Revised 2023 and Later Model Year Light'
Duty Vehicle GHG Emissions Standards
Regulatory Impact Analysis
&EPA
United States
Environmental Protection
Agency

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Revised 2023 and Later Model Year Light-
Duty Vehicle GHG Emissions Standards
Regulatory Impact Analysis
Assessment and Standards Division
Office of Transportation and Air Quality
U.S. Environmental Protection Agency
EPA-420-R-21 -018
August 2021

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Table of Contents
List of Tables	v
List of Figures	xi
Executive Summary	xii
Proposed Revisions to Light-duty GHG Emissions Standards	xiii
Proposed Compliance Incentives and Flexibilities	xv
Summary of Proposal Costs and Benefits	xvi
Summary of the Analysis of Alternatives to the Proposal	xix
Description of Alternatives	xix
Summary of Costs and Benefits of the Alternatives	xxii
Summary of the Proposal's Costs and Benefits Compared to the Alternatives	xxvii
RIA Chapter Summary	xxxii
Chapter 1: Background on the 2017 and Later Light-duty Vehicle GHG Standards and
Midterm Evaluation	xxxii
Chapter 2: Technology Feasibility, Effectiveness, Costs, and Lead-time	xxxii
Chapter 3: Economic and Other Key Inputs	xxxii
Chapter 4: Modeling GHG Compliance	xxxii
Chapter 5: Projected Impacts on Emissions, Fuel Consumption, and Safety	xxxii
Chapter 6: Vehicle Program Costs and Fuel Savings	xxxii
Chapter 7: Non-GHG Health and Environmental Impacts	xxxii
Chapter 8: Vehicle Sales, Employment, and Affordability and Equity Impacts	xxxii
Chapter 9: Small Business Flexibilities	xxxiii
Chapter 10: Summary of Costs and Benefits	xxxiii
Chapter 1: Background on the 2017 and Later Light-duty Vehicle GHG Standards and
Midterm Evaluation	1-1
1.1	Summary of 2012 Final Rulemaking	1-2
1.1.1	Light-duty Vehicle GHG Emissions Standards	1-2
1.1.2	Flexibilities	1-5
1.2	2016-2018 Midterm Evaluation of 2021-2025 Light-duty Vehicle GHG Standards.... 1-5
1.2.1	Updated EPA 2018 MTE Analysis	1-6
1.2.2	Comparison of Analytical Results Between the 2012 FRM and the MTE	1-9
1.3	Agency Actions, March 2017 - April 2020	1-12
1.3.1	2017 Reconsideration of the MTE Final Determination and 2018 MTE Final
Determination	1-13
1.3.2	SAFE2	1-13
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References for Chapter 1	1-15
Chapter 2: Technology Feasibility, Effectiveness, Costs, and Lead-time	2-1
2.1	Proposed Standards	2-1
2.1.1 Proposed Compliance Incentives and Flexibilities	2-4
2.2	Light-duty Vehicle Technology Feasibility	2-5
2.2.1	Feasibility of the Proposed Standards	2-5
2.2.2	Alternatives to the Proposed Standards	2-7
2.3	Vehicle Technologies	2-10
2.3.1	Recent Advances in Internal Combustion Engines	2-11
2.3.2	Changes to Engine Technologies Represented in the Analysis for the Proposal .2-13
2.3.3	Vehicle Electrification	2-13
2.4	Analysis of Manufacturers Generation and Use of GHG Credit	2-15
References for Chapter 2	2-19
Chapter 3: Economic and Other Key Inputs	3-1
3.1	Rebound	3-1
3.1.1	Accounting for the Fuel Economy Rebound Effect	3-1
3.1.2	Summary of Historical Literature on the LDV Rebound Effect	3-2
3.1.3	Review of Recent Literature on LDV Rebound	3-5
3.1.4	Basis for Rebound Effect Used in this Proposed LDV Rule	3-12
3.2	Energy Security Impacts	3-15
3.2.1	Review of Historical Energy Security Literature	3-16
3.2.2	Review of Recent Energy Security Literature	3-18
3.2.3	Cost of Existing U.S. Energy Security Policies	3-21
3.2.4	U.S. Oil Import Reductions from this Proposed Rule	3-23
3.2.5	Oil Security Premiums Used for this Proposed Rule	3-24
3.2.6	Energy Security Benefits of the Proposed Rule	3-27
3.3	Social Cost of Greenhouse Gases	3-28
3.4	Drive Surplus, Congestion and Noise	3-42
References for Chapter 3	3-44
Chapter 4: Modeling GHG Compliance	4-1
4.1 Compliance Modeling, Analytical Updates, and Analytical Revisions	4-1
4.1.1	GHG Targets and Compliance Levels	4-5
4.1.2	Projected Compliance Costs per Vehicle	4-12
4.1.3	Technology Penetration Rates	4-17
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4.1.4 Sensitivities	4-22
4.2 Estimates of Fuel Economy Impacts	4-23
4.2.1	Proposal	4-23
4.2.2	Alternatives	4-25
References for Chapter 4	4-29
Chapter 5: Projected Impacts on Emissions, Fuel Consumption, and Safety	5-1
5.1	Projected Emissions Impacts	5-1
5.1.1	Greenhouse Gas Emissions	5-1
5.1.2	Non-Greenhouse Gas Emissions	5-4
5.2	Projected Fuel Consumption	5-7
5.2.1	Proposal	5-7
5.2.2	Alternatives	5-8
5.3	Projected Safety Impacts	5-9
References for Chapter 5	5-12
Chapter 6: Vehicle Costs, Fuel Savings and Non-Emission Benefits	6-1
6.1	Costs	6-2
6.1.1	Proposal	6-2
6.1.2	Alternatives	6-3
6.2	Fuel Savings	6-5
6.2.1	Proposal	6-5
6.2.2	Alternatives	6-6
6.3	Non-Emission Benefits	6-7
6.3.1	Proposal	6-8
6.3.2	Alternatives	6-9
Chapter 7: Non-GHG Health and Environmental Impacts	7-1
7.1	Health and Environmental Impacts of Non-GHG Pollutants	7-1
7.1.1	Background on Non-GHG Pollutants Impacted by the Proposed Standards	7-1
7.1.2	Health Effects Associated with Exposure to Non-GHG Pollutants	7-3
7.1.3	Environmental Effects Associated with Exposure to Non-GHG Pollutants	7-16
7.2	Non-GHG Monetized Health Benefits	7-19
7.2.1 Uncertainty	7-29
References for Chapter 7	7-31
Chapter 8: Vehicle Sales, Employment, Environmental Justice, and Affordability and
Equity Impacts 8-1
8.1 Sales Impacts	8-1
in

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8.1.1	Conceptual Framework	8-1
8.1.2	How Sales Impacts were Modeled	8-6
8.1.3	Sales Impacts	8-8
8.2	Employment Impacts	8-9
8.2.1	Conceptual Framework	8-9
8.2.2	How Employment Impacts were Modeled	8-11
8.2.3	Employment Impacts	8-11
8.3	Environmental Justice	8-13
8.3.1	GHG Impacts	8-14
8.3.2	Non-GHG Impacts	8-17
8.4	Affordability and Equity Impacts	8-18
8.4.1	Effects on Lower-Income Households	8-20
8.4.2	Effects on the Used Vehicle Market	8-21
8.4.3	Effects on Access to Credit	8-22
8.4.4	Effects on Low-Priced Cars	8-23
8.4.5	Effects of Electric Vehicles on Affordability	8-24
8.4.6	Summary of Affordability and Equity Effects	8-24
References for Chapter 8	8-25
Chapter 9: Small Business Flexibilities	9-1
References for Chapter 9	9-3
Chapter 10: Summary of Costs and Benefits	10-1
10.1	Proposal	10-1
10.2	Alternative 1	10-6
10.3	Alternative 2	10-13
10.4	Sensitivities	10-18
iv

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List of Tables
TABLE 1 PROJECTED INDUSTRY FLEET-WIDE C02 COMPLIANCE TARGETS (GRAMS/MI)	XIV
TABLE 2: PROJECTED INDUSTRY FLEET AVERAGE TARGET YEAR-OVER-YEAR PERCENT
REDUCTIONS	XIV
TABLE 3: EPA PROPOSED EXTENSION OF CREDIT CARRY-FORWARD PROVISIONS	XVI
TABLE 4 MONETIZED DISCOUNTED COSTS, BENEFITS, AND NET BENEFITS OF THE PROPOSED
PROGRAM FOR CALENDAR YEARS THROUGH 2050 (BILLIONS OF 2018 DOLLARS)ab c d e	XVII
TABLE 5 GHG ANALYSIS OF LIFETIME COSTS & BENEFITS TO MEET THE PROPOSED MY 2023-2026
GHG STANDARDS, 3% DISCOUNT RATE (FOR VEHICLES PRODUCED IN MY 2023-2026)A h '; IJ
(BILLIONS OF 2018$)	XVIII
TABLE 6 GHG ANALYSIS OF LIFETIME COSTS & BENEFITS TO MEET THE PROPOSED MY 2023-2026
GHG STANDARDS, 7% DISCOUNT RATE (FOR VEHICLES PRODUCED IN MY 2023-2026)AB C D
(BILLIONS OF 2018$)	XIX
TABLE 7 APPLICABILITY OF PROGRAM PROVISIONS TO THE PROPOSAL AND ALTERNATIVES	XX
TABLE 8 PROJECTED FLEET AVERAGE TARGET LEVELS FOR PROPOSED STANDARDS AND
ALTERNATIVES (C02 GRAMS/MILE)	XXI
TABLE 9 MONETIZED DISCOUNTED COSTS, BENEFITS, AND NET BENEFITS OF ALTERNATIVE 1
FOR CALENDAR YEARS THROUGH 2050 (BILLIONS OF 2018 DOLLARS)abcde	XXII
TABLE 10 GHG ANALYSIS OF LIFETIME COSTS & BENEFITS TO MEET THE ALTERNATIVE 1 MY
2023-2026 GHG STANDARDS, 3% DISCOUNT RATE (FOR VEHICLES PRODUCED IN MY 2023-
2026)ABCD (BILLIONS OF 2018$)	XXIII
TABLE 11 GHG ANALYSIS OF LIFETIME COSTS & BENEFITS TO MEET THE ALTERNATIVE 1 MY
2023-2026 GHG STANDARDS, 7% DISCOUNT RATE (FOR VEHICLES PRODUCED IN MY 2023-
2026)AB CD (BILLIONS OF 2018$)	XXIV
TABLE 12 MONETIZED DISCOUNTED COSTS, BENEFITS, AND NET BENEFITS OF ALTERNATIVE 2
FOR CALENDAR YEARS THROUGH 2050 (BILLIONS OF 2018 DOLLARS)ab cd e	XXV
TABLE 13 GHG ANALYSIS OF LIFETIME COSTS & BENEFITS TO MEET THE ALTERNATIVE 2 MY
2023-2026 GHG STANDARDS, 3% DISCOUNT RATE (FOR VEHICLES PRODUCED IN MY 2023-
2026)AB CD (BILLIONS OF 2018$)	XXVI
TABLE 14 GHG ANALYSIS OF LIFETIME COSTS & BENEFITS TO MEET THE ALTERNATIVE 2 MY
2023-2026 GHG STANDARDS, 7% DISCOUNT RATE (FOR VEHICLES PRODUCED IN MY 2023-
2026)AB CD (BILLIONS OF 2018$)	XXVII
TABLE 15 PRESENT VALUE MONETIZED DISCOUNTED COSTS, BENEFITS, AND NET BENEFITS OF
THE PROPOSED PROGRAM AND ALTERNATIVES FOR CALENDAR YEARS THROUGH 2050
(BILLIONS OF 2018 DOLLARS)abcde	XXVIII
TABLE 16 ANNUALIZED MONETIZED DISCOUNTED COSTS, BENEFITS, AND NET BENEFITS OF THE
PROPOSED PROGRAM AND ALTERNATIVES FOR CALENDAR YEARS THROUGH 2050
(BILLIONS OF 2018 DOLLARS)A,B,C,D,E	XXIX
TABLE 17 PRESENT VALUE GHG ANALYSIS OF LIFETIME COSTS & BENEFITS FOR MY 2023-2026
GHG STANDARDS UNDER THE PROPOSAL AND ALTERNATIVES, (FOR VEHICLES PRODUCED
IN MY 2023-2026)A B C D (BILLIONS OF 2018$)	XXX
TABLE 18 ANNUALIZED GHG ANALYSIS OF LIFETIME COSTS & BENEFITS FOR MY 2023-2026 GHG
STANDARDS UNDER THE PROPOSAL AND ALTERNATIVES, (FOR VEHICLES PRODUCED IN MY
2023-2026)AB CD (BILLIONS OF 2018$)	XXXI
TABLE 1-1: PROJECTED FLEET-WIDE EMISSIONS COMPLIANCE TARGETS UNDER THE FOOTPRINT-
BASED C02 STANDARDS IN THE 2012 FRM	1-3
TABLE 1-2: PROJECTED VS. ACTUAL CAR/TRUCK SALES SHARE, 2016-2019 MODEL YEARS	1-3
TABLE 1-3: COMPARISON OF TECHNOLOGY PENETRATIONS INTO THE LIGHT-DUTY FLEET AND
PER VEHICLE COSTS IN 2025 (INCREMENTAL TO 2021) FOR THE 2012 FRM COMPARED TO
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ANALYSES CONDUCTED BY EPA UNDER THE MTE. ALL PER VEHICLE COSTS ARE SHOWN IN
2018$ TO MAINTAIN CONSISTENCY WITH OTHER ANALYSES WITHIN THIS DRAFT RIA	1-11
TABLE 1-4: COMPARISON OF FUEL PRICE, PERCENTAGE OF CARS AND TRUCKS IN THE FLEET,
AND C02 FLEET AVERAGE EMISSIONS TARGETS WHEN TAKING INTO CONSIDERATION THE
CAR AND TRUCK FLEET MIX FOR THE 2012 FRM COMPARED TO ANALYSES CONDUCTED BY
EPA UNDER THE MTE	 1-12
TABLE 1-5: COMPARISON PER VEHICLES COSTS FOR PASSENGER CARS, LIGHT-DUTY TRUCKS
AND THE COMBINED LIGHT-DUTY VEHICLE FLEET IN 2025 (INCREMENTAL TO 2021) FOR THE
2012 FRM COMPARED TO ANALYSES CONDUCTED BY EPA UNDER THE MTE. PER VEHICLE
COSTS ARE SHOWN IN 2018$ TO MAINTAIN CONSISTENCY WITH OTHER ANALYSES WITHIN
THIS DRAFT RIA	 1-12
TABLE 2-1: PROPOSED COEFFICIENTS FOR PASSENGER CAR GHG TARGETS	2-1
TABLE 2-2: PROPOSED COEFFICIENTS FOR LIGHT-DUTY TRUCK GHG TARGETS	2-2
TABLE 2-3: ESTIMATES OF EPA'S PROPOSED STANDARDS IN TERMS OF THE PROJECTED OVERALL
FLEETWIDE C02-EQUIVALENT G/MI EMISSION COMPLIANCE TARGET LEVELS	2-4
TABLE 2-4: EPA PROPOSED EXTENSION OF CREDIT CARRY-FORWARD PROVISIONS	2-5
TABLE 2-5: PROJECTED FLEET AVERAGE TARGET LEVELS FOR PROPOSED STANDARDS AND
ALTERNATIVES (C02 GRAMS/MILE)	2-8
TABLE 2-6: PRODUCTION SHARE BY ENGINE TECHNOLOGIES FOR MY2015-2020	 2-11
TABLE 2-7: PRODUCTION SHARE BY TRANSMISSION TECHNOLOGIES FOR MY2015-2020	 2-11
TABLE 2-8: DISTRIBUTION OF 2021 MY VEHICLE MODELS AND NUMBER OF VEHICLES WHICH
GENERATE CREDITS VS. 2023 MY STANDARDS (ALL VEHICLES)	2-16
TABLE 2-9: DISTRIBUTION OF 2021 MY VEHICLE MODELS AND NUMBER OF VEHICLES WHICH
GENERATE CREDITS VS. 2023 MY STANDARDS (GASOLINE ICE AND HYBRID VEHICLES).... 2-17
TABLE 2-10: DISTRIBUTION OF 2021 MY VEHICLE MODELS AND NUMBER OF VEHICLES WHICH
GENERATE CREDITS VS. 2022 MY STANDARDS (ALL VEHICLES)	2-18
TABLE 3-1: ESTIMATES OF THE REBOUND EFFECT USING U.S. AGGREGATE TIME-SERIES DATA ON
VEHICLE TRAVEL	3-2
TABLE 3-2: ESTIMATES OF THE REBOUND EFFECT USING U.S./STATE AND CANADIAN/PROVINCE
LEVEL DATA	3-2
TABLE 3-3: ESTIMATES OF THE REBOUND EFFECT USING U.S. HOUSEHOLD SURVEY DATA	3-3
TABLE 3-4: STUDIES GIVEN SIGNIFICANT WEIGHT IN DEVELOPING AN ESTIMATE OF THE VMT
REBOUND EFFECT FOR THIS PROPOSED RULE	3-13
TABLE 3-5: PROJECTED TRENDS IN U.S. OIL EXPORTS/IMPORTS, NET OIL PRODUCT EXPORTS, NET
CRUDE OIL/PRODUCT EXPORTS, OIL CONSUMPTION AND U.S. OIL IMPORT REDUCTIONS
RESULTING FROM THE PROPOSED LDV GHG RULE FROM 2023 TO 2050 (MILLIONS OF
BARRELS PER DAY (MMBD))*	3-24
TABLE 3-6: MACROECONOMIC OIL SECURITY PREMIUMS FOR SELECTED YEARS FROM 2023-2050
(2018$/BARREL)*	3-27
TABLE 3-7: ANNUAL ENERGY SECURITY BENEFITS OF THE PROPOSED LDV GHG/FUEL ECONOMY
PROPOSED RULE FOR SELECTED YEARS 2023-2050 (IN BILLIONS OF 2018$)	3-27
TABLE 3-8: INTERIM GLOBAL SOCIAL COST OF CARBON VALUES, 2020-2070 (2018$/METRIC TONNE
CO2)98	3-31
TABLE 3-9: INTERIM GLOBAL SOCIAL COST OF METHANE VALUES, 2020-2070 (2018$/METRIC
TONNE CH4)98	3-32
TABLE 3-10: INTERIM GLOBAL SOCIAL COST OF NITROUS OXIDE VALUES, 2020-2070 (2018$/METRIC
TONNE N20)98 	 3-32
TABLE 3-11: ESTIMATED GLOBAL CLIMATE BENEFITS FROM CHANGES IN C02 EMISSIONS 2023 -
2050 FOR THE PROPOSAL (BILLIONS OF 2018$)	3-36
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TABLE 3-12: ESTIMATED GLOBAL CLIMATE BENEFITS FROM CHANGES IN CH4 EMISSIONS 2023 -
2050 FOR THE PROPOSAL (BILLIONS OF 2018$)	3-37
TABLE 3-13: ESTIMATED GLOBAL CLIMATE BENEFITS FROM CHANGES IN N20 EMISSIONS 2023 -
2050 (BILLIONS OF 2018$)	3-38
TABLE 3-14: ESTIMATED GLOBAL CLIMATE BENEFITS FROM CHANGES IN GHG EMISSIONS 2023 -
2050 (BILLIONS OF 2018$)	3-39
TABLE 3-15 ESTIMATED GLOBAL CLIMATE BENEFITS FROM CHANGES IN GHG EMISSIONS 2023 -
2050 FOR ALTERNATIVE 1 (BILLIONS OF 2018$)	3-40
TABLE 3-16: ESTIMATED GLOBAL CLIMATE BENEFITS FROM CHANGES IN GHG EMISSIONS 2023 -
2050 FOR ALTERNATIVE 2 (BILLIONS OF 2018$)	3-41
TABLE 3-17: COSTS ASSOCIATED WITH CONGESTION AND NOISE (2018 DOLLARS PER VEHICLE
MILE)	3-43
TABLE 4-1: CHANGES MADE TO CCEMS INPUTS FOR ALL MODEL RUNS	4-3
TABLE 4-2 PROPOSED CAR TARGETS (C02 GRAM/MILE)	4-6
TABLE 4-3 PROPOSED TRUCK TARGETS (C02 GRAM/MILE)	4-6
TABLE 4-4 PROPOSED SALES WEIGHTED FLEET TARGETS (C02 GRAM/MILE)	4-7
TABLE 4-5 PROPOSED CAR ACHIEVED (C02E GRAM/MILE)	4-7
TABLE 4-6 PROPOSED TRUCK ACHIEVED (C02E GRAM/MILE)	4-8
TABLE 4-7 PROPOSED SALES WEIGHTED FLEET ACHIEVED (C02E GRAM/MILE)	4-8
TABLE 4-8 CAR TARGETS UNDER ALTERNATIVES 1 AND 2 (C02 GRAM/MILE)	4-9
TABLE 4-9 TRUCK TARGETS UNDER ALTERNATIVES 1 AND 2 (C02 GRAM/MILE)	4-10
TABLE 4-10 FLEET TARGETS UNDER ALTERNATIVES 1 AND 2 (C02 GRAM/MILE)	4-10
TABLE 4-11 CAR ACHIEVED UNDER ALTERNATIVES 1 AND 2 (C02E GRAM/MILE)	4-11
TABLE 4-12 TRUCK ACHIEVED UNDER ALTERNATIVES 1 AND 2 (C02E GRAM/MILE)	4-11
TABLE 4-13 FLEET ACHIEVED UNDER ALTERNATIVES 1 AND 2 (C02E GRAM/MILE)	4-12
TABLE 4-14 CAR COSTS/VEHICLE RELATIVE TO THE NO ACTION SCENARIO (2018 DOLLARS)	4-13
TABLE 4-15 TRUCK COST/VEHICLE RELATIVE TO THE NO ACTION SCENARIO (2018 DOLLARS) ..4-13
TABLE 4-16 FLEET AVERAGE COST/VEHICLE RELATIVE TO THE NO ACTION SCENARIO (2018
DOLLARS)	4-14
TABLE 4-17 FLEET AVERAGE COST/VEHICLE RELATIVE TO THE SAFE FRM (2018 DOLLARS)	4-15
TABLE 4-18 CAR AVERAGE COST/VEHICLE FOR ALTERNATIVES 1 AND 2 RELATIVE TO THE NO
ACTION SCENARIO (2018 DOLLARS)	4-15
TABLE 4-19 TRUCK AVERAGE COST/VEHICLE FOR ALTERNATIVES 1 AND 2 RELATIVE TO THE NO
ACTION SCENARIO (2018 DOLLARS)	4-16
TABLE 4-20 FLEET AVERAGE COST/VEHICLE FOR ALTERNATIVES 1 AND 2 RELATIVE TO THE NO
ACTION SCENARIO (2018 DOLLARS)	4-16
TABLE 4-21 CAR BEV+PHEV PENETRATION RATES UNDER THE PROPOSED STANDARDS	4-17
TABLE 4-22 TRUCK BEV+PHEV PENETRATION RATES UNDER THE PROPOSED STANDARDS	4-18
TABLE 4-23 FLEET BEV+PHEV PENETRATION RATES UNDER THE PROPOSED STANDARDS	4-18
TABLE 4-24 FLEET ICE TECHNOLOGY PENETRATION RATES UNDER THE PROPOSAL	4-19
TABLE 4-25 IMPACT OF ADVANCED TECHNOLOGY MULTIPLIERS ON THE PENETRATION OF BEV
AND PHEV TECHNOLOGY	4-20
TABLE 4-26 CAR BEV+PHEV PENETRATION RATES UNDER THE ALTERNATIVE STANDARDS	4-20
TABLE 4-27 TRUCK BEV+PHEV PENETRATION RATES UNDER THE ALTERNATIVE STANDARDS . 4-21
TABLE 4-28 FLEET BEV+PHEV PENETRATION RATES UNDER THE ALTERNATIVE STANDARDS... 4-21
TABLE 4-29 COSTS PER VEHICLE FOR THE PROPOSAL AND SENSITIVITIES RELATIVE TO THEIR NO
ACTION SCENARIOS (2018 DOLLARS)*	4-22
TABLE 4-30 MY 2026 TECHNOLOGY PENETRATION RATES FOR THE NO-ACTION CASE, PROPOSAL
AND SENSITIVITIES*	4-23
TABLE 4-31 FUEL ECONOMY (MPG) ESTIMATES BASED ON THE GHG STANDARDS *	4-24
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TABLE 4-32 FUEL ECONOMY (MPG) ESTIMATES ASSUMING FULL USE OF AC LEAKAGE CREDITS *4-
24
TABLE 4-33 FUEL ECONOMY (MPG) ESTIMATED "LABEL VALUE" *	4-25
TABLE 4-34 FUEL ECONOMY (MPG) ESTIMATES BASED ON THE GHG STANDARDS OF
ALTERNATIVE 1 *	4-25
TABLE 4-35 FUEL ECONOMY (MPG) ESTIMATES ASSUMING FULL USE OF AC LEAKAGE CREDITS IN
ALTERNATIVE 1 *	4-26
TABLE 4-36 FUEL ECONOMY (MPG) ESTIMATED "LABEL VALUE" UNDER ALTERNATIVE 1 *	4-26
TABLE 4-37 FUEL ECONOMY (MPG) ESTIMATES BASED ON THE GHG STANDARDS OF
ALTERNATIVE 2 *	4-27
TABLE 4-38 FUEL ECONOMY (MPG) ESTIMATES ASSUMING FULL USE OF AC LEAKAGE CREDITS IN
ALTERNATIVE 2 *	4-27
TABLE 4-39 FUEL ECONOMY (MPG) ESTIMATED "LABEL VALUE" UNDER ALTERNATIVE 2 *	4-28
TABLE 5-1 IMPACTS ON GHG EMISSIONS UNDER THE PROPOSED STANDARDS RELATIVE TO THE
NO ACTION SCENARIO	5-1
TABLE 5-2 IMPACTS ON GHG EMISSIONS UNDER ALTERNATIVE 1 RELATIVE TO THE NO ACTION
SCENARIO	5-2
TABLE 5-3 IMPACTS ON GHG EMISSIONS UNDER ALTERNATIVE 2 RELATIVE TO THE NO ACTION
SCENARIO	5-3
TABLE 5-4 IMPACTS ON NON-GHG EMISSIONS UNDER THE PROPOSAL RELATIVE TO THE NO
ACTION SCENARIO	5-4
TABLE 5-5 IMPACTS ON NON-GHG EMISSIONS UNDER ALTERNATIVE 1 RELATIVE TO THE NO
ACTION SCENARIO	5-5
TABLE 5-6 IMPACTS ON NON-GHG EMISSIONS UNDER ALTERNATIVE 2 RELATIVE TO THE NO
ACTION SCENARIO	5-6
TABLE 5-7 IMPACTS ON FUEL CONSUMPTION FOR THE PROPOSAL RELATIVE TO THE NO ACTION
SCENARIO	5-7
TABLE 5-8 IMPACTS ON FUEL CONSUMPTION FOR ALTERNATIVE 1 RELATIVE TO THE NO ACTION
SCENARIO	5-8
TABLE 5-9 IMPACTS ON FUEL CONSUMPTION FOR ALTERNATIVE 2 RELATIVE TO THE NO ACTION
SCENARIO	5-9
TABLE 6-1 COSTS ASSOCIATED WITH THE PROPOSED PROGRAM RELATIVE TO THE NO ACTION
SCENARIO ($BILLIONS OF 2018 DOLLARS)	6-2
TABLE 6-2 COSTS ASSOCIATED WITH ALTERNATIVE 1 RELATIVE TO THE NO ACTION SCENARIO
($BILLIONS OF 2018 DOLLARS)	6-3
TABLE 6-3 COSTS ASSOCIATED WITH ALTERNATIVE 2 RELATIVE TO THE NO ACTION SCENARIO
($BILLIONS OF 2018 DOLLARS)	6-4
TABLE 6-4 FUEL SAVINGS ASSOCIATED WITH THE PROPOSED PROGRAM ($BILLIONS OF 2018
DOLLARS)	6-5
TABLE 6-5 FUEL SAVINGS ASSOCIATED WITH ALTERNATIVE 1 ($BILLIONS OF 2018 DOLLARS).... 6-6
TABLE 6-6 FUEL SAVINGS ASSOCIATED WITH ALTERNATIVE 2 ($BILLIONS OF 2018 DOLLARS).... 6-7
TABLE 6-7 CCEMS INPUTS USED TO ESTIMATE REFUELING TIME COSTS	6-8
TABLE 6-8 BENEFITS FROM NON-EMISSION SOURCES UNDER THE PROPOSAL ($BILLIONS OF 2018
DOLLARS)	6-8
TABLE 6-9 BENEFITS FROM NON-EMISSION SOURCES UNDER ALTERNATIVE 1 ($BILLIONS OF 2018
DOLLARS)	6-9
TABLE 6-10 BENEFITS FROM NON-EMISSION SOURCES UNDER ALTERNATIVE 2 ($BILLIONS OF 2018
DOLLARS)	6-10
TABLE 7-1: HEALTH EFFECTS OF AMBIENT PM2 5	7-21
TABLE 7-2: HEALTH EFFECTS OF AMBIENT OZONE	7-22
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TABLE 7-3: ADDITIONAL UNQUANTIFIED HEALTH AND WELFARE BENEFITS CATEGORIES	7-23
TABLE 7-4 PM-RELATED BENEFIT-PER-TON VALUES (2018$)A	7-25
TABLE 7-5: UNDISCOUNTED STREAM, PRESENT AND ANNUALIZED VALUE OF PM25-RELATED
BENEFITS FROM 2023 THROUGH 2050 FOR THE PROPOSAL (DISCOUNTED AT 3 PERCENT AND 7
PERCENT; $BILLIONS OF 2018$)A	7-26
TABLE 7-6 UNDISCOUNTED STREAM, PRESENT AND ANNUALIZED VALUE OF PM2.5-RELATED
BENEFITS FROM 2023 THROUGH 2050 FOR ALTERNATIVE 1 (DISCOUNTED AT 3 PERCENT AND
7 PERCENT; $BILLIONS OF 2018$)A	7-27
TABLE 7-7 UNDISCOUNTED STREAM, PRESENT AND ANNUALIZED VALUE OF PM2 5-RELATED
BENEFITS FROM 2023 THROUGH 2050 FOR ALTERNATIVE 2 (DISCOUNTED AT 3 PERCENT AND
7	PERCENT; $BILLIONS OF 2018$)A	7-28
TABLE 8-1: SALES IMPACTS, 2.5 YEARS OF FUEL SAVINGS IN NET PRICE, DEMAND ELASTICITY -1 8-
8
TABLE 8-2: SALES IMPACTS, 2.5 YEARS OF FUEL SAVINGS IN NET PRICE, DEMAND ELASTICITY -0.4
	8-9
TABLE 8-3: EMPLOYMENT IMPACTS, BASED ON SALES ESTIMATES IN TABLE 8-1 (DEMAND
ELASTICITY-1)	8-12
TABLE 8-4: EMPLOYMENT IMPACTS, BASED ON SALES ESTIMATES IN TABLE 8-2 (DEMAND
ELASTICITY-0.4)	8-12
TABLE 9-1: PRIMARY VEHICLE SBA SMALL BUSINESS CATEGORIES	9-1
TABLE 9-2 SMALL BUSINESS ENTITIES	9-2
TABLE 10-1: COSTS ASSOCIATED WITH THE PROPOSED PROGRAM ($BILLIONS OF 2018 DOLLARS)
	 10-1
TABLE 10-2: FUEL SAVINGS ASSOCIATED WITH THE PROPOSED PROGRAM ($BILLIONS OF 2018
DOLLARS)	 10-2
TABLE 10-3: BENEFITS FROM NON-EMISSION SOURCES FOR THE PROPOSAL ($BILLIONS OF 2018
DOLLARS)	 10-2
TABLE 10-4: PM25-RELATED EMISSION REDUCTION BENEFITS OF THE PROPOSAL ($BILLIONS OF
2018 DOLLARS)	 10-3
TABLE 10-5: CLIMATE BENEFITS FROM REDUCTION IN GHG EMISSIONS ($BILLIONS OF 2018
DOLLARS)	 10-3
TABLE 10-6: NET BENEFITS (EMISSION BENEFITS + NON-EMISSION BENEFITS + FUEL SAVINGS -
COSTS) ASSOCIATED WITH THE PROPOSED PROGRAM ($BILLIONS OF 2018 DOLLARS)	10-4
TABLE 10-7: MONETIZED COSTS, FUEL SAVINGS, BENEFITS, AND NET BENEFITS ASSOCIATED
WITH THE LIFETIMES OF 2023-2026 MODEL YEAR LIGHT-DUTY VEHICLES ($BILLIONS, 2018$; 3
PERCENT DISCOUNT RATE)AB	 10-5
TABLE 10-8: MONETIZED COSTS, FUEL SAVINGS, BENEFITS, AND NET BENEFITS ASSOCIATED
WITH THE LIFETIMES OF 2023-2026 MODEL YEAR LIGHT-DUTY VEHICLES ($BILLIONS, 2018$; 7
PERCENT DISCOUNT RATE)AB	 10-6
TABLE 10-9: COSTS ASSOCIATED WITH ALTERNATIVE 1 ($BILLIONS OF 2018 DOLLARS)	10-6
TABLE 10-10: FUEL SAVINGS ASSOCIATED WITH ALTERNATIVE 1 ($BILLIONS OF 2018 DOLLARS) 10-
7
TABLE 10-11: BENEFITS FROM NON-EMISSION SOURCES ASSOCIATED WITH ALTERNATIVE 1
($BILLIONS OF 2018 DOLLARS)	 10-7
TABLE 10-12: PM2 5-RELATED EMISSION REDUCTION BENEFITS ASSOCIATED WITH ALTERNATIVE 1
($BILLIONS OF 2018 DOLLARS)	 10-8
TABLE 10-13: CLIMATE BENEFITS FROM REDUCTION IN GREENHOUSE GAS EMISSIONS
ASSOCIATED WITH ALTERNATIVE 1 ($BILLIONS OF 2018 DOLLARS)	10-9
TABLE 10-14: NET BENEFITS (EMISSION BENEFITS + NON-EMISSION BENEFITS + FUEL SAVINGS -
COSTS) FOR ALTERNATIVE 1 ($BILLIONS OF 2018 DOLLARS)	10-10
IX

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TABLE 10-15: MONETIZED COSTS, FUEL SAVINGS, BENEFITS, AND NET BENEFITS ASSOCIATED
WITH THE LIFETIMES OF 2023-2026 MODEL YEAR LIGHT-DUTY VEHICLES, ALTERNATIVE 1
($BILLIONS, 2018$; 3 PERCENT DISCOUNT RATE)AB	10-11
TABLE 10-16: MONETIZED COSTS, FUEL SAVINGS, BENEFITS, AND NET BENEFITS ASSOCIATED
WITH THE LIFETIMES OF 2023-2026 MODEL YEAR LIGHT-DUTY VEHICLES, ALTERNATIVE 1
($BILLIONS, 2018$; 7 PERCENT DISCOUNT RATE)A	10-12
TABLE 10-17: COSTS ASSOCIATED WITH ALTERNATIVE 2 ($BILLIONS OF 2018 DOLLARS)	10-13
TABLE 10-18: FUEL SAVINGS ASSOCIATED WITH ALTERNATIVE 2 ($BILLIONS OF 2018 DOLLARS) 10-
13
TABLE 10-19: BENEFITS FROM NON-EMISSION SOURCES ASSOCIATED WITH ALTERNATIVE 2
($BILLIONS OF 2018 DOLLARS)	 10-14
TABLE 10-20: PM2 5-RELATED EMISSION REDUCTION BENEFITS ASSOCIATED WITH ALTERNATIVE 2
($BILLIONS OF 2018 DOLLARS)	 10-14
TABLE 10-21: CLIMATE BENEFITS FROM REDUCTION IN GREENHOUSE GAS EMISSIONS
ASSOCIATED WITH ALTERNATIVE 2 ($BILLIONS OF 2018 DOLLARS)	10-15
TABLE 10-22: NET BENEFITS (EMISSION BENEFITS + NON-EMISSION BENEFITS + FUEL SAVINGS -
COSTS) ASSOCIATED WITH ALTERNATIVE 2 ($BILLIONS OF 2018 DOLLARS)	10-16
TABLE 10-23: MONETIZED COSTS, FUEL SAVINGS, BENEFITS, AND NET BENEFITS ASSOCIATED
WITH THE LIFETIMES OF 2023-2026 MODEL YEAR LIGHT-DUTY VEHICLES, ALTERNATIVE 2
($BILLIONS, 2018$; 3 PERCENT DISCOUNT RATE)AB	10-17
TABLE 10-24: MONETIZED COSTS, FUEL SAVINGS, BENEFITS, AND NET BENEFITS ASSOCIATED
WITH THE LIFETIMES OF 2023-2026 MODEL YEAR LIGHT-DUTY VEHICLES, ALTERNATIVE 2
($BILLIONS, 2018$; 7 PERCENT DISCOUNT RATE)AB	10-17
TABLE 10-25 MONETIZED DISCOUNTED COSTS, BENEFITS, AND NET BENEFITS OF THE PROPOSED
PROGRAM AND EACH SENSITIVITY FOR CALENDAR YEARS THROUGH 2050 ($BILLIONS OF
2018 DOLLARS, 3 PERCENT DISCOUNT RATE)AB CD	10-18
TABLE 10-26 MONETIZED DISCOUNTED COSTS, BENEFITS, AND NET BENEFITS OF THE PROPOSED
PROGRAM AND EACH SENSITIVITY FOR CALENDAR YEARS THROUGH 2050 ($BILLIONS OF
2018 DOLLARS, 7 PERCENT DISCOUNT RATE)AB CD	10-19
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List of Figures
FIGURE 1 EPA PROPOSED INDUSTRY FLEET-WIDE C02 COMPLIANCE TARGETS, COMPARED TO
2012 AND SAFE RULES, GRAMS/MILE, 2021-2026	XIV
FIGURE 2 PROPOSED STANDARDS FLEET AVERAGE TARGETS COMPARED TO ALTERNATIVES... XXI
FIGURE 1-1: REGULATORY TIMELINE FOR THE MODEL YEAR (MY) 2017 AND LATER LIGHT-DUTY
VEHICLE (LDV) GREENHOUSE GAS (GHG) EMISSIONS STANDARDS, THE MIDTERM
EVALUATION, AND SUBSEQUENT REGULATORY ACTIONS. THE TOP ROW REPRESENTS
AGENCY ACTIONS THAT USED EPA'S MTE ANALYSES AS THE BASIS	1-2
FIGURE 1-2: 2012 FRM FOOTPRINT CURVES FOR PASSENGER CAR C02 (G/MILE) STANDARDS	1-4
FIGURE 1-3: 2012 FRM FOOTPRINT CURVES FOR LIGHT-DUTY TRUCK C02 (G/MILE) STANDARDS. 1-4
FIGURE 1-4: COMPARISON OF FLEET AVERAGE (CAR AND TRUCK), PER-VEHICLE TECHNOLOGY
COSTS IN 2025 FROM THE 2012 FRM TO SUBSEQUENT ANALYSES CONDUCTED BY EPA
DURING THE MTE (2018$). VERTICAL LINES ON TOP OF THE BARS REPRESENT THE RANGE OF
SENSITIVITY ANALYSES CONDUCTED	1-10
FIGURE 2-1: C02 VS FOOTPRINT COMPLIANCE CURVES FOR CARS	2-2
FIGURE 2-2: C02 VS FOOTPRINT COMPLIANCE CURVES FOR TRUCKS	2-3
FIGURE 2-3: PROPOSED FLEET-WIDE C02-EQUIVALENT G/MI COMPLIANCE TARGETS, COMPARED
TO 2012 AND SAFE RULES, 2021-2026	 2-4
FIGURE 2-4: PROPOSED STANDARDS FLEET AVERAGE TARGETS COMPARED TO ALTERNATIVES 2-9
FIGURE 3-1: FREQUENCY DISTRIBUTION OF SC-C02 ESTIMATES FOR 2030	 3-33
FIGURE 3-2FREQUENCY DISTRIBUTION OF SC-CH4 ESTIMATES FOR 2030M	3-34
FIGURE 3-3: FREQUENCY DISTRIBUTION OF SC-N20 ESTIMATES FOR 2030M	3-34
FIGURE 4-1: COMPARISON OF FLEET AVERAGE PROPOSED REVISED STANDARDS (RED LINE) TO
THE SAFE FRM, THE CALIFORNIA FRAMEWORK AGREEMENT, AND THE 2012 FRM	4-5
XI

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Executive Summary
This Draft Regulatory Impact Analysis (RIA) contains supporting documentation to the EPA
NPRM and addresses requirements in Clean Air Act Section 317. The preamble to the Federal
Register notice associated with this document provides the full context for the EPA proposed
rule, and it references this Draft RIA throughout.
The Environmental Protection Agency (EPA) is proposing to revise existing national
greenhouse gas (GHG) emissions standards for passenger cars and light trucks under section
202(a) of the Clean Air Act (CAA). Section 202(a) requires EPA to establish standards for
emissions of air pollutants from new motor vehicles which, in the Administrator's judgment,
cause or contribute to air pollution which may reasonably be anticipated to endanger public
health or welfare. 42 U.S.C. 7521(a).
This proposal also responds to Executive Order (E.O.) 13990, "Protecting Public Health and
the Environment and Restoring Science To Tackle the Climate Crisis" (Jan. 20, 2021), which
directs EPA to consider taking the action proposed in this notice:a
"[T]he head of the relevant agency, as appropriate and consistent with applicable law, shall
consider publishing for notice and comment a proposed rule suspending, revising, or rescinding
the agency action[s set forth below] within the time frame specified."
"Establishing Ambitious, Job-Creating Fuel Economy Standards: ... 'The Safer Affordable
Fuel-Efficient (SAFE) Vehicles Rule for Model Years 2021-2026 Passenger Cars and Light
Trucks,' 85 FR 24174 (April 30, 2020), by July 2021. ... In considering whether to propose
suspending, revising, or rescinding the latter rule, the agency should consider the views of
representatives from labor unions, States, and industry."
The proposed program would revise the light-duty vehicle GHG standards previously revised
by the SAFE rule and builds upon earlier EPA actions and supporting analyses that established
or maintained stringent light-duty vehicle GHG emissions standards. For example, in 2012, EPA
issued a final rule establishing light-duty vehicle GHG standards for model years (MY) 2017-
2025,b which were supported in analyses accounting for compliance costs, lead time and other
relevant factors.0 That rule and its analyses also accounted for the development and availability
of advanced GHG emission-reducing technologies for gasoline-fueled vehicles, which
demonstrated that the standards were appropriate under section 202(a) of the CAA.d This
proposed rule provides additional analysis that takes into consideration updated data and recent
developments. Auto manufacturers are currently implementing an increasing array of advanced
gasoline vehicle GHG emission reduction technologies at a rapid pace throughout their vehicle
fleets. Vehicle electrification technologies are also advancing rapidly, as battery costs have
continued to decline, and automakers have announced an increasing diversity and volume of
zero-emission vehicle models. Meanwhile, in 2019, several auto manufacturers voluntarily
a https://www.federalregister.gov/documents/2021/01/25/2021-01765/protecting-public-health-and-the-
environment-and-restoring-science-to-tackle-the-climate-crisis
b EPA's model year emission standards also apply in subsequent model years, unless revised, e.g., MY 2025
standards issued in the 2012 rule also applied to MY 2026 and beyond.
c 77 FR 62624, October 15, 2012.
A Id.
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entered into agreements with the State of California to comply with GHG emission reduction
targets through MY 2026 across their national vehicle fleets (the "California Framework
Agreements") that are more stringent than the EPA standards as revised by the SAFE rule. These
developments further support EPA's decision to reconsider and propose revising the existing
EPA standards to be more stringent, particularly in light of factors indicating that more stringent
near-term standards are feasible at reasonable cost and would achieve significantly greater GHG
emissions reductions and public health and welfare benefits than the existing program. In
developing this proposal, EPA has conducted outreach with a wide range of interested
stakeholders, including labor unions, states, and industry as provided in E.O. 13990, and we will
continue to engage with these and other stakeholders as part of our regulatory development
process.
This proposal is limited to MYs 2023-2026, given lead time considerations under the CAA,
which is consistent with E.O. 13990's direction to review the SAFE rule standards. We have
designed the proposed program based on our assessment that the proposed standards are
reasonable and appropriate and will achieve a significant level of GHG reductions for MYs
2023-2026 vehicles, with the expectation that a future, longer-term program for MYs 2027 and
later will build upon these near-term standards.
Proposed Revisions to Light-duty GHG Emissions Standards
As with EPA's previous light-duty GHG programs, EPA is proposing footprint-based
standards curves for both passenger cars and trucks. Each manufacturer would have a unique
standard for the passenger cars category and another for the truck category6 for each MY based
on the sales-weighted footprint-based CO2 targetsf of the vehicles produced in that MY. Figure 1
shows EPA's proposed standards, expressed as average fleetwide GHG emissions targets (cars
and trucks combined), projected through MY 2026. For comparison, the figure also shows the
corresponding targets for the SAFE final rulemaking (FRM) and the 2012 FRM. The projected
fleet targets for this proposed rule increase in stringency in MY 2023 by about 10 percent (from
the existing SAFE rule standards in MY2022), followed by stringency increases thereafter of
nearly 5 percent year over year from MY2024 through MY2026. As with all EPA vehicle
emissions standards, the proposed MY2026 standards would then remain in place for all
subsequent MYs, unless and until they are revised in a subsequent rulemaking. Table 1 presents
the estimates of EPA's proposed standards presented in Figure 1, again in terms of the projected
overall industry fleetwide CCh-equivalent emission compliance target levels. The industry fleet-
wide estimates in Table 1 are projections based on modeling EPA conducted for the proposed
rule, taking into consideration projected fleet mix and footprints for each manufacturer's fleet in
each model year. Figure 1 presents projected industry fleet average year-over-year percent
reductions comparing the existing standards under the SAFE rule and the proposed revised
standards. See Chapter 2 for a full discussion of the proposed standards.
e Passenger cars include cars and smaller cross-overs and SUVs, while the truck category includes larger cross-overs
and SUVs, minivans, and pickup trucks.
f Because compliance is based on the full range of vehicles in a manufacturer's car and truck fleets, with lower-
emitting vehicles compensating for higher-emitting vehicles, the emission levels of specific vehicles within the fleet
are referred to as targets, rather than standards.
Xlll

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240
230
220
2012 FRM
SAFE FRM
Proposal
210
_QJ
J!S
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Proposed Compliance Incentives and Flexibilities
The existing Light-duty GHG program established in the 2010 and 2012 rules includes several
key flexibilities, such as credit programs and technology incentives, including:
•	Credit Averaging, Banking, and Trading (ABT) with credit carry-forward, credit
carry-back, transferring of credits between a manufacturer's car and truck fleets, and
credit trading between manufacturers (see Chapter 2.1.1)
•	Off-cycle credits for GHG emissions reductions not captured by the test procedures
used for fleet average compliance with the footprint-based standards
•	Air conditioning credits for system efficiency improvements and reduced refrigerant
leakage or use of low global warming potential refrigerants
•	Multiplier incentives for advanced technology vehicles including electric vehicles,
fuel cell vehicles, and plug-in hybrid-electric vehicles
•	Multiplier incentives for natural gas fueled vehicles (MY 2021-2026)
•	Full-size pick-up incentives for hybridization or GHG improvements equivalent to
hybridization
EPA is proposing a limited, targeted set of extended or additional compliance flexibilities and
incentives that we believe are appropriate given the stringency and lead time of the proposed
standards. We are proposing four types of flexibilities/incentives, in addition to
flexibilities/incentives that already will be available for these MYs under EPA's existing
regulations: 1) a limited extension of carry-forward credits generated in MYs 2016 through
2020; 2) an extension of the advanced technology vehicle multiplier credits for MYs 2022
through 2025 with a cumulative credit cap; 3) restoration of the 2012 rule's full-size pickup truck
incentives for strong hybrids or similar performance-based credit for MYs 2022 through 2025
(provisions which were removed in the SAFE rule); and 4) an increase of the off-cycle credits
menu cap from 10 g/mile to 15 g/mile. We summarize these flexibilities and incentives below
and provide further detail in Chapter 2.1.1.
The GHG program includes existing provisions initially established in the 2010 rule, which
set the MY 2012-2016 GHG standards, for how credits may be used within the program. These
averaging, banking, and trading (ABT) provisions include credit carry-forward, credit carry-back
(also called deficit carry-forward), credit transfers (within a manufacturer), and credit trading
(across manufacturers). These ABT provisions define how credits may be used and are integral
to the program. The current program limits credit carry-forward to 5 years. EPA is proposing a
limited extension of credit carry-forward for credits generated in MYs 2016 through 2020. The
proposal would change the credit carry-forward time limitation for MY 2016 credits from five to
seven years and the carry-forward limit for MYs 2017-2020 from 5 to 6 years, as shown in Table
3 below.
xv
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Table 3: EPA Proposed Extension of Credit Carry-forward Provisions
MY Credits
are Banked
MYs Credits Are Valid Under EPA's Proposed Extension
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2016

X
X
X
X
X
+
+



2017


X
X
X
X
X
+



2018



X
X
X
X
X
+


2019




X
X
X
X
X
+

2020





X
X
X
X
X
+
2021






X
X
X
X
X
x = Current program. + = Proposed additional years.
The existing GHG program also includes temporary incentives through MY 2021 that
encourage the use of advanced technologies such as electric, hybrid, and fuel cell vehicles, as
well as incentives for full-size pickups using strong hybridization or technologies providing
similar emissions reductions to hybrid technology. The full-size pickup incentives originally
were available through MY 2025, but the SAFE rule removed these incentives for MYs 2022
through 2025. When EPA established these incentives in the 2012 rule, we recognized that they
would reduce the effective stringency of the standards and have the potential to increase or
decrease the costs of achieving a particular standard depending upon how the manufacturers
respond to the incentives. However, we believed that it was worthwhile to have a limited near-
term loss of emissions reduction benefits to increase the potential for far greater emissions
reduction and technology diffusion benefits in the longer term. Our rationale was that the
temporary regulatory incentives would help bring low emission technologies to market more
quickly than in the absence of incentives. With these same goals in mind for this program, we
are proposing multiplier incentives from MY 2022 though MY 2025 with a cap on multiplier
credits and to reinstate the full-size pickup incentives removed from the program by the SAFE
rule. These proposed incentives are intended as a temporary measure supporting the transition to
zero-emission vehicles and to provide additional flexibility in meeting the MY 2023-2026
proposed standards. For further details, see Chapters 1.1.2 and 2.1.1; and also see Section II.B.l
within the Preamble to this proposed rule.
The current program also includes credits for real-world emissions reductions not reflected on
the test cycles used for measuring CO2 emissions for compliance with the fleet average
standards. There are credits for using technologies that reduce GHG emissions that aren't
captured on EPA tests ("off-cycle" technologies) and improvements to air conditioning systems
that increase efficiency and reduce refrigerant leakage. These credit opportunities do not sunset
under the existing regulations, remaining a part of the program through MY 2026 and beyond
unless the program is changed by regulatory action. EPA is proposing to modify an aspect of the
off-cycle credits program to provide additional opportunities for manufacturers to generate
credits by increasing the pre-defined menu credit cap from 10 to 15 g/mile. EPA is also
proposing to modify some of the regulatory definitions that are used to determine whether a
technology is eligible for the menu credits. EPA is not proposing changes to the air conditioning
credit elements of the program.
Summary of Proposal Costs and Benefits
We estimate that this proposal would result in significant present value net benefits of $86
billion to $140 billion (annualized net benefits of $4.2 billion to $7.3 billion) - that is, the total
benefits far exceed the total costs of the program. Table 4 below summarizes EPA's estimates of
xvi
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total discounted costs, fuel savings, and benefits. The results presented here project the
monetized environmental and economic impacts associated with the proposed standards during
each calendar year through 2050. The proposal also would have significant benefits for
consumers, as the fuel savings for American drivers would total $120 billion to $250 billion in
present-value through 2050. With these fuel savings, consumers would benefit from reduced
operating costs over the vehicle lifetime.
The benefits include climate-related economic benefits from reducing emissions of GHGs that
otherwise contribute to climate change, reductions in energy security externalities caused by U.S.
petroleum consumption and imports, the value of certain particulate matter-related health
benefits (including premature mortality), the value of additional driving attributed to the rebound
effect, and the value of reduced refueling time needed to fill up a more fuel efficient vehicle. The
analysis also includes estimates of economic impacts stemming from additional vehicle use, such
as the economic damages caused by crashes, congestion, and noise (from increased rebound
driving). See Chapter 10 for more information regarding these estimates.
Table 4 Monetized Discounted Costs, Benefits, and Net Benefits of the Proposed Program for Calendar
Years through 2050 (Billions of 2018 dollars)a'b'c'd'e
Present Value	Annualized Value
3% Discount Rate 7% Discount Rate 3% Discount Rate 7% Discount Rate
Costs
$240
$150
$12
$12
Fuel Savings
$250
$120
$13
19
Benefits
$130
$110
5.9
5.3
Net Benefits
$140
$7.3
$4.2
Notes:
a Values rounded to two significant figures; totals may not sum due to rounding. Present and annualized values
are based on the stream of annual calendar year costs and benefits included in the analysis (2021 - 2050) and
discounted back to year 2021.
b Climate benefits are based on reductions in CO2, CH4 and N20 emissions and are calculated using four
different estimates of the social cost of each greenhouse gas (SC-GHG model average at 2.5%, 3%, and 5%
discount rates; 95th percentile at 3% discount rate), which each increase over time. For the presentational
purposes of this table, we show the benefits associated with the average SC-GHGs at a 3% discount rate but the
Agency does not have a single central SC-GHG point estimate. We emphasize the importance and value of
considering the benefits calculated using all four SC-GHG estimates and present them later in this preamble. As
discussed in Chapter 3.3 of the RIA, a consideration of climate benefits calculated using discount rates below 3
percent, including 2 percent and lower, are also warranted when discounting intergenerational impacts.
°The same discount rate used to discount the value of damages from future GHG emissions (SC-GHGs at 5, 3,
and 2.5 percent) is used to calculate the present and annualized values of climate benefits for internal consistency,
while all other costs and benefits are discounted at either 3% or 7%.
dNet benefits reflect the fuel savings plus benefits minus costs.
e Non-GHG impacts associated with the standards presented here do not include the full complement of health
and environmental effects that, if quantified and monetized, would increase the total monetized benefits. Instead,
the non-GHG benefits are based on benefit-per-ton values that reflect only human health impacts associated with
reductions inPM2.5 exposure.	
Table 5 and Table 6 summarize EPA's estimates of total discounted costs, fuel savings, and
benefits through the full lifetime of vehicles projected to be sold in MYs 2023-2026. g The
g In the MY lifetime analysis, we look only at specific model-year vehicles and sum the costs and benefits of those
model-year vehicles over their full lifetimes. In the calendar year analysis, we sum the costs and benefits of all
vehicles of all vintages (i.e., all model-years and ages), that are in-service during the calendar years noted (in this
case calendar years 2021 through 2050).
xvii
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estimated results presented here project the monetized environmental and economic impacts
associated with the proposed standards. Note that standards continue at their MY2026 levels
beyond MY2026 in any scenario. At both a 3% and 7% discount rate all model years show
substantial fuel savings and net benefits.
Table 5 GHG Analysis of Lifetime Costs & Benefits to Meet the Proposed MY 2023-2026 GHG Standards,
3% discount rate (for Vehicles Produced in MY 2023-2026)a b c d (Billions of 2018$)
MY
Costs
Fuel Savings
Benefits
Net Benefits
Present Values
2023
$4.8
$3.6
$1.9
$0.68
2024
$5.9
$7
$3.6
$4.7
2025
$6.7
$8.6
$4.4
$6.2
2026
$8.1
$13
$7.2
$12
Sum
$26
$33
$17
$24
Annualized Values
2023
$0.21
$0.16
$0.08
$0,029
2024
$0.26
$0.3
$0.16
$0.2
2025
$0.29
$0.37
$0.19
$0.27
2026
$0.35
$0.58
$0.31
$0.54
Sum
$1.1
$1.4
$0.74
$1
Notes:
" The lifetime costs and benefits of each MY vehicle are discounted back to 2021.
b Climate benefits are based on reductions in CO2, CH4 and N20 emissions and are calculated
using four different estimates of the social cost of each greenhouse gas (SC-GHG model average at
2.5%, 3%, and 5% discount rates; 95th percentile at 3% discount rate), which each increase over
time. For the presentational purposes of this table, we show the benefits associated with the
average SC-GHGs at a 3% discount rate, but the Agency does not have a single central SC-GHG
point estimate. We emphasize the importance and value of considering the benefits calculated
using all four SC-GHG estimates and present them later in this preamble. As discussed in Chapter
3.3 of the RIA, a consideration of climate benefits calculated using discount rates below 3 percent,
including 2 percent and lower, are also warranted when discounting intergenerational impacts.
0 The same discount rate used to discount the value of damages from future GHG emissions is used
to calculate the present and annualized value of SC-GHGs for internal consistency, while all other
costs and benefits are discounted at 3% in this table.
dNon-GHG impacts associated with the standards presented here do not include the full
complement of health and environmental effects that, if quantified and monetized, would increase
the total monetized benefits. Instead, the non-GHG benefits are based on benefit-per-ton values
that reflect only human health impacts associated with reductions in PM2.5 exposure.
xviii
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Table 6 GHG Analysis of Lifetime Costs & Benefits to Meet the Proposed MY 2023-2026 GHG Standards,
7% discount rate (for Vehicles Produced in MY 2023-2026)a b c d (Billions of 2018$)
MY
Costs
Fuel Savings
Benefits
Net Benefits
Present Values
2023
$4.4
$2.6
$1.7
-$0.14
2024
$5.5
$4.7
$3.3
$2.4
2025
$6.1
$5.5
$3.9
$3.4
2026
$7.3
$8.2
$6.2
$7.2
Sum
$23
$21
$15
$13
Annualized Values
2023
$0.33
$0.19
$0,085
-$0,053
2024
$0.41
$0.35
$0.16
$0.1
2025
$0.45
$0.41
$0.19
$0.15
2026
$0.55
$0.62
$0.31
$0.38
Sum
$1.7
$1.6
$0.75
$0.58
Notes:
a The lifetime costs and benefits of each MY vehicle are discounted back to 2021.
b Climate benefits are based on reductions in CO2, CH4 and N20 emissions and are calculated using
four different estimates of the social cost of each greenhouse gas (SC-GHG model average at 2.5%,
3%, and 5% discount rates; 95th percentile at 3% discount rate), which each increase over time. For
the presentational purposes of this table, we show the benefits associated with the average SC-GHGs
at a 3% discount rate, but the Agency does not have a single central SC-GHG point estimate. We
emphasize the importance and value of considering the benefits calculated using all four SC-GHG
estimates and present them later in this preamble. As discussed in Chapter 3.3 of the RIA, a
consideration of climate benefits calculated using discount rates below 3 percent, including 2 percent
and lower, are also warranted when discounting intergenerational impacts.
0 The same discount rate used to discount the value of damages from future GHG emissions is used
to calculate the present and annualized value of SC-GHGs for internal consistency, while all other
costs and benefits are discounted at 7% in this table.
dNon-GHG impacts associated with the standards presented here do not include the full complement
of health and environmental effects that, if quantified and monetized, would increase the total
monetized benefits. Instead, the non-GHG benefits are based on benefit-per-ton values that reflect
only human health impacts associated with reductions in PM2.5 exposure.
Summary of the Analysis of Alternatives to the Proposal
Description of Alternatives
Along with the proposed standards, we analyzed both a more stringent and a less stringent
alternative. For the less stringent alternative, Alternative 1, we used the coefficients from the
California Framework for the 2.7 percent effective stringency level as the basis for the MY 2023
stringency level11 and the 2012 rule MY 2025 standards1 as the basis for the MY 2026 stringency
level, with linear year-over-year reductions between the two points for MYs 2024 and 2025. We
view the California Framework as a reasonable basis for the least stringent alternative since it
represents a level of stringency that five manufacturers have already committed to achieving.
h California Air Resources Board. California Framework Agreements on Clean Cars. Last accessed on the Internet
on 7/23/2021 at the following URL: https://ww2.arb.ca.gov/sites/default/files/2020-08/clean-car-framework-
documents-all-bmw-ford-honda-volvo-vw.pdf.
1 77 FR 62624
xix
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We did not include incentive multipliers within the analysis for Alternative 1, as doing so would
only further reduce the effective stringency of this Alternative. Alternative 1 is the lower end of
stringency that we believe is appropriate through 2026.
For the more stringent alternative, Alternative 2, we used the 2012 rule standards as the basis
for MY 2023-2025 targets, with the standards continuing to increase in stringency in a linear
fashion for MY2026. Alternative 2 adopts the 2012 rule stringency levels in MY 2023 and
follows the 2012 rule standard target levels through MY2025. EPA extended the same linear
average year-over-year trajectory for MYs 2023-2025 to MY2026 for the final standards under
Alternative 2. We believe that it is important to continue to make progress in MY2026 beyond
the MY2025 standard levels in the 2012 rule. As with the proposal, Alternative 2 meets this
objective. We also did not include in Alternative 2 the proposed incentive multipliers with the
proposed cumulative credit cap in MYs 2022-2025, which would have had the effect of making
Alternative 2 less stringent.
EPA is proposing several changes to program flexibilities. Further details regarding program
flexibilities can be found in Chapter 2.1.1. These proposed program changes would apply to the
alternatives as well and the proposal except for the advanced technology multipliers. Table 7
below provides a list of the proposed flexibilities and their proposed applicability to the proposal
and alternatives.
Table 7 Applicability of Program Provisions to the Proposal and Alternatives
Provision
Proposal
Alternative 1
Alternative 2
Extension of credit carry-forward for MY 2016-
Yes
Yes
Yes
2020 credits



Advanced technology incentive multipliers for MYs
2022-2025 with cap
Yes
No
No
Increase of off-cycle menu cap from 10 to 15 g/mile
Yes
Yes
Yes
Reinstatement of full-size pickup incentive for
Yes
Yes
Yes
strong hybrids or equivalent technologies for MYs
2022-2025



Note:



EPA's technical analysis, presented in Chapter 4, consists of model runs using a model capable of reflecting some
but not all of these provisions. The modeling includes consideration of advanced technology incentive multipliers
for the proposal but not for the alternatives. The model runs also include the 15 grams per mile off-cycle menu
cap as appropriate given the standards or targets to which a fleet being modeled is complying. Not included in the
model runs are the full-size pickup truck technology incentive credit or the extension of the emissions credit
carry-forward.



The fleet average targets for the two alternatives compared to the proposed standards are
provided in Table 8 below. As discussed in detail in Chapter 2.3.3, there has been a proliferation
of recent announcements from automakers signaling a rapidly growing shift in investment away
from internal-combustion technologies and toward high levels of electrification. EPA has also
heard from a wide range of stakeholders over the past several months, including but not limited
to the automotive manufacturers and the automotive suppliers, that the significant investments
being made now to develop and launch new EV product offerings and in the expansion of EV
charging infrastructure could enable higher levels of EV penetration to occur in the market place
by the MY 2026 time frame than we have projected in this proposal for both the proposed
MY2026 standards and the Alternative 2 MY2026 standards.
xx
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Table 8 Projected Fleet Average Target Levels for Proposed Standards and Alternatives (C02 grams/mile)
Model Year
Proposal Projected
Targets
Alternative 1
Projected Targets
Alternative 2
Projected Targets
2021
223*
223*
224*
2022
220*
220*
220*
2023
199
203
195
2024
189
194
186
2025
180
185
177
2026
171
177
169
* SAFE rule standards included here for reference.
240
230
220
210
= 200
J!S
0~ 190
u
180
170
160
150
2020	2021	2022	2023	2024	2025	2026	2027
Model Year
Figure 2 Proposed Standards Fleet Average Targets Compared to Alternatives
As shown in Figure 2, the range of alternatives that EPA has analyzed differ from the
proposed standard targets in any given MY in 2023-2026 by 2 to 6 g/mile. EPA believes this
approach is reasonable and appropriate considering the relatively limited lead time for the
proposed standards (especially for MYs 2023-2025), our assessment of feasibility, the existing
automaker commitments to meet the California Framework (representing about one-third of the
auto market), the standards adopted in the 2012 rule, and the need to reduce GHG emissions.
EPA provides further discussion of the feasibility of the proposed standard and alternatives and
the selection of the proposed standards within Chapter 2.2. The analysis of costs and benefits of
Alternatives 1 and 2 is shown in the Chapters 4, 5, 6, and 10.

••• SAFE FRM

2012 FRM
v..
V--..
			
Proposal
••• Alternative 1
\ V	
— — Alternative 2


	



xxi
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Summary of Costs and Benefits of the Alternatives
EPA estimates that Alternative 1 would result in significant present value net benefits of $76
to $130 billion (annualized net benefits of $4.1 to $6.6 billion) - that is, the total benefits far
exceed the total costs of the program. Table 9 below summarizes EPA's estimates of total
discounted costs, fuel savings, and benefits for Alternative 1. The results presented here project
the monetized environmental and economic impacts associated with the proposed standards
during each calendar year through 2050. Alternative 1 also would have significant benefits for
consumers, as the fuel savings for American drivers would total $98 to $200 billion through
2050. With these fuel savings, consumers would benefit from reduced operating costs over the
vehicle lifetime.
The benefits include climate-related economic benefits from reducing emissions of GHGs that
otherwise contribute to climate change, reductions in energy security externalities caused by U.S.
petroleum consumption and imports, the value of certain particulate matter-related health
benefits (including premature mortality), the value of additional driving attributed to the rebound
effect, and the value of reduced refueling time needed to fill up a more fuel efficient vehicle. The
analysis also includes estimates of economic impacts stemming from additional vehicle use, such
as the economic damages caused by crashes, congestion, and noise (from increased rebound
driving). See the Chapters 4, 5, 6, and 10 for more information regarding these estimates.
Table 9 Monetized Discounted Costs, Benefits, and Net Benefits of Alternative 1 for Calendar Years through
2050 (Billions of 2018 dollars)a'b'c'd'e

Present Value
Annualized Value
3% Discount Rate
7% Discount Rate
3% Discount Rate
7% Discount Rate
Costs
$190
$110
$9.5
$9.2
Fuel Savings
$200
$98
$10
$7.9
Benefits
$120
$93
$6
$5.4
Net Benefits
$130
$76
$6.6
$4.1
Notes:
a Values rounded to two significant figures; totals may not sum due to rounding. Present and annualized values
are based on the stream of annual calendar year costs and benefits included in the analysis (2021 - 2050) and
discounted back to year 2021.
b Climate benefits are based on reductions in C02, CH4 and N20 emissions and are calculated using four
different estimates of the social cost of each greenhouse gas (SC-GHG model average at 2.5%, 3%, and 5%
discount rates; 95th percentile at 3% discount rate), which each increase over time. For the presentational
purposes of this table, we show the benefits associated with the average SC-GHGs at a 3% discount rate but the
Agency does not have a single central SC-GHG point estimate. We emphasize the importance and value of
considering the benefits calculated using all four SC-GHG estimates and present them later in this preamble. As
discussed in Chapter 3.3 of the RIA, a consideration of climate benefits calculated using discount rates below 3
percent, including 2 percent and lower, are also warranted when discounting intergenerational impacts.
°The same discount rate used to discount the value of damages from future GHG emissions (SC-GHGs at 5, 3,
and 2.5 percent) is used to calculate the present and annualized values of climate benefits for internal consistency,
while all other costs and benefits are discounted at either 3% or 7%.
dNet benefits reflect the fuel savings plus benefits minus costs.
eNon-GHG impacts associated with the standards presented here do not include the full complement of health
and environmental effects that, if quantified and monetized, would increase the total monetized benefits. Instead,
the non-GHG benefits are based on benefit-per-ton values that reflect only human health impacts associated with
reductions inPM2.5 exposure.	
XXll
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Table 10 and Table 11 summarize EPA's estimates of total discounted costs, fuel savings, and
benefits through the full lifetime of vehicles projected to be sold in MYs 2023-2026 under
Alternative 1. The estimated results presented here project the monetized environmental and
economic impacts associated with the Alternative 1 standards. Note that standards continue at
their MY2026 levels beyond MY2026 in any scenario. At both a 3% and 7% discount rate all
model years show substantial fuel savings and net benefits.
Table 10 GHG Analysis of Lifetime Costs & Benefits to Meet the Alternative 1 MY 2023-2026 GHG
Standards, 3% discount rate (for Vehicles Produced in MY 2023-2026)a b c d (Billions of 2018$)
MY
Costs
Fuel Savings
Benefits
Net Benefits
Present Values
2023
$3.9
$3.4
$2
$1.5
2024
$4.9
$6.5
$3.7
$5.3
2025
$5.6
$7.7
$4.5
$6.5
2026
$6.4
$10
$6
$9.7
Sum
$21
$28
$16
$23
Annualized Values
2023
$0.17
$0.15
$0,085
$0,067
2024
$0.21
$0.28
$0.16
$0.23
2025
$0.24
$0.33
$0.19
$0.28
2026
$0.28
$0.44
$0.26
$0.42
Sum
$0.9
$1.2
$0.7
$1
Notes:
aThe lifetime costs and benefits of each MY vehicle are discounted back to 2021.
b Climate benefits are based on reductions in C02, CH4 and N20 emissions and are calculated using four
different estimates of the social cost of each greenhouse gas (SC-GHG model average at 2.5%, 3%, and 5%
discount rates; 95th percentile at 3% discount rate), which each increase over time. For the presentational
purposes of this table, we show the benefits associated with the average SC-GHGs at a 3% discount rate, but the
Agency does not have a single central SC-GHG point estimate. We emphasize the importance and value of
considering the benefits calculated using all four SC-GHG estimates and present them later in this preamble. As
discussed in Chapter 3.3 of the RIA, a consideration of climate benefits calculated using discount rates below 3
percent, including 2 percent and lower, are also warranted when discounting intergenerational impacts.
0 The same discount rate used to discount the value of damages from future GHG emissions is used to calculate
the present and annualized value of SC-GHGs for internal consistency, while all other costs and benefits are
discounted at 3% in this table.
dNon-GHG impacts associated with the standards presented here do not include the full complement of health
and environmental effects that, if quantified and monetized, would increase the total monetized benefits. Instead,
the non-GHG benefits are based on benefit-per-ton values that reflect only human health impacts associated with
reductions inPM2.5 exposure.
XXlll
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Table 11 GHG Analysis of Lifetime Costs & Benefits to Meet the Alternative 1 MY 2023-2026 GHG
Standards, 7% discount rate (for Vehicles Produced in MY 2023-2026)a b c d (Billions of 2018$)
MY
Costs
Fuel Savings
Benefits
Net Benefits
Present Values
2023
$3.7
$2.4
$1.7
$0.4
2024
$4.7
$4.3
$3.2
$2.8
2025
$5.1
$4.9
$3.8
$3.6
2026
$5.6
$6.2
$5
$5.6
Sum
$19
$18
$14
$12
Annualized Values
2023
$0.28
$0.18
$0,091
-$0.0084
2024
$0.35
$0.32
$0.17
$0.14
2025
$0.38
$0.37
$0.2
$0.19
2026
$0.42
$0.47
$0.26
$0.31
Sum
$1.4
$1.3
$0.72
$0.63
Notes:
aThe lifetime costs and benefits of each MY vehicle are discounted back to 2021.
b Climate benefits are based on reductions in C02, CH4 and N20 emissions and are calculated using four
different estimates of the social cost of each greenhouse gas (SC-GHG model average at 2.5%, 3%, and 5%
discount rates; 95th percentile at 3% discount rate), which each increase over time. For the presentational
purposes of this table, we show the benefits associated with the average SC-GHGs at a 3% discount rate, but the
Agency does not have a single central SC-GHG point estimate. We emphasize the importance and value of
considering the benefits calculated using all four SC-GHG estimates and present them later in this preamble. As
discussed in Chapter 3.3 of the RIA, a consideration of climate benefits calculated using discount rates below 3
percent, including 2 percent and lower, are also warranted when discounting intergenerational impacts.
0 The same discount rate used to discount the value of damages from future GHG emissions is used to calculate
the present and annualized value of SC-GHGs for internal consistency, while all other costs and benefits are
discounted at 7% in this table.
dNon-GHG impacts associated with the standards presented here do not include the full complement of health
and environmental effects that, if quantified and monetized, would increase the total monetized benefits. Instead,
the non-GHG benefits are based on benefit-per-ton values that reflect only human health impacts associated with
reductions inPM2.5 exposure.	
We estimated that Alternative 2 would result in significant present value net benefits of $110
to $180 billion (annualized net benefits of $5.7 to $9.1 billion) - that is, the total benefits far
exceed the total costs of the program. Table 12 below summarizes EPA's estimates of total
discounted costs, fuel savings, and benefits for Alternative 2. The results presented here project
the monetized environmental and economic impacts associated with the proposed standards
during each calendar year through 2050. Alternative 2 also would have significant benefits for
consumers, as the fuel savings for American drivers would total $150 to $290 billion through
2050. With these fuel savings, consumers would benefit from reduced operating costs over the
vehicle lifetime.
The benefits include climate-related economic benefits from reducing emissions of GHGs that
otherwise contribute to climate change, reductions in energy security externalities caused by U.S.
petroleum consumption and imports, the value of certain particulate matter-related health
benefits (including premature mortality), the value of additional driving attributed to the rebound
effect, and the value of reduced refueling time needed to fill up a more fuel efficient vehicle. The
analysis also includes estimates of economic impacts stemming from additional vehicle use, such
as the economic damages caused by crashes, congestion, and noise (from increased rebound
driving). See the Chapters 4,5,6, and 10 for more information regarding these estimates.
xxiv
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Table 12 Monetized Discounted Costs, Benefits, and Net Benefits of Alternative 2 for Calendar Years
through 2050 (Billions of 2018 dollars)a'b'c'd'e

Present Value
Annualized Value
3% Discount Rate
7% Discount Rate
3% Discount Rate
7% Discount Rate
Costs
$290
$180
$15
$14
Fuel Savings
$290
$150
$15
$12
Benefits
$170
$140
$8.8
$8
Net Benefits
$180
$110
$9.1
$5.7
Notes:
a Values rounded to two significant figures; totals may not sum due to rounding. Present and annualized values
are based on the stream of annual calendar year costs and benefits included in the analysis (2021 - 2050) and
discounted back to year 2021.
b Climate benefits are based on reductions in C02, CH4 and N20 emissions and are calculated using four
different estimates of the social cost of each greenhouse gas (SC-GHG model average at 2.5%, 3%, and 5%
discount rates; 95th percentile at 3% discount rate), which each increase over time. For the presentational
purposes of this table, we show the benefits associated with the average SC-GHGs at a 3% discount rate but the
Agency does not have a single central SC-GHG point estimate. We emphasize the importance and value of
considering the benefits calculated using all four SC-GHG estimates and present them later in this preamble. As
discussed in Chapter 3.3 of the RIA, a consideration of climate benefits calculated using discount rates below 3
percent, including 2 percent and lower, are also warranted when discounting intergenerational impacts.
°The same discount rate used to discount the value of damages from future GHG emissions (SC-GHGs at 5, 3,
and 2.5 percent) is used to calculate the present and annualized values of climate benefits for internal consistency,
while all other costs and benefits are discounted at either 3% or 7%.
dNet benefits reflect the fuel savings plus benefits minus costs.
e Non-GHG impacts associated with the standards presented here do not include the full complement of health
and environmental effects that, if quantified and monetized, would increase the total monetized benefits. Instead,
the non-GHG benefits are based on benefit-per-ton values that reflect only human health impacts associated with
reductions inPM2.5 exposure.	
Table 13 and Table 14 summarize EPA's estimates of total discounted costs, fuel savings, and
benefits through the full lifetime of vehicles projected to be sold in MYs 2023-2026 under
Alternative 2. The estimated results presented here project the monetized environmental and
economic impacts associated with the proposed standards. Note that standards continue at their
MY2026 levels beyond MY2026 in any scenario. At both a 3% and 7% discount rate all model
years show substantial fuel savings and net benefits.
xxv
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Table 13 GHG Analysis of Lifetime Costs & Benefits to Meet the Alternative 2 MY 2023-2026 GHG
Standards, 3% discount rate (for Vehicles Produced in MY 2023-2026)a b c d (Billions of 2018$)
MY
Costs
Fuel Savings
Benefits
Net Benefits
Present Values
2023
$6.8
$7.7
$4.6
$5.5
2024
$7.7
$9.8
$5.7
$7.8
2025
$8.4
$11
$6.5
$9.1
2026
$9.2
$13
$7.8
$12
Sum
$32
$42
$25
$34
Annualized Values
2023
$0.3
$0.33
$0.2
$0.24
2024
$0.33
$0.42
$0.25
$0.34
2025
$0.37
$0.48
$0.28
$0.39
2026
$0.4
$0.57
$0.34
$0.51
Sum
$1.4
$1.8
$1.1
$1.5
Notes:




aThe lifetime costs and benefits of each MY vehicle are discounted back to 2021.

b Climate benefits are based on reductions in C02, CH4 and N20 emissions and are calculated using four
different estimates of the social cost of each greenhouse gas (SC-GHG model average at 2.5%, 3%, and 5%
discount rates; 95th percentile at 3% discount rate), which each increase over time. For the presentational
purposes of this table, we show the benefits associated with the average SC-GHGs at a 3% discount rate, but the
Agency does not have a single central SC-GHG point estimate. We emphasize the importance and value of
considering the benefits calculated using all four SC-GHG estimates and present them later in this preamble. As
discussed in Chapter 3.3 of the RIA, a consideration of climate benefits calculated using discount rates below 3
percent, including 2 percent and lower, are also warranted when discounting intergenerational impacts.
0 The same discount rate used to discount the value of damages from future GHG emissions is used to calculate
the present and annualized value of SC-GHGs for internal consistency, while all other costs and benefits are
discounted at 3% in this table.



dNon-GHG impacts associated with the standards presented here do not include the full complement of health
and environmental effects that, if quantified and monetized, would increase the total monetized benefits. Instead,
the non-GHG benefits are based on benefit-per-ton values that reflect only human health impacts associated with
reductions inPM2.5 exposure.
xxvi
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Table 14 GHG Analysis of Lifetime Costs & Benefits to Meet the Alternative 2 MY 2023-2026 GHG
Standards, 7% discount rate (for Vehicles Produced in MY 2023-2026)a b c d (Billions of 2018$)
MY
Costs
Fuel Savings
Benefits
Net Benefits
Present Values
2023
$6.3
$5.4
$4
$3.1
2024
$7
$6.5
$5
$4.4
2025
$7.4
$7.1
$5.5
$5.2
2026
$7.9
$8.2
$6.6
$6.9
Sum
$29
$27
$21
$20
Annualized Values
2023
$0.48
$0.4
$0.21
$0.14
2024
$0.53
$0.49
$0.26
$0.22
2025
$0.56
$0.54
$0.29
$0.27
2026
$0.59
$0.61
$0.34
$0.37
Sum
$2.2
$2
$1.1
$1
Notes:
aThe lifetime costs and benefits of each MY vehicle are discounted back to 2021.
b Climate benefits are based on reductions in C02, CH4 and N20 emissions and are calculated using four
different estimates of the social cost of each greenhouse gas (SC-GHG model average at 2.5%, 3%, and 5%
discount rates; 95th percentile at 3% discount rate), which each increase over time. For the presentational
purposes of this table, we show the benefits associated with the average SC-GHGs at a 3% discount rate, but the
Agency does not have a single central SC-GHG point estimate. We emphasize the importance and value of
considering the benefits calculated using all four SC-GHG estimates and present them later in this preamble. As
discussed in Chapter 3.3 of the RIA, a consideration of climate benefits calculated using discount rates below 3
percent, including 2 percent and lower, are also warranted when discounting intergenerational impacts.
0 The same discount rate used to discount the value of damages from future GHG emissions is used to calculate
the present and annualized value of SC-GHGs for internal consistency, while all other costs and benefits are
discounted at 7% in this table.
dNon-GHG impacts associated with the standards presented here do not include the full complement of health
and environmental effects that, if quantified and monetized, would increase the total monetized benefits. Instead,
the non-GHG benefits are based on benefit-per-ton values that reflect only human health impacts associated with
reductions inPM2.5 exposure.
Summary of the Proposal's Costs and Benefits Compared to the Alternatives
Table 15 through Table 16 provide summaries of the proposal's costs and benefits compared
to the costs and benefits of the two alternatives that were analyzed. The benefits include climate-
related economic benefits from reducing emissions of GHGs that otherwise contribute to climate
change, reductions in energy security externalities caused by U.S. petroleum consumption and
imports, the value of certain particulate matter-related health benefits (including premature
mortality), the value of additional driving attributed to the rebound effect, and the value of
reduced refueling time needed to fill up a more fuel efficient vehicle. The analysis also includes
estimates of economic impacts stemming from additional vehicle use, such as the economic
damages caused by crashes, congestion, and noise (from increased rebound driving). See
Chapters 4, 5, 6 and Chapter 10 for more information regarding these estimates.
xxvii
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Table 15 Present Value Monetized Discounted Costs, Benefits, and Net Benefits of the Proposed Program
and Alternatives for Calendar Years through 2050 (Billions of 2018 dollars)a'b'c'd'e

3% Discount Rate
7% Discount Rate
Proposal
Alternative 1
Alternative 2
Proposal
Alternative 1
Alternative 2
Costs
$240
$190
$290
$150
$110
$180
Fuel
Savings
$250
$200
$290
$120
$98
$150
Benefits
$130
$120
$170
$110
$93
$140
Net Benefits
$140
$130
$180
$86
$76
$110
Notes:
a Values rounded to two significant figures; totals may not sum due to rounding. Present and annualized values
are based on the stream of annual calendar year costs and benefits included in the analysis (2021 - 2050) and
discounted back to year 2021.
b Climate benefits are based on reductions in C02, CH4 and N20 emissions and are calculated using four
different estimates of the social cost of each greenhouse gas (SC-GHG model average at 2.5%, 3%, and 5%
discount rates; 95th percentile at 3% discount rate), which each increase over time. For the presentational
purposes of this table, we show the benefits associated with the average SC-GHGs at a 3% discount rate but the
Agency does not have a single central SC-GHG point estimate. We emphasize the importance and value of
considering the benefits calculated using all four SC-GHG estimates and present them later in this preamble. As
discussed in Chapter 3.3 of the RIA, a consideration of climate benefits calculated using discount rates below 3
percent, including 2 percent and lower, are also warranted when discounting intergenerational impacts.
°The same discount rate used to discount the value of damages from future GHG emissions (SC-GHGs at 5, 3,
and 2.5 percent) is used to calculate the present and annualized values of climate benefits for internal consistency,
while all other costs and benefits are discounted at either 3% or 7%.
dNet benefits reflect the fuel savings plus benefits minus costs.
e Non-GHG impacts associated with the standards presented here do not include the full complement of health
and environmental effects that, if quantified and monetized, would increase the total monetized benefits. Instead,
the non-GHG benefits are based on benefit-per-ton values that reflect only human health impacts associated with
reductions inPM2.5 exposure.	
xxviii
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Table 16 Annualized Monetized Discounted Costs, Benefits, and Net Benefits of the Proposed Program and
Alternatives for Calendar Years through 2050 (Billions of 2018 dollars)a,b,c,d,e

3% Discount Rate
7% Discount Rate
Proposal
Alternative 1
Alternative 2
Proposal
Alternative 1
Alternative 2
Costs
$12
$9.5
$15
$12
$9.2
$14
Fuel
Savings
$13
$10
$15
$9.9
$7.9
$12
Benefits
$6.9
$6
$8.8
$6.3
$5.4
$8
Net Benefits
$7.3
$6.6
$9.1
$4.2
$4.1
$5.7
Notes:
a Values rounded to two significant figures; totals may not sum due to rounding. Present and annualized values
are based on the stream of annual calendar year costs and benefits included in the analysis (2021 - 2050) and
discounted back to year 2021.
b Climate benefits are based on reductions in C02, CH4 and N20 emissions and are calculated using four
different estimates of the social cost of each greenhouse gas (SC-GHG model average at 2.5%, 3%, and 5%
discount rates; 95th percentile at 3% discount rate), which each increase over time. For the presentational
purposes of this table, we show the benefits associated with the average SC-GHGs at a 3% discount rate but the
Agency does not have a single central SC-GHG point estimate. We emphasize the importance and value of
considering the benefits calculated using all four SC-GHG estimates and present them later in this preamble. As
discussed in Chapter 3.3 of the RIA, a consideration of climate benefits calculated using discount rates below 3
percent, including 2 percent and lower, are also warranted when discounting intergenerational impacts.
0 The same discount rate used to discount the value of damages from future GHG emissions (SC-GHGs at 5, 3,
and 2.5 percent) is used to calculate the present and annualized values of climate benefits for internal consistency,
while all other costs and benefits are discounted at either 3% or 7%.
dNet benefits reflect the fuel savings plus benefits minus costs.
e Non-GHG impacts associated with the standards presented here do not include the full complement of health
and environmental effects that, if quantified and monetized, would increase the total monetized benefits. Instead,
the non-GHG benefits are based on benefit-per-ton values that reflect only human health impacts associated with
reductions inPM2.5 exposure.	
Table 17 and Table 18 summarize EPA's estimates of total discounted costs, fuel savings, and
benefits through the full lifetime of vehicles projected to be sold in MYs 2023-2026. The
estimated results presented here project the monetized environmental and economic impacts
associated with the proposed standards. Note that standards continue at their MY2026 levels
beyond MY2026 in any scenario. At both a 3% and 7% discount rate all model years show
substantial fuel savings and net benefits.
xxix
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Table 17 Present Value GHG Analysis of Lifetime Costs & Benefits for MY 2023-2026 GHG Standards
under the Proposal and Alternatives, (for Vehicles Produced in MY 2023-2026)a b c d (Billions of 2018$)
3% Discount Rate	7% Discount Rate
MY
Costs
Fuel
Savings
Benefits
Net
Benefits
Costs
Fuel
Savings
Benefits
Net
Benefits
Proposal
2023
$4.8
$3.6
$1.9
$0.68
$4.4
$2.6
$1.7
-$0.14
2024
$5.9
$7
$3.6
$4.7
$5.5
$4.7
$3.3
$2.4
2025
$6.7
$8.6
$4.4
$6.2
$6.1
$5.5
$3.9
$3.4
2026
$8.1
$13
$7.2
$12
$7.3
$8.2
$6.2
$7.2
Sum
$26
$33
$17
$24
$23
$21
$15
$13
Alternative 1
2023
$3.9
$3.4
$2
$1.5
$3.7
$2.4
$1.7
$0.4
2024
$4.9
$6.5
$3.7
$5.3
$4.7
$4.3
$3.2
$2.8
2025
$5.6
$7.7
$4.5
$6.5
$5.1
$4.9
$3.8
$3.6
2026
$6.4
$10
$6
$9.7
$5.6
$6.2
$5
$5.6
Sum
$21
$28
$16
$23
$19
$18
$14
$12
Alternative 2
2023
$6.8
$7.7
$4.6
$5.5
$6.3
$5.4
$4
$3.1
2024
$7.7
$9.8
$5.7
$7.8
$7
$6.5
$5
$4.4
2025
$8.4
$11
$6.5
$9.1
$7.4
$7.1
$5.5
$5.2
2026
$9.2
$13
$7.8
$12
$7.9
$8.2
$6.6
$6.9
Sum
$32
$42
$25
$34
$29
$27
$21
$20
Notes:
aThe lifetime costs and benefits of each MY vehicle are discounted back to 2021.
b Climate benefits are based on reductions in C02, CH4 and N20 emissions and are calculated using four
different estimates of the social cost of each greenhouse gas (SC-GHG model average at 2.5%, 3%, and 5%
discount rates; 95th percentile at 3% discount rate), which each increase over time. For the presentational
purposes of this table, we show the benefits associated with the average SC-GHGs at a 3% discount rate, but the
Agency does not have a single central SC-GHG point estimate. We emphasize the importance and value of
considering the benefits calculated using all four SC-GHG estimates and present them later in this preamble. As
discussed in Chapter 3.3 of the RIA, a consideration of climate benefits calculated using discount rates below 3
percent, including 2 percent and lower, are also warranted when discounting intergenerational impacts.
0 The same discount rate used to discount the value of damages from future GHG emissions is used to calculate
the present and annualized value of SC-GHGs for internal consistency, while all other costs and benefits are
discounted at 3% in this table.
dNon-GHG impacts associated with the standards presented here do not include the full complement of health
and environmental effects that, if quantified and monetized, would increase the total monetized benefits. Instead,
the non-GHG benefits are based on benefit-per-ton values that reflect only human health impacts associated with
reductions inPM2.5 exposure.	
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Table 18 Annualized GHG Analysis of Lifetime Costs & Benefits for MY 2023-2026 GHG Standards under
the Proposal and Alternatives, (for Vehicles Produced in MY 2023-2026)a b c d (Billions of 2018$)
3 % Discount Rate	7% Discount Rate
MY
Costs
Fuel
Savings
Benefits
Net
Benefits
Costs
Fuel
Savings
Benefits
Net
Benefits
Proposal
2023
$0.21
$0.16
$0.08
$0,029
$0.33
$0.19
$0,085
-$0,053
2024
$0.26
$0.3
$0.16
$0.2
$0.41
$0.35
$0.16
$0.1
2025
$0.29
$0.37
$0.19
$0.27
$0.45
$0.41
$0.19
$0.15
2026
$0.35
$0.58
$0.31
$0.54
$0.55
$0.62
$0.31
$0.38
Sum
$1.1
$1.4
$0.74
$1
$1.7
$1.6
$0.75
$0.58
Alternative 1
2023
$0.17
$0.15
$0,085
$0,067
$0.28
$0.18
$0,091
-$0.0084
2024
$0.21
$0.28
$0.16
$0.23
$0.35
$0.32
$0.17
$0.14
2025
$0.24
$0.33
$0.19
$0.28
$0.38
$0.37
$0.2
$0.19
2026
$0.28
$0.44
$0.26
$0.42
$0.42
$0.47
$0.26
$0.31
Sum
$0.9
$1.2
$0.7
$1
$1.4
$1.3
$0.72
$0.63
Alternative 2
2023
$0.3
$0.33
$0.2
$0.24
$0.48
$0.4
$0.21
$0.14
2024
$0.33
$0.42
$0.25
$0.34
$0.53
$0.49
$0.26
$0.22
2025
$0.37
$0.48
$0.28
$0.39
$0.56
$0.54
$0.29
$0.27
2026
$0.4
$0.57
$0.34
$0.51
$0.59
$0.61
$0.34
$0.37
Sum
$1.4
$1.8
$1.1
$1.5
$2.2
$2
$1.1
$1
Notes:
aThe lifetime costs and benefits of each MY vehicle are discounted back to 2021.
b Climate benefits are based on reductions in C02, CH4 and N20 emissions and are calculated using four
different estimates of the social cost of each greenhouse gas (SC-GHG model average at 2.5%, 3%, and 5%
discount rates; 95th percentile at 3% discount rate), which each increase over time. For the presentational
purposes of this table, we show the benefits associated with the average SC-GHGs at a 3% discount rate, but the
Agency does not have a single central SC-GHG point estimate. We emphasize the importance and value of
considering the benefits calculated using all four SC-GHG estimates and present them later in this preamble. As
discussed in Chapter 3.3 of the RIA, a consideration of climate benefits calculated using discount rates below 3
percent, including 2 percent and lower, are also warranted when discounting intergenerational impacts.
0 The same discount rate used to discount the value of damages from future GHG emissions is used to calculate
the present and annualized value of SC-GHGs for internal consistency, while all other costs and benefits are
discounted at 3% in this table.
dNon-GHG impacts associated with the standards presented here do not include the full complement of health
and environmental effects that, if quantified and monetized, would increase the total monetized benefits. Instead,
the non-GHG benefits are based on benefit-per-ton values that reflect only human health impacts associated with
reductions inPM2.5 exposure.
xxxi
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RIA Chapter Summary
This document contains the following Chapters:
Chapter 1: Background on the 2017 and Later Light-duty Vehicle GHG Standards and
Midterm Evaluation
This chapter provides background on previous Agency actions with respect to the light-duty
vehicle GHG emissions program and summaries of previous EPA analyses.
Chapter 2: Technology Feasibility, Effectiveness, Costs, and Lead-time
This chapter summarizes the proposed revisions to the model year 2023 and later light-duty
vehicle GHG standards. It also includes a summary of proposed GHG compliance incentives
and flexibilities and discusses technological feasibility and manufacturer's lead-time
considerations.
Chapter 3: Economic and Other Key Inputs
This chapter provides EPA's analyses of rebound effects, energy security impacts, the social
cost of greenhouse gases, and the costs associated with congestion and noise.
Chapter 4: Modeling GHG Compliance
This chapter discusses the analytical methodology used to model GHG emissions compliance
of the light-duty vehicle fleet with the proposed standards and then summarizes the resulting
estimated compliance costs and associated technology pathways necessary to comply with the
proposed revisions to the model year 2023 and later GHG standards.
Chapter 5: Projected Impacts on Emissions, Fuel Consumption, and Safety
This chapter documents EPA's analysis of the emission, fuel consumption and safety impacts
of the emission standards for light-duty vehicles. Light-duty vehicles include passenger vehicles
such as cars, sport utility vehicles, vans, and pickup trucks. Such vehicles are used for both
commercial and personal uses and are significant contributors to the total United States (U.S.)
GHG emission inventory.
Chapter 6: Vehicle Program Costs and Fuel Savings
In this chapter, EPA presents our estimated costs associated with the proposed vehicle
program. This includes summaries of the vehicle level costs associated with new technologies
expected to be added to meet the model year 2023 and later GHG standards. The analysis also
provides costs associated with congestion, noise, fatalities and non-fatal crashes.
Chapter 7: Non-GHG Health and Environmental Impacts
In this chapter we discuss the health effects associated with non-GHG pollutants, specifically:
particulate matter, ozone, nitrogen oxides (NOx), sulfur oxides (SOx), carbon monoxide and air
toxics. These pollutants will not be directly regulated by the proposed revisions to the GHG
standards, but the proposed standards will affect emissions of these pollutants and precursors.
Chapter 8: Vehicle Sales, Employment, and Affordabilitv and Equity Impacts
xxxii
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This chapter presents the methodology and analytical results for EPA's modeling of vehicle
sales and employment impacts. It also includes an analysis of affordability and the potential
impacts on lower-income households, the used vehicle market, access to credit, and the low-
priced new vehicle segment.
Chapter 9: Small Business Flexibilities
This chapter discusses the flexibilities provided to small businesses under the proposed
revisions to the model year 2023 and later light-duty GHG standards.
Chapter 10: Summary of Costs and Benefits
This Chapter presents a summary of costs, benefits, and net benefits of the proposed program
and the alternatives.
XXXlll
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xxxiv
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Chapter 1: Background on the 2017 and Later Light-duty Vehicle GHG
Standards and Midterm Evaluation
This proposal marks the fifth time that EPA has analyzed the feasibility and cost associated
with meeting stringent GHG standards in the 2021/2022 through 2025/2026 timeframe. These
analyses include the 2012 FRM, the 2016 Draft Technical Assessment Report (DTAR), the 2016
MTE Proposed Determination and a 2018 analysis performed to update the MTE analyses for the
previous administration. Through these five analyses EPA has applied four different initial fleets
and updated critical inputs such as fuel costs. We have continued to develop our cost and
effectiveness assessments and we have refined our analytical tools. With this proposal, we have
also used two different fleet analysis models and a subset of the inputs used by NHTSA in
developing the SAFE rule. As discussed below and summarized in Figure 1-1, the results have
been remarkably consistent.
In 2012, EPA established greenhouse gas (GHG) emissions standards for model year 2017
and later new passenger cars, light-duty trucks, and medium-duty passenger vehicles.1 The
program was projected to reduce GHG emissions from model year 2025 light-duty vehicles by
50 percent relative to model year 2010 vehicles.
As part of the 2012 Final Rule, EPA made a regulatory commitment to conduct a Midterm
Evaluation (MTE) of the standards for MY 2022-2025. As a part of this process, EPA examined
a wide range of factors, such as developments in powertrain technology, vehicle electrification,
vehicle mass reduction and potential vehicle safety impacts, the penetration of fuel efficient
technologies in the marketplace, consumer acceptance of fuel efficient technologies, trends in
fuel prices and the vehicle fleet, employment impacts, and many other factors.
The 2012 Final Rule established three formal steps for the MTE process:
1.	Draft Technical Assessment Report (TAR) to be issued jointly with the National
Highway Traffic Safety Administration (NHTSA) and the California Air Resources
Board (CARB) with opportunity for public comment. This was completed in July 2016.
2.	The EPA Administrator was to make a Proposed Determination with opportunity for
public comment. The Proposed Determination was completed in November 2016.
3.	The EPA Administrator was to make a final determination with regard to whether the
standards remained appropriate or should be changed no later than April 1, 2018. This
was completed in January 2017.
There were opportunities for public input on the Draft TAR and on Proposed Determination
and a formal Response to Comments document was issued by EPA along with a Final
Determination in January 2017.
A timeline for the original final rule, the MTE through January 2017, and subsequent MTE
and regulatory actions is summarized within Figure 1-1. Despite the extensive EPA economic,
scientific, and engineering analyses published as part of the MTE process through January 2017,
and the availability of an updated 2018 EPA MTE Analysis completed in January 2018, these
prior EPA analyses were not used as the basis of the Agency's March 2017 MTE
Reconsideration, April 2018 MTE Final Determination or the Agency's subsequent 2018 through
2020 regulatory actions under the Safer Affordable Fuel Efficient (SAFE) Vehicle Program.
1-1
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April 2020
SAFE Vehicles
Final Rule for
Model Years 2021-
2026
July 2016
Fhe Midterm
Evaluation (MTE)
Draft Fechnical
Assessment Report
November 2016
Proposed
Determination of the
2022-2025 LDV
GHG Standards
imder the MFE
August 2018
SAFE Vehicles
Proposed Rule for
Model Years 2021-
2026
October 2012
Final Rule for
MY2017 and Later
LDV GHG
Emissions and
CAFE Standards
January 2017
Final Determination
of the 2022-2025
LDV GHG
Standards under the
MTE
March 2017
Reconsideration of
the Midterm
Evaluation Final
Determination
April 2018
Midterm Evaluation
Final Determination
Figure 1-1: Regulatory Timeline for the Model Year (MY) 2017 and Later Light-duty Vehicle (LDV)
Greenhouse Gas (GHG) Emissions Standards, the Midterm Evaluation, and Subsequent Regulatory Actions.
The top row represents Agency actions that used EPA's MTE analyses as the basis.
1.1 Summary of 2012 Final Rulemaking
1.1.1 Light-duty Vehicle GHG Emissions Standards
The 2017 and later light-duty vehicle GHG standards were established within the 2012 Final
Rulemaking (2012 FRM) based upon CO2 emissions-footprint curves, where each vehicle uses a
different CO2 emissions compliance target depending on its characteristic footprint (i.e., the area
contained within the vehicle wheelbase and track width). In general, vehicles with a larger
footprint would meet higher corresponding vehicle CO2 emissions standards. As a result, the
burden of compliance within this program was distributed across all vehicles and all
manufacturers and each manufacturer would have its own fleet-wide standard that reflects the
vehicles it chooses to produce. The program also provided a wide range of credit programs and
flexibilities for manufacturers to meet 2017 and later GHG standards.
Table 1-1 shows the projected fleet-wide CO2 emission targets under footprint-based
approach used in the 2012 FRM. Passenger car CO2 emission levels were projected to increase in
stringency from 212 to 143 grams per mile (g/mi) between MY s 2017 and 2025. Similarly, fleet-
wide CO2 emission levels for trucks were projected to increase in stringency from 295 in MY
2017 to 203 g/mi in MY 2025. EPA projected that the average light-duty vehicle (combined car
and truck) tailpipe CO2 compliance level in MY 2017 would be 243 g/mi, phasing down by MY
2025 to 163 g/mi. These projected targets in the first three rows include the effects of credits and
flexibilities. In contrast, the final row provides the actual tailpipe emissions achieved by
manufacturers for 2016-2019 based on certification data and excludes the effects of credits and
flexibilities.
1-2
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Table 1-1: Projected Fleet-Wide Emissions Compliance Targets under the Footprint-Based CO2 Standards in
the 2012 FRM

2016
base
2017
2018
2019
2020
2021
2022
2023
2024
2025
Passenger Cars (g/mi)
225
212
202
191
182
172
164
157
150
143
Light Trucks (g/mi)
298
295
285
277
269
249
237
225
214
203
Combined Cars &
Trucks (g/mi)
250
243
232
222
213
199
190
180
171
163
Actual Tailpipe C02,
Cars & Trucks (g/mi)
285
284
280
282






Table Notes:
Actual Tailpipe CO2 adapted from the 2020 EPA Automotive Trends Report.25
The difference between actual tailpipe CO2 emissions and the projected standards is due to
not only the credits and flexibilities, but also the difference between the projected car/truck sales
mix at the time of the 2012 FRM, and the actual sales mix for each model year. The 2012 FRM
projected car sales greater than 60% for all model years. However, manufacturers sales have
shifted significantly to more light trucks which has caused an effective increase in the standards.
Table 1-2 shows the projected sales mix from the original rule, the actual sales mix achieved, and
the effective increase in industry standards (in g/mi) for years 2016-2019 due solely to the
increase in truck sales share. For example, the combined standard of 222 g/mi projected for
2019 MY increased by 21 g/mi - to 243 g/mi - solely due to the 40% / 60% sales shares of
passenger vehicles and light trucks, respectively.
Table 1-2: Projected vs. Actual Car/Truck Sales Share, 2016-2019 Model Years

2016
base
2017
2018
2019
Proj Passenger Car Share
66%
63%
64%
64%
Proj Light Truck Share
34%
37%
36%
36%
Actual Passenger Car Share
51%
48%
43%
40%
Actual Light Truck Share
49%
52%
57%
60%
Car/Truck Shift Effect on Stds (g/mi)
+11
+12
+17
+21
Figure 1-2 and Figure 1-3 show the vehicle footprint vs. CO2 emissions standards curves for
cars and trucks, respectively, from the 2012 FRM. For passenger cars, the CO2 compliance
values associated with the footprint curves reduced on average by approximately 5 percent per
year from the MY 2016 projected passenger car industry-wide compliance level through MY
2025. To separately address GHG compliance challenges faced while preserving the utility of
light-duty trucks (e.g., towing and payload capabilities), the GHG standards in the 2012 FRM
provided a lower annual rate of improvement for light-duty trucks during the initial years of the
program. The average annual rate of CO2 emissions reduction in MYs 2017 through 2021 were
3.5 percent per year, increasing to 5 percent per year for MYs 2022 through 2025.
1-3
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«
3 250
i
-2016
201?
-- 201S
"2319
• ZOZO
2021
2022
2023
- 2324
-2025
Figure 1-2: 2012 FRM Footprint Curves for Passenger Car CO2 (g/mile) Standards
350
§ 250
200
150
IOC- 			'	*	j	'	j
35	40	45	50	55	60	65	70	7S	SO
Footprint jsfj
Figure 1-3: 2012 FRM Footprint Curves for Light-duty Truck CO2 (g/mile) Standards
1-4
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1.1.2 Flexibilities
EPA created flexibilities to ensure manufacturers could comply with the light-duty vehicle
GHG standards. Manufacturers create product plans with the goal of full compliance with EPA
regulations, however these product plans span many years and the flexibility to both bank credits
some years (over comply) and create debits (under comply) is a key flexibility within many EPA
regulations. Credit programs allow manufacturer to phase in new technologies during product
redesigns and new product introductions instead of redesigning all vehicles to comply at once.
Also, when executing product plans to meet our standards, manufacturers may need to respond to
changes in fuel prices, changes in consumer demand, and parts shortages such as the recent
semiconductor chip shortage2 directly affect what manufacturers can build. EPA has anticipated
that manufacturers would need and would take advantage of program flexibilities within its light-
duty GHG programs. This includes both credits and incentives, such as car/truck credit transfers,
air conditioning credits, off-cycle credits, advanced technology vehicle multipliers, intermediate
volume manufacturer lead-time provisions, and hybrid and performance-based incentives for full
size pick-up trucks. See the 2017-2025 Preamble section III.C (E012866 2017-2025 GHG-
CAFE Standards_2060-AQ54_FRM_FRN_20120827_Final) for an extended discussion of these
credits.
1.2 2016-2018 Midterm Evaluation of 2021-2025 Light-duty Vehicle GHG Standards
The Draft Technical Assessment Report (TAR), issued jointly by EPA, NHTSA, and CARB
for public comment, was the first formal step in the MTE process.3'4 A wide range of technical
and economic issues relevant to the light-duty GHG emissions standards for MY2022-2025 were
examined and shared with the public within the Draft TAR. The analyses contained within the
approximately 1,200 pages of the Draft TAR and the subsequent public comments received on
the Draft TAR informed the EPA's development of the Proposed Determination (PD)56 and the
Final Determination (FD).7'8 The primary conclusions of the Draft TAR were:
•	A wider range of technologies exist for manufacturers to use to meet the MY2022-
2025 standards, and at costs that are similar or lower, than those projected in the 2012
rule;
•	Advanced gasoline vehicle technologies will continue to be the predominant
technologies, with modest levels of strong hybridization and very low levels of full
electrification (plug-in vehicles) needed to meet the standards;
•	The car/truck mix reflects updated consumer trends that are informed by a range of
factors including economic growth, gasoline prices, and other macro-economic trends.
However, as the standards were designed to yield improvements across the light-duty
vehicle fleet, irrespective of consumer choice, updated trends are fully accommodated
by the footprint-based standards.
The analyses from the Draft TAR were further updated and published as part of an
approximately 700-page Technical Support Document9 (TSD) released in conjunction with the
PD and referenced within the FD. Key updates within the TSD included:
•	Use of the fuel prices, vehicle sales volumes, and car/truck mix, from the 2016 Energy
Information Administration's Annual Energy Outlook (AEO2016)10
•	Use of MY2015 for the base year vehicle fleet
1-5
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•	Changes to EPA's vehicle simulation model to include the most recent data on
technology effectiveness from the EPA vehicle benchmarking testing program and
other sources
•	Changes battery costs for electrified vehicles based on updated data from the ANL
BatPaC model
•	Building in additional quality assurance checks of technology effectiveness estimates
•	Changes to vehicle class definitions for effectiveness modeling and a greater
resolution of vehicle types to provide more accuracy and precision in representing
technology cost and effectiveness for the future vehicle fleet
•	Better accounting for tire and aerodynamic improvements in the base year vehicle
fleet
The Administrator's November 2016 Proposed Determination was the following:
1.	The MY2022-2025 light-duty GHG standards are feasible
2.	The standards will achieve significant CO2 and oil reductions
3.	The standards will provide significant benefits to consumers and the public
4.	The auto industry is thriving and meeting the standards more quickly than required
5.	Continued reductions in CO2 emissions are essential to help address the threat of climate
change
The Administrator also determined that there was ample evidence that supported
strengthening the standards; however, she chose not to propose revising the levels of the GHG
standards finalized in 2012. Comments received on the Draft TAR were addressed as part of a
formal response to comments within the appendices of the TSD. 11 Comments on the PD and
TSD were addressed within a separate Response to Comments Document released as part of the
FD.12
The Administrator's January 2017 Final Determination was:
1.	The MY2022-2025 standards remain appropriate under section 202 (a)(1) of the Clean
Air Act.
2.	The standards are feasible at reasonable cost, without need for extensive electrification
3.	The standards will achieve significant CO2 and oil reductions
4.	The standards will provide significant benefits to consumers and to the public
5.	The auto industry is thriving and meeting the standards more quickly than required
1.2.1 Updated EPA 2018 MTE Analysis
EPA completed an analysis in January 2018 that further updated the analyses from the TSD.13
Although conducted by EPA as part of the MTE and to inform an anticipated SAFE NPRM, the
updated EPA analysis was not used as part of the revised Final Determination of April 2018, the
SAFE NPRM, or the SAFE2 FRM. The following updates to the November 2016 TSD were
included within EPA's updated January 2018 MTE analysis:
1-6
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1.2.1.1	Updated Base Year Fleet Data:
The base year vehicle fleet was updated from MY2015 to MY2016. The EIA AEO sales
projections, car and truck percentages within the fleet, and fuel prices were updated to
AEO2017.14 The OEM and vehicle class market share projections were updated using data
purchased from IHS Markit, Ltd. Improvements were also implemented in EPA's ALPHA
vehicle model and OMEGA compliance model to better characterize technologies within the
base year fleet. The improvements included conducting ALPHA vehicle model runs for each
vehicle configuration in the base year (road loads; engine, transmission, and accessory models)
and confirming model alignment with CO2 from EPA vehicle emissions certification data.
The resolution of technology characterization was improved within EPA's ALPHA vehicle
model and OMEGA GHG compliance model via the following changes:
•	Increased number of engine maps for turbocharged/downsized engines (i.e., 3 different
engine maps, vs. 1 for TSD)
•	Increased number of engine maps to represent port-fuel-injected (PFI) and gasoline
direct injection (GDI) engines (2 different engine maps each vs. 1 for the TSD)
•	Use of fleet-wide technology characterization to characterize the GHG performance of
the 2016 fleet based primarily on certification data submitted by manufacturers to
EPA's VERIFY Database
Additional data was also obtained from EPA's Test Car Database and technical specifications
that were not available in either the EPA VERIFY or Test Car databases (e.g. curb weight,
dimensions, power steering type) were obtained via other public and commercially available
sources of vehicle data such as Edmunds.com©, Wards Automotive (Penton©) and AllData
Repair (AllData LLC©). Further details of the 2016 base year fleet characterization can be
found in Bolon et al.15
1.2.1.2	Updated Fuel Price and Fleet Projections
Future fuel prices were updated to reflect AEO2017 projections.14 Updated fleet volume and
car/truck percentage projections were based on preliminary AEO2018 projections and updated
IHS Markit forecasting.16
1.2.1.3	Other Updates to the ALPHA Vehicle Model
ALPHA modeling process improvements were put into place to implement cloud computing
and improve computational efficiency. This allowed full combinatorial modeling of vehicle
technology packages, including all combinations of engines, transmissions, accessories and road
loads. The introduction of full combinatorial modeling allowed replacement of the Lumped
Parameter Model (LPM) previously used within the OMEGA model with peer-reviewed
response surface equations (RSEs) based entirely on ALPHA modeling. Under this approach,
packages applied to future vehicles contained only the technology combinations reflected within
ALPHA runs. This also eliminated any manual calibration of the LPM.
Mass reduction (MR) was applied in predefined steps based on the amount of MR required to
move a vehicle into a new estimated test weight (ETW) bin. Mass reduction in passenger cars
was thus not constrained by lower curb weight limits as was done for the previous TSD safety
analysis.
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BEVs, PHEVs and HEVs were mapped into unique vehicle types rather than being mapped
into ICE vehicle types. The total number of vehicle types increased to 42 from the 29 vehicle
types used within the TSD analysis, which allowed for greater granularity in both cost and
effectiveness calculations.
1.2.1.4	Updates to the Technologies Considered and Technology Effectiveness
Additional technologies were used in the updated analysis that were not used within the TSD
to better reflect recent vehicle product introductions. Effectiveness for engine technologies was
also updated based on EPA engine and chassis dynamometer benchmarking. Updates included:
•	Addition of a new, dynamically-controlled cylinder deactivation technology
(deacFC)17 based on vehicle benchmarking of Tula's Dynamic Skip Fire system, with
greater effectiveness than traditional fixed cylinder deactivation (deacPD), although at
higher costs due to the necessity for deactivation hardware for each cylinder
•	Addition of a 2nd generation turbocharged downsized engine package based on EPA
benchmark testing of the Honda L15B7 1.5L turbocharged, direct-injection engine18
•	ALPHA modeling of 12V Start-Stop and 48V Mild Hybrids for every combination of
engine/trans/vehicle class instead of using constant effectiveness for these
technologies applied to each vehicle class within the TSD
•	Use of an engine map for Atkinson (ATK2+CEGR) technology based on EPA
benchmark testing of the MY2018 Camry 2.5L A25A FKS engine17 in place of using
developmental engine test data and GT-POWER engine modeling within the TSD
•	Updates to both aerodynamic drag technologies and other road-load reducing
technologies19
1.2.1.5	Updates to Cost Analysis
A significant number of updates were included within the cost analysis. This included updates
to the costs of vehicle electrification and other technology, some changes to indirect costs, and
use of a 2016 dollar basis in order to be consistent with AEO2017. The changes to the cost
analysis relative to the TSD included:
•	Use of an updated ANL BatPaC model (BatPaC Version 3.1,9 October 2017) as the
basis for BEV, PHEV, HEV and mild HEV battery costs
•	The learning curves for battery costs were adjusted to ensure consistency between
BatPaC and OMEGA
•	Non-battery BEV and PHEV costs were updated based on more recent teardown data
from California Air Resources Board, UBS, and other references.20,21,22
•	Level 2 home charging costs were updated based on data provided by the California
Air Resources board on the cost of electric vehicle service equipment (EVSE).23
•	BEV/PHEV battery and non-battery integration efforts were changed within OMEGA
to a "medium complexity" as opposed to the "high complexity" used in the TSD,
resulting in application of a reduced indirect cost markup
•	Some additional cost savings were applied for BEVs since they did not need to add
additional technology to comply with light-duty Tier 3 criteria pollutant emissions
standards. Such costs were found to have been applied to BEVs within the TSD.
1-8
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•	Markups on emerging and future technologies remained at near-term levels through
2025 instead of using near-term levels through 2018 or 2024 as was done in the TSD
•	LUB2 & EFR2 were added as incremental technologies to LUB1 and EFR1 in
OMEGA and both LUB1 and EFR1 were included in all base year Exemplar vehicles.
•	Cooled EGR costs were changed to a single EGR loop when applied to ATK2 engines.
Previous Cooled EGR costs had assumed a higher cost low-pressure/higher pressure
dual loop system for application to highly boosted (27-bar BMEP) turbocharged
engines no longer used within the analysis.
1.2.1.6 Sensitivity Analyses
The range of sensitivities analyzed within the OMEGA model for the Updated EPA 2018
MTE Analysis included:
•	AEO2016 central, high, and low fuel price scenarios10'21
•	No additional mass reduction beyond what existed in MY2016 base year fleet
•	Technology adoption for 20 percent of trucks constrained to 2021 standards level
•	Limiting the adoption of advanced, non-turbo engine technology to 10 percent of fleet
•	No new adoption of advanced transmission technologies
•	No new adoption of advanced turbocharged/downsized engines
•	Added consideration of credit trading between manufacturers
•	No car-truck credit transfers within a manufacturer's fleet
1.2.2 Comparison of Analytical Results Between the 2012 FRM and the MTE
Table 1-3 provides a comparison of MY2025 light-duty vehicle fleet-average technology
penetrations and per-vehicle costs for the central analytical case from the 2012 FRM and for
central analytical cases and sensitivity analyses for the Draft TAR, TSD, and EPA's Updated
2018 Analysis. Although EPA is proposing new standards for MY's 2023 through 2026, a
comparison of the CEMMS analytical results for the proposed MY2025 and MY2026 standards
(see draft RIA Chapter 4.1.2) shows remarkable consistency with analytical results over the last
10 years. Figure 1-4 shows a graphical representation comparing per vehicle costs for the same
2012 through 2018 EPA analyses. Table 1-4 compares the fuel prices, assumed car/truck fleet
mix and resulting fleet average CO2 g/mile emissions targets between these analyses and
Table 1-5 provides per vehicle costs in 2025 broken down separately for cars and trucks in the
light-duty vehicle fleet. The CEMMS analysis in draft RIA Chapter 4.1.2 found fleet-level per
vehicle costs of $771 and $1044 for MY2025 and MY2026, respectively, and previous EPA
analyses ranged from $922 to $1228 per vehicle for a roughly comparable level of stringency.13
a The CCEMS analysis for this proposal described in Chapter 2 uses AEO2021 for estimating gasoline prices. In
general, AEO2021 reference, high, and low estimates for the retail price of gasoline are lower than comparable cases
within AEO2016 and AEO 2017. For example, in 2025 the AEO2021 reference retail gasoline price in 2018$ is
estimated to be $2.44 per gallon vs. $3.13 and $3.05 per gallon for AEO2016 and AEO2017, respectively.
b Please note, however, that there are differences in the "no action" cases used for determining costs between the
proposal and the previous 2012 - 2018 EPA analyses. For a complete description of the "no action" case used for
this proposal, please see draft RIA Chapter 4.
1-9
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$1,400 r
$1,200
$1.228
~	2012 FRM
~	Draft Tar
~	PD/FDTSD
~	2018 Analysis
| Sensitivities
8 $1,000
$969
$922
$976
$800
$600
$400
$200
2012 FRM
Draft TAR
(July 2016)
PD/FD TSD 2018 EPA Analysis
(Nov. 2016)	(January 2018)
Figure 1-4: Comparison of fleet average (car and truck), per-vehicle technology costs in 2025 from the 2012
FRM to subsequent analyses conducted by EPA during the MTE (2018$). Vertical lines on top of the bars
represent the range of sensitivity analyses conducted.
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Table 1-3: Comparison of technology penetrations into the light-duty fleet and per vehicle costs in 2025
(incremental to 2021) for the 2012 FRM compared to analyses conducted by EPA under the MTE. All per
vehicle costs are shown in 2018$ to maintain consistency with other analyses within this draft RIA.
Technology
[1]
2012
Final
Rule [2]
Draft TAR [3]
PD/FD TSD [4]
Updated EPA 2018
Analysis [51,[61,[71
Primary
Analysis
Range of
Sensitivities
Analyzed
Primary
Analysis
Range of
Sensitivities
Analyzed
Primary
Analysis
Range of
Sensitivities
Analyzed
Advanced High-
efficiency
Engines [8]
93%
81%
58% to 86%
62%
36% to 82%
67%
56% to 73%
Cylinder
Deactivation
not modeled
not
modeled
not modeled
49%
43% to 55%
28%
24% to 31%
8 speed and
other advanced
transmissions
(%) [91
91%
90%
89% to 91%
93%
92% to 94%
90%
90% to 94%
Mass reduction
(%) [10]
-7%
6%
2% to 6%
8%
1% to 9%
4%
2% to 5%
Off-cycle
technology (%)
not modeled
not
modeled
not modeled
26%
8% to 53%
not
modeled
not modeled
Stop-start (%)
15%
20%
15% to 31%
15%
12% to 39%
16%
12% to 20%
Mild Hybrid (%)
26%
18%
13% to 38%
18%
16% to 27%
1%
0% to 3%
Strong Hybrid
(%)
5%
2.6%
2.0% to 3.0%
2%
2% to 3%
2%
1% to 2%
PHEV (%) [11]
0%
1.7%
2% to 2%
2%
2% to 2%
1%
1% to 1%
BEV (%) [11]
2%
2.6%
2.0% to 3.0%
3%
2% to 4%
2%
1% to 2%
Per vehicle cost
(2018$)
$1,228
$969
$938 to $1,125
$922
$840 to $1,175
$976
$942 to $1,242
Table Notes:
[1]	Technology penetrations shown are absolute and MY2025 vehicle costs are incremental to MY2021.
[2]	The 2012 FRM values are based on the AEO2012 Early Release "Reference Case" and analytical results were originally reported as average
per vehicle costs of $1070 in 2010$.
[3]	The Draft TAR values are based on the AEO 2015 "Reference Case" and analytical results were originally reported as average per vehicle
costs of $894 in 2013$.
[4]	The Proposed/Final Determination values are based on the AEO 2016 "Reference Case", which included a 53%/47% car/truck mix.
Analytical results were originally reported as average per vehicle costs of $875 in 2015$.
[5]	The 2018 Updated Analysis values are based on the AEO 2017 "Reference Case", which included a 42%/58% car/truck mix. Analytical
results were originally reported as average per vehicle costs of $935 in 2016$.
[6]	Advanced high-efficiency engines updated based on benchmarking of MY2016 and MY2017 OE engines.
[7]	Lumped parameter modeling was completely removed in favor of peer reviewed response surface equations based entirely on ALPHA
vehicle modeling.
[8]	Includes both turbocharged/downsized and Atkinson Cycle engines.
[9]	Including continuously variable transmissions (CVT).
[10]	The mass reductions are fleet average % reduction in curb weight relative to the 'null' package.
[11]	BEV and PHEV penetrations include the California Zero Emission Vehicles (ZEV) program.
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As EPA analyses were updated for the MTE through 2018, projected 2025 fuel prices
decreased, the car/truck fleet mix shifted to a higher percentage of trucks, and the fleet CO2
g/mile targets increased relative to the analysis for the 2012 FRM (Table 1-4).
The MTE analyses reflect an estimate of an approximately $200 decrease in per vehicle fleet
costs relative to the 2012 FRM analysis. Some of the MTE sensitivity analyses have per vehicle
costs that are approaching or approximately equivalent to that of the 2012 FRM analysis. Despite
considerable updates to the EPA analyses between 2012 and 2018, and a significant increase in
the percentage of trucks in light-duty fleet (from 33 percent in the 2012 FRM analysis to 58
percent in the 2018 analysis), per vehicle costs for either the light-duty vehicle fleet (Figure 1-4)
or separately for light-duty car or trucks (Table 1-5) have remained remarkably stable as EPA
analyses and EIA-AEO projections were updated from 2012 to 2018.
Table 1-4: Comparison of fuel price, percentage of cars and trucks in the fleet, and CO2 fleet average
emissions targets when taking into consideration the car and truck fleet mix for the 2012 FRM compared to
analyses conducted by EPA under the MTE.

2012 Final Rule
[1]
Draft TAR [2]
PD/FD TSD [3]
Updated EPA 2018
Analysis [4]
2025 Fuel Price
$4.44
$3.20
$3.13
$3.05
2025 Car/Truck Fleet Mix[5]
67%/33%
52%/48%
53%/47%
42%/58%
2025 Fleet CO2 Target
(g C02/mi)
163
175
173
180
Table Notes:
[1]	AEO 2011 Reference Case, fuel price converted to 2018$
[2]	AEO 2015 Reference Case, fuel price converted to 2018$
[3]	AEO 2016 Reference Case, fuel price converted to 2018$
[4]	AEO 2017 Reference Case, fuel price converted to 2018$
[5]	Car/Truck definitions used by EPA for GHG standards differ from those used by AEO. The Car/Truck Fleet Mix in 2025 is based upon
EPA's regulatory car and truck definitions.
Table 1-5: Comparison per vehicles costs for passenger cars, light-duty trucks and the combined light-duty
vehicle fleet in 2025 (incremental to 2021) for the 2012 FRM compared to analyses conducted by EPA under
the MTE. Per vehicle costs are shown in 2018$ to maintain consistency with other analyses within this draft
RIA.

2012 Final Rule [1]
Draft TAR [2]
PD/FD TSD [3]
Updated EPA 2018 Analysis [4]
(sensitivity range in parentheses)
Car
$1,101
$766
$790
$805
($805 - $1,021)
Truck
$1,487
$1,191
$1,073
$1,098
($1,010-$1,454)
Fleet
$1,228
$969
$922
$976
($942 - $1,242)
Table Notes:
[1]	AEO 2011 Reference Case, converted to 2018$	[3] AEO 2016 Reference Case, converted to 2018$
[2]	AEO 2015 Reference Case, converted to 2018$	[4] AEO 2017 Reference Case, converted to 2018$
1.3 Agency Actions, March 2017 - April 2020
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1.3.1	2017 Reconsideration of the MTE Final Determination and 2018 MTE Final
Determination
On March 15, 2017 EPA announced that the final determination, issued on January 12, 2017,
would be reconsidered in coordination with NHTSA. On April 2, 2018, a new Mid-term
Evaluation Final Determination was signed, which withdrew the previous Final Determination
and found that the model year 2022-2025 greenhouse gas standards were not appropriate and
should be revised.24
1.3.2	SAFE2
In April 2020, EPA published " The Safer Affordable Fuel-Efficient (SAFE) Vehicles Rule
for Model Years 2021-2026 Passenger Cars and Light Trucks", a final rule amending the 2012
FRM beginning with MY2021 by establishing new and substantially less stringent GHG
standards for MY2021 and later light-duty vehicles.25
On January 20, 2021, President Biden signed Executive Order 13990, which issued the
following directives to EPA and other federal agencies:
"Section 1. Policy. Our Nation has an abiding commitment to empower our workers and
communities; promote and protect our public health and the environment; and conserve our
national treasures and monuments, places that secure our national memory. Where the Federal
Government has failed to meet that commitment in the past, it must advance environmental
justice. In carrying out this charge, the Federal Government must be guided by the best science
and be protected by processes that ensure the integrity of Federal decision-making. It is,
therefore, the policy of my Administration to listen to the science; to improve public health and
protect our environment; to ensure access to clean air and water; to limit exposure to dangerous
chemicals and pesticides; to hold polluters accountable, including those who disproportionately
harm communities of color and low-income communities; to reduce greenhouse gas emissions;
to bolster resilience to the impacts of climate change; to restore and expand our national
treasures and monuments; and to prioritize both environmental justice and the creation of the
well-paying union jobs necessary to deliver on these goals.
To that end, this order directs all executive departments and agencies (agencies) to
immediately review and, as appropriate and consistent with applicable law, take action to
address the promulgation of Federal regulations and other actions during the last 4 years that
conflict with these important national objectives, and to immediately commence work to confront
the climate crisis.
Sec. 2. Immediate Review of Agency Actions Taken Between January 20, 2017, and January
20, 2021. (a) The heads of all agencies shall immediately review all existing regulations, orders,
guidance documents, policies, and any other similar agency actions (agency actions)
promulgated, issued, or adopted between January 20, 2017, and January 20, 2021, that are or
may be inconsistent with, or present obstacles to, the policy set forth in section 1 of this order.
For any such actions identified by the agencies, the heads of agencies shall, as appropriate and
consistent with applicable law, consider suspending, revising, or rescinding the agency actions.
In addition, for the agency actions in the 4 categories set forth in subsections (i) through (iv) of
this section, the head of the relevant agency, as appropriate and consistent with applicable law,
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shall consider publishing for notice and comment a proposed rule suspending, revising, or
rescinding the agency action within the time frame specified.
... (ii) Establishing Ambitious, Job-Creating Fuel Economy Standards: 'The Safer Affordable
Fuel-Efficient (SAFE) Vehicles Rule Part One: One National Program,' 84 FR 51310
(September 27, 2019), by April 2021; and 'The Safer Affordable Fuel-Efficient (SAFE) Vehicles
Rule for Model Years 2021-2026 Passenger Cars and Light Trucks,' 85 FR 24174 (April
30,2020), by July 2021. In considering whether to propose suspending, revising, or rescinding
the latter rule, the agency should consider the views of representatives from labor unions, States,
and industry. "26
With respect to § 2(ii) of Executive Order 13990, we are referring to 84 FR 51310 as
"SAFE1" and 85 FR 24174 as "SAFE2". The revision of MY2023 to MY2026 Light-duty
Vehicle GHG standards under SAFE2 is the purpose of the Notice of Proposed Rulemaking of
which this draft Regulatory Impact Analysis is a part. Reconsideration of SAFE1 is the subject of
a separate Agency action.27
In response to this Executive Order, EPA has considered taking action under the Clean Air
Act relating to the SAFE GHG emissions standards. As described in further detail in Preamble
Section VI and elsewhere in the preamble to this proposed rulemaking, we are proposing revised,
more stringent GHG standards under our Clean Air Act authority.
1.3.2.1 New GHG Compliance Flexibilities Established Under SAFE2
As part of the amendment of MY2021 and later GHG emissions standards under SAFE2, a
small number of flexibilities related to real world fuel efficiency improvements were finalized.
EPA continued to allow manufacturers to make improvements related to air conditioning
refrigerants and leakage and credit those improvements toward compliance with GHG standards.
EPA made no changes to the 10 g-C02/mi off-cycle credit cap. EPA also extended the "0 g/mi
upstream" incentive for electric vehicles through 2026 beyond its original sunset of MY 2021
and established a new credit multiplier for natural gas vehicles through the 2026 model year. For
natural gas vehicles, both dedicated and dual-fueled, EPA established a multiplier of 2.0 for
model years 2022-2026.
In order to support easier use of certain real world fuel efficiency improvements, EPA added
high efficiency alternators and advanced A/C compressors to the off-cycle credit menu to help
streamline the program by allowing manufacturers to select the menu credit g/mi values rather
than continuing to seek credits through the public approval process. The credit levels added to
the menu were based on data previously submitted by multiple manufacturers through the off-
cycle credits application process. The high efficiency alternator credit is scalable with
efficiency, providing an increasing credit value of 0.16 grams/mile CO2 per percent improvement
as the efficiency of the alternator increases above a baseline level of 67 percent efficiency. The
advanced A/C compressor credit value is 1.1 grams/mile for both cars and light trucks. For more
information on any aspect of these changes see 84 FR 24174, April 30, 2020.25 For a summary
of the proposed revisions to flexibilities and incentives for MY2023 and later, see Chapter 2.1.1.
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References for Chapter 1
1	U.S. EPA and U.S. DOT/NHTSA. 2017 and Later Model Year Light-Duty Vehicle Greenhouse Gas Emissions
and Corporate Average Fuel Economy Standards. U.S. Federal Register, Vol. 77, No. 199, pp. 62624-63200,
October 15, 2012.
2	LaReau, J.L. "Everything you need to know about the chip shortage that's plaguing automakers", Detroit Free
Press June 15, 2021 Last accessed on the internet 7/16/2021 URL:
https://www.freep.com/story/money/cars/2021/06/15/car-chip-shortage-2021/7688773002/
3	U.S. EPA and U.S. DOT/NHTSA. Notice of Availability of Midterm Evaluation Draft Technical Assessment
Report for Model Year 2022-2025 Light Duty Vehicle GHG Emissions and CAFE Standards. U.S. Federal Register,
Vol. 81, No. 144, pp. 49217-49220, July 27, 2016.
4	U.S. EPA, CA-EPA/ARB, U.S. DOT/NHTSA. Draft Technical Assessment Report: Midterm Evaluaton of Light-
Duty Vehicle Greenhouse Gas Emission Standards and Corporate Average Fuel Economy Standards for Model
Years 2022-2025. EPA-420-D-16-900, July 2016.
5	U.S. EPA. Proposed Determination on the Appropriateness of the Model Year 2022-2025 Light-Duty Vehicle
Greenhouse Gas Emissions Standards Under the Midterm Evaluation. U.S. Federal Register, Vol. 81, No. 234, pp.
87927-87928, December 6, 2016.
6	U.S. EPA Proposed Determination on the Appropriateness of the Model Year 2022-2025 Light-Duty Vehicle
Greenhouse Gas Emissions Standards under the Midterm Evaluation. EPA-420-R-16-020, November 2016.
7	U.S. EPA. EPA Determination Letter from Administrator Gina McCarthy to Stakeholders Regarding Model Year
2022-2025 Light-Duty Greenhouse Gas Standards, January 12, 2017. Last accessed on the Internet on April 23,
2021 at the following URL: https://www.epa.gov/sites/production/files/2017-01/documents/mte-stakeholder-letter-
2017-01-12.pdf
8	U.S. EPA. Final Determination on the Appropriateness of the Model Year 2022-2025 Light-Duty Vehicle
Greenhouse Gas Emissions Standards under the Midterm Evaluation. EPA-420-R-17-001, January 2017.
9	U.S. EPA. Proposed Determination on the Appropriateness of the Model Year 2022-2025 Light-Duty Vehicle
Greenhouse Gas Emissions Standards under the Midterm Evaluation: Technical Support Document. EPA-420-R-16-
021, November, 2016.
10	U.S. Energy Information Administration. Annual Energy Outlook 2016 with Projections to 2040. DOE/EIA-
0383(2016). August 2016.
11	U.S. EPA. Proposed Determination on the Appropriateness of the Model Year 2022-2025 Light-Duty Vehicle
Greenhouse Gas Emissions Standards under the Midterm Evaluation: Technical Support Document, Appendices A
through D. EPA-420-R-16-021, November, 2016.
12	U.S. EPA. Final Determination on the Appropriateness of the Model Year 2022-2025 Light-Duty Vehicle
Greenhouse Gas Emissions Standards under the Midterm Evaluation - Response to Comments. EPA-420-R-17-002,
January 2017.
13	McDonald, J. Memo to Docket, No. EPA-HQ-OAR-2021-0208, EPA/OAR/OTAQ January 9, 2018 Briefing for
OAR Assistant Administrator Wehrum.
14	U.S. Energy Information Administration. Annual Energy Outlook 2017 with Projections to 2050. January 2017.
15	Bolon, K., Moskalik, A., Newman, K., Hula, A., Neam, A. (2018). Characterization of GHG Reduction
Technologies in the Existing Fleet. SAE Technical Paper 2018-01-1268, doi: 10.4271/2018-01-1268.
16	U.S. Energy Information Administration. Annual Energy Outlook 2018 with Projections to 2050. February 2018.
17	Kargul, J., Stuhldreher, M., Barba, D., Schenk, C., Bohac, S., McDonald, J., & Dekraker, P. (2019).
Benchmarking a 2018 Toyota Camry 2.5-liter Atkinson Cycle Engine with Cooled-EGR. SAE International Journal
of Advances and Current Practices in Mobility, 1(2), 601.
18	Stuhldreher, M., Kargul, J., Barba, D., McDonald, J., Bohac, S., Dekraker, P., & Moskalik, A. (2018).
Benchmarking a 2016 Honda Civic 1.5-liter L15B7 turbocharged engine and evaluating the future efficiency
potential of turbocharged engines. SAE International journal of engines, 11(6), 1273.
19	Moskalik, A., Bolon, K., Newman, K., & Cherry, J. (2018). Representing GHG Reduction Technologies in the
Future Fleet with Full Vehicle Simulation. SAE International journal of fuels and lubricants, 11(4), 469.
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20	California Air Resources Board. Advanced Strong Hybrid and Plug-In Hybrid Engineering Evaluation and Cost
Analysis, CARB Agreement 15CAR018, prepared for CARB and California EPA by Munro & Associates, Inc. and
Ricardo Strategic Consulting, April 25, 2017.
21	Hummel, P., Lesne, D., Radlinger, J., Golbaz, C., Langan, C., Takahashi, K.,... & Shaw, L. (2017). UBS
Evidence Lab Electric Car Teardown—Disruption Ahead. UBS report, Basel.
22	Safoutin, M.J. (2018) Predicting Powertrain Costs for Battery Electric Vehicles Based on Industry Trends and
Component Teardowns. Proceedings of the 31 st International Electric Vehicle Symposium & Exhibition and
International Electric Vehicle Technology Conference. Society of Automotive Engineers of Japan, 2018. ISBN:
9781510891579.
23	California Air Resources Board. California's Advanced Clean Cars Midterm Review - Summary Report for the
Technical Analysis of the Light-duty Vehicle Standards. (2017). Last accessed on the Internet on 5/6/2021 at the
following URL: https://ww2.arb.ca.gov/resonrces/documents/2017-niidterm-review-report.
24	U.S. EPA. Mid-Term Evaluation of Greenhouse Gas Emissions Standards for Model Year 2022-2025 Light-Duty
Vehicles. U.S. Federal Register, Vol.83, No. 72, pp 16077-16087, April 13, 2018.
25	U.S. EPA and U.S. DOT/NHTSA. The Safer Affordable Fuel-Efficient (SAFE) Vehicles Rule for Model Years
2021-2026 Passenger Cars and Light Trucks. U.S. Federal Register, Vol.85, No. 84, pp 24174-25278, April 30,
2020.
26	Order, Executive. 13990. Protecting Public Health and the Environment and Restoring Science to Tackle the
Climate Crisis. U.S. Federal Register, Vol. 86., No. 14, pp 7037-7043, January 25, 2021.
27	U.S. EPA. California State Motor Vehicle Pollution Control Standards; Advanced Clean Car Program;
Reconsideration of a Previous Withdrawal of a Waiver of Preemption; Opportunity for Public Hearing and Public
Comment. U.S. Federal Register, Vol. 86, No. 80, pp 22421-22430, April 28, 2021.
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Chapter 2: Technology Feasibility, Effectiveness, Costs, and Lead-time
EPA is proposing to revise existing national greenhouse gas (GHG) emissions standards for
passenger cars and light trucks under section 202(a) of the Clean Air Act (CAA). Section 202(a)
requires EPA to establish standards for emissions of air pollutants from new motor vehicles
which, in the Administrator's judgment, cause or contribute to air pollution which may
reasonably be anticipated to endanger public health or welfare. The transport sector is currently
the largest source of anthropogenic GHG emissions in the U.S. There are technically feasible and
cost-effective technologies to achieve additional reductions for MY2023 through MY2026 light-
duty vehicles at reasonable cost per vehicle and without compromise to vehicle utility or safety.
As in many prior EPA mobile source rulemakings, the decision on what standard to set is largely
based on the availability, capability, and cost of the emissions control technology along with the
need for reductions of GHG and the benefits of doing so. This proposal would also establish a
path toward more significant reductions in the years following 2026.
2.1 Proposed Standards
As with the existing and previous light-duty vehicle GHG standards, EPA is proposing
separate car and truck standards; that is, vehicles defined as cars would have one set of footprint-
based curves, and vehicles defined as trucks would have a different set. In general, for a given
footprint, the CO2 g/mile targeta for trucks is higher than for a car with the same footprint. The
curves are described mathematically by a family of piecewise linear functions (with respect to
vehicle footprint) that gradually and continually ramp down from the MY 2022 curve established
in the SAFE rule.
Written in mathematic notation, the function is as follows:b
Passenger Car Target = min (b,max(a, c * footprint+d))
Table 2-1: Proposed Coefficients for Passenger Car GHG Targets

Model Year
Coefficient
2023
2024
2025
2026+
a
145.6
138.6
131.9
125.6
b
199.1
189.5
180.3
171.6
c
3.56
3.39
3.23
3.07
d
-0.4
-0.4
-0.3
-0.3
a Because compliance is based on the full range of vehicles in a manufacturer's car and truck fleets, with lower
emitting vehicles compensating for higher emitting ones, the emission levels of specific vehicles within the fleet are
referred to as targets, rather than standards.
b See Regulatory text for the official proposed coefficients and equation. The information presented here is a
summary.
2-1
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230
210
190
u 150
130
90
70
50
^HrMro^LntDr^oocno^HrMro^LntDr^oo
^^^^^^^^^LnLDLnLnLnLnLnLnLn
footprint (ft2)
Figure 2-1: CO2 vs Footprint Compliance Curves for Cars
Light Truck Target = min (b,max(a, c * footprint+d))
Table 2-2: Proposed Coefficients for Light-duty Truck GHG Targets

Model Year
Coefficient
2023
2024
2025
2026+
a
181.1
172.1
163.5
155.4
b
312.1
296.5
281.8
267.8
c
3.97
3.77
3.58
3.41
d
18.4
17.4
16.6
15.8
-------
350
300
* 250
r\l
O
U
E 200
~E
rc
ob 15Q
100
50
footprint (ft2)
Figure 2-2: CO2 vs Footprint Compliance Curves for Trucks
The MY 2023 car curve shape is similar to the MY 2022 curve. By contrast, the truck curve
returns to the cutpoint of 74 sq ft originally established in the 2012 rule, but changed in the
SAFE rule, for MY2023.1 The gap between the 2022 curves and the 2023 curves is indicative of
design of the standards described earlier, where the increase in stringency in MY 2023 is roughly
twice that of MYs 2024-2026. EPA is proposing to return the truck cutipoint to 74 sq ft because
it is consistent both with the 2012 FRM standards for MYs beyond 2022 and the California
Framework agreements cutpoint value, and because EPA is relying heavily on its past analyses
of technological feasibility and those analyses were done in consideration of the 74 sq ft
cutpoint.
Figure 2-3 shows EPA's proposed standards, expressed as year-over-year fleetwide GHG
emissions targets (cars and trucks combined), projected through model year 2026 and beyond.
For comparison, the figure also shows the corresponding targets for the recent SAFE FRM and
the 2012 FRM. The proposed fleet targets pick up from the existing SAFE FRM targets for
model years 2021 and 2022, but then ramp down considerably in model year 2023, nearly
reaching the 2012 FRM targets for that model year. The proposed fleet targets then parallel the
2012 FRM targets through model year 2025 (the last year of that program), and then continue
that same downward slope for one additional model year, to model year 2026 (the last year of the
SAFE program). As with all EPA light-duty GHG rules, the targets would then remain in place at
the same level for all subsequent model years unless replaced by a subsequent rulemaking.
Table 2-3 presents the estimates of EPA's proposed standards presented in Figure 2-3, again
in terms of the projected overall fleetwide CCh-equivalent emission compliance target levels. See
Section II.B below for a full discussion of the proposed standards and presentations of the
footprint curves.
•2021
¦2022
2023
2024
•2025
•2026+
-------
240

230 ^
220 \ 	y...
V 		

210 Xnn\

200

190
	2012 FRM
180
Proposed
170 ^

160

150

2020 2021 2022 2023 2024 2025 2026 2027

Figure 2-3: Proposed Fleet-Wide CCh-Equivalent g/mi Compliance Targets, Compared to 2012 and SAFE
Rules, 2021-2026.
Table 2-3: Estimates of EPA's proposed standards in terms of the projected overall fleetwide CCh-equivalent
g/mi emission compliance target levels.
Model Year
2023
2024
2025
2026
Cars
165
157
149
142
Trucks
232
221
210
199
Combined Cars and Trucks
198
189
180
171
Car Share
50.0%
49.9%
49.7%
49.7%
Truck Share
50.0%
50.1%
50.3%
50.3%
2.1.1 Proposed Compliance Incentives and Flexibilities
The proposed Light-duty GHG standards include flexibilities initially established in the 2010
Light-Duty GHG Final Rule for how credits may be used within the program.2 Averaging,
banking, and trading (ABT) provisions include credit carry-forward, credit carry-back (also
called deficit carry-forward), credit transfers (within a manufacturer), and credit trading (across
manufacturers). These ABT provisions define how credits may be used and are integral to the
program. The current program limits credit carry-forward to 5 years. EPA is proposing a limited
extension of credit carry-forward for credits generated in model years 2016 through 2020. The
proposal would change the credit carry-forward for MY2016 credits from five to seven years and
the carry-forward limit for MYs 2017-2020 from 5 to 6 years, as shown in Table 2-4.
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Table 2-4: EPA Proposed Extension of Credit Carry-forward Provisions
MY Credits
are Banked
MYs Credits Are Valid Under EPA's Proposed Extension

2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2016

X
X
X
X
X
/
/



2017


X
X
X
X
X
/



2018



X
X
X
X
X
/


2019




X
X
X
X
X
/

2020





X
X
X
X
X
/
2021






X
X
X
X
X
x = Current program. «/= Proposed additional years.
The current Light-duty GHG program includes temporary incentives through model year 2021
that encourage the use of advanced technologies such as electric, hybrid electric, and fuel cell
vehicles, as well as incentives for full-size pickups using strong hybridization or technologies
providing similar emissions reductions to hybrid technology. The full-size pickup incentives
originally extended out through model year 2025, but the SAFE rule removed the incentives for
model years 2022 through 2025. When EPA established these incentives in the 2012 rule, EPA
recognized that temporary regulatory incentives would reduce the effective stringency of the
standards, but believed that such incentives would lead to greater benefits in the longer run by
encouraging the development and broader application of advanced vehicle technologies.3 EPA
believed that such temporary regulatory incentives might help bring some technologies to market
more quickly than in the absence of incentives.4 With these same goals in mind for this
program, EPA is proposing to increase and extend multiplier incentives though model year 2025
with a cap on multiplier credits and also to reinstate the full-size pickup incentives removed from
the program by the SAFE rule.
The current program also includes credits for real-world emissions reductions not reflected on
the test cycles used for determining CO2 emissions compliance with fleet average GHG
standards. These are credits for using technologies that reduce emissions that aren't captured on
EPA tests ("off-cycle" technologies) and improvements to air conditioning systems that increase
efficiency and reduce HFC refrigerant leakage. These credit opportunities currently do not
sunset, remaining a part of the program through model year 2026 and beyond unless the program
is changed as part of a regulatory action. EPA is proposing to modify the off-cycle credits
program to provide additional opportunities for manufacturers to generate credits based on the
current pre-defined credits menu by increasing the menu credit cap from 10 to 15 g/mile. EPA is
also proposing to modify some of the regulatory definitions that are used to determine whether a
technology is eligible for the menu credits. EPA is not proposing changes to the air conditioning
credits elements of the program.
2.2 Light-duty Vehicle Technology Feasibility
2.2.1 Feasibility of the Proposed Standards
Based upon the light-duty vehicle fleet compliance analysis summarized within Chapter 4 of
this draft RIA, and consistent with the extensive public record established by EPA with its
publication of the 2012 FRM, July 2016 Draft TAR, November 2016 Proposed Determination
and the January 2017 Final Determination, the proposed revisions of the MY2023 and later light-
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duty GHG standards are feasible using existing vehicle technologies that are already widely
available within the current light-duty vehicle fleet.
The feasibility of the proposed standards is best understood within the context of the decade-
long light-duty vehicle GHG emissions reduction program in which the automotive industry has
innovated a wide range of GHG-reducing technologies. Over this time period, the industry has
had the ability to plan for increasingly stringent GHG emissions requirements. The result has
been the widespread and continual introduction of new and improved GHG-reducing
technologies across the industry, many of which were in the early stages of development at the
beginning of the program in 2012. See Chapter 2.3 for a discussion of technological progression,
status of technology penetration, and Chapter 4.1.3 for our assessment of the continuing
technology penetration across the fleet required to meet the proposed standards.
The technological achievements already developed and already increasing in application to
vehicles within the current new vehicle fleet (Chapter 2.3) will enable the industry to achieve the
proposed standards even without the development and implementation of additional
technologies. Compliance with the proposed standards, adjustment to the pace of technology
penetration of existing GHG reduction technologies, and adjustment to the management of both
existing GHG credits and generation of credits under the revised light-duty GHG program
particularly should be considered within the full context of the revised incentives and flexibilities
that will be available under the proposal. As we discuss in Chapter 2.3.2, our assessment shows
that a large portion of the current fleet (MY2021 vehicles), across a wide range of vehicle
segments, already meets future standards and that there are clearly opportunities for automakers
to focus their sales and marketing on these more efficient products.
The multi-year nature of automotive design and engineering development also means that the
industry's product plans that were developed in response to the EPA's GHG standards finalized
in 2012 for MYs 2017-2025 has largely continued despite the relaxation of GHG standards under
SAFE that were promulgated in April 2020 with implementation beginning in MY 2021. This
can also be seen within the increased penetration of GHG reducing technologies (Chapter 2.3). In
previous comments on EPA's light-duty GHG and other light-duty vehicle programs, automakers
have broadly stated that they require approximately five years to design, develop, and produce a
new vehicle model. Thus, in most cases, vehicles that automakers intend to sell during the first
years of these proposed MY2023 and later GHG standards were already designed under the
original, and more stringent, GHG standards finalized in 2012 for those model years. At the time
of this proposal, the relaxed GHG standards under the SAFE rule have been in place for little
more than one year, and the ability of the industry to rely on the standards relaxed under SAFE,
especially for MY2023 and later, were under a cloud of regulatory uncertainty in light of pending
litigation. During this time, the automobile industry continually expressed concern over the
uncertain future of the SAFE standards. In fact, due in part to this uncertainty surrounding the
SAFE standards, five automakers voluntarily agreed to more stringent national standards under
the California Framework Agreement.5 Therefore, based on the automakers' own past
comments regarding product plan development and the regulatory and litigation history of the
GHG standards since 2012, we believe that automakers continue to be largely on track in terms
of technological readiness within their product plans to meet the approximate trajectory of
increasingly stringent light-duty vehicle GHG standards initially promulgated in 2012.
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Although we do not believe that automakers have significantly changed their product plans
yet in response to the SAFE final rule issued in 2020, any that may have would have done so
relatively recently and we would anticipate that their earlier product plans could be reinstated or
adapted with minimal change. It is important to note that we have considered the need for
manufacturers to transition from the SAFE standards (or the California Framework Agreement)
to standards that are similar in stringency to the 2012 standards and have structured the proposed
standards to be less stringent than the 2012 standards for model years 2023 2024, and 2025.
EPA considers this an important aspect of its analysis that mitigates concerns about lead-time for
manufacturers to meet the proposed standards beginning with the 2023 model year. We see no
reason to expect that the major GHG-reducing technologies that automakers have already
developed and have increasingly introduced (see Chapter 2.3), or have already been planning for
near-term implementation, will not be available for MY2023 and later vehicles. Thus, in contrast
to the situation that existed prior to EPA's adoption of the initial light-duty GHG standards in the
2012 rule, automakers now have had the benefit of at least 8-9 years of planning and
development in preparation for meeting these proposed standards.
Another important factor in considering the feasibility of the proposed standards is the fact
that five automakers voluntarily entered into the California Framework Agreements with the
California Air Resources Board, first announced in July 2019, to meet more stringent GHG
standards nationwide than those relaxed by the SAFE rule.5 These voluntary actions by
automakers that collectively represent approximately one-third of the U.S. light-duty vehicle
market speak directly to the feasibility of meeting standards at least as stringent as those under
the California Framework. The California Framework voluntary targets were a key
consideration in our development and assessment of the proposed EPA light-duty vehicle GHG
standards.
It is important to note that our conclusion that the proposed program is technologically
feasible is based in part on a projection that the standards will be met largely with the kinds of
advanced light-duty vehicle engine technologies, transmission technologies, electric drive
systems, aerodynamics, tires, and vehicle mass reduction already in place in vehicles within
today's fleet. Furthermore, the proposed standards do not rely on a significant penetration of
electric vehicles into the fleet during the 2023-2026 model years. Our updated analysis projects
that approximately 8 percent of vehicles meeting the MY 2026 proposed standards would be
EV/PHEVs (see Chapter 4). Given manufacturers' public announcements about their ambitious
plans to transition fleets to electric vehicles, we believe it is likely that an even higher percentage
of the industry-wide light-duty vehicle fleet could be electrified during the time period of our
proposed MY2023 and later standards. Moreover, EPA is committed to encouraging the rapid
development and broad acceptance of zero-emission vehicles (ZEVs), and the proposed light-
duty vehicle GHG program includes incentives to support this transition (see Chapter 2.1.1).
2.2.2 Alternatives to the Proposed Standards
In addition to the proposed standards, we analyzed both a more stringent and a less stringent
alternative. For the less stringent alternative, Alternative 1, we used the coefficients in the
California Framework5 for the 2.7 percent effective stringency level as the basis for the MY 2023
stringency level and the 2012 rule MY 2025 standards as the basis for the MY 2026 stringency
level, with linear year-over-year reductions between the two points for MYs 2024 and 2025.
EPA views the California Framework as a reasonable basis for the least stringent alternative that
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we would consider finalizing, since it represents a level of stringency that five manufacturers
have already committed to achieving. We did not include incentive multipliers for Alternative 1,
as doing so would only further reduce the effective stringency of this Alternative, and we view
Alternative 1 as the lower end of stringency that we believe is appropriate through 2026.
For the more stringent alternative, Alternative 2, we used the 2012 rule standards as the basis
for MY 2023-2025 targets, with the standards continuing to increase in stringency in a linear
fashion for MY2026. Alternative 2 adopts the 2012 rule stringency levels in MY 2023 and
follows the 2012 rule standard target levels through MY2025. We extended the same linear
average year-over-year trajectory for MYs 2023-2025 to MY2026 for the final standards under
Alternative 2. We believe that it is important to continue to make progress in MY2026 beyond
the MY2025 standard levels in the 2012 rule, and as with the proposal, Alternative 2 meets this
objective. We also did not include in Alternative 2 the proposed incentive multipliers with the
proposed cumulative credit cap in MYs 2022-2025, which would have had the effect of making
Alternative 2 less stringent.
The fleet average targets for the two alternatives compared to the proposed standards are
provided in Table 2-5 below. As discussed in detail in detail in Chapter 2.3.3, there has been a
proliferation of recent announcements from automakers signaling a rapidly growing shift in
investment away from internal-combustion technologies and toward high levels of
electrification. EPA has also heard from a wide range of stakeholders over the past several
months, including but not limited to the automotive manufacturers and the automotive suppliers,
that the significant investments being made now to develop and launch new EV product
offerings and in the expansion of EV charging infrastructure could enable higher levels of EV
penetration to occur in the market place by the MY 2026 time frame than we have projected as
the basis for both the proposed MY2026 standards and the Alternative 2 MY2026
standards. The information concerning the investment landscape potentially accelerating to an
even greater extent market penetration of EV products is the basis on which EPA is relying on
for Alternative 2 with respect to the potential for a more stringent MY2026 standard that would
reflect this information and related considerations.
Table 2-5: Projected Fleet Average Target Levels for Proposed Standards and Alternatives (C02 grams/mile)
Model Year
Proposal Projected
Targets
Alternative 1
Projected Targets
Alternative 2
Projected Targets
2021
223*
223*
224*
2022
220*
220*
220*
2023
199
203
195
2024
189
194
186
2025
180
185
177
2026
171
177
169
* SAFE rule standards included here for reference.
2-8
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240
230
220
210
E
>n 200
no
rsl
o
u
190
180
170
160
2020 2021 2022 2023 2024 2025 2026 2027
Model Year
Figure 2-4: Proposed Standards Fleet Average Targets Compared to Alternatives
As shown in Figure 2-4, the range of alternatives that we are considering is fairly narrow,
with the proposed standard targets differing from the alternatives in any given MY in 2023-2026
by 2 to 6 g/mile. We believe that this approach is reasonable and appropriate considering the
relatively short lead time for the proposed standards, especially for MYs 2023-2025, our
assessment of feasibility, the existing automaker commitments to meet the California Framework
(representing about one-third of the auto market), the standards adopted in the 2012 rule, and the
need to reduce GHG emissions. The analysis of costs and benefits of Alternatives 1 and 2 is
shown in the Chapters 4, 5, 6, and 10.
The proposed standards and both alternatives all incorporate year-over-year increases in GHG
stringency, with varying starting stringencies in MY2023, and varying ending stringencies in
MY2026, and with fairly linear increases in stringency between MY2023 and 2026 that would
essentially follow the same slope as the 2012 program. All three potential programs would also
result, by MY2026, in standards at least as stringent as the last year (MY2025) of the 2012
program.
For Alternative 1, the standards would reach the model year 2025 level of the 2012 rule (the
final increase in stringency of the 2012 program) in model year 2026, resulting in a less stringent
program compared to the 2012 rule until MY2026. Chapter 5.1.1.2 shows the associated lower
amount of GHG reductions achieved under Alternative 1 compared to the proposal. Again, given

	2012 FRM

••• SAFE FRM
v.,
V*-..
Proposal


\ 		
California Framework
2-9
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the urgent need for GHG reductions to address the climate crisis, we believe Alternative 1 does
not go far enough and would thus be inappropriate.
For Alternative 2, the standards by MY2025 would nearly match the stringency level of the
MY2025 standards in the 2012 rule and would continue to increase in stringency for one
additional year in MY2026. Consistent with EPA's previous discussions regarding feasibility,
compliance costs, and lead time, we believe that Alternative 2 may be feasible. Several
arguments can be made in support of Alternative 2 that are similar to those that support the
proposed standards.
2.3 Vehicle Technologies
For a summary of the effectiveness and cost of technologies used by EPA for modeling
compliance with the proposed standards, see Chapter 4.1 of this draft RIA. A complete summary
of vehicle technologies and associated GHG effectiveness for internal combustion engine
technologies, transmission technologies, vehicle electrification, aerodynamics, tires, and vehicle
mass reduction can be found within Chapter 2.2 of the Technical Support Document (TSD) for
the November 2016 Proposed Determination.6 We still believe this document to be a sound and
thorough examination of the available technologies and their GHG effectiveness for the
timeframe of this rulemaking. In fact, some vehicle manufacturers have recently made public
statements regarding their plans to discontinue the development of conventional, internal
combustion engine-based technologies to focus on the electrified vehicle technologies7. If the
automakers choose to follow through with their announcements, this will further support that the
technologies described in the TSD are comprehensive of all technologies that the manufacturers
will apply on the road to full electrification. In their press release announcing their goal to be
carbon neutral in 2040, GM stated that "The company will also continue to increase fuel
efficiency of its traditional internal combustion vehicles in accordance with regional fuel
economy and greenhouse gas regulations. Some of these initiatives include fuel economy
improvement technologies, such as Stop/Start, aerodynamic efficiency enhancements, downsized
boosted engines, more efficient transmissions and other vehicle improvements, including mass
reduction and lower rolling resistance tires."8 Although some manufacturers have indicated a
reduced focus on ICE technologies, EPA has continued its independent evaluation of advanced
engine and transmission technologies and update and improve our assessment of light-duty
vehicle GHG emissions over the intervening 4 years since publication of the TSD.9 The results
of these analyses have been published in over a dozen peer-reviewed technical and journal
nanaro 10,11,12,13,14,15,16,17,18,19,20,21,22,23,24
pdpcl
The percentage share of specific MY2015 to MY2020 engine and transmission technologies
are summarized from EPA Automotive Trends Report data in Table 2-6 and Table 2-7
respectively.25 In MY2020, hybrid electric vehicles (HEV) accounted for approximately 6.5
percent of vehicle sales, while plug-in electric hybrids (PHEV) and battery electric vehicles
(BEV) together comprised 4 percent of sales. Thus, powertrain electrification of all types has
increased more than 3-fold from MY2015 to MY2020. The pace of introduction of new EV
models is rapidly increasing. Nearly 100 pure electric EV models are expected to be introduced
in the United States by the end of 2024.26 The sales of vehicles with 12V start/stop systems has
increased from approximately 7 percent to approximately 42 percent between MY2015 and
MY2020.
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Table 2-6: Production Share by Engine Technologies for MY2015-2020

Powertrain Technologies
Engine Technologies
Model Year
Gasoline
Gasoline
HEV
Diesel
PHEV
BEV
GDI
Port
Avg. Displ.
(L)
HP
VVT
CD
Turbo
Stop/ Start
2015
95.9%
2.4%
0.9%
0.2%
0.5%
41.9%
56.7%
2.90
229
97.2%
10.5%
15.7%
7.1%
2016
96.9%
1.8%
0.5%
0.3%
0.5%
48.0%
51.0%
2.85
230
98.0%
10.4%
19.9%
9.6%
2017
96.1%
2.3%
0.3%
0.8%
0.6%
49.7%
49.4%
2.85
234
98.1%
11.9%
23.4%
17.8%
2018
95.1%
2.3%
0.4%
0.8%
1.4%
50.2%
48.0%
2.82
241
96.4%
12.5%
30.0%
29.8%
2019
94.4%
3.8%
0.1%
0.5%
1.2%
52.9%
45.7%
2.85
245
97.2%
14.9%
30.0%
36.9%
2020 (prelim)
88.5%
6.5%
1.0%
0.7%
3.3%
55.3%
40.3%
2.75
247
94.0%
13.8%
35.3%
42.2%
Note: Adapted from the 2020 EPA Automotive Trends Report.25
As of MY2020, more than half of light-duty gasoline spark ignition engines now use direct
injection (GDI) and more than a third are turbocharged.0"27 Nearly half of all light-duty vehicles
have planetary automatic transmissions with 8 or more gear ratios, and a fourth are using
continuously variable transmissions (CVT). We anticipate that these GHG reducing technologies
will continue to increasingly penetrate into the light-duty vehicle fleet for MY2023-2026.
Table 2-7: Production Share by Transmission Technologies for MY2015-2020
Model Year
Manual
Automatic
with Lockup
Automatic
without
Lockup
CVT
(Hybrid)
CVT
(Non-
Hybrid)
4 Gears
Or Fewer
5 Gears
6 Gears
7 Gears
8+ Gears
Average
No. of
Gears
2015
2.6%
72.3%
1.4%
2.2%
21.5%
1.5%
4.5%
54.2%
3.1%
13.0%
5.9
2016
2.2%
72.3%
2.6%
1.7%
21.2%
1.1%
3.0%
54.9%
2.9%
15.3%
6.0
2017
2.1%
71.5%
2.6%
1.9%
21.8%
1.0%
2.4%
49.0%
3.4%
20.5%
6.1
2018
1.6%
72.8%
3.2%
1.7%
20.6%
1.9%
2.0%
37.6%
3.7%
32.5%
6.4
2019
1.4%
72.1%
2.4%
2.2%
21.9%
1.5%
1.6%
26.1%
2.6%
44.0%
6.6
2020 (prelim)
1.5%
66.1%
4.4%
3.1%
25.0%
3.4%
1.3%
15.8%
2.4%
49.0%
6.6
Note: Adapted from the 2020 EPA Automotive Trends Report.25
2.3.1 Recent Advances in Internal Combustion Engines
The Automotive Trends Report does not separately track the introduction of HEV and non-
HEV applications of Atkinson Cycle and Miller Cycle engines, however their application has
been increasing over the past five years. Atkinson Cycle and Miller Cycle engines represent
technologies that improve efficiency via use of increased expansion when compared to
convention (Otto cycle) spark ignition engines. Although Atkinson and Miller Cycles are
sometimes used interchangeably, EPA's use of the nomenclature refers specifically to either
0 A technical assessment of the particulate matter (PM) emissions impacts of MY2020-2021 light-duty vehicles
using engines equipped with gasoline direct injection (GDI) and port fuel injection is included within a memo to the
docket for this NPRM.
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naturally-aspirated (Atkinson) or turbocharged (Miller) implementations. Recent
implementations also include use of fast, wide-range of authority camshaft phasing to allow
variation of effective compression ratio for load control and additional reduction of pumping
losses. Most implementations over the last six years use gasoline direct injection (GDI) for
additional knock mitigation.d For additional information on these technologies, see Chapter
2.2.1.2 "Descriptions of Technologies and Key Developments since the FRM" within the
Technical Support Document for the November 2016 Proposed Determination (2016 TSD).6
Atkinson Cycle engines have been common in HEV applications for more than two decades.
More recently, Toyota, Mazda, and Hyundai/Kia have been expanding the use of these engines
in non-HEV applications to reduce fuel consumption and comply with GHG emissions
standards. Since the publication of the 2016 TSD, there has also been a broader range of product
introductions with Atkinson Cycle engines combined with gasoline direct injection (GDI) and
either cylinder deactivation or cooled EGR. Mazda introduced fixed cylinder deactivation® on
the base 2.5L Atkinsons Cycle engine in the MY2018 CX-5 CUV and Mazda 6 passenger car. It
was also introduced in the MY2019 Mazda 3. Based on comparisons of certification data for
comparable chassis and trim levels, Mazda's implementation of fixed cylinder deactivation
provides an incremental effectiveness of approximately 2 percent beyond that of a 4-cylinder
Atkinson Cycle engine without fixed cylinder deactivation.
Atkinson Cycle with cooled EGR has been applied to a broad range of both HEV and non-
HEV passenger cars and crossover utility vehicles (CUV). Examples include the Toyota's
"Dynamic Force" range of engines added as part of the Toyota New Global Architecture
(TNGA).28'29'30'31'32' Cooled EGR is used to reduce pumping losses and to mitigate combustion
knock. These include the following Toyota engines: the M15A-FKS, M20A-FKS, and A25A-
FKS non-HEV engines; and the M15A-FXE, M20A-FXS, and A25A-FXS HEV-specific engines
used in the Toyota Corolla, Camry, Aval on, C-HR, RAV4, Highlander, Lexus ES and Lexus
UX. In 2018, EPA conducted engine dynamometer benchmark testing of the Toyota 2.5L
A25A-FKS engine with Atkinson Cycle and cooled EGR.19 During testing on Federal Tier 2
certification fuel, the Toyota A25A-FKS engine demonstrated a peak break thermal efficiency
(BTE) of approximately 40 percent, the highest published BTE for a production, non-HEV
engine. This represents a significant improvement over the peak BTE (typically 35-37 percent)
of the naturally aspirated GDI engines that make up a majority of MY2020 vehicle fleet.
Atkinson Cycle engines were estimated to have GHG effectiveness of approximately 3.2 to 3.8
percent relative to over otherwise comparable naturally-aspirated GDI engines in non-HEV
applications. EPA estimates that the addition of cooled EGR to an Atkinson Cycle engine further
reduces 2-cycle GHG emissions by an additional 4.4 percent over Atkinson Cycle alone.
Both engine-dynamometer developmental work and benchmarking of production engines by
EPA identified synergies between the use of fixed cylinder deactivation and cooled EGR on
Atkinson Cycle engines when used in non-HEV applications.33'34'35'19 Both EPA and other
researchers have also identified synergies between the use of dynamic cylinder deactivation and
cooled EGR on Atkinson Cycle engines.19,36 EPA estimates that the addition of either fixed
d Knock is an abnormal and potentially damaging form of combustion characterized by a very high rate of increase
in cylinder pressure and high peak cylinder pressure.
e Fixed cylinder deactivation disables a fixed number of engine cylinders to reduce pumping losses at light load.
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cylinder deactivation or dynamic cylinder deactivationf to an Atkinson Cycle engine with cooled
EGR would provide an additional 2.3 percent or 7.9 percent reduction in 2-cycle GHG
emissions, respectively.19
VW now offers EA888-3B 2.0L Miller Cycle engine as the base engine in the Passat and
Arteon passenger cars and the Atlas and Tiguan CUVs. The MY2022 Taos CUV will use the
EA211 1.5L evo Miller Cycle engine as the base engine, which has a peak break thermal
efficiency of 38.1 percent.37 A hybrid-specific version of this engine is under development by
VW. When equipped with cooled-EGR and a variable-geometry turbo, it demonstrated a peak
BTE of 41.5 percent.37
2.3.2	Changes to Engine Technologies Represented in the Analysis for the Proposal
Analytical revisions to the modeling of light-duty vehicle compliance with the proposed
standards and the resulting GHG emissions and vehicle technology costs are summarized within
Chapter 4.1. Key revisions to engine technologies are summarized within Table 4-1.
Within EPA's analysis, HCRO represents an implementation of Atkinson Cycle with GDI.
HCR1 represents, on average, the addition of either cooled EGR or fixed cylinder deactivation to
an Atkinson Cycle engine with GDI and thus represents most non-HEV implementations of
Atkinson Cycle within the light-duty vehicle fleet starting in MY2018. HCR2 represents the
addition of dynamic cylinder deactivation and cooled EGR within non-HEV Atkinson Cycle
engine applications. For HEV applications, HCR2 represents the application of GDI, cooled
EGR, higher compression and expansion ratio, and the use of a dedicated hybrid electric/engine
powertrain strategy.8
The restriction within the analysis of HCR technologies to naturally aspirated engines with
cylinder counts of 6 or less during compliance modeling was a means of restricting Atkinson
Cycle from application to trucks and other applications having a specific need for additional
torque reserve (e.g., trailer towing or high payload applications).
2.3.3	Vehicle Electrification
While we anticipate that the proposed standards will be met primarily through the continued
penetration of conventional powertrain (e.g., internal combustion engine, transmission)
improvements and road-load reductions as outlined previously within the draft TAR,38 the PD
TSD,39 and in the previous sections of this chapter, we anticipate that the design of a future,
longer-term program beyond 2026 will further incorporate accelerating advances in zero-
emission technologies.
A proliferation of recent announcements from automakers signals a rapidly growing shift in
investment away from internal-combustion technologies and toward high levels of
electrification. These automaker announcements are supported by continued advances in
f Dynamic cylinder deactivation is a newer, more capable system than fixed cylinder deactivation. Any number of
cylinders can be deactivated or activated on a cycle resolved basis. The first production examples became available
on GM full-frame trucks in MY2019.
g Dedicated hybrid engines combine an engine and electric drive within a powertrain and calibrated in a synergistic
manner that increases engine efficiency and avoids areas of engine operation prone to knock and/or low-speed
preignition.
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automotive electrification technologies, and further driven by the need to compete in a global
market as other countries implement aggressive zero-emission transportation policies.
For example, in January 2021, General Motors announced plans to become carbon neutral by
2040, including an aspirational goal to shift its light-duty vehicles entirely to zero-emissions by
2035.40 In March 2021, Volvo announced plans to make only electric cars by 2030,41 and
Volkswagen announced that it expects half of its U.S. vehicle sales will be all-electric by 2030.42
In April 2021, Honda announced a full electrification plan to take effect by 2040, with 40 percent
of its North American vehicle sales expected to be fully electric or fuel cell vehicles by 2030, 80
percent by 2035 and 100 percent by 2040.43 In May 2021, Ford announced that they expect 40
percent of their global light-duty vehicle sales will be all-electric by 2030.44 In June 2021, Fiat
announced a move to all electric vehicles by 2030,45 and in July 2021 its parent corporation
Stellantis announced an intensified focus on electrification across all of its brands.46 Also in July
2021, Mercedes-Benz announced that all of its new architectures would be electric-only from
2025, with plans to become ready to go all-electric by 2030 where possible.47
These announcements and others like them continue a pattern over the past several years of
many manufacturers taking steps to aggressively pursue zero-emission technologies, introduce a
wide range of ZEV models, and reduce their reliance on the internal-combustion engine in
various markets around the globe.48'49 These goals and investments have been coupled with a
rapidly increasing availability of plug-in vehicle models in the U.S.50 For example, the number
of battery electric vehicle (BEV) and plug-in hybrid electric vehicle (PHEV) models available
for sale in the U.S. more than doubled from about 24 in MY 2015 to about 60 in MY 2021, with
offerings in a growing range of vehicle segments.51,52 Recent model announcements indicate
that this number will increase to more than 80 models by MY 2023, with many more expected to
reach production before the end of the decade.53 Many of the ZEVs already on the market today
cost less to drive than conventional vehicles,54'55 offer improved performance and handling,56
and can be charged at a growing network of public chargers as well as at home.57
Recent BEV product announcements also include a growing number of dedicated battery
electric vehicle platforms, such as the GM BEV2 light-duty vehicle (LDV) and BEV3 light-duty-
truck (LDT) platforms, the Tesla Model 3/Model Y LDV and crossover utility vehicle (CUV)
platform, the VW MEB LDV and CUV platform, and the Hyundai E-GMP LDV and CUV
platform.58 Dedicated BEV platforms eliminate provisions for internal combustion engine (ICE)
powertrain, exhaust emissions, evaporative emissions, and fuel systems that would otherwise
need to be accommodated on platforms that are shared between BEV, PHEV, HEV, and
conventional ICE vehicle models. This dedicated BEV platform approach typically allows
integration of the battery pack entirely within the vehicle floor structure, reduces vehicle weight,
reduces manufacturing costs, increases available passenger and cargo volume, and in some cases,
has the battery pack integrated as part of the vehicle's crash mitigation structure.
An increasing number of global jurisdictions and U.S. states are planning to take actions to
shift the light-duty fleet toward zero-emissions technology. In 2020, California announced an
intention to require increasing volumes of ZEVs to meet the goal that, by 2035, all new light-
duty vehicles sold in the state be ZEVs. Massachusetts and New York are also poised to adopt
similar targets and requirements to take effect by 2035.59'60'61 Several other states may adopt
similar provisions by 2050 as members of the International Zero-Emission Vehicle Alliance.62
Globally, at least 12 countries, as well as numerous local jurisdictions, have announced similar
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goals to shift all new passenger car sales to ZEVs in the coming years, including Norway (2025),
the Netherlands, Denmark, Iceland, Ireland, Sweden, and Slovenia (2030), Canada and United
Kingdom (2035), France and Spain (2040) and Costa Rica (2050).63'64 Together, these countries
represent approximately 13 percent of the global market for passenger cars, in addition to that
represented by the aforementioned U.S. states and other global jurisdictions.65
The BEV production announcements by manufacturers - combined with the regulatory actions
announced by governing bodies worldwide regarding ZEVs - could mean that there will be less
of a need for regulatory incentives for producing ZEVs. However, long-term GHG reduction
goals will require a far greater penetration of ZEVs than this proposal would require through
MY2026. The need for substantial increases in fleet penetration of ZEVs over the long term is
supported by the recommendations of the National Academy of Sciences, which states in its
2021 Light-duty Vehicle Technology Assessment: "The agencies should use all their delegated
authority to drive the development and deployment of ZEVs, because they represent the long-
term future of energy efficiency, petroleum reduction, and greenhouse gas emissions reduction in
the light-duty fleet".66 EPA believes that the inclusion of regulatory incentives for ZEVs in this
proposal through MY2026 is consistent with the need to promote rapid development and
deployment of ZEVs over the longer term.
2.4 Analysis of Manufacturers Generation and Use of GHG Credit
EPA believes that the multi-year nature of auto design and development means that the
industry's product plans originally developed in response to the EPA's 2012 GHG standards
rulemaking for MYs 2017-25 have largely continued notwithstanding the SAFE rule that was
promulgated in April 2020 and relaxed standards beginning in MY 2021. Thus, in most cases,
the vehicles that automakers will be producing during the first years of the proposed MY 2023-
26 program were already designed under the original, more stringent GHG standards for those
model years finalized in 2012. Manufacturers are also already demonstrating the ability to
comply with the proposed 2023 model year standards with many vehicles currently for sale.
For this proposal, EPA performed an analysis of 2021 model year vehicles to assess how
changes in sales mix could help facilitate vehicle manufacturer compliance to more stringent
standards. This analysis analyzed certification and projected sales data for 2021 model year
vehicles. EPA assumed that manufacturers continue to utilize credits for off-cycle technologies,
as well as A/C credits for reduced refrigerant leakage and improved efficiency. The level of off-
cycle credits was based on average manufacturer's MY2019 off-cycle credits for cars and trucks,
respectively (so it does not reflect the proposed increased cap to 15 g/mi of menu off-cycle
credits). EPA applied the industry-average of 19 g/mi and 24 g/mi of total A/C credits for car
and truck models, respectively. Table 2-8 and Table 2-9 show the availability of "credit
generators" (vehicle models that outperform their proposed individual footprint-based standard
for 2023 model year), grouped by market segment. The smallest market segments, by total sales
volume, are shaded in gray and collectively represent only about 5 percent of all sales. Projected
performance is based on actual 2021 tailpipe CO2 emissions and adjusting for assumed A/C and
off-cycle credits.
The analysis accounted for the various trim levels by manufacturers, as there are 1370 unique
vehicle model types in the 2021 model year. Of those 1370 unique vehicles, 216 models (over
16 percent of all models sold) already outperform the proposed 2023 standards. 125 of these
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models are advanced gasoline or hybrid vehicles while an additional 91 models are plug-in
hybrids or battery electric vehicles.
Table 2-8: Distribution of 2021 MY Vehicle Models and Number of Vehicles Which Generate Credits vs. 2023
MY Standards (All Vehicles)


Credit

% of 2021
Vehicle Category
Total
Generators
% CG's
Sales
Minicompact Cars
35
1
v„
<)"„
Subcompact Cars
1 l«>
l>
8" ii
2".,
Compact Cars
1 l(.
15
1 V'„

Two Seaters
(4
u

<)"„
Midsize Cars
158
28
18%
13%
Large Cars
87
21
24%
5%

.¦)
-S"..

Vans
h.
u

I-.,
Small SUVs
140
32
23%
28%
Standard SUVs
288
34
12%
25%
Small Pick-up Trucks
40
u

¦!"„
Standard Pick-up Trucks
256
61
24%
13%
Totals
1370
216
16%

Note: Gray shading denotes niche vehicle segments at or below 3 percent of total sales
2-16
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Table 2-9: Distribution of 2021 MY Vehicle Models and Number of Vehicles Which Generate Credits vs. 2023
MY Standards (Gasoline ICE and Hybrid Vehicles)


Credit

% of2021
Vehicle Category
Total
Generators
% CG's
Sales
Minicompact Cars
U
(i

<)"„
Subcompact Cars
1 lu
(i


Compact Cars
In"
(.


Two Seaters

(i
<)"„
<)"„
Midsize Cars
143
13
9%
13%
Large Cars
70
7
10%
5%
Small Station Wagons
22
o
.¦)
14";,

Midsize Station Wagons
i:
(i
<)"„
<)"„
Minivans
7
:
:
(i
<)"„
I"u
Small SUVs
131
23
18%
28%
Standard SUVs
264
10
4%
25%
Small Pick-up Trucks
4(1
(i
<)"„
¦!"„
Standard Pick-up Trucks
256
61
24%
13%
Totals
1275
125
10%

Note: Gray shading denotes niche vehicle segments at or below 3 percent of total sales
Some niche market segments (shaded in gray within Table 2-8throughTable 2-10), including
the smallest vehicles (minicompact and subcompact cars), two-seaters, and small pickup trucks;
show limited or no credit-generating models. However, credit-generators are currently available
to manufacturers in market segments that total nearly 95 percent of the total sales volume.
Manufacturers are already well-positioned to earn significant credits against the 2022 model
year standards with their 2021 vehicles. These credits can be banked to provide margin for later
years as a potential compliance strategy.
Using the same analytical approach, these 2021 model year vehicles offer additional credits
and more opportunities against the 2022 model year standards. Table 2-10 displays the 2021
model year offerings against the 2022 model year standards.
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Table 2-10: Distribution of 2021 MY Vehicle Models and Number of Vehicles Which Generate Credits vs.
2022 MY Standards (All Vehicles)

Total
Credit
Generators
% cg's
% of 2021
Sales
Minicompact Cars
35
1
v„
()" „
Subcompact Cars
1 l«>
l>
X"„
2".,
Compact Cars
1 l(.
25
22"..

Two Seaters
(4
u


Midsize Cars
158
40
25%
13%
Large Cars
87
29
33%
5%
Small Station Wagons
^1
14
45%
3%
Midsize Station Wagons
i:
0

<)".,
Minivans
s
s
(.V'„
2".,
Vans
i(>
s
5n"..
1".,
Small SUVs
140
52
37%
28%
Standard SUVs
288
61
21%
25%
Small Pick-up Trucks
40
0

v„
Standard Pick-up Trucks
256
92
36%
13%
Totals
1370
336
25%

Note: Gray shading denotes niche vehicle segments at or below 3 percent of total sales
2-18
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References for Chapter 2
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Future Fleet with Full Vehicle Simulation. SAE International journal of fuels and lubricants, 11(4), 469.
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Hybrid Lithium-Ion Battery Pack. SAE International Journal of Alternative Powertrains, 7(3), 273-288.
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Benchmarking a 2018 Toyota camry 2.5-liter atkinson cycle engine with cooled-EGR. SAE International journal of
advances and current practices in mobility, 1(2), 601.
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to EIVC strategy on a 1.6 L turbocharged GDI engine (No. 2019-01-0192). SAE Technical Paper.
21	Moskalik, A., & Newman, K. (2020). Assessment of Changing Relationships between Vehicle Fuel Consumption
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Automatic Transmission (No. 2020-01-1286). SAE Technical Paper.
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23	Moskalik, A. (2020). Using Transmission Data to Isolate Individual Losses in Coastdown Road Load
Coefficients. SAE International Journal of Advances and Current Practices in Mobility, 2(2020-01-1064), 2156-
2171.
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Road Portable Emission Measurement Systems Test Data Analysis and Light-Duty Vehicle In-Use Emissions
Development. SAE International Journal of Electrified Vehicles, 9(2), 111.
25	U.S. EPA. The 2020 EPA Automotive Trends Report - Greenhouse Gas Emissions, Fuel Economy, and
Technology since 1975. EPA-420-R-21-003, January 2021.
26	Consumer Reports. "Hot New Electric Cars That Are Coming Soon - Automakers are offering new models in all
shapes and sizes for 2021 and beyond." May 20, 2021. Last accessed on the Internet on 5/26/2021 at the following
URL: https://www.consumerreports.org/hybrids-evs/hot-new-electric-cars-are-coming-soon/
27	McDonald, J. (2021) Particulate Matter Emissions from Light-duty Vehicles Equipped with Port Fuel Injection
and Gasoline Direct Injection. Memo to the Docket.
28	Toyota Motor Corporation. Toyota Develops TNGA-based Powertrain Units for Smooth, Responsive, 'As
Desired' Driving. December 06, 2016. Last accessed on the Internet on 5/26/2021 at the following URL:
https://pressroom.toyota.com/toyota-tnga-powertrain-responsive-driving/
29	Itabashi, S., Murase, E., Tanaka, H., Yamaguchi, M., & Muraguchi, T. (2017). New combustion and powertrain
control technologies for fun-to-drive dynamic performance and better fuel economy (No. 2017-01-0589). SAE
Technical Paper.
30	Hakariya, M., Toda, T., & Sakai, M. (2017). The new Toyota inline 4-cylinder 2.5 L gasoline engine (No. 2017-
01-1021). SAE Technical Paper.
31	Toyota Motor Corporation. Toyota Announces New Powertrain Units Based on TNGA. February 26, 2018. Last
accessed on the Internet on 5/26/2021 at the following URL:
https://global.toyota/en/newsroom/corporate/21179861 .html
32	Yamaji, K., Tomimatsu, M., Takagi, I., Higuchi, A., Yoshida, T., & Murase, E. (2018). New 2.0 L14 gasoline
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Greenhouse Gas Emissions Standards under the Midterm Evaluation: Technical Support Document. Chapter
2.3.4.1.8.1 - Effectiveness Data Used and Basis for Assumptions. EPA-420-R-16-021, November 2016.
34	Lee, S., Schenk, C., & McDonald, J. (2016). Air Flow Optimization and Calibration in High-Compression-Ratio
Naturally Aspirated SI Engines with Cooled-EGR (No. 2016-01-0565). SAE Technical Paper.
35	Schenk, C., & Dekraker, P. (2017). Potential fuel economy improvements from the implementation of cEGR and
CDA on an Atkinson Cycle Engine (No. 2017-01-1016). SAE Technical Paper.
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Engine Concept with Atkinson Cycle, Cooled EGR and Dynamic Skip Fire (No. 2021-01-0459). SAE Technical
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37	Brannys, S., Gehrke, S., Hoffmeyer, H., Hentschel, L., Blumenroder, K., Helbing, C., & Dinkelacker, F. (2019,
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Automobile and Engine Technology.
38	U.S. EPA, CA-EPA/ARB, U.S. DOT/NHTSA. Draft Technical Assessment Report: Midterm Evaluaton of Light-
Duty Vehicle Greenhouse Gas Emission Standards and Corporate Average Fuel Economy Standards for Model
Years 2022-2025. EPA-420-D-16-900, July 2016.
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40	See RIA Chapter 2 Endnote No.8.
41	Volvo Car Group, "Volvo Cars to be fully electric by 2030," Press Release, March 2, 2021.
42	Volkswagen Newsroom, "Strategy update at Volkswagen: The transformation to electromobility was only the
beginning," March 5, 2021. Accessed June 15, 2021 at https://www.volkswagen-newsroom.com/en/stories/strategy-
update-at-volkswagen-the-transformation-to-electromobility-was-only-the-beginning-6875
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43	Honda News Room, "Summary of Honda Global CEO Inaugural Press Conference," April 23, 2021. Accessed
June 15, 2021 athttps://global.honda/newsroom/news/2021/c210423eng.html
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of Today's 'Delivering Ford+' Capital Markets Day," Press Release, May 26, 2021.
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Conversation to Rewrite the Future of Cities," Press Release, June 4, 2021.
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Income Margins in the Mid-Term," Press Release, July 8, 2021.
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48	Environmental Defense Fund and M.J. Bradley & Associates, "Electric Vehicle Market Status - Update,
Manufacturer Commitments to Future Electric Mobility in the U.S. and Worldwide," April 2021.
49	International Council on Clean Transportation, "The end of the road? An overview of combustion-engine car
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(2021), March 25, 2021. Accessed July 15, 2021 at https://iopscience.iop.org/article/10.1088/2516-1083/abe0ad
51	Fueleconomy.gov, 2015 Fuel Economy Guide.
52	Fueleconomy.gov, 2021 Fuel Economy Guide.
53	See RIA Chapter 2 Endnote No. 48.
54	Department of Energy Vehicle Technologies Office, Transportation Analysis Fact of the Week #1186, "The
National Average Cost of Fuel for an Electric Vehicle is about 60% Less than for a Gasoline Vehicle," May 17,
2021.
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Electric Vehicles Have Lower Scheduled Maintenance Costs than Other Light-Duty Vehicles," June 14, 2021.
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1, 2021 from https://www.reuters.com/world/americas/canada-ban-sale-new-fuel-powered-cars-light-trucks-2035-
2021-06-29/
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https://doi.org/10.17226/26092
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Chapter 3: Economic and Other Key Inputs
3.1 Rebound
3.1.1 Accounting for the Fuel Economy Rebound Effect
In the context of light-duty vehicles (LDVs), rebound effects might occur when an increase in
vehicle fuel efficiency results in individuals driving more as a result of the lower cost per mile of
driving. Because this additional driving consumes fuel and generates emissions, the magnitude of
the rebound effect is one determinant of the actual fuel savings and emission reductions that will
result from adopting GHG emissions standards. The rebound effect generally refers to the
additional energy consumption that may arise from the introduction of a more efficient, lower
cost energy service. This effect offsets, to some degree, the energy savings benefits of that
efficiency improvement.1,2,3
The rebound effect for personal vehicles can, in theory, be estimated directly from the change
in vehicle use, in terms of vehicle miles traveled (VMT), which results from a change in vehicle
fuel efficiency.21 In practice, any attempt to quantify this "VMT rebound effect" (sometimes also
labeled the "direct rebound effect," or "direct VMT rebound effect") is complicated by the
difficulty in identifying an applicable data source from which the response to a significant
improvement in fuel efficiency can be estimated.b'4 Analysts, instead, often estimate the VMT
rebound indirectly, as the change in vehicle use that results from a change in fuel cost per mile
driven or a change in fuel price. When a fuel cost per mile approach is used, it does not
distinguish the relative contributions of changes in fuel efficiency and changes in fuel price to
the rebound effect, since both factors are determinants of fuel cost per mile.c When expressed as
positive percentages, the elasticities give the percentage increase in vehicle use that is presumed
to result from an increase in fuel efficiency or a decrease in fuel price.
The VMT rebound effect can also be divided into: (1) the short- to medium-run and (2) the
long-run rebound effect. Typically, studies estimating the short- to medium-run VMT rebound
effect are based upon a time period of roughly one to two years when the vehicle stock and land
use patterns are not changing significantly. The long-run rebound effect is estimated over a
longer time period when households can adjust where they work and live and the vehicle stock
can change more significantly than in the short/medium-run time frame.
While we focus on the VMT rebound effect in our analysis of this LDV proposal, there are at
least two other types of rebound effects discussed in the transportation policy and economics
literature: the "indirect rebound effect," which typically refers to the purchase of other energy-
consuming goods or services using the cost savings from energy efficiency improvements and
a Vehicle fuel efficiency is sometimes measured in terms of fuel consumption (gallons per mile) rather than fuel
economy (miles per gallon) in rebound estimates.
b Many of time series studies of the LDV rebound effect examine time periods before 2010. U.S. LDV fleet-wide
fuel economy has only been increasing since 2005. From 2005 to 2010, U.S. LDV fleet-wide fuel economy
improvements were fairly modest. Thus, there may be insufficient variability in LDV fuel economy to estimate a
relationship between fuel economy and VMT. See reference citation [4],
0 Fuel cost-per mile is equal to the price of fuel in dollars per gallon divided by fuel economy in miles per gallon (or
multiplied by fuel consumption in gallons per mile), so this figure declines when a vehicle's fuel efficiency
increases.
3-1
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the "economy-wide rebound effect." The economy-wide rebound effect refers to the increased
demand for energy throughout the whole economy, in response to the reduced market price of
energy that results from energy efficiency improvements.
Because research on indirect and economy-wide rebound effects is scant, the rebound effect
discussed in this section refers solely to the effect of increased fuel efficiency on vehicle use.
The terms "VMT rebound effect," "direct VMT rebound effect," and "rebound effect" are used
interchangeably, and are distinguished from other rebound effects that could potentially impact
the fuel savings and emissions reductions from EPA's proposed LDV standards, including the
indirect and the economy-wide rebound effects.
3.1.2 Summary of Historical Literature on the LDV Rebound Effect
This section provides a brief summary of historical literature on the LDV rebound effect. It
is important to note that a majority of the studies previously conducted rely on data from the
1950 -1990s. While these older studies provide useful information on the potential magnitude of
the rebound effect, studies based on more recent information (e.g., within the last decade)
provide more reliable estimates of how the proposed standards will affect future driving
behavior. A number of more recent studies on LDV rebound effects (i.e., after 2010) are
summarized in Section 3.1.3 below.
Estimates from published studies covering the period from roughly 2010 and earlier using
data from 1950-2004 have found long-run rebound effects on the order of 10-30 percent. Some
of these studies are summarized in Table 3-1, Estimates of the Rebound Effect Using U.S.
Aggregate Time-Series Data on Vehicle Travel and Table 3-2, Estimates of the Rebound Effect
Using U.S./State and Canadian/Province Level Data. In addition, Table 3-3 provides estimates of
the rebound effect using U.S. household survey data.
Table 3-1: Estimates of the Rebound Effect Using U.S. Aggregate Time-Series Data on Vehicle Travel
Author (year)
Short-Run
Long-Run
Time Period
Mayo & Mathis (1988)
22%
26%
1958-1984
Gately (1992)
9%
9%
1966-1988
Greene (1992)
Linear 5-19%
Log-linear 13%
Linear 5-19%
Log-linear 13%
1957-1989
Jones (1992)
13%
30%
1957-1989
Schimek (1996)
5-7%
21-29%
1950-1994
Source: Sorrell and Dimitropolous (2007) Table 4.6.5
Table 3-2: Estimates of the Rebound Effect Using U.S./State and Canadian/Province Level Data
Author (year)
Short-Run
Long-Run
Time Period
Haughton & Sarkar
9-16%
22%
1973-1992
(1996)



Small and Van
5%
22%
1966-2001
Dender (2007)
2%
11%
1997-2001
Hymel, Small and
3%
14%
1966-2004
Van Dender (2010)
5%
16%
1984-2004
Barla et al. (2009)
8%
18%
1990-2004
Source: Sorrell and Dimitropolous (2007) Table 4.7, with the addition of Small and Van Dender (2007),
Hymel, Small and Van Dender (2010) and Barla et al. (2009). The Barla et al. study is based upon Canadian
Province data.



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Table 3-3: Estimates of the Rebound Effect Using U.S. Household Survey Data
Author (year)
Estimate of Rebound Effect
Time Period
Goldberg (1996)
0%
1984-1990
Greene, Kahn, and
Gibson (1999)
23%
1979-1994
Pickrell & Schimek
(1999)
4-34%
1995
Puller & Greening
(1999)
49%
1980-1990
West (2004)
87%
1997
Bento et al. (2009)
34%
2001
Source: Sorrell and Dimitropolous (2007) with the addition of Bento et al. (2009).
While studies using national (Table 3-1) and state-level (Table 3-2) data have found a
relatively consistent range of long-run estimates of the rebound effect, household surveys display
more variability (Table 3-3). One explanation for this variability is that these studies consistently
find that the magnitude of the rebound effect differs according to the number of household
vehicles, and the average number of household vehicles differs among the surveys used to derive
these estimates. Still another possibility is that it is difficult to distinguish the impact of fuel cost
per mile on vehicle use from other, unobserved factors. For example, commuting distance might
influence both the choice of the vehicle and VMT. Residential density may also influence both
fuel cost per mile and VMT since households in urban areas are likely to simultaneously face
both higher fuel prices and shorter travel distances. Also, given that household data tends to be
collected on an annual basis, there may not be enough variability in the fuel price data to
estimate the magnitude of the rebound effect.6
Since there has been little variation in fuel economy over the time frame of most studies,
isolating the impact of fuel economy on VMT can be difficult using econometric analysis of
historical data. Therefore, studies that estimate the rebound effect using time series data often
examine the impact of gasoline prices or fuel cost per mile (i.e., the combined impact of both
gasoline prices and fuel economy) on VMT. However, if drivers are more responsive to changes
in fuel price or the cost of driving than to the variable directly of interest, fuel economy, these
studies may overstate the potential impact of the rebound effect resulting from this proposed
rule. For example, drivers may respond more to changes in fuel prices that are highly visible
(i.e., salient) than to changes in fuel economy from vehicle standards that are gradually
implemented over time.
Another important distinction among studies is whether they assume that the rebound effect is
constant or varies over time in response to the absolute levels of fuel costs, personal income, or
household vehicle ownership. Most studies using aggregate annual data for the U.S. assume a
constant rebound effect, although some test whether the effect can vary as changes in retail fuel
prices or average fuel efficiency alter fuel cost per mile driven. Many studies using household
survey data estimate significantly different rebound effects for households owning varying
numbers of vehicles, with most finding that the rebound effect is larger among households that
own more vehicles.
Some of the studies, such as Small and Van Dender (2007) and Hymel, Small and Van
Dender (2010), using a combination of state-level and national data, conclude that the rebound
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effect varies directly in response to changes in personal income, as well as fuel costs. These
studies indicate that the rebound effect has decreased over time as incomes have risen. One
reason that the rebound effect could vary over time is that the responsiveness to the fuel cost of
driving will be larger when it is a larger proportion of the total cost of driving. For example, as
incomes rise, the responsiveness to the fuel cost per mile of driving will decrease if households
view the time cost of driving - which is likely to be related to their income levels - as a larger
component of the total cost.
Small and Van Dender (2007) combine time series data for each of the 50 states and the
District of Columbia to estimate the rebound effect, allowing the magnitude of the rebound to
vary over time.7 For the time period 1966-2001, their study finds a long-run rebound effect of
22 percent, which is generally consistent with previously published studies. But for the five-year
period (1997-2001) estimated in their study, the long-run rebound effect decreases to 11 percent.
Hymel, Small and Van Dender (2010) extend the Small and Van Dender model by adding
congestion's impact on driving behavior.8 Controlling for congestion modestly increases their
estimates of the rebound effect in the study. For the time period 1966-2004, they estimate a
long-run rebound effect of 14 percent. For the time period, 1984-2004, they find a long-run
rebound effect of 16 percent, while for the most recent year in their data set, 2004, they estimate
a long-run rebound effect of 9 percent.
Barla et al. (2009) uses Canadian, province-level, panel data from 1990-2004 of light-duty
vehicles to estimate a VMT rebound effect.9 The model uses a similar methodological approach
as Small and Van Dender (2007) use, with a simultaneous three-equation model of aggregate
demand for vehicle kilometers traveled, vehicle stock and fuel efficiency. Barla et al. find short-
and long-run VMT rebound effects of 8 percent and 18 percent, respectively.
Of the studies listed in Table 3-3, one of the more recent is by Bento et al. (2009).10 Bento et
al. combine, for more than 20,000 U.S. households, their demographic characteristics, the
manufacturer and model of each vehicle they owned, and the annual usage of each vehicle from
the 2001 National Household Travel Survey (NHTS), with detailed data on fuel economy and
other attributes for each vehicle model. The authors aggregate vehicle models into 350
categories, representing combinations of manufacturer, vehicle type, and age. They use the
resulting data to estimate the parameters of a complex model of households' joint choices of the
number and types of vehicles to own, and their annual use of each vehicle. Bento et al. find an
estimate of a rebound effect of 34 percent, depending upon household composition, vehicle size
and type, and vehicle age.
There is some evidence in the literature that consumers are more responsive to an increase in
fuel prices than to a decrease in fuel prices. At the aggregate level, Dargay and Gately (1997)
and Sentenac-Chemin (2012) provide some evidence that demand for transportation fuel is
asymmetric.11 In other words, given the same magnitude of change, the response to a decrease
in gasoline price is smaller than the response to an increase. Gately (1993) shows that the
response to an increase in oil prices can be on the order of five times larger than the response to a
price decrease.12 Furthermore, Dargay and Gately and Sentenac-Chemin also find evidence that
consumers respond more to a large shock than to a small, gradual change in fuel prices. Since
these proposed standards would decrease the cost of driving gradually over time, it is possible
that the rebound effect would be much smaller than some of the historical estimates included in
the literature.
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3.1.3 Review of Recent Literature on LDV Rebound
More recent studies on LDV rebound effects have become available in the last decade (i.e.,
since 2010) and are summarized in Section 3.1.3 below.
A national, U.S. study by Greene (2012) concludes that the magnitude of the rebound effect
"is by now on the order of 10 percent."13 In this study, Greene looks at how VMT is influenced
by the gasoline price fluctuations, light-duty fuel consumption patterns, U.S. real personal
income, and the number of registered vehicles in the U.S., among other factors. Over the entire
time period analyzed, 1966-2007, Greene finds that fuel prices have a statistically significant
impact on VMT, while fuel efficiency did not. From this perspective, if the impact of fuel
efficiency on VMT is not statistically significant, the VMT rebound effect could be zero. Like
Small and Van Dender, Greene finds that the VMT rebound effect is declining modestly over
time as household incomes rise and travel costs increase. When using Greene's preferred
functional form, the projected rebound effect is approximately 12 percent in 2008, and drops to
10 percent in 2020 and to 9 percent in 2030.
Using data on household characteristics and vehicle use from the 2009 NHTS, Su (2012)
analyzes the effects of locational and demographic factors on household vehicle use and
investigates how the magnitude of the rebound effect varies with vehicles' annual use.14 Using
variation in the fuel economy and per-mile cost of driving and detailed controls for the
demographic, economic, and locational characteristics of the households that owned them (e.g.,
road and population density) and each vehicle's main driver (as identified by survey
respondents), Su employs specialized regression methods to capture the variation in the rebound
effect across ten different categories of vehicle use.
Su estimates that the overall rebound effect for all vehicles in the sample averages 13 percent,
and that its magnitude varies from 11-19 percent among the ten different categories of annual
vehicle use. The smallest rebound effects were estimated for vehicles at the two extremes of the
distribution of annual use - those vehicles driven comparatively little, and those vehicles used
most intensively - while the largest estimated effects applied to vehicles that were driven slightly
more than average. Controlling for the possibility that high-mileage drivers respond to the
increased importance of fuel costs by choosing vehicles that offer higher fuel economy narrowed
the range of Su's estimates of rebound effects slightly (to 11-17 percent), but did not alter the
finding that they are smallest for lightly- and heavily-driven vehicles and largest for those with
slightly above average use. The 2009 NHTS is based upon data collected from April 2008 to
April 2009. This time period may have been an unusual time period, since it was during the time
period of the Great Recession. It is not clear how the impacts of employment and output losses
from the Great Recession influenced, and resulted in, unusual travel patterns in the U.S.
Frondel and Vance (2013) use panel estimation methods and household diary travel data
collected in Germany between 1997-2009 to identify an estimate of a private transport rebound
value.15 The study focuses on single-car households that did not change their car ownership over
the timeframe each household was surveyed, up to a maximum of three years. Frondel and
Vance find a rebound effect for single-vehicle households of 46-70 percent.
Liu et al. (2014) employ the 2009 NHTS to develop an elaborate model of an individual
household's choices about how many vehicles to own, what types and ages of vehicles to
purchase, and how much combined driving to do using all of the household's vehicles.16 Their
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analysis uses a complex mathematical formulation and statistical methods to represent and
measure the interdependence among households' choices of the number, types, and ages of
vehicles to purchase, as well as how intensively to use them. The complexity of the relationships
among a number of factors incorporated in their model - the number of vehicles owned, their
specific types and ages, fuel economy levels, and use - requires them to measure these effects by
introducing variation in income, neighborhood attributes, and fuel costs, and observing the
response of households' annual driving. Their results imply a rebound effect of approximately
40 percent in response to a significant (25-50 percent) variation in fuel costs, with almost exactly
symmetrical responses to increases and declines in fuel costs.
Like Su and Liu et al., Linn (2016) also uses the 2009 NHTS to develop an approach to
estimate the relationship between the VMT of vehicles belonging to each household and a
variety of different factors: fuel costs, vehicle characteristics other than fuel economy (e.g.,
horsepower, the overall "quality" of the vehicle), and household characteristics (e.g., age,
income).17 Linn reports a fuel economy rebound effect with respect to VMT of between 20-40
percent.
One interesting result of the study is that when the fuel efficiency of all vehicles on the road
increases - which would be the long-run effect of rising fuel efficiency standards - two factors
have opposing effects on the VMT of a particular vehicle in a multi-vehicle household. First,
VMT increases when a vehicle's own fuel economy increases. But the increase in fuel economy
of the household's other vehicles cause the vehicle's own VMT to decrease. Since the vehicle's
own VMT response to a fuel economy increase is larger in magnitude than the VMT response to
changes in other vehicles' fuel economy, VMT increases if the fuel economy of all vehicles
increases proportionately. Linn also finds that VMT responds much more strongly to vehicle fuel
economy than to gasoline prices, which is at variance with the Hymel et al. and Greene results
discussed above.
Gillingham (2014) examines a period of significant swings in retail gasoline prices, along
with media reports of changing household driving habits, to examine how households respond to
changes in gasoline prices.18 This study uses a vehicle-level dataset of all new vehicles registered
in California in 2001-2003, and subsequently given a smog check (i.e., odometer readings) over
the 2005-2009 time frame, a period of steady economic growth but rapidly increasing gasoline
prices. Gillingham estimates the effect of differences in average monthly fuel price on monthly
vehicle use - at a county level. The primary empirical result of the responsiveness of new vehicle
VMT to gasoline prices is a medium-run estimate of 22 percent. There is evidence of
considerable heterogeneity in this responsiveness across buyer types, demographics, and
geographic conditions.
In a follow-up paper, Gillingham (2020) states that this 2014 study examines the response to
the 2008 gasoline price shock in California, an unusual period when gasoline prices were
particularly salient to consumers.19 Thus, according to Gillingham, the results of his 2014 study
should not be used for developing an estimate of the VMT rebound effect for fuel
economy/GHG standards. Gillingham points to his own PhD dissertation (2011) which examines
travel patterns for California drivers from 2001 to 2009 using odometer readings as more
suggestive of the VMT rebound effect of LDV fuel economy/GHG standards.20 His PhD
estimates a VMT rebound effect of one percent.
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Gillingham's results in his 2014 paper find that vehicle-level responsiveness to fuel price
increases with income, which is the opposite of the conclusions that Hymel, Small and Van
Dender and Greene find in previous studies. Gillingham hypothesizes that the increase in the
per-vehicle rebound effect with higher incomes may relate to wealthier households having more
discretionary driving, or to switching between flying and driving. Alternatively, wealthier
households tend to own more vehicles, and it is possible that within-household switching of
vehicles may account for the greater responsiveness at higher income levels.
Wang and Chen (2014) examine the responsiveness of VMT to fuel prices across income
groups, using a system of structural equations with VMT and fuel efficiency (i.e., miles per
gallon) from the 2009 NHTS.21 They find that the rebound effect is only significant for the
lowest income households (up to $25,000). Wang and Chen hypothesize that low income
households have numerous unfilled travel needs. Thus, fuel efficient vehicles spur more driving
by low income households.
Hymel and Small (2015) revisit the simultaneous equations methodology of Small and Van
Dender (2007) and Hymel, Small and Van Dender (2010), to see whether their previous
estimates of the VMT rebound effect have changed by adding in more recent data (2005-
2009).22 Their estimates of the long-run light-duty vehicle rebound effect over 2000-2009 is 4-
18 percent, when evaluated at average values of income, fuel cost, and urbanization in the U.S.
during this time period. These results show strong evidence of asymmetry in responsiveness to
fuel price increases and decreases. Results suggest that a rebound adjustment to fuel price rises
takes place quickly; the rebound response is large in the year of, and the first year following, a
price rise, then diminishes to a smaller value. The rebound response to price decreases occurs
more slowly. GHG standards result in lowering the price of driving, so the lower end of the 4-18
percent range is likely most applicable when assessing GHG standards. Consistent with previous
results using the same modeling framework used previously in other published studies, the VMT
rebound effect declines with increasing income and urbanization and increases with increasing
fuel cost. By far the most important of these sources of variation is income, the effect of which
is large enough to reduce the projected rebound effect for time periods of interest for this
proposed rule.
The study by Hymel and Small also finds a strengthening of the VMT rebound effect for the
years 2003-2009 when compared to their earlier results, suggesting that some additional,
unaccounted for factors have increased the rebound effect. Three potential factors are
hypothesized to have caused the upward shift in the VMT rebound effect in the 2003-2009 time
period: (1) media coverage, (2) price volatility, and (3) asymmetric responses to fuel price
changes.d While media coverage and volatility are important for understanding the rebound
effect based upon fuel prices, they may not be as relevant to influencing the rebound effect due
to fuel efficiency from LDV standards.
Hymel and Small find that there is an upward shift in the rebound effect of 2.5-2.8 percent
starting in 2003. Results suggest that the media coverage and volatility variables may explain
d The media coverage variable is measured by constructing measures of media coverage based upon gasoline-price
related articles appearing in the New York Times newspaper. Using the ProQuest historical database, they tally the
annual number of article titles containing the words gasoline (or gas) and price (or cost). They then form a variable
equal to the annual fraction of all New York Times articles that are gasoline-price-related. This fraction ranged from
roughly 1/4000 during the 1960s to a high of 1/500 in 1974.
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about half of the upward shift in LDV rebound in the 2003-2009 time period. Nevertheless,
these influences are small enough that they do not fully offset the downward trend in VMT
response due to higher incomes and other factors. Hence, even assuming that the variables retain
their 2003-2009 values into the indefinite future, they will not prevent a further diminishing of
the magnitude of the rebound effect if incomes continue to grow through time.
West et al. (2017) attempt to estimate the VMT rebound effect with household level data from
Texas, using a discontinuity in the eligibility requirements for the 2009 U.S. Car Allowance
Rebate System (CARS). This program, known as "Cash for Clunkers," incentivized eligible
households to purchase more fuel-efficient vehicles.23 Households that owned "clunkers" -
defined as vehicles with a fuel economy of 18 miles per gallon (MPG) or less - were eligible for
the subsidy, as long as their replacement vehicle was at least 22 MPG. The empirical strategy of
the paper is to compare the fuel economy of vehicle purchases and subsequent VMT of "barely
eligible" households to those households who were "barely ineligible."
Based upon odometer data reporting VMT, the paper finds a meaningful discontinuity in the
fuel economy of new vehicles purchased by CARS-eligible relative to ineligible households.
West et al. report that the increases in fuel economy realized by households who utilized the
program were not accompanied by increased use of the higher-MPG replacement vehicles. They
suggest this is because of the replacement vehicles' other attributes. Because households chose to
buy cheaper, smaller, and lower-performing vehicles, they did not drive any additional miles
after the purchase of the fuel-efficient vehicle. They conclude there is no evidence of a rebound
effect in response to improved fuel economy from the CARS program.
It is difficult to generalize the VMT response from the CARS program to a program for LDV
GHG standards. This was a one-time program for a fixed fleet of existing vehicles with specific
characteristics. The change in vehicle attributes from the program may not be representative of
any vehicle attribute changes from LDV GHG standards. Thus, this study does not provide
useful implications about the likely response of vehicle use to increases in LDV GHG standards.
Gillingham et al. (2015) use detailed annual vehicle-level emissions inspection test data from
Pennsylvania for 2000-2010 - including odometer readings, inspection zip codes, and extensive
vehicle characteristics - to examine both the responsiveness of driving to changing gasoline
prices, and heterogeneity in this responsiveness by geography, the fuel economy of the vehicle,
and the age of the vehicle.24 The study finds a short-run driving response (i.e., VMT) to gasoline
prices of 10 percent.
Leung (2015) examines how VMT is allocated across a typical household's vehicles in
response to a gasoline price increase.25 Leung uses 2009 NHTS data to decompose household
decreased demand for gasoline in response to a gasoline price increase into: (1) changes to VMT
and (2) changes to fuel economy or MPG (via a household reallocating its VMT to a vehicle with
a different MPG). Leung finds a VMT responsiveness to gasoline prices of 10 percent.
Langer et al. (2017) develop a model of motorists' demand for automobile travel that
explicitly accounts for heterogeneity across drivers and their vehicles for the state of Ohio. The
study estimates drivers' responses to changes in the marginal cost of driving. The study is based
upon data from State Farm Mutual Automobile Insurance Company on individual drivers who, in
return for a discount on their insurance, allowed a private firm to remotely record their vehicles'
VMT from odometer readings from 2009-2013. The model allows for a comparison of the
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effects of gasoline and VMT taxes on fuel consumption, among other factors. They find a
responsiveness of VMT with respect to the price of automobile travel is 12 percent.26
Knittel and Sandler (2018) estimate the VMT responsiveness to gasoline price, in the context
of the gasoline tax as an emission reduction policy tool. The study looks at California LDVs over
the period of 1998-2008, using odometer readings (i.e., Smog Check data).27 They find an
average VMT responsiveness of 13 percent. They also observe significant heterogeneity across
different types of vehicles, suggesting that VMT responsiveness to gasoline prices can vary
significantly based upon the specific sub-classes of vehicles considered.
One interesting study of VMT rebound is by De Borger et al. (2016). They analyze the
response of vehicle use to changes in fuel economy among a sample of nearly 350,000 Danish
households owning a single vehicle, of which almost one-third replaced it with a different model
during the 2001-2011 time period.28 By comparing the change in households' driving between
those who replaced their vehicles during the intervening period to those who did not, the authors
attempt to isolate the effect of changes in fuel economy on vehicle use from those of other
factors. Their data allow them to control for the effects of household characteristics and vehicle
features other than fuel economy on vehicle use. The authors use complex statistical methods to
account for the fact that some households replacing their vehicles may have done so in
anticipation of changes in their driving demands (rather than the reverse), as well as for the
possibility that some households who replaced their cars may be doing so because their driving
behavior is more sensitive to fuel prices than other households.
De Borger et al. measure the rebound effect from the change in households' vehicle use in
response to changes in fuel economy that are a consequence of their decisions to replace their
vehicles. Thus, the authors are able to directly estimate the fuel economy rebound effect itself, in
contrast to studies that rely on indirect measures, such as fuel prices or the costs per mile of
driving. Their preferred estimates of the fuel economy rebound effect range from 8-10 percent.
De Borger et al. also find no evidence that the rebound effect is smaller among lower-income
households than among their higher-income counterparts.
Gillingham et al. (2016) undertake a summary and review of the general rebound literature,
including rebound effects from LDV studies considered for this proposed rule, as well as
electricity used in stationary applications.29 According to Gillingham et al., the literature
suggests that differences in estimates of the rebound effect stem from its varying definitions, as
well as variation in the quality of data and empirical methodologies used to estimate it.
Gillingham et al. seek to clarify the definition of each of the channels of the rebound effect, and
to critically assess the state of the literature that estimates its magnitude.
Gillingham et al. note that most analyses assume a "zero cost breakthrough" (ZCB) - their
term for an improvement in efficiency that results in energy savings and related energy or fuel
cost savings but does not have associated increased costs of technology or implementation. Thus,
the authors argue, most analyses do not reflect the true costs of a "policy-induced improvement."
Gillingham et al. also caution that failing to account for the increased costs of equipment and/or
implementation of a policy-induced improvement may result in different estimates of the
rebound effect, compared to a ZCB improvement in efficiency.
Wenzel and Fujita (2018) examine the responsiveness of driving to changes in the price of
gasoline and driving costs.30 Using detailed odometer readings from over 30 million vehicles in
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four urban areas of Texas from 2005-2010, they estimate that the responsiveness of the demand
for VMT with respect to the price of gasoline in Texas is 9 percent, after accounting for
differences in vehicle models. They also use the rated combined city/highway fuel economy of
each vehicle to calculate the cost of driving, in cents per mile, since a vehicle's previous
inspection. They find a VMT responsiveness with respect to the cost of driving of 16 percent.
A study by Gillingham and Munk-Nielsen (2019) provides an estimate for the fuel price
elasticity of driving for Denmark in the time period from 1998-2011.31 They find a one-year
elasticity of 30 percent. An interesting aspect of this study is that it finds two tails of more
responsive drivers. The first tail is drivers living in the outskirts of cities with long commutes,
but with adequate access to public transport. The second tail is composed of drivers who
commute very little and tend to live in cities. Households with long commutes can readily switch
to public transport, while households who commute very little largely use their vehicles for a
diverse set of non-work trips, many of which can be readily switched to other modes of transport.
The finding of the two tails may explain differences in the results in fuel price elasticities
between the U.S. and Europe, according to Gillingham and Munk-Nielsen. The Gillingham and
Munk-Nielsen study finds a price responsiveness of driving of 30 percent for Denmark drivers
but, if ample access to public transport is eliminated, this responsiveness changes to 13 percent.
This is more in line with recent estimates from the U.S. for the fuel price responsiveness of
driving.
In an additional study, Gillingham (2020) develops a rationale for the use of a 10 percent
VMT rebound effect, and argues that the 20 percent used by the agencies in the most recent joint
LDV rulemaking for the 2020-2026 GHG/fuel economy standards is too high.32'33 Gillingham
points out that the agencies argue that odometer reading data is the most reliable data when they
are discussing the relationship between vehicle miles traveled and vehicle age, but do not make
this distinction in the discussion of the VMT rebound effect. Gillingham argues that, when
reviewing VMT rebound studies and attempting to develop a single value of a VMT rebound
effect, studies based upon odometer readings should be given greater weight. This is because
odometer reading data is more reliable, since it is measured rather than self-reported, and may be
more representative of travel behavior by covering nearly the entire LDV fleet in a region.
Based upon a list of recent VMT studies that the agencies reviewed in the proposed 2022-
2026 LDV standards, Gillingham presents a summary of literature relevant for his central
estimate of the rebound effect of fuel economy standards in the U.S. He restricts his review to
publicly available U.S.-based literature from the past decade. His review excludes estimates from
outside of the U.S., in particular Europe, as travel behavior has been shown to be different due to
a variety of factors including different urban forms and public transportation access. Second,
Gillingham excludes some estimates from unpublished work that is inaccessible, or that
estimates something other than the VMT rebound effect (i.e., response of gasoline demand to
fuel price). Third, Gillingham excludes estimates that are inappropriate for using as an estimate
of the rebound effect, based upon individual author's judgements. For example, as mentioned
above, Gillingham excludes his own study published in 2014, which examines the driving
response to the 2008 gasoline price shock, an unusual period when gasoline prices were
particularly salient to consumers.
According to Gillingham, a few clear findings are apparent. First, there is a relatively wide
range of estimates. In general, studies using household survey data tend to have much higher
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rebound effect estimates than those using odometer reading data. Second, the average rebound
effect over all studies that are considered by Gillingham is 14 percent, and the average over all
studies using just odometer readings is 8 percent. According to Gillingham, based upon his
review of relevant studies, he casts doubt on the argument for a central case estimate of 20
percent for the VMT rebound effect of U.S. LDV GHG/fuel economy standards.34
A study by the Dimitropoulos et al. (2018) presents a meta-analysis of 76 empirical studies
and 1,138 estimates of elasticities of travel from 18 countries (i.e., the U.S., European countries,
China and India) over the last fifty years, which can serve as possible measures of the VMT
rebound effect.35 Some of the most recent U.S. state-level studies using odometer readings data
such as Knittel and Sandler (2018), Langer et al. (2017) and Wenzel and Fujita (2018) are not
included in the meta-analysis. The meta-analysis uses an econometric approach to assess the
sources of heterogeneity in rebound effect estimates across the studies. The overall world VMT
rebound effect is estimated to be, on average, around 12 percent in the short-run, and roughly 32
percent in the long-run, across all of the studies considered. Other findings by Dimitropoulos et
al. suggest that studies using household survey data typically produce long-run rebound
estimates twice or more as large as studies based on aggregate data. The meta-analysis also finds
that the VMT rebound effect is declining worldwide, at a rate of roughly 0.7 percentage points
per year.
Dimitropoulos et al. provide VMT rebound estimates that vary by the price of gasoline,
population density, and gross domestic product (GDP) per capita, based upon the meta-analysis
results. They conclude that the VMT rebound effect increases with the price of gasoline and
population density, and decreases with per capita GDP, making rebound estimates from different
countries not directly comparable. Using 2018 U.S. values for gasoline price ($0.63/liter),
population density (33.75/km2), and GDP per capita ($51,552) for the U.S., Dimitropoulos et al.
results predict a long-run VMT rebound effect of roughly 20 percent for the U.S.36
In a Report entitled, "Science Advisory Board (SAB) Consideration of the Scientific and
Technical Basis of the EPA's Proposed Rule titled The Safer Affordable Fuel-Efficient (SAFE)
Vehicles Rule for Model Years 2021-2026 Passenger Cars and Light Trucks",37 EPA's
Scientific Advisory Board (SAB) provides comments on the VMT rebound estimate used in the
proposed SAFE rule. On the magnitude of the 20 percent rebound value used, the SAB provides
several recommendations. First, the SAB suggests that the Agency's consider several recent
odometer-based VMT rebound studies (e.g., Langer et al. (2017); Knittel and Sandler (2018);
and Wenzel and Fujita (2018)) 26>27>30 which were not considered for the proposed SAFE rule.
The SAB also recommends that the Agency not over-generalize on the importance of the
rebound effect, assuming the implications of increased efficiency will be seen uniformly across
sectors. Finally, the SAB recommends that the Agency consider the relative saturation of
demand for VMT, the increasing role that the travel behaviors of Millennials, Baby Boomers and
ride sharing services have in reducing the magnitude of the U.S. VMT rebound effect. In a
concluding statement, the SAB comments, "Due to these concerns, the SAB recommends that
the rebound estimate be reconsidered to account for the broader literature, and that it be
determined through a full assessment of the quality and relevance of the individual studies rather
than a simple average of results. A more in-depth analysis will allow the Agency to weight
papers based on their quality and applicability: recent papers using strong methodology and U.S.
data should be weighted more heavily than older papers, or those from outside the U.S., or those
with weaker methodology".
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3.1.4 Basis for Rebound Effect Used in this Proposed LDV Rule
EPA uses a single point estimate for the direct VMT rebound effect as an input to the agency's
analyses for this proposed LDV GHG rule (2023-2026). Based on a review of estimates of the
VMT rebound effect from recent analyses (i.e., since 2010) and some of the insights from VMT
rebound studies completed before 2010, EPA is using a value of 10 percent for the long-run
direct rebound effect for this proposed rule. In Chapter 10.4, as sensitivities, EPA presents
estimates of the impacts of using a five percent and 15 percent VMT rebound effect.
There is a wide variety of estimates of the VMT rebound effect from the recent analyses, in
part, due to the many different methodologies and data sources used to try to quantify this
impact. Given the broad range of values, EPA believes it is important to critically evaluate which
studies are most likely to be reflective of the rebound effect that is relevant to the proposed
standards (2023-2026). In other words, one cannot just take the "average" rebound estimates
from literature to use for the VMT rebound effect for this proposed rule.
EPA weights the following critical factors when choosing a VMT rebound estimate for this
proposed rulemaking:
1.	Geographic/Timespan relevance: Priority is given to U.S., as opposed to international
rebound studies, since U.S. studies are based upon U.S. LDV travel, land use patterns,
and socio-economic conditions. U.S. national-level studies are most useful since they are
based upon the geographic scale of this proposed rulemaking. Priority is given to studies
that are based on U.S. demographic/land use patterns over timespans most relevant to this
rulemaking's analytical timeframe (e.g., 2023-2050). Thus, we focus on studies relying
upon time series data rather than single-year studies. Even well-executed single year
studies have difficulty in controlling for confounding factors influencing the VMT
rebound effect, so these studies are not given significant weight;
2.	Time period of study: Priority is given to more recent rebound studies in the last decade,
since their driving patterns are more likely to resemble driving patterns over the time
frame of this upcoming proposed LDV rule;
3.	Reliability/Replicability of study: Priority is given to studies that use measured
odometer reading data for VMT. Many household survey studies rely on self-reported
VMT data, which may not produce as reliable estimates of the VMT rebound effect as
studies based on measured data. Also, odometer reading data is likely to more
representative of travel behavior by covering nearly the entire LDV fleet in a region.
Finally, the 2009 NHTS data was collected during the Great Recession time period. It is
not clear how representative travel patterns in the U.S. were during this time period for
developing estimates over timespans most relevant to this rulemaking's analytical
timeframe (e.g., 2023-2050); and
4.	Strong statistical/methodological basis: Priority is given to studies using strong
statistical methods that effectively attempt to control and isolate the impacts of the VMT
rebound effect.
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The critical factors listed above are consistent with the SAB's recommendations on how to
determine a preferred estimate of the magnitude of the VMT rebound effect for use in this
proposed LDV GHG rule. EPA undertakes a comprehensive, overall, in-depth assessment of the
full range of VMT rebound studies relevant for developing a preferred VMT rebound estimate
for this proposed rule. EPA weighs the applicability and quality of each individual VMT rebound
study in this overall assessment. EPA does not simply average the results of the relevant VMT
studies in developing a VMT rebound estimate for this proposed rule. EPA gives more weight to
U.S. rebound studies as opposed to international VMT rebound studies. In addition, EPA gives
more weight to recent rebound studies (i.e., in the last decade). The application of the critical
factors listed above to the relevant VMT rebound literature is presented below.
Studies that provide a U.S. estimate of the LDV VMT rebound effect are most applicable to
estimating the overall VMT effects of the proposed LDV standards. The most recent national,
U.S. studies are by Hymel and Small (2015), which estimates a rebound effect ranging from 4-
18 percent, and Greene (2012), which concludes that the rebound effect "is by now on the order
of 10 percent." Since GHG standards, which result in improved vehicle efficiency, lower the cost
of driving, and Hymel and Small found an asymmetric response to the costs of driving, the lower
end of the range in the Hymel and Small estimates is more applicable for evaluating the proposed
LDV GHG standards.
Both studies, Greene (2012) and Hymel and Small (2015), are based upon U.S. vehicle travel
patterns, as opposed to relying on international (i.e., outside the U.S.) travel patterns. Both
studies have been published in the last decade and are based upon the geographic scale of this
proposed rulemaking - the national, U.S. level. Both studies estimate the VMT rebound effect
looking at travel behavior over many years, as opposed to studies that rely on only a single year.
As noted above, even well executed, single year studies may have difficulty in controlling for
confounding factors influencing the VMT rebound effect. Both studies use solid statistical
methods that are generally effective at isolating the impacts of the VMT rebound effect. See
Table 3-4 below for the list of national, U.S. studies given significant weight in developing an
estimate of the VMT rebound effect for this proposed rule.
The set of studies at the U.S. state-level using odometer readings further support the 10
percent VMT rebound estimate for the U.S. as a whole. These studies, for Pennsylvania:
Gillingham et al. (2015); for California: Gillingham (2011)/Knittel and Sandler (2018); for Ohio:
Langer et al. (2017); and for Texas: Wenzel and Fujita (2018), find VMT rebound effects of 10,
1, 13, 12, and 9-16 percent, respectively. See Table 3-4 below for the list of U.S. state-level,
odometer studies given significant weight in developing an estimate of the VMT rebound effect
for this proposed rule.
Table 3-4: Studies Given Significant Weight in Developing an Estimate of the VMT Rebound Effect for this
Proposed Rule
Author
Year
Estimate of Rebound Effect
Description/



Time Period
U.S. National
Greene
2012
10%
Aggregate



1966-2007
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Hymel and
2015
4-18%
State-level
Small


2000-2009
State-Level Odometer
Gillingham
2011
1%
California
2001-2009
Gillingham et al.
2015
10%
Pennsylvania
2000-2010
Langer et al.
2017
12%
Ohio
2009-2013
Wenzel and
2018
9-16%
Texas
Fujita


2005-2010
Knittel and
2018
13%
California
Sandler


1998-2010
All of the state-level studies are based upon U.S. vehicle travel patterns, as opposed to relying
on international (i.e., outside the U.S.) travel patterns. All five of the studies have been published
in the past decade. These state-level studies use odometer readings to measure VMT, as opposed
to self-reported data, which provides more confidence in the reliability of their results. In
addition, odometer reading data is likely to be more representative of travel behavior by covering
nearly the entire LDV fleet in a region. Also, these studies all use time series, rather than single
year, data to estimate the VMT rebound effect, avoiding possible confounding effects of using a
single year's data. All of the U.S. state-level studies use solid statistical methods that are
generally effective at isolating the impacts of the VMT rebound effect. The Gillingham (2014)
study, which found a 22 percent VMT rebound effect in California, is excluded from
consideration in the set of state-level rebound studies using odometer data. As Gillingham points
out, this study assesses the response to driving from a salient 2008 gasoline price shock, which is
quite different than gradual changes in fuel economy from the proposed LDV standards.
The four states considered in the studies - Pennsylvania, Ohio, Texas and California - are
geographically diverse, with different population sizes, incomes, demographic characteristics,
and vehicle fleet characteristics. Nevertheless, these studies provide estimates of VMT rebound
effects that are roughly clustered in the 10 percent range. Thus, these U.S. state-level studies,
based on odometer readings, provide support for the use of a 10 percent rebound effect in
developing a single VMT rebound estimate for the U.S. nation as a whole.
The West et al. study (2017) on the CARS (Cash for Clunkers) program did not find a VMT
rebound effect (i.e., a VMT rebound effect of zero). This study uses odometer data from the state
of Texas. But the VMT response to a vehicle scrappage program could be very different than for
a program that results in a gradual increase in fuel economy over time, such as the LDV proposal
considered here. For example, West el al. find that vehicle attribute changes (i.e., lower curb
weight/horsepower) offset the lower costs of driving, resulting in a zero rebound effect. It is not
clear how vehicle attributes will change with this proposed LDV rule. Therefore, little to no
weight is given to the West et al. study in determining a VMT rebound effect for this proposed
rule.
Su (2012), Liu et al. (2014), Leung (2015) and Linn (2016), each using NHTS 2009 data, find
rebound effects that vary from 10-40 percent. Wang and Chen (2014), using the 2009 NHTS
data as well, find a rebound effect only for low income households. These widely different
results based upon the same dataset suggest that these studies may not provide reliable estimates
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of the VMT rebound effect. The concern is that different methodological approaches with the
same set of data yield different results. All of the household survey studies are based on self-
reported VMT data, suggesting that the results may not be as reliable as studies based on
odometer readings. Further, the NHTS data is for a single year. Even well executed studies based
upon a single year of data may have difficulty controlling for confounding factors influencing
estimates of the VMT rebound effect. Also, travel and household data from the 2009 NHTS was
collected while the U.S. was in the midst of the Great Recession. The Great Recession led to
significant employment and output losses in the U.S., which may have possibly led to unusual
travel patterns.
This proposed rule uses AEO 2021 as the basis for projecting economic and fuel market
trends during time frame of analysis of this proposed rule.38 The AEO 2021 projects that U.S.
Gross Domestic Product will increase over time. Some of the national, aggregate studies of the
U.S., Hymel and Small (2015) and Greene (2012), find that the VMT rebound effect decreases as
household incomes rise. As incomes rise, the value of time spent driving is typically assumed to
rise as well. Thus, the time cost of travel becomes a larger fraction of total travel costs, so
vehicle use may become less responsive to variations in fuel costs. Wang and Chen find that only
low-income households have a rebound effect, which is consistent with the VMT rebound effect
diminishing with increases in income. On the other hand, Gillingham, (2014) finds that the VMT
rebound effect increases with household income. But the Gillingham (2014) study examines the
travel response to a salient gasoline price increase, which is somewhat different than a gradual
improvement in fuel economy from this proposed LDV rule. Thus, the evidence of how the
rebound effect varies with income is somewhat mixed. While the relationship between the VMT
rebound effect and income is supported by some of the national, aggregate studies, less weight is
given to this factor in determining a VMT rebound value for this proposed rule.
In summary, the 10 percent VMT rebound value chosen for use in these proposed LDV GHG
standards (2023-2026) is based upon applying a set of critical factors - geographic/timespan
relevance, time period, repeatability/reliability, and statistical/methodological basis - and the
weight of evidence from multiple recent studies (i.e., studies since 2010), based upon an updated
and rigorous review of the large body of literature on this topic. A combination of the recent
U.S., national VMT rebound studies and recent, odometer-based, VMT rebound studies for
different states - Pennsylvania, Ohio, Texas and California - that are geographically diverse,
with different population sizes, incomes, demographic characteristics, and vehicle fleet
characteristics, support a single point value of 10 percent for the direct VMT rebound effect. All
of the studies estimate the VMT rebound effect over many years, as opposed to a single year, and
use strong statistical methods. Thus, we believe that this combination of studies provides a very
reliable estimate of the VMT rebound effect, 10 percent, and we have used this value within this
LDV GHG proposal.
3.2 Energy Security Impacts
This NPRM is designed to require improvements in the fuel economy of light-duty vehicles
(LDV) and thereby reduce fuel consumption and GHG emissions. In turn, this proposed rule
helps to reduce U.S. petroleum imports. A reduction of U.S. petroleum imports reduces both
financial and strategic risks caused by potential sudden disruptions in the supply of imported
petroleum to the U.S., thus increasing U.S. energy security. In other words, reduced U.S. oil
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imports act as a "shock absorber" when there is a supply disruption in world oil markets. This
section summarizes the agency's estimates of U.S. oil import reductions and energy security
benefits of the proposed light-duty GHG standards for model years 2023-2026.
Energy independence and energy security are distinct but related concepts, and an analysis of
energy independence informs our analysis of energy security.39 The goal of U.S. energy
independence is the elimination of all U.S. imports of petroleum and other foreign sources of
energy.40 U.S. energy security is broadly defined as the continued availability of energy sources
at an acceptable price.41 Most discussions of U.S. energy security revolve around the topic of the
economic costs of U.S. dependence on oil imports.6'42
The U.S.'s oil consumption has been gradually increasing in recent years (2015-2019) before
dropping dramatically as a result of the Covid pandemic in 2020.43 The U.S. has increased its
production of oil, particularly "tight" (i.e., shale) oil, over the last decade.44 As a result of the
recent increase in U.S. oil production, the U.S. became a net exporter of crude oil and product in
2020 and is now projected to be a net exporter of crude oil and product through 2023 to 2050, the
time frame of this analysis.45 This is a significant reversal of the U.S.'s net export position since
the U.S. has been a substantial net importer of crude oil and product starting in the early 1950s.46
Given that the U.S. is projected to be a net exporter of crude oil and product for the
foreseeable future, one could reason that the U.S. does not have a significant energy security
problem anymore. However, U.S. refineries still rely on significant imports of heavy crude oil
from potentially unstable regions of the world. Also, oil exporters with a large share of global
production have the ability to raise or lower the price of oil by exerting the monopoly power
associated with a cartel, the Organization of Petroleum Exporting Countries (OPEC), to restrict
oil supply relative to demand. These factors contribute to the vulnerability of the U.S. economy
to episodic oil supply shocks and price spikes, even when the U.S. is projected to be an overall
net exporter of crude oil and product.
3.2.1 Review of Historical Energy Security Literature
Energy security discussions are typically based around the concept of the oil import premium.
The oil import premium is the extra cost of importing oil beyond the price of the oil itself as a
result of: (1) potential macro-economic 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 around of the oil shocks of the 1970's (Bohi and Montgomery 1982, EMF 1982).47
Plummer (1982) provided valuable discussion of many of the key issues related to the oil import
premium as well as the analogous oil stockpiling premium.48 Bohi and Montgomery (1982)
detailed the theoretical foundations of the oil import premium and established many of the
critical analytic relationships.49 Hogan (1981) and Broadman and Hogan (1986, 1988) revised
and extended the established analytical framework to estimate optimal oil import premia with a
more detailed accounting of macroeconomic effects.50'51 Since the original work on energy
e 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.
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security was undertaken in the 1980's, there have been several reviews on this topic by Leiby,
Jones, Curlee and Lee (1997) and Parry and Darmstadter (2004).52'53
The economics literature on whether oil shocks are the same level of threat to economic
stability as they once were, is mixed. Some of the literature asserts that the macroeconomic
component of the energy security externality is small. For example, the National Research
Council (2009) argued that the non-environmental externalities associated with dependence on
foreign oil are small, and potentially trivial.54 Analyses by Nordhaus (2007) and Blanchard and
Gali (2010) questioned the impact of oil price shocks on the economy in the early 2000 time
frame.55 They were motivated by attempts to explain why the economy actually expanded
during the oil shock in the early 2000 time frame, and why there was no evidence of higher
energy prices being passed on through higher wage inflation. One reason, according to Nordhaus
and Blanchard and Gali, is that monetary policy has become more accommodating to the price
impacts of oil shocks. Another reason is that consumers have simply decided that such
movements are temporary and have noted that price impacts are not passed on as inflation in
other parts of the economy.
Hamilton (2012) reviewed the empirical literature on oil shocks and suggested that the results
are mixed, noting that some work (e.g. Rasmussen and Roitman (2011)) finds less evidence for
economic effects of oil shocks or declining effects of shocks (Blanchard and Gali (2010)), while
other work continues to find evidence regarding the economic importance of oil shocks.56 For
example, Baumeister and Peersman (2011) find 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
means 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".57 Hamilton observed that "a negative effect of oil
prices on real output has also been reported for a number of other countries, particularly when
nonlinear functional forms have been employed" (citing as examples Kim (2012), Engemann,
Kliesen, and Owyang (2011)).58'59 Alternatively, rather than a declining effect, Ramey and Vine
(2010) find "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."60
Some of the literature on oil price shocks emphasizes 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. Recent 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 (2010)).61 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.62 Kilian and
Vigfussen 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
2009a)".63
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 1979-2011.64 They
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stated: "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 for an
oil-demand disturbance.
EPA's assessment of the energy security literature finds that there are benefits to the U.S.
from reductions in oil imports. But there is some debate as to the magnitude, and even the
existence, of energy security benefits from U.S. oil import reductions. Over the last decade,
differences in economic impacts from oil demand and oil supply shocks have been distinguished.
The oil security premium calculations in this analysis are based on price shocks from potential
future supply events only. Oil supply shocks, which reduce economic activity, have been the
predominant focus of oil security issues since the oil price shocks/oil embargoes of the 1970's.
3.2.2 Review of Recent Energy Security Literature
There have also been a handful of more recent studies undertaken in the last few years that are
relevant for the issue of energy security: one by Resources for the Future (RFF), two studies by
Oak Ridge National Laboratory (ORNL), and a couple of studies, Newell and Prest and
Bjornland et al., on the responsiveness of U.S. tight oil (i.e., shale oil) to world oil price changes.
We provide a brief review and high-level summary of each of these studies below.
The RFF study (2017) attempts 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.65 The RFF work argues that there have been major changes that have occurred in
recent years which have reduced the impacts of oil shocks on the U.S. economy. First, the U.S. is
less dependent on imported oil than in the early 2000s due in part to the 'Tracking revolution"
(i.e., tight/shale oil), and to a lesser extent, increased production of renewable fuels. In addition,
RFF argues that the U.S. economy is more resilient to oil shocks than in the earlier 2000 time
frame. Some of the factors that make 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), many of the same factors that Nordhaus and Blanchard and Gali pointed to as
discussed above.
In the RFF effort, a number of comparative modeling scenarios are 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 is a dynamic stochastic general equilibrium model
developed by Balke and Brown.66 The second set of modeling frameworks use alternative
structural vector autoregressive models of the global crude oil market.67'68'69 The last of the
models utilized is the National Energy Modeling System (NEMS).70
Two key parameters are 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 will be 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.
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For oil price responsiveness, RFF reports 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 examines 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 is that the large variations in oil price
over the last fifteen years are believed to be predominantly "demand shocks": for example, a
rapid growth in global oil demand followed by the Great Recession and then the post-recession
recovery.
The only supply-side oil shock in the last several years was the attack on the Saudi Aramco
Abqaiq oil processing facility and the Khurais oil field (which took place after the publication of
RFF's study). 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 is the largest crude oil processing and stabilization plant in the
world, with a capacity of roughly 7 MMBD or roughly seven percent of global crude oil
production capacity.71 On September 16th, the first full day of commodity trading after the
attack, both Brent and West Texas Intermediate (WTI) crude oil prices surged by $7.17/barrel
and $8.34/barrel, respectively, in response to the attack, the largest price increase in roughly a
decade.
However, by September 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.72 Tanker loading estimates from third-party data sources indicated that loadings at
two Saudi Arabian export facilities were restored to the pre-attack levels.73 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 price 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. Thus, there is little
recent empirical evidence to estimate the response of the U.S. economy to an oil supply shock of
a significant magnitude/
A second set of recent studies related to energy security are from ORNL. In the first study,
ORNL (2018) undertakes a quantitative meta-analysis of world oil demand elasticities based
upon the recent economics literature.74 The ORNL study estimates oil demand elasticities for
f The Hurricanes Katrina/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 petroleum product exceeded
the loss of crude oil, and the regional impact varied even within the U.S. The Katrina/Rita Hurricanes were a
different type of oil disruption event than is quantified in the Stanford EMF risk analysis framework, which provides
the oil disruption probabilities than ORNL is using.
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two sectors (transportation and non-transportation) and by world regions (OECD and Non-
OECD) by meta-regression. To establish the dataset for the meta-analysis, ORNL undertakes a
literature search of peer reviewed journal articles and working papers between 2000 and 2015
that contain estimates of oil demand elasticities. The dataset consisted of 1,983 observations
from 75 published studies. The study finds a weighted short-run price elasticity of world oil
demand of -0.07 and a long-run price elasticity of world oil demand of -0.26.
The second relevant ORNL (2018) study from the standpoint of energy security is 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.75 Nineteen studies after the year 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 finds is a
short-run oil price elasticity of U.S. GDP, roughly one year after an oil shock, of -0.021, with a
68 percent confidence interval of -0.006 to -0.036.
Only in recent years have the implications of the "tight oil revolution" been felt in the
international oil market where U.S. production of oil is rising to be roughly on par with Saudi
Arabia and Russia. Recent economics literature of the tight (i.e., shale/unconventional) 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 shale oil has eroded the ability of OPEC to set world oil prices to some
degree, since OPEC cannot directly influence shale oil production decisions. Also, the growth in
U.S. oil supply is reducing the share of global oil supply controlled by OPEC, also possibly
limiting OPEC's degree of market power. But given that the shale oil expansion is a relatively
recent trend, it is difficult to know how much of an impact the increase in shale oil is having, or
will have, on OPEC behavior.
Two 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, the question that arises is: can
tight oil respond to an oil price shock more quickly and substantially than conventional oil?76 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.
Newell and Prest (2019) look at differences in the price responsiveness for oil wells, using a
detailed dataset of 164,000 oil wells, during the time frame of 2000-2015 in five major oil-
producing states: Texas, North Dakota, California, Oklahoma, and Colorado.77 They find that
unconventional oil wells are more price responsive than conventional oil wells, mostly due to
their much higher productivity, but the estimated price elasticity is still small. Newell and Prest
also estimate a medium-run price elasticity of oil supply of 0.12. Newell and Prest note that the
shale 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 to 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 a short period of time. From the standpoint of energy
security, the most relevant time frame of analysis is roughly a year, considered the short-run
responsiveness of oil demand to price.
Another study, by Bjornland et al. (2021), uses a well-level monthly production data set
covering more than 15,000 crude oil wells in North Dakota to examine differences in supply
responses between conventional and tight oil/shale oil.78 They find a short-run (i.e., one-month)
supply elasticity with respect to oil price for tight oil wells of 0.076, whereas the one-month
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response of conventional oil supply was not statistically different from zero. Both the results
from the Newell and Prest and Bjornland et al. 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.
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. U.S. oil markets are expected to
remain tightly linked to trends in the world crude oil market. 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. 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, this assessment of oil costs focuses
on those incremental social costs that follow from the resulting changes in net imports,
employing the usual oil import premium measure.
3.2.3 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. 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.
EPA also has considered the possibility of quantifying the military benefits components of
energy security but has 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 hard to quantify. These include possible costs of U.S.
military programs to secure oil supplies from unstable regions of the world, the energy security
costs associated with the U.S. military's reliance on petroleum to fuel its operations, possible
national security costs associated with expanded oil revenues to "rogue states" and relatedly the
foreign policy costs of oil insecurity.
Of these categories listed above, the one that is most clearly connected to petroleum use and
is, in principle, quantifiable is the first: the cost of military programs to secure oil supplies and
stabilize oil supplying regions. There is an 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
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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 proposed rule.
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.79
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).80
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).81
Stern (2010), on the other hand, argues 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.82 Stern presents an
estimate of military cost for Persian Gulf force projection, addressing the challenge of cost
allocation with an activity-based cost method. He uses 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 to 10 times, than other estimates. Stern also provides some insight on the analysis of
incremental effects, by estimating that Persian Gulf force projection costs are relatively strongly
correlated to Persian Gulf petroleum export values and volumes. Still, the issue remains of the
marginality of these costs with respect to Persian Gulf oil supply levels, the level of U.S. oil
imports, or U.S. oil consumption levels.
Delucchi and Murphy (2008) seek 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.83 Excluding an estimate of cost for missions
unrelated to oil, and for the protection of oil in the interest of other countries, Delucchi and
Murphy estimated military costs for all U.S. domestic oil interests of between $24 and $74
billion annually. Delucchi and Murphy assume that military costs from oil import reductions can
be scaled proportionally, attempting to address the incremental issue.
Crane et al. considers 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 percent of the current U.S. defense budget, could be
reduced.
Finally, an Issue Brief by Securing America's Energy Future (SAFE) (2018) found a
conservative estimate of approximately $81 billion per year spent by the U.S. military protecting
global oil supplies.84 This is approximately 16 percent 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 concludes that the implicit subsidy for all petroleum consumers is approximately
$11.25 per barrel of crude oil, or $0.28 per gallon. According to SAFE, a more comprehensive
estimate suggests the costs could be greater than $30 per barrel, or over $0.70 per gallon.85
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As in the examples above, an incremental analysis can estimate how military costs would vary
if the oil security mission is 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
are partially reduced, as is projected to be a consequence of this proposed rule. Partial reduction
of U.S. oil use surely diminishes the magnitude of the 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.86 EPA is 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 proposal.
3.2.4 U.S. Oil Import Reductions from this Proposed Rule
Over the time frame of analysis for this proposed rule, 2023-2050, the U.S. Department of
Energy's (DOE) Energy Information Administration's (EIA) Annual Energy Outlook (AEO)
2021 (Reference Case) projects that the U.S. will be both an exporter and an importer of crude
oil.87 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 oils 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
gradually increasing from 3 million barrels per day (MMBD) in 2023 to 3.5 MMBD in 2026 and
remain above 3 MMBD through 2050. U.S. crude oil imports, meanwhile, are projected to
decline modestly from 7.8 MMBD in 2023 to 7.5 MMBD in 2026. U.S. crude oil imports
continue to decrease modestly to 6.9 MMBD by 2030, before rising to the 7.6 MMBD in 2050.
The AEO 2021 projects that U.S. net oil product exports will be 5.3 MMBD in 2023 and rise
modestly to 5.6 MMBD in 2026. After 2026, U.S. net oil product exports are projected to be
somewhat greater than five MMBD until 2045, before decreasing modestly to 4.6 MMBD in
2050. Given the pattern of U.S. crude oil exports/imports, and U.S. net oil product exports, the
U.S. is projected to be a net petroleum (crude oil and product) exporter from 2023 through 2050.
For example, from 2023 to 2026, the U.S. net crude oil and product exports increase steadily
from 0.5 to 1.6 MMBD. U.S. net crude oil and product exports increase to roughly 2 MMBD in
the 2030 to 2035 time frame, before tapering off to 0.1 MMBD by 2050.
Since the U.S. is projected to continue importing significant quantities of crude oil through
2050, EPA's assessment is that the U.S. it is not expected to achieve the overall goal of U.S.
energy independence during the analytical time frame of this rule. However, the U.S. is projected
to be a net exporter of crude oil and products through 2050.
U.S. oil consumption is projected to be fairly steady for the time period from 2023 to 2050.
From 2023 to 2040, projected U.S. oil consumption is fairly constant at roughly 20 MMBD
before increasing modestly to roughly 21 MMBD in the 2045-2050 time period. During the
2023-2050 time frame, the AEO projects that the U.S. will continue to consume significant
quantities of oil and will likewise continue to rely on significant quantities of crude oil imports.
Estimated petroleum consumption changes from this proposed GHG rule are presented in
Chapter 6.2. Based on a detailed analysis of differences in U.S. fuel consumption, crude oil
imports/exports and exports of petroleum products for the time frame 2023-2050, and using the
AEO 2021 (Reference Case) and two alternative sensitivity cases, i.e., (Low Economic Growth)
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and (High Economic Growth), EPA estimates that approximately 91 percent of the change in
fuel consumption resulting from the proposed LDV GHG standards is likely to be reflected in
reduced U.S. imports of crude oil.g The 91 percent oil import factor is calculated by taking the
ratio of the changes in U.S. net crude oil and product imports divided by the change in U.S. oil
consumption in the different AEO cases. Thus, on balance, each gallon of petroleum reduced as a
result of the proposed LDV GHG Rule is anticipated to reduce total U.S. imports of petroleum
by 0.91 gallons.
Based upon the changes in oil consumption estimated in Chapter 6.2 and the 91 percent oil
import reduction factor, the reduction in U.S. oil imports as a result of the proposed LDV GHG
standards are estimated in Table 3-5 below for the 2023-2050 time frame. For comparison
purposes, based upon the AEO 2021 (Reference Case), Table 3-5 also shows the U.S.'s
projected crude oil exports and imports, net oil product exports, net crude oil/product exports and
U.S. oil consumption for the years 2023-2050.88
Table 3-5: Projected Trends in U.S. Oil Exports/Imports, Net Oil Product Exports, Net Crude Oil/Product
Exports, Oil Consumption and U.S. Oil Import Reductions Resulting from the Proposed LDV GHG rule
from 2023 to 2050 (Millions of barrels per day (MMBD))*




U.S. Net Crude

U.S. Oil Import

U.S. Crude
U.S. Crude
U.S.Net Oil
Oil and Product
U.S. Oil
Reductions from
Year
Oil Exports
Oil Imports
Product Exports
Exports
Consumption
Proposal
2023
3.0
7.8
5.3
0.5
20.0
0.0
2024
3.4
7.8
5.4
0.9
20.1
0.0
2025
3.3
7.5
5.6
1.4
20.2
0.1
2026
3.5
7.5
5.6
1.6
20.2
0.1
2030
3.1
6.9
5.9
2.0
20.2
0.3
2035
3.3
7.0
5.6
1.9
20.4
0.5
2040
3.2
7.5
5.5
1.2
20.6
0.6
2045
3.1
7.3
5.1
1.0
21.0
0.6
2050
3.1
7.6
4.6
0.1
21.6
0.6
3.2.5 Oil Security Premiums Used for this Proposed Rule
In order to understand the energy security implications of reducing U.S. oil imports, EPA has
worked with Oak Ridge National Laboratory (ORNL), which has developed approaches for
evaluating the social costs and energy security implications of oil use. The energy security
estimates provided below are based upon a methodology developed in a peer-reviewed study
entitled, "The Energy Security Benefits of Reduced Oil Use, 2006-2015," completed in 2008.89
This ORNL study is an updated version of the approach used for estimating the energy security
benefits of U.S. oil import reductions developed in a 1997 ORNL Report.90 This approach has
been used to estimate energy security benefits for the LDV GHG and fuel economy standards
(2012-2016)/(2017-2025) and the HDV GHG/fuel economy standards Phase I (2014-2018) and
Phase II (2018 and later).91'92'93
g We looked at changes in U.S. crude oil imports/exports and net petroleum products in the AEO 2021 Reference
Case, Table 11. Petroleum and Other Liquids Supply and Disposition, in comparison to two alternative Cases from
the AEO 2021. See the spreadsheet, "AEO 2021 Change in oil product demand on imports".
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When conducting this analysis, ORNL considers the full cost of importing petroleum into the
U.S. The full economic cost (i.e., labeled oil security premiums below) is defined to include two
components in addition to the purchase price of petroleum itself. These are: (1) the higher
costs/benefits for oil imports resulting from the effect of U.S. demand on the world oil price (i.e.,
the "demand" or "monopsony" costs/benefits); and (2) the risk of reductions in U.S. economic
output and disruption to the U.S. economy caused by sudden disruptions in the supply of
imported oil to the U.S. (i.e., the avoided macroeconomic disruption/adjustment costs).
For this proposed LDV GHG rule, EPA is using oil security premiums estimated using
ORNL's methodology, which incorporates the oil price projections and energy market and
economic trends, particularly regional oil supplies and demands at a global level (i.e.,
U.S./OPEC/rest of the world), from the AEO 2018 into its model. For the final LDV GHG rule,
EPA plans to update its energy security analysis based upon the AEO 2021. EPA only
considered the avoided macroeconomic disruption/adjustment costs oil security premiums (i.e.,
labeled macroeconomic oil security premiums below), since the monopsony impacts of this
proposed rule are considered transfer payments. See the EPA analysis within the draft TAR for a
discussion of the monopsony oil security premiums.94
In addition, EPA and ORNL have worked together to revise the oil security premiums based
upon recent energy security literature. Based upon EPA/ORNL's review of the recent energy
security literature, EPA is updating its macroeconomic oil security premiums for this proposed
LDV GHG rule. The recent economics literature (discussed in Section 3.2.2 above) focuses on
three factors that can influence the macroeconomic oil security premiums. We discuss each
factor below and provide a rationale for how we are updating two out of three of the factors to
develop new estimates of the macroeconomic oil security premiums. We are not accounting for
how shale oil is influencing the macroeconomic oil security premiums in this proposed rule.
First, we assess the price elasticity of demand for oil. In previous EPA vehicle rulemakings,
EPA has used a short-run elasticity of demand for oil of -0.045.95 From the recent RFF study,
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
currently limited alternatives to oil use in this sector. According to the IEA, the share of global
oil consumption attributed to the transportation sector grew from 60 percent in 2000 to 66
percent in 2018.96 The next largest sector by oil consumption, and an area of recent growth, is
petrochemicals. Thus, we believe it would be surprising if short-run oil demand responsiveness
has changed in a dramatic fashion. Increases in future electric vehicle use could influence the
price elasticity of demand for oil, but there is little empirical evidence available to assess this
issue. We may attempt to address this issue in the future if new information and data becomes
available.
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, we are increasing
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the short-run price elasticity of demand for oil from -0.045 to -0.07, a 56 percent increase.11 This
increase has the effect of lowering the macroeconomic oil security premiums estimated for this
rulemaking.
Second, we consider the elasticity of GDP to an oil price shock. For previous EPA vehicle
rulemakings, a GDP elasticity to an oil shock of -0.032 was used.97 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 recent literature
on this topic since it considers a wider 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, EPA is using a GDP elasticity of -0.023, a 28 percent reduction from
the GDP elasticity used previously (i.e., the -0.032 value).1 This GDP elasticity is in between the
ORNL meta-analysis estimate and the elasticity EPA has used in previous vehicle rulemakings.
Finally, we have not factored in how increases in U.S. tight oil might influence U.S. oil security
values, other than how they significantly reduce net oil imports, given the complexity of this
issue.
Table 3-6 below provides estimates of ORNL's macroeconomic oil security premium
estimates for selected years from 2023-2050. For the final rule, EPA intends to update the
macroeconomic oil security premium methodology to use the AEO 2021. As U.S. oil production
has increased and U.S. oil imports have declined steadily through the last decade, the
macroeconomic oil security premiums have been declining modestly over time. Thus, the use of
the AEO 2018 for calculating macroeconomic oil security premiums may modestly overestimate
the energy security benefits of this proposed rule.
In terms of cents per gallon, the macroeconomic oil security premiums range from 8.6
cents/gallon in 2023 to 9 cents/gallon in 2026. In the later years of the time frame of this
analysis, the macroeconomic oil security premiums range from 9.5 cents/gallon in 2030 to 13.2
cents/gallon in 2050.
h 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.
1 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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Table 3-6: Macroeconomic Oil Security Premiums for Selected Years from 2023-2050 (2018$/Barrel)*
Year (range)
Macroeconomic Oil Security Premiums
(Range)
2023
$3.63
($1.22-$6.13)
2024
$3.68
($1.20-$6.20)
2025
$3.72
($1.18-$6.27)
2026
$3.78
($1.17-$6.37)
2030
$3.99
($1.13-$6.74)
2035
$4.30
($1.14-$7.35)
2040
$4.66
($1.26-$7.96)
2045
$5.12
($1.52-$8.72)
2050
$5.57
($1.89-$9.53)
* Top values in each cell are the midpoints, the values in parentheses are the 90 percent confidence
intervals. The macroeconomic oil security premium estimates for the years 2023, 2024 and 2026 are
linearly interpolated values from ORNL estimates, which are reported in five-year time intervals.
3.2.6 Energy Security Benefits of the Proposed Rule
Using the ORNL oil security premium methodology with: (1) estimated oil savings calculated
by EPA, (2) an oil import reduction factor of 91 percent, and (3) updated oil security premium
estimates based upon the recent energy security literature and the AEO 2018, EPA presents the
annual energy security benefits of the proposed LDV GHG standards for selected years from
2023-2050 in Table 3-7 below. We do not consider military cost impacts or the monopsony
effect of oil import changes in the energy security benefits provided below.
Table 3-7: Annual Energy Security Benefits of the Proposed LDV GHG/Fuel Economy Proposed Rule for
Selected Years 2023-2050 (in Billions of 2018$)
Year
Benefits (2018$)
2023
0.0
2026
0.1
2030
0.5
2035
0.8
2040
1.1
2050
1.5
PV, 3%
12.5
PV, 7%
6.1
Annualized, 3%
0.6
Annualized, 7%
0.5
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3.3 Social Cost of Greenhouse Gases
We estimate the climate benefits for this proposed rulemaking using measures of the social
cost of three greenhouse gases: carbon, methane, and nitrous oxide. The social cost of each gas
(i.e., the social cost of carbon (SC-CO2), methane (SC-CH4), and nitrous oxide (SC-N2O)) is the
monetary value of the net harm to society associated with a marginal increase in emissions in a
given year, or the benefit of avoiding that increase. Collectively, these values are referenced as
the "social cost of greenhouse gases" (SC-GHG). In principle, SC-GHG includes the value of all
climate change impacts, including (but not limited to) changes in net agricultural productivity,
human health effects, property damage from increased flood risk and natural disasters, disruption
of energy systems, risk of conflict, environmental migration, and the value of ecosystem
services. The SC-GHG therefore, reflects the societal value of reducing emissions of the gas in
question by one metric ton. The SC-GHG is the theoretically appropriate values to use in
conducting benefit-cost analyses of policies that affect CO2, CH4, and N2O emissions.
We estimate the global social benefits of CO2, CH4, and N2O emission reductions expected
from this proposed rule using the SC-GHG estimates presented in the Technical Support
Document: Social Cost of Carbon, Methane, and Nitrous Oxide Interim Estimates under
Executive Order 13990.98 These SC-GHG estimates are interim values developed under
Executive Order (E.O.) 13990 for use in benefit-cost analyses until updated estimates of the
impacts of climate change can be developed based on the best available science and economics.
The SC-GHG estimates used in this draft RIA are the same as those used in the July 2016 Draft
Technical Assessment Report: Midterm Evaluation of Light-Duty Vehicle Greenhouse Gas
Emission Standards and Corporate Average Fuel Economy Standards for Model Years 2022-
2025, adjusted for inflation to 2018 dollars.
The SC-GHG estimates presented here were developed over many years, using transparent
process, peer-reviewed methodologies, the best science available at the time of that process, and
with input from the public. Specifically, in 2009, an interagency working group (IWG) that
included the EPA and other executive branch agencies and offices was established to ensure that
agencies were using the best available science and to promote consistency in the SC-CO2 values
used across agencies. The IWG published SC-CO2 estimates in 2010 that were developed from
an ensemble of three widely cited integrated assessment models (IAMs) that estimate global
climate damages using highly aggregated representations of climate processes and the global
economy combined into a single modeling framework. The three IAMs were run using a
common set of input assumptions in each model for future population, economic, and CO2
emissions growth, as well as equilibrium climate sensitivity (ECS) - a measure of the globally
averaged temperature response to increased atmospheric CO2 concentrations. These estimates
were updated in 2013 based on new versions of each jam.99,100:101 In August 2016 the IWG
published estimates of the social cost of methane (SC-CH4) and nitrous oxide (SC-N2O) using
methodologies that are consistent with the methodology underlying the SC-CO2 estimates. In
2015, as part of the response to public comments received to a 2013 solicitation for comments on
the SC-CO2 estimates, the IWG announced a National Academies of Sciences, Engineering, and
Medicine review of the SC-CO2 estimates to offer advice on how to approach future updates to
ensure that the estimates continue to reflect the best available science and methodologies. In
January 2017, the National Academies released their final report, Valuing Climate Damages:
Updating Estimation of the Social Cost of Carbon Dioxide, and recommended specific criteria
for future updates to the SC-CO2 estimates, a modeling framework to satisfy the specified
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criteria, and both near-term updates and longer-term research needs pertaining to various
components of the estimation process.102 Shortly thereafter, in March 2017, President Trump
issued Executive Order 13783, which disbanded the IWG, withdrew the previous TSDs, and
directed agencies to ensure SC-CO2 estimates used in regulatory analyses are consistent with the
guidance contained in OMB's Circular A-4, "including with respect to the consideration of
domestic versus international impacts and the consideration of appropriate discount rates" (E.O.
13783, Section 5(c)). Benefit-cost analyses following E.O. 13783, including the benefit-cost
analysis in the SAFE rule RIA,1 used SC-CO2 estimates that attempted to focus on the domestic
impacts of climate change as estimated by the models to occur within U.S. borders and were
calculated using two discount rates recommended by Circular A-4, 3 percent and 7 percent. All
other methodological decisions and model versions used in SC- CO2 calculations remained the
same as those used by the IWG in 2010 and 2013, respectively.
On January 20, 2021, President Biden issued Executive Order 13990, which re-established the
IWG and directed it to ensure that the U.S. Government's estimates of the social cost of carbon
and other greenhouse gases reflect the best available science and the recommendations of the
National Academies.102 The IWG was tasked with first reviewing the SC-GHG estimates
currently used in Federal analyses and publishing interim estimates within 30 days of the E.O.
that reflect the full impact of GHG emissions, including by taking global damages into account.
The interim SC-GHG estimates published in February 2021 are used here to estimate the climate
benefits for this proposed rulemaking. The E.O. instructs the IWG to undertake a fuller update of
the SC-GHG estimates by January 2022 that takes into consideration the advice of the National
Academies and other recent scientific literature.102
The February 2021 TSD provides a complete discussion of the IWG's initial review
conducted under E.O. 13990. In particular, the IWG found that the SC-GHG estimates used
under E.O. 13783 fail to reflect the full impact of GHG emissions in multiple ways. First, the
IWG found that a global perspective is essential for SC-GHG estimates because climate impacts
occurring outside U.S. borders can directly and indirectly affect the welfare of U.S. citizens and
residents. Thus, U.S. interests are affected by the climate impacts that occur outside U.S.
borders. Examples of affected interests include direct effects on U.S. citizens and assets located
abroad, international trade, and tourism, and spillover pathways such as economic and political
destabilization and global migration. In addition, assessing the benefits of U.S. GHG mitigation
activities requires consideration of how those actions may affect mitigation activities by other
countries, as those international mitigation actions will provide a benefit to U.S. citizens and
residents by mitigating climate impacts that affect U.S. citizens and residents. Therefore, in this
proposed rule EPA centers attention on a global measure of SC-GHG. This approach is the same
as that taken in EPA regulatory analyses over 2009 through 2016. As noted in the February 2021
TSD, the IWG will continue to review developments in the literature, including more robust
methodologies for estimating SC-GHG values based on purely domestic damages, and explore
ways to better inform the public of the full range of carbon impacts, both global and domestic.
As a member of the IWG, EPA will continue to follow developments in the literature pertaining
to this issue.
J The values used in the SAFE rule RIA were interim values developed under E.O. 13783 for use in regulatory
analyses. EPA followed E.O. 13783 by using SC-CO2 estimates reflecting impacts occurring within U.S. borders
and 3% and 7% discount rates in our central analysis for the proposal RIA.
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Second, the IWG found that the use of the social rate of return on capital (7 percent under
current OMB Circular A-4 guidance) to discount the future benefits of reducing GHG emissions
inappropriately underestimates the impacts of climate change for the purposes of estimating the
SC-GHG. Consistent with the findings of the National Academies and the economic literature,
the IWG continued to conclude that the consumption rate of interest is the theoretically
appropriate discount rate in an intergenerational context (IWG 2010, 2013, 2016a, 2016b), and
recommended that discount rate uncertainty and relevant aspects of intergenerational ethical
considerations be accounted for in selecting future discount rates.k'102'103'104'105'106 As a member
of the IWG involved in the development of the February 2021 TSD, EPA agrees with this
assessment and will continue to follow developments in the literature pertaining to this issue.
While the IWG works to assess how best to incorporate the latest, peer reviewed science to
develop an updated set of SC-GHG estimates, it set the interim estimates to be the most recent
estimates developed by the IWG prior to the group being disbanded in 2017. The estimates rely
on the same models and harmonized inputs and are calculated using a range of discount rates. As
explained in the February 2021 TSD, the IWG has determined that it is appropriate for agencies
to revert to the same set of four values drawn from the SC-GHG distributions based on three
discount rates as were used in regulatory analyses between 2010 and 2016 and subject to public
comment. For each discount rate, the IWG combined the distributions across models and
socioeconomic emissions scenarios (applying equal weight to each) and then selected a set of
four values for use in benefit-cost analyses: an average value resulting from the model runs for
each of three discount rates (2.5 percent, 3 percent, and 5 percent), plus a fourth value, selected
as the 95th percentile of estimates based on a 3 percent discount rate. The fourth value was
included to provide information on potentially higher-than-expected economic impacts from
climate change, conditional on the 3 percent estimate of the discount rate. As explained in the
February 2021 TSD, this update reflects the immediate need to have an operational SC-GHG for
use in regulatory benefit-cost analyses and other applications that was developed using a
transparent process, peer-reviewed methodologies, and the science available at the time of that
process. Those estimates were subject to public comment in the context of dozens of proposed
rulemakings as well as in a dedicated public comment period in 2013.
Table 3-8, Table 3-9, and Table 3-10 summarize the interim global SC-CO2, SC-CH4, and SC-
N2O estimates for the years 2015 to 2070.1 These estimates are reported in 2018 dollars but are
k GHG emissions are stock pollutants, where damages are associated with what has accumulated in the atmosphere
over time, and they are long lived such that subsequent damages resulting from emissions today occur over many
decades or centuries depending on the specific greenhouse gas under consideration. In calculating the SC-GHG, the
stream of future damages to agriculture, human health, and other market and non-market sectors from an additional
unit of emissions are estimated in terms of reduced consumption (or consumption equivalents). Then that stream of
future damages is discounted to its present value in the year when the additional unit of emissions was released.
Given the long time horizon over which the damages are expected to occur, the discount rate has a large influence
on the present value of future damages.
1 The February 2021 TSD provides SC-GHG estimates through emissions year 2050. Estimates were extended for
the period 2051 to 2070 using the IWG methods, assumptions, and parameters identical to the 2020-2050 estimates.
Specifically, 2051-2070 SC-GHG estimates were calculated in Mimi.jl, an open-source modular computing platform
used for creating, running, and performing analyses on IAMs (www.mimiframework.org). For CO2, the 2051-2054
SC-GHG values were calculated by linearly interpolating between the 2050 TSD values and the 2055 Mimi-based
values. The annual unrounded 2051-2070 values used in the calculations in this RIA are available in the rule docket,
and the replication code is available upon request.
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otherwise identical to those presented in the IWG's 2016 TSD. For purposes of capturing
uncertainty around the SC-GHG estimates in analyses, the IWG's February 2021 TSD
emphasizes the importance of considering all four of the SC-GHG values. The SC-GHG
increases over time within the models - i.e., the societal harm from one metric ton emitted in
2030 is higher than the harm caused by one metric ton emitted in 2025 - because future
emissions produce larger incremental damages as physical and economic systems become more
stressed in response to greater climatic change, and because GDP is growing over time and many
damage categories are modeled as proportional to GDP.
Table 3-8: Interim Global Social Cost of Carbon Values, 2020-2070 (2018$/Metric Tonne CO2)98
Emissions

Discount Rate and Statistic

Year





5%
3%
2.5%
3%

Average
Average
Average
95th Percentile
2020
$14
$50
$74
$147
2025
$16
$55
$81
$164
2030
$19
$60
$87
$181
2035
$22
$66
$93
$200
2040
$24
$71
$100
$218
2045
$28
$77
$107
$235
2050
$31
$82
$113
$252
2055
$34
$86
$119
$258
2060
$37
$91
$124
$268
2065
$42
$98
$132
$292
2070
$48
$105
$139
$318
Note: The 2020-2050 SC-C02 values are identical to those reported in the 2016 TSD (IWG 2016a) adjusted for
inflation to 2018 dollars using the annual GDP Implicit Price Deflator values in the U.S. Bureau of Economic
Analysis' (BEA) NIPA Table 1.1.9 (U.S. BEA 2021). The estimates were extended for the period 2051 to 2070
using methods, assumptions, and parameters identical to the 2020-2050 estimates. The values are stated in
$/metric tonne CO2 and vary depending on the year of CO2 emissions. This table displays the values rounded to
the nearest dollar; the annual unrounded values through 2050 are available on OMB's website:
. The annual unrounded 2051-2070 values used in the calculations in
this draft RIA are available in the rule docket.


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Table 3-9: Interim Global Social Cost of Methane Values, 2020-2070 (2018$/Metric Tonne CH4)98
Emissions

Discount Rate and Statistic

Year





5%
3%
2.5%
3%

Average
Average
Average
95th Percentile
2020
$650
$1,400
$1,900
$3,800
2025
$780
$1,700
$2,200
$4,400
2030
$910
$1,900
$2,400
$5,000
2035
$1,100
$2,200
$2,700
$5,800
2040
$1,200
$2,400
$3,100
$6,500
2045
$1,400
$2,700
$3,400
$7,200
2050
$1,600
$3,000
$3,700
$7,900
2055
$1,700
$3,100
$3,800
$8,100
2060
$1,800
$3,300
$4,000
$8,300
2065
$2,400
$4,100
$4,800
$11,000
2070
$3,000
$4,800
$5,700
$14,000
Note: The 2020-2050 SC-CH4 values are identical to those reported in the 2016 TSD (IWG 2016a) adjusted for
inflation to 2018 dollars using the annual GDP Implicit Price Deflator values in the U.S. Bureau of Economic
Analysis' (BEA) NIPA Table 1.1.9 (U.S. BEA 2021). The estimates were extended for the period 2051 to 2070
using methods, assumptions, and parameters identical to the 2020-2050 estimates. The values are stated in
$/metric tonne CH4 and vary depending on the year of CH4 emissions. This table displays the values rounded to
the nearest dollar; the annual unrounded values through 2050 are available on OMB's website:
. The annual unrounded 2051-2070 values used in the calculations in
this draft RIA are available in the rule docket.


Table 3-10: Interim Global Social Cost of Nitrous Oxide Values, 2020-2070 (2018$/Metric Tonne N2O)98
Emissions

Discount Rate and Statistic

Year





5%
3%
2.5%
3%

Average
Average
Average
95th Percentile
2020
$5,600
$18,000
$26,000
$47,000
2025
$6,600
$20,000
$29,000
$53,000
2030
$7,600
$22,000
$32,000
$59,000
2035
$8,800
$24,000
$35,000
$65,000
2040
$10,000
$27,000
$38,000
$72,000
2045
$11,000
$29,000
$41,000
$79,000
2050
$13,000
$32,000
$44,000
$86,000
2055
$14,000
$35,000
$47,000
$92,000
2060
$16,000
$37,000
$50,000
$98,000
2065
$19,000
$42,000
$55,000
$110,000
2070
$22,000
$46,000
$60,000
$130,000
Note: The 2020-2050 SC-N20 values are identical to those reported in the 2016 TSD (IWG 2016a) adjusted for
inflation to 2018 dollars using the annual GDP Implicit Price Deflator values in the U.S. Bureau of Economic
Analysis' (BEA) NIPA Table 1.1.9 (U.S. BEA 2021). The estimates were extended for the period 2051 to 2070
using methods, assumptions, and parameters identical to the 2020-2050 estimates. The values are stated in
$/metric tonne N20 and vary depending on the year of N20 emissions. This table displays the values rounded to
the nearest dollar; the annual unrounded values through 2050 are available on OMB's website:
. The annual unrounded 2051-2070 values used in the calculations in
this draft RIA are available in the rule docket.


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There are a number of limitations and uncertainties associated with the SC-GHG estimates
presented in Table 3-8 through Table 3-10. Some uncertainties are captured within the analysis,
while other areas of uncertainty have not yet been quantified in a way that can be modeled.
Figure 3-1, Figure 3-2, and Figure 3-3 present the quantified sources of uncertainty in the form
of frequency distributions for the SC-CO2, SC-CFU, and SC-N2O estimates for emissions in
2030. The distributions of SC-GHG estimates reflect uncertainty in key model parameters such
as the equilibrium climate sensitivity, as well as uncertainty in other parameters set by the
original model developers. To highlight the difference between the impact of the discount rate
and other quantified sources of uncertainty, the bars below the frequency distributions provide a
symmetric representation of quantified variability in the SC-GHG estimates for each discount
rate. As illustrated by the figures, the assumed discount rate plays a critical role in the ultimate
estimate of the SC-GHG. This is because GHG emissions today continue to impact society far
out into the future, so with a higher discount rate, costs that accrue to future generations are
weighted less, resulting in a lower estimate. As discussed in the February 2021 TSD, there are
other sources of uncertainty that have not yet been quantified and are thus not reflected in these
estimates.
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80 100 120 140 160 180 200 220 240 260 280 300 320
Social Cost of Carbon in 2030 [2018$ / metric ton C02]
Figure 3-1: Frequency Distribution of SC-CO2 Estimates for 20301
m Although the distributions and numbers are based on the full set of model results (150,000 estimates for each
discount rate and gas), for display purposes the horizontal axis is truncated with 0.02 to 0.68 percent of the estimates
falling below the lowest bin displayed and 0.12 to 3.11 percent of the estimates falling above the highest bin
displayed, depending on the discount rate and GHG.
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CO
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3% Average = $2000
2.5% Average = $2500
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95th Pet. = $5200
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Figure 3-2:Frequency Distribution of SC-CEU Estimates for 2030"
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5% Average = $7800
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Discount Rate
~	5.0%
~	3.0%
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Social Cost of Nitrous Oxide in 2030 [2018$ / metric ton N20]
1e+05
Figure 3-3: Frequency Distribution of SC-N2O Estimates for 2030™
The interim SC-GHG estimates presented in Table 3-8 through Table 3-10 have a number of
other limitations. First, the current scientific and economic understanding of discounting
approaches suggests discount rates appropriate for intergenerational analysis in the context of
climate change are likely to be less than 3 percent, near 2 percent or lower.106 Second, the IAMs
used to produce these interim estimates do not include all of the important physical, ecological,
and economic impacts of climate change recognized in the climate change literature and the
science underlying their "damage functions" -i.e., the core parts of the IAMs that map global
mean temperature changes and other physical impacts of climate change into economic (both
market and nonmarket) damages-lags behind the most recent research. For example, limitations
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include the incomplete treatment of catastrophic and non-catastrophic impacts in the integrated
assessment models, their incomplete treatment of adaptation and technological change, the
incomplete way in which inter-regional and intersectoral linkages are modeled, uncertainty in the
extrapolation of damages to high temperatures, and inadequate representation of the relationship
between the discount rate and uncertainty in economic growth over long time horizons.
Likewise, the socioeconomic and emissions scenarios used as inputs to the models do not reflect
new information from the last decade of scenario generation or the full range of projections.
The modeling limitations do not all work in the same direction in terms of their influence on
the SC-GHG estimates. However, as discussed in the February 2021 TSD, the IWG has
recommended that, taken together, the limitations suggest that the SC-GHG estimates used in
this proposed rule likely underestimate the damages from GHG emissions. In particular, the
Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report, which was the
most current IPCC assessment available at the time when the IWG decision over the ECS input
was made, concluded that SC-CO2 estimates "very likely.. .underestimate the damage costs" due
to omitted impacts.107 Since then, the peer-reviewed literature has continued to support this
conclusion, as noted in the IPCC's Fifth Assessment report and other recent scientific
assessments.108'109'110'111'112'113'114'115 These assessments confirm and strengthen the science,
updating projections of future climate change and documenting and attributing ongoing changes.
For example, sea level rise projections from the IPCC's Fourth Assessment report ranged from
18 to 59 centimeters by the 2090s relative to 1980-1999, while excluding any dynamic changes
in ice sheets due to the limited understanding of those processes at the time.107 A decade later,
the Fourth National Climate Assessment projected a substantially larger sea level rise of 30 to
130 centimeters by the end of the century relative to 2000, while not ruling out even more
extreme outcomes.113 The February 2021 TSD briefly previews some of the recent advances in
the scientific and economic literature that the IWG is actively following and that could provide
guidance on, or methodologies for, addressing some of the limitations with the interim SC-GHG
estimates. The IWG is currently working on a comprehensive update of the SC-GHG estimates
(to be released by January 2022 under E.O. 13990) taking into consideration recommendations
from the National Academies of Sciences, Engineering and Medicine, recent scientific literature,
public comments received on the February 2021 TSD and other input from experts and diverse
stakeholder groups.116 To complement the IWG process, and as an active member of the IWG,
the EPA is also soliciting comment on the interim SC-GHG estimates presented in this draft
RIA.
Table 3-11 through Table 3-13 shows the estimated global climate benefits from changes in
CO2, CH4, N2O,respectively and Table 3-14 through Table 3-16 shows the combined total
climate benefits expected to occur over 2023-2070 under the proposed GHG standards and the
two alternatives considered (see Chapter 2.2.2 and also Sections I.G and II.C of the preamble to
the proposed rule for more detail on the alternatives considered by EPA). EPA estimated the
dollar value of the GHG-related effects for each analysis year between 2023 through 2050 by
applying the SC-GHG estimates, shown in Tables 3-8 through 3-10, to the estimated changes in
GHG emissions inventories resulting from including tailpipe emissions from light-duty cars and
trucks, and the upstream emissions associated with the fuels used to power those vehicles." EPA
n According to OMB's Circular A-4 (2003), an "analysis should focus on benefits and costs that accrue to citizens
and residents of the United States", and international effects should be reported separately. To correctly assess the
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then calculated the present value and annualized benefits from the perspective of 2021 by
discounting each year-specific value to the year 2021 using the same discount rate used to
calculate the SC-GHG.
Table 3-11: Estimated Global Climate Benefits from Changes in CO2 Emissions 2023 - 2050 for the Proposal
(Billions of 2018$)
Discount Rate and Statistic
Calendar Year
5% Average
3% Average
2.5% Average
% 95th percentile
2023
$0.1
$0.2
$0.3
$0.6
2024
$0.1
$0.4
$0.5
$1.0
2025
$0.2
$0.6
$0.9
$2.0
2026
$0.3
$1.0
$1.0
$3.0
2027
$0.4
$1.0
$2.0
$4.0
2028
$0.6
$2.0
$3.0
$6.0
2029
$0.8
$2.0
$4.0
$7.0
2030
$0.9
$3.0
$4.0
$9.0
2031
$1.0
$4.0
$5.0
$11.0
2032
$1.0
$4.0
$6.0
$13.0
2033
$2.0
$5.0
$7.0
$14.0
2034
$2.0
$5.0
$7.0
$16.0
2035
$2.0
$6.0
$8.0
$17.0
2036
$2.0
$6.0
$9.0
$19.0
2037
$2.0
$7.0
$9.0
$20.0
2038
$2.0
$7.0
$10.0
$21.0
2039
$3.0
$7.0
$10.0
$22.0
2040
$3.0
$8.0
$11.0
$24.0
2041
$3.0
$8.0
$11.0
$24.0
2042
$3.0
$8.0
$11.0
$25.0
2043
$3.0
$8.0
$12.0
$26.0
2044
$3.0
$9.0
$12.0
$27.0
2045
$3.0
$9.0
$12.0
$27.0
2046
$3.0
$9.0
$12.0
$28.0
2047
$3.0
$9.0
$13.0
$28.0
2048
$3.0
$9.0
$13.0
$29.0
2049
$4.0
$9.0
$13.0
$29.0
2050
$4.0
$10.0
$13.0
$30.0
PV
$20.0
$87.0
$130.0
$260.0
Annualized
$1.3
$0.4
$6.4
$13.0
Climate benefits are based on changes (reductions) in C02 emissions and are calculated using four different estimates of the social cost of
carbon (SC-CO2) (model average at 2.5 percent, 3 percent, and 5 percent discount rates; 95th percentile at 3 percent discount rate). We
emphasize the importance and value of considering the benefits calculated using all four SC-CO2 estimates. As discussed in the Technical
Support Document: Social Cost of Carbon, Methane, and Nitrous Oxide Interim Estimates under Executive Order 13990 (IWG 2021), a
consideration of climate benefits calculated using discount rates below 3 percent, including 2 percent and lower, are also warranted when
discounting intergenerational impacts.	
total climate damages to U.S. citizens and residents, an analysis must account for impacts that occur within U.S.
borders, climate impacts occurring outside U.S. borders that directly and indirectly affect the welfare of U.S. citizens
and residents, and spillover effects from climate action elsewhere. The SC-GHG estimates used in regulatory
analysis under revoked E.O. 13783, including in the RIA for the SAFE rule, were an approximation of the climate
damages occurring within U.S. borders only (e.g., $7/mtC02 and $1 l/mtC02 (2018 dollars) using a 3% discount rate
for emissions occurring in 2023 and 2050, respectively; $207/mtCH4 and $376/mtCH4 (2018 dollars) using a 3%
discount rate for emissions occurring in 2023 and 2050, respectively; and $2437/mtN20 and $3986/mtN20 (2018
dollars) using a 3% discount rate for emissions occurring in 2023 and 2050, respectively). Applying the same
estimates (based on a 3% discount rate) to the GHG emission reduction expected from the proposed rule would yield
benefits from climate impacts within U.S borders of $28 million in 2023, increasing to $1.3 billion in 2050 for CO2;
$1 million in 2023, increasing to $54 million in 2050 for CH4; $0.25 million in 2023, increasing to $16 million in
2050 for N2O; and combined GHG benefits of $30 million in 2023, increasing to $1.4 billion in 2050. However, as
discussed at length in the IWG's February 2021 TSD, estimates focusing on the climate impacts occurring solely
within U.S. borders are an underestimate of the benefits of CO2 mitigation accruing to U.S. citizens and residents, as
well as being subject to a considerable degree of uncertainty due to the manner in which they are derived.
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Table 3-12: Estimated Global Climate Benefits from Changes in CH4 Emissions 2023 - 2050 for the Proposal
(Billions of 2018$)
Discount Rate and Statistic
Calendar Year	5% Average	3% Average	2.5% Average	3% 95th percentile
2023
$0.0
$0.0
$0.0
$0.0
2024
$0.0
$0.0
$0.0
$0.0
2025
$0.0
$0.0
$0.0
$0.1
2026
$0.0
$0.0
$0.1
$0.1
2027
$0.0
$0.1
$0.1
$0.1
2028
$0.0
$0.1
$0.1
$0.2
2029
$0.1
$0.1
$0.1
$0.3
2030
$0.1
$0.1
$0.2
$0.3
2031
$0.1
$0.1
$0.2
$0.4
2032
$0.1
$0.2
$0.2
$0.4
2033
$0.1
$0.2
$0.2
$0.5
2034
$0.1
$0.2
$0.3
$0.6
2035
$0.1
$0.2
$0.3
$0.6
2036
$0.1
$0.3
$0.3
$0.7
2037
$0.1
$0.3
$0.3
$0.7
2038
$0.1
$0.3
$0.4
$0.8
2039
$0.2
$0.3
$0.4
$0.8
2040
$0.2
$0.3
$0.4
$0.9
2041
$0.2
$0.3
$0.4
$0.9
2042
$0.2
$0.4
$0.4
$0.9
2043
$0.2
$0.4
$0.5
$1.0
2044
$0.2
$0.4
$0.5
$1.0
2045
$0.2
$0.4
$0.5
$1.0
2046
$0.2
$0.4
$0.5
$1.0
2047
$0.2
$0.4
$0.5
$1.0
2048
$0.2
$0.4
$0.5
$1.0
2049
$0.2
$0.4
$0.5
$1.0
2050
$0.2
$0.4
$0.5
$1.0
PV
$1.3
$3.6
$5.0
$9.7
Annualized
$0.1
$0.2
$0.2
$0.5
Climate benefits are based on changes (reductions) in CH4 emissions and are calculated using four different
estimates of the social cost of methane (SC-CH4) (model average at 2.5 percent, 3 percent, and 5 percent discount
rates; 95th percentile at 3 percent discount rate). We emphasize the importance and value of considering the
benefits calculated using all four SC-CH4 estimates. As discussed in the Technical Support Document: Social
Cost of Carbon, Methane, and Nitrous Oxide Interim Estimates under Executive Order 13990 (IWG 2021), a
consideration of climate benefits calculated using discount rates below 3 percent, including 2 percent and lower,
are also warranted when discounting intergenerational impacts.	
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Table 3-13: Estimated Global Climate Benefits from Changes in N2O Emissions 2023 - 2050 (Billions of
2018$)
Discount Rate and Statistic
Calendar Year	5% Average	3% Average	2.5% Average	3% 95th percentile
2023
$0.0
$0.0
$0.0
$0.0
2024
$0.0
$0.0
$0.0
$0.0
2025
$0.0
$0.0
$0.0
$0.0
2026
$0.0
$0.0
$0.0
$0.0
2027
$0.0
$0.0
$0.0
$0.0
2028
$0.0
$0.0
$0.0
$0.1
2029
$0.0
$0.0
$0.0
$0.1
2030
$0.0
$0.0
$0.1
$0.1
2031
$0.0
$0.0
$0.1
$0.1
2032
$0.0
$0.1
$0.1
$0.1
2033
$0.0
$0.1
$0.1
$0.1
2034
$0.0
$0.1
$0.1
$0.2
2035
$0.0
$0.1
$0.1
$0.2
2036
$0.0
$0.1
$0.1
$0.2
2037
$0.0
$0.1
$0.1
$0.2
2038
$0.0
$0.1
$0.1
$0.2
2039
$0.0
$0.1
$0.1
$0.2
2040
$0.0
$0.1
$0.1
$0.2
2041
$0.0
$0.1
$0.1
$0.3
2042
$0.0
$0.1
$0.1
$0.3
2043
$0.0
$0.1
$0.1
$0.3
2044
$0.0
$0.1
$0.2
$0.3
2045
$0.0
$0.1
$0.2
$0.3
2046
$0.0
$0.1
$0.2
$0.3
2047
$0.1
$0.1
$0.2
$0.3
2048
$0.1
$0.1
$0.2
$0.3
2049
$0.1
$0.1
$0.2
$0.3
2050
$0.1
$0.1
$0.2
$0.3
PV
$0.3
$1.1
$1.6
$2.8
Annualized
$0.0
$0.1
$0.1
$0.1
Climate benefits are based on changes (reductions) in N20 emissions and are calculated using four different
estimates of the social cost of nitrous oxide (SC-N20) (model average at 2.5 percent, 3 percent, and 5 percent
discount rates; 95th percentile at 3 percent discount rate). We emphasize the importance and value of considering
the benefits calculated using all four SC-N20 estimates. As discussed in the Technical Support Document: Social
Cost of Carbon, Methane, and Nitrous Oxide Interim Estimates under Executive Order 13990 (IWG 2021), a
consideration of climate benefits calculated using discount rates below 3 percent, including 2 percent and lower,
are also warranted when discounting intergenerational impacts.	
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Table 3-14: Estimated Global Climate Benefits from Changes in GHG Emissions 2023 - 2050 (Billions of
2018$)
Discount Rate and Statistic
Calendar Year	5% Average	3% Average	2.5% Average	3% 95th percentile
2023
$0.1
$0.2
$0.3
$0.6
2024
$0.1
$0.4
$0.6
$1.0
2025
$0.2
$0.6
$0.9
$2.0
2026
$0.3
$1.0
$1.0
$3.0
2027
$0.5
$1.0
$2.0
$4.0
2028
$0.6
$2.0
$3.0
$6.0
2029
$0.8
$3.0
$4.0
$8.0
2030
$1.0
$3.0
$5.0
$10.0
2031
$1.0
$4.0
$5.0
$11.0
2032
$1.0
$4.0
$6.0
$13.0
2033
$2.0
$5.0
$7.0
$15.0
2034
$2.0
$5.0
$8.0
$17.0
2035
$2.0
$6.0
$8.0
$18.0
2036
$2.0
$6.0
$9.0
$20.0
2037
$2.0
$7.0
$10.0
$21.0
2038
$3.0
$7.0
$10.0
$22.0
2039
$3.0
$8.0
$11.0
$24.0
2040
$3.0
$8.0
$11.0
$25.0
2041
$3.0
$8.0
$12.0
$26.0
2042
$3.0
$9.0
$12.0
$26.0
2043
$3.0
$9.0
$12.0
$27.0
2044
$3.0
$9.0
$13.0
$28.0
2045
$3.0
$9.0
$13.0
$28.0
2046
$4.0
$9.0
$13.0
$29.0
2047
$4.0
$10.0
$13.0
$30.0
2048
$4.0
$10.0
$14.0
$30.0
2049
$4.0
$10.0
$14.0
$30.0
2050
$4.0
$10.0
$14.0
$31.0
PV
$22.0
$91.0
$140.0
$280.0
Annualized
$1.4
$4.7
$6.7
$14.0
Climate benefits are based on changes (reductions) in CO2, CH4, and N20 emissions and are calculated using four
different estimates of the social cost of carbon (SC-CO2), the social cost of methane (SC-CH4), and the social cost
of nitrous oxide (SC-N20) (model average at 2.5 percent, 3 percent, and 5 percent discount rates; 95th percentile
at 3 percent discount rate). We emphasize the importance and value of considering the benefits calculated using
all four SC-CO2, SC-CH4, and SC-N20 estimates. As discussed in the Technical Support Document: Social Cost
of Carbon, Methane, and Nitrous Oxide Interim Estimates under Executive Order 13990 (IWG 2021), a
consideration of climate benefits calculated using discount rates below 3 percent, including 2 percent and lower,
are also warranted when discounting intergenerational impacts.	
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Table 3-15 Estimated Global Climate Benefits from Changes in GHG Emissions 2023 - 2050 for Alternative 1
(Billions of 2018$)
Discount Rate and Statistic
Calendar Year	5% Average	3% Average	2.5% Average	3% 95th percentile
2023
$0.1
$0.2
$0.3
$0.6
2024
$0.1
$0.3
$0.5
$1.0
2025
$0.2
$0.6
$0.8
$2.0
2026
$0.3
$0.8
$1.0
$2.0
2027
$0.4
$1.0
$2.0
$4.0
2028
$0.5
$2.0
$2.0
$5.0
2029
$0.6
$2.0
$3.0
$6.0
2030
$0.8
$2.0
$4.0
$7.0
2031
$0.9
$3.0
$4.0
$9.0
2032
$1.0
$3.0
$5.0
$10.0
2033
$1.0
$4.0
$5.0
$11.0
2034
$1.0
$4.0
$6.0
$13.0
2035
$2.0
$5.0
$6.0
$14.0
2036
$2.0
$5.0
$7.0
$15.0
2037
$2.0
$5.0
$7.0
$16.0
2038
$2.0
$6.0
$8.0
$17.0
2039
$2.0
$6.0
$8.0
$18.0
2040
$2.0
$6.0
$9.0
$19.0
2041
$2.0
$6.0
$9.0
$20.0
2042
$2.0
$7.0
$9.0
$20.0
2043
$2.0
$7.0
$9.0
$21.0
2044
$3.0
$7.0
$10.0
$21.0
2045
$3.0
$7.0
$10.0
$22.0
2046
$3.0
$7.0
$10.0
$22.0
2047
$3.0
$7.0
$10.0
$22.0
2048
$3.0
$7.0
$10.0
$23.0
2049
$3.0
$8.0
$10.0
$23.0
2050
$3.0
$8.0
$10.0
$23.0
PV
$17.0
$70.0
$110.0
$210.0
Annualized
$1.1
$3.6
$5.1
$11.0
Climate benefits are based on changes (reductions) in CO2, CH4, and N20 emissions and are calculated using four
different estimates of the social cost of carbon (SC-CO2), the social cost of methane (SC-CH4), and the social cost
of nitrous oxide (SC-N20) (model average at 2.5 percent, 3 percent, and 5 percent discount rates; 95th percentile
at 3 percent discount rate). We emphasize the importance and value of considering the benefits calculated using
all four SC-CO2, SC-CH4, and SC-N20 estimates. As discussed in the Technical Support Document: Social Cost
of Carbon, Methane, and Nitrous Oxide Interim Estimates under Executive Order 13990 (IWG 2021), a
consideration of climate benefits calculated using discount rates below 3 percent, including 2 percent and lower,
are also warranted when discounting intergenerational impacts.	
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Table 3-16: Estimated Global Climate Benefits from Changes in GHG Emissions 2023 - 2050 for Alternative
2 (Billions of 2018$)
Discount Rate and Statistic
Calendar Year	5% Average	3% Average	2.5% Average	3% 95th percentile
2023
$0.2
$0.6
$0.9
$2.0
2024
$0.3
$0.9
$1.0
$3.0
2025
$0.4
$1.0
$2.0
$3.0
2026
$0.5
$2.0
$2.0
$5.0
2027
$0.6
$2.0
$3.0
$6.0
2028
$0.8
$3.0
$4.0
$8.0
2029
$1.0
$3.0
$5.0
$10.0
2030
$1.0
$4.0
$5.0
$11.0
2031
$1.0
$4.0
$6.0
$13.0
2032
$2.0
$5.0
$7.0
$15.0
2033
$2.0
$6.0
$8.0
$17.0
2034
$2.0
$6.0
$9.0
$19.0
2035
$2.0
$7.0
$10.0
$20.0
2036
$2.0
$7.0
$10.0
$22.0
2037
$3.0
$8.0
$11.0
$23.0
2038
$3.0
$8.0
$11.0
$25.0
2039
$3.0
$8.0
$12.0
$26.0
2040
$3.0
$9.0
$12.0
$27.0
2041
$3.0
$9.0
$13.0
$28.0
2042
$3.0
$9.0
$13.0
$29.0
2043
$4.0
$10.0
$14.0
$30.0
2044
$4.0
$10.0
$14.0
$30.0
2045
$4.0
$10.0
$14.0
$31.0
2046
$4.0
$10.0
$14.0
$32.0
2047
$4.0
$11.0
$15.0
$33.0
2048
$4.0
$11.0
$15.0
$33.0
2049
$4.0
$11.0
$15.0
$34.0
2050
$4.0
$11.0
$16.0
$35.0
PV
$25.0
$100.0
$160.0
$320.0
Annualized
$1.6
$5.3
$7.7
$16.0
Climate benefits are based on changes (reductions) in CO2, CH4, and N20 emissions and are calculated using four
different estimates of the social cost of carbon (SC-CO2), the social cost of methane (SC-CH4), and the social cost
of nitrous oxide (SC-N20) (model average at 2.5 percent, 3 percent, and 5 percent discount rates; 95th percentile
at 3 percent discount rate). We emphasize the importance and value of considering the benefits calculated using
all four SC-CO2, SC-CH4, and SC-N20 estimates. As discussed in the Technical Support Document: Social Cost
of Carbon, Methane, and Nitrous Oxide Interim Estimates under Executive Order 13990 (IWG 2021), a
consideration of climate benefits calculated using discount rates below 3 percent, including 2 percent and lower,
are also warranted when discounting intergenerational impacts.	
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3.4 Drive Surplus, Congestion and Noise
As discussed in Chapter 3.1, the assumed rebound effect might occur when an increase in
vehicle fuel efficiency encourages people to drive more as a result of the lower cost per mile of
driving. Along with the safety considerations associated with increased vehicle miles traveled
(described in Chapter 5.3), additional driving can lead to other costs and benefits that can be
monetized.
The increase in travel associated with the rebound effect produces additional benefits to
vehicle drivers, which reflect the value of the added (or more desirable) social and economic
opportunities that become accessible with additional travel. Consistent with assumptions used in
the SAFE FRM, this analysis estimates the economic benefits from increased rebound-effect
driving as the owner/operator surplus from the additional accessibility it provides.
The equation for the calculation of the Drive Value:
Drive Value = (1/2) (VMTrebound) [(S/ot/Z^noAction -	Action]
The economic value of the increased owner/operator surplus provided by added driving is
estaimted as one half of the product of the decline in vehicle operating costs per vehicle-mile and
the resulting increase in the annual number of miles driven. Because it depends on the extent of
improvement in fuel consumption, the value of benefits from increased vehicle use changes by
model year and varies among alternative standards.
In contrast to the benefits of additional driving are the costs associated with that driving. If net
operating costs of the vehicle decline, then we expect a positive rebound effect. Increased vehicle
use associated with a positive rebound effect also contributes to increased traffic congestion and
highway noise. Depending on how the additional travel is distributed throughout the day and
where it takes place, additional vehicle use can contribute to traffic congestion and delays by
increasing traffic volumes on facilities that are already heavily traveled during peak periods.
These added delays impose higher costs on other road users in the form of increased travel time
and operating expenses. Because drivers do not take these external costs into account in deciding
when and where to travel, they must be accounted for separately as a cost of the added driving
associated with the rebound effect.
EPA relies on estimates of congestion and noise costs developed by the Federal Highway
Administration to estimate the increased external costs caused by added driving due to the
rebound effect.117 EPA employed estimates from this source previously in the analysis
accompanying the light-duty 2010 and 2012 vehicle rulemakings and the 2016 Draft TAR and
Proposed Determination. We continue to find them appropriate for this analysis after reviewing
the procedures used by FHWA to develop them and considering other available estimates of
these values.
FHWA's congestion cost estimates focus on freeways because non-freeway effects are less
serious due to lower traffic volumes and opportunities to re-route around the congestion. The
agencies, however, applied the congestion cost to the overall VMT. The results of this analysis
potentially overestimate the congestions costs associated with increased vehicle use, and thus
lead to a conservative estimate of net benefits.
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EPA has used FHWA's "Middle" estimates for marginal congestion and noise costs caused by
increased travel from vehicles. This approach is consistent with the methodology used in our
prior analyses. The values used are shown in Table 3-17.
These congestion costs differ from those used in the SAFE FRM and, as stated, are consistent
with those used in the 2016 Draft TAR and the Proposed Determination. For this proposal, EPA
has chosen not to adopt the approach from the SAFE FRM where scaling factors were used to
adjust the underlying FHWA congestion cost estimates. In particular, EPA concluded that
scaling the marginal per-mile congestion costs by the change in VMT per lane-mile on US
highways from 1997 to 2017 does not account for changes in average speeds and improved road
design, and may have the potential to over-estimate costs. We are using the FHWA congestion
estimates without scaling, consistent with the SAFE NPRM and prior EPA rulemakings, and
adjusting to represent 2018 dollars.
Table 3-17: Costs Associated with Congestion and Noise (2018 dollars per vehicle mile)

Passenger cars
Van/SUVs
Pickups
Congestion
0.0634
0.0634
0.0566
Noise
0.0009
0.0009
0.0009
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36	See pp. 11-351-11-352. 2021. Assessment of Technologies for Improving Light-Duty Vehicle Fuel
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38	U.S. Energy Information Administration (EIA), U.S. Department of Energy (DOE), Annual Energy Outlook,
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39	Greene, D. 2010. Measuring energy security: Can the United States achieve oil independence? Energy Policy 38,
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40	Ibid.
41	Ibid.
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Overview. April.
44	Ibid.
10-45
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45	U.S. Energy Information Administration. 2021. Annual Energy Outlook 2021. Reference Case. Table A11.
Petroleum and Other Liquids Supply and Disposition.
46	See EIA https://www.eia.gov/energvexplained/oil~and~petroleiHn~products/imports~and-exports.php
47	Bohi, D. and Montgomery, D. 1982. Social Cost of Imported and U.S. Import Policy, Annual Review of Energy,
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W. 1988. "Is an Oil Import Tariff Justified? An American Debate: The Numbers Say 'Yes'." The Energy Journal 9:
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51	Hogan, W. 1981. "Import Management and Oil Emergencies", Chapter 9 in Deese, 5 David and Joseph Nye, eds.
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6851, Oak Ridge National Laboratory, November.
53	Parry, I. and Darmstadter, J. 2004. "The Costs of U.S. Oil Dependency," Resources for the Future, November 17,
2004. Also published as NCEP Technical Appendix Chapter 1: Enhancing Oil Security, the National Commission
on Energy Policy 2004 Ending the Energy Stalemate - A Bipartisan Strategy to Meet America's Energy Challenges.
54	National Research Council. 2009. Hidden Costs of Energy: Unpriced Consequences of Energy Production and
Use. National Academy of Science, Washington, DC.
55	Nordhaus, W. 2007. "Who's Afraid of a Big Bad Oil Shock?," Brookings Papers on Economic Activity.
Economic Studies Program, The Brookings Institution, vol. 38(2), pp. 219-240.
56	Rasmussen, T. and Roitman, A. 2011. Oil Shocks in a Global Perspective: Are We Really That Bad. IMF
Working Paper Series.
57	Hamilton, J. 2012. Oil Prices, Exhaustible Resources, and Economic Growth. In Handbook of Energy and Climate
Change. Retrieved from http://econweb.ucsd.edu/~jhamilto/handbook_climate.pdf
58Kim, D. 2012. What is an oil shock? Panel data evidence. Empirical Economics, Volume 43, pp. 121-143.
59	Engemann, K., Kliesen. K. and Owyang, M. 2011. Do Oil Shocks Drive Business Cycles, Some U.S. and
International Evidence. Federal Reserve Bank of St. Louis, Working Paper Series. No. 2010-007D.
60	Ramey, V. and Vine, D. 2010. "Oil, Automobiles, and the U.S. Economy: How Much have Things Really
Changed?", National Bureau of Economic Research Working Papers, WP 16067 (June). Retrieved from
http://www.nber.org/papers/wl6067.pdf
61	Baumeister C., Peersman, G. and Van Robays, I. 2010. "The Economic Consequences of Oil Shocks: Differences
across Countries and Time." RBA Annual Conference Volume in: Renee Fry & Galium Jones & Christopher Kent
(ed.), Inflation in an Era of Relative Price Shocks, Reserve Bank of Australia.
62	Kilian, L. and Vigfusson, R. 2014. "The role of oil price shocks in causing U.S. recessions." CFS Working Paper
Series 460, Center for Financial Studies.
63	Kilian, L. 2009. "Not All Oil Price Shocks Are Alike: Disentangling Demand and Supply Shocks in the Crude Oil
Market." American Economic Review, 99 (3): pp. 1053-69.
64	Cashin, P., Mohaddes, K., and Raissi, M. 2014. The Differential Effects of Oil Demand and Supply Shocks on the
Global Economy, Energy Economics, 12 (253), DPI: .1.0. .1.0.1.6/i.eneco.2014.03.014
65	Krupnick, A., Morgenstern, R., Balke, N., Brown, S., Herrara, M. and Mohan, S. 2017. "Oil Supply Shocks, U.S.
Gross Domestic Product, and the Oil Security Problem", Resources for the Future Report.
66	Balke, N. and Brown, S. 2018. "Oil Supply Shocks and the U.S. Economy: An Estimated DSGE Model". Energy
Policy, 116, pp. 357-372.
67	Kilian, L. 2009. Not All Oil Price Shocks are Alike: Disentangling Demand and Supply Shocks in the Crude Oil
Market, American Economic Review, 99:3, pp., 1053-1069.; and
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68	Kilian, L. and Murphy, D. 2013. "The Role of Inventories and Speculative Trading in the Global Market for
Crude Oil", Journal of Applied Economics, https://doi.org/10.1002/jae.2322.
69	Baumeister, C. and Hamilton, J. 2019. "Structural Interpretation of Vector Autoregressions with Incomplete
Identification: Revisiting the Role of Oil Supply and Demand Shocks", American Economic Review, 109(5),
pp.1873-1910.
70	Mohan, S. 2017. "Oil Price Shocks and the U.S. Economy: An Application of the National Energy Modeling
System". Resources for the Future Report Appendix.
71	U.S. Energy Information Administration. September 23, 2019. "Saudi Arabia crude oil production outage affects
global crude oil and gasoline prices". Today in Energy.
72	Ibid.
73	Ibid.
74	Uria-Martinez, R., Leiby, P., Oladosu, G., Bowman, D., Johnson, M. 2018. Using Meta-Analysis to Estimate
World Oil Demand Elasticity, ORNL Working Paper.
75	Oladosu, G., Leiby, P., Bowman, D., Uria-Martinez, R., Johnson, M. 2018. Impacts of oil price shocks on the U.S.
economy: a meta-analysis of oil price elasticity of GDP for net oil-importing economies, Energy Policy 115. pp.
523-544.
76	Union of Concerned Scientist, "What is Tight Oil?". 2015. "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."
77	Newell, R. and Prest, B. 2019. The Unconventional Oil Supply Boom: Aggregate Price Response from Microdata,
The Energy Journal, Volume 40, Issue Number 3.
78	Bjemland, H., Nordvik, F. and Rohrer, M. 2021. "Supply flexibility in the shale patch: Evidence from North
Dakota." Journal of Applied Economics, February, https://doi.org/10.1002/iae.2808.
79	Crane, K., Goldthau, A., Toman, M., Light, T., Johnson, S., Nader, A., Rabasa, A. and Dogo, H. 2009. Imported
oil and US national security. RAND, 2009. http://www.stormingmedia.us/62/6279/A627994.pdf.
80	Koplow, D. and Martin, A. 1998. Fueling Global Warming: Federal Subsidies to Oil in the United States.
Greenpeace, Washington, DC.
81	Moore, J., Behrens, C. and Blodgett, J. 1997. "Oil Imports: An Overview and Update of Economic and Security
Effects." CRS Environment and Natural Resources Policy Division report 98, no. 1: pp. 1-14.
82	Stern, R. 2010. "United States cost of military force projection in the Persian Gulf, 1976-2007." Energy Policy
38, no. 6. June: 2816-2825. doi:10.1016/j.enpol.2010.01.013.
http ://linkinghub. elsevier. com/retrieve/pii/S0301421510000194.
83	Delucchi, M. and Murphy, J. 2008. "US military expenditures to protect the use of Persian Gulf oil for motor
vehicles." Energy Policy 36, no. 6. June: 2253-2264. doi:10.1016/j.enpol.2008.03.006,
84	Securing America's Energy Future. 2018. Issue Brief. The Military Cost of Defending the Global Oil Supply.
85	Ibid.
86	Crane, K., Goldthau, A., Toman, M., Light, T., Johnson, S., Nader, A., Rabasa, A. and Dogo, H. 2009. Imported
oil and US national security. 2009. RAND. http://www.stormingmedia.us/62/6279/A627994.pdf.
87	U.S. Energy Information Administration. 2021. Annual Energy Outlook 2021. Reference Case. Table All.
Petroleum and Other Liquids Supply and Disposition.
88	U.S. Energy Information Administration. 2021. Annual Energy Outlook 2021. Reference Case. Table All.
Petroleum and Other Liquids Supply and Disposition.
89	Leiby, Paul N. 2008. Estimating the Energy Security Benefits of Reduced U.S. Oil Imports, Final Report,
ORNL/TM-2007/028, Oak Ridge National Laboratory, Rev. March 14.
90	Leiby, P., Jones, D., Curlee, R., and Lee, R. 1997. Oil Imports: An Assessment of Benefits and Costs, ORNL-
6851, Oak Ridge National Laboratory, November.
91	Joint Technical Support Document, Final Rulemaking to Establish Light-Duty Vehicle Greenhouse Gas Emission
Standards and Corporate Average Fuel Economy Standards, EPA-420-R-10-901, April 2010. Final Rulemaking for
2017-2025 Light-Duty Vehicle Greenhouse Gas Emission Standards, EPA-420-R-12, August 2012.
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92	Final Rulemaking to Establish Greenhouse Gas Emissions Standards and Fuel Efficiency Standards for Medium-
and Heavy-Duty Engines and Vehicles Regulatory Impact Analysis, U.S. EPA/NHTSA, EPA-420-R-11-901,
August 2011.
93	Greenhouse Gas Emissions and Fuel Efficiency Standards for Medium- and Heavy-Duty Engines and Vehicles -
Phase 2 Regulatory Impact Analysis, U.S. EPA/NHTSA, EPA-420-R-16-900 August 2016.
94	Energy Security Impacts. Effect of Oil Use on the Long-Run Oil Price. Section 10. 5.2.1. pp. 10-25. 2016. Draft
Technical Assessment Report: Midterm Evaluation of Light-Duty Vehicle Greenhouse Gas Emission Standards and
Corporate Average Fuel Economy Standards for Model Years 2022-2025. EPA-420-D-16-900.
95	Regulation of Fuels and Fuel Additives: Changes to Renewable Fuel Standard Program; Proposed Rule, 40 C.F.R.
§80 (2010).
96IEA, Data and Statistics, https://www.iea.org/data~and~
statistics?countrv=WORLD&fuel=Oil&indicator=OiLProductsConsBvSector: Accessed March 2021.
97	Regulation of Fuels and Fuel Additives: Changes to Renewable Fuel Standard Program; Proposed Rule, 40 C.F.R.
§80. 2010.
98	Interagency Working Group on Social Cost of Greenhouse Gases (IWG). 2021. Technical Support Document:
Social Cost of Carbon, Methane, and Nitrous Oxide Interim Estimates under Executive Order 13990. February.
United States Government. Available at: 
-------
Waterfield (eds.)].
110	Intergovernmental Panel on Climate Change (IPCC). 2019a. Climate Change and Land: an IPCC special
report on climate change, desertification, land degradation, sustainable land management, food
security, and greenhouse gas fluxes in terrestrial ecosystems [P.R. Shukla, J. Skea, E. Calvo Buendia, V.
Masson-Delmotte, H.-O. Portner, D. C. Roberts, P. Zhai, R. Slade, S. Connors, R. vanDiemen, M. Ferrat, E.
Haughey, S. Luz, S. Neogi, M. Pathak, J. Petzold, J. Portugal Pereira, P. Vyas, E. Huntley, K. Kissick, M.
Belkacemi, J. Malley, (eds.)].
111	Intergovernmental Panel on Climate Change (IPCC). 2019b. IPCC Special Report on the Ocean and
Cryosphere in a Changing Climate [H.-O. Portner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor,
E. Poloczanska, K. Mintenbeck, A. Alegria, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)].
112	U.S. Global Change Research Program (USGCRP). 2016. The Impacts of Climate Change on Human Health
in the United States: A Scientific Assessment. Crimmins, A., J. Balbus, J.L. Gamble, C.B. Beard, J.E. Bell, D.
Dodgen, R.J. Eisen, N. Fann, M.D. Hawkins, S.C. Herring, L. Jantarasami, D.M. Mills, S. Saha, M.C. Sarofim,
J. Trtanj, andL. Ziska, Eds. U.S. Global Change Research Program, Washington, DC, 312 pp.
https://dx.dio.org/10.7930/J0R49NQX.
113	U.S. Global Change Research Program (USGCRP). 2018. Impacts, Risks, and Adaptation in the United
States: Fourth National Climate Assessment, Volume II [Reidmiller, D.R., C.W. Avery, D.R. Easterling, K.E.
Kunkel, K.L.M. Lewis, T.K. Maycock, and B.C. Stewart (eds.)]. U.S. Global Change Research Program,
Washington, DC, USA, 1515 pp. doi: 10.7930/NCA4.2018.
114	National Academies of Sciences, Engineering, and Medicine (National Academies). 2016b. Attribution of
Extreme Weather Events in the Context of Climate Change. Washington, DC: The National Academies
Press, https://dio.org/10.17226/21852.
115	National Academies of Sciences, Engineering, and Medicine (National Academies). 2019. Climate Change
and Ecosystems. Washington, DC: The National Academies Press, https://doi.org/10.17226/25504.
116	National Academies of Sciences, Engineering, and Medicine (National Academies). 2017. Valuing Climate
Damages: Updating Estimation of the Social Cost of Carbon Dioxide. Washington, D.C.: National
Academies Press.
117	Federal Highway Administration, 1997 Federal Highway Cost Allocation Study,
http://www.fhwa.dot.gov/policy/hcas/final/index.htm, Tables V-22, V-23, and V-24 (last accessed Sept. 9, 2011).
3-49
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Chapter 4: Modeling GHG Compliance
4.1 Compliance Modeling, Analytical Updates, and Analytical Revisions
The modeling runs presented within this draft RIA are not meant to be the sole technical
justification underlying the proposed revisions to the 2023-2026 GHG standards. That
justification is based upon nearly a decade of analyses presented by EPA in the 2010 and 2012
final rules, the 2016 Draft TAR, during both the Proposed and Final Determinations.1'2'3'4'5
Please see Chapter 1.2 for further discussion of these EPA analyses. The analysis represented
within this chapter of the draft RIA is intended primarily for direct comparison to the SAFE final
rulemaking (FRM). EPA's extensive record has made clear that more stringent GHG standards
are both feasible and at reasonable costs and result in significant GHG emission reductions and
public health and welfare benefits.. The analysis presented here is meant to show that, once
again, when estimating standards of similar stringency to those set forth in the 2012 rule, the
results are similar to those presented within previous EPA analyses. Those previous analytical
results are summarized and discussed in Chapter 1 of this draft RIA.
To estimate compliance costs and the associated technology pathways that manufacturers
might choose to comply with proposed GHG standards, EPA has traditionally used its
Optimization Model for reducing Emissions of Greenhouse Gases (OMEGA). However, in
considering modeling tools to support the analysis for today's proposed GHG standards, EPA
has chosen to use the CAFE Compliance and Effects Modeling System (CCEMS) for modeling
light-duty GHG compliance and costs for the revised MY2023-2026 GHG standards uses and to
use the same version of that model used in support of the SAFE FRM. EPA has made this choice
for a number of reasons:
•	The proposed GHG emissions standards are meant to revise the standards put into
place in the SAFE FRM. EPA's OMEGA model was not used to support the analyses
conducted for the SAFE FRM. Instead, CCEMS was used. EPA believes that using
that same compliance modeling tools, with changes to a number of critical inputs as
discussed in Table 4-1, provides strong support for the changes proposed here.
•	Direct comparisons between the analysis presented within this draft RIA and the
analysis presented in support of the SAFE FRM are inevitable, particularly since the
SAFE FRM was published only a year ago. Those comparisons are made easier if the
same tool is used.
•	By using the same tool along with a select set of inputs considered more appropriate
by EPA than the inputs used for last year's SAFE analyses, we can illustrate the
importance of carefully selecting model inputs, carefully considering and responding
to stakeholder comments regarding the inputs, and providing a complete rationale and
documentation of the inputs selected.
To be clear, modeling inputs are critically important to EPA analyses. As long as the
underlying structure of a modeling tool is sound, which is the case with both CCEMS and with
the OMEGA model, then it is not so much the specific tool used by EPA that is of paramount
importance but the inputs for the tools that are of the most importance within our GHG
compliance modeling efforts. This was made clear within the preamble to the SAFE FRM which
stated, ".. .inputs do not define models; models use inputs. Therefore, disagreements about inputs
do not logically extend to disagreements about models. Similarly, while models determine
4-1
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resulting outputs, they do so based on inputs."6 This statement was a response to public
comments received on the SAFE NPRM, some of which argued that EPA should use its own
modeling tools to support EPA actions. During development of the SAFE FRM, EPA staff had
significant input on the CCEMS and considered the FRM version of the model, given changes
made in response to public comments and EPA staff, to be an suitable modeling tool for that
analysis. Similarly, we believe the SAFE FRM model and inputs, together with the key changes
we've made since the SAFE FRM, are appropriate for the particular analysis at hand in assessing
standards through 2026.
EPA is also currently developing an updated version of OMEGA. In the development of this
updated model, OMEGA2, we are placing emphasis on the treatment of BEVs, the interaction
between consumer and producer decisions, and the capability to consider a wider range of GHG
program options. We intend to submit the model for peer review later this year.
As previously noted, we are using the version of the CCEMS docketed by NHTSA in support
of the SAFE FRM. CCEMS itself has been extensively documented by NHTSA in support of
the SAFE FRM and the documentation used there is applicable to the analysis presented here.7
Importantly, the following changes have been made to the inputs for this analysis (Table 4-1).
Consistent with the SAFE FRM, EPA is using a MY2017 base year fleet and allowing the
model to determine the future fleet based on the consumer choice model and scrappage
models.7'a As such, we have not changed the data contained within the market file (the base year
fleet) from what was used in the SAFE FRM other than as described in Table 4-1 and to split the
market file into separate framework-OEM and non-framework-OEM fleets for some model runs
to account for the impacts of the California Framework Agreement.8 Note that the scrappage
model received many negative comments following the SAFE NPRM, but the FRM version of
the model incorporated changes such that it no longer generates the inexplicable sales and VMT
results of the NPRM version.9
As mentioned, for some model runs, including the No Action case, we have split the fleet in
two, one fleet consisting of California Framework OEMs (FW-OEMs) and the other consisting
of the non-Framework OEMs (NonFW-OEMs). This was necessary since, for years that we are
modeling previous to the proposed MY 2023 start of this program, we modeled the FW-OEMs
meeting the more stringent Framework emission targets (as set in the scenarios file) while having
access to the additional (15 g/mi) off-cycle credits (as set in the market and scenarios file) and
the additional advanced technology incentive multipliers of the Framework. We modeled the
NonFW-OEMs meeting less stringent (SAFE) standards while having access to just 10 g/mi off-
cycle credits and no advanced technology multipliers. For such model runs, a post-processing
step was necessary to properly sales-weight the two sets of model outputs into a single fleet of
results. This post-processing tool is in the docket for this rule.10
" See Chapter 8.1 for discussion of modeling of vehicle sales, as well as references to reviews of the literature that
EPA has conducted.
4-2
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Table 4-1: Changes made to CCEMS Inputs for all Model Runs
Input file
Changes
Parameters
file
Global social cost of GHG $/ton values used in place of domestic values (see Chapter 3.3).
Inclusion of global social cost of methane (CH4) and nitrous oxide (N20) $/ton values (see
Chapter 3.3).
Updated PM2 5 cost factors (benefit per ton values, see Chapter 7)
Rebound effect of -0.10 rather than -0.20 (see Chapter 3.1).
AEO2021 fuel prices (expressed in 2018 dollars) rather than AEO2019.
Update energy security cost per gallon factors (see Chapter 3.2).
Congestion cost factors of 6.34/6.34/5.66 (car/van-SUV/truck) cents/mile rather than
15.4/15/4/13.75 (see Chapter 3.4).
Discounting values to calendar year 2021 rather than calendar year 2019.
The following fuel import and refining inputs have been changed based on AEO2021 (see
Chapter 3.2):
Share of fuel savings leading to lower fuel imports:
Gasoline 7%; E85 19%; Diesel 7% rather than 50%; 7.5%; 50%
Share of fuel savings leading to reduced domestic fuel refining:
Gasoline 93%; E85 25.1%; Diesel 93% rather than 50%; 7.5%; 50%
Share of reduced domestic refining from domestic crude:
Gasoline 9%; E85 2.4%; Diesel 9% rather than 10%; 1.5%; 10%
Share of reduced domestic refining from imported crude:
Gasoline 91%; E85 24.6%; Diesel 91% rather than 90%; 13.5%; 90%
Technology
file
High Compression Ratio level 2 (HCR2, sometimes referred to as Atkinson level 2) technology
allowance set to TRUE for all engines beginning in 2018 (see Chapter 2).
Market file
On the Engines sheet, we allow HCR1 and HCR2 technology on all 6-cylinder and smaller
engines rather than allowing it on no engines (see Chapter 2).
Change the off-cycle credit values on the Credits and Adjustments sheet to 15 grams/mile for
2020 through 2026 (for the CARB-OEM framework) or to 15 gram/mile for 2023 through 2026
(for the proposed option) depending on the model run.
The scenario input file includes BEV and PHEV multipliers available to manufacturers in
facilitating compliance. However, the CCEMS version of the model we used does not allow for
implementing the backstop, as discussed in the in Section II.B.l of the Preamble to this proposal,
against over use of those multipliers. To mimic that backstop, we have used "effective
multipliers" in the scenario input file that serve to mimic the backstop. As such, the actual
multipliers are not used in the modeling of the proposal (or the CARB-OEM framework) and,
instead, effective multipliers have been used.
Importantly, our primary model runs consist of a "no action" case and an "action" case. The
results, or impact of our proposed standards, are measured relative to the no action case. Our no
action case consists of the FW-OEMs (roughly 29 percent of fleet sales) meeting the framework
while NonFW-OEMs (roughly 71 percent of fleet sales) meet the SAFE FRM standards. Our
action case consists of the whole fleet meeting our proposal for model years 2023 and later.
Throughout this preamble, our no action case refers to this FW-OEM/NonFW-OEM compliance
split.
EPA has chosen not to make other SAFE FRM model input changes largely because
additional changes would not result in significant differences in the analytical results supporting
the proposed standards. For example, the technology effectiveness estimates used to support the
SAFE FRM relied on dated engine efficiency maps for both the future and baseline technologies
represented. However, the effectiveness values (e.g., incremental from future technology back to
4-3
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the 2017 base year fleet) are of primary importance within analyses using CCEMS and, while the
maps were dated, the effectiveness values derived from them were, on increment, in rough
agreement with values derived from the more up-to-date baseline and future engine maps used
within OMEGA (see Chapter 1.2.1). In other words, the absolute engine technology brake
thermal efficiency for both the baseline and future technologies were artificially low, however
the incremental differences in brake thermal efficiency between the baseline and future
technologies are very similar to those used by EPA in previous analyses for the MTE.
Likewise, the battery costs used in the SAFE FRM were considered too high by EPA.
However, given that significant levels of vehicle electrification will not be necessary in order to
comply with the proposed standards (past analyses by EPA have estimated BEV penetrations of
less than 5 percent, in general), we did not consider updating vehicle electrification costs to be of
paramount importance for this proposal, although we may update battery and other vehicle
electrification costs for the final rule.
The decision to allow for more broad application of HCR1 and HCR2 technologies as a
compliance choice within the model was considered by EPA to be of significant importance to
update relative to the SAFE FRM. We made that choice because it is a very cost-effective ICE
technology that is currently in use and already in broad application with no consumer choice
concerns such as those that might be argued for BEV technology. In short, there are modeling
inputs that EPA has chosen not to change given the very large number of inputs required to run
any model as complex as the CCEMS. EPA's decision not to change those inputs should not be
taken as blanket agreement by EPA staff with those inputs. For further discussion of these
technologies, see Chapters 2.3.1 and 2.3.2.
Lastly, to calculate the full program costs, benefits and net benefits, EPA has developed and
made use of an aforementioned post-processing tool.11 For many benefit-cost metrics, the post-
processing tool follows the calculation approach employed within the CCEMS model. For
example, costs associated with application of technology, foregone consumer sales surplus,
congestion, noise, fatalities and non-fatal crashes are all handled within the CCEMS model and
are taken "as-is" in the post-processing tool and transferred through to the final cost-benefit
analysis. However, the calculation of emissions benefits is handled entirely within the post-
processing tool by applying EPA's preferred $/ton benefit values (for both criteria air pollutants
(CAP) and GHGs) and discounting those values exclusively at their internally consistent
discount rates. In other words, the social cost of GHG $/ton values are generated using discount
rates equal to 2.5 percent, 3 percent and 5 percent. Each of those streams of benefit values will
always be discounted, whenever discounting is employed (for net present and/or annualized
valuations) using the internally consistent discount rate. CCEMS uses this same approach.
However, CCEMS can calculate only a single GHG valuation in each run of the model. As such,
to monetize 4 GHG streams (2.5 percent, 3 percent, 5 percent, 3 percent-95th percentile) would
require 4 separate runs of the model despite the fact that the tons do not differ between runs.
Therefore, EPA has chosen to post-process the results such that all 4 streams could be monetized
without re-running the full CCEMS. The post-processing tool also allows for valuation of
upstream CAP benefits separately from tailpipe CAP benefits which the SAFE FRM version of
CCEMS does not allow.
4-4
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4.1.1 GHG Targets and Compliance Levels
4.1.1.1 Proposal
The proposed curve coefficients are presented in preamble Section II.A.2. Here we present the
fleet targets for each manufacturer. Figure 4-1 depicts the fleet targets of the SAFE FRM and
today's proposed standards. Also shown are the 2012 FRM targets and the California Framework
targets for comparison. As can be seen, the proposal standards move from the SAFE FRM to the
Framework 3.7 percent year-over-year targets between MYs 2022 and 2023. It then achieves
greater stringency than the Framework 3.7 percent and surpasses the 2012 FRM targets by
MY2026.
240
230
220
210
'v
rsj
o
u
190
180
170
160
2020 2021 2022 2023 2024 2025 2026 2027
Model Year
Figure 4-1: Comparison of Fleet Average Proposed Revised Standards (Red Line) to the SAFE FRM, the
California Framework Agreement, and the 2012 FRM.
These targets are dependent on each manufacturer's car/truck fleets and their sales weighted
footprints. As such, each manufacturer has a set of targets unique to them. Those targets are
shown by manufacturer for MYs 2023 through 2026 in Table 4-2 for cars, Table 4-3 for trucks,
and Table 4-4 for the combined fleet.

2012 FRM

••• SAFE FRM
v#,
V-...
\ 	„
	 Proposal
— — California Framework
V 		

s\ \

v\
	


N NS.
	
4-5
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Table 4-2 Proposed Car Targets (CO2 gram/mile)

2023
2024
2025
2026
BMW
166
158
150
143
Daimler
173
165
157
149
FCA
169
161
153
146
Ford
167
159
151
144
General Motors
166
158
151
143
Honda
163
155
147
140
Hyundai Kia-H
165
157
149
142
Hyundai Kia-K
164
156
149
142
JLR
174
166
158
150
Mazda
163
155
147
140
Mitsubishi
151
143
136
130
Nissan
164
156
148
141
Subaru
160
152
145
138
Tesla
191
182
173
165
Toyota
162
154
147
140
Volvo
172
164
156
148
VWA
160
152
145
138
TOTAL
165
157
149
142
Table 4-3 Proposed Truck Targets (CO2 gram/mile)

2023
2024
2025
2026
BMW
219
208
198
188
Daimler
225
214
203
193
FCA
233
222
211
200
Ford
246
234
222
211
General Motors
252
239
228
216
Honda
215
205
195
185
Hyundai Kia-H
214
203
193
183
Hyundai Kia-K
217
206
196
186
JLR
221
210
199
190
Mazda
206
196
186
177
Mitsubishi
194
184
175
166
Nissan
225
214
203
193
Subaru
197
187
178
169
Tesla




Toyota
227
216
205
195
Volvo
222
211
200
190
VWA
218
207
196
187
TOTAL
232
221
210
199
-------
Table 4-4 Proposed Sales Weighted Fleet Targets (CO2 gram/mile)

2023
2024
2025
2026
BMW
187
178
169
161
Daimler
195
186
177
168
FCA
221
210
200
190
Ford
215
205
195
185
General Motors
215
204
195
185
Honda
185
176
167
159
Hyundai Kia-H
168
160
152
145
Hyundai Kia-K
177
169
161
153
JLR
211
200
190
181
Mazda
176
167
159
151
Mitsubishi
168
160
152
145
Nissan
185
176
167
159
Subaru
187
178
169
161
Tesla
191
182
173
165
Toyota
194
185
176
167
Volvo
205
195
185
176
VWA
179
171
162
155
TOTAL
198
189
180
171
The actual achieved CCte-equivalent (CCtee) levels, which include credit programs and how
those factor into compliance, are shown in Table 4-5 for cars, Table 4-6 for trucks, and Table 4-7
for the combined fleets.
Table 4-5 Proposed Car Achieved (CChe gram/mile)

2023
2024
2025
2026
BMW
173
168
168
131
Daimler
184
169
166
168
FCA
183
178
178
171
Ford
168
160
159
151
General Motors
152
136
133
132
Honda
161
161
161
130
Hyundai Kia-H
162
147
146
145
Hyundai Kia-K
138
134
134
137
JLR
217
162
158
165
Mazda
156
156
156
146
Mitsubishi
136
136
129
129
Nissan
165
153
147
147
Subaru
193
193
193
174
Tesla
-20
-20
-20
-20
Toyota
161
143
135
133
Volvo
185
185
184
145
VWA
146
144
143
135
TOTAL
161
150
147
141
4-7
-------
Table 4-6 Proposed Truck Achieved (CChe gram/mile)

2023
2024
2025
2026
BMW
220
210
156
161
Daimler
206
206
151
126
FCA
218
217
217
207
Ford
245
234
234
216
General Motors
270
261
245
224
Honda
212
210
210
210
Hyundai Kia-H
222
129
129
140
Hyundai Kia-K
225
209
209
209
JLR
210
210
176
187
Mazda
177
177
177
176
Mitsubishi
194
194
185
185
Nissan
220
218
198
192
Subaru
187
187
187
168
Tesla




Toyota
239
231
224
204
Volvo
181
180
176
183
VWA
240
200
173
122
TOTAL
233
226
218
203
Table 4-7 Proposed Sales Weighted Fleet Achieved (CChe gram/mile)

2023
2024
2025
2026
BMW
192
184
163
143
Daimler
194
185
159
150
FCA
211
210
210
200
Ford
215
205
205
190
General Motors
220
208
197
185
Honda
183
181
182
164
Hyundai Kia-H
166
146
145
145
Hyundai Kia-K
160
153
153
156
JLR
212
200
172
182
Mazda
162
162
162
155
Mitsubishi
159
160
152
152
Nissan
184
175
164
163
Subaru
189
189
189
170
Tesla
-20
-20
-20
-20
Toyota
199
186
179
168
Volvo
182
182
179
170
VWA
178
163
153
131
TOTAL
197
188
183
172
Note that the values shown in Table 4-5 through Table 4-7 are modeled tailpipe certification
values considering use of AJC leakage credits and other off-cycle credits apart from AJC leakage
This explains the negative 20 grams/mile CChe shown for Tesla cars. That value reflects the 15
g/mi of off-cycle credit available under the proposal and 5 g/mi AJC efficiency credit under the
existing and proposed standards. To date, Tesla has not been a major user of the off-cycle credit
program given that they make nothing but BEVs. However, when running the model, we have
chosen to apply 15 g/mi off-cycle credit both on the credit side and the cost side for any year in
which that credit is available. We have done this rather than predicting which manufacturers
4-8
-------
might choose to use the full 15 g/mi credit and which manufacturers might choose to use fewer.
This decision is reflected in the cost/vehicle tables in the next section where every manufacturer
is modeled as earning 15 g/mi off-cycle credit and incurring costs associated with that 15 g/mi on
every vehicle.
4.1.1.2 Alternatives
Table 4-8, Table 4-9 and Table 4-10 show the car, truck and fleet targets, respectively, for
Alternatives 1 and 2. The actual achieved CChe levels, which include credit programs and how
those factor into compliance, are shown in Table 4-11, Table 4-12 and Table 4-13 for cars, trucks
and the combined fleet.
Table 4-8 Car Targets under Alternatives 1 and 2 (CO2 gram/mile)

Alternative 1
Alternative 2

2023
2024
2025
2026
2023
2024
2025
2026
BMW
169
162
155
149
163
156
149
142
Daimler
177
169
162
155
170
163
156
148
FCA
173
166
159
152
167
159
152
145
Ford
170
163
156
150
164
157
150
143
General Motors
170
163
156
149
164
157
149
142
Honda
166
159
153
146
160
153
146
139
Hyundai Kia-H
168
161
154
148
162
155
148
141
Hyundai Kia-K
168
161
154
148
162
155
148
141
JLR
178
170
163
157
172
164
157
149
Mazda
166
159
152
146
160
153
146
139
Mitsubishi
154
147
141
135
148
142
135
129
Nissan
167
160
154
147
161
154
147
140
Subaru
164
157
150
144
158
151
144
137
Tesla
195
187
179
172
189
180
172
164
Toyota
166
159
152
146
160
153
146
139
Volvo
175
168
161
154
169
162
154
147
VWA
163
156
150
144
158
151
144
137
TOTAL
168
161
154
148
162
155
148
141
4-9
-------
Table 4-9 Truck Targets under Alternatives 1 and 2 (CO2 gram/mile)

Alternative 1
Alternative 2

2023
2024
2025
2026
2023
2024
2025
2026
BMW
224
213
203
194
214
203
194
184
Daimler
229
219
209
199
219
209
199
189
FCA
238
227
216
206
228
217
206
197
Ford
251
240
228
218
240
229
218
208
General Motors
257
245
234
223
246
234
223
213
Honda
220
210
200
191
210
200
190
181
Hyundai Kia-H
218
208
198
189
208
198
189
180
Hyundai Kia-K
222
211
202
192
212
202
192
183
JLR
226
215
205
195
215
205
195
186
Mazda
210
200
191
182
201
191
182
173
Mitsubishi
198
189
180
171
189
179
171
163
Nissan
230
219
209
199
220
209
199
190
Subaru
201
192
183
174
192
183
174
166
Tesla








Toyota
232
221
211
201
222
211
201
191
Volvo
226
216
206
196
216
206
196
187
VWA
222
212
202
193
212
202
192
183
TOTAL
237
226
215
205
227
216
205
196
Table 4-10 Fleet Targets under Alternatives 1 and 2 (CO2 gram/mile)

Alternative 1
Alternative 2

2023
2024
2025
2026
2023
2024
2025
2026
BMW
191
182
174
167
183
175
167
159
Daimler
199
191
182
174
191
183
175
166
FCA
226
215
205
196
216
206
196
187
Ford
219
210
200
192
210
201
192
183
General Motors
220
210
201
191
211
201
192
183
Honda
189
180
173
165
181
173
165
157
Hyundai Kia-H
171
164
157
151
165
158
151
144
Hyundai Kia-K
182
174
166
159
175
167
159
152
JLR
215
205
196
187
206
196
187
178
Mazda
179
171
164
157
172
164
157
149
Mitsubishi
172
164
157
150
165
157
150
143
Nissan
188
180
173
165
181
173
165
157
Subaru
191
183
174
166
183
175
166
159
Tesla
195
187
179
172
189
180
172
164
Toyota
198
190
181
173
191
182
174
165
Volvo
209
200
191
182
200
191
182
174
VWA
183
175
168
161
176
168
160
153
TOTAL
203
194
185
177
195
186
177
169
4-10
-------
Table 4-11 Car Achieved under Alternatives 1 and 2 (CChe gram/mile)

Alternative 1
Alternative 2

2023
2024
2025
2026
2023
2024
2025
2026
BMW
179
173
173
141
154
148
147
115
Daimler
177
165
162
161
172
155
152
152
FCA
184
180
180
173
178
174
173
169
Ford
173
166
166
152
166
156
156
149
General Motors
153
140
135
129
142
130
124
118
Honda
163
162
162
143
161
161
161
125
Hyundai Kia-H
161
154
153
152
159
153
152
149
Hyundai Kia-K
142
139
139
139
142
138
138
137
JLR
196
164
162
162
200
142
140
139
Mazda
159
158
158
156
157
156
156
143
Mitsubishi
136
136
129
129
130
130
123
123
Nissan
171
160
152
151
162
150
144
144
Subaru
193
193
193
183
190
190
190
174
Tesla
-20
-20
-20
-20
-20
-20
-20
-20
Toyota
165
149
139
135
152
150
140
137
Volvo
185
185
185
145
185
185
184
145
VWA
152
150
149
138
142
139
138
127
TOTAL
163
155
151
144
156
149
146
137
Table 4-12 Truck Achieved under Alternatives 1 and 2 (CChe gram/mile)

Alternative 1
Alternative 2

2023
2024
2025
2026
2023
2024
2025
2026
BMW
222
211
166
166
222
211
172
172
Daimler
223
223
195
156
216
216
184
140
FCA
224
223
223
215
214
213
213
208
Ford
246
238
238
226
238
230
230
223
General Motors
276
268
255
237
267
259
246
230
Honda
214
212
212
212
212
210
210
210
Hyundai Kia-H
222
177
177
177
220
132
132
132
Hyundai Kia-K
227
211
211
211
225
209
209
209
JLR
213
213
190
189
208
208
185
185
Mazda
183
183
183
183
174
174
174
173
Mitsubishi
194
194
185
185
176
176
169
169
Nissan
221
218
209
200
218
215
199
197
Subaru
189
189
189
180
184
184
184
166
Tesla








Toyota
240
231
230
212
232
229
228
208
Volvo
205
205
201
201
190
190
186
186
VWA
241
218
202
145
239
217
201
144
TOTAL
236
230
225
213
229
224
218
207
4-11
-------
Table 4-13 Fleet Achieved under Alternatives 1 and 2 (CChe gram/mile)

Alternative 1
Alternative 2

2023
2024
2025
2026
2023
2024
2025
2026
BMW
196
188
170
151
181
173
157
138
Daimler
197
190
176
159
191
182
166
147
FCA
217
215
215
207
207
206
206
201
Ford
218
210
210
197
210
201
201
195
General Motors
223
213
204
191
214
204
195
183
Honda
184
183
183
172
183
182
182
161
Hyundai Kia-H
165
156
155
154
163
152
151
148
Hyundai Kia-K
164
157
158
158
163
157
157
156
JLR
209
202
184
183
207
194
175
175
Mazda
166
166
166
164
162
161
161
152
Mitsubishi
159
160
152
152
149
149
142
142
Nissan
188
180
171
168
181
173
163
162
Subaru
190
190
190
181
186
186
186
168
Tesla
-20
-20
-20
-20
-20
-20
-20
-20
Toyota
202
190
184
174
192
189
184
173
Volvo
198
198
196
182
188
188
186
172
VWA
182
173
167
141
174
166
160
133
TOTAL
200
193
189
179
193
187
183
173
4.1.2 Projected Compliance Costs per Vehicle
4.1.2.1 Proposal
EPA has performed an updated assessment of the per vehicle costs for manufacturers to meet
the proposed MY2023-2026 standards. Importantly, we applied off-cycle credits at the levels
entered in the market file and applied costs for those credits at the levels entered in the scenarios
file. In conducting model runs for the proposed option, since we are proposing 15 grams/mile
available credit in MYs 2023 through 2026, those credits are applied and they incur costs at the
rates entered in the scenarios file (on average, those off-cycle credits are valued at $78/gram/mile
during the four model years of implementation of the proposed option). The car costs per vehicle
are shown in Table 4-14, followed by truck in Table 4-15 and combined fleet costs in Table
4-16. As shown in Table 4-14, Tesla incurs nearly $400 per vehicle despite being a pure electric
vehicle maker (0 grams/mile) and despite there being no upstream emissions accounting under
the proposal. The costs shown for Tesla represent the costs of 15 grams/mile of off-cycle credit
that we estimate Tesla would incur to generate additional GHG credits which it could sell to
other manufacturers.
4-12
-------
Table 4-14 Car Costs/Vehicle Relative to the No Action Scenario (2018 dollars)

2023
2024
2025
2026
BMW
$64
$40
$42
$254
Daimler
$37
$414
$490
$487
FCA
$465
$525
$511
$823
Ford
$22
$234
$228
$458
General Motors
$662
$1,351
$1,354
$1,512
Honda
$39
$44
$43
$766
Hyundai Kia-H
$457
$845
$847
$878
Hyundai Kia-K
$395
$406
$396
$416
JLR
-$510
$1,075
$1,076
$1,006
Mazda
$510
$522
$517
$745
Mitsubishi
$870
$860
$993
$985
Nissan
$614
$825
$940
$912
Subaru
$403
$397
$392
$710
Tesla
$398
$393
$387
$382
Toyota
$470
$822
$958
$979
Volvo
$212
$210
$222
$211
VWA
$158
$168
$177
$185
TOTAL
$383
$643
$682
$846
Table 4-15 Truck Cost/Vehicle Relative to the No Action Scenario (2018 dollars)

2023
2024
2025
2026
BMW
$270
$264
$1,080
$1,037
Daimler
$1,641
$1,582
$2,964
$4,233
FCA
$1,074
$1,022
$974
$1,423
Ford
$34
$279
$267
$500
General Motors
$786
$977
$1,350
$2,100
Honda
$25
$64
$63
$62
Hyundai Kia-H
$398
$3,370
$3,170
$2,995
Hyundai Kia-K
$435
$482
$475
$468
JLR
$752
$740
$2,140
$2,007
Mazda
$787
$783
$777
$788
Mitsubishi
$440
$434
$599
$592
Nissan
$556
$590
$978
$1,178
Subaru
$415
$410
$404
$808
Tesla
$0
$0
$0
$0
Toyota
$440
$590
$763
$1,081
Volvo
$1,193
$1,140
$1,040
$997
VWA
$35
$1,028
$1,595
$2,148
TOTAL
$546
$682
$855
$1,232
4-13
-------
Table 4-16 Fleet Average Cost/Vehicle Relative to the No Action Scenario (2018 dollars)

2023
2024
2025
2026
BMW
$145
$129
$459
$566
Daimler
$727
$917
$1,567
$2,123
FCA
$957
$927
$886
$1,309
Ford
$29
$261
$252
$485
General Motors
$733
$1,138
$1,353
$1,854
Honda
$33
$52
$52
$467
Hyundai Kia-H
$454
$1,006
$997
$1,015
Hyundai Kia-K
$404
$424
$413
$426
JLR
$471
$813
$1,904
$1,784
Mazda
$591
$599
$595
$758
Mitsubishi
$697
$688
$833
$825
Nissan
$595
$746
$954
$1,005
Subaru
$412
$406
$401
$783
Tesla
$398
$393
$387
$382
Toyota
$456
$709
$863
$1,033
Volvo
$860
$827
$766
$731
VWA
$116
$456
$656
$853
TOTAL
$465
$663
$771
$1,044
Overall, EPA estimates the costs of today's proposal at $1,044 per vehicle relative to the no
action scenario. The large jump in costs between MYs 2025 and 2026 under the proposal is a
result of the elimination of advanced technology multiplier credits in combination with the need
to apply considerable amounts of technology in that final year to "make up" the CO2 no longer
available via those credits.
Of note is the difference in costs per vehicle for the FW-OEMs (BMW, Ford, Honda, Volvo
and VWA) and the NonFW-OEMs. On a sales weighted basis, the FW-OEM costs/vehicle, in
MY 2026, are $532/vehicle, while the MY2026 NonFW-OEM costs/vehicle are estimated to be
$l,248/vehicle. Since the FW-OEMs are incurring costs associated with the Framework, their
incremental costs to meet the proposed standards, which are more stringent than the Framework,
are lower than for those NonFW-OEMs that have chosen to comply with the SAFE FRM.
Also of interest is the cost per vehicle if we assume that the full fleet is actually meeting the
SAFE FRM standards rather than a portion of the fleet meeting the CA-OEM Framework
agreements. The full fleet costs per vehicle under that no action scenario are shown in Table
4-17.
Interestingly, while not presented here but discussed in Chapter 1.2.2 of this draft RIA, in
MY2025, EPA's OMEGA model runs show a MY2025 cost for the 2012 FRM relative to the
SAFE FRM of $922 to $1,228 per vehicle, depending on the specific analysis. Thus the
MY2025 per vehicle costs of $942 for this proposal relative to a full fleet meeting the SAFE
FRM are comparable to our past analyses of standards for the similar level of stringency. The
large jump in costs between MYs 2025 and 2026 under the proposal is a result of the elimination
of advanced technology multiplier credits in combination with the need to apply considerable
amounts of technology in that final year to "make up" the CO2 no longer available via those
credits. Note that the MY2025 $942/vehicle results are within the bounds of per-vehicle costs of
previous EPA analyses (see Chapter 1.2.2).
4-14
-------
Table 4-17 Fleet Average Cost/Vehicle Relative to the SAFE FRM (2018 dollars)

2023
2024
2025
2026
BMW
$663
$783
$1,400
$2,305
Daimler
$721
$941
$1,580
$2,123
FCA
$967
$938
$897
$1,320
Ford
$769
$1,059
$993
$1,628
General Motors
$712
$1,111
$1,353
$1,831
Honda
$396
$396
$387
$1,049
Hyundai Kia-H
$430
$1,010
$1,001
$1,009
Hyundai Kia-K
$439
$456
$443
$455
JLR
$512
$844
$1,923
$1,800
Mazda
$590
$599
$594
$758
Mitsubishi
$682
$673
$828
$821
Nissan
$591
$744
$947
$998
Subaru
$411
$406
$400
$782
Tesla
$398
$393
$387
$382
Toyota
$452
$705
$860
$1,031
Volvo
$1,506
$1,447
$1,492
$1,739
VWA
$663
$1,097
$1,393
$2,268
TOTAL
$625
$837
$949
$1,333
4.1.2.2 Alternatives
Car, truck, and fleet average vehicle costs for Alternatives 1 and 2 relative to the no action
scenario (framework OEMs meeting the framework, non-framework OEMs meeting SAFE) are
summarized in Table 4-18, Table 4-19, and Table 4-20.
Table 4-18 Car Average Cost/Vehicle for Alternatives 1 and 2 Relative to the No Action Scenario (2018
dollars)

Alternative 1
Alternative 2

2023
2024
2025
2026
2023
2024
2025
2026
BMW
$33
$13
$23
-$131
$1,289
$1,241
$1,187
$1,004
Daimler
$503
$743
$804
$791
$734
$1,184
$1,213
$1,176
FCA
$542
$530
$521
$645
$802
$855
$853
$893
Ford
$22
$175
$165
$371
$246
$556
$534
$576
General Motors
$777
$1,272
$1,284
$1,434
$1,213
$1,853
$1,895
$2,002
Honda
$5
$5
$5
$125
$39
$44
$44
$1,039
Hyundai Kia-H
$493
$621
$648
$657
$549
$670
$696
$762
Hyundai Kia-K
$395
$390
$380
$374
$393
$403
$395
$415
JLR
$398
$1,121
$1,143
$1,121
$194
$2,307
$2,233
$2,114
Mazda
$443
$438
$432
$478
$511
$524
$518
$848
Mitsubishi
$870
$860
$994
$985
$1,069
$1,057
$1,191
$1,182
Nissan
$495
$684
$837
$828
$685
$959
$1,050
$1,014
Subaru
$404
$398
$393
$493
$439
$433
$427
$712
Tesla
$398
$393
$387
$382
$398
$393
$387
$382
Toyota
$419
$724
$900
$927
$639
$682
$873
$908
Volvo
$212
$210
$191
$31
$212
$210
$222
$303
VWA
$223
$255
$221
$16
$802
$793
$738
$542
TOTAL
$393
$574
$625
$685
$625
$823
$868
$1,010
4-15
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Table 4-19 Truck Average Cost/Vehicle for Alternatives 1 and 2 Relative to the No Action Scenario (2018
dollars)

Alternative 1
Alternative 2

2023
2024
2025
2026
2023
2024
2025
2026
BMW
$270
$264
$739
$694
$270
$264
$622
$624
Daimler
$1,449
$1,398
$2,050
$3,410
$1,830
$1,755
$2,567
$4,122
FCA
$977
$938
$902
$1,082
$1,403
$1,344
$1,290
$1,328
Ford
$44
$244
$240
$58
$292
$510
$501
$121
General Motors
$602
$809
$1,101
$1,538
$982
$1,174
$1,462
$1,847
Honda
$1
$7
$7
$7
$25
$64
$63
$62
Hyundai Kia-H
$398
$1,905
$1,880
$1,856
$437
$3,748
$3,540
$3,357
Hyundai Kia-K
$398
$416
$410
$404
$435
$466
$459
$452
JLR
$776
$755
$1,899
$1,816
$993
$966
$2,109
$1,986
Mazda
$557
$549
$541
$542
$857
$852
$844
$858
Mitsubishi
$440
$434
$599
$592
$971
$960
$1,123
$1,114
Nissan
$538
$597
$743
$974
$606
$655
$1,074
$1,022
Subaru
$398
$393
$387
$543
$481
$475
$468
$848
Tesla
$0
$0
$0
$0
$0
$0
$0
$0
Toyota
$421
$578
$590
$787
$539
$577
$602
$901
Volvo
$480
$487
$443
$449
$956
$913
$887
$851
VWA
$35
$692
$494
$1,095
$86
$741
$539
$1,150
TOTAL
$479
$595
$680
$853
$718
$827
$927
$1,044
Table 4-20 Fleet Average Cost/Vehicle for Alternatives 1 and 2 Relative to the No Action Scenario (2018
dollars)

Alternative 1
Alternative 2

2023
2024
2025
2026
2023
2024
2025
2026
BMW
$126
$112
$311
$198
$885
$851
$967
$850
Daimler
$911
$1,025
$1,345
$1,936
$1,210
$1,431
$1,808
$2,481
FCA
$894
$860
$829
$998
$1,289
$1,251
$1,207
$1,245
Ford
$36
$217
$211
$180
$274
$528
$514
$298
General Motors
$677
$1,009
$1,181
$1,498
$1,081
$1,463
$1,648
$1,919
Honda
$3
$6
$6
$75
$33
$53
$53
$621
Hyundai Kia-H
$487
$703
$728
$735
$542
$870
$882
$933
Hyundai Kia-K
$395
$395
$385
$379
$401
$416
$408
$421
JLR
$692
$835
$1,730
$1,660
$817
$1,256
$2,132
$2,009
Mazda
$477
$471
$465
$498
$614
$623
$617
$852
Mitsubishi
$697
$688
$833
$825
$1,030
$1,018
$1,164
$1,155
Nissan
$510
$655
$806
$880
$659
$856
$1,060
$1,020
Subaru
$400
$394
$388
$529
$470
$464
$457
$813
Tesla
$398
$393
$387
$382
$398
$393
$387
$382
Toyota
$421
$653
$749
$861
$591
$632
$741
$910
Volvo
$389
$394
$359
$307
$705
$679
$667
$664
VWA
$160
$401
$313
$383
$559
$770
$668
$755
TOTAL
$436
$584
$654
$772
$672
$826
$900
$1,030
4-16
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4.1.3 Technology Penetration Rates
4.1.3.1 Proposal
Many manufacturers have projected aggressive moves toward electrification in the coming
years, with GM projecting 100 percent of their sales to be battery electric by 2035. Today's
proposal would set new standards through 2026, however it is intended to begin a future
transition toward electrification. Table 4-21, Table 4-22, and Table 4-23 show the penetration
rate of BEV+PHEV technology with the remaining share being traditional ICE and/or advanced
ICE technology under today's proposal. Values shown reflect fleet penetration and are not
increments from the SAFE standards or other standards. The combined fleet technology
penetrations for ICE vehicles are shown in Table 4-24.
Table 4-21 Car BEV+PHEV Penetration Rates under the Proposed Standards

2023
2024
2025
2026
BMW
8.4%
8.4%
8.4%
19.5%
Daimler
7.2%
8.0%
8.0%
8.0%
FCA
4.3%
6.3%
6.2%
6.2%
Ford
7.7%
9.3%
9.6%
9.6%
General Motors
6.1%
12.2%
12.1%
13.3%
Honda
0.1%
0.1%
0.1%
12.7%
Hyundai Kia-H
0.3%
3.4%
3.8%
3.8%
Hyundai Kia-K
9.2%
9.2%
9.1%
9.1%
JLR
0.5%
11.2%
11.2%
11.2%
Mazda
0.0%
0.0%
0.0%
0.0%
Mitsubishi
0.0%
0.0%
0.0%
0.0%
Nissan
1.0%
1.2%
1.2%
1.2%
Subaru
0.0%
0.0%
0.0%
0.0%
Tesla
100.0%
100.0%
100.0%
100.0%
Toyota
2.6%
4.0%
4.4%
4.4%
Volvo
0.0%
0.0%
0.0%
16.6%
VWA
15.4%
15.5%
15.5%
17.2%
TOTAL
4.6%
6.3%
6.4%
8.4%
4-17
-------
Table 4-22 Truck BEV+PHEV Penetration Rates under the Proposed Standards

2023
2024
2025
2026
BMW
4.3%
4.3%
8.9%
8.9%
Daimler
28.8%
28.8%
38.3%
39.6%
FCA
5.6%
5.6%
5.6%
5.6%
Ford
1.8%
4.8%
4.8%
7.3%
General Motors
2.3%
3.7%
5.0%
11.0%
Honda
0.0%
0.0%
0.0%
0.0%
Hyundai Kia-H
0.0%
20.6%
20.6%
20.6%
Hyundai Kia-K
0.0%
0.0%
0.0%
0.0%
JLR
13.0%
13.0%
24.6%
24.6%
Mazda
0.0%
0.0%
0.0%
0.0%
Mitsubishi
0.0%
0.0%
0.0%
0.0%
Nissan
0.0%
0.0%
3.7%
5.9%
Subaru
0.0%
0.0%
0.0%
0.0%
Tesla
0.0%
0.0%
0.0%
0.0%
Toyota
0.0%
0.0%
1.9%
1.9%
Volvo
15.6%
15.6%
17.3%
17.3%
VWA
1.2%
20.8%
20.8%
39.5%
TOTAL
2.6%
4.0%
5.1%
7.2%
Table 4-23 Fleet BEV+PHEV Penetration Rates under the Proposed Standards

2023
2024
2025
2026
BMW
6.8%
6.8%
8.6%
15.2%
Daimler
16.5%
17.0%
21.2%
21.8%
FCA
5.3%
5.7%
5.7%
5.7%
Ford
4.1%
6.5%
6.7%
8.2%
General Motors
3.9%
7.4%
8.0%
12.0%
Honda
0.1%
0.1%
0.1%
7.3%
Hyundai Kia-H
0.2%
4.5%
4.9%
4.9%
Hyundai Kia-K
6.9%
6.9%
6.8%
6.8%
JLR
10.2%
12.6%
21.7%
21.7%
Mazda
0.0%
0.0%
0.0%
0.0%
Mitsubishi
0.0%
0.0%
0.0%
0.0%
Nissan
0.6%
0.8%
2.1%
2.8%
Subaru
0.0%
0.0%
0.0%
0.0%
Tesla
100.0%
100.0%
100.0%
100.0%
Toyota
1.3%
2.0%
3.1%
3.1%
Volvo
10.3%
10.3%
11.5%
17.0%
VWA
10.7%
17.3%
17.3%
24.7%
TOTAL
3.6%
5.1%
5.8%
7.8%
4-18
-------
Table 4-24 Fleet ICE Technology Penetration Rates under the Proposal

2023
2024
2025
2026

No-action
Proposal
No-action
Proposal
No-action
Proposal
No-action
Proposal
Gasoline Direct Injection
(without turbo, HCR, HEV,
etc.)
12%
12%
11%
11%
10%
10%
8%
8%
Cylinder deactivation
16%
16%
16%
16%
15%
15%
15%
14%
Turbocharging level 1
20%
19%
18%
16%
18%
14%
14%
10%
Turbocharging level 2
0%
0%
0%
0%
0%
0%
0%
0%
Cooled EGR
0%
0%
0%
0%
0%
0%
0%
0%
High compression ratio level 1
19%
19%
20%
21%
20%
23%
20%
22%
High compression ratio level 2
6%
8%
7%
13%
8%
15%
10%
24%
Mild hybrid
0%
0%
0%
0%
1%
1%
1%
2%
Strong hybrid P2
0%
0%
0%
1%
1%
1%
1%
3%
Strong hybrid Powersplit
2%
2%
2%
2%
2%
2%
2%
2%
Strong hybrid P2 with HCR
0%
0%
0%
0%
0%
0%
0%
0%
Strong hybrid P2 with HCR1
0%
0%
0%
0%
0%
0%
0%
0%
Mass reduction 0
22%
18%
21%
13%
18%
12%
15%
8%
Mass reduction 1
44%
45%
44%
48%
46%
47%
46%
39%
Mass reduction 2
11%
10%
11%
10%
11%
10%
11%
10%
Mass reduction 3
14%
17%
14%
19%
14%
20%
17%
26%
Mass reduction 4
10%
10%
11%
11%
12%
12%
12%
18%
Mass reduction 5
0%
0%
0%
0%
0%
0%
0%
0%
Mass reduction 6
0%
0%
0%
0%
0%
0%
0%
0%
Curb Weight reduction
(relative to MRO)
4.2%
4.4%
4.2%
4.7%
4.4%
4.8%
4.7%
5.6%
This proposed rule includes advanced technology multipliers. A recent working paper by
Gillingham (2021) uses a stylized model to examine the effects of EV multipliers on EV
adoption and conventional vehicle emission reductions.12 He finds that, under some conditions,
multipliers may reduce EV adoption and increase vehicle emissions; under other conditions, they
may increase EV adoption and decrease vehicle emissions. In particular, under the conditions of
low levels of EV market share and EV costs higher than those of conventional vehicles, EV
multiplier incentives are expected to increase EV penetration. Gillingham (2021) states that
tightening the standards in addition to allowing multipliers will "offset the standard-weakening
effect of the generous crediting." Gillingham acknowledges the stylized nature of his model, and
suggests examining the effectiveness of advanced technology multipliers with more detailed
models in regulatory analyses, citing as an example the NHTSA CAFE model. As part of the
analysis for this proposed rule, EPA has estimated the benefits and costs of this proposal rule
with and without the advanced technology multipliers in a memo to the docket.b For reasons
discussed in Preamble section II.B. 1, we are proposing the limited use of multipliers to promote
commercialization of advanced technologies and to provide compliance flexibility.
To help shed light on the impact of advanced technology multipliers on the penetration rates
of BEVs and PHEVs, we conducted model runs without the multipliers. Those results along with
the results of the runs with multipliers are shown in Table 4-25. The results presented in this
b Sherwood, Todd (2021). "Benefits and Costs of the EPA Light-duty Vehicle GHG Proposal with and without
Advanced Technology Multipliers." Memo to Docket EPA-HQ-OAR-2021-0208.
4-19
-------
table suggest that the advanced technology multipliers are not expected to have a large impact on
BEV and PHEV technology penetration.
Table 4-25 Impact of Advanced Technology Multipliers on the Penetration of BEV and PHEV Technology

2023
2024
2025
2026
Proposal, with multipliers
3.6%
5.1%
5.8%
7.8%
Proposal, without multipliers
3.5%
4.9%
5.3%
7.4%
4.1.3.2 Alternatives
Table 4-26, Table 4-27, and Table 4-28 show the penetration rate of BEV+PHEV technology
with the remaining share being traditional ICE and/or advanced ICE technology for Alternative 1
and Alternative 2. Values shown reflect fleet penetration and are not increments from the SAFE
standards or other standards.
Table 4-26 Car BEV+PHEV Penetration Rates under the Alternative Standards

Alternative 1
Alternative 2

2023
2024
2025
2026
2023
2024
2025
2026
BMW
8.0%
8.0%
8.0%
12.5%
22.8%
22.9%
23.0%
28.3%
Daimler
10.4%
10.6%
10.6%
10.6%
13.8%
14.6%
14.6%
14.6%
FCA
4.4%
6.3%
6.3%
6.3%
4.3%
6.3%
6.2%
6.2%
Ford
7.7%
8.9%
8.8%
8.8%
7.7%
10.6%
10.5%
10.5%
General Motors
7.3%
13.4%
13.3%
14.5%
11.1%
17.1%
17.4%
18.5%
Honda
0.1%
0.1%
0.1%
6.3%
0.1%
0.1%
0.1%
15.6%
Hyundai Kia-H
0.3%
0.3%
0.6%
0.6%
0.2%
0.2%
0.6%
0.7%
Hyundai Kia-K
9.2%
9.2%
9.1%
9.1%
9.1%
9.1%
9.1%
9.1%
JLR
8.6%
8.6%
8.6%
8.6%
6.7%
18.7%
18.7%
18.7%
Mazda
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
1.5%
Mitsubishi
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
Nissan
1.0%
1.0%
1.0%
1.0%
1.0%
2.0%
2.0%
2.0%
Subaru
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
Tesla
100.0%
100.0%
100.0%
100.0%
100.0%
100.0%
100.0%
100.0%
Toyota
2.6%
2.6%
2.7%
2.7%
2.5%
2.5%
2.7%
2.7%
Volvo
0.0%
0.0%
0.0%
16.6%
0.0%
0.0%
0.0%
16.5%
VWA
15.7%
15.9%
15.9%
16.5%
18.4%
18.6%
18.6%
19.4%
TOTAL
4.8%
5.9%
6.0%
7.0%
5.9%
7.4%
7.5%
9.6%
4-20
-------
Table 4-27 Truck BEV+PHEV Penetration Rates under the Alternative Standards

Alternative 1
Alternative 2

2023
2024
2025
2026
2023
2024
2025
2026
BMW
4.3%
4.3%
10.8%
10.8%
4.3%
4.3%
4.3%
4.3%
Daimler
21.4%
21.4%
21.4%
23.1%
28.8%
28.8%
28.8%
33.0%
FCA
4.1%
4.1%
4.1%
4.1%
5.6%
5.6%
5.6%
5.6%
Ford
1.8%
4.3%
4.3%
4.3%
1.8%
4.8%
4.8%
4.8%
General Motors
0.0%
1.4%
1.4%
3.8%
0.0%
1.4%
1.4%
3.7%
Honda
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
Hyundai Kia-H
0.0%
0.0%
0.0%
0.0%
0.0%
20.6%
20.6%
20.6%
Hyundai Kia-K
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
JLR
14.3%
14.3%
23.7%
23.7%
14.3%
14.4%
23.8%
23.8%
Mazda
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
Mitsubishi
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
Nissan
0.0%
0.0%
1.1%
3.3%
0.0%
0.0%
3.7%
3.7%
Subaru
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
Tesla
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
Toyota
0.0%
0.0%
0.2%
0.2%
0.0%
0.0%
0.2%
0.2%
Volvo
5.6%
5.6%
7.3%
7.3%
13.0%
13.0%
14.6%
14.6%
VWA
1.2%
1.2%
1.2%
19.9%
1.2%
1.2%
1.2%
19.9%
TOTAL
1.7%
2.4%
2.7%
3.7%
2.2%
3.1%
3.5%
4.4%
Table 4-28 Fleet BEV+PHEV Penetration Rates under the Alternative Standards

Alternative 1
Alternative 2

2023
2024
2025
2026
2023
2024
2025
2026
BMW
6.6%
6.5%
9.1%
11.8%
15.5%
15.5%
15.5%
18.6%
Daimler
15.1%
15.3%
15.3%
16.0%
20.3%
20.8%
20.9%
22.8%
FCA
4.1%
4.5%
4.5%
4.5%
5.3%
5.7%
5.7%
5.7%
Ford
4.1%
6.1%
6.1%
6.1%
4.1%
7.0%
7.0%
7.0%
General Motors
3.2%
6.6%
6.5%
8.3%
4.7%
8.1%
8.1%
10.0%
Honda
0.1%
0.1%
0.1%
3.7%
0.1%
0.1%
0.1%
9.0%
Hyundai Kia-H
0.2%
0.2%
0.5%
0.5%
0.2%
1.6%
1.9%
2.0%
Hyundai Kia-K
6.9%
6.9%
6.8%
6.8%
6.8%
6.8%
6.7%
6.7%
JLR
13.0%
13.0%
20.4%
20.4%
12.6%
15.3%
22.7%
22.7%
Mazda
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
1.1%
Mitsubishi
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
Nissan
0.6%
0.6%
1.0%
1.8%
0.6%
1.3%
2.6%
2.6%
Subaru
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
Tesla
100.0%
100.0%
100.0%
100.0%
100.0%
100.0%
100.0%
100.0%
Toyota
1.3%
1.3%
1.5%
1.5%
1.3%
1.3%
1.5%
1.5%
Volvo
3.7%
3.7%
4.8%
10.4%
8.6%
8.6%
9.7%
15.2%
VWA
10.9%
11.0%
11.0%
17.6%
12.6%
12.7%
12.7%
19.5%
TOTAL
3.3%
4.2%
4.3%
5.4%
4.0%
5.2%
5.4%
6.9%
Note that Alternative 2, the more stringent alternative, has slightly lower BEV+PHEV
penetration in MY2026 than does the proposal (6.9 percent versus 7.8 percent). This can be
explained by, at least, three important considerations. The first of these being the advanced
technology multipliers in the proposal which Alternative 2 does not have. Those multipliers
provide incentives for BEVs and PHEVs. The second being the earlier introduction of BEVs and
PHEVs in Alternative 2 due to the forward looking features within the model to comply with the
4-21
-------
more stringent standards under Alternative 2. This forward looking feature, in combination with
the refresh and redesign cycles within the model, is adding slighly more BEVs and PHEVs in the
early years of Alternative 2 relative to the proposal, even with the proposal's multipliers, which
tends to decrease the need for BEVs and PHEVs in the later years (due to credit generation). The
third reason would be that Alternative 2 results in a slightly higher market share of trucks than
does the proposal. This happens because the trucks tend to have greater fuel economy
improvements (on a percentage improvement basis) than do cars and, as such, the model projects
a higher truck share under Alternative 2 than the proposal. While Alternative 2 has a higher car
BEV+PHEV penetration it also has a lower truck BEV+PHEV penetration. These penetrations
are offset by the larger truck market share under Alternative 2.
4.1.4 Sensitivities
We have conducted the following sensitivies:
•	AEO high oil price (AEO High)
•	AEO low oil price (AEO Low)
•	Mass safety coefficients at the 5th percentile (Mass Safety 5th %ile)
•	Mass safety coefficients at the 95th percentile (Mass Safety 95th %ile)
•	No HCR2 availability (No HCR2)
o This represents no further progression of Atkinson Cycle technology beyond
HCR1, i.e., technologies that are currently in today's light-duty vehicle fleet (see
Chapter 2.3.2)
•	Perfect trading amongst manufacturers (Perfect Trading)
•	Price elasticity equal to -0.4 (rather than -1.0)
•	Rebound effect equal to -0.05 (rather than -0.1)
•	Rebound effect equal to -0.15 (rather than -0.1)
Each sensitivity is compared to its own no action scenario. In other words, the no action
standards were used but the no action scenario was run using the same set of sensitivity
parameters as used for the action scenario.
4.1.4.1 Compliance Costs per Vehicle and Technology Penetration
The per-vehicle compliance costs for the proposal and for each of the analyzed sensitivities
are shown in Table 4-29. The technology penetration rates for the proposal and for each of the
analyzed sensitivities are shown in Table 4-30.
Table 4-29 Costs per Vehicle for the Proposal and Sensitivities relative to their No Action Scenarios (2018
dollars)*
Model Year
Proposal
AEO High
AEO Low
No HCR2
Perfect Trading
Price Elasticity
2023
$465
$458
$509
$498
$415
$465
2024
$663
$595
$693
$690
$612
$663
2025
$771
$655
$780
$750
$736
$769
2026
$1,044
$873
$1,107
$1,071
$1,024
$1,041
*Note that the costs per vehicle for the mass safety and rebound sensitivities are identical to the proposal since those
sensitivities have no impact on compliance; therefore, those costs per vehicle are not shown.
4-22
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Table 4-30 MY 2026 Technology Penetration Rates for the No-Action case, Proposal and Sensitivities*

No-Action
Proposal
AEO High
AEO Low
No HCR2
Perfect Trading
Price Elasticity
DEAC
15%
14%
13%
15%
14%
13%
14%
TURBO1
14%
10%
10%
9%
10%
13%
10%
HCR1
20%
22%
27%
25%
41%
13%
22%
HCR2
10%
24%
19%
19%
0%
27%
24%
AT8
53%
41%
41%
42%
40%
41%
41%
AT8L2
3%
5%
6%
8%
3%
6%
5%
AT8L3
0%
1%
1%
0%
1%
0%
1%
AT9L2
0%
0%
0%
0%
0%
0%
0%
AT10L2
9%
7%
10%
9%
13%
16%
7%
AT10L3
4%
10%
4%
6%
6%
1%
10%
SS12V
16%
16%
15%
16%
15%
20%
16%
BISG
1%
2%
3%
3%
2%
5%
2%
SHEVP2
1%
3%
3%
3%
2%
1%
3%
SHEVPS
2%
2%
2%
2%
2%
1%
2%
P2HCR1
0%
0%
0%
0%
0%
0%
0%
PHEV
0%
1%
1%
1%
1%
1%
1%
BEV
3%
7%
6%
8%
8%
6%
7%
MRO
15%
8%
7%
8%
8%
5%
8%
MR1
46%
39%
38%
35%
36%
30%
39%
MR2
11%
10%
10%
9%
9%
5%
10%
MR3
17%
26%
25%
28%
24%
37%
26%
MR4
12%
18%
20%
20%
23%
23%
18%
MR5
0%
0%
0%
0%
0%
0%
0%
MR6
0%
0%
0%
0%
0%
0%
0%
*Note that the technology penetration rates for the mass safety and rebound sensitivities are identical to the proposal
since those sensitivities have no impact on technology application; therefore, those penetration rates are not shown.
4.2 Estimates of Fuel Economy Impacts
4.2.1 Proposal
While the proposal sets new GHG standards, those standards will impact fuel economy since
reducing fuel consumption is one of the primary means of reducing CO2 emissions. The
estimated impacts on fuel economy associated with our No Action scenario and the proposal are
shown in Table 4-31. Importantly, these fuel economy values are based on the standards and the
model's estimated achieved levels, or rating, and therefore do not consider use of AC leakage
credits. The fuel economy values are estimated using the average CO2 content of the gasoline
used for compliance testing (8887 grams CChper gallon of certification gasoline). Table 4-32
presents the fuel economy values assuming full use of AC leakage credits where we have
calculated the fuel economy again using the CO2 content of certification gasoline and adding to
the values shown in Table 4-31 the AC leakage credit. Because we expect full use of the AC
leakage credit, the values shown in Table 4-32 are considered to be more indicative of the actual
fuel economy values in compliance testing.
Perhaps of most interest are the estimated fuel economy impacts on-the-road, or the expected
"EPA label values." Those fuel economy values are shown in Table 4-33 where we have
multiplied the values shown in Table 4-32 by the traditional "gap" of 0.8 to reflect the estimated
real world values relative to the test cycle values.
4-23
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Table 4-31 Fuel Economy (MPG) Estimates based on the GHG Standards *
Regulatory
Class
MY
No Action Scenario
Proposal
Standard
Rating
Standard
Rating
Car
2023
51
53
54
55
2024
52
53
57
59
2025
54
54
60
60
2026
55
56
63
63
Truck
2023
36
36
38
38
2024
37
37
40
39
2025
37
37
42
41
2026
38
38
45
44
Combined
2023
42
43
45
45
2024
43
44
47
47
2025
44
44
49
49
2026
45
45
52
52
* Calculated as 8887 divided by CChe. Note that the "rating" is the estimated compliance value and, as such,
includes possible under compliance due to use of banked credits and/or over compliance for earning credits for
future use.
Table 4-32 Fuel Economy (MPG) Estimates assuming full use of AC Leakage Credits *
Regulatory
Class
MY
No Action Scenario
Proposal
Standard
Rating
Standard
Rating
Car
2023
48
49
50
51
2024
48
49
52
54
2025
50
50
55
55
2026
51
51
57
58
Truck
2023
33
34
36
36
2024
34
34
37
37
2025
35
35
39
38
2026
36
36
41
40
Combined
2023
39
40
42
42
2024
40
40
44
44
2025
41
41
46
45
2026
42
42
48
47
* Calculated as 8887 divided by (CChe + AC Leakage Credit). Note that the "rating" is the estimated compliance
value and, as such, includes possible under compliance due to use of banked credits and/or over compliance for
earning credits for future use.
4-24
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Table 4-33 Fuel Economy (MPG) Estimated "Label Value" *
Regulatory
Class
MY
No Action Scenario
Proposal
Standard
Rating
Standard
Rating
Car
2023
38
39
40
41
2024
39
39
42
43
2025
40
40
44
44
2026
40
41
46
46
Truck
2023
27
27
29
28
2024
27
27
30
29
2025
28
28
31
30
2026
28
29
33
32
Combined
2023
31
32
33
33
2024
32
32
35
35
2025
33
33
36
36
2026
33
34
38
38
* Calculated as 8887 divided by (CChe + AC Leakage Credit) then multiplied by 0.8. Note that the "rating" is the
estimated compliance value and, as such, includes possible under compliance due to use of banked credits and/or
over compliance for earning credits for future use.
4.2.2 Alternatives
Here we present the analogous series of tables presented in Chapter 4.2.1 but for each of the
alternatives.
Table 4-34 Fuel Economy (MPG) Estimates based on the GHG Standards of Alternative 1 *
Regulatory
Class
MY
No Action Scenario
Proposal
Standard
Rating
Standard
Rating
Car
2023
51
53
53
54
2024
52
53
55
57
2025
54
54
58
59
2026
55
56
60
62
Truck
2023
36
36
38
38
2024
37
37
39
39
2025
37
37
41
39
2026
38
38
43
42
Combined
2023
42
43
44
44
2024
43
44
46
46
2025
44
44
48
47
2026
45
45
50
50
* Calculated as 8887 divided by CC>2e. Note that the "rating" is the estimated compliance value and, as such,
includes possible under compliance due to use of banked credits and/or over compliance for earning credits for
future use.
4-25
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Table 4-35 Fuel Economy (MPG) Estimates assuming full use of AC Leakage Credits in Alternative 1 *
Regulatory Class
MY
No Action Scenario
Proposal
Standard
Rating
Standard
Rating
Car
2023
48
49
49
50
2024
48
49
51
53
2025
50
50
53
54
2026
51
51
55
56
Truck
2023
33
34
35
35
2024
34
34
37
36
2025
35
35
38
37
2026
36
36
40
39
Combined
2023
39
40
41
41
2024
40
40
42
43
2025
41
41
44
43
2026
42
42
46
46
* Calculated as 8887 divided by (CChe + AC Leakage Credit). Note that the "rating" is the estimated compliance value and,
as such, includes possible under compliance due to use of banked credits and/or over compliance for earning credits
for future use.
Table 4-36 Fuel Economy (MPG) Estimated "Label Value" under Alternative 1 *
Regulatory Class
MY
No Action Scenario
Proposal
Standard
Rating
Standard
Rating
Car
2023
38
39
39
40
2024
39
39
41
42
2025
40
40
42
43
2026
40
41
44
45
Truck
2023
27
27
28
28
2024
27
27
29
29
2025
28
28
31
29
2026
28
29
32
31
Combined
2023
31
32
33
33
2024
32
32
34
34
2025
33
33
35
35
2026
33
34
37
37
* Calculated as 8887 divided by (C02e +ac Leakage credit) then multiplied by 0 8 Note that the "rating" is the estimated
compliance value and, as such, includes possible under compliance due to use of banked credits and/or over
compliance for earning credits for future use.
4-26
-------
Table 4-37 Fuel Economy (MPG) Estimates based on the GHG Standards of Alternative 2 *
Regulatory Class
MY
No Action Scenario
Proposal
Standard
Rating
Standard
Rating
Car
2023
51
53
55
57
2024
52
53
57
60
2025
54
54
60
61
2026
55
56
63
65
Truck
2023
36
36
39
39
2024
37
37
41
40
2025
37
37
43
41
2026
38
38
45
43
Combined
2023
42
43
46
46
2024
43
44
48
47
2025
44
44
50
49
2026
45
45
53
51
* Calculated as 8887 divided by CC>2e. Note that the "rating" is the estimated compliance value and, as such,
includes possible under compliance due to use of banked credits and/or over compliance for earning credits for
future use.
Table 4-38 Fuel Economy (MPG) Estimates assuming full use of AC Leakage Credits in Alternative 2 *
Regulatory Class
MY
No Action Scenario
Proposal
Standard
Rating
Standard
Rating
Car
2023
48
49
51
52
2024
48
49
53
55
2025
50
50
55
56
2026
51
51
58
59
Truck
2023
33
34
36
36
2024
34
34
38
37
2025
35
35
40
38
2026
36
36
42
40
Combined
2023
39
40
42
43
2024
40
40
44
44
2025
41
41
46
45
2026
42
42
48
47
* Calculated as 8887 divided by (CChe + AC Leakage Credit). Note that the "rating" is the estimated compliance value and,
as such, includes possible under compliance due to use of banked credits and/or over compliance for earning credits
for future use.
4-27
-------
Table 4-39 Fuel Economy (MPG) Estimated "Label Value" under Alternative 2 *
Regulatory Class
MY
No Action Scenario
Proposal
Standard
Rating
Standard
Rating
Car
2023
38
39
40
42
2024
39
39
42
44
2025
40
40
44
45
2026
40
41
46
47
Truck
2023
27
27
29
29
2024
27
27
31
29
2025
28
28
32
30
2026
28
29
33
32
Combined
2023
31
32
34
34
2024
32
32
35
35
2025
33
33
37
36
2026
33
34
39
38
* Calculated as 8887 divided by (CChe + AC Leakage Credit) then multiplied by 0.8. Note that the "rating" is the estimated
compliance value and, as such, includes possible under compliance due to use of banked credits and/or over
compliance for earning credits for future use.
4-28
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References for Chapter 4
1	75 FR 25324.
2	77 FR 62624.
3	EPA-420-D-16-900, July 2016.
4	EPA-420-R-16-020, November 2016.
5	EPA-420-R-17-001, January 2017.
6	85 FR 24218.
7	CAFE Model Documentation, DOT HS 812 934, March 2020.
8	California Air Resources Board. Framework Agreements on Clean Cars. August 17, 2020. Last accessed on the
Internet on 5/25/2021 at the following URL: https://ww2.arb.ca.gov/sites/default/files/2020-08/clean-car-
framework-documents-all-bmw-ford-honda-volvo-vw.pdf
9	85 FR 24647.
10	EPA CCEMS Post-Processing Tool, available in the docket and at
https://github.com/LISEPA/.	IEMS PostProcessingToo!
11	ibid.
12	Gillingham, K. (2021). "Designing Fuel-Economy Standards in Light of Electric Vehicles." NBER working paper
#29067.
4-29
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Chapter 5: Projected Impacts on Emissions, Fuel Consumption, and Safety
This chapter documents EPA's analysis of the emission, fuel consumption and safety impacts
of the emission standards for light-duty vehicles. Light-duty vehicles include passenger vehicles
such as cars, sport utility vehicles, vans, and pickup trucks. Such vehicles are used for both
commercial and personal uses and are significant contributors to the total United States (U.S.)
GHG emission inventory.
5.1 Projected Emissions Impacts
5.1.1 Greenhouse Gas Emissions
5.1.1.1 Proposal
EPA estimated the GHG emissions impacts associated with the proposed standard, including
impacts on tailpipe emissions from light-duty cars and trucks and the upstream emissions
associated with the fuels used to power those vehicles (both at the refinery and the electricity
generating unit). The tailpipe emissions of GHGs are estimated internal to the model based on
the policy scenario(s) being run (as controlled via the scenarios input file) and the projected
compliance pathway which impacts the projected technology mix. The upstream emissions are
then calculated using emission factors applied to the gallons of liquid fuels projected to be
consumed and the kilowatt hours of electricity projected to be consumed. The upstream
emission factors used in the modeling are identical to those used in support of the SAFE FRM
and were generated using the DOE/Argonne GREET model as described in the SAFE FRM.1
Table 5-1 Impacts on GHG Emissions under the Proposed Standards relative to the No Action Scenario

C02 Upstream
(MMT)
CH4 Upstream
(metric tons)
N20 Upstream
(metric tons)
C02 Tailpipe
(MMT)
CH4 Tailpipe
(metric tons)
N20 Tailpipe
(metric tons)
2023
0
-4,819
-102
-3
-2
-2
2024
-1
-8,549
-189
-6
-11
-12
2025
-1
-13,389
-301
-10
-23
-29
2026
-1
-21,109
-477
-16
-44
-57
2027
-2
-30,629
-692
-23
-74
-93
2028
-2
-40,915
-922
-31
-104
-129
2029
-3
-51,470
-1,158
-39
-137
-168
2030
-4
-61,849
-1,388
-46
-165
-204
2031
-5
-71,947
-1,610
-54
-191
-237
2032
-5
-81,652
-1,824
-61
-221
-272
2033
-6
-90,831
-2,026
-67
-248
-306
2034
-7
-99,320
-2,214
-74
-277
-341
2035
-8
-106,685
-2,374
-79
-296
-366
2036
-8
-113,496
-2,523
-84
-316
-393
2037
-9
-119,605
-2,661
-88
-347
-429
2038
-9
-124,915
-2,781
-92
-377
-464
2039
-9
-129,273
-2,877
-95
-402
-491
2040
-10
-132,923
-2,958
-98
-424
-516
2041
-10
-135,964
-3,026
-100
-441
-538
2042
-10
-137,984
-3,072
-102
-457
-558
2043
-10
-139,581
-3,112
-103
-479
-581
2044
-10
-140,731
-3,143
-104
-499
-603
2045
-10
-141,408
-3,164
-105
-521
-626
2046
-10
-141,772
-3,179
-106
-542
-647
2047
-10
-142,303
-3,199
-107
-566
-673
2048
-9
-142,357
-3,208
-107
-585
-693
2049
-9
-142,560
-3,220
-108
-608
-718
2050
-9
-143,037
-3,246
-109
-643
-755
Sum
-186
-2,711,072
-60,645
-2,019
-9,000
-10,898
5-1
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5.1.1.2 Alternatives
Table 5-2 Impacts on GHG Emissions under Alternative 1 relative to the No Action Scenario

C02 Upstream
(MMT)
CH4 Upstream
(metric tons)
N20 Upstream
(metric tons)
C02 Tailpipe
(MMT)
CH4 Tailpipe
(metric tons)
N20 Tailpipe
(metric tons)
2023
-1
-4,493
-93
-3
5
3
2024
-1
-7,853
-166
-5
5
3
2025
-1
-12,144
-258
-8
7
1
2026
-2
-18,026
-382
-12
9
-3
2027
-3
-25,423
-536
-17
7
-9
2028
-4
-33,280
-699
-23
3
-15
2029
-4
-41,556
-871
-28
-2
-23
2030
-5
-49,640
-1,039
-34
-6
-30
2031
-6
-57,549
-1,203
-39
-12
-35
2032
-7
-65,064
-1,358
-44
-18
-40
2033
-8
-72,169
-1,504
-48
-23
-45
2034
-9
-78,701
-1,640
-53
-30
-52
2035
-10
-84,308
-1,753
-56
-30
-51
2036
-10
-89,567
-1,861
-60
-34
-55
2037
-11
-94,449
-1,968
-63
-56
-74
2038
-11
-98,605
-2,060
-66
-76
-92
2039
-11
-102,010
-2,134
-68
-92
-105
2040
-12
-104,864
-2,196
-70
-107
-118
2041
-12
-107,320
-2,250
-72
-119
-131
2042
-12
-108,938
-2,289
-74
-133
-147
2043
-12
-110,133
-2,319
-75
-151
-164
2044
-12
-110,948
-2,342
-75
-167
-180
2045
-12
-111,454
-2,362
-76
-189
-203
2046
-12
-111,221
-2,358
-76
-198
-212
2047
-11
-111,177
-2,361
-76
-211
-228
2048
-11
-110,732
-2,354
-76
-220
-238
2049
-11
-110,687
-2,360
-77
-237
-258
2050
-11
-110,794
-2,374
-77
-267
-289
Sum
-230
-2,143,105
-45,089
-1,452
-2,342
-2,786
5-2
-------
Table 5-3 Impacts on GHG Emissions under Alternative 2 relative to the No Action Scenario

C02 Upstream
(MMT)
CH4 Upstream
(metric tons)
N20 Upstream
(metric tons)
CO, Tailpipe
(MMT)
CH4 Tailpipe
(metric tons)
N20 Tailpipe
(metric tons)
2023
-2
-14,336
-290
-9
16
12
2024
-2
-19,560
-405
-13
13
9
2025
-3
-25,750
-540
-17
11
2
2026
-3
-33,283
-704
-23
3
-11
2027
-4
-42,818
-909
-30
-8
-29
2028
-5
-54,099
-1,151
-37
-24
-50
2029
-6
-65,258
-1,390
-45
-39
-69
2030
-7
-76,263
-1,626
-53
-56
-89
2031
-8
-86,885
-1,852
-60
-74
-109
2032
-9
-96,974
-2,068
-67
-94
-130
2033
-10
-106,480
-2,272
-74
-114
-151
2034
-11
-115,223
-2,460
-80
-136
-174
2035
-12
-122,843
-2,622
-85
-151
-189
2036
-13
-129,719
-2,767
-90
-165
-205
2037
-13
-135,868
-2,902
-94
-190
-229
2038
-14
-141,075
-3,016
-98
-212
-252
2039
-14
-145,376
-3,108
-101
-231
-269
2040
-15
-149,051
-3,188
-104
-250
-287
2041
-15
-152,182
-3,256
-106
-267
-304
2042
-15
-154,366
-3,307
-108
-286
-324
2043
-15
-155,993
-3,347
-109
-307
-343
2044
-15
-157,150
-3,377
-110
-326
-362
2045
-15
-157,890
-3,402
-111
-353
-388
2046
-14
-158,950
-3,443
-113
-395
-430
2047
-14
-159,880
-3,482
-114
-437
-473
2048
-13
-160,295
-3,507
-115
-473
-508
2049
-13
-161,117
-3,544
-117
-517
-554
2050
-12
-162,297
-3,598
-119
-575
-619
Sum
-296
-3,140,980
-67,532
-2,202
-5,637
-6,528
5-3
-------
5.1.2 Non-Greenhouse Gas Emissions
5.1.2.1 Proposal
Table 5-4 Impacts on Non-GHG Emissions under the Proposal relative to the No Action Scenario

Upstream Emissions (US tons)
Tailpipe Emissions (US tons)

pm25
NOx
so2
voc
CO
pm25
NOx
so2
VOC
CO
2023
-56
-628
-36
-1,211
-334
17
1,037
-24
1,345
12,884
2024
-97
-1,040
282
-2,245
-539
37
2,385
-45
3,255
29,814
2025
-150
-1,570
699
-3,595
-802
50
3,270
-72
4,501
41,380
2026
-236
-2,454
1,183
-5,699
-1,251
58
4,032
-114
5,583
50,655
2027
-342
-3,546
1,730
-8,279
-1,807
57
4,356
-166
6,183
52,764
2028
-457
-4,747
2,167
-11,023
-2,429
40
4,010
-220
5,817
43,400
2029
-575
-5,973
2,611
-13,840
-3,065
24
3,656
-276
5,491
34,336
2030
-690
-7,182
2,963
-16,588
-3,699
5
3,072
-331
4,889
21,673
2031
-806
-8,419
3,094
-19,228
-4,342
-16
2,359
-383
4,105
7,504
2032
-917
-9,601
3,248
-21,779
-4,952
-41
1,506
-433
3,137
-8,754
2033
-1,023
-10,726
3,340
-24,183
-5,533
-70
573
-480
2,048
-26,420
2034
-1,121
-11,756
3,468
-26,425
-6,058
-101
-401
-525
904
-44,195
2035
-1,207
-12,685
3,364
-28,315
-6,542
-128
-1,265
-561
-116
-59,229
2036
-1,286
-13,520
3,349
-30,084
-6,969
-156
-2,094
-596
-1,085
-74,202
2037
-1,355
-14,232
3,506
-31,727
-7,319
-188
-2,951
-629
-2,088
-90,292
2038
-1,416
-14,846
3,646
-33,163
-7,616
-219
-3,746
-657
-3,021
-105,517
2039
-1,466
-15,374
3,601
-34,301
-7,878
-246
-4,394
-679
-3,809
-117,461
2040
-1,508
-15,804
3,594
-35,264
-8,085
-272
-4,963
-699
-4,502
-127,860
2041
-1,544
-16,174
3,571
-36,067
-8,264
-295
-5,463
-714
-5,091
-138,174
2042
-1,569
-16,411
3,581
-36,619
-8,371
-316
-5,901
-726
-5,600
-147,394
2043
-1,588
-16,573
3,706
-37,098
-8,429
-336
-6,304
-735
-6,065
-156,119
2044
-1,602
-16,679
3,831
-37,464
-8,458
-356
-6,662
-743
-6,472
-164,134
2045
-1,610
-16,714
4,022
-37,729
-8,443
-374
-6,983
-749
-6,834
-171,092
2046
-1,615
-16,711
4,249
-37,913
-8,381
-390
-7,269
-753
-7,153
-177,417
2047
-1,622
-16,708
4,571
-38,172
-8,310
-408
-7,590
-759
-7,507
-185,213
2048
-1,624
-16,659
4,821
-38,284
-8,219
-424
-7,855
-762
-7,801
-191,667
2049
-1,627
-16,620
5,110
-38,450
-8,129
-440
-8,138
-766
-8,100
-198,645
2050
-1,632
-16,556
5,686
-38,781
-8,000
-460
-8,501
-774
-8,475
-207,606
5-4
-------
5.1.2.2 Alternatives
Table 5-5 Impacts on Non-GHG Emissions under Alternative 1 relative to the No Action Scenario

Upstream Emissions (US tons)
Tailpipe Emissions (US tons)

pm25
NOx
so2
voc
CO
pm25
NOx
so2
VOC
CO
2023
-53
-605
-144
-1,095
-326
18
1,039
-21
1,323
13,252
2024
-91
-1,022
-109
-1,954
-547
40
2,301
-38
3,088
29,773
2025
-141
-1,563
-115
-3,044
-836
55
3,051
-60
4,113
40,445
2026
-209
-2,328
-246
-4,504
-1,251
67
3,624
-88
4,892
48,758
2027
-295
-3,299
-473
-6,324
-1,782
70
3,722
-124
5,094
49,480
2028
-386
-4,333
-764
-8,243
-2,352
62
3,377
-161
4,632
42,518
2029
-481
-5,406
-1,021
-10,278
-2,944
59
3,193
-200
4,438
38,639
2030
-574
-6,449
-1,298
-12,261
-3,524
57
2,923
-239
4,133
33,944
2031
-668
-7,513
-1,696
-14,180
-4,110
53
2,564
-275
3,694
28,545
2032
-757
-8,522
-2,076
-16,005
-4,666
49
2,155
-310
3,173
22,382
2033
-841
-9,480
-2,466
-17,726
-5,194
41
1,694
-343
2,574
14,955
2034
-918
-10,349
-2,770
-19,326
-5,670
31
1,215
-374
1,946
7,290
2035
-985
-11,127
-3,219
-20,649
-6,106
25
828
-399
1,420
1,704
2036
-1,048
-11,835
-3,540
-21,917
-6,497
17
453
-423
921
-4,018
2037
-1,104
-12,431
-3,520
-23,192
-6,808
3
-13
-448
328
-12,129
2038
-1,152
-12,930
-3,480
-24,287
-7,065
-12
-445
-470
-221
-19,930
2039
-1,191
-13,356
-3,553
-25,156
-7,289
-25
-787
-487
-679
-25,765
2040
-1,224
-13,701
-3,580
-25,893
-7,467
-37
-1,086
-501
-1,077
-31,001
2041
-1,253
-14,003
-3,575
-26,538
-7,619
-50
-1,380
-514
-1,438
-37,191
2042
-1,272
-14,175
-3,442
-27,009
-7,693
-62
-1,667
-524
-1,775
-43,356
2043
-1,285
-14,288
-3,272
-27,383
-7,731
-75
-1,936
-531
-2,086
-49,325
2044
-1,294
-14,351
-3,096
-27,661
-7,744
-88
-2,187
-537
-2,366
-55,063
2045
-1,299
-14,346
-2,761
-27,912
-7,707
-103
-2,467
-543
-2,663
-61,138
2046
-1,298
-14,311
-2,698
-27,876
-7,660
-112
-2,651
-542
-2,862
-65,161
2047
-1,298
-14,280
-2,532
-27,922
-7,605
-124
-2,880
-544
-3,109
-70,745
2048
-1,294
-14,210
-2,432
-27,843
-7,535
-133
-3,063
-543
-3,307
-75,171
2049
-1,294
-14,156
-2,166
-27,923
-7,460
-147
-3,312
-545
-3,556
-81,376
2050
-1,295
-14,073
-1,662
-28,120
-7,348
-163
-3,612
-550
-3,863
-88,420
5-5
-------
Table 5-6 Impacts on Non-GHG Emissions under Alternative 2 relative to the No Action Scenario

Ui
astream Emissions (US tons)
Tailpipe Emissions (US tons)

pm25
NOx
so2
voc
CO
pm25
NOx
so2
VOC
CO
2023
-173
-2,033
-923
-3,391
-1,111
36
1,946
-65
2,462
25,083
2024
-232
-2,661
-784
-4,753
-1,443
63
3,526
-92
4,686
45,620
2025
-302
-3,412
-670
-6,362
-1,841
80
4,448
-124
5,959
58,602
2026
-387
-4,335
-566
-8,308
-2,331
92
5,084
-162
6,844
67,196
2027
-496
-5,524
-538
-10,749
-2,968
94
5,237
-210
7,151
67,566
2028
-625
-6,931
-549
-13,622
-3,725
86
4,988
-267
6,862
60,643
2029
-752
-8,320
-608
-16,450
-4,478
83
4,822
-322
6,743
56,309
2030
-876
-9,672
-640
-19,249
-5,214
75
4,342
-377
6,198
46,889
2031
-1,000
-11,040
-841
-21,929
-5,948
65
3,725
-429
5,469
36,289
2032
-1,118
-12,326
-977
-24,494
-6,633
49
2,950
-479
4,526
22,508
2033
-1,229
-13,544
-1,149
-26,906
-7,282
30
2,096
-526
3,452
7,387
2034
-1,331
-14,650
-1,246
-29,146
-7,866
8
1,192
-569
2,316
-8,429
2035
-1,421
-15,645
-1,521
-31,050
-8,400
-10
396
-606
1,303
-21,627
2036
-1,502
-16,530
-1,744
-32,773
-8,872
-29
-363
-639
355
-34,387
2037
-1,573
-17,288
-1,776
-34,368
-9,264
-52
-1,138
-671
-615
-48,185
2038
-1,633
-17,924
-1,804
-35,724
-9,590
-76
-1,865
-697
-1,518
-61,536
2039
-1,683
-18,468
-1,957
-36,810
-9,874
-97
-2,460
-718
-2,285
-71,975
2040
-1,726
-18,917
-2,030
-37,755
-10,103
-117
-2,986
-737
-2,964
-81,226
2041
-1,763
-19,306
-2,064
-38,572
-10,297
-136
-3,461
-753
-3,552
-90,585
2042
-1,789
-19,554
-1,980
-39,184
-10,408
-154
-3,899
-765
-4,079
-99,429
2043
-1,808
-19,728
-1,870
-39,661
-10,477
-172
-4,289
-775
-4,547
-107,607
2044
-1,822
-19,842
-1,756
-40,019
-10,514
-190
-4,631
-782
-4,950
-115,081
2045
-1,831
-19,864
-1,435
-40,336
-10,487
-209
-4,989
-789
-5,352
-122,877
2046
-1,842
-19,848
-645
-40,871
-10,378
-234
-5,430
-802
-5,823
-133,018
2047
-1,851
-19,815
138
-41,373
-10,257
-260
-5,871
-813
-6,293
-143,579
2048
-1,856
-19,742
778
-41,698
-10,125
-282
-6,246
-821
-6,690
-152,475
2049
-1,864
-19,689
1,557
-42,177
-9,988
-308
-6,692
-832
-7,144
-163,326
2050
-1,875
-19,606
2,700
-42,870
-9,801
-344
-7,305
-848
-7,747
-178,749
5-6
-------
5.2 Projected Fuel Consumption
5.2.1 Proposal
The proposed standards will reduce not only greenhouse gas emissions but also fuel
consumption. Reducing fuel consumption is one of, although not the only, means of reducing
greenhouse gas emissions from the transportation fleet. Table 5-7 shows the estimated fuel
consumption changes, including rebound effects, credit usage and advanced technology
multiplier use, under the proposed standards relative to the no action scenario.
Table 5-7 Impacts on Fuel Consumption for the Proposal relative to the No Action Scenario

Gasoline
% of 2020
Electricity
% of 2020

(Million Barrels)
US Consumption
(Gigawatt hours)
US Consumption
2023
-9
-0.3%
929
0.0%
2024
-17
-0.6%
2,338
0.1%
2025
-27
-0.9%
4,197
0.1%
2026
-43
-1.5%
6,798
0.2%
2027
-62
-2.1%
9,908
0.3%
2028
-83
-2.8%
12,965
0.3%
2029
-104
-3.5%
16,101
0.4%
2030
-124
-4.2%
19,017
0.5%
2031
-144
-4.9%
21,619
0.6%
2032
-163
-5.5%
24,234
0.6%
2033
-181
-6.1%
26,654
0.7%
2034
-197
-6.7%
29,044
0.8%
2035
-211
-7.2%
30,735
0.8%
2036
-224
-7.6%
32,413
0.9%
2037
-237
-8.1%
34,329
0.9%
2038
-247
-8.4%
36,052
0.9%
2039
-256
-8.7%
37,173
1.0%
2040
-263
-8.9%
38,228
1.0%
2041
-269
-9.2%
39,096
1.0%
2042
-273
-9.3%
39,812
1.0%
2043
-277
-9.4%
40,699
1.1%
2044
-280
-9.5%
41,491
1.1%
2045
-282
-9.6%
42,335
1.1%
2046
-283
-9.6%
43,229
1.1%
2047
-286
-9.7%
44,396
1.2%
2048
-287
-9.8%
45,292
1.2%
2049
-288
-9.8%
46,327
1.2%
2050
-291
-9.9%
48,122
1.3%
Sum
-5,409

813,534

Table Notes:
One barrel (BBL) contains 42 gallons of gasoline; according to the Energy Information Administration (EIA), US
gasoline consumption in 2020 was 123.49 billion gallons, roughly 16 percent less (due to the coronavirus
pandemic) than the highest consumption on record (2018). According to the Department of Energy, there are
0.031 kWh of electricity per gallon gasoline equivalent, the metric reported by CCEMS for electricity
consumption and used here to convert to kWh. According to statista.com, the US consumed 3,802 terawatt hours
of electricity in 2020.
5-7
-------
5.2.2 Alternatives
Table 5-8 Impacts on Fuel Consumption for Alternative 1 relative to the No Action Scenario

Gasoline
% of 2020
Electricity
% of 2020

(Million Barrels)
US Consumption
(Gigawatt hours)
US Consumption
2023
-8
-0.3%
643
0.0%
2024
-14
-0.5%
1,408
0.0%
2025
-22
-0.8%
2,291
0.1%
2026
-33
-1.1%
3,257
0.1%
2027
-46
-1.6%
4,349
0.1%
2028
-60
-2.1%
5,407
0.1%
2029
-75
-2.6%
6,624
0.2%
2030
-90
-3.0%
7,758
0.2%
2031
-104
-3.5%
8,695
0.2%
2032
-117
-4.0%
9,604
0.3%
2033
-129
-4.4%
10,419
0.3%
2034
-141
-4.8%
11,305
0.3%
2035
-150
-5.1%
11,692
0.3%
2036
-159
-5.4%
12,242
0.3%
2037
-169
-5.7%
13,456
0.4%
2038
-177
-6.0%
14,564
0.4%
2039
-183
-6.2%
15,266
0.4%
2040
-189
-6.4%
15,952
0.4%
2041
-194
-6.6%
16,613
0.4%
2042
-197
-6.7%
17,381
0.5%
2043
-200
-6.8%
18,142
0.5%
2044
-202
-6.9%
18,836
0.5%
2045
-204
-6.9%
19,833
0.5%
2046
-204
-6.9%
20,063
0.5%
2047
-205
-7.0%
20,581
0.5%
2048
-204
-6.9%
20,854
0.5%
2049
-205
-7.0%
21,619
0.6%
2050
-207
-7.0%
22,994
0.6%
Sum
-3,889

351,847

5-8
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Table 5-9 Impacts on Fuel Consumption for Alternative 2 relative to the No Action Scenario

Gasoline
% of 2020
Electricity
% of 2020

(Million Barrels)
US Consumption
(Gigawatt hours)
US Consumption
2023
-24
-0.8%
1,154
0.0%
2024
-35
-1.2%
2,511
0.1%
2025
-47
-1.6%
4,032
0.1%
2026
-61
-2.1%
5,817
0.2%
2027
-79
-2.7%
7,879
0.2%
2028
-100
-3.4%
10,230
0.3%
2029
-121
-4.1%
12,460
0.3%
2030
-142
-4.8%
14,714
0.4%
2031
-161
-5.5%
16,706
0.4%
2032
-180
-6.1%
18,756
0.5%
2033
-198
-6.7%
20,645
0.5%
2034
-214
-7.3%
22,559
0.6%
2035
-228
-7.8%
23,880
0.6%
2036
-241
-8.2%
25,080
0.7%
2037
-252
-8.6%
26,548
0.7%
2038
-262
-8.9%
27,832
0.7%
2039
-270
-9.2%
28,649
0.8%
2040
-277
-9.4%
29,506
0.8%
2041
-283
-9.6%
30,317
0.8%
2042
-288
-9.8%
31,183
0.8%
2043
-292
-9.9%
31,990
0.8%
2044
-294
-10.0%
32,708
0.9%
2045
-297
-10.1%
33,810
0.9%
2046
-301
-10.3%
36,121
1.0%
2047
-306
-10.4%
38,412
1.0%
2048
-309
-10.5%
40,273
1.1%
2049
-313
-10.6%
42,584
1.1%
2050
-319
-10.8%
45,872
1.2%
Sum
-5,895

662,229

5.3 Projected Safety Impacts
EPA has long considered the safety implications of its emission standards. Section 202(a) of
the Clean Air Act specifically prohibits the use of an emission control device, system or element
of design that will cause or contribute to an unreasonable risk to public health, welfare, or safety.
With respect to its light-duty greenhouse gas emission regulations, EPA has historically
considered the potential impacts of GHG standards on safety including the: the 2010 rule which
established the 2012-2016 light-duty vehicle GHG standards, the 2012 rule which previously
established 2017-2025 light-duty vehicle GHG standards, the 2017 MTE Proposed
Determination and the 2020 SAFE Rulemaking.
The potential relationship between GHG emissions standards and safety is multi-faceted, and
can be influenced not only by control technologies, but also by consumer decisions about vehicle
ownership and use. EPA has estimated the impacts of this proposal on safety by accounting for
changes in new vehicle purchase, changes in vehicle scrappage, fleet turnover and VMT, and
changes in vehicle weight as an emissions control strategy. Safety impacts relate to changes in
the use of vehicles in the fleet, relative mass changes, and the turnover of fleet to newer and safer
vehicles fleet turnover have been estimated and considered in the standard setting process.
The GHG emissions standards are attribute-based standards, using vehicle footprint as the
attribute. Footprint is defined as a vehicle's wheelbase multiplied by its average track width—in
other words, the area enclosed by the points at which the wheels meet the ground. The standards
are therefore generally based on a vehicle's size: larger vehicles have numerically higher GHG
5-9
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emissions targets and smaller vehicles have numerically lower GHG emissions targets.
Footprint-based standards help to distribute the burden of compliance across all vehicle
footprints and across all manufacturers. Manufacturers are not compelled to build vehicles of any
particular size or type, and each manufacturer has its own fleetwide standard for its car and truck
fleets in each year that reflects the light-duty vehicles it chooses to produce.
Consistent with previous light-duty GHG analyses, EPA has assessed the potential of the
proposed MY2023-2026 standards to affect vehicle safety. EPA applied the same historical
relationships between mass, size, and fatality risk that were established and documented in the
SAFE rulemaking. These relationships are based on the statistical analysis of historical crash
data, which included an analysis performed by using the most recently available crash studies
based on data for model years 2007 to 2011. EPA used the findings of this analysis to estimate
safety impacts of the modeled mass reductions over the lifetimes of new vehicles in response to
MY2023-2026 standards. As in initially promulgating the GHG standards, the MTE proposed
determination and this proposal, EPA's assessment is that manufacturers can achieve the
MY2023-2026 standards while using modest levels of mass reduction as one technology option
among many. On the whole, EPA considers safety impacts in the context of all projected health
impacts from the proposal including public health benefits from the projected reductions in air
pollution.
The projected change in risk of fatal and non-fatal injuries is influenced by changes in fleet
mix (car/truck share), vehicle scrappage rates, distribution of VMT among vehicles in the fleet
and vehicle mass. EPA estimates that these factors together will result in an average 0.07 percent
increase (with results from sensitivity cases ranging from a decrease of 0.25 percent to an
increase of 0.38 percent) in the annual fatalities per billion miles driven through 2050.2 In
addition to changes in risk, EPA also considered the projected impact of the proposed standards
on the absolute number of fatal and non-fatal injuries. The majority of the fatalities projected
would result from the projected increased driving - i.e., people choosing to drive more due to the
lower operating costs of more efficient vehicles. Our cost-benefit analysis accounts for both the
value of this additional driving and its associated risk, which we assume are considerations in the
decision to drive. The risk valuation associated with this increase in driving partially offsets the
associated increase in societal costs due to increased fatalities and non-fatal injuries.
This analysis projects that there will be an increase in vehicle miles traveled (VMT) under the
proposed standards of 449 billion miles compared to the No Action case through 2050 (an
increase of about 0.5 percent). EPA estimates that vehicle safety, in terms of risk measured as the
total fatalities per the total distance travelled over this period, will remain almost unchanged at
4.642 fatalities per billion miles under the proposal, compared to 4.640 fatalities per billion miles
for the no-action case. EPA has also estimated, over the same 30-year period, that total fatalities
will increase by 2,288, with 1,952 deaths attributed to increased driving and 336 deaths
attributed to the increase in fatality risk. In other words, approximately 85 percent of the change
in fatalities under these proposed standards is due to projected increases in VMT and mobility
(i.e., people driving more). Our analysis also considered the increase in non-fatal injuries.
Consistent with the SAFE FRM, EPA assumed that non-fatal injuries scale with fatal injuries.
EPA also estimated the societal costs of these safety impacts using assumptions consistent
with the SAFE FRM (see Table 39) Specifically, we are continuing to use the cost associated
with each fatality of $10.4 million. We have also continued to use a scalar of approximately 1.6
5-10
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applied to fatality costs to estimate non-fatal injury costs. In addition, we have accounted for the
driver's inherent valuation of risk when making the decision to drive more due to rebound. This
risk valuation partially offsets the fatal and non-fatal injury costs described above, and,
consistent with the SAFE FRM, is calculated as 90 percent of the fatal and non-fatal injury costs
due to rebound to reflect the fact that consumers do not fully evaluate the risks associated with
this additional driving.
5-11
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References for Chapter 5
1	85 FR 24271.
2	This range of fatality risk values is based on a sensitivity analysis using the 5% to 95% confidence interval of
mass-safety coefficients presented in the SAFE FRM.
5-12
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Chapter 6: Vehicle Costs, Fuel Savings and Non-Emission Benefits
In this chapter, EPA presents our estimate of the costs, the fuel savings and the non-emission
benefits associated with the proposal. The presentation here summarizes the vehicle level costs
associated with the new technologies expected to be added to meet the MY2023 and later GHG
standards, including hardware costs to comply with the AJC credit program. The analysis
summarized here also provides costs associated with congestion and noise (see Chapter 0), and
for fatalities and non-fatal crashes and includes rebound effects.
For our analysis of safety impacts and how they are reflected in the benefit cost analysis,
having used the CCEMS, we have also used the safety-related inputs consistent with the SAFE
FRM. For example, we have used the costs associated with fatalities of $10.4 million, as was
done in the SAFE FRM. We have also used, as mentioned above, the scaler of approximately 1.6
applied to fatality costs to estimate non-fatal crash costs. In addition, we have offset the fatality
costs with a fatality risk value calculated as the fatalities due to rebound driving multiplied by the
fatality costs scaled by 90 percent to reflect the fact that consumers do not fully evaluate the risks
associated with driving. The same non-fatal crash risk scaler was applied to the fatality risk value
to estimate the non-fatal crash risk value. All of this is done exactly as was done in the SAFE
FRM with the exception that, rather than presenting fatality costs and non-fatal crash costs as
costs and fatality risk value and non-fatal crash risk value as benefits, we have calculated the net
of these and present the net result as a cost.
6-1
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6.1 Costs
6.1.1 Proposal
Table 6-1 Costs Associated with the Proposed Program relative to the No Action Scenario (SBillions of 2018
dollars)
Calendar
Year
Foregone
Consumer Sales
Surplus
Technology
Costs
Congestion
Noise
Fatality
Costs
Non-fatal
Crash
Costs
Total
Costs
2023
$0.26
$6.7
$0,046
$0.00073
$0.16
$0.26
$7.4
2024
$0.37
$9.5
$0,079
$0.0013
$0.36
$0.61
$11
2025
$0.44
$11
$0.12
$0.0019
$0.49
$0.82
$13
2026
$0.64
$15
$0.19
$0,003
$0.61
$1
$18
2027
$0.48
$14
$0.27
$0.0044
$0.68
$1.1
$16
2028
$0.49
$14
$0.37
$0,006
$0.64
$1.1
$17
2029
$0.48
$15
$0.47
$0.0075
$0.63
$1
$17
2030
$0.43
$14
$0.59
$0.0095
$0.58
$0.96
$17
2031
$0.4
$14
$0.68
$0,011
$0.52
$0.85
$17
2032
$0.36
$14
$0.79
$0,013
$0.44
$0.73
$16
2033
$0.33
$13
$0.88
$0,014
$0.36
$0.6
$15
2034
$0.3
$13
$0.97
$0,016
$0.28
$0.46
$15
2035
$0.28
$12
$1
$0,017
$0.2
$0.33
$14
2036
$0.26
$12
$1.1
$0,018
$0.13
$0.22
$14
2037
$0.25
$12
$1.2
$0,019
$0,074
$0.12
$13
2038
$0.24
$11
$1.2
$0.02
$0,025
$0,041
$13
2039
$0.23
$11
$1.3
$0.02
$-0,012
$-0,019
$13
2040
$0.21
$11
$1.3
$0,021
$-0,038
$-0,062
$12
2041
$0.2
$11
$1.3
$0,022
$-0,055
$-0,089
$12
2042
$0.19
$10
$1.4
$0,022
$-0,065
$-0.11
$12
2043
$0.18
$10
$1.4
$0,022
$-0,069
$-0.11
$12
2044
$0.18
$10
$1.4
$0,022
$-0,069
$-0.11
$11
2045
$0.18
$9.9
$1.4
$0,022
$-0,064
$-0.1
$11
2046
$0.17
$9.9
$1.4
$0,022
$-0,055
$-0,089
$11
2047
$0.17
$10
$1.4
$0,022
$-0,044
$-0,071
$11
2048
$0.17
$9.8
$1.4
$0,022
$-0,033
$-0,053
$11
2049
$0.16
$9.8
$1.3
$0,022
$-0,021
$-0,033
$11
2050
$0.16
$9.9
$1.3
$0,021
$-0.0093
$-0,015
$11
PV, 3%
$5.7
$210
$15
$0.24
$4.5
$7.6
$240
PV, 7%
$3.7
$130
$7.3
$0.12
$3.4
$5.6
$150
Annualized,
3%
$0.29
$11
$0.75
$0,012
$0.23
$0.39
$12
Annualized,
7%
$0.3
$10
$0.59
$0.0095
$0.27
$0.45
$12
Table Notes:
"Foregone Consumer Sales Surplus" refers to the difference between a vehicle's price and the buyer's willingness
to pay for the new vehicle; the impact reflects the reduction in new vehicle sales described in Chapter 8.1.
6-2
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6.1.2 Alternatives
Table 6-2 Costs Associated with Alternative 1 relative to the No Action Scenario (SBillions of 2018 dollars)
Calendar
Year
Foregone
Consumer Sales
Surplus
Technology
Costs
Congestion
Noise
Fatality
Costs
Non-fatal
Crash
Costs
Total
Costs
2023
$0.24
$6.3
$0,044
$0.00071
$0.15
$0.25
$6.9
2024
$0.32
$8.4
$0,079
$0.0013
$0.33
$0.56
$9.7
2025
$0.35
$9.5
$0.12
$0,002
$0.43
$0.72
$11
2026
$0.43
$11
$0.19
$0,003
$0.5
$0.83
$13
2027
$0.26
$9
$0.26
$0.0042
$0.5
$0.84
$11
2028
$0.27
$9.6
$0.35
$0.0057
$0.44
$0.73
$11
2029
$0.29
$10
$0.45
$0.0071
$0.42
$0.69
$12
2030
$0.26
$10
$0.56
$0.0089
$0.38
$0.63
$12
2031
$0.25
$10
$0.64
$0.01
$0.33
$0.55
$12
2032
$0.23
$10
$0.74
$0,012
$0.28
$0.46
$12
2033
$0.22
$9.8
$0.82
$0,013
$0.22
$0.37
$11
2034
$0.21
$9.6
$0.9
$0,014
$0.17
$0.28
$11
2035
$0.2
$9.2
$0.97
$0,015
$0.12
$0.2
$11
2036
$0.19
$9.1
$1
$0,016
$0,075
$0.12
$11
2037
$0.18
$9.1
$1.1
$0,017
$0,037
$0,061
$10
2038
$0.17
$9
$1.1
$0,018
$0.0062
$0.01
$10
2039
$0.17
$8.7
$1.2
$0,019
-$0,016
-$0,027
$10
2040
$0.16
$8.7
$1.2
$0,019
-$0,033
-$0,053
$9.9
2041
$0.15
$8.6
$1.2
$0.02
-$0,043
-$0.07
$9.8
2042
$0.14
$8.2
$1.2
$0.02
-$0,052
-$0,084
$9.5
2043
$0.14
$8
$1.3
$0.02
-$0,057
-$0,093
$9.3
2044
$0.13
$7.9
$1.3
$0.02
-$0,059
-$0,096
$9.1
2045
$0.13
$7.8
$1.2
$0.02
-$0,059
-$0,096
$9
2046
$0.13
$7.5
$1.2
$0.02
-$0,056
-$0,091
$8.8
2047
$0.13
$7.6
$1.2
$0.02
-$0,051
-$0,083
$8.9
2048
$0.12
$7.5
$1.2
$0.02
-$0,045
-$0,074
$8.8
2049
$0.12
$7.5
$1.2
$0.02
-$0.04
-$0,065
$8.8
2050
$0.12
$7.6
$1.2
$0.02
-$0,035
-$0,057
$8.9
PV, 3%
$3.9
$160
$14
$0.22
$3.2
$5.4
$190
PV, 7%
$2.6
$98
$6.8
$0.11
$2.5
$4.1
$110
Annualized,
3%
$0.2
$8.2
$0.69
$0,011
$0.16
$0.27
$9.5
Annualized,
7%
$0.21
$7.9
$0.55
$0.0088
$0.2
$0.33
$9.2
Table Notes:
"Foregone Consumer Sales Surplus" refers to the difference between a vehicle's price and the buyer's willingness
to pay for the new vehicle; the impact reflects the reduction in new vehicle sales described in Chapter 8.1.
6-3
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Table 6-3 Costs Associated with Alternative 2 relative to the No Action Scenario (SBillions of 2018 dollars)
Calendar
Year
Foregone
Consumer Sales
Surplus
Technology
Costs
Congestion
Noise
Fatality
Costs
Non-fatal
Crash
Costs
Total
Costs
2023
$0.37
$9.6
$0.15
$0.0023
$0.27
$0.46
$11
2024
$047
$12
$0.2
$0.0031
$0.5
$0.84
$14
2025
$0.51
$13
$0.26
$0.0042
$0.62
$1
$15
2026
$0.62
$15
$0.33
$0.0053
$0.7
$1.2
$18
2027
$0.47
$14
$0.43
$0.0069
$0.72
$1.2
$16
2028
$0.56
$16
$0.55
$0.0088
$0.7
$1.2
$19
2029
$0.54
$16
$0.66
$0,011
$0.69
$1.2
$19
2030
$0.51
$16
$0.8
$0,013
$0.65
$1.1
$19
2031
$0.47
$16
$0.91
$0,015
$0.59
$0.98
$19
2032
$0.43
$15
$1
$0,017
$0.52
$0.85
$18
2033
$0.41
$15
$1.1
$0,018
$0.44
$0.72
$18
2034
$0.38
$15
$1.2
$0.02
$0.36
$0.59
$17
2035
$0.35
$14
$1.3
$0,021
$0.29
$0.47
$16
2036
$0.33
$14
$1.4
$0,023
$0.22
$0.36
$16
2037
$0.32
$14
$1.5
$0,024
$0.16
$0.27
$16
2038
$0.3
$13
$1.5
$0,025
$0.12
$0.19
$15
2039
$0.29
$13
$1.6
$0,025
$0,082
$0.13
$15
2040
$0.28
$13
$1.6
$0,026
$0,057
$0,093
$15
2041
$0.26
$13
$1.7
$0,027
$0,041
$0,067
$15
2042
$0.25
$12
$1.7
$0,027
$0,031
$0.05
$14
2043
$0.24
$12
$1.7
$0,027
$0,026
$0,043
$14
2044
$0.23
$12
$1.7
$0,028
$0,027
$0,044
$14
2045
$0.23
$12
$1.7
$0,027
$0,031
$0,051
$14
2046
$0.22
$12
$1.7
$0,027
$0,037
$0,061
$14
2047
$0.22
$12
$1.7
$0,027
$0,046
$0,076
$14
2048
$0.22
$12
$1.6
$0,027
$0,057
$0,093
$14
2049
$0.21
$12
$1.6
$0,026
$0,068
$0.11
$14
2050
$0.21
$12
$1.6
$0,026
$0,076
$0.12
$14
PV, 3%
$6.8
$240
$19
$0.31
$6.3
$10
$290
PV, 7%
$4.5
$150
$9.8
$0.16
$4.5
$7.4
$180
Annualized,
3%
$0.35
$12
$0.97
$0,016
$0.32
$0.53
$15
Annualized,
7%
$0.36
$12
$0.79
$0,013
$0.36
$0.6
$14
Table Notes:
"Foregone Consumer Sales Surplus" refers to the difference between a vehicle's price and the buyer's willingness
to pay for the new vehicle; the impact reflects the reduction in new vehicle sales described in Chapter 8.1.
6-4
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6.2 Fuel Savings
Fuel savings presented here include fuel expenditure impacts for all fuels, including increased
expenditures on electricity and include rebound effects, credit usage and advanced technology
multiplier use.
6.2.1 Proposal
Table 6-4 Fuel Savings Associated with the Proposed Program ($Billions of 2018 dollars)
Calendar Year
Retail Fuel Savings
Fuel Tax Savings
Pre-Tax Fuel Savings
2023
$0.78
$0.2
$0.58
2024
$1.4
$0.38
$1
2025
$2.2
$0.61
$1.6
2026
$3.5
$0.95
$2.6
2027
$5.2
$1.4
$3.8
2028
$7.1
$1.8
$5.3
2029
$9.2
$2.3
$6.9
2030
$12
$2.7
$8.9
2031
$14
$3.1
$10
2032
$16
$3.5
$12
2033
$18
$3.8
$14
2034
$20
$4.2
$15
2035
$21
$4.4
$17
2036
$23
$4.7
$18
2037
$24
$4.9
$19
2038
$26
$5.1
$21
2039
$27
$5.2
$22
2040
$28
$5.4
$23
2041
$29
$5.5
$24
2042
$30
$5.5
$24
2043
$30
$5.6
$25
2044
$31
$5.6
$25
2045
$31
$5.6
$25
2046
$31
$5.6
$26
2047
$31
$5.6
$26
2048
$32
$5.6
$26
2049
$32
$5.6
$26
2050
$32
$5.6
$26
PV, 3%
$310
$62
$250
PV, 7%
$150
$32
$120
Annualized, 3%
$16
$3.2
$13
Annualized, 7%
$12
$2.5
$9.9
6-5
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6.2.2 Alternatives
Table 6-5 Fuel Savings Associated with Alternative 1 (SBillions of 2018 dollars)
Calendar Year
Retail Fuel Savings
Fuel Tax Savings
Pre-Tax Fuel Savings
2023
$0.73
$0.18
$0.54
2024
$1.3
$0.33
$0.94
2025
$2
$0.5
$1.5
2026
$3
$0.74
$2.3
2027
$4.3
$1
$3.2
2028
$5.7
$1.3
$4.4
2029
$7.3
$1.6
$5.7
2030
$9.2
$1.9
$7.3
2031
$11
$2.2
$8.5
2032
$12
$2.5
$9.9
2033
$14
$2.7
$11
2034
$15
$3
$12
2035
$16
$3.1
$13
2036
$18
$3.3
$14
2037
$19
$3.5
$15
2038
$20
$3.6
$16
2039
$21
$3.8
$17
2040
$22
$3.9
$18
2041
$23
$3.9
$19
2042
$23
$4
$19
2043
$23
$4
$19
2044
$24
$4
$20
2045
$24
$4.1
$20
2046
$24
$4
$20
2047
$24
$4
$20
2048
$24
$4
$20
2049
$24
$4
$20
2050
$24
$4
$20
PV, 3%
$240
$45
$200
PV, 7%
$120
$23
$98
Annualized, 3%
$12
$2.3
$10
Annualized, 7%
$9.7
$1.8
$7.9
6-6
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Table 6-6 Fuel Savings Associated with Alternative 2 (S Bill ions of 2018 dollars)
Calendar Year
Retail Fuel Savings
Fuel Tax Savings
Pre-Tax Fuel Savings
2023
$2.3
$0.56
$1.8
2024
$3.2
$0.78
$2.4
2025
$4.2
$1
$3.2
2026
$5.5
$1.4
$4.2
2027
$7.2
$1.7
$5.5
2028
$9.4
$2.2
$7.2
2029
$12
$2.6
$8.9
2030
$14
$3.1
$11
2031
$16
$3.5
$13
2032
$19
$3.8
$15
2033
$20
$4.2
$16
2034
$22
$4.5
$18
2035
$24
$4.8
$19
2036
$26
$5
$21
2037
$27
$5.2
$22
2038
$29
$5.4
$23
2039
$30
$5.5
$24
2040
$31
$5.7
$25
2041
$32
$5.8
$26
2042
$33
$5.8
$27
2043
$33
$5.9
$28
2044
$34
$5.9
$28
2045
$34
$5.9
$28
2046
$35
$6
$29
2047
$35
$6
$29
2048
$35
$6
$29
2049
$35
$6.1
$29
2050
$36
$6.1
$30
PV, 3%
$360
$69
$290
PV, 7%
$180
$36
$150
Annualized, 3%
$18
$3.5
$15
Annualized, 7%
$15
$2.9
$12
6.3 Non-Emission Benefits
Non-emission benefits include the drive value, or drive surplus (see Chapter 0), the refueling
time savings and the energy security benefits (see Chapter 3.2). With changes in fuel
consumption come associated changes in the amount of time spent refueling vehicles. Consistent
with the assumptions used in the SAFE FRM (and presented in Table 6-7), the costs of time
spent refueling are calculated as the total amount of time the driver of a typical vehicle would
spend refueling multiplied by the value of their time. If less time is spent refueling vehicles
under the proposed standards, then a refueling time savings would be incurred.
6-7
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Table 6-7 CCEMS Inputs used to Estimate Refueling Time Costs

Cars
Vans/SUVs
Pickups
Fixed Component of Average Refueling Time in Minutes (by Fuel Type)
Gasoline
3.5
3.5
3.5
Ethanol-85
3.5
3.5
3.5
Diesel
3.5
3.5
3.5
Electricity
3.5
3.5
3.5
Hydrogen
0
0
0
Compressed Natural Gas
0
0
0
Average Tank Volume Refueled
65%
65%
65%
Value of Travel Time per Vehicle (2018 $/hour)
20.46
20.79
20.79
6.3.1 Proposal
Table 6-8 Benefits from Non-Emission Sources under the Proposal (SBillions of 2018 dollars)
Calendar Year
Drive
Refueling Time
Energy Security
Total Non-Emission

Value
Savings
Benefits
Benefits
2023
$0,065
-$0,019
$0.03
$0,076
2024
$0.1
-$0,049
$0,057
$0.11
2025
$0.16
-$0,094
$0,093
$0.16
2026
$0.25
-$0.12
$0.15
$0.28
2027
$0.35
-$0.13
$0.22
$0.44
2028
$0.49
-$0.13
$0.29
$0.65
2029
$0.63
-$0.14
$0.36
$0.85
2030
$0.83
-$0.15
$0.46
$1.1
2031
$0.97
-$0.14
$0.53
$1.4
2032
$1.1
-$0.13
$0.6
$1.6
2033
$1.3
-$0.13
$0.66
$1.8
2034
$1.4
-$0.13
$0.72
$2
2035
$1.6
-$0.1
$0.83
$2.3
2036
$1.7
-$0,092
$0.88
$2.5
2037
$1.8
-$0,081
$0.93
$2.7
2038
$2
-$0,073
$0.98
$2.9
2039
$2
-$0,044
$1
$3
2040
$2.1
-$0,017
$1.1
$3.2
2041
$2.2
$0.0093
$1.1
$3.4
2042
$2.3
$0,029
$1.2
$3.5
2043
$2.3
$0,052
$1.2
$3.5
2044
$2.3
$0,064
$1.2
$3.6
2045
$2.3
$0,075
$1.3
$3.7
2046
$2.3
$0,083
$1.3
$3.8
2047
$2.3
$0,084
$1.3
$3.8
2048
$2.3
$0,092
$1.3
$3.8
2049
$2.3
$0,097
$1.4
$3.8
2050
$2.3
$0.1
$1.5
$3.9
PV, 3%
$23
-$0.94
$13
$35
PV, 7%
$11
-$0.72
$6.1
$17
Annualized,
$1.2
-$0,048
$0.64
$1.8
3%




Annualized,
$0.92
-$0,058
$0.49
$1.4
7%




6-8
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6.3.2 Alternatives
Table 6-9 Benefits from Non-Emission Sources under Alternative 1 (SBillions of 2018 dollars)
Calendar Year
Drive
Refueling Time
Energy Security
Total Non-Emission

Value
Savings
Benefits
Benefits
2023
$0,067
$0.0052
$0,027
$0,099
2024
$0.11
-$0.00047
$0,049
$0.16
2025
$0.17
-$0.00029
$0,077
$0.25
2026
$0.25
$0.02
$0.12
$0.39
2027
$0.36
$0.06
$0.16
$0.58
2028
$0.49
$0.1
$0.21
$0.8
2029
$0.62
$0.12
$0.26
$1
2030
$0.81
$0.15
$0.33
$1.3
2031
$0.94
$0.19
$0.38
$1.5
2032
$1.1
$0.22
$0.43
$1.8
2033
$1.2
$0.26
$0.47
$2
2034
$1.4
$0.28
$0.51
$2.2
2035
$1.5
$0.33
$0.59
$2.4
2036
$1.6
$0.35
$0.63
$2.6
2037
$1.7
$0.36
$0.67
$2.8
2038
$1.8
$0.37
$0.7
$2.9
2039
$1.9
$0.39
$0.72
$3
2040
$2
$0.41
$0.81
$3.2
2041
$2.1
$0.43
$0.83
$3.3
2042
$2.1
$0.42
$0.84
$3.4
2043
$2.2
$0.41
$0.85
$3.4
2044
$2.2
$0.4
$0.86
$3.4
2045
$2.1
$0.38
$0.96
$3.5
2046
$2.2
$0.37
$0.96
$3.5
2047
$2.2
$0.36
$0.96
$3.5
2048
$2.2
$0.36
$0.96
$3.5
2049
$2.2
$0.34
$0.96
$3.5
2050
$2.2
$0.32
$1.1
$3.5
PV, 3%
$22
$4.2
$9
$35
PV, 7%
$11
$2.1
$4.4
$17
Annualized,
$1.1
$0.21
$0.46
$1.8
3%




Annualized,
$0.88
$0.17
$0.36
$1.4
7%




6-9
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Table 6-10
Benefits from
Non-Emission Sources under Alternative 2
(SBillions of 2018 dollars)
Calendar Year
Drive
Refueling Time

Value
Savings
2023
$0.21
$0.08
2024
$0.28
$0,083
2025
$0.37
$0,087
2026
$0.47
$0.11
2027
$0.6
$0.15
2028
$0.78
$0.19
2029
$0.96
$0.23
2030
$1.2
$0.27
2031
$1.4
$0.32
2032
$1.6
$0.35
2033
$1.8
$0.39
2034
$1.9
$0.41
2035
$2.1
$0.45
2036
$2.3
$0.5
2037
$2.4
$0.54
2038
$2.5
$0.58
2039
$2.6
$0.63
2040
$2.8
$0.68
2041
$2.9
$0.71
2042
$2.9
$0.72
2043
$3
$0.74
2044
$3
$0.75
2045
$3
$0.75
2046
$3
$0.73
2047
$3
$0.71
2048
$3
$0.69
2049
$2.9
$0.67
2050
$2.9
$0.61
PV, 3%
$31
$7.5
PV, 7%
$16
$3.8
Annualized,
$1.6
$0.38
3%


Annualized,
$1.3
$0.31
7%


Energy Security
Total Non-Emission
Benefits
Benefits
$0,082
$0.37
$0.12
$0.48
$0.16
$0.62
$0.21
$0.79
$0.27
$1
$0.35
$1.3
$0.42
$1.6
$0.52
$2
$0.59
$2.3
$0.66
$2.6
$0.72
$2.9
$0.78
$3.1
$0.9
$3.5
$0.95
$3.7
$0.99
$3.9
$1
$4.2
$1.1
$4.3
$1.2
$4.6
$1.2
$4.8
$1.2
$4.9
$1.2
$5
$1.3
$5
$1.4
$5.1
$1.4
$5.1
$1.4
$5.1
$1.4
$5.1
$1.5
$5
$1.6
$5.1
$14
$53
$6.8
$26
$0.7
$2.7
$0.55
$2.1
6-10
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Chapter 7: Non-GHG Health and Environmental Impacts
In this chapter we discuss the health effects associated with non-GHG pollutants, specifically:
particulate matter, ozone, nitrogen oxides (NOx), sulfur oxides (SOx), carbon monoxide and air
toxics. These pollutants will not be directly regulated by the standards, but the standards will
affect emissions of these pollutants and precursors.
7.1 Health and Environmental Impacts of Non-GHG Pollutants
7.1.1 Background on Non-GHG Pollutants Impacted by the Proposed Standards
7.1.1.1 Particulate Matter
Particulate matter (PM) is a complex mixture of solid particles and liquid droplets distributed
among numerous atmospheric gases which interact with solid and liquid phases. Particles in the
atmosphere range in size from less than 0.01 to more than 10 micrometers (|im) in diameter.1
Atmospheric particles can be grouped into several classes according to their aerodynamic and
physical sizes. Generally, the three broad classes of particles include ultrafine particles (UFPs,
generally considered as particles with a diameter less than or equal to 0.1 |im [typically based on
physical size, thermal diffusivity or electrical mobility]), "fine" particles (PM2.5; particles with a
nominal mean aerodynamic diameter less than or equal to 2.5 |im), and "thoracic" particles
(PM10; particles with a nominal mean aerodynamic diameter less than or equal to 10 |im).
Particles that fall within the size range between PM2.5 and PM10, are referred to as "thoracic
coarse particles" (PM10-2.5, particles with a nominal mean aerodynamic diameter greater than 2.5
|im and less than or equal to 10 |im). EPA currently has NAAQS for PM2.5 and PMio.a
Most particles are found in the lower troposphere, where they can have residence times
ranging from a few hours to weeks. Particles are removed from the atmosphere by wet
deposition, such as when they are carried by rain or snow, or by dry deposition, when particles
settle out of suspension due to gravity. Atmospheric lifetimes are generally longest for PM2.5,
which often remains in the atmosphere for days to weeks before being removed by wet or dry
deposition.2 In contrast, atmospheric lifetimes for UFP and PM10-2.5 are shorter. Within hours,
UFP can undergo coagulation and condensation that lead to formation of larger particles in the
accumulation mode, or can be removed from the atmosphere by evaporation, deposition, or
reactions with other atmospheric components. PM10-2.5 are also generally removed from the
atmosphere within hours, through wet or dry deposition.3
Particulate matter consists of both primary and secondary particles. Primary particles are
emitted directly from sources, such as combustion-related activities (e.g., industrial activities,
motor vehicle operation, biomass burning), while secondary particles are formed through
atmospheric chemical reactions of gaseous precursors (e.g., sulfur oxides (SOx), nitrogen oxides
(NOx) and volatile organic compounds (VOCs)). From 2000 to 2017, national annual average
a Regulatory definitions of PM size fractions, and information on reference and equivalent methods for measuring
PM in ambient air, are provided in 40 CFR Parts 50, 53, and 58. With regard to national ambient air quality
standards (NAAQS) which provide protection against health and welfare effects, the 24-hour PMio standard
provides protection against effects associated with short-term exposure to thoracic coarse particles (i.e., PMi0-2.5).
7-1
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ambient PM2.5 concentrations have declined by over 40 percent,4 largely reflecting reductions in
emissions of precursor gases.
7.1.1.2	Ozone
Ground-level ozone pollution forms in areas with high concentrations of ambient NOx and
VOCs when solar radiation is strong. Major U.S. sources of NOx are highway and nonroad
motor vehicles, engines, power plants and other industrial sources, with natural sources, such as
soil, vegetation, and lightning, serving as smaller sources. Vegetation is the dominant source of
VOCs in the U.S. Volatile consumer and commercial products, such as propellants and solvents,
highway and nonroad vehicles, engines, fires, and industrial sources also contribute to the
atmospheric burden of VOCs at ground-level.
The processes underlying ozone formation, transport, and accumulation are complex.
Ground-level ozone is produced and destroyed by an interwoven network of free radical
reactions involving the hydroxyl radical (OH), NO, NO2, and complex reaction intermediates
derived from VOCs. Many of these reactions are sensitive to temperature and available sunlight.
High ozone events most often occur when ambient temperatures and sunlight intensities remain
high for several days under stagnant conditions. Ozone and its precursors can also be transported
hundreds of miles downwind which can lead to elevated ozone levels in areas with otherwise low
VOC or NOx emissions. As an air mass moves and is exposed to changing ambient
concentrations of NOx and VOCs, the ozone photochemical regime (relative sensitivity of ozone
formation to NOx and VOC emissions) can change.
When ambient VOC concentrations are high, comparatively small amounts of NOx catalyze
rapid ozone formation. Without available NOx, ground-level ozone production is severely
limited and VOC reductions will have little impact on ozone concentrations. Photochemistry
under these conditions is said to be "NOx-limited." When NOx levels are sufficiently high,
faster NO2 oxidation consumes more radicals, dampening ozone production. Under these " VOC-
limited" conditions (also referred to as "NOx-saturated" conditions), VOC reductions are
effective in reducing ozone, and NOx can react directly with ozone resulting in suppressed ozone
concentrations near NOx emission sources. Under these NOx-saturated conditions, NOx
reductions can actually increase local ozone under certain circumstances, but overall ozone
production (considering downwind formation) decreases and even in VOC-limited areas, NOx
reductions are not expected to increase ozone levels if the NOx reductions are sufficiently large -
large enough to become NOx-limited.
7.1.1.3	Nitrogen Oxides
Oxides of nitrogen (NOx) refers to nitric oxide (NO) and nitrogen dioxide (NO2). Most NO2
is formed in the air through the oxidation of nitric oxide (NO) emitted when fuel is burned at a
high temperature. NOx is a criteria pollutant, regulated for its adverse effects on public health
and the environment, and highway vehicles are an important contributor to NOx emissions.
NOx, along with VOCs, are the two major precursors of ozone and NOx is also a major
contributor to secondary PM2.5 formation.
7.1.1.4	Sulfur Oxides
Sulfur dioxide (SO2), a member of the sulfur oxide (SOx) family of gases, is formed from
burning fuels containing sulfur (e.g., coal or oil), extracting gasoline from oil, or extracting
7-2
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metals from ore. SO2 and its gas phase oxidation products can dissolve in water droplets and
further oxidize to form sulfuric acid which reacts with ammonia to form sulfates, which are
important components of ambient PM.
7.1.1.5	Carbon Monoxide
Carbon monoxide (CO) is a colorless, odorless gas emitted from combustion processes.
Nationally, particularly in urban areas, the majority of CO emissions to ambient air come from
mobile sources.5
7.1.1.6	Air Toxics
Light-duty engine emissions contribute to ambient levels of air toxics that are known or
suspected human or animal carcinogens, or that have noncancer health effects. These
compounds include, but are not limited to, benzene, formaldehyde, acetaldehyde, naphthalene,
and 1,3-butadiene. These compounds were identified as national or regional risk drivers or
contributors in the 2014 National-scale Air Toxics Assessment and have significant inventory
contributions from mobile sources.6'7
7.1.2 Health Effects Associated with Exposure to Non-GHG Pollutants
7.1.2.1 Particulate Matter
Scientific evidence spanning animal toxicological, controlled human exposure, and
epidemiologic studies shows that exposure to ambient PM is associated with a broad range of
health effects. These health effects are discussed in detail in the Integrated Science Assessment
for Particulate Matter (PM ISA), which was finalized in December 2019.8 The PM ISA
characterizes the causal nature of relationships between PM exposure and broad health categories
(e.g., cardiovascular effects, respiratory effects, etc.) using a weight-of-evidence approach.b>9
Within this characterization, the PM ISA summarizes the health effects evidence for short- and
long-term exposures to PM2.5, PM10-2.5, and ultrafine particles, and concludes that human
exposures to ambient PM2.5 are associated with a number of adverse health effects. The
discussion below highlights the PM ISA's conclusions pertaining to the health effects evidence
for both short- and long-term PM exposures. Further discussion of PM-related health effects can
also be found in the 2020 Policy Assessment for the review of the PM NAAQS.10
EPA has concluded that recent evidence in combination with evidence evaluated in the 2009
PM ISA supports a "causal relationship" between both long- and short-term exposures to PM2.5
and premature mortality and cardiovascular effects and a "likely to be causal relationship"
between long- and short-term PM2.5 exposures and respiratory effects.11 Additionally, recent
experimental and epidemiologic studies provide evidence supporting a "likely to be causal
relationship" between long-term PM2.5 exposure and nervous system effects, and long-term
b The causal framework draws upon the assessment and integration of evidence from across scientific disciplines,
spanning atmospheric chemistry, exposure, dosimetry and health effects studies (i.e., epidemiologic, controlled
human exposure, and animal toxicological studies), and assess the related uncertainties and limitations that
ultimately influence our understanding of the evidence. This framework employs a five-level hierarchy that
classifies the overall weight-of-evidence with respect to the causal nature of relationships between criteria pollutant
exposures and health and welfare effects using the following categorizations: causal relationship; likely to be causal
relationship; suggestive of, but not sufficient to infer, a causal relationship; inadequate to infer the presence or
absence of a causal relationship; and not likely to be a causal relationship.
7-3
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PM2.5 exposure and cancer. In contrast, EPA determined that the more limited and uncertain
evidence was "suggestive of, but not sufficient to infer, a causal relationship" for long-term
PM2.5 exposure and reproductive and developmental effects (i.e., male/female reproduction and
fertility; pregnancy and birth outcomes), long- and short-term exposures and metabolic effects,
and short-term exposure and nervous system effects.
As discussed extensively in the 2019 PM ISA, recent studies continue to support and extend
the evidence base linking short- and long-term PM2.5 exposures and premature mortality.8 For
short-term PM2.5 exposure, recent multi-city studies, in combination with single- and multi-city
studies evaluated in the 2009 PM ISA, provide evidence of consistent, positive associations
across studies conducted in different geographic locations, populations with different
demographic characteristics, and studies using different exposure assignment techniques.
Additionally, the consistent and coherent evidence across scientific disciplines for cardiovascular
morbidity, particularly ischemic events and heart failure, and to a lesser degree for respiratory
morbidity, with the strongest evidence for exacerbations of chronic obstructive pulmonary
disease (COPD) and asthma, provide biological plausibility for cause-specific mortality and
ultimately total mortality.
In addition to re-analyses and extensions of the American Cancer Society (ACS) and Harvard
Six Cities (HSC) cohorts, multiple new cohort studies conducted in the U.S. and Canada
consisting of people employed in a specific job (e.g., teacher, nurse), and that apply different
exposure assignment techniques provide evidence of positive associations between long-term
PM2.5 exposure and mortality. Biological plausibility for mortality due to long-term PM2.5
exposure is provided by the coherence of effects across scientific disciplines for cardiovascular
morbidity, particularly for coronary heart disease (CHD), stroke and atherosclerosis, and for
respiratory morbidity, particularly for the development of COPD. Additionally, recent studies
provide evidence indicating that as long-term PM2.5 concentrations decrease there is an increase
in life expectancy.
A large body of recent studies examining both short- and long-term PM2.5 exposure and
cardiovascular effects supports and extends the evidence base evaluated in the 2009 PM ISA.
Some of the strongest evidence from both experimental and epidemiologic studies examining
short-term PM2.5 exposures are for ischemic heart disease (IHD) and heart failure. The evidence
for cardiovascular effects is coherent across studies of short-term PM2.5 exposure that have
observed associations with a continuum of effects ranging from subtle changes in indicators of
cardiovascular health to serious clinical events, such as increased emergency department visits
and hospital admissions due to cardiovascular disease and cardiovascular mortality. For long-
term PM2.5 exposure, there is strong and consistent epidemiologic evidence of a relationship with
cardiovascular mortality. This evidence is supported by epidemiologic and animal toxicological
studies demonstrating a range of cardiovascular effects including coronary heart disease, stroke,
impaired heart function, and subclinical markers (e.g., coronary artery calcification,
atherosclerotic plaque progression), which collectively provide coherence and biological
plausibility.
Recent studies continue to provide evidence of a relationship between both short- and long-
term PM2.5 exposure and respiratory effects. Epidemiologic and animal toxicological studies
examining short-term PM2.5 exposure provide consistent evidence of asthma and COPD
exacerbations, in children and adults, respectively. This evidence is supported by epidemiologic
7-4
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studies examining asthma and COPD emergency department visits and hospital admissions, as
well as, respiratory mortality. Epidemiologic studies conducted in the U.S. and abroad provide
evidence of a relationship between long-term PM2.5 exposure and respiratory effects, including
consistent changes in lung function and lung function growth rate, increased asthma incidence,
asthma prevalence, and wheeze in children; acceleration of lung function decline in adults; and
respiratory mortality. The epidemiologic evidence is supported by animal toxicological studies,
which provide coherence and biological plausibility for a range of effects including impaired
lung development, decrements in lung function growth, and asthma development.
Since the 2009 PM ISA, a growing body of scientific evidence examined the relationship
between long-term PM2.5 exposure and nervous system effects, resulting for the first time in a
causality determination for this health effects category. Epidemiologic studies provide consistent
evidence for cognitive decrements and reductions in brain volume in adults. The effects observed
in epidemiologic studies are supported by animal toxicological studies demonstrating effects on
the brain of adult animals including inflammation, morphologic changes, and neurodegeneration
of specific regions of the brain.
Building off the decades of research demonstrating mutagenicity, DNA damage, and
endpoints related to genotoxicity due to whole PM exposures, recent experimental and
epidemiologic studies focusing specifically on PM2.5 provide evidence of a relationship between
long-term PM2.5 exposure and cancer. Epidemiologic studies examining long-term PM2.5
exposure and lung cancer incidence and mortality provide evidence of generally positive
associations in cohort studies spanning different populations, locations, and exposure assignment
techniques. Additionally, there is evidence of positive associations in analyses limited to never
smokers. The epidemiologic evidence is supported by both experimental and epidemiologic
evidence of genotoxicity, epigenetic effects, carcinogenic potential, and that PM2.5 exhibits
several characteristics of carcinogens, which collectively provides biological plausibility for
cancer development.
For the additional health effects categories evaluated for PM2.5 in the 2019 PM ISA,
experimental and epidemiologic studies provide limited and/or inconsistent evidence of a
relationship with PM2.5 exposure. As a result, the 2019 PM ISA concluded that the evidence is
"suggestive of, but not sufficient to infer a causal relationship" for short-term PM2.5 exposure
and metabolic effects and nervous system effects, and long-term PM2.5 exposures and metabolic
effects as well as reproductive and developmental effects.
In addition to evaluating the health effects attributed to short- and long-term exposure to
PM2.5, the 2019 PM ISA also conducted an extensive evaluation as to whether specific
components or sources of PM2.5 are more strongly related with health effects than PM2.5 mass.
An evaluation of those studies resulted in the 2019 PM ISA concluding that "many PM2.5
components and sources are associated with many health effects, and the evidence does not
indicate that any one source or component is consistently more strongly related to health effects
than PM2.5 mass."8
For both PM10-2.5 and UFPs, for all health effects categories evaluated, the 2019 PM ISA
concluded that the evidence was "suggestive of, but not sufficient to infer, a causal relationship"
or "inadequate to determine the presence or absence of a causal relationship." For PM10-2.5,
although a Federal Reference Method (FRM) was instituted in 2011 to measure PM10-2.5
concentrations nationally, the causality determinations reflect that the same uncertainty identified
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in the 2009 PM ISA with respect to the method used to estimate PM10-2.5 concentrations in
epidemiologic studies persists. Specifically, across epidemiologic studies, different approaches
are used to estimate PM10-2.5 concentrations (e.g., direct measurement of PM10-2.5, difference
between PM10 and PM2.5 concentrations), and it remains unclear how well correlated PM10-2.5
concentrations are both spatially and temporally across the different methods used.
For UFPs, the uncertainty in the evidence for the health effect categories evaluated across
experimental and epidemiologic studies reflects the inconsistency in the exposure metric used
(i.e., particle number concentration, surface area concentration, mass concentration) as well as
the size fractions examined. In epidemiologic studies the size fraction can vary depending on the
monitor used and exposure metric, with some studies examining number count over the entire
particle size range, while experimental studies that use a particle concentrator often examine
particles up to 0.3 |im. Additionally, due to the lack of a monitoring network, there is limited
information on the spatial and temporal variability of UFPs within the U.S., as well as population
exposures to UFPs, which adds uncertainty to epidemiologic study results.
The 2019 PM ISA cites extensive evidence indicating that "both the general population as
well as specific populations and lifestages are at risk for PM2.5-related health effects."8'10 For
example, in support of its "causal" and "likely to be causal" determinations, the ISA cites
substantial evidence for (1) PM-related mortality and cardiovascular effects in older adults ; (2)
PM-related cardiovascular effects in people with pre-existing cardiovascular disease ; (3) PM-
related respiratory effects in people with pre-existing respiratory disease, particularly asthma
exacerbations in children ; and (4) PM-related impairments in lung function growth and asthma
development in children . The ISA additionally notes that stratified analyses (i.e., analyses that
directly compare PM-related health effects across groups) provide strong evidence for racial and
ethnic differences in PM2.5 exposures and in the risk of PM2.5-related health effects, specifically
within Hispanic and non-Hispanic black populations. Additionally, evidence spanning
epidemiologic studies that conducted stratified analyses, experimental studies focusing on animal
models of disease or individuals with pre-existing disease, dosimetry studies, as well as studies
focusing on differential exposure suggest that populations with pre-existing cardiovascular or
respiratory disease, populations that are overweight or obese, populations that have particular
genetic variants, populations that are of low socioeconomic status, and current/former smokers
could be at increased risk for adverse PM2.5-related health effects.
7.1.2.2 Ozone
This section provides a summary of the health effects associated with exposure to ambient
concentrations of ozone.12 The information in this section is based on the information and
conclusions in the April 2020 Integrated Science Assessment for Ozone (Ozone ISA).13 The
Ozone ISA concludes that human exposures to ambient concentrations of ozone are associated
with a number of adverse health effects and characterizes the weight of evidence for these health
effects.14 The discussion below highlights the Ozone ISA's conclusions pertaining to health
effects associated with both short-term and long-term periods of exposure to ozone.
For short-term exposure to ozone, the Ozone ISA concludes that respiratory effects, including
lung function decrements, pulmonary inflammation, exacerbation of asthma, respiratory-related
hospital admissions, and mortality, are causally associated with ozone exposure. It also
concludes that metabolic effects, including metabolic syndrome (i.e., changes in insulin or
glucose levels, cholesterol levels, obesity and blood pressure) and complications due to diabetes
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are likely to be causally associated with short-term exposure to ozone and that evidence is
suggestive of a causal relationship between cardiovascular effects, central nervous system effects
and total mortality and short-term exposure to ozone.
For long-term exposure to ozone, the Ozone ISA concludes that respiratory effects, including
new onset asthma, pulmonary inflammation and injury, are likely to be causally related with
ozone exposure. The Ozone ISA characterizes the evidence as suggestive of a causal
relationship for associations between long-term ozone exposure and cardiovascular effects,
metabolic effects, reproductive and developmental effects, central nervous system effects and
total mortality. The evidence is inadequate to infer a causal relationship between chronic ozone
exposure and increased risk of cancer.
This section provides a summary of the health effects associated with exposure to ambient
concentrations of ozone.c The information in this section is based on the information and
conclusions in the April 2020 Integrated Science Assessment for Ozone (Ozone ISA).15 The
Ozone ISA concludes that human exposures to ambient concentrations of ozone are associated
with a number of adverse health effects and characterizes the weight of evidence for these health
effects.d The discussion below highlights the Ozone ISA's conclusions pertaining to health
effects associated with both short-term and long-term periods of exposure to ozone.
For short-term exposure to ozone, the Ozone ISA concludes that respiratory effects, including
lung function decrements, pulmonary inflammation, exacerbation of asthma, respiratory-related
hospital admissions, and mortality, are causally associated with ozone exposure. It also
concludes that metabolic effects, including metabolic syndrome (i.e., changes in insulin or
glucose levels, cholesterol levels, obesity and blood pressure) and complications due to diabetes
are likely to be causally associated with short-term exposure to ozone and that evidence is
suggestive of a causal relationship between cardiovascular effects, central nervous system effects
and total mortality and short-term exposure to ozone.
For long-term exposure to ozone, the Ozone ISA concludes that respiratory effects, including
new onset asthma, pulmonary inflammation and injury, are likely to be causally related with
ozone exposure. The Ozone ISA characterizes the evidence as suggestive of a causal
relationship for associations between long-term ozone exposure and cardiovascular effects,
metabolic effects, reproductive and developmental effects, central nervous system effects and
total mortality. The evidence is inadequate to infer a causal relationship between chronic ozone
exposure and increased risk of cancer.
Finally, interindividual variation in human responses to ozone exposure can result in some
groups being at increased risk for detrimental effects in response to exposure. In addition, some
groups are at increased risk of exposure due to their activities, such as outdoor workers and
children. The Ozone ISA identified several groups that are at increased risk for ozone-related
0 Human exposure to ozone varies over time due to changes in ambient ozone concentration and because people
move between locations which have notably different ozone concentrations. Also, the amount of ozone delivered to
the lung is influenced not only by the ambient concentrations but also by the breathing route and rate.
dThe ISA evaluates evidence and draws conclusions on the causal relationship between relevant pollutant exposures
and health effects, assigning one of five "weight of evidence" determinations: causal relationship, likely to be a
causal relationship, suggestive of a causal relationship, inadequate to infer a causal relationship, and not likely to be
a causal relationship. For more information on these levels of evidence, please refer to Table II in the Preamble of
the ISA.
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health effects. These groups are people with asthma, children and older adults, individuals with
reduced intake of certain nutrients (i.e., Vitamins C and E), outdoor workers, and individuals
having certain genetic variants related to oxidative metabolism or inflammation. Ozone
exposure during childhood can have lasting effects through adulthood. Such effects include
altered function of the respiratory and immune systems. Children absorb higher doses
(normalized to lung surface area) of ambient ozone, compared to adults, due to their increased
time spent outdoors, higher ventilation rates relative to body size, and a tendency to breathe a
greater fraction of air through the mouth. Children also have a higher asthma prevalence
compared to adults. Recent epidemiologic studies provide generally consistent evidence that
long-term ozone exposure is associated with the development of asthma in children. Studies
comparing age groups reported higher magnitude associations for short-term ozone exposure and
respiratory hospital admissions and emergency room visits among children than for adults. Panel
studies also provide support for experimental studies with consistent associations between short-
term ozone exposure and lung function and pulmonary inflammation in healthy children.
7.1.2.3 Nitrogen Oxides
The most recent review of the health effects of oxides of nitrogen completed by EPA can be
found in the 2016 Integrated Science Assessment for Oxides of Nitrogen - Health Criteria
(Oxides of Nitrogen ISA).16 The primary source of NO2 is motor vehicle emissions, and ambient
NO2 concentrations tend to be highly correlated with other traffic-related pollutants. Thus, a key
issue in characterizing the causality of N02-health effect relationships consists of evaluating the
extent to which studies supported an effect of NO2 that is independent of other traffic-related
pollutants. EPA concluded that the findings for asthma exacerbation integrated from
epidemiologic and controlled human exposure studies provided evidence that is sufficient to
infer a causal relationship between respiratory effects and short-term NO2 exposure. The
strongest evidence supporting an independent effect of NO2 exposure comes from controlled
human exposure studies demonstrating increased airway responsiveness in individuals with
asthma following ambient-relevant NO2 exposures. The coherence of this evidence with
epidemiologic findings for asthma hospital admissions and ED visits as well as lung function
decrements and increased pulmonary inflammation in children with asthma describe a plausible
pathway by which NO2 exposure can cause an asthma exacerbation. The 2016 ISA for Oxides of
Nitrogen also concluded that there is likely to be a causal relationship between long-term NO2
exposure and respiratory effects. This conclusion is based on new epidemiologic evidence for
associations of NO2 with asthma development in children combined with biological plausibility
from experimental studies.
In evaluating a broader range of health effects, the 2016 ISA for Oxides of Nitrogen
concluded that evidence is "suggestive of, but not sufficient to infer, a causal relationship"
between short-term NO2 exposure and cardiovascular effects and mortality and between long-
term NO2 exposure and cardiovascular effects and diabetes, birth outcomes, and cancer. In
addition, the scientific evidence is inadequate (insufficient consistency of epidemiologic and
toxicological evidence) to infer a causal relationship for long-term NO2 exposure with fertility,
reproduction, and pregnancy, as well as with postnatal development. A key uncertainty in
understanding the relationship between these non-respiratory health effects and short- or long-
term exposure to NO2 is co-pollutant confounding, particularly by other roadway pollutants. The
available evidence for non-respiratory health effects does not adequately address whether NO2
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has an independent effect or whether it primarily represents effects related to other or a mixture
of traffic-related pollutants.
The 2016 ISA for Oxides of Nitrogen concluded that people with asthma, children, and older
adults are at increased risk for NCh-related health effects. In these groups and lifestages, NO2 is
consistently related to larger effects on outcomes related to asthma exacerbation, for which there
is confidence in the relationship with NO2 exposure.
7.1.2.4 Sulfur Oxides
This section provides an overview of the health effects associated with SO2. Additional
information on the health effects of SO2 can be found in the 2017 Integrated Science Assessment
for Sulfur Oxides - Health Criteria (SOx ISA).17 Following an extensive evaluation of health
evidence from animal toxicological, controlled human exposure, and epidemiologic studies, the
EPA has concluded that there is a causal relationship between respiratory health effects and
short-term exposure to SO2. The immediate effect of SO2 on the respiratory system in humans is
bronchoconstriction. People with asthma are more sensitive to the effects of SO2, likely resulting
from preexisting inflammation associated with this disease. In addition to those with asthma
(both children and adults), there is suggestive evidence that all children and older adults may be
at increased risk of S02-related health effects. In free-breathing laboratory studies involving
controlled human exposures to SO2, respiratory effects have consistently been observed
following 5-10 min exposures at SO2 concentrations > 400 ppb in people with asthma engaged in
moderate to heavy levels of exercise, with respiratory effects occurring at concentrations as low
as 200 ppb in some individuals with asthma. A clear concentration-response relationship has
been demonstrated in these studies following exposures to SO2 at concentrations between 200
and 1000 ppb, both in terms of increasing severity of respiratory symptoms and decrements in
lung function, as well as the percentage of individuals with asthma adversely affected.
Epidemiologic studies have reported positive associations between short-term ambient SO2
concentrations and hospital admissions and emergency department visits for asthma and for all
respiratory causes, particularly among children and older adults (> 65 years). The studies provide
supportive evidence for the causal relationship.
For long-term SO2 exposure and respiratory effects, the EPA has concluded that the evidence
is suggestive of a causal relationship. This conclusion is based on new epidemiologic evidence
for positive associations between long-term SO2 exposure and increases in asthma incidence
among children, together with animal toxicological evidence that provides a pathophysiologic
basis for the development of asthma. However, uncertainty remains regarding the influence of
other pollutants on the observed associations with SO2 because these epidemiologic studies have
not examined the potential for co-pollutant confounding.
Consistent associations between short-term exposure to SO2 and mortality have been observed
in epidemiologic studies, with larger effect estimates reported for respiratory mortality than for
cardiovascular mortality. While this finding is consistent with the demonstrated effects of SO2
on respiratory morbidity, uncertainty remains with respect to the interpretation of these observed
mortality associations due to potential confounding by various co-pollutants. Therefore, the EPA
has concluded that the overall evidence is suggestive of a causal relationship between short-term
exposure to SO2 and mortality.
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7.1.2.5 Carbon Monoxide
Information on the health effects of carbon monoxide (CO) can be found in the January 2010
Integrated Science Assessment for Carbon Monoxide (CO ISA).18 The CO ISA presents
conclusions regarding the presence of causal relationships between CO exposure and categories
of adverse health effects.19 This section provides a summary of the health effects associated with
exposure to ambient concentrations of CO, along with the CO ISA conclusions.20
Controlled human exposure studies of subjects with coronary artery disease show a decrease
in the time to onset of exercise-induced angina (chest pain) and electrocardiogram changes
following CO exposure. In addition, epidemiologic studies observed associations between short-
term CO exposure and cardiovascular morbidity, particularly increased emergency room visits
and hospital admissions for coronary heart disease (including ischemic heart disease, myocardial
infarction, and angina). Some epidemiologic evidence is also available for increased hospital
admissions and emergency room visits for congestive heart failure and cardiovascular disease as
a whole. The CO ISA concludes that a causal relationship is likely to exist between short-term
exposures to CO and cardiovascular morbidity. It also concludes that available data are
inadequate to conclude that a causal relationship exists between long-term exposures to CO and
cardiovascular morbidity.
Animal studies show various neurological effects with in-utero CO exposure. Controlled
human exposure studies report central nervous system and behavioral effects following low-level
CO exposures, although the findings have not been consistent across all studies. The CO ISA
concludes that the evidence is suggestive of a causal relationship with both short- and long-term
exposure to CO and central nervous system effects.
A number of studies cited in the CO ISA have evaluated the role of CO exposure in birth
outcomes such as preterm birth or cardiac birth defects. There is limited epidemiologic evidence
of a CO-induced effect on preterm births and birth defects, with weak evidence for a decrease in
birth weight. Animal toxicological studies have found perinatal CO exposure to affect birth
weight, as well as other developmental outcomes. The CO ISA concludes that the evidence is
suggestive of a causal relationship between long-term exposures to CO and developmental
effects and birth outcomes.
Epidemiologic studies provide evidence of associations between short-term CO
concentrations and respiratory morbidity such as changes in pulmonary function, respiratory
symptoms, and hospital admissions. A limited number of epidemiologic studies considered
copollutants such as ozone, SO2, and PM in two-pollutant models and found that CO risk
estimates were generally robust, although this limited evidence makes it difficult to disentangle
effects attributed to CO itself from those of the larger complex air pollution mixture. Controlled
human exposure studies have not extensively evaluated the effect of CO on respiratory
morbidity. Animal studies at levels of 50-100 ppm CO show preliminary evidence of altered
pulmonary vascular remodeling and oxidative injury. The CO ISA concludes that the evidence
is suggestive of a causal relationship between short-term CO exposure and respiratory morbidity,
and inadequate to conclude that a causal relationship exists between long-term exposure and
respiratory morbidity.
Finally, the CO ISA concludes that the epidemiologic evidence is suggestive of a causal
relationship between short-term concentrations of CO and mortality. Epidemiologic evidence
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suggests an association exists between short-term exposure to CO and mortality, but limited
evidence is available to evaluate cause-specific mortality outcomes associated with CO exposure.
In addition, the attenuation of CO risk estimates which was often observed in co-pollutant
models contributes to the uncertainty as to whether CO is acting alone or as an indicator for other
combustion-related pollutants. The CO ISA also concludes that there is not likely to be a causal
relationship between relevant long-term exposures to CO and mortality.
7.1.2.6 Air Toxics
7.1.2.6.1	Health Effects Associated with Exposure to Benzene
EPA's Integrated Risk Information System (IRIS) database lists benzene as a known human
carcinogen (causing leukemia) by all routes of exposure, and concludes that exposure is
associated with additional health effects, including genetic changes in both humans and animals
and increased proliferation of bone marrow cells in mice.21,22:23 EPA states in its IRIS database
that data indicate a causal relationship between benzene exposure and acute lymphocytic
leukemia and suggest a relationship between benzene exposure and chronic non-lymphocytic
leukemia and chronic lymphocytic leukemia. EPA's IRIS documentation for benzene also lists a
range of 2.2 x 10-6 to 7.8 x 10-6 per |ig/m3 as the unit risk estimate (URE) for benzene.24'25 The
International Agency for Research on Cancer (IARC) has determined that benzene is a human
carcinogen, and the U.S. Department of Health and Human Services (DHHS) has characterized
benzene as a known human carcinogen.26'27
A number of adverse noncancer health effects, including blood disorders such as preleukemia
and aplastic anemia, have also been associated with long-term exposure to benzene.28'29 The
most sensitive noncancer effect observed in humans, based on current data, is the depression of
the absolute lymphocyte count in blood.30'31 EPA's inhalation reference concentration (RfC) for
benzene is 30 |ig/m3. The RfC is based on suppressed absolute lymphocyte counts seen in
humans under occupational exposure conditions. In addition, studies sponsored by the Health
Effects Institute (HEI) provide evidence that biochemical responses occur at lower levels of
benzene exposure than previously known.32'33'34'35 EPA's IRIS program has not yet evaluated
these new data. EPA does not currently have an acute reference concentration for benzene. The
Agency for Toxic Substances and Disease Registry (ATSDR) Minimal Risk Level (MRL) for
acute exposure to benzene is 29 |ig/m3 for 1-14 days exposure.36'37
7.1.2.6.2	Health Effects Associated with Exposure to Formaldehyde
In 1991, EPA concluded that formaldehyde is a Class B1 probable human carcinogen based
on limited evidence in humans and sufficient evidence in animals.38 An Inhalation URE for
cancer and a Reference Dose for oral noncancer effects were developed by EPA and posted on
the IRIS database. Since that time, the NTP and IARC have concluded that formaldehyde is a
known human carcinogen.39'40'41
The conclusions by IARC and NTP reflect the results of epidemiologic research published
since 1991 in combination with previous animal, human and mechanistic evidence. Research
conducted by the National Cancer Institute reported an increased risk of nasopharyngeal cancer
and specific lymphohematopoietic malignancies among workers exposed to formaldehyde.42'43'44
A National Institute of Occupational Safety and Health study of garment workers also reported
increased risk of death due to leukemia among workers exposed to formaldehyde.45 Extended
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follow-up of a cohort of British chemical workers did not report evidence of an increase in
nasopharyngeal or lymphohematopoietic cancers, but a continuing statistically significant excess
in lung cancers was reported.46 Finally, a study of embalmers reported formaldehyde exposures
to be associated with an increased risk of myeloid leukemia but not brain cancer.47
Health effects of formaldehyde in addition to cancer were reviewed by the Agency for Toxics
Substances and Disease Registry in 1999, supplemented in 2010, and by the World Health
Organization. 48>49>50 These organizations reviewed the scientific literature concerning health
effects linked to formaldehyde exposure to evaluate hazards and dose response relationships and
defined exposure concentrations for minimal risk levels (MRLs). The health endpoints reviewed
included sensory irritation of eyes and respiratory tract, reduced pulmonary function, nasal
histopathology, and immune system effects. In addition, research on reproductive and
developmental effects and neurological effects were discussed along with several studies that
suggest that formaldehyde may increase the risk of asthma - particularly in the young.
In June 2010, EPA released a draft Toxicological Review of Formaldehyde - Inhalation
Assessment through the IRIS program for peer review by the National Research Council (NRC)
and public comment.51 That draft assessment reviewed more recent research from animal and
human studies on cancer and other health effects. The NRC released their review report in April
2011.52	EPA's draft assessment, which addresses NRC recommendations, was suspended in
2018.53	The draft assessment was unsuspended in March 2021.
7.1.2.6.3	Health Effects Associated with Exposure to Acetaldehyde
Acetaldehyde is classified in EPA's IRIS database as a probable human carcinogen, based on
nasal tumors in rats, and is considered toxic by the inhalation, oral, and intravenous routes.54
The URE in IRIS for acetaldehyde is 2.2 x 10-6 per |ig/m3.55 Acetaldehyde is reasonably
anticipated to be a human carcinogen by the NTP in the 14th Report on Carcinogens and is
classified as possibly carcinogenic to humans (Group 2B) by the IARC.56'57
The primary noncancer effects of exposure to acetaldehyde vapors include irritation of the
eyes, skin, and respiratory tract.58 In short-term (4 week) rat studies, degeneration of olfactory
epithelium was observed at various concentration levels of acetaldehyde exposure.59'60 Data
from these studies were used by EPA to develop an inhalation reference concentration of 9
|ig/m3. Some asthmatics have been shown to be a sensitive subpopulation to decrements in
functional expiratory volume (FEV1 test) and bronchoconstriction upon acetaldehyde
inhalation.61 Children, especially those with diagnosed asthma, may be more likely to show
impaired pulmonary function and symptoms of asthma than are adults following exposure to
acetaldehyde.62
7.1.2.6.4	Health Effects Associated with Exposure to Naphthalene
Naphthalene is found in small quantities in gasoline and diesel fuels. Naphthalene emissions
have been measured in larger quantities in both gasoline and diesel exhaust compared with
evaporative emissions from mobile sources, indicating it is primarily a product of combustion.
Acute (short-term) exposure of humans to naphthalene by inhalation, ingestion, or dermal
contact is associated with hemolytic anemia and damage to the liver and the nervous system.63
Chronic (long term) exposure of workers and rodents to naphthalene has been reported to cause
cataracts and retinal damage.64 EPA released an external review draft of a reassessment of the
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inhalation carcinogenicity of naphthalene based on a number of recent animal carcinogenicity
studies.65 The draft reassessment completed external peer review.66 Based on external peer
review comments received, EPA was developing a revised draft assessment that considers all
routes of exposure, as well as cancer and noncancer effects; this reassessment was suspended in
2018.67 The external review draft does not represent official agency opinion and was released
solely for the purposes of external peer review and public comment. The NTP listed naphthalene
as "reasonably anticipated to be a human carcinogen" in 2004 on the basis of bioassays reporting
clear evidence of carcinogenicity in rats and some evidence of carcinogenicity in mice.68
California EPA has released a new risk assessment for naphthalene, and the IARC has
reevaluated naphthalene and re-classified it as Group 2B: possibly carcinogenic to humans.69
Naphthalene also causes a number of chronic non-cancer effects in animals, including
abnormal cell changes and growth in respiratory and nasal tissues.70 The current EPA IRIS
assessment includes noncancer data on hyperplasia and metaplasia in nasal tissue that form the
basis of the inhalation RfC of 3 |ig/m3.71 The ATSDR MRL for acute exposure to naphthalene is
0.6 mg/kg/day.
7.1.2.6.5	Health Effects Associated with Exposure to 1.3-Butadiene
EPA has characterized 1,3-butadiene as carcinogenic to humans by inhalation.72'73 The IARC
has determined that 1,3-butadiene is a human carcinogen and the U.S. DHHS has characterized
1,3-butadiene as a known human carcinogen.74'75'76' 77 There are numerous studies consistently
demonstrating that 1,3-butadiene is metabolized into genotoxic metabolites by experimental
animals and humans. The specific mechanisms of 1,3-butadiene-induced carcinogenesis are
unknown; however, the scientific evidence strongly suggests that the carcinogenic effects are
mediated by genotoxic metabolites. Animal data suggest that females may be more sensitive
than males for cancer effects associated with 1,3-butadiene exposure; there are insufficient data
in humans from which to draw conclusions about sensitive subpopulations. The URE for 1,3-
butadiene is 3 x 10-5 per |ig/m3.78 1,3-butadiene also causes a variety of reproductive and
developmental effects in mice; no human data on these effects are available. The most sensitive
effect was ovarian atrophy observed in a lifetime bioassay of female mice.79 Based on this
critical effect and the benchmark concentration methodology, an RfC for chronic health effects
was calculated at 0.9 ppb (approximately 2 |ig/m3).
7.1.2.6.6	Health effects Associated with exposure to other air toxics
In addition to the compounds described above, other compounds found in gaseous
hydrocarbon and PM emissions from engines will be affected by this proposed rulemaking.
Mobile source air toxic compounds that would potentially be affected include acrolein,
ethylbenzene, propionaldehyde, toluene, and xylene. Information regarding the health effects of
these compounds can be found in EPA's IRIS database.80
7.1.2.7 Exposure and Health Effects Associated with Traffic
Locations in close proximity to major roadways generally have elevated concentrations of
many air pollutants emitted from motor vehicles. Hundreds of such studies have been published
in peer-reviewed journals, concluding that concentrations of CO, CO2, NO, NO2, benzene,
aldehydes, particulate matter, black carbon, and many other compounds are elevated in ambient
air within approximately 300-600 meters (about 1,000-2,000 feet) of major roadways. The
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highest concentrations of most pollutants emitted directly by motor vehicles are found at
locations within 50 meters (about 165 feet) of the edge of a roadway's traffic lanes.
A large-scale review of air quality measurements in the vicinity of major roadways between
1978 and 2008 concluded that the pollutants with the steepest concentration gradients in
vicinities of roadways were CO, ultrafine particles, metals, elemental carbon (EC), NO, NOx,
and several VOCs.81 These pollutants showed a large reduction in concentrations within 100
meters downwind of the roadway. Pollutants that showed more gradual reductions with distance
from roadways included benzene, NO2, PM2.5, and PM10. In the review article, results varied
based on the method of statistical analysis used to determine the gradient in concentration. More
recent studies continue to show significant concentration gradients of traffic-related air pollution
around major roads 82=83=84=85=86;87-88-89 There is evidence that EPA's regulations for vehicles have
lowered the near-road concentrations and gradients.90
For pollutants with relatively high background concentrations relative to near-road
concentrations, detecting concentration gradients can be difficult. For example, many aldehydes
have high background concentrations as a result of photochemical breakdown of precursors from
many different organic compounds. However, several studies have measured aldehydes in
multiple weather conditions and found higher concentrations of many carbonyls downwind of
roadways.91'92 These findings suggest a substantial roadway source of these carbonyls.
In the past 20 years, many studies have been published with results reporting that populations
who live, work, or go to school near high-traffic roadways experience higher rates of numerous
adverse health effects, compared to populations far away from major roads.93 In addition,
numerous studies have found adverse health effects associated with spending time in traffic, such
as commuting or walking along high-traffic roadways.94'95'96'97 The health outcomes with the
strongest evidence linking them with traffic-associated air pollutants are respiratory effects,
particularly in asthmatic children, and cardiovascular effects.
Numerous reviews of this body of health literature have been published as well. In 2010, an
expert panel of the Health Effects Institute (HEI) published a review of hundreds of exposure,
epidemiology, and toxicology studies.98 The panel rated how the evidence for each type of
health outcome supported a conclusion of a causal association with traffic-associated air
pollution as either "sufficient," "suggestive but not sufficient," or "inadequate and insufficient."
The panel categorized evidence of a causal association for exacerbation of childhood asthma as
"sufficient." The panel categorized evidence of a causal association for new onset asthma as
between "sufficient" and "suggestive but not sufficient." "Suggestive of a causal association"
was how the panel categorized evidence linking traffic-associated air pollutants with
exacerbation of adult respiratory symptoms and lung function decrement. It categorized as
"inadequate and insufficient" evidence of a causal relationship between traffic-related air
pollution and health care utilization for respiratory problems, new onset adult asthma, chronic
obstructive pulmonary disease (COPD), nonasthmatic respiratory allergy, and cancer in adults
and children. Currently, HEI is conducting another expert review of health studies associated
with traffic-related air pollution published after the studies included in their 2010 review.99
Other literature reviews have been published with conclusions generally similar to the 2010 HEI
panel's.100'101'102'103 However, in 2014, researchers from the U.S. Centers for Disease Control
and Prevention (CDC) published a systematic review and meta-analysis of studies evaluating the
risk of childhood leukemia associated with traffic exposure and reported positive associations
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between "postnatal" proximity to traffic and leukemia risks, but no such association for
"prenatal" exposures.104 The U.S. Department of Health and Human Services' National
Toxicology Program (NTP) recently published a monograph including a systematic review of
traffic-related air pollution (TRAP) and its impacts on hypertensive disorders of pregnancy.
NTP concluded that exposure to TRAP is "presumed to be a hazard to pregnant women" for
developing hypertensive disorders of pregnancy.105
Health outcomes with few publications suggest the possibility of other effects still lacking
sufficient evidence to draw definitive conclusions. Among these outcomes with a small number
of positive studies are neurological impacts (e.g., autism and reduced cognitive function) and
reproductive outcomes (e.g., preterm birth, low birth weight). 106>107>108>109
In addition to health outcomes, particularly cardiopulmonary effects, conclusions of numerous
studies suggest mechanisms by which traffic-related air pollution affects health. Numerous
studies indicate that near-roadway exposures may increase systemic inflammation, affecting
organ systems, including blood vessels and iungS.110>m>112=113 Long-term exposures in near-road
environments have been associated with inflammation-associated conditions, such as
atherosclerosis and asthma.114'115'116
Several studies suggest that some factors may increase susceptibility to the effects of traffic-
associated air pollution. Several studies have found stronger respiratory associations in children
experiencing chronic social stress, such as in violent neighborhoods or in homes with high
family stress.117'118'119
The risks associated with residence, workplace, or schools near major roads are of potentially
high public health significance due to the large population in such locations. Every two years
from 1997 to 2009 and in 2011, the U.S. Census Bureau's American Housing Survey (AHS)
conducted a survey that includes whether housing units are within 300 feet of an "airport,
railroad, or highway with four or more lanes."120 The 2013 AHS was the last AHS that included
that question. The 2013 survey reports that 17.3 million housing units, or 13 percent of all
housing units in the U.S., were in such areas. Assuming that populations and housing units are
in the same locations, this corresponds to a population of more than 41 million U.S. residents in
close proximity to high-traffic roadways or other transportation sources. According to the
Central Intelligence Agency's World Factbook, based on data collected between 2012-2014, the
United States had 6,586,610 km of roadways, 293,564 km of railways, and 13,513 airports. As
such, highways represent the overwhelming majority of transportation facilities described by this
factor in the AHS.
On average, populations near major roads have higher fractions of minority residents and
lower socioeconomic status (see Chapter 8.3) 12U22,123,124,125 Furthermore, on average,
Americans spend more than an hour traveling each day, bringing nearly all residents into a high-
exposure microenvironment for part of the day.126
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7.1.3 Environmental Effects Associated with Exposure to Non-GHG Pollutants
7.1.3.1	Visibility
Visibility can be defined as the degree to which the atmosphere is transparent to visible
light.127 Visibility impairment is caused by light scattering and absorption by suspended
particles and gases. It is dominated by contributions from suspended particles except under
pristine conditions. Visibility is important because it has direct significance to people's
enjoyment of daily activities in all parts of the country. Individuals value good visibility for the
well-being it provides them directly, where they live and work, and in places where they enjoy
recreational opportunities. Visibility is also highly valued in significant natural areas, such as
national parks and wilderness areas, and special emphasis is given to protecting visibility in these
areas. For more information on visibility see the final 2019 PM ISA.8
EPA is working to address visibility impairment. Reductions in air pollution from
implementation of various programs associated with the Clean Air Act Amendments of 1990
(CAAA) provisions have resulted in substantial improvements in visibility and will continue to
do so in the future. Because trends in haze are closely associated with trends in particulate
sulfate and nitrate due to the relationship between their concentration and light extinction,
visibility trends have improved as emissions of SO2 and NOx have decreased over time due to air
pollution regulations such as the Acid Rain Program.8
In the Clean Air Act Amendments of 1977, Congress recognized visibility's value to society
by establishing a national goal to protect national parks and wilderness areas from visibility
impairment caused by manmade pollution.128 In 1999, EPA finalized the regional haze program
to protect the visibility in Mandatory Class I Federal areas.129 There are 156 national parks,
forests and wilderness areas categorized as Mandatory Class I Federal areas.130 These areas are
defined in CAA Section 162 as those national parks exceeding 6,000 acres, wilderness areas and
memorial parks exceeding 5,000 acres, and all international parks which were in existence on
August 7, 1977.
EPA has also concluded that PM2.5 causes adverse effects on visibility in other areas that are
not targeted by the Regional Haze Rule, such as urban areas, depending on PM2.5 concentrations
and other factors such as dry chemical composition and relative humidity (i.e., an indicator of the
water composition of the particles). EPA revised the PM2.5 NAAQS in 2012, retained it in 2020,
and established a target level of protection that is expected to be met through attainment of the
existing secondary standards for PM2.5.131
7.1.3.2	Ozone Effects on Ecosystems
The welfare effects of ozone include effects on ecosystems, which can be observed across a
variety of scales, i.e. subcellular, cellular, leaf, whole plant, population, and ecosystem. Ozone
effects that begin at small spatial scales, such as the leaf of an individual plant, when they occur
at sufficient magnitudes (or to a sufficient degree) can result in effects being propagated along a
continuum to higher and higher levels of biological organization. For example, effects at the
individual plant level, such as altered rates of leaf gas exchange, growth and reproduction, can,
when widespread, result in broad changes in ecosystems, such as productivity, carbon storage,
water cycling, nutrient cycling, and community composition.
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Ozone can produce both acute and chronic injury in sensitive plant species depending on the
concentration level and the duration of the exposure.132 In those sensitive species,133 effects
from repeated exposure to ozone throughout the growing season of the plant can tend to
accumulate, so that even relatively low concentrations experienced for a longer duration have the
potential to create chronic stress on vegetation.134,135 Ozone damage to sensitive plant species
includes impaired photosynthesis and visible injury to leaves. The impairment of
photosynthesis, the process by which the plant makes carbohydrates (its source of energy and
food), can lead to reduced crop yields, timber production, and plant productivity and growth.
Impaired photosynthesis can also lead to a reduction in root growth and carbohydrate storage
below ground, resulting in other, more subtle plant and ecosystems impacts.136 These latter
impacts include increased susceptibility of plants to insect attack, disease, harsh weather,
interspecies competition and overall decreased plant vigor. The adverse effects of ozone on
areas with sensitive species could potentially lead to species shifts and loss from the affected
ecosystems,137 resulting in a loss or reduction in associated ecosystem goods and services.
Additionally, visible ozone injury to leaves can result in a loss of aesthetic value in areas of
special scenic significance like national parks and wilderness areas and reduced use of sensitive
ornamentals in landscaping.138 In addition to ozone effects on vegetation, newer evidence
suggests that ozone affects interactions between plants and insects by altering chemical signals
(e.g., floral scents) that plants use to communicate to other community members, such as
attraction of pollinators.
The Ozone ISA presents more detailed information on how ozone affects vegetation and
ecosystems.13 The Ozone ISA reports causal and likely causal relationships between ozone
exposure and a number of welfare effects and characterizes the weight of evidence for different
effects associated with ozone.® The ISA concludes that visible foliar injury effects on vegetation,
reduced vegetation growth, reduced plant reproduction, reduced productivity in terrestrial
ecosystems, reduced yield and quality of agricultural crops, alteration of below-ground
biogeochemical cycles, and altered terrestrial community composition are causally associated
with exposure to ozone. It also concludes that increased tree mortality, altered herbivore growth
and reproduction, altered plant-insect signaling, reduced carbon sequestration in terrestrial
ecosystems, and alteration of terrestrial ecosystem water cycling are likely to be causally
associated with exposure to ozone.
7.1.3.3 Deposition
The Integrated Science Assessment for Oxides of Nitrogen, Oxides of Sulfur, and Particulate
Matter - Ecological Criteria documents the ecological effects of the deposition of these criteria
air pollutants.139 It is clear from the body of evidence that oxides of nitrogen, oxides of sulfur,
and particulate matter contribute to total nitrogen (N) and sulfur (S) deposition. In turn, N and S
deposition cause either nutrient enrichment or acidification depending on the sensitivity of the
landscape or the species in question. Both enrichment and acidification are characterized by an
alteration of the biogeochemistry and the physiology of organisms, resulting in harmful declines
in biodiversity in terrestrial, freshwater, wetland, and estuarine ecosystems in the U.S. Decreases
e The Ozone ISA evaluates the evidence associated with different ozone related health and welfare effects, assigning
one of five "weight of evidence" determinations: causal relationship, likely to be a causal relationship, suggestive of
a causal relationship, inadequate to infer a causal relationship, and not likely to be a causal relationship. For more
information on these levels of evidence, please refer to Table II of the ISA.
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in biodiversity mean that some species become relatively less abundant and may be locally
extirpated. In addition to the loss of unique living species, the decline in total biodiversity can be
harmful because biodiversity is an important determinant of the stability of ecosystems and their
ability to provide socially valuable ecosystem services.
Terrestrial, wetland, freshwater, and estuarine ecosystems in the U.S. are affected by nitrogen
enrichment/eutrophication caused by nitrogen deposition. These effects have been consistently
documented across the U.S. for hundreds of species. In aquatic systems increased nitrogen can
alter species assemblages and cause eutrophication. In terrestrial systems nitrogen loading can
lead to loss of nitrogen-sensitive lichen species, decreased biodiversity of grasslands, meadows
and other sensitive habitats, and increased potential for invasive species.
The sensitivity of terrestrial and aquatic ecosystems to acidification from nitrogen and sulfur
deposition is predominantly governed by geology. Prolonged exposure to excess nitrogen and
sulfur deposition in sensitive areas acidifies lakes, rivers and soils. Increased acidity in surface
waters creates inhospitable conditions for biota and affects the abundance and biodiversity of
fishes, zooplankton and macroinvertebrates and ecosystem function. Over time, acidifying
deposition also removes essential nutrients from forest soils, depleting the capacity of soils to
neutralize future acid loadings and negatively affecting forest sustainability. Major effects in
forests include a decline in sensitive tree species, such as red spruce (Picea rubens) and sugar
maple (Acer saccharum).
Building materials including metals, stones, cements, and paints undergo natural weathering
processes from exposure to environmental elements (e.g., wind, moisture, temperature
fluctuations, sunlight, etc.). Pollution can worsen and accelerate these effects. Deposition of PM
is associated with both physical damage (materials damage effects) and impaired aesthetic
qualities (soiling effects). Wet and dry deposition of PM can physically affect materials, adding
to the effects of natural weathering processes, by potentially promoting or accelerating the
corrosion of metals, by degrading paints and by deteriorating building materials such as stone,
concrete and marble.140 The effects of PM are exacerbated by the presence of acidic gases and
can be additive or synergistic due to the complex mixture of pollutants in the air and surface
characteristics of the material. Acidic deposition has been shown to have an effect on materials
including zinc/galvanized steel and other metal, carbonate stone (as monuments and building
facings), and surface coatings (paints).141 The effects on historic buildings and outdoor works of
art are of particular concern because of the uniqueness and irreplaceability of many of these
objects. In addition to aesthetic and functional effects on metals, stone and glass, altered energy
efficiency of photovoltaic panels by PM deposition is also becoming an important consideration
for impacts of air pollutants on materials.
7.1.3.4 Environmental Effects of Air Toxics
Emissions from producing, transporting and combusting fuel contribute to ambient levels of
pollutants that contribute to adverse effects on vegetation. Volatile organic compounds (VOCs),
some of which are considered air toxics, have long been suspected to play a role in vegetation
damage.142 In laboratory experiments, a wide range of tolerance to VOCs has been observed.143
Decreases in harvested seed pod weight have been reported for the more sensitive plants, and
some studies have reported effects on seed germination, flowering and fruit ripening. Effects of
individual VOCs or their role in conjunction with other stressors (e.g., acidification, drought,
temperature extremes) have not been well studied. In a recent study of a mixture of VOCs
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including ethanol and toluene on herbaceous plants, significant effects on seed production, leaf
water content and photosynthetic efficiency were reported for some plant species.144
Research suggests an adverse impact of vehicle exhaust on plants, which has in some cases
been attributed to aromatic compounds and in other cases to nitrogen oxides.145'146'147 The
impacts of VOCs on plant reproduction may have long-term implications for biodiversity and
survival of native species near major roadways. Most of the studies of the impacts of VOCs on
vegetation have focused on short-term exposure and few studies have focused on long-term
effects of VOCs on vegetation and the potential for metabolites of these compounds to affect
herbivores or insects.
7.2 Non-GHG Monetized Health Benefits
It is important to quantify the health and environmental impacts associated with the proposed
program because a failure to adequately consider ancillary impacts could lead to an incorrect
assessment of a program's costs and benefits. Moreover, the health and other impacts of
exposure to criteria air pollutants and airborne toxics tend to occur in the near term, while most
effects from reduced climate change are likely to occur only over a time frame of several decades
or longer. Ideally, human health benefits would be estimated based on changes in ambient PM2.5
and ozone as determined by full-scale air quality modeling. However, the projected non-GHG
emissions impacts associated with the proposal would be expected to contribute to only very
small changes in ambient air quality (see Section V.C of the preamble for more detail).
In lieu of air quality modeling, we use a reduced-form benefit-per-ton (BPT) approach to
inform our assessment of health impacts, which is conceptually consistent with EPA's use of
BPT estimates in several previous RIAs.148'149In this approach, the PM2.5-related BPT values are
the total monetized human health benefits (the sum of the economic value of the reduced risk of
premature death and illness) that are expected from reducing one ton of directly-emitted PM2.5 or
PM2.5 precursor such as NOx or SO2. We note, however, that the complex, non-linear
photochemical processes that govern ozone formation prevent us from developing reduced-form
ozone BPT values. This is an important limitation to recognize when using the BPT approach.
For tailpipe emissions, we apply national PM2.5-related BPT values that were recently derived
for the "Onroad Light-duty Vehicle" sector. 150'f The onroad light-duty vehicle BPT values were
derived using detailed mobile sector source-apportionment air quality modeling, and apply
EPA's existing method for using reduced-form tools to estimate PM2.5-related benefits. 151>152
Compared to values EPA has used in the past,153 these BPT values provide better resolution by
mobile sector and geographic area, two features that make them especially useful for quantifying
the benefits of reducing emissions from the onroad light-duty sector.
To monetize the PM2.5-related impacts of upstream emissions, we apply BPT values that were
developed for the refinery sector.154 While total upstream emissions also include electricity
generating unit sources, petroleum extraction, storage and transport sources, as well as sources
upstream from the refinery, the modeling tool used to support this analysis only provides
estimates of upstream emissions impacts aggregated across all sources. Furthermore, we assume
the majority of upstream emission reductions associated with the proposal would be related to
f Available for download here: https://www.epa.gov/benmap/mobile-sector-source-apportionment-air-quality-and-
benefits-ton.
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domestic onsite refinery emissions and domestic crude production. We therefore believe it is
appropriate to apply the refinery values to all upstream emissions.
EPA bases its benefits analyses on peer-reviewed studies of air quality and health effects and
peer-reviewed studies of the monetary values of public health and welfare improvements. Very
recently, EPA updated its approach to estimating the benefits of changes in PM2.5 and
ozone.155'156 These updates were based on information drawn from the recent 2019 PM2.5 and
2020 Ozone Integrated Science Assessments (ISAs), which were reviewed by the Clean Air
Science Advisory Committee (CASAC) and the public.157'158 Unfortunately, EPA has not had an
opportunity to update its BPT estimates to reflect these updates in time for this proposal. Instead,
we use PM2.5 BPT estimates that are based on the review of the 2009 PM ISA159 and include a
mortality risk estimate derived from the Krewski et al. (2009)160 analysis of the American
Cancer Society (ACS) cohort and nonfatal illnesses consistent with benefits analyses performed
for the analysis of the final Tier 3 Vehicle Rule,161 the final 2012 PM NAAQS Revision,162 and
the final 2017-2025 Light-duty Vehicle GHG Rule.163 We expect this lag in updating our BPT
estimates to have only a minimal impact on total PM benefits, since the underlying mortality risk
estimate based on the Krewski study is identical to an updated PM2.5 mortality risk estimate
derived from an expanded analysis of the same ACS cohort.164 The Agency is currently working
to update its BPT estimates to reflect these recent updates for use in future rulemaking analyses.
Table 7-1 and Table 7-2 displays the health effects associated with human exposure to
ambient concentrations of PM2.5 and ozone, respectively, including the quantified PM2.5-related
benefits included in the BPT estimates used in this analysis and the unquantified PM2.5 and
ozone health effects the BPT estimates do not capture. Table 7-3 also displays additional criteria
pollutant-related health and environmental effects not captured in the BPT estimates.
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Table 7-1: Health Effects of Ambient PM2.5
Category
Effect
Effect
Quantified
Effect
Monetized
More
Information
Premature mortality
from exposure to PM25
Adult premature mortality from long-term exposure (age 25-99 or
age 30-99)
Infant mortality (age <1)
/
/
/
/
2009 PM
ISA1®
2009 PM ISA



2019 PM
ISA1®

Adult premature mortality from long-term exposure (age 65-99)
—
—

Non-fatal heart attacks (age > 18)
/
/
2009 PM ISA

Hospital admissions - respiratory (all ages)
/
/
2009 PM ISA

Hospital admissions - cardiovascular (age > 20)
/
/
2009 PM ISA

Emergency department visits—respiratory (all ages)
/
/
2009 PM ISA

Acute bronchitis (age 8-12)
/
/
2009 PM ISA

Lower respiratory symptoms (age 7-14)
/
/
2009 PM ISA

Upper respiratory symptoms (asthmatics age 9-11)
/
/
2009 PM ISA

Asthma exacerbation (asthmatics age 6-18)
/
/
2009 PM ISA

Lost work days (age 18-65)
/
/
2009 PM ISA

Minor restricted-activity days (age 18-65)
/
/
2009 PM ISA

Hospital admissions—cardiovascular (ages 65-99)
—
—
2019 PM ISA

Emergency department visits— cardiovascular (age 0-99)
—
—
2019 PM ISA

Hospital admissions—respiratory (ages 0-18 and 65-99)
—
—
2019 PM ISA
Nonfatal morbidity
Cardiac arrest (ages 0-99; excludes initial hospital and/or emergency
department visits)
—
—
2019 PM ISA
from exposure to PM2.5
Stroke (ages 65-99)
—
—
2019 PM ISA

Asthma onset (ages 0-17)
—
—
2019 PM ISA

Asthma symptoms/exacerbation (6-17)
—
—
2019 PM ISA

Lung cancer (ages 30-99)
—
—
2019 PM ISA

Allergic rhinitis (hay fever) symptoms (ages 3-17)
—
—
2019 PM ISA

Hospital admissions—Alzheimer's disease (ages 65-99)
—
—
2019 PM ISA

Hospital admissions—Parkinson's disease (ages 65-99)
—
—
2019 PM ISA

Other cardiovascular effects (e.g., other ages)
—
—
2019 PM ISA

Other respiratory effects (e.g., pulmonary function, non-asthma ER
visits, non-bronchitis chronic diseases, other ages and populations)
—
—
2019 PM ISA

Other nervous system effects (e.g., autism, cognitive decline,
dementia)
—
—
2019 PM ISA

Metabolic effects (e.g., diabetes)
—
—
2019 PM ISA

Reproductive and developmental effects (e.g., low birth weight, pre-
term births, etc.)
—
—
2019 PM ISA

Cancer, mutagenicity, and genotoxicity effects
—
—
2019 PM ISA
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Table 7-2: Health Effects of Ambient Ozone
Mortality from exposure to ozone
Premature respiratory mortality from short-term exposure (0-99)
2020 Ozone
~~ ~~ ISA167
Premature respiratory mortality from long-term exposure (age 30-
99)
— — 2020 Ozone ISA

Hospital admissions—respiratory (ages 65-99)
— — 2020 Ozone ISA

Emergency department visits—respiratory (ages 0-99)
— — 2020 Ozone ISA

Asthma onset (0-17)
— — 2020 Ozone ISA

Asthma symptoms/exacerbation (asthmatics age 5-17)
— — 2020 Ozone ISA

Allergic rhinitis (hay fever) symptoms (ages 3-17)
— — 2020 Ozone ISA
Nonfatal morbidity from exposure to
Minor restricted-activity days (age 18-65)
— — 2020 Ozone ISA
ozone
School absence days (age 5-17)
— — 2020 Ozone ISA

Decreased outdoor worker productivity (age 18-65)
— — 2020 Ozone ISA

Metabolic effects (e.g., diabetes)
— — 2020 Ozone ISA

Other respiratory effects (e.g., premature aging of lungs)
— — 2020 Ozone ISA

Cardiovascular and nervous system effects
— — 2020 Ozone ISA

Reproductive and developmental effects
— — 2020 Ozone ISA
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Table 7-3: Additional Unquantified Health and Welfare Benefits Categories
Category
Effect
Effect
Quantified
Effect
Monetized
More
Information
Improved Human Health

Asthma hospital admissions
—
—
2016 N02
ISA168

Chronic lung disease hospital admissions
—
—
2016NO2 ISA
Reduced incidence of
morbidity from exposure
to N02
Respiratory emergency department visits
—
—
2016 N02 ISA
Asthma exacerbation
—
—
2016 N02 ISA
Acute respiratory symptoms
—
—
2016 N02 ISA

Premature mortality
—
—
2016 N02 ISA

Other respiratory effects (e.g., airway hyperresponsiveness
and inflammation, lung function, other ages and
populations)
—
—
2016 N02 ISA
Improved Environment
Reduced visibility
Visibility in Class 1 areas
—
—
2019 PM ISA
impairment
Visibility in residential areas
—
—
2019 PM ISA
Reduced effects on
Household soiling
—
—
2019 PM ISA
materials
Materials damage (e.g., corrosion, increased wear)
—
—
2019 PM ISA
Reduced effects from PM
deposition (metals and
organics)
Effects on Individual organisms and ecosystems
—
—
2019 PM ISA

Visible foliar injury on vegetation
—
—
2020 Ozone ISA

Reduced vegetation growth and reproduction
—
—
2020 Ozone ISA

Yield and quality of commercial forest products and crops
—
—
2020 Ozone ISA
Reduced vegetation and
ecosystem effects from
Damage to urban ornamental plants
—
—
2020 Ozone ISA
Carbon sequestration in terrestrial ecosystems
—
—
2020 Ozone ISA
exposure to ozone
Recreational demand associated with forest aesthetics
—
—
2020 Ozone ISA

Other non-use effects


2020 Ozone ISA

Ecosystem functions (e.g., water cycling, biogeochemical
cycles, net primary productivity, leaf-gas exchange,
community composition)
—
—
2020 Ozone ISA

Recreational fishing
—
—
2008 NOx SOx
ISA1®

Tree mortality and decline
—
—
2008 NOx SOx
ISA
Reduced effects from
Commercial fishing and forestry effects
—
—
2008 NOx SOx
ISA
acid deposition
Recreational demand in terrestrial and aquatic ecosystems
—
—
2008 NOx SOx
ISA

Other non-use effects


2008 NOx SOx
ISA

Ecosystem functions (e.g., biogeochemical cycles)
—
—
2008 NOx SOx
ISA

Species composition and biodiversity in terrestrial and
estuarine ecosystems
—
—
2008 NOx SOx
ISA

Coastal eutrophication
—
—
2008 NOx SOx
ISA
Reduced effects from
nutrient enrichment
Recreational demand in terrestrial and estuarine
ecosystems
—
—
2008 NOx SOx
ISA

Other non-use effects


2008 NOx SOx
ISA

Ecosystem functions (e.g., biogeochemical cycles, fire
regulation)
—
—
2008 NOx SOx
ISA
Reduced vegetation
effects from ambient
Injury to vegetation from S02 exposure
—
—
2008 NOx SOx
ISA
exposure to S02 and
NOx
Injury to vegetation from NOx exposure
—
—
2008 NOx SOx
ISA
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In addition to omitting ozone-related impacts from this analysis, there are other impacts
associated with reductions in exposure to NO2, ecosystem benefits, and visibility improvement
that EPA is unable to quantify due to data, resource, and methodological limitations. Chapter 7.1
provides a qualitative description of both the health and environmental effects of the criteria
pollutants controlled by the proposed program.
There would also be impacts associated with reductions in air toxic pollutant emissions that
result from the proposed program (see Chapters 5.1 and 7.1), but the Agency does not attempt to
monetize those impacts. This is because currently available tools and methods to assess air toxics
risk from mobile sources at the national scale are not adequate for extrapolation to incidence
estimation or benefits assessment. While EPA has worked to improve these tools, there remain
critical limitations for estimating incidence and assessing benefits of reducing mobile source air
toxics.
The PM-related BPT estimates used in this analysis are provided in Table 7-4. We multiply
these BPT values by national changes in projected NOx, SO2 and directly-emitted PM2.5, in tons,
to estimate the total PIVfo.s-related monetized human health benefits associated with the proposed
program. As the table indicates, these values differ among pollutants and depend on their original
source, because emissions from different sources can result in different degrees of population
exposure and resulting health impacts. The BPT values for emissions of non-GHG pollutants
from both onroad light-duty vehicle use and upstream sources such as fuel refineries will
increase over time. These projected increases reflect rising income levels, which increase
affected individuals' willingness to pay for reduced exposure to health threats from air pollution.
The BPT values also reflect future population growth and increased life expectancy, which
expands the size of the population exposed to air pollution in both urban and rural areas,
especially among older age groups with the highest mortality risk.170
Table 7-5 through Table 7-7 display the total undiscounted stream of PIVb.s-related benefits
and the present value of those benefits for the proposal and two alternatives. Using PIVfo.s-related
BPT estimates to monetize the non-GHG impacts of the proposed standards omits ozone-related
impacts as well as other impacts associated with reductions in exposure to air toxics, ecosystem
benefits, and visibility improvement. Draft RIA Chapter 7.1 provides a qualitative description of
both the health and environmental effects of the non-GHG pollutants impacted by the proposed
program.
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Table 7-4 PM-related Beneflt-per-ton Values (2018$)a
Year
Onroad Light-duty Vehicles'3
Upstream Sourcesc d

Direct PM2 5
S02
NOX
Direct PM2 5
S02
NOX
Estimated Using a 3 Percent Discount Rate
2020
$600,000
$150,000
$6,400
$380,000
$81,000
$8,100
2025
$660,000
$170,000
$6,900
$420,000
$90,000
$8,800
2030
$740,000
$190,000
$7,600
$450,000
$98,000
$9,600
2035
$830,000
$210,000
$8,400
-
-
-
2040
$920,000
$230,000
$9,000
-
-
-
2045
$1,000,000
$250,000
$9,600
-
-
-
Estimated Using a 7 Percent Discount Rate
2020
$540,000
$140,000
$5,800
$350,000
$74,000
$7,300
2025
$600,000
$150,000
$6,200
$380,000
$80,000
$7,900
2030
$660,000
$170,000
$6,800
$410,000
$88,000
$8,600
2035
$750,000
$190,000
$7,500
-
-
-
2040
$830,000
$210,000
$8,200
-
-
-
2045
$900,000
$230,000
$8,600
-
-
-
Table Notes:





a The benefit-per-ton estimates presented in this table are based on estimates derived from the American Cancer
Society cohort study (Krewski et al., 2009). They also assume either a 3 percent or 7 percent discount rate in the
valuation of premature mortality to account for a twenty-year segmented premature mortality cessation lag.
b Benefit-per-ton values for onroad light-duty vehicles were estimated for the years 2020, 2025, 2030, 2035,
2040, and 2045. We hold values constant for intervening years (e.g., the 2020 values are assumed to apply to
years 2021-2024; 2025 values foryears 2026-2029; and 2045 values foryears 2046 and beyond).
0 Benefit-per-ton values for upstream sources were estimated only for the years 2020, 2025 and 2030. We hold
values constant for intervening years and 2030 values are applied to years 2031 and beyond.
d We assume for the purpose of this analysis that total "upstream emissions" are most appropriately monetized
using refinery sector benefit per-ton values.
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Table 7-5: Undiscounted Stream, Present and Annualized Value of PIVh.s-related Benefits from 2023 through
2050 for the Proposal (Discounted at 3 percent and 7 percent; SBillions of 2018$)a
Calendar Year
Tailpipe
Upstream
Total

3% DR
7% DR
3% DR
7% DR
3% DR
7% DR
2023
-$0,013
-$0,012
$0,029
$0,027
$0,016
$0,015
2024
-$0,031
-$0,028
$0,023
$0,021
-$0.0081
-$0.0069
2025
-$0,043
-$0,039
$0,014
$0,014
-$0,029
-$0,026
2026
-$0,047
-$0,042
$0,014
$0,015
-$0,033
-$0,028
2027
-$0.04
-$0,036
$0,019
$0.02
-$0,021
-$0,016
2028
-$0,017
-$0,016
$0,039
$0,038
$0,022
$0,022
2029
$0.0053
$0.0048
$0,059
$0,057
$0,064
$0,061
2030
$0,035
$0,032
$0,089
$0,084
$0.12
$0.12
2031
$0,066
$0,059
$0.14
$0.13
$0.21
$0.19
2032
$0.1
$0.09
$0.19
$0.17
$0.29
$0.26
2033
$0.14
$0.12
$0.24
$0.22
$0.37
$0.34
2034
$0.18
$0.16
$0.28
$0.26
$0.45
$0.41
2035
$0.23
$0.21
$0.34
$0.31
$0.57
$0.52
2036
$0.27
$0.25
$0.38
$0.35
$0.65
$0.59
2037
$0.31
$0.28
$0.4
$0.37
$0.72
$0.65
2038
$0.35
$0.32
$0.42
$0.39
$0.77
$0.7
2039
$0.38
$0.35
$0.45
$0.42
$0.84
$0.76
2040
$0.46
$0.41
$0.48
$0.44
$0.94
$0.85
2041
$0.49
$0.44
$0.5
$0.46
$0.99
$0.9
2042
$0.51
$0.46
$0.51
$0.47
$1
$0.93
2043
$0.54
$0.49
$0.51
$0.47
$1
$0.95
2044
$0.56
$0.51
$0.51
$0.46
$1.1
$0.97
2045
$0.63
$0.57
$0.49
$0.45
$1.1
$1
2046
$0.65
$0.59
$0.47
$0.43
$1.1
$1
2047
$0.68
$0.61
$0.44
$0.41
$1.1
$1
2048
$0.69
$0.63
$0.42
$0.38
$1.1
$1
2049
$0.71
$0.64
$0.39
$0.36
$1.1
$1
2050
$0.74
$0.67
$0.34
$0.31
$1.1
$0.98
PV
$4.3
$1.6
$4.5
$2
$8.8
$3.6
Annualized
$0.22
$0.13
$0.23
$0.16
$0.45
$0.29
Table Notes:






a Note that the non-GHG impacts associated with the standards presented here do not include the full complement
of health and environmental effects that, if quantified and monetized, would change the total monetized estimate
of rule-related impacts. Instead, the non-GHG benefits are based on benefit-per-ton values that reflect only
human health impacts associated with reductions in PM2 5 exposure.



b Calendar year non-GHG benefits presented in this table assume either a 3 percent or 7 percent discount rate in
the valuation of PM-related premature mortality to account for a twenty-year segmented cessation lag. Note that
annual benefits estimated using a 3 percent discount rate were used to calculate the present and annualized values
using a 3 percent discount rate and the annual benefits estimated using a 7 percent discount rate were used to
calculate the present and annualized values using a 7 percent discount rate.


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Table 7-6 Undiscounted Stream, Present and Annualized Value of PIVh.s-related Benefits from 2023 through
2050 for Alternative 1 (Discounted at 3 percent and 7 percent; SBillions of 2018$)a
Calendar Year
Tailpipe
Upstream
Total
3% DR
7% DR
3% DR
7% DR
3% DR
7% DR
2023
-$0,014
-$0,013
$0,037
$0,034
$0,022
$0,021
2024
-$0,033
-$0.03
$0,052
$0,047
$0,019
$0,018
2025
-$0,047
-$0,043
$0,083
$0,075
$0,036
$0,032
2026
-$0,054
-$0,049
$0.13
$0.12
$0,076
$0,068
2027
-$0,051
-$0,046
$0.2
$0.18
$0.14
$0.13
2028
-$0,037
-$0,034
$0.27
$0.24
$0.23
$0.21
2029
-$0,028
-$0,025
$0.34
$0.31
$0.31
$0.28
2030
-$0,019
-$0,017
$0.45
$0.41
$0.43
$0.39
2031
-$0.0071
-$0.0064
$0.54
$0.49
$0.53
$0.48
2032
$0,006
$0.0054
$0.63
$0.57
$0.63
$0.57
2033
$0,022
$0,019
$0.71
$0.64
$0.73
$0.66
2034
$0,038
$0,034
$0.78
$0.71
$0.82
$0.74
2035
$0,056
$0,051
$0.87
$0.78
$0.92
$0.83
2036
$0,071
$0,064
$0.93
$0.84
$1
$0.91
2037
$0,092
$0,083
$0.96
$0.87
$1.1
$0.95
2038
$0.11
$0.1
$0.98
$0.89
$1.1
$0.99
2039
$0.13
$0.12
$1
$0.92
$1.1
$1
2040
$0.16
$0.15
$1
$0.93
$1.2
$1.1
2041
$0.18
$0.16
$1
$0.95
$1.2
$1.1
2042
$0.19
$0.18
$1
$0.95
$1.2
$1.1
2043
$0.21
$0.19
$1
$0.94
$1.2
$1.1
2044
$0.23
$0.2
$1
$0.93
$1.2
$1.1
2045
$0.27
$0.24
$0.99
$0.9
$1.3
$1.1
2046
$0.28
$0.25
$0.99
$0.89
$1.3
$1.1
2047
$0.29
$0.26
$0.97
$0.88
$1.3
$1.1
2048
$0.3
$0.27
$0.96
$0.87
$1.3
$1.1
2049
$0.32
$0.29
$0.93
$0.84
$1.2
$1.1
2050
$0.34
$0.3
$0.88
$0.8
$1.2
$1.1
PV
$1.4
$0.42
$11
$5.1
$13
$5.6
Annualized
$0.07
$0,034
$0.57
$0.41
$0.64
$0.45
Table Notes:
a Note that the non-GHG impacts associated with the standards presented here do not include the full
complement of health and environmental effects that, if quantified and monetized, would change the total
monetized estimate of rule-related impacts. Instead, the non-GHG benefits are based on benefit-per-ton values
that reflect only human health impacts associated with reductions in PM2 5 exposure.
b Calendar year non-GHG benefits presented in this table assume either a 3 percent or 7 percent discount rate in
the valuation of PM-related premature mortality to account for a twenty-year segmented cessation lag. Note
that annual benefits estimated using a 3 percent discount rate were used to calculate the present and annualized
values using a 3 percent discount rate and the annual benefits estimated using a 7 percent discount rate were
used to calculate the present and annualized values using a 7 percent discount rate.
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Table 7-7 Undiscounted Stream, Present and Annualized Value of PIVh.s-related Benefits from 2023 through
2050 for Alternative 2 (Discounted at 3 percent and 7 percent; SBillions of 2018$)a
Calendar Year
Tailpipe
Upstream
Total

3% DR
7% DR
3% DR
7% DR
3% DR
7% DR
2023
-$0,024
-$0,022
$0.16
$0.14
$0.13
$0.12
2024
-$0,046
-$0,042
$0.17
$0.16
$0.13
$0.12
2025
-$0,063
-$0,057
$0.22
$0.2
$0.15
$0.14
2026
-$0,069
-$0,062
$0.25
$0.23
$0.18
$0.16
2027
-$0,063
-$0,057
$0.31
$0.28
$0.24
$0.22
2028
-$0,046
-$0,042
$0.37
$0.34
$0.33
$0.29
2029
-$0,034
-$0,031
$0.44
$0.4
$0.41
$0.37
2030
-$0,018
-$0,016
$0.55
$0.5
$0.53
$0.48
2031
$0.0044
$0,004
$0.64
$0.58
$0.64
$0.58
2032
$0,031
$0,028
$0.72
$0.65
$0.75
$0.68
2033
$0.06
$0,054
$0.8
$0.72
$0.86
$0.78
2034
$0,091
$0,083
$0.86
$0.78
$0.95
$0.86
2035
$0.13
$0.12
$0.94
$0.85
$1.1
$0.97
2036
$0.16
$0.15
$1
$0.91
$1.2
$1.1
2037
$0.19
$0.17
$1
$0.95
$1.2
$1.1
2038
$0.23
$0.2
$1.1
$0.98
$1.3
$1.2
2039
$0.25
$0.23
$1.1
$1
$1.4
$1.2
2040
$0.31
$0.28
$1.2
$1
$1.5
$1.3
2041
$0.33
$0.3
$1.2
$1.1
$1.5
$1.4
2042
$0.36
$0.32
$1.2
$1.1
$1.5
$1.4
2043
$0.38
$0.34
$1.2
$1.1
$1.6
$1.4
2044
$0.4
$0.36
$1.2
$1.1
$1.6
$1.4
2045
$0.46
$0.41
$1.2
$1
$1.6
$1.5
2046
$0.49
$0.44
$1.1
$0.98
$1.6
$1.4
2047
$0.52
$0.47
$1
$0.92
$1.5
$1.4
2048
$0.55
$0.5
$0.95
$0.86
$1.5
$1.4
2049
$0.58
$0.53
$0.88
$0.8
$1.5
$1.3
2050
$0.63
$0.57
$0.77
$0.7
$1.4
$1.3
PV
$2.7
$0.92
$13
$6.3
$16
$7.2
Annualized
$0.14
$0,074
$0.67
$0.51
$0.81
$0.58
Table Notes:






a Note that the non-GHG impacts associated with the standards presented here do not include the full complement
of health and environmental effects that, if quantified and monetized, would change the total monetized estimate
of rule-related impacts. Instead, the non-GHG benefits are based on benefit-per-ton values that reflect only
human health impacts associated with reductions in PM2 5 exposure.



b Calendar year non-GHG benefits presented in this table assume either a 3 percent or 7 percent discount rate in
the valuation of PM-related premature mortality to account for a twenty-year segmented cessation lag. Note that
annual benefits estimated using a 3 percent discount rate were used to calculate the present and annualized values
using a 3 percent discount rate and the annual benefits estimated using a 7 percent discount rate were used to
calculate the present and annualized values using a 7 percent discount rate.

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7.2.1 Uncertainty
Uncertainties and limitations exist at each stage of the emissions-to-health benefit analysis
pathway (e.g., projected emissions inventories, air quality modeling, health impact assessment,
economic valuation). The BPT approach to monetizing benefits relies on many assumptions;
when uncertainties associated with these assumptions are compounded, even small uncertainties
can greatly influence the size of the total quantified benefits. Some key assumptions associated
with PM2.5-related health benefits and uncertainties associated with the BPT approach are
described below.
We assume that all fine particles, regardless of their chemical composition, are equally potent
in causing premature mortality. Support for this assumption comes from the 2019 PM ISA,
which concluded that "many PM2.5 components and sources are associated with many health
effects and that the evidence does not indicate that any one source or component is consistently
more strongly related with health effects than PM2.5 mass."171
We assume that the health impact function for fine particles is log-linear without a threshold.
Thus, the estimates include health benefits from reducing fine particles in areas with different
concentrations of PM2.5, including both areas with projected annual mean concentrations that are
above the level of the fine particle standard and areas with projected concentrations below the
level of the standard.
We also assume that there is a "cessation" lag between the change in PM exposures and the
total realization of changes in mortality effects. Specifically, we assume that some of the
incidences of premature mortality related to PM2.5 exposures occur in a distributed fashion over
the 20 years following exposure based on the advice of the Science Advisory Board Health
Effect Subcommittee,172 which affects the valuation of mortality benefits at different discount
rates. The above assumptions are subject to uncertainty.
In general, we are more confident in the magnitude of the risks we estimate from simulated
PM2.5 concentrations that coincide with the bulk of the observed PM concentrations in the
epidemiological studies that are used to estimate the benefits. Likewise, we are less confident in
the risk we estimate from simulated PM2.5 concentrations that fall below the bulk of the observed
data in these studies. There are uncertainties inherent in identifying any particular point at which
our confidence in reported associations decreases appreciably, and the scientific evidence
provides no clear dividing line. Applying BPT values to estimates of changes in policy-related
emissions precludes us from assessing the distribution of risk as it relates to the associated
distribution of baseline concentrations of PM2.5.
Another limitation of using the BPT approach is an inability to provide estimates of the health
benefits associated with exposure to ozone, ambient NOx, and air toxics. Furthermore, the air
quality modeling that underlies the PM2.5 BPT value did not provide estimates of the PM2.5-
related benefits associated with reducing VOC emissions, but these unquantified benefits are
generally small compared to benefits associated with other PM2.5 precursors.173
National-average BPT values reflect the geographic distribution of the underlying modeled
emissions used in their calculation, which may not exactly match the geographic distribution of
the emission reductions that would occur due to a specific rulemaking. Similarly, BPT estimates
may not reflect local variability in population density, meteorology, exposure, baseline health
7-29
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incidence rates, or other local factors for any specific location. For instance, even though we
assume that all fine particles have equivalent health effects, the BPT estimates vary across
precursors depending on the location and magnitude of their impact on PM2.5 levels, which
drives population exposure. The emissions and photochemically-modeled PM2.5 concentrations
used to derive the BPT values may not match the changes in air quality that would result from
the proposal.
Finally, as mentioned earlier in this Chapter, EPA recently updated its approach to estimating
the benefits of changes in PM2.5 and ozone. Unfortunately, EPA has not had an opportunity to
update its BPT estimates to reflect these updates in time for this proposal. The Agency is
currently working to update its BPT estimates to reflect these changes for use in future
rulemaking analyses.
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References for Chapter 7
1	U.S. EPA. Policy Assessment (PA) for the Review of the National Ambient Air Quality Standards for Particulate
Matter (Final Report, 2020). U.S. Environmental Protection Agency, Washington, DC, EPA/452/R-20/002, 2020.
2	U.S. EPA. Integrated Science Assessment (ISA) for Particulate Matter (Final Report, 2019). U.S. Environmental
Protection Agency, Washington, DC, EPA/600/R-19/188, 2019. Table 2-1.
3	U.S. EPA. Integrated Science Assessment (ISA) for Particulate Matter (Final Report, 2019). U.S. Environmental
Protection Agency, Washington, DC, EPA/600/R-19/188, 2019. Table 2-1.
4	See https://www.epa.gov/air-trends/particulate-matter-pm25-trends and https://www.epa.gov/air-trends/particulate-
matter-pm25-trends#pmnat for more information.
5	U.S. EPA, (2010). Integrated Science Assessment for Carbon Monoxide (Final Report). U.S. Environmental
Protection Agency, Washington, DC, EPA/600/R-09/019F, 2010.
http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=218686. See Section 2.1.
6	U.S. EPA (2018) Technical Support Document EPA's 2014 National Air Toxics Assessment.
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8U.S. EPA. Integrated Science Assessment (ISA) for Particulate Matter (Final Report, 2019). U.S. Environmental
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move between locations which have notably different ozone concentrations. Also, the amount of ozone delivered to
the lung is influenced not only by the ambient concentrations but also by the breathing route and rate.
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20	Personal exposure includes contributions from many sources, and in many different environments. Total personal
exposure to CO includes both ambient and non-ambient components; and both components may contribute to
adverse health effects.
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37	A minimal risk level (MRL) is defined as an estimate of the daily human exposure to a hazardous substance that is
likely to be without appreciable risk of adverse noncancer health effects over a specified duration of exposure.
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156	U.S. Environmental Protection Agency (U.S. EPA). 2021b. Estimating PM2.5- and Ozone-Attributable Health
Benefits. Technical Support Document (TSD) for the Final Revised Cross-State Air Pollution Rule Update for the
2008 Ozone Season NAAQS. EPA-HQ-OAR-2020-0272. March.
157	U.S. Environmental Protection Agency (U.S. EPA). 2019a. Integrated Science Assessment (ISA) for Particulate
Matter (Final Report, 2019). U.S. Environmental Protection Agency, Washington, DC, EPA/600/R-19/188, 2019.
158	U.S. Environmental Protection Agency (U.S. EPA). 2020b. Integrated Science Assessment (ISA) for Ozone and
Related Photochemical Oxidants (Final Report). U.S. Environmental Protection Agency, Washington, DC,
EPA/600/R-20/012, 2020.
159	U.S. Environmental Protection Agency (U.S. EPA). 2009. Integrated Science Assessment for Particulate Matter
(Final Report). EPA-600-R-08-139F. National Center for Environmental Assessment - RTP Division, Research
Triangle Park, NC. December. Available at: .
160	Krewski D., M. Jerrett, R.T. Burnett, R. Ma, E. Hughes, Y. Shi, et al. 2009. Extended Follow-Up and Spatial
Analysis of the American Cancer Society Study Linking Particulate Air Pollution and Mortality. HEI Research
Report, 140, Health Effects Institute, Boston, MA.
161	U.S. Environmental Protection Agency. (2014). Control of Air Pollution from Motor Vehicles: Tier 3 Motor
Vehicle Emission and Fuel Standards Final Rule: Regulatory Impact Analysis, Assessment and Standards Division,
Office of Transportation and Air Quality, EPA-420-R-14-005, March 2014. Available on the internet:
http://www3.epa.gOv/otaq/documents/tier3/420rl4005.pdf.
162	U.S. Environmental Protection Agency. (2012). Regulatory Impact Analysis for the Final Revisions to the
National Ambient Air Quality Standards for Particulate Matter, Health and Environmental Impacts Division, Office
of Air Quality Planning and Standards, EPA-452-R-12-005, December 2012. Available on the internet:
http://www3.epa.gov/ttnecasl/regdata/RIAs/finalria.pdf.
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163	U.S. Environmental Protection Agency (U.S. EPA). (2012). Regulatory Impact Analysis: Final Rulemaking for
2017-2025 Light-Duty Vehicle Greenhouse Gas Emission Standards and Corporate Average Fuel Economy
Standards, Assessment and Standards Division, Office of Transportation and Air Quality, EPA-420-R-12-016,
August 2012. Available on the Internet at: http://www3.epa.gov/otaq/climate/documents/420rl2016.pdf.
164	Turner, MC, Jerrett, M, Pope, A, III, Krewski, D, Gapstur, SM, Diver, WR, Beckerman, BS, Marshall, JD, Su, J,
Crouse, DL and Burnett, RT (2016). Long-term ozone exposure and mortality in a large prospective study. Am J
Respir Crit Care Med 193(10): 1134-1142.
165	U.S. Environmental Protection Agency (U.S. EPA). 2009. Integrated Science Assessment for Particulate Matter
(Final Report). EPA-600-R-08-139F. National Center for Environmental Assessment - RTP Division, Research
Triangle Park, NC. December. Available at: .
166	U.S. Environmental Protection Agency (U.S. EPA). 2019. Integrated Science Assessment (ISA) for Particulate
Matter (Final Report, 2019). U.S. Environmental Protection Agency, Washington, DC, EPA/600/R-19/188, 2019.
167	U.S. Environmental Protection Agency (U.S. EPA). 2020. Integrated Science Assessment (ISA) for Ozone and
Related Photochemical Oxidants (Final Report). U.S. Environmental Protection Agency, Washington, DC,
EPA/600/R-20/012, 2020.
168	U.S. Environmental Protection Agency (U.S. EPA). 2016. Integrated Science Assessment for Oxides of Nitrogen
- Health Criteria (Final Report). National Center for Environmental Assessment, Research Triangle Park, NC. July.
Available at: < https://cfpub.epa.gov/ncea/isa/recordisplay.cfm?deid=310879>.
169	U.S. Environmental Protection Agency (U.S. EPA). 2008. Integrated Science Assessment for Oxides of Nitrogen
and Sulfur-Ecological Criteria National (Final Report). National Center for Environmental Assessment - RTP
Division, Research Triangle Park, NC. EPA/600/R-08/139. December. Available at:
.
170	For more information about income growth adjustment factors and EPA's population projections, please refer to
the following: https://www.epa.gov/sites/production/files/2015-04/documents/benmap-
ce_user_manual_march_2015 .pdf.
171	U.S. Environmental Protection Agency (U.S. EPA). 2019. Integrated Science Assessment (ISA) for Particulate
Matter (Final Report, 2019). U.S. Environmental Protection Agency, Washington, DC, EPA/600/R-19/188, 2019.
172	U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 2004. Advisory Council on
Clean Air Compliance Analysis Response to Agency Request on Cessation Lag. EPA-COUNCIL-LTR-05-001.
December. Available at:
.
173	U.S. EPA. 2012. Regulatory Impact Analysis for the Proposed Revisions to the National Ambient Air Quality
Standards for Particulate Matter.
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Chapter 8: Vehicle Sales, Employment, Environmental Justice, and
Affordability and Equity Impacts
8.1 Sales Impacts
8.1.1 Conceptual Framework
A significant question in vehicle GHG rules has been why there have appeared to be existing
technologies that, if adopted, would reduce fuel consumption enough to pay for themselves in
short periods, but which were not widely adopted. If the benefits to vehicle buyers outweighed
the costs to those buyers of the new technologies, economic principles suggest that automakers
would provide them, and people would buy them. Yet engineering analyses have identified a
number of technologies, such as downsized-turbocharged engines, gasoline direct injection, and
improved aerodynamics, with short payback periods that were not widely adopted before the
standards, but which were adopted rapidly afterwards.1 Why did markets fail, on their own, to
adopt these technologies?
This question, termed the "energy paradox" or "energy efficiency gap,"2 has received a great
deal of discussion in previous rulemakings.3 The gap exists if the estimates of net benefits of
these new technologies are correct, and if there are no major adverse effects associated with the
technologies (hidden costs) that provide clear disincentives to adopt the technologies. A separate
question is to explain why the gap exists.
8.1.1.1 Existence of the Energy Efficiency Gap
EPA has previously explored the existence of the paradox, including in the Midterm
Evaluation.4 In terms of the costs and effectiveness of the fuel-saving technologies, EPA has
relied on published research, highly-regarded teardown studies,5 and extensive testing to ensure
the best available estimates for its analyses. In the MTE's TAR and Proposed Determination
TSD, EPA undertook retrospective analysis of its cost and effectiveness estimates and generally
confirmed the previous estimates. See Chapter 4 of this draft RIA for more discussion of the
technology and cost estimates for the current proposal.
The 2021 National Academies of Science (NAS) report6 (p. 11-348) raises the issue of
tradeoffs between improved performance and fuel economy and recommends that "agencies
should collect further evidence on the influence of vehicle performance trade-offs on automaker
compliance strategies and consumers, and reassess whether forgone performance improvements
should be included in benefit-cost analysis of the standards. The agencies should assess how new
technologies penetrating the market will affect the trade-offs among greenhouse gas (GHG)
emissions rates, performance, and other attributes."
EPA has considered evidence related to potential adverse effects on other vehicle attributes.
First, EPA sponsored research to evaluate how auto reviewers — professionals expected to be
especially sensitive to vehicle performance and attributes — evaluated MY 2014 and 2015
vehicles with fuel-saving technologies (Helfand et al. 2016, Huang et al. 2018).7 These studies
found that all technologies were evaluated positively more often than they were evaluated
negatively, suggesting that it is possible to implement these technologies without imposing
hidden costs. In addition, they looked for correlations between evaluations of each technology
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and a range of operational characteristics (handling, acceleration, noise, etc.). Both papers found
few correlations between the existence of a technology and negative rating of operational
characteristics; and even fewer of those were consistently correlated in both model years. In
addition, Huang et al. (2018) found that overall evaluation of a vehicle's quality is more
associated with operational characteristics than with the technologies themselves; as noted in the
previous paragraph, there is little association between operational characteristics and the
technologies.
Additional research (Huang et al. 2018a)8 explored the use of results from a consumer
satisfaction survey conducted by Strategic Vision to look at how vehicle buyers responded to the
presence of fuel-saving technologies. Preliminary results were developed using a subset of the
data, due to incomplete matching of technology information with survey information. Overall,
people were highly satisfied with their newly purchased vehicles; less than 3 percent of owners
expressed dissatisfaction. This result is not surprising; people are unlikely to buy new vehicles
that they find unsatisfactory. Further, comparing negative satisfaction ratings from before and
after the presence of fuel saving technology, results show little correlation between the presence
of a technology and a change in satisfaction ratings for overall experience, power and pickup,
driving performance, noise/vibration/harshness, or fuel economy. EPA continues to explore these
data.
In these three studies, a limitation is that it is not possible to demonstrate causally that the
presence or absence of a technology affects people's perceptions of vehicle quality. For instance,
it may be that some of the fuel-saving technologies are used primarily in market segments that
would, regardless of the presence of the technology, not be considered as high-quality as vehicles
in other market segments; an association between the presence of the technology and an
evaluation of quality may be based, not on a causal effect of the technology on quality, but rather
a correlation due to its use in that market segment. Nevertheless, the research indicates that it has
been possible for these technologies to be adopted without observed adverse impacts on
assessments by expert reviewers or on consumer satisfaction.
Some research9 has argued that reducing fuel consumption must come at the expense of either
vehicle acceleration or vehicle weight, however the existing literature has only been able to
examine trade-offs between limited numbers of factors. The concept is based in the principle that
energy is required to move the vehicle, so that heavier and faster vehicles will require more
energy use. While this statement is true when all else (e.g., powertrain, body style) is held
constant, it is uncommon that, in fact, all else is held constant. The Midterm Evaluation Proposed
Determination TSD, Chapter 4.1.2, discussed some concerns and limitations with the existing
literature. One issue is that the papers typically assume that the tradeoff between power and fuel
economy, or between weight and fuel economy, does not vary over time. MacKenzie and
Hey wood (2015) further point out that these studies are based on horsepower and weight, two
measures that may not accurately reflect the characteristics sought by vehicle buyers. If, as they
have found, the relationship between acceleration (measured as 0-to-60 speed) and horsepower
divided by weight has changed over time, then studies holding constant the relationship between
horsepower-to-weight and fuel consumption are not accurately measuring the tradeoff of concern
to vehicle buyers.
Recent work by Moskalik et al. (2018)10 suggests that using historic data to estimate tradeoffs
may miss changes in the relationship between acceleration and CO2 emissions with new
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technologies. Moskalik et al. (2018) shows results using the ALPHA model for trade-off curves
between CO2 emissions and 0-to-60 acceleration time for five different engine types covering a
range of production years: carbureted, port fuel injection (PFI), gasoline direct injection (GDI),
Atkinson, and turbo-downsized (TDS) engines. These engines have different operating efficiency
characteristics, and thus different tradeoff curves. Most notably, the newest TDS engines have
much flatter tradeoffs than earlier carbureted, GDI and PFI; in fact, the "future" TDS engine
shows almost no change in CO2 emissions over a wide range of acceleration times. Thus, the
assumption in the previous research that the tradeoffs among acceleration, fuel economy, and
weight are constant does not appear to accurately represent the new technologies, and in fact may
substantially overestimate the magnitude of the performance-fuel economy tradeoff.
Watten et al. (2021)11 develops a theoretical framework that incorporates producer decisions
on technological adoption and attribute production, taking into account consumer preferences
toward performance and fuel economy. This paper distinguishes between technologies that
improve, or do not adversely affect, both performance and fuel economy, and reducing engine
displacement, which does trade off improved fuel economy for performance. Following
Moskalik et al. (2018), it observes that the "marginal rate of attribute substitution" between
power and fuel economy has changed substantially over time. In particular, it has become
relatively more costly to improve efficiency by reducing power, and relatively less costly to add
technologies that improve efficiency. These technology improvements do not reduce power and
in some cases may increase it. It supports the concept that automakers take consumer preferences
into account in identifying where to add technology.
Regarding the NAS's recommendation to evaluate whether forgone performance
improvements should be included in the benefit-cost analysis, the agencies have typically
included the costs of holding performance constant in the rulemakings. As discussed in previous
paragraphs, ways exist to enhance performance without adverse effects on fuel economy. In that
case, Helfand and Dorsey-Palmateer (2015)12 argue that the only additional cost due to the
standards associated with additional performance would occur if adding that performance is
more expensive for a vehicle with higher fuel economy. In addition, it would be important to add
benefits associated with improvements in other attributes due to fuel-saving technologies.
Whitefoot et al. (2017)13 find that allowing for tradeoffs between performance and fuel economy
reduces the costs of the standards, by allowing an additional way to achieve compliance. EPA
has not included either these increased costs or increased benefits in its analysis, due to lack of
sufficient data to estimate these effects.
This discussion does not reject the observation that the energy efficiency gap has existed for
light-duty vehicles. Cost and effectiveness values for the technologies have not been shown to
have significant errors. Helfand and Dorsey-Palmateer (2015) conclude that, in response to the
standards, automakers have improved fuel economy without adversely affecting other vehicle
attributes, and any remaining tradeoffs are likely to be included in the costs of the technologies.
Thus, it appears that markets on their own have not led to adoption of a number of technologies
with short payback periods in the absence of the standards.
8.1.1.2 Potential Explanations for the Existence of the Energy Efficiency Gap
Previous rules have discussed a number of hypotheses for this apparent market failure.14 Here
we summarize the theories.
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On the consumer side, the 2021 NAS Report (p. 11-355) observes that "the literature has not
settled on a single explanation for potential consumer undervaluation of fuel cost savings."
Hypotheses include:
•	Consumers might lack the information necessary to estimate the value of future fuel
savings, not have a full understanding of this information even when it is presented, or
not trust the presented information
•	Consumers might be "myopic" and hence undervalue future fuel savings in their
purchasing decisions
•	Consumers may be accounting for uncertainty in future fuel savings when comparing
upfront cost to future returns
•	Consumers may consider fuel economy after other vehicle attributes and, as such, not
optimize the level of this attribute (instead "satisficing" - that is, selecting a vehicle
that is acceptable rather than optimal — or selecting vehicles that have some sufficient
amount of fuel economy)
•	Consumers might be especially averse to the short-term losses associated with the
higher prices of energy efficient products relative to the long-term gains of future fuel
savings (the behavioral phenomenon of "loss aversion")
•	Consumers might associate higher fuel economy with inexpensive, less well designed
vehicles
•	When buying vehicles, consumers may focus on visible attributes that convey status,
such as size, and pay less attention to attributes such as fuel economy that typically do
not visibly convey status
•	Even if consumers have relevant knowledge, selecting a vehicle is a highly complex
undertaking, involving many vehicle characteristics. In the face of such a complicated
choice, consumers may use simplified decision rules
•	Because consumers differ in how much they drive, they may already sort themselves
into vehicles with different, but individually appropriate, levels of fuel economy in
ways that an analysis based on an average driver does not identify.
EPA has explored the evidence on how consumers evaluate fuel economy in their vehicle
purchase decisions.15 Some research finds that vehicle buyers consider close to all fuel
consumption over a vehicle's lifetime in the purchase decision.16 Others find that vehicle buyers
consider only a small share of that future consumption in the purchase decision.17 The variation
in estimates is very large, even after outliers are removed; Greene et al. (2018) estimated a mean
willingness to pay for a $0.01 reduction in fuel cost per mile among published estimates of
$1880, a median of $990, and a standard deviation of $6880, compared to a reference value of
$1150 for the value of reducing fuel costs by $0.01/mile over the lifetime of an average vehicle.21
The estimates vary based on the type of study (revealed preference, stated preference, or market
sales), and the form of statistical model used to analyze the data. These observations provide
little guidance on whether consumers overvalue or undervalue fuel economy, or get the estimates
" It also provides a reference value of $1150 for the value of reducing fuel costs by $0.01/mile over the lifetime of an
average vehicle.
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approximately right. Thus, it is not clear whether consumer behavior is responsible for the
energy efficiency gap, much less which hypotheses might explain it.
For possible explanations on the producer side, two major themes arise: the role of market
structure and business strategy, and the nature of technological invention and innovation.
•	Light-duty vehicle production involves significant fixed costs, and automakers strive
to differentiate their products from each other. These observations suggest that
automakers can act strategically in how they design and market products. In this
context, the fuel economy of a vehicle can become a factor in product differentiation
rather than a decision based solely on cost-effectiveness of a fuel-saving technology.18
Product differentiation carves out corners of the market for different automobile
brands and models. For instance, automakers may emphasize luxury characteristics in
some vehicles to attract people with preferences for those characteristics, and they
may emphasize cost and fuel economy for people attracted to frugality. By separating
products into different market segments, producers both provide consumers with
goods targeted for their tastes, and may reduce competition among vehicle models,
creating the possibility of greater profits. From the producer perspective, fuel economy
is not necessarily closely related to the cost-effectiveness of the technologies to
consumers, but rather is one of many factors that manufacturers use to market their
models to different consumer groups. As Fischer (2005) points out, this strategy can
lead to inefficiencies in the market: an under-supply of fuel economy relative to what
is cost-effective to consumers in some segments, and an over-supply of fuel economy
in other sectors.19 The structure of the automobile industry may inefficiently allocate
car attributes—fuel economy among them—and help to explain the existence of an
energy efficiency gap.
•	Innovation - the first commercialization of a new product - occurs on a continuum
between two extremes: "major" innovation where product characteristics change, and
"incremental" innovation13 which exploits relatively minor changes to the existing
product.20 In the absence of standards, automakers have seemed willing to invest in
small improvements upon existing technologies ("incremental" technologies) that can
be used to improve fuel economy or other vehicle attributes (Helfand and Dorsey-
Palmateer 2015).However, they may be more hesitant to invest in "major" innovations
in the absence of standards, for several reasons, including being the first (or one of the
first) to invest in a new technology.
•	There may be first-mover disadvantages to investing in new technologies. Many
manufacturers prefer to observe the market and follow other manufacturers rather than
be the first to market with a specific technology. The "first-mover disadvantage" has
been recognized in other research where the "first-mover" pays a higher proportion of
the costs of developing technology, but loses the long-term advantage when other
businesses follow quickly.21
•	There could be "dynamic increasing returns" to adopting new technologies, wherein
the value of a new technology may depend on how many other companies have
adopted the technology — for instance, creating multiple suppliers for a technology
b Abernathy and Utterback use "major" and "incremental" Henderson and Clark, with a two-dimensional framework,
use "radical" and "incremental."
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should increase competition, improve quality, and reduce price. This could be due to
network effects or learning-by-doing. In a network effects situation, the usefulness of
the technology depends on others' adoption of the technology: e.g., a telephone is only
useful if other people also have telephones. Learning by doing is the concept that the
costs (benefits) of using a particular technology decrease (increase) with use. Both of
these incentivize firms to pursue a "wait and see" strategy when it comes to adopting
new technologies.22
•	There can be synergies when companies work on the same technologies at the same
time.23 Research among multiple parties can be a synergistic process: ideas by one
researcher may stimulate new ideas by others, and more and better results occur than if
the one researcher operated in isolation.0 Standards can promote research into low-
CO2 technologies that would not take place in the absence of the standards. Because
all companies (both auto firms and auto suppliers) have incentives to find better, less
expensive ways of meeting the standards, the possibilities for synergistic interactions
may increase. Thus, the standards, by focusing all companies on finding more efficient
ways of achieving the standards, may lead to better outcomes than if any one company
operated on its own.
•
Much less research has been conducted to evaluate the producer side of the market. The 2015
NAS report (cited in the 2021 NAS report) observes that automakers "perceive that typical
consumers would pay upfront for only one to four years of fuel savings" (p. 9-10),24 a range of
values within that identified in Greene et al. (2018) for consumer response, but well below the
median or mean. It may be possible, though puzzling, that automakers operate under a
misperception of consumer willingness to pay for additional fuel economy. The 2021 NAS
Report (p. 11-356) observes that the auto industry is concentrated, "in part owing to the large
capital investments necessary to enter the automotive market," and raises the "first-mover
disadvantage" argument. In addition, it discusses the challenges associated with a "disruptive"
technology such as the transition to electrification (p. 11-358). Thus, it supports the concept that
there potentially are barriers to adoption of new technologies on the part of automakers, though it
also does not provide conclusive evidence.
In sum, it continues to be an open question why there was limited adoption of cost-effective
fuel-saving technologies before the implementation of more stringent standards, that were
adopted without serious disruption to the vehicle market after the standards became effective.
Nevertheless, it appears to have happened.
8.1.2 How Sales Impacts were Modeled
As discussed in Chapter 4:, EPA is using the CAFE Compliance and Effects Modeling
System (CCEMS) model for this analysis. The FRIA for the SAFE rule (starting p. 871)
describes the approach to vehicle sales impacts used in the model. First, it projects future new
vehicle sales in the reference case based on projections of macroeconomic variables. Second, it
0 Powell and Giannella (2010) discuss how a "collective momentum" has led uncoordinated research efforts among
a diverse set of players to develop advances in a number of technologies (such as electricity and telephones). They
contrast this view of technological innovation with that of proprietary research in corporate laboratories, where the
research is part of a corporate strategy. Such momentum may result in part from alignment of economic, social,
political, and other goals.
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applies a demand elasticity of -1 (that is, a one percent increase in price produces a one percent
decrease in the quantity sold) to the change in net price, where net price is the difference in
technology costs less an estimate of the change in fuel costs over 2.5 years. This approach
assumes that vehicle buyers and automakers take into consideration the fuel savings that
consumers expect to accrue over the first 2.5 years of vehicle ownership — an assumption that
warrants further evaluation as discussed below. This assumption applies to both the without-
program and with-program calculations. It does not allow for different perceptions of the value
of fuel economy to buyers on the part of automakers, in providing fuel-saving technologies, and
those buyers.
As discussed in Chapter 8.1, there does not yet appear to be consensus around the role of fuel
consumption in people's vehicle purchase decisions, and the assumption that 2.5 years of fuel
consumption is the right number deserves further evaluation. As noted there, this assumption is
consistent with automakers' statements of their perceptions of consumer behavior. Also as noted
there, Greene et al. (2018) provides a reference value of $1,150 for the value of reducing fuel
costs by $0.01/mile over the lifetime of an average vehicle; for comparison, 2.5 years of fuel
savings is about 30 percent of that value, or about $333.d This value is within the large standard
deviation in Greene et al. (2018) for the willingness to pay to reduce fuel costs, but it is lower
than both the mean of $1,880 (160 percent of the reference value) and the median of $990 (85
percent of the reference value) per one cent per mile in the paper. EPA estimates that the present
value of 85 percent of fuel consumption is about 10 years of fuel consumption, using Greene et
al.'s assumptions.6 It appears possible that automakers may operate under a different perception
of consumer willingness to pay for additional fuel economy than how consumers actually
behave. CCEMS does not allow automaker perception to differ from consumer behavior.
EPA recognizes that setting the elasticity of demand at -1 is based on literature more than 25
years old, and thus, EPA is currently working to review more recent estimates of the elasticity of
demand for new vehicles; for instance, Leard (2021)25 estimates a new vehicle demand elasticity
of -0.4. The elasticity does not affect whether the sales increase or decrease, but it does affect the
magnitude of those increases: a 1 percent change in sales for a 1 percent change in net price is
larger than a 0.4 percent change for a 1 percent change in net price. In recognition of newer
elasticity of demand estimates, EPA presents a sensitivity analysis with an elasticity of -0.4, as in
Leard (2021).
CCEMS also makes use of a dynamic fleet share model (FRIA p. 877) that estimates,
separately, the shares of passenger cars and light trucks based on vehicle characteristics, and then
adjusts them so that the market shares sum to one. The model also includes the effects of the
standards on vehicle scrappage based on a statistical analysis (FRIA starting p. 926). The model
looks for associations between age, change in new vehicle prices, fuel prices, cost per mile of
d Greene et al. (2018) does not provide enough detail to replicate their analysis perfectly. The 30 percent estimate is
calculated by assuming, following assumptions in Greene et al. (2018), that a vehicle is driven 15,000 miles peryear
for 13.5 years, 10 percent discount rate. Those figures produce a "present value of miles" of 108,600; thus, a
$0.01/mile change in the cost of driving would be worth $1086. In contrast, saving $0.01/mile for 2.5 years is worth
about $318, or 29 percent of the value over 13.5 years. Here we use 29 percent of Greene et al.'s estimate
($1150*0.29 = $333).
e With a 10 percent discount rate, the present value of 15,000 miles per year at age 10 is 85 percent of the present
value of 15,000 miles peryear at age 13.5. For comparison, a 5 percent discount rate achieves 85 percent of the 13.5
years of present value at roughly age 11.
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driving, and macroeconomic measures and the scrappage rate, with different equations for cars,
SUVs/vans, and pickups. Because the scrappage model was revised from the version in the
SAFE proposal due to public comments received, the current version has not been subject to
review/EPA's project to review new vehicle demand elasticities is also reviewing the literature
on the relationship between new and used vehicle markets and scrappage.
For the purposes of this proposal, EPA is maintaining these assumptions for its modeling. We
will continue to evaluate these assumptions for the final rule.
8.1.3 Sales Impacts
With the modeling assumption, described in Chapter 8.1.2, that vehicle buyers consider 2.5
years of future fuel consumption in the purchase decision, the sales impacts projected by the
model are in Table 8-1. Vehicle sales decrease by roughly 2 percent compared to sales in the
baseline SAFE rule.
Table 8-1: Sales Impacts, 2.5 Years of Fuel Savings in Net Price, Demand Elasticity -1

SAFE
Proposal
Difference
Percent Change
2022
15,199,000
15,166,000
-33,000
-0.2%
2023
15,126,000
14,944,000
-182,000
-1.2%
2024
15,214,000
14,966,000
-248,000
-1.6%
2025
15,379,000
15,092,000
-287,000
-1.9%
2026
15,475,000
15,097,000
-378,000
-2.4%
2027
15,625,000
15,317,000
-308,000
-2.0%
2028
15,737,000
15,422,000
-315,000
-2.0%
2029
15,743,000
15,429,000
-314,000
-2.0%
2030
15,812,000
15,518,000
-294,000
-1.9%
2031
15,861,000
15,580,000
-281,000
-1.8%
2032
15,857,000
15,596,000
-261,000
-1.6%
2033
15,880,000
15,633,000
-247,000
-1.6%
2034
15,879,000
15,649,000
-230,000
-1.4%
2035
15,829,000
15,613,000
-216,000
-1.4%
Table 8-2 examines the impact of using an elasticity of -0.4 on sales. As expected, the smaller
(in absolute value) elasticity produces lower sales impacts. Sales under both the SAFE program
and the proposed program would be higher with the smaller elasticity. In addition, the difference
in sales between the proposal and the SAFE rule shrinks; the effects of the standards peak at -1
percent, and drop to about -0.5 percent.
f Details on the changes made to the scrappage model can be found in the SAFE FRIA.
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Table 8-2: Sales Impacts, 2.5 Years of Fuel Savings in Net Price, Demand Elasticity -0.4

SAFE
Proposal
Difference
Percent Change
2022
15,531,000
15,518,000
-13,000
-0.1%
2023
15,454,000
15,381,000
-73,000
-0.5%
2024
15,541,000
15,442,000
-99,000
-0.6%
2025
15,701,000
15,593,000
-115,000
-0.7%
2026
15,833,000
15,681,000
-151,000
-1.0%
2027
15,969,000
15,846,000
-123,000
-0.8%
2028
16,083,000
15,957,000
-126,000
-0.8%
2029
16,079,000
15,953,000
-126,000
-0.8%
2030
16,137,000
16,019,000
-118,000
-0.7%
2031
16,178,000
16,066,000
-113,000
-0.7%
2032
16,166,000
16,061,000
-105,000
-0.6%
2033
16,190,000
16,091,000
-99,000
-0.6%
2034
16,181,000
16,093,000
-93,000
-0.6%
2035
16,130,000
16,043,000
-87,000
-0.5%
As discussed above, the use of 2.5 years by consumers for consideration of future fuel
consumption is smaller than the mean or median estimates in the Greene et al. (2018) meta-
analysis for consumer valuation of fuel savings, though it appears to reflect automakers'
perception of that value. In addition, it is possible that automakers and vehicle buyers may differ
in their practices relating to consumers' willingness to pay for fuel economy. If automakers
underestimate consumers' valuation of fuel economy, then sales may increase relative to the
baseline under the proposed standards. EPA will continue to evaluate the sales impacts of the
standards.
8.2 Employment Impacts
8.2.1 Conceptual Framework
Economic theory of labor demand indicates that employers affected by environmental
regulation may increase their demand for some types of labor, decrease demand for other types
of labor, or for still other types, not change it at all. A variety of conditions can affect
employment impacts of environmental regulation, including baseline labor market conditions
and employer and worker characteristics such as industry, region, and skill level.
A growing literature has investigated employment effects of environmental regulation.
Morgenstern et al. (2002)26 decompose the labor consequences in a regulated industry facing
increased abatement costs into three separate components. First, there is a demand effect caused
by higher production costs raising market prices. Higher prices reduce consumption (and
production) reducing demand for labor within the regulated industry. Second, there is a cost
effect where, as production costs increase, plants use more of all inputs, including labor, to
produce the same level of output. Third, there is a factor-shift effect where post-regulation
production technologies may have different labor intensities. These three effects outlined by
Morgenstern et al. (2002) provides the theoretical foundation for EPA's analysis of the impacts
of the proposed regulation on labor throughout Chapter 8.2.26
Additional papers approach employment effects through similar frameworks. Berman and Bui
(2001) model two components that drive changes in firm-level labor demand: output effects and
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substitution effects.27,8 If regulation causes marginal cost to increase, it will place upward
pressure on output prices, leading to a decrease in the quantity demanded, and resulting in a
decrease in production that they term the output effect. The substitution effect describes how,
holding output constant, regulation affects labor intensity of production. Deschenes describes
environmental regulations as requiring additional capital equipment for pollution abatement that
does not increase labor productivity.28 These higher production costs induce regulated firms to
reduce output and decrease labor demand (an output effect) while simultaneously shifting away
from the use of more expensive capital towards increased labor demand (a substitution effect).
At the industry level, labor demand is more likely to be responsive to regulatory costs if: (1) the
elasticity of labor demand is high relative to the elasticity of labor supply, and (2) labor costs are
a large share of total production costs.29 Labor demand might also respond to regulation if
compliance activities change labor intensity in production.
To study labor demand impacts empirically, researchers have compared employment levels at
facilities subject to an environmental regulation to employment levels at similar facilities not
subject to that environmental regulation; some studies find no employment effects, and others
find statistically significant, usually small differences. For example, see Berman and Bui,
Greenstone (2002), Ferris et al. (2014), Walker (2013), and Curtis (2018, 2020).30
Workers affected by changes in labor demand due to regulation may experience a variety of
impacts including job gains or involuntary job loss and unemployment. Localized reductions in
employment may adversely impact individuals and communities just as localized increases may
have positive impacts. Workforce adjustments in response to decreases in labor demand can be
costly to firms as well as workers, so employers may choose to adjust their workforce over time
through natural attrition or reduced hiring, rather than incur costs associated with job separations
(see, for instance, Curtis (2018) and Hafstead and Williams (2018).31
In addition to impacts on labor demand in directly regulated industries, impacts on related
industries are possible too. Industries operating upstream or downstream from the regulated
industries may experience changes in labor demand. For example, as described elsewhere in this
draft RIA, we expect the proposed rule to cause a small decline in extracting, refining,
transporting, and storing of petroleum fuels, and a small increase in electricity generation which
may have consequences for labor demand in those upstream industries. Or lower per-mile fuel
costs could lead to increases in demand for ride-sharing or ride-hailing services and cause
increases in demand for drivers in those jobs. Firms producing substitutes or complements to the
goods produced by the regulated industry may also experience changes in demand for labor. For
example, the expected decline in gas station visits may lead to reduced demand for labor in that
sector. The magnitude of these impacts depends on a variety of factors including the labor
intensities of the related sectors as well as the nature of the linkages (which can be reflected in
measures of elasticity) between them and the regulated firms.
As suggested in this discussion, the overall employment effects of environmental regulation
are difficult to estimate. Estimation is difficult due to the multitude of small changes in different
sectors related to the regulated industry, and because employment impacts are hard to
disentangle from other economic changes and business decisions that affect employment, over
g Berman and Bui (2001) also discuss a third component, the impact of regulation on factor prices, but conclude that
this effect is unlikely to be important for large competitive factor markets, such as labor and capital.
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time and across regions and industries. If the U.S. economy is at full employment, even a large-
scale environmental regulation is unlikely to have a noticeable impact on aggregate net
employment.11 Instead, labor would primarily be reallocated from one productive use to another,
and net national employment effects from environmental regulation would be small and
transitory (e.g., as workers move from one job to another).32 However, localized reductions in
employment may adversely impact individuals and communities just as localized increases may
have positive impacts. If the economy is operating at less than full employment, economic theory
does not clearly indicate the direction or magnitude of the net impact of environmental regulation
on employment; it could cause either a short-run net increase or short-run net decrease.33
Because it is challenging to know the state of the macroeconomy when these standards
become effective, and also because of the difficulties of modeling impacts on employment in a
complex national economy, we focus our analysis on the direct impacts in closely affected
sectors, as described in the next section.
8.2.2	How Employment Impacts were Modeled
The SAFE FRIA (starting p. 1067) describes the calculation of employment impacts for three
sets of affected sectors: automotive dealers, final assembly labor and parts production, and fuel-
saving (or GHG-reducing) technology labor. The first two of these (automobile dealers and final
assembly) are examples of demand-effect employment, while the third (technology labor)
reflects cost-effect employment. For automotive dealers, the model estimates the hours involved
in each new vehicle sale. Estimating the labor involved in final assembly used average labor
hours per vehicle at a sample of U.S. assembly plants, adjusted by the ratio of vehicle assembly
manufacturing employment to employment for total vehicle and equipment manufacturing for
new vehicles. Finally, for fuel-saving technology labor, the analysis calculated the average
revenue per job-year for automakers, and used the revised revenue estimates for calculation of
the change in job-years. These estimates are still in use for this proposal.
8.2.3	Employment Impacts
Table 8-3 below provides the results of these calculations, combined for these three sectors. It
indicates a very small effect on employment, initially negative but becoming increasingly
positive over time, when using the estimate that both automakers and vehicle buyers take 2.5
years of fuel savings into consideration in the purchase decision. The estimated employment
impact starts out negative in the early years, where the negative demand effect due to decreased
sales outweighs the positive cost effect due to increased technology costs. This relationship flips
in 2026.
h Full employment is a conceptual target for the economy where everyone who wants to work and is available to do
so at prevailing wages is actively employed. The unemployment rate at Ml employment is not zero.
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Table 8-3: Employment Impacts, Based on Sales Estimates in Table 8-1 (Demand Elasticity -1)
Year
SAFE
Proposal
Difference
Percent Difference
2022
1,105,000
1,105,000
0
0.0%
2023
1,100,000
1,099,000
-1,000
-0.1%
2024
1,108,000
1,106,000
-2,000
-0.2%
2025
1,120,000
1,119,000
-1,000
-0.1%
2026
1,130,000
1,132,000
2,000
0.2%
2027
1,143,000
1,149,000
6,000
0.5%
2028
1,152,000
1,160,000
8,000
0.7%
2029
1,153,000
1,162,000
9,000
0.8%
2030
1,156,000
1,166,000
10,000
0.9%
2031
1,159,000
1,169,000
10,000
0.9%
2032
1,157,000
1,167,000
10,000
0.9%
2033
1,158,000
1,169,000
11,000
0.9%
2034
1,157,000
1,168,000
11,000
1.0%
2035
1,153,000
1,164,000
11,000
1.0%
Table 8-4 shows the effects of using the sales estimates based on an elasticity of -0.4, as
shown in Table 8-2. As with the sales impacts, employment under both the SAFE program and
the proposal are higher with the smaller (in absolute value) elasticity. The effects on employment
due to the proposed standards, with this lower elasticity, are projected to be positive, gradually
increasing to 2 percent. Due to the smaller (in absolute value) demand elasticity in this analysis
than in the analysis above, here the positive cost effect outweighs the negative demand effect
across all analyzed years.
Table 8-4: Employment Impacts, Based on Sales Estimates in Table 8-2 (Demand Elasticity -0.4)
Year
SAFE
Proposal
Difference
Percent Difference
2022
1,130,000
1,131,000
2, 000
0.1%
2023
1,124,000
1,131,000
7, 000
0.6%
2024
1,132,000
1,141,000
9, 000
0.8%
2025
1,144,000
1,156, 000
12, 000
1.0%
2026
1,157,000
1,176, 000
19, 000
1.6%
2027
1,169,000
1,189, 000
20, 000
1.7%
2028
1,178,000
1,200, 000
22, 000
1.9%
2029
1,178,000
1,201,000
23, 000
2.0%
2030
1,180,000
1,204, 000
23, 000
2.0%
2031
1,182, 000
1,205, 000
23, 000
1.9%
2032
1,180, 000
1,202, 000
22, 000
1.9%
2033
1,181,000
1,203, 000
22, 000
1.9%
2034
1,180, 000
1,201,000
22, 000
1.8%
2035
1,175,000
1,196, 000
21, 000
1.8%
If automakers underestimate consumers' valuation of fuel economy, as noted in Chapter 8.2.3,
then demand-effect employment is likely to be higher, and employment impacts are likely to be
more positive.
As mentioned, we are only providing partial estimates of employment impacts in the directly
regulated sector, plus the impacts for automotive dealers. These do not include economy-wide
labor impacts. As discussed in Chapter 8.2.1, economy-wide impacts on employment are
generally driven by broad macroeconomic effects. It also does not reflect employment effects
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due to impacts on related sectors other than car dealerships (those that are upstream or
downstream, or producing substitutes and complements). For example, we have not estimated
the impacts of reduced spending on fuel consumption. Those changes may lead to some
reductions in employment in gas stations, and some increases in other sectors to which people
reallocate those expenditures.
Electrification of the vehicle fleet is likely to affect both the number and the nature of
employment in the auto and parts sectors and related sectors, such as providers of charging
infrastructure. Because this proposal projects relatively minor increases in penetration of plug-in
electric vehicles, from 3.6 percent of the fleet in MY 2023 to 7.8 percent of the fleet in MY 2026
(see Table 4-23), we do not predict major changes in the composition of employment in these
sectors for MYs 2023-2026. EPA will continue to assess changes in employment as
electrification of the auto industry proceeds.
8.3 Environmental Justice
Executive Order 12898 (59 FR 7629, February 16, 1994) establishes federal executive policy
on environmental justice. It directs federal agencies, to the greatest extent practicable and
permitted by law, to make achieving environmental justice part of their mission by identifying
and addressing, as appropriate, disproportionately high and adverse human health or
environmental effects of their programs, policies, and activities on minority populations and low-
income populations in the United States. EPA defines environmental justice as the fair treatment
and meaningful involvement of all people regardless of race, color, national origin, or income
with respect to the development, implementation, and enforcement of environmental laws,
regulations, and policies.1
Executive Order 14008 (86 FR 7619, February 1, 2021) also calls on Agencies to make
achieving environmental justice part of their missions "by developing programs, policies, and
activities to address the disproportionately high and adverse human health, environmental,
climate-related and other cumulative impacts on disadvantaged communities, as well as the
accompanying economic challenges of such impacts." It also declares a policy "to secure
environmental justice and spur economic opportunity for disadvantaged communities that have
been historically marginalized and overburdened by pollution and under-investment in housing,
transportation, water and wastewater infrastructure and health care." Executive Order 13563
directs federal agencies to consider equity, human dignity, fairness, and distributional
considerations.
1 Fair treatment means that "no group of people should bear a disproportionate burden of environmental harms and
risks, including those resulting from the negative environmental consequences of industrial, governmental and
commercial operations or programs and policies.". Meaningful involvement occurs when "1) potentially affected
populations have an appropriate opportunity to participate in decisions about a proposed activity [e.g., rulemaking]
that will affect their environment and/or health; 2) the public's contribution can influence [the EPA's rulemaking]
decision; 3) the concerns of all participants involved will be considered in the decision-making process; and 4) [the
EPA will] seek out and facilitate the involvement of those potentially affected" A potential EJ concern is defined as
"the actual or potential lack of fair treatment or meaningful involvement of minority populations, low-income
populations, tribes, and indigenous peoples in the development, implementation and enforcement of environmental
laws, regulations and policies." See "Guidance on Considering Environmental Justice During the Development of an
Action." Environmental Protection Agency, www.epa.gov/environmentaljustice/guidanceconsidering-
environmental-justice-duringdevelopment-action. See also https://www.epa.gov/environmentaljustice.
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EPA also released its "Technical Guidance for Assessing Environmental Justice in Regulatory
Analysis" providing recommendations on conducting the highest quality analysis feasible,
recognizing that data limitations, time and resource constraints, and analytic challenges will vary
by media and regulatory context.34
When assessing the potential for disproportionately high and adverse health or environmental
impacts of regulatory actions on minority populations, low-income populations, tribes, and/or
indigenous peoples, the EPA strives to answer three broad questions: (1) Is there evidence of
potential EJ concerns in the baseline (the state of the world absent the regulatory action)?
Assessing the baseline will allow the EPA to determine whether pre-existing disparities are
associated with the pollutant(s) under consideration (e.g., if the effects of the pollutant(s) are
more concentrated in some population groups). (2) Is there evidence of potential EJ concerns for
the regulatory option(s) under consideration? Specifically, how are the pollutant(s) and its effects
distributed for the regulatory options under consideration? And, (3) do the regulatory option(s)
under consideration exacerbate or mitigate EJ concerns relative to the baseline? It is not always
possible to quantitatively assess these questions.
EPA's 2016 Technical Guidance does not prescribe or recommend a specific approach or
methodology for conducting an environmental justice analysis, though a key consideration is
consistency with the assumptions underlying other parts of the regulatory analysis when
evaluating the baseline and regulatory options. Where applicable and practicable, the Agency
endeavors to conduct such an analysis. Going forward, EPA is committed to conducting
environmental justice analysis for rulemakings based on a framework similar to what is outlined
in EPA's Technical Guidance, in addition to investigating ways to further weave environmental
justice into the fabric of the rulemaking process. EPA greatly values input from EJ stakeholders
and communities and looks forward to engagement as we consider the impacts of light-duty
vehicle emissions.
8.3.1 GHG Impacts
In 2009, under the Endangerment and Cause or Contribute Findings for Greenhouse Gases
Under Section 202(a) of the Clean Air Act ("Endangerment Finding"), the Administrator
considered how climate change threatens the health and welfare of the U.S. population. As part
of that consideration, she also considered risks to minority and low-income individuals and
communities, finding that certain parts of the U.S. population may be especially vulnerable based
on their characteristics or circumstances. These groups include economically and socially
disadvantaged communities; individuals at vulnerable lifestages, such as the elderly, the very
young, and pregnant or nursing women; those already in poor health or with comorbidities; the
disabled; those experiencing homelessness, mental illness, or substance abuse; and/or Indigenous
or minority populations dependent on one or limited resources for subsistence due to factors
including but not limited to geography, access, and mobility.
Scientific assessment reports produced over the past decade by the U.S. Global Change
Research Program (USGCRP),35'36 the Intergovernmental Panel on Climate Change
(IPCC),37'38'39'40 and the National Academies of Science, Engineering, and Medicine41'42 add
more evidence that the impacts of climate change raise potential environmental justice concerns.
These reports conclude that poorer or predominantly non-White communities can be especially
vulnerable to climate change impacts because they tend to have limited adaptive capacities and
are more dependent on climate-sensitive resources such as local water and food supplies, or have
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less access to social and information resources. Some communities of color, specifically
populations defined jointly by ethnic/racial characteristics and geographic location, may be
uniquely vulnerable to climate change health impacts in the United States. In particular, the 2016
scientific assessment on the Impacts of Climate Change on Human Health43 found with high
confidence that vulnerabilities are place- and time-specific, lifestages and ages are linked to
immediate and future health impacts, and social determinants of health are linked to greater
extent and severity of climate change-related health impacts.
8.3.1.1 Effects on Specific Populations of Concern
Individuals living in socially and economically disadvantaged communities, such as those
living at or below the poverty line or who are experiencing homelessness or social isolation, are
at greater risk of health effects from climate change. This is also true with respect to people at
vulnerable lifestages, specifically women who are pre- and perinatal, or are nursing; in utero
fetuses; children at all stages of development; and the elderly. Per the Fourth National Climate
Assessment, "Climate change affects human health by altering exposures to heat waves, floods,
droughts, and other extreme events; vector-, food- and waterborne infectious diseases; changes in
the quality and safety of air, food, and water; and stresses to mental health and well-being."44
Many health conditions such as cardiopulmonary or respiratory illness and other health impacts
are associated with and exacerbated by an increase in greenhouse gases and climate change
outcomes, which is problematic as these diseases occur at higher rates within vulnerable
communities. Importantly, negative public health outcomes include those that are physical in
nature, as well as mental, emotional, social, and economic.
To this end, the scientific assessment literature, including the aforementioned reports,
demonstrates that there are myriad ways in which these populations may be affected at the
individual and community levels. Individuals face differential exposure to criteria pollutants, in
part due to the proximities of highways, trains, factories, and other major sources of pollutant-
emitting sources to less-affluent residential areas. Outdoor workers, such as construction or
utility crews and agricultural laborers, who frequently are comprised of already at-risk groups,
are exposed to poor air quality and extreme temperatures without relief. Furthermore, individuals
within EJ populations of concern face greater housing and clean water insecurity and bear
disproportionate economic impacts and health burdens associated with climate change effects.
They have less or limited access to healthcare and affordable, adequate health or homeowner
insurance. Finally, resiliency and adaptation are more difficult for underserved communities:
They have less liquidity, individually and collectively, to move or to make the types of
infrastructure or policy changes to limit or reduce the hazards they face. They frequently are less
able to self-advocate for resources that would otherwise aid in resiliency and hazard reduction
and mitigation.
The assessment literature cited in EPA's 2009 and 2016 Endangerment Findings, as well as
Impacts of Climate Change on Human Health,43 also concluded that certain populations and life
stages, including children, are most vulnerable to climate-related health effects. The assessment
literature produced from 2016 to the present strengthens these conclusions by providing more
detailed findings regarding related vulnerabilities and the projected impacts youth may
experience. These assessments - including the Fourth National Climate Assessment (2018) and
The Impacts of Climate Change on Human Health in the United States (2016) - describe how
children's unique physiological and developmental factors contribute to making them
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particularly vulnerable to climate change. Impacts to children are expected from heat waves, air
pollution, infectious and waterborne illnesses, and mental health effects resulting from extreme
weather events. In addition, children are among those especially susceptible to allergens, as well
as health effects associated with heat waves, storms, and floods. Additional health concerns may
arise in low-income households, especially those with children, if climate change reduces food
availability and increases prices, leading to food insecurity within households.
The Impacts of Climate Change on Human Health!also found that some communities of
color, low-income groups, people with limited English proficiency, and certain immigrant groups
(especially those who are undocumented) live with many of the factors that contribute to their
vulnerability to the health impacts of climate change. While difficult to isolate from related
socioeconomic factors, race appears to be an important factor in vulnerability to climate-related
stress, with elevated risks for mortality from high temperatures reported for Black or African
American individuals compared to White individuals after controlling for factors such as air
conditioning use. Moreover, people of color are disproportionately exposed to air pollution based
on where they live, and disproportionately vulnerable due to higher baseline prevalence of
underlying diseases such as asthma, so climate exacerbations of air pollution are expected to
have disproportionate effects on these communities.
Native American Tribal communities possess unique vulnerabilities to climate change,
particularly those impacted by degradation of natural and cultural resources within established
reservation boundaries and threats to traditional subsistence lifestyles. Tribal communities whose
health, economic well-being, and cultural traditions depend upon the natural environment will
likely be affected by the degradation of ecosystem goods and services associated with climate
change. The IPCC indicates that losses of customs and historical knowledge may cause
communities to be less resilient or adaptable45. The Fourth National Climate Assessment (2018)
noted that while Indigenous peoples are diverse and will be impacted by the climate changes
universal to all Americans, there are several ways in which climate change uniquely threatens
Indigenous peoples' livelihoods and economies46. In addition, there can institutional barriers to
their management of water, land, and other natural resources that could impede adaptive
measures.
For example, Indigenous agriculture in the Southwest is already being adversely affected by
changing patterns of flooding, drought, dust storms, and rising temperatures leading to increased
soil erosion, irrigation water demand, and decreased crop quality and herd sizes. The
Confederated Tribes of the Umatilla Indian Reservation in the Northwest have identified climate
risks to salmon, elk, deer, roots, and huckleberry habitat. Housing and sanitary water supply
infrastructure are vulnerable to disruption from extreme precipitation events.
NCA4 noted that Indigenous peoples often have disproportionately higher rates of asthma,
cardiovascular disease, Alzheimer's, diabetes, and obesity, which can all contribute to increased
vulnerability to climate-driven extreme heat and air pollution events. These factors also may be
exacerbated by stressful situations, such as extreme weather events, wildfires, and other
circumstances.
NCA4 and IPCC AR547 also highlighted several impacts specific to Alaskan Indigenous
Peoples. Coastal erosion and permafrost thaw will lead to more coastal erosion, exacerbated risks
of winter travel, and damage to buildings, roads, and other infrastructure - these impacts on
archaeological sites, structures, and objects that will lead to a loss of cultural heritage for
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Alaska's Indigenous people. In terms of food security, the NCA discussed reductions in suitable
ice conditions for hunting, warmer temperatures impairing the use of traditional ice cellars for
food storage, and declining shellfish populations due to warming and acidification. While the
NCA also noted that climate change provided more opportunity to hunt from boats later in the
fall season or earlier in the spring, the assessment found that the net impact was an overall
decrease in food security.
8.3.2 Non-GHG Impacts
In addition to significant climate change benefits, the proposed standards would also impact
non-GHG emissions. In general, we expect small non-GHG emissions reductions from the
combination of "upstream" emissions sources related to extracting, refining, transporting, and
storing petroleum fuels. We also expect small increases in emissions from upstream electricity
generating units (EGUs). For on-road light-duty vehicles, the proposed standards would reduce
total non-GHG emissions, though we expect small increases in some non-GHG emissions in the
years immediately following implementation of the proposal, followed by growing decreases in
non-GHG emissions in later years. This is due to our assumptions about increased "rebound"
driving. See "Chapter 7: Health and Environmental Impacts of Non-GHG Pollutants" for more
detail on the estimated non-GHG emissions impacts of the proposal.1 As discussed in Section
I. A. 1 of the preamble Executive Summary, future EPA actions that would result in increased
ZEVs and emissions reductions from the power sector would more significantly change the non-
GHG impacts of transportation and electricity generation, and those impacts will be analyzed in
more detail in those future actions.
There is evidence that communities with EJ concerns could be impacted by the non-GHG
emissions from light-duty vehicles. 48 Numerous studies have found that environmental hazards
such as air pollution are more prevalent in areas where racial/ethnic minorities and persons with
low socioeconomic status (SES) represent a higher fraction of the population compared with the
general population.49'50'51 Consistent with this evidence, a recent study found that most
anthropogenic sources of PM2.5, including industrial sources, and light- and heavy-duty vehicle
sources, disproportionately affect people of color.52
Analyses of communities in close proximity to upstream sources, such as EGUs, have found
that a higher percentage of communities of color and low-income communities live near these
sources when compared to national averages.53 Vulnerable populations near upstream refineries
may experience potential disparities in pollution-related health risk from that source.54 In this
proposal we expect that small increases in non-GHG emissions from EGUs and small reductions
in petroleum-sector emissions would lead to small changes in exposure to these non-GHG
pollutants for people living in the communities near these facilities.
There is also substantial evidence that people who live or attend school near major roadways
are more likely to be of a racial minority, Hispanic ethnicity, and/or low socioeconomic
status.55'56 We would expect that communities near roads will benefit from reductions of non-
GHG pollutants as fuel efficiency improves and the use of ZEVs (such as full battery electric
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vehicles) increases, though increased rebound driving may offset some of these emission
reductions, especially in the years immediately after finalization of the proposed standards.
Although proximity to an emissions source is a useful indicator of potential exposure, it is
important to note that the impacts of emissions from both upstream and tailpipe sources are not
limited to communities in close proximity to these sources. The effects of potential increases and
decreases in emissions from the sources affected by this proposal might also be felt many miles
away, including in communities with EJ concerns. The spatial extent of these impacts from
upstream and tailpipe sources depend on a range of interacting and complex factors including the
amount of pollutant emitted, atmospheric chemistry and meteorology.
In summary, we expect this proposed rule would result in both small reductions and small
increases of non-GHG emissions that could impact communities with EJ concerns, though not
necessarily immediately and not equally in all locations. For this rulemaking, the air quality
information needed to perform a quantified analysis of the distribution of such impacts was not
available. We therefore recommend caution when interpreting these broad, qualitative
observations. EPA intends to develop a future rule to control emissions of GHGs, criteria
pollutants, and air toxic pollutants from light-duty vehicles for model years beyond 2026. We are
considering how to project air quality impacts from the changes in non-GHG emissions for that
future rulemaking (see Section V.C of the preamble).
8.4 Affordability and Equity Impacts
The impacts of these proposed standards on social equity depend in part on their effects on the
affordability of vehicles and impacts on lower-income households.
Access to transportation improves the ability of people, including those with low income, to
pursue jobs, education, health care, and necessities of daily life such as food and housing. These
standards might affect affordability of vehicles and their impacts on low-income households in
particular. We acknowledge that vehicles, especially household vehicle ownership, are only a
portion of the larger issue of access to transportation and mobility services, which also takes into
consideration public transportation and urban design. In addition, online working and shopping
may provide alternative ways to accomplish some goals, for those with stable access to internet
services. Though these issues are inextricably linked, the following discussion focuses on effects
related to private vehicle ownership and use. We also acknowledge that the emissions of
vehicles, both local pollutants and greenhouse gases, can have disproportionate impacts on
lower-income and minority communities; see Chapter 8.3 and Preamble Section VII.L. for
further discussion of these topics.
The SAFE rule discussed affordability primarily as relating to the up-front purchase price of a
new vehicle: if the up-front price increased, due to addition of fuel-saving technologies, then
vehicles became less affordable. E.g., "technologies added to comply with fuel economy
standards have an impact on vehicle prices, and, by extension, on the affordability of newer,
safer vehicles, and therefore on the rates at which newer vehicles are acquired and older, less
safe vehicles are retired from use" (85 FR 24742). While this is one use of the concept of
affordability, it is not the only one.
Cassidy et al. (2016)57 and the TSD for the MTE Proposed Determination,58 Chapter 4.3.1,
discussed the lack of specificity in the concept of affordability in academic literature. For
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instance, Bradley (2008)59 identified affordability as "a vague concept... When pundits use the
word 'afford,' there is no clear definition of affordability; it is at best a subjective notion." Bartl
(2010)60 declared that "affordability is a new 'alien' concept penetrating the field of contract and
consumer law." Researchers have nevertheless grappled with attempting to define the term for
goods including energy, food, telephone service, health insurance, and housing. Some themes
that appear in the different definitions of affordability include:
•	Instead of focusing on the traditional economic concept of willingness to pay, any
consideration of affordability must also consider the ability to pay for a socially
defined minimum level of a good, especially of a necessity.
•	Although the ability to pay is often based on the proportion of income devoted to
expenditures on a particular good, this ratio approach is widely criticized for not
considering expenditures on other possibly necessary goods, for not considering
quality differences in the good, and for not considering heterogeneity of consumer
preferences for the good.
•	Assessing affordability should take into account both the short-term costs and long-
term costs associated with consumption of a particular good.
These themes were all developed in the context of goods typically deemed essential, such as
food and housing. There is very little literature applying the concept of affordability to
transportation, much less to vehicle ownership. Thakuriah and Liao (2006)61 attempted to define
ability to pay for transportation expenditures, but do not offer a definition of affordable
transportation. A report by the Manhattan Strategy Group for the Department of Transportation
and the Department of Housing and Urban Development (HUD) (Schanzenbach and
McGranahan, 2012)62 attempted to create metrics of various types of vehicle costs to be included
in HUD's Location Affordability Index, which considers housing and transportation costs based
on location. However, this report also did not attempt to define vehicle affordability.
It is not clear how to identify the socially acceptable minimum level of transportation service.
It seems reasonable to assume that such a socially acceptable minimum level should allow access
to employment, education, and basic services like buying food, but it is not clear where
consumption of transportation moves from necessity to optional. Normatively defining the
minimum adequate level of transportation consumption is difficult given the heterogeneity of
consumer preferences and living situations. As a result, it is challenging to define how much
residual income should remain with each household after transportation expenditures. It is
therefore not surprising that academic and policy literature have largely avoided attempting to
define transportation affordability.
We do not here propose a quantitative measure of the affordability of new vehicles. Instead,
we follow the approach in the Proposed Determination for the Midterm Evaluation63 of
considering four questions that relate to the effects of the LDV GHG standards on new vehicle
affordability and equity: how the standards affect low-income households; how the standards
affect the used vehicle market; how the standards affect access to credit; and how the standards
affect the low-priced vehicle segment. These questions are intended to examine some ways in
which the standards might influence the distribution of access to transportation across the public,
especially those who might disproportionately suffer from low access.
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8.4.1 Effects on Lower-Income Households
As noted in Chapter 8.3, there is no commonly accepted definition of affordable access to
transportation. Access to transportation involves access to any form of transportation, not only
vehicle ownership and access, but also public transportation, ride-sharing, and ride-hailing
services. Within vehicle ownership, access does not involve only the up-front costs of purchasing
a vehicle, but also the operating and maintenance costs of a vehicle. Trying to define a socially
acceptable minimum level for access to transportation services is even more difficult, because
such requirements will vary with geography and personal needs. People in rural areas are
unlikely to be able to rely on public transit, for instance. Though nutritious food is a generally
acknowledged necessity, people who live in urban food deserts may suffer in health and quality
of life due to the transportation time and cost of accessing adequate and nutritious food. On the
other hand, those who live in areas with good, inexpensive public transportation and easy access
to stores and other desired destinations may be able to rely on public transportation, bicycling, or
walking to meet their needs, and not need a personally owned vehicle. How the standards might
affect affordable access to transportation is thus a complex question.
A first point to note is that the standards on average are projected to have fuel savings over
the lifetime of the vehicles that exceed the up-front costs (see Chapter 6: and Preamble Section
VII.H). Thus, on average, the standards are expected to reduce the total cost of ownership of new
vehicles subject to the standards. This metric on its own implies that vehicle affordability is
enhanced by the standards. This metric is nevertheless likely to be overly simplistic for the
purposes of understanding the distributional effects of the standards on equitable access to
transportation, and specifically the effects on lower-income households. It does not measure, for
instance, who is likely to get the benefits of the fuel savings, and who bears the increased up-
front costs of the vehicle. If those groups are different, then it is not initially obvious who earns
the net benefits.
It should also be noted that low-income households, defined as households having annual
after-tax income below the current-year's median after-tax income level, are much more likely to
have used vehicles than new ones. For instance, 70 percent of new vehicle buyers have income
above $75,000;64 median household income in 2019 was about $68,700.65 Thus, lower-income
households will eventually feel the effects of reduced fuel consumption in new vehicles over
time, when those vehicles are resold on the used market. Lower-income households are also
more likely to experience the effects of price changes in the used vehicle market as explained in
Chapter 8.4.2, below.
A few recent papers have asked whether fuel economy standards are progressive or regressive
— that is, having greater beneficial effects or more adverse effect on lower-income households
than on higher-income households. Jacobsen (2013)66 finds, for the flat (not footprint-based)
standard used in the CAFE program before MY 2011, the standards were regressive by implicitly
discouraging more desired larger vehicles. The subsequent use of the footprint-based standard is
intended to reduce the disincentives for larger vehicles. Levinson (2019)67 as well as Davis and
Knittel (2019),68 on the other hand, criticize the use of footprint-based standards for not
providing incentives for people to buy smaller vehicles. These papers argue that the standards are
more harmful to lower-income households than a gasoline tax would be, in part because a
gasoline tax is more economically efficient, in part because higher-income households can better
afford the up-front cost increase, and in part because the revenues from a gasoline tax can be
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redistributed in ways to reduce (or eliminate) the regressivity of the tax. Neither of these papers
addresses the reduction in fuel costs associated with the standards, though, and thus they omit a
significant effect of the standards.
The focus on vehicle ownership does not account for how the standards' effects on reduction
in per-mile costs might affect access to transportation for lower-income households. If these
reductions in operating costs are passed along to users, ride-hailing and ride-sharing services
might become less expensive and thus more accessible than before. Vehicles used in these
services are likely to have higher mileage more quickly than personally owned vehicles; as a
result, up-front costs are likely to be recovered more quickly than the costs for personally owned
vehicles.
Greene and Welch (2018)69 include both fuel consumption and up-front costs in the
calculation of distributional effects. They find that higher-income households experience
decreases in fuel consumption due to the standards before lower-income households, because the
latter are more likely to own used vehicles and thus get the fuel savings with a lag. However,
they estimate that the ratio of fuel savings to costs, as well as the ratio of net savings to income,
is higher for lower-income households than higher-income households.
Vaidyanathan et al. (2021) observe that gasoline burden — the share of gasoline in income —
is more than three times higher for lower-income households than higher-income households.70
Thus, reducing per-mile costs may disproportionately benefit lower-income households, both
through more efficient vehicles gradually entering the used vehicle fleet, as well as through
reduced operating costs for other providers of transportation services. Cassidy et al. (2016),71 the
Proposed Determination72 and Consumer Federation of America (2018)73 observed that, on an
annual basis, lower-income households spent more on gasoline than on vehicles, either new or
used. In addition, lower-income households spent more per year on used vehicles than new ones.
Expenditures on fuel also fluctuate more than expenditures on vehicles, suggesting more
uncertainty for fuel costs.
In sum, the effects of the standards on low-income households are likely to be felt primarily
through effects on operating costs, and the effects of the standards on the used vehicle market.
While the standards are projected to reduce per-mile operating costs, and thus potentially
increase access to mobility, increases in new vehicle costs are likely to affect the used vehicle
market as well. This is discussed in Chapter 8.4.2.
8.4.2 Effects on the Used Vehicle Market
According to Consumer Federation of America, in 2015 about 92 percent of vehicle purchases
by low-income households were used vehicles.74 Thus, the effects of the standards on lower-
income households depends on its impacts, not only in the new vehicle market, but also in the
used vehicle market. The effect of the standards on the used vehicle market will be related to the
effects of the standards on new vehicle prices, the fuel efficiency of new vehicle models, the fuel
efficiency of used vehicles, and the total sales of new vehicles. On one hand, if the consumer
value of fuel savings resulting from improved fuel efficiency outweighs the average increase in
new models' prices to potential buyers of new vehicles, sales of new vehicles could rise, and the
used vehicle market may increase in volume as new vehicle buyers sell their older vehicles. If
this is the case, lower-income households are likely to benefit from the increased availability of
used vehicles. On the other hand, if potential buyers value future fuel savings resulting from the
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increased fuel efficiency of new models at less than the increase in their average selling price,
sales of new vehicles could decline, and the used vehicle market may decrease in volume as
people hold onto their vehicles longer. In this case, lower-income households could face
increased costs due to reduced availability of used vehicles.
If the only effect of the standards on total cost of ownership were the up-front costs, then the
standards might also encourage people to hold onto their used vehicles longer. This effect,
sometimes termed the "Gruenspecht effect" after Gruenspecht (1982),75 would lead to both
slower adoption of vehicles subject to the new standards, and more use of older vehicles not
subject to the new standards, with associated higher emissions. Two older studies examine the
effects of new vehicle prices on scrappage: Miaou (1995)76 estimates an elasticity of -0.2 (that is,
a 1 percent increase in new vehicle price leads to a 0.2 percent decrease in scrappage), while
Greenspan and Cohen (1999)77 estimate an elasticity of -0.8. Two newer studies estimate the
effects of changes in used vehicle prices on scrappage; Jacobsen and van Benthem (2015)78
estimate an elasticity of -0.7, and Bento et al. (2018)79 estimate an elasticity of -0.4. These
estimates suggest that scrappage rates are likely to change a relatively small amount in response
to changes in the new vehicle market.
The NAS 2021 Report80 (p. 11-357) notes the possibility of the Gruenspecht effect on
scrappage. In addition, it notes that, if people find the reduced fuel consumption of new vehicles
attractive, new vehicle sales would increase, and reduced scrappage would not be expected.
As discussed in Chapter 8.1.2, EPA is working to understand better the connections between
the new and the used vehicle market. Changes in the new vehicle market are expected not only to
have immediate effects on the prices of used vehicles, but also to affect the market over time, as
the supply of used vehicles in the future depends on how many new vehicles are sold.
8.4.3 Effects on Access to Credit
Another question is whether higher vehicle prices may exclude some prospective consumers
from the new vehicle market through effects on consumers' ability to finance vehicles. It is
possible that lenders focus solely on the amount of the vehicle loan, the person's current debt,
and the person's income when issuing loans, and not the costs associated with fuel consumption.
If lenders restrict consideration to the amount of the loan, the borrower's debt, and the borrower's
income, then increased up-front costs of new vehicles subject to the standards will reduce
buyers' ability to get loans. However, if fuel savings are factored into lenders' decisions, reduced
fuel costs increase a borrower's capacity to repay a loan and therefore increase the likelihood of
getting a loan. Ignoring fuel savings could prevent a buyer's ability to get a loan, even if fuel
savings exceed the increase in loan payments due to higher purchase price. Thus, if lenders do
not take fuel savings into account in providing loans, households that are borrowing near the
limit of their abilities to borrow will either have to buy a different vehicle than intended, or not
buy a vehicle at all.
On the other hand, some lenders give discounts for loans to purchase more fuel-efficient
vehicles.81 In addition, subsidies exist from the federal government, and some state governments,
for plug-in electric vehicles.82 When automakers comply with the proposed standards through
production of plug-in vehicles, these subsidies reduce the costs of these vehicles and facilitate
their purchases. Concerns have been raised that these subsides go primarily to wealthier
households, who are more likely to purchase new vehicles in general and may be an expensive
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way to promote adoption of these vehicles. Sheldon and Dua (2019)83 find that subsidies targeted
by income and other factors may be more cost-effective and progressive financially than
untargeted policies.
The Proposed Determination TSD84 examined the question whether the higher up-front cost
might create an obstacle if borrowers face a ceiling on the debt-to-income ratio (DTI), which
affects a borrower's access to credit. At the time, evidence suggested that lenders may not give
loans to consumers who have a DTI above 36 percent; more recent evidence suggests that
lenders consider 43 percent the maximum.85 Evidence presented in the TSD, using data from the
U.S. Bureau of Labor Statistics' Consumer Expenditure Survey, indicated that, from 2007 to
2015, 28 percent of lower-income households and 7 percent of higher-income households who
both had a DTI of over 36 percent and purchased at least one new vehicle financed their vehicle
purchases. Thus, a DTI above 36 percent may not always be a disqualifying threshold in
financing a new vehicle
It is worth mentioning that in addition to the four factors discussed here, there are other
factors that may influence access to credit, such as race, ethnicity, gender, gender identity,
residential location, religion, or other factors. It is unclear whether or to what extent these
possible limitations on access to credit may affect access to auto loans.
Although access to credit is a potential barrier to purchase of vehicles whose up-front costs
increase, it may be a less impenetrable barrier when those up-front costs come with reduced fuel
consumption.
8.4.4 Effects on Low-Priced Cars
Average transaction price for a new vehicle in February 2021 was $41,000, an increase of 6.5
percent from February 2020. That increase, though, masks great diversity in vehicle prices; for
instance, the average transaction price for subcompacts at the same time was $18,300.86 For that
reason, low-priced vehicles may be considered an entry point for people into buying new
vehicles instead of used ones; automakers may seek to entice people to buy new vehicles through
a low price point. It is possible that higher costs associated with the standards could affect the
ability of automakers to maintain vehicles in this segment.
In the past, when CAFE standards did not vary by footprint, not only was the low-priced
vehicle segment a way to encourage first-time new vehicle purchasers, but it also tended to
include more fuel-efficient vehicles that assisted automakers in achieving CAFE standards.87 The
footprint-based standards, by encouraging improvements in GHG emissions and fuel economy
across the vehicle fleet, reduce the need for smaller, and by extension, low-priced vehicles to be
a primary means of compliance with the standards. This change in incentives for the marketing
of this segment may contribute to the increases in the prices of vehicles previously in this
category. Both MotorTrend88 and Car and Driver89 provide a list of the ten least expensive new
vehicles for MY 2021. The lowest priced, the Chevrolet Spark, is listed at under $15,000. Car
and Driver's list has prices all below $20,000; MotorTrend includes two with prices between
$20,000 and $21,000. In addition, these vehicles appear to be gaining more content, such as
improved entertainment systems and electric windows; they may be developing an identity as a
desirable market segment without regard to their historic role in enabling the sales of less
efficient vehicles and compliance with CAFE standards.90 Both MotorTrend and Car and Driver
note that these vehicles come with the latest safety, comfort, and entertainment features. It may
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be that the small, fuel-efficient vehicles previously sold with low prices are evolving to fit
consumer demand that prefers content to low prices.
In sum, the low-priced vehicle segment still exists. Whether it continues to exist, and in what
form, may depend on the marketing plans of manufacturers: whether benefits are greater from
offering basic new vehicles to first-time new-vehicle buyers, or from making small vehicles
more attractive by adding more desirable features to them.
8.4.5	Effects of Electric Vehicles on Affordabilitv
Electric vehicles create some novel questions for affordability. Their up-front costs tend to be
higher than those of comparable gasoline vehicles, and their operating costs tend to be much
lower. Qualitatively, these characteristics are similar to gasoline vehicles subject to the
standards, as discussed above. In addition, though, electric vehicles require access to charging.
Home charging can be very convenient but requires the ability to park where charging is
available; a number of people, such as those who rely on on-street parking, may not have such
access. As discussed in Chapter 4.1.3, under this proposal the penetration of plug-in electric
vehicles is projected to increase slightly, from 3.6 percent in MY 2023 to 7.8 percent in MY
2026 (see Table 4-23). EPA plans to study and monitor these concerns as the prevalence of
electric vehicles increases.
8.4.6	Summary of Affordabilitv and Equity Effects
As with the effects of the standards on vehicle sales discussed in Chapter 8.1, the effects of
the standards on affordability depend on two countervailing effects: the increase in the up-front
costs of the vehicles, and the decrease in operating costs. The increase in up-front costs has the
potential to increase the prices of used vehicles, to make credit more difficult to obtain, and to
make the least expensive new vehicles less desirable compared to used vehicles. The reduction in
operating costs has the potential to mitigate or reverse all these effects. Lower operating costs on
their own increase mobility (see Chapter 3.1 for a discussion of rebound driving).
The effects of the standards on lower-income households are of great importance for social
equity and reflect these contrasting forces. The overall effects of the standards on vehicle
ownership, including for lower-income communities, depend heavily, as discussed in Chapter
8.1, on the role of fuel consumption in vehicle sales decisions. At the same time, lower-income
households own fewer vehicles per household and are more likely to buy used vehicles than new
compared to higher-income households, and they spend more on fuel than on vehicles on an
annual basis. As a result, lower-income households may benefit more from the reduction in
operating costs than the increase in up-front costs of either new or used vehicles. Finally, we note
that effects on social equity involve impacts beyond those on lower-income households. EPA
will continue to examine these impacts.
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Chapter 9: Small Business Flexibilities
The Regulatory Flexibility Act, as amended by the Small Business Regulatory Enforcement
Fairness Act of 1996 (SBREFA), generally requires an agency to prepare a regulatory flexibility
analysis of any rule subject to notice-and-comment rulemaking requirements under the
Administrative Procedure Act or any other statute. As a part of this analysis, an agency is
directed to convene a Small Business Advocacy Review Panel (SBAR Panel or 'the Panel'),
unless the agency certifies that the rule will not have a significant economic impact on a
substantial number of small entities. During such a Panel process, the agency would gather
information and recommendations from Small Entity Representatives (SERs) on how to reduce
the impact of the rule on small entities. As discussed below, EPA is certifying that this rule will
not have a significant economic impact on a substantial number of small entities, and thus we
have not conducted an SBAR Panel for this rulemaking.
The following discussion provides an overview of small entities in the vehicle market. Small
entities include small businesses, small organizations, and small governmental jurisdictions. For
the purposes of assessing the impacts of the rule on small entities, a small entity is defined as: (1)
a small business that meets the definition for business based on the Small Business
Administration's (SBA) size standards (see Table 9-1); (2) a small governmental jurisdiction that
is a government of a city, county, town, school district or special district with a population of less
than 50,000; and (3) a small organization that is any not-for-profit enterprise which is
independently owned and operated and is not dominant in its field. This chapter provides an
overview of the primary SBA small business categories potentially affected by this regulation.
Table 9-1: Primary Vehicle SBA Small Business Categories
Industry a
Defined as Small Entity by
SBA if Less Than or Equal to:
NAICS Codes b
Vehicle manufacturers (including small
volume manufacturers)
1,500 employees
336111,336112
Independent commercial importers
$8 million annual sales
$27 million annual sales
250 employees
811111,811112,811198
441120
423110
Alternative Fuel Vehicle Converters
1,000 employees
1,250 employees
$8 million annual sales
336312,336322,336399
335312
811198
Table Notes:
a.	Light-duty vehicle entities that qualify as small businesses are not be subject to this rule. We are exempting
small business entities from the GHG standards.
b.	North American Industrial Classification System
We compiled a list of vehicle manufacturers, independent commercial importers (ICIs), and
alternative fuel converters that would be potentially affected by the rule from our 2019 and 2021
model year certification databases. These companies are already certifying their vehicles for
compliance with applicable EPA emissions standards (e.g., Tier 3). We then identified
companies that appear to meet the definition of small business provided in the table above. We
were able to identify companies based on certification information and previous rulemakings
where we conducted Regulatory Flexibility Analyses.
Based on this assessment, EPA identified a total of about 19 entities that appear to fit the
Small Business Administration (SBA) criterion of a small business. EPA estimates there are
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about 7 small vehicle manufacturers , 4 independent commercial importers (ICIs), and 8
alternative fuel vehicle converters in the light-duty vehicle market which may qualify as small
businesses (see Table 9-2 for a list of current entities). Independent commercial importers (ICIs)
are companies that hold a Certificate (or Certificates) of Conformity permitting them to import
nonconforming vehicles and to modify these vehicles to meet U.S. emission standards. ICIs are
not required to meet the emission standards in effect when the vehicle is modified, but instead
they must meet the emission standards in effect when the vehicle was originally produced (with
an annual production cap of a total of 50 light-duty vehicles and trucks). Alternative fuel vehicle
converters are businesses that convert gasoline or diesel vehicles to operate on alternative fuel
(e.g., compressed natural gas), and converters must seek a certificate for all of their vehicle
models. Model year 1993 and newer vehicles that are converted are required to meet the
standards applicable at the time the vehicle was originally certified. Converters serve a niche
market, and these businesses primarily convert vehicles to operate on compressed natural gas
(CNG) and liquefied petroleum gas (LPG), on a dedicated or dual fuel basis.
Table 9-2 Small Business Entities
Small Vehicle
Manufacturers
Alternative Fuel Converters
Independent Commercial Importers
Ineos Automotive
Karma Automotive
Koenigsegg
Pagani
RUF
Workhorse Group
Rimac
AGA Systems, LLC
Agility Powertrain Systems, LLC
Altech-Eco Corporation
Blossman Services, Inc.
Eco Vehicle Systems, LLC
Encore TEC LLC
Landi Renzo USA Corporation
Westport Dallas, Inc
DRPC, LLC
G&K Automotive Conversions, Inc
Wallace Environmental Testing Laboratories, Inc
JK Technologies, LLC
EPA is exempting from the GHG standards any manufacturer, domestic or foreign, meeting
SBA's size definitions of small business as described in 13 CFR 121.201. EPA adopted the
same type of exemption for small businesses in the MY 2012-2016 rulemaking.1 Together, we
estimate that small entities comprise less than 0.1 percent of total annual vehicle sales and
exempting them will have a negligible impact on the GHG emissions reductions from the
standards. Because we are exempting small businesses from the GHG standards, we are
certifying that the rule will not have a significant economic impact on a substantial number of
small entities. Therefore, EPA has not conducted a Regulatory Flexibility Analysis or a
SBREFA SBAR Panel for the rule.
EPA allows small businesses to voluntarily waive their small business exemption and
optionally certify to the GHG standards. This will allow small entity manufacturers to earn CO2
credits under the GHG program, if their actual fleetwide CO2 performance is better than their
fleetwide CO2 target standard. Manufacturers waiving their small business exemption are
required to meet all aspects of the GHG standards and program requirements across their entire
product line. However, the exemption waiver is optional for small entities and thus we believe
that manufacturers would only opt into the GHG program if it is economically advantageous for
them to do so, for example to generate and sell CO2 credits. Therefore, EPA believes having this
voluntary option does not affect EPA's determination that the standards will impose no
significant adverse impact on small entities.
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References for Chapter 9
1 75 FR 25424, May 7, 2010.
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Chapter 10: Summary of Costs and Benefits
This Chapter presents a summary of costs, benefits, and net benefits of the proposed program
and the alternatives. This rule is not expected to have measurable inflationary or recessionary
effects.
10.1 Proposal
Table 10-1 shows the estimated annual monetized costs of the proposed program for the
indicated calendar years. The table also shows the present values (PV) of those costs for the
calendar years 2021-2050 using both 3 percent and 7 percent discount rates.a The table includes
an estimate of foregone consumer sales surplus, which measures the loss in benefits attributed to
consumers who would have purchased a new vehicle in the absence of the proposed standards.
Table 10-1: Costs Associated with the Proposed Program ($Billions of 2018 dollars)
Calendar
Year
Foregone Consumer
Sales Surplus [1]
Technology
Costs
Congestion
Noise
Fatality
Costs
Non-fatal
Crash Costs
Total
Costs
2023
$0.26
$6.7
$0,046
$0,000
73
$0.16
$0.26
$7.4
2026
$0.64
$15
$0.19
$0,003
$0.61
$1
$18
2030
$0.43
$14
$0.59
$0,009
5
$0.58
$0.96
$17
2035
$0.28
$12
$1
$0,017
$0.2
$0.33
$14
2040
$0.21
$11
$1.3
$0,021
-$0,038
-$0,062
$12
2050
$0.16
$9.9
$1.3
$0,021
-$0.0093
-$0,015
$11
PV, 3%
$5.7
$210
$15
$0.24
$4.5
$7.6
$240
PV, 7%
$3.7
$130
$7.3
$0.12
$3.4
$5.6
$150
Annualiz
ed, 3%
$0.29
$11
$0.75
$0,012
$0.23
$0.39
$12
Annualiz
ed, 7%
$0.3
$10
$0.59
$0,009
5
$0.27
$0.45
$12
[1] "Foregone Consumer Sales Surplus" refers to the difference between a vehicle's price and the buyer's
willingness to pay for the new vehicle; the impact reflects the reduction in new vehicle sales described in Chapter
8.1. See Section 8 of CAFE Model Documentation FR 2020.pdf in the docket for more information.
Table 10-2 shows the undiscounted annual monetized fuel savings of the proposed program.
The table also shows the present value of those fuel savings for the same calendar years using
both 3 percent and 7 percent discount rates. The aggregate value of fuel savings is calculated
using pre-tax fuel prices since savings in fuel taxes do not represent a reduction in the value of
economic resources utilized in producing and consuming fuel. Note that the fuel savings shown
in Table 10-2 result from reductions in fleet-wide fuel use and include rebound effects, credit
usage and advanced technology multiplier use. Thus, fuel savings grow over time as an
increasing fraction of the fleet is projected to meet the proposed standards.
" For the estimation of the stream of costs and benefits, we assume that after implementation of the proposed MY
2023-2026 standards, the 2026 standards apply to each year thereafter.
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Table 10-2: Fuel Savings Associated with the Proposed Program ($Billions of 2018 dollars)
Calendar Year
Retail Fuel Savings
Fuel Tax Savings
Total Fuel Savings
2023
$0.78
$0.2
$0.58
2026
$3.5
$0.95
$2.6
2030
$12
$2.7
$8.9
2035
$21
$4.4
$17
2040
$28
$5.4
$23
2050
$32
$5.6
$26
PV, 3%
$310
$62
$250
PV, 7%
$150
$32
$120
Annualized, 3%
$16
$3.2
$13
Annualized, 7%
$12
$2.5
$9.9
Table Note:
Electricity expenditure increases are included.
Table 10-3 presents estimated annual monetized benefits from non-emission sources for the
indicated calendar years. The table also shows the present value of those benefits for the
calendar years 2021-2050 using both 3 percent and 7 percent discount rates.
Table 10-3: Benefits from Non-Emission Sources for the Proposal (SBillions of 2018 dollars)
Calendar Year
Drive
Value
Refueling Time
Savings
Energy Security
Benefits
Total Non-Emission
Benefits
2023
$0,065
-$0,019
$0.03
$0,076
2026
$0.25
-$0.12
$0.15
$0.28
2030
$0.83
-$0.15
$0.46
$1.1
2035
$1.6
-$0.1
$0.83
$2.3
2040
$2.1
-$0,017
$1.1
$3.2
2050
$2.3
$0.1
$1.5
$3.9
PV, 3%
$23
-$0.94
$13
$35
PV, 7%
$11
-$0.72
$6.1
$17
Annualized,
3%
$1.2
-$0,048
$0.64
$1.8
Annualized,
7%
$0.92
-$0,058
$0.49
$1.4
Table 10-4 presents estimated annual monetized benefits from emission sources for the
indicated calendar years. The table also shows the present value of those benefits for the
calendar years 2021-2050 using both 3 percent and 7 percent discount rates.
Table 10-5 shows the benefits of reduced GHG emissions, and consequently the annual
quantified benefits (i.e., total GHG benefits), for each of the four interim social cost of GHG
(SC-GHG) values estimated by the interagency working group. As discussed in draft RIA
Chapter 3.3 there are some limitations to the SC-GHG analysis, including the incomplete way in
which the integrated assessment models capture catastrophic and non-catastrophic impacts, their
incomplete treatment of adaptation and technological change, uncertainty in the extrapolation of
damages to high temperatures, and assumptions regarding risk aversion.
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Table 10-4: PIVh.s-related Emission Reduction Benefits of the Proposal (SBillions of 2018 dollars)
Calendar Year
Tailpipe
Benefits
Upstream Benefits
Total PM2 5-related Benefits
3% DR
7% DR
3% DR
7% DR
3% DR
7% DR
2023
-$0,013
-$0,012
$0,029
$0,027
$0,016
$0,015
2026
-$0,047
-$0,042
$0,014
$0,015
-$0,033
-$0,028
2030
$0,035
$0,032
$0,089
$0,084
$0.12
$0.12
2035
$0.23
$0.21
$0.34
$0.31
$0.57
$0.52
2040
$0.46
$0.41
$0.48
$0.44
$0.94
$0.85
2050
$0.74
$0.67
$0.34
$0.31
$1.1
$0.98
PV
$4.3
$1.6
$4.5
$2
$8.8
$3.6
Annualized
$0.22
$0.13
$0.23
$0.16
$0.45
$0.29
Table Notes:






a Note that the non-GHG impacts associated with the standards presented here do not include the full complement
of health and environmental effects that, if quantified and monetized, would increase the total monetized benefits.
Instead, the non-GHG benefits are based on benefit-per-ton values that reflect only human health impacts
associated with reductions in PM2 5 exposure.




b Calendar year non-GHG benefits presented in this table assume either a 3 percent or 7 percent discount rate in
the valuation of PM-related premature mortality to account for a twenty-year segmented cessation lag. Note that
annual benefits estimated using a 3 percent discount rate were used to calculate the present and annualized values
using a 3 percent discount rate and the annual benefits estimated using a 7 percent discount rate were used to
calculate the present and annualized values using a 7 percent discount rate.


Table 10-5: Climate Benefits from Reduction in GHG Emissions (SBillions of 2018 dollars)
Calendar Year
Discount Rate and Statistic
5% Average
3% Average
2.5% Average
3% 95th percentile
2023
$0,063
$0.21
$0.31
$0.63
2026
$0.31
$1
$1.5
$3
2030
$1
$3.2
$4.6
$9.5
2035
$2
$6
$8.5
$18
2040
$2.8
$8.1
$11
$25
2050
$3.9
$10
$14
$31
PV
$22
$91
$140
$280
Annualized
$1.4
$4.7
$6.7
$14
Table Notes:
Climate benefits are based on changes (reductions) in CO2, CH4, and N20 emissions and are calculated using four
different estimates of the social cost of carbon (SC-CO2), the social cost of methane (SC-CH4), and the social cost
of nitrous oxide (SC-N20) (model average at 2.5 percent, 3 percent, and 5 percent discount rates; 95th percentile
at 3 percent discount rate). We emphasize the importance and value of considering the benefits calculated using
all four SC-CO2, SC-CH4, and SC-N20 estimates. As discussed in the Technical Support Document: Social Cost
of Carbon, Methane, and Nitrous Oxide Interim Estimates under Executive Order 13990 (IWG 2021), a
consideration of climate benefits calculated using discount rates below 3 percent, including 2 percent and lower,
are also warranted when discounting intergenerational impacts.
The same discount rate used to discount the value of damages from future emissions (SC-GHGs at 5, 3, 2.5
percent) is used to calculate the present value of SC-GHGs for internal consistency Annual benefits shown are
undiscounted values.
10-3
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Table 10-6 presents estimated annual net benefits for the indicated calendar years. The table
also shows the present value of those net benefits for the calendar years 2021-2050 using both 3
percent and 7 percent discount rates. The table includes the benefits of reduced GHG emissions
(and consequently the annual net benefits) for each of the four SC-GHG values considered by
EPA. We estimate that the proposed program would result in a net present value of benefits that
ranges between $17-$330 billion; that is, the total benefits would far exceed the costs of the
program.
Table 10-6: Net Benefits (Emission Benefits + Non-Emission Benefits + Fuel Savings - Costs) Associated with
the Proposed Program ($Billions of 2018 dollars)
Calendar
Year
Net Benefits,
with Climate
Benefits based on
5% discount rate
Net Benefits,
with Climate
Benefits based on
3% discount rate
Net Benefits,
with Climate
Benefits based
on 2.5% discount
rate
Net Benefits,
with Climate Benefits based
on 3% discount rate, 95th
percentile SC-GHG
2023
-$6.6
-$6.5
-$6.4
-$6.1
2026
-$14
-$14
-$13
-$12
2030
-$5.8
-$3.7
-$2.3
$2.7
2035
$7.6
$12
$14
$24
2040
$17
$22
$26
$39
2050
$23
$30
$34
$51
PV, 3%
$73
$140
$190
$330
PV, 7%
$17
$86
$140
$270
Annualized,
3%
$4.1
$7.3
$9.4
$17
Annualized,
7%
$1
$4.2
$6.3
$14
Table Notes:
Climate benefits are based on changes (reductions) in CO2, CH4, and N20 emissions and are calculated using four
different estimates of the social cost of carbon (SC-CO2), the social cost of methane (SC-CH4), and the social cost
of nitrous oxide (SC-N20) (model average at 2.5 percent, 3 percent, and 5 percent discount rates; 95th percentile
at 3 percent discount rate). We emphasize the importance and value of considering the benefits calculated using
all four SC-CO2, SC-CH4, and SC-N20 estimates. As discussed in the Technical Support Document: Social Cost
of Carbon, Methane, and Nitrous Oxide Interim Estimates under Executive Order 13990 (IWG 2021), a
consideration of climate benefits calculated using discount rates below 3 percent, including 2 percent and lower,
are also warranted when discounting intergenerational impacts. The same discount rate used to discount the value
of damages from future emissions (SC-GHG at 5, 3, 2.5 percent) is used to calculate present value of SC-GHGs
for internal consistency, while all other costs and benefits are discounted at either 3% or 7%. Annual costs and
benefits shown are undiscounted values. Note that the non-GHG impacts associated with the standards included
here do not include the full complement of health and environmental effects that, if quantified and monetized,
would increase the total monetized benefits. Instead, the non-GHG benefits are based on benefit-per-ton values
that reflect only human health impacts associated with reductions in PM2 5 exposure.
EPA also conducted a separate analysis of the total benefits over the model year lifetimes of
the 2021 through 2026 model year vehicles. In contrast to the calendar year analysis presented
above (in Table 10-3 through Table 10-6), the model year lifetime analysis below shows the
impacts of the proposed program on vehicles produced during each of the model years 2021
through 2026 over the course of their expected lifetimes. The net societal benefits over the full
lifetimes of vehicles produced during each of the six model years are shown in Table 10-7 and
Table 10-8 at both 3 percent and 7 percent discount rates, respectively. Similar to the calendar
year analysis, the net benefits would far exceed the costs of the program.
10-4
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Table 10-7: Monetized Costs, Fuel Savings, Benefits, and Net Benefits Associated with the Lifetimes of 2023-
2026 Model Year Light-Duty Vehicles (SBillions, 2018$; 3 percent Discount Rate)a'b
MY
Costs
Fuel Savings
Benefits
Net Benefits
Present Values
2023
$4.8
$3.6
$0.89 to $4.5
-$0.29 to $3.3
2024
$5.9
$7
$1.8 to $8.8
$2.8 to $9.8
2025
$6.7
$8.6
$2 to $11
$3.9 to $13
2026
$8.1
$13
$3.6 to $17
$8.8 to $22
Sum
$26
$33
$8.2 to $41
$15 to $48
Annualized Values
2023
$0.21
$0.16
$0,044 to $0.19
-$0.0072 to $0.14
2024
$0.26
$0.3
$0,086 to $0.38
$0.13 to $0.43
2025
$0.29
$0.37
$0.1 to $0.46
$0.18 to $0.55
2026
$0.35
$0.58
$0.17 to $0.73
$0.4 to $0.96
Sum
$1.1
$1.4
$0.4 to $1.8
$0.71 to $2.1
Table Notes:
a Note that the non-GHG impacts associated with the standards included here do not include the Ml complement
of health and environmental effects that, if quantified and monetized, would increase the total monetized benefits.
Instead, the non-GHG benefits are based on benefit-per-ton values that reflect only human health impacts
associated with reductions in PM2 5 exposure.
b Climate benefits are based on reductions in CO2, CH4, and N20 emissions and are calculated using four
different estimates of the social cost of each greenhouse gas (SC-GHG) (model average at 2.5 percent, 3 percent,
and 5 percent discount rates; 95th percentile at 3 percent discount rate), which each increase over time. For the
presentational purposes of this table, the range of benefits and net benefits reflects the low to high range of SC-
GHG values. We emphasize the importance and value of considering the climate benefits calculated using all
four SC-GHG estimates which are available in Chapter 3. As discussed in Chapter 3.3, a consideration of climate
benefits calculated using discount rates below 3 percent, including 2 percent and lower, are also warranted when
discounting intergenerational impacts. The same discount rate used to discount the value of damages from future
emissions (SCGHG at 5, 3, 2.5 percent) is used to calculate net present value of SCGHG for internal consistency,
while all other costs and benefits are discounted at 3 percent in this table.
10-5
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Table 10-8: Monetized Costs, Fuel Savings, Benefits, and Net Benefits Associated with the Lifetimes of 2023-
2026 Model Year Light-Duty Vehicles (SBillions, 2018$; 7 percent Discount Rate) a'b
MY
Costs
Fuel Savings
Benefits
Net Benefits
Present Values
2023
$4.4
$2.6
$0.72 to $4.3
-$1.1 to $2.5
2024
$5.5
$4.7
$1.4 to $8.4
$0.54 to $7.6
2025
$6.1
$5.5
$1.6 to $10
$1 to $9.7
2026
$7.3
$8.2
$2.6 to $16
$3.6 to $17
Sum
$23
$21
$6.3 to $39
$4 to $37
Annualized Values
2023
$0.33
$0.19
$0,048 to $0.2
-$0,089 to $0,061
2024
$0.41
$0.35
$0,092 to $0.39
$0,029 to $0.32
2025
$0.45
$0.41
$0.1 to $0.47
$0,064 to $0.43
2026
$0.55
$0.62
$0.18 to $0.74
$0.25 to $0.81
Sum
$1.7
$1.6
$0.42 to $1.8
$0.25 to $1.6
Table Notes:




a Note that the non-GHG impacts associated with the standards presented here do not include the full complement of health and
environmental effects that, if quantified and monetized, would increase the total monetized benefits. Instead, the non-GHG benefits are based
on benefit-per-ton values that reflect only human health impacts associated with reductions in PM2.5 exposure.
b Climate benefits are based on reductions in C02, CH4, and N20 emissions and are calculated using four different estimates of the social cost
of each greenhouse gas (SC-GHG) (model average at 2.5 percent, 3 percent, and 5 percent discount rates; 95th percentile at 3 percent discount
rate), which each increase over time. For the presentational purposes of this table, the range of benefits and net benefits reflects the low to
high range of SC-GHG values. We emphasize the importance and value of considering the climate benefits calculated using all four SC-GHG
estimates which are available in Chapter 3. As discussed in Chapter 3.3, a consideration of climate benefits calculated using discount rates
below 3 percent, including 2 percent and lower, are also warranted when discounting intergenerational impacts. The same discount rate used
to discount the value of damages from future emissions (SCGHG at 5, 3, 2.5 percent) is used to calculate net present value of SCGHG for
internal consistency, while all other costs and benefits are discounted at 7 percent in this table.
10.2 Alternative 1
The same series of tables as presented in Chapter 10.1 for the proposal are presented here for
Alternative 1. Note that Table 10-9 includes an estimate of foregone consumer sales surplus,
which measures the loss in benefits attributed to consumers who would have purchased a new
vehicle in the absence of the proposed standards.
Table 10-9: Costs Associated with Alternative 1 (S Bil lions of 2018 dollars)
Calendar
Year
Foregone Consumer
Sales Surplus [11
Technology
Costs
Congestion
Noise
Fatality
Costs
Non-fatal
Crash Costs
Total
Costs
2023
$0.24
$6.3
$0,044
$0,000
71
$0.15
$0.25
$6.9
2026
$0.43
$11
$0.19
$0,003
$0.5
$0.83
$13
2030
$0.26
$10
$0.56
$0,008
9
$0.38
$0.63
$12
2035
$0.2
$9.2
$0.97
$0,015
$0.12
$0.2
$11
2040
$0.16
$8.7
$1.2
$0,019
-$0,033
-$0,053
$9.9
2050
$0.12
$7.6
$1.2
$0.02
-$0,035
-$0,057
$8.9
PV, 3%
$3.9
$160
$14
$0.22
$3.2
$5.4
$190
PV, 7%
$2.6
$98
$6.8
$0.11
$2.5
$4.1
$110
Annualiz
ed, 3%
$0.2
$8.2
$0.69
$0,011
$0.16
$0.27
$9.5
Annualiz
ed, 7%
$0.21
$7.9
$0.55
$0,008
8
$0.2
$0.33
$9.2
[1] "Foregone Consumer Sales Surplus" refers to the difference between a vehicle's price and the buyer's
willingness to pay for the new vehicle; the impact reflects the reduction in new vehicle sales described in Chapter
8.1. See Section 8 of CAFE Model Documentation FR 2020.pdf in the docket for more information.
10-6
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Table 10-10 shows the undiscounted annual monetized fuel savings of the Alternative 1. The
table also shows the present value of those fuel savings for the same calendar years using both 3
percent and 7 percent discount rates. The aggregate value of fuel savings is calculated using pre-
tax fuel prices since savings in fuel taxes do not represent a reduction in the value of economic
resources utilized in producing and consuming fuel. Note that the fuel savings shown in Table
10-10 result from reductions in fleet-wide fuel use. Thus, fuel savings grow over time as an
increasing fraction of the fleet is projected to meet the proposed standards.
Table 10-10: Fuel Savings Associated with Alternative 1 (SBillions of 2018 dollars)
Calendar Year
Retail Fuel Savings
Fuel Tax Savings
Total Fuel Savings
2023
$0.73
$0.18
$0.54
2026
$3
$0.74
$2.3
2030
$9.2
$1.9
$7.3
2035
$16
$3.1
$13
2040
$22
$3.9
$18
2050
$24
$4
$20
PV, 3%
$240
$45
$200
PV, 7%
$120
$23
$98
Annualized, 3%
$12
$2.3
$10
Annualized, 7%
$9.7
$1.8
$7.9
Table 10-11 presents estimated annual monetized benefits from non-emission sources for the
indicated calendar years. The table also shows the present value of those benefits for the
calendar years 2021-2050 using both 3 percent and 7 percent discount rates.
Table 10-11: Benefits from Non-Emission Sources Associated with Alternative 1 (SBillions of 2018 dollars)
Calendar Year
Drive
Refueling Time
Energy Security
Total Non-Emission

Value
Savings
Benefits
Benefits
2023
$0,067
$0.0052
$0,027
$0,099
2026
$0.25
$0.02
$0.12
$0.39
2030
$0.81
$0.15
$0.33
$1.3
2035
$1.5
$0.33
$0.59
$2.4
2040
$2
$0.41
$0.81
$3.2
2050
$2.2
$0.32
$1.1
$3.5
PV, 3%
$22
$4.2
$9
$35
PV, 7%
$11
$2.1
$4.4
$17
Annualized,
$1.1
$0.21
$0.46
$1.8
3%




Annualized,
$0.88
$0.17
$0.36
$1.4
7%




10-7
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Table 10-12 presents estimated annual monetized benefits from emission sources for the
indicated calendar years. The table also shows the present value of those benefits for the
calendar years 2021-2050 using both 3 percent and 7 percent discount rates.
Table 10-12: PIVh.s-related Emission Reduction Benefits Associated with Alternative 1 (SBillions of 2018
dollars)
Calendar Year
Tailpipe
Benefits
Upstream Benefits
Total PM2 5-related Benefits
3% DR
7% DR
3% DR
7% DR
3% DR
7% DR
2023
-$0,014
-$0,013
$0,037
$0,034
$0,022
$0,021
2026
-$0,054
-$0,049
$0.13
$0.12
$0,076
$0,068
2030
-$0,019
-$0,017
$0.45
$0.41
$0.43
$0.39
2035
$0,056
$0,051
$0.87
$0.78
$0.92
$0.83
2040
$0.16
$0.15
$1
$0.93
$1.2
$1.1
2050
$0.34
$0.3
$0.88
$0.8
$1.2
$1.1
PV
$1.4
$0.42
$11
$5.1
$13
$5.6
Annualized
$0.07
$0,034
$0.57
$0.41
$0.64
$0.45
Notes:






a Note that the non-GHG impacts associated with the standards presented here do not include the full complement
of health and environmental effects that, if quantified and monetized, would increase the total monetized benefits.
Instead, the non-GHG benefits are based on benefit-per-ton values that reflect only human health impacts
associated with reductions in PM2 5 exposure.




b Calendar year non-GHG benefits presented in this table assume either a 3 percent or 7 percent discount rate in
the valuation of PM-related premature mortality to account for a twenty-year segmented cessation lag. Note that
annual benefits estimated using a 3 percent discount rate were used to calculate the present and annualized values
using a 3 percent discount rate and the annual benefits estimated using a 7 percent discount rate were used to
calculate the present and annualized values using a 7 percent discount rate.


Table 10-13 shows the benefits of reduced GHG emissions, and consequently the annual
quantified benefits (i.e., total benefits), for each of the four interim social cost of GHG (SC-
GHG) values estimated by the interagency working group. As discussed in draft RIA Chapter
3.3 there are some limitations to the SC-GHG analysis, including the incomplete way in which
the integrated assessment models capture catastrophic and non-catastrophic impacts, their
incomplete treatment of adaptation and technological change, uncertainty in the extrapolation of
damages to high temperatures, and assumptions regarding risk aversion.
10-8
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Table 10-13: Climate Benefits from Reduction in Greenhouse Gas Emissions Associated with Alternative 1
(SBillions of 2018 dollars)
Calendar Year
Discount Rate and Statistic
5% Average
3% Average
2.5% Average
3% 95th percentile
2023
$0,058
$0.19
$0.29
$0.58
2026
$0.26
$0.83
$1.2
$2.5
2030
$0.78
$2.4
$3.5
$7.4
2035
$1.5
$4.5
$6.4
$14
2040
$2.2
$6.2
$8.6
$19
2050
$2.9
$7.6
$10
$23
PV
$17
$70
$110
$210
Annualized
$1.1
$3.6
$5.1
$11
Table Notes:
Climate benefits are based on changes (reductions) in CO2, CH4, and N20 emissions and are calculated using four
different estimates of the social cost of carbon (SC-CO2), the social cost of methane (SC-CH4), and the social cost
of nitrous oxide (SC-N20) (model average at 2.5 percent, 3 percent, and 5 percent discount rates; 95th percentile
at 3 percent discount rate). We emphasize the importance and value of considering the benefits calculated using
all four SC-CO2, SC-CH4, and SC-N20 estimates. As discussed in the Technical Support Document: Social Cost
of Carbon, Methane, and Nitrous Oxide Interim Estimates under Executive Order 13990 (IWG 2021), a
consideration of climate benefits calculated using discount rates below 3 percent, including 2 percent and lower,
are also warranted when discounting intergenerational impacts.
The same discount rate used to discount the value of damages from future emissions (SC-GHGs at 5, 3, 2.5
percent) is used to calculate the present value of SC-GHGs for internal consistency Annual benefits shown are
undiscounted values.
10-9
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Table 10-14 presents estimated annual net benefits for the indicated calendar years. The table
also shows the present value of those net benefits for the calendar years 2021-2050 using both 3
percent and 7 percent discount rates. The table includes the benefits of reduced GHG emissions
(and consequently the annual net benefits) for each of the four SC-GHG values considered by
EPA.
Table 10-14: Net Benefits (Emission Benefits + Non-Emission Benefits + Fuel Savings - Costs) for Alternative
1 (S Bill ions of 2018 dollars)
Calendar
Year
Net Benefits,
with Climate
Benefits based on
5% discount rate
Net Benefits,
with Climate
Benefits based on
3% discount rate
Net Benefits,
with Climate
Benefits based
on 2.5% discount
rate
Net Benefits,
with Climate Benefits based
on 3% discount rate, 95th
percentile SC-GHG
2023
-$6.2
-$6.1
-$6
-$5.7
2026
-$10
-$9.6
-$9.2
-$8
2030
-$2.4
-$0.78
$0.3
$4.1
2035
$7.4
$10
$12
$20
2040
$14
$18
$21
$31
2050
$19
$23
$26
$39
PV, 3%
$77
$130
$170
$270
PV, 7%
$24
$76
$110
$220
Annualized,
3%
$4.2
$6.6
$8.2
$14
Annualized,
7%
$1.6
$4.1
$5.7
$11
Table Notes:
a Climate benefits are based on changes (reductions) in CO2, CH4, and N20 emissions and are calculated using
four different estimates of the social cost of carbon (SC-CO2), the social cost of methane (SC-CH4), and the social
cost of nitrous oxide (SC-N20) (model average at 2.5 percent, 3 percent, and 5 percent discount rates; 95th
percentile at 3 percent discount rate). We emphasize the importance and value of considering the benefits
calculated using all four SC-CO2, SC-CH4, and SC-N20 estimates. As discussed in the Technical Support
Document: Social Cost of Carbon, Methane, and Nitrous Oxide Interim Estimates under Executive Order 13990
(IWG 2021), a consideration of climate benefits calculated using discount rates below 3 percent, including 2
percent and lower, are also warranted when discounting intergenerational impacts. The same discount rate used to
discount the value of damages from future emissions (SC-GHG at 5, 3, 2.5 percent) is used to calculate present
value of SC-GHGs for internal consistency, while all other costs and benefits are discounted at either 3 percent or
7 percent. Annual costs and benefits shown are undiscounted values.
EPA also conducted a separate analysis of the total benefits over the model year lifetimes of
the 2021 through 2026 model year vehicles. In contrast to the calendar year analysis presented
above (in Table 10-9 through Table 10-14), the model year lifetime analysis below shows the
impacts of the proposed program on vehicles produced during each of the model years 2021
through 2026 over the course of their expected lifetimes. The net societal benefits over the full
lifetimes of vehicles produced during each of the six model years are shown in
10-10
-------
Table 10-15 and Table 10-16 at both 3 percent and 7 percent discount rates, respectively.
Similar to the calendar year analysis, the net benefits would far exceed the costs of the program.
Table 10-15: Monetized Costs, Fuel Savings, Benefits, and Net Benefits Associated with the Lifetimes of 2023-
2026 Model Year Light-Duty Vehicles, Alternative 1 (SBillions, 2018$; 3 percent Discount Rate)ab
MY
Costs
Fuel Savings
Benefits
Net Benefits
Present Values
2023
$3.9
$3.4
$1.1 to $4.4
$0.65 to $4
2024
$4.9
$6.5
$2 to $8.3
$3.6 to $9.8
2025
$5.6
$7.7
$2.5 to $9.9
$4.6 to $12
2026
$6.4
$10
$3.4 to $13
$7.1 to $17
Sum
$21
$28
$9 to $36
$16 to $43
Annualized Values
2023
$0.17
$0.15
$0,051 to $0.19
$0,033 to $0.17
2024
$0.21
$0.28
$0,097 to $0.36
$0.16 to $0.43
2025
$0.24
$0.33
$0.12 to $0.43
$0.21 to $0.52
2026
$0.28
$0.44
$0.16 to $0.57
$0.32 to $0.73
Sum
$0.9
$1.2
$0.43 to $1.5
$0.73 to $1.8
Table Notes:




a Note that the non-GHG impacts associated with the standards presented here do not include the full
complement of health and environmental effects that, if quantified and monetized, would increase the total
monetized benefits. Instead, the non-GHG benefits are based on benefit-per-ton values that reflect only human
health impacts associated with reductions in PM2 5 exposure.
b Climate benefits are based on reductions in CO2, CH4, and N20 emissions and are calculated using four
different estimates of the social cost of each greenhouse gas (SC-GHG) (model average at 2.5 percent, 3 percent,
and 5 percent discount rates; 95th percentile at 3 percent discount rate), which each increase over time. For the
presentational purposes of this table, the range of benefits and net benefits reflects the low to high range of SC-
GHG values. We emphasize the importance and value of considering the climate benefits calculated using all
four SC-GHG estimates which are available in Chapter 3. As discussed in Chapter 3.3, a consideration of climate
benefits calculated using discount rates below 3 percent, including 2 percent and lower, are also warranted when
discounting intergenerational impacts. The same discount rate used to discount the value of damages from future
emissions (SCC at 5, 3, 2.5 percent) is used to calculate net present value of SCC for internal consistency, while
all other costs and benefits are discounted at 3 percent in this table.
10-11
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Table 10-16: Monetized Costs, Fuel Savings, Benefits, and Net Benefits Associated with the Lifetimes of 2023-
2026 Model Year Light-Duty Vehicles, Alternative 1 (SBillions, 2018$; 7 percent Discount Rate)3
MY
Costs
Fuel Savings
Benefits
Net Benefits
Present Values
2023
$3.7
$2.4
$0.83 to $4.2
-$0.49 to $2.8
2024
$4.7
$4.3
$1.5 to $7.8
$1.2 to $7.4
2025
$5.1
$4.9
$1.8 to $9.2
$1.7 to $9.1
2026
$5.6
$6.2
$2.4 to $12
$3 to $13
Sum
$19
$18
$6.6 to $33
$5.3 to $32
Annualized Values
2023
$0.28
$0.18
$0,057 to $0.2
-$0,042 to $0,097
2024
$0.35
$0.32
$0.1 to $0.37
$0,077 to $0.34
2025
$0.38
$0.37
$0.12 to $0.43
$0.11 to $0.42
2026
$0.42
$0.47
$0.17 to $0.57
$0.21 to $0.61
Sum
$1.4
$1.3
$0.45 to $1.6
$0.36 to $1.5
Table Notes:




a Note that the non-GHG impacts associated with the standards presented here do not include the full
complement of health and environmental effects that, if quantified and monetized, would change the total
monetized estimate of rule-related impacts. Instead, the non-GHG benefits are based on benefit-per-ton values
that reflect only human health impacts associated with reductions in PM2 5 exposure,
b Climate benefits are based on reductions in CO2, CH4, and N20 emissions and are calculated using four
different estimates of the social cost of each greenhouse gas (SC-GHG) (model average at 2.5 percent, 3 percent,
and 5 percent discount rates; 95th percentile at 3 percent discount rate), which each increase over time. For the
presentational purposes of this table, the range of benefits and net benefits reflects the low to high range of SC-
GHG values. We emphasize the importance and value of considering the climate benefits calculated using all
four SC-GHG estimates which are available in Chapter 3. As discussed in Chapter 3.3, a consideration of climate
benefits calculated using discount rates below 3 percent, including 2 percent and lower, are also warranted when
discounting intergenerational impacts. The same discount rate used to discount the value of damages from future
emissions (SCC at 5, 3, 2.5 percent) is used to calculate net present value of SCC for internal consistency, while
all other costs and benefits are discounted at 7 percent in this table.
10-12
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10.3 Alternative 2
The same series of tables as presented in Chapter 10.1 for the proposal are presented here for
Alternative 2. Note that Table 10-17 includes an estimate of foregone consumer sales surplus,
which measures the loss in benefits attributed to consumers who would have purchased a new
vehicle in the absence of the proposed standards.
Table 10-17: Costs Associated with Alternative 2 (SBillions of 2018 dollars)
Calendar
Year
Foregone Consumer
Sales Surplus Til
Technology
Costs
Congestion
Noise
Fatality
Costs
Non-fatal
Crash Costs
Total
Costs
2023
$0.37
$9.6
$0.15
$0,002
3
$0.27
$0.46
$11
2026
$0.62
$15
$0.33
$0,005
3
$0.7
$1.2
$18
2030
$0.51
$16
$0.8
$0,013
$0.65
$1.1
$19
2035
$0.35
$14
$1.3
$0,021
$0.29
$0.47
$16
2040
$0.28
$13
$1.6
$0,026
$0,057
$0,093
$15
2050
$0.21
$12
$1.6
$0,026
$0,076
$0.12
$14
PV, 3%
00
VO
&
$240
$19
$0.31
$6.3
$10
$290
PV, 7%
$4.5
$150
$9.8
$0.16
$4.5
$7.4
$180
Annualiz
ed, 3%
$0.35
$12
$0.97
$0,016
$0.32
$0.53
$15
Annualiz
ed, 7%
$0.36
$12
$0.79
$0,013
$0.36
$0.6
$14
[1] "Foregone Consumer Sales Surplus" refers to the difference between a vehicle's price and the buyer's
willingness to pay for the new vehicle; the impact reflects the reduction in new vehicle sales described in Chapter
8.1. See Section 8 of CAFE Model Documentation FR 2020.pdf in the docket for more information.
Table 10-18 shows the undiscounted annual monetized fuel savings of Alternative 2. The
table also shows the present value of those fuel savings for the same calendar years using both 3
percent and 7 percent discount rates. The aggregate value of fuel savings is calculated using pre-
tax fuel prices since savings in fuel taxes do not represent a reduction in the value of economic
resources utilized in producing and consuming fuel. Note that the fuel savings shown in Table
10-18 result from reductions in fleet-wide fuel use. Thus, fuel savings grow over time as an
increasing fraction of the fleet is projected to meet the proposed standards.
Table 10-18: Fuel Savings Associated with Alternative 2 (SBillions of 2018 dollars)
Calendar Year
Retail Fuel Savings
Fuel Tax Savings
Total Fuel Savings
2023
$2.3
$0.56
$1.8
2026
$5.5
$1.4
$4.2
2030
$14
$3.1
$11
2035
$24
$4.8
$19
2040
$31
$5.7
$25
2050
$36
$6.1
$30
PV, 3%
$360
$69
$290
PV, 7%
$180
$36
$150
Annualized, 3%
$18
$3.5
$15
Annualized, 7%
$15
$2.9
$12
10-13
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Table 10-19 presents estimated annual monetized benefits from non-emission sources for the
indicated calendar years. The table also shows the present value of those benefits for the
calendar years 2021-2050 using both 3 percent and 7 percent discount rates.
Table 10-19: Benefits from Non-Emission Sources Associated with Alternative 2 (SBillions of 2018 dollars)
Calendar Year
Drive
Value
Refueling Time
Savings
Energy Security
Benefits
Total Non-Emission
Benefits*
2023
$0.21
$0.08
$0,082
$0.37
2026
$0.47
$0.11
$0.21
$0.79
2030
$1.2
$0.27
$0.52
$2
2035
$2.1
$0.45
$0.9
$3.5
2040
$2.8
$0.68
$1.2
$4.6
2050
$2.9
$0.61
$1.6
$5.1
PV, 3%
$31
$7.5
$14
$53
PV, 7%
$16
$3.8
00
vd
&
$26
Annualized,
3%
$1.6
$0.38
$0.7
$2.7
Annualized,
7%
$1.3
$0.31
$0.55
$2.1
Table 10-20 presents estimated annual monetized benefits from emission sources for the
indicated calendar years. The table also shows the present value of those benefits for the
calendar years 2021-2050 using both 3 percent and 7 percent discount rates.
Table 10-20: PIVh.s-related Emission Reduction Benefits Associated with Alternative 2 (SBillions of 2018
dollars)
Calendar Year
Tailpipe
Benefits
Upstream Benefits
Total PM2 5-related Benefits
3% DR
7% DR
3% DR
7% DR
3% DR
7% DR
2023
-$0,024
-$0,022
$0.16
$0.14
$0.13
$0.12
2026
-$0,069
-$0,062
$0.25
$0.23
$0.18
$0.16
2030
-$0,018
-$0,016
$0.55
$0.5
$0.53
$0.48
2035
$0.13
$0.12
$0.94
$0.85
$1.1
$0.97
2040
$0.31
$0.28
$1.2
$1
$1.5
$1.3
2050
$0.63
$0.57
$0.77
$0.7
$1.4
$1.3
PV
$2.7
$0.92
$13
$6.3
$16
$7.2
Annualized
$0.14
$0,074
$0.67
$0.51
$0.81
$0.58
Notes:






a Note that the non-GHG impacts associated with the standards presented here do not include the full complement
of health and environmental effects that, if quantified and monetized, would increase the total monetized benefits.
Instead, the non-GHG benefits are based on benefit-per-ton values that reflect only human health impacts
associated with reductions in PM2 5 exposure.




b Calendar year non-GHG benefits presented in this table assume either a 3 percent or 7 percent discount rate in
the valuation of PM-related premature mortality to account for a twenty-year segmented cessation lag. Note that
annual benefits estimated using a 3 percent discount rate were used to calculate the present and annualized values
using a 3 percent discount rate and the annual benefits estimated using a 7 percent discount rate were used to
calculate the present and annualized values using a 7 percent discount rate.


10-14
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Table 10-21 shows the benefits of reduced GHG emissions, and consequently the annual
quantified benefits (i.e., total benefits), for each of the four interim social cost of GHG (SC-
GHG) values estimated by the interagency working group. As discussed in draft RIA Chapter
3.3 there are some limitations to the SC-GHG analysis, including the incomplete way in which
the integrated assessment models capture catastrophic and non-catastrophic impacts, their
incomplete treatment of adaptation and technological change, uncertainty in the extrapolation of
damages to high temperatures, and assumptions regarding risk aversion.
Table 10-21: Climate Benefits from Reduction in Greenhouse Gas Emissions Associated with Alternative 2
(SBillions of 2018 dollars)
Calendar Year
Discount Rate and Statistic
5% Average
3% Average
2.5% Average
3% 95th percentile
2023
$0.18
$0.61
$0.9
$1.8
2026
$0.47
$1.5
$2.2
$4.6
2030
$1.2
$3.8
$5.5
$11
2035
$2.3
$6.7
$9.5
$20
2040
$3.1
$8.8
$12
$27
2050
$4.4
$11
$16
$35
PV
$25
$100
$160
$320
Annualized
$1.6
$5.3
$7.7
$16
Table Notes:
Climate benefits are based on changes (reductions) in C02, CH4, and N20 emissions and are calculated using four
different estimates of the social cost of carbon (SC-CO2), the social cost of methane (SC-CH4), and the social cost
of nitrous oxide (SC-N20) (model average at 2.5 percent, 3 percent, and 5 percent discount rates; 95th percentile
at 3 percent discount rate). We emphasize the importance and value of considering the benefits calculated using
all four SC-CO2, SC-CH4, and SC-N20 estimates. As discussed in the Technical Support Document: Social Cost
of Carbon, Methane, and Nitrous Oxide Interim Estimates under Executive Order 13990 (IWG 2021), a
consideration of climate benefits calculated using discount rates below 3 percent, including 2 percent and lower,
are also warranted when discounting intergenerational impacts.
The same discount rate used to discount the value of damages from future emissions (SC-GHGs at 5, 3, 2.5
percent) is used to calculate the present value of SC-GHGs for internal consistency Annual benefits shown are
undiscounted values.
Table 10-22 presents estimated annual net benefits for the indicated calendar years. The table
also shows the present value of those net benefits for the calendar years 2021-2050 using both 3
percent and 7 percent discount rates. The table includes the benefits of reduced GHG emissions
(and consequently the annual net benefits) for each of the four SC-GHG values considered by
EPA.
10-15
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Table 10-22: Net Benefits (Emission Benefits + Non-Emission Benefits + Fuel Savings - Costs) Associated
with Alternative 2 (SBillions of 2018 dollars)
Calendar
Year
Net Benefits,
with Climate
Benefits based on
5% discount rate
Net Benefits,
with Climate
Benefits based on
3% discount rate
Net Benefits,
with Climate
Benefits based
on 2.5% discount
rate
Net Benefits,
with Climate Benefits based
on 3% discount rate, 95th
percentile SC-GHG
2023
-$8.4
-$8
-$7.7
OO
VO
&
1
2026
-$12
-$11
-$10
-$8
2030
-$4.5
-$2
-$0.28
$5.7
2035
$9.6
$14
$17
$28
2040
$20
$25
$29
$43
2050
$26
$33
$38
$57
PV, 3%
$100
$180
$240
$390
PV, 7%
$30
$110
$170
$320
Annualized,
3%
$5.4
$9.1
$11
$20
Annualized,
7%
$2
$5.7
$8.1
$17
Table Notes:
Climate benefits are based on changes (reductions) in CO2, CH4, and N20 emissions and are calculated using four
different estimates of the social cost of carbon (SC-CO2), the social cost of methane (SC-CH4), and the social cost
of nitrous oxide (SC-N20) (model average at 2.5 percent, 3 percent, and 5 percent discount rates; 95th percentile
at 3 percent discount rate). We emphasize the importance and value of considering the benefits calculated using
all four SC-CO2, SC-CH4, and SC-N20 estimates. As discussed in the Technical Support Document: Social Cost
of Carbon, Methane, and Nitrous Oxide Interim Estimates under Executive Order 13990 (IWG 2021), a
consideration of climate benefits calculated using discount rates below 3 percent, including 2 percent and lower,
are also warranted when discounting intergenerational impacts. The same discount rate used to discount the
value of damages from future emissions (SC-GHG at 5, 3, 2.5 percent) is used to calculate present value of SC-
GHGs for internal consistency, while all other costs and benefits are discounted at either 3 percent or 7 percent.
Annual costs and benefits shown are undiscounted values.
EPA also conducted a separate analysis of the total benefits over the model year lifetimes of
the 2021 through 2026 model year vehicles. In contrast to the calendar year analysis presented
above (in Table 10-3 through Table 10-6), the model year lifetime analysis below shows the
impacts of the proposed program on vehicles produced during each of the model years 2021
through 2026 over the course of their expected lifetimes. The net societal benefits over the full
lifetimes of vehicles produced during each of the six model years are shown in Table 10-23 and
Table 10-24 at both 3 percent and 7 percent discount rates, respectively. Similar to the calendar
year analysis, the net benefits would far exceed the costs of the program.
10-16
-------
Table 10-23: Monetized Costs, Fuel Savings, Benefits, and Net Benefits Associated with the Lifetimes of 2023-
2026 Model Year Light-Duty Vehicles, Alternative 2 (SBillions, 2018$; 3 percent Discount Rate)a'b
MY
Costs
Fuel Savings
Benefits
Net Benefits
Present Values
2023
$6.8
$7.7
$2.6 to $10
$3.5 to $11
2024
$7.7
$9.8
$3.2 to $13
$5.3 to $15
2025
$8.4
$11
$3.6 to $14
$6.2 to $17
2026
$9.2
$13
$4.3 to $17
$8.4 to $21
Sum
$32
$42
$14 to $54
$23 to $64
Annualized Values
2023
$0.3
$0.33
$0.12 to $0.44
$0.16 to $0.47
2024
$0.33
$0.42
$0.15 to $0.55
$0.24 to $0.64
2025
$0.37
$0.48
$0.17 to $0.62
$0.28 to $0.74
2026
$0.4
$0.57
$0.21 to $0.75
$0.38 to $0.92
Sum
$1.4
$1.8
$0.65 to $2.4
$1.1 to $2.8
Table Notes:




a Note that the non-GHG impacts associated with the standards presented here do not include the full complement of health and
environmental effects that, if quantified and monetized, would increase the total monetized benefits. Instead, the non-GHG benefits are based
on benefit-per-ton values that reflect only human health impacts associated with reductions in PM2.5 exposure.
b C02 Climate benefits are based on reductions in C02, CH4, and N20 emissions and are calculated using four different estimates of the social
cost of each greenhouse gas (SC-GHG) (model average at 2.5 percent, 3 percent, and 5 percent discount rates; 95th percentile at 3 percent
discount rate), which each increase over time. For the presentational purposes of this table, the range of benefits and net benefits reflects the
low to high range of SC-GHG values. We emphasize the importance and value of considering the climate benefits calculated using all four
SC-GHG estimates which are available in Chapter 3. As discussed in Chapter 3.3, a consideration of climate benefits calculated using
discount rates below 3 percent, including 2 percent and lower, are also warranted when discounting intergenerational impacts The same
discount rate used to discount the value of damages from future emissions (SCC at 5, 3, 2.5 percent) is used to calculate net present value of
SCC for internal consistency, while all other costs and benefits are discounted at 3 percent in this table.
Table 10-24: Monetized Costs, Fuel Savings, Benefits, and Net Benefits Associated with the Lifetimes of 2023-
2026 Model Year Light-Duty Vehicles, Alternative 2 (SBillions, 2018$; 7 percent Discount Rate)a'b
MY
Costs
Fuel Savings
Benefits
Net Benefits
Present Values
2023
$6.3
$5.4
$2 to $9.4
$1.1 to $8.5
2024
$7
$6.5
$2.4 to $12
$1.9 to $11
2025
$7.4
$7.1
$2.6 to $13
$2.3 to $13
2026
$7.9
$8.2
$3.1 to $16
$3.4 to $16
Sum
$29
$27
$10 to $51
$8.6 to $49
Annualized Values
2023
$0.48
$0.4
$0.14 to $0.45
$0,067 to $0.38
2024
$0.53
$0.49
$0.16 to $0.56
$0.13 to $0.53
2025
$0.56
$0.54
$0.18 to $0.63
$0.16 to $0.61
2026
$0.59
$0.61
$0.21 to $0.75
$0.23 to $0.77
Sum
$2.2
$2
$0.69 to $2.4
$0.58 to $2.3
Table Notes:




a Note that the non-GHG impacts associated with the standards presented here do not include the full complement of health and
environmental effects that, if quantified and monetized, would increase the total monetized benefits. Instead, the non-GHG benefits are based
on benefit-per-ton values that reflect only human health impacts associated with reductions in PM2.5 exposure.
b C02Climate benefits are based on reductions in C02, CH4, and N20 emissions and are calculated using four different estimates of the social
cost of each greenhouse gas (SC-GHG) (model average at 2.5 percent, 3 percent, and 5 percent discount rates; 95th percentile at 3 percent
discount rate), which each increase over time. For the presentational purposes of this table, the range of benefits and net benefits reflects the
low to high range of SC-GHG values. We emphasize the importance and value of considering the climate benefits calculated using all four
SC-GHG estimates which are available in Chapter 3. As discussed in Chapter 3.3, a consideration of climate benefits calculated using
discount rates below 3 percent, including 2 percent and lower, are also warranted when discounting intergenerational impacts. The same
discount rate used to discount the value of damages from future emissions (SCC at 5, 3, 2.5 percent) is used to calculate net present value of
SCC for internal consistency, while all other costs and benefits are discounted at 7 percent in this table.
10-17
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10.4 Sensitivities
We have conducted the following sensitivites:
•	AEO high oil price (AEO High)
•	AEO low oil price (AEO Low)
•	Mass safety coefficients at the 5th percentile (Mass Safety 5th %ile)
•	Mass safety coefficients at the 95th percentile (Mass Safety 95th %ile)
•	No HCR2 availability (No HCR2)
o This represents no further progression of Atkinson Cycle technology beyond
HCR1, i.e., technologies that are currently in today's light-duty vehicle fleet (see
Chapter 2.3.2)
•	Perfect trading amongst manufacturers (Perfect Trading)
•	Price elasticity equal to -0.4 (rather than -1.0)
•	Rebound effect equal to -0.05 (rather than -0.1)
•	Rebound effect equal to -0.15 (rather than -0.1)
Each sensitivity is compared to its own no action scenario. In other words, the no action
standards were used but the no action scenario was run using the same set of sensitivity
parameters as used for the action scenario.
Table 10-25 Monetized Discounted Costs, Benefits, and Net Benefits of the Proposed Program and each
Sensitivity for Calendar Years through 2050 ($Billions of 2018 dollars, 3 percent Discount Rate)a'b'c'd

Proposal
AEO
AEO
Mass
Mass
No
Perfect
Price
Rebound -
Rebound -


High
Low
Safety
5th %ile
Safety
95th %ile
HCR2
Trading
Elasticity
0.05
0.15
Costs
$240
$200
$270
$220
$270
$250
$260
$250
$230
$250
Fuel
$250
$300
$160
$250
$250
$250
$260
$250
$260
$240
Savings










Benefits
$130
$110
$140
$130
$130
$130
$150
$140
$130
$140
Net
$140
$210
$37
$170
$120
$130
$150
$150
$150
$140
Benefits










Table Notes










" Present and annualized values are based on the stream of annual calendar year costs and benefits included in the analysis (2021
- 2050)and
discounted back to year 2021.








b Climate benefits are based on reductions in C02, N20 and methane emissions and are calculated using four different estimates of the per-ton
social cost of each greenhouse gas (model average at 2.5 percent, 3 percent, and 5 percent discount rates; 95th percentile at 3 percent discount
rate), which each increase over time. For the purposes of this summary presentation of estimated costs and benefits, however, we include the
benefits associated with the model average at a 3 percent discount rate. Section VII. D below provides a complete list of the social cost of
GHG values for the 4 estimates.








c Note that the present and annualized values of reduced GHG emissions is calculated differently than other benefits and costs. The same
discount rate used to discount the value of damages from future emissions (social costs at 5, 3, and 2.5 percent) is used to calculate the present
and annualized values of climate benefits for internal consistency, while all other costs and benefits are discounted at either 3 percent or 7
percent.
11 Net benefits reflect the fuel savings plus benefits minus costs.





10-18
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Table 10-26 Monetized Discounted Costs, Benefits, and Net Benefits of the Proposed Program and each
Sensitivity for Calendar Years through 2050 ($Billions of 2018 dollars, 7 percent Discount Rate)a'b'c'd

Proposal
AEO
High
AEO
Low
Mass
Safety 5th
%ile
Mass
Safety
95th %ile
No HCR2
Perfect
Trading
Price
Elasticity
Rebound
-0.05
Reboun
d - 0.15
Costs
$150
$120
$160
$140
$160
$150
$150
$150
$140
$150
Fuel
Savings
$120
$150
$80
$120
$120
$120
$130
$120
$130
$120
Benefits
$110
$89
$120
$110
$110
$110
$120
$110
$110
$110
Net
Benefits
$86
$120
$39
$97
$75
$80
$91
$89
$90
$82
Table Notes:
a Present and annualized values are based on the stream of annual calendar year costs and benefits included in the analysis (2021 - 2050) and
discounted back to year 2021.
b Climate benefits are based on reductions in CO2, N2O and methane emissions and are calculated using four different estimates of the per-ton
social cost of each greenhouse gas (model average at 2.5 percent, 3 percent, and 5 percent discount rates; 95th percentile at 3 percent discount
rate), which each increase over time. For the purposes of this summary presentation of estimated costs and benefits, however, we include the
benefits associated with the model average at a 3 percent discount rate. Section VII.D below provides a complete list of the social cost of
GHG values for the 4 estimates.
c Note that the present and annualized values of reduced GHG emissions is calculated differently than other benefits and costs. The same
discount rate used to discount the value of damages from future emissions (social costs at 5, 3, and 2.5 percent) is used to calculate the present
and annualized values of climate benefits for internal consistency, while all other costs and benefits are discounted at either 3 percent or 7
percent.
d Net benefits reflect the fuel savings plus benefits minus costs.	
10-19
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