions at this step include evaporative losses during fueling, permeation,
leaking, and diurnal tank venting, as well as exhaust emissions from combustion during vehicle
operation. 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 certain ethanol blends on emissions from vehicles meeting Tier 2 and Tier 3
standards.169 170 171 172 173 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 proposal are
relatively small increases in ethanol used as El5 and E85. These small increases in El5 and E85
use, as discussed in Chapter 6, are not expected to have a significant impact on overall vehicle
evaporative and exhaust emissions.
169 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.
1711 EPA, "EPAct/V2/E-89: Assessing the Effect of Five Gasoline Properties onExhaust 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.
171 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/Q5/CRC-E-94-3 Final-Report 2018-06-26.pdf.
172 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.
173 Karavalakis, Georgios, Thomas Durbin, Tianbo Tang. "Comparison of Exhaust Emissions Between E10 CaRFG
and Splash Blended E15." June 2022. https://ww2.arb.ca.gov/sites/default/files/2022-07/E15 Final Report 7-14-
22 O.pdf.
139
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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
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 4.1.2.3.2-1.174 The biodiesel effects implemented in MOVES were
obtained from an analysis conducted as part of the 2010 RFS2 Rule.175 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 very high cetane index and very low aromatics and sulfur content in comparison to
petroleum diesel fuel but is chemically analogous to petroleum diesel blendstocks. Studies
indicate no impact, and in some cases reductions, of regulated pollutant and toxic emissions from
vehicles operating on renewable diesel as compared to petroleum diesel 176-177-178-179-180
174 EPA, "Fuel Effects on Exhaust Emissions from Onroad Vehicles in MOVES3," EPA-420-R-20-016, November
2020.
175 RFS2 Rule RIA Appendix A.
176 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.
177 Coordinating Research Council, "Biodiesel and Renewable Diesel Characterization and Testing in Modern LD
Diesel Passenger Cars and Trucks," Project CRC AVFL-17b, November 2014.
178 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.
179 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.
1811 California EPA, "Staff Report - Multimedia Evaluation of Renewable Diesel," May 2015.
https://ww2.arb.ca.gov/sites/default/files/2018-08/Renewable Diesel Multimedia Evaluation 5-21-15.pdf.
140
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Therefore, we do not expect an emissions impact from the end use of renewable diesel from this
proposal.
4.1.3 Air Quality Impacts of Proposed Volumes
The geographic distribution of emissions impacts due to the Proposed 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 proposed 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 Proposed
Volumes as compared to the No RFS Baseline.
Comparing the Proposed 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, but we would expect increases in emissions at production
facilities, due to the Proposed Volumes, that 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.181 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, renewable CNG/LNG, or corn ethanol, and
negligible impacts from vehicles running on fuel with renewable diesel. Overall, we expect the
emission impacts from the Proposed Volumes to be variable in how they affect ambient
concentrations of ozone and PM2.5 in specific locations across the U.S.
The per gallon results of the LCA modeling included in the RtC3 indicate that we would
expect that increased volumes of biofuels would lead to increased emissions and air quality
impacts, however as the biofuels industry continues to mature 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 proposed 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
i8i Further discussion on the potential impacts of the Proposed Volumes on the production of petroleum fuels can be
found in Chapter 6.4.1.
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impact on BBD than what is being proposed 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: First Triennial Report to Congress182 and the Biofuels and the Environment:
Second Triennial Report to Congress.183 These reports are led by EPA's Office of Research and
Development (ORD) in accordance with Section 204 of EISA, which requires that EPA assess
and report to Congress every three years on the current and potential future environmental and
resource conservation impacts associated with increased biofuel production and use.
The first and second Reports to Congress 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 third
report 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.
EPA further assessed how the Set 1 Rule for years 2023-2025 may increase cropland in
the Set 1 Rule RIA and Biological Evaluation,184 the latter of which was completed in
accordance with the Endangered Species Act (ESA) Section 7 consultation process. The findings
and conclusions from these documents, as well as all three Reports to Congress, relied heavily on
studies from the peer reviewed literature in addition to additional analyses completed by the
EPA.
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
come out in 2023-2024 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).
4.2.1 Natural Land Conversion Effects
The aforementioned documents (Biofuels and the Environment: Reports to Congress, Set
1 Rule RIA, and Biological Evaluation) have greatly contributed to EPA's understanding of how
182 EPA, "Biofuels and the Environment: First Triennial Report to Congress," EPA/600/R-10/183F, December 2011.
183 EPA, "Biofuels and the Environment: Second Triennial Report to Congress," EPA/600/R-18/195F, June 2018.
184 EPA, "Biological Evaluation of the Renewable Fuel Standard Set Rule and Addendum," EPA-420-R-23-029,
May 2023.
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agriculture, biofuel production and consumption, and past RFS renewable volume obligations
influenced the conversion of natural lands. 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 crop-based feedstocks used for biofuel production, followed by canola. As such, the
production of these three feedstocks is the main concern when it comes to conversion of natural
lands.185
The RtC3 discusses historical trends from several land cover federal 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.186 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)187 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 forested areas and
2% of wetlands were converted to agriculture.
Similarly, Lark et al. (2020)188 found that 88% of grasslands were the source of new
cropland when looking at a longer timeframe, from 2008-2016. A total of 2.8 million acres of
new cropland (28%) originated from longstanding habitat sites, of which 2.3 million acres, or
81%, were long-term grasslands. They found that, relative to all converted land, 26% of
converted grasslands, 29% of converted wetlands, 44% of converted forest, and 52% of
converted shrublands were previously categorized as long-term sites.
185 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.
186 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.
187 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.
188 Lark, Tyler J., Seth A. Spawn, 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.
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The studies referenced above examined historical land use changes and natural land
conversion patterns that can be attributed to various causes. One potential cause for the observed
land use changes is demand for renewable fuel and production of crop-based feedstocks.
Regarding the potential natural land conversion effects from the RFS program alone, however, it
is 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 proposal, 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.189 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.190 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.
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's RIA and Biological Evaluation discussed the potential for an
associated increase in crop production from the 2023-2025 Set 1 Rule alone. In the Set 1 Rule
189 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.
1911 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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Biological Evaluation, 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 Biological Evaluation.
• Additional analyses supporting the Biological Evaluation 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 and 2024 have come to pass, we can look at BBD supply data from
those years to infer what may have actually happened. For example, in the year 2023 alone,
additional BBD supply came from a significant increase in biodiesel imports. There was very
little increase in domestic feedstock production; instead, feedstock was sourced from increased
FOG imports, canola imports from Canada, and a diversion of soybean oil from other uses. In the
case of FOG as an example, imports have risen gradually since 2014 followed by rapid increase
in more recent years (2022 and 2023) in particular. This rise is likely due to multiple factors,
including a rapid increase in renewable diesel production capacity domestically, greater
incentives from California's LCFS program and other state clean fuels programs for BBD
produced from FOG, the anticipated changes to the federal tax credit in 2025, and biofuel
policies internationally. This and other information and data regarding imports of BBD supply is
discussed in more detail in Chapter 7.
With regard to BBD supply, Chapter 7 also discusses trends in exports. Soybean oil
exports peaked in 2009/2010 and since then exports have generally decreased as the quantity of
soybean oil used for domestic biofuel production has gone up. USDA estimates that in the
2022/2023 agricultural marketing year soybean oil exports decreased by approximately 90%.
Given these significant changes in soybean oil exports, and well as increases in imports as
described above, it is very possible that very minimal to no land use impacts have occurred in
years 2023 and 2024 so far from the final BBD volumes.
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 lands were converted due to the RFS program alone. To date,
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
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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.191 It is currently not possible to know effects at an even smaller scale, such
as the field or 30-meter scale, for example, due to many degrees of freedom leading to
irreducible uncertainty.
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 Triennial Reports to Congress and Set 1 Biological Evaluation. To keep
abreast of the latest science, EPA also completed a literature review of research of articles and
other federal agency assessments published in 2023-2024.
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),192
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 half of 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."
191 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
Biological Evaluation, so EPA was not able to accomplish county-level estimates for this. See Li et al. (2019) and
updated analyses by Madliu Klianna as described in the Set 1 Rule Biological Evaluation for more information.
192 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)193 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 soy and palm oil, 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 published in the year 2023 or 2024 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.194195
Bedrosian et al. (2024)196 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 From This Rule
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. In the process of developing proposed volume requirements for this rule, EPA
completed 5-year analyses for two Volume Scenarios. EPA then completed additional analyses
for the Proposed Volumes for 2026 and 2027. The projected BBD and conventional renewable
fuel volume changes for the Low Volume Scenario, High Volume Scenario, and Proposed
Volumes are shown in Tables 4.2-1 and 2. More detailed information can be found in Chapter 3.
193 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.
194 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.
195 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.
196 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 4.2-1: Total BBD Renewable Fuel Volume Changes Relative to the No RFS Baseline
and 2025 Baseline (million gallons)
Low Volume Scenario - Total Biomass-Based
)iesel Volumes
2026
2027
2028
2029
2030
Relative to the No RFS Baseline
3,379
3,595
3,833
4,030
4,255
Relative to the 2025 Baseline
986
1,298
1,611
1,923
2,236
High Volume Scenario - Total Biomass-Based
Jiesel Volumes
2026
2027
2028
2029
2030
Relative to the No RFS Baseline
3,691
4,220
4,770
5,280
5,818
Relative to the 2025 Baseline
1,298
1,923
2,548
3,173
3,798
Proposed Volumes - Total Biomass-Based Diesel Volumes
2026
2027
2028
2029
2030
Relative to the No RFS Baseline
4,817
5,050
n/a
n/a
n/a
Relative to the 2025 Baseline
2,424
2,753
n/a
n/a
n/a
Table 4.2-2: Conventional Renewable Fuel Volume Changes Relative to the No RFS
Baseline and 2025 Baseline (million gallons)
Low Volume Scenario - Conventional Volumes
2026
2027
2028
2029
2030
Relative to the No RFS Baseline
212
228
238
252
266
Relative to the 2025 Baseline
-158
-279
-425
-589
-769
High Volume Scenario - Conventional Volumes
2026
2027
2028
2029
2030
Relative to the No RFS Baseline
212
228
238
252
266
Relative to the 2025 Baseline
-158
-279
-425
-589
-769
Proposed Volumes - Conventional Volumes
2026
2027
2028
2029
2030
Relative to the No RFS Baseline
212
228
n/a
n/a
n/a
Relative to the 2025 Baseline
-156
-277
n/a
n/a
n/a
Based on the values in Table 4.2-1, for all scenarios we would expect increases in BBD
volumes attributable to this rule. As expected, compared to the Low Volume Scenario, the High
Volume Scenario volume increases would be higher, which could potentially lead to greater land
use effects. Since this proposal would reduce the number of RINs generated for imported
renewable fuel and renewable fuel produced from imported feedstocks, the analyses demonstrate
that we would see relatively high BBD volume increases for 2026 and 2027 years as well, even
higher than the numbers for the High Volume Scenario in those two years. Even with lower
RINs for imported renewable fuel and feedstocks, however, 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 lead to a potential increase in land conversion for agricultural lands to
produce more feedstock (soy and canola, specifically) to meet extra BBD volume demands
generated by this rule. An increase in land conversion for agricultural lands, as a result, 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 different story (Table 4.2-2). For all scenarios, the numbers suggest we would see
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an increase in volumes from this rule relative to the No RFS Baseline. However, compared to the
existing 2025 Baseline as it exists following the Set 1 Rule and previous RFS annual rules, this
rule would not lead to higher convention renewable fuel volumes as seen by the negative values
in the table. As such, with respect to potential increases in agricultural conversion to meet
conventional volume demands from this rule, we would not expect to see any increases because
this rule would not generate additional demand for conventional biofuel.
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.
Figure 4.2-1. Causal chain between RFS standards and impacts on land used to grow crops
Refiner access to renew able fuel
1
RFS standards
Avaiabikty.type; and price of RINs
Reiatwe costs between different renew able fuels
Refiner decisonsabout the mix of biofueitypes and/or RINs
needed to meet the RFS standards
Relative retail price cf fossi-based gasoline
anddiesei versusrenewablefuel
infrastructur eto support d istribut ion, b lend ing,
d ispensing, and con sumption of renew able fuel
Total consumption of renewablefuel in the U.&
Statutory andregulatoryconstrantson renewable
fuels blended hto transportation fueis
Imports and exportsof renewablefuel
Total production of r enewab le fuel in the U.S.
Producton of norxr op- basedfeed stocks
for renewablefuel production
Production of crop-based feedstocks
for renewablefuel production
Size of carryover RIN bank
Other state and federal programs
that require renew able fuels
Federal and state tax mcentivesand grants
Consumer attitudes and preferences
Domestic renewablefuel production capacity
Importsand exportsof crops
Total production of crops
E xtensficatbn vs intensification
Crop production for human
consumption andanimaifeed
Suitability of land for growing crops
Alternative uses for land
Land used to gr ow 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, additional U.S. soybean oil production
could be possible in the future if we crushed more of our soybeans domestically and decreased
exportation of whole soybeans. Furthermore, additional quantities of soybean oil could be made
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available for biofuel production from decreased exports of soybean oil itself. 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 past is prologue, any future
expansion of agriculture from this rule would most likely 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 or
22% of all lands 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 plans to further explore the potential land use change effects from this rule in a
Biological Evaluation document for this rule to be completed in consultation with the Fish and
Wildlife and National Marine Fisheries Services (NMFS). EPA expects to largely use the same
analytical approaches that were used in the Set 1 Biological Evaluation. For the Set 1 Biological
Evaluation, we leveraged econometric analyses available in published literature (Li et al. 2019)
combined with updated data from Dr. Madhu Khanna to estimate the change in corn acres and
total cropland per billion gallons of ethanol production. EPA is currently working to update this
data again with more recent years of data and explore whether the analyses could be modified
and used in combination with other observed trends to estimate the potential change in soybean
acres and total cropland in response to soybean oil production from this rule. EPA anticipates
finishing these analyses before this rule is finalized.
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
water bodies. EPA defines water quality as the condition of water to serve human or ecological
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needs, while USD A defines soil quality as the ability of soil to function, including its capacity to
support plant life.
On the topic of how biofuel production and use may impact soil and water quality, like
the topic of conversion of natural lands this has been discussed in detail in the three Biofuels and
the Environment: Triennial Reports to Congress, in addition to the 2023-2025 Set 1 Rule RIA
and Biological Evaluation. The past effects of the RFS program alone have also been assessed in
more recent years, and in particular are discussed in the RtC3 as well as the Biological
Evaluation for the Set 1 Rule.
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 2023-2024
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
A summary of findings and EPA's understanding of how agriculture, biofuel production,
and the RFS program have historically impacted soil and water quality is included below.
First, it is well understood that soil and water quality effects from biofuels are largely
associated with production of crop-based feedstocks (corn, soybean, canola) rather than waste
fats, oils and greases, or biogas. The conversion of grasslands or other lands to production of
agriculture for biofuel feedstocks adversely affects soil quality, with increases in erosion and the
loss of soil nutrients, soil 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
Mexico and other water bodies. An increase in cropland also typically corresponds with 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)197 simulated greater erosion and loss of
soil carbon and nitrogen from converting low productivity, highly sloped Conservation Reserve
Program grasslands compared to those with higher productivity soils and lower slopes.
197 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. Ill 1/gcbb. 12352.
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The type of feedstock being grown 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.198
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 Mexico.199 200
It is also important to recognize other potential effects from biofuel production and
consumption that may affect to 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,201 depending on the plant species being grown and the type of land being converted.202
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, biogas used that is upgraded to RNG may have localized soil or water
impacts. The associated manure collection and agricultural anaerobic digesters may decrease
pathogen risk in water, but without proper treatment, excess nutrient pollution can also be a
concern.
198 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.
199 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.ihvdrol.2023.130281.
21111 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.
2111 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.
2112 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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Lastly, palm oil production for biodiesel is an established industry in Southeast Asia for
exportation to other countries such as the U.S. and should be considered. There is strong
evidence that expanded palm oil production adversely affects soil and water quality in Southeast
Asia as well as carbon sequestration.
For the purposes of this rulemaking, however, we are most interested in the potential
effects from domestic production of crop-based feedstocks. An increase in cropland acreage for
renewable fuel production and consumption in the U.S. would generally be expected to lead to
more negative soil and water quality impacts. There are many factors that influence cropland
acreage in the U.S., and the RFS program is only one factor. The EPA has also worked in recent
years to evaluate the potential effects of the RFS program specifically on soil and water quality,
and in particular from past RFS volumes.
As described in the RtC3, EPA ran 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 modeling 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 biodiesel and soybean acreage, nor any
effect from crop switching on existing cropland. The report also notes that this finding
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 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 (6.0% and 6.5%); and then conversion to corn/wheat (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, EPA's Biological Evaluation for the Set 1 Rule leveraged the Missouri
River Basin SWAT analysis from the Triennial Report to Congress 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 Biological Evaluation that
localized water quality impacts from the Set 1 Rule were likely to be discountable as defined
under the ES A.
As discussed in Chapter 4.2.1, based on what we know happened in 2023 and 2024, those
estimates from the Set 1 Biological Evaluation likely overestimated the actual effects from the
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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.
Beyond EPA's work in these areas, a study by Lark and coauthors (2022)203 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 explains in the Set 1
Biological Evaluation how the coefficients Lark et al. used for estimating these effects compare
to the coefficients EPA used for its evaluation for the Set 1 Rule. With respect to EPA's findings
from the same years in the RtC3, we find that they are similar to those from Lark et al. (2022),
though lower because we account for other factors like MTBE effects on corn price.
4.3.2 New Literature on Soil and Water Quality Effects
To assess the current state of the science, EPA also completed a literature review of
research of articles related to agriculture and biofuel production soil and water quality effects.
EPA looked for articles published in 2023-2024. EPA found no studies that directly linked
potential soil and water quality effects to the RFS program.
One study by Byers et al. (2024)204 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.
The research 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.
2113 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.
2114 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)205 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)206 estimated that a reduction of 24% of U.S. demand for corn as a renewable fuel
feedstock would sustain land use and nitrogen leaching below 2020 levels through the year 2025.
Further, they found that a 41% reduction would do the same but 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)207
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 eutrophication 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
2115 Zhou, Wei, Mengmeng Li, and Varenyam Achal. "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.
2116 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.
2117 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 From This Rule
As was done in Section 4.2.3 for potential natural land conversion effects, 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 understand potential soil and water quality effects.
EPA completed these analyses for the Volume Scenarios and Proposed Volumes. 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. However, relative to the
2025 Baseline, we would not see additional conventional volumes attributable to this rule. As
such, this rule would not lead to additional demands for conventional fuel as things currently
stand and would likely not contribute to further domestic land use changes that impact soil and
water quality.
Of course, the true impacts on land use change and subsequent soil and water quality
impacts from this rule would also depend on imports and exports of BBD supplies in the coming
years. Decreasing exportation of whole soybeans and crushing more soybeans domestically
would allow for greater U.S. soybean oil production. FOG supplies have been imported at
greater quantities in recent years, and their continued importation, as well as decreased
exportation of whole soybeans, could also provide greater BBD supplies in the domestic market.
If BBD is largely supplied by these changing import and export dynamics, then it could mean
fewer land use impacts may be expected, and minimal soil and water quality effects from this
rule.
It is difficult to say for certain what will occur in the future, and it is still possible that
land use changes could occur from increased BBD volumes attributable to this rule. If so, some
soil and water quality effects would likely occur. Based on results from the EPIC modeling work
done in the RtC3 that was summarized previously, if past is prologue the volume increases from
this rule could continue to contribute to small percentage increases in erosion, nutrient loss, and
soil organic carbon loss.
In considering these potential impacts, is important to consider the baseline nutrients,
sediment, and pesticide runoff from existing land uses. Even forests, which provide the highest
water quality among all land cover types,208 contribute to nutrient loadings in watersheds. As
2118 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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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 from the BBD
volume increases in this rule. 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.
The magnitude of effects 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.
As explained in Section 4.2.3, EPA plans to further explore the potential land use change
effects from this rule in a Biological Evaluation. EPA is currently working with a contractor to
update this econometric data to estimate maximum potential land use changes from the Proposed
Volumes. These analyses will further contribute to our understanding of the potential soil and
water quality effects from crop-based feedstocks.
Soil and water quality effects from other issues beyond agriculture could occur in
connection to this rulemaking. This includes 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.
Lastly, palm oil production in Southeast Asia could lead to soil and water quality
degradation abroad. At this time, however, EPA is unable to evaluate potential effects from this
rule. As described in the RtC3, attribution of international effects to the RFS program remains
challenging due to complex interrelationships among other major drivers of observed change.
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.
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4.4.1 Water and Biofuel Crop Growth
Growth of biofuel crops such as corn and soybeans is the primary use of water in the
process of creating renewable fuels. Although there are several other "fuel crops" used in the
RFS program, these are the two that will be the main focus of our evaluation on water quantity.
4.4.1.1 Corn
Historically, corn has been grown in mostly rain-fed locations such as in Iowa and
Minnesota. Because of this, corn is considered to have a low to modest water footprint currently.
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 into locations where more irrigation is needed to produce this crop.
As discussed in the Set 1 Rule Biological Evaluation, several studies evaluated land use
change as a result of volumes from the Set 1 Rule. More recently analyzed data concluded that
corn acreage growth did not necessarily result in total crop land acreage growth. 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 crops growth 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 are therefore grown in the same regions, which typically receive higher
rainfall.
Projections for biodiesel and renewable diesel volumes suggest an increase in production
in the proposed years analyzed. However, imports of used cooking oil (UCO) have significantly
increased in the past year. Access to UCO supply from China has increased drastically after
European intake was paused in 2023. The majority of the projected increase in renewable fuel
production is expected to be met mostly with this increased supply in UCO. The remainder of
supply would then be meet with 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 stated above, the irrigation of corn, soybeans, and other biofuel crops is the
predominant driver of water quantity impacts. Some studies show land use change over time
coincided with areas experiencing groundwater depletion, 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.209
2119 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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4.4.2
Use of Water in Production Facilities
Production of biofuels requires water use for both the growth of crop feedstocks and the
actual production of fuels at the biofuel facility. With increases in potential volumes in biofuel
production, an increase in need for water can be assumed, not just for crop production but also in
the fuel production process.
Similar to petroleum-based fuels, biofuel production requires the use of water to produce
the 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 feed crops. That said, biofuel facility water use, even with the implementation 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.
In summary, based on the approaches above, there will likely be some increased
irrigation pressure on water resources due to the Proposed Volumes. Specifically, the volume
increases for 2026-2085 compared to the No RFS Baseline that is described in Section 4.2.3 due
to biofuels produced from agricultural feedstocks (especially corn and soybeans) would suggest
the potential for some associated increase in crop production, which in turn would likely increase
irrigation pressure on water resources. The increased volume requirements, especially that of
renewable diesel, could incent greater production of its underlying feedstock (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 to air
quality, wetland and other natural land loss, soil, and water quality, and water quantity. Changes
to any of these environmental end points could subsequently impact ecosystems, defined as a
biological community of interacting organisms and their physical environment. This may include
impacts to 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 three Biofuels and the Environment: Triennial Reports to
Congress. The RtC3, the Set 1 Rule RIA, and Biological Evaluation further evaluate impacts
from the RFS program, of which the latter two examine potential impacts from the Set 1 Rule
specifically. The Biological Evaluation 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
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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.
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's RIA and Biological Evaluation, finalized in 2023, included a
literature review and information examining wildlife impacts, we also reviewed and discuss new
literature from 2023-2024 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
A summary of findings and EPA's current understanding of how agriculture, biofuel
production, and the RFS program historically impacted ecosystems and wildlife habitat is
included below.
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 habitat loss, for example. 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.
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Furthermore, excess nutrients (eutrophication) and sediment in places like the Gulf of
Mexico 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
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. 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 Biological Evaluation. 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 not likely to adversely affect listed species
(NLAA) and their designated critical habitats. The 259-page Biological Evaluation document
details the specific analyses and findings that led to this conclusion. In accordance with the
Endangered Species Act (ESA), EPA submitted this Biological Evaluation 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.
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 and is still not possible at this
time 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 is at the county scale, though such analyses for the Set 1 Rule rendered
limited information210 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.
210 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
Biological Evaluation, 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 Biological Evaluation for more information.
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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
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 of articles to
assess the current state of the science related to agriculture and biofuel production effects on
habitat and species. Since the Set 1 Rule RIA included a literature review and was published in
mid-2023, EPA searched for articles published in 2023-2024. 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).211 In this article, Lark explores how the RFS 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. The article's examination of how land use change from biofuel crops relates to potential
species impacts is in congruence with what EPA has written about in the three Biofuels and the
Environment: Reports to Congress. 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 uphold a lot 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)212 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
211 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.
212 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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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 do to features on small grain fields that mimic the
vegetation structure and phenology of grasslands.
In another study, Crawford and Alexander (2024)213 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 From This Rule
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 Volume Scenarios and Proposed 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 and canola, then this rule could contribute to additional
land use change, declines in soil and water quality, and impacts to wildlife and habitat.
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. However, relative to the
2025 Baseline, we would not see additional conventional volumes attributable to this rule. As
such, this rule would not lead to additional demands for conventional fuel as things currently
stand and would likely not contribute to further domestic land use changes that impact wildlife
and habitat.
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. U.S. capacity for soybean crushing has increased in recent years. Further,
as described in more detail in Chapter 7, FOG supplies have been imported at significantly
greater quantities in recent years. Should this trend continue, this could provide greater BBD
supplies in the domestic market. If BBD is largely supplied by these changing import and export
213 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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dynamics, then it could mean fewer land use impacts may be expected, and minimal wildlife and
ecosystem effects from this rule.
EPA is currently working to update econometric data used to estimate maximum
potential land use changes from the Proposed Volumes. The results from this analysis will be
included in a Biological Evaluation for this rule, in accordance with the ESA Section 7. As
discussed in Chapters 4.2 and 4.3, this analysis will help contribute to our understanding of this
rulemaking's potential impacts to land use conversion to agriculture as well as potential soil and
water quality impacts, which consequently affect wildlife and habitat. Further, as was done for
the Set 1 Rule Biological Evaluation, in the Biological Evaluation for this rule EPA will apply
probabilistic analyses to select available lands for conversion and estimate the overlap between
potential cropland changes and critical habitats or listed species' ranges. The probabilistic
analyses will be repeated 100-500 times to generate an estimated probability of impact.
Impacts to air quality or water quantity from this rulemaking could also affect wildlife
and ecosystems. However, any effects to water quantity and air quality impacts would likely be
highly variable and dependent on what is going on at the local level. 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, considering end use, transport, and production, emission changes are expected to
have variable impacts on ambient concentrations of pollutants 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.214 The United Nations Millennium Ecosystem Assessment215 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 ecosystems loss identified in the Millennium Ecosystem
Assessment, such as climate change, pollution, and land-use change, are expected to be impacted
by the production of renewable fuels generally and may be impacted by the Proposed Volumes
in this rule specifically.
214 EPA, "Ecosystem Services." https://www.epa.gov/eco-researcli/ecosYStem-services.
215 Millennium Ecosystem Assessment, "Ecosystems and Human Well-being: Synthesis," 2005.
https://www.millenniumassessment.org/documents/document.356.aspx.pdf.
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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)
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 ecosystem 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 the well-being to the
environment. Water scarcity and land conversion are two of the most prominent 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 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, NH3
Water Quality and Aquatic
Habitats
• Fertilizer and Pesticide
Runoff
• Sediment Runoff
• Habitat and Associated
Filtration
• Leakage from
Underground Storage
Tanks
• Atmospheric
Deposition
r ,
Human Well-Being
Changes
Monetary Value
Changes
Social Effects from Climate
Change
Social Cost of GHGs
Property Effects
Property Values
Morbidity and Mortality Effects
Health Values
Energy, Transportation, and
Drinking Water Production
Effects
Agricultural Product Value
Recreation Effects
Wildlife Product Value
Wildlife Existence Value
Recreation Value
Hydrology, Water Quantity,
and Flood Risk
• Tilling
• Land Use/Habitat Change
• Irrigation
Wildlife and Habitat
• Pollinating Insects
• Commercial Species
• Species of Public Interest
• Pest Control Species
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 proposal. While the
statute requires that EPA base its determinations, in part, on an analysis of the climate change
impact of renewable fuels, it does not require a specific type of analysis.
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 Volume
Scenarios assessed in this proposal. This section describes the methods for evaluating the GHG
emissions associated with two different categories of biofuels (crop-based fuels and
waste/byproduct-based fuels). Chapter 5.2 presents the results of modeling the Volume Scenarios
relative to the No RFS Baseline. Results are presented in tons of GHG emissions changes.
Chapter 5.3 describes how the analyses of the Volume Scenarios were used to assess the GHG
impacts of the Proposed Volumes. This section also summarizes those impacts in tons of GHG
emissions. Appendix 5-A discusses a sensitivity analysis which provides information on the
sensitivity of the cumulative emissions estimates to uncertainty in model parameters.
5.1 Methodology
In this rule, our methodology for assessing climate change impacts advances the science
of estimating climate impacts of biofuel policies in several key aspects discussed in the sections
below. Our assessment of the climate impacts of the Volume Scenarios and Proposed Volumes
includes: (1) new economic modeling of the combined impact of changes in volumes of fuels
produced from crop-based feedstocks; and (2) new 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, the two categories of fuels and methods noted above, and the scenarios modeled
in our analysis. Chapter 5.1.2 focuses on the methodology of assessing emissions impacts of
volumes of biofuels produced from wastes and byproducts. Chapter 5.1.3 focuses on the
methodology of assessing emissions impacts of volumes of fuels produced from crops.
5.1.1 Overview
Estimating the GHG emissions associated with the production and use of renewable fuels
is an integral component of the Renewable Fuel Standard program. Multiple analyses requiring
assessment of the GHGs associated with biofuels are prescribed in the Clean Air Act (CAA),
including biofuel lifecycle assessments for the purpose of determining qualification of a fuel
under the RFS program,216 and, as required by CAA section 21 l(o)(2)(B)(ii), assessments of
216 "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).
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climate change impacts of setting annual volume standards. These two analyses in particular
serve different purposes under the statute. Thus, while there are many methodological
similarities between the two, there are also important differences. 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).
The Energy Independence and Security Act of 2007 (EISA) required substantial changes
to the existing RFS program; the updated program also included statutorily established volumes
of different categories of renewable fuels through 2022. The changes necessary to implement
EISA's updates were implemented in the RFS2 Rule. In accordance with Executive Order
12866,217 which provides guidance on conducting cost benefit analysis for significant regulatory
actions, EPA developed and applied a methodology for assessing the climate impacts of volumes
established under EISA in the RFS2 Rule.218 EPA did not conduct a quantitative assessment of
the potential climate change impacts of subsequent annual volume standards rules until the 2020-
2022 Final Volume Standards Rule219 in which EPA conducted an illustrative climate impacts
analysis, again under the guidance of E.O. 12866. For continuation of the RFS program after
2022, the 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."
Thus, for the Set 1 Rule, EPA assessed the potential climate change impacts of those volume
standards. We again assess the potential climate impacts of proposed 2026 and 2027 standards
under this rule, as required by the CAA.
The climate change assessment methodology under the RFS2 rule relied on 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 available models and
science available at the time. While our 2010 approach represented a best-in-class approach at
the time of publication, evidence from expert discussions, input from public stakeholders, and
EPA's review of the available literature subsequently laid plain that this approach required
updating. 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 were available which integrated key economic
sectors and global trade. 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 error. 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. By 2022, we had access to
the most recent data on crop yields, trade flows, and other key factors which improved the
217 Executive Order 12866: Regulatory Planning and Review, https://www.federalregister.gov/executive-
order/12866.
218 RFS2 Rule RIA. Chapter 2.7.
219 87 FR 39600 (July 1, 2022).
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accuracy of our estimates of economic activity in that year and onward. All of these factors led to
the conclusion not only that our 2010 methodology was out of date, but that better tools were
available to meet our statutory obligations under the RFS program.
Recognizing that public input on models and methods available would be integral to
incorporating the latest scientific advancements, EPA co-hosted a two-day public workshop with
DOE and USD A, on February 28 and March 1, 2022, on biofuel GHG modeling. At this
workshop, speakers within and outside of the federal government presented on available data,
models, methods and uncertainties related to the assessment of GHG impacts of land-based
biofuels. EPA also opened a public docket for the workshop (86 FR 73757) and requested that
stakeholders submit any input or suggestions they might have regarding the best available
scientific approaches for conducting biofuel GHG modeling under the RFS program, including,
but not limited to, any suggested models, data sources, or interpretive methods. We received 29
public comments with 550 pages of technical input and recommendations in response to this
request. The workshop proceedings and public comments showed that there continued to be
substantial variation in estimates of the climate effects of biofuels, especially for emissions
associated with biofuel-induced land use changes and other market-mediated effects.220 A
general theme that emerged from the workshop process was that, in support of a better
understanding of the climate impacts of biofuels, it would be helpful to compare available
models, identify how and why the modeled estimates differ, and evaluate which models and
estimates align best with available science and data. Recognizing this need, EPA conducted a
model comparison exercise ("MCE") to better understand these scientific questions.
The MCE effort started in May 2022 and culminated in the MCE Technical Report
published in July 2023 along with the Set 1 Rule.221 The goals of the MCE were to advance our
scientific understanding of available models capable of assessing GHG impacts of biofuels and
how differences between these models contributes to varying results. The MCE included five
models:
• The Greenhouse gases, Regulated Emissions, and Energy use in Technologies
Model (GREET)222
• The Global Change Analysis Model (GCAM)223
• The Global Biosphere Management Model (GLOBIOM)224
2211 See, e.g., Daioglou, Vassilis. "Review of Land Use Change Emission Estimates." Workshop on Biofuel
Greenhouse Gas Modeling. March 1, 2022. https://www.epa.gov/svstem/files/documents/2022-03/biofuel-ghg-
model-workshop-luc-emission-estiim-2022-03 -01 .pdf.
221 EPA, "Model Comparison Exercise Technical Document," EPA-420-R-23-017, June 2023.
https://nepis.epa.gov/Exe/ZvPDF.cgi?Dockev=P1017P9B.pdf.
222 Wang, Michael, Elgowainy, Amgad, Lee, Uisung, Baek, Kwang H., Bafana, Adarsh, Benavides, Pahola T„
Burnham, Andrew, Cai, Hao, Cappello, Vincenzo, Chen, Peter, Gan, Yu, Gracida-Alvarez, Ulises R., Hawkins,
Troy R., Iyer, RakeshK., Kelly, Jarod C., Kim, Taemin, Kumar, Shishir, Kwon, Hoyoung, Lee, Kyuha, Liu, Xinyu,
Lu, Zifeng, Masum, Farhad, Ng, Clarence, Ou, Longwen, Reddi, Krishna, Siddique, Nazib, Sun, Pingping,
Vyawahare, Pradeep, Xu, Hui, and Zaimes, George. "Greenhouse gases. Regulated Emissions, and Energy use in
Technologies Model ® (2022 Excel)." Computer software. October 10, 2022. https://doi.org/10.11578/GREET-
Excel-2022/dc.20220908.1.
223 JGCRI, "GCAM Documentation (Version 7.0)," September 13, 2024. https://doi.org/10.5281/zenodo.11377813.
224 IIASA, "Global Biosphere Management Model (GLOBIOM) Documentation 2023 - Version 1.0," 2023.
https://pure.iiasa.ac.at/id/eprint/18996/l/GLOBIOM Documentation.pdf.
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• The GTAP-BIO225 model - an extension of the Global Trade Analysis Project
(GTAP) model
• The Applied Dynamic Analysis of the Global Economy (ADAGE) model226
The MCE led to several findings which inform our analytical approach for this proposed
rule, including the following conclusions:
• Economic models are best suited for estimating GHG emissions resulting from a
change in biofuel consumption levels.
• Land use change estimates vary significantly among the models.
• Economic modeling of the energy sector provides important insights into the overall
GHG impacts of a change in biofuel volumes.
• The MCE did not attempt to determine which of these economic models are more
likely to be correct—doing so would require extensive validation tools that are not
currently available. However, among the economic models included in the MCE, the
GCAM, GLOBIOM, and GTAP-BIO models provide a strong level of detail in key
sectors. The MCE observed that GCAM and GLOBIOM are dynamic models that can
estimate impacts over time, whereas GTAP-BIO represents one historical year (i.e.,
2014 in the version evaluated). Due to structural differences, these models estimate
differing market-mediated effects.
During the same time period that EPA was conducting the technical work for the MCE,
the National Academy of Sciences, Engineering and Medicine (NASEM) initiated a committee
to write a report on lifecycle analysis (LCA) methods for low-carbon transportation fuel policies
(hereafter the "the NASEM LCA Report").227 The NASEM LCA Report, published in October
2022, did not reach a consensus on the best available model or any particular estimates, but it did
include several recommendations that informed our analytical approach for this proposed rule
climate change analysis, including:
• Regulatory impact analyses should evaluate market-mediated impacts to assess the
extent to which a given policy design will result in reduced GHG emissions
(Conclusion 3-1, Recommendations 2-2, 3-2).
• Policies should strive to reduce model uncertainties and compare results from
multiple economic modeling approaches and transparently communicate the estimates
(Recommendation 4-2).
225 See, e.g., Taheripour, Farzad, XinZhao, and Wallace E. Tyner. "The Impact of Considering Land Intensification
and Updated Data on Biofuels Land Use Change and Emissions Estimates." Biotechnology for Biofuels 10, no. 1
(July 20, 2017). https://doi.org/10.1186/sl3068-017-Q877-Y. Model versions relying on the GTAP database are
discussed in detail in the MCE technical report.
226 Cai, Yongxia, Jared Woollacott, Robert H. Beach, Lauren E. Rafelski, Christopher Ramig, and Michael Shelby.
"Insights From Adding Transportation Sector Detail Into an Economy-wide Model: The Case of the ADAGE CGE
Model." Energy Economics 123 (May 8, 2023): 106710. https://doi.Org/10.1016/i.eneco.2023.106710.
227 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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Given that the models used in the GHG impacts analysis conducted for the RFS2 Rule
were no longer operational when technical work commenced on the Set 1 Rule and given that the
MCE and NASEM investigation were still ongoing, EPA was unable to conduct new GHG
impacts modeling for the volume scenarios for 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 RVO
standards.
While the literature review-based approach was necessary to assess GHG impacts under
the Set 1 Rule for the reasons outline above, it does have several deficiencies which are
addressed in the climate impacts analysis in this rule. 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. 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 introduces inconsistencies between simulations, and
can substantially affect the overall emissions estimates, as was recognized in the RFS2 Rule.228
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 RFS2 Rule analysis reported annual
emissions impacts.229 Thus, the illustrative GHG emissions scenarios presented in Set 1 Rule
represented only the results of EPA's 2010 dated 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
review-based approach presented a summary of the state of recent literature on biofuel lifecycle
228 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
can not be broken out into per fuels impacts, while the threshold values represent impacts of specific fuels but do not
account for all the interactions... [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).
229 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).
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analysis and the wide breadth of potential impacts estimated therein.230 For these reasons
(analysis did not capture interactions between fuels and volumes, relied on outdated estimates,
did not address differences in methods), 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.
For the GHG impacts assessment in this 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: crop-based fuels and
waste- and byproduct-based fuels.
Based on our review of the available science referenced above, we continue to conclude
that, because of interactions with complex global agricultural and feed systems and feedstock
land use requirements, production and use of crop-based fuels has the potential for substantial
market-mediated effects with significant implications for GHG emissions. 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. For this analysis, we conduct
new modeling of changes in crop-based fuels using two of the models considered in the 2023
MCE Technical Report: GCAM and GLOBIOM. This modeling incorporates several important
advancements over past climate impacts 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 and land use change emissions. Second, these models have both
benefitted from significant ongoing development over the last decade, incorporating the latest
science and agricultural and energy system data into their simulations. Finally, use of these
models allows for representation of 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 GCAM and GLOBIOM models, their relative strengths
and reasons for selection, and implementation for the Volume Scenarios considered in this
proposal are discussed in Chapter 5.1.3.
However, the MCE did not conclude which model(s) were most appropriate to use for the
RFS nor does it conclude that crop-based biofuels have significant indirect emissions.
Accordingly, we are soliciting public comment on the following issues:
• The methodologies and/or models that are most appropriate, accurate, and best-suited to
be used to determine whether crop-based biofuels have significant indirect emissions,
230 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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• The most effective way to consider the uncertainties in quantifying indirect emissions.
Are indirect emissions most appropriately characterized in the RFS with precise
numerical values or with risk-based classification schemes?
• What are the system boundaries for the attribution of indirect emissions? Should
emissions outside of the United States be considered? Indirect emissions assigned to
biofuels in one region represent the direct emissions from other sectors and regions.
Since the attribution of indirect emissions is not placed on the party that caused them,
crop-based biofuels cannot mitigate indirect emissions.
• Should policies of foreign governments be considered in indirect emission
determinations? Policies of foreign governments can significantly increase or decrease
deforestation and land-use change.
Waste- and byproduct-based fuels 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. For these fuels and feedstocks, our review of the best available
science has led us to conclude that economic modeling of global markets is not necessary.
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. Additionally, global economic models as a class tend to lack detail on the supply
chains for key non-crop-based fuels (e.g., waste fats, oils, and greases; 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. In the climate impacts assessment for the RFS2
Rule we used aspects of the GREET supply chain model to assess this category of fuels. In our
climate impacts assessment for this rule, we rely more fully on a recent release of the R&D
GREET model. More specifically, for this analysis we use the R&D GREET 2023 Revision 1
version of the model.231 Although the 2024 version of the R&D GREET model is now available,
the analytical work for this proposed rule was substantively completed before its release. 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. For the purposes of this proposal, we hereafter
use the terms "the R&D GREET model" to specifically mean R&D GREET 2023 Revision 1,
unless otherwise noted. Implementation and additional assumptions for our assessment of waste-
and byproduct-based fuel pathways are detailed in Chapter 5.1.2.
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 difficult and significant uncertainties remain. A sensitivity
231 Wang, Michael, Elgowainy, Amgad, Lee, Uisung, Baek, Kwang H., Balchandani, Sweta, Benavides, Pahola T„
Burnham, Andrew, Cai, Hao, Chen, Peter, Gan, Yu, Gracida-Alvarez, Ulises R., Hawkins, Troy R., Huang, Tai-
Yuan, Iyer, RakeshK., Kar, Saurajyoti, Kelly, Jarod C., Kim, Taemin, Kolodziej, Christopher, Lee, Kyuha, Liu,
Xinyu, Lu, Zifeng, Masum, Farhad, Morales, Michele, Ng, Clarence, Ou, Longwen, Poddar, Tuliin, Reddi, Krishna,
Shukla, Siddharth, Singh Udayan, Sun, Lili, Sun, Pingping, Sykora, Tom, Vyawahare, Pradeep, and Zhang, Jingyi.
"Greenhouse gases. Regulated Emissions, and Energy use in Technologies Model ® (2023 Excel)." Computer
software. October 09, 2023. https://doi.Org/10.11578/GREET-Excel-2023/dc.20230907.l.
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analysis considering uncertainty in this methodology is presented in Appendix 5-A at the end of
this chapter.
5.1.1.1 Scenarios Assessed
Scenarios described in Section III and Section V of this proposal include estimates of
volumes of different qualifying biofuels that would be expected to be consumed in the United
States under alternative standards levels (i.e., the Low and High Volume Scenarios and the
Proposed 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 each of these scenarios.
5.1.1.1.1 Volumes Analyzed
Chapter 3 presents the renewable fuel volumes represented by each of the scenarios
assessed for this proposed rule. For the Low and High Volume Scenarios, volumes by RIN
category are presented in Tables 3.1-1 and 2 respectively. 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 3.3-1 (Low Volume Scenario) and Table
3.3-2 (High Volume Scenario) and form the basis of our analysis of the potential climate change
impacts of the analytical Volume Scenarios.
The volume differences specified in the tables in Chapter 3.3 include values for 2026-
2030, i.e., volumes in these tables could be used to construct scenarios in which standards are set
for anywhere between one and five years. However, the modeling necessary for climate impacts
assessment would require separate simulations for each of the alternative durations (i.e., one
year, two years, etc.) of standards under the Low Volume Scenario and High Volume Scenario.
Completing the economic modeling described in Chapter 5.1.3 requires substantial lead time and
resources. Additionally, based on findings in previous modeling efforts, we believe increasing
the scope of this modeling to include scenarios representing multiple durations would yield
limited additional insight.232 For these reasons, we have only analyzed one duration for the Low
and High Volume Scenarios; standards set for three years, 2026 through 2028. Additionally, at
the time the technical specification of these scenario analyses was completed, the volumes
proposed in this NPRM had not yet been determined, so assessing the three-year version of the
scenarios provided analyses most applicable to the range of alternative durations. We
acknowledge that this assumed three-year duration does not align perfectly with the two-year
duration being proposed in this rule. However, for the reasons described above we nonetheless
232 Chapter 8.1 of EPA's 2023 MCE Technical Report presented scenarios that investigated the sensitivity of per-
megajoule CI results to the overall size of shock implemented. Based on these sensitivities, the MCE report
conclude that "[the volume sensitivity scenario] results indicate a linear effect between shock size and most output
values for ADAGE, GCAM, and GTAP results. GLOBIOM results show somewhat more nonlinearity with shock
size for certain output parameters, which leads to differences in the GHG emissions. But the nonlinearities observed
in the GLOBIOM results tend to be minor." Thus, we expect that emissions estimates form modeling scenarios that
represent continued growth in volume standards through 2030 would scale roughly proportional to the marginal
increase in fuel volumes over the 2028 volumes represented in our assessed scenarios.
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believe this analysis is appropriate and provides useful information regarding the potential
impacts of this proposal.
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 3.3-1 and Table 3.3-2 with one exception: "Other
Advanced Biofuels - Other" shows a relatively small volume (52 million RINs 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;
we do not believe this exclusion meaningfully changes the results of our analysis, or the
conclusions stakeholders may draw from them, in any way. Additionally, for the purposes of our
climate change analysis we disaggregate estimated volumes of biofuels produced from waste fats
oils and greases (FOG) into fuels produced from animal tallow, and fuels produced from used
cooking oil. To do this, we assume 52% of fuels produced from waste FOG are produced from
used cooking oil, and 48% are produced from tallow. This assumption is based on EIA data and
is described in additional detail in Chapter 3.3. 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 purposes 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 assessment of the Low and High Volume Scenarios in our
climate change analysis are presented in Table 5.1.1-1. Our assessment of the climate impacts
under these scenarios is presented in Chapter 5.2.
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Table 5.1.1-1: Difference in Consumption of Renewable Fuels (Trillion BTUs) in the Low
Volume Scenario and High Volume Scenario Relative to the No RFS Baseline
Assessed
Low Volume
High Volume
market-
Scenario Minus
Scenario Minus
mediated
No RFS Baseline
No RFS Baseline
GHG
Fuel
emissions?
2026
2027
2028
2026
2027
2028
CNG/LNG from biogas
55
57
59
55
57
59
Biodiesel from Corn oil
4
7
4
4
7
4
Biodiesel from Used Cooking Oil
-3
-3
-3
-3
-3
-3
Biodiesel from Tallow
No
-3
-3
-3
-3
-3
-3
Renewable Diesel from Corn Oil
10
5
6
10
5
6
Renewable Diesel from Used Cooking
Oil
61
74
87
61
74
87
Renewable Diesel from Tallow
57
69
80
57
69
80
Biodiesel from Soybean oil
138
137
138
138
137
138
Biodiesel from Canola oil
41
41
41
41
41
41
Renewable Diesel from Soybean oil
Yes
87
91
96
113
144
174
Renewable Diesel from Canola oil
16
16
16
28
41
53
Ethanol from Corn starch
16
18
18
16
18
18
In addition to the Low and High Volume Scenarios, Chapter 3 presents the volumes of
specific fuels which are estimated to comprise the volume standards proposed in this rule. If
these proposed standards are finalized, the actions of RIN generators and obligated parties will
ultimately determine the exact volumes of each of these fuels which contribute to these standards
in practice. However, Chapter 3 describes in detail that we believe these estimated volumes are
appropriate for the purposes of estimating the impacts of these proposed standards. Parallelling
the above discussion, volumes by RIN category are presented in Table 3.2-1 while projections of
volumes by fuel and feedstock compared against similar estimates in the No RFS Baseline are
presented in Table 3.3-5. The volumes used in our assessment of the climate change impacts of
the Proposed Volumes are presented in Table 5.1.1-2.
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Table 5.1.1-2: Difference in Consumption of Renewable Fuels (Trillion BTUs) in the
Proposed Volumes Relative to the No B
LFS Baseline
Fuel
Assessed market-
mediated GHG
emissions?
Proposed Volumes
Minus No RFS Baseline
2026
2027
CNG/LNG from biogas
No
55
57
Biodiesel from Corn oil
14
16
Biodiesel from Used Cooking Oil
-8
-8
Biodiesel from Tallow
-7
-8
Renewable Diesel from Corn Oil
59
55
Renewable Diesel from Used Cooking
Oil
15
14
Renewable Diesel from Tallow
14
13
Biodiesel from Soybean oil
Yes
119
120
Biodiesel from Canola oil
19
19
Renewable Diesel from Soybean oil
153
184
Renewable Diesel from Canola oil
52
52
Ethanol from Corn starch
16
18
The Proposed Volumes are for years 2026 and 2027 only. The components of our
analysis which rely on economic modeling have only been completed for the three-year
analytical Volume Scenarios, as discussed above. See Chapter 5.3 for information on how the
modeling for the analytical Volume Scenarios was used to estimate emissions impacts of the
Proposed Volumes.
5.1.1.1.2 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
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 (e.g., crops). If
increased demand for biofuels leads to land conversion, 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 continues, the GHG benefits of displacing fossil fuels may
eventually "pay back" the initial increase in GHG emissions from the initial expansion of
cropland. It is therefore important that an analysis of the GHG emissions impacts of biofuels
formulate these assumptions intentionally and then describe these choices transparently, as we do
in this subsection.
In the specific context of this proposal and EPA's recurring responsibility to set RVOs
under the RFS program, EPA must determine more specifically 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 proposal, 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
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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 proposing volume standards. Nor would this choice
be the norm for assessing the impacts of a policy with effects that continue over time. When
impacts of a proposed 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 proposal 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 proposed standards.
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.233 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
science indicating the benefits of reducing emissions in the near term.234 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,235 and
setting volumes standards is a key mechanism through which the RFS program promotes the use
of those fuels, we believe that our accounting for the climate benefits of increasing volumes of
those fuels should be consistent in temporal scope with the 30-year period of analysis.
Thus, the climate change analyses for this proposal consider a time horizon of 30 years of
impacts of renewable fuel consumption. The Low and High Volume Scenarios assessed in
Chapter 5.2 represent the volumes for 2026, 2027, and 2028 presented in Table 5.1.1-1, then
hold constant volumes of U.S. renewable fuel consumption from 2028-2055. This comprises in
total a 30-year period of analysis from 2026 to 2055. The Proposed Volumes assessed in Chapter
5.3 represents the volumes for 2026 and 2027 presented in Table 5.1.1-2, then hold constant
volumes of U.S. renewable fuel consumption from 2027-2055, comprising an identical 30-year
period of analysis to that analyzed for the Low and High Volume Scenarios.
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.
233 See discussion of the selection of the 30-year period of analysis in the RFS2 Rule DRIA Chapter 2.4.5.
234 Id.
235 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).
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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, and that such analysis could provide potentially useful sensitivities for the
analysis of the potential climate impacts of volume standards.
5.1.2 Waste- and Byproduct-based Fuels
For the purposes of defining categories of renewable fuel feedstocks in this climate
change analysis, waste and byproduct materials are considered to not be the primary driver of an
economic activity; they are produced as secondary or tertiary output of a primary activity which
is responsive to market pressures.236 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 that are wastes, residues, and byproducts for use as feedstock to produce renewable
fuels and we do not conduct any assessment of market-mediated impacts associated with these
feedstocks and fuels. 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 waste and residue-based fuels,
this represents all the relevant categories of emissions that should be considered as this type of
analysis estimates the emissions associated with all of 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
wastes and byproducts and which are assessed in our climate change analysis (i.e., fuels with
significant volume differences between the assessed Volume Scenarios and the No RFS
Baseline).
• CNG/LNG produced from biogas from landfills and waste digesters
• 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 waste- and byproduct-based 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 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. 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 coproduct accounting
methods as appropriate based on the purpose of our analysis. This section describes our analysis
with the R&D GREET model and the resulting estimates.
To estimate the GHG emissions impacts associated with changes in the volumes of these
fuels consumed in the U.S., we multiply the volume changes, converted to megajoules, with the
236 See discussion of wastes and byproducts in the RFS2 Rule.
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lifecycle GHG emissions factor for each fuel pathway generated in the R&D GREET model. 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.237 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 5.1.2-1.
Additional information about the assumptions used for each of these estimates is presented in the
subsections below.
Table 5.1.2-1: Emissions Factors Used for Climate Impacts Analysis of Fuel Pathways Not
Expected to Have Significant Market-Mediated Emissions Impacts (g/M.
fuel used
Fuel Pathway
CO2
CH4
N2O
COiea
CNG/LNG: Biogas
11.0
0.50
0.001
26.3
Biodiesel: Distillers Corn Oil
16.3
0.07
0.032
26.8
Biodiesel: Used Cooking Oil
11.6
0.06
0.000
13.7
Biodiesel: Tallow
13.4
0.07
0.001
15.6
Renewable Diesel: Distillers Corn Oil
19.5
0.07
0.033
30.4
Renewable Diesel: Used Cooking Oil
15.2
0.07
0.001
17.4
Renewable Diesel: Tallow
16.1
0.07
0.001
18.3
Gasoline (E0)
88.9
0.11
0.001
92.5
Diesel (B0)
87.0
0.13
0.000
91.0
Natural Gas (CNG Vehicle)
65.3
0.27
0.002
74.1
a Estimates presented in CO2C are calculated using 100-year global wanning potentials from the IPCC Fifth
Assessment Report (AR5) and are provided for informational purposes only. The climate change analysis in this
chapter uses emissions factors for each individual GHG and applies social cost factors specific to emissions changes
in each individual gas.
5.1.2.1 CNG/LNG 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 then applied technical adjustments based on
a combination of available industry data, petition submission data, and other recent scientific
literature to construct the CNG pathway in R&D GREET. Importantly, 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,238 and flared. This assumption is consistent
with the approach that EPA adopted for Pathways II Rule.239 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.240
237 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/LNG derived from biogas displaces use of fossil natural gas and biodiesel and
renewable diesel of all sources displaces use of diesel produced from petroleum.
238 61 FR 9905 (March 12, 1996).
239 79 FR 42128 (July 18, 2014).
2411 "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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Our analysis of converting biogas to compressed natural gas (CNG) involves several key
stages. First, biogas is generated at a landfill and collected using a gas collection system. The
collected biogas then diverted to a purification process to be upgraded into renewable natural gas
(RNG). The upgrading process requires electricity, which is supplied by the grid, based on the
average U.S. energy mix. Once purified, the RNG is transported via pipelines to CNG stations,
where it is compressed for vehicle fueling.
When considering the baseline emissions associated with diverting biogas from landfills
for energy production, we assume that biogas is already being collected at landfills and sent to a
flare. In this counterfactual, the carbon dioxide (CO2) present in the flared biogas is emitted
without conversion, while the methane (CH4) is flared into CO2 with a 99.96% CH4 destruction
efficiency. A small emission credit is applied to the biogas in the fuel production stage for
avoiding unburned methane being released into the atmosphere when sent to a flare. During the
upgrade process to RNG, we account for a 2% biomethane leakage. The process energy needed
for the upgrading stage is supplied by the grid as electricity. For the RNG pipeline injection and
delivery stages, we assume an RNG compression efficiency of 99.2% and that the RNG travels
680 miles241 through pipelines, with a 0.31% leakage rate, before reaching off-site refueling
stations. The delivered RNG is then compressed to CNG for vehicle fueling using the default
values in R&D GREET.
Lastly, the tailpipe emissions associated with CNG use as a transportation fuel were
modeled using a passenger vehicle from the 2017 model year that utilizes a spark ignition system
in its internal combustion engine. Using the above methodology, we calculate that CNG from
biogas has a lifecycle emission intensity of 26.3 g C02e/MJ. Our estimates of the supply chain
GHG emissions associated with CNG Fuel produced from Landfill Biogas are summarized in
Table 5.1.2.1-1.
Table 5.1.2.1-1: Supply Chain Emissions Associated with CNG Fuel from Landfill Biogas
(gCChe/MJ)
Supply Chain Stage
Renewable CNG
Fuel Production
18.3
Fuel Transport and Distribution
6.9
Fuel Use
1.1
Total
26.3
5.1.2.2 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 that the biodiesel is produced through a standard
241 The average natural gas transmission pipeline distance from the field to end-use is 680 miles. This value is based
on the national ton-miles of natural gas freight via pipeline as reported by the US Bureau of Transportation Statistics
in 2009 and tons of dry natural gas production in the same year as reported by EI A. Dunn JB, Elgowainy A, Vyas A,
et al. "Update to Transportation Parameters in GREET," October 25, 2013.
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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 that is used as a
feedstock to produce biodiesel and renewable diesel. At dry mill ethanol plants, corn grain is
ground and fermented to produce ethanol with coproduct distillers grains and solubles (hereafter
"distillers grains"), a protein-rich livestock feed. Most dry mill ethanol plants extract distillers
corn oil from the distillers grains. The distillers corn oil is used as feedstock to produce biomass-
based diesel 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,
coproduct 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.242 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 coproducts on an energy basis. Thus, we allocate approximately 4% of the supply chain
corn farming and transport emissions to the distillers corn oil.243
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
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 coproduct accounting. The
NASEM LCA Report discusses the various coproduct accounting methods used in policy and the
242 ISO 14044 defines unit process as the "smallest element considered in the life cycle inventory analysis for which
input and output data are quantified."
243 This ensures that the supply chain emissions associated with distillers corn oil production are not double counted
when we sum the GLOBIOM 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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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 coproduct accounting, reflecting a lack of consensus on
the most appropriate approach.
While there are many viable options for coproduct 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,
which is an energy carrier. The energy allocation method is based on the physical properties of
the coproducts, 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 highly 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.244 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
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 R&D
GREET model, we assume that 20% of distillers corn oil used as biofuel feedstock is transported
244 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/10.1002/bbb.2225.
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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 coproducts. 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, 147 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 0.97 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
coproduced 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 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-CCh emissions associated with biodiesel combustion. Consistent with the
methodology developed for the RFS2 Rule, we do not include the biodiesel combustion CO2
emissions as 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."
For this analysis, we do not include the CO2 emissions from combustion of the non-
biogenic methanol portion of the biodiesel, because the purpose of this analysis is to estimate the
emissions associated RIN generating volumes of fuel. Only the biogenic portion of biodiesel
generates RINs as the equivalence value for biodiesel, 1.5 RINs per gallon, accounts for the
renewable content of the fuel.
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For this analysis we use emissions factors from the R&D GREET model representing
current average U.S. industry operations. To evaluate the emissions associated with electricity,
we use the emissions factor from the R&D GREET model for U.S. grid average electricity (128
gCChe/mmBtu). For natural gas, we use the emissions factor for conventional North American
natural gas used at a biofuel plant (13,413 gCChe/mmBtu well-to-gate plus 59,587
gCChe/mmBtu from combustion). We assume that the methanol used for biodiesel production is
produced from conventional natural gas (25,560 gCChe/mmBtu), and that hydrogen for
renewable diesel production is produced by steam methane reforming of conventional natural gas
(9,449 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.
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 5.1.2.2-1.
Although our climate analysis uses estimates broken out by gas, for brevity this table presents the
estimates in carbon dioxide-equivalent emissions (CChe) using global warming potential values
from the IPCC Fifth Assessment Report (AR5).245 The calculations upon which these estimates
are based are contained in spreadsheets that are available in the public docket for this proposed
rule.
Table 5.1.2.2-1: Supply Chain Emissions Associated with Corn Oil-based Fuels (gC02e/MJ)
Supply Chain Stage
Biodiesel
Renewable Diesel
Corn Production and Transport
17
17
Corn Oil Extraction
1
1
Corn Oil Transport
0.3
0.3
Fuel Production
7
10
Fuel Transport
0.3
0.3
Fuel Use
1
1
Total
27
30
5.1.2.3 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 used cooking oil (UCO). For
this analysis, we assume the feedstocks and fuels are produced and used in the U.S., using
industry average production practices. To the extent that feedstocks are imported, the estimates
for some supply chain stages are likely underestimates. For example, imported UCO would
likely have higher emissions associated with transportation than domestic UCO. 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.
245 IPCC, "Climate Change 2013: The Physical Science Basis," Working Group I Contribution to the
Fifth Assessment Report of the Intergovernmental Panel on Climate Change, 2014.
https://doi.org/10.1017/cbo9781107415324.
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Tallow is produced through rendering of the animal by-products from cattle
slaughtering.246 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 tallow rendering, we assume 1,052 Btu of natural gas and 307 Btu of
electricity is used per pound of rendered tallow. R&D GREET assumes the follow average
transportation modes and distances for transporting tallow from rendering facilities to biofuel
plants: 20% by rail for 400 miles, and 80% by truck for 100 miles.
For 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 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.
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 coproducts. For tallow biodiesel production, we assume
the process inputs per pound of biodiesel output are 1.05 pounds of tallow, 1,137 Btu of natural
gas, 147 Btu of electricity and 896 Btu of methanol. For UCO biodiesel production, we assume
the process inputs per pound of biodiesel output are 1.05 pounds of UCO, 1,075 Btu of natural
gas, 138 Btu of electricity and 847 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 1.3 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.
246 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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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 5.1.2.2. For this analysis we use emissions factors from the R&D GREET model
representing current average U.S. industry operations (see Chapter 5.1.2.2 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 tallow and UCO are summarized in Table 5.1.2.3-1. The
calculations upon which these estimates are based are contained in spreadsheets that are
available in the public docket for this proposed rule.
Table 5.1.2.3-1: Supply Chain Emissions Associated with Tallow and UCO-Based Fuels
(gCChe/MJ)
Tallow-
jased Fuels
UCO-based Fuels
Renewable
Renewable
Supply Chain Stage
Biodiesel
Diesel
Biodiesel
Diesel
Collection, Rendering and Transport
7
7
7
6
Fuel Production
7
10
6
10
Fuel Transport
0.3
0.4
0.3
0.4
Fuel Use
1
1
1
1
Total
16
18
14
18
5.1.2.4 Fossil Fuel Baselines
For this climate change analysis, we assume that waste and byproduct-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
market-mediated transportation sector effects of these particular biofuels, we believe that the
energy-equivalent displacement assumption is appropriate for the purposes of this analysis.
For this analysis, we assume that biodiesel and renewable diesel replace conventional
diesel fuel and that renewable CNG displaces conventional CNG. For the GLOBIOM-based
analysis described in Chapter 5.2.2, 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,247 248 249 and a detailed assessment of the energy intensities of 27 oil
sands projects.250 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.251252 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.
247 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.
248 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.energyluels.6b02916.
249 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.
2511 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.
251 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.
252 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.
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Table 5.1.2.4-1: Supply Chain Emissions Associated with Conventional Fuels (gCChe/MJ)
Supply Chain Stage
Gasoline
Diesel
CNG
Feedstock
6
8
14
Fuel
13
8
3
Vehicle Operation
73
76
57
Total
93
91
74
5.1.3 Crop-based Fuels
As discussed in Chapter 5.1.1, our analysis of the climate change impacts of crop-based
fuels is based on new economic modeling that represents the specific fuel volumes under
consideration in this proposal. The use of economic modeling for this assessment is well aligned
with the recommendations of the 2022 NASEM LC A report, which concluded that regulatory
impact analyses should evaluate market-mediated impacts to assess the extent to which a given
policy design will result in reduced GHG emissions (Conclusion 3-1, Recommendations 2-2, 3-
2).
5.1.3.1 Models Used
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, and therefore does not satisfy the
NASEM recommendation to use consequential modeling for the purposes of regulatory impact
analyses.
Estimating impacts of changes in GHG emissions over the 30-year scenarios assessed in
this climate change analysis requires estimates of changes in emissions of each GHG between
2026 and 2055. GTAP-BIO is a static comparative model; biofuel modeling using the GTAP-
BIO model represents alternative versions of the world under different assumed volumes of
biofuel consumption in a single year—2014 for the version of GTAP-BIO assessed in the MCE,
2017 in more recent work.253 Thus, GTAP-BIO does not provide estimates that are suitable for
use in an RIA climate change analysis.
Next, while ADAGE does provide dynamic over time results that could be used to
estimate emissions impacts in individual years over the period of analysis for this proposal, the
analytical work undertaken as part of the MCE identified several model updates that we believe
would be necessary before results from ADAGE 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 proposed rule.
253 See, e.g., DOE, "Guidelines To Determine Life Cycle Greenhouse Gas Emissions of Clean Transportation Fuel
Production Pathways Using 45ZCF-GREET," January 2025. https://www.energy.gov/sites/default/files/2025-
01/45zcf-greet user-manual.pdf.
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Finally, undertaking analyses with each model discussed in this section requires
significant effort and resources. We believe that representing two models appropriately balances
the NASEM recommendations to consider framework uncertainty by comparing results using
multiple models, with the goal of having a flexible and responsive approach that allows updating
analyses as appropriate for each proposed and final volume rule within given timing and resource
constraints. For these reasons, we use the GLOBIOM and GCAM models in our assessment of
the climate change impacts of volumes of crop-based biofuels under this proposal. The
GLOBIOM and GCAM models are described in detail in the subsections below. Considerations
for scenario implementation specific to each of GLOBIOM and GCAM follow in Chapters
5.1.3.2 and 5.1.3.3 respectively.
5.1.3.1.1 GLOBIOM
The Global Biosphere Management Model (GLOBIOM) is a partial equilibrium,
recursive dynamic model with detailed grid cell land representation that captures the agricultural,
forest and bioenergy sectors. It was developed by and continues to be managed by the
International Institute for Applied Systems Analysis (IIASA). A sample of GLOBIOM code is
available to the public, and an open-source version is under development.254
The climate change analysis undertaken in this proposal uses a version of GLOBIOM
which is nearly identical to the version of GLOBIOM reviewed in EPA's 2023 MCE. The most
notable difference is that, while the modeling in the MCE considered GLOBIOM scenarios that
ran in ten-year timesteps though 2050, the version of GLOBIOM used in this analysis has been
extended to run through model year 2060.255
As a partial equilibrium model, GLOBIOM does not have feedback from labor, capital or
other parts of the economy. The model finds market equilibria that maximize the sum of
producer and consumer surplus subject to resource, technological, demand and policy constraints
at a country/regional level. Producer surplus is defined as the difference between market prices at
a regional level and the product's supply curve at the regional level. The supply curve accounts
for labor, land, capital and other purchased input. Consumer surplus is based on the level of
consumption of each market and is arrived at by integrating the difference between the demand
function of a good and its market price. The model uses linear programming to solve, although it
also contains some non-linear functions that have been linearized using stepwise
approximation 256
The detailed grid cell-level spatial coverage for GLOBIOM includes more than 10,000
spatial units worldwide. The model represents 18 crops globally (and nine additional crops in
Europe) using FAOSTAT as the primary database for crop statistics. Crop modeling includes
differentiation in management systems and multi-cropping.
254 GLOBIOM, "Model Code." https://iiasa.github.io/GLOBIOM/model code.html.
255 Scaling of GLOBIOM results to the analytical timeframe is discussed in Chapter 5.1.3.2.
256 Documentation of GLOBIOM's economic principles, sectors, and representation can be found in: IIASA,
"Global Biosphere Management Model (GLOBIUM) Documentation 2023 - Version 1.0," 2023.
https://pure.iiasa.ac.at/id/eprint/18996/l/GLOBIOM Documentation.pdf.
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GLOBIOM also features highly detailed livestock representation based on FAOSTAT
data and represented at the grid cell level. For ruminants there are 8 production system
possibilities, including grazing systems in different climatic locations such as arid and humid,
mixed crop-livestock systems, and others. Pigs and poultry are classified under either small
holder or industrial systems. Based on the production system, animal species, and region,
GLOBIOM differentiates diets, yields, and GHG emissions.257 Livestock production is allowed
to intensify or extensify, thereby altering the amount of feed or grass consumed.258 Since for
ruminants this is modeled spatially, any changes in grassland consumed due to changes in
production systems, animal type, yield, and GHGs is captured in the spatially-relevant areas.
Forestry in GLOBIOM is captured through the G4M module259 and includes detailed
representation of the sector and its supply chain and a differentiation between managed and
unmanaged forest areas. GLOBIOM includes bilateral trade for agricultural and wood products.
The model also includes a bioenergy sector with first- and second-generation biofuels and
biomass power plants. GLOBIOM represents biofuel coproducts including distillers grains,
oilseed meals, and sugar beet fibers. These coproducts can be traded either in their processed or
whole forms. Coproducts that can be used for livestock feed can substitute other forms of feed
depending on protein and metabolizable energy content.260
There are nine land cover types in GLOBIOM, and six of these are modeled dynamically:
cropland, grassland, short rotation plantations, managed forests, unmanaged forests, and other
natural vegetation land. The other three land cover categories are represented in the model but
kept constant; they include other agricultural land, wetlands, and not relevant (ice, water bodies
etc.). GHG emission coverage includes 12 sources of emissions that represent crop cultivation,
livestock, above- and below-ground biomass, soil-organic carbon, and peatland. Although
GLOBIOM does not track terrestrial carbon stocks dynamically, carbon fluxes from land use
change are calculated with equations, following IPCC guidelines, that estimate changes over
time and allocate the average annual emissions to the time period in which the land use change
occurs.
Land use in GLOBIOM allows for both intensification and extensification.261 Land
conversion is endogenously determined based on conversion costs and the profitability of
primary products, coproducts and final products. Costs increase as the area converted expands.
257 For instance, dairy and meat herds are modeled separately, and their diets are differentiated. Poultry in industrial
systems is split into laying hens and broilers, again with different dietary needs.
258 Intensifying involves increasing livestock output without expanding the area of pasture land by grazing more
livestock per area of land, increasing feed relative to grazing, or using feedlots. Extensifying is the opposite—it
involves expanding pasture area in order to increase livestock production.
259 IIASA, "Global Forest Model (G4M)." https://iiasa.ac.at/models-tools-data/g4m.
2611 Valin, Hugo, et al. "Improvements to GLOBIOM for Modelling of Biofuels Indirect Land Use Change," ILUC
Quantification Consortium, September 17, 2014.
261 We define intensification as an increase in the amount of crop production on a given area of land, and
extensification as an increase in the total area used to grow the crop of interest. Where we use the term
extensification, we are including both non-cropland that was converted to cropland and shifting of cropland from
one type of crop to another. However, our discussion of the results shows cropland shifting and land conversion to
cropland separately.
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Additionally, there are biophysical land suitability and production potential restrictions. Land use
change is determined at the grid cell level.262 There is a land transition matrix that sets the
options for land conversion for each cell and is based on land conversion patterns specific to that
region and conversion costs depending on the type of land converted. In the U.S. and EU
regions, GLOBIOM, by default, does not allow forest conversion and restricts natural land
conversion though these assumptions can be changed.
5.1.3.1.2 GCAM
The Global Change Analysis Model (GCAM) is a partial equilibrium, dynamic recursive,
multi-sector dynamic model that represents human and Earth system dynamics. The core GCAM
is developed and maintained at the Joint Global Change Research Institute, a partnership
between Pacific Northwest National Laboratory (PNNL) and the University of Maryland (UMD)
in College Park, Maryland. PNNL is the primary steward of the model, though members of a
larger GCAM Community also contribute to its development.263 GCAM is an open-source
community model that can be downloaded from a public repository.264 The model documentation
is also publicly available265 and includes a partial list of GCAM publications.266
The climate change analysis undertaken in this proposal uses a version of GCAM which
is nearly identical to the version of GCAM reviewed in EPA's 2023 MCE technical document.
That version of GCAM, referred to as "GCAM-T" in the MCE, was based on the GCAM core
model version 5.3,267 with a number of enhancements to better capture the energy, land, and
atmospheric impacts of biofuel production. Additional documentation for the version of GCAM-
T used in the MCE is included as a memorandum to the docket.268 One revision has been made
to the version of GCAM-T described above in the version of GCAM used in the climate change
analysis for this proposal: parameters governing the effective elasticity of land use transitions
have been updated to align with previous updates to GCAM's representation of land
suitability.269 The rest of this section describes the GCAM modeling framework broadly,
including recent core versions of GCAM and the specific version of GCAM used in this
proposal.
GCAM represents five systems—energy, economy, agriculture and land use, water, and
atmosphere—and structurally represents key interactions between these systems through a fully
262 GLOBIOM represents most land in the world using 5 arcminutes by 5 arcminutes grid. At the equator, this is
roughly 9 km by 9 km.
263 For more information, see GCIMS, "Community." https://gcims.pnnl.gov/communitv.
264 See https://doi.org/10.5281/zenodo.1042788
265 See https://igcri.github.io/gcam-doc/index.html.
266 See, more specifically, https://igcri.github.io/gcam-doc/references.html.
267 GCAM version 5.3 is available at: https://doi.org/10.5281/zenodo.3908600.
268 See "GCAM-T 2022.0 Documentation" available in the docket for this action.
269 These land transition parameter updates were released in core model version 7.1. While GCAM-T is built off of a
prior core version of GCAM (v5.3), land transitions are a central component of biofuel modeling and, for this
reason, we have included this isolated update in the version of GCAM used in the analyses in this proposal. These
updates are described in Section 2.1 of JGCRI, "GCAM Core Model Proposal #393: Update AgLU parameters for
land-based mitigation measures," March 20, 2024. https://igcri.github.io/gcam-doc/cmp/393-
AgLU Parameters Update.pdf.
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integrated computational system.270 It encompasses all human systems and economic sectors that
produce or consume energy or emit GHGs. The model operates at a global level, with differing
levels of spatial resolution across the different systems; there are 32 socioeconomic (market)
regions, 235 water basins, and 384 distinct land use regions globally which are generated as the
intersection of socioeconomic and water basin regions.
GCAM operates as a dynamic recursive model, solving for market equilibria in 5-year
time steps, such that the information from one time period is passed forward to subsequent time
periods. In practice, the model is often run from a base year in the recent past through the years
2050 or 2100. In our analysis for this proposal, it is run through 2060. However, time step and
scenario length are flexible input assumptions to GCAM, and the framework can support
scenario analysis across a wide range of time scales. For each modeled time period, GCAM
iterates until it finds a vector of prices that clears all markets and satisfies all consistency
conditions.
The energy and agricultural systems in GCAM are represented as distinct, interacting
sectors, wherein the output of one sector can be an input to other sectors. Within each sector,
specific production technologies compete for market share using a multi-level nesting approach
that allows competition between different nodes at each level, and any number of levels. This
nested competition follows a discrete logit271 or modified logit model,272 depending on the
object. The market share of each discrete technology is determined by: (1) Relative costs, which
include exogenous and endogenous components; (2) Calibration-derived "share-weight"
parameters and capital carryover from prior time periods; and (3) An exogenous logit exponent
that determines the price responsiveness of the competition. Additionally, technologies that are
introduced in future time periods are assigned exogenous share-weights in each model time
period. In the end, market shares of competing technologies are influenced by a number of both
endogenous and exogenous parameters—including fuel and non-fuel costs, efficiency or input-
output coefficients, share-weights, and logit exponents.
Inter-regional trade of energy and agricultural commodities in GCAM is specified using a
hybrid of the Armington approach273 and the Heckscher Ohlin theorem.274 Traded commodities
include crop and livestock products, forestry products, primary energy goods such as coal and
oil, and selected secondary energy and industrial commodities such as refined fuels and
nitrogenous fertilizers. Trade of each commodity is represented using a pooled global market; the
inter-regional allocation of exports is based on relative costs of production and base-year
2711 https://igcri.github.io/gcam-doc/overview.html.
271 McFadden Daniel. "Conditional logit analysis of qualitative choice behavior." 1973.
https://eml.berkelev.edu/reprints/mcfadden/zarembka.pdf.
272 Clarke, John F., and J. A. Edmonds. "Modelling Energy Technologies in a Competitive Market." Energy
Economics 15, no. 2 (April 1, 1993): 123-29. https://doi.org/10.1016/0140-9883(93)90031-l.
273 The Armington approach to modeling international trade is based on the premise that products traded
internationally are differentiated by country of origin. This is in contrast to models that assume perfect substitution
between products produced in different countries. Armington, Paul S. "A Theory of Demand for Products
Distinguished by Place of Production." IMF Staff Papers, 1969 (001). https://doi.org/10.5089/9781451956245.Q24.
274 Note that the most recent public version of GCAM trades all energy goods through the Annington-like approach,
rather than through homogenous markets. This version of the model was not released in time for inclusion in this
exercise.
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calibration, and on the consumption side, each region's choice of imports versus domestic
sourcing is similarly determined by relative costs and calibrated base-year decisions.
The energy system in GCAM is detailed and consists of depletable and renewable
resources (including primary biomass), energy transformation and distribution sectors
(electricity, refining, gas processing, hydrogen production, and district services), and final energy
demand sectors (buildings, industry, and transportation). For transportation biofuels specifically
(referred to in the GCAM documentation as "biomass liquids"), the default model includes a
total of 11 biofuel production technologies. These include four "first generation" technologies,
representing ethanol and biodiesel products produced from agricultural commodity crops, and
seven "second generation" technologies representing fuels produced from a variety of
feedstocks, including energy crops and residues. By default, the technology assumptions for
second generation represent the inputs and outputs of cellulosic ethanol and Fischer-Tropsch
fuels. However, the input assumptions for these technologies can be modified to represent other
fuel production pathways. Further description of these technological representations is available
in the online GCAM documentation.275
The agriculture and land use module differentiates 384 land use regions globally,
generated as the intersection of 32 socioeconomic regions with 235 water basins. Within each
land use region, up to 25 land use types compete for land share based on the relative profitability
of each use, using a nested land allocator tree structure.276 277 Land use conversion in GCAM is
driven by the logit structure of the model coupled with the land nesting structure. Further,
GCAM land categories are structured in sub-nests, with easier conversion between land types
within a sub-nest than across sub-nests. Land use types include exogenous land types (tundra,
desert, urban), commercial and non-commercial pasture and forest lands, grasslands and
shrublands, and a detailed set of agricultural crop commodities, including bioenergy crops,
classified by irrigation type and management intensity.278 Major global commodity crops, such
as corn, rice, soybeans and wheat are modeled individually, while all other crops are modeled as
a series of thematic aggregations.
Within this nesting structure, the allocations of land to each land use type are calibrated
in the model base year, and in the future, changes from the base-year allocations are driven by
changes in the relative profitability of each land use type, including both commercial and natural
lands. Profitability of lands in agricultural and forestry production changes over time as a
function of future commodity prices, yields, and costs of production (including endogenous costs
of fertilizer, fuel, and irrigation water). The intrinsic profitability or value of natural lands is
275 See https://igcri.github.io/gcam-doc/supplY energy.html.
276 Wise, Marshall, Kate Calvin, Page Kyle, Patrick Luckow, and Jae Edmonds. "Economic and physical modeling
of land use in GCAM 3.0 and an application to agricultural productivity, land, and terrestrial carbon." Climate
Change Economics 05, no. 02 (May 1, 2014): 1450003. https://doi.org/10.1142/s2010007814500031.
277 Zhao, Xin, Katherine V. Calvin, and Marshall A. Wise. "The critical role of conversion cost and comparative
advantage in modeling agricultural land use change." Climate Change Economics 11, no. 01 (January 30, 2020):
2050004. https://doi.org/10.1142/s2010007820500Q49.
278 A complete description of the land use module can be found in the online documentation
(https://i gcri. github.io/gcam-doc/land.html) and in Kyle, G. Page, Patrick Luckow, Katherine V. Calvin, William R.
Emanuel, Mayda Nathan, and Yuyu Zhou. "GCAM 3.0 Agriculture and Land Use: Data Sources and Methods,"
December IZ 2011. https://doi.org/10.2172/1036082.
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inferred from the base year profitability of proximate land used for agriculture and forestry in
each region. The logit competition for land is non-linear and exhibits diminishing marginal
returns to expansion of each use as well as non-constant elasticities.279 This nonlinear nature
allows the land shares to be solved based on equal value at the margin without need for the
explicit constraints used in linear models.
GCAM also uses land suitability and land protection assumptions to determine what land
is available for expansion. All versions of GCAM divide land into arable and non-arable
categories and, by default, protect some portion of the arable land from conversion to agricultural
or silvicultural use. In the version of GCAM used for this exercise, GCAM-T, other assumptions
limit the suitability of arable lands for crop production based on biophysical limitations (e.g.,
slope, annual rainfall) and human-imposed limitations such as land protection policies. The latter
are parameterized using the International Union for Conservation of Nature's (IUCN) World
Database of Protected Areas.280
5.1.3.2 Scenario Implementation: GLOBIOM
Because GLOBIOM represents decadal timesteps (i.e., model outputs are given for
modeled years 2020, 2030, 2040, 2050, and 2060), representing biofuel volume changes in only
a few consecutive years—three years (2026, 2027, and 2028) in the case of the Low and High
Volume Scenarios and in only two years (2026 and 2027) in the case of the Proposed Volumes—
requires making post-hoc translations, adjustments and interpretations of the native model
outputs.
For model year 2020, we specify volumes of U.S. consumption for each of the five
biofuels represented in GLOBIOM using RFS administrative data by taking a five-year average
(2018-2022) of RINs generated for that fuel and feedstock. Because GLOBIOM does not
represent the year 2028 specifically, in order to represent the 2028 volumes in the three-year
Low and High Volume Scenarios and the accompanying No RFS Baseline, we specify volumes
in model year 2030 to match the corresponding values for each scenario in 2028 (see Table
5.1.1-1 for volumes differences compared to No RFS).281 We then assume that all effects in the
model results are lagged by two years from the analytical scenario year (e.g., GLOBIOM outputs
for model year 2050 represent analytical scenario year 2048, interpolated GLOBIOM outputs for
year 2036 represent analytical scenario year 2034).
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 are summarized in Table
5.1.3.2-1.
279 See Wise et al (2020).
2811 For more information, see documentation provided at: https://github.com/gcamt/gcam-core/tree/GCAM-T-2020.
281 All volume specifications for GCAM and GLOBIOM scenarios are contained in "Set 2 NPRM Climate Change -
Economic Model Scenario Specifications," available in the docket for this action.
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Table 5.1.3.2-1: Summary of Emissions Categories and Adjustments for GHG Emissions
Estimates Using GLOI
IIOM
Emissions Category
Interpolation Notes
Adjustments
Land Use Change
Linear interpolation of area of land
use change between model years.
This results in constant LUC
emissions between model years.
N/A
Crop Production
Linear interpolation of crop
production emissions between
model years.
Model outputs supplemented with
estimates for several components of
crop production emissions using
externally developed emissions
factors.
Livestock Production
Linear interpolation of livestock
production emissions between
model years.
N/A
Fuel Production,
Transport,
Distribution &Use
Emissions calculated external to
model outputs using the R&D
GREET model based on volume
assumptions.
Calculated using R&D GREET
model-based emissions factors.
Fossil Fuel Use
Emissions calculated external to
model outputs using the R&D
GREET model based on volume
assumptions.
Use of fossil fuels is not represented
in GLOBIOM. We assume biofuels
displace use of fossil fuels on a one-
for-one energy equivalent basis.
GLOBIOM model outputs for land use change (LUC) emissions represent cumulative
emissions over the prior decade (e.g., the GLOBIOM output in 2040 represents emissions from
2031-2040). To estimate LUC emissions for unrepresented years, we assume a constant rate of
change in land use area over each decade, i.e., we use a linear interpolation assumption for total
area of each land type. Since emissions are determined based on the area of annual land
transitions, and we assume the amount and types of land changing in each individual year
throughout a decade are the same, LUC emissions estimates are constant for each decade, with
one exception. For the decade 2021-2030, we make alternative assumptions to represent the
specific volume changes in 2026, 2027, and 2028. The model year 2030 outputs represent
analytical year 2028 fuel volumes and their associated land use requirements. Thus, differences
in land use change emissions outputs between the Volume Scenarios and the No RFS Baseline
represent the LUC emissions associated with the difference in land use requirements to produce
the specified 2028 fuel volumes. To allocate those emissions to individual years, we scale the
2030 LUC emissions estimate by the year-over-year volume difference for each year as
percentage of 2028 volume difference. In other words, we allocate the LUC emissions
proportionally to the three years 2026, 2027, and 2028 based on how much of the total change
(relative to the No RFS Baseline) in volumes happens in those years. These scalar factors are
presented in the top row of Table 5.1.3.2-2.
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Table 5.1.3.2-2: Emissions Adjustment Scalars for Economic Modeling of the Low and
High Volume Scenarios Compared to the No RFS Baseline
Low Volume Minus
No RFS
High Volume Minus
No RFS
2026
2027
2028
2026
2027
2028
Year-over-year volume difference as
percentage of 2028 volume difference
96.3%
1.6%
2.1%
79.2%
10.2%
10.6%
Cumulative volume difference as
percentage of 2028 volume difference
96.3%
97.9%
100.0%
79.2%
89.4%
100.0%
For livestock production emissions and those crop production emissions components that
are endogenously represented in GLOBIOM, we linearly interpolate emissions estimates for non-
modeled years. This represents an assumption of linear change in crop production and livestock
production activity between modeled years. For 2026, 2027, and 2028 we follow a similar
approach to allocation of LUC emissions estimates, with one key difference: we scale the 2030
crop / livestock production emissions estimates by the cumulative volume difference for each
year as percentage of 2028 volume difference rather than the year-over-year difference. These
emissions correspond with ongoing activity (crop / livestock production), rather than one-time
releases of stored carbon as is the case for LUC emissions, so we adjust by the cumulative total
change in that activity. These scalar factors are presented in the bottom row of Table 5.1.3.2-2.
Components of crop production emissions that are endogenously represented in
GLOBIOM outputs are limited to non-C02 emissions associated with application of manure and
fertilizer, and CH4 emissions associated with the cultivation of rice. Other components of crop
production emissions that are not represented in GLOBIOM include non-C02 emissions from
burning or decomposition of crop residues, pesticide use and on-farm energy use (e.g., use of
diesel fuel to operate farm equipment). We estimate crop production emissions from these
unrepresented components using emissions factors developed for Argonne National Laboratory
and for use in implementation of the 45Z tax credits.282 These factors are defined by region and
by crop as emissions by GHG per metric ton of production. These emissions factors and
necessary region mappings are contained in an Excel workbook in the docket for this proposal.283
As discussed above, GLOBIOM does not endogenously represent the production,
transport, distribution, or use of fuels, including biofuels. To represent emissions associated with
those lifecycle stages for the assessed volumes in the GLOBIOM scenarios, we use estimated
emissions factors for those stages of the assessed crop-based fuels to make post-hoc adjustments.
The emissions factors used for these adjustments are presented in Table 5.1.3.2-3.
282 Crop production emissions estimates under the 45Z tax credits are documented in: DOE, "Guidelines To
Determine Life Cycle Greenhouse Gas Emissions of Clean Transportation Fuel Production Pathways Using 45ZCF-
GREET," January 2025. https://www.energy.gov/sites/default/files/2025-01/45zcf-greet user-manual.pdf.
283 Factors are defined using the GTAP region definitions. In order to apply factors, we aggregate crop production
outputs by GLOBIOM region to regions consistent with GTAP definitions. See details in "Set 2 NPRM Climate
Change - Crop Production Emissions Factors," available in the docket for this action.
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Table 5.1.3.2-3: Emissions Factors (gCChe/MJ fuel used) Associated With Fuel Production
Fuel
Production
Transport,
Distribution & Use
Subtotal
Biodiesel: Soybean Oil
3.8
1.1
4.9
Biodiesel: Canola Oil
3.8
1.1
4.9
Renewable Diesel: Soybean Oil
10.1
1.2
11.3
Renewable Diesel: Canola Oil
10.1
1.2
11.3
Ethanol: Corn Starch
25.5
1.4
26.9
We estimated all these emissions factors using data and emissions factors from the R&D
GREET model. The fuel production emissions include the emissions associated with converting
the feedstock to the finished fuel. The fuel production emissions do not include emissions
associated with feedstock production or delivering the feedstocks to the biofuel plant, as those
emissions are estimated with GLOBIOM. The fuel production emissions estimates use energy
allocation to account for coproducts (see Chapter 5.1.2.2 for further discussion on the choice of
energy allocation). For biodiesel and renewable diesel production, we use the simple system-
level allocation method whereby the total fuel production emissions are allocated among the
coproducts on an energy basis. For ethanol production, we allocate all the fuel production
emissions to the ethanol, except that we allocate all of the emissions associated with corn oil
extraction to the distillers corn oil (see Chapter 5.1.2.2 for further discussion on ethanol
production allocation methods).
The transportation, distribution and use emissions include all the emissions downstream
of the fuel production facility all the way to ultimate use of the fuel. As discussed above in
Chapter 5.1.2.2, we exclude the biogenic CO2 emissions from fuel combustion as we treat this as
biogenic carbon recently removed from the atmosphere. Note that the GLOBIOM analysis
estimates all the significant emissions associated with producing the feedstocks for these biofuels
(including market-mediated land use changes). For these reasons, it would not be accurate to
characterize our analysis as assuming that these fuels are "carbon neutral". Rather, we account
explicitly for uptake of carbon in biomass and the release of that carbon as biomass degrades, is
transformed into other states, and/or is consumed, along with all other relevant emissions and
sinks associated with the production and use of these fuel products.
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
volumes) in the United States and apply emissions factors representing process energy inputs for
renewable fuel production as estimated in R&D GREET (See Chapter 5.1.2.4). These emissions
factors are similarly used in our assessment of waste- and byproduct-based fuels.
5.1.3.3 Scenario Implementation: GCAM
While GCAM operates in five-year model timesteps instead of the 10-year steps that
GLOBIOM uses, the scaling, translating, and interpolation necessary for implementing the Low
Volume Scenario, High Volume Scenario, and No RFS Baseline is broadly similar. Differently
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from GLOBIOM, the version of GCAM used in this analysis represents production, trade and
consumption of biodiesel produced from various vegetable oils. However, it does not represent
renewable diesel produced from vegetable oils separately from biodiesel. To implement the
volumes specified in the Volume Scenarios and Proposed Volumes in GCAM, we assume that
all volumes (by energy content) of renewable diesel are instead biodiesel. This assumption
ensures that roughly similar demands for feedstock vegetable oils are represented in our GCAM
simulations and that an equivalent amount of biofuel on an energy equivalent basis enters the
market to displaced fossil-based alternatives.
For GCAM model year 2020, we specify volumes of U.S. consumption for each of the
represented biofuels using RFS administrative data by taking a five-year average (2018-2022) of
RINs generated for that fuel and feedstock. For GCAM model year 2025, U.S. biofuel
consumption levels we specify volumes of corn ethanol and biomass-based diesel (represented as
biodiesel in GCAM) using the updated projections for 2025 biofuel consumption levels
discussed in Chapter 2.2 and presented in Table 2.2-3. Because GCAM does not represent the
year 2028 specifically, in order to represent the 2028 volumes in the three-year Low and High
Volume Scenarios and the accompanying No RFS Baseline, we specify volumes in model year
2030 to match the corresponding values for each scenario in 2028 (see Table 5.1.1-1 for volumes
differences compared to No RFS) 284 We then assume that all effects in the model results are
lagged by two years from the analytical scenario year (e.g., GCAM outputs for model year 2045
represent analytical scenario year 2043, interpolated GCAM outputs for year 2036 represent
analytical scenario year 2034).
We use GCAM model outputs to estimate five different categories of emissions, either as
is or using post hoc adjustments or assumptions to supplement components which are not
represented endogenously in GCAM. These categories are summarized in Table 5.1.3.3-1.
284 All volume specifications for GCAM and GLOBIOM scenarios are contained in "Set 2 NPRM Climate Change -
Economic Model Scenario Specifications," available in the docket for this action.
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Table 5.1.3.3-1: Summary of Emissions Categories and Adjustments for GHG Emissions
Estimates Using GCAM
Emissions Category
Interpolation Notes
Adjustments
Land Use Change
Annual emissions reported from
GCAM based on an assumed linear
interpolation of area of land use change
between model years.
N/A
Crop Production
Linear interpolation of crop production
emissions between model years.
N/A
Livestock Production
Linear interpolation of livestock
production emissions between model
years.
N/A
Other Industrial3
Linear interpolation of other industrial
emissions between model years.
N/A
Fossil Fuel Use
Linear interpolation of emissions from
fossil fuel use between model years.
Adjustment using emissions
factor from the R&D GREET
model accounting for difference
between biodiesel and
renewable diesel in production
& downstream emissions.
a "Other Industrial" emissions in GCAM represent changes in emissions from other industrial processes that are
impacted by market-mediated effects in biofuel scenarios. These effects are very small relative to the other
categories of emissions. For this reason, these emissions are aggregated with other energy sector emissions in our
presentation of scenario results in Chapters 5.2 and 5.3.
While GCAM operates in five-year timesteps, LUC emissions are estimated within a land
allocation module that results in annual emissions estimates, including for non-modeled years.
These annual estimates implicitly assume a linear interpolation of land area change between
model years, but account for non-linear processes affecting CO2 emissions, including vegetative
carbon uptake and soil carbon loss after land transitions.285 Because GCAM outputs provide
annual estimates of LUC emissions, no interpolation is needed: we instead simply translate the
stream of emissions outputs by two years (i.e., analytical year 2029 corresponds with GCAM
model year 2031 outputs). However, for analytical years 2026, 2027, and 2028 we need to make
similar adjustments as were done in the GLOBIOM modeling to account for the specific biofuel
consumption volumes in those years which are unable to be represented in GCAM. Because
GCAM's LUC emissions reporting implicitly assumes linear changes in land area between non-
modeled years, the emissions reported for 2026 represents emissions associated with roughly one
fifth of the land area change that takes place between the 2025 and 2030 model time steps.286 We
assume the total land area change in 2030 represents the area change necessary to meet the 2028
volume specifications implemented as 2030 targets in these scenarios. Thus, we estimate the
total LUC emissions between analytical years 2026 and 2028 to be five times the LUC emissions
reported in model year 2026. We then allocate those LUC emissions between 2026, 2027, and
285 GCAM's land allocation module and land use change emissions are documented in the GCAM online
documentation at: https://igcri.github.io/gcam-doc/land.html.
286 This assumption ignores the non-linear effects accounted for in the GCAM land allocation and emissions
accounting module, but is necessary for estimating three years of scaled emissions from emissions reporting for a
five-year modeled timestep.
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2028 using the same scalars used in the GLOBIOM modeling, presented in the top row of Table
5.1.3.2-2.
For livestock production emissions, crop production emissions, other industrial
emissions, and emissions from fossil fuel use, we linearly interpolate emissions estimates for
non-modeled years. For 2026, 2027, and 2028 we follow the same emissions allocation approach
as was used in the GLOBIOM modeling for interpolated emissions categories; we scale the 2030
emissions estimates by the cumulative volume difference for each year as percentage of 2028
volume difference. These scalar factors are presented in the bottom row of Table 5.1.3.2-2.
Note that, because detailed energy demands are endogenously represented in GCAM, the
emissions estimates for differences in 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. However, using biodiesel volumes as a proxy for renewable diesel production in
our scenarios does not account for the different input and energy requirements between biodiesel
and renewable diesel production. Given the substantial volumes of renewable diesel in the
scenarios we are assessing, this is a necessary source of emissions to represent in our assessment.
We account for these emissions by comparing estimates from GREET of the emissions of
producing, distributing, and using renewable diesel versus the emissions from the same lifecycle
stages for biodiesel. We then apply factors developed using the R&D GREET model
representing the marginal additional emissions associated with producing renewable diesel rather
than biodiesel to the volume of renewable diesel in the assessed scenarios. These factors are
calculated simply as the difference between emissions factors for renewable diesel and biodiesel
produced from a given feedstock in Table 5.1.3-3.
5.2 Assessment of Analytical Volume Scenarios
This section presents results of the climate change analysis for the Low and High Volume
Scenarios relative to the No RFS Baseline under an assumed three-year standards rule (i.e.,
setting volumes for 2026, 2027, and 2028). Modeling methods, assumptions and scenario
implementation are described in Chapter 5.1. Chapter 5.2.1 provides a summary of the emissions
impacts of waste- and byproduct-based fuels under these scenarios. Chapter 5.2.2 provides an
extensive description of the modeling undertaken to assess the emissions impacts of crop-based
fuels.
5.2.1 Waste- and Byproduct-based Fuels
As discussed in Chapter 5.1.2, estimates of changes in emissions between scenarios for
fuels produced from waste and byproduct feedstocks are calculated by comparing emissions
intensity estimates from R&D GREET for the biofuel to emissions intensity estimates for the
fossil fuel their use is assumed to displace. This results in a per-megajoule emissions impact
factor for each renewable fuel product assessed in this manner, which is then multiplied by the
difference in volumes of those renewable fuels for the scenario under consideration. However,
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volumes of all the waste- and byproduct-based fuels are identical under the Low and High
Volume Scenarios, so the emissions impacts of these fuel volumes relative to the No RFS
Baseline are identical under either scenario. These emissions impacts, assuming 30 years of
continued volumes as described in Chapter 5.1.1.1, are presented in CChe287 in Table 5.2.1-1.
Table 5.2.1-1: GHG Emissions (Million Metric Tons CChe) for Waste- and Byproduct-
Based Fuels in the Low and High Volume Scenarios (2026-2028 Standards) Estimated
Using R&D GREET
Renewable
Renewable
Renewable
CNG/LNG:
Biodiesel:
Biodiesel:
Biodiesel:
Diesel:
Diesel:
Diesel:
Year
Biogas
Corn Oil
UCO
Tallow
Corn Oil
UCO
Tallow
Total
2026
-2.8
-0.3
0.2
0.2
-0.6
-4.8
-4.3
-12.3
2027
-2.9
-0.4
0.3
0.2
-0.3
-5.8
-5.3
-14.2
2028
-3.0
-0.3
0.2
0.2
-0.4
-6.8
-6.2
-16.2
2029
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2031
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2032
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2033
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2034
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2035
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2036
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2037
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2038
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2039
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2040
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2041
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2042
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2043
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2044
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2045
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2046
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2048
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5.2.2 Crop-based Fuels
In this section we discuss results of the scenarios described above when modeled in the
GCAM and GLOBIOM frameworks. Based on the differing scopes, designs, strengths and
287 For simplicity, we report all emissions in this chapter in terms of CO2 equivalence using GWPs published in
AR5. However, emissions estimates within these analyses are calculated for three major GHGs: CO2, CH4 and N20.
Estimates disaggregated by gas are provided, where available, in "Set 2 NPRM Climate Change Analyses,"
available in the docket for this action.
202
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limitations of these frameworks, GCAM and GLOBIOM scenario results provide a range of
potential effects of the assessed volume changes on the energy, agriculture, land use, and
livestock sectors, with corresponding differences in GHG emissions associated with those
effects. While the sections below compare and discuss the model results in detail for each of
these sectors, it is useful for understanding the intertwined effects to start from a narrative
summary comparison of the scenario results in each model.
At a high level, in the GCAM simulations, the additional demand for biofuels is met
through a combination of expansion of cropland both within and outside the U.S., some
"swapping out" of soybean oil for other vegetable oils from food usage, some increased
imports288 of fuels currently produced in non-U. S. regions, and decreased net exports of
feedstock crops. In the GLOBIOM simulations the additional demand for biofuels is met by
producing additional feedstock crops through crop switching in the U.S. and cropland expansion
outside the U.S., and through diversion of soybean and canola oil from food and other uses
which are subsequently backfilled with expanded meat and dairy consumption and greater
consumption of other vegetable oils.
Energy sector impacts (Chapter 5.2.2.1) illustrate a significant distinction between the
scope of these two modeling frameworks. Energy demands and trade in energy commodities are
not represented within GLOBIOM, so the entirety of the increased biofuel demand represented in
the scenarios must be met by increasing U.S. production of those fuels. In contrast, GCAM
represents global energy demands and trade, thus allowing a portion of the additional demand for
biofuels to be met through increased U.S. net imports, effectively lowering the overall global
increase in biofuel usage. In terms of crop production (Chapter 5.2.2.2) and land use (Chapter
5.2.2.3), GCAM tells a story primarily of cropland expansion, and to a lesser extent, crop
switching and substitution between vegetable oils, while GLOBIOM results emphasize greater
crop switching and substitution between vegetable oils, with cropland expansion playing a lesser
but still substantial role outside the U.S. Finally, impacts on livestock production (Chapter
5.2.2.4) are minimal in GCAM, with additional oilseed meals replacing other feed commodities
while overall livestock production remains relatively unchanged. In contrast, in GLOBIOM
simulates livestock production increases in response to the availability of additional oilseed meal
for feed use. This shift towards more meat and dairy consumption for food partially offsets the
decrease in consumption of vegetable oils for food.
The sections below present figures and data describing these observations in detail.
Unless otherwise noted, figures and values discussed in this section represent unadjusted model
outputs from the GCAM and GLOBIOM simulations. This discussion of model outputs
describes estimated tradeoffs and responses associated with the modeled scenarios and should be
understood within the boundaries of each respective model. Additionally, the GCAM and
GLOBIOM simulations discussed in this section do not explicitly represent the "import RIN
reduction" discussed in Preamble Section VIII. Trade in fuels and feedstock commodities may be
expected to differ from the results shown if we were able to represent the "import RIN
reduction" proposal in the simulations discussed in this section.
288 Note that the GCAM and GLOBIOM simulations undertaken for this analysis do not explicitly represent the
proposed "import RIN reduction" discussed in Preamble Section VIII.
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In some cases, model outputs provide incomplete accounting of all categories of GHG
impacts associated with the volumes of renewable fuels under assessment. For example,
emissions associated with fossil fuel use and emissions associated with some categories of crop
production are not endogenously represented in GLOBIOM; these results are instead accounted
for in post-hoc adjustments to model outputs. These adjustments are described where relevant in
the sections below and in Chapters 5.1.3.2 and 5.1.3.3.
Additionally, model outputs are reported in model years which approximate the analytical
year for which the volumes in the Low and High Volume Scenarios are defined. For example,
GCAM and GLOBIOM provide outputs for a 2030 time step, which are then translated to
correspond with analytical year 2028. See Chapter 5.1.3 for additional explanation of this
adjustment.
Unless otherwise indicated, figures below represent the Low Volume Scenario for
standards that would apply to three years: 2026, 2027, and 2028, and assume 2028 volumes are
held constant in future years. Results from modeling the High Volume Scenario show similar
directional effects, with magnitudes roughly proportional to the greater volumes. Emissions
impact estimates for both scenarios are presented Chapter 5.2.3. Importantly, the volumes and
years modeled in the scenarios discussed in this section do not match the Proposed Volumes and
years. Due to the significant lead time required to complete complex global economic simulation
modeling, this analysis was completed before the Proposed Volumes were determined. While we
were unable to complete additional modeling in the economic modeling frameworks used to
assess the impacts of crop-based fuels, we have used the completed simulations described in this
section to derive an estimate of the impacts of the Proposed Volumes. That derivation and
resulting emissions estimates are described in Chapter 5.3. We intend to revise this analysis for a
final rule to reflect finalized volume standards.
5.2.2.1 Energy Use
Figure 5.2.2.1-1 illustrates how the Low Volume Scenario and the No RFS Baseline are
implemented in GCAM and GLOBIOM, along with several key differences between these
models and how they represent energy commodities. The left-most panes of Figure 5.2.2.1-1
show the difference between the Low Volume Scenario and the No RFS Baseline in
consumption of corn starch ethanol and soy- and canola-based diesel products—roughly 0.31
quadrillion BTUs combined in 2028, which is represented in model year 2030. The version of
GCAM used in this analysis represents biodiesel but not renewable diesel,289 so all volumes of
soy- and canola-based biomass-based diesel are represented as biodiesel in GCAM.290 In
GLOBIOM, biodiesel and renewable diesel are differentiated, as indicated in the figure.
289 See Chapter 5.1.3.1 for a description GCAM and the specific version used in this analysis.
290 Renewable diesel and biodiesel have different process input requirements, which is accounted for in the final
emissions estimates. This is described in more detail in Chapter 5.1.3.2.
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Figure 5.2.2.1-1: Difference in Consumption of Energy Commodities (Quadrillion BTUs) in
the Low Volume Scenario Relative to the No RFS Baseline
Biofuels
USA I IMon-USA
Fossil Fuels
USA I Non-USA
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A key difference between the GCAM and GLOBIOM models is that GCAM
endogenously represents energy demands, production, trade and use of fuels, and GHG
emissions associated with producing and using fuels, and GLBOIOM does not. This difference
in scope results in several important differences in modeled outcomes which are apparent in
Figure 5.2.2.1-1. First, because GLOBIOM does not represent energy commodities within its
economic logic—the model includes only exogenously defined additional demand for
agricultural feedstocks to produce bioenergy products—GLOBIOM outputs do not include any
displacement or other economic effects associated with use of fossil fuels, nor do GLOBIOM
simulations allow for any changes in renewable fuel production and use in the non-U. S. regions
as market-mediated responses to the exogenously defined consumption shock within the U.S.
This boundary in the scope of GLOBIOM is apparent in the absence of effects depicted in the
bottom three rightmost panels in Figure 5.2.2.1-1. Because these effects are not represented in
the GLOBIOM results, we exogenously account for displacement of refined oil and process
energy requirements in our emissions accounting for the GLOBIOM scenarios. We assume a
straightforward one-for-one energy equivalent displacement of fossil gasoline (for ethanol
volumes) and fossil diesel (for BBD volumes) in the U.S. and apply emissions factors
representing process energy inputs for renewable fuel production as estimated in R&D GREET
(see Chapter 5.1.3.2 for explanation of these emissions factors). In contrast, GCAM
endogenously represents effects on fossil fuel consumption and markets in the U.S. and non-U.S.
regions and effects on renewable fuel use in non-U.S. regions in its economic logic. The
remainder of the discussion in this section focuses on these effects in the GCAM scenarios.
Within the GCAM simulations, the primary effect of greater use of biofuels in the U.S. is
displacement of petroleum-based fuel consumption. This is seen in the significant decline in use
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of refined oil291 in the U.S. in Figure 5.2.2.1-1 (top middle-right panel). Outside of the U.S.,
there is a substantial decline in consumption of soy- and canola-based biodiesel (top middle-left
panel). This decline represents biofuels that were produced and consumed in non-U.S. regions in
the No RFS Baseline, but which were instead traded and consumed in the U.S. to meet the higher
consumption targets under the Low Volume Scenario.292 The decline in consumption of these
biofuels in non-U.S. regions corresponds with a similar increase in use of refined oil to meet
energy demand (top right panel). Observed changes in natural gas consumption in the U.S. and
non-U.S. correspond with changes in natural gas demand for biofuel production in the regions
respectively.
Figure 5.2.2.1-2: Difference in Liquid Fuel Consumption Relative to the Total Cumulative
Difference in U.S. Biofuel Consumption for the Low Volume Scenario in GCAM
100%
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USA
Non-USA
67%
-38%
-50%
-100%
-105%
Biofuels
Refined Oil
In addition to the first order displacement and backfilling effects observed above, subtler
demand shifts responding to market signals also affect consumption of refined oil in the GCAM
simulations. Figure 5.2.2.1-2 illustrates the cumulative differences in consumption of biofuels
and refined oil in the U.S. and non-U.S., expressed as a percentage of the cumulative difference
in U.S. biofuel consumption, i.e., the "shock." Note that the difference in consumption of
"Biofuels" in the U.S. in the figure is, by definition, 100%. First, the cumulative decline in
refined oil consumption within the U.S. is 105% of the full cumulative shock—i.e., 5% greater
than one-for-one energy equivalent displacement. The decline in fuel consumption is greater than
one-for-one because the additional renewable fuels being blended into the U.S. fuel supply in the
assessed Volume Scenarios are estimated by GCAM to be costlier on a per-unit-of-energy-basis
than the petroleum gasoline and diesel they displace. This estimate generally comports with our
analysis and findings regarding the costs of these fuels presented in Chapter 10 of the DRIA. As
these fuels displace less costly fuels in the domestic transportation fuel pool, the average cost of
291 "Refined oil" is an aggregated commodity in GCAM representing gasoline, diesel, jet fuel, and other fuels
produced from crude oil. End use sectors, including transportation, residential, and industrial sectors, represent
different energy demands that can be met with refined oil, biofuels, and other sources of energy.
292 Note that the GCAM and GLOBIOM simulations undertaken for this analysis do not explicitly represent the
proposed "import RIN reduction" discussed in Preamble Section VIII.
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transportation fuel increases, which in turn causes transportation fuel demand to decline on the
margin.
Additionally, Figure 5.2.2.1-2 shows that, over the full analytical timeframe (2026-
2055), the decline in consumption of biofuels in non-U.S. regions is equivalent to 52% of the
increase in U.S. consumption of biofuels. In other words, the total global cumulative change in
biofuel consumption is 48% of the cumulative U.S. volume shock. Furthermore, we see that the
increase in use of refined oil in non-U.S. regions exceeds the decrease in use of biofuels in those
regions—67% vs 52% respectively. In other words, the increase in refined oil use outside of the
U.S. goes beyond backfilling for the decrease in biofuel use by an additional 15% of the shock.
This effect—often described in the literature as oil rebound—represents increased demand for
refined oil outside the U.S. because of lower oil and refined oil consumption in the U.S. and
consequently, lower global prices for those commodities. As the U.S. decreases its consumption
of refined petroleum, this increases the supply of petroleum to the rest of the global market. This
increase in supply in turn depresses prices, which stimulates additional petroleum demand on the
margin.
5.2.2.2 Crop Production
The impacts of differing biofuel volumes on agricultural commodity demand are
predominantly driven through additional demand for vegetable oils because oilseed oil-based
biofuels represent the substantial majority of the difference in consumption of crop-based
biofuels between the Low Volume Scenario and the No RFS Baseline. Thus, we begin our
consideration of effects on crop production and use with an examination of changes in vegetable
oil consumption. Figure 5.2.2.2-1 illustrates shifts in consumption in model year 2030 of the four
categories of vegetable oils represented in GCAM and GLOBIOM (soybean oil, canola oil, palm
fruit oil, and oil from other oil crops) across three end uses—fuel production, food, and "other
uses" which represents non-fuel industrial uses and use in other commercial products such as
cosmetics.
First, we observe that use of soybean oil and canola oil to produce biofuels aligns with
the observations in Chapter 5.2.2.1; because GLOBIOM does not represent trade in fuels, all the
additional biofuel consumption in the U.S. must be met through fuel production using soybean
oil and canola oil in the U.S., and no shifts in non-U.S. biofuel production are possible. In
GCAM, non-U.S. production of soybean oil- and canola-based biofuels is affected, with canola
BBD volumes being met almost entirely through imports of fuels—sourced from both existing
and expanded production—and soybean oil-based BBD volumes being met through increased
imports of oil that is refined into BBD in the U.S. rather than in non-U.S. regions.
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Figure 5.2.2.2-1: Difference in Consumption of Oilseed Oils (Million Metric Tons) by End
Use in the Low Volume Scenario Relative to the No RFS Baseline in Model Year 2030 a
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¦ Other Oil Crops Oil ¦CanolaOil
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a Reference lines represent the net difference in consumption of a oilseed oils for a given end use and region.
Consumption of vegetable oils for food shows shifts in both models. In GCAM
simulations, in both the U.S. and non-U.S. more soybean oil is used to produce fuel rather than
consumed as food. That food consumption is replaced with consumption of the other three
represented categories of vegetable oils, but with total net consumption of vegetable oils for food
remaining roughly the same in all regions. In GLOBIOM simulations, however, while some
backfilling does occur, overall consumption of vegetable oils for food decreases as soybean oil is
shifted towards fuel production. This decrease in human consumption of vegetable oils
represents a shift in caloric intake away from vegetable oils and towards greater meat and dairy
consumption. This shift is discussed in greater detail in Chapter 5.2.2.4. Finally, GLOBIOM
simulations show much greater substitutability between vegetable oils used for "other uses," with
no significant substitution in GCAM, but a substantial shift from soybean oil to palm oil in
GLBOIOM in this use category. Based on the scale of the difference between the two models,
we believe the representation of substitutability of vegetable oils in this category warrants further
review in the future.
Next, we consider how the shifts in consumption of vegetable oils and the greater
demand for corn to produce corn ethanol impact production and trade of key commodity crops.
Figure 5.2.2.2-2 illustrates production, trade, and consumption differences for key crops in the
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Low Volume Scenario relative to the No RFS Baseline in model year 2030, which represents the
analytical scenario volumes in 2028. For each of the crop categories depicted, the darker color
represents changes in production in a region (U.S. on the left, non-U.S. on the right), while the
lighter color represents changes in net imports of that crop for the given region. Because neither
GCAM nor GLOBIOM depict inter-year storage or spoilage of commodities, all of a commodity
available after trade must be consumed. In other words, the difference in consumption in a region
is the sum of the differences in production and net imports, which is depicted in the figure with
reference lines in each column.
Figure 5.2.2.2-2: Difference in Production and Net Imports of Crops (Million Metric Tons)
in the Low Volume Scenario Relative to the No RFS Baseline in Model Year 2030a
USA | Non-USA
o
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¦ Corn, Production
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10 II® Palm Fruit, Production
15
i- ¦ Other Oil Crops, Net Imports
5 J: 5 ¦ ¦ Other Oil Crops, Production
5 § I I ® Wheat, Net Imports
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a Reference lines represent differences in consumption. Neither GCAM nor GLOBIOM represent inter-year storage
or spoilage of commodities, so consumption is, by definition, equal to production plus net import.
Several important aspects of each model's respective response to the shock are illustrated
in this figure. First, oilseed oils—primarily soybean oil—represent a significant majority of the
additional feedstocks required to meet renewable fuel volume targets in the Low Volume
Scenario relative to the No RFS Baseline. Consequently, both models show substantial increases
in soybean crushing in the U.S., with roughly one third of the additional soybeans being sourced
from changes in reduced U.S. exports (depicted as increased net imports in Figure 5.2.2.2-2) in
GCAM and roughly one half in GLOBIOM. However, as noted in Chapter 5.2.2.1, a portion of
the additional biodiesel consumed in the U.S. in the Low Volume Scenario in GCAM is
imported from non-U. S. regions. Thus, the total additional demand for soybean oil and for
soybeans to crush in the U.S. is lower in GCAM than in GLOBIOM. Additionally, the increase
209
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in soybean production and imports in the U.S. has notably differing impacts in the two models.
In GCAM, increased U.S. soybean production comes from: 1) Cropland expansion, which is
discussed in Chapter 5.2.2.3, and 2) Switching from corn to soybean production, as some use of
corn in feed can be replaced with additional soybean meal available from increased crushing. In
GLOBIOM, increased U.S. production of soybeans is achieved almost entirely from crop
switching, primarily away from wheat and "other crops." Outside of the U.S., GCAM
simulations show greater production of soybeans to make up for reduced exports from the U.S.,
with a marginal net increase in use of soybeans in non-U.S. regions reflecting increased crushing
for exporting soybean oil rather than soybeans. In GLOBIOM, there is both a substantial
decrease in imports of soybeans in non-U. S. regions, and a decrease in production of soybeans in
those regions, roughly commensurate with the increase in soy production in the U.S. As shown
in Figure 5.2.2.2-2, the GLOBIOM simulations show a more pronounced substitution effect of
palm and other vegetable oils for soybean and canola oil to meet global food and other industrial
demand, which is reflected in greater production of those crops in non-U. S. regions.
In order to meet increasing demand for canola-based diesel products, simulations in both
models show increased canola production in non-U.S. regions, primarily in Canada. Results
related to canola differ between the models largely from differences in the stage at which trade
takes place; GCAM's structure allows for more flexibility, since canola-based fuels can be
sourced from imported canola seeds (rapeseed), imported canola oil, or imported finished
biofuels, whereas in GLOBIOM, the biofuel required to meet the shock must be produced in the
U.S. so can be sourced from imported canola seeds or canola oil only.
GCAM and GLOBIOM each include estimates of GHG emissions associated with the
changes in crop production observed above. However, the categories of crop production
emissions represented in GLOBIOM are incomplete, excluding important categories such as
emissions from on farm energy use (e.g., emissions associated with using diesel fuel to run
tractors). For those categories of emissions, we use external estimates of emissions factors
associated with production of crops in different regions. Those adjustments are presented in
greater detail in Chapter 5.1.3.2.
5.2.2.3 Land Use
Changes in cropland area correspond with the production changes discussed in Chapter
5.2.2.2. Differences in cropland area between the Low Volume Scenario and the No RFS
Baseline are illustrated in Figure 5.2.2.3-1. In GCAM simulations, extensification (i.e.,
expansion of cropland area) is the most significant trend, with expansion in the harvested area of
soybeans in the U.S. and expansion of soybeans, canola and other oil crops in non-U.S. regions
being the predominant effects. Switching cropland from cultivation of corn and other crops to
accommodate expanded oilseed production is a notable but less prominent impact in the GCAM
simulations. In GLOBIOM simulations, there is substantially more crop switching and less
expansion of new cropland when compared to the GCAM results. Expansion of soybean
cultivation in the U.S. replaces primarily cultivation of wheat and other crops. In non-U.S.
regions, the substitution of non-soy vegetable oils for soybean oil in food and other end uses
corresponds with significant crop switching away from soybeans and towards palm and canola
cultivation. Displaced production of wheat and other crops in the U.S. is also made up for
210
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through increasing cultivation of these crops in non-U. S. regions, which have more elastic land
transition assumptions in GLOBIOM.293
Figure 5.2.2.3-1: Difference in Cropland Area (Million Hectares) in the Low Volume
Scenario Relative to the No RFS Baseline Over Time3
USA
Non-USA
World
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3 Reference lines represent the net difference in cropland area in a given region and time period.
Cropland expansion differs in magnitude between simulations in the two models, as
noted above, but also in which land types are replaced. Differences in land cover area between
the Low Volume Scenario and the No RFS Baseline are illustrated in Figure 5.2.2.3-2. In
GCAM, expansion of cropland replaces substantial quantities of less intensively used or natural
lands, including unmanaged forest, unmanaged pasture, grassland and shrubland. While
vegetative and soil carbon sequestered in the landscape differs greatly across climates, regions
and land cover types, in general natural lands, and especially unmanaged (i.e., not commercially
productive) forests, hold more carbon than other land cover categories. Consequently, the
expansion of cropland into these natural land types results in substantial carbon releases in the
GCAM simulations (Figure 5.2.2.3-3), with emissions taking place across most regions.
293 GLOBIOM includes assumptions that preclude expansion of cropland and other commercially productive land
cover types into natural areas in the USA, including into natural forest and grassland. This is an important
assumption that we believe warrants further investigation and consideration of alternative implementation methods
in future analyses. A consequence of this assumption is that most changes in crop production in the U.S. are
achieved through switching cropland from one crop to another (i.e., "crop switching"), rather than through
expanding cropland into other land cover types.
211
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Figure 5.2.2.3-2: Difference in Land Area (Million Hectares) in the Low Volume Scenario
Relative to the No RFS Baseline Over Time
USA
Non-USA
World
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Grassland &Shrubland
Other Arable Land
Forest (managed)
Forest (unmanaged)
Pasture (managed)
Pasture (unmanaged)
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00 ^ uo ID 00 LO 10 00 ^ LO "0
o o o o o o o o o o o o
C\J CSJ C\J OsJ CM CVJ CM (\J C\J CM CM CM
As discussed above, in GLOBIOM simulations cropland expansion is constrained within
the U.S., with most of the expansion seen happening at the expense of "other arable land"—a
category that includes cropland pasture and other unused cropland, with lower carbon densities
compared with other unmanaged land cover types. Most cropland expansion in GLOBIOM
simulations occurs in non-U. S. regions, replacing unmanaged forest, other arable land and,
increasingly, managed pasture. Additionally, GLOBIOM simulations include greater substitution
between vegetable oils compared with GCAM, with this substitution effect increasing over time.
As a result, the difference in cropland area between the Low Volume Scenario and the No RFS
Baseline increases gradually over time, resulting in additional land use change emissions in
future years (Figure 5.2.2.3-3). Additionally, this vegetable oil substitution effect results in a
greater production of palm oil which primarily takes place in Southeast Asia (included in "Rest
of Asia" in the regional aggregation in Figure 5.2.2.3-3). Regions with the highest levels of oil
palm production—Indonesia and Malaysia—also have high concentrations of peat soils. When
peat lands are developed for crop cultivation, e.g., for oil palm cultivation, carbon within the peat
can continue to oxidize (be released into the atmosphere as CO2) for several decades. This
phenomenon is represented in both GCAM and GLOBIOM, and can be seen in the continued
land use change emissions in the Rest of Asia region through 2060 in Figure 5.2.2.3-3.
212
-------
Figure 5.2.2.3-3: Difference in GHG Emissions (Million Metric Tons of CChe) From Land
Use Change (LUC) in the Low Volume Scenario Relative to the No RFS Baseline Over
Time, and by Region in Which the Emissions Take Place a
GCAM GLOBIOM Rscjion
¦ USA
¦ China
¦ Europe
¦ Africa
¦ Rest of World
¦ Rest of Latin America
¦ Brazil
¦ Rest of Asia
o 50
u
U)
.0 40
5 20
2030 2040 2050 2060 | 2030 2040 2050 2060
1 Reference lines represent the net difference in LUC emissions in a given region and time period.
5.2.2.4 Livestock Production
Impacts of the Low Volume Scenario relative to the No RFS Baseline on livestock
production can be understood through examination of use of agricultural commodities for
livestock feed and for human food consumption. This is illustrated in Figure 5.2.2.4-1, with
consumption for feed displayed in the left pane and consumption for food in the right pane.
213
-------
Figure 5.2.2.4-1: Difference in Commodities Used for Livestock Feed and Food
Consumption (Million Metric Tons) in the Low Volume Scenario Relative to the No RFS
Baseline Over Time a
USA
Feed
Non-USA
World
10
o 5
-5
10
2 £
CO
O ^
w O
uufmllii
...Him
"5
o o o o
m ^ in ID
o o o o
(\j c\j <\j c\j
o
n-;
O
CM
o
-------
Figure 5.2.2.4-2: Difference in Meat and Dairy Production (Million Metric Tons) in the
Low Volume Scenario Relative to the No RFS Baseline Over Time3
Meat
USA
Non-USA
0.6
0.4
10
USA
Dairy
Non-USA
<
U
0.2
<
u
0.5
0.0
0.0
CO
O
i
ID
0.6
0.4
0.2
0.0
o
CO
o
(\l
O
o
C\J
o
LO
o
(\J
o
<£)
O
CM
O
CO
o
CM
o
o
CM
O
m
o
CM
o
o
CM
CQ CL>
1.0
O 5 0.5
« §
0.0
O
CO
O
CM
O
o
CM
O
m
o
CM
o
o
CM
O
m
o
CM
o
o
CM
o
m
o
CM
o
o
CM
Beef
Sheep and Goat
Pork
Poultry
Dairy
a Reference lines in the "Meat" panel represent the net difference in meat production across all categories of
livestock for a given region and time period.
The differences in meat and dairy consumption noted above are also illustrated in Figure
5.2.2.4-2, which shows differences between the Low Volume Scenario and No RFS Baseline in
meat and dairy production over time. Again, we observe relatively small changes in meat and
dairy production in the GCAM simulations. In the GLOBIOM simulations, meat and dairy
production expands to meet food demand as vegetable oils are shifted from food to biofuel
consumption. However, over time additional demand for soybean oil to meet U.S. BBD
production targets is increasingly met by shifting soybean oil out of "other uses" rather than
away from food usage, so less meat and dairy is needed by 2060 to meet human food demand.
5.2.2.5 Emissions
Because emissions outputs from GCAM and GLOBIOM are not comparable without first
performing the post-hoc adjustments, interpolation, scaling and translation described in Chapter
5.1.3.2 and Chapter 5.1.3.3, in this section we provide the adjusted model results. This differs
from the unadjusted model outputs presented and discussed in Chapters 5.2.2.1 through 5.2.2.4.
215
-------
Tables 5.2.2.5-1 through 4 provide 30-year annual GHG emissions estimates, reported in
CO2Q,294 for the Low and High Volume Scenarios relative to the No RFS Baseline.
For GCAM results, emissions reported under Fossil Fuel Use include all endogenously
determined impacts on energy consumption, an adjustment for emissions associated with
renewable diesel versus biodiesel production, and relatively small emissions impacts reported
under the "other industrial" category in GCAM. For GLOBIOM results, emissions reported
under Fossil Fuel Use include assumed one-for-one displacement of fossil fuels using emissions
factors derived from R&D GREET and biofuel production and downstream emissions estimated
using factors derived from R&D GREET. Additionally, Crop Production emissions results for
the GLOBIOM modeling have been supplemented with estimated emissions factors for
categories of crop production emissions that are not represented in GLOBIOM. All of these
adjustments are described in detail in Chapter 5.1.3.3.
294 For simplicity we report all emissions in this chapter in terms of CO2 equivalence using GWPs published in AR5.
However, emissions estimates within these analyses are calculated for three major GHGs: CO2, CH4 and N20.
Estimates disaggregated by gas are provided, where available, in "Set 2 NPRM Climate Change Analyses,"
available in the docket for this action.
216
-------
Table 5.2.2.5-1: GHG Emissions (Million Metric Tons CChe) for the Low Volume Scenario
(2026-2C
128 Standards) Estimated Using GCAM
Land Use
Crop
Livestock
Fossil Fuel
Year
Change
Production
Production
Usea
Total
2026
287.4
4.7
-0.4
-10.0
281.7
2027
4.9
4.8
-0.4
-10.2
-0.8
2028
6.2
4.9
-0.4
-10.4
0.4
2029
8.1
4.9
-0.4
-10.2
2.3
2030
5.8
4.8
-0.4
-10.1
0.2
2031
4.0
4.8
-0.4
-10.0
-1.6
2032
2.3
4.7
-0.3
-9.9
-3.2
2033
0.9
4.7
-0.3
-9.8
-4.6
2034
0.7
4.6
-0.3
-9.6
-4.6
2035
-0.1
4.6
-0.3
-9.5
-5.3
2036
-0.8
4.6
-0.3
-9.3
-5.9
2037
-1.4
4.5
-0.3
-9.2
-6.4
2038
-2.0
4.5
-0.3
-9.0
-6.8
2039
-2.0
4.4
-0.3
-8.9
-6.7
2040
-2.4
4.4
-0.3
-8.8
-7.1
2041
-2.7
4.4
-0.3
-8.7
-7.4
2042
-3.0
4.3
-0.3
-8.6
-7.6
2043
-3.3
4.3
-0.3
-8.5
-7.8
2044
-2.6
4.3
-0.3
-8.5
-7.1
2045
-2.7
4.3
-0.3
-8.5
-7.3
2046
-2.9
4.2
-0.3
-8.5
-7.4
2047
-3.1
4.2
-0.3
-8.4
-7.6
2048
-3.2
4.2
-0.3
-8.4
-7.7
2049
-0.2
4.2
-0.3
-8.4
-4.7
2050
-0.2
4.2
-0.3
-8.4
-4.7
2051
-0.1
4.1
-0.3
-8.4
-4.6
2052
-0.1
4.1
-0.3
-8.4
-4.6
2053
-0.1
4.1
-0.3
-8.4
-4.6
2054
0.0
4.1
-0.3
-8.4
-4.5
2055
0.0
4.1
-0.3
-8.4
-4.6
a Emissions reported under Fossil Fuel Use include all endogenously determined impacts on energy consumption, an
adjustment for emissions associated with renewable diesel versus biodiesel production, and relatively small
emissions impacts reported under the "other industrial" category in GCAM.
217
-------
Table 5.2.2.5-2: GHG Emissions (Million Metric Tons CChe) for the High Volume Scenario
(2026-2C
128 Standards) Estimated Using GCAM
Land Use
Crop
Livestock
Fossil
Year
Change
Production
Production
Fuel Usea
Total
2026
334.0
5.5
-0.4
-11.2
327.9
2027
43.2
6.2
-0.4
-12.5
36.5
2028
44.5
6.9
-0.5
-13.8
37.2
2029
11.7
6.9
-0.5
-13.6
4.4
2030
8.5
6.8
-0.5
-13.5
1.3
2031
5.8
6.7
-0.5
-13.3
-1.3
2032
3.4
6.7
-0.5
-13.1
-3.5
2033
1.4
6.6
-0.5
-12.9
-5.4
2034
1.4
6.6
-0.5
-12.7
-5.3
2035
0.2
6.5
-0.4
-12.5
-6.3
2036
-0.9
6.4
-0.4
-12.2
-7.1
2037
-1.8
6.4
-0.4
-12.0
-7.9
2038
-2.6
6.3
-0.4
-11.8
-8.5
2039
-2.5
6.3
-0.4
-11.7
-8.3
2040
-3.1
6.2
-0.4
-11.5
-8.8
2041
-3.6
6.2
-0.4
-11.4
-9.2
2042
-4.1
6.2
-0.4
-11.3
-9.6
2043
-4.5
6.1
-0.4
-11.1
-9.9
2044
-3.5
6.1
-0.4
-11.1
-8.9
2045
-3.8
6.1
-0.4
-11.0
-9.1
2046
-4.0
6.0
-0.4
-11.0
-9.3
2047
-4.2
6.0
-0.4
-10.9
-9.5
2048
-4.4
6.0
-0.4
-10.9
-9.7
2049
-0.3
5.9
-0.4
-10.8
-5.6
2050
-0.2
5.9
-0.4
-10.8
-5.5
2051
-0.2
5.9
-0.4
-10.8
-5.4
2052
-0.1
5.9
-0.4
-10.8
-5.4
2053
-0.1
5.9
-0.4
-10.8
-5.4
2054
-0.1
5.8
-0.4
-10.7
-5.3
2055
-0.1
5.8
-0.4
-10.6
-5.3
a Emissions reported under Fossil Fuel Use include all endogenously determined impacts on energy consumption, an
adjustment for emissions associated with renewable diesel versus biodiesel production, and relatively small
emissions impacts reported under the "other industrial" category in GCAM.
218
-------
Table 5.2.2.5-3: GHG Emissions (Million Metric Tons CChe) for the Low Volume Scenario
(2026-2C
128 Standards) Estimated Using GLOBIO
M
Land Use
Crop
Livestock
Fossil Fuel
Year
Change
Production
Production
Usea
Total
2026
168.1
5.8
1.4
-26.0
149.3
2027
2.8
5.9
1.5
-26.4
-16.3
2028
3.6
6.0
1.5
-27.0
-15.9
2029
8.3
5.8
1.4
-27.0
-11.5
2030
8.3
5.7
1.3
-27.0
-11.7
2031
8.3
5.6
1.2
-27.0
-11.9
2032
8.3
5.4
1.1
-27.0
-12.1
2033
8.3
5.3
1.0
-27.0
-12.4
2034
8.3
5.2
0.9
-27.0
-12.6
2035
8.3
5.0
0.8
-27.0
-12.8
2036
8.3
4.9
0.7
-27.0
-13.0
2037
8.3
4.8
0.6
-27.0
-13.3
2038
8.3
4.6
0.5
-27.0
-13.5
2039
12.7
4.5
0.5
-27.0
-9.2
2040
12.7
4.4
0.4
-27.0
-9.4
2041
12.7
4.4
0.3
-27.0
-9.5
2042
12.7
4.3
0.3
-27.0
-9.7
2043
12.7
4.2
0.2
-27.0
-9.9
2044
12.7
4.1
0.1
-27.0
-10.1
2045
12.7
4.0
0.1
-27.0
-10.2
2046
12.7
3.9
0.0
-27.0
-10.4
2047
12.7
3.8
-0.1
-27.0
-10.6
2048
12.7
3.7
-0.2
-27.0
-10.7
2049
9.5
4.4
-0.4
-27.0
-13.5
2050
9.5
5.1
-0.7
-27.0
-13.1
2051
9.5
5.8
-1.0
-27.0
-12.7
2052
9.5
6.5
-1.3
-27.0
-12.3
2053
9.5
7.2
-1.6
-27.0
-11.8
2054
9.5
7.9
-1.9
-27.0
-11.4
2055
9.5
8.6
-2.2
-27.0
-11.0
a Emissions reported under Fossil Fuel Use in GLOBIOM results include assumed one-for-one displacement of
fossil fuels using emissions factors derived from R&D GREET and biofuel production and downstream emissions
estimated using factors derived from R&D GREET.
219
-------
Table 5.2.2.5-4: GHG Emissions (Million Metric Tons CChe) for the High Volume Scenario
(2026-2C
128 Standards) Estimated Using GLOBIO
M
Land Use
Crop
Livestock
Fossil Fuel
Year
Change
Production
Production
Usea
Total
2026
177.6
6.5
2.0
-29.3
156.9
2027
23.0
7.3
2.1
-32.9
-0.5
2028
23.7
8.2
2.1
-36.7
-2.7
2029
8.1
2.1
-36.7
-15.3
2030
8.0
2.1
-36.7
-15.4
2031
8.0
2.1
-36.7
-15.5
2032
7.9
2.1
-36.7
-15.6
2033
7.8
2.1
-36.7
-15.6
2034
7.7
2.2
-36.7
-15.7
2035
7.7
2.2
-36.7
-15.8
2036
7.6
2.2
-36.7
-15.8
2037
7.5
2.2
-36.7
-15.9
2038
7.4
2.2
-36.7
-16.0
2039
8.2
7.3
2.1
-36.7
-19.1
2040
8.2
7.1
2.1
-36.7
-19.3
2041
8.2
6.9
2.0
-36.7
-19.5
2042
8.2
6.8
2.0
-36.7
-19.7
2043
8.2
6.6
1.9
-36.7
-19.9
2044
8.2
6.4
1.9
-36.7
-20.2
2045
8.2
6.3
1.8
-36.7
-20.4
2046
8.2
6.1
1.7
-36.7
-20.6
2047
8.2
6.0
1.7
-36.7
-20.8
2048
8.2
5.8
1.6
-36.7
-21.0
2049
15.7
6.7
1.4
-36.7
-12.9
2050
15.7
7.6
1.1
-36.7
-12.3
2051
15.7
8.6
0.8
-36.7
-11.6
2052
15.7
9.5
0.5
-36.7
-11.0
2053
15.7
10.4
0.3
-36.7
-10.4
2054
15.7
11.3
0.0
-36.7
-9.7
2055
15.7
12.2
-0.3
-36.7
-9.1
a Emissions reported under Fossil Fuel Use in GLOBIOM results include assumed one-for-one displacement of
fossil fuels using emissions factors derived from R&D GREET and biofuel production and downstream emissions
estimated using factors derived from R&D GREET.
220
-------
5.2.3 Summary of GHG Emission Impacts Estimates
In this section we summarize combined emissions impacts estimates for both waste- and
byproduct-based biofuels and crop-based fuels for the Low and High Volume Scenarios relative
to the No RFS Baseline. Tables 5.2.3-1 and 2 label these combined net emissions estimates as
"Estimate A," which uses the GCAM model to assess market-mediated emissions for crop-based
fuels, and "Estimate B," which uses the GLOBIOM model to assess market-mediated emissions
for crop-based fuels. Neither estimate should be interpreted as EPA's central or favored
assessment of the likely GHG impacts of these scenarios. Rather, it is EPA's assessment that
each of the two economic models provides plausible projections of the potential impacts of the
analytical Volume Scenarios. As described in the section above, the two economic models used
in this analysis each have relative strengths for assessing the GHG impacts of increased use of
biofuels. Consistent with recommendations found in the 2022 NASEM technical report, we
provide results using both methodologies, reflecting alternative complex mathematical
representations of earth and human systems and recognizing the uncertainty that is inherent in
any modeling exercise or comparison of model results (i.e., "model uncertainty") 295 All
emissions are presented in million metric tons CChe.296
295 See, for example, Recommendation 4-3: "LCA studies used to inform policy should explicitly consider parameter
uncertainty, scenario uncertainty, and model uncertainty." NASEM Report at 58. The NASEM Report explains:
"Ideally, model structural uncertainty would be assessed through comparisons between models with fundamentally
different approaches, such that there would not be common errors made by both approaches. In reality it is often not
possible to estimate LCA model outputs through approaches that do not share many of the same assumptions." Id. at
57. It therefore provides that, "in some cases, it is more informative to simply compare discrete model runs with
different assumptions, rather than parsing the average output of multiple models." Id. at 56.
296 For simplicity we report all emissions in this chapter in terms of CO2 equivalence using GWPs published in AR5.
However, emissions estimates within these analyses are calculated for three major GHGs: CO2, CH4 and N20.
Estimates disaggregated by gas are provided, where available, in "Set 2 NPRM Climate Change Analyses,"
available in the docket for this action.
221
-------
Table 5.2.3-1: Summary of GHG Emissions Estimates (Million Metric Tons CChe) for the
Low Volume Scenario (2026-2028 Si
tandards) Re
ative to the No RFS Baseline"
Crop-based
Crop-based
Waste- and
Estimate A
Estimate B
Fuels
Fuels
Byproduct-
Annual
Cumulative
Annual
Cumulative
Year
(GCAM)
(GLOBIOM)
based Fuels
Total
Total
Total
Total
2026
281.7
149.3
-12.3
269.4
269.4
137.0
137.0
2027
-0.8
-16.3
-14.2
-15.1
254.4
-30.5
106.5
2028
0.4
-15.9
-16.2
-15.8
238.6
-32.0
74.5
2029
2.3
-11.5
-16.2
-13.8
224.8
-27.6
46.9
2030
0.2
-11.7
-16.2
-16.0
208.8
-27.8
19.0
2031
-1.6
-11.9
-16.2
-17.8
191.0
-28.1
-9.0
2032
-3.2
-12.1
-16.2
-19.4
171.7
-28.3
-37.3
2033
-4.6
-12.4
-16.2
-20.7
151.0
-28.5
-65.8
2034
-4.6
-12.6
-16.2
-20.7
130.2
-28.7
-94.6
2035
-5.3
-12.8
-16.2
-21.5
108.8
-29.0
-123.6
2036
-5.9
-13.0
-16.2
-22.0
86.7
-29.2
-152.8
2037
-6.4
-13.3
-16.2
-22.5
64.2
-29.4
-182.2
2038
-6.8
-13.5
-16.2
-23.0
41.2
-29.7
-211.8
2039
-6.7
-9.2
-16.2
-22.9
18.4
-25.4
-237.2
2040
-7.1
-9.4
-16.2
-23.2
-4.9
-25.5
-262.7
2041
-7.4
-9.5
-16.2
-23.5
-28.4
-25.7
-288.4
2042
-7.6
-9.7
-16.2
-23.8
-52.1
-25.9
-314.3
2043
-7.8
-9.9
-16.2
-24.0
-76.1
-26.0
-340.3
2044
-7.1
-10.1
-16.2
-23.2
-99.4
-26.2
-366.5
2045
-7.3
-10.2
-16.2
-23.4
-122.8
-26.4
-392.9
2046
-7.4
-10.4
-16.2
-23.6
-146.3
-26.5
-419.5
2047
-7.6
-10.6
-16.2
-23.7
-170.0
-26.7
-446.2
2048
-7.7
-10.7
-16.2
-23.8
-193.9
-26.9
-473.1
2049
-4.7
-13.5
-16.2
-20.9
-214.8
-29.7
-502.7
2050
-4.7
-13.1
-16.2
-20.8
-235.6
-29.3
-532.0
2051
-4.6
-12.7
-16.2
-20.8
-256.4
-28.8
-560.8
2052
-4.6
-12.3
-16.2
-20.8
-277.2
-28.4
-589.2
2053
-4.6
-11.8
-16.2
-20.7
-297.9
-28.0
-617.2
2054
-4.5
-11.4
-16.2
-20.7
-318.6
-27.6
-644.8
2055
-4.6
-11.0
-16.2
-20.7
-339.3
-27.1
-671.9
a "Estimate A" represents the estimates using the GCAM model. "Estimate B" represents estimates using the
GLOBIOM model.
222
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Table 5.2.3-2: Summary of GHG Emissions Estimates (Million Metric Tons CChe) for the
Crop-based
Crop-based
Waste- and
Estimate A
Estimate B
Fuels
Fuels
Byproduct-
Annual
Cumulative
Annual
Cumulative
Year
(GCAM)
(GLOBIOM)
based Fuels
Total
Total
Total
Total
2026
327.9
156.9
-12.3
315.6
315.6
144.6
144.6
2027
36.5
-0.5
-14.2
22.3
337.9
-14.8
129.8
2028
37.2
-2.7
-16.2
21.0
358.9
-18.8
111.0
2029
4.4
-15.3
-16.2
-11.7
347.2
-31.5
79.5
2030
1.3
-15.4
-16.2
-14.8
332.4
-31.6
47.9
2031
-1.3
-15.5
-16.2
-17.4
315.0
-31.6
16.3
2032
-3.5
-15.6
-16.2
-19.6
295.3
-31.7
-15.4
2033
-5.4
-15.6
-16.2
-21.6
273.8
-31.8
-47.2
2034
-5.3
-15.7
-16.2
-21.4
252.4
-31.8
-79.0
2035
-6.3
-15.8
-16.2
-22.4
229.9
-31.9
-110.9
2036
-7.1
-15.8
-16.2
-23.3
206.6
-32.0
-142.9
2037
-7.9
-15.9
-16.2
-24.0
182.6
-32.1
-175.0
2038
-8.5
-16.0
-16.2
-24.6
158.0
-32.1
-207.1
2039
-8.3
-19.1
-16.2
-24.5
133.6
-35.2
-242.4
2040
1
OO
00
-19.3
-16.2
-24.9
108.6
-35.4
-277.8
2041
-9.2
-19.5
-16.2
-25.4
83.3
-35.7
-313.5
2042
-9.6
-19.7
-16.2
-25.7
57.6
-35.9
-349.3
2043
-9.9
-19.9
-16.2
-26.0
31.5
-36.1
-385.4
2044
-8.9
-20.2
-16.2
-25.0
6.5
-36.3
-421.7
2045
-9.1
-20.4
-16.2
-25.3
-18.8
-36.5
-458.2
2046
-9.3
-20.6
-16.2
-25.5
-44.2
-36.7
-495.0
2047
-9.5
-20.8
-16.2
-25.7
-69.9
-36.9
-531.9
2048
-9.7
-21.0
-16.2
-25.8
-95.8
-37.2
-569.1
2049
-5.6
-12.9
-16.2
-21.7
-117.5
-29.1
-598.2
2050
-5.5
-12.3
-16.2
-21.6
-139.1
-28.4
-626.6
2051
-5.4
-11.6
-16.2
-21.6
-160.7
-27.8
-654.4
2052
-5.4
-11.0
-16.2
-21.5
-182.3
-27.1
-681.5
2053
-5.4
-10.4
-16.2
-21.5
-203.8
-26.5
-708.0
2054
-5.3
-9.7
-16.2
-21.4
-225.2
-25.9
-733.9
2055
-5.3
-9.1
-16.2
-21.5
-246.7
-25.2
-759.1
a "Estimate A" represents the estimates using the GCAM model. "Estimate B" represents estimates using the
GLOBIOM model.
In both estimates for both scenarios, a similar pattern is present in the emissions
estimates: an immediate pulse of emissions in 2026, driven by land use change emissions
impacts from the difference in volumes of crop-based fuels relative to the No RFS Baseline,
followed by continual emissions benefits as biofuels produced on the additional acreage of
cropland continue to accrue emissions benefits by displacing use of fossil fuels. One way of
comparing these estimates is through the consideration the "payback period" —measured as the
year in which net emissions become negative. Under the Low Volume Scenario with 2026-2028
standards, net negative cumulative GHG emissions are achieved in 2040 under Estimate A and in
2031 under Estimate B. Under the High Volume Scenario with 2026-2028 standards, net
223
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negative cumulative GHG emissions are achieved in 2045 under Estimate A and in 2032 under
Estimate B.
5.3 Assessment of Proposed Volumes
Table 5.1.1-1 presents the volume differences between the Proposed Volumes for 2026
and 2027 and the volume in the No RFS Baseline. For waste- and byproduct-based fuels, we
follow the same methodology described in the sections above; we apply emissions factors
developed using the R&D GREET model for 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 5.1.2.
Because of the lead time required to specify and complete economic modeling of
volumes of crop-based fuels using GCAM and GLOBIOM, we were unable to complete
simulations representing the 2026 and 2027 Proposed Volumes in either of the economic models.
However, the simulations used to assess the Low and High Volume Scenarios relative to the No
RFS Baseline can be adjusted to approximate the results under the proposed volumes by scaling
all impacts proportionally based on the total net difference in modeled biofuel volumes (assessed
in energy equivalence) that would be implemented in model year 2030 in the GCAM and
GLOBIOM simulations. For the Low and High Volume Scenarios, that is the difference in crop-
based biofuels from No RFS Baseline levels in analytical year 2028; 310 trillion BTUs in the
Low Volume Scenario and 425 trillion BTUs in the High Volume Scenario. For the Proposed
Volumes, we consider the difference in crop-based biofuels from No RFS Baseline levels in
analytical year 2027; 392 trillion BTUs. Because the total volume difference that would have
been modeled for the Proposed Volumes is closer to the High Volume Scenario than to the Low
Volume Scenario, we use scaled High Volume Scenario modeling results to approximate the
effects under the Proposed Volumes. However, neither the Low Volume Scenario nor the High
Volume Scenario represents the same ratio of volumes of fuels as the Proposed Volumes, so the
scaling is a necessary, if imperfect representation of what a more specific analysis of the
Proposed Volumes might estimate. We also note that the total magnitude of the Proposed
Volumes is in between those of the Low and High Volume Scenarios, so considering the full
range of potential results estimated across the Low and High Scenarios may therefore provide a
more fulsome sense for the potential impacts of this proposal, across the many attendant
uncertainties that exist when modeling future GHG emissions impacts of renewable fuels.297 To
the extent possible, we intend to align our simulations with the volumes in the final rule.
Scaling of the High Volume Scenario results to estimate the impacts of the Proposed
Volumes largely follows the methodology described in Chapter 5.1.3, with a few exceptions of
note. First, all model results are translated by three years instead of two (e.g., model year 2030
corresponds with analytical year 2027 instead of 2028). All interpolation between model years
after 2030 is identical to the methods described in Chapters 5.1.3.2 and 5.1.3.3. In order to
297 Additionally, the economic modeling of crop-based fuels undertaken for this proposal does not explicitly
represent the proposed "import RIN reduction" discussed in Preamble Section VIII, nor do these simulations
endogenously determine the mix of fuels expected to be used to meet the volume standards. Rather, volumes of
crop-based fuels expected to be used to meet the standards are projected external to the models (see Chapter
5.1.1.1.1) and used as exogenously specified consumption targets.
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represent the specific volumes in 2026 and 2027, we use scalar factors calculated using the same
methods as described in Chapters 5.1.3.2 and 5.1.3.3, but using the 2026 and 2027 Proposed
Volumes relative to the No RFS Baseline. These factors are presented in Table 5.3-1.
Table 5.3.1-1: Emissions Adjustment Scalars for Economic Modeling of the Proposed
Volumes Relative to the No RFS Baseline
Proposed
Minus
Volumes
\o RFS
2026
2027
Year-over-year volume difference as
percentage of 2027 volume difference
91.6%
8.4%
Cumulative volume difference as
percentage of 2027 volume difference
91.6%
100.0%
Finally, all impacts are scaled by the ratio of the total volume difference in 2027 in the
Proposed Volumes to the total volume difference in 2028 in the High Volume Scenario—a factor
of approximately 0.92 (392 trillion BTUs / 425 trillion BTUs). All calculations are provided in
an Excel workbook in the docket for this proposal.298 Table 5.3.1-2 provides the stream of
estimated emissions impacts reported in CChe.299 Under the Proposed Volumes for 2026 and
2027, net negative cumulative GHG emissions are achieved in 2053 under Estimate A and in
2034 under Estimate B.
298 See "Set 2 NPRM Climate Change Analyses," available in the docket for this action.
299 For simplicity, we report all emissions in this chapter in terms of CO2 equivalence using GWPs published in
AR5. However, emissions estimates within these analyses are calculated for three major GHGs: CO2, CH4 and N20.
Estimates disaggregated by gas are provided, where available, in "Set 2 NPRM Climate Change Analyses,"
available in the docket for this action.
225
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Table 5.3-2: Summary of GHG Emissions Estimates (Million Metric Tons CChe) for the
Crop-based
Crop-based
Waste- and
Estimate A
Estimate B
Fuels
Fuels
Byproduct-
Annual
Cumulative
Annual
Cumulative
Year
(GCAM)
(GLOBIOM)
based Fuels
Total
Total
Total
Total
2026
350.1
167.3
-8.5
341.6
341.6
158.8
158.8
2027
25.9
-6.8
-8.3
17.6
359.2
-15.1
143.6
2028
4.2
-13.9
-8.3
-4.0
355.2
-22.2
121.4
2029
1.4
-14.0
-8.3
-6.9
348.3
-22.3
99.1
2030
-1.0
-14.1
-8.3
-9.3
339.0
-22.4
76.7
2031
-3.0
-14.1
-8.3
-11.3
327.7
-22.4
54.3
2032
-4.8
-14.2
-8.3
-13.1
314.6
-22.5
31.8
2033
-4.7
-14.3
-8.3
-13.0
301.6
-22.6
9.2
2034
-5.6
-14.3
-8.3
-13.9
287.7
-22.6
-13.4
2035
-6.4
-14.4
-8.3
-14.7
273.1
-22.7
-36.1
2036
-7.1
-14.5
-8.3
-15.3
257.7
-22.8
-58.8
2037
-7.6
-14.5
-8.3
-15.9
241.8
-22.8
-81.7
2038
-7.5
-17.4
-8.3
-15.8
226.0
-25.7
-107.3
2039
-7.9
-17.6
-8.3
-16.2
209.8
-25.9
-133.2
2040
-8.3
-17.8
-8.3
-16.6
193.2
-26.1
-159.3
2041
-8.6
-18.0
-8.3
-16.9
176.3
-26.3
-185.6
2042
-8.9
-18.2
-8.3
-17.2
159.1
-26.5
-212.1
2043
-8.0
-18.4
-8.3
-16.3
142.8
-26.7
-238.7
2044
-8.2
-18.6
-8.3
-16.5
126.3
-26.9
-265.6
2045
-8.4
-18.8
-8.3
-16.7
109.6
-27.1
-292.7
2046
-8.6
-19.0
-8.3
-16.9
92.7
-27.3
-319.9
2047
1
OO
00
-19.2
-8.3
-17.0
75.6
-27.5
-347.4
2048
-5.0
-11.7
-8.3
-13.3
62.4
-20.0
-367.4
2049
-4.9
-11.1
-8.3
-13.2
49.2
-19.4
-386.8
2050
-4.8
-10.5
-8.3
-13.1
36.1
-18.8
-405.6
2051
-4.8
-9.9
-8.3
-13.1
23.0
-18.2
-423.8
2052
-4.8
-9.3
-8.3
-13.0
10.0
-17.6
-441.5
2053
-4.7
1
OO
00
-8.3
-13.0
-3.0
-17.0
-458.5
2054
-4.7
-8.2
-8.3
-13.0
-16.0
-16.5
-475.0
2055
-4.7
-7.6
-8.3
-13.0
-29.0
-15.9
-490.8
a "Estimate A" represents the estimates using the GCAM model. "Estimate B" represents estimates using the
GLOBIOM model.
For this proposed rule we are not monetizing the estimated GHG emissions. There are
significant uncertainties related to monetization of greenhouse gases 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;
• Future population growth;
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• 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 pubic 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.
Appendix 5-A: Sensitivity Analysis for Economic Modeling
We simulated a Monte Carlo simulation (MCS) with GCAM to explore the influence of a
range of parameters on the estimates. The goals of the MCS are to test the behavior of the model,
evaluate the overall sensitivity of the estimates to variations in the input parameters, and to test
which parameters tend to have the largest influence on the results for this specific model.300
We conducted this analysis using methods and software consistent with the MCS
described in Plevin et al. (2022).301 We ran the MCS by applying random values drawn from
distributions across 37 parameters. In this case, we use the term parameter to refer to a set of
related values in GCAM's input files. For example, for this analysis we call "biomass carbon
density of cropland" one parameter, even though GCAM uses independent cropland biomass
carbon input values for each water basin region. For each of the three MCE scenarios (i.e.,
reference, low-growth biofuel shock, high-growth biofuel shock), we ran 1,000 trials (3,000 total
model simulations). The same set of randomly drawn parameter values were used for each of the
three scenarios. We consulted with the GCAM developers to determine the likely range of
legitimate values for each parameter and then set selected distributions for each parameter based
on our own judgements. In some cases, we were able to leverage previous research to determine
empirically based distribution shapes. Table 5.A-1 describes the parameters and distributions
used in our MCS.
31111 The NASEM LCA Report highlights the importance of investigating and transparently communicating
uncertainty in impacts modeling of renewable fuels (see recommendation 4-2, page 57). Use of MCS methods for
characterizing variance in GHG impacts of biofuels based on parametric uncertainty are discussed on pages 55-56 in
the NASEM LCA Report.
3111 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. Section 2.5 describes the
MCS.
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Table 5.A-1: GCAM Monte Carlo Simulation Parameter Distributions
Name
Distribution
Description
corn-etoh-corn-
coef
Triangle(0.98, 1, 1.02)
Million metric tons of corn required to produce an exajoule of
corn ethanol.
cropland-soil-c
Uniform(0.5, 0.99)
Defines a distribution for percentiles to draw from the Beta
distribution implied by the statistics gleaned from the moirai
data.
cropland-veg-c
Uniform(0.5, 0.99)
Defines a distribution for percentiles to draw from the Beta
distribution implied by the statistics gleaned from the moirai
data.
forest-soil-c
Uniform(0.5, 0.99)
Defines a distribution for percentiles to draw from the Beta
distribution implied by the statistics gleaned from the moirai
data.
forest-veg-c
Uniform(0.5, 0.99)
Defines a distribution for percentiles to draw from the Beta
distribution implied by the statistics gleaned from the moirai
data.
grass-shrab-soil-c
Uniform(0.5, 0.99)
Defines a distribution for percentiles to draw from the Beta
distribution implied by the statistics gleaned from the moirai
data.
grass-shrab-veg-c
Uniform(0.5, 0.99)
Defines a distribution for percentiles to draw from the Beta
distribution implied by the statistics gleaned from the moirai
data.
pasture-soil-c
Uniform(0.5, 0.99)
Defines a distribution for percentiles to draw from the Beta
distribution implied by the statistics gleaned from the moirai
data.
pasture-veg-c
Uniform(0.5, 0.99)
Defines a distribution for percentiles to draw from the Beta
distribution implied by the statistics gleaned from the moirai
data.
peat-C02-
emissions
Uniform(0.31, 1.75)
C02 emissions from peatland conversion.
peat-C02-
emissions-linked
Linked(peat-C02-
emissions)
C02 emissions from peatland conversion on unmanaged land.
N-fertilizer-rate
Triangle(0.7, 1, 1.3)
Quantity of N fertilizer required per mass of crop harvested.
crop-productivity
Triangle(0.7, 1, 1.3)
Annual change in agricultural productivity (yield).
irrig-rainfed-logit-
exp
Triangle(0.333, 1, 3.0)
Logit exponent controlling competition between irrigated and
rainfed land.
mgmt-level-logit-
exp
Triangle(0.333, 1, 3.0)
Logit exponent controlling competition between high and low
crop management levels.
n2o-emissions
Triangle(0.5, 1, 2.0)
N20 emissions intensity of agricultural production.
veg-oil-demand-
logit-exp
Triangle(0.333, 1.0,2.0)
Controls substitution among types of vegetable oil
water-wd-price
Triangle(0.333, 1, 3.0)
The price of withdrawn water.
agro-forest-logit-
exp
Triangle(0.333, 1, 3.0)
Logit exponent controlling competition between forest-grass-
crop and pasture.
cow-sheepgoat-
feed-logit
Triangle(0.5, 1, 2.0)
Logit exponent controlling competition between Beef, Dairy,
and SheepGoat, which determines the sharing between Mixed
and Pastoral subsectors.
crop-logit-exp
Triangle(0.333, 1, 3.0)
Logit exponent controlling competition among crops.
forest-grass-crop-
logit-exp
Triangle(0.1, 1.0, 3.0)
Logit exponent controlling competition among forest, grassland,
and cropland.
forest-logit-exp
Triangle(0.333, 1, 3.0)
Logit exponent controlling competition between managed and
unmanaged forest.
pasture-logit-exp
Triangle(0.333, 1, 3.0)
Logit exponent controlling competition between managed and
unmanaged pasture.
228
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Name
Distribution
Description
regional-crop-
logit-exp
Triangle(0.333, 1.0,2.0)
Logit exponent controlling competition between imports and
domestic ag products. (10/3/22 - Reduced upper end to try to
reduce the number of model failures.)
traded-
commodity-logit-
exp
Triangle(0.333, 1.0,2.0)
Logit exponent controlling competition in traded ag
commodities. (10/3/22 - Reduced upper end to try to reduce the
number of model failures.)
traded-
commodity-
subsector-logit-
exp
Triangle(0.333, 1.0,2.0)
Logit exponent controlling competition among exports in each
traded commodity sector. (10/3/22 - Reduced upper end to try to
reduce the number of model failures.)
ng-upstream-ch4
Unifonn(0.9, 1.3)
CH4 emissions upstream from natural gas production processes
and transport.
population-factor
Triangle(0.0, 0.5, 1.0)
Defines a path between the lower and higher bounds of the
UNDP 95% confidence interval around population projections.
biodiesel-
competition-logit-
exp
Triangle(0.5, 1, 2.0)
Controls substitution among types of biodiesel
pass-road-ldv-
4W-logit-exp
Triangle(0.5, 1, 2.0)
Logit exponent controlling substitution among Compact Car,
Midsize Car, Large Car, Light Truck and SUV.
pass-road-ldv-
4W-vehicle-logit-
exp
Triangle(0.5, 1, 2.0)
Logit exponent controlling substitution among 4WD vehicle
fuel technology options include BEV, FCEV, Hybrid liquids.
Liquids, and NG.
pass-road-ldv-
logit-exp
Triangle(0.5, 1, 2.0)
Logit exponent controlling substitution between 2- and 4-wheel
light-duty vehicles.
ref-fuel-enduse-
ex-US
Triangle(0.333, 1, 3.0)
Controls substitution in supplies of refined fuel for "end use"
outside the U.S.
staples-price-elast
empirical
Price elasticity of demand for staple foods
non-staples-price-
elast
empirical
Own price elasticity of non-staple food demand.
non-staples-
income-elast
empirical
Income elasticity of non-staple food demand.
a Unless the parameter name includes an asterisk, the draws from the given distributions were multiplied by the
GCAM default values to produce values for each trial. For parameter names with an asterisk, values from the
distribution were used directly, replacing the default values.
Most of the parameters above are applied directly to values in GCAM's extensible
markup language (XML) input files. Parameters for vegetative and soil carbon are handled
differently though, as these distributions are applied to the comma-separated value (CSV) data in
the GCAM data system, which is then run to regenerate consistent XML files with values
reflecting the distributions.302
In some cases, combinations of parameters push the model beyond its ability to match
supply and demand in all markets simultaneously, in which case the model fails to solve. As
shown in Table 5.A-1, we primarily used triangular distributions to reduce the likelihood,
relative to normal distributions, of outlier parameter draws, thus reducing the number of model
failures. Nonetheless, some of the trials failed to solve; the actual number of trials completed for
each model version was 938 for the No RFS Baseline (93.8%), 919 for the High Volume
Scenario (91.9%), and 916 for the Low Volume Scenario (91.6%).
3112 Additional documentation of the software and methods used in this analysis are available at: pygcam, "Running
the GCAM data system." https://pvgcain.readthedocs.io/en/inaster/incs/datasvstem.html.
229
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The following figure presents the results of our MCS experiment with GCAM as
distributions of cumulative GHG estimates from 2026-2055. Although the figure presents the
MCS results in probabilistic terms, the actual probability of any given GHG emissions impact
cannot be determined from this analysis. Our sensitivity analysis only reveals the likelihood of
an outcome given all the inputs into our analysis, such as the version of GCAM, the reference
parameter values, the solution technique, the definitions chosen for the parameters evaluated, and
the distributions for the parameters evaluated. Although the figure does not tell us the actual
probability of a given outcome, it provides information about the general tendency of the model
and the variance of results due to parametric uncertainty.
Figure 5.A-1: Distribution of Cumulative (2026-2055) GHG Emissions (Million Metric
Tons CChe) Difference Estimates from GCAM Modeling of Crop-Based Fuels Using
Monte-Carlo Analysis
A Cumulative COje
high
tow
-400 -200 0 200 400 600 800
Tg CC>2e
"low" indicates estimates from the Low Volume Scenario relative to the No RFS Baseline, "high" indicates
estimates from the High Volume Scenario relative to the No RFS Baseline. Boxes indicate interquartile range;
whiskers indicate 5th and 95th percentiles; vertical line indicates median value.
Based on Figure 5.A-1, we observe that GCAM tends to estimate a net increase in
cumulative GHG emissions for both the High and Low Volume Scenarios relative to the No RFS
Baseline. However, for both sets of scenarios there are a minority of parameters values that cause
GCAM to estimate net decreases in the cumulative emissions.
As part of the MCS experiment, we identified the parameters most strongly influencing
the variance in GHG emissions results. We did this by computing the rank correlations between
the values for each random variable and the resulting GHG emissions across all MCS trials. The
rank correlations are squared and normalized to sum to one to produce an approximate
"contribution to variance." In Figure 5.A-2, the sign of the correlation is applied after
normalization. This figure shows the strength of the influence of the 15 most influential input
parameters on the variance in the output (cumulative GHG emissions), in descending order, with
the magnitude and direction corresponding to the strength and direction of the correlation
respectively. A contribution to variance further from zero indicates that the parameter is more
influential. A positive contribution to variance indicates that as the parameter value increases or
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decreases, the cumulative GHG estimates tend to move in the same direction. A negative
contribution to variance indicates the opposite. Following the figure, we discuss our
interpretation of the findings. We only present the figure associated with the High Volume
Scenario relative to the baseline, as the same figure or the Low Volume Scenario is nearly
identical.
Figure 5.A-2: Tornado Chart of Most the Influential Parameters on Cumulative (2026-
2055) GHG Emissions (Million Metric Tons CChe) Difference Estimates from GCAM
Modeling of Crop-based Fuels Using Monte-Carlo Analysis
forest-grass-crop-logit-exp
cropland-soil-c
n2oemissions
forest-soil-c
pasture-soil-c
ag ro-forest-logit-exp
cnop-log it-exp
pasture-veg-c
population-factor
staples-price-elast
crop-productivity
veg-oil-demand-logit-exp
mgmt-level-logit-exp
non-staples-income-elast
non-staples-price-elast
Sensitivity of delta-cumulative-C02
-0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6
—I 1 1 1 1 1 1 1—
0.8
I
I
Contribution to variance
Figure 5.A-2 shows that, for this MCS experiment, about 5 parameters have an outsized
influence on the estimates. This does not mean the other parameters have no effect, but rather
that their influence is much smaller than that of the 5 most influential parameters. The most
influential parameter is forest-grass-crop-logit-exp, the parameter controlling the flexibility of
competition among forest, grassland, and cropland. Higher values for this parameter mean more
flexibility for price-driven land use changes among these land categories. For example, given an
increase in crop prices, higher values for this parameter will translate to larger increases in crop
area at the expense of grassland and forest area. The other most influential parameters are: (1)
cropland-soil-c, the soil carbon density of cropland, (2) n2o-emissions, the N2O emissions
intensity of agriculture, (3) forest-soil-c, the soil carbon density of forestland, and (4) pasture-
soil-c, the soil carbon density of pastureland. All the most influential parameters appear to be
primarily related to land use change and land use change emissions.
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Chapter 6: Energy Security Impact
The CAA 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 Volume Scenarios relative to the No RFS Baseline.
In addition, this chapter provides energy security estimates of the Proposed Volumes.
U.S. energy security is broadly defined as the uninterrupted availability of energy sources
at an acceptable price.303 Most discussions of U.S. energy security have historically revolved
around the topic of the economic costs of U.S. dependence on oil imports.304 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
sensitivity 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.305 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. Similarly, as described in
Preamble Section VIII, we are also proposing to reduce the number of RINs generated for
imported renewable fuel and renewable fuel produced from foreign feedstocks, which is intended
to reduce America's reliance on such fuels in future years consistent with the statutory goals of
energy security and independence. In addition to evaluating impacts on energy security, we have
also considered the impacts of the Volume Scenarios 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.306 As stated above, energy independence
and energy security are distinct but related concepts, implying that an 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 largely focuses on the role of oil imports in energy security and energy
independence. The growing role of imported renewable fuels and renewable fuel feedstocks, and
the interplay between reductions in imports of oil, renewable fuels, and renewable fuel
3113IEA, "Energy Security." https://www.iea.org/topics/energy-securitv.
3114 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.
3115 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.
3116 See Americans for Clean Energy v. Env'tProt. 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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feedstocks on U.S. energy security and independence, is also a significant factor to consider.
However, we currently lack the tools to assess these impacts.
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 2023, U.S. production of crude oil and petroleum products
grew at an average annual rate of approximately 7.4%, 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.307 As of 2023, as a result of growing oil production, the U.S. was the largest
producer of oil in the world, producing 12.9 million barrels per day (MMBD), followed by
Russia producing 10.1 MMBD and Saudi Arabia producing 9.7 MMBD.308
During this same time frame, U.S. consumption of crude oil and petroleum products
remained fairly flat, with an average annual growth rate of 0.6%, as shown in Figure 6.1. The
significant increase in U.S. oil production and relatively flat U.S. oil consumption resulted in a
significant reversal in the U.S.'s petroleum trade balance position. Prior to 2020, the U.S. had
been a net importer of crude oil and petroleum products (i.e., net petroleum importer) since the
early 1950s.309 However, as also depicted in Figure 6.1, the U.S. became a net exporter of crude
oil and petroleum products (i.e., a net petroleum exporter) starting in 2020. Thus, the U.S. has
achieved a greater degree of energy independence with respect to petroleum by reducing
dependence on imports.
From 2026-2030, EIA estimates that U.S. oil consumption will gradually decline from
18.6 to 18.4 MMBD, roughly similar to the amount of oil that the U.S. consumed in 2010.
However, in 2010, the U.S. imported roughly 9.4 MMBD of petroleum.310 In contrast, the U.S. is
now anticipated to be a modest net petroleum exporter of roughly 2.3 MMBD in 2030.311
3117 EIA, "Tight oil production estimates by play," Petroleum & Other Liquids, May 2025.
https://www.eia.gov/petroleum/data.php.
3118 EIA, "United States produces more crude oil than any country, ever," Today in Energy, March 11, 2024.
https://www.eia.gov/todavinenergy/detail.php?id=61545. Oil production estimates for the U.S. include crude oil and
lease condensate.
3119 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.
310 Id.
311 Calculated from AEO2023, Table 11 as Total Net Exports minus Ethanol, Biodiesel, Renewable Diesel, and
Other Biomass-derived Liquid Net Exports.
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Figure 6-1: U.S. Consumption, Production, and Net Imports of Crude Oil and Petroleum
Products
8000
Year
U.S. Production of Crude Oil and Petroleum Products (Millions of Barrels)
U.S. Consumption of Crude Oil and Petroleum Products (Millions of Barrels)
U.S. Net Imports of Crude Oil and Petroleum Products (Millions of Barrels)
Source: EIA, "Supply and Disposition," Petroleum & Other Liquids, April 30, 2025.
https://www.eia.gov/dnav/pet/pet sum snd d nus mbbl a cur.htm.
The Volume Scenarios represent net increases in renewable fuels in comparison to
previous years and, also, net increases in comparison to the No RFS Baseline. Increasing the use
of renewable fuels in the U.S. displaces domestic consumption of petroleum-based fuels. Given
the U.S.'s projected pattern of petroleum trade with other countries, reductions in U.S. oil
consumption will result in increases in U.S. gross petroleum exports as well as a reduction in
U.S. gross petroleum imports. A reduction in U.S. net petroleum imports (i.e., from the
combined increase in U.S. gross petroleum exports and reductions in U.S. gross petroleum
imports) reduces both financial and strategic risks caused by potential sudden disruptions in the
supply of petroleum to the U.S., increasing 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., Preamble
Section III.B.2), have implications for energy security and energy independence.
Increasing reliance on renewable fuels and renewable fuel feedstocks, and imports of
both, to meet the RFS volume obligations will likely influence the U.S.'s energy security and
independence. 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
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shocks, blending renewable fuels with petroleum fuels will likely provide energy security
benefits. The 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. However, the energy security risks of using renewable fuels/feedstocks are
not well understood, nor well studied. As a result, this chapter focuses 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 petroleum consumption/net imports that
would result from this proposal.
After considering recent changes in the U.S. trade balances of oil, renewable fuels, and
renewable fuel feedstocks and the greater degree of independence from foreign oil, energy
security risks still remain. There are three main reasons why energy security is still a concern,
despite the reduction in U.S. net imports of petroleum. 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 become equally 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 uncorrelated, but will
result in new exposure in the renewable fuel markets.
Second, 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,
gross petroleum imports totaled roughly 8.4 MMBD.312 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 the Organization of Petroleum
Exporting Countries (OPEC) to alter oil supply relative to demand. It is somewhat uncertain how
much market power OPEC will have in the time frame of this proposed rule. 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.
312 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
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To summarize, we recognize that 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.313 But the potential for petroleum supply
disruptions due to supply shocks has been diminished due to the increase in U.S. tight oil
production and due to increased consumption of renewable fuels, among other factors. These
factors have collectively shifted the U.S. to being a modest net petroleum exporter in the world
petroleum market in 2026-2027.314 At the same time, a trend of increasing imports of renewable
fuel feedstocks has emerged, raising concerns about the impacts of renewable fuels and
renewable fuel feedstocks on energy security and independence. The potential for petroleum
supply disruptions has also not been eliminated, however, due to the continued need to import
petroleum to satisfy the demands of the U.S. petroleum industry and because the U.S. continues
to consume substantial quantities of oil.315
6.1 Review of Historical Energy Security Literature (1981 to 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),316 EMF (1982),317
and Plummer (1982)318). 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)319 and Broadman and Hogan
(1986,320 1988321) revised and extended the established analytical framework to estimate optimal
oil import premia with a more detailed accounting of macroeconomic effects. Since the original
313 Bordoff, Jason. "The Myth of U.S. Energy Independence Has Gone up in Smoke." Foreign Policy, September
18, 2019. https://foreignpolicY.com/2019/09/18/the-mYth-of-u-s-energy-independence-has-gone-up-in-smoke.
314 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.
315 Foreman, Dean. "Why the US must Import and Export Oil," American Petroleum Institute, June 14, 2018.
316 Bohi, D.R. and W.D. Montgomery. "Social Cost of Imported Oil and US Import Policy." Annual Review of
Energy 7, no. 1 (November 1, 1982): 37-60. https://doi.org/10.1146/annurev.eg.07.110182.000345.
317 "World Oil: Summary Report." Energy Policy 10, no. 4 (December 1, 1982): 367. https://doi.org/10.1016/Q301-
4215(82)90059-3.
318 Plummer, James L. Energy Vulnerability. Ballinger Publishing Company, 1982.
319 Hogan, W. "Import Management and Oil Emergencies," Chapter 9 in D. Deese and J. Nye, eds. Energy and
Security, Cambridge: Ballinger Press, 1981.
3211 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.
321 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.
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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).322 323
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
Gali (2010)), while other work found more evidence regarding the economic importance of oil
shocks.324 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."325 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."326
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)).327 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.328 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)."329
322 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.
323 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.
324 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.
325 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.
326 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.
327 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-vanrobaYS.htnil.
328 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.
329 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.
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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 1979-2011.330
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
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
proposed 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.331 In a follow-on study, Brown summarized the RFF study results as well.332 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
3311 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.
331 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.
332 Brown, Stephen P. A. "New Estimates of the Security Costs of U.S. Oil Consumption." Energy Policy 113
(November22, 2017): 171-92. https://doi.Org/10.1016/i.enpol.2017.ll.003.
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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
time frame. 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
stochastic general equilibrium model developed by Balke and Brown.333 The second set of
modeling frameworks used alternative structural vector autoregressive models of the global
crude oil market.334 The last of the models utilized was the National Energy Modeling System
(NEMS).335
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
333 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.
334 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.
335 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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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 two 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 14th, 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
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.336 On September 16th, the
first full day of commodity trading after the attack, both Brent and 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 17th, 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.337 Tanker loading estimates from third-party data sources indicated that loadings
at two Saudi Arabian export facilities were restored to the pre-attack levels.338 As a result, both
Brent and WTI crude oil prices fell on September 17th, 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.339 The WTI oil price
increased to roughly $124/barrel on March 8th, 2022, a 63% increase.340 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.341 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.342 More recently, as of September 9, 2024, the WTI crude
oil price was $70/barrel, a somewhat lower price than before the Russian invasion of Ukraine.343
336 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.
337 Id.
338 Id.
339 EIA, "Spot Prices," Petroleum & Other Liquids, May 14, 2025.
https://www.eia.gov/dnav/pet/pet pri spt si d.htm.
340 Id.
341 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.
342 Id.'
343 EIA, "Spot Prices," Petroleum & Other Liquids, May 14, 2025.
https://www.eia.gov/dnav/pet/pet pri spt si d.htm.
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Oil prices at present are relatively low mainly because of projected slowdown in world oil
demand growth, particularly in China.344 Crude oil prices (i.e., the WTI crude oil price) are
expected to be flat in the 2026-2027 time frame of this proposed rule, in the $85-86 per barrel
(2022$) range.345
Geopolitical disruptions that occurred in 2022 are likely to continue to affect global trade of
crude oil and refined petroleum products in 2023 and beyond. 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 sanctions against Russia's petroleum industry.346 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
20 23.347 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 a modest increase in refined petroleum product exports in the time frame of
this proposed rule, 2026-2027.348
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.349
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
the recent economics literature.350 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.
344 EIA, "Short-Term Energy Outlook," September 2024. https://www.eia.gov/outlooks/steo/arcliives/sep24.pdf.
345 AEO2023, Table 12 - Petroleum and Other Liquids Prices, https://www.eia.gov/outlooks/archive/aeo23.
346 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.
347 Id.
348 AEO2023, Table 11 - Petroleum and Other Liquids Supply and Disposition.
349 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.
3511 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.
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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.351 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.352
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 6.353 According to the GAO, the ban was
lifted in part due to increases in tight (i.e., shale) oil.354 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
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.355
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.356 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 2024.357
351 Oladosu, Gbadebo A., Paul N. Leiby, David C. Bowman, Rocio Una-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.
352 "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.
353 Pub. L. 114-113 (December 18, 2015).
354 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."
355 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-vear-Mgh-kemp-2021-06-04.
356 EIA, "Crude Oil Production," Petroleum & Other Liquids, April 30, 2025.
https://www.eia.gov/dnav/pet/pet crd crpdn adc mbblpd a.htm.
357 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.
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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 (less than 10% in 2024)
of global liquid fuel supply.358
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.359 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.360
358 The 2024 global crude oil production value used to compute the U.S. tight oil share (102.8 mb/d) is from EIA,
"Petroleum and other liquids (production)," International, May 15, 2025.
https://www.eia.gov/international/data/world/petroleum-and-other-liauids/annual-petroleum-and-other-liauids-
production.
359 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.
3611 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
¦ Bakken Niobrara-Codell I Eagle Ford Wy,
(ND & MT) (CO & WY) I ¦ (TX)
¦ Permian n
,TV „ . n . Rest of US
(TX & NM Permian)
Source: EIA, "Tight oil production estimates by play," Petroleum & Other Liquids, May 2025.
https://www.eia.gov/petroleum/data.pli . EIA, "Spot Prices," Petroleum & Other Liquids, May 14, 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 is rising to be roughly on par with Saudi
Arabia and Russia. 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 to some degree, since OPEC cannot directly influence
tight oil production decisions. 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.361 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.
361 "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 the 2005-2017 time
frame in five major oil-producing states: Texas, North Dakota, California, Oklahoma, and
Colorado.362 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.363 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.364
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
362 Newell, Richard, and Brian Prest. "The Unconventional Oil Supply Boom: Aggregate Price Response From
Microdata," October 1, 2017. https://doi.org/10.3386/w23973.
363 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.
364 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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2020s does not appear to be consistent with that observed during the episodes of crude oil price
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.365
Most U.S. tight oil producers did not generate positive cashflow.366 As of 2021, U.S. shale oil
producers have pledged to repay their debt and reward shareholders through dividends and stock
buybacks.367 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.
In its first quarter 2022 energy survey, the Dallas Fed (i.e., the Federal Reserve Bank of
Dallas) asked oil exploration and production (E&P) firms about the WTI price levels needed to
cover operating expenses for existing wells or to profitably drill a new well. The average
breakeven price to continue operating existing wells in the shale oil regions ranged from $23-
35/barrel. To profitably drill new wells, the required average WTI prices ranged from $48-
69/barrel. For both types of breakeven prices, there was substantial variation across companies,
even within the same region. The actual WTI price level observed in the first quarter of 2022 has
been roughly $95/barrel, substantially larger than the breakeven price to drill new wells.
However, the median production growth expected by the respondents to the Dallas Fed Energy
Survey from the fourth quarter of 2021 to the fourth quarter of 2022 is modest (6% among large
firms and 15% among small firms). Investor pressure to maintain capital discipline was cited by
59% of respondents as the primary reason why publicly traded oil producers are restraining
growth despite high oil prices. The other reasons cited included supply chain constraints,
difficulty in hiring workers, environmental, social, and governance concerns, lack of access to
financing, and government regulations.368 Given the recent behavior of tight oil producers, we do
not believe that tight oil will provide additional significant energy security benefits in 2026-2027
due to its lack of price responsiveness. The ORNL model still accounts for U.S. tight oil
production increases on U.S. net oil imports and, in turn, the U.S.'s energy security position.
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.
365 McLean, Bethany. "The Next Financial Crisis Lurks Underground," New York Times, September 1, 2018.
https://www.nYtimes.com/2018/09/01/opinion/the-next-financial-crisis-lurks-underground.html.
366 Id.
367 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-heavYweights-shower-investors-with-
dividends-on-oil-rallv.
368 Federal Reserve Bank of Dallas, "Oil and Gas Expansion Accelerates as Outlooks Improve Significantly," Dallas
Fed Energy Survey. First Quarter, March 23, 2022. https://www.dallasfed.org/researcli/survevs/des/2022/2201.
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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.369
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.370 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 2022.371 In 2023,
the DOE sold 26 million barrels of oil between April and June.372 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
369 Energy Policy and Conservation Act 42 U.S.C. § 6241(d) (1975).
3711 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.
371 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.
372 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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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
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.373
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).374
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).375 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.376 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.
373 Crane, Keith, Andreas Goldthau, Michael Toman, Thomas Light, Stuart Johnson Alireza Nader, Angel Rabasa,
and Harun Dogo. "Imported Oil and U.S. National Security." RAND Corporation, 2009.
https://doi.org/10.7249/mg838.
374 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.
375 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.
376 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.
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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.377 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,
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.378 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.379
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.380 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.
377 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.
378 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.
379 Id.
3811 Crane, Keith, Andreas Goldthau, Michael Toman, Thomas Light, Stuart Johnson Alireza Nader, Angel Rabasa,
and Harun Dogo. "Imported Oil and U.S. National Security." RAND Corporation, 2009.
https://doi.org/10.7249/mg838.
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6.4 Energy Security Impacts
6.4.1 U.S. Oil Import Reductions
From 2026-2030, the time frame of the analysis of this proposed rule, the AEO2023
Reference Case projects that the U.S. will be a net exporter of petroleum, both an exporter and an
importer of crude oil and petroleum products.381 The U.S. produces more light crude oil than its
refineries can refine. 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.
U.S. crude oil exports are projected to be fairly stable at 3.2 MMBD in 2026 and 3.4 MMBD in
2030. U.S. crude oil imports, meanwhile, are projected to range between 6.8 MMBD and 7.1
MMBD over the 2026-2030 time period. AEO2023 also projects that net U.S. exports of
petroleum products will increase from 5.7 MMBD in 2026 to 6.0 MMBD in 2030. Given the
pattern of stable U.S. crude oil imports, and the projected growth in the U.S.'s net petroleum
product exports, the U.S. is projected to have constant net petroleum exports of 2.3 MMBD for
both 2026 and 2027.
Currently, the U.S. is the largest oil consumer in the world, consuming 20.3 MMBD of
oil.382 U.S. oil consumption is anticipated to gradually decline during the time frame of this
proposed rule from 18.6 MMBD in 2026 to 18.4 MMBD in 2030.383 It is not just U.S. crude oil
imports alone, but both imports and consumption of petroleum from all sources and their role in
economic activity, that exposes the U.S. to risk from price shocks in the world oil price. In 2026-
2027, the U.S. is projected to continue to consume significant quantities of oil and to rely on
significant quantities of crude oil imports. As a result, U.S. oil markets are expected to remain
tightly linked to trends in the world crude oil market.
EPA estimates changes in U.S. petroleum consumption as a result of this proposed rule.
EPA uses an oil import reduction factor to estimate how changes in U.S. refined product demand
from this rule (i.e., changes in U.S. oil consumption) influences U.S. net oil imports (i.e.,
changes in U.S. oil imports). In Chapter 10, EPA is estimating an oil import reduction factor of
98.3%. See Chapter 10.4.2.1.1 for how the 98.3% is estimated.
We also estimate how lower U.S. petroleum demand would affect U.S. refinery
production, partially due to its impact on imports, but also to understand how lower petroleum
demand would impact U.S. refinery's production capacity for energy security reasons. Based on
an industry study, EPA believes that U.S. refinery output would decline by half (50%) of that
reduced oil demand (it is likely that much of this decline would be due to U.S. refineries
converting from refining crude oil to instead produce renewable diesel fuel), while increases in
refined product net exports (i.e., equivalently a decline in net refined product imports) would
account for the other half (50%) of that reduced oil demand. See Chapter 10 and a Memorandum
381 AEO2023, Table 11 - Petroleum and Other Liquids Supply and Disposition.
382 EIA, "Petroleum and other liquids (consumption)," International, May 15, 2025.
https://www.eia.gov/international/data/world/petroleum-and-other-liauids/annual-refined-petroleum-products-
consumption.
383 AEO2023, Table 11 - Petroleum and Other Liquids Supply and Disposition.
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to the Docket for more discussion on this topic.384 There are good economic reasons why U.S.
refineries might continue to operate despite reduced U.S. product demand as a result of this
proposed rule. Principally due to lower natural gas and crude oil prices available in the U.S., U.S.
refineries generally have lower production costs compared to other refinery regions around the
world. Lower refinery production costs are attributed to the lower feedstock costs in the U.S.385
Even with a decrease in the U.S. product demand from this proposed rule, we anticipate that U.S.
refineries would be quite economically competitive compared to refineries operating around the
world. Thus, U.S. refineries would largely continue to operate and export refined products to the
rest of the world.
Based upon the changes in oil consumption estimated by EPA and the 98.3% oil import
reduction factor, the reductions in U.S. net oil imports as a result of the Low and High Volume
Scenarios and the renewable fuel volumes for the proposed RFS Set 2 Rule are estimated in
Table 6.4.1-1 for the 2026-2030 timeframe. Included in this table are estimates of U.S. crude oil
exports and imports, net oil refined product exports, net crude oil and refined petroleum product
exports and U.S. oil consumption for the years 2026-2030, the time frame of the analysis of this
proposed rule, based on the AEO2023.
Table 6.4.1-1: Projected Trends in U.S. Oil Exports/Imports, Net Oil Refined Product
Exports, Net Crude Oil and Refined Petroleum Product Exports, U.S. Oil Consumption
and Reductions in U.S. Oil imports Resulting from Volume Scenarios and Proposed
Volumes (MMBD)
2026
2027
2028
2029
2030
U.S. Crude Oil Exports
3.2
3.3
3.3
3.3
3.4
U.S. Crude Oil Imports
6.8
6.9
6.9
7.0
7.1
U.S. Net Petroleum Refined Product Exports3
5.7
5.8
5.8
5.9
6.0
U.S. Net Crude Oil and Refined Petroleum
Product Exports'3
2.1
2.1
2.2
2.2
2.3
U.S. Oil Consumption0
18.6
18.6
18.6
18.5
18.4
Reduction in U.S. Net Oil Imports from:
Low Volume Scenario
0.10
0.11
0.12
0.12
0.13
High Volume Scenario
0.11
0.13
0.15
0.16
0.18
Proposed Volumes
0.15
0.15
a Calculated from AEO2023, Table 11 as Net Product Exports minus Ethanol, Biodiesel, Renewable Diesel, and
Other Biomass-derived Liquid Net Exports.
b Calculated from AEO2023, Table 11 as Total Net Exports minus Ethanol, Biodiesel, Renewable Diesel, and Other
Biomass-derived Liquid Net Exports.
0 Calculated from AEO2023, Table 11 as Total Primary Supply minus Biofuels.
Of course, the impact on crude oil and refined petroleum product exports does not tell the
whole story. The proposed RFS Set 2 Rule will likely result in substantial imports of feedstocks
that are used to produce renewable fuels to meet the RFS renewable fuel volume requirements.
384 Ding, Cherry, Alexandre Ferro, Tim Fitzgibbon, and Piotr Szabat. "Refining in the Energy Transition Through
2040," McKinsev & Company, November 3, 2022. https://www.mckinsev.com/industries/oil-and-gas/our-
insights/refining-in-the-energy-transition-through-2040.
385 EIA, "Lower crude feedstock costs contribute to North American refinery profitability," Today in Energy, June 5,
2014. https://www.eia.gov/todavinenergy/detail.php?id=16571.
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As discussed in Chapter 10.4.2.1, we expect that up to 40% of the renewable fuels used to meet
the renewable fuel volumes will be derived from imported feedstocks.
While imported feedstocks will move the U.S. away from the goal of energy
independence, it is not clear how imported feedstocks will influence U.S. energy security. The
energy security implications of using imported feedstocks to make renewable fuels used in the
U.S. are not well understood or studied. To estimate the energy security impacts of imported
feedstocks on the U.S.'s energy security, one would need to have information on the variability
of imported feedstock prices. In addition, one would need to know how prices of imported
feedstocks and the biofuels produced from them are correlated with world oil prices. For
example, consider canola oil. Price variability in canola oil is likely related to weather-related
events, while price increases in world oil markets are influenced largely by geopolitical events
such as wars that cause disruptions in the world oil markets. From an overall perspective,
however, imported feedstocks will provide fuel supply diversity to the U.S., which may bring
some modest energy security benefits.
6.4.2 Oil Import Premiums Used for This Proposed Rule
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.386
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.387
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
proposed rule, EPA has updated the ORNL methodology using the AEO2023.
The ORNL methodology is used to compute the oil import security premium per barrel of
imported oil.388 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
386 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.
387 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.
388 The oil import premium concept is defined in Chapter 6.1.
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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.389
In the context of this proposed 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 the 2026-2030 time frame of analysis of this proposed rule. 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 proposed 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,
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 proposed 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
389 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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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-0.045.390 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 the 2026-2027 timeframe of this proposed rule. According to IEA, the share of global oil
consumption attributed to the transportation sector grew from 60% in 2000 to 66% in 2019.391
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 the 2026-2027
timeframe. 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 proposed 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.392 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.393 The RFF
"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 proposed rule and consistent with
the 2020-2022 RFS Rule and 2023-2025 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).394 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
390 See, e.g., 75 FR 26049, May 10, 2010.
391 IEA, "World Energy Statistics and Balances." https://www.iea.org/data-and-statistics/data-product/world-energy-
statistics-and-balances.
392 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.
393 See, e.g., 75 FR 26049 (May 10, 2010).
394 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.
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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 2020.395 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 proposed/final 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 $3.7/barrel.
395 The short-run oil supply elasticity assumed in the ORNL model is 0.06 and is applied to production from both
conventional and shale oil wells.
255
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Figure 6.4.2-1: Comparison of Oil Security Premiums of This Rule and Previous Rules
(2022$)
c.
d.
e.
h.
RFS1: Final Rule. (2007). Based on AE02006.
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.
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.
2020-2022 RFS Rule (2022). Based on AEO2021.
Final Rule to Revise Existing National GHG Emissions Standards for Passenger Cars and Light Trucks
Through Model Year 2026 (2023). Based on AEO2021.
Set 1 Rule (2023). Based on AEO2023.
Greenhouse Gas Emissions Standards for Heavy-Duty Vehicles - Phase 3 (2024). Based on AEO2023.
Final Rule: Multi-Pollutant Emissions Standards for Model Years 2027 and Later Light-Duty and Medium-
Duty Vehicles (2024). Based on AEO2023.
RFS Set 2 Rule 2026-2027; Proposal. Based on AEO2023.
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
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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
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.
Table 6.4.2-1 provides EPA's estimates of the macroeconomic oil security premiums for
2026-2030 for this RFS proposed rulemaking, showing that they are gradually increasing over
this time period. The macroeconomic oil security premiums range from $3.65/barrel in 2026 to
$3.92/barrel in 2030. In terms of cents per gallon, the macroeconomic oil security premiums
range from 8.6 cents per gallon in 2026 to 9.3 cents per gallon in 2030. These estimates of the
macroeconomic oil security premiums are actual values as opposed to discounted values,
implying that they do not reflect the time value of money.
Table 6.4.2-1: Macroeconomic Oil Security Premiums (2022$/barrel)a
Year
Macroeconomic Oil Security Premiums
(2022$/Barrel of Reduced Oil Imports)
2026
$3.65
($0.47-$6.89)
2027
$3.73
($0.51-$7.02)
2028
$3.78
($0.51-$7.15)
2029
$3.87
($0.54-$7.31)
2030
$3.92
($0.51-$7.46)
a Top-values in each cell are mean values. Values in parentheses are 90 percent confidence intervals.
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6.4.3 Energy Security Benefits
Estimates of the total annual energy security benefits of the Low and High Volume
Scenarios and the proposed RFS renewable fuel volumes are based on the ORNL oil import
premium methodology with updated oil import premium estimates reflecting the energy security
literature and using AEO2023. To calculate total energy security benefits, annual
macroeconomic oil security premiums (Table 6.4.2-1) are multiplied by the annual reduction in
U.S. net oil imports (Table 6.4.1-1). The total annual energy security benefits are presented in
Tables 6.4.3-1 and 2. We do not consider military cost impacts or the monopsony effect of U.S.
crude oil and refined petroleum product import changes. These benefit estimates are actual
values as opposed to discounted values, implying that they do not reflect the time value of
money.
Table 6.4.3-1: Total Annual Energy Security Benefits of the Low and High Volume
Scenarios (millions 2022$, undiscounted)a'b
Year
Total Energy Security Benefits
Low Volume Scenario
Total Energy Security Benefits
High Volume Scenario
2026
$138
C$18—$261)
$151
($19-$284)
2027
$150
($21-$283)
$176
($24-$331)
2028
$162
($22-$307)
$201
($27-$380)
2029
$175
($24-$331)
$228
($32-$430)
2030
$187
($24-$357)
$254
($33-$484)
a Top-values in each cell are the mean values, while the values in parentheses define 90 percent confidence intervals.
b U.S. net oil import reductions used for the energy security analysis in this section are a combination of reduced
U.S. imports of gasoline, diesel fuel, and crude oil from Chapter 10.4.2.1.1 converted to crude oil-equivalent barrels.
In Table 6.4.3-2, we present the energy security benefits for the proposed RFS renewable
fuel volumes. These benefit estimates are actual values as opposed to discounted values,
implying that they do not reflect the time value of money. The present value and annualized
value of estimated energy security impacts of the Proposed Volumes using 3% and 7% discount
rates are presented in Preamble Section V.H.
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Table 6.4.3-2: Total Annual Energy Security Benefits of the Proposed Renewable Fuel
Volumes for 2026-2027 (millions 2022$, undiscounted)a'b
Year
Total Energy Security Benefits
Proposed Volumes
2026
$196
($25-$369)
2027
$210
($29-$395)
a Top-values in each cell are the mean values, while the values in parentheses define 90 percent confidence intervals.
b 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.
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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
biomass-based diesel. While we are proposing to establish RFS standards for a two-year period,
we are presenting projections of the rate of production and consumption of renewable fuels
through 2030, consistent with the Low and High Volume Scenarios. Consequently, we evaluated
production trends for each year from 2026 to 2030. 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.396
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 11. For 2026-2030, 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 2030.
We discuss the production and use of each major type of biofuel in turn: cellulosic
biofuel (Chapter 7.1), biomass-based diesel (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
Over the past few years, cellulosic biofuel production has steadily increased, reaching
record levels in 2024. This growth has been primarily driven by biogas-derived compressed
natural gas (CNG) and liquified natural gas (LNG).397 However, small volumes of liquid
cellulosic biofuels, particularly ethanol produced from corn kernel fiber (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 2025-2030, along with
some of the uncertainties associated with the projected volume for these years.
396 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.
397 The majority of the cellulosic RINs generated for CNG/LNG are sourced frombiogas 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.
260
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Figure 7.1-1
1100
1000
900
800
£ 700
S 600
I 500
| 400
300
200
100
0
Cellulosic RINs Generated (2013-2024)
2<>13 20]4 20js 20j6 2oj7 2q}8 2f)]9 202q 2()?j 2q22 2023 2024
~ CNG/LNG Derived from Biogas
BLiquid Cellulosic Biofuels
Source: EMTS.
To project the volume of cellulosic biofuel production in 2025-2030, 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
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 2030.
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
RNGused as CNG/LNG from 2026-2030. Chapter 7.1.5 focuses on the projected production of
liquid cellulosic biofuels from 2026-2030. Finally, Chapter 7.1.6 summarizes the overall
projected rate of cellulosic biofuel production for 2026-2030.
7.1.1 Cellulosic Biofuel Industry Assessment
This section evaluates the cellulosic biofuel producers expected to generate qualifying
cellulosic biofuel between 2026 and 2030. This includes producers of both D3 RIN-generating
cellulosic biofuels and D7 RIN-generating cellulosic diesel. Analysis of existing RIN generation
data shows two primary contributors: biogas-derived compressed natural gas (CNG) and
liquified natural gas (LNG), as well as ethanol produced from corn kernel fiber (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 Biogas-derived Compressed Natural Gas and Liquefied Natural Gas
In July 2014, EPA approved cellulosic biofuel pathways under the "Pathways II" Rule,
allowing CNG and LNG derived from biogas to generate cellulosic biofuel (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 1,013 million RINs in 2024. Notably, about 95%
of all cellulosic RINs generated in 2024 were attributed to CNG/LNG derived from biogas (see
Figure 7.1-1). Biogas-derived CNG/LNG is expected to remain the primary source of cellulosic
RIN generation through 2030.
7.1.1.2 Ethanol from Corn Kernel Fiber
Outside of biogas-derived CNG/LNG, few additional sources of cellulosic biofuel exist.
One notable exception is ethanol produced from corn kernel fiber (CKF). During the corn
ethanol production process, a fraction of the cellulosic component of corn kernel fiber can be co-
processed with the corn starch to produce cellulosic ethanol. Thus, with minimal additional
processing or modifications, meaningful volumes of cellulosic ethanol could be co-produced
alongside starch ethanol production. However, facilities must accurately determine the amount of
ethanol specifically derived from the cellulosic portion to qualify for generating cellulosic
biofuel (D3) RINs. This requires reliable and precise methods to distinguish ethanol produced
from the cellulosic component from that derived 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 corn kernel fiber and corn
starch.398 EPA has also engaged with facility owners registered as cellulosic biofuel producers.
As a result of these efforts, EPA anticipates that most facilities currently producing corn starch
ethanol will generate D3 RINs for cellulosic ethanol during the years analyzed in this proposed
rule. Given the significant volume 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 2030, EPA expects that commercial-scale production of cellulosic
biofuel—beyond CNG/LNG derived from biogas and ethanol produced from CKF—to remain
very limited. 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,399 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 commercial scale volumes of cellulosic biofuel by 2030. These
facilities primarily focus on producing cellulosic hydrocarbons from feedstocks such as
398 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.
399 EMTS data reports that from 2020 to 2024, annual D7 RIN generation varied from 55,892 to 283,259 RINs.
262
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separated municipal solid waste (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 2030.
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 2030.
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. These data show that EPA projections underestimated
RIN availability in 2015, 2018, and 2022, while overestimating it in 2016, 2017, 2019, 2020,400
2023, and 2024. 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.
400 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.
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Table 7.1.2-1: Projected and Actual Cellulosic Biofuel Production (million ethanol-
equivalent gallons)
Year
Projected Volume
Actual Volume3
Source
Liquid
Cellulosic
Biofuel
RNG used
as
CNG/LNG
Total
Cellulosic
Biofuelb
Liquid
Cellulosic
Biofuel
RNG used
as
CNG/LNG
Total
Cellulosic
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
1
51
1,039
1,090
43
971
1,014
2025
l,m
77
1,299
1,380
-
-
-
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 RNG used as
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 As discussed in Preamble Section VII and Chapter 7.1.3, EPA is proposing to revise the 2025 cellulosic biofuel
volume requirement in this rule.
Examining the data in this table, we find that EPA projections for liquid cellulosic biofuel
were consistently higher than actual production volumes each year from 2015 to 2017. In
response to the over-projections in 2015-2017, EPA revised our methodology in the 2018 final
rule to incorporate the most recent data and improve the accuracy of our projections. This
updated approach involves first categorizing potential liquid cellulosic biofuel producers into
two groups: those with a proven track record of commercial-scale production ("consistent
producers") and those still working toward it ("new producers"). For each group, we defined a
likely production range and then applied a percentile value to estimate a single projected
production volume based on each group's historical performance relative to its projected range.
Despite these adjustments, EPA continued to overestimate liquid cellulosic biofuel
production from 2018-2020. The year 2020, however, posed particular challenges due to the
impacts of Covid-19—an unexpected disruption that could not be predicted in our projections. In
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2022, EPA under-projected liquid cellulosic biofuel volumes using the revised 2018
methodology. For the 2023-2025 rule, EPA once again used the 2018 projection methodology.
While only two full years of data (2023 and 2024) are available as of this proposal, this
information shows that EPA overestimated liquid cellulosic biofuel production for both years.
Next, we turn to the projection of RNG used as CNG/LNG. From 2015 to 2017, EPA
applied a facility-by-facility approach to project CNG/LNG production from RNG, estimating
volumes for individual companies or facilities. However, this methodology also significantly
overestimated 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 RNG production rate. Specifically, EPA analyzes RIN
generation data from the most recent 24 months available at the time of each rulemaking and
calculated 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 RNG industry used as CNG/LNG, 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 RNG used as CNG/LNG in 2018,
2019, and 2022. Though, 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
biogas production potential more accurately. While only two full years of generation data (2023
and 2024) are available as of this proposal, this information shows that EPA overestimated RNG
production for all years projected in the 2023-2025 rulemaking. 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 RNG used as CNG/LNG has consistently exceeded
that of liquid cellulosic biofuel. This difference likely results from several factors, including the
maturity of RNG production technology relative to liquid cellulosic biofuel technologies, the
lower production costs for RNG used as CNG/LNG (see Chapter 11), and the relatively high
value of the cellulosic RIN. While we project liquid cellulosic biofuel and RNG volumes
separately, the overall accuracy of the combined cellulosic biofuel volume projection is
ultimately what matters for obligated parties.
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7.1.3 Projection of the 2025 Cellulosic Biofuel Volumes
As discussed in the previous section, EPA overestimated total cellulosic RIN generation
for 2023 and 2024. This overestimation was largely driven by an over projection of RNG
production, which makes up a significant portion of the total cellulosic biofuel volumes.
Specifically for 2023, RNG volumes were insufficient to meet the cellulosic volume requirement
set by the prior RFS rulemaking. As a result, a deficit in cellulosic RINs was carried forward into
2024. Looking at 2024, RNG production—and, consequently, total cellulosic biofuel volumes—
are again expected to fall short of the required volumes. Given this anticipated shortfall, the
existing RIN deficit from 2023, and the limited availability of 2023 carryover RINs, the
cellulosic RIN deficit in 2024 could be substantial. This shortfall may force some obligated
parties that carried forward a deficit from 2023 into noncompliance with their 2024 obligations.
In response, EPA proposed adjusting the 2024 cellulosic biofuel volume requirements.401
Given the shortfalls in projecting the 2023 and 2024 volumes, EPA has reason to believe
that cellulosic biofuel volumes could also fall short in 2025. While the exact causes of past
deficits remain unclear and may stem from multiple factors, EPA has long been aware that the
RNG market could eventually reach a "saturation point"—where nearly all RFS-eligible
CNG/LNG vehicles are fueled entirely with RNG. Since biogas-derived CNG/LNG can only
generate RINs when it is used in CNG/LNG vehicles as a transportation fuel, RIN generation
from biogas-derived CNG/LNG past this saturation point would be constrained by the expansion
of the total CNG/LNG vehicle market. While EPA had anticipated this eventual limitation, we
did not believe the market had reached this point when establishing the 2023-2025 volume
targets. At the time of that rulemaking, EPA projected future volumes based on the assumption
that RNG production capacity—not the RNG market consumption—would be the primary
constraint on cellulosic RIN generation. Though, in that rulemaking EPA acknowledged that this
methodology might become less appropriate as RNG usage in CNG/LNG vehicles approaches
the total volume of CNG/LNG used as transportation.402 With cellulosic biofuel volumes falling
short of projections for both 2023 and 2024, there is now strong evidence to suggest that the
market is, in fact, demand-limited. In light of this shift, EPA has reevaluated 2025 volume
projections under both a supply-limited and a demand-limited scenario, using the most recent
generation data available.
Under a demand-limited scenario—where RNG consumption is the limiting factor—EPA
projects the 2025 volumes shown in Table 7.1.3-1. Additional details on how this volume was
calculated can be found in Chapter 7.1.4.1. Conversely, under a supply-limited scenario—where
RNG production capacity is the primary constraint—EPA estimates that 2025 RNG volumes will
align with the data presented in Table 7.1.3-1. For details on how this estimate was determined,
see Chapter 7.1.4.2.
401 89 FR 100442 (December 12, 2024).
402 Set 1 Rule RIA Chapter 6.1.3.
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Table 7.1.3-1: Projected 2025 Biogas-derived CNG/LNG Volumes (million ethanol-
Volume
Biogas-derived CNG/LNG Volumes
Under Demand-limited Scenario
1,113
Biogas-derived CNG/LNG Volumes
Under Supply-limited Scenario
1,206
Combining this estimate for the future biogas-derived CNG/LNG volume with the
estimate of the future volume of cellulosic ethanol from the previous rulemaking, (see Table
7.1.2-1), EPA estimates that 2025 total cellulosic volumes will align with the data presented in
Table 7.1.3-2.
Table 7.1.3-2: Projected 2025 Cellulosic Volumes (million ethanol-equivalent gallons)
Biogas-derived
CNG/LNG
Ethanol from
CKF
Total
Cellulosic
Demand-limited Scenario
1,113
77
1,190
Supply-limited Scenario
1,206
77
1,283
Because the demand for biogas-derived CNG/LNG is lower than the projected supply, we
believe that the market has effectively reached the above-mentioned saturation point, with nearly
all RFS-eligible CNG/LNG vehicles being fueled primarily by biogas-derived CNG/LNG.
Accordingly, we are proposing in this action to adjust the cellulosic fuel volume for 2025.403
Table 7.1.3-3: Projected 2025 Cellulosic Volumes (million ethanol-equivalent gallons)
Biogas-derived
CNG/LNG
Ethanol from
CKF
Total
Cellulosic
2025 Cellulosic Volumes
1,113
77
1,190
7.1.4 Projecting the Biogas-derived CNG and LNG Market
As discussed in the previous section, biogas-derived CNG/LNG can only qualify for RIN
generation when it is used by CNG/LNG vehicles as a 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 renewable natural gas (RNG).404 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
403 See Preamble Section VII.
404 40 CFR 80.2.
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at landfills and wastewater treatment plants across the U.S., with further potential for biogas
generation from manure and other agricultural residues.405 Although the quantity of biogas from
qualifying sources potentially far exceeds current CNG/LNG usage as transportation fuel,406
much of this biogas is not being upgraded to RNG407—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.408
Despite these challenges, the incentive created by the cellulosic biofuel RIN has led to
rapid growth in RNG409 use as CNG/LNG since 2014 (see Table 7.1.4-1). Considering this
incentive, we believe that the volume of RNG used as CNG/LNG can continue to grow under the
influence of the RFS through 2030. 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 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 RNG.
Table 7.1.4-1: RIN Generation (Million RINs) and Annual Growth Rate for RNG used as
CNG/LNG
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
RIN
Generation
140
189
240
304
404
504
567
667
773
971
Annual
Growth Rate
-
35%
27%
27%
33%
25%
13%
18%
16%
26%
7.1.4.1 Projected Demand of Biogas-derived CNG and LNG
To estimate the future demand of RNG used in CNG/LNG vehicles, we first looked to
identify an appropriate estimate for all CNG/LNG usage in transportation, including both fossil
and biogas-derived sources. Because RINs under the RFS can only be generated for CNG/LNG
used as transportation fuel, the maximum potential volume of CNG/LNG in transportation
represents the upper limit for RNG volumes. Several projections exist for CNG/LNG usage in
the 2026-2030 period. One key source is AEO2023, which projects nationwide CNG/LNG use as
transportation fuel at: 1,752; 1,778; 1,845; 1,893; and 1,909 million ethanol-equivalent gallons
for the years 2026, 2027, 2028, 2029, and 2030, respectively (see Table 7.1.4.1-1). However,
these AEO projections include all transportation-related energy usage, including sectors like
4115 American Biogas Council, "Biogas Market Snapshot," April 2025. https://americanbiogascouncil.org/biogas-
market-snapshot.
4116 A discussion of EPA's estimates for current and future CNG/LNG usage as transportation fuel is in Chapter
7.1.4.1.
4117 EPA, "LFG Energy Project Development Handbook," January 2024.
https://www.epa.gov/sYStem/files/documents/2024-01/pdh full.pdf.
4118 EPA, "LMOP Landfill and Project Database." https://www.epa.gov/lmop/lmop-landfill-and-proiect-database.
4119 We note that RNG is defined as biogas that has been upgraded to commercial pipeline quality and placed onto the
natural gas commercial pipeline system. We also define the term "treated biogas" to refer to biogas that has
undergone treatment for use as transportation fuel but that is not placed onto the natural gas commercial pipeline
system (i.e., it is distributed via a closed, private distribution system). Although they are defined differently in the
regulations, we use the term "RNG" to collectively refer to both treated biogas and RNG in this document.
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international shipping, which are outside the RFS scope.410 After adjusting the AEO estimates to
exclude non-relevant CNG/LNG volumes,411 the revised projections indicate more conservative
estimates of: 1,131; 1,136; 1,141; 1,143; and 1,145 million ethanol-equivalent gallons for the
years 2026-2030, as shown in Table 7.1.4.1-1.
Table 7.1.4.1-1: Projected CNG/LNG Transportation Usage from EIA's 2023 AEO412
(million ethanol-equivalent gallons)
2026
2027
2028
2029
2030
CNG/LNG
Transportation Usage
1,752
1,778
1,845
1,893
1,909
Adjusted3 CNG/LNG
Transportation Usage
1,131
1,136
1,141
1,143
1,145
a Usage adjusted to exclude volumes attributed to international and domestic shipping.
Additionally, given the high likelihood of nationwide consumption limitations emerging
by the mid-to-late 2020s, we believe it would be valuable to develop an alternative estimate of
future CNG/LNG demand. This would allow for a more comprehensive assessment of potential
saturation points by providing a basis for comparison with the AEO estimate. To achieve this,
EPA created a separate estimate of future CNG/LNG demand entirely independent of the AEO
estimate. Referred to in this section as the "EPA Estimate," this independent estimate was
developed from a combination of data sources and modeling techniques specifically tailored to
different vehicle categories.
The vehicle categories chosen were primarily based 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.413 The HPMS classifications include light-duty vehicles with a
short wheelbase, light-duty vehicles with a long wheelbase, motorcycles, buses, single unit
trucks,414 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
4111 Under the definition of transportation fuel in 40 CFR 80.2, fuel for use in ocean-going vessels is excluded as a
transportation fuel.
411 Volumes attributed to: Light-Duty Vehicle, Commercial Light Trucks, Freight Trucks, Freight Rail, Transit
Buses, and School Buses were included. Volumes attributed to: International Shipping and Domestic Shipping were
excluded.
412 AEO2023, Table 36 - Transportation Sector Energy Use by Fuel Type Within a Mode.
413 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.
414 Single-Unit: single frame trucks that have 2-axles and at least 6 tires or a gross vehicle weight rating exceeding
10,000 lbs.
269
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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 Department of Energy (DOE) as the primary source for vehicle count information.415
This vehicle count data for natural gas vehicles was integrated with assumptions for average
vehicle miles traveled (VMT)416 and average fuel efficiency417 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 Model Year (MY) 2022.418 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)3
Year
Data Type
Vehicle Count
CNG/LNG Usage
2022
Actual
24,700
23.4
2023-2030
Projected
24,700
23.4
a Calculated using an average efficiency of 17.8 miles per gasoline-equivalent gallon and an average VMT of
11,318 miles per vehicle.
To estimate consumption from public transit, EPA utilized data from the American
Public Transportation Association's 2023 Public Transportation Fact Book, which provides
energy consumption data separated by fuel type.419 Data from this source indicates significant
variability in annual fuel usage, with no clear trend beyond a noticeable reduction in usage
during the Covid-19 pandemic period. Given this volatility and the fact that the most recent data
available are from 2021 (which would still reflect the impacts of Covid-19), EPA opted to
calculate an average annual growth rate based on data from 2014 onward. This starting point
aligns with the classification of RNG as a cellulosic biofuel under the RFS program. The
resulting average growth rate of 0.9% per year was applied to each subsequent year to project
415 AFDC, "Vehicle Registration Counts by State," 2022. https ://afdc. energy.gov/vehicle-registration?vear=2022.
416 AFDC, "Average Annual Vehicle Miles Traveled by Major Vehicle Category," September 2024.
https://afdc.energv.gov/data/10309.
417 AFDC, "Average Fuel Economy by Major Vehicle Category," January 2024. https://afdc.energy.gov/data/10310.
418 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). and Model Year 2024
(https ://afdc. energy. gov/vehicles/search/download.pdf?vear=2024).
419 American Public Transportation Association, "2023 Public Transportation Fact Book," Appendix A: Historical
Tables, Table 58 - Non-Diesel Fossil Fuel Consumption by Fuel Type, https://www.apta.com/research-teclinical-
resources/transit-statistics/public-transportation-fact-book.
270
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future CNG/LNG consumption in public transportation. The projected CNG/LNG usage data for
public transportation are presented in Table 7.1.4.1-3.
Table 7.1.4.1-3: CNG/LNG Usage from the Public Transportation Sector (million ethanol-
CNG/LNG
Year-over Year
Year
Data Type
Usage
Growth
2014
Actual
284.2
N/A
2015
Actual
297.9
4.8%
2016
Actual
316.8
6.3%
2017
Actual
312.7
-1.3%
2018
Actual
323.4
3.4%
2019
Actual
342.2
5.8%
2020
Actual
310.1
-9.4%
2021
Actual
299.2
-3.5%
2022
Projected
301.8
0.9%
2023
Projected
304.5
0.9%
2024
Projected
307.2
0.9%
2025
Projected
309.9
0.9%
2026
Projected
312.7
0.9%
2027
Projected
315.5
0.9%
2028
Projected
318.3
0.9%
2029
Projected
321.1
0.9%
2030
Projected
323.9
0.9%
For school buses, EPA is using data from the World Resources Institute's Dataset of U.S.
School Bus Fleets,420 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, data
for four states—Colorado, Hawaii, Louisiana, and New Hampshire—are not available. To
address this limitation, EPA used state population data alongside state-level CNG bus counts to
estimate the number of CNG school buses in the states with missing data. The vehicle count data
for CNG buses was then combined with average VMT421 and average fuel efficiency specific to
school buses.422 This approach resulted in an estimate of total CNG/LNG consumption for the
school bus sector. Since this dataset does not reflect changes over time—data were collected
between March and November 2022, capturing vehicle counts only for that period—EPA applied
the same annual growth rate (0.9%) as used for the public transportation sector to estimate year-
over-year growth in CNG/LNG usage. For simplicity, we have also chosen to exclude future
scrappage from the analysis, as fuel consumption in the school bus category is already minimal.
The estimated consumption data for the school bus sector are presented in Table 7.1.4.1-4.
4211 Lazer, Leah, Lydia Freehafer, and Jessica Wang. "Dataset of U.S. School Bus Fleets Version 2," World
Resources Institute, February 17, 2023. https://datasets.wri.org/datasets/usa-school-bus-fleets.
421 AFDC, "Average Annual Vehicle Miles Traveled by Major Vehicle Category," September 2024.
https://afdc.energy.gov/data/10309.
422 AFDC, "Average Fuel Economy by Major Vehicle Category," January 2024. https://afdc.energy.gov/data/10310.
271
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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
5,564
N/A
18.1
2023
Projected
5,614
0.9%
18.3
2024
Projected
5,664
0.9%
18.4
2025
Projected
5,714
0.9%
18.6
2026
Projected
5,764
0.9%
18.8
2027
Projected
5,816
0.9%
18.9
2028
Projected
5,867
0.9%
19.1
2029
Projected
5,919
0.9%
19.3
2030
Projected
5,972
0.9%
19.4
a Calculated using an average efficiency of 6.46 miles per gasoline-equivalent gallon.
b Calculated using an average VMT of 14,084 miles per vehicle.
For refuse trucks, EPA derived vehicle count estimates from fleet information reported in
the sustainability reports of the largest waste management companies in the U.S. For many
companies, especially smaller ones, data were more limited, and historical data were unavailable
for several of the years reviewed. In such cases, EPA applied average growth rates from
companies with available data to estimate vehicle counts for periods with missing information.
Following this approach, total vehicle counts from the analyzed companies were aggregated 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 VMT423 and average fuel
efficiency for refuse haulers,424 were used to estimate total CNG/LNG consumption within the
refuse hauler sector. In 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]"425
This figure is lower than EPA's estimate of approximately 21,000 vehicles in 2023. 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.
423 AFDC, "Average Annual Vehicle Miles Traveled by Major Vehicle Category," September 2024.
https://afdc.energy.gov/data/10309.
424 AFDC, "Average Fuel Economy by Major Vehicle Category," January 2024. https://afdc.energy.gov/data/10310.
425 The Transport Project, "Vehicles for every route", https://transportproiect.org/veliicles.
272
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Table 7.1.4.1-5: Estimated Refuse Hauler Vehicle Counts3
Company
2018
2019
2020
2021
2022
2023
Waste Management
7,944
8,924
10,388
10,832
11,307
12,119
Republic Services
3,200
3,200
3,423
3,444
3,380
3,440
Waste Connections
1,070
1,119
1,166
1,090
1,070
1,13 4b
Clean Harbors
-
-
-
-
13
14b
GFL Environmental
-
776
983
1,179
1,238
1,312b
Recology
-
1,950
2,080
2,158
2,314
2,453b
Waste Pro USA
800
800b
800b
800b
800b
848b
Casella Waste Systems
-
-
-
30
44
47b
a Vehicle counts are estimated based on limited data, including information available only from earlier years.
b Data sources: 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-recology): Waste Pro USA (https://www.wasteprousa.com/blog/waste-
pro-recognized-for-eco-friendlv-operations): Casella Waste Systems SASB Reports.
Table 7.1.4.1-6: CNG/LNG Usage from the Refuse Hauler Sector (million ethanol-
Year-over Year
CNG/LNG
Year
Data Type
Vehicle Count
Growth
Usage
2019
Actual
16,769
N/A
252.5
2020
Actual
18,840
12.3%
283.7
2021
Actual
19,533
3.7%
294.1
2022
Actual
20,166
3.2%
303.7
2023
Actualb
21,367
6.0%
321.7
2024
Projected
22,715
6.3%
342.0
2025
Projected
24,147
6.3%
363.6
2026
Projected
25,670
6.3%
386.5
2027
Projected
27,288
6.3%
410.9
2028
Projected
29,009
6.3%
436.8
2029
Projected
30,838
6.3%
464.4
2030
Projected
32,783
6.3%
493.6
a Calculated using an average efficiency of 2.48 miles per gasoline-equivalent gallon and an average VMT of 25,000
miles per vehicle.
b Calculated using both projected and actual data.
For single-unit trucks (excluding refuse haulers) and combination trucks, EPA estimated
CNG/LNG vehicle counts for each calendar year using national vehicle registration data from
2014, 2020, and 2023.426 To fill in the gaps, data was linearly interpolated to estimate
426 Vehicle count data shown in Tables 7.1.4.1-7 and 8 are from the Motor Vehicle Emission Simulator (M0VES5),
(https://www.epa.gov/inoves/latest-version-inotor-veliicle-einission-simulator-moves). For information on how this
data was derived, see EPA, "Population and Activity of Onroad Vehicles in M0VES5," EPA-420-R-24-019,
November 2024, Chapters 4 and 5. https://nepis.epa.gov/Exe/ZvPDF.cgi?Dockev=P101CUN7.pdf.
273
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registrations for all years between 2014 and 2022. In addition to registration data, EPA
incorporated average VMT and fuel efficiency data from the Bureau of Transportation Statistics'
National Transportation Statistics publication.427 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.
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)3
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
a 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)3
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
a Diesel-equivalent gallons (DGE) converted to ethanol-equivalent gallons (EGE) using 1 EGE = 0.59 DGE.
427 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).
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Table 7.1.4.1-9: CNG/LNG Usage from the Single Unit Truck Sector (million ethanol-
equivalent gallons)
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%
2028
Projected
144.2
9.4%
2029
Projected
157.8
9.4%
2030
Projected
172.6
9.4%
Table 7.1.4.1-10: CNG/LNG Usage from the Combination Truck Sector (million ethanol-
equivalent gallons)
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%
2028
Projected
407.5
8.2%
2029
Projected
440.9
8.2%
2030
Projected
477.1
8.2%
In addition to the above scenario using a year-over-year growth projection for total
CNG/LNG usage, EPA conducted an alternative analysis incorporating higher future CNG/LNG
vehicle counts to account for potential accelerated adoption in this sector. In particular, this
analysis focused on the potential market impact of the Cummins XI5N engine,428 assuming
exponential growth in CNG engine adoption among freight trucks, with market penetration
428 Cummins, "Engines - X15N (2024)." https://www.cummins.com/engines/xl5n-2024.
275
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potentially reaching 10% of new vehicles by 2030.429 Under this scenario, estimated fuel
volumes increased significantly (Table 7.1.4.1-11). This outcome highlights the challenges of
accurately forecasting CNG/LNG consumption, particularly as emerging technologies shape
market trends. While this aggressive growth scenario was not included in our final consumption
estimate—given that we did not want to base RNG consumption potential on a single new engine
technology—it is presented here for context. With that stated, stakeholder feedback has shown
strong interest in this new engine, suggesting that future adoption rates may warrant revisions to
these estimates as market dynamics evolve.
Table 7.1.4.1-11: CNG/LNG Usage from both Single Unit and Combination Trucking
CNG/LNG Usage Under
CNG/LNG Usage Under
Year
Standard Growth Scenario
High Penetration Scenario
2022
338.1
338.1
2023
366.8
421.6
2024
398.0
487.1
2025
431.8
568.8
2026
468.4
671.7
2027
508.2
802.5
2028
551.4
970.6
2029
598.2
1,188.9
2030
649.0
1,476.0
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
7.1.4.1-12. This aggregated volume is generally consistent with, but slightly higher than, the
AEO estimate, shown in Table 7.1.4.1-1.
Table 7.1.4.1-12: Total CNG/LNG Usage for the "EPA Estimate" (million ethanol-
equivalent gallons)
2025
2026
2027
2028
2029
2030
Light-duty Vehicles
23.4
23.4
23.4
23.4
23.4
23.4
Public Transportation
309.9
312.7
315.5
318.3
321.1
323.9
School Buses
18.6
18.8
18.9
19.1
19.3
19.4
Refuse Trucks
363.6
386.5
410.9
436.8
464.4
493.6
Single Unit Trucks
110.1
120.5
131.8
144.2
157.8
172.6
Combination Trucks
321.7
348.1
376.6
407.5
440.9
477.1
Total3
1,147
1,210
1,277
1,349
1,426
1,509
a Total may not be precisely equal to the sum of each vehicle sector due to rounding.
In addition to the EPA Estimate, we wanted to develop an alternative volume projection
that considered a different potential market constraint: the limitation of existing CNG/LNG
429 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://voutu.be/fOH6i lcclkl (19:15 in video).
276
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fueling infrastructure. Specifically, we examined how the growth of RNG used as CNG/LNG
might be constrained by the existing CNG/LNG fueling infrastructure. While natural gas and
natural gas pipelines are widely accessible, there are only around 1,400 public and private
fueling stations in the U.S.430 compared to roughly 145,000 gasoline stations.431 Some fleets
interested in using CNG/LNG invest in private fueling infrastructure so they can fuel onsite;
however, this can be a significant investment that not all businesses can afford. To account for
this potential constraint, EPA developed an additional projection based on fueling infrastructure
capacity.
By analyzing California's CNG/LNG usage data and station numbers, EPA calculated an
average fuel throughput per station. This throughput was then applied to the total number of
CNG/LNG stations nationwide to estimate overall U.S. CNG/LNG throughput of CNG/LNG, as
shown in Table 7.1.4.1-13. This "station throughput" method was not used to project future
volumes, as it assumes every U.S. station would dispense fuel at California's rates, which would
likely overestimate consumption due to California's outsized market. It also is heavily based on
the number of CNG/LNG refueling stations, an estimate which experiences a reasonable amount
of volatility year-to-year. However, comparing this throughput estimate to actual RIN data for
each corresponding year shows an interesting insight: although this volume exceeds the total
RINs generated in each year, even if every U.S. station dispensed fuel at California's rates, the
estimated volume totals over the past five years still only ranges between 1,186 and 1,558
million EGE.
Table 7.1.4.1-13: Projected Consumption of CNG/LNG Used as Transportation Fuel Using
"Station Throughput" Method i
Units
2019
2020
2021
2022
2023
CNG/LNG used as transportation
fuel in California3
Million EGE
305
278
303
335
356
CNG/LNG refueling stations in
California13
Station Count
365
363
364
352
341
Average annual throughput per
station in California
Million EGE per
Station
0.84
0.77
0.83
0.95
1.04
CNG/LNG refueling stations in
the U.S.
Station Count
1576
1549
1510
1399
1492
Projected CNG/LNG used as
transportation fuel in the U.S.
Million EGE
1,317
1,186
1,257
1,331
1,558
RIN Generation for RNG used as
CNG/LNG
Million RINs
404
504
568
667
773
" California LCFS Reporting Tool Quarterly Summaries, https://ww2.arb.ca.gov/resources/documents/low-carbon-
fuel-standard-reporting-tool-auarterlY-summaries.
b AFDC, "Alternative Fueling Station Locator". https://afdc.energv.gov/stations#/analvze?countrv=US®ion=US-
CA&tab=location&fuel=CNG&fuel=LNG&access=public&access=private.
4311 AFDC, "Alternative Fueling Station Locator."
https://afdc.energv.gov/stations#/analvze?fuel=LNG&fuel=CNG&access=public&access=private&countrv=US&tab
=fuel.
431 API, "Service Station FAQs." https://www.api.org/oil-and-natural-gas/consumer-information/consumer-
resources/service-station-faa s.
277
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Because we are not using the station throughput method, we return to the previous
estimates of total CNG/LNG consumption, specifically EIA's AEO estimate (Table 7.1.4.1-1)
and the EPA estimate (Table 7.1.4.1-12). To develop a single projection for future CNG/LNG
consumption, we have chosen to rely solely on the EPA estimate for the demand side of the
market. This was done due to the similarity of the values and our level of understanding of their
derivation.
With the consumption estimate selected, we next looked to determine how much of the
total CNG/LNG market could be met with RNG. In a model scenario where all fossil-based
CNG/LNG could be fully replaced by RNG, this total CNG/LNG estimates would serve as the
maximum potential RNG volumes, with no further adjustment needed. However, due to practical
facility-level constraints like infrastructure limitations, costs, and other variables, it's unlikely the
market would achieve 100% replacement efficiency, and some fossil-based CNG/LNG would
likely remain in use. Thus, to better estimate realistic RNG consumption in a saturated market,
we applied an efficiency factor based on observed market conditions. Specifically, we looked at
California's LCFS program to better understand RNG consumption in a saturated market. 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 RNG
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 RNG-based 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 RNG-based,
with volumes accounting for an average of 97% of the total market from 2021 through 2023.
Thus, we assumed that California represents a mature, fully saturated market.
Table 7.1.4.1-14: California Low Carbon Fuel Standard Program Data (million ethanol-
equivalent gallons)432 i
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
RNG-based
CNG/LNG
49
113
151
181
203
236
257
296
323
344
Fossil-based
CNG/LNG
164
122
95
87
82
69
21
7
12
12
Total
CNG/LNG
213
234
247
268
285
305
278
303
335
356
Year-over-year
Growth of Total
-
10%
5%
8%
6%
7%
-9%
9%
11%
6%
RNG Blend
Rate
23%
48%
61%
68%
71%
77%
92%
98%
96%
97%
Subsequently, we assume that any fully saturated CNG/LNG market would consist of
approximately 97% RNG. Using this approach, we applied a 97% efficiency factor to the EPA
projections for future CNG/LNG volumes to estimate the potential RNG consumption under
saturated market conditions. These consumption estimates for RNG are detailed in Table 7.1.4.1-
15.
432 Id.
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Table 7.1.4.1-15: Projected Maximum Amount of RNG That Could Be Used As CNG/LNG
Due to Market Replacement Efficiency (million ethanol-equivalent gallons)
2025
2026
2027
2028
2029
2030
EPA CNG/LNG Consumption
Estimate
1,147
1,210
1,277
1,349
1,426
1,509
Potential RNG Usage Assuming
97% Replacement Efficiency
1,113
1,174
1,239
1,309
1,384
1,464
7.1.4.2 Projected Supply of Biogas-derived CNG and LNG
In addition to projecting future demand for biogas-derived CNG/LNG, EPA also
analyzed the potential production capacity of biogas-derived CNG/LNG 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 projection methodology that has been employed in the
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 biogas (see Chapter 7.1.2 for more
information on this methodology). Specifically, EPA used RIN generation data from the most
recent 24 months and multiplied the observed growth rate during that period with the most recent
full calendar year of data available. This growth rate was then repeatedly applied to each
progressive year to project future production. Using this method, the growth rate calculated is
24.2%, shown in Table 7.1.4.2-1.
Table 7.1.4.2-1: RIN Generation for D3 RNG (million ethanol-equivalent gallons)
Volume Generated Between
Feb.2023 - Jan. 2024
Volume Generated Between
Feb.2024 - Jan. 2025a
Y ear-Over-Y ear
Increase
778
966
24.2%
a This was the most recent 12 months for which data were available at the time of this analysis.
EPA then applied this 24.2% year-over-year growth rate to the total number of 2024
cellulosic RINs generated and available for compliance for CNG/LNG. That is, in this proposed
rule, as in the 2018-2022 final rules, we are multiplying the calculated year-over-year rate of
growth by the volume of CNG/LNG supplied in the most recent calendar year for which data is
available (in this case 2024), considering actual RIN generation. The ethanol equivalent RNG
volume potential projected using this methodology are shown in Table 7.1.4.2-2.
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Table 7.1.4.2-2: Projected Production Potential of RNG (million ethanol-equivalent gallons)
Year
Date Type
Growth Rate
Volume
2024
Actual
N/A
971
2025
Projected
24.2%
1,206
2026
Projected
24.2%
1,497
2027
Projected
24.2%
1,859
2028
Projected
24.2%
2,308
2029
Projected
24.2%
2,866
2030
Projected
24.2%
3,559
The projected production shown in Table 7.1.4.2-2 serves as the estimated volume of
RNG that could be produced absent any constraint on demand for use as transportation fuel.
7.1.4.3 Projected Volume of Biogas-derived CNG and LNG
With the consumption estimate selected from Chapter 7.1.4.1, we combined it with the
production estimates from Chapter 7.1.4.2, with this combination shown in Table 7.1.4.3-1.
Table 7.1.4.3-1: Estimated Production of RNG and Estimated Consumption of RNG By
2026
2027
2028
2029
2030
RNG Production
1,497
1,859
2,308
2,866
3,559
RNG Consumption
1,174
1,239
1,309
1,384
1,464
Analyzing both the consumption and production estimates shows that for 2026-2030,
potential RNG production exceeds the likely theoretical maximum for RNG consumption over
this period. Therefore, we expect the RNG market to be limited by the overall CNG/LNG market
size. Thus, EPA is projecting future RNG volumes based on the estimated future consumption of
RNG. These estimated volumes are shown in in Table 7.1.4.3-2.
Table 7.1.4.3-2: Projected Volume Biogas-derived CNG and LNG (million ethanol-
2026
2027
2028
2029
2030
Volume of RNG
1,174
1,239
1,309
1,384
1,464
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 2030. One notable exception is the production of ethanol from corn kernel fiber
(CKF), for which several companies have developed 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
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analytical methods that could be used to quantify the amount of ethanol produced when co-
processing corn kernel fiber and corn starch.433
EPA has also 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 might be feasible at all
existing corn ethanol facilities with minimal additional processing units or modifications.
However, for the purposes of this analysis, we assume that only 90% of facilities will actually be
able to produce cellulosic ethanol during the years analyzed for this proposed rule due to
potential facility-specific challenges that may prevent 100% adoption.
Additionally, while technology providers have indicated that the use of analytical
methods consistent with EPA's guidance allows for demonstrating that approximately 1.5% of
the ethanol produced at existing corn ethanol facilities comes from cellulosic biomass, the
current industry-wide average for registered facilities is closer to 1%. Therefore, for the purposes
of this analysis, we are using a 1% conversion rate.
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 7.1.6 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: Projected Production of Ethanol from CKF (ethanol-equivalent gallons)
Year
Volume
2026
124
2027
123
2028
122
2029
120
2030
119
7.1.6 Projected Rate of Cellulosic Biofuel Production for 2026-2030
After projecting production of cellulosic biofuel from liquid cellulosic biofuels and
CNG/LNG derived from biogas, EPA combined these estimates to project total cellulosic biofuel
production for 2026-2030. These projections are shown in Table 7.1.6-1.
433 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.
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Table 7.1.6-1: Projected Production of Cellulosic Biofuel in 2026-2030 (million ethanol-
2026
2027
2028
2029
2030
CNG/LNG Derived from Biogas
1,174
1,239
1,309
1,384
1,464
Ethanol from CKF
124
123
122
120
119
Total Cellulosic Biofuel
1,298
1,362
1,431
1,504
1,583
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 and import of renewable diesel are expected to continue to increase
in future years. Along with biodiesel and renewable diesel, there are also very 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 2026-2030. First, we present the available data on
biodiesel and renewable diesel production, import, and use in previous years (Chapter 7.2.1).
Next, we provide an updated projection of the supply of BBD through 2025 based on recent data
(Chapter 7.2.2) and assess the current and projected future production capacity for biodiesel and
renewable diesel (Chapter 7.2.3). The availability of qualifying feedstocks for biodiesel and
renewable diesel production (Chapter 7.2.4) and potential imports and exports of BBD (Chapter
7.2.5) are in the following sections. Finally, we describe our assessment of the rate of production
and use of qualifying biomass-based diesel biofuel in 2026-2030 based on this information
(Chapter 7.2.6) 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 2026-2030, 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 historic 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
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biofuel, and total renewable fuel requirements), the availability of BBD feedstocks,434 demand
for those feedstocks in other markets and internationally, the federal tax credits available 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 historic data and trends
alone are insufficient to project the volumes of biodiesel and renewable diesel that could be
provided in future years, historic 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 2012 to 2022 (million
gallons)3
2016
2017
2018
2019
2020
2021
2022
2023
Domestic Biodiesel
1,581
1,552
1,841
1,706
1,802
1,701
1,614
1,661
(Annual Change)
(+336)
(-29)
(+289)
(-135)
(+96)
(-101)
(-87)
(+47)
Imported Biodiesel
562
462
175
185
209
208
240
501
(Annual Change)
(+301)
(-100)
(-287)
(+10)
(+24)
(-1)
(+32)
(+261)
Exported Biodiesel
89
129
74
76
88
91
117
97
(Annual Change)
(+16)
(+40)
(-55)
(+2)
(+12)
(+3)
(+26)
(-20)
Total Biodiesel
2,054
1,885
1,942
1,815
1,924
1,817
1,738
2,065
(Annual Change)0
(+621)
(-169)
(+57)
(-127)
(+109)
(-107)
(-79)
(+327)
Domestic Renewable
Diesel
(Annual Change)
231
252
282
454
472
777
1,369
2,345
(+62)
(+21)
(+30)
(+172)
(+18)
(+305)
(+592)
(+976)
Imported Renewable
165
191
176
267
280
362
311
361
Diesel (Annual Change)
(+45)
(+26)
(-15)
(+91)
(+13)
(+82)
(-51)
(+50)
Exported Renewable
Diesel
(Annual Change)
40
37
80
145
223
241
326
414
(+19)
(-3)
(+43)
(+65)
(+78)
(+18)
(+85)
(+88)
Total Renewable Diesel
356
406
378
576
529
897
1,354
2,292
(Annual Change)0
(+88)
(+50)
(-28)
(+198)
(-47)
(+368)
(+457)
(+938)
Total BBDd
2,412
2,293
2,322
2,393
2,457
2,717
3,106
4,378
(Annual Change)
(+711)
(-119)
(+29)
(+71)
(+64)
(+260)
(+389)
(+1,272)
a 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.
0 Total is equal to domestic production plus imports minus exports.
d Total BBD includes some small volumes (<20 million gallons per year) of D4 jet fuel.
434 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 80.1426).
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Since 2016, the year-over-year changes in the volume of BBD used in the U.S. have
varied greatly, from a low of 119 million fewer gallons from 2016 to 2017 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
in future years. Rather, these data illustrate both the magnitude of the changes in biomass-based
diesel 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 2023 production and net imports of renewable diesel had increased
not only in absolute terms (from 365 million gallons in 2016 to 2.29 billion gallons in 2023), but
also as a percentage of the BBD pool. In 2023 approximately 52% of all BBD was renewable
diesel, while the remaining 48% was biodiesel. As discussed further in the following sections,
we expect that renewable diesel will represent an increasing percentage of total BBD in future
years.
The historic data indicates 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 this tax credit has also provided biodiesel and renewable diesel with a competitive
advantage relative to other biofuels that do not qualify for the tax 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 has applied in each year since 2010, it has
historically only been prospectively in effect during the calendar year in 2011, 2013, 2016, and
2020-2025, while other years it has been applied retroactively. Years in which the biodiesel
blenders tax credit was in effect during the calendar year (2013, 2016, 2020-2023) 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,435 2 60 million gallons, 389 million
gallons, and 1,272 million 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, and other economic factors.
Beginning in 2025, the structure of the federal tax credit available to biodiesel and
renewable diesel producers is scheduled to change significantly. Prior to 2025 all qualifying
biodiesel and renewable diesel (including biodiesel and renewable diesel co-processed with
petroleum) was eligible for a $1 per gallon tax credit. This tax credit was available for biodiesel
435 This is the volume increase in 2020, which was impacted by the Covid-19 pandemic.
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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 will come 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 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 transportation fuel, potentially including
ethanol. Second, the tax credit is available only for transportation fuel produced in the United
States. 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 for non-
aviation fuel and up to $1.75 per gallon for aviation fuel, provided certain wage and labor
requirements are met. The structure of the 45Z tax 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.436 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
2017.437 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.438 Biodiesel imports from the EU increased to
approximately 114 million gallons in 2022 and then quite dramatically to approximately 320
million gallons in 2023.439 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. These impacts are discussed in greater detail in Chapters 7.2.4
and 7.2.3 respectively.
436 USITC, "Biodiesel from Argentina and Indonesia Injures U.S. Industry, says USITC," December 5, 2017.
https://www.usitc.gov/press room/news release/2017/erl20511876.htm.
437 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.
438 Id. Total reported biodiesel imports from the EU include imports from Belgium, Finland, France, Germany, Italy,
the Netherlands, Norway, Portugal, and Spain.
439 Id.
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7.2.2 Biomass-Based Diesel Supply in 2024 and 2025
In addition to the data on BBD production, imports and exports discussed in Chapter
7.2.1, we also considered more recent data from 2024 in projecting the production and
consumption of BBD in 2024, 2025, and beyond. At the time the analyses for this rulemaking
were completed EPA had RIN generation data for the first five months of 2024 (January - May).
While RIN generation and retirement data for the first five months of 2024 are not determinative
of RIN generation and retirement though the remainder of the year, we can use this data to
inform the supply of BBD in 2024. The simplest way to project BBD RIN supply in 2024 using
this data is to assume the average monthly RIN generation observed in the first five months of
the year continues through the end of 2024. This projection methodology, however, ignores the
observed seasonality in BBD RIN generation. To better account for the observed seasonality in
BBD RIN generation we compared RIN generation in the first 5 months of 2024 to RIN
generation during the first 5 months of 2023. From this data we can calculate a percentage
increase (or decrease) that can be applied to the total BBD RIN supply in 2023 to project the
total BBD RIN supply in 2024. Because the recent trends in the supply of BBD are significantly
different for biodiesel and renewable diesel we calculated percentage increases separately for
these fuels. We included the small volume of renewable jet fuel produced in 2024 in the
renewable diesel total. These calculations, and the resulting projecting of BBD RIN supply in
2024 are shown in Table 7.2.2-1.
Table 7.2.2-1: Projected BBD Supply for 2023 Based on 2024 Data Through May 2024
(Million RINs)
RIN Generation
(Jan. - May
2023)
RIN Generation
(Jan. - May
2024)
Percent
Change
2023
RIN
Supply
2024 RIN
Supply
(Projected)
Biodiesel
1,271
1,292
+1.7%
3,097
3,150
Renewable DieseP
1,746
2,236
+28.7%
3,891
5,008
a Includes a small volume of renewable jet fuel.
At the time this analysis was completed we did not have sufficient data to determine the
feedstocks used to produce BBD in 2024, nor do we have sufficient data to determine whether
there is any seasonality in the feedstocks used to produce BBD. We therefore applied the
projected percent changes in biodiesel and renewable diesel production from 2023 to 2024
equally to each of the feedstocks used to produce BBD in 2023. At the time this analysis was
completed we did not have any RIN generation data for 2025 to use to further project BBD
growth from 2024 to 2025. We considered using the same percentage growth rates we used to
project the BBD supply in 2024 based on data through May 2024 to project further growth in
2025. There are several factors that suggest this may over-estimate the BBD supply in 2025.
First, the overall growth rate for BBD through May 2024 (17.3%), while significant, is notably
lower than the observed increase in the supply of BBD from 2022 to 2023 (42%). Second, the
switch from the biodiesel blenders tax credit to the CFPC is expected to reduce the federal tax
incentives available to BBD producers, particularly for fuels produced from virgin vegetable oils,
and eliminate the incentives available for imported BBD. In light of these anticipated changes,
we have projected that the BBD supply in 2025 will be equal to the projected BBD supply in
2024. This projection reflects both the incentives provided by the RFS program and the reduced
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incentives provided by the CFPC relative to the biodiesel blenders tax credit it replaces. The
projected supply of BBD for 2024 and 2025 using this methodology is shown in Table 7.2.2-2.
Table 7.2.2-2: Projected BBD Supply for 2024 and 2025 (Million RINs)
2024 and 2025
Fuel Type
2023 Supply
Growth Rate
Projected Supply
BBD (total)
6,988
+17.3%
8,157
Biodiesel (total)
3,097
+1.7%
3,150
Soybean Oil
1,883
+1.7%
1,915
FOG
505
+1.7%
514
Corn Oil
183
+1.7%
186
Canola Oil
526
+1.7%
535
Renewable Diesel/Jet Fuel (total)
3,891
+28.7%
5,008
Soybean Oil
870
+28.7%
1,120
FOG
2,489
+28.7%
3,203
Corn Oil
362
+28.7%
466
Canola Oil
170
+28.7%
219
7.2.3 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.440 Domestic biodiesel production capacity, domestic biodiesel
production, and the utilization rate of the existing biodiesel production capacity each year is
shown in Figure 7.2.3-1. 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 2019.441 As of August 2024, active biodiesel production capacity has decreased
to approximately 2.0 billion gallons.442 While production of biodiesel has generally increased
during this time period, 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
4411EMTS 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.
441 EIA, "Monthly Biodiesel Production Report," February 2021.
https://www.eia.gov/biofuels/biodiesel/production/arcliive/2020/202Q 12/biodiesel.pdf.
442 EIA, "U.S. Total Biofuels Operable Production Capacity," Petroleum & Other Liquids, April 30, 2025.
https://www.eia.gov/dnav/pet/pet pnp capbio dcu nus m.htm.
287
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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. These data suggest
that domestic biodiesel production capacity is unlikely to limit biodiesel production in future
years, and that factors other than production capacity limit domestic biodiesel production.
Figure 7.2.3-1: U.S. Biodiesel Production Capacity, Production, and Capacity Utilization
3.00 90%
C
o
§ 2.50
T3
O
^ 2.00
u
ro
9- 1-50
(U
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80%
70%
60%
50%
40%
30%
20%
10%
0%
2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023
I Production Capacity Biodiesel Production Percent Utilization
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 4.6 billion gallons in August 2024 (Figure 7.2.3-2).443 Domestic
renewable diesel production has increased along with production capacity in recent years, and
capacity utilization at domestic renewable diesel production facilities has been high,
approximately 80% from 2017-2022. Further, much of the unused capacity was likely the result
of facilities ramping up new capacity to full production rates. Unlike the bi odi esel industry, in
which unused production capacity has persisted for many years, since 2017 production of
renewable diesel has consistently neared or exceeded the production capacity from the previous
year. As renewable diesel production capacity continues to expand aggressively, it is unclear if
this trend will continue in future years, particularly as affordable feedstocks may become more
scarce with increasing renewable diesel production (see Chapter 7.2.4 for further discussion of
available feedstocks).
443 RFS facility registration data and EIA, "U.S. Total Biofuels Operable Production Capacity," Petroleum & Other
Liquids, April 30, 2025. ittps://www.eia.gov/dnav/pet/pet pnp capbio dcu nus m.htm.
288
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Figure 1.2.3-2°. U.S. Renewable Diesel Production Capacity, Production, and Capacity
Utilization
4,000
120%
c 3,500
O
-O 3,000
o
CL
g 2,500
'u
8" 2,000
U
to
o 1,500
U
c 1,000
o
500
0%
2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023
Production Capacity m Renewable Diesel Production
Percent Utilization
Source: Renewable diesel production volumes are from EIA, "Monthly Energy Review." March 2025, Table 10.4b.
https://www.eia.gov/totalenergv/data/monthlv/arcliive/00352503.pc . Renewable diesel production capacity for
2012-2020 is from EMTS. Renewable diesel production capacity for 2021-2023 is from EIA, "U.S. Total Biofuels
Operable Production Capacity," Petroleum & Other Liquids, April 30, 2025.
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. EIA currently projects that renewable diesel
production capacity will continue to expand and could reach nearly 6 billion gallons by 2025.444
A recent report published by the National Renewable Energy Laboratory (NREL) found that by
2028 the domestic production capacity for renewable diesel and jet fuel could increase to 9.6
billion gallons per year.445 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 7.2.3-3.
We note, however, that despite the potential for rapidly increasing production capacity
through 2028, feedstock limitations (discussed in Chapter 7.2.4) are not expected to support all
of these facilities. It is also possible that some of these projects may be delayed or cancelled.
444 EIA, "Domestic renewable diesel capacity could more than double through 2025,", Today in Energy, Febmarv 2,
2023. https://www.eia.gov/todavinenergy/detail.php?id=55399.
445 Calderon, Oscar Rosales, Ling Tao, Zia Abdullah, Michael Talmadge, Anelia Milbrandt, Sharon Smolinski,
Kristi Moriarty. et at. "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.
289
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Thus, it is likely that the domestic renewable diesel production will fall short of the 9.6 billion
gallons implied by the sum current production capacity and announced new and expanded
facilities. Nevertheless, it appears unlikely that domestic production capacity will limit
renewable diesel production through 2030. Rather, it is more likely that the feedstock limitations
may limit production.
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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Source: Calderon, Oscar Rosales, Ling Tao, Zia Abdullah, Michael Talmadge, Anelia Milbrandt, Sharon Smolinski,
Kristi Moriarly. 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.
httt>s://doi.org/10.2172/2426563.
7.2.4 Biomass-Based Diesel Feedstock Availability to Domestic Biofuel
Producers
As EPA considered the rate of production of BBD through 2025, a central and critical
factor influencing final volume requirements was our assessment of the availability of qualifying
feedstocks. To assess the availability of feedstocks for producing BBD through 2030, 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 2023 is shown in Figure 7.2.4-1.
290
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Figure 7.2.4-1: Feedstocks Used to Produce BBD in the U.S.
4,000
3,500
3,000
l/l
c 2,500
u 2,000
C
o
% 1,500
1,000
500
¦ FOG ¦ Distillers Corn Oil ¦ Soybean Oil ¦ Canola Oil
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 in 2023. 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
291
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changes to biofuel policies in both the U.S. and other countries, such that the U.S. has become a
preferred destination for these feedstocks. If these market conditions continue in future years we
would expect to see increasing imports of FOG for biofuel production. However, 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 7.2.4 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 Proposed Volumes, can be found in Chapter 7.2.6 and Preamble Section V.C, 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 differ significantly 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 7.2.5.
7.2.4.1 Domestic BBD Feedstocks
Domestic feedstocks used for BBD production have historically come from three
different 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. 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 USD A and other sources, and import data from UN Comtrade.
Domestic BBD production from fats, oils, and greases (FOG) in the U.S. was mostly
from domestically sourced feedstocks and was relatively stable from 2014 through 2020.
However, beginning in 2022 it increased rapidly, driven primarily by FOG imports (see Chapter
7.2.4.2 for more information on FOG imports). These feedstocks are generally by-products 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
292
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Chapter 7.2.4.2 for a discussion of the availability of imported FOG to domestic BBD
producers).
To project increases in the supply of FOG from the U.S. to domestic BBD producers we
relied primarily on historical data. Table 7.2.4.1-1 shows the total quantity of FOG used by
domestic BBD producers each year from 2014 - 2023. 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 2030.446 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 2030.
Table 7.2.4.1-1:
domestic BBD production from FOG (mi
lion gallons)
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
391
406
477
469
518
516
455
587
869
1,395
Production of BBD from distillers corn oil has also generally increased through 2023.
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 7.2.4.1-2). 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 2023 was approximately 2.15 million tons,447 or
enough corn oil to produce about 540 million gallons of BBD. This suggests that distillers corn
oil could be used to produce over 200 million gallons of additional BBD, but that would require
shifting distillers corn oil from other existing uses, which would then have to be backfilled with
other new sources.448 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 2030.
Table 7.2.4.1-2: Domestic BBD production from Distillers
Corn Oi
(million gallons)
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
187
183
224
245
308
278
226
299
325
332
446 Global Data, "UCO Supply Outlook," August 2023. https://cleanfuels.org/wp-content/uploads/GlobalData UCO-
SuppIy-Outlook Sep2023.pdf. Annual growth in UCO collection based on estimated growth in per capita UCO
collection rates from 2022-2030.
447 USD A, "Grain Crushings and Co-Products Production 2023 Summary," September 2024.
https://downloads.usda.librarv.cornell.edu/usda-esmis/files/v979v304g/m326nt02n/r781z807m/cagcan24.pdf.
448 For a discussion of backfilling when oil is removed from dried distillers grains, see 83 FR 37735 (August 2,
2018).
293
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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
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.449 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 2013/2014,450
and we are therefore not projecting any increase in the availability of domestic canola oil to U.S.
biofuel producers through 2030. Our projections of potential increases in imported canola oil
from Canada are covered in Chapter 7.2.4.2. 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.451 Less than half of that volume was
used as biofuel feedstock. In a hypothetical scenario where all canola oil was shifted to biofuel
production, there would be sufficient supply to produce about 662 million gallons of BBD from
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 12.5 billion pounds in the
2022/2023 agricultural marketing year.452 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 27.5% from 2013/2014 (1,734 million bushels) to 2022/2023 (2,212
million bushels) with a corresponding 30% increase in soybean oil production over these years.
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 47% in 2022/2023.
As a point of reference, if all the soybean oil produced in the U.S. in 2022/2023 (26.6
billion pounds) were used to produce BBD, this quantity of feedstock could be used to produce
approximately 3.3 billion gallons of renewable diesel. If all soybeans grown in the U.S. in
2022/2023 were crushed domestically (rather than exported) we project that domestic soybean
oil production would be approximately 50.6 billion pounds, enough feedstock to produce
approximately 6.3 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
possible to divert all soybean oil to biofuel production due to strong demand in other non-biofuel
industries. 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
449 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.
450 Id.
451 Id.
452 Id.
294
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markets for increased biofuel production could result in these markets turning to other sources of
vegetable oil such as palm oil, potentially impacting the GHG benefits.
Additional soybean oil production in future years is primarily expected to come from
increased domestic soybean oil production. While additional quantities could be made available
through shifting the use of soybean oil from other markets (discussed further below) this seems
relatively unlikely as the total use of soybean oil in non-biofuel markets has remained relatively
stable at approximately 14 billion pounds per year since the 2008/2009 agricultural marketing
year even as the use of soybean oil for biofuel production has increased by approximately 10
billion pounds.453 In contrast, 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.454 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 66.3
million tons (approximately 2.2 billion bushels) in the 2022/2023 agricultural marketing year and
is expected to increase to 67 million tons (2.3 billion bushels in 2023/2024).455 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. 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) 456 If this entire increase in soybean oil production
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
453 Id.
454 Id.
455 Id.
456 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.
295
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50 million gallons per year.457 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 2028. 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 7.2.4.1-3) 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, however, indicate that this rate of
growth may be accelerating. 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.
Table 7.2.4.1-3:
domestic BBD production from Soybean
)il (million gallons)
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
641
616
774
745
984
987
1,123
1,072
1,159
1,418
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. In the Set 1 Rule RIA, EPA projected the increase in the
domestic production of soybean oil based on publicly available announcements of capacity
expansion (including both new facilities and expanded facilities). In the Set 1 rule we projected
that from 2022-2025 the production of renewable diesel from domestic soybean oil would
increase by approximately 580 million gallons, or approximately 190 million gallons per year. In
comments on that rule stakeholders identified several other similar estimates of growth in
domestic soybean oil production. 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.458 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.459 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.460 These estimates
are summarized in Table 7.2.4.1-4.
457 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.
458 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.
459 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.
4611 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.
296
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Table 7.2.4.1-4: 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
Setl RIA
580
2022-2025
190
American Soybean Association
700
2023-2025
350
LMC
750-800
2021-2025
200
S&P Global
1,000
2024-2027a
250
a Estimate includes expansion in 2024.
The higher observed and projected increases in domestic soybean oil production occurred
following a period where soybean oil prices were historically high. From 2013/2014 through
2018/2019 the average price of soybean oil was approximately $0.31 per pound.461 Starting in
2019/2020 soybean oil prices increased dramatically and remained high for several years,
averaging $0.61 per pound from 2019/2020 through 2023/2024.462 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.
While there are some slight variations in these estimates, the data submitted by
commenters demonstrates that domestic soybean oil production is likely to increase beyond 2025
levels. With the exception of the USDA estimate from the agricultural projections to 2033 and
the EMTS data from 2014-2023, all of these estimates cover a time period during which high
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.463 Since that time soybean oil exports have generally decreased as the quantity of
soybean oil used for domestic biofuel production has increased. USDA estimates that in the
2022/2023 agricultural marketing year soybean oil exports decreased by approximately 90
percent to 0.4 billion pounds.464 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
461 USDA, "Oil Crops Yearbook," March 2025. https://www.ers.usda.gov/data-products/oil-crops-Yearbook.
462 Id.
463 Id.
464 Id.
297
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from 2022/2023 to biofuel production would only increase BBD production by approximately 50
million gallons per year.
Based on our review of the 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 2030 if supported by increases in demand.
Conversely, absent increased demand from the BBD producers increases in domestic soybean
production are likely to be much smaller, closer to 50 million gallons per year, driven by
increased demand for soybean meal from the livestock industry and other markets.
7.2.4.2 Imported BBD Feedstocks
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 380 million pounds in the agricultural marketing year 2022/2023.465 Even if this
entire volume were used for BBD production it would result in approximately 50 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 2030 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.466 This chapter focuses on projected imports of FOG and
canola oil, which are projected to be the dominant sources if imported BBD feedstocks through
2030.
Imports of FOG to the U.S. have, historically, been relatively small. From 2014-2021
imports of FOG increased gradually, reaching a total of about 0.5 million metric tons in 2021.467
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,468 greater incentives from California's LCFS program
and other state clean fuels programs for BBD produced from FOG, the changes to the federal tax
credit in 2025 which is expected to further advantage biofuels produced from FOG relative to
those produced from virgin vegetable oils, and biofuel policies internationally.
465 Id.
466 Colussi, Joana, Nick Paulson, Gary Schnitkey, and Jim Baltz. "Brazil Emerges as Corn-Ethanol Producer with
Expansion of Second Crop Cornfarm doc 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.
467 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.
468 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.
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Table 7.2.4.2-]
: U.S. Imports of FOG i
million metric tons)
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
UCO
0.02
0.02
0.02
0.04
0.06
0.09
0.08
0.13
0.40
1.41
Animal Fats
0.06
0.06
0.08
0.08
0.14
0.19
0.24
0.33
0.55
0.79
Total
0.08
0.09
0.10
0.12
0.20
0.28
0.33
0.46
0.95
2.20
Projecting the future supply of 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. 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. remains the
preferred destination for these feedstocks. 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 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. From 2021-2023 UCO imports increased at an annual average rate of
approximately 175 million gallons per year and imports of animal fats increased at a rate of
approximately 64 million gallons per year. In both cases the increase from 2022-2023 was
higher than the increase from 2021-2022 demonstrating an ongoing trend of increasing FOG
imports to the U.S. The total increase in FOG imports from 2021-2023 (478 million gallons)
were lower than the total increase in domestic BBD production from FOG (808 million gallons).
The fact that the increase in the quantity of BBD produced from FOG from 2021 to 2023 is
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 (million gallons)
2019
2020
2021
2022
2023
UCO Importsa
24
23
36
109
387
Animal Fat Importsa
53
67
91
152
218
Total FOG Importsa
77
90
127
261
605
BBD Produced from FOGb
516
455
587
869
1,395
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
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per year.469 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).470 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.
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.
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. Total UCO exports in
2023 were more than 80% higher than in 2019. At the same time, U.S. imports of FOG increased
significantly since 2021, both in absolute terms and as a percentage of total FOG market. The
projected potential increases in U.S. FOG imports (250 million gallons per year) through 2030
469 Global Data, "UCO Supply Outlook," August 2023. https://cleanfuels.org/wp-content/uploads/GlobalData UCO-
SuppIy-Outlook Sep2023.pdf.
4711 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.
300
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are approximately equal to the observed average annual increases in total FOG exports from
2019-2023 (260 million gallons per year). If global FOG exports continue to increase at the
observed rate from the past five years, the projected FOG imports could occur without diverting
FOG from existing markets. Alternatively, if demand for these feedstocks from other markets
increases this may limit the quantity of these feedstocks available to U.S. BBD producers. These
projections therefore assume continued incentives for the production of BBD from FOG
sufficient to ensure that the U.S. remains the preferred global destination for these feedstocks.
Policy changes by the U.S. to discourage imports or by other countries to discourage the export
of FOG to foreign markets, or to increase their domestic incentives offered for biofuels produced
from these feedstocks, could have a significant impact on the available supply in future years.
Figure 7.2.4.2-1: Global FOG Exports and U.S. vs. Non-U.S. Imports (Million Gallons)
3,000
2,500
Q
g 2,000
c
v)
_o
ro 1,500
13
c
o
1,000
5
500
2019 2020 2021 2022 2023
Global UCO Exports Global Tallow Exports
^^-•US UCO and Tallow Imports NorvU.S. Imports
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.
Production of BBD from canola oil increased significantly in 2023, to approximately 340 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.471 Since
the 2010/2011 agricultural marketing year approximately 70% of all canola oil supplied to the
U.S. has been imported,472 and greater than 95% of the imported canola oil is imported from
Canada.4'3 We anticipate that through 2030 Canada will be the predominant source of any
® 87 FR 73956 (December % 2022).
4?2 USD A, "Oil Crops Yearbook," March 2025. itps://www.ers.usda.gov/data-products/oil-crops-vearbook.
4?3 Data from UN Comtrade.
301
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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
(Million Gallons BBD Equivalent)
2014
2015
2106
2017
2018
2019
2020
2021
2022
2023
Domestic BBD
Production
from Canola Oil
141
115
207
177
159
157
159
166
174
344
Canola Oil
Imports for All
Uses
433
470
509
541
505
493
506
524
610
809
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). The estimate that only half of the increased in
Canadian canola oil production would be available to U.S. BBD producers reflects our
expectation that there will continue to be strong demand for canola oil in both the food market
and from Canadian biofuel producers.
More recently 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.474 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 5.475 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
474 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.
475 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-energv-use-could-double-canadas-net-zero-future.html.
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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.
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 Setl
283
2022-2025
94
S&P Global (Total)a
700
2024-2027
175
S&P Global (50%)a
350
2024-2027
88
a Estimate includes expansion in 2024.
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 100 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. It is lower than the observed annual increase in total canola oil imports since 2021 and the
projected total increase of canola oil production in Canada, reflecting continuing strong demand
for canola oil from Canadian biofuel producers and non-biofuel markets. As with the previous
feedstock projections, these projections assume continued incentives for the production of BBD
from FOG sufficient to ensure that the U.S. remains the preferred global destination for these
feedstocks.
7.2.4.3 Emerging Oilseed Feedstocks
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 pathways for some of these
emerging oilseeds, 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 2030.
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
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used to produce BBD since the beginning of the RFS program (FOG, distillers corn oil, soybean
oil, and canola oil) from both domestic and imported feedstocks. Our projections assume that
there continues to be sufficient incentives for BBD production in the U.S. to drive investment in
domestic oilseed crushing, and that the U.S. remains the preferred destination for exported BBD
feedstocks from other countries. As such, they 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. Our projected annual
increases of the potential BBD feedstocks are summarized in Table 7.2.4.4-1. 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 2030 (million
Feedstock
Projected Annual
Average Increase
Domestic FOG
25
Domestic Distillers Corn Oil
0
Domestic Soybean Oil
250
Imported UCO
200
Imported Tallow/Animal Fats
50
Imported Canola Oil
100
Emerging Vegetable Oils
0
Total
625
7.2.5 Imports and Exports of Biomass-Based Diesel
In evaluating the potential consumption of BBD through 2030 we also examined BBD
imports and exports in previous years. Since 2014 biodiesel imports have generally averaged
about 200 million gallons per year, with the exception of 2015-2017. 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.476 These
tariffs were subsequently confirmed in April 2018.477 Since the time the preliminary tariffs were
announced, EIA has not reported any biodiesel imported from these countries.478 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. Most of the increase in biodiesel imports
476 82 FR 40748 (August 28, 2017).
477 83 FR 18278 (April 26, 2018).
478 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.
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were supplied by countries in the EU, including Germany, Italy, and Spain.479 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.
Renewable diesel imports have generally increased from 2014-2021. Since 2021,
renewable diesel imports have been relatively stable, ranging between 300 and 400 million
gallons per year. A significant factor in the increasing imports of renewable diesel appears to be
the California Low Carbon Fuel Standard (LCFS), 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.480 We
expect that, as the CI requirements in California's LCFS program continue to decrease, and as
similar LCFS programs are taken up in other states (e.g., New Mexico, Oregon and Washington),
these programs, in conjunction with the RFS program and the federal tax credit, will continue to
provide an attractive market for both domestically produced and imported renewable diesel.
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
Regulations that require increasing volumes of low-carbon fuels in future years.481 Biodiesel and
renewable diesel imports, exports, and net imports are shown in Figure 7.2.5-1.
479 Id.
4811 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.
481 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-california-stvle-fuel-rules-to-cut-emissions.
305
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Figure 7.2.5-1: Biodiesel and Renewable Diesel Imports, Exports, and Net Imports
1000
800
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 B BD from different
feedstocks in different foreign and domestic markets. One factor likely supporting simultaneous
imports and exports of biodiesel and renewable diesel is the structure of the biodiesel tax credit.
The U.S. tax credit for biodiesel and renewable diesel applies to fuel either used or produced in
the U.S. Thus, by importing foreign produced biodiesel and renewable diesel for domestic use
and then exporting domestically produced biodiesel and renewable diesel to other countries,
parties are able to claim the biodiesel tax credit on both the imported and the exported volumes.
This dynamic, however, is about to change. The biodiesel blenders tax credit expired at
the end of 2024 and was replaced by the CFPC.482 The CFPC differs from the biodiesel blenders
tax credit is several significant ways. The CFPC is not limited to producers of biodiesel and
renewable diesel, rather it is available to all transportation fuels that meet a specified emission
factor. The CFPC is also only available for fuels produced in the U.S. Finally, the CFPC is not a
fixed amount per gallon of qualifying fuel produced, but instead offers greater incentives to fuels
with lower GHG emissions.
Because the CFPC is only available for fuels produced in the U.S., we expect the change
in the tax credit will negatively impact imports of biodiesel and renewable diesel in future years.
It may simultaneously increase the imports of feedstocks for use by domestic biofuel producers
as these producers seek additional quantities of feedstocks with lower carbon intensities to
482 See Chapter 1.6 for a further discussion of the CFPC, including estimates of the value available to different types
of biofuels.
306
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maximize the value of the tax credit and result in reduced exports of biodiesel and renewable
diesel as U.S. markets seek alternatives to imported biodiesel and renewable diesel that are no
longer eligible for the tax credit. As discussed further in Chapter 7.2.6, the CFPC may also result
in greater demand for low-carbon feedstocks such as FOG and the fuels produced from these
feedstocks with relatively less demand for biofuels produced from virgin vegetable oils such as
soybean oil and canola oil.
In projecting net imports of BBD through 2030 we considered both the historical trends
and expected impact of the change to the CFPC in 2025. Net BBD imports have been relatively
stable at around 200 million gallons per year since 2018. As imported BBD is not be eligible for
the CFPC, we 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. Further, as the
CFPC is expected to provide relatively little value for BBD produced from virgin vegetable oils,
imported BBD produced from these feedstocks will be at a relatively small disadvantage when
competing with domestic fuels produced from the same or similar feedstocks. 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 2030.
7.2.6 Projected Rate of Production and Use of Biomass-Based Diesel
The preceding Chapters describe the factors EPA considered when projecting the rate of
production and use of BBD through 2030. 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 2030 is the
availability of qualifying feedstocks at economically sustainable prices.
The availability of qualifying feedstocks is not a hard limit, but rather reflects EPA's
judgement about the quantity of feedstocks that would be reasonable to assume are available to
domestic BBD producers in light of the expected impacts of supplying higher or lower quantities
of feedstock. For example, the global production of vegetable oil in the 2023/2024 agricultural
marketing year was approximately 223 million metric tons, or enough vegetable oil to produce
over 60 billion gallons of BBD. While this entire quantity of vegetable oil may be technically
available for use to BBD producers, the cost of out-bidding existing markets for this entire
volume of vegetable oil would be extreme, as would be the environmental and social impacts.
Our projections of available feedstock generally assume consistent demand for potential BBD
feedstocks from food markets and other industries, as well as consistent demand for biofuels in
other countries. In other words, we have focused our assessment of BBD feedstock availability
on projections of increasing feedstock production rather than diversion from existing uses and
have generally not considered feedstocks or biofuels currently being used in non-U.S. biofuel
307
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markets to be available. 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 a
projection of the BBD supply in 2025, discussed in greater detail in Chapter 7.2.5. From that
starting point we applied the projected annual growth rates for the major feedstocks used to
produce BBD in the U.S. (FOG, distillers corn oil, soybean oil, and canola oil), discussed in
greater detail in Chapter 7.2.3. Based on the supply trends since 2018 (discussed in Chapter
7.2.1) and the projected changes in production capacity beyond 2025 (discussed in 7.2.2) we
project that all of the growth in the BBD category through 2027 will come from renewable diesel
and jet fuel, and that domestic biodiesel production will remain constant during these years. The
annual supply of BBD through 2027 by fuel type and feedstock that result from these projections
are shown in Tables 7.2.6-1 (in million RINs) and 7.2.6-2 (in million gallons).
Table 7.2.6-1: Projected Supply of BBD Through 2027 (million RINs)
Fuel Type
2026
2027
BBD (total)
8,690
9,190
Biodiesel (total)
2,600
2,620
Soybean Oil
1,664
1,684
FOG
384
384
Corn Oil
311
311
Canola Oil
241
241
Renewable Diesel/Jet Fuel (total)
6,090
6,570
Soybean Oil
1,990
2,390
FOG
2,335
2,415
Corn Oil
1,087
1,087
Canola Oil
678
678
Table 7.2.6-2: Projected Supply of BBD
Through 2027 (million gallons)
Fuel Type
2026
2027
BBD (total)
6,826
7,155
Biodiesel (total)
2,116
2,145
Soybean Oil
1,220
1,249
FOG
366
366
Corn Oil
208
208
Canola Oil
322
322
Renewable Diesel/Jet Fuel (total)
4,710
5,010
Soybean Oil
1,294
1,544
FOG
2,119
2,169
Corn Oil
681
681
Canola Oil
616
616
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,
federal tax credit, and other state level incentives and volume mandates) continue to provide a
308
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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. While the market has
demonstrated a willingness and ability to make these investments, a lack of support for biofuels
produced from these vegetable oils would be expected to negatively impact the profitability of
oilseed crushing in North America and any future investment in soybean and canola crushing
capacity in the U.S. and Canada. Alternatively, even if investment in oilseed crushing were to
continue, it is likely that the increased vegetable oil production from these facilities and/or
biofuels produced from the increased supply of vegetable oils would be exported to other
markets absent strong incentives for these products in the U.S.
The second assumption is that the U.S. remains the preferred destination for imported
biofuel feedstocks such as UCO and animal fats. Current policies, such as the CFPC and state
programs in California, Oregon, and Washington, provide greater incentives for BBD produced
from these feedstocks relative to BBD produced from virgin vegetable oils. The observed
increase in the import volumes of these feedstocks in 2022 and 2023 suggests that these
incentives, when combined with declining demand for BBD in the EU, are capable of drawing
increasing volumes of these feedstocks to U.S. markets in future years. Conversely, the proposed
reduction in the number of RINs generated for renewable fuel produced from foreign feedstocks
is expected to result in decreased incentives for continued feedstock imports, which may offset,
in part or in whole, any incentives renewable fuel produced from these feedstocks receive from
other programs.
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 2030 the rate of increase in the imported volumes of
FOG and animal fats 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.
Because these projections assume strong incentives for the production and use of BBD in
the U.S. and that the U.S. remains the preferred destination for feedstock exports from other
countries the projection can be viewed as something akin to an upper bound or best-case scenario
for the supply of BBD to the U.S. through 2030. That is not to say that higher volumes are not
possible, but rather that higher volumes would likely require the diversion of BBD and/or
feedstock from existing markets. It is also possible that lower volumes of BBD that projected in
the chapter may be supplied in future years. All else equal, lower RFS volume requirements are
expected to result in a lower supply of BBD. Even if U.S. demand remains strong, increased
incentives or use mandates in other countries may similarly result in a lower supply of BBD to
the U.S. While it is possible to project the domestic and global production of BBD in the near to
mid-term with some degree of confidence based on projections of biofuel production capacity
and feedstock supplies, projecting where in the world these biofuels will be consumed
309
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necessarily requires making assumptions about a number of factors, including potential changes
to national policies related to biofuel use and broader trade policies, that are inherently uncertain.
7.3 Imported Sugarcane Ethanol
The predominant available source of advanced biofuel other than cellulosic biofuel and
BBD has historically been imported sugarcane ethanol. Imported sugarcane ethanol from Brazil
is the predominant form of imported ethanol and the only significant source of advanced ethanol.
However, data through 2023 demonstrates considerable variability in imports of sugarcane
ethanol.
Figure 6.3-1: Historical Sugarcane Ethanol Imports
800
700
600
c
_o
500
75
e?
400
c
o
=
300
1
200
100
0
II
h
1 1
l.li
III.
¦^LnkDr^oocno-HrMm^mujr^ooCTiO'HfNpn
OO OOO O*—rH t-H t-H «—I t-ItHtH (N (\ (N fNJ
oooooooooooooooooooo
(N(N(N(NrMfN(NrM(NrM(NrMrs|
-------
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
sugarcane 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 58 million gallons. As we
are projecting volumes for 2026-2030 in this action, and this is the latest data available, the same
projection applies for all three years.
Table 7.3-1: Annual Advanced Ethanol Imports and Weighting Factors
Year
Imported advanced
ethanol9 (million gallons)
Weighting factor
2015
89
0.00391
2016
34
0.00781
2017
74
0.0156
2018
78
0.03125
2019
196
0.0625
2020
185
0.125
2021
60
0.25
2022
81
0.5
2023
21
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 sugarcane ethanol is inherently
imprecise, and actual imports in years 2026-2030 could be lower or higher than 58 million
gallons. Factors that could affect import volumes include uncertainty in the Brazilian political
climate, weather and harvests in Brazil, world ethanol demand and prices, constraints associated
with the E10 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.
7.4 Other Advanced Biofuel
In addition to cellulosic biofuel, imported sugarcane ethanol, and BBD, there are other
advanced biofuels that can be supplied in the years after 2022. 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.
311
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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
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
192 million RINs. This volume of other advanced biofuel is composed of 28 million RINs of
domestic advanced ethanol, 111 million RINs of co-processed renewable diesel, and 52 million
RINs of other advance biofuels (non-cellulosic RNG, heating oil, and naphtha). We have used
these values in our candidate volumes for each of the years addressed in this action. We do not
believe the available data and the methodology we employed can reasonably be used to project
future volumes that change over time for other advanced biofuels.
We recognize that the potential exists for additional volumes of advanced biofuel from
sources such as D5 jet fuel, liquefied petroleum gas (LPG), butanol, and liquefied natural gas (as
distinct from CNG), as well as non-cellulosic CNG from biogas produced in digesters. However,
since they 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 the timeframe of this
rulemaking (2026-2030).
7.5 Total Ethanol Consumption
Total ethanol consumption is the sum of ethanol blended with fossil fuel 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.484 This decision was the result of poor
data availability for sales volumes at the retail station level especially for higher blends and
unsatisfactory insight into the distribution of total sales volumes aggregated by blend level. In
reevaluating our methodology for this proposed rulemaking, EPA found the percent ethanol
concentration in the gasoline pool to be unrealistically high using the prior approach. To produce
a more reasonable projection of total ethanol consumption that brings percent ethanol
concentration in the national motor gasoline pool more in line with recent observations, EPA
opted to take a different approach. For this proposal, volume data from the HBIIP program and
484 For more details on our prior methods, see Set 1 Rule RIA Chapter 6.5.1.
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volume data acquired directly from six states with high volumes of higher-level ethanol blends
(CA, KS, IA, MN, NY, and ND) has enabled EPA to employ a data-driven, bottom-up approach
to projecting total ethanol consumption for the years 2026-2030.
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, cellulosic biomass, non-
cellulosic portions of separated food waste, and sugarcane, 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 E15 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 proposed
rulemaking. 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.485 Projected volumes for each blend level were generated using the equation:
2030
Znnhn 4- nn hn 4- nn hn 4- nn hn
a0°0 t a10D10^ a15°15 ^ a85°85
i=2026
For each year (2026-2030), the total amount of ethanol is found by the above equation,
where a is the total number of stations for the applicable fuel proof (E0.. .E85, denoted by the
subscripts) in year n and b is average station throughput (gallons per station) observed for the
appropriate proof in year n. Summing the products of a and b for each blend value within a year
yields the total amount of fossil fuel gasoline and biofuel from ethanol projected to be sold for
that year. Performing the summation across all years yields the total expected volume sold
during the proposed rule's timeframe. 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 proposal, EPA
projected the stations located 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.
485 See Chapter 10 for more detailed analyses of renewable fuel costs.
313
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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 2021-2023 to extrapolate projections of E85 stations nationally and E85
volumes for California.
Table 7.5.1-1: Main Variables and Data Sources Used for the Projection of Poolwide
Ethanol Consumption (2026-2(
)30)
Variable
Data Source or Derivation
EO Stations
Pure-Gas.org486
El5 Stations
E85 Stations (Non-CA)
E85 Stations (CA)
AFDC487/Prime the Pump488
EO Throughput
Iowa Department of Revenue489
El5 Throughput
E85 Throughput (Non-CA)
Derived from BIP490/HBIIP491
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 AEO2023492 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)
CARB493
The annual average number of stations offering E15 and E85 in the U.S. are shown in
Table 7.5.1-2. Historical annual averages of E15 station populations based on interpolations of
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 El5 blends for sale in 2023. Based on communications with stakeholders and
recently updated numbers from Prime the Pump, EPA expects just over 3,700 El 5 fueling
486 Pure-Gas.org, "Stations." https://www.rore-gas.org.
487 AFDC, "Historical Alternative Fueling Station Counts." https://afdc.energy. gov/stations/states.
488 Prime the Pump is anbiofuels industry-led program seeking to encourage and expand retail adoption of E15 by
building out infrastructure.
489 Iowa Department of Revenue, "2023 Retailers Fuel Gallons Annual Report," March 2024.
https://revenue.iowa.gov/media/3846/download7inline.
4911 USD A, "Biofuel Infrastructure Partnership." https://sandbox.fsa.usda.gov/programs-and-services/energy-
pro grams/bip/index.
491 USD A, "Higher Blends Infrastructure Incentive Program." https://www.rd.usda. gov/hbiip.
492 AEO2023, Table 11 - Petroleum and Other Liquids Supply and Disposition.
493 CARB, "Annual E85 Volumes," April 11, 2025. https://ww2.arb.ca.gov/resources/documents/alternative-fuels-
annual-e85 -volumes.
314
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stations in operation in 33 states at the end of 2024.494 This reported datapoint is in good
agreement with our projections, which represent annual averages of station populations.
Table 7.5.1-2: Annual Average Number of Stations Offering Higher Level Ethanol Blends
Year
E15 Stations
E85 Stations
2014
88
2,839
2015
145
3,013
2016
308
3,095
2017
776
3,419
2018
1,376
3,627
2019
1,838
3,786
2020
2,180
3,946
2021
2,461
4,351
2022
2,724
4,452
2023
3,181
4,495
2024
3,521*
4,705*
2025
3,862*
5,052*
* Future value projected for this rule.
Source: AFDC, "Historical Alternative Fueling Station Counts." https://afdc.energv.gov/stations/states.
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 2026-2030 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
2028
2029
2030
E0
17,494
17,741
17,987
18,234
18,480
E15
4,202
4,512
4,883
5,223
5,564
E85 (non-CA)
5,248
5,397
5,512
5,859
6,055
E85 (CA)
633
708
783
858
933
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).495 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 these 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 2015-2023 were used to calculate an average E85 sales per station figure for each year.
494 Number based on communication between EPA and Growth Energy, January 2025.
495 See "E85 Consumption Based on State Data for RFS Set 2 NPRM," available in the docket for this action.
315
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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 2026-2030. The
result shows very little growth in E85 throughput outside of California across the years covered
in this proposed rulemaking. El5 throughput was projected based on BIP/HBIIP data provided to
EPA. Retail sales data for E0 stations is sparse, but Iowa's Retailer Fuel Gallons annual report
provides the basis for calculating E0 throughput in Iowa, which we treated as representative of
national average E0 throughput mirroring the methods used in the Set 1 Rule. Annual E0
throughput declines from 110,635 gallons per station in 2026 to 102,937 gallons per station in
2030, as more volumes of ethanol-free gasoline are replaced by increased sales of higher-level
ethanol blends as availability of these fuels continues to grow.
Table 7.5.1-4: Projected Average Annual Throughput Volume by Gasoline Blend (gallons
per station)
Blend
2026
2027
2028
2029
2030
E0
110,635
108,711
106,786
104,861
102,937
E15
218,163
228,928
239,693
250,458
261,224
E85 (non-CA)
49,335
49,463
49,592
49,721
49,850
E85 (CA)
333,840
341,303
347,373
352,406
356,648
The total volumes of each gasoline blend that EPA projects to be consumed during the
time covered by this proposed rule are shown in Table 7.5.1-5. E10 volumes are the remainder of
motor gasoline volumes that are not E0, E15, or E85. Projected volumes of E10 are the
calculated difference between projected overall national motor gasoline consumption as
published by EI A and the sum of EPA's projected volumes of E0, El 5, and E85.496 Increasing
station counts and increasing throughputs of El 5 and E85 gasoline, coupled with decreasing
throughputs of E0 gasoline and increased penetration of electric vehicles, causes overall gasoline
consumption to decrease across these years while El 5 and E85 grow as a share of the total.
Table 7.5.1-5: Projection of Total Motor Gasoline Consumption by Blend Level (million
gallons)
Blend
2026
2027
2028
2029
2030
E0
1,936
1,929
1,921
1,912
1,902
E10
132,991
131,219
129,371
127,238
124,939
E15
917
1,102
1,170
1,308
1,453
E85 (non-CA)
253
263
274
285
295
E85 (CA)
211
242
272
302
333
496 AEO2023, Table 11 - Petroleum and Other Liquids Supply and Disposition.
316
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Table 7.5.1-6: Projection of Total Motor Gasoline Consumption Relative to 2025 Baseline
Blend
2026
2027
2028
2029
2030
E0
-5
-12
-20
-29
-39
E10
-1,897
-3,669
-5,517
-7,650
-9,949
E15
116
301
369
507
652
E85
41
82
123
164
205
There is inherent uncertainty in any projection of future conditions. Market dynamics can
shift rapidly with new policy signals and political pressure. For example, El5 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 El 5 gasoline to be sold in California.497 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 proposal. 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
El5 from at least one dispenser beginning in 2026.498 This would likewise increase the volume
of El 5 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 percent denaturant), El5 contains 15%
ethanol by volume, and E85 contains 74% ethanol by volume (consistent with EIA assumptions).
Our projection results in just under 14 billion gallons of ethanol consumed in 2026, declining to
13.72 billion gallons in 2028 and 13.38 billion gallons in 2030. 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: Projection of
Blend
2026
2027
2028
2029
2030
E10
13,512
13,332
13,144
12,927
12,694
E15
138
165
176
196
218
E85
343
374
404
434
465
Total
13,993
13,871
13,724
13,558
13,376
otal Ethanol Consumption by Blend Level (million gallons)
Table 7.5.2-2 shows EPA's projection of total ethanol consumption (equating to the
proposed volumes for ethanol) and the difference in these proposed volumes from the No RFS
and 2025 Baselines. Based on our projections, we expect to see a pool-wide ethanol
concentration that rises to 10.32% in 2028 and 10.38% in 2030.
497 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.
498 AFDC, "Iowa Retail E15 Access Requirements." https://afdc.energy.gov/laws/12998.
317
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Table 7.5.1-5: Total Ethanol Consumption Pro.
ection (million ga
Ions)
Year
Ethanol
consumption
Difference from
No RFS Baseline
Difference from
2025 Baseline
Ethanol
concentration
2026
13,993
212
-145
10.27%
2027
13,871
228
-267
10.29%
2028
13,724
238
-414
10.32%
2029
13,558
252
-580
10.35%
2030
13,376
266
-761
10.38%
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 2024, production capacity exceeded 18 billion gallons.499 Actual production of
ethanol in the U.S. reached 16.22 billion gallons in 2024.500 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 2026-2030 for determining potential volumes.
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. In 2024, ethanol
exported from the U.S. to foreign markets were 1.9 billion gallons.501 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 beyond 2030. Exports of fuel ethanol form the U.S. reached
record levels in 2024. In the first 10 months of 2024, U.S. fuel ethanol exports were nearly 35%
higher than the first 10 months of 2023, exceeding 1.155 billion gallons.502
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 is 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 explode in recent years due to growth in
hydrous E100 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
499 EIA, "U.S. Fuel Ethanol Plant Production Capacity," Petroleum & Other Liquids, August 15, 2024.
https://www.eia.gov/petroleuin/ethanolcapacitv.
sou £[a "Monthly Energy Review," March 2025, Table 10.3.
https://www.eia.gov/totalenergy/data/montlilv/arcliive/00352503.pdf.
5111 EIA, "Exports by Destination" Petroleum & Other Liquids, April 30, 2025.
https://www.eia.gov/dnav/pet/pet move expc dc NUS-Z00 mbbl a.htm.
5112 EIA, "U.S. Exports of Fuel Ethanol," Petroleum & Other Liquids, April 30, 2025.
https://www.eia.gov/dnav/pet/liist/LeafHandler.aslix?n=PET&s=M EPOOXE EEX NUS-Z00 MBBL&f=a.
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demand for imported ethanol that has been met by increasing supplies from the American
ethanol industry.503
As described in Chapter 7.5.1, we estimated total ethanol consumption for 2026-2030 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 corn
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 and/or
LCFS credits and that conventional corn ethanol will comprise the remainder. Our estimate of
corn ethanol consumption for 2026-2030 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
'rojected Corn Ethanol Consumption i
million ga
2026
2027
2028
2029
2030
Total ethanol
13,993
13,871
13,724
13,558
13,376
Imported sugarcane ethanol
58
58
58
58
58
Domestic advanced ethanol
28
28
28
28
28
Ethanol from CKF
126
125
124
122
120
Corn ethanol
13,781
13,660
13,514
13,350
13,170
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 2014-2023 is shown in
Table 7.7-1.
5113 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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Table 7.7-1: Conventional Biodiesel and Renewable Diesel Used in the U.S. (million gallons)
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
Domestic D6
Biodiesel
1
0
0
0
0
0
0
0
0
3
Domestic D6
Renewable Diesel
0
0
0
0
0
0
0
0
0
1
Imported D6
Biodiesel
52
74
113
0
0
0
0
0
0
6
Imported D6
Renewable Diesel
2
86
45
2
0
0
0
0
0
0
All D6 Biodiesel and
Renewable Diesel
55
160
158
2
0
0
0
0
0
10
In 2014-2016 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, though the overall supply
remained small (less than 0.1% of the total biofuel supply to the U.S.). Nearly all of the
conventional biodiesel and renewable diesel used in the U.S. has been imported, with the only
exceptions being less than 5 million gallons per year in 2014 and 2023. However, conventional
(D6) RINs have continued to be generated for biodiesel and renewable diesel in recent years.
From 2018 through 2023 the volumes of renewable diesel for which conventional biofuel RINs
were generated each year (in million gallons) were 107, 116, 76, 135, 75, and 69 respectively.
Nearly all 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
agricultural marketing year.504 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.505 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 2030 is not without limit, but this data suggests that large
5114 USDA, "World Agricultural Supply and Demand Estimates," November 8, 2024.
https://downloads.usda.librarv.cornell.edu/usda-esmis/files/3t945a76s/s4657804b/z029qx92b/latest.pdf.
5115 This calculation assumes one gallon of renewable diesel can be produced from 8 pounds of vegetable oil.
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quantities of this fuel are being or could be produced,506 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.
506 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
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 the 2026-2027 timeframe. 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 and 2027 in comparison to their recent or current status.
8.1 Biogas
Renewable biogas infrastructure considerations differ from those for other biofuels not
only because it is a gas rather than a liquid, but also because renewable biogas can be processed
to be physically identical to natural gas, which is used for many purposes including
transportation.507 Natural gas was used in CNG/LNG vehicles for many years prior to the
introduction of renewable biogas. The RFS program allows RINs to be generated for renewable
biogas 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.508 As the cost of running spur pipelines for anything beyond
short distances becomes prohibitively expensive, only those biogas 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 network, renewable biogas 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 natural gas. According to data from the AFDC, there are currently
507 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.
508 See 40CFR 80.1426(f).
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approximately 1,300 public and private CNG fueling stations and approximately 100 public and
private LNG refueling stations in the U.S.509
Once the processed biogas is in the gas pipeline, it is virtually indistinguishable from
natural gas. However, expanding CNG/LNG vehicle infrastructure to support growth in the
renewable biogas beyond the current level of CNG/LNG used in the transportation sector—
estimated at 1.17-1.2 billion ethanol-equivalent gallons of CNG/LNG per year in 2026 and
2027—would represent a substantial challenge.510 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 associated with removing the impurities
in renewable biogas to make it suitable for use in CNG/LNG vehicles and to facilitate its
fungible transportation in the natural gas distribution system could also be a barrier to its
expanded use. Nevertheless, we do not expect infrastructure to constrain the use of CNG/LNG
derived from biogas 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.511
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
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
5119 AFDC, "Alternative Fueling Station Locator."
https://afdc.energv.gov/stations#/analvze?fuel=LNG&fuel=CNG&access=public&access=private&countrv=US&tab
=fuel.
5111 See Chapter 7.1.4 for further discussion of the estimated use of CNG/LNG as transportation fuel in 2026-2027
and Chapter 10.1.4 for discussion of the costs associated with refueling stations.
511 See RFS2 RIA Chapter 1.2.2.
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could be resolved.512 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
2022.513 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.514 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.515
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 B5.516
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.517
While we are projecting that the Proposed Volumes for 2026 and 2027 would require
substantial biodiesel volumes relative to the No RFS Baseline, we are also projecting very small
increases in the volume of biodiesel relative to the volume of biodiesel projected to be used in
2025 in the Set 1 Rule. The primary expansion of BBD is projected to occur through renewable
diesel, 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.
However, it is possible that domestic biodiesel production and/or biodiesel imports may
increase in 2026 and 2027. As discussed in Chapter 7.2, domestic biodiesel production 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.518 For
512 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.
513 Biodiesel consumption numbers based on EMTS data.
514 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.
515 ASTM specifications currently limit biodiesel contamination in jet fuel to 50 mg/kg (ASTM D1655-24b).
si6 --Pilot Flying J Fuel Offerings," Docket Item No. EPA-HQ-OAR-2021-0427-0065.
https://www.regulations.gov/document/EPA-HO-OAR-2021-0427-0Q65.
517 "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-2021-0427-0119.
518 EIA, "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.
324
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example, over 700 million gallons of biodiesel was imported in 2016.519 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. Some additional expansion in import
infrastructure may also occur through 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.520
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.521
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.522 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.523 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
in some cases.524 Therefore, a substantial increase in the use of biodiesel, especially biodiesel
produced from palm oil, during the winter may be a challenge.
519 Id.
5211 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.
521 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.
522 B5 blend levels can typically be maintained.
523 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.
524 Verma, Puneet, M.P. Shanna, 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.
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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 2022.525
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.526
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.527
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
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.
525 See RFS2 RIA Chapter 1.2.2.
526 Such drop-in fuels are typically blended with petroleum-based diesel prior to use.
527 See RFS2 RIA Chapter 1.6.
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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 2026-2027
is already being used in 2022; 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
coo
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 2022.529
The ORNL analysis analyzed 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.
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
528 A nationwide average ethanol concentration above 10.00% can only occur insofar as there is consumption of E15
and/or E85.
529 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.
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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.530
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.531 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 where
the rail industry was not able to fully accommodate the expansion of inter-regional trade in
ethanol. 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.
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
530 See RFS2 RIA Chapter 1.6.
531 ICF, "Task 5: Impact of Biofuels on Infrastructure," January 2018.
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vehicles.532 533 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.534 However, California is
seeing a resurgence in use of E85 which may push a small increase in FFVs for the region.535
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 has been certified to operate safely with the high ethanol concentrations.536 As shown
in Figure 8.4.2-2, stations offering E85 have increased steadily since about 2005. By November
2024, the total number of stations offering E85 had reached 4,683.
532 AFDC, "Light-Duty AFV Registrations," June 2024. https://afdc.energy.gov/data/10861.
533 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.
534 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.
535 Renewable Fuels Association, "RFA Calls on California to Expand Flex Fuel Vehicles for Lower Costs, Cleaner
Air," January 16, 2024. https://etlianolrfa.org/media-and-news/categorv/news-releases/article/2024/01/rfa-calls-on-
california-to-expand-flex-fuel-veliicles-for-lower-costs-cleaner-air.
536 EPA, "UST System Compatibility with Biofuels," EPA-510-K-20-001, July 2020.
329
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Figure 8.4.2-2: Number of Public and Private Retail Service Stations Offering E85a
a Data through 2007 is annual, whereas data for 2008 and later is monthly.
Source: AFDC, "Historical Alternative Fueling Station Counts." https://afdc.energv.gov/stations/states.
Grant programs such as the USDA Biofuels Infrastructure Partnership (BIP) 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 effect 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 can be
attributed to growth in California which has seen a large private incentivization for retail stations
to supply this blend.537 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
5,248
633
2027
5,397
708
8.4.3 Infrastructure for E15
E15 is permitted to be used only in MY2001 and newer light-duty motor vehicles.538 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
through blender pumps—which can produce El5 on demand for consumers through the
537 AFDC, "Historical Alternative Fueling Station Counts." https://afdc.energy. gov/stations/states.
538 76 FR 4662 (January 26, 2011).
330
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combination of E10 (or EO) and E85—the number of terminals offering preblended E15 directly
to service stations has been increasing.539
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
100%
95%
90%
85%
80%
75%
70%
65%
60%
55%
50%
2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
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 El 5 production (terminals and blender pumps at retail
stations), and the fact that the majority of in-use vehicles are legally permitted to use El 5, it
appears that the primary constraint on the consumption of E15 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 E15 was slow
until the BIP and Prime the Pump programs began providing funding for station conversions in
2016.
539 Renewable Fuels Association, "Terminal Availability of E15 Grows as EPA Prepares to Remove RVP Barrier,"
March 12, 2019. https://etlianolrfa.org/media-and-news/categorv/blog/article/2019/03/terminal-availabilitv-of-el5-
grows-as-epa-prepares-to-remove-rvp-barrier.
Fuel volume consumed
\/phirlp rni int
331
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Table 8.4.3-1: Number of Retail Stations Offering E15
Year
E15 Stations
2012
2
2013
70
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
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 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 NREL540 and Stillwater Associates,541
compatibility with E15 is not the same as being approved for E15 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:542
• 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
documentation for their design and the types of materials used, and even their installation dates,
5411 Moriarty, K., and J. Yanowitz. "E15 And Infrastructure," National Renewable Energy Laboratory, NREL/TP-
5400-64156, May 21. 2015. https://doi.org/10.2172/1215238.
541 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.
542 EPA, "UST System Compatibility with Biofuels," EPA-510-K-20-001, July 2020.
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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.543 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 El 5 represents a
significant constraint on the expansion of E15 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 Number of Retail Stations Offering E15
Year
E15 Stations
2026
4,202
2027
4,812
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.544 This indicates that the shipment of the statutory biofuel volumes
could be accommodated without impacting the deliverability of other items. However, as
discussed in Chapter 8.5.1, ORNL noted that significant investment would be needed to
543 See, e.g., 40 CFR 1090.1420 and 1090.1510.
544 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
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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.545
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 3.1-8, corn ethanol consumption volumes are expected to decrease in
2026-2030, with projected volumes between 13.1-13.8 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.
We estimate that the use of FOG for the production of biofuel will increase slightly, from
approximately 2.4 billion gallons in 2024 to approximately 2.54 billion gallons in 2027 (see
Chapter 3). The projected increase in the use of FOG for biofuel production is consistent with the
observed trend in the domestic supply of FOG for biofuel production from 2014-2021, before
the rapid increase in FOG imports. 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
545 ICF, "Impact of Biofuels on Infrastructure," January 2018.
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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 2.05 billion gallons in 2024 to approximately 2.88 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).
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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.546 547 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 Proposed 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 Volume Scenarios and Proposed Volumes on the prices of agricultural
commodities and food, respectively.
9.1 Employment and Rural Economic Development Impacts
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 a new renewable fuel
production plant, including increases in employment, output and income, and the subsequent
increases in demand for local goods and services all create additional beneficial ripple effects.548
Having said that, 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%.549 A report by the National Research Council (NRC)550 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.551 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
546 As explained in Preamble Section II, we also consider several other factors besides those enumerated in the
statute.
547 The impacts evaluated in this chapter are for volume increases for 2026-2030 compared to the No RFS Baseline.
548 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.
549 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.
5511 National Research Council. Renewable fuel Standard. National Academies Press eBooks, 2011.
https://doi.org/10.17226/13105.
551 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.
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be small. Studies have found that, from a global perspective, higher crop prices might lead to
higher rates of malnutrition in developing countries.552 553 554
Some research has also suggested the growth of biofuels may also contribute to the
ongoing trend of U.S. farmland consolidation.555 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.556 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.557 558 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.559 During the nearly four decades examined, the overall number of farms has
decreased while farm sizes have tripled.560 This consolidation of farmland also impacts
agricultural communities by driving up real estate prices and making it difficult for small-scale
farmers to buy or lease land.561 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.
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.
552 IIASA, "Biofuels and Food Security - Implications of an accelerated biofuels production," March 2009.
https://pure.iiasa.ac.at/id/eprint/8984/l/XQ-09-062.pdf.
553 EPA, "Economics of Biofuels." https://19ianuarv2021snapshot.epa.gov/environmental-economics/economics-
biofuels .html.
554 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.
555 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.
556 Union of Concerned Scientists. "Losing Ground," April 14, 2021. https://www.ucs.org/resources/losing-ground.
557 Id.
558 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.
559 Union of Concerned Scientists. "Losing Ground," April 14, 2021. https://www.ucs.org/resources/losing-ground.
560 Id.
561 Scafidi, Angela. "Increased Biofuel Production in the US Midwest May Hann Fanners and the Climate." World
Resources Institute, February 27, 2024. https://www.wri.org/insights/increased-biofuel-production-impacts-climate-
change-fanners.
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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, the industry requires technologies that can convert biomass
more efficiently and cost-effectively for various applications.562 These trends will result in
shifting employment across sectors with subsequent impacts to local and regional spending in
these impacted areas.
In this section, we focus on the gross employment impacts, not net impacts, and the
income impacts that follow from increased investment in renewable fuels. Job creation is an
important part of economic impact analysis since it directly addresses "well-being"—a critical
aspect of economic growth and development. To that end, this section describes our evaluation
of the impacts of renewable fuels on employment and on rural economic development. In
subsequent sections (Chapters 9.2, 9.3, and 9.4), we talk about the impacts to prices, supply of
agricultural commodities and 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
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.563 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 (10) 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
562 DOE, "Jobs & Economic Impact of a Billion-Ton Bioeconomy," June 2017.
https://www.energv.gov/eere/bioenergv/articles/iobs-economic-impact-billion-ton-bioeconomY.
563 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.
338
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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).564 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.565 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
computed estimates would then be interpreted as jobs at risk were the RFS program
discontinued.
564 Demski, Joe. "Understanding IMPLAN: Direct, Indirect, and Induced Effects," I MP I.. IV. April 18, 2025.
https://blog.implan.com/understanding-implan-effects.
565 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
339
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Figure 9.1-1: Direct, Indirect, and Induced Economic Impacts in the Construction Phase of
a Biofuel Facility
Indirect Effect
Investment in new
biofuel facility
Increased output of
construction sector
Increased Demand
for Construction
Sector Input
Increased
Employment in
Sectors producing
Construction Sector
inputs
Increased
Employment in the
Construction Sector
Direct Effect
1
r
Increase in
Consumption
>
r
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
340
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community.566 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,567 cross disciplinary models,568
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.
proscriptive569), and data availability. Table 9.1.1-1 describes a non-exhaustive list of these
approaches based on ease of use.
566 Social For Action, "How to Measure Rural Development: Key Indicators and Metrics," November 17, 2024.
https://www.socialforaction.com/blog/liow-to-measure-rural-development.
567 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." RAND Corporation, 2022.
https://doi.org/10.7249/pea828-l.
568 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-Q141-x.'
569 Home, Christine. "Norms." Data set. Oxford Bibliographies Online Datasets, November 27, 2013.
https://doi.org/10.1093/obo/9780199756384-0Q91.
341
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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: NREL, "Assessment of the Value, Impact, and Validity of the Jobs and Economic Development Impacts
(JEDI) Suite of Models," August 2013. https://www.nrel.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 our volume scenarios relative to the No RFS Baseline: corn ethanol, biodiesel and
renewable diesel (from soybean oil, FOG, corn oil, and canola oil), and renewable natural gas.570
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.
5711 The impacts evaluated in this chapter are for volume increases for 2026-2030 compared to the No RFS Baseline.
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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
approach—to draw conclusions about the economic impacts. We utilize the results of existing
studies for estimates of multipliers or the impact per unit of biofuel and then apply these to the
projected volumes to derive employment and other economic impacts.
For the corn ethanol case alone, we have relied on the use of two separate methods: a
basic method (rule-of-thumb) and NREL's JEDI model (an input-output modeling approach).
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 compare 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
chain571 and logistic networks572), 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 10 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),573 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),574 and a 2024 study on renewable natural gas economic impact
analysis by Guidehouse (hereafter the Guidehouse study).575 A fourth study by PWC compares
the impacts of renewable fuels to those of oil and gas, employing a similar 10 approach to the
571 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.
572 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.
573 ABF Economics, "Contribution of the Ethanol Industry to the Economy of the United States in 2023," February
1, 2024. https://d35tlsYewk4d42.cloudfront.net/file/2659/RFA%202023%20Economic%20Impact%20Final.pdf.
574 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.
575 Guidehouse, "Renewable Natural Gas Economic Impact Analysis," December 2024.
https://staticl.sauarespace.eom/static/53a09c47e4b050b5ad5bf4f5/t/67577elc8695832cc7125f86/1733787172143/2
024+RNG+Economic+Impact+Report FINAL.pdf.
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three fuel-specific studies (hereafter the PWC study).576 The analysis in this chapter uses the
results of these studies to parameterize our rule-of-thumb analysis.
While other methods have been applied to estimate impacts on job creation and rural
economic development, we choose to focus on the results of 10 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 10 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 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 the 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 job
impact was 392,371 in 2023. See Table 9.1.1.2.1-1.
576 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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Table 9.1.1.2.1-1: 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
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 $54,226 million from the ethanol industry in 2023. With the assumptions stated above, we can
roughly estimate that the impact on rural economies is about 78% (= (27,916+14,602)/54,226) of
the total GDP impact of ethanol. The job impact is even larger in relative terms, about 91% (=
(264,464+95,166)/394,464) of the total economy-wide employment impact. See Table 9.1.1.2.1-
2.
Table 9.1.1.2.1-2: Job Creation and GDP Impacts of Ethanol Production by Sector
Job Creat
ion (FTE)
GDP (million 2023$)
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 reply 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 LMC Study of Biodiesel and Renewable Diesel Impacts
The LMC study assesses the economic impact of BBD, including both renewable diesel
(RD) and biodiesel (BD). 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
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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.
• Baseline: 3.1 billion gallon 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 RD
representing half of U.S. domestic production in all except the baseline scenario (which assumes
67% BD and 33% RD).
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 Annual Job Creation Impacts by Scenario (FTE)
Scenario (billion gallons of BBD
)
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 plan, this implies 72,250 full-time
equivalent [FTE] jobs lasting two years).
Table 9.1.1.2.2-2: The One-Off Job Creation Effects from Construction (FTE)
Scenario (billion
gallons of BBD)
4.0
6.0
Total temp 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
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value chain compared to the total estimated impact of BBD economy wide. For the purposes of
our analysis, we assume that oilseed production, crushing, and processing occurs 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 wages estimates.
Table 9.1.1.2.2-3: 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 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 $7.2 billion in GDP in that year. Table
9.1.1.2.3-1 summarizes the three components of the job creation impacts.
Table 9.1.1.2.3-1: Creai
tion Impacts of RNG Production
Direct
Indirect
Induced
Total
Job Creation (FTE)
23,359
13,395
18,910
55,664
GDP (billion 2024$)
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.
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Table 9.1.1.2.3-2: The Planned and Existing RNG Projects' Job Creation Impacts by
Facility 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
identifies four categories of biogas feedstocks for RNG: municipal solid waste, food waste,
agricultural digesters (termed "agricultural waste" in the study), and wastewater.577 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 created the
second largest economic impacts; 19,751 jobs or 35% of the total job impact, and $2.6 billion or
36% of the total GDP impact. Table 9.1.1.2.3-3 summarizes these impacts.
Table 9.1.1.2.3-3: Agricultural Job Creation and Rural Economy Impacts
Direct
Indirect
Induced
Total
Job creation (FTE)
8,297
4,612
6,843
19,752
GDP (billion 2024$)
1.2
0.7
0.8
2.6
9.1.1.2.4 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 full-time
equivalents (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 $1 billion GDP than fossil
fuels. Construction impacts, which are one-time effects, are excluded from this comparison.
Table 9.1.1.2.4-1 shows employment per billion dollars GDP for the oil and gas, ethanol, BBD,
and RNG industries.
577 These categories differ somewhat from the categories established by EPA in Table 1 to 40 CFR 80.1426 in both
wording and substance, but are a reasonable general guide for this purpose.
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Table 9.1.1.2.4-1: Employment per GDP for Fossil Fuel and Renewable Fuel Production
Oil & Gas
Ethanol
BBD
RNG
Employment (FTE)
9,400,000
392,371
75,196
23,917
GDP (billion 2021$)
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
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: Job Creation and GDP Impacts of Agricultural Production Supported by
Renewable Fuel Production
RNG
BBD
Ethanol
Job creation (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 Report578 finds that
clean energy has "surpassed the 50% mark for its share of total energy employment" and has the
biggest potential for job creation.579 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.580 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 2023581), the International Renewable Energy Agency (IRENA) estimated that
liquid biofuels supported 2.421 million jobs globally in 2021 and most of these were in planting
and harvesting feedstock,582 implying that expansion of the biofuel industry will likely have the
biggest job impacts to the agricultural and rural community.
578 IEA, "World Energy Employment," August 2022. https://doi.org/10.1787/5d44ff7f-en.
579 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.
5811 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.
581 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.
582 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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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 Chapter 9.2.1, we combine the estimates derived above from each of these three
studies with the projected production increases associated with our Low and High Volume
Scenarios relative to the No RFS Baseline to estimate the potential impacts of our proposal on
jobs and rural economic development.
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 Jobs and Economic Development Impacts (JEDI)
model. The JEDI modeling suite was developed by NREL 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
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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.
10 modeling is a data-intensive effort and requires access to sector specific multipliers583
that permit us to compute rates of change for several different variables—output, employment,
labor income, and value added. In the case of corn ethanol, IMPLAN584 maintains a database of
multipliers that is available for purchase and NREL has developed an 10 model for Dry Mill
Corn Ethanol585 using these multipliers,586 results of which have been validated with both
modeled job results as well as empirical employment data.587 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,588
EMPLOY, and others589 for some of the other biofuel categories and technologies are either in
the R&D phase or employ slightly different modeling capabilities compared with JEDI, and
could be used as well in future analyses if appropriate.590
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 NREL's JEDI
module for dry mill corn ethanol.
583 Multipliers are rates of change that describe how a given change in a particular industry generates impacts in the
overall economy.
584 https ://implan. com.
585 NREL, "JEDI Corn Ethanol Model rel. CE 12.23.16." https://www.nrel.gov/docs/libraries/analvsis/Old-iedi-corn-
ethanol-model-rel-ce 12-23 -16. xlsm.
586 NREL, "Jobs and Economic Development Impact Models," April 21, 2025. https://www.nrel.gov/analYsis/iedi.
587 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.
588 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.
589 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.
5911 WIRED - an updated regional 10 tool much like the JEDI suite of models, 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 NREL 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.
352
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Changes in ethanol volumes evaluated in this proposed rule result from increased
consumption of higher-level ethanol motor gasoline blends (e.g., El 5 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 presented in our volume scenarios 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 Chapter
9.1.1.2. The impact estimates from these three studies are based on total production of these
renewable fuels. Tables 9.1.2-la and 9.1.2-lb summarize the volume increases of RNG, BBD,
and ethanol attributable to the RFS volume requirements relative to the No RFS Baseline under
the Volume Scenarios and Proposed Volumes.
Table 9.1.2-la: Projected Production Increases Under Volume Scenarios (million ethanol-
Low
High
2026
2027
2028
2029
2030
2026
2027
2028
2029
2030
RNG
716
743
772
802
834
716
743
772
802
834
BBD
3,382
3,598
3,836
4,033
4,258
3,695
4,223
4,774
5,283
5,821
Ethanol
212
228
238
252
266
212
228
238
252
266
Table 9.1.2-lb: Projected Production Increases Under Proposed Volumes (million ethanol-
2026
2027
RNG
716
743
BBD
4,817
5,050
Ethanol
212
228
To generate impact estimations based on projected production increases in million
ethanol equivalent gallons (in Tables 9.1.2-la and lb), we calculate the impact per million
ethanol equivalent gallons for each renewable fuel. We make the following assumptions:
• Linear impacts - 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 linear impacts,
due to that prior research does not provide sufficient data to help accurately estimate
potential nonlinear impacts.
353
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• Impacts from operations - Since the projections under all scenarios, including the High
Volume Scenario, are generally below production capacity (see Table 9.1.2-2), there may
not be a need to construct new facilities even under the High Volume Scenario. As such,
we focus on impacts from operations only. This is also conservative and may help
mitigate potential overestimation (due to the linear assumption we make).
Table 9.1.2-2: Production Capacity and Projections by Fuel
Year
Production
Capacity
Production
Projection 2026
(High Volume)
Original Unit
Original Unit
Million Gallons
(Ethanol
Equivalent)
Million Gallons
(Ethanol
Equivalent)
RNG
2024
133 tril Btu
878 mil gal
878
1,174
BBD
2021
4.6 bil gal
2.5 bil gal
3,925
5,701
Ethanol
2023
17.8 bil gal
15.6 bil gal
15,600
13,993
The impacts on employment (FTE) of the production of the renewable fuels based on the
10 models discussed in the section above 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 (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 the job creation impacts in the studies summarized in Table 9.1.2-3 represent the
impacts of differing quantities of biofuel production, we next normalized these studies to
calculate the job 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 (FTE) per Million Ethanol Equivalent Gallons
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
354
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We then estimate the impacts of the projected 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 directed effects only and the other based
on all effects (i.e., direct, indirect, and induced effects). Tables 9.1.2-5a and 9.1.2-5b show the
projected job impacts relative to the No RFS Baseline accounting for only the direct effects
while Tables 9.1.2-6a and 9.1.2-6b show 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.
Table 9.1.2-5a: Job Creation Impacts of the Projected Production Increases Based on
Direct Ef
ects Only Under Volume Scenarios (FTE)
Low
2026
2027
2028
2029
2030
RNG
6,482
6,726
6,988
7,260
7,550
BBD
16,198
17,233
18,373
19,316
20,394
Ethanol
971
1,044
1,090
1,154
1,218
High
2026
2027
2028
2029
2030
RNG
6,482
6,726
6,988
7,260
7,550
BBD
17,697
20,226
22,865
25,303
27,880
Ethanol
971
1,044
1,090
1,154
1,218
Table 9.1.2-5b: Job Creation Impacts of the Projected Production Increases Based on
Direct
2026
2027
RNG
6,482
6,726
BBD
23,071
24,187
Ethanol
971
1,044
All Fuels
30,524
31,957
Table 9.1.2-6a: Job Creation Impacts of the Projected Production Increases Based on All
Effects Under Volume Scenarios (FTE)
Low
2026
2027
2028
2029
2030
RNG
19,504
20,240
21,030
21,847
22,718
BBD
64,793
68,931
73,491
77,265
81,576
Ethanol
5,332
5,735
5,986
6,338
6,690
High
2026
2027
2028
2029
2030
RNG
19,504
20,240
21,030
21,847
22,718
BBD
70,790
80,905
91,461
101,213
111,520
Ethanol
5,332
5,735
5,986
6,338
6,690
355
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Table 9.1.2-6b: Job Creation Impacts of
Effects Under Proposed Volumes (FTE)
Direct + Indirect + Induced
2026
2027
RNG
19,504
20,240
BBD
92,285
96,749
Ethanol
5,332
5,735
All Fuels
117,121
122,723
Projected Production Increases Based on All
9.1.3 Employment Impacts using NREL'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 NREL's JEDI model to produce a second estimate.
We proceed under the assumption that the volumes for this proposal 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 Volume Scenarios and Proposed Volumes under these assumptions. In this subsection, we
report the cumulative impact to direct gross jobs that result from the Volume Scenarios and
Proposed 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 Volume
Scenarios 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.
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.591 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
591 Ethanol plant employment data obtained via Ethanol Producer Magazine. See "Employment information sources
for corn-ethanol facilities," available in the docket for this action.
356
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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
c
o
2.0
1.8
Employees per million gal/yr capacity
= 8.30(million gal/yr capacity)-0-6
75 1.5 R2 = 0.72
ej
C
.2 1.3
1.0
s. *••••»..*
8 0 8 ••
CD
10.5 • * r .• »
Urbanchuk-based estimate of
o.
E
^ 0.3 0.65 plotted here at 90 million
gals/yr average plant capacity
0.0
VV •
0 25 50 75 100 125 150
Plant Capacity (Million Gallons per Year)
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.592 Uncertainty regarding the size of the facility or facilities which will provide
the incremental volume of ethanol projected in the Volume Scenarios is therefore relevant to our
analysis of employment impacts. To help address this uncertainty, we show the cumulative job
impacts assuming the construction and/or operation of both a single large facility and 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
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 the time frame of the analysis in each of the two scenarios
presented.
592 EIA, "U.S. Fuel Ethanol Plant Production Capacity," Petroleum & Other Liquids, August 15, 2024.
https://www.eia.gov/petroleuin/ethanolcapacitv.
357
-------
Table 9.1.3-1: Cumulative Direct Permanent Annual Operations Jobs, Temporary
Construction Jobs & Total Direct Jobs for Volume Scenarios (FTE)
Year
Aggregate Volume
in mil gal
(Multi-plant
Volumes in mil gal)
Single Facility
Mu
ti-plant Facility
Range
(min-max)
Cumulative
Operations
Jobs
(Aggregate)
Cumulative
Operations
Jobs
(Aggregate)
Construction
Jobs
Total
(Direct)
Jobs
2026
212
(90,90,32)
4
141
504
645
4-645
2027
228
(90,90,48)
5
294
538
832
5-832
2028
238
(90,90,58)
5
452
554
1,006
5-1,006
2029
252
(90,90,72)
6
612
570
1,182
6-1,182
2030
266
(90,90,86)
11
773
592
1,365
11-1,365
Table 9.1.3-2: Cumulative Direct Permanent Annual Operations Jobs, Temporary
Construction Jobs & Total Direct Jobs for Proposed Volumes (FTE)
Year
Aggregate Volume
in mil gal
(Multi-plant
Volumes in mil gal)
Single Facility
Mu
ti-plant Facility
Range
(min-max)
Cumulative
Operations
Jobs
(Aggregate)
Cumulative
Operations
Jobs
(Aggregate)
Construction
Jobs
Total
(Direct)
Jobs
2026
212
(90,90,32)
4
141
504
645
4-645
2027
228
(90,90,48)
5
294
538
832
5-832
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
(biodiesel and renewable diesel) production for 2026-2030 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 Agricultural Employment Impacts Using the Rule-of-thumb Approach
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.
358
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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
As 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 these studies in million ethanol equivalent gallons to estimate
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 Volume
Scenarios and Proposed 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(a and b) and 9.1.4.1-4(a and b)
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 production increases relative to the baseline.
Table 9.1.4.1-3a: Agricultural Employment Impacts of the Projected Production Increases
Low
High
2026
2027
2028
2029
2030
2026
2027
2028
2029
2030
RNG
2,302
2,389
2,482
2,579
2,682
2,302
2,389
2,482
2,579
2,682
BBD
6,082
6,471
6,899
7,253
7,658
6,645
7,595
8,586
9,501
10,469
Ethanol
793
852
890
942
994
793
852
890
942
994
359
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Table 9.1.4.1-3b: Agricultural Employment Impacts of the Projected Production Increases
Direct
2026
2027
RNG
2,302
2,389
Biodiesel
8,663
9,082
Ethanol
793
852
All Fuels
11,758
12,324
Table 9.1.4.1-4a: Agricultural Employment Impacts of the Projected Production Increases
Low
High
2026
2027
2028
2029
2030
2026
2027
2028
2029
2030
RNG
6,907
7,168
7,447
7,737
8,046
6,907
7,168
7,447
7,737
8,046
BBD
24,330
25,884
27,596
29,013
30,632
26,581
30,380
34,344
38,005
41,876
Ethanol
3,594
3,865
4,035
4,272
4,509
3,594
3,865
4,035
4,272
4,509
Table 9.1.4.1-4b: Agricultural Employment Impacts of the Projected Production Increases
Based on A
1 Effects Under Proposed Volumes (FTE)
Direct + Indirect + Induced
2026
2027
RNG
6,907
7,168
Biodiesel
34,653
36,329
Ethanol
3,594
3,865
All Fuels
45,154
47,362
9.1.4.2 Agricultural Employment Impacts Using NREL' s JEDI model for Dry
Mill Corn Ethanol
Once again, relying on NREL's JEDI model (as discussed in Chapter 9.1.2) and using the
incremental corn ethanol volumes (compared with the No RFS Baseline) to infer the size of the
policy shock, 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.593 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.594 Different modeling approaches
593 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:
NREL, "Limitations of JEDI Models." https://www.nrel.gov/analYsis/iedi/limitations.html
594 EPA, "Model Comparison Exercise Technical Document," EPA-420-R-23-017, June 2023.
https://nepis.epa.gov/Exe/ZvPDF.cgi?Dockev=P1017P9B.pdf.
360
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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 proposed 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. Tables 9.1.4.2-1 and 2 show 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 case of the Volume Scenarios and the Proposed Volumes, respectively,
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.
Table 9.1.4.2-1 Annual Cumulative Indirect and Induced (Gross) Jobs in Agriculture,
Industry and Other Sectors for Volume Scenarios
Indirect
Indirect
Operations Jobs
(Agriculture)
Operations Jobs
(Industry)
Induced
Operations Jobs
Year
Single
Large
Facility
Multiple
Smaller
Facilities
Single
Large
Facility
Multiple
Smaller
Facilities
Single
Large
Facility
Multiple
Smaller
Facilities
2026
648
648
429
573
477
580
2027
1,345
1,345
870
1,190
978
1,203
2028
2,072
2,073
1,316
1,832
1,493
1,853
2029
2,843
2,843
1,767
2,509
2,027
2,537
2030
3,656
3,657
2,220
3,220
2,579
3,256
FTE)
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
648
648
429
573
477
580
2027
1,345
1,345
870
1,190
978
1,203
361
-------
Since the purpose of the sensitivity analysis was to demonstrate primarily how changes in
the sourcing assumption (from JEDI's default values) of this corn ethanol shock will impact
gross jobs, it was applied only for 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 9.1.4.2-1 shows the number of gross indirect operations
jobs that emerge out of this sensitivity analysis for agriculture, industry, and gross induced
operations jobs sectors. The second column in Table 9.1.4.2-1 shows the outcome/impacts when
47.4% of this corn (for ethanol) is sourced from new agricultural production/new plantings, the
third column shows the results when 25% is from new agricultural production, and the last
column shows the impacts when 1% is sourced from new agricultural production/new
plantings.595
Table 9.1.4.2-1: Results of Sensitivity Analysis on Cumulative (Indirect & Induced) Jobs
for the Volume Scenarios (FTE)
Indirect Operations Jobs (Agriculture) - Cumulative FTE
Year
High Sourcing Value
(47%)
JEDI Default Value"
(25%)
Low Sourcing Value
(1%)
2026
1,229
648
26
2027
2,550
1,345
54
2028
3,929
2,072
83
2029
5,390
2,843
114
2030
6,931
3,656
146
Indirect Operations Jobs (Industry) - Cumulative FTE
2026
429
429
429
2027
870
870
870
2028
1,316
1,316
1,316
2029
1,767
1,767
1,767
2030
2,220
2,220
2,220
Induced Operations Jobs - Cumulative Fr
rE
2026
658
477
283
2027
1,354
978
753
2028
2,073
1,493
1,051
2029
2,822
2,027
1,354
2030
3,602
2,579
1,663
a The results in this column correspond to the incremental output coming entirely from one single facility using
JEDI's default sourcing assumption estimates.
595 These percentages are the results of two competing models (PNNL's GCAM model and IIASA's GLOBIOM
model) that was used by EPA in its previously published MCE.
362
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Table 9.1.4.2-1: Results of Sensitivity Analysis on Cumulative (Indirect & Induced) Jobs
for the Proposed Volumes (FTE)
Indirect Operations Jo
)s (Agriculture) Cumulative FTE
Year
High Sourcing Value
(47%)
JEDI Default Value"
(25%)
Low Sourcing Value
(1%)
2026
1,229
648
26
2027
2,550
1,345
54
Indirect Operations Jobs (Industry) Cumulative FTE
2026
429
429
429
2027
870
870
870
Induced Operations Jobs Cumulative FTE
2026
658
477
283
2027
1,354
978
753
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. Our volume scenarios project greater
consumption of ethanol, BBD (including biodiesel and renewable diesel), and RNG used as
CNG/LNG in 2026-2030 relative to the No RFS Baseline. However, the majority of the
production growth (and therefore consumption growth) when considered relative to 2025 is due
to expansions in renewable diesel. As discussed in Chapter 9.1.1, the impact of the RFS volumes
for 2026-2030 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 additional jobs created relative to the
state of the industry in 2025, all this renewable fuel production 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 three specific IO studies we identified in Chapter 9.1.1.2 provide a basis for
estimating the impact of renewable fuels on rural economic development. From these studies, we
have estimated the 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.
363
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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)
Feedstock
Direct
Indirect
Induced
Indirect +
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$)
Feedstock (mostly corn)
0.20
0.92
0.67
1.79
The GDP impacts in Table 9.1.5.1-2 are based on data from different years. We use the
GDP price index from the Federal Reserve Economic Data (FRED)596 to compute the ratio of the
GDP price index in base year (2022) to the GDP price index in year t (2021-2024), as shown in
Table 9.1.5.1-3.
Table 9.1.5.1-3: GDP Price Index Ratios (Base Year 2022)
Year
GDP Price Index
GDP Price Index Ratio
2021
110
1.071
2022
118
1.000
2023
122
0.965
2024
125
0.942
We then use the ratios to compute the GDP impacts in 2022 dollars as shown in Table
9.1.5.1-4. For example, to derive 0.71 (the real value measured in 2022 dollars in Table 9.1.5.1-
4), we multiply 0.75 (the nominal value in Table 9.1.5.1-2) by 0.942 (the ratio for 2024 in Table
9.1.5.1-3). Compared with RNG, BBD and ethanol have higher impacts per million ethanol
equivalent gallons on rural economic development.
596 Federal Reserve Economic Data, "Gross domestic product (implicit price deflator)," March 27, 2025.
https://fred.stlouisfed.org/series/A191RD3A086NBEA.
364
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Table 9.1.5.1-4: Rural GDP Impacts (million 2022$ per million ethanol-equivalent gallons)
Feedstock
Direct
Indirect
Induced
Indirect +
Induced
Total
RNG
Agricultural waste
0.71
0.41
0.38
1.50
BBD
Oilseed production
0.51
1.52
2.02
Ethanol
Feedstock (mostly corn)
0.19
0.88
0.65
1.73
We next estimate the impacts of the projected 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 (Table 9.1.5.1-5) and
the other based on all effects (i.e., direct, indirect, and induced effects, Table 9.1.5.1-6). 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.
Table 9.1.5.1-5: Rural GDP Impacts of the Projected Production Increases Based on Direct
Effects Only Under Volume Scenarios (million 2022$)
Low
High
2026
2027
2028
2029
2030
2026
2027
2028
2029
2030
RNG
505
524
545
566
588
505
524
545
566
588
BBD
1,710
1,819
1,940
2,039
2,153
1,868
2,135
2,414
2,671
2,943
Ethanol
41
44
46
49
52
41
44
46
49
52
Table 9.1.5.1-6: Rural GDP Impacts of the Projected Production Increases Based on All
Effects Under Volume Scenarios (million 2022$)
Low
High
2026
2027
2028
2029
2030
2026
2027
2028
2029
2030
RNG
1,072
1,113
1,156
1,201
1,249
1,072
1,113
1,156
1,201
1,249
BBD
6,840
7,277
7,758
8,157
8,612
7,473
8,541
9,655
10,685
11,773
Ethanol
366
394
411
435
459
366
394
411
435
459
For the Proposed Volumes, we have performed the discounting analysis using discount
rates of 3% and 7%. We report the results separately from those of the Volume Scenarios in
Table 9.1.5.1-7. Without discounting and based only on the direct impacts, the Proposed
Volumes are projected to create $2.98 billion and $3.12 billion in 2026 and 2027, respectively. If
we discount these direct impacts at 3%, the total impact is $5.84 billion over the two-year
horizon, or $2.92 billion per year. If we also account for indirect and induced effects, the total
impact without discounting is $11.18 billion and $11.72 billion in 2026 and 2027; with
discounting, it is $21.90 billion over the two-year horizon, or $10.95 billion per year. In addition,
if we amortize $21.90 billion over the two-year horizon, the annualized value is $11.45 billion.597
597 An annualized value is the amount one would have to pay (or receive) at the end of each time period so that the
sum of all payments in present value terms equals the original stream of values. Computing annualized costs and
benefits from present values spreads the costs and benefits equally over each period, taking account of the discount
rate.
365
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Table 9.1.5.1-7: Rural GDP Impacts of the Projected Production Increases (million 2022$)
Direct
Direct + Indirect + Induced
2026
2027
2026
2027
RNG
505
524
1,072
1,113
BBD
2,436
2,553
9,742
10,214
Ethanol
41
44
366
394
All Fuels
2,982
3,122
11,181
11,720
Present Value at 3%
2,895
2,943
10,855
11,047
All fuels for two years
5,838
21,902
All fuels per year
2,919
10,951
Present Value at 7%
2,787
2,727
10,449
10,237
All fuels for two years
5,514
20,686
All fuels per year
2,757
10,343
9.1.5.2 Rural Economic Development Impacts using NREL' s JEDI Model for
Dry Mill Corn Ethanol
We also estimated annual cumulative earnings impacts associated with corn ethanol using
the NREL JEDI model. These estimates assume the ethanol volumes are associated with new
domestic production. 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 and Table 9.1.5.2-2 show the economic impacts of continued operations at higher
volumes from existing facilities and from multiple new average sized facilities for the Volume
Scenarios and Proposed Volumes, respectively.
Table 9.1.5.2-1: Annual Cumulative Earnings From All Indirect and Induced Jobs for Both
Analytical Scenarios (million 2022$)
Year
Indirect Operations
Earnings
(Agriculture)
Indirect C
Earnings
~perations
Industry)
Induced
Operations
Earnings
Single
Large
Facility
Multiple
Smaller
Facilities
Single
Large
Facility
Multiple
Smaller
Facilities
Single
Large
Facility
Multiple
Smaller
Facilities
2026
33
32
39
49
34
40
2027
68
66
78
102
69
84
2028
104
102
118
157
105
129
2029
142
140
158
215
142
176
2030
182
181
199
275
180
226
366
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Table 9.1.5.2-2: Annual Cumulative Earnings From All Indirect and Induced Jobs for the
Proposed Volumes (million 2022$)
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
33
32
39
49
34
40
2027
68
66
78
102
69
84
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 for more details on this discussion). 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 47.4% 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.598
598 These percentages are the results of two competing models (PNNL's GCAM model and IIASA's GLOBIOM
model) that was used by EPA in its previously published MCE.
367
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Table 9.1.5.2-1: Results of Sensitivity Analysis on Cumulative (Indirect & Induced)
Earnings (million 2022$)
Indirect Earnings (Agriculture)
Year
High Sourcing
Value
(47%)
JEDI Default
Value
(25%)
Low Sourcing
Value
d%)
2026
60.7
33.4
1.3
2027
99.8
67.8
2.7
2028
167.9
103.7
4.1
2029
240.0
141.7
5.6
2030
316.2
181.9
46.6
Indirect Earnings (Industry)
2026
37.9
38.7
37.9
2027
86.6
77.8
77.1
2028
126.4
117.6
116.9
2029
166.8
158.1
157.4
2030
207.8
199.1
178.5
Induced Earnings
2026
46.0
34.1
19.4
2027
94.6
68.9
39.3
2028
144.8
104.7
59.7
2029
197.2
141.8
80.5
2030
251.7
180.2
101.6
9.1.6 Summary of Employment and Economic Impacts
Chapter 9.1 contains our analysis of the employment, agricultural employment, and rural
economic development for the Volume Scenarios and the Proposed Volumes. In this section, we
summarize our main results in Tables 9.1.6-2, 4, and 6. Our analyses are based on existing
studies using a rule-of-thumb method (for ethanol, BBD, and RNG) and the NREL's JEDI model
approach (for corn ethanol).
The "rule-of-thumb" type approach uses job and income impact estimates from previous
studies, expressed in jobs and/or dollars per unit of biofuel production, and multiplies these
estimated impacts by the projected volumes to arrive at employment estimates. This approach is
taken to produce estimates for the impacts of the quantities of ethanol, BBD, and RNG fuels in
the Low and High Volume Scenarios relative to the No RFS Baseline. The JEDI model approach
is a slightly more nuanced approach that relies on the use of an input-output modeling
methodology developed specifically for analysis of dry mill corn ethanol, which is applied to the
volumes of that fuel in the Low and High Volume Scenarios relative to the No RFS Baseline. In
some cases, we have developed ranges of impacts for fuel volumes based on uncertainty
regarding how the volumes will be provided. For example, volumes associated with new
production capacity would also be associated with some number of temporary construction jobs,
while expanded capacity utilization at existing facilities would not. Additionally, we were also
able to carry out a sensitivity analysis on the results of these model runs using research from the
MCE. This approach illustrates both how results from a simple rule-of-thumb type approach
368
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compare 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.
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 Volume Scenarios and Proposed 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.
Table 9.1.6-1: Job Creation Impacts (FTE) per Million Ethanol-Equivalent Gallons
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 all of the Volume
Scenarios and Proposed Volumes (Tables 9.1.6-2a and b).
Using the JEDI model for corn ethanol, we created the following two scenarios to
estimate the impacts of the analytical and Proposed Volumes on the economy: (1) we assume
there is no new construction of ethanol facilities and the increased ethanol volume associated
with the volume scenarios (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-
2c and d report the cumulative number of total jobs (in FTE) that would result under the Volume
Scenarios and the Proposed Volumes.
369
-------
Table 9.1.6-2a: Employment Impacts of the Volume Scenario Using the Rule-of-thumb
Approach (FTE)
Low Volume Scenario
2026
2027
2028
2029
2030
Ethanol
5,332
5,735
5,986
6,338
6,690
BBD
64,793
68,931
73,491
77,265
81,576
RNG
19,504
20,240
21,030
21,847
22,718
Total
89,629
94,906
100,507
105,450
110,984
High Volume Scenario
Ethanol
5,332
5,735
5,986
6,338
6,690
BBD
70,790
80,905
91,461
101,213
111,520
RNG
19,504
20,240
21,030
21,847
22,718
Total
95,626
106,880
118,477
129,398
140,928
Table 9.1.6-2b: Employment Impacts of the Proposed Volumes Using the Rule-of-thumb
Approach (FTE)
Proposed Volumes
2026
2027
Ethanol
5,332
5,735
BBD
92,285
96,749
RNG
19,504
20,240
Total
117,121
122,724
Table 9.1.6-2c: Employment Impacts of the Volume Scenarios Using NREL's JEDI Model
for Dry Mill Corn Ethanol (FTE)a
Low Volume Scenario
2026
2027
2028
2029
2030
Ethanol
1,558-2,446
3,198-4,570
4,886-6,764
6,643-9,071
8,466-11,498
High Volume Scenario
Ethanol
1,558-2,446
3,198-4,570
4,886-6,764
6,643-9,071
8,466-11,498
a 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.
Table 9.1.6-2d: Employment Impacts of the Proposed Volumes Using NREL's JEDI Model
for Dry Mill Corn Ethanol (FTE)a
Proposed Volumes
2026
2027
Ethanol
1,558-2,446
3,198-4,570
a 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.
370
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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 all of the Volume Scenarios
and Proposed Volumes. See Tables 9.1.6-4a and b.
Once again, using the JEDI model for corn ethanol, we created the following two
scenarios to estimate the impacts of the analytical and Proposed Volumes on the economy: (1)
we assume there is no new construction of ethanol facilities and the increased ethanol volume
associated with the volume scenarios (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-
4 c and d report the cumulative number of total indirect operations (in agriculture and industry)
jobs and the total induced operations jobs that would result under the Volume Scenarios and
Proposed 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 Volume Scenarios Using the Rule-
Low Volume Scenario
2026
2027
2028
2029
2030
Ethanol
3,594
3,865
4,035
4,272
4,509
BBD
24,330
25,884
27,596
29,013
30,632
RNG
6,907
7,168
7,447
7,737
8,046
Total
34,831
36,917
39,078
41,022
43,187
High Volume Scenario
Ethanol
3,594
3,865
4,035
4,272
4,509
BBD
26,581
30,380
34,344
38,005
41,876
RNG
6,907
7,168
7,447
7,737
8,046
Total
37,082
41,413
45,826
50,014
54,431
371
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Table 9.1.6-4b: Agricultural Employment Impacts of the Proposed Volumes Using the
Proposed Volumes
2026
2027
Ethanol
3,594
3,865
BBD
34,653
36,329
RNG
6,907
7,168
Total
45,154
47,362
Table 9.1.6-4c: Agricultural Employment Impacts of the Volume Scenarios Using NREL's
JEDI Model for Dry Mill Corn Ethanol (FTE)a
Low Volume Scenario
2026
2027
2028
2029
2030
Ethanol
1,554- 1,801
3,193 -3,738
4,881 -5,758
6,637-7,889
8,455 - 10,133
High Volume Scenario
Ethanol
1,554- 1,801
3,193 -3,738
4,881 -5,758
6,637-7,889
8,455 - 10,133
a 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.
Table 9.1.6-4d: Agricultural Employment Impacts of the Proposed Volumes Using NREL's
JEDI Model for Dry Mill Corn Ethanol (F
Proposed Volumes
2026
2027
Ethanol
1,554- 1,801
3,193 -3,738
'E)a
a 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
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
0.71
0.41
0.38
1.50
BBD
Oilseed production
0.51
1.52
2.02
Ethanol
Feedstock (mostly corn)
0.19
0.88
0.65
1.73
quivalent Gallons (million 2022$)
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 Tables 9.1.6-6a and b. Note
that the estimates of rural GDP impacts are actual values as opposed to discounted values,
implying that they do not reflect the time value of money.
372
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Once again, using the JEDI model for corn ethanol, we created the following two
scenarios to estimate the impacts of the analytical and Proposed Volumes on the economy: (1)
we assume there is no new construction of ethanol facilities and the increased ethanol volume
associated with the volume scenarios (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-
6c and d report 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 Volume Scenarios and the Proposed Volumes. For the results of the sensitivity
analysis, please refer to Tables 9.1.5.2-1 and 9.1.5.2-2.
Table 9.1.6-6a: Rural Economic Development Impacts of the Volume Scenarios (million
2022$)
Low Volume Scenario
2026
2027
2028
2029
2030
Ethanol
366
394
411
435
459
BBD
6,840
7,277
7,758
8,157
8,612
RNG
1,072
1,113
1,156
1,201
1,249
Total
8,278
8,784
9,325
9,793
10,320
E
igh Volume Scenario
Ethanol
366
394
411
435
459
BBD
7,473
8,541
9,655
10,685
11,773
RNG
1,072
1,113
1,156
1,201
1,249
Total
8,911
10,048
11,222
12,321
13,481
Table 9.1.6-6b: Rural Economic Development Impacts of the Proposed Volumes (million
2022$)
Proposed Volumes
2026
2027
Ethanol
366
394
BBD
9,742
10,214
RNG
1,072
1,113
Total
11,180
11,721
373
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Table 9.1.6-6c: Rural Economic Development Impacts of the Volume Scenarios Using
NREL's JEDI Model for Dry Mill Corn Ethanol (million 2022$)a
Low Volume Scenario
2026
2027
2028
2029
2030
Ethanol
106-121
215-252
327-388
442-531
561 -682
High Volume Scenario
Ethanol
106-121
215-252
327-388
442-531
561 -682
a The estimates are presented as ranges corresponding to the single facility outcome as the lower bound of the range
and the multi facility outcome as the upper bound of the range.
Table 9.1.6-6d: Rural Economic Development Impacts of the Proposed Volumes Using
NREL's JEDI Model for Dry Mill Corn Ethanol (million 2022$)a
Proposed Volumes
2026
2027
Ethanol
106-121
215-252
a 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 estimates and not net estimates.
While we have also not been able to quantify the impacts of this proposed rule on small entities
in rural areas, we note that we do anticipate that small entities (such as farms and supporting
industries) would experience benefits from this proposed 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.599 600 The expectation is that there will be a movement of labor
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.601
599 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-analYsis-in-enviromnental-health-and-safetv-regulation 161535983778.pdf.
61111 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.
6111 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/aie/ait022.
374
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9.2 Supply of Agricultural Commodities
Changes in biofuel production can have an impact on the supply of agricultural
commodities. The Volume Scenarios in this proposed rulemaking 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 1995—
2024.602 This data suggests domestic corn production has grown steadily at a 25-year average
rate of around 2% year over year, or 219 million bushels added annually.
Figure 9.2-1: Corn Production and Usage
ethanol food feed exports total corn
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 production.
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 rule
relative to 2025 are likely to have minimal impact on the supply of corn to food, exports, or other
uses.
6112 USD A, "U.S. Bioenergy Statistics," October 2024, Table 16 - Biodiesel and Diesel Prices.
https://www.ers.usda.gov/data-products/us-bioenergy-statistics.
375
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Soybean production has risen steadily over time, similar to the trend for corn production,
according to data from USD A.603 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 beans has grown by about 25% since 2005, which is mirrored in growth of
crush products, soy meal and oil. These data also show that exports of soy meal nearly doubled
during this time, which together with the growth in whole bean 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.
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. As with corn, when considering the relatively small changes in the supply of
soybean oil for food and other non-biofuel uses since 2008, we expect the Proposed Volumes
would likely have minimal impact on the supply of soybean oil for food and other uses. While
annual gross exports of soybean oil have declined in recent years, they reached nearly zero in
2022 and have remained there since. Because of this, we believe it is unlikely that these
standards will have any significant impact on gross exports of soybean oil. The continued
expansion of biofuel demand has, however, begun to shift the relative value relationship between
the oil and meal crush products, as discussed in Chapter 9.3.
Soybean Oil Production Soybean Oil for Biofuel Exports Other Domestic Use Ending Stocks
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
6113 USD A, "Oil Crops Yearbook," March 2025. https://www.ers.usda.gov/data-products/oil-crops-Yearbook.
376
-------
specific demand perturbations, necessarily contain high levels of uncertainty. This section
reviews historical trends and presents key observations from the literature.
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.604 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 RFA in 2016 examined
the historical relationship between corn usage, stocks, and futures prices.605 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
§ $7.00
Q.
£ $6.00
2012
• _2011
• • 2010
FF = 0.8255 \
£ $5.00
• 2007
f $4.00
01
| $3.00
<
2} $2.00
>
2 $1-00
0
• 2013
2008
. !%V009
2006 2015 ..
'"•-2005
\
2004
$-
3% 8% 13% 18% 23%
US Corn Ending Stocks to Use Ratio
Source: USDA, Informa Economics IEG
61,1 USD A, "Feed Grains Yearbook," May 2025. iitps://www.ers.usda.gov/data-products/feed-grains-database/feed-
grains-vearbook-tables.
6P5 Informa Economics IEG, "The Impact of Ethanol Industry Expansion on Food Prices: A Retrospective Analysis,"
2016. https://d35tlsvewk4d42.cloudfront.net/file/975/Retrospective-of-Impact-of-Ethanol-on-Food-Prices-2016.pdf.
377
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To make more specific quantitative estimates of the impact of increased biofuel
production on corn prices, we considered two meta-studies. Condon, et al., reviewed 29
published papers in 2015 and found a central estimate of 3-5% increase in corn prices per billion
gallons of ethanol.606 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 several newer
studies.607 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.
EPA is projecting a marginal increase in ethanol volumes for years 2026-2030 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 2026-2030, as our projected consumption volumes remain below USDA's
projected domestic production volumes for these years.608 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, it may be 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 decrease in corn demand and corn prices.
To illustrate the potential impact of the volume scenarios on corn prices, we have
calculated the projected impact in 2026-2030 if these 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 candidate volumes on
corn prices relative to the No RFS Baseline are shown in Table 9.3-1.
6116 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.
6117 FAPRI, "Literature Review of Estimated Market Effects of U.S. Corn Starch Ethanol." FAPRI-MU Report #01-
16, February 2016. https://etlianolrfa.org/file/2007/FAPRI-Report-01-16.pdf.
6118 USD A, "USDA Agricultural Projections to 2034," OCE-2025-1, February 2025.
https://doi.org/10.32747/2025.9015815.ers.
378
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Table 9.3-1: Projected Impact
on Corn Prices Relai
tive to No RFS I
»aseline
Units
2026
2027
2028
2029
2030
Corn Price
(All Volume Scenarios)3
$ per Bushel
$3.97
$4.07
$4.17
$4.27
$4.30
Corn Price Response
(%) per Billion
Gallons of Ethanol
3%
3%
3%
3%
3%
Corn Ethanol Volume
Increase Relative to No RFS
Baseline
Billion Gallons
0.212
0.228
0.238
0.252
0.266
Corn Price Increase Relative
to No RFS Baseline
$ per Bushel
$0.03
$0.03
$0.03
$0.03
$0.03
a Corn prices are from: USD A, "USDA Agricultural Projections to 2034," OCE-2025-1, February 2025.
https://doi.org/10.32747/2025.9015815.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).
With biodiesel and renewable diesel production, the commodity input of interest is
soybean oil, which has an indirect link to soybean production. Soybean oil is produced by
crushing soybeans, which also creates soy 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 soy oil and meal price
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.609
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 relatively minor outlet and had remained fairly consistent for many years until falling following
steep price increases since 2020. Noting the lack of correlation between soybean oil price and its
use in biofuel production historically, we conclude that 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 a significant factor.610
6119 USDA, "Oil Crops Yearbook," March 2025. https://www.ers.usda.gov/data-products/oil-crops-Yearbook.
6111 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.
379
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Figure 9.3-2: Soybean Oil Price and Allocation to Biofuel and Exports
60
50
40
30
20
10
0
0
1980
1990
2000
2010
2020
There are relatively few quantitative studies on the impacts of BBD production on soy oil
and bean prices, and they show a range of results. This is in part because these studies have
included a variety of different policy combinations, none of which separated out just 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 different scenario volumes on soybean prices, we
calculated the projected price effects for the 2026-2030 period relative to the No RFS Baseline.
As in the Set 1 Rule, our projections are primarily based on modeling by Lusk, et al., which
estimates the price impact of a 20% shock to current biofuel volumes—equivalent to
approximately 243 million gallons of soy-derived BBD.611 This model links such a shock to
changes in soybean oil prices and related commodities. The projected impacts of the volume
scenarios on soybean oil and soybean meal prices at the time of this proposal are shown in
Tables 9.3-2, 3, and 4 for the Low Volume Scenario, High Volume Scenario, and Proposed
Volumes, respectively.
611 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.
380
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Table 9.3-2: Projected Impact of the Low Volume Scenario on Soybean Oil and Meal
Prices Relative to the No RFS Baseline
Units
2026
2027
2028
2029
2030
Soybean Oil Price3
$ per Pound
$0.39
$0.37
$0.37
$0.36
$0.36
Soybean Oil Price
Response13
(%) per Billion
Gallons of Biofuel
35.7%
35.7%
35.7%
35.7%
35.7%
Soybean Oil Biofuel
Increase Relative to No
Million Gallons
1,898
1,929
1,975
2,012
2,045
RFS Baseline
Soybean Oil Price
Increase Relative to No
$ per Pound
$0.26
$0.26
$0.26
$0.26
$0.26
RFS Baseline
Soybean Meal Price3
$ per Ton
$324
$331
$339
$347
$355
Soybean Meal Price
Response
(%) per Billion
Gallons of Biofuel
-7.94%
-7.94%
-7.94%
-7.94%
-7.94%
Soybean Meal Price
Change Relative to No
$ per Ton
-$49
-$51
-$53
-$55
-$58
RFS Baseline
a Prices are from: USD A, "USDA Agricultural Projections to 2034," OCE-2025-1, February 2025.
https://doi.org/10.32747/2025.9015815.ers. Prices represent the average price for a calendar year. For soybean oil,
the price is calculated using 1/4 of the price for the first agricultural marketing year (e.g., 2025/2026 for 2026) and
3/4 of the price for the second agricultural marketing year (e.g., 2026/2027 for 2026).
b This number is based on a modified shock from Lusk equivalent to 1 billion gallons (as opposed to approximately
240 million gallons in the Lusk paper).
381
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Table 9.3-3: Projected Impact of the High Volume Scenario on Soybean Oil and Meal
Prices Relative to the No RFS Baseline
Units
2026
2027
2028
2029
2030
Soybean Oil Price3
$ per Pound
$0.39
$0.37
$0.37
$0.36
$0.36
Soybean Oil Price
Response13
(%) per Billion
Gallons of Biofuel
35.7%
35.7%
35.7%
35.7%
35.7%
Soybean Oil Biofuel
Increase Relative to No
Million Gallons
2,111
2,354
2,612
2,862
3,108
RFS Baseline
Soybean Oil Price
Increase Relative to No
$ per Pound
$0.29
$0.31
$0.34
$0.37
$0.40
RFS Baseline
Soybean Meal Price3
$ per Ton
$324
$331
$339
$347
$355
Soybean Meal Price
Response
(%) per Billion
Gallons of Biofuel
-7.94%
-7.94%
-7.94%
-7.94%
-7.94%
Soybean Meal Price
Change Relative to No
$ per Ton
-$54
-$62
-$70
-$79
-$88
RFS Baseline
a Prices are from: USD A, "USDA Agricultural Projections to 2034," OCE-2025-1, February 2025.
https://doi.org/10.32747/2025.9015815.ers. Prices represent the average price for a calendar year. For soybean oil,
the price is calculated using 1/4 of the price for the first agricultural marketing year (e.g., 2025/2026 for 2026) and
3/4 of the price for the second agricultural marketing year (e.g., 2026/2027 for 2026).
b This number is based on a modified shock from Lusk equivalent to 1 billion gallons (as opposed to
approximately240 million gallons in the Lusk paper).
382
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Table 9.3-3: Projected Impact of the Proposed Volumes on Soybean Oil and Meal Prices
Relative to the No RFS Baseline
Units
2026
2027
Soybean Oil Price3
$ per Pound
$0.39
$0.37
Soybean Oil Price
Response13
(%) per Billion
Gallons of Biofuel
35.7%
35.7%
Soybean Oil Biofuel
Increase Relative to No
Million Gallons
2,433
2,705
RFS Baseline
Soybean Oil Price
Increase Relative to No
$ per Pound
$0.33
$0.36
RFS Baseline
Soybean Meal Price3
$ per Ton
$324
$331
Soybean Meal Price
Response
(%) per Billion
Gallons of Biofuel
-7.94%
-7.94%
Soybean Meal Price
Change Relative to No
$ per Ton
-$63
-$71
RFS Baseline
a Prices are from: USD A, "USDA Agricultural Projections to 2034," OCE-2025-1, February 2025.
https://doi.org/10.32747/2025.9015815.ers. Prices represent the average price for a calendar year. For soybean oil,
the price is calculated using 1/4 of the price for the first agricultural marketing year (e.g., 2025/2026 for 2026) and
3/4 of the price for the second agricultural marketing year (e.g., 2026/2027 for 2026).
b This number is based on a modified shock from Lusk equivalent to 1 billion gallons (as opposed to
approximately240 million gallons in the Lusk paper).
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.612 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.613
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
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.
612 Irwin, Scott, "The Value of Soybean Oil in the Soybean Crush: Further Evidence on the Impact of the U.S.
Biodiesel Boom." farmcloc daily (7): 169, September 14, 2017. https://farmdocdailv.illinois.edu/2017/09/the-value-
of-sovbean-oil-in-the-sovbean-crush.html.
613 USDA, "Oil Crops Yearbook," March 2025. https://www.ers.usda.gov/data-products/oil-crops-vearbook.
383
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Figure 9.3-3: Relative Values of Soybean Oil and Soybean Meal
Hfli
0% L 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 candidate volumes and adjusted the
projected prices for these commodities lower in our price projections for the No RFS Baseline.
The projected impact of the Proposed Volumes on sorghum, barley, oat, and DDG prices are
shown in Table 9.3-4.
384
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Table 9.3-4: Projected Impact of All Volume Scenarios on Prices of Other Commodities
Relative to No RFS Baseline
2026
2027
2028
2029
2030
Price Change Factor Relative to Corn Price C
iangea
Corn; $/bushel
$1.00
$1.00
$1.00
$1.00
$1.00
Sorghum; $/bushel
$0.93
$0.93
$0.93
$0.93
$0.93
Barley; $/bushel
$0.88
$0.88
$0.88
$0.88
$0.88
Oats; $/bushel
$0.72
$0.72
$0.72
$0.72
$0.72
Distillers Grains; $/ton
$0.02
$0.02
$0.02
$0.02
$0.02
Projected Price Impact Relative to No RFS Baseline
Corn; $/bushel
$0.03
$0.03
$0.03
$0.03
$0.03
Sorghum; $/bushel
$0.02
$0.03
$0.03
$0.03
$0.03
Barley; $/bushel
$0.02
$0.02
$0.03
$0.03
$0.03
Oats; $/bushel
$0.02
$0.02
$0.02
$0.02
$0.02
Distillers Grains; $/ton
$0.90
$0.99
$1.05
$1.15
$1.23
a These factors were developed in conjunction with USD A 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-2012-
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 Volume Scenarios
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 Volume Scenarios 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).614
To project the impact of the candidate 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.615 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. For context, 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.
614 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.
615 77 FR 70752 (November 27, 2012).
385
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In Chapter 9.3, we presented estimates of the impact of the volume scenarios on
commodity prices relative to the No RFS Baseline These estimates are the starting point for our
estimate of the impact of the RFS scenario 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 through Table 9.4-
3. 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.616
EPA recognizes that projecting that the price of distillers grains (DDG) increases
proportionally to the price of corn may overstate the impact of this proposed 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 Proposed Volumes could be expected.
This methodology assumes no response by producers or consumers to changes in
commodity prices 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.617 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.
616 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.
617 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.
386
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Table 9.4-1: Changes in Food Expenditures of the Low Volume Scenario 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
$0.26 per Pound
14,438 million Pounds
$3,766 million
Soybean Meal
-$48.79 per Ton
42,344 thousand Short Tons
-$2,066 million
DDG
$0.90 per Short Ton
47,122 million Short Tons
$42 million
Total
$1,938 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
$0.03 per Bushel
158 million Bushels
$4 million
Oats
$0.02 per Bushel
128 million Bushels
$3 million
Soybean Oil
$0.26 per Pound
14,488 million Pounds
$3,716 million
Soybean Meal
-$50.70 per Ton
42,950 thousand Short Tons
-$2,177 million
DDG
$0.99 per Short Ton
47,175 million Short Tons
$47 million
Total
$1,802 million
2028
Corn
$0.03 per Bushel
7,510 million Bushels
$224 million
Grain Sorghum
$0.03 per Bushel
110 million Bushels
$3 million
Barley
$0.03 per Bushel
157 million Bushels
$4 million
Oats
$0.02 per Bushel
129 million Bushels
$3 million
Soybean Oil
$0.26 per Pound
14,538 million Pounds
$3,754 million
Soybean Meal
-$53.16 per Ton
43,550 thousand Short Tons
-$2,315 million
DDG
$1.06 per Short Ton
47,175 million Short Tons
$50 million
Total
$1,723 million
2029
Corn
$0.03 per Bushel
7,574 million Bushels
$245 million
Grain Sorghum
$0.03 per Bushel
110 million Bushels
$3 million
Barley
$0.03 per Bushel
160 million Bushels
$5 million
Oats
$0.02 per Bushel
129 million Bushels
$3 million
Soybean Oil
$0.26 per Pound
14,588 million Pounds
$3,785 million
Soybean Meal
-$55.43 per Ton
44,150 thousand Short Tons
-$2,447 million
DDG
$1.15 per Short Ton
47,175 million Short Tons
$54 million
Total
$1,648 million
2030
Corn
$0.03 per Bushel
7,679 million Bushels
$264 million
Grain Sorghum
$0.03 per Bushel
110 million Bushels
$4 million
Barley
$0.03 per Bushel
160 million Bushels
$5 million
Oats
$0.02 per Bushel
128 million Bushels
$3 million
Soybean Oil
$0.26 per Pound
14,638 million Pounds
$3,847 million
Soybean Meal
-$57.64 per Ton
44,750 thousand Short Tons
-$2,579 million
DDG
$1.23 per Short Ton
47,175 million Short Tons
$58 million
Total
$1,601 million
a Quantity used for food and feed was calculated from: USD A, "USDA Agricultural Projections to 2034," OCE-
2025-1, February 2025. https://doi.org/10.32747/2025.9015815.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.
387
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Table 9.4-2: Changes in Food Expenditures of the High Volume Scenario 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
$0.29 per Pound
14,438 million Pounds
$4,189 million
Soybean Meal
-$54.26 per Ton
42,344 thousand Short Tons
-$2,298 million
DDG
$0.90 per Short Ton
47,122 million Short Tons
$42 million
Total
$2,129 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
$0.31 per Pound
14,488 million Pounds
$4,535 million
Soybean Meal
-$61.87 per Ton
42,950 thousand Short Tons
-$2,657 million
DDG
$0.99 per Short Ton
47,175 million Short Tons
$47 million
Total
$2,141 million
2028
Corn
$0.03 per Bushel
7,510 million Bushels
$224 million
Grain Sorghum
$0.03 per Bushel
110 million Bushels
$3 million
Barley
$0.03 per Bushel
157 million Bushels
$4 million
Oats
$0.02 per Bushel
129 million Bushels
$3 million
Soybean Oil
$0.34 per Pound
14,538 million Pounds
$4,965 million
Soybean Meal
-$70.31 per Ton
43,550 thousand Short Tons
-$3,062 million
DDG
$1.06 per Short Ton
47,175 million Short Tons
$50 million
Total
$2,187 million
2029
Corn
$0.03 per Bushel
7,574 million Bushels
$245 million
Grain Sorghum
$0.03 per Bushel
110 million Bushels
$3 million
Barley
$0.03 per Bushel
160 million Bushels
$5 million
Oats
$0.02 per Bushel
129 million Bushels
$3 million
Soybean Oil
$0.37 per Pound
14,588 million Pounds
$5,384 million
Soybean Meal
-$78.85 per Ton
44,150 thousand Short Tons
-$3,481 million
DDG
$1.15 per Short Ton
47,175 million Short Tons
$54 million
Total
$2,213 million
2030
Corn
$0.03 per Bushel
7,679 million Bushels
$264 million
Grain Sorghum
$0.03 per Bushel
110 million Bushels
$4 million
Barley
$0.03 per Bushel
160 million Bushels
$5 million
Oats
$0.02 per Bushel
128 million Bushels
$3 million
Soybean Oil
$0.40 per Pound
14,638 million Pounds
$5,847 million
Soybean Meal
-$87.61perTon
44,750 thousand Short Tons
-$3,920 million
DDG
$1.23 per Short Ton
47,175 million Short Tons
$58 million
Total
$2,260 million
a Quantity used for food and feed was calculated from: USD A, "USDA Agricultural Projections to 2034," OCE-
2025-1, February 2025. https://doi.org/10.32747/2025.9015815.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.
388
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Table 9.4-3: Changes in Food Expenditures of the Proposed 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
$0.33 per Pound
14,438 million Pounds
$4,828 million
Soybean Meal
-$62.54 per Ton
42,344 thousand Short Tons
-$2,648 million
DDG
$0.90 per Short Ton
47,122 million Short Tons
$42 million
Total
$2,418 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
$0.36 per Pound
14,488 million Pounds
$5,211 million
Soybean Meal
-$71.09 per Ton
42,950 thousand Short Tons
-$3,053 million
DDG
$0.99 per Short Ton
47,175 million Short Tons
$47 million
Total
$2,421 million
a Quantity used for food and feed was calculated from: USD A, "USDA Agricultural Projections to 2034," OCE-
2025-1, February 2025. https://doi.org/10.32747/2025.9015815.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 2023 survey.618 We used
the ratio of the estimated change in food expenditures to the total food expenditures to estimate
the change in food expenditures for the average consumer unit (households shown in Tables 9.4-
4, 9.4-5, and 9.4-6 for the Low Volume Scenario, High Volume Scenario, and Proposed
Volumes, respectively).
618 Bureau of Labor and Statistics, Consumer Expenditures in 2023, Table 1101 - Quintiles of income before taxes:
Shares of annual aggregate expenditures and sources of income, 2023. https://www.bls.gov/cex/tables/calendar-
vear/aggregate-group-share/cu-income-auintiles-before-taxes-2023.xlsx.
389
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Table 9.4-4: Change in Food Expenditures per Consumer Unit of the Low Volume
Scenario Relative to No
*FS Baseline
2026
2027
2028
2029
2030
Number of Consumer
Units (thousands)
134,556
134,556
134,556
134,556
134,556
Food Expenditures per
Consumer Unit
$9,985
$9,985
$9,985
$9,985
$9,985
Total Food
Expenditures (millions)
$1,343,542
$1,343,542
$1,343,542
$1,343,542
$1,343,542
Change in Food
Expenditures (millions)
$1,938
$1,802
$1,723
$1,648
$1,601
Percent Change in
Food Expenditures
0.14%
0.13%
0.13%
0.12%
0.12%
Projected Food
Expenditure Increase
$14.41
$13.40
$12.80
$12.25
$11.90
Table 9.4-5: Change in Food Expenditures per Consumer Unit of the High Volume
Scenario Relative to No RFS Baseline
2026
2027
2028
2029
2030
Number of Consumer
Units (thousands)
134,556
134,556
134,556
134,556
134,556
Food Expenditures per
Consumer Unit
$9,985
$9,985
$9,985
$9,985
$9,985
Total Food Expenditures
(millions)
$1,343,542
$1,343,542
$1,343,542
$1,343,542
$1,343,542
Change in Food
Expenditures (millions)
$2,129
$2,141
$2,187
$2,213
$2,260
Percent Change in Food
Expenditures
0.16%
0.16%
0.16%
0.16%
0.17%
Projected Food
Expenditure Increase
$15.82
$15.92
$16.25
$16.45
$16.79
Table 9.4-6: Change in Food Expenditures per Consumer Unit of the Proposed Volumes
Relative to No RFS Baseline
2026
2027
Number of Consumer Units (thousands)
134,556
134,556
Food Expenditures per Consumer Unit
$9,985
$9,985
Total Food Expenditures (millions)
$1,343,542
$1,343,542
Change in Food Expenditures (millions)
$2,418
$2,421
Percent Change in Food Expenditures
0.18%
0.18%
Projected Food Expenditure Increase
$17.97
$18.00
390
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Chapter 10: Estimated 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. 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.
Although we are proposing to set RFS volume requirements for 2026 and 2027, we first
analyzed the Low and High Volume Scenarios for 2026-2030, which informed our decisions for
the rule. We assessed costs for each Volume Scenario 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 relied on AEO2023, which was the most recently published version of the AEO at the time
the analyses were conducted. For the final rule we anticipate updating our analyses based on
AEO2025 and other relevant information. While it is difficult to predict the impacts of updating
to AEO2025 due to the wide range of factors that impact the projected cost of this rule, we
anticipate that, all else equal, the costs for the final rule will be higher due to lower projected
crude oil prices in AEO2025.
The large increase in domestic vegetable oil and animal fat volumes for the Proposed
Volumes could pose a challenge for the agricultural sector to provide the required volumes of
domestically sourced feedstocks incentivized by the proposed standards and thus cause price
increases for those feedstocks. We therefore conducted a sensitivity analysis at higher prices for
those feedstocks. The costs for the Proposed Volumes and the Low and High Volume Scenarios
are all summarized in this chapter. Chapter 10.4.2 contains subsections that summarize the
changes in renewable fuel volumes relative to the No RFS and 2025 Baselines, as well as the
estimated change in fossil fuel volumes displaced by the change in volume of renewable fuels.619
In all cases, costs are reported in 2022 dollars.
10.1 Renewable Fuel Costs
10.1.1 F eedstock 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 estimate the production cost for these feedstocks
prior to providing information for the production, distribution and blending costs for the various
renewable fuels.
For estimating feedstock costs, we used projections of feedstock prices for 2026-2030
from multiple sources, including EIA and USDA.620 We also made adjustments to account for
differences between these projections. Crude oil prices affect the cost for growing renewable fuel
619 The spreadsheet used to estimate the costs for the Volume Scenarios relative to the No RFS and 2025 Baselines
can be found in "Estimated Fuel Costs for Set 2 Proposed Rule," available in the docket for this action.
6211 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.
391
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feedstocks, the cost to transport them to the renewable fuel production plants, the cost for
transporting the produced renewable fuel from the plant to market, and may impact the cost for
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 in
an attempt to remove this potential bias in the cost analysis. We also adjusted the projected
nominal prices to constant year 2022 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 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
2026-2030 USDA projected corn prices (row #1).621 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
2022 dollars used across this cost analysis (row #2).622
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).623 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 2022 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 2022 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. 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:624-625-626
Corn Price ($/bushel) = Crude Oil Price ($/bbl) x 0.0445 + 1.54
621 USDA, "USDA Agricultural Projections to 2033," OCE-2024-1, February 2024.
https://www.usda.gov/sites/default/files/documents/USDA-Agricultural-Proiections-to-2033.pdf.
622 USDA reports estimated future inflation rates that are used for adjusting nominal dollar values to 2022$.
623 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.
624 We chose the years from 2012-2024 because of the wide range in crude oil and corn prices that existed over this
time period.
625 USDA, "Corn Prices Received by Fanners," Quick Stats, 2024.
https://auickstats.nass.usda.gov/results/3538FFA4-F207-383E-A9CF-09AlF14Q8C77.
626 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.
392
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The corn prices estimated by this regression was not used directly for the cost analysis
because farmers are more efficient at producing corn today than in the past, and corn production
is likely to be on a different supply/demand point on the corn price curve as evidenced by
ongoing corn production efficiency improvements. Instead, the difference in regressed corn
prices (row #8) was added to the USDA corn prices adjusted to 2022 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)
Row #
2026
2027
2028
2029
2030
Corn Prices
USDA Nominal $
1
4.30
4.30
4.30
4.30
4.30
USDA 2022$
2
4.10
4,01
3.93
3.86
3.78
Crude Oil
Prices
USDA Nominal $
3
93
96
98
101
104
USDA 2022$
4
88.6
89.6
89.7
90.6
91.4
EIA2022$
5
83.9
84.3
84.6
85.2
85.7
Regressed
Corn Prices
Based on USDA 2022
6
5.06
5.09
5.10
5.13
5.16
Based on EIA 2022
7
4.88
4.90
4.91
4.93
4.95
Corn Prices
Difference in Regressed
Corn Prices EIA - USDA
8
-0.17
-0.19
-0.19
-0.20
-0
Corn Prices
Adjusted USDA 2022$
9
3.92
3.82
3.75
3.66
3.57
Both the inflation and crude oil price adjustment are modest, and their effects cause
offsetting effects. Also, these adjustments are well within the recent variation in corn prices.
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).627 The FAPRI DDGS projected prices are
reported in nominal dollars, so we adjusted the price projections to 2022 dollars. Table 10.1.1.1-
2 summarizes DDGS prices used in the cost analysis.
Table 10.1.1.1-2: DDGS Prices ($/drv ton; 2022$)
Year
2026
2027
2028
2029
2030
Nominal
147.1
146.5
147.9
1498.3
147.1
2022$
132.8
129.6
128.2
126.0
122.5
10.1.1.2 Soybean Oil, Corn Oil and Fats, Oil and Grease Prices
Soybean oil, waste fats, oils, and greases (FOG), corn oil, and canola oil were identified
in Chapter 2 as the feedstocks for producing biodiesel and renewable diesel fuel. For the cost
627 FAPRI, "2024 U.S. Agricultural Market Outlook," FAPRI-MU Report #01-24, March 2024.
https://fapri.missouri.edu/wp-content/uploads/2024/03/2024-Baseline-Outlook.pdf.
393
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analysis, canola oil volumes are combined with the soybean oil volume to estimate a single
volume of virgin oil, but we refer to it solely as soybean oil. 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.628 Soybean oil price projections made by USDA are
used as a starting point for this cost analysis.629
We followed the same methodology we used for corn prices as described above for soy
oil prices; this process is summarized in Table 11.1.1.2-1 and the description that follows
references the rows in that Table to aid in understanding. The first step required converting
USDA projected soy oil prices in nominal dollars (row #1) to 2022 dollars (row #2), and then
adjusting for the differences in crude oil prices (row #4 for USDA in 2022 dollars) and EIA (row
#5). When adjusting for the differences in crude oil prices, a regression of monthly soy oil and
crude oil prices between January 2012 and November 2024 yielded the following
equation:630-631-632
Soy Oil Price ($/lb) = Crude Oil Price ($/bbl) x 0.259 + 19.06
The soy oil prices (row #6) based on USDA crude oil prices and the soy oil prices (row
#7) based on EIA crude oil prices were not used in the cost analysis directly. Rather the
difference in regressed soy oil prices (row #8) was added to the adjusted USDA soy prices (row
#2) to derive the adjusted soy oil prices (row #9).
628 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.
629 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.
6311 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.
631 Federal Reserve Economic Data, "Global price of Soybeans Oil," May 13, 2025.
https://fred.stlouisfed.org/series/PSOILUSDM.
632 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=ROOOO 3&f=a.
394
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Table 10.1.1.2-1: Derivation of Soy Oil Feedstock Production Costs (cents/pound for soy oil,
$/bbl for crude oil)
Row #
2026
2027
2028
2029
2030
Soy Oil Prices
USDA Nominal $
1
0.50
0.46
0.45
0.44
0.43
USDA 2022$
2
0.48
0.43
0.41
0.40
0.38
Crude Oil
Prices
USDA Nominal $
3
93
96
98
101
104
USDA 2022$
4
88.6
89.6
89.7
90.6
91.4
EIA2022$
5
83.9
84.3
84.6
85.2
85.7
Regressed
Soy Oil Prices
Based on USDA 2022$
6
0.420
0.423
0.423
0.425
0.428
Based on EIA 2022$
7
0.408
0.409
0.410
0.411
0.413
Soy Oil Prices
Difference in Regressed
Soy Oil Prices EIA - USDA
8
-0.012
-0.012
-0.012
-0.012
-0.012
Soy Oil Prices
Adjusted USDA 2022$
9
0.46
0.42
0.40
0.38
0.36
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.633 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 (ranged from 29-680 per pound. Corn oil and FOG prices were compared to soy oil
prices, and these were priced at 82.7% and 75.4% of soybean oil, respectively. The projected soy
oil, FOG, and corn oil prices used in this cost analysis are summarized in Table 10.1.1.2-2.
Table 10.1.1.2-2: Projected Vegetable Oil Production Costs (2022 $/lb)
Projected Vegetable C
>il Prices
Year
Soybean Oil
FOG
Corn Oil
2026
0.46
0.36
0.38
2027
0.42
0.33
0.34
2028
0.40
0.31
0,33
2029
0.38
0.30
0.31
2030
0.36
0.28
0.30
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 the 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 the biogas are all discussed under the sections discussing 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 the capital, fixed, and operating costs. We generally express the
633 USDA, "Oil Crops Yearbook," March 2025. https://www.ers.usda.gov/data-products/oil-crops-Yearbook.
395
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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 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 2026.634
10.1.2.1 Cost Factors
10.1.2.1.1 Capital and Fixed Costs
The economic assumptions used to amortize capital costs over the production volume of
renewable fuels are summarized in Table 10.1.2.1.1-1. These capital amortization cost factors are
used in the following section for converting the one-time, total capital cost to an equivalent per-
gallon cost.635 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
636,637,638,639,640
pdpcl
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 2022 dollars for this analysis. The Chemical Engineering
Plant Index (CEPI) capital cost index was used to adjust capital costs to 2022 dollars. Consistent
with the increased inflation observed over recent years, the CEPI capital cost index for 2022
represents a large increase in capital costs when adjusting capital costs to the year 2022.
Fixed operating costs include the maintenance costs, insurance costs, rent, laboratory
charges and miscellaneous chemical supplies.641 Maintenance costs can range from 1% to 8% for
634 All the costs summarized in this chapter are calculated in the spreadsheet, "Estimated Fuel Costs for Set 2
Proposed Rule," available in the docket for this action.
635 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.
636 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;.
637 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.
638 Wyborny, Lester. "Cost Estimates of Long-Term Options for Addressing Boutique Fuels," EPA, October 22,
2001.
639 EPA, "Final Regulatory Analysis - Control of Emissions from Nonroad Diesel Engines," EPA-420-R-04-007,
May 2004.
640 RFS2 Rule RIA.
641 Peters, Klaus D., Max S. Timmerhaus, and Ronald E. West. Plant Design and Economics for Chemical
Engineers. 5th ed. McGraw Hill, 2003.
396
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industrial processes.642 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 Utility and Fuel 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
AEO2023. 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.643 644 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 (2022$)
Year
Natural Gas
($/MMBtu)
Electricity
(jif/kWh)
Water
(S/1000 gals)
2025
4.48
6.88
3.0
2026
4.22
6.69
3.0
2027
4.13
6.56
3.0
2028
4.15
6.49
3.0
2029
4.21
6.48
3.0
10.1.2.2 Corn Ethanol Production Costs
Corn ethanol plant input and output information were based on a 2019 survey of corn
ethanol plants, although some plant information was sourced from an older analysis.645 646
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
642 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.
643 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.
644 DOE, "Water and Wastewater Annual Escalation Rates for Selected Cities across the United States," September
2017. https://doi.org/10.2172/1413878.
645 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.
646 Mueller, Steffen. "2012 Corn Ethanol: Emerging Plant Energy and Enviromnental Technologies." April 29,
2013.
397
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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.647 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.648 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.
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. 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.649 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.
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).
647 Irwin, Scott. "Weekly Output: Ethanol Plants Remain Barely Profitable," Successful Farming. March 16, 2018.
https://www.agriculture.com/news/business/weeklY-outlook-ethanol-plants-remain-barelY-profitable.
648 Edwards, William. "Estimating Grain Transportation Costs," Ag Decision Maker File A3-41, August 2017.
https://www.extension.iastate.edu/agdm/crops/litml/a3 -41 .html.
649 Shurson, Jerry. "DDGS present handling and storage considerations," National Hog Farmer. May 29, 2019.
https://www.nationalhogfanner.com/hog-nutrition/ddgs-present-handling-and-storage-considerations.
398
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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.
Table 10.1.2.2-3: Corn Ethanol Plant Demands, Production Levels, and Capital Costs for
2026 (2022$)
Category of Plant
Plant
Cost
Cost
Input/Output
Inputs/Outputs
Cost per Input
(MM$)
($/gal)
Ethanol Yield
2.86 gal/bushel
$3.98/bushel
106
1.39
DDG Yield
4.4 lb/gal
$132.8/ton
-22.2
-0.29
Corn Oil Yield
0.27 lb/gal
380/lb
-7.9
-0.10
C02 Yield
1 lb/gal
$12/ton
Thermal Demand
22,480 Btu/gal
$4.32/MMBtu
7.1
0.09
Electricity Demand
0.63 kWh/gal
6.880/kwh
3.3
0.04
Water Use
2.7 gal/gal
$3/1000 gals
0.6
0.01
Labor Cost
$0.07/gal
-
5.3
0.07
Capital Cost (2022$,
76 million Gals/Yr)
$3.27/gal Plant
Capital Cost
32.5
0.43
Annual Fixed Cost
5.5% of Total
Capital Cost
16.3
0.21
Denaturant
2 vol%
0.6
0.01
Total Cost
138
1.83
The projected corn ethanol social production cost for an 85 million gallon capacity
ethanol plant producing 76 million gallons per year of ethanol is $1.83 per gallon of denatured
ethanol for 2026, $1.80 for 2027, $1.78 for 2028, $1.76 for 2029 and $1.72 for 2030. The
downward trend in estimated per-gallon production costs reflect the expected downward trend in
corn prices.
10.1.2.3 Biodiesel Production Costs
Biodiesel production costs for this rule were estimated using an ASPEN cost model
developed by USD A for a 38 million gallon-per-y ear transesterification biodiesel plant
processing degummed soybean oil as feedstock. Details on the model are given in a 2006
399
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technical publication by Haas.650'651 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.652
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 of 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 in 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 2022 dollars using a ratio of the capital cost index from the Chemical
Engineering Cost Index. This adjustment increased installed capital cost from $11.9 million to
650 Haas, M.J, A process model to estimate biodiesel production costs, Bioresource Technology 97 (2006) 671-678.
651 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.
652 Haas, M.J, A process model to estimate biodiesel production costs, Bioresource Technology 97 (2006) 671-678.
400
-------
$14.5 million. Fixed operating costs are estimated to comprise 5.5% of the plant cost. Prices
were found for methanol,653 sodium methoxide,654 hydrochloric acid,655 sodium hydroxide,656
and glycerine.657 658 The value of methanol is from a Methanex report, plus 150 added on for
distribution costs.659 Prices for sodium methoxide, hydrochloric acid, and sodium hydroxide are
all bulk prices from a chemicals supplier.660
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.661 We can
expect that new uses for glycerin will continue to be found as long as it is plentiful and cheap.
We use recent cost information of about 250 per pound for glycerine.
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.
653 Methanex, "Methanex Methanol Price Sheet," January 31, 2023. https://www.methanex.com/about-
methanol/pricing.
654 Alibaba, "Food Grade Purity 28%-31% Colorless Transparent Sodium Methoxide," February 2023.
https://www.alibaba.com/product-detail/Food-Grade-Puritv-28-31-Colorless 1600468349215.html.
655 ChemAnalyst, "Hydrochloric Acid Price Trend and Forecast," February 2023.
https://www.chemanalYSt.com/Pricing-data/liYdrocliloric-acid-61.
656 eBioChem, "wholesale Caustic Soda; Sodium Hydroxide," February 2023.
http://www.ebiochem.coni/product/caustic-soda-sodium-hYdroxide-16515.
657 Alibaba, "Competitive Price 99.7% Refined Food/USP/Industry Grade Glycerol Glycerine," February 2023.
https://www.alibaba.coni/product-detail/Competitive-Price-80-99-7-Refined 1600713799582.html.
658 Irwin, Scott. "2021 Was a Devastating Year for Biodiesel Production Profits." farmdoc daily (12):21, February
16, 2022. https://farmdocdailv.illinois.edu/2022/02/2021-was-a-devastating-vear-for-biodiesel-production-
profits.html.
659 Methanex, "Current Posted Prices," January 31, 2023. https://www.methanex.coni/about-methanol/pricing.
6611 https://www.alibaba.com.
661 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.
401
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Table 10.1.2.3-1: Soy-1
Jiodiesel Production Cost for 2026 (2C
22$)
Unit Demands
Cost per Unit
Cost (MM$)
Cost ($/gal)
Soybean Oil Feed
76,875 (1000 lb)
46.40/lb
35,215
3.52
Methanol
7422 (1000 lb)
$1.88/gal
2,170
0.22
Sodium Methoxide
927 (1000 lb)
$800/ton
371
0.037
Hydrochloric Acid
529 (1000 lb)
$150/MT
36.1
0.004
Sodium Hydroxide
369 (1000 lb)
$420/ton
77.5
0.008
Water
2478 (1000 lb)
$3/1000 gals
1.2
0.00
Glycerine
9000 (1000 lb)
240/lb
(2160)
(0.22)
Natural Gas
66.9 million SCF
4.32 $/MMBtu
289
0.029
Electricity
1008 kW
6.88 0/kWh
607
0.061
Labor
0.05
Capital Cost 2006$
11.35 ($million)
-
-
-
Capital Cost 2022$
18.54 ($million)
2,039
0.20
Fixed Cost
5.5%
1,019
0.10
Total Cost
40,130
4.02
As shown in Table 10.1.2.3-1, biodiesel produced from soybean oil is estimated to cost
4.060 per gallon in 2026. The estimated biodiesel production cost for all vegetable oil types and
for all five years is summarized in Table 10.1.2.3-2.
Table 10.1.2.3-2: Summary of Estimated Biodiesel Production Costs ($/gal)
Year
Soy Oil
Corn Oil
FOG
2026
4.02
3.41
3.15
2027
3.64
3.09
2.86
2028
3.51
2.98
2.76
2029
3.36
2.87
2.66
2030
3.22
2.75
2.55
As depicted in the table, there is a substantial production cost decline from 2026-2030,
and this is almost entirely due to the declining vegetable oil prices summarized in Table 10.1.1.2-
2.
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
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 cracking that occurs over the hydrotreating catalyst. Besides the renewable diesel
402
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product, propane (light gas output), water, and carbon dioxide are also produced. The byproducts
created from that first reactor are separated from the renewable diesel in a separation unit.
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.662 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 2024.663 The capital cost for this smaller
renewable diesel fuel plant is estimated to be $768 million.
In addition to feedstock and facility costs, another significant cost input is hydrogen. We
used an estimate provided by Duke Biofuels for our hydrogen consumption estimate for
producing renewable diesel. On average, vegetable and waste oil feedstocks require 2,000
SCF/bbl of feedstock processed.664 Hydrogen costs are estimated based on a 50 million SCF/day
steam methane reforming hydrogen plant, adjusted to represent a 32 million SCF/day plant,
which would be the quantity of hydrogen required for a typical sized 220 million gallon per year
renewable diesel plant.665
Table 10.1.2.4-1: Hydrogen Plant Costs for 2026
Unit Demands for a 50
MM SCF/day plant
Cost per Unit
Cost for a 32
MMSCF/day plant
MM$
S/MSCF
Feed Natural Gas
730 MMBtu/hr
$4.32/MMBtu
17.7
1.51
Fuel Gas for Heat
150 MMBtu/hr
$4.32/MMBtu
3.6
0.31
Power
1200 KW
6.880/kWh
0.5
0.04
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
-3.8
-0.32
Capital Cost
$70 MM in 2016
For a 50 MMSCF/
day plant
$81 MM in 2022
For a 32 MMSCF/
day plant
8.9
0.76
Fixed Cost
6.7%
5.4
0.46
Total Cost
33.5
2.87
Based on our cost analysis, hydrogen is estimated to cost $2.90/MSCF in 2026. If
renewable fuel producers elect to produce and use renewable hydrogen as a feedstock to their
662 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-
technology-helps-diamond-green-diesel-become-one-of-the-worlds-largest-renewable-diesel-plants.
663 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.
664 Conversation with Mike Ackerson, Duke Biofuels, May 2020.
665 Meyers, Robert A. Handbook of Petroleum Refining Processes, Fourth Edition. McGraw-Hill Education, 2016.
403
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renewable diesel plant, 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 is higher
than fossil natural gas, thus the hydrogen produced from the biogas would also be more
expensive. Also, the cost of producing hydrogen from electrolysis is more expensive than steam
methane reforming of natural gas.666
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.667 Despite the age of the
reference, the underlying chemistry is unlikely to have changed appreciably.
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
We derived a cost of 6.90 per gallon of renewable diesel product to cover other costs:
utilities, labor, and other operating costs.668 Finally, the total cost per gallon was estimated at
$4.64. Table 10.1.2.4-3 provides more details for the process assumed in this analysis and
summarizes the total and per-gallon costs for the year 2026.
Table 10.1.2.4-3: Renewable Diesel Production Cost Estimate for a Greenfield 220 Million
Gallons/Yr Plant Processing Soy Oil in 2026 (2022$)
Stream
Estimated value
MM$/yr
$/gal
Soy Oil input
198 MMgals/yr
460/lb
697
3.52
Naphtha output
11.8 MMgals/yr
1.810/gal
(20.4)
(0.10)
Light fuel gas output
11.8 MMgals/yr
173.70/gal
(20.4)
(0.09)
Hydrogen input
4,760 SCF/100 gals
$2.87/MSCF
27.1
0.14
Other Operating Costs
15.2
0.07
Capital Costs (2022$)
$1,052 million
115.7
0.58
Fixed Costs
5.5%
57.8
0.29
Total Costs
1020
4.40
The estimated renewable diesel production cost for all vegetable oil types and for all the
years analyzed is summarized in Table 10.1.2.4-5.
666 Congressional Research Service, "Hydrogen Production: Overview and Issues for Congress," R48196, October 3,
2024. https://www.congress.gov/crs-product/R48196.
667 Holmgren, Jennifer, Chris Gosling, Rich Mariangeli, Terry Marker, Giovanni Faraci, and Carlo Perego. "A New
Development in Renewable Fuels: Green Diesel," National Petrochemical & Refiners Association AM-07-10, 2007.
668 Estimated based on the utility cost for an FCC naphtha hydrotreater. EPA, "Control of Air Pollution from Motor
Vehicles: Tier 3 Motor Vehicle Emission and Fuel Standards Final Rule - Regulatory Impact Analysis," EPA-420-
R-14-005, March 2014. https://nepis.epa.gov/Exe/ZvPDF.cgi/P100ISWM.PDF?Dockev=P100ISWM.PDF.
404
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Table 10.1.2.4-5 Summary of Estimated Renewable Diesel Production Costs ($/gal)
Year
Soy Oil
Corn Oil
FOG
2026
4.40
3.79
3.54
2027
4.02
3.48
3.25
2028
3.89
3.37
3.15
2029
3.78
3.29
3.08
2030
3.64
3.17
2.97
As depicted in the table, there is a substantial production cost decline from 2026-2030,
and this is almost entirely due to the declining vegetable oil prices summarized in Table 10.1.1.2-
2.
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 for doing so using vegetable oil and animal
fats 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. 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 from the diesel
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 only
make a rough cut between the two hydrocarbon cuts. This type of distillation column would not
be designed to boil off much 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 a fewer number of trays and therefore not be very
tall. EPA obtained cost information from Mobil Oil for such a column for the 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.669
669 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.
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The capital and operating cost information, and cost to 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 costs are for a 50 thousand bbl/day unit from the
year 2000; thus it was necessary to adjust the costs to 2022 dollars and base it on 14.4 thousand
barrels per day (220 million gallon per year). A 20% factor is added on for contingency costs,
and a 40% factor is added on to cover 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
Annual Cost
(MM$)
Cost to Jet Fuel
(j^/gal)
Capacity Basis
50,000 bbl/day
Capital Cost 2000$
$4.1 million
Capital Cost for a
14.5 bbl/day unit
$2.2 million
0.24
0.54
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 diesel from soy
oil in 2026 as $4.40 per gallon. Thus, adding a distillation column at a renewable diesel
production facility is estimated to result in a jet fuel production cost of $4.44 per gallon.
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.670 A hydrocracker can produce a range of products depending on the
operating conditions and catalyst used. For this analysis, a hydrocracker processing waste
cooking oil is estimated to produce 64% jet fuel, 20% naphtha, 6% light hydrocarbons, and 10%
670 Meyers, Robert A. Handbook of Petroleum Refining Processes, Fourth Edition. McGraw-Hill Education, 2016.
406
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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.671 The mild hydrocracking reaction is estimated to cause the product to swell
3 volume-percent 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
Annual Cost
(MM$)
Cost to Jet
Fuel (^/gal)
Capacity Basis
14.4 kbbl/day
Capital Cost 2016$
$5000/bbl/day
Capital Cost 2022$
$98 million
10.7
8.2
Fixed Cost
5% of capital
2.9
3.7
Feedstock
12,916 bbl/day
$4.40/gal
872.8
667.9
Hydrogen
250 SCF/bbl
$2.87/MSCF
3.38
2.6
Natural Gas
90,000 Btu/bbl
$4.48/MMBtu
1.90
1.5
Electricity
8.4 kWh/bbl
6.88 0/kWh
2.72
2.1
Cooling Water
2 gal/bbl
$3/1000 ft3
0.03
0.22
Steam Export
10 lb/bbl
$4.48/MMBtu
-0.16
-0.1
Byproducts
Other
798
$1.74/gallon
-21.3
-16.3
Naphtha - Low
Naphtha - High
2,661
$1.74/gallon
$4.60/gallon
-70.9
-179.6
-54.3
-137.6
Renewable Diesel
1,330
$4.40/gallon
-89.8
-68.8
Total Cost - Naphtha Low
713.4
546.6
Total Cost - Naphtha High
604.7
463.3
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 per gallon
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 230 per gallon above the assumed renewable
671 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/s42269-020-00425-6.
407
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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 and 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 alcohol to jet fuel based on a study which developed
an Aspen Plus technical model for the cost analysis. The model assumed a smaller sized 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 ethanol-to-jet
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 alcohol to jet process.
Table 10.1.2.5.3-1 Estimated Cost to Convert
Corn Ethanol to Renewable Jet Fuel
Cost to Jet and
Annual Cost
Diesel Fuels
(MM$)
(j^/gal)
Capacity Basis
Capital Cost 2022$
$21.0 million
2.3
0.24
Fixed Cost
6% of capital
1.3
0.13
Corn Ethanol
185 tons/day
(20.5 million gals/yr)
$1.83/gal
37.4
3.82
Hydrogen
1.1 tons/day
$2.9/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
$1.74/gal
-1.9
-0.19
Diesel
2.26 million gal/yr
-
Jet Fuel
7.55 million gal/yr
-
Total cost to jet and
diesel (2900 bbl/day)
42.8
4.36
408
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The cost analysis estimates that producing jet fuel from corn ethanol using the alcohol to
jet process costs $4.36/gallon. This is slightly less expensive than producing jet fuel from
soybean oil, although more expensive than producing jet fuel from corn and used cooking oil.
10.1.2.6 Biogas
Biogas is the result of anaerobic digestion of organic matter, including municipal waste,
manure, agricultural waste, and food waste.672 The primary product of this anaerobic digestion of
waste 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.673
The largest source of biogas, which is already being collected to avoid releasing methane
into the environment, is from landfills.674 Since landfill gas is the largest source of biogas
available for the motor vehicle fleet, this cost analysis makes the simplifying assumption that the
biogas will solely be provided by landfills.
While in some cases biogas can be used in local fleet vehicles which are operated at the
landfill site, in most cases, a new pipeline would need to be constructed to transport the cleaned-
up biogas to a nearby common carrier pipeline. Gas 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 the biogas 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 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 using Version 3.5 of the Landfill Gas Energy
Cost Model (LFGcost-Web).675-676 The throughput volume of landfill gas was estimated to be
8,000 SCF/min, which is at the upper end of the range of production volumes from biogas
production facilities.677 The equations from the LFGcost-Web model for biogas clean-up and
interconnection 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 $0.09/MSCF.678 The estimated production and clean-
672 Wikipedia, "Biogas." https://en.wikipedia.org/wiki/Biogas.
673 LeFevers, Daniel. "Landfill Gas to Renewable Energy," Waste Management. April 26, 2013.
https://www.eesi.org/files/042613 Daniel LeFevers.pdf.
674 EIA, "Biomass explained - Landfill gas and biogas," November 19, 2024.
https://www.eia.gov/energyexplained/biomass/landfill-gas-and-biogas.php.
675 The current version of this model and user's manual are available at: https://www.epa.gov/lmop/lfgcost-web-
landfill-gas-energy-cost-model.
676 This cost estimate does not include the cost for complying with California's more stringent natural gas pipeline
specifications designed to address harmful contaminants in some sources of biogas.
677 The Coalition for Renewable Natural Gas, "Economic Analysis of the US Renewable Natural Gas Industry,"
December 2021. https://guidehouse.com/-/media/www/site/insights/energy/2022/guidehouse-esirng-coalition-final-
reportl22022.pdf.
678 EPA, "LFG Energy Project Development Handbook," January 2024, Chapter 4.
https://www.epa.gov/svstem/files/documents/2024-01/pdh full.pdf.
409
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up costs for landfills is summarized in Table 10.1.6.2-2. Distribution and retail costs are
estimated for biogas in Chapter 10.1.4.3.
Table 10.1.2.6-1: Biogas Cleanup Cost Information3
Cost Factors (2019$)
Interconnection
$400,000
Capital Costs
6,000000*e(0 00°3*SCF/min)
Operating and Maintenance
250 x SCF/min+148,000
Electricity Costs
0.009 kWh/SCF
a Excludes any new offsite pipeline costs and retailing costs.
Table 10.1.2.6-2 Biogas Collection and Cleanup costs
Cost
Cost
(MM$)
($/MMBtu)
Capital Cost
9.8
4.49
Operating and Maintenance
2.1
0.99
Electricity Costs
4.1
1.86
Interconnection
0.05
0.03
Total Clean-up Cost
16.1
4.62
Collection Cost
0.4
0.09
Collection and Clean-up Cost
16.5
7.36
The combined biogas collection and cleanup costs for a typical sized landfill amount to
$7.36 per million Btu.
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 is typically combusted to
produce the energy needed for the process.
We estimated the cost to produce sugarcane ethanol two different ways. The first way is
based on recent data on sugarcane ethanol prices which we receive in the EPA Moderated
Transaction System (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 to the cost of corn starch ethanol.
This is due to favorable growing conditions, relatively low-cost feedstock and energy inputs, and
other cost reductions gained from years of experience. The average of recent sugarcane ethanol
prices from EMTS was $2.73 per gallon. Other price data which EPA receives from OPIS
showed a similar average price which helps to corroborate the price data from EMTS.
The second way we estimated the cost of producing sugarcane ethanol is 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,
which is shown in Table 10.1.2.6-1, primarily relies on the analysis made for that study. The
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original cost estimate reported in the RFS2 Rule assumes an ethanol-dedicated mill and is based
off an internal rate of return of 12%, a debt/equity ratio of 50% with an 8% interest rate, and a
selling of surplus power at $57 per MWh. We revised the capital and operating costs higher by
63% to account for the effects of inflation from 2006 to 2022. When we estimated 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 and amortized the capital costs using our capital cost
amortization parameters. 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
($2022)
Capital Costs
($ million)
Investment cost in million$
97
158
Investment cost for sugarcane
production
36
59
Per Gallon
Costs ($/gal)
Operating & maintenance costs
0.26
0.42
Variable sugarcane production costs
0.64
1.05
Capital costs
0.49
0.64
Total production costs
1.40
2.11
Shipping Costs to U.S.
0.15
Delivered Cost
2.26
The average FOB ethanol price of $2.73/gallon in Brazil is somewhat higher than the
estimated sugarcane ethanol production cost of $2.26/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. When we
use a more typical 0.16 after-tax capital amortization factor used by industry, the per-gallon costs
increase to $2.55 per gallon. Normally we would use the bottom-up cost estimate; 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 dried
distiller grains (DDGS) can also be converted to ethanol. This additional ethanol from corn fiber
is considered cellulosic ethanol and earns D3 RINs. Historically, this cellulosic conversion step
of the fiber to ethanol was thought of as a separate step than the starch to ethanol conversion, and
therefore would require a separate reactor vessel and require additional operating costs.
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However, one or more companies have found 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 this single reactor design is what will be used to produce the cellulosic ethanol volumes in
the timeframe of this rulemaking.679-680 Anticipating that this cellulosic ethanol would be
produced in an existing starch to ethanol reactor provides a cost efficiency which 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.681
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. Since we
already estimate the capital, fixed, and variable operating cost of producing ethanol from corn
starch, we simply apply those same cost estimates to the corn fiber ethanol. There are other cost
factors to consider, which are the potential cost for the additional enzyme added to convert corn
fiber to ethanol, and a cost savings due to increased corn oil production.682 It appears that the
cost of the additional enzyme is approximately equally offset by the cost savings of additional
corn oil production. Therefore, we simply use the cost for producing ethanol from corn starch for
the cost of producing ethanol from cellulosic ethanol.
10.1.3 Blending and Fuel Economy Cost
Certain renewable fuels, namely gasoline, 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 El0, El5, 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 Reid Vapor
Pressure (RVP) when blended into gasoline at low concentrations, and is low in energy content
relative to the gasoline pool that 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
679 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.
6811 National Corn to Ethanol Research Center, "Conversion of Corn-Kernel Fiber in Conventional Fuel-Ethanol
Plants," Project No. 0340-19-03, November 11, 2018.
681 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.
682 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.
412
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into (reformulated gasoline (RFG) versus conventional gasoline (CG), winter versus summer
gasoline, premium versus regular, and blended at 10% versus E15 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. However, refiners are 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
E10. 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 this 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, although 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.683 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.684 Due to the options
available to refiners to replace ethanol's octane, ICF/Mathpro ran two ethanol replacement cases.
In the lower per-gallon 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.
ICF/Mathpro modeled a second case. Instead of relying on large butane purchases for
producing alkylate, the model increased the throughput to, and turned up the 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, 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. 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
683 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.
684 EPA's contract was with ICF Incorporated, LLC, which in turn retained Mathpro for some aspects of the work.
413
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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 and
ethanol's marginal costs for two ethanol removal cases: different gasoline types and refinery
regions. For the two ethanol removal cases the refinery modeling for both the reference case (all
gasoline with ethanol) and the Low Biofuel cases (conventional gasoline without ethanol), which
replaced ethanol in the gasoline pool with refinery sourced alternatives, Low Biofuel #1 is the
reformate-centric case while Low Biofuel #2 is the alkylate-centric case. The lower marginal
values for PADD 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.
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
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
PADD 1
Reg
91.45
81.35
115.98
105.97
CG
Prem
92.68
83.89
123.02
100.87
Reg
88.93
81.35
136.43
105.88
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
PADD 5
Reg
91.61
82.01
62.46
97.37
CG
Prem
77.63
83.00
118.14
98.01
Reg
73.38
81.12
126.14
97.68
414
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The gasoline-ethanol difference in marginal values is calculated and summarized in Table
10.1.3.1.1-2.
Table 10.1.3.1.1-2: Marginal Ethanol Replacement Cost by Gasoline Type and Season
PADD of
Gasoline
Origin
Type
Grade
Low Bi(
Reformat
)fuel #1
e-centric
Low Biofuel #2
Alkylate-centric
Summer
Winter
Summer
Winter
PADD 1
RFG
Prem
30.07
40.35
0
0
Reg
58.41
58.62
0
0
CG
Prem
72.23
40.43
0
0
Reg
113.10
58.42
0
0
PADD 2
RFG
Prem
105.56
68.08
60.39
35.55
Reg
144.23
86.31
90.61
52.00
CG
Prem
151.27
69.44
98.08
35.73
Reg
187.51
86.52
126.41
51.15
PADD 3
RFG
Prem
86.35
39.08
78.77
27.29
Reg
125.74
52.52
117.69
43.07
CG
Prem
119.78
38.93
108.86
26.50
Reg
159.07
51.68
147.69
42.38
PADD 4
CG
Prem
132.70
90.86
167.45
62.16
Reg
170.67
116.41
216.07
83.19
Low
RVP
Prem
100.19
0.00
135.02
0.00
Reg
123.27
0.00
168.77
0.00
PADD 5
RFG
Prem
-140.97
29.46
0
0
Reg
-69.39
36.56
0
0
CG
Prem
96.44
35.73
0
0
Reg
125.61
39.43
0
0
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 since the ethanol was only replaced in the
conventional portion of the gasoline pool in the study. 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 (c/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 for RFG
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
415
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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.
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 time 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.685 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
reformulated gasoline (RFG) as well, so we assumed that they were the same for RFG.686
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.
685 California relies on ethanol blended at 10% for compliance with its LCFS program; thus, removing E10 ethanol
from California gasoline is an unlikely possibility.
686 Both RFG and CG must meet many of the same gasoline property specifications, including sulfur and benzene, as
well as ASTM D4814.
416
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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. Therefore, for
evaluating the cost of using ethanol in gasoline at 10%, the lower cost, alkylate-centric cost of
68.650 per gallon was used for ethanol's blending cost for ethanol blended as E10. This 68.650
per gallon cost represents ethanol's average nationwide blending replacement cost in U.S.
gasoline. This 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. Furthermore, the congressional 1 psi RVP waiver which applies for blending
E10 gasoline in summer conventional gasoline does not apply to blending E15, 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 El5 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 2022-2024, EPA granted numerous emergency waivers to allow El 5 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 2025.687 As a result, the same lower RVP, higher cost gasoline blendstock is
required for both E10 and El5 ethanol blends in summertime conventional gasoline in those
states.
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
687 8 9 FR 14760 (February 29, 2024).
417
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lower octane BOB specially designed for producing E15 instead of E10.688 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%).689 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 than what it
can be priced at. In the summer months, El5 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 called natural gas liquids (NGL). The
corn ethanol plants add an additional quantity of the NGL, 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 on average over both the summer and winter, instead of 85%, to have
sufficiently high RVP to avoid RVP minimum limits.690
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).691 Accounting for ethanol's lower energy density adds about $1 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.
688 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.
689 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.
6911E85 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.
691 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.
418
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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.692 693 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.694 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.
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 E15.
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
692 Farm Energy, "Animal Fats for Biodiesel Production" January 31, 2014. https://farm-
energv.extension.org/animal-fats-for-biodiesel-production.
693 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.
694 ICF, "Modeling a 'No-RFS' Case," EPA Contract No. EP-C-16-020, July 17, 2018.
419
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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.695 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
(i.e., Colorado and Wyoming), and by manifest train for the adjacent areas further out (i.e., 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.696 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
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. A simple analysis revealed that each
unit train receiving terminal must then service, on average, an area of 32 thousand square miles
(equivalent to a 180 x 180 miles) to make the ethanol available to the various terminals in the
area. 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, and as revised to remove the 70 per gallon
transportation cost.
695 Id.
696 EIA, "Rail congestion, cold weather raise ethanol spot prices," Today in Energy, April 3, 2014.
https://www.eia. gov/todavinenergy/detail.php?id= 15691.
420
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Table 10.1.4.1.1-1: Ethanol Distribution Costs for Certain Cities or Areas (2017$)
Distribution Cost (^/gal) to:
Location
Hub/Terminal
Total (^/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 2022 dollars.
Table 10.1.4.1.1-2: Average Ethanol Distribution Cost by PADD and the U.S. (2022$)
Gasoline Volume
Average Ethanol
Distribution Cost
Region
(kgals/day)
(j^/gal)
PADD 1
123,700
22.0
PADD 2
102,400
11.0
PADD 3
68,500
15.5
PADD 4
15,100
17.2
PADD 5
63,400
24.4
U.S. Average (2017$)
373,100
18.1
U.S. Average 2022$
20.1
10.1.4.1.2 Retail Costs
The infrastructure at retail needed to make E10 available has been in place for many
years. As a result, no additional retail costs are assumed for E10. However, this is not the case
for E15 and E85. Additional investments are needed to make them available at retail. The E15
and E85 volumes that we are using in this cost analysis are summarized in Chapter 6.5.2.
The retail costs for El 5 and E85 are estimated based on the investments that are needed
to be made to offer such ethanol blends. To this end, we reviewed literature and conferred with
421
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EPA's Office of Underground Storage Tanks on what might be considered "typical" for El 5 and
E85 equipment installations for a typical sized retail station selling these blends.697-698 699 700 For
the typical retail station revamp to sell El5, the station is assumed to have an underground
storage tank already compatible with El5 that it would convert over to store El5, but would still
require 4 new dispensers to dispense the E15. Each dispenser is estimated to cost $20,000 for a
total cost of $80,000 (assuming only 4 dispensers for a retail outlet), and this cost per dispenser
increases to $29,500 when adjusted to 2022 dollars.701 In addition, these retail stations are
assumed to invest in additional equipment changes to make their hardware compatible with El5
(e.g., pipes, pipe connectors, sealants including pipe dope and elastomers, pumps, and hardware
associated with underground storage tanks) at a cost of $15,000. Thus, the total investment for a
typical retail station revamp is $132,900.
The E85 stations are also assumed to have an existing underground storage they could
use for storing E85, but they would require some equipment modification to allow the very high
ethanol concentration to be stored in that tank and other equipment. The E85 station would also
be required install a new E85-compatible dispenser, costing $29,500, for a total cost of $40,500
(assuming only one dispenser at a retail outlet is provided for E85).702
Retail stations can incur costs which are higher or lower than the retail revamp costs we
estimate for offering El 5 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 the retail station needs the new dispensers and also needs to install a separate
storage tank 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 representative costs for this large cost range.
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
697 Moriarty, K., and J. Yanowitz. "E15 And Infrastructure," National Renewable Energy Laboratory, NREL/TP-
5400-64156. May 21. 2015. https://doi.org/10.2172/1215238.
698 EPA, "E15's Compatibility withUST Systems," January 2020. https://www.epa.gov/sites/default/files/2020-
01/documents/el5-ust-compatibilitv-statement-l-23-20.pdf.
699 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.
71111 Conversations with Ryan Haerer, Office of Underground Storage Tanks; Spring 2022.
7111 Renkes, Robert. "Scenarios to Determine Approximate Cost forE15 Readiness," Petroleum Equipment Institute,
September 6, 2013.
7112 Because only a small percentage of the motor vehicle fleet is comprised of FFVs that can refuel on E85, typically
a retail station only offers E85 from a single dispenser at the retail station.
422
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data collected by USDA through their BIP program and made available to EPA.703 The total
volumes of E15 and E85 sold were divided by the estimated number of E15 and E85 retail
stations to estimate the volume per retail station. As a result, retail stations offering El5 are
estimated to sell 181 thousand gallons of E15 per year while retail stations offering E85 are
estimated to sell 39 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 inE15 andE85
(15% for El 5 and 74% for E85), covering the cost of capital for the retail equipment adds 430
and 90 per gallon to the ethanol portion of El 5 and E85, respectively. When solely amortizing
this retail cost solely over the 5% and 64% of ethanol that is incremental to E10, the cost is $1.28
and 100 per gallon of ethanol in El 5 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.
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.704 While biodiesel production is more spread out across the country than ethanol, a
significant amount must still be moved long distances to match the production to the 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 are 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 2022 dollars.
7113 "Communication with USDA on the BIP program 1-19-22," Docket Item No. EPA-HQ-OAR-2021-0324-0734.
https://www.re gulations. gov/document/EPA-HO-QAR-2021-0324-0734.
704 ICF, "Modeling a 'No-RFS' Case," EPA Contract No. EP-C-16-020, July 17, 2018.
423
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Table 10.1.4.2-1: Estimated Biodiesel and Renewable Diesel Fuel Distribution Cost by
PADD (2022$)
Destination
PADD Total
Location
Transportation Cost (^/gal)
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 2022$
19.7
10.1.4.3 Renewable Natural Gas (RNG)
10.1.4.3.1 Distribution Costs
RNG, which is gathered from landfill off-gassing and cleaned up, must then be
transported to where it can be used. Typically, 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 not in, urban areas to keep the transportation
costs lower for hauling the waste to the landfill. The landfill gas is estimated to be moved 5 miles
to access a commercial natural gas pipeline. For installing each mile of pipeline, it is estimated to
cost $1 million, and adds up to $6.7 million in 2022 dollars for the entire 5 mile pipeline.705 A
typical volume case was modeled of 600 SCF of renewable biogas being captured per minute to
estimate the cost for a typical sized landfill.706 When the pipeline capital costs are amortized over
that typical volume of cleaned up landfill gas, the pipeline capital cost is estimated to be $1.89
per million Btu.707 If the biogas generation 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 renewable natural gas 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 located near
urban areas which are destination areas for natural gas pipelines. This means that the distribution
costs for RNG in the natural gas pipeline would be less than that for natural gas which is being
7115 EPA, "LFGcost-Web - Landfill Gas Energy Cost Model." https://www.epa.gov/lmop/lfgcost-web-landfill-gas-
energy-cost-model.
7116 The Coalition for Renewable Natural Gas, "Economic Analysis of the US Renewable Natural Gas Industry,"
December 2021. https://guidehouse.com/-/media/www/site/insights/energy/2022/guidehouse-esirng-coalition-final-
reportl22022.pdf.
7117 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.
424
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distributed longer distances from natural gas production areas. 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 biogas 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 $5.58/MSCF. 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 $2.40/MSCF, is assumed to apply to
biogas for distribution to the natural gas pipeline.708
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.709
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.710
When adjusted to 2022 dollars, the estimated retail cost to dispense RNG is estimated to be $6.53
per million Btu.
708 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.
709 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.
710 Clean Fuel Connection, Inc. "Permitting CNG and LNG Stations, Best Practices Guide for Host Sites and Local
Permitting Authorities."
425
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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
demand for gasoline and diesel fuel is stable or somewhat in decline.711 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 AEO2023.712 The projected Brent crude oil prices and
gasoline and diesel fuel wholesale prices in 2026-2030 are summarized in Table 10.2.1.1-1.
Table 10.2.1.1-1: Estimated Gasoline Production Costs
2026
2027
2028
2029
2030
Brent Crude Oil Prices ($/bbl)
87.9
88.3
88.9
89.47
90.2
Wholesale Prices (Assumed to
be Production Costs) ($/gal)
Gasoline
2.24
2.22
2.23
2.24
2.25
Diesel Fuel
2.80
2.68
2.58
2.59
2.60
Since the EIA models much of the RFS program in its AEO modeling, some price impact
of the RFS program is likely represented in these wholesale gasoline and diesel fuel prices. The
AEO models the most recent RFS standards, so these wholesale price estimates 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.
711 AEO2023, Table 12 - Petroleum and Other Liquid Prices, and Table 57 - Component of Selected Petroleum
Product Prices.
712 AEO2023, Table 57 - Components of Selected Petroleum Product Prices.
426
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10.2.1.2 Natural Gas Production Cost
For estimating the cost of biogas 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 AEO2023, EIA projects the natural gas spot price for Henry Hub to
average $3.07/MSCF in 2026 and decrease somewhat in the following years.713 The Henry Hub
spot price most closely represents the natural gas field price, and thus is a proxy for its
production cost.
10.2.2 Gasoline, Diesel Fuel and Natural Gas Distribution and Blending Cost
10.2.2.1 Gasoline and Diesel Fuel
Gasoline and diesel fuel distribution costs from refineries to terminals are estimated as
the difference between wholesale prices and terminal prices (which we estimated based on
historical sales-for-resale prices). This results in estimated gasoline and diesel fuel distribution
costs to the terminal of 50 and 80 per gallon, respectively.
We also estimated the distribution costs from terminals to retail stations, which also
contains the retailing costs. To do so, we first calculated the retail costs of gasoline, less taxes.
We calculated this by subtracting average federal and state taxes, which are 550 per gallon for
gasoline and 640 gallon for diesel fuel, from historical gasoline and diesel fuel retail prices.
Then, we calculated the difference between historical retail prices (less taxes) and historical
terminal prices (estimated as sales for resales prices) to estimate the distribution costs from the
terminal to retail and retail costs. Price data for 2017 to 2019 was used to estimate the
distribution and retail costs—these years were chosen because they avoided the price distortions
associated with the Covid-19 pandemic and war in Ukraine. The resulting distribution costs for
gasoline and diesel fuel are 50 and 80 per gallon, respectively. The retail costs for gasoline and
diesel fuel are estimated to be 200 and 400 per gallon, respectively. These various prices and
estimated costs are summarized in Table 10.2.2.1-1.
Table 10.2.2.1-1: Estimated Gasoline and Diesel Fuel Distribution and Retail Costs ($/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 2022 dollars, to the projected wholesale gasoline and diesel fuel prices in Table 10.2.1.1-1 for
713 AEO2023, Table 13 - Natural Gas Supply, Disposition, and Prices.
427
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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.
Table 10.2.2.1-2: Projected Gasoline ant
Diesel Production Costs ($/gal)
2026
2027
2028
2029
2030
Brent Crude Oil Prices
87.9
88.3
88.9
89.47
90.2
Gasoline
Retail Cost minus taxes
2.52
2.50
2.51
2.52
2.53
Terminal and Retail Costs
0.22
0.22
0.22
0.22
0.22
Terminal Costs
2.30
2.28
2.29
2.30
2.31
Distribution Cost
0.06
0.06
0.06
0.06
0.06
Production Cost
(from Table 10.2.1.1-1)
2.24
2.22
2.23
2.24
2.25
Diesel Fuel
Retail Cost minus taxes
3.31
3.19
3.09
3.10
3.11
Terminal and Retail Costs
0.43
0.43
0.43
0.43
0.43
Terminal Costs
2.88
2.76
2.66
2.67
2.68
Distribution Cost
0.08
0.08
0.08
0.08
0.08
Production Cost
(from Table 10.2.1.1-1)
2.80
2.68
2.58
2.59
2.60
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.714 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.715
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 AEO2023 were subtracted from the commercial prices for
2026-2030. 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 biogas, we also add $6.53 per million Btu for
retail costs.716
714 AEO2023, Table 13 - Natural Gas Supply, Disposition, and Prices.
715 Taxes are not included in social cost estimates because they are not true costs, only transfer payments.
716 Clean Fuel Connection, Inc. "Permitting CNG and LNG Stations, Best Practices Guide for Host Sites and Local
Permitting Authorities.
428
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Table 10.2.2.2-1: Natural Gas
)istribution Cost ($/
MSCF)
2026
2027
2028
2029
2030
Commercial Prices
8.79
8.57
8.47
8.51
8.54
Henry Hub Prices $/MMBtu
$/MSCF
3.07
2.96
2.85
2.75
2.80
2.70
2.83
2.72
2.91
2.81
Pipeline Distribution Costs
5.83
5.83
5.82
5.77
5.79
Retail Station Costs
6.53
6.53
6.53
6.53
6.53
Total Average Distribution &
Retail Station Costs
12.11
12.11
12.11
12.11
12.11
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. 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
El 5 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 Diesel
122,887
0.960
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.
To account for the fuel economy effect for 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. However, 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. The fuel economy effect is therefore inherent in the cost analysis and is not reported
out separately.
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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
Costs are presented in several different ways. First, a per-gallon, individual renewable
fuels cost summary presents our analysis of each renewable fuel relative to the fossil fuel being
displaced.
Second, costs are presented for the Proposed Volumes and the Volume Scenarios, each
relative to the No RFS and 2025 Baselines. For each case, we first present the change in volume
for each renewable fuel and the fossil fuel it displaces. Then we present the costs for those
volume changes by cost category (production, distribution, 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.
In addition, to estimate the per-gallon cost on the total gasoline, diesel, and natural gas
pools, the projected total volumes for each of these fuels was obtained from AEO2023 and
summarized in Table 10.4-1.
Table 10.4-1: Total Gasoline, Diesel Fuel, and Natural Gas Volumes
2026
2027
2028
2029
2030
Units
Gasoline Volume
131.53
130.00
128.31
126.47
124.48
Billion gallons
Diesel Volume
52.43
51.97
51.66
51.20
50.74
Billion gallons
Natural Gas Volume
29.68
29.15
28.95
28.8
28.55
Trillion cubic feet
Source: AEO2023, 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) for the renewable fuels analyzed for this rulemaking
for the years 2026-2030. These costs do not account for the per-gallon federal cellulosic biofuel
and biodiesel 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 El5.717 A separate line item is added for
El 5 and E85 which only adds in V2 of the retail cost to help illustrate the impact that the HBIIP
program would have on the costs for these fuels. The costs of renewable fuels, other than biogas,
are primarily influenced by the feedstock costs, which can vary significantly depending on a
717 USD A, Higher Blends Infrastructure Incentive Program, https://www.rd.usda. gov/hbiip.
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wide range of factors domestically and internationally, especially since many of them are also
agricultural commodities.
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 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 therefore must be accounted for in a
social cost analysis. The cost associated with the impact of renewable fuels on fuel economy
costs are determined relative to the fuels they are assumed to displace; ethanol displaces
gasoline, biodiesel and renewable diesel displace diesel fuel, and RNG displaces natural gas.718
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 for the volumes and
timeframe of this action, and so is not included in this cost analysis.
The cost shown for RNG in two different units. The first is RNG dollars per million Btu
and dollars per ethanol-equivalent gallon. Table 10.4.1-1 is divided into two subparts, "a," "b"
and "c."
718 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.
431
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Table 10.4.1-la: Renewable Fuels Production Costs Estimated for 2026-2030 (2022$/gal
unless otherwise noted)
Production
Cost
2026
2027
2028
2029
2030
Corn Starch
Ethanol
E10
1.83
1.80
1.78
1.76
1.72
El5 w/ '/2 Retail Costs
1.83
1.80
1.78
1.76
1.72
El5 w/ Retail Costs
1.83
1.80
1.78
1.76
1.72
E85 w/ '/2 Retail Costs
1.83
1.80
1.78
1.76
1.72
E85 w/ Retail Costs
1.83
1.80
1.78
1.76
1.72
Biodiesel
Soy Oil
4.02
4.02
4.02
4.02
4.02
Corn Oil
3.41
3.41
3.41
3.41
3.41
Waste Oil
3.15
3.15
3.15
3.15
3.15
Renewable Diesel
Soy Oil
4.40
4.40
4.40
4.40
4.40
Corn Oil
3.79
3.79
3.79
3.79
3.79
Waste Oil
3.54
3.54
3.54
3.54
3.54
Other Advanced
Sugarcane Ethanol
2.73
2.73
2.73
2.73
2.73
Cellulosic Biofuel
RNG ($/gal Ethanol)
0.57
0.57
0.57
0.57
0.57
RNG ($/MMBtu)
7.46
7.46
7.46
7.46
7.46
Corn Kernel Fiber El0 Ethanol
1.83
1.80
1.78
1.76
1.72
a Fuel economy cost is per fuel being displaced—ethanol displaces gasoline, renewable diesel and biodiesel
displaces diesel fuel, and biogas displaces natural gas.
b 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 and this distinction is important for understanding ethanol's relative economic viability in the
marketplace.
0 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 E15 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 E15 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.
432
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Table 10.4.1-lb: Renewable Fuels Blending, Distribution, Retail and Fuel Economy Costs
Estimated for 2026-2030 (2022$/gal unless ot
herwise nol
ted)
Blending
Cost
Distribution
Cost
Retail
Cost
Fuel Economy
Cost
Corn
Starch
Ethanol
E10
-0.85
0.42
0.83
El 5 w/ '/2 Retail Costs
0.42
0.64
0.83
El 5 w/ Retail Costs
0.42
1.28
0.83
E85 w/ '/2 Retail Costs
0.42
0.05
0.83
E85 w/ Retail Costs
0.42
0.10
0.83
Biodiesel
Soy Oil
0.77
0.21
Corn Oil
0.77
0.21
Waste Oil
0.77
0.21
Renewable
Diesel
Soy Oil
0.77
0.13
Corn Oil
0.77
0.13
Waste Oil
0.77
0.13
Other
Advanced
Sugarcane Ethanol
-0.85
0.42
0.83
Cellulosic
Biofuel
RNG ($/gal Ethanol)
0.43
0.50
-
RNG ($/MMBtu)
5.58
6.53
-
Corn Kernel Fiber E10 Ethanol
-0.85
0.42
0.83
Table 10.4.1-lc: Renewable Fuels Total Costs Estimated for 2026-2030 (2022$/gal unless
otherwise noted)
"otal Cost
2026
2027
2028
2029
2030
Corn Starch
Ethanol
E10
2.23
2.20
2.18
2.16
2.12
El5 w/ '/2 Retail Costs
3.72
3.69
3.67
3.65
3.19
El5 w/ Retail Costs
4.36
4.33
4.31
4.29
3.83
E85 w/ '/2 Retail Costs
3.13
3.10
3.08
3.06
3.02
E85 w/ Retail Costs
3.18
3.16
3.14
3.11
3.07
Biodiesel
Soy Oil
5.01
5.01
5.01
5.01
5.01
Corn Oil
4.40
4.40
4.40
4.40
4.40
Waste Oil
4.14
4.14
4.14
4.14
4.14
Renewable
Diesel
Soy Oil
5.31
5.31
5.31
5.31
5.31
Corn Oil
4.70
4.70
4.70
4.70
4.70
Waste Oil
4.45
4.45
4.45
4.45
4.45
Other Advanced
Sugarcane Ethanol
3.13
3.13
3.13
3.13
3.13
Cellulosic
Biofuel
Biogas ($/gal ethanol)
1.49
1.49
1.49
1.49
1.49
Biogas ($/MMBtu)
19.57
19.57
19.57
19.57
19.57
Corn Kernel Fiber El0 Ethanol
2.23
2.20
2.18
2.16
2.12
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
433
-------
blended as E10 can vary from around $3.66-3.86 per gallon. Biogas distribution includes both
the amortized capital cost of transporting the biogas to a nearby pipeline as well as the amortized
retail distribution capital costs, since the retail facilities for natural gas trucks are relatively
expensive.
Tables 10.4. l-2a and Table 10.4.l-2b summarize the production, distribution, retail and
total costs for each category of fossil transportation fuel—gasoline, diesel fuel, and natural gas.
For gasoline and diesel, production costs are based on wholesale prices in AEO2023.719
Projected natural gas spot prices from AEO2023 are used to represent both feedstock and
production costs of fossil natural gas.
The distribution costs for gasoline and diesel fuel are typical for these fuels. While they
can vary depending on the transportation distance, the differences between high and low
distribution costs for gasoline and diesel fuel are likely lower than that for renewable fuels due to
the well-established pipeline distribution system for petroleum fuels. The natural gas distribution
costs are based on the difference between the projected price for natural gas sold to commercial
entities and the projected natural gas spot price, which reflects the price at the point of
production.
Table 10.4.1-2a: Gasoline, Diesel Fuel, and Natural Gas Production, Distribution and
Retail Costs for 2026-2030 (2022$)
Production
Cost
Distribution
Cost
Retail
Cost
2026
2027
2028
2029
2030
Gasoline ($/gal)
2.24
2.22
2.23
2.24
2.25
0.28
Diesel Fuel ($/gal)
2.80
2.68
2.58
2.59
2.6
0.51
Natural Gas $/gal ethanol
0.33
0.31
0.30
0.30
0.31
0.51
0.50
Natural Gas ($/MMBtu)
4.32
4.07
3.99
4.00
4.06
6.76
6.53
Table 10.4.1-2b: Gasoline, Diesel Fuel, and Natural Gas Total Costs for 2026-2030 (2022$)
Total Cost
2026
2027
2028
2029
2030
Gasoline ($/gal)
2.52
2.50
2.51
2.52
2.53
Diesel Fuel ($/gal)
3.31
3.19
3.09
3.10
3.11
Natural Gas $/gal ethanol
1.34
1.32
1.31
1.31
1.32
Natural Gas ($/MMBtu)
17.61
17.36
17.27
17.29
17.35
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.
719 AEO2023, Table 57 - Components of Selected Petroleum Product Prices.
434
-------
Table 10.4.1-3: Relative Renewable Fuel Costs for 2026-2030 (2022$/gal unless otherwise
noted)
Tol
tal Net Cost
2026
2027
2028
2029
2030
Corn Starch
Ethanol
E10
-0.29
-0.29
-0.32
-0.36
-0.41
El5 w/ '/2 Retail Costs
1.20
1.19
1.16
1.13
0.66
El5 w/ Retail Costs
1.84
1.83
1.80
1.77
1.30
E85 w/ '/2 Retail Costs
0.61
0.61
0.58
0.54
0.49
E85 w/ Retail Costs
0.67
0.66
0.63
0.60
0.55
Biodiesel
Soy Oil
1.69
1.81
1.91
1.90
1.89
Corn Oil
1.08
1.20
1.30
1.29
1.28
Waste Oil
0.83
0.95
1.05
1.04
1.03
Renewable Diesel
Soy Oil
2.00
2.12
2.22
2.21
2.20
Corn Oil
1.39
1.51
1.61
1.60
1.59
Waste Oil
1.13
1.25
1.35
1.34
1.33
Other Advanced
Sugarcane Ethanol
0.62
0.64
0.63
0.62
0.61
Cellulosic Biofuel
Biogas ($/gal ethanol)
0.16
0.17
0.18
0.18
0.18
Biogas ($/MMBtu)
1.96
2.21
2.30
2.28
2.22
Corn Kernel Fiber El0 Ethanol
-0.29
-0.29
-0.32
-0.36
-0.41
10.4.2 Costs for the Proposed Volumes
This chapter estimates the costs for the Proposed Volumes, which include a RIN
reduction for imported feedstocks and renewable fuels. The costs are analyzed relative to both
the No RFS Baseline as well as the 2025 Baseline. The costs are based on projected agricultural
feedstock prices; however, those price projections do not consider large increases in demand due
to large increases in biofuel demand. To understand the impact of the increased demand on the
cost of the Proposed Volumes, we include a sensitivity analysis in Chapter 10.4.2.3 at a higher
price for vegetable oils and animal fats.
10.4.2.1 Proposed 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 Proposed Volumes relative
to the No RFS Baseline are summarized in Tables 10.4.2.1.1-la and lb, respectively.
435
-------
Table 10.4.2.1.1-la: Proposed Volumes - Renewable Fuel Volume Changes Relative to the
No RFS Baseline (million gallons, exce
pt where noted)
Change in Renewable
Fuel Volumes
Fuel Type
2026
2027
Cellulosic Biofuel
CNG - Landfill Biogas (MMSCF)
52,804
54,795
Corn Kernel Fiber Ethanol
-2
-2
Non-cellulosic Advanced
Biodiesel - Soy
1,139
1,161
Biodiesel - FOG
-25
-28
Biodiesel - Corn Oil
118
141
Biodiesel - Canola
295
292
Renewable Diesel - Soy
1,294
1,544
Renewable Diesel - FOG
897
875
Renewable Diesel - Corn
483
448
Renewable Diesel - Canola
616
616
Sugarcane Ethanol
0
0
Conventional
Ethanol - E10
-111
-130
Ethanol - El5
138
165
Ethanol - E85
187
195
Change in Biogas Volume
52,804
54,795
Change in Ethanol Volume
214
230
Change in Biodiesel Volume
1,527
1,566
Change in Renewable Diesel Volume
3,290
3,483
436
-------
Table 10.4.2.1.1-lb: Proposed Volumes - Fossil Fuel Volume Changes Relative to the No
RFS Baseline
million gallons, except where notet
)
Change in Fossil
Fuel Volumes
Fuel Type
Fuel Displaced
2026
2027
Natural Gas
Cellulosic Biofuel
CNG - Landfill Biogas (MMSCF)
-52,804
-54,795
Gasoline
Corn Kernel Fiber Ethanol
-1
-1
Non-cellulosic Advanced
Diesel Fuel
Biodiesel - Soy
-1,061
-1,081
Diesel Fuel
Biodiesel - FOG
23
26
Diesel Fuel
Biodiesel - Corn Oil
-110
-131
Diesel Fuel
Biodiesel - Canola
-274
-272
Diesel Fuel
Renewable Diesel - Soy
-1,238
-1,477
Diesel Fuel
Renewable Diesel - FOG
-859
-838
Diesel Fuel
Renewable Diesel - Corn
-462
-429
Diesel Fuel
Renewable Diesel - Canola
-589
-589
Gasoline
Sugarcane Ethanol
0.0
0.0
Conventional
Gasoline
Ethanol - E10
74
87
Gasoline
Ethanol - El 5
-92
-111
Gasoline
Ethanol - E85
-125
-130
-
Change in Gasoline Volume
-145
-155
-
Change in Diesel Fuel Volume
-4,569
-4,791
-
Change in Natural Gas Volume
-52,804
-54,795
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 AEO2023.720 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 the AEO
estimates changes in reduced imports of crude oil refined products. The two AEO cases project
that for a volume of reduced gasoline or diesel fuel, 86% of that gasoline or diesel reduction
would be attributed to reduced crude oil imports and imports of refined product would decrease
by 11%.
The difference between the two AEO cases estimates that of the decrease in refined
product demanded, about 89% of that decreased demand (100% minus 11%) would be caused by
less gasoline and diesel fuel production by U.S. refineries. For a previous rulemaking we
assessed the likely impact of reduced U.S. product demand on U.S. refineries and derived an
720 "Change in Product Demand on Imports AEO2023 for Set 2 Proposed Rule," available in the docket for this
action.
437
-------
estimate based an analysis by a study conducted by McKinsey and Company.721 Based on the
McKinsey study, we estimate that of a given volume of reduced demand for gasoline and diesel
fuel, half of that reduced demand would be due to U.S. refineries reducing their production of
gasoline and diesel fuel (U.S. refineries produce less product or convert to produce renewable
diesel fuel or shutdown), and the other half would be attributed to reduced net imports (reduced
imports of gasoline and diesel fuel, or increased exports).
Relying on the McKinsey study for impacts on U.S. refinery production requires that we
adjust the initial estimated impact on imports based on the two refinery modeling cases from
AEO2023. These calculations are shown in Table 10.4.2.1.1-2. The first column in Table
10.4.2.1.1-2 summarizes the original AEO2023 estimates, the next two columns summarize the
McKinsey Study adjustments made to the AEO2023 estimates based on the 50%/50% impact
estimate on U.S. refineries, and the column furthest to the right summarizes the final estimated
impact on imports based on McKinsey study adjustments to the AEO2023 estimate.
For the first column of the McKinsey study adjustment, which assumes that U.S.
refineries reduce their output for 50% of the reduced refinery product demand, there would be no
impact on the volume of imported refined product. Thus, the reduced volume of imported crude
oil is estimated as 86.2/(86.2+3.0), which equates to 96.7%.
For the second column of the McKinsey study adjustment, which assumes that U.S.
refineries maintain their output for 50% of the reduced refinery product demand, there is only an
impact on the volume of net imported refined product, either decreased imports or increased
refined product exports. Thus, in this case impact on the net refined product import volume is
100%) of the reduced product demand.
The final estimated impact on imports is shown in the last column and is simply the
average of the two McKinsey adjustment columns.
Table 10.4.2.1.1-2 Summary of AEO2023 Estimate on Imports, the McKinsey Adjustments,
and Final Estimate of Impacts on Imports
AEO2023
McKinsey Study: (
prot
>f reduced refined
uct
Average
50%: U.S. refineries
reduce output
50%: U.S. refineries
stay operating
Percent reduction in
imported crude oil
86.2
96.7
48.3
Percent reduction in
domestic crude oil
3.0
3.3
1.7
Percent reduction in
net imported product
10.8
100.0
50.0
Total Percentage of
imported petroleum
97.0
96.7
100.0
98.3
721 "Estimate of the impact of decreased petroleum consumption on U. S. refinery production based on a study by
McKinsey and Co.," available in the docket for this action.
438
-------
As shown in Table 10.4.2.1.1-2, of a certain amount of reduced U.S. refined product
demand, 48.3% of that reduced product demand is attributed to lower demand of imported crude
oil and 50% is associated with lower net demand of imported refined product; thus, a total of
98.3%) of that reduced demand is estimated to be attributed to reduced imports.
Based on these correlations, Table 10.4.2.1.1-3 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.
Table 10.4.2.1.1-3: Proposed Volumes - Projected Change in Petroleum Imports Due to
2026
2027
Change in Imported Gasoline
-72
-78
Change in Imported Diesel Fuel
-2,285
-2,396
Total Change in Crude Oil
-2,327
-2,441
Change in Domestic Crude Oil
-77
-81
Change in Imported Crude Oil
-2,250
-2,360
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 the vegetable oil or seeds that are used
as feedstocks to produce renewable fuels may also be imported. Because renewable fuels
demand continues to comprise a much smaller portion of the U.S. fuels market, 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 any increases in imports
of renewable fuels and their feedstocks, However, we will nevertheless discuss possible changes
in imports of renewable fuels or their feedstocks. Since the federal BBD fuel subsidy has been
revised to incentivize the production of these renewable fuels in the U.S., we anticipate any
imports would most likely be vegetable oils, not the finished fuels.
Reviewing the projected increase in demand for renewable fuels, we identified that the
renewable diesel and biodiesel vegetable oil feedstocks of canola oil, which would likely be
supplied from Canada, and used cooking oil, most of which could be supplied from China, are
the most likely imported renewable fuel vegetable oil feedstocks. If, for example, we
conservatively assume that all the volume of canola oil and used cooking oil demanded to meet
the increased volume are imported, as much as 40%> of the increase vegetable oil demanded
under the Proposed Volumes could be imported.
10.4.2.1.2 Cost Impacts
The component cost (production, distribution, blending retail) of each biofuel type
compared to the fossil fuel it is displacing under the Proposed Volumes relative to the No RFS
Baseline is summarized in Tables 10.4.2.1.2-la and lb.
439
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Table 10.4.2.1.2-1: Proposed Volumes - Renewable and Petroleum Fuel Costs Relative to
the No RFS Baseline (million
Renewable Fuel
Petroleum Fuel
Production
Distribution
Blending
Production
Distribution
Total
Cellulosic Biofuel
CNG - Landfill Biogas
207
673
0
-228
-801
-150
Corn Kernel Fiber Ethanol
-4
-1
-2
3
0
-3
Non-cellulosic A dvanced
Biodiesel - Soy
4,575
731
0
-2,970
-543
1,792
Biodiesel - FOG
-79
-16
0
65
12
-18
Biodiesel - Corn Oil
403
76
0
-309
-56
114
Biodiesel - Canola
1,183
189
0
-768
-141
463
2026
Renewable Diesel - Soy
5,698
830
0
-3,466
-634
2,427
Renewable Diesel - FOG
3,186
578
0
-2,412
-441
910
Renewable Diesel - Corn
1,832
310
0
-1,293
-237
612
Renewable Diesel - Canola
2,712
395
0
-1,650
-302
1,155
Sugarcane Ethanol
0
0
0
0
0
0
Conventional
Ethanol - E10
-202
-47
94
166
21
32
Ethanol - El5
251
117
-78
-206
-25
58
Ethanol - E85
342
96
-22
-281
-35
100
Cellulosic Biofuel
CNG - Landfill Biogas
215
698
0
-223
-831
-142
Corn Kernel Fiber Ethanol
0
0
0
0
0
0
Non-cellulosic A dvanced
Biodiesel - Soy
4,222
745
0
-2,898
-554
1,515
Biodiesel - FOG
-80
-18
0
70
13
-15
Biodiesel - Corn Oil
436
90
0
-352
-67
107
Biodiesel - Canola
1,063
187
0
-729
-139
381
2027
Renewable Diesel - Soy
6,209
499
0
-3,959
-757
1,992
Renewable Diesel - FOG
2,854
564
0
-2,253
-431
734
Renewable Diesel - Corn
1,558
287
0
-1,148
-220
477
Renewable Diesel - Canola
2,477
83
0
-1,579
-302
679
Sugarcane Ethanol
0
0
0
0
0
0
Conventional
Ethanol - E10
-234
-55
111
194
24
39
Ethanol - El5
298
140
-94
-246
-31
68
Ethanol - E85
350
99
-22
-289
-36
102
The costs are aggregated for each fossil fuel type and shown as annual totals and per-
gallon and perMSCF costs in Table 10.4.2.1.2-3.
440
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Table 10.4.2.1.2-3: Total Annual and Per-Gallon Costs Relative to the No RFS Baseline
(2022$)
Total Cost
Per-Unit
Year
Fuel Type
(million $)
Cost
Units
2026
Gasoline
Diesel Fuel
188
7,456
0.14
14.22
0/gal gasoline
0/gal diesel
Natural Gas
-150
-0.50
$/MSCF natural gas
Total
7,494
4.07
0/gal gasoline and diesel
Gasoline
206
0.16
0/gal gasoline
2027
Diesel Fuel
5,871
11.30
0/gal diesel
Natural Gas
-142
-0.49
$/MSCF natural gas
Total
5,936
3.26
0/gal gasoline and diesel
10.4.2.2 Proposed Volumes Relative to 2025 Baseline
In this section, we summarize the estimated costs for the proposed 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, including production, blending,
and distribution costs, and differences in energy density.
10.4.2.2.1 Volumes
The renewable fuel and fossil fuel volume changes under the Proposed Volumes relative
to the 2025 Baseline are summarized in Tables 10.4.2.2.1-la and lb, respectively.
441
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Table 10.4.2.2.1-la: Proposed Volumes - Renewable Fuel Volume Changes Relative to the
Change in Renewable
Fuel Volumes
Fuel Type
2026
2027
Cellulosic Biofuel
CNG - Landfill Biogas (MMSCF)
-9,219
-4,425
Corn Kernel Fiber Ethanol
47
46
Non-cellulosic Advanced
Biodiesel - Soy
267
296
Biodiesel - FOG
81
81
Biodiesel - Corn Oil
145
145
Biodiesel - Canola
-1
-1
Renewable Diesel - Soy
356
606
Renewable Diesel - FOG
893
943
Renewable Diesel - Corn
392
392
Renewable Diesel - Canola
307
307
Sugarcane Ethanol
-37
-37
Conventional
Ethanol - E10
-204
-383
Ethanol - El 5
17
45
Ethanol - E85
30
61
Change in Biogas Volume
-9,219
-,4425
Change in Ethanol Volume
-109
-231
Change in Biodiesel Volume
492
521
Change in Renewable Diesel Volume
1,947
2,247
442
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Table 10.4.2.2.1-lb: Proposed Volumes - Fossil Fuel Volume Changes Relative to the 2025
Change in Fossil
Fuel Volumes
Fuel Type
Fuel Displaced
2026
2027
Natural Gas
Cellulosic Biofuel
CNG - Landfill Biogas (MMSCF)
9,219
4,425
Gasoline
Corn Kernel Fiber Ethanol
31
31
Non-cellulosic Advanced
Diesel Fuel
Biodiesel - Soy
248
275
Diesel Fuel
Biodiesel - FOG
76
76
Diesel Fuel
Biodiesel - Corn Oil
135
135
Diesel Fuel
Biodiesel - Canola
-1
-1
Diesel Fuel
Renewable Diesel - Soy
-340
-580
Diesel Fuel
Renewable Diesel - FOG
-854
-902
Diesel Fuel
Renewable Diesel - Corn
-375
-375
Diesel Fuel
Renewable Diesel - Canola
-293
-293
Gasoline
Sugarcane Ethanol
-25
-25
Conventional
Gasoline
Ethanol - E10
136
256
Gasoline
Ethanol - El 5
-12
-30
Gasoline
Ethanol - E85
-20
-41
-
Change in Gasoline Volume
111
191
-
Change in Diesel Fuel Volume
-1,404
-1,664
-
Change in Natural Gas Volume
9,219
4,425
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 demanded 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: Proposed Volumes - Projected Change in Petroleum Imports Due to
2026
2027
Change in Imported Gasoline
56
96
Change in Imported Diesel Fuel
-702
-832
Total Change in Crude Oil
-647
-740
Change in Domestic Crude Oil
-21
-25
Change in Imported Crude Oil
-625
-715
443
-------
10.4.2.2.2 Cost Impacts
The component cost (production, distribution, blending retail) of each biofuel type
compared to the fossil fuel it is displacing under the Proposed Volumes relative to the 2025
Baseline is summarized in Table 10.4.2.2.2-1.
Table 10.4.2.2.2-1: Proposed Volumes - Renewable and Petroleum Fuel Costs Relative to
the 2025 Baseline (million 2022$)
Renewable Fuel
Petroleum Fuel
Production
Distribution
Blending
Production
Distribution
Total
Cellulosic Biofuel
CNG - Landfill Biogas
-36
-117
0
40
140
26
Corn Kernel Fiber Ethanol
86
13
40
-71
-9
60
Non-cellulosic Advanced
Biodiesel - Soy
1071
171
0
-695
-127
420
Biodiesel - FOG
256
52
0
-377
-69
58
Biodiesel - Corn Oil
493
93
0
2
0
139
Biodiesel - Canola
-3
0
0
0
0
-1
2026
Renewable Diesel - Soy
1567
228
0
-953
-174
668
Renewable Diesel - FOG
3158
573
0
-1049
-192
902
Renewable Diesel - Corn
1486
251
0
-821
-150
496
Renewable Diesel - Canola
1350
197
0
0
0
575
Sugarcane Ethanol
-101
-16
31
56
7
-23
Conventional
Ethanol - E10
-372
-86
173
306
38
59
Ethanol - El5
32
15
-10
-26
-3
7
Ethanol - E85
55
15
-3
-46
-6
16
Cellulosic Biofuel
CNG - Landfill Biogas
-17
-56
0
18
67
11
Corn Kernel Fiber Ethanol
83
13
39
-68
-9
58
Non-cellulosic Advanced
Biodiesel - Soy
1075
190
0
-738
-141
386
Biodiesel - FOG
233
52
0
-203
-39
43
Biodiesel - Corn Oil
447
93
0
-361
-69
110
Biodiesel - Canola
-2
0
0
2
0
-1
2027
Renewable Diesel - Soy
2436
-103
0
-1553
-297
483
Renewable Diesel - FOG
3062
605
0
-2417
-462
788
Renewable Diesel - Corn
1362
251
0
-1004
-192
417
Renewable Diesel - Canola
1233
-115
0
-786
-150
181
Sugarcane Ethanol
-101
-16
31
55
7
-23
Conventional
Ethanol - E10
-689
-161
325
569
71
115
Ethanol - El5
81
38
-26
-67
-8
18
Ethanol - E85
109
31
-7
-90
-11
32
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.
444
-------
Table 10.4.2.2.2-2: Proposed Volumes - Total Annual and Per-Gallon Costs Relative to the
2025 Baseline (2022$)
Total Cost
Per-Unit
Year
Fuel Type
(million $)
Cost
Units
2026
Gasoline
Diesel Fuel
119
3,256
0.09
6.21
0/gal gasoline
0/gal diesel
Natural Gas
26
0.09
$/MSCF natural gas
Total
3,401
1.85
0/gal gasoline and diesel
Gasoline
200
0.15
0/gal gasoline
2027
Diesel Fuel
2,408
4.63
0/gal diesel
Natural Gas
11
0.04
$/MSCF natural gas
Total
2,619
1.42
0/gal gasoline and diesel
10.4.2.3 High Vegetable Oil Price Sensitivity Analysis
As summarized in Table 10.4.2.2.1-la, the proposed renewable fuels standard is
estimated to cause more than 1,4-billion-gallon increase in biodiesel and renewable diesel
consumption which will largely be supplied by domestic feedstock sources. This large increase
in demand could increase vegetable oil and animal fat feedstock prices higher than the price
projections made by USDA. For this reason, we conducted cost sensitivity analyses at higher
prices for those feedstocks. For 2026 we assumed a price of 750 per pound, and for 2027 we
assumed a price of 650 per pound, which equates to the soybean prices we modeled in the Set 1
Rule for 2023 and 2024, respectively. Although there could be higher prices for vegetable oils
and animal fats due to the large step increase in demand for domestic supplies, such price
increases are likely to be transitory as the market has a chance to rebalance around the increased
volumes.
10.4.2.3.1 Proposed Volumes 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 Proposed Volumes at High Prices relative to
the No RFS Baseline is summarized in Table 10.4.2.3.1-1.
445
-------
Table 10.4.2.3.1-1: Proposed Volumes at High Prices - Renewable and Petroleum Fuel
Renewable Fuel
Petroleum Fuel
Production
Distribution
Blending
Production
Distribution
Total
Cellulosic Biofuel
CNG - Landfill Biogas
207
673
0
-228
-801
-150
Corn Kernel Fiber Ethanol
-4
-1
-2
3
0
-3
Non-cellulosic Advanced
Biodiesel - Soy
7,128
731
0
-2,970
-543
4,346
Biodiesel - FOG
-157
-16
0
65
12
-96
Biodiesel - Corn Oil
741
76
0
-309
-56
451
Biodiesel - Canola
1,844
189
0
-768
-141
1,124
2026
Renewable Diesel - Soy
8,596
830
0
-3,466
-634
5,326
Renewable Diesel - FOG
5,982
578
0
-2,412
-441
3,706
Renewable Diesel - Corn
3,207
310
0
-1,293
-237
1,987
Renewable Diesel - Canola
4,092
395
0
-1,650
-302
2,535
Sugarcane Ethanol
0
0
0
0
0
0
Conventional
Ethanol - E10
-202
-47
94
166
21
32
Ethanol - El5
251
117
-78
-206
-25
58
Ethanol - E85
342
96
-22
-281
-35
100
Cellulosic Biofuel
CNG - Landfill Biogas
215
698
0
-223
-831
-142
Corn Kernel Fiber Ethanol
0
0
0
0
0
0
Non-cellulosic Advanced
Biodiesel - Soy
6,069
745
0
-2,898
-554
3,362
Biodiesel - FOG
-146
-18
0
70
13
-81
Biodiesel - Corn Oil
736
90
0
-352
-67
408
Biodiesel - Canola
1,528
187
0
-729
-139
846
2027
Renewable Diesel - Soy
9,060
499
0
-3,959
-757
4,843
Renewable Diesel - FOG
5,155
564
0
-2,253
-431
3,035
Renewable Diesel - Corn
2,628
287
0
-1,148
-220
1,548
Renewable Diesel - Canola
3,615
83
0
-1,579
-302
1,817
Sugarcane Ethanol
0
0
0
0
0
0
Conventional
Ethanol - E10
-234
-55
111
194
24
39
Ethanol - El5
298
140
-94
-246
-31
68
Ethanol - E85
350
99
-22
-289
-36
102
The costs are aggregated for each fossil fuel type and shown as annual totals and per-
gallon and perMSCF costs in Table 10.4.2.3.1-2.
446
-------
Table 10.4.2.3.1-2: Total Annual and Per-Gallon Costs Relative to the No RFS Baseline
(2022$)
Total Cost
Per-Unit
Year
Fuel Type
(million $)
Cost
Units
2026
Gasoline
Diesel Fuel
188
19,379
0.14
36.96
0/gal gasoline
0/gal diesel
Natural Gas
-150
-0.50
$/MSCF natural gas
Total
19,417
10.56
0/gal gasoline and diesel
Gasoline
206
0.16
0/gal gasoline
2027
Diesel Fuel
15,779
30.36
0/gal diesel
Natural Gas
-142
-0.49
$/MSCF natural gas
Total
15,843
8.71
0/gal gasoline and diesel
10.4.2.3.2 Proposed Volumes Relative to the 2025 Baseline
The component cost (production, distribution, blending retail) of each biofuel type
compared to the fossil fuel it is displacing under the Proposed Volumes at High Prices relative to
the 2025 Baseline is summarized in Table 10.4.2.3.2-1.
447
-------
Table 10.4.2.3.2-1: Proposed Volumes at High Prices - Renewable and Petroleum Fuel
Costs Relative to the 2025 Baseline (million 2022$)
Renewable Fuel
Petroleum Fuel
Total
Production
Distribution
Blending
Production
Distribution
Cellulosic Biofuel
CNG - Landfill Biogas
-36
-117
0
40
140
26
Corn Kernel Fiber Ethanol
86
13
40
-71
-9
60
Non-cellulosic Advanced
Biodiesel - Soy
1,669
171
0
-695
-127
1,017
Biodiesel - FOG
509
52
0
-377
-69
310
Biodiesel - Corn Oil
905
93
0
2
0
552
Biodiesel - Canola
-4
0
0
0
0
-3
2026
Renewable Diesel - Soy
2,364
228
0
-953
-174
1,465
Renewable Diesel - FOG
5,930
573
0
-1,049
-192
3,674
Renewable Diesel - Corn
2,602
251
0
-821
-150
1,612
Renewable Diesel - Canola
2,037
197
0
0
0
1,262
Sugarcane Ethanol
-101
-16
31
56
7
-23
Conventional
Ethanol - E10
-372
-86
173
306
38
59
Ethanol - El5
32
15
-10
-26
-3
7
Ethanol - E85
55
15
-3
-46
-6
16
Cellulosic Biofuel
CNG - Landfill Biogas
-17
-56
0
18
67
11
Corn Kernel Fiber Ethanol
83
13
39
-68
-9
58
Non-cellulosic Advanced
Biodiesel - Soy
1,545
190
0
-738
-141
856
Biodiesel - FOG
425
52
0
-203
-39
235
Biodiesel - Corn Oil
756
93
0
-361
-69
419
Biodiesel - Canola
-3
0
0
2
0
-2
2027
Renewable Diesel - Soy
3,555
-103
0
-1,553
-297
1,602
Renewable Diesel - FOG
5,532
605
0
-2,417
-462
3,257
Renewable Diesel - Corn
2,298
251
0
-1,004
-192
1,353
Renewable Diesel - Canola
1,799
-115
0
-786
-150
748
Sugarcane Ethanol
-101
-16
31
55
7
-23
Conventional
Ethanol - E10
-689
-161
325
569
71
115
Ethanol - El5
81
38
-26
-67
-8
18
Ethanol - E85
109
31
-7
-90
-11
32
The costs are aggregated for each fossil fuel type and shown as annual totals and per-
gallon and per MSCF costs in Table 10.4.2.3.2-2.
448
-------
Table 10.4.2.3.2-2: Proposed Volumes at High Prices - Total Annual and Per-Gallon Costs
Relative to the 2025 Baseline (2022$)
Total Cost
Per-Unit
Year
Fuel Type
(million $)
Cost
Units
2026
Gasoline
Diesel Fuel
119
9,890
0.09
18.86
0/gal gasoline
0/gal diesel
Natural Gas
26
0.09
$/MSCF natural gas
Total
10,035
5.45
0/gal gasoline and diesel
Gasoline
200
0.15
0/gal gasoline
2027
Diesel Fuel
8,469
16.30
0/gal diesel
Natural Gas
11
0.04
$/MSCF natural gas
Total
8,680
4.72
0/gal gasoline and diesel
10.4.3 Costs for the Low Volume Scenario
We analyzed the costs for the Low Volume Scenario relative to the No RFS Baseline, as
well as incremental to the 2025 Baseline.
10.4.3.1 Low Volume Scenario 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 described in
Chapter 2). For this analysis we considered all societal costs, including production, blending, and
distribution costs, and differences in energy density.
10.4.3.1.1 Volumes
The renewable fuel and fossil fuel volume changes under the Low Volume Scenario
relative to the No RFS Baseline are summarized in Tables 10.4.2.1.1-la and lb, respectively.
449
-------
Table 10.4.3.1.1-la: Low Volume Scenario - Renewable Fuel Volume Changes Relative to
Change in Renewable Fuel Volume
Fuel Type
2026
2027
2028
2029
2030
Cellulosic Biofuel
CNG - Landfill Biogas (MMSCF)
52,804
63,866
65,931
67,996
70,282
Corn Kernel Fiber Ethanol
-2
-2
-2
-2
-1
Non-cellulosic Advanced
Biodiesel - Soy
1,196
1,189
1,197
1,197
1,193
Biodiesel - FOG
-49
-52
-47
-53
-46
Biodiesel - Corn Oil
34
57
35
33
35
Biodiesel - Canola
330
327
330
330
328
Renewable Diesel - Soy
703
740
778
815
853
Renewable Diesel - FOG
956
1,159
1,359
1,545
1,743
Renewable Diesel - Corn
79
44
52
35
23
Renewable Diesel - Canola
130
130
130
130
130
Sugarcane Ethanol
0
0
0
0
0
Conventional
Ethanol - E10
-111
-130
-138
-153
-169
Ethanol - El5
138
165
176
196
218
Ethanol - E85
187
195
203
211
218
Change in Biogas Volume
52,804
63,866
65,931
67,996
70,282
Change in Ethanol Volume
214
230
240
254
267
Change in Biodiesel Volume
1,511
1,521
1,515
1,507
1,510
Change in Renewable Diesel Volume
1,868
2,073
2,318
2,525
2,749
450
-------
Table 10.4.3.1.1-lb: Low Volume Scenario - Fossil Fuel Volume Changes Relative to the
No RFS Base
ine (million gallons, except where noted)
Change in Fossil Fuel Volume
Fuel Type
Fuel Displaced
2026
2027
2028
2029
2030
Natural Gas
Cellulosic Biofuel
CNG - Landfill Biogas (MMSCF)
-52,804
-63,866
-65,931
-67,996
-70,282
Gasoline
Corn Kernel Fiber Ethanol
-1
-1
-1
-1
-1
Non-cellulosic Advanced
Diesel Fuel
Biodiesel - Soy
-1,114
-1,107
-1,115
-1,115
-1,111
Diesel Fuel
Biodiesel - FOG
46
48
44
49
43
Diesel Fuel
Biodiesel - Corn Oil
-32
-53
-32
-31
-32
Diesel Fuel
Biodiesel - Canola
-307
-305
-307
-307
-306
Diesel Fuel
Renewable Diesel - Soy
-673
-708
-744
-780
-816
Diesel Fuel
Renewable Diesel - FOG
-915
-1,109
-1,300
-1,478
-1,668
Diesel Fuel
Renewable Diesel - Corn
-75
-42
-50
-34
-22
Diesel Fuel
Renewable Diesel - Canola
-124
-124
-124
-124
-124
Gasoline
Sugarcane Ethanol
0.0
0.0
0.0
0.0
0.0
Conventional
Gasoline
Ethanol - E10
74
87
92
102
113
Gasoline
Ethanol - E15
-92
-111
-118
-131
-146
Gasoline
Ethanol - E85
-125
-130
-136
-141
-146
-
Change in Gasoline Volume
-145
-155
-162
-172
-180
-
Change in Diesel Fuel Volume
-3,194
-3,400
-3,629
-3,820
-4,036
-
Change in Natural Gas Volume
-52,804
-63,866
-65,931
-67,996
-70,282
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 demanded and its effect on
both imported crude oil, domestic crude oil, and imported petroleum products. Table 10.4.3.1.1-2
summarizes the projected change in petroleum imports expected from the increased consumption
of renewable biofuels relative to the No RFS Baseline.
Table 10.4.3.1.1-2: Low Volume Scenario - Projected Change in Petroleum Imports Due to
Increase in Renewable Fuel Consumption
Relative to the No RFS Base
ine (milli
2026
2027
2028
2029
2030
Change in Imported Gasoline
-72
-78
-81
-86
-90
Change in Imported Diesel Fuel
-1,597
-1,700
-1,814
-1,910
-2,018
Total Change in Crude Oil
-1,646
-1,753
-1,869
-1,967
-2,078
Change in Domestic Crude Oil
-55
-58
-62
-65
-69
Change in Imported Crude Oil
-1,591
-1,694
-1,807
-1,902
-2,009
10.4.3.1.2 Cost Impacts
The component cost (production, distribution, blending retail) of each biofuel type
compared to the fossil fuel it is displacing under the Low Volume Scenario relative to the No
RFS Baseline is summarized in Tables 10.4.2.1.2-la and lb.
451
-------
Table 10.4.3.1.2-la: Low Volume Scenario - Renewable and Petroleum Fuel Costs Relative
Renewable Fuel
Petroleum Fuel
Production
Distribution
Blending
Production
Distribution
Total
Cellulosic Biofuel
CNG - Landfill Biogas
207
673
0
-228
-801
-150
Corn Kernel Fiber Ethanol
-4
-1
-2
3
0
-3
Non-cellulosic Advanced
Biodiesel - Soy
4,804
767
0
-3,119
-571
1,882
Biodiesel - FOG
-155
-31
0
128
23
-35
Biodiesel - Corn Oil
117
22
0
-89
-16
33
Biodiesel - Canola
1,324
211
0
-859
-157
519
2026
Renewable Diesel - Soy
3,095
451
0
-1,883
-345
1,319
Renewable Diesel - FOG
3,394
616
0
-2,570
-470
969
Renewable Diesel - Corn
299
50
0
-211
-39
100
Renewable Diesel - Canola
572
83
0
-348
-64
244
Sugarcane Ethanol
0
0
0
0
0
0
Conventional
Ethanol - E10
-202
-47
94
166
21
32
Ethanol - El5
251
117
-78
-206
-25
58
Ethanol - E85
342
96
-22
-281
-35
100
Cellulosic Biofuel
CNG - Landfill Biogas
250
814
0
-260
-969
-165
Corn Kernel Fiber Ethanol
0
0
0
0
0
0
Non-cellulosic Advanced
Biodiesel - Soy
4,324
763
0
-2,968
-567
1,551
Biodiesel - FOG
-148
-33
0
129
25
-28
Biodiesel - Corn Oil
176
37
0
-142
-27
43
Biodiesel - Canola
1,190
210
0
-817
-156
427
2027
Renewable Diesel - Soy
2,976
499
0
-1,897
-363
1,215
Renewable Diesel - FOG
3,776
746
0
-2,981
-570
972
Renewable Diesel - Corn
153
28
0
-113
-22
47
Renewable Diesel - Canola
523
83
0
-333
-64
209
Sugarcane Ethanol
0
0
0
0
0
0
Conventional
Ethanol - E10
-234
-55
111
194
24
39
Ethanol - El5
298
140
-94
-246
-31
68
Ethanol - E85
350
99
-22
-289
-36
102
Cellulosic Biofuel
CNG - Landfill Biogas
258
840
0
-263
-1,000
-165
Corn Kernel Fiber Ethanol
0
0
0
0
0
0
Non-cellulosic Advanced
Biodiesel - Soy
4,197
768
0
-2,877
-571
1,517
Biodiesel - FOG
-131
-30
0
114
23
-25
Biodiesel - Corn Oil
104
22
0
-83
-17
26
Biodiesel - Canola
1,157
212
0
-793
-157
418
2028
Renewable Diesel - Soy
3,025
499
0
-1,920
-381
1,223
Renewable Diesel - FOG
4,286
874
0
-3,361
-667
1,132
Renewable Diesel - Corn
174
33
0
-128
-25
54
Renewable Diesel - Canola
506
83
0
-321
-64
204
Sugarcane Ethanol
0
0
0
0
0
0
Conventional
Ethanol - E10
-246
-58
117
206
26
45
Ethanol - El5
312
149
-99
-262
-33
67
Ethanol - E85
361
104
-23
-303
-38
101
452
-------
Table 10.4.3.1.2-lb: Low Volume Scenario - Renewable and Petroleum Fuel Costs Relative
to the No RFS Baseline (million 2022$)
Renewable Fuel
Petroleum Fuel
Production
Distribution
Blending
Production
Distribution
Total
Cellulosic Biofuel
CNG - Landfill Biogas
266
867
0
-272
-1,031
-171
Corn Kernel Fiber Ethanol
-4
-1
-2
3
0
-2
Non-cellulosic Advanced
Biodiesel - Soy
4,024
768
0
-2,887
-571
1,334
Biodiesel - FOG
-141
-34
0
128
25
-22
Biodiesel - Corn Oil
96
21
0
-81
-16
21
Biodiesel - Canola
1,109
212
0
-796
-157
368
2029
Renewable Diesel - Soy
3,082
523
0
-2,019
-399
1,186
Renewable Diesel - FOG
4,752
991
0
-3,828
-757
1,158
Renewable Diesel - Corn
116
23
0
-88
-17
34
Renewable Diesel - Canola
492
83
0
-322
-64
189
Sugarcane Ethanol
0
0
0
0
0
0
Conventional
Ethanol - E10
-268
-64
130
229
28
55
Ethanol - El5
345
166
-111
-294
-36
69
Ethanol - E85
371
108
-24
-316
-39
99
Cellulosic Biofuel
CNG - Landfill Biogas
275
896
0
-286
-1,066
-181
Corn Kernel Fiber Ethanol
0
0
0
0
0
0
Non-cellulosic Advanced
Biodiesel - Soy
3,846
765
0
-2,888
-569
1,154
Biodiesel - FOG
-118
-30
0
112
22
-14
Biodiesel - Corn Oil
96
22
0
-84
-17
17
Biodiesel - Canola
1,059
211
0
-795
-157
318
2030
Renewable Diesel - Soy
3,109
547
0
-2,122
-418
1,117
Renewable Diesel - FOG
5,183
1,118
0
-4,337
-855
1,110
Renewable Diesel - Corn
72
15
0
-56
-11
19
Renewable Diesel - Canola
474
83
0
-323
-64
170
Sugarcane Ethanol
0
0
0
0
0
0
Conventional
Ethanol - E10
-290
-71
144
255
31
68
Ethanol - El5
374
185
-124
-328
-40
67
Ethanol - E85
375
111
-25
-329
-40
92
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.
453
-------
Table 10.4.3.1.2-2: Total Annual and Per-Gallon Costs Relative to the No RFS Baseline
(2022$)
Total Cost
Per-Unit
Year
Fuel Type
(million $)
Cost
Units
2026
Gasoline
Diesel Fuel
188
5,030
0.14
9.59
0/gal gasoline
0/gal diesel
Natural Gas
-150
-0.50
$/MSCF natural gas
Total
5,068
2.76
0/gal gasoline and diesel
Gasoline
206
0.16
0/gal gasoline
2027.
Diesel Fuel
4,436
8.54
0/gal diesel
Natural Gas
-165
-0.57
$/MSCF natural gas
Total
4,477
2.46
0/gal gasoline and diesel
2028
Gasoline
Diesel Fuel
211
4,549
0.16
8.80
0/gal gasoline
0/gal diesel
Natural Gas
-165
-0.57
$/MSCF natural gas
Total
4,595
2.55
0/gal gasoline and diesel
Gasoline
220
0.17
0/gal gasoline
2029
Diesel Fuel
4,267
8.33
0/gal diesel
Natural Gas
-171
-0.59
$/MSCF natural gas
Total
4,316
2.43
0/gal gasoline and diesel
Gasoline
226
0.18
0/gal gasoline
2030
Diesel Fuel
3,891
7.67
0/gal diesel
Natural Gas
-181
-0.63
$/MSCF natural gas
Total
3,936
2.25
0/gal gasoline and diesel
10.4.3.2 Low Volume Scenario Relative to the 2025 Baseline
In this section, we summarize the estimated costs for the proposed changes in renewable
fuel volumes described in Chapter 3.2 (changes relative to the 2025 Baseline). For this analysis
we considered all societal costs, including production, blending, and distribution costs, and
differences in energy density.
10.4.3.2.1 Volumes
The renewable fuel and fossil fuel volume changes under the Low Volume Scenario
relative to the 2025 Baseline are summarized in Tables 10.4.2.2.1-la and lb, respectively.
454
-------
Table 10.4.3.2.1-la: Low Volume Scenario - Renewable Fuel Volume Changes Relative to
Change in Renewable Fuel Volume
Fuel Type
2026
2027
2028
2029
2030
Cellulosic Biofuel
CNG - Landfill Biogas (MMSCF)
-9,219
4,646
9,735
15,118
20,945
Corn Kernel Fiber Ethanol
47
46
45
43
42
Non-cellulosic Advanced
Biodiesel - Soy
324
324
324
324
324
Biodiesel - FOG
57
57
57
57
57
Biodiesel - Corn Oil
61
61
61
61
61
Biodiesel - Canola
34
34
34
34
34
Renewable Diesel - Soy
-235
-198
-160
-123
-85
Renewable Diesel - FOG
952
1,227
1,502
1,777
2,052
Renewable Diesel - Corn
-12
-12
-12
-12
-12
Renewable Diesel - Canola
-179
-179
-179
-179
-179
Sugarcane Ethanol
-37
-37
-37
-37
-37
Conventional
Ethanol - E10
-204
-383
-570
-784
-1,017
Ethanol - El 5
17
45
55
76
98
Ethanol - E85
30
61
91
121
152
Change in Biogas Volume
-9,219
4,646
9,735
15,118
20,945
Change in Ethanol Volume
-109
-231
-378
-544
-725
Change in Biodiesel Volume
476
476
476
476
476
Change in Renewable Diesel Volume
525
837
1,150
1,462
1,775
455
-------
Table 10.4.3.2.1-lb: Low Volume Scenario - Fossil Fuel Volume Changes Relative to the
2025 Baseline (million gallons, except where noted)
Change in Fossil Fuel Volume
Fuel Type
Fuel Displaced
2026
2027
2028
2029
2030
Natural Gas
Cellulosic Biofuel
CNG - Landfill Biogas (MMSCF)
9,219
-4,646
-9,735
-15,118
-20,945
Gasoline
Corn Kernel Fiber Ethanol
31
31
30
29
28
Non-cellulosic Advanced
Diesel Fuel
Biodiesel - Soy
301
301
301
301
301
Diesel Fuel
Biodiesel - FOG
53
53
53
53
53
Diesel Fuel
Biodiesel - Corn Oil
56
56
56
56
56
Diesel Fuel
Biodiesel - Canola
32
32
32
32
32
Diesel Fuel
Renewable Diesel - Soy
225
190
153
118
81
Diesel Fuel
Renewable Diesel - FOG
-911
-1,174
-1,437
-1,700
-1,963
Diesel Fuel
Renewable Diesel - Corn
12
12
12
12
12
Diesel Fuel
Renewable Diesel - Canola
172
172
172
172
172
Gasoline
Sugarcane Ethanol
-25
-25
-25
-25
-25
Conventional
Gasoline
Ethanol - E10
136
256
381
525
681
Gasoline
Ethanol - E15
-12
-30
-37
-51
-65
Gasoline
Ethanol - E85
-20
-41
-61
-81
-102
-
Change in Gasoline Volume
111
191
289
397
517
-
Change in Diesel Fuel Volume
-59
-357
-657
-955
-1,255
-
Change in Natural Gas Volume
9,219
-4,646
-9,735
-15,118
-20,945
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 demanded and its effect on
both imported crude oil, domestic crude oil, and imported petroleum products. Table 10.4.3.2.1-2
summarizes the projected change in petroleum imports expected from the increased consumption
of renewable biofuels relative to the 2025 Baseline.
Table 10.4.3.2.1-2: Low Volume Scenario - Projected Change in Petroleum Imports Due to
2026
2027
2028
2029
2030
Change in Imported Gasoline
56
96
144
199
259
Change in Imported Diesel Fuel
-29
-179
-328
-478
-627
Total Change in Crude Oil
20
-93
-198
-298
-394
Change in Domestic Crude Oil
1
-3
-7
-10
-13
Change in Imported Crude Oil
19
-90
-192
-288
-381
10.4.3.2.2 Cost Impacts
The component cost (production, distribution, blending retail) of each biofuel type
compared to the fossil fuel it is displacing under the Low Volume Scenario relative to the 2025
Baseline is summarized in Tables 10.4.2.2.2-la and lb.
456
-------
Table 10.4.3.2.2-la: Low Volume Scenario - Renewable and Petroleum Fuel Costs Relative
to the 2025 Baseline (million 2022$)
Renewable Fuel
Petroleum Fuel
Production
Distribution
Blending
Production
Distribution
Total
Cellulosic Biofuel
CNG - Landfill Biogas
-36
-117
0
40
140
26
Corn Kernel Fiber Ethanol
86
13
40
-71
-9
60
Non-cellulosic Advanced
Biodiesel - Soy
1,300
208
0
-844
-154
509
Biodiesel - FOG
181
37
0
-158
-29
41
Biodiesel - Corn Oil
207
39
0
-90
-16
58
Biodiesel - Canola
138
22
0
0
0
54
2026
Renewable Diesel - Soy
-1,035
-151
0
630
115
-441
Renewable Diesel - FOG
3,367
611
0
33
6
962
Renewable Diesel - Corn
-47
-8
0
480
88
-16
Renewable Diesel - Canola
-790
-115
0
0
0
-336
Sugarcane Ethanol
-101
-16
31
56
7
-23
Conventional
Ethanol - E10
-372
-86
173
306
38
59
Ethanol - El5
32
15
-10
-26
-3
7
Ethanol - E85
55
15
-3
-46
-6
16
Cellulosic Biofuel
CNG - Landfill Biogas
18
59
0
-19
-70
-12
Corn Kernel Fiber Ethanol
83
13
39
-68
-9
58
Non-cellulosic Advanced
Biodiesel - Soy
1,177
208
0
-808
-154
422
Biodiesel - FOG
164
37
0
-143
-27
30
Biodiesel - Corn Oil
188
39
0
-151
-29
46
Biodiesel - Canola
125
22
0
-86
-16
45
2027
Renewable Diesel - Soy
-797
-103
0
508
97
-294
Renewable Diesel - FOG
3,985
787
0
-3,145
-601
1,025
Renewable Diesel - Corn
-43
-8
0
32
6
-13
Renewable Diesel - Canola
-721
-115
0
460
88
-289
Sugarcane Ethanol
-101
-16
31
55
7
-23
Conventional
Ethanol - E10
-689
-161
325
569
71
115
Ethanol - El5
81
38
-26
-67
-8
18
Ethanol - E85
109
31
-7
-90
-11
32
Cellulosic Biofuel
CNG - Landfill Biogas
38
124
0
-39
-148
-24
Corn Kernel Fiber Ethanol
80
12
38
-67
-8
55
Non-cellulosic Advanced
Biodiesel - Soy
1,134
208
0
-778
-154
410
Biodiesel - FOG
158
37
0
-138
-27
30
Biodiesel - Corn Oil
181
39
0
-146
-29
45
Biodiesel - Canola
120
22
0
-82
-16
43
2028
Renewable Diesel - Soy
-623
-103
0
395
78
-252
Renewable Diesel - FOG
4,727
963
0
-3,707
-736
1,248
Renewable Diesel - Corn
-42
-8
0
31
6
-13
Renewable Diesel - Canola
-698
-115
0
443
88
-282
Sugarcane Ethanol
-101
-16
31
55
7
-23
Conventional
Ethanol - E10
-1,014
-240
484
851
106
186
Ethanol - El5
99
47
-31
-83
-10
21
Ethanol - E85
162
46
-10
-136
-17
45
457
-------
Table 10.4.3.2.2-lb: Low Volume Scenario - Renewable and Petroleum Fuel Costs Relative
to the 2025 Baseline (million 2022$)
Renewable Fuel
Petroleum Fuel
Production
Distribution
Blending
Production
Distribution
Total
Cellulosic Biofuel
CNG - Landfill Biogas
59
193
0
-61
-229
-38
Corn Kernel Fiber Ethanol
0
0
0
0
0
0
Non-cellulosic Advanced
Biodiesel - Soy
1,088
208
0
-781
-154
361
Biodiesel - FOG
152
37
0
-138
-27
23
Biodiesel - Corn Oil
174
39
0
-146
-29
37
Biodiesel - Canola
115
22
0
-83
-16
38
2029
Renewable Diesel - Soy
-466
-79
0
305
60
-179
Renewable Diesel - FOG
5,465
1,140
0
-4,402
-871
1,331
Renewable Diesel - Corn
-41
-8
0
31
6
-12
Renewable Diesel - Canola
-678
-115
0
444
88
-261
Sugarcane Ethanol
-101
-16
31
56
7
-23
Conventional
Ethanol - E10
-1,378
-330
667
1,176
145
280
Ethanol - El5
134
64
-43
-114
-14
27
Ethanol - E85
213
62
-14
-182
-22
57
Cellulosic Biofuel
CNG - Landfill Biogas
82
267
0
-85
-318
-54
Corn Kernel Fiber Ethanol
0
0
0
0
0
0
Non-cellulosic Advanced
Biodiesel - Soy
1,044
208
0
-784
-154
313
Biodiesel - FOG
146
37
0
-139
-27
17
Biodiesel - Corn Oil
167
39
0
-147
-29
30
Biodiesel - Canola
111
22
0
-83
-16
33
2030
Renewable Diesel - Soy
-310
-55
0
212
42
-111
Renewable Diesel - FOG
6,100
1,316
0
-5,104
-1,006
1,307
Renewable Diesel - Corn
-39
-8
0
31
6
-10
Renewable Diesel - Canola
-654
-115
0
446
88
-235
Sugarcane Ethanol
-101
-16
31
56
7
-23
Conventional
Ethanol - E10
-1,746
-428
864
1,532
188
410
Ethanol - El5
168
83
-55
-147
-18
30
Ethanol - E85
261
77
-17
-229
-28
64
The costs are aggregated for each fossil fuel type and shown as annual totals and per-
gallon and per MSCF costs in Table 10.4.3.2.2-2.
458
-------
Table 10.4.3.2.2-2: Low Volume Scenario - Total Annual and Per-Gallon Costs Relative to
the 2025 Baseline (2022$)
Total Cost
Per-Unit
Year
Fuel Type
(million $)
Cost
Units
2026
Gasoline
Diesel Fuel
119
831
0.09
1.58
0/gal gasoline
0/gal diesel
Natural Gas
26
0.09
$/MSCF natural gas
Total
976
0.53
0/gal gasoline and diesel
Gasoline
200
0.15
0/gal gasoline
2027.
Diesel Fuel
973
1.87
0/gal diesel
Natural Gas
-12
-0.04
$/MSCF natural gas
Total
1,161
0.63
0/gal gasoline and diesel
Gasoline
285
0.22
0/gal gasoline
2028
Diesel Fuel
1,230
2.38
0/gal diesel
Natural Gas
-24
-0.08
$/MSCF natural gas
Total
1,491
0.81
0/gal gasoline and diesel
2029
Gasoline
Diesel Fuel
393
1,339
0.31
2.62
0/gal gasoline
0/gal diesel
Natural Gas
-38
-0.13
$/MSCF natural gas
Total
1,694
0.95
0/gal gasoline and diesel
Gasoline
530
0.43
0/gal gasoline
2030
Diesel Fuel
1,343
2.65
0/gal diesel
Natural Gas
-54
-0.19
$/MSCF natural gas
Total
1,819
1.04
0/gal gasoline and diesel
10.4.4 Costs for the High Volume Scenario
We analyzed the costs for the High Volume Scenario relative to the No RFS Baseline, as
well as incremental to the 2025 Baseline.
10.4.4.1 High Volume Scenario Relative to No RFS Baseline
10.4.4.1.1 Volumes
The renewable fuel and fossil fuel volume changes under the High Volume Scenario
relative to the No RFS Baseline are summarized in Tables 10.4.4.1.1-la and lb, respectively.
459
-------
Table 10.4.4.1.1-la: High Volume Scenario - Renewable Fuel Volume Changes Relative to
Change in Renewable Fuel Volume
Fuel Type
2026
2027
2028
2029
2030
Cellulosic Biofuel
CNG - Landfill Biogas (MMSCF)
52,804
63,866
65,931
67,996
70,282
Corn Kernel Fiber Ethanol
-2
-2
-2
-2
-1
Non-cellulosic Advanced
Biodiesel - Soy
1,196
1,189
1,197
1,196
1,192
Biodiesel - FOG
-49
-51
-47
-53
-46
Biodiesel - Corn Oil
34
57
35
33
35
Biodiesel - Canola
329
327
330
329
328
Renewable Diesel - Soy
915
1,165
1,415
1,665
1,915
Renewable Diesel - FOG
957
1,160
1,359
1,545
1,744
Renewable Diesel - Corn
79
44
52
35
22
Renewable Diesel - Canola
230
330
430
530
630
Sugarcane Ethanol
0
0
0
0
0
Conventional
Ethanol - E10
-111
-130
-138
-153
-169
Ethanol - El 5
138
165
176
196
218
Ethanol - E85
187
195
203
211
218
Change in Biogas Volume
52,804
63,866
65,931
67,996
70,282
Change in Ethanol Volume
214
230
240
254
267
Change in Biodiesel Volume
1,511
1,521
1,515
1,507
1,509
Change in Renewable Diesel Volume
2,180
2,699
3,256
3,776
4,312
460
-------
Table 10.4.4.1.1-lb: High Volume Scenario - Fossil Fuel Volume Changes Relative to the
No RFS Baseline (million gallons, except where noted)
Change in Fossil Fuel Volume
Fuel Type
Fuel Displaced
2026
2027
2028
2029
2030
Natural Gas
Cellulosic Biofuel
CNG - Landfill Biogas (MMSCF)
-52,804
-63,866
-65,931
-67,996
-70,282
Gasoline
Corn Kernel Fiber Ethanol
-1
-1
-1
-1
-1
Non-cellulosic Advanced
Diesel Fuel
Biodiesel - Soy
-1,113
-1,107
-1,115
-1,114
-1,111
Diesel Fuel
Biodiesel - FOG
45
48
44
49
43
Diesel Fuel
Biodiesel - Corn Oil
-32
-53
-32
-31
-33
Diesel Fuel
Biodiesel - Canola
-307
-304
-307
-307
-306
Diesel Fuel
Renewable Diesel - Soy
-875
-1,115
-1,354
-1,593
-1,832
Diesel Fuel
Renewable Diesel - FOG
-915
-1,110
-1,300
-1,478
-1,668
Diesel Fuel
Renewable Diesel - Corn
-75
-42
-49
-34
-22
Diesel Fuel
Renewable Diesel - Canola
-220
-316
-411
-507
-603
Gasoline
Sugarcane Ethanol
0.0
0.0
0.0
0.0
0.0
Conventional
Gasoline
Ethanol - E10
74
87
92
102
113
Gasoline
Ethanol - E15
-92
-111
-118
-131
-146
Gasoline
Ethanol - E85
-125
-130
-136
-141
-146
-
Change in Gasoline Volume
-145
-155
-162
-172
-180
-
Change in Diesel Fuel Volume
-3,493
-3,999
-4,525
-5,016
-5,531
-
Change in Natural Gas Volume
-52,804
-63,866
-65,931
-67,996
-70,282
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 demanded and its effect on
both imported crude oil, domestic crude oil, and imported petroleum products. Table 10.4.4.1.1-2
summarizes the projected change in petroleum imports expected from the increased consumption
of renewable relative to the No RFS Baseline.
Table 10.4.4.1.1-2: High Volume Scenario - Projected Change in Petroleum Imports Due to
Increase in Renewable Fuel Consumption I
telative to the No B
LFS Baseline (millic
2026
2027
2028
2029
2030
Change in Imported Gasoline
-72
-78
-81
-86
-90
Change in Imported Diesel Fuel
-1,746
-1,999
-2,263
-2,508
-2,765
Total Change in Crude Oil
-1,794
-2,049
-2,313
-2,560
-2,818
Change in Domestic Crude Oil
-60
-68
-77
-85
-94
Change in Imported Crude Oil
-1,734
-1,981
-2,236
-2,475
-2,725
10.4.4.1.2 Cost Impacts
The component cost (production, distribution, blending retail) of each biofuel type
compared to the fossil fuel it is displacing under the High Volume Scenario relative to the No
RFS Baseline is summarized in Tables 10.4.4.1.2-la and lb.
461
-------
Table 10.4.4.1.2-la: High Volume Scenario - Renewable and Petroleum Fuel Costs Relative
to the No RFS Baseline (million 2022$)
Renewable Fuel
Petroleum Fuel
Production
Distribution
Blending
Production
Distribution
Total
Cellulosic Biofuel
CNG - Landfill Biogas
207
673
0
-228
-801
-150
Corn Kernel Fiber Ethanol
-4
-1
-2
3
0
-3
Non-cellulosic Advanced
Biodiesel - Soy
4,802
767
0
-3,118
-570
1,881
Biodiesel - FOG
-153
-31
0
127
23
-34
Biodiesel - Corn Oil
117
22
0
-90
-16
33
Biodiesel - Canola
1,322
211
0
-858
-157
518
2026
Renewable Diesel - Soy
4,029
587
0
-2,451
-448
1,716
Renewable Diesel - FOG
3,396
616
0
-2,571
-470
970
Renewable Diesel - Corn
298
50
0
-210
-38
100
Renewable Diesel - Canola
1,013
148
0
-616
-113
431
Sugarcane Ethanol
0
0
0
0
0
0
Conventional
Ethanol - E10
-202
-47
94
166
21
32
Ethanol - El5
251
117
-78
-206
-25
58
Ethanol - E85
342
96
-22
-281
-35
100
Cellulosic Biofuel
CNG - Landfill Biogas
250
814
0
-260
-969
-165
Corn Kernel Fiber Ethanol
-4
-1
-2
3
0
-3
Non-cellulosic Advanced
Biodiesel - Soy
4,323
763
0
-2,967
-567
1,551
Biodiesel - FOG
-147
-33
0
128
25
-27
Biodiesel - Corn Oil
176
37
0
-142
-27
43
Biodiesel - Canola
1,189
210
0
-816
-156
426
2027
Renewable Diesel - Soy
4,686
908
0
-2,988
-571
2,035
Renewable Diesel - FOG
3,778
746
0
-2,982
-570
972
Renewable Diesel - Corn
152
28
0
-112
-21
47
Renewable Diesel - Canola
1,327
276
0
-846
-162
595
Sugarcane Ethanol
0
0
0
0
0
0
Conventional
Ethanol - E10
-234
-55
111
194
24
39
Ethanol - El5
298
140
-94
-246
-31
68
Ethanol - E85
350
99
-22
-289
-36
102
Cellulosic Biofuel
CNG - Landfill Biogas
258
840
0
-263
-1,000
-165
Corn Kernel Fiber Ethanol
-4
-1
-2
3
0
-2
Non-cellulosic Advanced
Biodiesel - Soy
4,196
768
0
-2,876
-571
1,516
Biodiesel - FOG
-130
-30
0
113
22
-25
Biodiesel - Corn Oil
104
22
0
-84
-17
26
Biodiesel - Canola
1,156
212
0
-792
-157
418
2028
Renewable Diesel - Soy
5,504
908
0
-3,493
-694
2,225
Renewable Diesel - FOG
4,287
874
0
-3,362
-668
1,132
Renewable Diesel - Corn
174
33
0
-127
-25
54
Renewable Diesel - Canola
1,672
276
0
-1,061
-211
676
Sugarcane Ethanol
0
0
0
0
0
0
Conventional
Ethanol - E10
-246
-58
117
206
26
45
Ethanol - El5
312
149
-99
-262
-33
67
Ethanol - E85
361
104
-23
-303
-38
101
462
-------
Table 10.4.4.1.2-lb: High Volume Scenario - Renewable and Petroleum Fuel Costs
Relative to the No RFS Baseline (million 2022$)
Renewable Fuel
Petroleum Fuel
Production
Distribution
Blending
Production
Distribution
Total
Cellulosic Biofuel
CNG - Landfill Biogas
266
867
0
-272
-1,031
-171
Corn Kernel Fiber Ethanol
-4
-1
-2
3
0
-2
Non-cellulosic Advanced
Biodiesel - Soy
4,023
768
0
-2,886
-571
1,333
Biodiesel - FOG
-140
-34
0
127
25
-21
Biodiesel - Corn Oil
96
21
0
-81
-16
21
Biodiesel - Canola
1,108
211
0
-795
-157
367
2029
Renewable Diesel - Soy
6,298
1,068
0
-4,126
-816
2,424
Renewable Diesel - FOG
4,753
991
0
-3,829
-757
1,158
Renewable Diesel - Corn
116
23
0
-87
-17
34
Renewable Diesel - Canola
2,004
340
0
-1,313
-260
771
Sugarcane Ethanol
0
0
0
0
0
0
Conventional
Ethanol - E10
-268
-64
130
229
28
55
Ethanol - El5
345
166
-111
-294
-36
69
Ethanol - E85
371
108
-24
-316
-39
99
Cellulosic Biofuel
CNG - Landfill Biogas
275
896
0
-286
-1,066
-181
Corn Kernel Fiber Ethanol
-2
0
-1
2
0
-1
Non-cellulosic Advanced
Biodiesel - Soy
3,845
765
0
-2,887
-569
1,154
Biodiesel - FOG
-117
-30
0
111
22
-14
Biodiesel - Corn Oil
96
22
0
-85
-17
17
Biodiesel - Canola
1,058
210
0
-794
-157
317
2030
Renewable Diesel - Soy
6,981
1,229
0
-4,764
-939
2,507
Renewable Diesel - FOG
5,184
1,119
0
-4,338
-855
1,111
Renewable Diesel - Corn
71
14
0
-56
-11
19
Renewable Diesel - Canola
2,296
404
0
-1,567
-309
825
Sugarcane Ethanol
0
0
0
0
0
0
Conventional
Ethanol - E10
-290
-71
144
255
31
68
Ethanol - El5
374
185
-124
-328
-40
67
Ethanol - E85
375
111
-25
-329
-40
92
The costs are aggregated for each fossil fuel type and shown as annual totals and per-
gallon and per MSCF costs in Table 10.4.4.1.2-2.
463
-------
Table 10.4.4.1.2-2: High Volume Scenario - Total Annual and Per-Gallon Costs Relative to
No RFS Baseline (20225
S)
Total Cost
Per-Unit
Year
Fuel Type
(million $)
Cost
Units
2026
Gasoline
Diesel Fuel
188
5,615
0.14
10.71
0/gal gasoline
0/gal diesel
Natural Gas
-150
-0.50
$/MSCF natural gas
Total
5,653
3.07
0/gal gasoline and diesel
Gasoline
206
0.16
0/gal gasoline
2027.
Diesel Fuel
5,642
10.86
0/gal diesel
Natural Gas
-165
-0.57
$/MSCF natural gas
Total
5,683
3.12
0/gal gasoline and diesel
2028
Gasoline
Diesel Fuel
211
6,022
0.16
11.66
0/gal gasoline
0/gal diesel
Natural Gas
-165
-0.57
$/MSCF natural gas
Total
6,068
3.37
0/gal gasoline and diesel
2029
Gasoline
Diesel Fuel
220
6,087
0.17
11.89
0/gal gasoline
0/gal diesel
Natural Gas
-171
-0.59
$/MSCF natural gas
Total
6,135
3.45
0/gal gasoline and diesel
Gasoline
226
0.18
0/gal gasoline
2030
Diesel Fuel
5,936
11.70
0/gal diesel
Natural Gas
-181
-0.63
$/MSCF natural gas
Total
5,981
3.41
0/gal gasoline and diesel
10.4.4.2 High Volume Scenario Relative to the 2025 Baseline
10.4.4.2.1 Volumes
The renewable fuel and fossil fuel volume changes under the High Volume Scenario
relative to the 2025 Baseline are summarized in Tables 10.4.4.2.1-la and lb, respectively.
464
-------
Table 10.4.4.2.1-la: High Volume Scenario - Renewable Fuel Volume Changes Relative to
Change in Renewable Fuel Volume
Fuel Type
2026
2027
2028
2029
2030
Cellulosic Biofuel
CNG - Landfill Biogas
-9,219
4,646
9,735
15,118
20,945
Corn Kernel Fiber Ethanol
47
46
45
43
42
Non-cellulosic Advanced
Biodiesel - Soy
323
323
323
323
323
Biodiesel - FOG
58
58
58
58
58
Biodiesel - Corn Oil
61
61
61
61
61
Biodiesel - Canola
34
34
34
34
34
Renewable Diesel - Soy
-23
227
477
727
977
Renewable Diesel - FOG
952
1,227
1,502
1,777
2,052
Renewable Diesel - Corn
-13
-13
-13
-13
-13
Renewable Diesel - Canola
-79
21
121
221
321
Sugarcane Ethanol
-37
-37
-37
-37
-37
Conventional
Ethanol - E10
-204
-383
-570
-784
-1,017
Ethanol - El 5
17
45
55
76
98
Ethanol - E85
30
61
91
121
152
Change in Biogas Volume
-9,219
4,646
9,735
15,118
20,945
Change in Ethanol Volume
-109
-231
-378
-544
-725
Change in Biodiesel Volume
476
476
476
476
476
Change in Renewable Diesel Volume
837
1,462
2,087
2,712
3,337
465
-------
Table 10.4.4.2.1-lb: High Volume Scenario - Fossil Fuel Volume Changes Relative to the
2025 Baseline (million gallons, except where noted)
Change in Fossil Fuel Volume
Fuel Type
Fuel Displaced
2026
2027
2028
2029
2030
Natural Gas
Cellulosic Biofuel
CNG - Landfill Biogas (MMSCF)
9,219
-4,646
-9,735
-15,118
-20,945
Gasoline
Corn Kernel Fiber Ethanol
31
31
30
29
28
Non-cellulosic Advanced
Diesel Fuel
Biodiesel - Soy
301
301
301
301
301
Diesel Fuel
Biodiesel - FOG
54
54
54
54
54
Diesel Fuel
Biodiesel - Corn Oil
57
57
57
57
57
Diesel Fuel
Biodiesel - Canola
32
32
32
32
32
Diesel Fuel
Renewable Diesel - Soy
22
-217
-457
-696
-935
Diesel Fuel
Renewable Diesel - FOG
-911
-1,174
-1,437
-1,700
-1,963
Diesel Fuel
Renewable Diesel - Corn
12
12
12
12
12
Diesel Fuel
Renewable Diesel - Canola
76
-20
-115
-211
-307
Gasoline
Sugarcane Ethanol
-25
-25
-25
-25
-25
Conventional
Gasoline
Ethanol - E10
136
256
381
525
681
Gasoline
Ethanol - E15
-12
-30
-37
-51
-65
Gasoline
Ethanol - E85
-20
-41
-61
-81
-102
-
Change in Gasoline Volume
111
191
289
397
517
-
Change in Diesel Fuel Volume
-358
-956
-1,554
-2,152
-2,750
-
Change in Natural Gas Volume
9,219
-4,646
-9,735
-15,118
-20,945
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 demanded and its effect on
both imported crude oil, domestic crude oil, and imported petroleum products. Table 10.4.4.2.1-2
summarizes the projected change in petroleum imports expected from the increased consumption
of renewable biofuels relative to the 2025 Baseline.
Table 10.4.4.2.1-2: High Volume Scenario - Projected Change in Petroleum Imports Due to
2026
2027
2028
2029
2030
Change in Imported Gasoline
56
96
144
199
259
Change in Imported Diesel Fuel
-179
-478
-111
-1,076
-1,375
Total Change in Crude Oil
-128
-389
-642
-891
-1,134
Change in Domestic Crude Oil
-4
-13
-21
-30
-38
Change in Imported Crude Oil
-124
-376
-621
-861
-1,097
10.4.4.2.2 Cost Impacts
The component cost (production, distribution, blending retail) of each biofuel type
compared to the fossil fuel it is displacing under the High Volume Scenario relative to the 2025
Baseline is summarized in Tables 10.4.4.2.2-la and lb.
466
-------
Table 10.4.4.2.2-la: High Volume Scenario - Renewable and Petroleum Fuel Costs Relative
to the 2025 Baseline (million 2022$)
Renewable Fuel
Petroleum Fuel
Production
Distribution
Blending
Production
Distribution
Total
Cellulosic Biofuel
CNG - Landfill Biogas
-36
-117
0
40
140
26
Corn Kernel Fiber Ethanol
86
13
40
-71
-9
60
Non-cellulosic Advanced
Biodiesel - Soy
1,299
207
0
-843
-154
509
Biodiesel - FOG
182
37
0
-158
-29
41
Biodiesel - Corn Oil
207
39
0
-89
-16
59
Biodiesel - Canola
136
22
0
0
0
53
2026
Renewable Diesel - Soy
-102
-15
0
62
11
-43
Renewable Diesel - FOG
3,368
611
0
34
6
962
Renewable Diesel - Corn
-48
-8
0
213
39
-16
Renewable Diesel - Canola
-349
-51
0
0
0
-149
Sugarcane Ethanol
-101
-16
31
56
7
-23
Conventional
Ethanol - E10
-372
-86
173
306
38
59
Ethanol - El5
32
15
-10
-26
-3
7
Ethanol - E85
55
15
-3
-46
-6
16
Cellulosic Biofuel
CNG - Landfill Biogas
18
59
0
-19
-70
-12
Corn Kernel Fiber Ethanol
83
13
39
-68
-9
58
Non-cellulosic Advanced
Biodiesel - Soy
1,176
207
0
-807
-154
422
Biodiesel - FOG
165
37
0
-144
-28
31
Biodiesel - Corn Oil
188
39
0
-152
-29
46
Biodiesel - Canola
123
22
0
-85
-16
44
2027
Renewable Diesel - Soy
913
306
0
-582
-111
526
Renewable Diesel - FOG
3,986
787
0
-3,146
-601
1,026
Renewable Diesel - Corn
-44
-8
0
32
6
-13
Renewable Diesel - Canola
83
77
0
-53
-10
97
Sugarcane Ethanol
-101
-16
31
55
7
-23
Conventional
Ethanol - E10
-689
-161
325
569
71
115
Ethanol - El5
81
38
-26
-67
-8
18
Ethanol - E85
109
31
-7
-90
-11
32
Cellulosic Biofuel
CNG - Landfill Biogas
38
124
0
-39
-148
-24
Corn Kernel Fiber Ethanol
80
12
38
-67
-8
55
Non-cellulosic Advanced
Biodiesel - Soy
1,133
207
0
-777
-154
410
Biodiesel - FOG
160
37
0
-139
-28
30
Biodiesel - Corn Oil
181
39
0
-146
-29
45
Biodiesel - Canola
119
22
0
-82
-16
43
2028
Renewable Diesel - Soy
1,856
306
0
-1,178
-234
750
Renewable Diesel - FOG
4,728
964
0
-3,708
-736
1,248
Renewable Diesel - Corn
-42
-8
0
31
6
-13
Renewable Diesel - Canola
469
77
0
-298
-59
190
Sugarcane Ethanol
-101
-16
31
55
7
-23
Conventional
Ethanol - E10
-1,014
-240
484
851
106
186
Ethanol - El5
99
47
-31
-83
-10
21
Ethanol - E85
162
46
-10
-136
-17
45
467
-------
Table 10.4.4.2.2-lb: High Volume Scenario - Renewable and Petroleum Fuel Costs
Relative to the 2025 Baseline (million 2022$)
Renewable Fuel
Petroleum Fuel
Production
Distribution
Blending
Production
Distribution
Total
Cellulosic Biofuel
CNG - Landfill Biogas
59
193
0
-61
-229
-38
Corn Kernel Fiber Ethanol
76
12
37
-65
-8
52
Non-cellulosic Advanced
Biodiesel - Soy
1,087
207
0
-780
-154
360
Biodiesel - FOG
153
37
0
-139
-28
24
Biodiesel - Corn Oil
174
39
0
-147
-29
38
Biodiesel - Canola
114
22
0
-82
-16
38
2029
Renewable Diesel - Soy
2,750
467
0
-1,802
-356
1,058
Renewable Diesel - FOG
5,466
1,140
0
-4,403
-871
1,332
Renewable Diesel - Corn
-41
-8
0
31
6
-12
Renewable Diesel - Canola
834
142
0
-547
-108
321
Sugarcane Ethanol
-101
-16
31
56
7
-23
Conventional
Ethanol - E10
-1,378
-330
667
1,176
145
280
Ethanol - El5
134
64
-43
-114
-14
27
Ethanol - E85
213
62
-14
-182
-22
57
Cellulosic Biofuel
CNG - Landfill Biogas
82
267
0
-85
-318
-54
Corn Kernel Fiber Ethanol
72
12
36
-63
-8
48
Non-cellulosic Advanced
Biodiesel - Soy
1,043
207
0
-783
-154
313
Biodiesel - FOG
147
37
0
-140
-28
17
Biodiesel - Corn Oil
167
39
0
-147
-29
30
Biodiesel - Canola
110
22
0
-82
-16
33
2030
Renewable Diesel - Soy
3,562
627
0
-2,431
-479
1,279
Renewable Diesel - FOG
6,101
1,316
0
-5,104
-1,006
1,307
Renewable Diesel - Corn
-40
-8
0
31
6
-11
Renewable Diesel - Canola
1,169
206
0
-798
-157
420
Sugarcane Ethanol
-101
-16
31
56
7
-23
Conventional
Ethanol - E10
-1,746
-428
864
1,532
188
410
Ethanol - El5
168
83
-55
-147
-18
30
Ethanol - E85
261
77
-17
-229
-28
64
The costs are aggregated for each fossil fuel type and shown as annual totals and per-
gallon and per MSCF costs in Table 10.4.4.2.2-2.
468
-------
Table 10.4.4.2.2-2: High Volume Scenario - Total Annual and Per-Gallon Costs Relative to
2025 Baseline (2022$)
Total Cost
Per-Unit
Year
Fuel Type
(million $)
Cost
Units
2026
Gasoline
Diesel Fuel
119
1,415
0.09
2.70
0/gal gasoline
0/gal diesel
Natural Gas
26
0.09
$/MSCF natural gas
Total
1,560
0.85
0/gal gasoline and diesel
Gasoline
200
0.15
0/gal gasoline
2027
Diesel Fuel
2,178
4.19
0/gal diesel
Natural Gas
-12
-0.04
$/MSCF natural gas
Total
2,366
1.29
0/gal gasoline and diesel
Gasoline
285
0.22
0/gal gasoline
2028
Diesel Fuel
2,703
5.23
0/gal diesel
Natural Gas
-24
-0.08
$/MSCF natural gas
Total
2,964
1.61
0/gal gasoline and diesel
2029
Gasoline
Diesel Fuel
393
3,158
0.31
6.17
0/gal gasoline
0/gal diesel
Natural Gas
-38
-0.13
$/MSCF natural gas
Total
3,513
1.98
0/gal gasoline and diesel
Gasoline
530
0.43
0/gal gasoline
2030
Diesel Fuel
3,388
6.68
0/gal diesel
Natural Gas
-54
-0.19
$/MSCF natural gas
Total
3,864
2.21
0/gal gasoline and diesel
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.
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 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, oil prices, 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 March
2025) as an estimate of future RIN prices, as shown in Table 10.5.1-1.
469
-------
Table 10.5.1-1: Average RIN Prices (April 2024 - March 2025)
RFS Standard
RIN
Type
Average
RIN Price
2025
Standard
Proposed 2026
Standard
Proposed 2027
Standard
Cellulosic Biofuel
(D3)
D3
$3.01
0.70%°
0.87%
0.92%
Biomass-Based Diesel
(D4)
D4
$0.61
3.15%
4.75%
5.07%
Other Advanced
BiofueP (D5)
D5
$0.61
0.46%
0.40%
0.41%
Conventional
Renewable Fuelb (D6)
D6
$0.62
8.82%
10.00%
10.14%
a Other advanced biofuel is not a fuel category for which a percentage standard is established, but is calculated by
subtracting the cellulosic biofuel and BBD standards from the advanced biofuel standard.
b Conventional renewable fuel is not a fuel category for which a percentage standard is established, but is calculated
by subtracting the advanced biofuel standard from the total renewable fuel standard.
0 Reflects the proposed partial waiver of the 2025 cellulosic biofuel standard.
We then calculated the RIN cost for petroleum fuel by weighting the RIN price for each
D code by their respective RFS standard and summing the total. The results are shown in Table
10.5.1-2.
Table 10.5.1-2: Estimated RIN Costs for Petroleum Fuel for 2025-2027
Year
RIN Cost
($/Gallon)
2025
$0.10
2026
$0.12
2027
$0.12
Finally, we calculated RIN values for fuels. For gasoline-ethanol blends, we multiplied
the average D6 RIN price by the ethanol content of each blend (i.e., 10% for E10, 15% for E15,
and an average ethanol content of 74% for E85). For biodiesel and renewable diesel, we
multiplied the average D4 RIN price by the equivalence value of each fuel (i.e., 1.5 for biodiesel
and 1.6 for renewable diesel). The results are shown in Table 10.5.1-3.
Table 10.5.1-3: Estimated RIN Va
RIN Value
Fuel
($/Gallon)
E10
$0.06
E15
$0.09
E85
$0.46
Biodiesel
$0.91
Renewable Diesel
$0.97
ues for Fuels
10.5.2 Estimated Fuel Price Impacts (Gasoline)
In this section, we estimate the fuel price impacts of the Proposed Volumes on gasoline
relative to the No RFS and 2025 Baselines. First, we estimated the total cost of gasoline-ethanol
470
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blends for the Proposed Volumes. We began with the production cost for each fuel,722 added the
RIN cost associated with the gasoline portion of the fuel, and then subtracted the RIN value
associated with the ethanol portion of 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 6.5.2-3 to get the total cost for
each fuel. Finally, we calculated the average gasoline cost by dividing the total cost of all fuels
by the total volume of all fuels. As shown in Tables 10.5.2-1 and 2, we estimate that average
gasoline costs range from $2.43 to $2.45 per gallon.
Table 10.5.2-1: Gasoline <
^osts - 2026
E0
E10
E15
E85
Cost to Produce ($/gal)
$2.52
$2.41
$2.46
$2.30
RIN Cost ($/gal)
$0.12
$0.11
$0.10
$0.03
RIN Value ($/gal)
$0.00
-$0.06
-$0.09
-$0.46
Net Cost ($/gal)
$2.64
$2.45
$2.47
$1.87
Volume (mil gal)
1,936
132,991
917
464
Total Fuel Cost ($bil)
$5.1
$325.9
$2.3
$0.9
Average Cost ($/gal)
$2.45
Table 10.5.2-2: Gasoline Costs - 2027
E0
E10
E15
E85
Cost to Produce ($/gal)
$2.50
$2.39
$2.44
$2.28
RIN Cost ($/gal)
$0.12
$0.11
$0.10
$0.03
RIN Value ($/gal)
$0.00
-$0.06
-$0.09
-$0.46
Net Cost ($/gal)
$2.62
$2.43
$2.45
$1.86
Volume (mil gal)
1,929
131,219
1,102
505
Total Fuel Cost ($bil)
$5.1
$319.3
$2.7
$0.9
Average Cost ($/gal)
$2.43
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 and multiplied by the volume of each blend under the respective baseline to get the total
cost for each fuel.723 We then calculated the average gasoline cost by dividing the total cost of all
fuels by the total volume of all fuels. As shown in Tables 10.5.2-3 and 4, we estimate that
average gasoline costs under the No RFS Baseline range from $2.39 to $2.41 per gallon.
722 Note that for purposes of this fuel price impacts assessment, we only looked at the cost to produce and distribute
fuel to retail stations for sale to consumers (i.e., we subtracted out of the fuel economy cost for each fuel).
723 For purposes of the No RFS Baseline analysis, we assumed that E0 volumes were held constant relative to the
Proposed Volumes and that there would not be any volumes of E15 or E85. E10 volumes were calculated by totaling
ethanol production for each year from Table 2.1.5-2 and dividing by 0.1.
471
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Table 10.5.2-3: Gasoline <
^osts - 2026 (No RFS Baseline)
E0
E10
E15
E85
Cost to Produce ($/gal)
$2.52
$2.41
$2.46
$2.30
Volume (mil gal)
1,936
134,100
0
211
Total Fuel Cost ($bil)
$4.9
$322.7
$0.0
$0.5
Average Cost ($/gal)
$2.41
Table 10.5.2-4: Gasoline <
^osts - 2027 (No RFS Baseline)
E0
E10
E15
E85
Cost to Produce ($/gal)
$2.50
$2.39
$2.44
$2.28
Volume (mil gal)
1,929
132,520
0
242
Total Fuel Cost ($bil)
$4.8
$316.1
$0.0
$0.6
Average Cost ($/gal)
$2.39
For the 2025 Baseline, we used the same approach described above for No RFS
Baseline.724 As shown in Tables 10.5.2-5 and 6, we estimate that average gasoline costs under
the 2025 Baseline range from $2.43 to $2.45 per gallon.
Table 10.5.2-5: Gasoline <
^osts - 2026 (2025 Baseline)
E0
E10
E15
E85
Cost to Produce ($/gal)
$2.52
$2.41
$2.46
$2.30
RIN Cost ($/gal)
$0.12
$0.11
$0.10
$0.03
RIN Value ($/gal)
$0.00
-$0.06
-$0.09
-$0.46
Net Cost ($/gal)
$2.64
$2.45
$2.47
$1.87
Volume (mil gal)
1,941
134,888
801
423
Total Fuel Cost ($bil)
$5.1
$330.5
$2.0
$0.8
Average Cost ($/gal)
$2.45
Table 10.5.2-6: Gasoline <
^osts - 2027 (2025 Baseline)
E0
E10
E15
E85
Cost to Produce ($/gal)
$2.50
$2.39
$2.44
$2.28
RIN Cost ($/gal)
$0.12
$0.11
$0.10
$0.03
RIN Value ($/gal)
$0.00
-$0.06
-$0.09
-$0.46
Net Cost ($/gal)
$2.62
$2.43
$2.45
$1.86
Volume (mil gal)
1,941
134,888
801
423
Total Fuel Cost ($bil)
$5.1
$328.2
$2.0
$0.8
Average Cost ($/gal)
$2.43
Finally, we calculated the fuel price impacts on gasoline for each year by subtracting the
average gasoline cost for each baseline from the average gasoline cost for the Proposed
Volumes. As shown in Table 10.5.2-7, we estimate that the fuel price impacts on gasoline under
the No RFS Baseline range from 4.40 to 4.70 per gallon. As shown in Table 10.5.2-8, we
estimate that the fuel price impacts on gasoline under the 2025 Baseline are 0.00 per gallon.
724 2025 Baseline gasoline-ethanol blend volumes from Set 1 Rule RIA Table 6.5.2-3.
472
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2026
2027
Average Cost (No RFS Baseline) ($/gal)
$2.41
$2.39
Average Cost (Proposed Volumes) ($/gal)
$2.45
$2.43
Fuel Price Impact (0/gal)
4.40
4.70
Table 10.5.2-8: Gasoline Fuel Price Impaci
ts (2025 Baseline)
2026
2027
Average Cost (2025 Baseline) ($/gal)
$2.45
$2.43
Average Cost (Proposed Volumes) ($/gal)
$2.45
$2.43
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 Proposed 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 Proposed Volumes. We began with the production cost for each
fuel,725 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 average diesel costs range from
$3.32 to $3.41 per gallon.
Table 10.5.3-1: Diesel Costs - 2026
Diesel
Biodiesel
Renewable Diesel
Corn
FOG
Soybean/
Canola
Corn
FOG
Soybean/
Canola
Cost to Produce ($/gal)
$3.31
$4.19
$3.93
$4.80
$4.57
$4.32
$5.18
RIN Cost ($/gal)
$0.12
$0.00
$0.00
$0.00
$0.00
$0.00
$0.00
RIN Value ($/gal)
$0.00
-$0.91
-$0.91
-$0.91
-$0.97
-$0.97
-$0.97
Tax Credit ($/gal)
$0.00
-$0.70
-$0.59
-$0.20
-$0.73
-$0.62
-$0.15
Net Cost ($/gal)
$3.43
$2.58
$2.43
$3.69
$2.87
$2.73
$4.06
Volume (mil gal)
50,490
208
366
1,542
681
2,188
1,910
Total Fuel Cost ($bil)
$173.1
$0.5
$0.9
$5.7
$2.0
$6.0
$7.8
Total Cost ($/gal)
$3.41
725 Note that for purposes of this fuel price impacts assessment, we only looked at the cost to produce and distribute
fuel to retail stations for sale to consumers (i.e., we subtracted out of the fuel economy cost for each fuel).
473
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Table 10.5.3-2: Diesel Costs - 2027
Diesel
Biodiesel
Renewable Diesel
Corn
FOG
Soybean/
Canola
Corn
FOG
Soybean/
Canola
Cost to Produce ($/gal)
$3.19
$4.19
$3.93
$4.80
$4.57
$4.32
$5.18
RIN Cost ($/gal)
$0.12
$0.00
$0.00
$0.00
$0.00
$0.00
$0.00
RIN Value ($/gal)
$0.00
-$0.91
-$0.91
-$0.91
-$0.97
-$0.97
-$0.97
Tax Credit ($/gal)
$0.00
-$0.70
-$0.59
-$0.20
-$0.73
-$0.62
-$0.15
Net Cost ($/gal)
$3.31
$2.58
$2.43
$3.69
$2.87
$2.73
$4.06
Volume (mil gal)
49,746
208
366
1,571
681
2,238
2,160
Total Fuel Cost ($bil)
$164.8
$0.5
$0.9
$5.8
$2.0
$6.1
$8.8
Total Cost ($/gal)
$3.32
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.726 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 average diesel costs under the No RFS Baseline range from $3.21 to $3.32 per gallon.
Table 10.5.3-3: Diesel Costs - 2026
No RFS Baseline)
Diesel
Biodiesel
Renewable
Jiesel
Corn
FOG
Soybean/
Canola
Corn
FOG
Soybean/
Canola
Cost to Produce ($/gal)
$3.31
$4.19
$3.93
$4.80
$4.57
$4.32
$5.18
Tax Credit ($/gal)
$0.00
-$0.70
-$0.59
-$0.20
-$0.73
-$0.62
-$0.15
Net Cost ($/gal)
$3.31
$3.49
$3.34
$4.60
$3.84
$3.70
$5.03
Volume (mil gal)
55,043
90
391
108
198
1,288
0
Total Fuel Cost ($bil)
$182.2
$0.3
$1.3
$0.5
$0.8
$4.8
$0.0
Total Cost ($/gal)
$3.32
726 Biodiesel and renewable diesel volumes from Table 2.1.5-2. For purposes of the No RFS Baseline analysis, we
assumed that total diesel energy demand was held constant relative to the Proposed Volumes to calculate petroleum
diesel fuel volumes.
474
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Table 10.5.3-4: Diesel Costs - 2027 (No RFS Baseline)
Diesel
Biodiesel
Renewable Diesel
Corn
FOG
Soybean/
Canola
Corn
FOG
Soybean/
Canola
Cost to Produce ($/gal)
$3.19
$4.19
$3.93
$4.80
$4.57
$4.32
$5.18
Tax Credit ($/gal)
$0.00
-$0.70
-$0.59
-$0.20
-$0.73
-$0.62
-$0.15
Net Cost ($/gal)
$3.19
$3.49
$3.34
$4.60
$3.84
$3.70
$5.03
Volume (mil gal)
54,521
67
394
118
233
1,360
0
Total Fuel Cost ($bil)
$173.9
$0.2
$1.3
$0.5
$0.9
$5.0
$0.0
Total Cost ($/gal)
$3.21
For the 2025 Baseline, we used the same approach described above for the No RFS
Baseline.727 As shown in Tables 10.5.3-5 and 6, we estimate that average diesel costs under the
2025 Baseline range from $3.32 to $3.42 per gallon.
Table 10.5.3-5: Diesel Costs - 2026 (2025 Baseline)
Diesel
Biodiesel
Renewable Diesel
Corn
FOG
Soybean/
Canola
Corn
FOG
Soybean/
Canola
Cost to Produce ($/gal)
$3.31
$4.19
$3.93
$4.80
$4.57
$4.32
$5.18
RIN Cost ($/gal)
$0.12
$0.00
$0.00
$0.00
$0.00
$0.00
$0.00
RIN Value ($/gal)
$0.00
-$0.91
-$0.91
-$0.91
-$0.97
-$0.97
-$0.97
Tax Credit ($/gal)
$0.00
-$0.70
-$0.59
-$0.20
-$0.73
-$0.62
-$0.15
Net Cost ($/gal)
$3.43
$2.58
$2.43
$3.69
$2.87
$2.73
$4.06
Volume (mil gal)
53,244
63
285
1,276
289
1,291
1,248
Total Blend Cost ($bil)
$182.6
$0.2
$0.7
$4.7
$0.8
$3.5
$5.1
Average Cost ($/gal)
$3.42
Table 10.5.3-6: Diesel Costs - 2027 (2025 Baseline)
Diesel
Biodiesel
Renewable Diesel
Corn
FOG
Soybean/
Canola
Corn
FOG
Soybean/
Canola
Cost to Produce ($/gal)
$3.19
$4.19
$3.93
$4.80
$4.57
$4.32
$5.18
RIN Cost ($/gal)
$0.12
$0.00
$0.00
$0.00
$0.00
$0.00
$0.00
RIN Value ($/gal)
$0.00
-$0.91
-$0.91
-$0.91
-$0.97
-$0.97
-$0.97
Tax Credit ($/gal)
$0.00
-$0.70
-$0.59
-$0.20
-$0.73
-$0.62
-$0.15
Net Cost ($/gal)
$3.31
$2.58
$2.43
$3.69
$2.87
$2.73
$4.06
Volume (mil gal)
53,244
63
285
1,276
289
1,291
1,248
Total Blend Cost ($bil)
$176.4
$0.2
$0.7
$4.7
$0.8
$3.5
$5.1
Average Cost ($/gal)
$3.32
727 2025 Baseline biodiesel and renewable diesel volumes from Table 2.2-2. For purposes of the 2025 Baseline
analysis, we assumed that total diesel energy demand was held constant relative to the Proposed Volumes to
calculate petroleum diesel fuel volumes.
475
-------
Finally, we calculated the fuel price impacts on diesel for each year by subtracting the
average diesel cost for each baseline from the average diesel cost for the Proposed Volumes. As
shown in Table 10.5.3-7, we estimate that the fuel price impacts on diesel under the No RFS
Baseline range from 9.10 to 10.60 per gallon. As shown in Table 10.5.3-8, we estimate that the
fuel price impacts on diesel under the 2025 Baseline range from -1.00 to -0.20 per gallon.
Table 10.5.3-7: Diesel Fuel Price Impacts (No RFS I
•aseline)
2026
2027
Average Cost (No RFS Baseline) ($/gal)
$3.32
$3.21
Average Cost (Proposed Volumes) ($/gal)
$3.41
$3.32
Fuel Price Impact (0/gal)
9.10
10.60
Table 10.5.3-8: Diesel Fuel Price Impacts i
2025 Baseline)
2026
2027
Average Cost (2025 Baseline) ($/gal)
$3.42
$3.32
Average Cost (Proposed Volumes) ($/gal)
$3.41
$3.32
Fuel Price Impact (0/gal)
-1.00
-0.20
10.5.4 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 9.10 per gallon in 2026 to 10.60 per gallon in 2027.
As a worst-case scenario, we will use the projected diesel fuel price increase of 10.60 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 because crude oil prices increased from an average of
$30 per barrel to over $90 per barrel.728 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
$3.31/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 10.60 per gallon increase in diesel fuel price in 2027 amounts to a 2.7%
increase in diesel fuel prices. Applying the 25% ratio from the USDA study would indicate that
the 2026 Proposed Volumes incremental to the No RFS Baseline would then increase the
wholesale price of produce by about 0.7%. 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 $0.02 per pound.729 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
728 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 errl60.pdf.
Coupons.com, "Comparing Prices on Groceries," May 4, 2021.
476
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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 Proposed Volumes is expected to cause a
small decrease in diesel fuel prices, thus slightly decreasing the cost to transport goods.
10.6 Comparison of Societal Benefits and Costs
In this section, we summarize the projected societal benefits and costs of the three cases
we analyzed (the Proposed Volumes, the Low Volume Scenario, and the High Volume
Scenario). Table 10.6-1 summarizes only the projected societal benefits and costs of this rule. It
does not, for example, include the projected rural economic development impacts of the three
cases, 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 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.
We note that the societal benefits and costs of the Proposed Volumes are greater in
magnitude than the Low and High Volume Scenarios. This difference is primarily due to the fact
that only the Proposed Volumes consider the impact of the proposed import RIN reduction. We
project that the Proposed Volumes would result in a greater volume of renewable fuel supplied in
2026 and 2027 than either the Low or High Volume Scenario.730 Further, because the Proposed
Volumes only cover 2026-2027 while the Low and High Volume Scenarios cover 2026-2030, it
can be difficult to compare the annualized societal benefits and costs between the cases. To
better enable this comparison, we have only included the projected benefits and costs for 2026
and 2027 in Table 10.6-1. For further discussion of the societal benefits and costs of each case,
see Chapters 6.4 and 10.4.
730 See Chapters 3.1 and 3.2 for more detail on the volumes of renewable fuel by type we project would be supplied
for each case.
477
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Table 10.6-1: Net Benefits of the Proposed Volumes and Volume Scenarios in 2026 and
2027 (million 2022$)a
Present
Annualized
Type
Category
2026
2027
Value
Value
Proposed
Volumes
Societal Benefits
Energy Security Benefits
$196
$210
$387
$202
Societal Costs
Fuel Costs
$7,494
$5,936
$12,871
$6,726
Net Benefits
Total
-$7,297
-$5,726
-$12,484
-$6,524
Low Volume Scenario
Societal Benefits
Energy Security Benefits
$138
$150
$275
$144
Societal Costs
Fuel Costs
$5,068
$4,477
$9,140
$4,777
Net Benefits
Total
-$4,930
-$4,327
-$8,862
-$4,633
High Volume Scenario
Societal Benefits
Energy Security Benefits
$151
$176
$312
$163
Societal Costs
Fuel Costs
$5,653
$5,683
$10,845
$5,668
Net Benefits
Total
-$5,502
-$5,507
-$10,533
-$5,505
a 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 NPRM Costs and Benefits Summary," available in the
docket for this action.
478
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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%.731 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.732
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.
731 A 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.
732 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=P100TBGV.pdf.
479
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Table 11.1-1: Estimated Cost-to-Sales Ratios of the Proposed 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
0.10-0.55%
0.11-0.67%
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.733
733 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 10.5.4).
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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 SB A 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. 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 2024 EIA refinery data,734 EPA believes that there are
approximately 35-40 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.
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.735 While EPA is making revisions to the RFS
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.
734 EIA, "Refinery Capacity Report 2024," Petroleum & Other Liquids, January 1, 2024.
https://www.eia. gov/petroleum/refinervcar)acitv/arcliive/2024/refcar)2024.r)hr).
735 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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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.736 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.737 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
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 be complying with the
upper-bound estimate of the 2026 and 2027 RFS standards. As described in Preamble Section
VI.C, this estimate assumes that all eligible small refineries—including the 6 obligated parties
that are small refiners—receive a small refinery exemption (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 all
small refineries will receive an exemption, but they do not receive an exemption and have to
comply with their 2026 and 2027 RFS obligations.
736 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=P100TBGV.pdf.
737 Id.
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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 March 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
(April 2024 -
March 2025)
2025 RFS
Standard
2026
2027
Standard
Aa
Standard
Aa
Cellulosic
Biofuel
D3
$3.01
0.70%d
0.87%
0.17%
0.92%
0.22%
Biomass-Based
Diesel
D4
$0.61
3.15%
4.75%
1.60%
5.07%
1.92%
Other Advanced
Biofuelb
D5
$0.61
0.46%
0.40%
-0.06%
0.41%
-0.05%
Conventional
Renewable Fuel0
D6
$0.62
8.82%
10.00%
1.18%
10.14%
1.32%
a A represents the change relative to the baseline of the 2025 RFS standards.
b Other advanced biofuel is not a fuel category for which a percentage standard is established, but is calculated by
subtracting the cellulosic biofuel and biomass-based diesel standards from the advanced biofuel standard.
0 Conventional renewable fuel is not a fuel category for which a percentage standard is established, but is calculated
by subtracting the advanced biofuel standard from the total renewable fuel standard.
d Reflects the proposed partial waiver of the 2025 cellulosic biofuel standard.
Using 2023 compliance data and SRE petition materials where available, and assuming
that the total gasoline and diesel production for each of these small refiners remains unchanged,
we estimated their RVOs for 2026 and 2027. The difference between the estimated RVOs for
each year multiplied by the estimated RIN price for each standard then gives us the estimated
cost of the 2026 and 2027 RFS standards for each small refiner that chooses to meet their
obligations by purchasing separated RINs. The actual calculations for each small refiner are
provided in Chapter 11.6; a non-CBI example of these calculations is shown in Tables 11.3.3-2
and 3.
Table 11.3.3-2: Example Small Refiner Costs Calculation for 2026
Other Advanced
Conventional
Gas/
Cellulosic
BBD
Biofuel
Renewable Fuel
Diesel
(D3)
(D4)
(D5)
(D6)
Total
Prod
A (mil
Cost
A (mil
Cost
A (mil
Cost
A (mil
Cost
Cost
Company
(mil gal)
RINs)
($mil)
RINs)
($mil)
RINs)
($mil)
RINs)
($mil)
($mil)
Example
100
0.17
$0.51
1.60
$0.97
-0.06
-$0.04
1.18
$0.73
$2.17
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Table 11.3.3-3: Example Small Refiner Costs Calculation for 2027
Other
Advanced
Conventional
Gas/
Cellulosic
BBD
Biofuel
Renewable Fuel
Diesel
(D3)
(D4)
(D5)
(D6)
Total
Prod
A (mil
Cost
A (mil
Cost
A (mil
Cost
A (mil
Cost
Cost
Company
(mil gal)
RINs)
($mil)
RINs)
($mil)
RINs)
($mil)
RINs)
($mil)
($mil)
Example
100
0.22
$0.66
1.92
$1.16
-0.05
-$0.03
1.32
$0.81
$2.61
11.4 Cost-to-Sales Ratio Result
The final step in our methodology is to compare the total estimated costs from each of the
methods above to relevant total estimated revenue from the sales of gasoline and diesel in the
U.S. in 2026 and 2027. Since the RFS standards are proportional to the volume of gasoline and
diesel produced by each obligated party, all obligated parties (including small refiners) are
expected to experience costs (and recover those costs) to comply with the RFS standards that are
proportional to their sales volumes.
For Method 1, all obligated parties—including small refiners—recover their RFS
compliance costs and thus they have no net cost of compliance. For Method 2, we divided the
estimated costs for each small refiner by its total estimated annual sales.738 The resulting cost-to-
sales ratios for each method are shown in Table 11.4-1, along with a non-CBI example of these
calculations for Method 2 using the data from Tables 11.3.3-2 through 4.
Table 11.4-1: Estimated Cost
t-to-Sales Ratios of the 2026 and 2027 R
7S Standards
Method
Screening Analysis
Total Cost ($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
--
--
0.10-0.55%
0.11-0.67%
Full RIN Price as Cost
for Small Refiners
(Example)
$2.17
$2.34
$500
$500
0.43%
0.52%
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
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
738 Estimated annual sales data gathered from SRE petition materials.
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(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 0.67%.
11.6 Small Refiner CBI Data
[Information Redacted - Claimed as CBI]
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