&EPA
Multi-Pollutant Emissions Standards for
Model Years 2027 and Later Light-Duty
and Medium-Duty Vehicles
Draft Regulatory Impact Analysis
United States
Environmental Protection
Agency
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Multi-Pollutant Emissions Standards for
Model Years 2027 and Later Light-Duty
and Medium-Duty Vehicles
Draft Regulatory Impact Analysis
This technical report does not necessarily represent final EPA decisions
or positions. It is intended to present technical analysis of issues using
data that are currently available. The purpose in the release of such
reports is to facilitate the exchange of technical information and to
inform the public of technical developments.
Assessment and Standards Division
Office of Transportation and Air Quality
U.S. Environmental Protection Agency
NOTICE
4>EPA
United States
Environmental Protection
Agency
EPA-420-D-23-003
April 2023
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Table of Contents
List of Tables xvi
List of Figures xxxiii
Executive Summary xli
Chapter 1: Development of GHG Standards and BEV Durability Requirements 1-1
1.1 Development of the proposed GHG standards for Light-Duty Vehicles 1-1
1.1.1 Analysis of fleet changes since 2012 1-1
1.1.1.1 Car and Truck Regulatory Classes 1-2
1.1.2 Relationship between GHG curve shape, stringency, and BEV share 1-6
1.1.3 Development of appropriate GHG curve shape (slope and cut points) 1-6
1.1.3.1 Establishing slope of car curve 1-6
1.1.3.2 Development of truck curve 1-8
1.1.3.3 Analysis of Footprint Response to Proposed Standards 1-13
1.1.3.4 Cut points 1-14
1.2 Development of the proposed GHG standards for Medium-Duty Vehicles 1-16
1.2.1 History of GHG standards for Medium-Duty Vehicles 1-16
1.2.2 Development of the proposed standards for Medium-Duty Vehicles 1-18
1.2.2.1 Proposed MDV GHG Standards 1-20
1.3 Development of the proposed battery durability standards 1-22
1.3.1 United Nations Global Technical Regulation No. 22 on In-Vehicle Battery Durability
1-22
1.3.2 California Air Resources Board battery durability and warranty provisions under the
ACC II program 1-24
Chapter 2: Tools and Inputs Used for Modeling Technologies and Adoption Towards
Compliance 2-1
2.1 Overview of EPA's Compliance Modeling Approach 2-1
2.1.1 OMEGA Compliance and Model Overview 2-2
2.1.2 OMEGA Updates 2-2
2.2 OMEGA2 Model Structure and Operation 2-4
2.2.1 Inputs and Outputs 2-4
2.2.2 Model Structure and Key Modules 2-5
2.2.3 Iteration and Convergence 2-6
2.2.4 Analysis Resolution 2-6
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2.3 0MEGA2 Peer Review 2-6
2.3.1 Charge Questions for the Peer Review: 2-7
2.3.2 Information Received from Peer Review 2-7
2.4 ALPHA Full Vehicle Simulation and Response Surface Equations 2-8
2.4.1 General Description of ALPHA 2-9
2.4.2 Overview of Previous Versions of ALPHA 2-10
2.4.3 Current version of ALPHA 2-10
2.4.4 ALPHA Models for Conventional and Electrified Vehicle Architectures 2-11
2.4.4.1 Conventional Vehicle Architecture 2-12
2.4.4.2 Hybrid Electric Vehicle (HEV) Architectures 2-12
2.4.4.2.1 Mild Hybrid Architectures 2-13
2.4.4.2.2 Strong Hybrid Architectures 2-14
2.4.4.3 Battery Electric Vehicle Architecture (BEV) 2-16
2.4.5 Engine, E-motor, Transmission and Battery Components 2-17
2.4.5.1 Light-Duty Engines 2-17
2.4.5.2 Electric Drive Components 2-18
2.4.5.3 Transmissions 2-19
2.4.5.4 Batteries 2-20
2.4.6 Scaling rules for ALPHA input maps 2-21
2.4.7 Tuning ALPHA'S Electrified Vehicle Models Using Vehicle Validations 2-22
2.4.7.1 Verifying the Validated Strong Hybrid and BEVs Models against Variant
Vehicles 2-24
2.4.7.2 P0 Mild Hybrid Validation Efforts 2-25
2.4.8 Verifying ALPHA'S Ability to Simulate Entire Fleets 2-26
2.4.8.1 Data Sources to Determine 2019 Fleet Parameters 2-26
2.4.8.2 Vehicle Parameters 2-27
2.4.8.3 Electrified Powertrain Model Assignments 2-28
2.4.8.4 Modeling Conventional Vehicles in the Fleet 2-28
2.4.8.5 Modeling Mild Hybrids in the Fleet 2-30
2.4.8.6 Modeling Strong Hybrids in the Fleet 2-31
2.4.8.7 Modeling Battery Electric Vehicles in the Fleet 2-33
2.4.9 Peer-Reviewing ALPHA Electrified Models 2-34
2.4.10 Estimating CO2 emissions of Future Fleets 2-35
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2.4.10.1 Technology Packages used to create RSEs for OMEGA 2-35
2.4.10.2 Vehicle Parameter Sweeps for each Technology Package 2-38
2.4.10.2.1 Swept Vehicle Parameters and Their Values 2-38
2.4.10.2.2 Values of Parameters Used for ALPHA Simulations 2-40
2.4.10.2.3 ALPHA Simulation Outputs for RSEs 2-40
2.4.10.3 Transforming ALPHA Simulation Outputs into RSEs for OMEGA 2-40
2.4.10.3.1 Steps to Create a RSE from the RSM 2-40
2.5 Cost Methodology 2-42
2.5.1 Absolute vs. incremental cost approach 2-42
2.5.2 Direct manufacturing costs 2-43
2.5.2.1 Battery cost modeling methodology 2-43
2.5.2.1.1 Battery sizing 2-43
2.5.2.1.2 Base year battery cost estimation 2-44
2.5.2.1.3 Development of battery pack cost reduction factors for future years 2-49
2.5.2.1.4 Battery cost reductions due to Inflation Reduction Act 2-53
2.5.2.2 BEV Non-Battery Cost Approach 2-55
2.5.2.2.1 Use of teardown studies 2-55
2.5.2.2.2 Munro and Associates teardowns 2-56
2.5.2.2.3 EPA-FEV comparative BEV-ICE vehicle teardown 2-57
2.5.2.2.4 Other teardowns 2-58
2.5.2.2.5 Published and other sources 2-58
2.5.3 Approach to cost reduction through manufacturer learning 2-59
2.5.4 Indirect costs 2-60
2.6 Inputs and Assumptions for Compliance Modeling 2-62
2.6.1 Powertrain Costs 2-62
2.6.1.1 ICE Powertrain Costs 2-62
2.6.1.1.1 Cost per cylinder and cost per liter 2-64
2.6.1.1.2 Gasoline Direct Injection 2-64
2.6.1.1.3 Turbocharging 2-65
2.6.1.1.4 Cooled Exhaust Gas Recirculation 2-66
2.6.1.1.5 Cylinder Deactivation 2-66
2.6.1.1.6 Atkinson Cycle Engine 2-66
2.6.1.1.7 Transmissions 2-67
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2.6.1.1.8 High Efficiency Alternator 2-67
2.6.1.1.9 Start-Stop 2-68
2.6.1.1.10 Gasoline Particulate Filter 2-68
2.6.1.1.11 Three-way Catalyst 2-68
2.6.1.1.12 Diesel Exhaust Aftertreatment System 2-69
2.6.1.2 HEV-specific and Mild HEV-specific Powertrain Costs 2-70
2.6.1.2.1 HEV and MHEV Non-Battery 2-70
2.6.1.2.2 HEV and MHEV Battery 2-70
2.6.1.3 BEV Powertrain Costs 2-70
2.6.1.3.1 BEV Battery 2-71
2.6.1.3.1.1 Battery cost estimation curve by kWh 2-71
2.6.1.3.2 BEV Non-Battery 2-71
2.6.1.3.2.1 Power electronics costs 2-72
2.6.1.3.2.2 Gearbox costs 2-73
2.6.1.3.2.3 A WD costs 2-73
2.6.1.3.2.4 Summary of BEV non-battery costs 2-73
2.6.1.4 PHEV Powertrain Costs 2-74
2.6.1.4.1 PHEV Battery Costs 2-74
2.6.1.4.2 PHEV Non-Battery Costs 2-75
2.6.1.5 Powertrain Costs for All Vehicles 2-79
2.6.1.5.1 Air Conditioning 2-79
2.6.1.5.2 Low voltage battery 2-79
2.6.1.5.3 Heating and Ventilation 2-79
2.6.2 Glider Costs 2-80
2.6.3 Consumer demand assumptions and S-Curves 2-82
2.6.4 Consideration of constraints in modeling real-world technology adoption 2-82
2.6.4.1 Redesign schedules 2-82
2.6.4.2 Materials and mineral availability 2-83
2.6.5 Manufacturing capacity 2-84
2.6.6 Fuel Prices used in OMEGA 2-84
2.6.7 Gross Domestic Product Price Deflators 2-85
2.6.8 Inflation Reduction Act 2-86
Chapter 3: Analysis of Technologies for Reducing GHG and Criteria Pollutant Emissions 3-1
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3.1 Technology Feasibility 3-1
3.1.1 Light-duty Vehicle Technologies and Trends 3-2
3.1.1.1 Advanced ICE technologies 3-2
3.1.1.2 Hybrid Electric Technologies 3-3
3.1.1.3 Plug-in Electric Vehicle Technologies 3-4
3.1.2 Medium-duty Vehicle Technologies and Trends 3-8
3.1.3 PEVFeasibility 3-12
3.1.3.1 PEV Technological Feasibility 3-12
3.1.3.2 Critical Minerals and Manufacturing 3-19
3.1.3.3 Additional Information on Critical Mineral Supply Chain Development 3-26
3.2 Proposed Criteria and Toxic Pollutant Emissions Standards for Model Years 2027-2032.3-
29
3.2.1 Proposed NMOG+NOx standards 3-32
3.2.1.1 Proposed NMOG+NOx bin structure for light-duty and MDVs 3-34
3.2.1.2 Light-duty NMOG+NOx standards and test cycles 3-35
3.2.1.3 NMOG+NOx Standards for MDV at or below 22,000 lb GCWR 3-37
3.2.2 Proposed PM standards for light-duty and MDV at or below 22,000 pounds GCWR. 3-
39
3.2.3 Proposed CO and formaldehyde (HCHO) standards 3-40
3.2.4 Current ICE-based vehicle NMOG+ NOx emissions 3-40
3.2.4.1 Current ICE Emissions at -7°C FTP 3-42
3.2.4.2 Feasibility of a single numerical standard for FTP, HFET, SC03 and US06.... 3-43
3.2.4.3 Off-Cycle emission controls 3-43
3.2.5 Particulate Matter Emissions Control 3-47
3.2.5.1 Overview of GPF technology 3-48
3.2.5.2 GPF benefits 3-50
3.2.5.2.1 PM mass, BC, and PAH emissions reductions over a composite drive cycle 3-
50
3.2.5.2.2 Cycle-specific reduction in PM mass emissions from GPF application to three
vehicles 3-54
3.2.5.3 Importance of test cycles 3-57
3.2.5.4 Demonstration of the feasibility of the standard 3-58
3.2.5.4.1 Setup and Test Procedures 3-58
3.2.5.5 GPF cost 3-60
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3.2.5.6 GPF impact on CO2 emissions 3-62
3.2.6 Evaporative Emissions Control 3-65
3.2.6.1 Technologies to Address Evaporative and Refueling Emissions 3-66
3.2.6.2 Filler Pipe and Seal 3-67
3.2.6.3 ORVRFlow Control Valve 3-68
3.2.6.4 Canister 3-68
3.2.6.5 Purge Valve 3-68
3.2.6.6 Design considerations for Unique Fuel Tanks 3-69
3.2.6.7 Onboard Refueling Vapor Recovery Anticipated Costs 3-69
3.3 On-board Diagnostics 3-72
3.4 PHEV Accounting 3-73
3.4.1 Proposed Approach for the Revised PHEV Utility Factor 3-73
3.4.1.1 FUF Comparisons with Real World Data 3-75
3.5 GHG Emissions Control Technologies 3-80
3.5.1 Engine Technologies 3-80
3.5.1.1 2013 Chevrolet 2.5L Ecotec LCV Engine Reg El0 Fuel 3-81
3.5.1.2 GT Power Baseline 2020 Ford 7.3L Engine from Argonne Report Tier 3 Fuel.. 3-
81
3.5.1.3 2014 Chevrolet 4.3L EcoTec3 LV3 Engine LEVIII Fuel 3-83
3.5.1.4 2013 Ford 1.6L EcoBoost Engine LEV III Fuel 3-84
3.5.1.5 2015 Ford 2.7L EcoBoost Engine Tier 3 Fuel 3-85
3.5.1.6 2016 Honda 1.5L L15B7 Engine Tier 3 Fuel 3-86
3.5.1.7 Volvo VEP 2.0L LP Gen3 Miller Engine from 2020 Aachen Paper Octane
Modified for Tier3 Fuel 3-87
3.5.1.8 Geely 1.5L Miller GHE from 2020 Aachen Paper Octane Modified for Tier 3 Fuel
3-88
3.5.1.9 2018 Toyota 2.5L A25A-FKS Engine Tier 3 Fuel 3-89
3.5.1.10 Toyota 2.5L TNGA Prototype Hybrid Engine from 2017 Vienna Paper Octane
Modified for Tier 3 Fuel 3-90
3.5.2 Electrification Technologies 3-91
3.5.2.1 2010 Toyota Prius 60kW 650VMG2EMOT 3-91
3.5.2.2 Est 2010 Toyota Prius 60kW 650V MG1 EMOT 3-92
3.5.2.3 2011 Hyundai Sonata 30kW 270V EMOT 3-93
3.5.2.4 2012 Hyundai Sonata 8.5kW 270V BISG 3-94
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3.5.2.5 Generic IPM 150kWEDU 3-95
3.5.3 Vehicle Architectures 3-95
3.5.4 Other Vehicle Technologies 3-96
Chapter 4: Consumer Impacts and Related Economic Considerations 4-1
4.1 Modeling the Purchase Decision 4-1
4.1.1 Costs Incorporated in the Purchase Decision 4-2
4.1.2 Consumer Response to Costs and Perceptions of Technology 4-4
4.1.3 Sensitivities 4-9
4.2 Ownership Experience 4-13
4.2.1 Vehicle Miles Traveled and Rebound Effect 4-13
4.2.1.1 Basis for Vehicle Miles Traveled for Battery Electric Vehicles 4-14
4.2.1.2 Basis for the Rebound Effect for Internal Combustion Engines 4-15
4.2.1.3 Basis for Rebound Effect for Battery Electric Vehicles 4-16
4.2.2 Consumer Savings and Expenses 4-17
4.2.3 Other Ownership Considerations 4-22
4.3 Consumer-Related Social Benefits and Costs 4-24
4.3.1 Vehicle Technology Cost Impacts 4-24
4.3.2 Value of Rebound Driving 4-24
4.3.3 Fuel Consumption 4-25
4.3.4 Monetized Fuel Savings 4-26
4.3.5 Costs Associated with the Time Spent Refueling 4-27
4.3.6 Maintenance and Repair Costs 4-32
4.3.6.1 Maintenance Costs 4-32
4.3.6.2 Repair Costs 4-35
4.3.7 Costs Associated with Noise and Congestion 4-37
4.4 New Vehicle Sales 4-38
4.4.1 How Sales Impacts Were Modeled 4-41
4.4.1.1 The Role of Fuel Consumption in Vehicle Sales Estimates 4-41
4.4.1.2 Elasticity of Demand 4-42
4.4.2 New LD Vehicle Sales Estimates 4-43
4.5 Employment 4-46
4.5.1 Background and Literature 4-46
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4.5.2 Potential Employment Impacts from the Increasing Penetration of Electric Vehicles. 4-
48
4.5.3 Potential Employment Impacts of the Proposed Standards 4-50
4.5.3.1 The Factor Shift Effect 4-50
4.5.3.2 The Demand Effect 4-51
4.5.3.3 The Cost Effect 4-51
4.5.4 Partial Employment Effects of the Proposed Standards 4-54
4.5.4.1 Partial Employment Effects of the Alterative Scenarios 4-58
4.5.5 Employment Impacts on Related Sectors 4-59
Chapter 5: Electric Infrastructure Impacts 5-1
5.1 Modeling PEV Charge Demand and Regional Distribution 5-1
5.1.1 PEV Disaggregation and Charging Simulation 5-2
5.2 Electric Power Sector Modeling 5-8
5.2.1 Estimating Retail Electricity Prices 5-9
5.2.2 IPM emissions post-processing 5-9
5.2.3 IPM National-level Demand, Generation, Emissions and Costs 5-9
5.2.4 Retail Price Modeling Results 5-15
5.2.5 New Builds, Retrofits and Retirements of EGUs 5-18
5.2.6 Interregional Dispatch 5-21
5.3 Assessment of PEV Charging Infrastructure 5-22
5.3.1 Status and Outlook for PEV Charging Infrastructure 5-22
5.3.1.1 Definitions 5-22
5.3.1.2 Charging Types 5-22
5.3.1.2.1 PEV Charging Infrastructure Status and Trends 5-23
5.3.1.3 PEV Charging Infrastructure Investments 5-24
5.3.1.3.1 Bipartisan Infrastructure Law 5-25
5.3.1.3.2 Inflation Reduction Act 5-26
5.3.1.3.3 Equity Considerations in BIL and IRA 5-26
5.3.1.3.4 Other Public and Private Investments 5-26
5.3.2 PEV Charging Infrastructure Cost Analysis 5-28
5.3.2.1 Charging Demand Projections 5-28
5.3.2.2 EVSE Port Counts 5-29
5.3.2.3 Hardware & Installation Costs 5-32
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5.3.2.3.1 Home Charging Ports 5-33
5.3.2.3.2 Work and Public Level 2 Charging Ports 5-33
5.3.2.3.3 Public DC Fast Charging Ports 5-34
5.3.2.4 Will Costs Change Over Time? 5-34
5.3.2.5 Other Considerations 5-34
5.3.2.6 PEV Charging Infrastructure Cost Summary 5-36
5.4 Grid Resiliency 5-37
Chapter 6: [RESERVED] 6-1
Chapter 7: Health and welfare impacts 7-1
7.1 Climate Change Impacts from GHG Emissions 7-1
7.2 Health Effects Associated with Exposure to Criteria and Air Toxics Pollutants 7-2
7.2.1 Ozone 7-3
7.2.1.1 Background on Ozone 7-3
7.2.1.2 Health Effects Associated with Exposure to Ozone 7-4
7.2.2 Particulate Matter 7-5
7.2.2.1 Background on Particulate Matter 7-5
7.2.2.2 Health Effects Associated with Exposure to Particulate Matter 7-6
7.2.3 Nitrogen Oxides 7-10
7.2.3.1 Background on Nitrogen Oxides 7-10
7.2.3.2 Heath Effects Associated with Exposure to Nitrogen Oxides 7-10
7.2.4 Sulfur Oxides 7-11
7.2.4.1 Background on Sulfur Oxides 7-11
7.2.4.2 Health Effects Associated with Exposure to Sulfur Oxides 7-11
7.2.5 Carbon Monoxide 7-12
7.2.5.1 Background on Carbon Monoxide 7-12
7.2.5.2 Health Effects Associated with Exposure to Carbon Monoxide 7-12
7.2.6 Diesel Exhaust 7-13
7.2.6.1 Background on Diesel Exhaust 7-13
7.2.6.2 Health Effects Associated with Exposure to Diesel Exhaust 7-14
7.2.7 Air Toxics 7-15
7.2.7.1 Health Effects Associated with Exposure to Benzene 7-15
7.2.7.2 Health Effects Associated with Exposure to 1,3-Butadiene 7-16
7.2.7.3 Health Effects Associated with Exposure to Formaldehyde 7-17
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12.1 A Health Effects Associated with Exposure to Acetaldehyde 7-18
7.2.7.5 Health Effects Associated with Exposure to Naphthalene 7-18
7.2.7.6 Health Effects Associated with Exposure to Acrolein 7-19
7.2.7.7 Health Effects Associated with Exposure to Ethylbenzene 7-20
7.2.7.8 Health Effects Associated with Exposure to PAHs/POM 7-20
7.2.8 Exposure and Health Effects Associated with Traffic 7-21
7.3 Welfare Effects Associated with Exposure to Criteria and Air Toxics Pollutants 7-24
7.3.1 Visibility Degradation 7-24
7.3.1.1 Visibility Monitoring 7-26
7.3.2 Plant and Ecosystem Effects of Ozone 7-27
7.3.3 Deposition 7-28
7.3.3.1 Deposition of Nitrogen and Sulfur 7-28
7.3.3.1.1 Ecological Effects of Acidification 7-29
7.3.3.1.1.1 Aquatic Acidification 7-29
7.3.3.1.1.2 Terrestrial Acidification 7-30
7.3.3.1.2 Ecological Effects from Nitrogen Enrichment 7-30
7.3.3.1.2.1 Aquatic Enrichment 7-30
7.3.3.1.2.2 Terrestrial Enrichment 7-31
7.3.3.1.3 Vegetation Effects Associated with Gaseous Sulfur Dioxide, Nitric Oxide,
Nitrogen Dioxide, Peroxyacetyl Nitrate, and Nitric Acid 7-31
7.3.3.1.4 Mercury Methylation 7-32
7.3.3.2 Deposition of Metallic and Organic Constituents of PM 7-32
7.3.3.3 Materials Damage and Soiling 7-34
7.3.4 Welfare Effects of Air Toxics 7-35
7.4 Criteria Pollutant Human Health Benefits 7-35
7.4.1 Approach to Estimating Human Health Benefits 7-36
7.4.2 Estimating PM2.5-attributable Adult Premature Death 7-39
7.4.3 Economic Value of Health Benefits 7-40
7.4.4 Dollar Value per Ton of Directly-Emitted PM2.5 and PM2.5 Precursors 7-41
7.4.5 Characterizing Uncertainty in the Estimated Benefits 7-43
7.4.6 Benefit-per-Ton Estimate Limitations 7-44
Chapter 8: Illustrative Analysis of Air Quality Impacts of a Light- and Medium-Duty Vehicles
Regulatory Scenario 8-1
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8.1 Current Air Quality 8-1
8.1.1 PM2.5 Concentrations 8-1
8.1.2 Ozone Concentrations 8-2
8.1.3 NO2 Concentrations 8-4
8.1.4 SO2 Concentrations 8-4
8.1.5 CO Concentrations 8-4
8.1.6 Air Toxics Concentrations 8-5
8.1.7 Deposition 8-5
8.2 Emissions Modeling for Illustrative Air Quality Analysis 8-5
8.2.1 Onroad Vehicle Emission Estimates with MOVES 8-6
8.2.1.1 Overview 8-6
8.2.1.2 MOVES version used for air quality modeling 8-6
8.2.1.3 Modeling the Regulatory Case with MOVES 8-7
8.2.1.3.1 EV sales and stock 8-7
8.2.1.3.2 ICEV Energy Consumption 8-7
8.2.1.3.3 ICEV NMOG and NOx rates 8-7
8.2.1.3.4 ICEV PM rates 8-7
8.2.1.3.4.1 PM emission reduction fractions 8-8
8.2.1.3.4.2 PM reduction phase-in 8-9
8.2.1.3.4.3 PM update for LEV rates 8-9
8.2.2 Upstream Emission Estimates for AQ Modeling 8-9
8.2.2.1 Electricity Generating Units (EGUs) 8-10
8.2.2.2 Refineries 8-11
8.2.2.3 Crude Production Well Sites and Pipeline Pumps 8-12
8.2.2.4 Natural Gas Production Well Sites and Pipeline Pumps 8-13
8.2.2.5 Limitations of the Upstream Inventory 8-14
8.2.3 Combined Onroad and Upstream Emission Impacts 8-14
8.3 Air Quality Modeling Methodology 8-15
8.3.1 Air Quality Model 8-15
8.3.2 Model Domain and Configuration 8-15
8.3.3 Model Inputs 8-18
8.3.4 Model Evaluation 8-19
8.3.5 Model Simulation Scenarios 8-19
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8.4 Results of Illustrative Air Quality Analysis 8-20
8.4.1 PM2.5 8-21
8.4.2 Ozone 8-22
8.4.3 NO; 8-24
8.4.4 SO2 8-26
8.4.5 Air Toxics 8-27
8.4.6 Deposition 8-32
8.5 Illustrative Ozone and Particulate Matter Health Benefits 8-34
Chapter 9: OMEGA Physical Effects of the Proposed Standards and Alternatives 9-1
9.1 The OMEGA "Context" 9-1
9.2 The Analysis Fleet and the Legacy Fleet 9-1
9.3 Estimating Vehicle, Fleet and Rebound VMT 9-3
9.3.1 OMEGA "Context" VMT 9-4
9.3.2 Context Fuel Costs Per Mile 9-5
9.3.3 Rebound VMT 9-5
9.3.4 Summary of VMT in the Analysis 9-6
9.4 Estimating Safety Effects 9-7
9.4.1 Fatality Rates used in OMEGA 9-8
9.4.2 Calculating Safety Effects tied to Vehicle Weight Changes 9-9
9.4.3 Calculating Fatalities 9-11
9.4.4 Summary of Safety Effects in the Analysis 9-12
9.5 Estimating Fuel Consumption in OMEGA 9-13
9.5.1 Drive Cycles for Onroad Fuel Consumption 9-13
9.5.2 Electricity Consumption 9-20
9.5.3 Liquid-Fuel Consumption 9-21
9.5.4 Summary of Fuel Consumption in the Analysis 9-22
9.6 Estimating Emission Inventories in OMEGA 9-25
9.6.1 Calculating EGU Emission Rates in OMEGA 9-26
9.6.2 Calculating Refinery Emission Rates in OMEGA 9-28
9.6.3 Vehicle Emission Rates in OMEGA 9-29
9.6.4 Calculating Upstream Emission Inventories 9-32
9.6.5 Calculating Vehicle Emission Inventories 9-33
9.6.6 Summary of Inventories and Inventory Impacts 9-34
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9.6.6.1 Greenhouse Gas Inventory Impacts 9-34
9.6.6.2 Criteria Air Pollutant Inventory Impacts 9-42
9.7 Estimating Energy Security Effects 9-53
9.7.1 Calculating Oil Consumption from Fuel Consumption 9-53
9.7.2 Calculating Oil Imports from Oil Consumption 9-54
9.7.3 Summary of Energy Security Effects 9-55
Chapter 10: Costs and Benefits of the Proposed Standards in OMEGA 10-1
10.1 Costs 10-1
10.2 Fuel Savings 10-3
10.3 Non-Emission Benefits 10-5
10.4 Climate Benefits 10-8
10.5 Criteria Air Pollutant Benefits 10-24
10.6 Summary and Net Benefits 10-30
10.7 Transfers 10-33
Chapter 11: Energy Security Impacts 11-1
11.1 Review of Historical Energy Security Literature 11-2
11.2 Review of Recent Energy Security Literature 11-4
11.2.1 Recent Oil Security Studies 11-4
11.2.2 Recent Tight (i.e., Shale) Oil Studies 11-6
11.2.3 Recent Electricity Security Studies 11-10
11.2.3.1 Fuel Costs 11-11
11.2.3.2 Fuel Price Stability/Volatility 11-13
11.2.3.3 Electricity Reliability/Resiliency 11-13
11.2.3.4 Energy Independence 11-17
11.3 Electricity Security Impacts 11-18
11.3.1 Recent Fuel Costs for Gasoline-Powered Vehicles Compared to PEVs in the U.S. 11-
18
11.3.1.1 National (i.e., U.S.) Analysis 11-18
11.3.1.2 State-Level Analysis 11-20
11.3.2 Fuel Price Stability/Volatility 11-21
11.3.3 Energy Independence 11-23
11.4 Oil Security Impacts 11-25
11.4.1 U.S. Oil Import Reductions 11-25
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11.4.2 Oil Security Premiums Used for this Proposed Rule 11-26
11.4.3 Cost of Existing U.S. Oil Security Policies 11-30
11.4.4 Oil Security Benefits of Proposed Rule 11-32
Chapter 12: Small Business Flexibilities 12-1
Chapter 13: Compliance Effects 13-1
13.1 Light-Duty Vehicles 13-1
13.1.1 GHG Targets and Compliance Levels 13-1
13.1.1.1 CO; g/mi 13-1
13.1.1.1.1 Proposed standards 13-1
13.1.1.1.2 Alternative 1 13-4
13.1.1.1.3 Alternative 2 13-6
13.1.1.1.4 Alternative 3 13-8
13.1.1.2 CO; Ylg 13-10
13.1.1.2.1 Proposed standards 13-10
13.1.1.2.2 Alternative 1 13-14
13.1.1.2.3 Alternative 2 13-17
13.1.1.2.4 Alternative 3 13-21
13.1.2 Projected Manufacturing Costs per Vehicle 13-24
13.1.2.1 Proposed GHG Standards 13-25
13.1.2.2 Alternative 1 13-27
13.1.2.3 Alternative 2 13-29
13.1.2.4 Alternative 3 13-31
13.1.3 Technology Penetration Rates 13-33
13.1.3.1 No Action Case 13-33
13.1.3.2 Proposal 13-36
13.1.3.3 Alternative 1 13-38
13.1.3.4 Alternative 2 13-40
13.1.3.5 Alternative 3 13-42
13.1.4 Light-Duty Vehicle Sensitivities 13-44
13.1.4.1 State-level ZEVPolicies (ACC II) 13-44
13.1.4.2 Battery Costs 13-45
13.1.4.2.1 Low Battery Costs 13-45
13.1.4.2.2 High Battery Costs 13-46
xiv
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13.1.4.3 Consumer Acceptance 13-47
13.1.4.3.1 Faster BEV Acceptance 13-47
13.1.4.3.2 Slower BEV Acceptance 13-48
13.2 Medium-Duty Vehicles 13-49
13.2.1 GHG Targets and Compliance Levels 13-49
13.2.1.1 CO; g/mi 13-49
13.2.1.1.1 Proposed GHG standards 13-49
13.2.1.2 CO; Ylg 13-50
13.2.1.2.1 Proposed standards 13-50
13.2.2 Projected Manufacturing Costs per Vehicle 13-52
13.2.2.1 Proposed Standards 13-52
13.2.3 Technology Penetration Rates 13-53
13.2.3.1 No Action Case 13-53
13.2.3.2 Proposal 13-53
13.2.4 Medium-Duty Vehicle Sensitivities 13-54
13.2.4.1 Low Battery Costs 13-54
13.2.4.2 High Battery Costs 13-54
xv
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List of Tables
Table 1: Projected GHG emission impacts in 2055 from the proposed rule, light-duty and
medium-duty (Million metric tons) xliv
Table 2: Projected cumulative GHG emission impacts through 2055 from the proposed rule,
light-duty and medium-duty (Million metric tons) xliv
Table 3: Projected criteria air pollutant impacts in 2055 from the proposed rule, light-duty and
medium-duty (US tons) xliv
Table 4: Projected air toxic impacts from vehicles in 2055 from the proposed rule, light-duty and
medium-duty (US tons) xlv
Table 5: Monetized discounted costs, benefits, and net benefits of the proposed program for
calendar years 2027 through 2055, light-duty and medium-duty (Billions of 2020 dollars).^
xlvii
Table 6: Average incremental vehicle cost by reg class, relative to the No Action scenario (2020
dollars) xlviii
Table 7: Comparison of proposed car standards to alternatives xlviii
Table 8: Comparison of proposed truck standards to alternatives xlix
Table 9: Comparison of proposed combined fleet standards to alternatives xlix
Table 10: Combined fleet year-over-year decreases for proposed standards and alternatives 1
Table 11: Comparison of projected incremental per-vehicle costs relative to the No Action
scenario 1
Table 12: Projected GHG emission impacts in 2055 from the proposed rule, light-duty and
medium-duty (Million metric tons) li
Table 13: Projected cumulative GHG emission impacts through 2055 from the proposed rule,
light-duty and medium-duty (Million metric tons) li
Table 14: Projected criteria air pollutant impacts in 2055 from the proposed rule, light-duty and
medium-duty (US tons) lii
Table 15: Projected air toxic impacts from vehicles in 2055 from the proposed rule, light-duty
and medium-duty (US tons) lii
Table 16: Monetized discounted costs, benefits, and net benefits of Alternative 1 for calendar
years 2027 through 2055, light-duty and medium-duty (Billions of 2020 dollars)a'b'c liii
Table 17: Monetized discounted costs, benefits, and net benefits of Alternative 2 for calendar
years 2027 through 2055, light-duty and medium-duty (Billions of 2020 dollars)a'b'c liv
Table 18: Monetized discounted costs, benefits, and net benefits of Alternative 3 for calendar
years 2027 through 2055, light-duty and medium-duty (Billions of 2020 dollars)a'b'c lv
Table 1-1. Comparison of Car and Truck GHG Targets for 50 Square-Foot Vehicles 1-3
xvi
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Table 1-2: Comparison of MY 2032 Footprint to Base Year Footprint, Proposed Standards... 1-14
Table 1-3: Proposed Coefficients for MDV Target GHG Standards 1-21
Table 1-4. Battery durability performance requirements of UN GTRNo. 22 1-24
Table 1-5. CARB ACC II battery durability requirements 1-25
Table 1-6. CARB battery warranty requirements 1-25
Table 2-1: Percentage breakdown of mild and strong hybrids in the MY 2019 vehicle fleet.. 2-13
Table 2-2: Engine ALPHA input maps used to create ALPHA outputs for RSEs 2-18
Table 2-3: Electric motor/related ALPHA input maps for electrified vehicles used to create
ALPHA outputs for RSEs 2-18
Table 2-4: Transmission ALPHA inputs used to create ALPHA outputs for RSEs 2-19
Table 2-5: Battery ALPHA inputs used to create ALPHA outputs for RSEs 2-20
Table 2-6: Table of test data vehicles used to validate ALPHA 2-22
Table 2-7: Percent difference of ALPHA vehicle validation simulation versus benchmarking test
data 2-23
Table 2-8: Percent difference of variant vehicle ALPHA simulations versus certification data. 2-
25
Table 2-9 Estimated CO2 reductions with P0 mild hybrid & start-stop technology applied to the
comparable conventional vehicle 2-26
Table 2-10: Powertrain components and categories 2-27
Table 2-11: Vehicle parameters 2-27
Table 2-12: Electrified model assignments 2-28
Table 2-13: Assignments of engines used to simulate MY 2019 base year fleet conventional
vehicle model types, based on engines in Table 2-2 2-29
Table 2-14: Transmissions used to simulate MY 2019 base year fleet conventional vehicles,
based on transmissions given in Table 2-4 2-29
Table 2-15 Conventional vehicle model type ALPHA CO2 grams/mile values versus
certification CO2 gpm (2019 fleet) 2-29
Table 2-16 P0 ALPHA CO2 grams/mile values versus certification CO2 grams/mile (2019 fleet)
2-30
Table 2-17 PowerSplit ALPHA CO2 grams/mile values versus certification CO2 grams/mile
(2019 fleet) 2-32
Table 2-18 P2 ALPHA CO2 grams/mile values versus certification CO2 grams/mile (2019 Fleet)
2-32
xvii
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Table 2-19 BEV ALPHA kWh/100 mi values versus certification kWh/100 mi 2-33
Table 2-20: Details of ALPHA 3.0 models peer reviewed 2-35
Table 2-21: Technology packages for LDV/LDT RSEs 2-37
Table 2-22 Engine displacements used in RSE construction 2-39
Table 2-23 - Sample results 2-41
Table 2-24 - Tabular results 2-41
Table 2-25: Munro Teardown Reports Used in the Analysis 2-56
Table 2-26 Learning Factors Applied in OMEGA, Indexed to 2022a 2-60
Table 2-27 Retail Price Equivalent Factors in the Heavy-Duty and Light-Duty Industries
(Rogozhin 2009) 2-61
Table 2-28: ICE Powertrain Cost in OMEGA 2-63
Table 2-30: Cost per Cylinder and Cost per Liter in OMEGA 2-64
Table 2-31: Gasoline Direct Injection System Cost in OMEGA 2-64
Table 2-32: Turbocharging Costs in OMEGA 2-65
Table 2-33: Cylinder Deactivation Costs in OMEGA 2-66
Table 2-34: Atkinson Cycle Engine Costs in OMEGA 2-67
Table 2-35: Transmission Costs in OMEGA 2-67
Table 2-36: Start-stop System Costs in OMEGA 2-68
Table 2-37: HEV & MHEV Non-Battery Costs in OMEGA 2-70
Table 2-38: PMSM Motors Described in Munro Reports 2-71
Table 2-39: Induction Motors Described in Munro Reports 2-72
Table 2-40 BEV Non-battery Powertrain Costs in OMEGA 2-74
Table 2-41: Potential PHEV Non-battery/Non-ICE Powertrain Costs 2-77
Table 2-42: Potential PHEV ICE Costs 2-78
Table 2-43 Air Conditioning System Costs in OMEGA 2-79
Table 2-44 Glider Costs in OMEGA 2-81
Table 2-45 Mass Calculations in OMEGA 2-82
Table 2-46: MY 2032 Vehicles: Year of Last Redesign 2-83
Table 2-47: Industry Maximum Battery Production Limits (GWh), by Model Year 2-84
Table 2-48 AEO2021 Fuel Prices Used in OMEGA (2020 dollars) 2-85
xviii
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Table 2-49: Gross domestic product implicit price deflators 2-86
Table 2-50: IRA Battery Production Tax Credits in OMEGA 2-87
Table 2-51: IRS 30D and 45W Clean Vehicle Credit in OMEGA 2-87
Table 3-1: Percentage of MY2020 sales and sales volumes of pickup, van, and incomplete
MDVs by fuel type 3-10
Table 3-2: All combinations of criteria pollutant phase-in scenarios available to manufacturers213-
31
Table 3-3: LDV, LDT, MDPV and MDV fleet average, chassis dynamometer FTP NMOG+NOx
standards under the early compliance pathway 3-34
Table 3-4: LDV, LDT, MDPV and MDV fleet average, chassis dynamometer FTP NMOG+NOx
standards under the default compliance pathway 3-34
Table 3-5: Proposed LDV, LDT, MDPV and MDV":" NMOG+NOx bin structure 3-35
Table 3-6: LDV, LDT* and MDPV NMOG+NOx NMOG+NOx fleet average FTP standards .. 3-
37
Table 3-7: LDV, LDT* and MDPV* NMOG+NOx fleet average FTP standards 3-37
Table 3-8: MDV fleet average NMOG+NOx standards under the early compliance pathway!... 3-
38
Table 3-9: MDV fleet average chassis dynamometer FTP NMOG+NOx standards under the
default compliance pathway* 3-38
Table 3-10: Propsed light-duty PM standards 3-40
Table 3-11: Proposed PM standards for MDV at or below 22,000 pounds GCWR 3-40
Table 3-12: Light-duty CO and HCHO standards 3-40
Table 3-13: CO and HCHO standards for MDV at or below 22,000 pounds GCWR 3-40
Table 3-14: Examples of NMOG+NOx cert emissions 3-41
Table 3-15: Light-Duty Gasoline Vehicles - 7C FTP Emissions (mg/mi) 3-42
Table 3-16: Light-Duty Diesel Vehicles -7C FTP Emissions 3-42
Table 3-17: Comparison of FTP, HFET, SC03, US06 cert test results for LD vehicles 3-43
Table 3-18: SET Operation Mode Power Comparison 3-47
Table 3-19: Vehicle and GPF specifications 3-63
Table 3-20: Change in measured C02 emissions for each test cycle when GPFs are added,
averaged across four test vehicles (2022 F250, 2021 F150 HEV, 2019 F150, 2011 F150).3-65
Table 3-21: ORVR Specifications and Assumptions used in the Cost Analysis for Incomplete
MDVs ( 8501 lbs to 14,000 lb GVWR) 3-72
xix
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Table 3-22: Estimated Direct Manufacturing Costs for ORVR Over Tier 3 as Baseline 3-72
Table 3-23: CO2 Emissions [g/mi] Calculated using Existing FUF and Proposed FUF 3-80
Table 4-1: Consumer generalized cost inputs 4-3
Table 4-2: Central case shareweight values by body style for light-duty 4-6
Table 4-3: Faster BEV acceptance shareweight values by body style for light-duty 4-10
Table 4-4: Slower BEV acceptance shareweight values by body style for light-duty 4-12
Table 4-5: Recent scientific studies of eVMT 4-14
Table 4-6: Recent scientific studies of eVMT rebound 4-17
Table 4-7: National per vehicle ownership savings and expenses for new model year 2032
vehicles under the proposed standards (2020 dollars) 4-20
Table 4-8: Estimated average savings over the first 8 years of vehicle life when MY 2032 BEV
purchased instead of ICE vehicle (2020 dollars) 4-21
Table 4-9: Vehicle technology costs, light-duty and medium-duty (billions of 2020 dollars)..4-24
Table 4-10: Liquid-fuel consumption impacts, light-duty and medium-duty (billion gallons). 4-25
Table 4-11 Electricity consumption impacts, light-duty and medium-duty (terawatt hours)....4-26
Table 4-12: Retail fuel expenditure savings, light-duty and medium-duty (billions of 2020
dollars)* 4-27
Table 4-13: BEV recharging thresholds by body style and range 4-30
Table 4-14: Curve coefficients used to estimate charge frequency and share charged 4-31
Table 4-15: Maintenance service schedule by powertrain 4-34
Table 4-16: Repair cost per mile coefficient valuesa 4-36
Table 4-17: Costs associated with congestion and noise (2018 dollars per vehicle mile) 4-38
Table 4-18: LD sales impacts in the Proposal scenario 4-44
Table 4-19: LD sales impacts in the alternative scenarios 4-44
Table 4-20: Sectors and associated workers per million dollars in expenditures used in this
analysis 4-55
Table 4-21: Estimated partial employment effects in job-years for BEV and ICE sectors, sectors
common to BEV and ICE, and the net minimum and maximum across all sectors 4-57
Table 4-22: Estimated partial employment effects in job-years for BEV and ICE sectors, sectors
common to BEV and ICE, and the net minimum and maximum across all sectors for
Alternative 1 (-10) 4-58
xx
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Table 4-23: Estimated partial employment effects in job-years for BEV and ICE sectors, sectors
common to BEV and ICE, and the net minimum and maximum across all sectors for
Alternative 2 (+10) 4-58
Table 4-24: Estimated partial employment effects in job-years for BEV and ICE sectors, sectors
common to BEV and ICE, and the net minimum and maximum across all sectors for
Alternative 3 (Linear) 4-58
Table 5-1: Representative PEV examples for charging simulations 5-4
Table 5-2: National electric power sector emissions, demand, generation and cost for the no-
action case 5-14
Table 5-3: National electric power sector emissions, demand, generation and cost for the
proposal 5-14
Table 5-4: National Energy Modeling System's Electricity Market Module regions (U.S. Energy
Information Administraton 2019) 5-16
Table 5-5: Average retail electricity price by region for the proposal and a no-action case in 2030
and 2050 compared to AEO2021 5-17
Table 5-6: Newly modeled EGU capacity for the no-action case 5-19
Table 5-7:Newly modeled EGU capacity for the proposal 5-19
Table 5-8: EGU retirements for the no-action case 5-20
Table 5-9: EGU retirements for the proposal 5-20
Table 5-10: Incremental EGU retirements comparing the proposal to the no-action case 5-20
Table 5-11: Incremental new EGU capacity comparing the proposal to the no-action case
[Cumulative GW] 5-20
Table 5-12: IPM results for net export of electricity into the contiguous United States for the no-
action case.*'1' 5-21
Table 5-13: IPM results for net export of electricity into the contiguous United States for the
proposal.*'1' 5-21
Table 5-14: Cost (hardware and installation) per EVSE port 5-32
Table 5-15: EVSE costs for the proposal relative to no-action case (billions of 2020 dollars). 5-36
Table 7-1 Human Health Effects of PM2.5 7-38
Table 7-2 PM2.5-related Benefit Per Ton values (2020$) associated with the reduction of NOx,
SO2 and directly emitted PM2.5 emissions for (A) Onroad light-duty gasoline cars, (B)
Onroad light-duty gasoline trucks, (C) Onroad light-duty diesel cars/trucks, (D) Electricity
Generating Units, and (E) Refineries 7-42
Table 7-3: Unquantified Health and Welfare Benefits Categories 7-45
Table 8-1: PM reduction by MOVES operating mode 8-9
xxi
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Table 8-2: PM control fraction by MOVES reg class and model year 8-9
Table 8-3: Total upstream emissions increases in LMDV regulatory scenario in 2055 8-10
Table 8-4: Total upstream emission increases in 2055 assuming no change in refinery emissions
8-10
Table 8-5: EGU emissions increases in AQM inventories in 2055 8-11
Table 8-6: Adjustment factors to apply to 2050 refinery inventory 8-12
Table 8-7: Refinery emissions decreases in AQM inventories in 2055 8-12
Table 8-8: Adjustment factors to apply to 2050 crude production well and pipeline pump
inventory 8-12
Table 8-9: Crude production well site and pipeline pump decreases in AQM inventories in 2055
8-13
Table 8-10: Adjustment factors to apply to 2050 natural gas production well site and pipeline
pump inventory 8-13
Table 8-11: Natural gas production well and pipeline pump increases in AQM inventories in
2055 8-13
Table 8-12: Net impacts21 on criteria pollutant emissions from the LMDV regulatory scenario8-14
Table 8-13: Geographic elements of domains used in air quality modeling 8-15
Table 8-14: Vertical layer structure for CMAQ domain 8-17
Table 8-15: Health effects of ambient ozone and PM2.5 8-35
Table 8-16: Quantified and monetized avoided PM2.5-related premature mortalities and illnesses
of the illustrative scenario in 2055 (95% confidence interval)21 8-37
Table 8-17: Quantified and monetized avoided ozone-related premature mortalities and illnesses
of the illustrative scenario in 2055 (95% confidence interval)21 8-38
Table 8-18: Total PM2.5 and ozone benefits of the illustrative scenario in 2055 (95% confidence
interval, billions of 2020 dollars)a'b 8-39
Table 9-1 Mileage accumulation and re-registration rates used for light-duty 9-3
Table 9-2 Mileage accumulation and re-registration rates used for medium-duty 9-4
Table 9-3 VMT summary, light-duty and medium-duty (billion miles) 9-6
Table 9-4 Rebound VMT relative to no action, light-duty and medium-duty (billion miles) 9-6
Table 9-5 Safety values used in OMEGA (2022 CAFE I RI A 2022) 9-9
Table 9-6 Fatalities per year, light-duty and medium-duty 9-12
Table 9-7 Fatality rate impacts, light-duty and medium-duty (fatalities per billion miles) 9-12
Table 9-8 Fuel consumption impacts, proposed standards, light-duty and medium-duty 9-22
XXll
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Table 9-9 Fuel consumption impacts, Alternative 1 standards, light-duty and medium-duty... 9-23
Table 9-10 Fuel consumption impacts, Alternative 2 standards, light-duty and medium-duty. 9-24
Table 9-11 Fuel consumption impacts, Alternative 3 standards, light-duty and medium-duty. 9-25
Table 9-12 Select EGU emission rate curves used in OMEGA 9-26
Table 9-13 Refinery emissions in AQM inventories in 2055 9-28
Table 9-14 Refinery emission rates estimated using AQM results 9-28
Table 9-15 Refinery emission rate curves used in OMEGA 9-28
Table 9-16 Pollutants for which vehicle emission rate curves were generated for use in OMEGA
9-30
Table 9-17 Exhaust PM2.5 emission rates, cars, grams/mile 9-31
Table 9-18 Exhaust PM2.5 emission rates, light-duty trucks, grams/mile 9-31
Table 9-19 Exhaust PM2.5 emission rates, medium-duty vans, grams/mile 9-31
Table 9-20 Exhaust PM2.5 emission rates, medium-duty pickups, grams/mile 9-32
Table 9-21 Greenhouse gas emission inventory impacts, Proposed standards, light-duty and
medium-duty (million metric tons) * 9-34
Table 9-22 Greenhouse gas emission inventory impacts, Alternative 1 standards, light-duty and
medium-duty (million metric tons) * 9-35
Table 9-23 Greenhouse gas emission inventory impacts, Alternative 2 standards, light-duty and
medium-duty (million metric tons) * 9-36
Table 9-24 Greenhouse gas emission inventory impacts, Alternative 3 standards, light-duty and
medium-duty (million metric tons) * 9-37
Table 9-25 Net Greenhouse gas emission inventory impacts, Proposed standards, light-duty and
medium-duty * 9-38
Table 9-26 Net Greenhouse gas emission inventory impacts, Alternative 1 standards, light-duty
and medium-duty
and medium-duty
and medium-duty
.9-39
Table 9-27 Net Greenhouse gas emission inventory impacts, Alternative 2 standards, light-duty
.9-40
Table 9-28 Net Greenhouse gas emission inventory impacts, Alternative 3 standards, light-duty
.9-41
Table 9-29 Criteria air pollutant impacts from vehicles, Proposed standards, light-duty and
medium-duty 9-42
Table 9-30 Criteria air pollutant impacts from vehicles, Alternative 1 standards, light-duty and
medium-duty 9-43
XXlll
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Table 9-31 Criteria air pollutant impacts from vehicles, Alternative 2 standards, light-duty and
medium-duty.
Table 9-32 Criteria air pollutant impacts from vehicles, Alternative 3 standards, light-duty and
medium-duty.
Table 9-33 Criteria air pollutant impacts from EGUs and refineries, Proposed standards, light-
duty and medium-duty.
.9-44
.9-45
.9-46
Table 9-34 Criteria air pollutant impacts from EGUs and refineries, Alternative 1 standards,
light-duty and medium-duty 9-47
Table 9-35 Criteria air pollutant impacts from EGUs and refineries, Alternative 2 standards,
light-duty and medium-duty 9-48
Table 9-36 Criteria air pollutant impacts from EGUs and refineries, Alternative 3 standards,
light-duty and medium-duty 9-49
Table 9-37 Net criteria air pollutant impacts from vehicles, EGUs and refineries, Proposed
standards, light-duty and medium-duty * 9-50
Table 9-38 Net criteria air pollutant impacts from vehicles, EGUs and refineries, Alternative 1
standards, light-duty and medium-duty * 9-51
Table 9-39 Net criteria air pollutant impacts from vehicles, EGUs and refineries, Alternative 2
standards, light-duty and medium-duty * 9-52
Table 9-40 Net criteria air pollutant impacts from vehicles, EGUs and refineries, Alternative 3
standards, light-duty and medium-duty * 9-53
Table 9-41 Parameters used in estimating oil import impacts 9-54
Table 9-42 Impacts on oil consumption and oil imports, Proposed standards, light-duty and
medium-duty (millions)
Table 9-43 Impacts on oil consumption and oil imports, Alternative 1 standards, light-duty and
medium-duty (millions).
medium-duty (millions).
medium-duty (millions).
.9-55
.9-56
Table 9-44 Impacts on oil consumption and oil imports, Alternative 2 standards, light-duty and
.9-57
Table 9-45 Impacts on oil consumption and oil imports, Alternative 3 standards, light-duty and
.9-58
Table 10-1 Costs associated with the Proposed standards, light-duty and medium-duty (billions
of 2020 dollars) 10-1
Table 10-2 Costs associated with Alternative 1, light-duty and medium-duty (billions of 2020
dollars) 10-2
Table 10-3 Costs associated with Alternative 2, light-duty and medium-duty (billions of 2020
dollars) 10-2
xxiv
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Table 10-4 Costs associated with Alternative 3, light-duty and medium-duty (billions of 2020
dollars) 10-3
Table 10-5 Pretax fuel savings and EVSE port costs associated with the Proposed standards,
light-duty and medium-duty (billions of 2020 dollars) * 10-4
Table 10-6 Pretax fuel savings and EVSE port costs associated with Alternative 1, light-duty and
medium-duty (billions of 2020 dollars) * 10-4
Table 10-7 Pretax fuel savings and EVSE port costs associated with Alternative 2, light-duty and
medium-duty (billions of 2020 dollars) * 10-5
Table 10-8 Pretax fuel savings and EVSE port costs associated with Alternative 3, light-duty and
medium-duty (billions of 2020 dollars) * 10-5
Table 10-9 Non-emission benefits associated with the Proposed standards, light-duty and
medium-duty (billions of 2020 dollars) * 10-6
Table 10-10 Non-emission benefits associated with Alternative 1, light-duty and medium-duty
(billions of 2020 dollars) * 10-6
Table 10-11 Non-emission benefits associated with Alternative 2, light-duty and medium-duty
(billions of 2020 dollars) * 10-7
Table 10-12 Non-emission benefits associated with Alternative 3, light-duty and medium-duty
(billions of 2020 dollars) * 10-7
Table 10-13 Interim Social Cost of Carbon Values, 2027-2055 (2020$/Metric Ton CO2) 10-14
Table 10-14 Interim Social Cost of Carbon Values, 2027-2055 (2020$/Metric Ton CH4) 10-15
Table 10-15 Interim Social Cost of Carbon Values, 2027-2055 (2020$/Metric TonN20) .... 10-16
Table 10-16 Climate benefits from reductions in GHG emissions associated with the Proposed
standards, light-duty and medium-duty (billions of 2020 dollars) 10-21
Table 10-17 Climate benefits from reductions in GHG emissions associated with Alternative 1,
light-duty and medium-duty (billions of 2020 dollars) 10-22
Table 10-18 Climate benefits from reductions in GHG emissions associated with Alternative 2,
light-duty and medium-duty (billions of 2020 dollars) 10-23
Table 10-19 Climate benefits from reductions in GHG emissions associated with Alternative 3,
light-duty and medium-duty (billions of 2020 dollars) 10-24
Table 10-20 Monetized PM2.5 health benefits of onroad and upstream emissions reductions
associated with the Proposed standards (billions of 2020 dollars) 10-26
Table 10-21 Monetized PM2.5 health benefits of onroad and upstream emissions reductions
associated with Alternative 1 (billions of 2020 dollars) 10-27
Table 10-22 Monetized PM2.5 health benefits of onroad and upstream emissions reductions
associated with Alternative 2 (billions of 2020 dollars) 10-28
xxv
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Table 10-23 Monetized PM2.5 health benefits of onroad and upstream emissions reductions
associated with Alternative 3 (billions of 2020 dollars) 10-28
Table 10-24 Summary of costs, fuel savings and benefits of the Proposal standards, light-duty
and medium-duty (billions of 2020 dollars)a'b'c 10-30
Table 10-25 Summary of costs, fuel savings and benefits of Alternative 1, light-duty and
medium-duty (billions of 2020 dollars)a'b'c 10-31
Table 10-26 Summary of costs, fuel savings and benefits of Alternative 2, light-duty and
medium-duty (billions of 2020 dollars)a'b'c 10-32
Table 10-27 Summary of costs, fuel savings and benefits of Alternative 3, light-duty and
medium-duty (billions of 2020 dollars)a'b'c 10-33
Table 10-28 Transfers associated with the Proposed standards, light-duty and medium-duty
(billions of 2020 dollars) 10-34
Table 10-29 Transfers associated with Alternative 1, light-duty and medium-duty (billions of
2020 dollars) 10-34
Table 10-30 Transfers associated with Alternative 2, light-duty and medium-duty (billions of
2020 dollars) 10-35
Table 10-31 Transfers associated with Alternative 3, light-duty and medium-duty (billions of
2020 dollars) 10-35
Table 11-1 Projected trends in U.S. crude oil exports/imports, net refined oil product exports, net
crude oil and refined petroleum product imports, oil consumption and U.S. oil import
reductions resulting from the proposed LMDV rule from 2027 to 2050 (MMBD)a 11-26
Table 11-2 Macroeconomic oil security premiums for 2027-2055 (2020$/barrel)a,b 11-30
Table 1 Small Entity Production from 2017 to 2021 12-2
Table 2 ICI Import Records 12-2
Table 13-1: Projected GHG Targets, Proposed Standards - Cars 13-2
Table 13-2: Projected GHG Targets, Proposed Standards - Trucks 13-2
Table 13-3: Achieved GHG Levels, Proposed Standards - Cars 13-3
Table 13-4: Achieved GHG Levels, Proposed Standards - Trucks 13-3
Table 13-5: Projected GHG Targets, Alternative 1 - Cars 13-4
Table 13-6: Projected GHG Targets, Alternative 1 - Trucks 13-4
Table 13-7: Achieved GHG Levels, Alternative 1 - Cars 13-5
Table 13-8: Achieved GHG Levels, Alternative 1 - Trucks 13-5
Table 13-9: Projected GHG Targets, Alternative 2 - Cars 13-6
Table 13-10: Projected GHG Targets, Alternative 2 - Trucks 13-6
xxvi
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Table 13-11: Achieved GHG Levels, Alternative 2 - Cars 13-7
Table 13-12: Achieved GHG Levels, Alternative 2 - Trucks 13-7
Table 13-13: Projected GHG Targets, Alternative 3 - Cars 13-8
Table 13-14: Projected GHG Targets, Alternative 3 - Trucks 13-8
Table 13-15: Achieved GHG Levels, Alternative 3 - Cars 13-9
Table 13-16: Achieved GHG Levels, Alternative 3 - Trucks 13-9
Table 13-17: Projected GHG Targets (Mg), Proposed Standards - Cars 13-10
Table 13-18: Projected GHG Targets (Mg), Proposed Standards - Trucks 13-11
Table 13-19: Projected GHG Targets (Mg), Proposed Standards - Combined 13-11
Table 13-20: Achieved GHG Levels (Mg), Proposed Standards - Cars 13-12
Table 13-21: Achieved GHG Levels (Mg), Proposed Standards - Trucks 13-12
Table 13-22: Achieved GHG Levels (Mg), Proposed Standards - Combined 13-13
Table 13-23: GHG Credits/Debits Earned (Mg), Proposed Standards - Combined 13-13
Table 13-24: Projected GHG Targets (Mg), Alternative 1 - Cars 13-14
Table 13-25: Projected GHG Targets (Mg), Alternative 1 - Trucks 13-14
Table 13-26: Projected GHG Targets (Mg), Alternative 1 - Combined 13-15
Table 13-27: Achieved GHG Levels (Mg), Alternative 1 - Cars 13-15
Table 13-28: Achieved GHG Levels (Mg), Alternative 1 - Trucks 13-16
Table 13-29: Achieved GHG Levels (Mg), Alternative 1 - Combined 13-16
Table 13-30: GHG Credits/Debits Earned (Mg), Alternative 1 - Combined 13-17
Table 13-31: Projected GHG Targets (Mg), Alternative 2 - Cars 13-17
Table 13-32: Projected GHG Targets (Mg), Alternative 2 - Trucks 13-18
Table 13-33: Projected GHG Targets (Mg), Alternative 2 - Combined 13-18
Table 13-34: Achieved GHG Levels (Mg), Alternative 2 - Cars 13-19
Table 13-35: Achieved GHG Levels (Mg), Alternative 2 - Trucks 13-19
Table 13-36: Achieved GHG Levels (Mg), Alternative 2 - Combined 13-20
Table 13-37: GHG Credits/Debits Earned (Mg), Alternative 2 - Combined 13-20
Table 13-38: Projected GHG Targets (Mg), Alternative 3 - Cars 13-21
Table 13-39: Projected GHG Targets (Mg), Alternative 3 - Trucks 13-21
Table 13-40: Projected GHG Targets (Mg), Alternative 3 - Combined 13-22
xxvii
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Table 13-41: Achieved GHG Levels (Mg), Alternative 3 - Cars 13-22
Table 13-42: Achieved GHG Levels (Mg), Alternative 3 - Trucks 13-23
Table 13-43: Achieved GHG Levels (Mg), Alternative 3 - Combined 13-23
Table 13-44: GHG Credits/Debits Earned (Mg), Alternative 3 - Combined 13-24
Table 13-45: Projected Manufacturing Costs Per Vehicle, Proposed Standards 13-25
Table 13-46: Projected Manufacturing Costs Per Vehicle, Proposed Standards (by Body Style)
13-25
Table 13-47: Projected Manufacturing Costs Per Vehicle, Proposed Standards - Cars 13-26
Table 13-48: Projected Manufacturing Costs Per Vehicle, Proposed Standards - Trucks 13-26
Table 13-49: Projected Manufacturing Costs Per Vehicle, Proposed Standards - Combined. 13-27
Table 13-50: Projected Manufacturing Costs Per Vehicle, Alternative 1 13-27
Table 13-51: Projected Manufacturing Costs Per Vehicle, Alternative 1 (by Body Style) 13-27
Table 13-52: Projected Manufacturing Costs Per Vehicle, Alternative 1 - Cars 13-28
Table 13-53: Projected Manufacturing Costs Per Vehicle, Alternative 1 - Trucks 13-28
Table 13-54: Projected Manufacturing Costs Per Vehicle, Alternative 1 - Combined 13-29
Table 13-55: Projected Manufacturing Costs Per Vehicle, Alternative 2 13-29
Table 13-56: Projected Manufacturing Costs Per Vehicle, Alternative 2 (by Body Style) 13-29
Table 13-57: Projected Manufacturing Costs Per Vehicle, Alternative 2 - Cars 13-30
Table 13-58: Projected Manufacturing Costs Per Vehicle, Alternative 2 - Trucks 13-30
Table 13-59: Projected Manufacturing Costs Per Vehicle, Alternative 2 - Combined 13-31
Table 13-60: Projected Manufacturing Costs Per Vehicle, Alternative 3 13-31
Table 13-61: Projected Manufacturing Costs Per Vehicle, Alternative 3 (by Body Style) 13-31
Table 13-62: Projected Manufacturing Costs Per Vehicle, Alternative 3 - Cars 13-32
Table 13-63: Projected Manufacturing Costs Per Vehicle, Alternative 3 - Trucks 13-32
Table 13-64: Projected Manufacturing Costs Per Vehicle, Alternative 3 - Combined 13-33
Table 13-65: Projected BEV Penetrations, No Action - Cars 13-34
Table 13-66: Projected BEV Penetrations, No Action - Trucks 13-34
Table 13-67: Projected BEV Penetrations, No Action - Combined 13-35
Table 13-68: Projected Strong HEV Penetrations, No Action 13-35
Table 13-69: Projected TURB12 Penetrations, No Action 13-35
xxviii
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Table 13-70: Projected ATK Penetrations, No Action 13-35
Table 13-71: Projected BEV Penetrations, Proposed Standards - Cars 13-36
Table 13-72: Projected BEV Penetrations, Proposed Standards - Trucks 13-36
Table 13-73: Projected BEV Penetrations, Proposed Standards - Combined 13-37
Table 13-74: Projected Strong HEV Penetrations, Proposed Standards 13-37
Table 13-75: Projected TURB12 Penetrations, Proposed Standards 13-37
Table 13-76: Projected ATK Penetrations, Proposed Standards 13-37
Table 13-77: Projected BEV Penetrations, Alternative 1 - Cars 13-38
Table 13-78: Projected BEV Penetrations, Alternative 1 - Trucks 13-38
Table 13-79: Projected BEV Penetrations, Alternative 1 - Combined 13-39
Table 13-80: Projected Strong HEV Penetrations, Alternative 1 13-39
Table 13-81: Projected TURB12 Penetrations, Alternative 1 13-39
Table 13-82: Projected ATK Penetrations, Alternative 1 13-39
Table 13-83: Projected BEV Penetrations, Alternative 2 - Cars 13-40
Table 13-84: Projected BEV Penetrations, Alternative 2 - Trucks 13-40
Table 13-85: Projected BEV Penetrations, Alternative 2 - Combined 13-41
Table 13-86: Projected Strong HEV Penetrations, Alternative 2 13-41
Table 13-87: Projected TURB12 Penetrations, Alternative 2 13-41
Table 13-88: Projected ATK Penetrations, Alternative 2 13-41
Table 13-89: Projected BEV Penetrations, Alternative 3 - Cars 13-42
Table 13-90: Projected BEV Penetrations, Alternative 3 - Trucks 13-42
Table 13-91: Projected BEV Penetrations, Alternative 3 - Combined 13-43
Table 13-92: Projected Strong HEV Penetrations, Alternative 3 13-43
Table 13-93: Projected TURB12 Penetrations, Alternative 3 13-43
Table 13-94: Projected ATK Penetrations, Alternative 3 13-43
Table 13-95: Projected targets with ACC II, for No Action case, proposed and alternatives (CO2
grams/mile) - cars and trucks combined 13-44
Table 13-96 13-44
Table 13-97: Projected achieved levels with ACC II, for No Action case, proposed and
alternatives (CO2 grams/mile) - cars and trucks combined 13-44
XXIX
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Table 13-98: BEV penetrations with ACC II, for No Action case, proposed and alternatives -
cars and trucks combined 13-44
Table 13-99: Average incremental vehicle cost vs. No Action case with ACC II, proposed and
alternatives - cars and trucks combined 13-44
Table 13-100. Projected targets with Low Battery Costs for No Action case, proposed and
alternatives (CO2 grams/mile) - cars and trucks combined 13-45
Table 13-101. Projected achieved levels with Low Battery Costs, for No Action case, proposed
and alternatives (CO2 grams/mile) - cars and trucks combined 13-45
Table 13-102. BEV penetrations with Low Battery Costs, for No Action case, proposed and
alternatives - cars and trucks combined 13-45
Table 13-103. Average incremental vehicle cost vs. No Action case for Low Battery Costs,
proposed and alternatives - cars and trucks combined 13-45
Table 13-104. Projected targets with High Battery Costs for No Action case, proposed and
alternatives (CO2 grams/mile) - cars and trucks combined 13-46
Table 13-105. Projected achieved levels with High Battery Costs, for No Action case, proposed
and alternatives (CO2 grams/mile) - cars and trucks combined 13-46
Table 13-106. BEV penetrations with High Battery Costs, for No Action case, proposed and
alternatives - cars and trucks combined 13-46
Table 13-107. Average incremental vehicle cost vs. No Action case for High Battery Costs,
proposed and alternatives - cars and trucks combined 13-46
Table 13-108. Projected targets with Faster BEV Acceptance for No Action case, proposed and
alternatives (CO2 grams/mile) - cars and trucks combined 13-47
Table 13-109. Projected achieved levels with Faster BEV Acceptance, for No Action case,
proposed and alternatives (CO2 grams/mile) - cars and trucks combined 13-47
Table 13-110. BEV penetrations with Faster BEV Acceptance, for No Action case, proposed and
alternatives - cars and trucks combined 13-47
Table 13-111. Average incremental vehicle cost vs. No Action case for Faster BEV Acceptance,
proposed and alternatives - cars and trucks combined 13-47
Table 13-112. Projected targets with Slower BEV Acceptance for No Action case, proposed and
alternatives (CO2 grams/mile) - cars and trucks combined 13-48
Table 13-113. Projected achieved levels with Slower BEV Acceptance, for No Action case,
proposed and alternatives (CO2 grams/mile) - cars and trucks combined 13-48
Table 13-114. BEV penetrations with Slower BEV Acceptance, for No Action case, proposed
and alternatives - cars and trucks combined 13-48
Table 13-115. Average incremental vehicle cost vs. No Action case for Slower BEV Acceptance,
proposed and alternatives - cars and trucks combined 13-48
XXX
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Table 13-116: Projected GHG Targets, Proposed Standards - Medium Duty Vans 13-49
Table 13-117: Projected GHG Targets, Proposed Standards - Medium Duty Pickups 13-49
Table 13-118: Achieved GHG Levels, Proposed Standards - Medium Duty Vans 13-49
Table 13-119: Achieved GHG Levels, Proposed Standards - Medium Duty Pickups 13-50
Table 13-120: Projected GHG Targets (Mg), Proposed Standards - Medium Duty Vans 13-50
Table 13-121: Projected GHG Targets (Mg), Proposed Standards - Medium Duty Pickups.. 13-50
Table 13-122: Projected GHG Targets (Mg), Proposed Standards - Medium Duty Combined.. 13-
51
Table 13-123: Achieved GHG Levels (Mg), Proposed Standards - Medium Duty Vans 13-51
Table 13-124: Achieved GHG Levels (Mg), Proposed Standards - Medium Duty Pickups... 13-51
Table 13-125: Achieved GHG Levels (Mg), Proposed Standards - Medium Duty Combinedl3-51
Table 13-126: GHG Credits/Debits Earned (Mg), Proposed Standards - Medium Duty Combined
13-51
Table 13-127: Projected Manufacturing Costs Per Vehicle, Proposed Standards - Medium Duty
Vehicles 13-52
Table 13-128: Projected Manufacturing Costs Per Vehicle, Proposed Standards - Medium Duty
Vans 13-52
Table 13-129: Projected Manufacturing Costs Per Vehicle, Proposed Standards - Medium Duty
Pickups 13-53
Table 13-130: Projected Manufacturing Costs Per Vehicle, Proposed Standards - Medium Duty
Combined 13-53
Table 13-131: Projected BEV Penetrations, No Action - Medium Duty Vehicles 13-53
Table 13-132: Projected BEV Penetrations, Proposed Standards - Medium Duty Vehicles... 13-53
Table 13-133. Projected targets with Low Battery Costs for No Action case and proposed
standards (CO2 grams/mile) - Medium Duty Combined 13-54
Table 13-134. Projected achieved levels with Low Battery Costs for No Action case and
proposed standards (CO2 grams/mile) - Medium Duty Combined 13-54
Table 13-135. BEV penetrations with Low Battery Costs for No Action case and proposed
standards - Medium Duty Combined 13-54
Table 13-136. Average incremental vehicle manufacturing cost vs. No Action case for Low
Battery Costs, proposed standards - Medium Duty Combined 13-54
Table 13-137. Projected targets with High Battery Costs for No Action case and proposed
standards (CO2 grams/mile) - Medium Duty Combined 13-54
XXXI
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Table 13-138. Projected achieved levels with High Battery Costs for No Action case and
proposed standards (CO2 grams/mile) - Medium Duty Combined 13-55
Table 13-139. BEV penetrations with High Battery Costs for No Action case and proposed
standards - Medium Duty Combined 13-55
Table 13-140. Average incremental vehicle manufacturing cost vs. No Action case for High
Battery Costs, proposed standards - Medium Duty Combined 13-55
XXXll
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List of Figures
Figure 1-1. Light-Duty Sales by Vehicle Type (U.S. EPA 2022) 1-4
Figure 1-2. Change in Car and Truck Regulatory Class Market Share, 2012-2021 MY 1-4
Figure 1-3. Effect of Fleet Shift on Average CO2 Standard 1-5
Figure 1-4. Footprint Response to Slope Sweeps, Sedan/Wagon Body Style 1-7
Figure 1-5. Footprint Response to Slope Sweeps, (Car Reg Class) CUV/SUV Body Style 1-8
Figure 1-6. Increase in Tailpipe CO2 Emissions: MY 2019 AWD vs. 2WD Crossovers 1-9
Figure 1-7. GCWR-Torque Relationship, MY 2019 Light Truck Data 1-10
Figure 1-8. Incremental CO2 as a Function of Increased Towing Capacity 1-11
Figure 1-9. Tow Rating-Footprint Relationship, MY 2019 Trucks 1-12
Figure 1-10. AWD and Utility Offset Applied to Establish Truck Curve (100 percent ICE)... 1-12
Figure 1-11. AWD and Utility Offset Applied to Establish Truck Curve (Scaled) 1-13
Figure 1-12. Comparison of Average Footprint to Base Year Footprint for Proposed Standards. 1-
14
Figure 1-13. Sales-weighted Footprint of Full-Size Pickups, 2012-2021 MY 1-15
Figure 1-14. Car and Truck Curves, Scaled, with Cutpoints 1-16
Figure 1-15: Heavy-duty Phase 2 work factor-based GHG standards for medium-duty pickups
and vans (81 FR 73478 2016) 1-18
Figure 1-16: Proposed MDV GHG Target Standards 1-22
Figure 2-1. Compliance modeling workflow 2-2
Figure 2-2 - Comparison of OMEGA1 and OMEGA2 2-3
Figure 2-3. Relationship of ALPHA, RSEs and OMEGA 2-9
Figure 2-4: Summary of components and architectures used in ALPHA'S modeling for this
proposal 2-11
Figure 2-5: Conventional vehicle architecture 2-12
Figure 2-6: P0 Mild hybrid-electric vehicle architecture 2-13
Figure 2-7: PowerSplit strong hybrid-electric architecture (& planetary gear arrangement). ...2-15
Figure 2-8: P2 strong hybrid-electric architecture 2-16
Figure 2-9: Battery electric vehicle architecture 2-16
Figure 2-10: Schematic of equivalent circuit battery model used in ALPHA 2-21
Figure 2-11: Power scaling example - Electric drive unit 2-22
XXXlll
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Figure 2-12: Sample validation comparison of modeled versus measured data from a 2016
Toyota Prius Prime operating on the drive schedule between 680 to 820 seconds 2-23
Figure 2-13: Conventional vehicle ALPHA combined cycle CO2 grams/mile values versus
certification CO2 grams/mile (2019 fleet). Bubble sizes reflect sales volumes 2-30
Figure 2-14: P0 ALPHA Combined Cycle CO2 grams/mile values versus Certification CO2
grams/mile (2019 fleet). Bubble sizes reflect sales volumes 2-31
Figure 2-15: PowerSplit ALPHA combined cycle CO2 grams/mile values versus certification
CO2 grams/mile (2019 Fleet). Bubble sizes reflect sales volumes 2-32
Figure 2-16: P2 ALPHA combined cycle CO2 grams/mile values versus certification CO2
grams/mile (2019 fleet). Bubbles sizes reflect sales volumes 2-33
Figure 2-17: BEV ALPHA combined cycle kWh/100 mi values versus certification kWh/100 mi
(2019 fleet). Bubble sizes reflect sales volumes 2-34
Figure 2-18: Relationships between vehicle parameters for the MY 2021 fleet 2-39
Figure 2-19: Graphical results 2-42
Figure 2-20. Direct manufacturing cost estimates for BEV packs at various annual production
volumes for NMC811-G chemistry, base year 2022 2-46
Figure 2-21. Base year cost per pack for HEV batteries as a function of gross capacity 2-47
Figure 2-22. Base year cost per kWh for HEV batteries as a function of gross capacity 2-47
Figure 2-27: Direct manufacturing costs derived from BatPaC 5.0 for PHEV batteries 2-48
Figure 2- Projected battery pack costs from various sources summarized by EDF/ERM 2-51
Figure 2-. Reference trajectory of future battery pack manufacturing costs for a 75 kWh BEV
pack 2-52
Figure 2-25: Example of pack direct manufacturing cost per kWh and average pack kWh
generated by OMEGA 2-53
Figure 2-26: Volume weighted average pack direct manufacturing cost and marked-up cost per
kWh after application of 45X credit 2-55
Figure 2-: An example of a series/parallel hybrid drive system for a transverse/front-drive
application with a portion of the outer casing and stators removed to show internal details.
Adapted from a presentation by Prof. J.D. Kelly, Weber State University (Kelly 2020).... 2-76
Figure 2-. Redesign Years for Select Vehicles 2-83
Figure 3-1 Manufacturer Use of Key Technologies in Model Year 2021 3-3
Figure 3-2 Gasoline Hybrid Engine Production Share by Vehicle Type 3-4
Figure 3-3 Production Share of BEVs, PHEVs, and FCEVs 3-5
Figure 3-4 Electric Vehicle Production Share by Vehicle Type 3-6
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Figure 3-5 Plug-In Hybrid Vehicle Production Share by Vehicle Type 3-6
Figure 3-6 Charge Depleting Range and Fuel Economy for BEVs and PHEVs 3-7
Figure 3-7 Model Year 2021 Production of BEVs, PHEVs, and FCEVs 3-8
Figure 3-8: Examples of incomplete MDV chassis finished with customized bodies for specific
applications 3-9
Figure 3-9: Rivian EDV 700 (left) and GM BrightDrop ZEVO 600 MDV (right) vans operated
by Amazon and FedEx, respectively 3-11
Figure 3-10: Limit on battery GWh demand implemented in OMEGA, compared to projected
battery manufacturing capacity and excess lithium supply 3-25
Figure 3-11: MY2022-2023 MDV box and whisker plot showing the interquartile range of
certification NMOG+NOx data 3-42
Figure 3-12: Wall-flow GPF design 3-48
Figure 3-13: Composite cycle PM reduction at low and high GPF soot loading 3-51
Figure 3-14: Cycle-specific EC reduction 3-52
Figure 3-15: Composite cycle PAH reduction at low and high GPF soot loading. Sum of 26 filter
collected PAHs shown on the left and sum of 26 gas phase PAHs shown on the right 3-53
Figure 3-16: Filter-collected PAH emissions rates with no GPF, lightly loaded GPF, and heavily
loaded GPF 3-54
Figure 3-17: Cancer potency weighted toxicity of 20 filter-collected PAHs with no GPF, lightly
loaded GPF, and heavily loaded GPF 3-54
Figure 3-18: PM emissions from a MY 2019 F150, with and without a MY 2019 GPF 3-55
Figure 3-19: PM emissions from a MY 2021 F150 HEV, with and without a MY 2022 GPF. 3-56
Figure 3-20: PM emissions from a MY 2022 F250, with and without MY 2022 GPFs 3-57
Figure 3-21: Vapor pressure of toluene and n-decane as a function of temperature 3-58
Figure 3-22: GPF cost estimate 3-62
Figure 3-23: Cycle-average GPF pressure drop as a function of test cycle 3-63
Figure 3-24: Cycle-average GPF pressure drop as a function of the ratio of GPF size to average
power required to drive US06 cycle 3-64
Figure 3-25: CO2 increase caused by added GPF. Only the two light blue bars indicated are
statistically significant to 95% confidence (p<0.05) 3-65
Figure 3-26: Schematic of an ORVR system 3-67
Figure 3-27: Current and Proposed Fleet Utility Factor for PHEV Compliance 3-74
Figure 3-28: FUFs with various data filtering sensitivities 3-77
xxxv
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Figure 3-29: The Proposed FUF, SAE MDIUF/FUF, and ICCT-BAR/FUELLY Curves 3-79
Figure 3-30 2013 Chevrolet 2.5L Ecotec LCV Engine Reg E10 Fuel (U.S. EPA 2023b) 3-81
Figure 3-31 GT Power Baseline 2020 Ford 7.3L Engine from Argonne Report Tier 3 Fuel (U.S.
EPA 2023b) 3-82
Figure 3-32 2014 Chevrolet 4.3L EcoTec3 LV3 Engine LEV III Fuel - Cyl Deac Disabled (U.S.
EPA 2023b) 3-83
Figure 3-33 2013 Ford 1.6L EcoBoost Engine LEV III Fuel (U.S. EPA 2023b) 3-84
Figure 3-34 2015 Ford 2.7L EcoBoost V6 Engine Tier 3 Fuel (U.S. EPA 2023b) 3-85
Figure 3-35 2016 Honda 1.5L L15B7 Engine Tier 3 Fuel (U.S. EPA 2023b) 3-86
Figure 3-36 Volvo 2.0L VEP LP Gen3 Miller Engine from 2020 Aachen Paper Octane
Modified for Tier 3 Fuel (U.S. EPA 2023b) 3-87
Figure 3-37 Geely 3-cyl 1.5L Miller GHE from 2020 Aachen Paper Octane Modified for Tier 3
Fuel (U.S. EPA 2023b) 3-88
Figure 3-38 2018 Toyota 2.5L A25A-FKS Engine Tier3 Fuel (U.S. EPA 2023b) 3-89
Figure 3-39 Toyota 2.5L TNGA Prototype Hybrid Engine from 2017 Vienna Paper Octane
Modified for Tier 3 Fuel (U.S. EPA 2023b) 3-90
Figure 3-40 2010 Toyota Prius 60kW 650V MG2 EMOT (U.S. EPA 2023a) 3-91
Figure 3-41 Est 2010 Toyota Prius 60kW 650V MG1 EMOT (U.S. EPA 2023a) 3-92
Figure 3-42 2011 Hyundai Sonata 30kW 270V EMOT (U.S. EPA 2023a) 3-93
Figure 3-43 2012 Hyundai Sonata 8.5kW 270V BISG (U.S. EPA 2023a) 3-94
Figure 3-44 Generic IPM 150kW EDU (U.S. EPA 2023a) 3-95
Figure 4-1: Central case shareweight values by body style for LD BEVs 4-7
Figure 4-2: Calibration of No Action-No IRA case with ACC2 to third party projections 4-8
Figure 4-3: Comparison of BEV penetrations for No Action - No IRA and No Action - IRA
cases, both without ACC2 4-9
Figure 4-4: Faster BEV acceptance shareweight values by body style for light-duty 4-11
Figure 4-5: Slower BEV acceptance shareweight values by body style for light-duty 4-13
Figure 4-6: Curve fits for miles driven to a mid-trip charge event 4-30
Figure 4-7: Curve fits for the share of miles charged in mid-trip events 4-31
Figure 4-8: Maintenance cost per mile (2019 dollars) at various odometer readings 4-35
Figure 4-9: Repair cost per mile (2019 dollars) for a $35,000 Car, Van/SUV and Pickup with
various powertrains 4-37
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Figure 4-10: Total new LD vehicle sales impacts, percent change from the No Action case. ..4-45
Figure 4-11: Workers per million dollars in sales, adjusted for domestic production 4-53
Figure 5-1: Modeling process flow highlighting the primary components for translating
OMEGA's national PEV stock projections and PEV attributes into hourly load profiles. ... 5-2
Figure 5-2: Procedure for disaggregating OMEGA national PEV stock projections to IPM
regions 5-3
Figure 5-3: EVI-X National light-duty vehicle framework simulation showing spatiotemporal
energy demands for three separate use cases: typical daily travel (EVI-Pro), long-distance
travel (EVI-RoadTrip), and ride-hailing (EVI-OnDemand) 5-4
Figure 5-4: Annual PEV charging loads (2030 and 2050 are shown) for each IPM region in the
contiguous United States based on OMEGA charge demand for the proposal in 2030 (top)
and 2050 (bottom) 5-6
Figure 5-5: Yearly hourly (in EST) weekday and weekend load profiles for two IPM regions
(ERCWEST, west Texas; and ERCREST, east Texas) broken out by charger type for an
example OMEGA analytical scenario 5-8
Figure 5-6: Power sector modeling comparing results of the Bipartisan Infrastructure Law (BIL)
and the Inflation Reduction Act (IRA) 5-10
Figure 5-7: 2028 through 2050 power sector CO2 emissions for the proposal (orange line) and
no-action case (dashed line) 5-11
Figure 5-8: 2028 through 2055 power sector generation and grid mix 5-11
Figure 5-9: 2028 through 2050 power sector NOx emissions for the proposal (orange line) and
no-action case (dashed line) 5-12
Figure 5-10: 2028 through 2050 power sector PM2.5 emissions for the proposal (orange line) and
no-action case (dashed line) 5-12
Figure 5-11: 2028 through 2050 power sector SO2 emissions for the proposal (orange line) and
no-action case (dashed line) 5-13
Figure 5-12: Electricity Market Module Regions (U.S. Energy Information Administraton 2019).
5-15
Figure 5-13: U.S. Non-residential PEV Charging Infrastructure from 2011—2022 (Data Source:
(U.S. Department of Energy, Alternative Fuels Data Center 2023b) 5-24
Figure 5-14: Share of charging demand by location and type for the no-action case (left side of
each pair of bars) and proposal (right side of each pair of bars) for 2028—2055 5-29
Figure 5-15: EVSE port counts by charging type for proposal 2027—2055 5-31
Figure 5-16: EVSE port counts by charging type for the no-action case 2027—2055 5-31
Figure 7-1 Important Factors Involved in Seeing a Scenic Vista (Malm, 2016) 7-25
xxxvii
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Figure 7-2: Mandatory Class I Federal Areas in the U.S 7-26
Figure 7-3: Nitrogen and Sulfur Cycling, and Interactions in the Environment 7-29
Figure 8-1: Counties designated nonattainment for PM2.5 (1997, 2006, and/or 2012 standards).. 8-
2
Figure 8-2: 8-Hour ozone nonattainment areas (2008 Standard) 8-3
Figure 8-3: 8-Hour ozone nonattainment areas (2015 Standard) 8-3
Figure 8-4: counties designated nonattainment for SO2 (2010 standard) 8-4
Figure 8-5: Map of the CMAQ 12 km modeling domain (noted by the purple box) 8-18
Figure 8-6: Projected illustrative changes in annual average PM2.5 concentrations in 2055 due to
8-21
LMDV regulatory scenario.
"onroad-only" emissions changes.
Figure 8-7: Projected illustrative changes in annual average PM2.5 concentrations in 2055 from
.8-22
Figure 8-8: Projected illustrative changes in 8-hour maximum average ozone concentrations in
2055 due to LMDV regulatory scenario 8-23
Figure 8-9: Projected illustrative changes in 8-hour maximum average ozone concentrations in
2055 from "onroad-only" emissions changes 8-24
Figure 8-10: Projected illustrative changes in annual average NO2 concentrations in 2055 due to
8-25
LMDV regulatory scenario.
Figure 8-11: Projected illustrative changes in annual average NO2 concentrations in 2055 from
"onroad-only" emissions changes.
LMDV regulatory scenario.
"onroad-only" emissions changes.
.8-25
Figure 8-12: Projected illustrative changes in annual average SO2 concentrations in 2055 due to
.8-26
Figure 8-13: Projected illustrative changes in annual average SO2 concentrations in 2055 from
.8-27
Figure 8-14: Projected illustrative changes in annual average acetaldehyde concentrations in
2055 due to LMDV regulatory scenario 8-28
Figure 8-15: Projected illustrative changes in annual average benzene concentrations in 2055 due
to LMDV regulatory scenario 8-28
Figure 8-16: Projected illustrative changes in annual average formaldehyde concentrations in
2055 due to LMDV regulatory scenario 8-29
Figure 8-17: Projected illustrative changes in annual average naphthalene concentrations in 2055
due to LMDV regulatory scenario 8-29
Figure 8-18: Projected illustrative changes in annual average acetaldehyde concentrations in
2055 from "onroad-only" emissions changes 8-30
xxxviii
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Figure 8-19: Projected illustrative changes in annual average benzene concentrations in 2055
from "onroad-only" emissions changes 8-30
Figure 8-20: Projected illustrative changes in annual average formaldehyde concentrations in
2055 from "onroad-only" emissions changes 8-31
Figure 8-21: Projected illustrative changes in annual average naphthalene concentrations in 2055
from "onroad-only" emissions changes 8-31
Figure 8-22: Projected illustrative changes in annual nitrogen deposition in 2055 due to LMDV
8-32
regulatory scenario
Figure 8-23: Projected illustrative changes in annual sulfur deposition in 2055 due to LMDV
regulatory scenario 8-33
only" emissions changes.
Figure 8-24: Projected illustrative changes in annual nitrogen deposition in 2055 from "onroad-
8-33
Figure 8-25: Projected illustrative changes in annual sulfur deposition in 2055 from "onroad-
only" emissions changes 8-34
Figure 9-1 ICE vehicle stock used in OMEGA effects calculations 9-2
Figure 9-2 BEV stock used in OMEGA effects calculations 9-2
Figure 9-3 Recent and projected future fatality rates for cars and light trucks (2022 CAFE FRIA
2022, 109) 9-9
Figure 9-8 Energy distribution (top) and cumulative energy use (bottom) over 10,000 miles for
the MOVES onroad data, compared to FTP/HW regulatory cycles, weighted 55%/45%. .9-15
Figure 9-9 Energy distribution (top) and cumulative energy use (bottom) over 10,000 miles for
the new cycle mix (27% FTP, 6% US06 bag 1, 67% US06 bag 2) compared to the MOVES
onroad data 9-17
Figure 9-10 Speed distribution for the new cycle mix (27% FTP, 6% US06 bag 1, 67% US06 bag
2) compared to the MOVES onroad data 9-19
Figure 10-1: Frequency Distribution of SC-CO2 Estimates for 2030 10-17
Figure 10-2: Frequency Distribution of SC-CH4 Estimates for 203013 10-17
Figure 10-3: Frequency Distribution of SC-N20 Estimates for 203013 10-18
Figure 11-1. U.S. tight oil production by producing regions (in MMBD) and West Texas
Intermediate (WTI) crude oil spot price (in U.S. Dollars per Barrel) Source: (EIA 2022) (EIA
2022) 11-8
Figure 11-2. Average U.S. fuel cost per vehicle mile driven of gasoline-powered vehicles and
PEVs from 2011 to 2021 Sources: Electricity prices: (EIA 2022); Gasoline prices: (EIA
2022); Fuel economies: (EPA 2022) 11-20
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Figure 11-3. Fuel cost per mile driven by gasoline-powered vehicles and PEVs for six states
from 2011 to 2021 Sources: Electricity prices: (EIA 2022); Gasoline prices: (EIA 2022); Fuel
economies: (EPA 2022) 11-21
Figure 11-4. Monthly percentage changes in U.S. retail electricity and gasoline prices from 2011
to 2021 Source: (EIA 2022) 11-22
Figure 11-5. U.S. electricity net imports as percentage of total electricity use from 2011 to 2020
and projected U.S. electricity net imports from 2021 to 2050. Source: (EIA 2022), (EIA
2022), (EIA 2022), (EIA 2022) 11-24
xl
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Executive Summary
Under its Clean Air Act Section 202 authority, the Environmental Protection Agency (EPA) is
proposing new, more stringent emissions standards for criteria pollutants and greenhouse gases
(GHG) for light-duty vehicles and Class 2b and 3 ("medium-duty") vehicles that would phase-in
over model years 2027 through 2032. In addition, EPA is proposing GHG program revisions in
several areas, including off-cycle and air conditioning credits, the treatment of zero emissions
vehicles and plug-in hybrid electric vehicles in fleet average calculations, and vehicle
certification and compliance. EPA is also proposing new standards to control refueling emissions
from incomplete medium-duty vehicles, and battery durability and warranty requirements for
light-duty plug-in vehicles.
This Draft Regulatory Impact Analysis (DRIA) contains supporting documentation for the
EPA proposed rulemaking 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 DRIA throughout.
DRIA Chapter Summary
This document contains the following Chapters:
Chapter 1: Development of GHG Standards and BEV Durability Requirements
This chapter provides technical details supporting the development of the proposed GHG
standards for both Light Duty and Medium Duty Vehicles, and a separate section that provides
additional background on development of EPA's proposed battery durability standards compared
to those developed by the United Nations (UN) and California.
Chapter 2: Tools and Inputs Used for Modeling Technologies and Adoption
Towards Compliance
This chapter summarizes the tools and inputs used for modeling technologies, adoption of
technologies, and vehicle compliance with the proposed standards. This includes details
regarding the OMEGA model, ALPHA vehicle simulation tools, and the Agency's approach to
analyzing vehicle manufacturing costs, consumer demand, vehicle operational costs. The
chapter also includes a summary of modeling inputs that reflect our assessment of impacts due to
the implementation of the Inflation Reduction Act of 2022.
Chapter 3: Analysis of Technologies for Reducing GHG and Criteria Pollutant
Emissions
This chapter provides EPA's analysis of technologies available for further reducing both
GHG and criteria pollutant emissions and current technology trends. It also provides EPA's
analysis supporting the proposed revisions for on-board diagnostics and PHEV accounting (i.e.,
revised utility factor).
Chapter 4: Consumer Impacts and Related Economic Considerations
This chapter discusses consumer impacts of this proposed rule, including the consumer
purchase decision, the ownership experience, social benefits and costs, as well as the effect on
new vehicle sales, and estimated employment effects. In the discussion of the purchase decision,
xli
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we include costs consumers incorporate into their purchase decision, how consumer respond to
costs, and how consumer perception of technologies change, or do not, over time. Within our
discussion of ownership experience, we include vehicle use and the effect on private savings and
expenses, including vehicle miles traveled, rebound, fueling costs, maintenance and repair, and
noise and congestion costs due to this rule. Consumer related costs and benefits include
components of social costs and benefits that are included in the benefit-cost analysis and that
have direct consumer impacts. The discussion of new vehicle sales explains how vehicle sales
were modeled, including an explanation of the elasticity of demand used in our analysis, as well
as the estimated effect of the proposed rule on total vehicle sales. We conclude the chapter with a
description of employment effects, including potential impacts of the growing prevalence of
BEVs, a quantitative estimate of partial employment impacts on sectors directly impacted by this
proposed rule, and discuss potential impacts on other related sectors.
Chapter 5: Electric Infrastructure Impacts
This chapter provides EPA's analysis of plug-in electric vehicle (PEV) charge demand and
regional distribution, electric power sector modeling including estimating retail electricity prices,
and EPA's assessment of current and future PEV charging infrastructure. Finally, this chapter
discusses electric grid resiliency.
Chapter 6: [Blankl
This chapter is intentionally left blank.
Chapter 7: Health and Welfare Impacts
The proposed rule will impact emissions of GHGs, criteria pollutants, and air toxic pollutants.
There are health and welfare impacts associated with ambient concentrations of GHGs, criteria
pollutants and air toxics which are described in this chapter.
Chapter 8: Illustrative Analysis of Air Quality Impacts of a Light- and Medium-
Duty Vehicles Regulatory Scenario
This chapter provides information regarding current air quality including pollutant
concentrations and EPA's assessment of air quality impacts. EPA conducted an air quality
modeling analysis of an illustrative regulatory scenario involving light- and medium-duty vehicle
emission reductions and corresponding changes in electric generating unit (EGU) emissions,
refinery emissions, emissions from crude oil production sites and pipeline pumps, and emissions
from natural gas production sites and pipeline pumps. This analysis does not represent the
proposal's regulatory scenario, and it does not account for the impacts of the Inflation Reduction
Act (IRA); however, it provides some insights into potential air quality impacts associated with
emissions increases and decreases from these multiple sectors.
Chapter 9: OMEGA Physical Effects of the Proposed Standards and Alternatives
This chapter describes the methods and approaches used within the OMEGA model to
estimate physical effects of the proposed standards. Physical effects refer to emission
inventories, fuel consumption, oil imports, vehicle miles traveled including effects associated
with the rebound effect, and safety effects. The cost and benefits of the proposal are tied directly
to these physical effects and are discussed in Chapter 11 of this draft RIA.
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Chapter 10: Costs and Benefits of the Proposed Standards in OMEGA
This chapter presents the costs and benefits calculated within OMEGA. The results presented
here show the estimated annual costs, fuel savings and benefits of the program for the indicated
calendar years (CY). The results also show the present-values (PV) of those costs and the
equivalent annualized values (EAV) for the calendar years 2027-2055 using both 3 percent and 7
percent discount rates. For the estimation of the stream of costs and benefits, we assume that
after implementation of the MY 2027 and later standards, the MY 2032 standards apply to each
year thereafter.
Chapter 11: Energy Security Impacts
This chapter provides EPA's evaluation of the energy security impacts of the light- and
medium-duty vehicle proposed rule. It provides a review of historical and recent energy security
literature and EPA assessment of potential electricity and oil security impacts.
Chapter 12: Small Business Flexibilities
This chapter discusses the flexibilities EPA proposes to provide to small businesses for model
years 2027 and later for both the proposed GHG and criteria pollutant emissions standards.
Chapter 13: Compliance Effects
This chapter summarizes the outputs from OMEGA related to the proposed standards and the
two alternatives which were presented in III.E of the preamble. It provides EPA's detailed
modeling results of GHG targets, projected achieved compliance GHG rates, as well as vehicle
costs and technology penetrations. These projections are grouped by car and truck regulatory
classes, and in select tables, using EPA's classification of body style in its OMEGA model.
Summary of Emission Reductions, Costs, and Benefits
This section of the Executive Summary summarizes our analysis of the proposal's estimated
emission impacts, costs, and monetized benefits, which is described in more detail in DRIA
Chapters 9, 10, and 13; and also in Sections V through VIII of the Preamble to the proposed rule.
The proposed standards would result in net reductions of emissions of criteria air pollutants
and GHGs in 2055, considering the impacts from light- and medium-duty vehicles, power plants
(i.e., electric generating units (EGUs)), and refineries. Table 1 shows the GHG emission impacts
in 2055 while Table 2 shows the cumulative impacts for the years 2027 through 2055. We show
cumulative impacts for GHGs as elevated concentrations of GHGs in the atmosphere are
resulting in warming and changes in the Earth's climate. Table 3 shows the criteria pollutant
emissions impacts in 2055. As shown in Table 4, we also predict reductions in air toxic
emissions from light-and medium-duty vehicles. We project that GHG and criteria pollutant
emissions from EGUs would increase as a result of the increased demand for electricity
associated with the proposal, although those projected impacts decrease over time because of
projected increases in renewables in the future power generation mix. We also project that GHG
and criteria pollutant emissions from refineries would decrease as a result of the lower demand
for liquid fuel associated with the proposed GHG standards. Chapters 8, 9 and 10 of the DRIA
and also Sections VI and VII of the Preamble to the proposed rule provide more information on
the projected emission reductions for the proposed standards and alternatives.
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Table 1: Projected GHG emission impacts in 2055 from the proposed rule, light-duty and
medium-duty (Million metric tons)
Pollutant Vehicle EGU Refinery* Net Impact Net Impact (%)
CO2 "44() 16 0 -42() "47%
CH4 -0.0088 0.00038 0 -0.0084 -45%
N2O -0.0077 0.00003 0 -0.0077 -41%
Table 2: Projected cumulative GHG emission impacts through 2055 from the proposed
rule, light-duty and medium-duty (Million metric tons)
Pollutant Vehicle EGU Refinery* Net Impact Net Impact (%)
CO2 "8-()()() 710 () "7-3()() "26%
CH4 -0.16 0.035 0 -0.12 -17%
N2O -"'4 0.0045 0 -0.13 -25%
Table 3: Projected criteria air pollutant impacts in 2055 from the proposed rule, light-duty
and medium-duty (US tons)
Pollutant
Vehicle
EGU
Refinery
Net Impact
Net Impact (%)
PM2.5
-9.800
1.500
-6.900
-15.000
-35%
NOx
-44.000
2.600
-25.000
-66.000
-41%
voc
-200.000
1.000
-21.000
-220.000
-50%
SOx
-2.800
1.600
-1 1.000
-12.000
-42%
CO*
-1.800.000
0
0
-1.800.000
-49%
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Table 4: Projected air toxic impacts from vehicles in 2055 from the proposed rule, light-
duty and medium-duty (US tons)
Pollutant
Vehicle
Vehicle (%)
Acclaldchvde
-840
-49%
Acrolein
-55
-48%
Benzene
-2.900
-51%
Elhv lbcn/.cnc
-3.400
-50%
Formaldehyde
-510
-49%
Naphthalene
-100
-51%
1.3-Butadiene
-340
-51%
15 Polvaromalic Hydrocarbons
-5
-78%
The GHG emission reductions would contribute toward the goal of holding the increase in the
global average temperature to well below 2°C above pre-industrial levels and would
subsequently reduce the probability of severe climate change related impacts including heat
waves, drought, sea level rise, extreme climate and weather events, coastal flooding, and
wildfires.
The decreases in vehicle emissions would reduce traffic-related pollution in close proximity
to roadways. As discussed in DRIA Chapter 7, concentrations of many air pollutants are elevated
near high-traffic roadways, and 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.
The changes in emissions of criteria and toxic pollutants from vehicles, EGUs, and refineries
would also impact ambient levels of ozone, PM2.5, NO2, SO2, CO, and air toxics over a larger
geographic scale. As discussed in DRIA Chapters 8 and 9, we expect that in 2055 the proposal
would result in widespread decreases in ozone, PM2.5, N02, CO, and some air toxics, even when
accounting for the impacts of increased electricity generation. We expect that in some areas,
increased electricity generation would increase ambient S02, PM2.5, ozone, or some air toxics.
However, as the power sector becomes cleaner over time, these impacts would decrease.
Although the specific locations of increased air pollution are uncertain, we expect them to be in
more limited geographic areas, compared to the widespread decreases that we predict to result
from the reductions in vehicle emissions.
EPA estimates the present value of net benefits lies in the range of $850 billion to $1.6
trillion, with equivalent annualized net benefits in the range of $60 billion to $85 billion. EPA
estimates that the total benefits of this proposal far exceed the total costs: the present value of
benefits range from $350 billion to $590 billion, with pre-tax fuel savings providing another
$450 billion to $890 billion, and the present value of vehicle technology costs range from $180
billion to $280 billion, but the present value of repair and maintenance savings are estimated at
$280 billion to $580 billion. The results presented here project the monetized environmental and
economic impacts associated with the proposed program during each calendar year through
2055. Table 5 below summarizes EPA's estimates of total costs, savings, and benefits. Note EPA
projects lower maintenance and repair costs for several advanced technologies (e.g., battery
electric vehicles) and those societal maintenance and repair savings grow significantly over time,
and by 2040 and later are larger than our projected new vehicle technology costs.
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The benefits include climate-related economic benefits from reducing emissions of GHGs that
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, the value of additional driving attributed to the rebound effect, and the value of reduced
refueling time needed to refuel vehicles. Between $63 and $280 billion of the present value of
total benefits through 2055 (assuming a 7 percent and 3 percent discount rate, respectively, as
well as different long-term PM-related mortality risk studies) are attributable to reduced
emissions of criteria pollutants that contribute to ambient concentrations of smaller particulate
matter (PM2.5). PM2.5 is associated with premature death and serious health effects such as
hospital admissions due to respiratory and cardiovascular illnesses, nonfatal heart attacks,
aggravated asthma, and decreased lung function. The proposed program would also have other
significant social benefits including $330 billion in climate benefits (with the average SC-GHGs
at a 3 percent discount rate).
The analysis also includes estimates of economic impacts stemming from additional vehicle
use from increased rebound driving, such as the economic damages caused by crashes,
congestion, and noise. See Chapter 10 of the DRIA for more information regarding these
estimates.
Note that some non-emission costs are shown as negative values in Table 5. Those entries
represent savings but are included as costs because, traditionally, things like repair and
maintenance are viewed as costs of vehicle operation. Where negative values are shown, we are
estimating that those costs are lower in the proposal than in the no-action case. Congestion and
noise costs are attributable to increased congestion and roadway noise resulting from our
assumption that people may choose to drive more under the proposal versus the no action case.
Those increased miles are known as rebound miles and are discussed DRIA Chapter 4.
Similarly, some of the traditional benefits of rulemakings that result in lower fuel
consumption by the transportation fleet, i.e., the non-emission benefits, are shown as negative
values. Our past GHG rules have estimated that time spent refueling vehicles would be reduced
due to the lower fuel consumption of new vehicles; hence, a benefit. However, in this analysis,
we are estimating that refueling time would increase somewhat due to mid-trip recharging events
for electric vehicles. Therefore, the increased refueling time represents a disbenefit (a negative
benefit) as shown. As noted in Chapter 4 of the DRIA, we consider our refueling time estimate to
be dated considering the rapid changes taking place in electric vehicle charging infrastructure,
which is significantly driven by the Inflation Reduction Act.
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Table 5: Monetized discounted costs, benefits, and net benefits of the proposed program for
calendar years 2027 through 2055, light-duty and medium-duty (Billions of 2020
dollars) .abc
CY 2055
PV, 3%
PV, 7%
EAV, 3%
EAV, 7%
Non-Emission Costs
Vehicle Technology Costs
10
280
180
15
15
Repair Costs
-24
-170
-79
-8.9
-6.5
Maintenance Costs
-51
-410
-200
-21
-16
Congestion Costs
0.16
' 2.3
1.3
0.12
0.11
Noise Costs
0.0025
0.037
0.021
0.0019
0.0017
Sum of Non-Emission Costs
-65
-290
-96
-15
-7.8
Fueling Impacts
Pre-tax Fuel Savings
93
890
450
46
*2 37 ^
EVSE Port Costs
7.1
120
68
6.2
5.6
Sum of Fuel Savings less EVSE Port
86
770
380
40
31
Costs
Non-Emission Benefits
Drive Value Benefits
0.3 1
4.8
2.7
0.25
0.22
Refueling Time Benefits
-8.2
-85
-45
-4.4
-3.6
Energy Security Benefits
4.4
41
21
2.2
1.7
Sum of Non-Emission Benefits
-3.6
-39
-21
-2 ^
-1.7
Climate Benefits*
5% Average
15
82
82
5.4
5.4
3% Average
38
330
330^
17
17
2.5% Average
52
500
500
25
25
3% 95th Percentile
110
1.000
1.000
52
[ 7 52 T
Criteria Air Pollutant Benefits1'
PM2.5 Health Benefits - W11 et al..
16- 18
140
63
7.5
5.1
2020
PM2.5 Health Benefits - Pope III et al..
3 1 - 34
280
130
15
10
2019
Net Benefits*'*
Willi Climate 5% Average
180 -200
1.400
610
74
48
Willi Climate 3% Average
200 - 220
1.600
850
85
60
Willi Climate 2.5% Average
210-230
1.800
1.000
93
67
Willi Climate 3% 95th Percentile
280 - 290
2.300
1.500
120
95
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 and equivalent annualized values of SC-GHGs for internal consistency, while all other costs and benefits are discounted at either 3
percent or 7 percent.
k PM2.5-related health benefits are presented based on two different long-term exposure studies of mortality risk: a Medicare study (Wu et
al.. 2020) and a National Health Interview Survey study (Pope III et al., 2019). The criteria pollutant benefits associated with the standards
presented here do not include the full complement of health and environmental benefits that, if quantified and monetized, would increase the
total monetized benefits.
c For net benefits, the range in 2055 uses the low end of the Wu range and the high end of
the Pope III et al. range. The present and equivalent annualized value of net benefits for a
3 percent discount rate reflect benefits based on the Pope III et al. study while the present
and equivalent annualized values of net benefits for a 7 percent discount rate reflect
benefits based on the Wu et al. study.
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EPA estimates the average upfront per-vehicle cost to meet the proposed standards to be
approximately $1,400 in MY 2032, as shown in Table 6 below. We discuss this in more detail in
DRIA Chapter 13.
Table 6: Average incremental vehicle cost by reg class, relative to the No Action scenario
(2020 dollars)
2027
2028
2029
2030
2031
2032
Cars
$249
$102
$32
$100
$527
$844
T nicks
$891
f $767
$653
$821
$1,100
$1,385
Total
$633
$497
$401
f $526
$866
$1,164
In addition, the proposal would result in significant savings for consumers from fuel savings
and reduced vehicle repair and maintenance. These lower operating costs would offset the
upfront vehicle costs. Total retail fuel savings for consumers through 2055 are estimated at
$560billion to $1.1 trillion (7 percent and 3 percent discount rates). Reduced maintenance and
repair costs through 2055 are estimated at $320 billion to $650 billion (7 percent and 3 percent
discount rates, see Chapter 10 of the DRIA).
Analysis of Alternatives to the Proposal
EPA analyzed three alternatives to the proposed standards. Alternative 1 is more stringent
than the proposal across the MY 2027-2032 time period, and Alternative 2 is less stringent. The
proposal as well as Alternatives 1 and 2 all have a similar proportional ramp rate of year over
year stringency, which includes a higher rate of stringency increase in the earlier years (MYs
2027-2029) than in the later years (MY 2030-3032). Alternative 3 achieves the same stringency
as the proposed standards in MY 2032 but provides for a more consistent rate of stringency
increase for MY 2027-2031.
The Alternative 1 projected fleet-wide CO2 targets are 10 g/mi lower on average than the
proposed targets; Alternative 2 projected fleet-wide CO2 targets averaged 10 g/mi higher than the
proposed targets. While the 20 g/mi range of stringency options may appear fairly narrow, for
the MY 2032 standards the alternatives capture a range of 12 percent higher and lower than the
proposed standards in the final year. Our goal in selecting the alternatives was to identify a range
of stringencies that we believe are appropriate to consider for the final standards because they
represent a range of standards that are anticipated to be feasible and are highly protective of
human health and the environment.
While the proposed standards, Alternative 1 and Alternative 2 all have a larger increase in
stringency between MY 2026 and MY 2027, Alternative 3 was constructed with the goal of
evaluating roughly equal reductions in absolute g/mi targets over the duration of the program
while achieving the same overall targets by MY 2032. This has the effect of less stringent year-
over-year increases in the early years of the program.
Table 7, Table 8 and Table 9 compare the projected fleet average targets for cars, trucks, and
the combined fleet, respectively, across the proposed standards and the three alternatives for
model years 2027-2032. Table 10 compares the relative percentage year-over-year reductions of
the proposed standards and the three alternatives.
Table 7: Comparison of proposed car standards to alternatives
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Model Year
Proposed Stds
Alternative 1
Alternative 2
Alternative 3
CO2 (g/mile)
CO2 (g/mile)
CO2 (g/mile)
CO2 (g/mile)
2026 adjusted
152
152
152
152
2027
134
124
144
139
2028
116
106
126
126
2029
99
89
108
112
2030
91
81
100
99
203 1
82
72
92
86
2032 and later
73
63
83
73
% reduction vs.
52%
59%
46%
52%
2026
Table 8: Comparison of proposed truck standards to
alternatives
Model Year
Proposed Stds
Alternative 1
Alternative 2
Alternative 3
CO2 (g/mile)
CO2 (g/mile)
CO2 (g/mile)
CO2 (g/mile)
2026 adjusted
207
207
207
207
2027
163
153
173
183
2028
142
131
152
163
2029
120
110
130
144
2030
110
100
121
126
203 1
100
90
111
107
2032 and later
89
78
99
89
% reduction vs.
57%
62%
52%
57%
2026
Table 9: Comparison of proposed combined fleet standards to alternatives
Model Year
Proposed Stds
Alternative 1
Alternative 2
Alternative 3
CO2 (g/mile)
CO2 (g/mile)
CO2 (g/mile)
CO2 (g/mile)
2026 adjusted
186
186
186
186
2027
152
141
162
165
2028
131
121
141
148
2029
111
101
122
132
2030
102
92
112
115
203 1
93
83
103
99
2032 and later
82
72
92
82
% reduction vs.
56%
61%
50%
56%
2026
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Table 10: Combined fleet year-over-year decreases for proposed standards and alternatives
Model Year
Proposed Slds
Alternative 1
Alternative 2
Alternative 3
CO2 (g/mile)
CO2 (g/mile)
CO2 (g/mile)
CO2 (g/mile)
2027
-18%
-24%
-13%
-11%
2028
-13%
-14%
-13%
-10%
2029
-15%
-16%
-14%
-11%
2030
-8%
-9%
-8%
-12%
203 1
-9%
-10%
-8%
-15%
2032
-11%
-13%
-10%
-17%
Average YoY
-13%
-15%
-11%
-13%
The proposed standards will result in industry-wide average GHG emissions target for the
light-duty fleet of 82 g/mi in MY 2032, representing a 56 percent reduction in average emission
target levels from the existing MY 2026 standards established in 2021. Alternative 1 is projected
to result in an industry-wide average target of 72 grams/mile (g/mile) of CO2 in MY 2032,
representing a 61 percent reduction in projected fleet average GHG emissions target levels from
the existing MY 2026 standards. Alternative 2 is projected to result in an industry-wide average
target of 92 g/mile of CO2 in MY 2032, which corresponds to a 50 percent reduction in projected
fleet average GHG emissions target levels from the existing MY 2026 standards. Like the
proposed standards, Alternative 3 is projected to result in an industry-wide average target of 82
g/mile of CO2 in MY 2032, which corresponds to a 56 percent reduction in projected fleet
average GHG emissions target levels from the existing MY 2026 standards.
Table 11 gives a comparison of average incremental per-vehicle costs for the proposed
standards and the alternatives. As shown, the 2032 MY industry average vehicle cost increase
(compared to the No Action case) ranges from approximately $1,000 to $1,800 per vehicle for
the alternatives, compared to $1,200 per vehicle for the proposed standards. These projections
represent compliance costs to the industry and are not the same as the costs experienced by the
consumer when purchasing a new vehicle. For example, the costs presented here do not include
any state and Federal purchase incentives that are available to consumers. Also, the manufacturer
decisions for the pricing of individual vehicles may not align exactly with the cost impacts for
that particular vehicle.
Table 11: Comparison of projected incremental per-vehicle costs relative to the No Action
scenario
Model Year Proposed Stds Alternative 1 Alternative 2 Alternative 3
$/vehicle
$/vehicle
$/vehicle
$/vehicle
2027
$633
$668
$462
$189
2028
$497
$804
$355
$125
2029
$401
$1,120
$353
$45
2030
$526
$1,262
$337
$250
203 1
$866
$ 1.565
$718
$800
2032
$1,164
$1,775
$1,041
$1,256
Projected emissions reductions from the alternatives are shown in Table 12 through Table 15.
A summary of the costs, savings and benefits for alternatives 1, 2, and 3 are shown in Table 16,
Table 17, and Table 18, respectively.
1
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Table 12: Projected GHG emission impacts in 2055 from the proposed rule, light-duty and
medium-duty (Million metric tons)
Pollutant Vehicle EGU Refinery* Net Impact Net Impact (%)
Alternative 1
CO2
-480
18 0
-460
-52%
CH4
.0096
0.00043 0
-0.0092
-49%
N2O
.0084
0.000034 0
-0.0083
-44%
Alternative 2
CO2
-400
14 0
-380
-43%
CH4
.0081
0.00035 0
-0.0078
-42%
N2O
.0072
0.000027 0
-0.0072
-38%
Alternative 3
CO2
-440
16 0
-420
-47%
CH4
.0088
0.00039 ! 0
-0.0084
-45%
N2O -<
.0078
0.00003 0
-0.0077
-41%
*GHG emission rates were not available for calculating GHG inventories from refineries.
Table 13: Projected cumulative GHG emission impacts through 2055 from the proposed
rule, light-duty and medium-duty (Million metric tons)
Pollutant Vehicle
Alternative 1
C02 -8.wo
CH4 -0.17
N2O "0.15
Alternative 2
EGU Refi
780
0.039
0.005
CO2
i -7,200
630 0
-6.600
-23%
CH4
-0.14
0.032 0
-0.11
-15%
N2O
-0.13
0.004 0
-0.12
-23%
Alternative 3
CO2
-7.800
670 0
-7.100
-25%
CH4
-0.15
0.033 0
-0.12
-16%
N20
-0.13
0.0042 0
-0.13
-24%
ery* Net Impact Net Impact (%)
-8.100
-0.13
-0.14
-29%
-18%
-27%
*GHG emission rates were not available for calculating GHG inventories from refineries.
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Table 14: Projected criteria air pollutant impacts in 2055 from the proposed rule, light-
duty and medium-duty (US tons)
Pollutant Vehicle EGU Refinery Net Impact Net Impact (%)
Alternative 1
PM2.5
-9.800
1.700
-7.600
-16.000
-37%
NOx
-47.000
2.800
-27.000
-71.000
-44%
voc
¦230.000
1.100
-23.000
-250.000
-55%
SOx
-3.000
1.900
-12.000
-13.000
-46%
CO* -2.000.000
0
0
-2.000.000
-55%
ilternative 2
PM2.5
-9.800
1.400
-6.200
-15.000
-34%
NOx
-41.000
2.400
-22.000
-61.000
-38%
voc
¦190.000
950
-19.000
-200.000
-45%
SOx
-2.500
1.500
-9.500
-1 1.000
-38%
CO* -1
[,600,000
0
0
-1.600.000
-45%
ilternative 3
PM2.5
-9.800
1.500
-6.900
-15.000
-35%
NOx
-44.000
2.600
-25.000
-66.000
-41%
VOC
¦200.000
1.000
-21.000
-220.000
-50%
SOx
-2.800
1.700
-1 1.000
-12.000
-42%
CO* -1
1.800.000
0
0
-1.800.000
-50%
*EPA did not have data available to calculate CO impacts from EGUs or refineries.
Table 15: Projected air toxic impacts from vehicles in 2055 from the proposed rule, light-
duty and medium-duty (US tons)
Pollutant
Vehicle
Vehicle
Alternative 1
Acetaldehyde
....... ^
-53%
Acrolein
-60
-52%
Benzene
; -3,200
-56%
Ethylbenzene
; -3,700
-55%
Formaldehyde
-550
-53%
Naphthalene
-110
f -56%
1,3-Butadiene
-370
-56%
15 Polyaromatic Hydrocarbons
-5 J
; -80%
Alternative 2
Acetaldehyde
-780
.1 "45%
Acrolein
-51
-44%
Benzene
; -2,600
: -47%
Ethylbenzene
: -3,100
-46%
Formaldehyde
-470
-45%
Naphthalene
" -95 'J
-47%
1,3-Butadiene
f " -310
-47%
15 Polyaromatic Hydrocarbons
-5 "
; -77%
Alternative 3
Acetaldehyde
-850
; -49%
Acrolein
-55
; -48%
Benzene
f -2,900
-51%
Ethylbenzene
; -3,400
! -50%
Formaldehyde
-510 "
-49%
Naphthalene
-100
" -51%
1,3-Butadiene
-340
-51%
15 Polyaromatic Hydrocarbons
-5
-78%
lii
-------
Table 16: Monetized discounted costs, benefits, and net benefits of Alternative 1 for
calendar years 2027 through 2055, light-duty and medium-duty (Billions of 2020
dollars)abc
CY 2055
PV, 3%
PV, 7%
EAV, 3%
EAV, 7°/
Non-Eniission Costs
Vehicle Technology Costs
11
' 330
220
17
18
Repair Costs
-26
-180
-82
-9.3
-6.7
Maintenance Costs
-57
-450
-220
-24
-18
Congestion Costs
0.11
3.5
2.2
0.18
0.18
Noise Costs
0.0017
0.055
0.034
0.0028
0.0027
Sum of Non-Emission Costs
-71
-300
-82
-15
-6.7
Fueling Impacts
Pre-tax Fuel Savings
100
990
510
51
41
EVSE Port Costs
7.1
120
68
6.2
5.6
Sum of Fuel Savings less EVSE Port
95
870
440
45
36
Costs
Non-Emission Benefits
Drive Value Benefits
0.22
6.5
3.9
0.34
0.32
Refueling Time Benefits
-8.8
-90
-47
-4.7
-3.8
Energy Security Benefits
4.8
46
23
2.4
1.9
Sum of Non-Emission Benefits
-3.8
-38
-20
-2 'J
-1.6
Climate Benefits*
5% Average
16
91
91
6
6
3% Average
41
360
360
19
19
2.5% Average
57
560
560
27
27
3% 95th Percentile
120
1.100
1.100
58
58
Criteria Air Pollutant Benefits1'
PM2.5 Health Benefits - Wu et al..
16 - 18
150
66
7/7
5.3 '
2020
PM2.5 Health Benefits - Pope III et al..
32-35
290
130
15
11
2019
Net Benefits*'*
Willi Climate 5% Average
200 - 210
1.500
660
80
52
Willi Climate 3% Average
220 - 240
1.800
930
93
65
Willi Climate 2.5% Average
240 - 260
2.000
1.100
100
73
Willi Climate 3% 95th Percentile
300 - 320
2.500
1.700
130
100
a 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 and equivalent annualized values of SC-GHGs for internal consistency, while all other costs and benefits are discounted at either 3
percent or 7 percent.
k PM2.5-related health benefits are presented based on two different long-term exposure studies of mortality risk: a Medicare study (Wu et
al.. 2020) and a National Health Interview Survey study (Pope III et al.. 2019). The criteria pollutant benefits associated with the standards
presented here do not include the full complement of health and environmental benefits that if quantified and monetized, would increase the
total monetized benefits.
L For net benefits, the range in 2055 uses the low end of the Wu range and the high end of the Pope III et al. range. The present and equivalent
annualized values for 3 percent use the Pope III et al. values while the 7 percent values use the Wu values.
liii
-------
Table 17: Monetized discounted costs, benefits, and net benefits of Alternative 2 for
calendar years 2027 through 2055, light-duty and medium-duty (Billions of 2020
dollars)abc
CY 2055
PV, 3%
PV, 7%
EAV, 3%
EAV, 7%
Non-Emission Costs
Vehicle Technology Costs
8.8
230
140
12
12
Repair Costs
-22
-160
-74
-8.3
-6
Maintenance Costs
-47
-370 j
-180
-19
-14
Congestion Costs
0.064
0.74
0.48
0.039
0.039
Noise Costs
0.001
0.012
0.0078
0.00064
0.00064
Sum of Non-Emission Costs
-60
-300
-110
-16
-8.7 *'
Fueling Impacts
Pre-tax Fuel Savings
84
790
400
41
33
EVSE Port Costs
7.1
120
68
6.2
5.6
Sum of Fuel Savings less EVSE Port
77
680
330
35
27
Costs
Non-Emission Benefits
Drive Value Benefits
0.17
: 4
1.5
0.12
0.12
Refueling Time Benefits
-7.6
-79
-41
-4.1
-3.3
Energy Security Benefits
3.9
37 |
19
1.9
1.5
Sum of Non-Emission Benefits
........ '_15j
-39
-21
^ -2 ~Z ..
-1.7
Climate Benefits*
5% Average
13
74
74
4.9
4.9
3% Average
34
290
290
15
15
2.5% Average
47
450
450
22
22
3% 95th Percentile
100
900
900
47
47
Criteria Air Pollutant Benefits1'
PM2.5 Health Benefits - Wu et al..
15 - 17
140
61
7.2
4.9
2020
PM2.3 Health Benefits - Pope III et
7 30 - 33
270
120
14
10
al.. 2019
Net Benefits*'*
Willi Climate 5% Average
160 - 180
1.300
550
68
44
Willi Climate 3% Average
180 -200
1.500
780
78
54
Willi Climate 2.5% Average
200 -210
1.700
930
85
61
Willi Climate 3% 95th Percentile
: 250-270
2.100
1.400
110
86
a 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 and equivalent annualized values of SC-GHGs for internal consistency, while all other costs and benefits are discounted at either 3
percent or 7 percent.
k PM2.5-related health benefits are presented based on two different long-term exposure studies of mortality risk: a Medicare study (Wu et
al., 2020) and a National Health Interview Survey study (Pope III et al., 2019). The criteria pollutant benefits associated with the standards
presented here do not include the full complement of health and environmental benefits that, if quantified and monetized, would increase the
total monetized benefits.
For net benefits, the range in 2055 uses the low end of the Wu range and the high end of the Pope III et al. range. The present and equivalent
annualized values for 3 percent use the Pope III et al. values while the 7 percent values use the Wu values.
liv
-------
Table 18: Monetized discounted costs, benefits, and net benefits of Alternative 3 for
calendar years 2027 through 2055, light-duty and medium-duty (Billions of 2020
dollars)abc
CY 2055
PV, 3%
PV, 7%
EAV, 3%
EAV, 7S
Non-Emission Costs
Vehicle Technology Costs
11
' 270
170
14
14
Repair Costs
-24
-170
-77
-8.6
-6.3
Maintenance Costs
-51
-390
-190
-20
-15
Congestion Costs
0.11
1.5
0.82
0.078
0.066
Noise Costs
0.0016
0.024
0.013
0.0012
0.0011
Sum of Non-Emission Costs
-64
-290
-95
-15
-7.8
Fueling Impacts
Pre-tax Fuel Savings
93
850
430
45
35
EVSE Port Costs
7.1
120
68
6.2
5.6
Sum of Fuel Savings less EVSE Port
86
740
360
38
29
Costs
Non-Emission Benefits
Drive Value Benefits
0.21
3.2
1.8
0.17
0.15
Refueling Time Benefits
-8.2
-83
-43
-4.3
-3.5
Energy Security Benefits
4.4
40
20
2.1
1.6
Sum of Non-Emission Benefits
-3.6
-39
-21
-2.1
-1.7
Climate Benefits*
5% Average
15
80
80
53
5.3
3% Average
38
320
320
17
17
2.5% Average
52
490
490
24
24
3% 95th Percentile
110
970
970
51
51
Criteria Air Pollutant Benefits1'
PM2.5 Health Benefits - Wu et al..
16 - 18
140
62
7.3
5.0
2020
PM2.5 Health Benefits - Pope III et al..
3 1 - 34
280
120
14
10
2019
Net Benefits*'*
Willi Climate 5% Average
180 - 190
1.300
580
71
46
Willi Climate 3% Average
200 - 220
1.600
820
82
57
Willi Climate 2.5% Average
210 - 230
1.800
990
90
64
Willi Climate 3% 95th Percentile
270 - 290
2.200
1.500
120
91
a 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 and equivalent annualized values of SC-GHGs for internal consistency, while all other costs and benefits are discounted at either 3
percent or 7 percent.
k PM2.5-related health benefits are presented based on two different long-term exposure studies of mortality risk: a Medicare study (Wu et
al.. 2020) and a National Health Interview Survey study (Pope III et al., 2019). The criteria pollutant benefits associated with the standards
presented here do not include the full complement of health and environmental benefits that, if quantified and monetized, would increase the
total monetized benefits.
L For net benefits, the range in 2055 uses the low end of the Wu range and the high end of the Pope III et al. range. The present and equivalent
annualized values for 3 percent use the Pope III et al. values while the 7 percent values use the Wu values.
lv
-------
Chapter 1: Development of GHG Standards and BEV Durability
Requirements
This chapter provides technical details supporting the development of the proposed
greenhouse gas (GHG) standards for both Light Duty and Medium Duty Vehicles, and a separate
section that provides additional background on development of EPA's proposed battery
durability standards compared to those developed by the UN and California.
1.1 Development of the proposed GHG standards for Light-Duty Vehicles
As a prelude to the development of the standards for this proposal, EPA first evaluated how
the market (manufacturers and consumers) responded (and the implications on emissions) since
the footprint-based standards were first established for 2012 model year (MY). We have
witnessed a shift in sales mix from the car regulatory class to truck class (described in 1.3.1), and
an increase in average vehicle footprint. One of the issues we assessed for this proposal was
potential ways to minimize potential erosion of projected GHG reductions due to changes in fleet
mix that might be influenced by the program structure.
The Technical Support Document (TSD) supporting the 2017-2025 NPRM (U.S. EPA 2011)
outlined EPA's rationale in its selection of footprint as the attribute for its GHG standards and
provided a detailed discussion of the statistical methodology applied in fitting footprint curves to
fleet data. EPA continues to believe that footprint is appropriate for attribute-based standards.
In assessing new footprint curves for this proposal, EPA wanted to a) reduce the likelihood of
change to average vehicle footprint as a compliance strategy and b) to minimize the incentive to
shift vehicle attributes and the resulting car/truck classification as a compliance strategy. The
following steps were taken (discussed in 1.1.3):
• Establish a footprint slope for passenger vehicles (cars) that does not overly
incentivize upsizing or downsizing
• Identify an appropriate CO2 emissions offset for trucks (relative to passenger vehicles)
to recognize the incremental tailpipe CO2 due to inclusion of all-wheel drive (AWD)1
and nominal towing capability, and incorporate it into a footprint curve for trucks
• Assess whether these slopes, of their own accord, incentivize a fleet shift towards
larger or smaller vehicles
• Propose cutpoints based on observed trends in full size trucks, and reflective of equity
concerns for smaller vehicles
1.1.1 Analysis of fleet changes since 2012
During the past rulemakings for GHG standards, several stakeholders have urged the Agency
to address what they viewed as overly generous CO2 targets for light trucks. EPA received
several comments on its 2021 NPRM requesting that the nature of the footprint curves, and of
1 We use the term AWD to include all types of four-wheel drive systems, consistent with SAE standard J1952.
1-1
-------
the dual standards for cars and trucks, be re-examined. In collective response to these comments,
and as preliminary analysis for this proposal, EPA felt that it was appropriate to assess changes
in the fleet and their impact on performance of the light-duty GHG program. EPA has now
gathered almost 10 years of sales data since the attribute-based GHG standards for light duty
vehicles first took effect in 2012 MY. While the light-duty GHG program has achieved
significant emissions reductions over the past decade, EPA witnessed underperformance of
achieved tailpipe GHG emissions rates compared to those that were originally projected. This
underperformance can be attributed to the market shift towards SUVs and trucks, as well as a
modest increase in average vehicle size.
1.1.1.1 Car and Truck Regulatory Classes
The separate car and truck curves stem from regulatory class definitions originally established
by NHTSA in its corporate average fuel economy (CAFE) program for cars and trucks, as
directed by passage of Energy Policy and Conservation Act (EPCA) in 1975 (Public Law 94-163
1975). EPCA originally defined passenger automobiles ("cars") as "any automobile (other than
an automobile capable of off-highway operation) which the Secretary [i.e., NHTSA] decides by
rule is manufactured primarily for use in the transportation of not more than 10 individuals."
Under EPCA, there are two general groups of automobiles that qualify as non-passenger
automobiles or light trucks:
1) those defined by NHTSA in its regulations as other than passenger automobiles due to their
having not been manufactured "primarily" for transporting up to ten individuals; and
2) those expressly excluded from the passenger category by statute due to their capability for
off-highway operation, regardless of whether they were manufactured primarily for
passenger transportation. NHTSA's classification rule directly tracks those two broad groups
of non-passenger automobiles in subsections (a) and (b), respectively, of 49 CFR Part 523.5
(Title 49 CFR § 523.5 2022).
EPA stated the following reasons in its 2012 FRM (77 FR 62624 2012) as to why it adopted
separate car and truck regulatory classes, and separate standards for each:
• First, some vehicles classified as trucks (such as pick-up trucks) have certain attributes
not common on cars which attributes contribute to higher CO2 emissions - notably
high load carrying capability and/or high towing capability. Due to these differences,
it is reasonable to separate the light-duty vehicle fleet into two groups.
• Second, EPA wished to harmonize key program design elements of the GHG
standards with NHTSA's CAFE program where it was reasonable to do so. NHTSA
is required by statute to set separate standards for passenger cars and for non-
passenger cars.
• Finally, most of the advantages of a single standard for all light duty vehicles are also
present in the two-fleet standards. Because EPA allows unlimited credit transfer
between a manufacturer's car and truck fleets, the two fleets can essentially be viewed
as a single fleet when manufacturers consider compliance strategies. Manufacturers
can thus choose on which vehicles within their fleet to focus GHG reducing
1-2
-------
technology and then use credit transfers as needed to demonstrate compliance, just as
they will if there was a single fleet standard.
Historically, for the same footprint vehicle, truck standards have been higher (less stringent)
than their equivalent-sized car. For example, for a 50 sq. ft crossover vehicle, the AWD version
(almost always classified as a truck) would be subject to a standard 40 or more g/mi higher than
an equivalent 2WD version of that same model (classified as a car). Beyond MY 2021, the offset
between the two curves will start to reduce but it is still significant. Table 1-1 shows a
comparison of the GHG targets (and the calculated offset) for a 50-square foot car and truck
crossover through the years. Certification data for MY 2019 vehicles comparing tailpipe CO2
emissions of vehicle models which are sold as both cars and light trucks (such as crossovers),
depending on their drivetrain - suggests that the empirical tailpipe CO2 emissions offset is far
less than the compliance offset which has been provided to crossover vehicles.
Table 1-1. Comparison of Car and Truck GHG Targets for 50 Square-Foot Vehicles
Model Year Car Target g/mi Truck Target g/mi Offset g/mi
2012 287 331 44
2017 235 282 46
2021 197 247 50
2026 142 172 30
Since the footprint-based light duty GHG standards first took effect in MY 2012, the makeup
of the fleet has changed significantly. In 2012, 64 percent of new vehicle sales were classified as
passenger vehicles, with the remaining 36 percent of sales as light trucks. As of 2021, sales of
sedans have declined; from 55 percent in 2012, they now represent only 26 percent of fleet sales.
Sedans have largely been replaced with taller vehicles such as truck-like sport utility vehicles
(SUVs) and crossover utility vehicles (CUVs). There has also been an increase in pickup truck
share, from 10 percent to 16 percent in 2021. The shift in sales mix of vehicle types is shown in
Figure 1-1.
1-3
-------
100%
2? 75%
(0
-C
C/)
c
o
o
3
T5
O
50%
25%
0%-
/•»
Sedan / Wagon
Car SUV
.a m
Ju
\ A_ i m
\ »~» *
'»> * /
J Truck SUV '
1975 1 985 19 95 200 5 2015 2025
Model Year
Figure 1-1. Light-Duty Sales by Vehicle Type (U.S. EPA 2022)
In total, there has been a marked increase in the number of light truck sales: as of 2021, light
trucks now account for 63 percent of new sales, and passenger vehicles only account for 37
percent of sales. This is illustrated in Figure 1-2.
Car/Truck Market Share
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
20OS
2010
2012
2014 2016
Model Year
Whitli? Truci
2018
2020
Figure 1-2. Change in Car and Truck Regulatory Class Market Share, 2012-2021 MY
The impact of this shift to light trucks on CO2 emissions has been noteworthy. In its analysis
supporting the 2012 rulemaking (which set standards for MY 2017-2025 vehicles) EPA's
1-4
-------
projected fleet mix for future years was unchanged from MY 2012 at 64 percent car and 36
percent truck2. For the 2021 standards, EPA projected that the MY 2021 fleet (based on the
originally projected car/truck mix and average footprint) would need to meet an average CO2
target of 217 g/mile.3 However, the shift in actual car/truck mix to 37 percent car and 63 percent
truck alone resulted in 14 g/mi higher standards by MY 2021.
Meanwhile, the fleet has increased its overall average footprint by over 5 percent (from 48.9
sq ft in 2012 to 51.5 sq ft in 2021), due to fewer small sedans, and an increase in average full-
size pickup trucks. This shift has permitted compliance under higher numerical standards: the
result of the increased average footprint alone resulted in an 8 g/mi increase in the MY 2021 fleet
average GHG target compared to the MY 2012 average footprint.
In total, the sum of these effects has resulted in MY 2021 standards that are 22 g/mi higher on
a fleetwide average than were originally projected. The effects of car/truck shift and footprint
increase (combined) are illustrated in Figure 1-3. From 2012-2021, the GFIG program has
projected combined reductions in CO2 emissions rates of 28 percent (or an average annual rate of
3.5 percent per year). During this period, the achieved industry CO2 emissions performance
value for new vehicles has only decreased from 287 g/mi in 2012 to 239 g/mi in 2021 - an
average annual reduction of about 2 percent per year4 (U.S. EPA 2021).
360
-p 280
S 240
E
d»
> 220
180
Effective Industry C02 Standards
2012 2013 2014 2015 2016 2017 2018 2019 2020 2021
Model Year
•CarStd * 'truck Sid
•All $td
^11 Std - 2012 Sales Mij
Figure 1-3. Effect of Fleet Shift on Average CO2 Standard
2 For the 2020 rule the projected car/truck mix was revised to 54 percent car and 46 percent truck, but it still
underestimated the market share of trucks that would be sold.
3 This has been adjusted from the published values to reflect differences in expected lifetime VMT for trucks
compared to cars.
4 Note that the 2012 industry performance of 287 g/mi was lower than the 2012 standard of 299 g/mi (black line in
Figure 3). This resulted in generation of GHG credits.
1-5
-------
1.1.2 Relationship between GHG curve shape, stringency, and BEV share
It is important to note that for the earlier rulemakings, footprint was selected as an attribute
with a fleet that was almost exclusively comprised of internal combustion engine (ICE) vehicles.
In contrast, footprint does not have any relationship with tailpipe emissions from BEVs or any
other zero-emission vehicle. A fleet of exclusively battery electric vehicles would all emit zero
g/mi tailpipe GHG, regardless of attribute (vehicle size, weight, tow rating, etc.); mathematically,
the only appropriate "footprint curve" for an all-electric fleet would have a slope of zero (flat)
and be set to zero g/mi. And so, as the fleet transitions to an increasing percentage of ZEVs, the
appropriate slope for the fleet will need to consider not just the current available technology of
ICE vehicles, but the ratio of those ICE vehicles sold as a percentage of the entire fleet of new
vehicles (including BEVs). For example, if only 50 percent of new vehicles sold were ICE
vehicles, it would be reasonable to scale the slope of the curves by roughly 50 percent. In setting
future fleet average standards, the anticipated decreasing level of ICE vehicles are thus factored
into the setting of the car and truck slopes.
1.1.3 Development of appropriate GHG curve shape (slope and cut points)
EPA believes that footprint is still an appropriate attribute for its standards curves. However,
EPA assessed ways to modify the shape of the footprint curves and the relative difference
between cars and trucks to minimize the incentive for manufacturers to change vehicle size or
regulatory class as a compliance strategy, which is not a goal of the program and could in turn
potentially reduce the projected GHG emissions reductions.
Beginning with the premise that the primary objective of light-duty vehicles (regardless of
their car/truck regulatory class designation) is to move people and their incidental cargo, EPA
first determined an appropriate curve slope for passenger vehicles (cars). The distinguishing
features that provide more capability for trucks and the associated increase in tailpipe emissions
(for ICE vehicles) are then used to build out a separate a truck curve from the base car curve. The
steps and the analysis performed are described below.
1.1.3.1 Establishing slope of car curve
EPA's OMEGA model, in addition to modeling the application of vehicle technology, also
has the capability to project changes in vehicle size as a compliance response. In determining an
appropriate slope for the car curve, EPA modeled a range of car slopes to evaluate the footprint
response - that is, to assess the tendency of the fleet to upsize or downsize as a compliance
strategy.
In theory, for ICE vehicles, a footprint-based slope that is too steep will incentivize
manufacturers to increase the size of their vehicles as a compliance strategy, whereas a slope too
flat may encourage some downsizing. For BEVs (or any ZEV technology), there is no
relationship between footprint and tailpipe emissions, so any slope greater than zero should
provide manufacturers with a compliance incentive (at some level) to upsize BEVs. For a fleet
comprised of BEV and ICE vehicles subject to the same footprint curve, the best compromise for
determining a "neutral" slope is one that strikes a balance between upsizing incentives for BEVs
with downsizing incentives for ICE vehicles.
1-6
-------
For any given vehicle, a manufacturer may be incentivized to increase footprint if the
compliance benefit of higher GHG target values (and less potentially less costly technology
needed for compliance) and consumer valuation of vehicle size exceeds the additional cost of
producing a larger vehicle and higher emissions associated with greater vehicle mass. In the
OMEGA model inputs, we assumed a consumer valuation, or willingness-to-pay (WTP) of
$20Q/sq ft of vehicle footprint. While this is on the low end of the range suggested in the
literature (e. a. Greene 2018), a higher WTP would create a stronger upsizing tendency, which
would suggest an even flatter "size-neutral" slope than found in our analysis.
The slope that corresponded with a neutral response for ICE vehicles only (overall, no change
in the average footprint of ICE vehicles) was 0.8 g/mi/square foot. This slope was then scaled
down accordingly- for example, based on a nominal BEV sales penetration of around 50 percent,
this 0.8 slope would be scaled down to 0.4 (based on a remaining 50 percent of ICE vehicles).
To confirm that this slope would give us a neutral response over a mixed fleet with
approximately 50 percent BEVs, we reviewed the footprint response (at a consistent level of
stringency which corresponds with 50 percent BEV share) for slopes ranging from 0 to 0.8
g/mi/sq ft. Figure 1-4 (for sedans) and Figure 1-5 (car SUVs) show the final fleet average
footprint, compared to the base year average footprint (in orange) for each slope tested. The
overall fleet-neutral slope was determined to be 0.43 g/mi/sq ft. As can be seen, the shift in the
two body styles balance out (about 0.5 sq ft increase for sedans and a 0.5 sq ft decrease for car
SUVs).
sedan-wagon body style
48,0
47.5
47.0
¦p 46.5
M—
46.0
c 45.5
2" 45.0
O
° 44.5
44.0
43.5
43.0
o o o o oo o o o
Slope (g/mi/sq ft)
Figure 1-4. Footprint Response to Slope Sweeps, Sedan/Wagon Body Style
OLniHLnrMLnroLn^fnLTiLnLnu3Lnr^Lnoo
1-7
-------
47.0
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Figure 1-5. Footprint Response to Slope Sweeps, (Car Reg Class) CUV/SUV Body Style
1.1.3.2 Development of truck curve
Historically, there has been a significant increase (offset) between the car and light truck
footprint-based curves to reflect the additional utility of trucks. The large shift in sales from car
crossovers to truck crossovers might suggest that the size of this offset was not appropriate for
vehicles with similar towing and hauling capability - for example, crossover vehicle models
(trucks) equipped with AWD compared to those same models with 2WD (cars). Most of these
vehicles available with both driveline options exhibit the same tow rating and nearly identical
GCWR.
In redesigning the truck curve, EPA considered the "base utility" of moving people for
passenger vehicles and light trucks to be similar (this is especially true for crossover vehicles and
wagons, for example). However, larger trucks which are designed for more towing and hauling
capability do require design changes to allow for handling of these larger loads and this is
reflected in increased engine capability, body-on-frame design, and greater structural mass. EPA
analyzed empirical fleet data to quantify the additional tailpipe CO2 resulting from these required
design changes and use it as a basis for a "utility offset" that is built into the slope of the
proposed truck curves.
The truck curve is based on the car curve, but with additional allowances for 1) AWD and 2)
towing and hauling utility. The analysis that went into the determination of each proposed offset,
and the resulting truck slope, is detailed below.
3) AWD Offset
EPA analyzed certification data (Ellies 2023) from MY 2019 (the latest at the time the
analysis was completed) to compare the tailpipe CO2 emissions of crossover vehicle models with
1-8
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2WD and AWD driveline configurations and identical engines. In total, 32 vehicle models were
offered in both a 2WD and an AWD version and were subject to passenger vehicle and light
truck CO2 compliance targets, respectively.
AWD vs 2WD Tailpipe C02 Increase: 2019 Crossovers
6
5
Median increase: 12.5 g/mi
4
-20 -10 0 10 20 30 40
TP delta
Figure 1-6. Increase in Tailpipe CO2 Emissions: MY 2019 AWD vs. 2WD Crossovers
Figure 1-4 shows the distribution of tailpipe increase between unique 2WD and AWD vehicle
models. The median increase in tailpipe CO2 is 12.5 g/mi for these models, although several
models showed increases below 10 g/mi. As this characteristic is the only attribute distinguishing
a "truck" crossover from a "car" crossover that should produce measurable tailpipe CO2
differences, it forms the basis for the proposed offset between the car and truck curves for
vehicles of equivalent towing capacity. Based on this analysis, EPA's proposed footprint curves
reflect an offset between the car and truck curves of 10 g/mi for ICE vehicles equipped with
AWD.
4) Towing and Hauling Utility Offset
In determining an offset for truck utility, EPA reviewed vehicle specifications available in the
MY 2019 fleet data. One way to quantify a vehicle's utility (or maximum output) is by its gross
combined weight rating (GCWR).5 GCWR is the value specified by the vehicle manufacturer as
the maximum weight of a loaded vehicle and trailer. (Title 40 CFR § 86.1803-01 2023)
In its simplest form,
GCWR = GVWR + maximum loaded trailer weight,
where:
5 GVWR describes the maximum load that can be carried by a vehicle, including the weight of the vehicle itself.
GCWR describes the maximum load that the vehicle can haul, including the weight of a loaded trailer and the
vehicle itself. For more information, please refer to the Medium and Heavy-duty GHG Phase 2 FRM (81 FR 73478
2016).
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GVWR (gross vehicle weight rating) is the value specified by the manufacturer as the
maximum design loaded weight of a single vehicle (Title 40 CFR § 86.1803-01 2023).
EPA first reviewed MY 2019 vehicle models and plotted GCWR vs engine performance. Of
horsepower or engine torque, engine torque correlated best with a truck's utility. As shown in
Figure 1-5, there is a positive correlation between a vehicle's GCWR and its rated engine torque.
GCWR vs Torque: All
20000
18000
16000
14000
~ 12000
I 10000
^ 8000
6000
4000
2000
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0 50 100 150 200 250 300
Rated Torque (ft-lb)
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Figure 1-7. GCWR-Torque Relationship, MY 2019 Light Truck Data
As seen in the fleet data, vehicle models which are offered at a higher tow rating than the base
model will be equipped with a more powerful engine (and accompanying transmission, driveline
and chassis improvements). From a modeling perspective EPA focused on the increase in engine
torque based on the relationship observed above.
EPA then evaluated the increase in tailpipe CO2 for additional towing capacity using response
surface equations (RSEs) from ALPHA model results as follows:
• First, we estimated the required nominal engine torque for three vehicle models with
different body styles (small pickup, SUV, and full-size pickup) at various tow rating
levels by calculating the GCWR and applying the relationship seen in Figure 1-7.
• Then we scaled each engine model to an appropriate displacement (to match required
torque) for various modeled engine architectures6 based on each modeled engine's
BMEP. Test weight (curb + 300 pounds) was increased slightly to account for heavier
powertrain, driveline, suspension and brakes that are required for greater towing
capacity. Road loads were modified slightly based on this increased weight. We were
then able to predict CO2 based on the RSE results for a downsized turbocharged
engine and various gasoline GDI engine models.
6
ALPHA modeled engines include GDI with and without cylinder deactivation and Turbo Gas for pickups, and GDI
and Atkinson for CUVs.
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• The modeling results show the increase in CO2 as a function of an increase in towing
capacity in Figure 1-8. The data suggests that the average increase in CO2 for a given
vehicle is about 9 g/mi per additional 1000 pounds of tow capability.
Incremental C02 vs Incremental Towing
20
10
0 •
0
Figure 1-8. Incremental CO2 as a Function of Increased Towing Capacity
Finally, MY 2019 data shown in Figure 1-9 indicates that tow rating is directionally
proportional with footprint (as longer wheelbases are required for stability during increased
towing demands). The difference in towing capacity between a 70 square foot truck (at a sales-
weight average tow rating slightly over 9000 pound) and that of a 45 square foot truck (with
average tow rating just over 2000 pound) is 7000 pounds. Based on the relationship derived
above for CO2 vs. towing capacity, this would correspond to an addition 63 g/mi of tailpipe CO2
between 45 and 70 square feet7. EPA combined these relationships to establish an appropriate
footprint-based truck slope that is based on the additional utility that trucks are designed for. This
represents the full utility-based offset of the proposed truck curve for a 100 percent ICE vehicle
fleet.
1000 2000 3000 4000 5000 6000 7000
7 EPA is not considering towing differences for trucks greater than 70 square feet or smaller 45 square feet
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14000
12000
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i 8000
6000
4000
2000
0
Tow Rating vs Footprint
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Figure 1-9. Tow Rating-Footprint Relationship, MY 2019 Trucks
For a strictly ICE vehicle fleet, the AWD and utility offset would look as shown in Figure
1-10.
Buildup of Truck Curve from "Base" Car Curve
40
55 60
Footprint (sq ft)
Car no CP w/AWD Truck no CP w/AWD and Utility Offsets
Figure 1-10. AWD and Utility Offset Applied to Establish Truck Curve (100 percent ICE)*
However, as described in 1.3.2, we are proposing the scaling of the car and truck curves as
appropriate to reflect expected increased BEY penetration. For the 2030 fleet we are applying a
! For this figure and the subsequent figures, "no CP" indicates that no outpoints were reflected in these plots.
1-12
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50 percent factor to these offsets (i.e., a nominal penetration of 50 percent remaining ICE
vehicles), as well as a 50 percent factor to the base car slope. We recognize BEV penetration
may be higher or lower than this figure but we believe it is appropriate, as discussed above, to
reflect increased BEV penetration in the curves and this is a reasonable approach. This reduces
the AWD offset to 5 g/mi and the full-size truck utility offset to 31.5 g/mi as shown in Figure
1-11.
Buildup of Truck Curve from "Base" Car Curve
3$ 40 45 50 55 60 65 70 75 80
Footprint (sq ft)
Car no CP w/AWD Truck no CP w/AWD and Utility Offsets
Figure 1-11. AWD and Utility Offset Applied to Establish Truck Curve (Scaled).
1.1.3.3 Analysis of Footprint Response to Proposed Standards
To confirm that the proposed slopes for car and truck curves would not incentivize a shift in
vehicle size, we analyzed the projected trend in vehicle footprint for the proposed standards to
confirm a minimal overall change in vehicle size for the combined fleet. Figure 1-12 shows a
comparison of 2020 base year footprint (blue) compared to the MY 2032 average projected
footprint (orange) for the proposed standards, for BEV and ICE vehicles, by body style. As can
be seen, the BEVs increase slightly in size, while the ICE vehicles decrease slightly. These two
tendencies offset each other to minimize the overall change in fleet size.
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ICE
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ICE
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ICE
sedan cuv_suv pickup
Figure 1-12. Comparison of Average Footprint to Base Year Footprint for Proposed
Standards
Table 1-2 shows the numerical MY 2032 average footprint (FP) for the various body styles
for BEVs and ICE vehicles, and the fleet averages, compared to base year (MY 2020) footprint
for the proposed standards. The overall change in average footprint (51.3 square feet) compared
to the base year footprint (50.6) is minimal (an increase of 1 percent).
Table 1-2: Comparison of MY 2032 Footprint to Base Year Footprint, Proposed Standards
BEV
ICE
Combined
Base FP
MY 2032 FP
Base FP
MY 2032 FP
Base FP
MY 2032 FP
Sedan
46.5
48.1
46.0
43.7
46.4
47.1
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49.0
50.9
49.0
46.7
49.0
49.7
Pickup
65.8
69.1
65.5
63.2
65.7
65.7
Total
49.7
51.7
52.7
50.3
50.6
51.3
1.1.3.4 Cut points
EPA evaluated the sales weight-average footprint for full size pickups in determining the
appropriate upper truck cutpoint for this proposal. Figure 1-13 shows that the average footprint
has increased for full size pickups from 67 square feet to over 69 square feet in 2021. The upper
cutpoint has increased from 66 square feet in MY 2018 to 69 square feet in 2021. To avoid any
incentive to further upsize the full-size pickups, EPA is proposing to phase down the long-term
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upper truck cutpoint to 70 square feet9. The upper cutpoint for cars is unchanged at 56 square
feet.
70
2012 2013 2014 2015 2016 2017 2018 2019 2020 2021
Mode1! Vear
¦¦Full Truck • SWfP IT Upper Cutpt
Figure 1-13. Sales-weighted Footprint of Fall-Size Pickups, 2012-2021 MY
EPA proposes that vehicles smaller than 45 square feet should not necessarily be subject to
more stringent standards based on an extrapolation of the utility offset approach described above.
Many vehicle models smaller than 45 square feet, both cars and trucks, are offered and EPA does
not want to discourage vehicles in this segment for equity and affordability concerns. These
include popular vehicles such as the Subaru Crosstrek, Nissan Kicks, the Chevy Trax, and the
Honda HR-V.
Applying the cutpoints to the preceding methodology yields the final curve shape that is
shown in Figure 1-14.
9 In the 2021 rule, for MYs 2023 and beyond the upper truck cutpoint was restored to the original 74 square foot
value first finalized in 2012. EPA proposes to reduce the upper cutpoint beginning in MY 2027, with full phase
down (from 74 in 2026) to 70 square feet by 2030.
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Buildup of Truck Curve from "Base" Curve
35 40 45 50 55 60 65 70 75 SO
Footprint (sq ft)
Car no CP «—•w/AWO — — w/tow Carw/CP Trkw/CP
Figure 1-14. Car and Truck Curves, Scaled, with Cut points
1.2 Development of the proposed GHG standards for Medium-Duty Vehicles
1.2.1 History of GHG standards for Medium-Duty Vehicles
In the Phase 1 Heavy-duty rule, EPA established a GHG standards program structure for
complete Class 2b and 3 heavy-duty vehicles (referred to in this rule as 'medium duty pickups
and vans") as part of a joint GHG and CAFE program with NHTSA (76 FR 57106 2011). The
Phase 1 standards began to be phased-in for MY 2014 with the final Phase 1 stringency levels
stabilizing in MY 2018. The Phase 1 program worked well to establish a first time GHG
standards program for these work-oriented vehicles. The Phase 2 program established more
stringent standards for MY 2027, phased in over MYs 2021-2027, requiring additional GHG
reductions (81 FR 73478 2016). The MY 2027 standards will remain in place unless and until
amended by the agency. Medium duty vehicles (previously described as heavy-duty vehicles in
the Phase 1 and Phase 2 HD GHG rules) with a gross vehicle weight rating (GVWR) between
8,501 and 10,000 pounds are classified in the industry as Class 2b motor vehicles while vehicles
with GVWR between 10,001 and 14,000 pounds are classified as Class 3 motor vehicles. Class
2b includes vehicles classified as medium-duty passenger vehicles (MDPVs) such as very large
SUVs (Title 40 CFR § 86.1803-01 2023)lu. Because MDPVs are designed primarily to be used
as light-duty passenger vehicles, they are regulated under the light-duty vehicle rules. Thus, the
requirements for MDPVs in this rulemaking are the same as the light-duty pickups with respect
to both GHG and criteria emission standards.
10 We are proposing changes in the definition of MDPV in 40 CFR § 86.1803-01. See § III.D of the Preamble to
this proposed rale.
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Historically, about 90 percent of medium-duty pickups and vans have been what are often
referred to as "3/4-ton" and "1-ton" pickup trucks11, 12- and 15-passenger vans, and large work
vans that are sold by vehicle manufacturers as complete vehicles, with no secondary
manufacturer making substantial modifications prior to registration and use. Most of these
vehicles are produced by companies with major light-duty markets in the United States,
primarily Ford, General Motors, and Stellantis12. Often, the technologies available to reduce
GHG emissions from this segment are similar to the technologies used for the same purpose on
light-duty pickup trucks and vans, including both engine efficiency improvements (for gasoline
and diesel engines) and vehicle efficiency improvements. In the Heavy-Duty Phase 1 (76 FR
57106 201 l)and Phase 2 (81 FR 73478 2016) rules, EPA adopted GHG standards for medium-
duty pickups and vans based on the whole vehicle (including the engine), expressed as grams of
CO2 per mile, consistent with the way these vehicles are regulated by EPA today for criteria
pollutants.
Vehicle testing for both the medium-duty and light-duty vehicle programs is conducted on
chassis dynamometers using the drive cycles from the EPA Federal Test Procedure (Light-duty
FTP or "city" test) and Highway Fuel Economy Test (HFET or "highway" test) (Title 40 CFR
§ 1066.801 Subpart 12023). For the light-duty GHG standards, EPA factored vehicle attributes
into the standards by basing the GHG standards on vehicle footprint (the wheelbase times the
average track width). For those standards, passenger cars and light trucks with larger footprints
are assigned higher GHG targets (see Chapter 1.1.1.1). For HD pickups and vans, the agencies
also set GHG standards based on vehicle attributes but used a work-based metric as the attribute
rather than the footprint attribute utilized in the light-duty vehicle rulemaking. Work-based
measures such as payload and towing capability are key among the parameters that characterize
differences in the design of these vehicles, as well as differences in how the vehicles will be
utilized. Buyers consider these utility-based attributes when purchasing a HD pickup or van.
EPA therefore finalized Phase 1 and 2 standards for medium-duty pickups and vans based on a
"work factor" attribute that combines the vehicle's payload and towing capabilities, with an
added adjustment for 4-wheel drive vehicles.
For Phase 1 and 2, the agencies adopted provisions such that each manufacturer's fleet
average standard is based on production volume-weighting of target standards for all vehicles
that in turn are based on each vehicle's work factor (76 FR 57106 2011) (81 FR 73478 2016).
These target standards are taken from a set of curves (mathematical functions). The Phase 2 work
factor GHG standards are shown in Figure 1-15 for reference. The agencies established separate
standards for diesel and gasoline medium-duty pickups and vans. Note that this approach does
not create an incentive to reduce the capabilities of these vehicles because less capable vehicles
are required to have proportionally lower emissions and fuel consumption targets.
11 "3/4-ton" and "1-ton" are common industry terms, not regulatory definitions. These terms typically refer to Class
2b and Class 3 trucks, respectively. For specific regulatory definitions for Class 2b and Class 3, please refer to 40
CFR § 86.1803-01.
12 Formerly Fiat-Chrysler during the period when the Heavy-duty Phase 1 and 2 standards were developed.
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Work Factor
Work Factor
Figure 1-15: Heavy-duty Phase 2 work factor-based GHG standards for medium-duty
pickups and vans (81 FR 73478 2016).
1.2.2 Development of the proposed standards for Medium-Duty Vehicles
Medium-duty-vehicles (MDV)13 are similar to the light-duty trucks addressed in this program
with respect to both technological opportunity for electrification as well as in terms of how they
13 In our proposal we are defining a new MDV category that combines Class 2b and Class 3 and that excludes
MDPV. For the full definition, please refer to § III. A. 1 of the Preamble to this proposed rule.
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are manufactured. Several light-duty manufacturers also the primary manufacturers of the
majority of medium-duty pickups and vans. Medium-duty pickups and vans share close parallels
to the light-duty program regarding how EPA has developed our proposed medium-duty
standards and compliance structures with the penetration of new technologies such as
electrification. The primary difference between the light-duty and the MDV standards is that
MDV standards continue to be based on work attributes rather than vehicle footprint. MDV
pickups and vans are true work vehicles that are designed for much higher towing and payload
capabilities than are light-duty vehicles. The technologies applied to light-duty vehicles are not
all applicable to MDVs at the same adoption rates, and the internal combustion engine
technologies often produce a lower percent reduction in CO2 emissions when used in many
medium-duty vehicles. For example, electrification of a MDV pick-up designed and used solely
for high towing capacity may not be appropriate or acceptable to consumers at this time.
Conversely, delivery vans or payload-oriented pick-ups that operate over limited distances and
daily routes present a significant opportunity for electrification. Due to this expected usage
difference of MDVs, there are fewer parallels with the structure of the light-duty program. In
addition, the phase-in provisions in the MDV program, although structurally different from those
of the light-duty program due to CAA requirements, serve the same purpose, which is to allow
manufacturers to achieve large reductions in emissions while providing a broad mix of products
to their customers.
The form and stringency of the original Phase 1 and 2 standards curves were based on the
performance of a set of vehicle, engine, and transmission technologies expected (although not
required) to be used to meet the GHG emissions standards with full consideration of how these
technologies were likely to perform in medium-duty vehicle specific testing and use. The
technologies included:
• Advanced engine improvements for friction reduction and low friction lubricants
• Improved engine parasitics, including fuel pumps, oil pumps, and coolant pumps
• Valvetrain variable lift and timing • cylinder deactivation
• Direct gasoline injection
• Cooled exhaust gas recirculation
• Turbo downsizing of gasoline engines
• Diesel engine efficiency improvements
• Downsizing of diesel engines
• Electric power steering
• High efficiency transmission gear boxes and driveline
• Further improvements in accessory loads
• Additional improvements in aerodynamics and tire rolling resistance
• Low drag brakes
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• Mass reduction
• Mild hybridization
• Strong hybridization
• Advanced 8 and higher speed automatic transmissions
• Diesel aftertreatment optimization
• BEV
Substantial opportunity still exists to further implement and make improvements to most of
these technologies to achieve further reductions in GHG emissions beyond those achieved in the
initial implementation of the Heavy-duty Phase 2 program as it applies to Class 2b and Class 3
vehicles (81 FR 73478 2016). Many of these technologies have not yet been implemented since
the Phase 2 standards are still within a phase-in period continuing through MY 2027. The
agency still expects to see additional penetration of many of these technologies.
The electrification of MDVs in the form of BEVs, particularly in delivery vans some pick-
ups, has the highest potential for GHG reductions of all technologies investigated by the agency.
However, mild and strong hybridization and targeted PHEV implementation, particularly PHEV
Class 2b pickup trucks, may also provide substantial GHG emission reductions as well as
potential improvements in internal combustion engines, transmissions and vehicle technologies.
1.2.2.1 Proposed MDV GHG Standards
Our proposed GHG standards for all MDVs14 are entirely chassis-dynamometer based and
continue to be work-factor-based as with the previous heavy-duty Phase 2 standards. The
standards also continue to use the same work factor (WF) and GHG target definitions (81 FR
73478 2016). However, for MDVs with high towing capability at or above 22,000 pounds
GCWR, we are proposing to limit the GCWR input into the work factor equation to 22,000
pounds GCWR in order to prevent increases in the GHG emissions target standards that are not
fully captured within the loads and operation reflected during chassis dynamometer GHG
emissions testing. The chassis dynamometer testing methodology for MDVs does not directly
incorporate any GCWR related direct load or weight increases (e.g., trailer towing) however,
they would be reflected in the higher target standards when calculating the GHG targets using
GCWR values above 22,000 pounds. Without some limiting "cap", the resulting high target
standards relative to actual measured performance would be unsupported within the test data
used to demonstrate compliance and would generate windfall compliance credits for higher
GCWR ratings. The equations for MDV compliance with the proposed GHG standards are:
CChe Target (g/mi) = [a x WF] + b
WF = Work Factor = [0.75 x [Payload Capacity + xwd] + [0.25 x Towing Capacity]
Payload Capacity = GVWR (pounds) - Curb Weight (pounds)
14 Pickup trucks, vans, incomplete vehicles and other vehicles having GVWR between 8,501 and 14,000 pounds,
excluding MDPVs. See § III.A.l of the Preamble to this proposed rule.
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xwd = 500 pounds if equipped with 4-wheel-drive, otherwise 0 pounds
Towing Capacity = GCWR (pounds) - GVWR (pounds); with GCWR capped within the
calculation at 22,000 pounds for GCWR > 22,000 pounds
and with coefficients "a" and "b" as defined in Table 1-3:
Table 1-3: Proposed Coefficients for MDV Target GHG Standards
Model Year a b
2027 0.0348 268
2028 0.0339 261
2029 0.0310 239
2030 0.0280 216
2031 0.0251 193
2032 0.0221 170
The feasibility of the-2027 - 2032 GHG standards is based primarily upon an assessment of
the potential for a steady increase in MDV electrification, primarily within the van segment. The
feasibility of the initial year of compliance (2027) is from continued introduction of technologies
phasing into use for compliance with HD GHG Phase 2 as described in DRIA Chapter 1.2.2.
Note that the proposed fuel neutral standard in 2027 is a revision that would replace the last year
of phase-in into the HD Phase 2 GHG program and applies solely to MDVs within that program.
The primary assumptions within the work factor based GHG standards for MDV from 2028 to
2032 include an approximately 8 percent year over year improvement, to a large degree from
electrification of MDV vans and to a lesser degree electrification of a small fraction (<25
percent) of MDV pickups and adoption of other technologies. The MDV target GHG standards
are compared to the current HD Phase 2 gasoline standards in Figure 1-13. Note that the GHG
standards continue beyond the data markers shown in Figure 1-13. The data markers within the
figure reflect the approximate transition from light-duty trucks and MDPVs to MDVs at a WF of
approximately 3,000 pounds and the approximate location of 22,000 pounds GCWR in work
factor space (e.g., a WF of approximately 5,500 pounds). Beginning in 2027, the MDV GHG
program moves gasoline, diesel, and PEV MDVs to fuel-neutral standards, i.e., identical
standards regardless of the fuel or energy source used.
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500
450
400
=¦ 350
_og
O
u 300
250
200
150
2500 3000 3500 4000 4500 5000 5500 6000
Workfactor
Figure 1-16: Proposed MDV GHG Target Standards
1.3 Development of the proposed battery durability standards
As described in sections III.F.2 and III.F.3 of the Preamble, EPA is proposing new battery
durability and warranty standards for PEVs.
In developing the proposed standards, EPA took into consideration the provisions established
in United Nations Global Technical Regulation No. 22, as well as the California Air Resources
Board battery durability and warranty requirements under the Advanced Clean Cars II program.
Although EPA is not proposing provisions that are identical to either program, we recognize
the fact that automakers may be subject to GTR No. 22 in markets outside the U.S., and that
many may also be subject to the durability and warranty requirements under the State of
California ACC II program. In considering the design and feasibility of the proposed standards,
EPA has considered the specific features and purposes of both programs and has considered
opportunities for harmonization.
The following discussion provides background on GTR No. 22, and on the California Air
Resources Board ACC II durability and warranty requirements. For a complete discussion of the
proposed requirements under this proposal and their relation to these other programs, please refer
to Preamble III.F.2 and III.F.3.
1.3.1 United Nations Global Technical Regulation No. 22 on In-Vehicle Battery
Durability
For several years, EPA has worked closely with the United Nations Economic Commission
for Europe (UNECE) Working Party on Pollution and Energy (GRPE) to develop a world
harmonized Global Technical Regulation (GTR) for In-vehicle Battery Durability for Electrified
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Vehicles, or GTRNo. 22 (UN ECE 2022). This GTR was created within a GRPE Informal
Working Group (IWG) known as Electric Vehicles and the Environment (EVE).
The EPA proposal for the BEV and PHEV battery durability program is described primarily
in Section III.F.2 of the Preamble. The proposed program largely adopts the general framework
and requirements described in GTRNo. 22, with minor adaptations to incorporate established
EPA test procedures and to achieve specific program objectives. In addition to the reference
published GTR, the EVE also produced a document which outlines the technical justification and
the development process of the GTR requirements (UN ECE 2021).
In 2015 the UNECE began studying the need for a GTR governing battery durability in light-
duty vehicles. In 2021 it finalized GTRNo. 22, which provides a regulatory structure for
contracting parties to set standards for battery durability in light-duty BEVs and PHEVs. The
European Commission and other contracting parties are currently working to adopt this standard
in their local regulatory structures. EPA representatives chaired the informal working group that
developed this GTR and worked closely with global regulatory agencies and industry partners to
complete its development in a form that could be adopted in various regions of the world,
including potentially the United States.
GTR No. 22 establishes a framework for regulating battery durability of BEVs and PHEVs by
establishing durability metrics, durability performance monitoring requirements, minimum
performance requirements, and procedures for determining monitor accuracy and determining
compliance. It does not include battery warranty requirements. To monitor durability
performance, it requires that manufacturers implement two ways of monitoring battery state-of-
health (SOH): State of Certified Energy (SOCE) and State of Certified Range (SOCR). SOCE
(and potentially in the future, SOCR) is then used to determine compliance with a Minimum
Performance Requirement (MPR) at two points during the vehicle's life, as described below. In
the current version of the GTR, the monitor requirements apply to Category 1-1, 1-2, and
Category 2 vehicles. The MPR applies only to Category 1-1 and Category 1-2 vehicles. The
IWG chose not to set an MPR for Category 2 vehicles at this time, largely because the early
stage of adoption of these vehicles meant that in-use data regarding battery performance of these
vehicles was difficult to obtain, and because these vehicles are more likely to have auxiliary
work-related features that use power from the battery for non-propulsion purposes, and the
impact of these features on battery life was not currently well characterized. MPR requirements
for category 2 vehicles were therefore reserved for possible inclusion in a future amendment to
the GTR.
SOCE is an estimate of remaining usable battery energy (UBE) capacity at a point in the
vehicle's life, expressed as a percentage of the original UBE capacity when the vehicle was new.
In most jurisdictions, including the U.S. and those that have adopted the WLTP, original UBE is
already measured as part of the vehicle certification or range labeling process when the vehicle is
new. The GTR requires the SOCE monitor estimate of remaining UBE capacity to be readable
by the customer and by regulatory authorities. The algorithm for estimating and updating SOCE
during the lifetime of the vehicle is left to the manufacturer. The SOCE monitor value is required
to be on average no more than 5 percent higher than the actual value that would be obtained if
the true remaining UBE capacity were to instead be determined by the test procedure that was
used at certification. Accuracy is determined by a test program in which a statistical test is
applied to test results from a sample of test vehicles within a defined test group.
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SOCR is an estimate of the total electric driving range that the vehicle battery remains capable
of providing at a point in the vehicle's life, expressed as a percentage of the original electric
driving range when the vehicle was new. As with UBE, electric driving range is already
measured and collected under applicable regional certification or type approval procedures when
the vehicle is new. The GTR requires SOCR to be readable by regulatory authorities but not
necessarily by the consumer. The SOCR monitor is also subject to the requirements for
determination and reporting of monitor accuracy but is not currently subject to the accuracy
requirement.
The GTR establishes a Minimum Performance Requirement (MPR) that specifies a minimum
percentage retention of SOCE and SOCR at two points in the vehicle's life. During the first phase
of implementation of the GTR, only the SOCE MPR will be enforced, although SOCR will be
collected for information purposes. As shown in Table 1-2, the MPRs established by GTR No.
22 require retention of at least 80 percent SOCE at 5 years or 100,000 km (about 62,000 mi), and
70 percent SOCE at 8 years or 160,000 km (about 100,000 miles).
Table 1-4. Battery durability performance requirements of UN GTR No. 22
Percent retention of at Mileage Percent of sample
must pass
80% SOH (UBE) 5 years 100,000 km 90%
70% 8 years 160,000 km
In the GTR, compliance with the SOCE MPR is determined for the vehicles within a given
durability test group by collecting a large sample of SOCE monitor values from in-use vehicles
at appropriate points in their life. The test group is compliant if at least 90 percent of the vehicles
monitored meet the applicable SOCE MPR.
This section has outlined the requirements and framework of GTR No. 22. For a description
of the specifics of the proposed EPA battery durability program and how they compare to the
provisions of the GTR, please refer to Section III.F.2 of the Preamble and to the regulatory text.
1.3.2 California Air Resources Board battery durability and warranty provisions
under the ACC II program
In 2022, the California Air Resources Board (CARB), as part of its Advanced Clean Cars II
(ACC II) program, established battery durability and battery warranty requirements as part of a
suite of customer assurance provisions designed to ensure that zero-emission vehicles maintain
similar standards for usability, useful life, and maintenance as conventional vehicles. The
performance requirements under the initial proposed version of the CARB durability standard
were significantly more stringent than those of UN GTR No. 22. After taking public comment
and consulting with the Board, the performance requirements were modified to a level closer to
that of GTR No. 22, while certain aspects of the program remain more stringent than those of the
GTR.
In contrast to GTR No. 22, the CARB battery durability requirement applies to electric
driving range instead of capacity, and phases in according to model year (MY). As shown in
Table 1-5, for MYs 2026 through 2029, a vehicle test group is compliant if at least 70 percent of
the vehicles in the group maintain 70 percent of certified range after 10 years or 150,000 miles
(240,000 km). For MYs 2030 and later, a test group is compliant if, on average, the vehicles in
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the group maintain 80 percent of certified range after 10 years or 150,000 miles (240,000 km).
Details on monitor accuracy requirements, thresholds for determination of non-conformance, and
specific data reporting requirements are outlined in the regulations (California, California Code
of Regulations, title 13, section 1962.4. 2022a), (California 2022b).
The CARB warranty requirement also phases in by model year, but instead of range it refers
to a state of health as expressed by usable battery energy (UBE). As shown in Table 1-6, for
MYs 2026 to 2030, the battery must maintain 70 percent state of health after 8 years or 100,000
miles (160,000 km). For MYs 2031 and later, it increases to 75 percent state of health. The
warranty requirement applies to the first purchaser and each subsequent purchaser. The warranty
requirements are further outlined in the regulation (Title 13, California Code of Regulations
2022).
Model years
2026-2029
2030+
Table 1-5. CARB ACC II battery durability requirements
Percent of at Mileage
retention
70%
80%
Range
10 years
150,000 mi
Percent of
sample must
pass
70%
On average
Table 1-6. CARB battery warranty requirements
Model years Percent retention of at
2026-2030 70% SOH (UBE) 8 years
2031+ 75%
Mileage
100,000 mi
As described in the Preamble sections III.F.2 and III.F.3, EPA is proposing battery durability
and warranty standards that would differ to some degree from those of the CARB program, but
we have taken California's approach into consideration because we recognize that a substantial
number of vehicles sold in the United States will be subject to California's requirements. The
proposed battery warranty requirements would be implemented under the existing regulatory
structure that establishes a minimum warranty for major emission control components, and
would thus retain similarities to the requirements under that program. The proposed durability
requirements are less stringent than the CARB program and have a greater similarity to those of
GTRNo. 22. For a complete discussion of the proposed requirements under this proposal and
their relation to these other programs, please refer to Preamble III.F.2 and III.F.3.
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Chapter 1 References
76 FR 57106. 2011. (September 15). https://www.govinfo.gov/content/pkg/FR-2011-09-
15/pdf/20 ll-20740.pdf.
77 FR 62624. 2012.
81 FR 73478. 2016. (October 25). https://www.govinfo.gov/content/pkg/FR-2016-10-
25/pdf/2016-21203 .pdf.
California, State of. 2022a. "California Code of Regulations, title 13, section 1962.4."
—. 2022b. "California Code of Regulations, title 13, section 1962.7."
Ellies, B. 2023. "Docket CUV Compliance Data Memo (Docket EPA-HQ-OAR-2022-0829)."
Greene, et al. 2018. "Consumer willingness to pay for vehicle attributes: What do we know?"
Transportation Research Part A.
Public Law 94-163. 1975. (December 22). Accessed 1 8, 2023.
https://www.govinfo.gov/content/pkg/STATUTE-89/pdf/STATUTE-89-Pg871.pdf.
Title 13, California Code of Regulations. 2022. Section 1962.7
Title 40 CFR § 1066.801 Subpart I. 2023. (March 6). https://www.ecfr.gov/current/title-
40/chapter-I/subchapter-U/part-1066/subpart-I.
Title 40 CFR § 86.1803-01. 2023.
Title 49 CFR § 523.5. 2022. https://www.ecfr.gov/current/title-49/subtitle-B/chapter-V/part-
523/section-523.5.
U.S. EPA. 2011. "Draft Joint Technical Support Document: Proposed Rulemaking for 2017-
2025 Light-Duty Vehicle Greenhouse Gas Emission Standards and Corporate Average Fuel
Economy Standards."
U.S. EPA. 2021. The 2021 EPA Automotive Trends Report: Greenhouse Gas Emissions, Fuel
Economy, and Technology since 1975. U.S. EPA.
U.S. EPA. 2022. "The EPA Automotive Trends Report: Greenhouse Gas Emissions, Fuel
Economy, and Technology since 1975."
UN ECE. 2021. "Proposal for a technical report on the development of a new UN Global
Technical Regulation on In-Vehicle Battery Durability for Electrified Vehicles." November.
https://unece.org/sites/default/files/2021-10/GRPE-84-02e_0.pdf.
—. 2022. "United Nations Global Technical Regulation on In-vehicle Battery Durability for
Electrified Vehicles." April, https://unece.org/transport/documents/2022/04/standards/un-gtr-
no22-vehicle-battery-durability-electrified-vehicles.
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Chapter 2: Tools and Inputs Used for Modeling Technologies and Adoption
Towards Compliance
This chapter summarizes the tools and inputs used for modeling technologies, adoption of
technologies, and vehicle compliance with the proposed standards. This includes details
regarding the OMEGA model, ALPHA vehicle simulation tools, and the Agency's approach to
analyzing vehicle manufacturing costs, consumer demand, vehicle operational costs. The chapter
also includes a summary of modeling inputs that reflect our assessment of impacts due to the
implementation of the Inflation Reduction Act of 2022.
2.1 Overview of EPA's Compliance Modeling Approach
EPA's technical analysis supporting the proposed emissions standards, at its highest level, is
based on the following major tools that are used in the assessment of emissions reduction
technologies and costs. These are, in order of execution: ALPHA, response surface modeling,
and OMEGA. They are used in an integrated fashion as follows:
• EPA's ALPHA model is our vehicle simulation tool used to predict tailpipe CO2
emissions and energy consumption for advanced technologies. ALPHA is detailed in
2.4.
• Response surface methodology (RSM) incorporates ALPHA results for various
vehicle technologies over thousands of vehicle combinations into response surface
equations (RSE) which can be quickly referenced to characterize any future vehicle's
GHG emissions based on its size, weight, power and road loads. This approach is
described in 2.4.10.
• EPA's manufacturer compliance model, OMEGA, incorporates RSEs, technology
costs and other inputs into its algorithms for finding cost-efficient pathways for
manufacturers to achieve compliance with desired emissions standards. The
compliance modeling produces a fleet of new light- and medium-duty vehicles for
each analyzed model year, which OMEGA integrates into projections of the on-road
vehicle stock and VMT. Finally, OMEGA tabulates the emissions inventories,
physical effects, and costs and benefits that arise from the usage of vehicles over their
lifetimes. A schematic of the overall analytical workflow is provided in Figure 2-1.
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"N
ALPHA & OMEGA Models
Inputs
V
ALPHA
Vehicle M(Hiding
Response Surface
Methodology
(RSM)
Other
Inputs
\/
OMEGA
(rcate Response Sucfucc Equations Compliance Modeling
_y
Figure 2-1. Compliance modeling workflow.
Finally, the results from OMEGA are used to inform its fleet onroad vehicle emissions model
(MOVES) to generate fleet vehicle emissions and project benefits due to the proposed standards.
A discussion of MOVES is provided in 8.2.1.
2.1.1 OMEGA Compliance and Model Overview
The OMEGA model has been developed by EPA to evaluate policies for reducing greenhouse
gas (GHG) emissions from light duty vehicles. Like the prior releases, this latest version is
intended primarily to be used as a tool to support regulatory development by providing estimates
of the effects of policy alternatives under consideration. These effects include the costs
associated with emissions-reducing technologies and the monetized effects normally included in
a societal benefit-cost analysis, as well as physical effects that include emissions quantities, fuel
consumption, and vehicle stock and usage. In developing OMEGA version 2.0 (OMEGA2), the
goal was to improve modularity, transparency, and flexibility so that stakeholders can more
easily review the model, conduct independent analyses, and potentially adapt the model to meet
their own needs.
2.1.2 OMEGA Updates
EPA created OMEGA version 1.0 (OMEGA1) to analyze new GHG standards for light-duty
vehicles proposed in 2011. The 'core' model performed the function of identifying
manufacturers' cost-minimizing compliance pathways to meet a footprint-based fleet emissions
standard specified by the user. A preprocessing step involved ranking the technology packages to
be considered by the model based on cost-effectiveness. Postprocessing of outputs was
performed separately using a spreadsheet tool, and later a scripted process which generated table
summaries of modeled effects. An overview of OMEGA1 is shown on the left of Figure 2-2.
In the period since the release of OMEGA1, there have been significant changes in the light
duty vehicle market including technological advancements and the introduction of new mobility
services. Advancements in battery electric vehicles (BEVs) with greater range, faster charging
capability, and expanded model availability, as well as potential synergies between BEVs, ride-
hailing services and autonomous driving are particularly relevant when considering pathways for
greater levels of emissions reduction in the future. OMEGA2 has been developed with these
trends in mind. The model's interaction between consumer and producer decisions allows a user
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to represent consumer responses to these new vehicles and services. The model now also has
been designed to have expanded capability to model a wider range of GHG program options,
which is especially important for the assessment of policies that are designed to address future
GHG reduction goals. In general, with the release of OMEGA2, the goal is to improve usability
and flexibility while retaining the primary functions of OMEGA1. The right side of Figure 2-2
shows the overall model flow for OMEGA2 and highlights the main areas that have been revised
and updated.
Technologies
Policy Alternatives +
Base Year Fleet
OMEGA ver.l
(core model)
Technology
Preprocessing
Ranked
Technologies
Producers
New Vehicles with
, Applied Technologies
Effects
Postprocessing
Societal
Costs
Environmental
Effects
OMEGA ver. 2
(D
Analysis
Context
Policy
Alternatives
® ©(¦
Compliance
Iteration
Environmental
Effects
Figure 2-2 - Comparison of OMEGA! and OMEGA2.
Update #1: Expanded model boundaries. In defining the scope of this model version, EPA has
attempted to simplify the process of conducting a run by incorporating into the model some of
the pre- and post-processing steps that had previously been performed manually. At the same
time, EPA recognizes that an overly expansive model boundary can result in requirements for
inputs that are difficult to specify. To avoid this, the input boundary has been set only so large as
to capture the elements of the system assumed are responsive to policy. This approach helps to
ensure that model inputs such as technology costs and emissions rates can be quantified using
data for observable, real-world, characteristics and phenomena, and in that way enable
transparency by allowing the user to maintain the connection to the underlying data. For the
assumptions and algorithms within the model boundary, the aim is transparency through well-
organized model code and complete documentation.
Update #2: Independent Policy Module. OMEGA1 was designed to analyze a very specific
GHG policy structure in which the vehicle attributes and regulatory classes used to determine
emissions targets were incorporated into the code throughout the model. To make it easier to
define and analyze other policy structures, the details regarding how GHG emissions targets are
determined and how compliance credits are treated over time are now included in an independent
Policy Module and associated policy inputs. This allows the user to incorporate new policy
structures without requiring revisions to other code modules. Specifically, the producer decision
module no longer contains any details specific to a GHG program structure, and instead
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functions only on very general program features such as fleet averaging of absolute GHG credits
and required technology shares.
Update #3: Modeling of multi-year strategic producer decisions. As a policy analysis tool,
OMEGA is intended to model the effect of policies that may extend well into the future, beyond
the timeframe of individual product cycles. OMEGA2 is structured to consider a producer
objective function to be optimized over the entire analysis period. Year-by-year compliance
decisions account for management of credits which can carry across years in the context of
projections for technology cost and market conditions which change over time. The timeframe of
a given analysis can be specified anywhere from near-term to long-term, with the length limited
only by inputs and assumptions provided by the user.
Update #4: Addition of a consumer response component. The light-duty vehicle market has
evolved significantly in the time since the initial release of OMEGA1. As the range of available
technologies and services has grown wider, so has the range of possible responses to policy
alternatives. The model structure for this version includes a Consumer Module that can be used
to project how the light-duty vehicle market would respond to policy-driven changes in new
vehicle prices, fuel operating costs, trip fees for ride hailing services, and other consumer-facing
elements. The Consumer Module outputs the estimated consumer responses, such as overall
vehicle sales and sales shares, as well as vehicle re-registration and use, which together
determine the stock of new and used vehicles and the associated allocation of total VMT.
Update #5: Addition of feedback loops for producer decisions. OMEGA2 is structured around
modeling the interactions between vehicle producers responding to a policy and consumers who
own and use vehicles affected by the policy. These interactions are bi-directional, in that the
producer's compliance planning and vehicle design decisions will both influence, and be
influenced by, the sales and shares of vehicles demanded and the GHG credits assigned under the
policy. Iterative feedback loops have now been incorporated; between the Producer and
Consumer modules to ensure that modeled vehicles would be accepted by the market at the
quantities and prices offered by the producer, and between the Producer and Policy modules to
account for the compliance implications of each successive vehicle design and production option
considered by the producer. This update has been peer reviewed as detailed in Section 2.3.
Update #6: Use of absolute vehicle costs and emissions rates. OMEGA1 modeled the
producer application of technologies to a fleet of vehicles that was otherwise held fixed across
policy alternatives. With the addition of a consumer response component that models market
share shifts, OMEGA2 utilizes absolute costs and emissions rates to compare vehicle design and
purchase decisions across vehicle types and market classes.
2.2 OMEGA2 Model Structure and Operation
2.2.1 Inputs and Outputs
Like other models, OMEGA relies on the user to specify appropriate inputs and assumptions.
Some of these may be provided by direct empirical observations, for example the number of
currently registered vehicles. Others might be generated by modeling tools outside of OMEGA,
such as physics-based vehicle simulation results produced by EPA's ALPHA model, or
transportation demand forecasts from DOE's NEMS model. OMEGA has adopted data elements
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and structures that are generic, wherever possible, so that inputs can be provided from whichever
sources the user deems most appropriate.
The inputs and assumptions are categorized according to whether they define the policies
under consideration or define the context within which the analysis occurs. Policy alternative
inputs describe the standards themselves, including the program elements and methodologies for
determining compliance as would be defined for an EPA rule in the Federal Register and Code of
Federal Regulations. Analysis context inputs and assumptions cover the range of factors that the
user assumes are independent of the policy alternatives. The context inputs may include fuel
costs, costs and emissions rates for a particular vehicle technology package, attributes of the
existing vehicle stock, consumer demand parameters, existing GHG credit balances, producer
decision parameters, and many more. The user may project changes in the context inputs over
the analysis timeframe based on other sources, but for a given analysis year the context definition
requires that these inputs are common across the policy alternatives being compared.
The primary outputs are the environmental effects, societal costs and benefits, and producer
compliance status for a set of policy alternatives within a given analysis context. These outputs
are expressed in absolute values, so that incremental effects, costs, and benefits can be evaluated
by comparing two policy alternatives for a given analysis context. For example, comparing a No
Action scenario to an Action (or Policy) Alternative. Those same policy alternatives can also be
compared using other analysis context inputs to evaluate the sensitivity of results to uncertainty
in particular assumptions. For example, comparing the incremental effects of a new policy in
high fuel price and low fuel price analysis contexts.
2.2.2 Model Structure and Key Modules
OMEGA2 has been set up so that primary components of the model are clearly delineated in
such a way that changing one component of the model will not require code changes throughout
the model. The four main modules — Producer, Consumer, Policy, and Effects — are each
defined along the lines of their real-world analogs. Producers and consumers are represented as
distinct decision-making agents, which each exist apart from the regulations defined in the Policy
Module. Similarly, the effects, both environmental and societal, exist apart from producer and
consumer decision-making agents and the policy. This structure allows a user to analyze policy
alternatives with consistently defined producer and consumer behavior. It also provides users the
option of interchanging any of OMEGA's default modules with their own, while preserving the
consistency and functionality of the larger model.
Producer Module: This module projects the decisions of the regulated entities (producers) in
response to policy alternatives, while accounting for consumer demand. The regulated entities
can be specified as individual companies, or considered in aggregate as a collection of
companies, depending on the assumptions made by the user regarding how GHG credits are
averaged or transferred between entities.
Consumer Module: This module projects demand for vehicle sales, ownership and use in
response to changes in vehicle characteristics such as price, ownership cost, and other key
attributes.
Policy Module: This module determines the compliance status for a producer's possible fleet
of new vehicles based on the characteristics of those vehicles and the policy defined by the user.
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Policies may be defined as performance-based standards using fleet averaging (for example,
determining compliance status by the accounting of fungible GHG credits), as a fixed
requirement without averaging (for example, a minimum required share of BEVs), or as a
combination of performance-based standards and fixed requirements.
Effects Module: This module projects the physical and cost effects that result from the
modeling of producers, consumers, and policy within a given analysis context. Examples of
physical effects include the stock and use of registered vehicles, electricity, and gasoline
consumption, and the GHG and criteria pollutant emissions from tailpipe and upstream sources.
Examples of cost effects include vehicle production costs, ownership and operation costs,
societal costs associated with GHG and criteria pollutants, and other societal costs associated
with vehicle use.
2.2.3 Iteration and Convergence
OMEGA2 is intended to find a solution which simultaneously satisfies producer, consumer,
and policy requirements while minimizing the producer generalized costs. OMEGA2's Producer
and Consumer modules represent distinct decision-making entities, with behaviors defined
separately by the user. Without some type of interaction between these modules, the model
would likely not arrive at an equilibrium of vehicles supplied and demanded. For example, a
compliance solution which only minimizes producer generalized costs without consideration of
consumer demand may not satisfy the market requirements at the fleet mix and level of sales
preferred by the consumer. Similarly, the interaction between Producer and Policy modules
ensures that that with each subsequent iteration, the compliance status for the new vehicle fleet
under consideration is correctly accounted for by the producer. Since there is no general
analytical solution to this problem of alignment between producers, consumers, and policy which
also allows model users to independently define producer and consumer behavior and the policy
alternatives, OMEGA2 uses an iterative search approach.
2.2.4 Analysis Resolution
The policy response projections generated by OMEGA2 are centered around the modeled
production, ownership, and use of light-duty vehicles. It would not be computationally feasible
(nor would it be necessary) to distinguish between the nearly 20 million light-duty vehicles
produced for sale each year in the US, and hundreds of millions of vehicles registered for use at
any given time. Therefore, OMEGA is designed to operate using 'vehicles' which are aggregate
representations of individual vehicles, while still retaining sufficient detail for modeling producer
and consumer decisions, and the policy response. The resolution of vehicles can be set for a
given analysis and will depend on the user's consideration of factors such as the availability of
detailed inputs, the requirements of the analysis, and the priority of reducing model run time.
2.3 OMEGA2 Peer Review
In parallel to the OMEGA2 development process, an early version of the model and
documentation was submitted to peer review. This process was intended to gain additional
insights for the updated structure, new modules, processing methods, and reporting methodology
of OMEGA2.
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2.3.1 Charge Questions for the Peer Review:
• The overall approach to the specified modeling purposes, the specific approaches
chosen for modeling individual modules, and the methodologies chosen to achieve
that purpose.
• The appropriateness and completeness of the contents of the input files.
• The types of information which can be input to the model point to both the flexibilities
and constraints of the model.
• The accuracy and appropriateness of the model's conceptual algorithms and equations
for technology application, market impacts, and calculation of compliance.
• The congruence between the conceptual methodologies and the program execution.
• Clarity, completeness, and accuracy of the model's visualization output, in which the
technology application is displayed.
• Recommendations for any functionalities beyond what EPA has described as "future
work."
2.3.2 Information Received from Peer Review
EPA's charge to the peer reviewers requested their expert opinions on the concepts and
methodologies upon which the model relies and whether the OMEGA2 model correctly executes
the associated algorithms. EPA's charge also asked the peer reviewers to comment on specific
aspects of the model's design, execution, outputs, and documentation.
All peer reviewers commented favorably that they appreciated the increased capability and
complexity of OMEGA2 over the previous OMEGA1 version. In general, the peer reviewers
provided numerous specific detailed, complex, and nuanced comments and recommendations
that indicated a good understanding of the model's design. The most common category of
comments consisted of recommendations for improving the model's documentation by adding
further explanations or specifics to enhance the user's understanding.
The second most common category of peer reviewer comments concerned the model's overall
approach, including the functions of each module. Reviewers commented on specific details,
recommended improvements, and noted inputs and results that would benefit from further
explanation. Many peer reviewer recommendations for new or additional functionality focused
on specific enhancements of the existing modules. Reviewers did not recommend additions that
deviated significantly from the current model's scope. EPA has addressed the recommendations
significant to the current application of the OMEGA2 model.
Certain topics were raised by multiple peer reviewers. For example, all peer reviewers
commented on some aspect of the model's handling of greenhouse gas (GHG) emissions credits,
especially as it relates to manufacturers banking these credits from one year to the next and, in
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some cases, how credit banking would interact with manufacturers' multi-year model
development cycle.
Peer reviewers indicated that it was likely that wide-scale implementation of the technologies
available in OMEGA2 could cause a significant change to overall fuel prices that should be
considered. Also, peer reviewers indicated the OMEGA2 model would benefit from further
consideration of VMT rebound due to increased vehicle fuel economy. The peer reviewers also
requested further explanation of how the OMEGA2 model processes hauling/non-hauling
vehicles and all-wheel drive (AWD).
Finally, all reviewers commented on some aspect of the OMEGA2 model algorithm's
treatment of iterative convergence on a final result and how additional documentation of this
process would be helpful.
In addition to the key themes and most common comments summarized here, reviewers
provided numerous other specific observations and recommendations for the OMEGA2 model in
response to EPA's individual charge questions, as documented in the peer review report.
2.4 ALPHA Full Vehicle Simulation and Response Surface Equations
ALPHA is a physics-based, forward-looking, full vehicle computer simulation capable of
analyzing various vehicle types with different powertrain technologies, showing realistic vehicle
behavior. The software tool is a MATLAB/Simulink based simulation.
ALPHA is capable of estimating CO2 emission values for many different vehicle types and
technology packages. OMEGA needs to quickly estimate the CO2 emission values for each
future vehicle considered along with estimates for future fleets. Because operating ALPHA in
real time to conduct full vehicle simulations is time prohibitive, EPA developed a methodology
of reproducing ALPHA model CO2 values using an industry standard statistical technique known
as response surface methodology (RSM). (Kleijnen 2015) This methodology is used to
computationally access CO2 results from a complete set of ALPHA model results by generating a
collection of response surface equations (RSEs) that represent those simulation results. In 2018,
EPA commissioned RTI International to conduct an independent peer review of an earlier
version of the RSE methodology. (RTI International 2018)
ALPHA simulates a single combination of technologies (known as a technology package)
across different combinations of vehicle parameters. Each set of ALPHA simulation outputs are
processed to create the RSEs needed for each technology package addressed. These RSEs are
subsequently used within OMEGA to quickly reproduce the ALPHA model estimates for CO2 in
real time for the various vehicle technologies. ALPHA'S role in the creation of the RSEs for use
within OMEGA is shown in Figure 2-3 and described in detail in Section 2.4.10.
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ALPHA Simulation Scripts
for Various Technology Packages!
Conventional Vehicles
Mild Hybrid Vehicles
Strong Hybrid Vehicles
Battery Electric Vehicles
ALPHA Inputs
for Various Components 2
engines
eMotors
batteries
transmissions
Notes:
1 - shown in Table ?-2 J
2- shown in Tables 2-2 through 2*5
3- discussed in section 2.4.11. 3
Relationship of ALPHA, RSEs & OMEGA
Inputs
V
ALPHA
Vehicle Modeling
Other
Inputs
V
Response Surface
Methodology
(RSM)
Create Response Surface Equations
OMEGA
Compliance Modeling
ALPHA Simulation Outputs
(or each Technology Package
Response Surface Equations (RSE);
for esch Technology Package
_y
Figure 2-3. Relationship of ALPHA, RSEs and OMEGA.
2.4.1 General Description of ALPHA
Within ALPHA, an individual vehicle is defined by specifying the appropriate vehicle road
loads (inertia weight and coast-down coefficients) and specifications of the powertrain
components. Powertrain components (e.g., engines, transmissions, e-motors) are individually
parameterized and can be exchanged within the model draft.
Vehicle control strategies are also modeled, including engine accessory loading, deceleration
fuel cut off (DFCO), hybrid behavior, torque converter lockup, and transmission shift strategy.
Transmission shifting is parameterized and controlled by ALPHAshift, (Newman, K., Kargul, J.,
and Barba, D. 2015a) a shifting strategy algorithm that ensures an appropriate shifting strategy
when engine size or vehicle loading changes. The control strategies used in ALPHA are modeled
after strategies observed during actual vehicle testing.
The performance of vehicle packages defined within ALPHA can be modeled over any pre-
determined vehicle drive cycle. To determine fuel consumption values used to calculate LD
GliG rule CO2 values, the FTP and HWFET cycles are simulated, separated by a HWFET prep
cycle as normally run during certification testing. ALPHA does not include a temperature model,
so the FTP is simulated assuming warm component efficiencies for all bags. Additional fuel
consumption due to the FTP cold start is calculated in post-processing by applying a fuel
consumption penalty to bags 1 and 2, depending on the assumed warmup strategy (refer to
Section 5.3.3.2.5 of the 2016 Draft Technical Assessment Report (U.S. EPA; U.S. DOT-NHTSA;
CARB 2016)). In addition, supporting vehicle drive cycles are defined and fuel economy
simulated in ALPHA. For example, the results from the US06, NEDC, and WLTP cycles (among
others) are used to tune vehicle control strategy parameters to match simulation results to
measured vehicle test results across a variety of conditions. In addition, performance cycles have
been defined, which are used to determine acceleration performance metrics.
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2.4.2 Overview of Previous Versions of ALPHA
The Advanced Light-Duty Powertrain and Hybrid Analysis (ALPHA) tool was created by
EPA to evaluate the Greenhouse Gas (GHG) emissions of Light-Duty (LD) vehicles. In addition,
to provide additional flexibilities and transparency, EPA developed this in-house full vehicle
simulation model that could freely be released to the public. Model development, along with the
data collection and benchmarking that comes along with model calibration, is an extremely
effective means of developing expertise and deeper understanding of technologies and their
interactions. Better understanding of technologies makes for more robust regulatory analysis.
Having a model available in-house allows EPA to make rapid modifications as new data is
collected.
EPA began developing both light-and heavy-duty vehicle models simultaneously as these
vehicles share many of the same basic components. The light-duty vehicle model (ALPHA), and
the heavy-duty model (GEM), share much of the same basic underlying architecture.15 ALPHA
2.1 and 2.2 were developed and used previously under the EPA's 2016Draft Technical
Assessment Report (U.S. EPA; U.S. DOT-NHTSA; CARB 2016), the 2016 Proposed
Determination (U.S. EPA 2016a) (U.S. EPA 2016b), and the 2017 Final Determination (U.S.
EPA 2017a) (U.S. EPA 2017b).
As part of the Midterm Evaluation, EPA validated the ALPHA model using several sources
including vehicle benchmarking, stakeholder data, and industry literature. To further enhance
transparency, in May 2016, EPA completed an external peer review of ALPHA 2.0 (U.S. EPA
2023a). This peer review package included runnable MatLab Simulink source code along with
the input data provided as part of the review.
2.4.3 Current version of ALPHA
ALPHA 3.0 is the current version of the simulation tool used for this proposal. The two
primary changes in ALPHA 3.0 compared to the previous version of ALPHA (ALPHA 2.2) are
the addition of electrified vehicle architectures (including hybrid, plug-in hybrid, and battery
electric vehicles) and the addition of a robust structure to allow large numbers of simulations to
characterize current and future fleets. A basic description of how ALPHA 3.0 works can be
found online (U.S. EPA 2022c).
While ALPHA 3.0 continues to be refined and calibrated, the new electrified vehicle models
of the version in use as of October 9, 2022, were externally peer-reviewed (U.S. EPA 2023a).
The concepts and methodologies upon which the model relies were examined by peer reviewers
to determine if these algorithms can deliver sufficiently accurate results. The results of the peer
review are discussed in section 2.4.9.
Throughout this section, details are provided on the major technology assumptions built into
ALPHA 3.0. EPA has also provided technical details in Section 3.5 which summarizes the
ALPHA inputs used for this proposal. In the time since ALPHA development began, EPA has
15 The GEM model has also been peer reviewed multiple times and was the subject of comment during the
rulemaking adopting the second phase of GHG standards for heavy duty vehicles and engines. See 81 FR 73530-
531, 538-549. (U.S. EPA 2022b)
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published over twenty peer-reviewed papers describing ALPHA and the results of key testing,
validation, and analyses (U.S. EPA 2023a) (U.S. EPA 2022b).
2.4.4 ALPHA Models for Conventional and Electrified Vehicle Architectures
One of the most significant changes in ALPHA 3.0 is the addition of new electrified vehicle
architecture models. Early in the development phase of ALPHA 3.0, EPA conducted research to
determine which electrified vehicle architectures should be included in ALPHA'S suite of models
(W. Zhuanga, S. Li (Eben), X. Zhangc, D. Kum, Z. Song, G. Yin, F. Ju, 2020). Based on trends
of the various hybrid and electric vehicles available for sale in the US in recent years, the
conclusion was the electrified vehicle market could be modeled with the addition of three hybrid
vehicle architectures and one battery electric vehicle architecture to the base conventional
vehicle architecture.
Figure 2-4 summarizes the five vehicle models used to simulate vehicle efficiency for this
proposal, including the conventional model used in previous versions of ALPHA, the three new
hybrid models, and the one new battery electric vehicle model added for ALPHA 3.0. A
summary of these five vehicle architectures used in ALPHA 3.0 is provided in the sections
below.
Conventional
Vehicle
PO Mild
Hybrid Vehicle
P2 Strong
Hybrid Vehicle
PowerSplit Strong
Hybrid Vehicle
Battery Electric
Vehicle
Components
Engine
V*
oooo
electric
starter
generator
D
Engine
wjcu
t
electric
starter
generator
(optional)
e-motor
HV JF HI CJCJOO
Battery
0
electric
generator
Architecture
e-motor
planetary
gear
n a
e-motor
I I
Figure 2-4: Summary of components and architectures used in ALPHA'S modeling
for this proposal.
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2.4.4.1 Conventional Vehicle Architecture
The CO2 performance for all conventional vehicles is modeled using the basic engine plus
transmission architecture shown in Figure 2-5. Different types of engines and transmissions
(including their many operational strategies such as cylinder deactivation, engine stop/start
control, engine deceleration fuel cut off) can be scaled to suit the different vehicle models. For
this proposal, conventional vehicles are modeled using the same model described in section
2.3.3.3 of the 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 (U.S. EPA 2016b).
Figure 2-5: Conventional vehicle architecture.
2.4.4.2 Hybrid Electric Vehicle (HEV) Architectures
There are a wide variety of possible hybrid-electric vehicle architectures, many of which are
or have been represented in the fleet. To assess the scope of this variety, EPA used a recent
hybrid architecture survey paper (W. Zhuanga, S. Li (Eben), X. Zhangc, D. Kum, Z. Song, G.
Yin, F. Ju, 2020). Although other researchers may use a different terminology for specific
architectures, in the interest of consistency EPA adopted the categorization and nomenclature of
the authors in this survey paper for further discussion of hybrid-electric vehicle architectures.
The CO2 performance of hybrid vehicles in ALPHA is modeled using one mild and two
strong hybrid architectures. The mild hybrid architecture chosen was a parallel P0 configuration
(referred to later as simply "P0"). The two strong hybrid architectures chosen were a parallel P2
configuration (referred to later as simply "P2") and a PowerSplit configuration patterned after the
Toyota Prius (referred to later as simply "PowerSplit").
While other mild and strong hybrid architectures also exist in the fleet, for example parallel
PI configurations (referred to later as "PI"), series configurations, and series-parallel multi-mode
configurations (referred to later as "series-parallel"), EPA's analysis in section 2.4.8.5 and 2.4.8.6
shows that these hybrid variations can be adequately modeled using the three core hybrid
architectures chosen for incorporation into ALPHA 3.0.
An analysis of the MY 2019 vehicle fleet revealed that nearly 30 percent of all hybrid
vehicles in the MY 2019 fleet were mild hybrids, and the remaining 70 percent were strong
hybrids (Table 2-1). Of the strong hybrids, 68 percent were based on PowerSplit architecture, 16
percent were based on P2 hybrid technology, and the remaining 16 percent were based on other
architectures such as series-parallel and pure series architectures. The following will discuss the
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different hybrid models incorporated into ALPHA 3.0 to simulate these different types of hybrid
vehicles.
Table 2-1: Percentage breakdown of mild and strong hybrids in the MY 2019 vehicle fleet
ALPHA'S Mild Hybrid Model
% of Mild
Hybrids
% of all Hybrid
Vehicles
PC) Mild Hybrids
94.9%
28.0%
PI Mild Hybrids
5.1%
15%
ALPHA'S Strong Hybrid Model
% of Strong
Hybrids
% of all Hybrid
Vehicles
PowerSplit Strong Hybrids
67.8%
47.8%
PowerSplit PHEVs
P2 Strong Hybrids
16.3%
11.5%
P2 PHEVs
Other Hybrids
16.0%
11.2%
Other PHEVs
2.4.4.2.1 Mild Hybrid Architectures
Mild hybrids are modeled within ALPHA using a 48V P0 architecture, which includes a
conventional engine and transmission along with a starter/generator mounted on the front of the
engine and connected through a belt and pulley, as shown in Figure 2-6. The battery energy
capacity of a typical mid-sized mild hybrid vehicle is around 0.25 kWh.
belt starter
generator
Figure 2-6: P0 Mild hybrid-electric vehicle architecture.
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Table 2-1 shows that 95 percent of the mild hybrids in the MY 2019 LD fleet are based on a
P0 design. FCA/Ram and Volkswagen were the two biggest producers of P0 mild hybrids
vehicles in the fleet. The other 5 percent of mild hybrids were based on a PI design, where the
starter generator is directly mounted on the backside of the engine without the use of a belt.
Mercedes was the only manufacturer of PI mild hybrids in 2019.
Analysis of P0 and PI hybrids in the MY 2019 fleet presented later in this chapter (section
2.4.8.5) indicates the PI variant of mild hybrids, although more efficient than the P0 architecture,
can be reasonably represented by ALPHA'S P0 mild hybrid model. Consequently, the ALPHA
P0 model was chosen to simulate all the mild hybrids associated with this proposal.
2.4.4.2.2 Strong Hybrid Architectures
ALPHA 3.0 uses two distinct models to simulate strong hybrid-electric vehicles in the U.S.
vehicle fleet.
The PowerSplit hybrid architecture is shown in Figure 2-7. This architecture includes a
dedicated hybrid engine specifically designed to provide higher efficiency at the more stable
engine loads possible with a PowerSplit powertrain. ALPHA 3.0 models the PowerSplit device
using a planetary arrangement like that in the third-generation Prius, with the engine mated to the
planetary's carrier gear, Motor/Generator 1 (MG1) connected the sun gear, and Motor/Generator
2 (and its associated planetary gear) connected to both the ring gear and drive axle (through the
final drive gear). The PowerSplit device balances the torque between the engine, MG1 and
MG2/drive axle to provide the needed torque to the wheels while optimizing efficiency of the
powertrain components. The battery for a typical mid-sized PowerSplit hybrid electric vehicle is
around 1.6 kWh. The battery capacity of a similar sized plug-in hybrid version of PowerSplit
hybrid is around 10 kWh.
Table 2-1 illustrates that 68 percent of the strong hybrid vehicles in the MY 2019 fleet are the
PowerSplit architecture. The biggest producer of PowerSplit hybrids in MY 2019 by far (both by
number of vehicle models and total sales) was Toyota. Ford, FCA, and Subaru also offered a
plug-in version of the PowerSplit architecture on at least one vehicle model, and GM sold a
multi-mode version of the PowerSplit design.
The PowerSplit model also delivered suitable CO2 predictions for other strong hybrids design
(e.g., series-parallel and pure series architecture), which represent 16 percent16 of the remaining
MY 2019 hybrid fleet. In total, ALPHA'S PowerSplit strong hybrid model was used to simulate
84 percent of the MY 2019 strong hybrid fleet.
16 Slightly more than half of these remaining vehicles are based on a series-parallel hybrid design like the Honda
Accord hybrid. While the CO2 performance of a series-parallel hybrid can be estimated using a PowerSplit hybrid
architecture, EPA is developing a dedicated series-parallel model for future use in ALPHA.
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e-motor/
generator 2
e-motor/
generator 1
PowerSplit
Device
engine
Figure 2-7: PowerSplit strong hybrid-electric architecture (& planetary gear
arrangement).
The P2 hybrid architecture illustrated in Figure 2-8 is the second strong hybrid-electric
model used within ALPHA. This hybrid architecture uses a conventional or a dedicated hybrid
engine and a conventional 6 speed (or higher) automatic transmission with a clutch and electric
motor/generator in place of the standard torque converter for a conventional vehicle. The P2
architecture has higher power and torque capability due to the full power engine and
transmission and is suitable for truck and large SUV applications with towing capability. The
battery energy capacity of a typical P2 strong hybrid vehicle is around 1.6 kWh (same as the
PowerSplit strong hybrid). The battery capacity of a similar sized plug-in hybrid version of P2
hybrid is around 10 kWh. Table 2-1 shows that 16 percent of the strong hybrids in the MY 2019
fleet are based on a P2 design. Leading manufacturers of P2 hybrid and plug-in hybrid vehicles
include Hyundai/Kia, BMW, Mercedes, and Porsche AG.
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Figure 2-8: P2 strong hybrid-electric architecture.
2.4.4.3 Battery Electric Vehicle Architecture (BEV)
The energy consumption performance of battery electric vehicles (BEVs) is modeled using a
battery and an electric drive unit (EDU) consisting of inverter, motor/generator, and gearing
assembly as shown in Figure 2-9. The battery capacity for a typical mid-sized vehicle with a
300-mile range is around 80 kWh.
e-motor/
generator
Figure 2-9: Battery electric vehicle architecture.
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2.4.5 Engine. E-motor. Transmission and Battery Components
ALPHA stores engine, transmission, e-motor and battery component data in ALPHA input
files. The data included in each of the ALPHA inputs comes from various sources including EPA
and other national laboratory benchmark testing, GT-Power modeling, contracted benchmark
testing, and technical papers. Each input dataset receives extensive quality analysis from EPA's
benchmarking and engineering team to identify and remove any errors, document primary
sources of data, apply best practices when extrapolating to very low or high speeds/torques, and
ensure consistency between similar ALPHA input files.
This rest of this section discusses the various ALPHA input files for the internal combustion
engines, electric inverters/motors, batteries, and transmissions used for this proposal. These
ALPHA inputs are listed in Table 2-2 through Table 2-5 and described in detail in section 3.5.
2.4.5.1 Light-Duty Engines
Table 2-2 identifies the internal combustion engines that ALPHA uses for this proposal. The
details of each engine ALPHA input listed are described in the section 3.5.1 of the RIA. Detailed
information about the engines (engine efficiency map, inertia, DFCO, fuel penalties, cylinder
deactivation features, fuel used, etc.) can be found in the data packet associated with each engine
(U.S. EPA 2023a) (U.S. EPA 2023c).
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Table 2-2: Engine ALPHA input maps used to create ALPHA outputs for RSEs
Type ALPHA Component Name
PFI Large Bore GT Power Baseline 2020 Ford 7.3L Engine from Argonne
Report Tier 3 Fuel17
GDI 2013 Chevrolet 2.5L Ecolcc LCV Engine Reg E10 Fuel
GDI 2014 Chevrolet 4.3L EcoTcc3 LV3 Engine LEVIII Fuel
Turbo Gas 2013 Ford 1.6L EcoBoosl Engine LEV III Fuel17
Turbo Gas 2015 Ford 2.7L EcoBoosl Engine Tier 3 Fuel
Data Source
Technical Report
(Argonne/SwRl)
Contracted Testing
(FEV)
EPA-NCAT Testing
EPA-NCAT Testing
EPA-NCAT Testing
Turbo Gas
Turbo Gas Miller
Turbo Gas Miller
Dedicated Hybrid
Atkinson
2016 Honda 1.5L L15B7 Engine Tier 3 Fuel
Volvo 2.0L VEP LP Gcn3 Miller Engine from 2020 Aachen
Paper Octane Modified for Tier 3 Fuel
Gccly 1.5L Miller GHE from 2020 Aachen Paper Octane
Modified for Tier 3 Fuel
2018 Toyota 2.5L A25A-FKS Engine Tier 3 Fuel
EPA-NCAT Testing
Technical Paper (2020
Aachen)
Technical Paper (2020
Aachen)
EPA-NCAT Testing
Atkinson
Dedicated Hvbrid
Toyota 2.5L TNGA Prototype Hybrid Engine from 2017
Vienna Paper Octane Modified for Tier 3 Fuel
Technical Paper (2017
Vienna)
2.4.5.2 Electric Drive Components
Table 2-3 shows the three types of electric drive components that ALPHA uses for this proposal.
• BISG - Belt Integrated Starter Generator consisting of an inverter, an electric motor,
and the engine's front-end pulley/belt drive.
• EDU - Electric Drive Unit consisting of an inverter, an electric motor, and the drive
gearing.
• EMOT - Electric Motor consisting of an inverter and an electric motor (the gear
losses are not accounted for within this device).
The details of each electric motor ALPHA input listed are described in the section 3.5.2 of the
RIA. Detailed information about the electric component (efficiency map, losses, gear ratios, etc.)
can be found in the data package associated with each component (U.S. EPA 2023a) (U.S. EPA
2023b).
Table 2-3: Electric motor/related ALPHA input maps for electrified vehicles
used to create ALPHA outputs for RSEs
17 Not included in the draft but are likely to be added to the analysis for the FRM.
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Type
E-molor
E-molor
E-molor
Bell Integrated Starter
Generator
Electric Drive Unit
ALPHA Component Name Data Source
2010 Toyota Prius 60kW 650V MG2 EMOT ORNL
Est 2010 Toyota Prius 60kW 650V MG1 EMOT ORNL / NCAT
2011 Hyundai Sonata 30kW 270V EMOT ORNL
2012 Hyundai Sonata 8.5kW 270V BISG ORNL
Generic IPM 150kW EDU NCAT
2.4.5.3 Transmissions
Table 2-4 identifies the automatic transmissions used for this proposal. These transmission
models are all traditional step automatic transmissions and are used to represent all drivetrains in
conventional and electrified vehicles (except for PowerSplit vehicles and BEVs). Transmission
losses as a function of load and gear number are built into the ALPHA input. The torque
converter efficiency and lockup logic are also programmed into each ALPHA input. The shifting
logic for each transmission is built into a function called ALPHA-shift. The TRXECVTFWD
transmission supplies the planetary gear ratios and the gear mesh efficiency for the PowerSplit
drivetrain. EPA did not perform any additional transmission testing for this rulemaking.
For more information on most of these transmissions, please refer to the description of
ALPHA in the 2016 Final Determination (U.S. EPA 2017b).
Table 2-4: Transmission ALPHA inputs used to create ALPHA outputs for RSEs
Type
AI.I'IIA Component Name
5-spd FWD AT
TRX10FWD
5-spdRWD AT
TRX10RWD
6-spd FWD AT
TRX11FWD
6-spdRWD AT
TRX11RWD
Adv 6-spd FWD AT (no torque converter)
TRX12_F WD_P2_Hybrid
Adv 6-spd FWD AT
TRX12FWD
Adv 6-spd RWD AT
TRX12RWD
8-spd FWD AT
TRX21FWD
8-spd RWD AT
TRX21RWD
Adv 8-spd FWD AT (no torque converter)
TRX22_FWD_P2_Hybrid
Adv 8-spd FWD AT
TRX22FWD
Adv 8-spd RWD AT
TRX22RWD
PS Planetary Gear
TRXECVTFWD
Surrogate for BEV Transmission
BEV transmission
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2.4.5.4 Batteries
Table 2-5 lists the drive battery packs used in electrified vehicles. EPA did not test any battery
packs for this rulemaking. We relied on battery RC data provided by Southwest Research
Institute and other sources.
Table 2-5: Battery ALPHA inputs used to create ALPHA outputs for RSEs
Type ALPHA Component Name Used For
48-Volt Battery battery _base_A 12348V8 Ah PO
High-Voltage battery _basc_Samsung_LI_Powcr_mod2 PowcrSplit
Battery
High-Voltage battery _base_9p8_kWh_NCM P2
Battery
High-Voltage battery _pack_NMC_58kWh BEV
Batterv
An equivalent circuit model, as shown in Figure 2-10 is used for the battery cells in the
ALPHA. The following parameters are used to define the high voltage battery model:
• Open circuit voltage (OCV V)
• Series resistance (RS) to model ohmic effects
• Short time constant resistor and capacitor (RP ST and CP ST) to model charge
transfer dynamics
• Long time constant resistor and capacitor (RP LT and CP LT) to model diffusion
dynamics
The ALPHA framework allows for these parameters to be a function of multiple variables
such as SoC, temperature, etc. The state of charge (SoC) is estimated based on coulomb
counting. Additionally, the model also contains a basic thermal model that estimates battery
temperature based on the losses.
Specifically, for the HEV and BEV models validated for the program, the propulsion battery
parameters are a function of SoC (at minimum) and temperature (when data was available).
Further, it was decided that using a series resistance (RS) was sufficient based on the
performance of the model compared to the vehicle test data, so the short and long-time constants
are disabled in such scenarios but can be enabled if data is available.
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RS RP_LT RP_ST
OCU_U f\/\, ~ M\r ~—
batt volts U
-------
Generic 150 kW EDU
Generic 200 kW EDU
667 kW
533 kW
400 kW
267 kW
133kW
67 kW
0
-67 kW
-133 kW
-267 kW
-400 kW
-533 kW
-667 kW
300 kW
200 kW
400 kW
Efficiency (%)
100 kW
50 kW
-50 kW
•100 kW
-100
-200 kW
J -300 kW
J -400 kW
-^-500 kW
-200
-300
5000 10000
Speed (RPM)
5000 10000
Speed (RPM)
Figure 2-11: Power scaling example - Electric drive unit.
Efficiency (%)
500 kW
2.4.7 Tuning ALPHA'S Electrified Vehicle Models Using Vehicle Validations
Using the architectures and ALPHA component input data described above, the P0, P2,
PowerSplit, and BEV models were developed, calibrated, tuned, and validated using detailed test
data measured in a laboratory from specific vehicles listed in Table 2-6 while driven over the
EPA city, highway and US06 regulatory drive cycles.
Table 2-6: Table of test data vehicles used to validate ALPHA
Model
Validation Vehicle
Notes
P0
Mild Hybrid
2013 Chevrolet
Malibu Eco
-Validation of ALPHA'S P0 mild hybrid model was previously
completed during the Midterm Evaluation. [9]
- Slight updates have been made since then based on data from chassis
testing done on 2018 Jeep Wrangler eTorque and 2020 Dodge Ram
eTorque vehicles.
PowerSplit
Strong Hybrid
2017 Toyota Prius
Prime PHEV
- While this vehicle is a PHEV, the ALPHA validation of ALPHA'S
PowerSplit model primarily focused on "charge sustaining" operation.
P2
Strong Hybrid
2016 Hyundai
Sonata PHEV
- While this vehicle is a PHEV, the ALPHA validation of ALPHA'S P2
hybrid model primarily focused on "charge sustaining" operation.
Battery Electric
Vehicle (BEV)
2018 Tesla Model 3
Each electrified vehicle model was tuned to achieve similar operational behavior for the
engine, transmission, electric motors, and battery, as observed in actual vehicle test data. For
example, Figure 2-12 compares data from the PowerSplit model against the corresponding
measured test data on a 2016 Toyota Prius Prime. This validation process similar to what was
done in previously for conventional vehicles. (Newman, K., Kargul, J., and Barba, D. 2015b)
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O 0.14
O
£>, u. I jO
0)
ro 0.13
E
— 2000
a. 1000
W
E -50
680
700
720
740 760
Time [s]
780
800
EG
700
760
800
-0.1
_210
O)
"53 200
190
model v test diff = -1.8 °/
720
740 760
Time [s]
model v test diff = -0.3 1
800
=2
680 700 720 740 760 780 800
Time [s]
ft— n
680 700 720 740 760 780 800
680 700 720 740 760 780 800
Figure 2-12: Sample validation comparison of modeled versus measured data from a 2016
Toyota Prius Prime operating on the drive schedule between 680 to 820 seconds.
Table 2-7_summarizes the final results of the strong hybrid and BEV models. For the
PowerSplit strong hybrid model, the ALPHA simulated combined city-highway CO2 grams per
mile was 3.5 percent higher than that of the 2017 Toyota Prius Prime driven on the
dynamometer. For the P2 strong hybrid model, the combined city-highway simulation results
were -4.4 percent lower than the 2016 Hyundai Sonata PHEV tested on the chassis dyno. Finally,
the combined results from the BEV model were 1.1 percent higher than the test data from the
2018 Tesla Model 3.
Table 2-7: Percent difference of ALPHA vehicle validation simulation versus
benchmarking test data
Model: Validation Vehicle
Hot
UDDS
HW
US06
Combined
(hot-UDDS
& HW)
Units
Power Split Strong Hybrid:
2017 Tovota Prius Prime PHEV
3.2%
3.9%
-2.5%
3.5%
% Diff CO2 g/nii
P2 Strong Hybrid:
2016 Hyundai Sonata PHEV
-8.3%
0.4%
-6.6%
-4.4%
% Diff CO2 g/mi
Battery Electric Vehicle:
2018 Tesla Model 3
-0.8%
3.4%
1.6%
1.1%
%Diff kWli/mi
Comments regarding the P2 validation results: Normally EPA targets +/- 4 percent
difference between the simulation and the test vehicle for each drive cycle in its validation
efforts. In case of the P2 model, Table 2-7 shows a -8.3 percent difference for the hot HDDS
cycle and a -6.6 percent difference for the US06 cycle. While the combined UDDS/HW
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result is close to 4 percent difference, there could be two reasons for this wider difference of
the values for the individual cycles.
The Toyota A25A-FKS engine was used as a surrogate for the 2016 Hyundai Sonata engine,
which was not available as an ALPHA input. Without the actual Hyundai engine map, it
would be expected the simulation results would be slightly different than the test vehicle
data.
It is possible that coastdown coefficient adjustments for P2 strong hybrids do not adequately
account for the losses that occur when the electric motor is always connected to the input of
the transmission. (Moskalik 2020)
Typical test-to-test variation of chassis dynamometer testing can be +/-3 percent due to a variety
of factors such as different drivers, measurement equipment, fuel, and facilities. Since the
test vehicle test data used in the P2 model came from several different laboratories, and other
differences between the dynamometer results and simulation results (as noted above) would
lead to even greater variation, the combined vehicle validation differences are reasonable
when considering the factors listed. However, EPA intends to continue working to refine
ALPHA'S P2 strong hybrid model for the final rulemaking.
2.4.7.1 Verifying the Validated Strong Hybrid and BEVs Models against Variant
Vehicles
Since ALPHA architecture models are intended to simulate a range of vehicles, it is helpful to
compare ALPHA results to data from multiple tests on multiple vehicles. Therefore, the next step
in the validation process was to verify the ALPHA model against a number of similar, but
different, vehicles (think of these other vehicles as "sibling" or "cousin" vehicles). These variant
vehicles were selected because they have very similar powertrain designs and control strategies
to the initial validation vehicle, yet they may of different size and make. Additionally, the
Certification data originates from different vehicles, drivers, equipment, and laboratories, all of
which increases the variability of the comparisons, and can yield a measure of how well the
validated model can simulate other vehicles.
Once each vehicle model was developed and tuned to provide similar behavior as its test vehicle,
CO2 (for hybrid operation) and energy consumption (for BEVs and PHEVs running in charge
depleting mode) results were compared for other "variant" vehicles from the same
manufacturer with very similar powertrain designs as the original validation vehicle. Since
there were no dynamometer test data for these variant vehicles, the ALPHA simulation
results were checked to see how close they agreed with available vehicle Certification data.
These results of the ALPHA model validations and their variant verifications for the strong
hybrids and BEVs are summarized in Table 2-8.
• The top row of Table 2-8 summarizes the average difference between ALPHA
estimated CO2 gpm and Certification CO2 gpm for four Toyota variants of the Prius
Prime PowerSplit design operating in charge sustaining mode. The comparison shows
the average CO2 percent difference over the three drive cycles (FTP, HW and US06)
to be 1.7 percent, -1.1 percent and -3.3 percent, respectively. The average percent
difference of the combined (FTP-HW) CO2 values is shown to be -0.1 percent. The
standard deviation of these combined averages is shown to be 2.2 percent.
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• The center row of Table 2-8 summarizes the average difference between a P2 strong
hybrid vehicle's ALPHA estimated CO2 gpm and its Certification CO2 gpm for five
Hyundai/Kia variants of the Sonata P2 Hybrid design operating in charge sustaining
mode. This comparison shows the average CO2 percent difference over the three drive
cycles (FTP, HW and US06) to -6.1 percent, 2.2 percent and -10.1 percent,
respectively. Again, as mentioned in the discussion of the P2 validation, the primary
reasons this lower FTP and US06 differences is because the Toyota A25A-FKS engine
was used as a surrogate for the 2016 Hyundai Sonata engine. Without the actual
Hyundai engine map, it would be expected the simulation results would be slightly
different than the certification data. The average percent difference of the combined
(cold FTP-HW) CO2 values is shown to be -2.6 percent. The standard deviation of
these combined averages is shown to be 3.6 percent.
• The bottom row of Table 2-8 summarizes the average difference between a Tesla
BEV's ALPHA estimated energy consumption (kWh/mi) and its Certification energy
consumption (kWh/mi) for 14 variants of the Tesla Model 3 design. This comparison
shows the average kWh/mi percent difference over the three drive cycles (FTP and
HW) to 4.1 percent, 2.6 percent, respectively. No US06 Certification data were
available for this comparison. The average percent difference of the combined (FTP-
HW) CO2 values is shown to be 3.4 percent. The standard deviation of these combined
averages is shown to be 4.3 percent.
Comparing the combined city-highway averages of the variant vehicle simulations in Table
2-8 to the vehicle validation combined averages in Table 2-7 shows a slight increase in
variability, which was expected given the validated model was tuned using a specific vehicle, yet
it being asked to estimate results for slightly different vehicles. Consequently, the results in
Table 2-8 are considered quite good.
Table 2-8: Percent difference of variant vehicle ALPHA simulations versus
certification data
Verification of Variant
Vehicles
City
HW
US06
Combined
(City & HW)
units
#
vehs.
Power Split Strong Hybrid Variants:
2017 Toyota Prius Prime PHEV
0.7%*
-1.1%
-3.3%
-0.1%
Avg % diff CO2 g/mi
4
1.9%*
2.6%
2.7%
2.2%
Std-dev of % diff CO2 g/mi
P2 Strong Hybrid variants:
2016 Hyundai Sonata PHEV
-6.1%*
2.2%
-10.1%
-2.6%
Avg % diff CO2 g/mi
5
3.8%*
3.4%
1.6%
3.6%
Std-dev of % diff CO2 g/mi
Battery Electric Vehicle:
4.1%**
2.6%
n/a
3.4%
Avg % diff kWli/mi
14
2018 Tesla Model 3
5.6%**
3.2%
n/a
4.3%
Std-dev of % diff kWli/mi
* cold-start FTP ** warm-UDDS
2.4.7.2 PO Mild Hybrid Validation Efforts
The ALPHA validation for PO mild hybrid vehicles was done during the Midterm Evaluation
(Lee, SoDuk; Cherry, Jeff; Safoutin, Michael; Neam, Anthony; McDonald, Joseph; Newman,
2-25
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Kevin; 2018), consequently there is no recent P0 vehicle validation data shown in Table 2-9.
Instead, a different approach to validating the accuracy of the P0 model. The first part of Table
2-9 summarizes the differences between comparisons of 24 ALPHA CO2 simulations of P0 mild
hybrids with engine start-stop applied against the ALPHA CO2 simulations of the same vehicles
without P0 and start-stop technology. The ALPHA simulation data shows an average combined
(FTP-HW) CO2 reduction of 9.3 percent when applying P0 and start-stop technology to a
conventional vehicle.
The second part of Table 2-9 documents the differences between five comparisons of EPA
certification results of P0 mild hybrids with engine start-stop applied against the EPA
certification results of similar conventional vehicles without P0 and start-stop. The EPA
certification data shows an average combined (FTP-HW) CO2 reduction of 10.9 percent when
applying P0 with start-stop technology to a conventional vehicle. These results verify that
ALPHA simulates a P0 with start-stop technology within -1.6 percent.
Table 2-9 Estimated CO2 reductions with PO mild hybrid & start-stop technology
applied to the comparable conventional vehicle
MY 2019 P0 Mild
Cold-
HW
US06
Combined
units
; #
Hybrids
Start
FTP
(cold-FTP
& HW)
vehs.
ALPHA of P0 vs ALPHA
sim of conv vehicles
13.3%
2.1%
n/;i
9.3%
avg % diff C02 for all pairs of sims.
24
2.4%
0.5%
n/;i
1.8%
std-dev of $diff C02 for all pairs of sims.
Cert of P0 vs Cert of conv
vehicles
13.8%
5.1%
n/ii
10.9%
avg % diff C02 for all pairs of Cert data
5
1 2.7% ;
2.0% :
n/ii
1.5%
std-dev of $diff C02 for all pairs of Cert data
Difference of C02 averages
-0.5%
-3.0% :
n/ii
-1.6%
difference of avg % diff C02
-
2.4.8 Verifying ALPHA'S Ability to Simulate Entire Fleets
With the validated conventional and electrified models, ALPHA3 was used to simulate the
entire MY 2019 base year fleet. To model the performance of these vehicles, data collected by
EPA for compliance purposes, together with information from other sources including laboratory
vehicle benchmarking, were used to calculate various metrics for vehicle and technology
characteristics that are related to fuel economy and GHG emissions. The process used was
similar to that used by the EPA in 2018. (Kevin Bolon, Andrew Moskalik, Kevin Newman,
Aaron Hula, Anthony Neam, and Brandon Mikkelsen 2018)
2.4.8.1 Data Sources to Determine 2019 Fleet Parameters
Vehicle specification data that is relevant to characterizing emissions-reducing technologies
are available from multiple sources. Because these data sources were generally not originally
developed for this particular use, any single source will often provide only partial coverage of
vehicle models over the years of interest, and production volume data necessary for generating
aggregate statistics is often lacking. This section describes a methodology for consolidating data
from multiple sources, while maintaining the integrity of the original data.
The most basic obstacle to consolidating data sets is variation in how vehicles are classified in
different data sources. This might include variation in the level of detail as well as variation in
the particular dimensions along which vehicles are characterized. Even when various data sets
2-26
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share a common categorization method, merging multiple sources may still be complicated when
one or more of the data sets does not include the entire range of vehicles.
The primary data source used by EPA to characterize the GHG performance of the existing
fleet is the certification data submitted by manufacturers to EPA's VERIFY database. The data
pertain mainly to vehicle emissions performance collected in dynamometer testing, and include a
general classification of engines, transmissions, and drive systems. Also included are vehicle
characteristics related to road loads: dynamometer target and set coefficients, road load
horsepower, and test weights. Additional data is obtained from EPA's Test Car database, which
is publicly available.
In addition to the information in datasets maintained by EPA, additional vehicle specifications
and technology details can be obtained through other public and commercially available sources
of vehicle data such as Edmunds.com©, Wards Automotive (Penton©) and AllData Repair
(AllData LLC©).
For the MY 2019 base year fleet, there were a total of 1341 distinct vehicle model types.
2.4.8.2 Vehicle Parameters
Using these data sources, for each vehicle model type the powertrain components were
categorized and vehicle parameters were determined. The categories of powertrain components
used are shown in Table 2-10.
Table 2-10: Powertrain components and categories
Component Category
Level of electrification
Start-stop
Type of hybridization
Engine type
Transmission type
Number of gears
Cylinder deactivation
Engine power
Engine displacement
Engine number of
cylinders
Electric motor power
Applicable to
All vehicles
Conventional vehicles
Mild hybrids
Strong hvbrids/PHEVs
Non-BEVs
Conventional and mild hybrids
Strong hvbrids/PHEVs
BEVs
Step transmissions
Non-BEVs
Non-BEVs
Non-BEVs
BEVs and hvbrids
Values
Conventional, mild hybrid, strong hybrid, strong
PHEV. or battery electric vehicle
YorN
PO or PI
PowerSplit. P2. series-parallel, or series
dicscl. PFI naturally aspirated. GDI naturally
aspirated, turbocharged. supercharged, none
AT. CVT. DCT. manual"
specialty
none
Number
Discrete, continuous, or none
Power (HP)
Displacement (liters)
3/4/6/8
Power (kW)
In addition, other vehicle parameters were defined for each vehicle model type. The
parameters defined are shown in Table 2-11.
Table 2-11: Vehicle parameters
Parameter Values / Units
Equivalent test weight (ETW) Pounds
Drive type FWD. RWD. or AWD
2-27
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Vehicle coastdown target values (A. B. C)
n/v ratio
Footprint
Production volume
Frame stvle
A (pounds). B (pounds/mph). C (pounds/mph2)
rpm/mph
Square feet
Number of units
Unibodv v. bodv on frame
2.4.8.3 Electrified Powertrain Model Assignments
Based on the level of electrification and the type of hybridization, vehicle model types in the
fleet were separated into individual groups to which to apply the appropriate ALPHA model.
These groups are shown in Table 2-12.
Table 2-12: Electrified model assignments
Vehicle architecture groups
Conventional vehicles, with or
without stop-start
Mild hybrids (PO and PI)
PowerSplit and other strong hybrids
(series and series-parallel)
P2 hybrids
BEVs
ALPHA model
Conventional vehicle model
PO model
PowerSplit model
P2 model
BEV model
Number of vehicle model types
1199
24
46
2.4.8.4 Modeling Conventional Vehicles in the Fleet
To model conventional vehicles, available ALPHA maps for powertrain components were
assigned to each vehicle, depending on which map had attributes closest to the specific
vehicle being modeled. Engines in conventional vehicles were mapped to the ALPHA input
engine maps given in Table 2-2. For some engine categories, different engines were specified
depending on whether the modeled vehicle was categorized as a "truck" or not. For this
purpose, all body-on-frame SUVs and pickup trucks, as well as large vans, were classified as
"trucks," while the remaining vehicles were categorized as "cars." The assignment of
ALPHA engines to conventional base year fleet vehicles is given in Table 2-13.
For each vehicle model type, the engine model was scaled to match either the given power of
the engine (power scaling), or to match the engine displacement (displacement scaling) as
described in section 2.4.6 (Paul Dekraker, John Kargul, Andrew Moskalik, Kevin Newman,
Mark Doorlag, and Daniel Barba 2017).
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Table 2-13: Assignments of engines used to simulate MY 2019 base year fleet conventional
vehicle model types, based on engines in Table 2-2
Engine Categories
Modeled As
Scaling
ALPHA engine input
Diesel engines
Miller cycle engine
power
Volvo 4-cyl 2.0L 2020 paper
PFI and GDI NA engines (cars)
GDI engine
power
2013 Chevrolet 2.5L Ecotcc LCV
PFI and GDI NA engines (trucks)
GDI engine
displacement
GTPowcr 2020 Ford 7.3L
Atkinson engines
Atkinson
power
2018 Toyota 2.5L A25A-FKS
Turbochargcd engines (cars)
TDS engine
power
2013 Ford Ecoboost 1.6L
Turbochargcd engines (trucks)
TDS engine
power
2015 Ford EcoBoost 2.7L
Supercharged engines
TDS engine
displacement
2013 Ford Ecoboost 1.6L
Advanced turbochargcd engines
Adv. TDS
power
2016 Honda 1.5L L15B7
Transmissions in conventional vehicle model types were mapped to one of five automatic step
transmissions given in Table 2-4. Losses in the transmission and differential were modified
depending on whether the vehicle was a front or rear wheel drive. This mapping is very similar
to the process used by EPA in earlier rulemakings (U.S. EPA 2016b). Losses in the transmission
were scaled to the peak torque of the engine.
Table 2-14: Transmissions used to simulate MY 2019 base year fleet conventional vehicles,
based on transmissions given in Table 2-4
Transmission Categories Modeled As
4- and 5-spd ATs. 5- and 6-spd manuals TRX10
6-spd ATs TRX 11
All DCTs. 7-spd manuals TRX 12
7-spd and above ATs. older CVTs TRX21
Newer CVTs TRX22
Source / Notes
Five-speed from 2007 Toyota Camrv
Six-speed GM 6T40
Six-speed with advanced loss reduction
Eight-speed FCA 845RE
Eight-speed with advanced loss reduction
With the appropriate powertrain assigned, each vehicle was simulated in ALPHA over the
FTP and HWFET cycles, using the vehicle parameters in Table 2-11.
The grams/mile CO2 values from the ALPHA simulation were compared to certification
values; the sales-weighted average of the difference is given in Table 2-15. A scatter plot of the
ALPHA versus certification values is shown in Figure 2-13. The sizes of the bubbles in the plot
reflect sales volumes for each vehicle.
Table 2-15 Conventional vehicle model type ALPHA CO2 grams/mile values versus
certification CO2 gpm (2019 fleet)
FTP HW Combined
Sales-weighted average -3.8% +4.8% -0.8%
Sales-weighted std. dev. 6.1% 6.2% 5.2%
2-29
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600
550
5 500
Q-
< 450
o 400
"U 350
>.
u
-o 300
<]J
J 250
E
Q 200
150
100
100 150 200 250 300 350 400 450 500 550 600
Combined Cycle C02: Certification
Figure 2-13: Conventional vehicle ALPHA combined cycle CO2 grams/mile values
versus certification CO2 grams/mile (2019 fleet). Bubble sizes reflect sales volumes.
2.4.8.5 Modeling Mild Hybrids in the Fleet
All mild hybrids were modeled using a P0 BISG model, (Lee, SoDuk; Cherry, Jeff; Safoutin,
Michael; Neam, Anthony; McDonald, Joseph; Newman, Kevin; 2018) using the BISG motor
from Table 2-3 and the 48V battery from Table 2-5. All mild hybrids in the fleet have 48V,
however, these vehicles are from multiple manufacturers with different operational strategies and
configurations (some mild hybrids have a P0 configuration, and some have a PI configuration).
However, a single P0 model was judged to be reasonably representative of all mild hybrid
vehicles.
The engines and transmissions for mild hybrids were assigned and sized in the same way as
for conventional vehicles. Both electric motor and battery components were sized as a function
of the rated engine power to keep the power values proportional. Each vehicle was simulated in
ALPHA over the FTP and HWFET cycles, using the parameters in Table 2-11.
Each vehicle was simulated in ALPHA over the FTP and HWFET cycles, using the
parameters in Table 2-11. The grams/mile CO2 values from the ALPHA simulation were
compared to certification values; the sales-weighted average of the difference is given in Table
2-16. A scatter plot of the ALPHA versus certification values is shown in Figure 2-14.
Table 2-16 PO ALPHA CO2 grains/mile values versus certification CO2 grams/mile (2019
fleet)
FTP
HW
Combined
Sales-weighted average
-5.0%
+8.5%
-0.1%
Sales-weighted std. dev.
6.3%
5.3%
5.5%
2-30
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400
<
n:
j 350
<
(N
O
u
a>
u 300
o
X5
o>
c
^ 250
o
u
200
200 250 300 350 400
Combined Cycle C02: Certification
Figure 2-14: PO ALPHA Combined Cycle CO2 grams/mile values versus Certification CO2
grams/mile (2019 fleet). Bubble sizes reflect sales volumes.
2.4.8.6 Modeling Strong Hybrids in the Fleet
As shown in Table 2-12, strong hybrids were divided into parallel P2 hybrids (modeled as
P2s) and the remainder of the strong hybrid fleet (modeled as PowerSplits). For these strong
hybrids, it was assumed that the engine was a dedicated hybrid engine (DHE), utilizing either an
Atkinson cycle or (in the case of turbocharged engines) a Miller cycle, based on the two
dedicated hybrid engines given in Table 2-2. Likewise, the electric motors for strong hybrids are
based on the motors shown in Table 2-3, and the batteries are based on the batteries from Table
2-5.
The range of strong hybrids in the fleet covers multiple manufacturers, vehicle applications,
hybrid configurations and operational strategies. Additionally, not all strong hybrids have a
dedicated hybrid engine as modeled in ALPHA. However, it was judged that using these two
strong hybrid models would be reasonably representative of the fleet.
In a similar way to conventional vehicles, the engine model in each hybrid vehicle was
resized to match the given power of the vehicle engine as discussed above. For the vehicles
modeled as a P2, the chosen engine was coupled to a six-speed transmission, based on the
TRX12. PowerSplit vehicles used a planetary gearset based on the Toyota Prius. The electric
motors were sized using the values reported by the manufacturers. PowerSplit generators were
sized as a function of the rated engine power to keep the power values proportional.
Additionally, the motor sizes for the series-parallel vehicles (modeled as PowerSplits) were also
sized as a function of the rated engine power, to maintain reasonable motor sizes for that
configuration. Battery sizes were assigned either according to the given value for the vehicle
from EPA's 2019 fleet parameter file (described in section 2.4.8.1) or assigned a default value
(1.62 kWh for HEVs and 9.18 kWh for PHEVs).
2-31
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Each vehicle was simulated in ALPHA over the FTP and HWFET cycles, using the vehicle
parameters in Table 2-11. The grams/mile CO2 values from the ALPHA simulation were
compared to certification values; the sales-weighted average of the difference is given in Table
2-17. A scatter plot of the ALPHA versus certification values for vehicles modeled as PowerSplit
hybrids in the 2019 fleet is shown in Figure 2-15.
Table 2-17 PowerSplit ALPHA CO2 grams/mile values versus certification CO2 grams/mile
(2019 fleet)
FTP
HW
Combined
Sales-weighted average
+0.5%
+1.6%
+1.0%
Sales-weighted std. dev.
5.0%
4.1%
4.2%
300
<
5 250
<
A1
o
o
O 200
o
XI
-------
FTP
HW
Combined
Sales-weighted average
-13.5%
-0.2%
-7.7%
Sales-weighted std. dev.
10.1%
8.2%
9.3%
400
< 350
CL
I
<
¦A, 300
O
u
-
u
"§ 200
15
E
8 150
100
100 150 200 250 300 350 400
Combined Cycle C02: Certification
Figure 2-16: P2 ALPHA combined cycle CO2 grams/mile values versus certification CO2
grams/mile (2019 fleet). Bubbles sizes reflect sales volumes.
2.4.8.7 Modeling Battery Electric Vehicles in the Fleet
A single model was used to represent all battery electric vehicles. This model used an electric
drive unit, as shown in Table 2-3, which was resized to match the rated power of each BEV. The
ratio of DC electric energy used to AC energy used to charge the vehicle was assumed to be
0.87, based on an average of available vehicle data.
The kWh/100 mi values from the ALPHA simulation were compared to certification values;
the sales-weighted average of the difference is given in Table 2-19. A scatter plot of the ALPHA
versus certification values is shown in Figure 2-17.
Table 2-19 BEV ALPHA kWh/100 mi values versus certification kWh/100 mi
UDDS
HW
Combined
Sales-weighted average
+4.2%
+1.7%
+3.0%
Sales-weighted std. dev.
9.3%
7.4%
8.3%
2-33
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Figure 2-17: BEV ALPHA combined cycle kWh/100 mi values versus certification
kWh/100 mi (2019 fleet). Bubble sizes reflect sales volumes.
2.4.9 Peer-Reviewing ALPHA Electrified Models
After preparing ALPHA 3.0 to correctly simulate electrified vehicles, it was submitted to a
peer review process to examine its structure, operation, and simulation results to determine the
effectiveness of various vehicle technologies via simulation. The scope of the peer-review was
limited to the concepts and methodologies upon which the model relies and whether or not the
model can be expected to execute these algorithms correctly for the new electrified vehicle
architectures added to ALPHA. (ICF International 2022)
The peer review is centered on the five vehicle models detailed in Table 2-20. The table
summarizes the configuration of each model provided for the peer review. The ETW and road
loads provided in the peer review were for a generalized mid-sized car and do not correspond to
any particular vehicle in the fleet. The Toyota Atkinson 2.5L engine was chosen based on the
base conventional vehicle and maintained for the electrified models to allow the CO2
performance of each model to be directly compared without the confounding factor of changing
engines. The transmission selected was a 6-speed automatic transmission (TRX12) and again
maintained for the P0 and P2 models. The PowerSplit, P2, and BEV models (including engine
and e-motor scaling) used for the peer review was the same as that described in the sections
above.
2-34
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Table 2-20: Details of ALPHA 3.0 models peer reviewed
Model ETW
Conv.
PO
PS
PHEV
P2
PHEV
EV
3500
3500
3500
3500
4250
Road
Load
(A, B, C
terms)
30. 0. 0.02
30. 0. 0.02
Engine Component
Name
engine_2018_T ov ota_A2 5 AFKS
_2L5_Tier2.m (scaled to 150kw)
engine_2018_T ov ota_A2 5 AFKS
_2L5_Tier2.m (scaled to 150kw)
30.0.0.02 : engine_2018_Tov ota_A25AFKS
_2L5_Tier2.m (scaled to 150kw)
30,0,0.02 : engine_2018_Tov ota_A25AFKS
| _2L5_Tier2.m (scaled to 150kw)
30.0,0.02 : NA
Trail
TRX12
TRX12
Internal
to PS
model
TRX12
9.5:1
single
speed
E-inotor/EDU
Component Name
NA
emachine_2012_Hvundai
_Sonata_8p5kW_270V_
BISG.m
MG1 and MG2:
emachine_2010_T ov ota_
Prius_60kW_650vjMG2
EMOT.m
emachine_201 1 Hyundai
_Sonata_30kW_270V_E
MOT.m
emachinelPMl 50kW_
350V EDU.m
Engine and E-inotor
scaling for
peer review vehicle
Engine: power scale
Engine: power scale
E-motor: power scaling
based on engine size (1 lkw
for peer review)
Engine: Displacement
scaling
E-motor: Power scaling
based on engine size (MG1
86kw MG2 106kw)
Engine: power scale
E-motor: power scaling
based on engine size (65kw)
E-motor: Power scaling
based on road load 150kw
Each sub-model provided to the peer reviewers was validated against a combination of
internally and externally collected vehicle operational data while running the vehicle on a vehicle
dynamometer over the USEPA city, highway and US06 regulatory cycles (as described above in
section 2.4.7). EPA's approach for validations was to use detailed lOhz CAN and discrete sensor
vehicle benchmarking data to set up the model structure and tune it based on e-motor and battery
current and voltage, engine speed and load, battery SOH, etc. to generally achieve within 2- 4
percent agreement with the CO2 measured over the city, highway and US06 EPA regulatory
cycles as shown in Table 2-7. Once the benchmarking test vehicle validation target was achieved
(generally after 3-6 months of work), the validated model was applied to variant vehicles with
the same powertrain design from the same manufacturer to achieve within 3-6 percent agreement
on CO2 with EPA certification data over the combined FTP/Highway cycle. Then the validated
model was applied to the broader fleet of similar technology hybrid/BEV vehicles to understand
the variation in the hybrid/BEV performance (CO2 g/mile or kWh/mile) across manufacturers.
Highlights of the peer reviewer comments were not ready at the time of this draft. However,
the peer review of the added electrified models in ALPHA can be found on EPA ALPHA
webpage (U.S. EPA 2023a).
2.4.10 Estimating CO2 emissions of Future Fleets
To estimate CO2 emissions in future fleets, OMEGA uses a set of response surface equations
(RSEs) based on ALPHA simulation outputs (results). To define each RSE, technology packages
(consisting of specific combinations of components) were identified. Then, an ALPHA
simulation matrix run was created, sweeping vehicle parameters over a defined range so that the
RSE could be applied to any vehicle. A unique ALPHA simulation output is created for each
vehicle parameter setting of the sweep.
2.4.10.1 Technology Packages used to create RSEs for OMEGA
ALPHA simulation outputs used to create the RSEs consisted of CO2 emissions or electric
energy consumption over each bag of the standard dynamometer cycles (FTP, HWFET, and
2-35
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US06), as well as acceleration times. Each RSE represents the results from simulating a single
combination of technologies known as a technology package across different combinations of
vehicle parameters.
The components used to create each technology package are shown below in Table 2-21.
2-36
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Table 2-21: Technology packages for LDV/LDT RSEs
Configu ration
Conventional
GDI-car
GDI+fixed CDA-car
GDI+dyn CDA-car
ATK-car
ATK+dyn CDA-car
GDI-truck
GDI+fixed CDA-
truck
GDI+dyn CDA-truck
TDS12:car
| ])S 1 I truck
Miller: car + truck
Strong Hybrid'.
Atkinson Dedicated
Hybrid
Miller Dedicated
Hybrid
BaUcry Elcdric
Vehicles
LDV/LDT BEV
EDU
Engine Name
11
2013 Chevrolet 2.5L Ecotec
LCV Engine Reg E10 Fuel
2013 Chevrolet 2.5L Ecotec
LCV Engine Reg E10 Fuel
+ fixed CDA modifier
2013 Chevrolet 2.5L Ecotec
LCV Engine Reg E10 Fuel
+ dyn CDA modifier
2018 Toyota 2.5LA25A-
FKS Engine Tier 3 Fuel
2018 Toyota 2.5LA25A-
FKS Engine Tier 3 Fuel +
dyn CDA modifier
2014 Chevrolet 4.3L
EcoTec3 LV3 Engine
LEVIII Fuel
2014 Chevrolet 4.3L
EcoTec3 LV3 Engine
LEVIII Fuel + fixed CDA
2014 Chevrolet 4.3L
EcoTec3 LV3 Engine
LEVIII Fuel + dyn CDA
modifier
2016 Honda 1.5LL15B7
Engine Tier 3 Fuel
2015 Ford EcoBoost 2.7L
Engine Tier 3 Fuel
Volvo 2.0L VEP LP Gen3
Miller Engine from 2020
Aachen Paper Octane
Modified for Tier 3 Fuel
2
Toyota 2.5L TNG A
Prototype Hybrid Engine
from 2017 Vienna Paper
Tier 3 Fuel
Geely 1.5LGHE Miller
from 2020 Aachen Paper
Tier 3 Fuel
Transmission
Drive
1.09
Electrification
Elcdric molor RSEs
180
No stop-start
TRX10 (5-spd)
TRX11 (6-spd) '
TRX12 (6-spd adv) : 1 WD
TRX21 (8-spd)
TRX22 (8-spd adv)
RWD
Stop-start
P0 MHEV (48V)
[2012 Hyundai
BISG]
2010 Prius
MG1 + MG2
EMOT
IPM 150 kW
EDU
For conventional and mild hybrid (P0) vehicles, powertrain technology packages were created
for each combination of engine and transmission shown under the "conventional" heading in
Table 2-21. For the combinations that were not exclusively cars or trucks, separate packages
were created for both front- and rear-wheel drive (truck RSEs were rear-wheel-drive only and car
RSEs were front-wheel-drive only). Finally, for each combination, different packages were
created (a) without stop-start technology, (b) with stop-start technology, and (c) with a mild
hybrid P0 technology.
Two technology packages were created for the strong hybrid, using each of the two dedicated
hybrid engines in Table 2-21. For this proposal, the strong hybrid packages created were
modeled as regular (non-plug-in) hybrids only using the PowerSplit model. A single additional
technology package was created for battery electric vehicles (BEVs).
2-37
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2.4.10.2 Vehicle Parameter Sweeps for each Technology Package
For each technology package, ALPHA 3.0 was used to provide data with which to construct
an RSE. To construct each RSE, a series of ALPHA simulations were performed using the same
technology package, but with different combinations of vehicle parameters, so that a single RSE
could be used to accurately characterize the performance of a range of different vehicles.
2.4.10.2.1 Swept Vehicle Parameters and Their Values
The vehicle parameters chosen for RSE development directly relate to vehicle parameters
used in certification dynamometer testing. These parameters were:
• Equivalent test weight (ETW).
• Road load horsepower at 20 mph, calculated from the target coefficients (this value is
substantially dominated by rolling resistance losses).
• Road load horsepower at 60 mph, calculated from the target coefficients (this value is
substantially dominated by aerodynamic losses).
• Rated power of primary power source (engine or electric motor).
The calculated road loads at 20 mph and 60 mph were chosen to characterize vehicle losses
rather than the coefficient of rolling resistance and drag coefficient. The choice of road loads to
characterize losses ensures that all road load losses in base year vehicles are correctly accounted
for, while the choice of two widely separated speeds gives parameters combinations where road
loads dominated by rolling resistance (at low speed) and aero resistance (at high speed) can be
separately altered.
In choosing which combinations of parameters to simulate, combinations of parameters that
would not appear in the real fleet were avoided. For example, vehicles with very high ETW
would not also have low road loads, as both weight and road load are correlated to vehicle size.
With that in mind, rather than independently setting the value of each parameter, values of road
loads and engine/motor power were chosen to be proportional to ETW. In other words, the
parameters set were:
• Equivalent test weight (ETW)
• Road load horsepower at 20 mph / ETW (RLHP@20/ETW)
• Road load horsepower at 60 mph / ETW (RLHP@60/ETW)
• ETW / rated power (ETW/HP)
To determine the ranges of these parameters for light-duty RSEs, the values of ETW, target
coefficients, and rated power from the EPA's publicly available "Data on Cars used for Testing
Fuel Economy from MY 2021" were used. (U.S. EPA 2022e) As shown in Figure 2-18, ETW
values range from 2500 pounds to 7000 pounds, and the values of RLHP@20/ETW,
RLHP@60/ETW, and ETW/HP are roughly consistent across the span of ETW.
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Additionally, the road loads at 20 mph and 60 mph are related, and thus so are the values of
RLHP@20/'ETW and RLHP@60/ETW. When choosing parameters to simulate, only
combinations of RLHP@20/ETW and RLHP@60/ETW that were near the envelope of points
shown in Figure 2-18 were chosen.
0.0014
0.0012
¦g 0.001
g 0.0008
I1 0.0006
X
a 0.0004
RLHP@20/ETW
~
RLHP@60/ETW
2000 3000
4000 5000
ETW
6000 7000
0.009
0.008
0.007
j? 0.006
o 0.005
0.004
a.
5 0.003
CC
0.002
0.001
0
4000 5000
ETW
6000 7000
0.008
0.007
:
0.003
0.002
0.0003 0.0005 0.0007 0.0009 0.0011 0.0013
RLHP20/ETW
Figure 2-18: Relationships between vehicle parameters for the MY 2021 fleet.
For each RSE, discrete values of ETW, corresponding to test weight bins, were chosen
spanning from 3000 pounds to 10,000 pounds. Four values of RLFIP@20/ETW were chosen
(0.0003, 0.0005, 0.00075, and 0.001 HP/lb), and for each value of RLFIP@20/ETW, three values
ofRLHP@60/ETW were chosen which spanned the point cloud shown in Figure 2-18.
Finally, for conventional vehicles, engine sizes were assigned so that the ETW/HP spanned
the values shown in Figure 2-18. The engines sizes were chosen from the displacements listed in
Table 2-22. For hybrid vehicles, the same set of engine sizes were used. For BE Vs. electric
motor sizes were chosen in 50kW increments from 100 kW to 400 kW.
Table 2-22 Engine displacements used in RSE construction
Engine configuration
13
Displacements
1.0L, 1.41,. 1.8L
14
1.6L, 2.21,. 2.8L
V6
2.5L. 3.4L. 4.3L
V8
4.0L, 5.5L. 7.0L
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2.4.10.2.2 Values of Parameters Used for ALPHA Simulations
The ALPHA simulation uses various input parameters to set up the vehicle simulation. A
batch run was created for each RSE, using the powertrain configuration for that RSE. For each
combination of swept vehicle parameters, the powertrain was sized according to the engine
displacement or BEV EDU power specified.
The quadratic target coefficients (designated "A," "B," and "C" for the constant, linear, and
quadratic term, respectively) were determined using the values of RLHP@20 and RLHP@60. To
do so, the linear (B) coefficient was assumed to be 0.22 pounds/mph, representing the average
value for that coefficient in the MY 2021 fleet. The constant (A) and quadratic (C) coefficients
were then calculated to give the correct values for RLHP@20 and RLHP@60. Although this
method does constrain the range of coefficients generated (as the linear term is always 0.22
lbs/mph), the resulting quadratic target force curve accurately reflects a wide range of target
curves. For example, recalculating the target coefficients of the MY 2021 fleet with this
methodology results in a difference between the original and recalculated curves at 40 mph
(midway between 20 and 60) of less than 4 percent for over 97 percent of the vehicles.
For hybrid vehicles, the electric machines and batteries used in the RSE ALPHA runs were
also scaled. For each hybrid configuration, the electric machine power was maintained as a
constant percentage of the engine power.
2.4.10.2.3 ALPHA Simulation Outputs for RSEs
The ALPHA runs for each RSE consisted of a series of simulations using a single powertrain,
but with different combinations of swept parameters. Each run simulated vehicle performance on
the FTP, HWFET, and US06 cycles, as well as a "performance cycle" which was used to
determine acceleration times.
For conventional and hybrid vehicles, the ALPHA 3.0 outputs consisted of CO2 emissions for
each bag of each simulated cycle, and acceleration times. For electric vehicles, the ALPHA3
outputs consisted of energy usage for each bag of each simulated cycle, and acceleration times.
2.4.10.3 Transforming ALPHA Simulation Outputs into RSEs for OMEGA
The OMEGA model requires a complete set of full-vehicle efficiency simulations for the
entire vehicle fleet represented in this proposal. To create this full set of simulations using a tool
such as the ALPHA model alone would require an unrealistic number of resources as millions of
simulation runs would be required to generate the resolution required to satisfy the requirements
of the OMEGA model.
To provide the necessary resolution for the OMEGA model while maintaining a realistic
number of ALPHA simulations, EPA implemented a peer reviewed (RTI International 2018)
Response Surface Methodology (RSM) (Kleijnen 2015). As described above, the inputs to the
RSM are a controlled set of ALPHA simulation outputs. The output from the RSM is a set of
Response Surface Equations (RSEs) suitable for the OMEGA model.
2.4.10.3.1 Steps to Create a RSE from the RSM
For this example, 157 ALPHA model results were generated from CO2 Bag 1 representing
cars with GDI engine, Continuous DEAC, TRX21 Transmission, FWD, and Start-Stop.
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Step 1: Compile the ALPHA model results. Table 2-23 contains a sample of the 157 results
from Bag 1 CO2 showing the 4 inputs and the CO2 output:
Table 2-23 - Sample results
RLHP20
RLHP60
HP_ETW
ETW
C02
0.0005
0.003
0.032258
3250
204.8645
0.0005
0.0065
0.032258
3250
248.2313
0.00075
0.004
0.032258
4250
293.0054
0.00075
0.006
0.032258
3250
254.2941
0.00075
0.006
0.032258
4250
326.5969
0.001
0.005
0.032258
3250
252.8225
Step 2: Generate the RSE from a commercial or open-source product. EPA utilized the
popular open-source R language (Foundation 2022) including the RSM library (Lenth 2021) to
generate the RSE:
C02-RSE = (11.6954329620654 + RLHP20 * -19931.541254933 + RLHP60 * -
3972.87047794276 + HP_ETW * 491.637862683 + ETW * 3.59189981164081E-02 + RLHP20
* RLHP60 * -605147.450118866+ RLHP20 * HP ETW * -114528.849261411 +RLHP20 *
ETW * 16.4326074843966 + RLHP60 * HP ETW * -14364.8208136198 + RLHP60 * ETW *
3.88487044872695 + HP_ETW * ETW * 9.75218337820661E-02 + RLHP20 * RLHP20 *
19630639.3025487 + RLHP60 * RLHP60 * 495588.873797923 + HP ETW * HP ETW *
700.465061662175 + ETW * ETW * -7.38489713757848E-09)
Step 3: Verify the output. Table 2-24 adds an additional column containing the results from
the RSE and Figure 2-19 shows all 157 ALPHA results vs 157 RSE results.
Table 2-24 - Tabular results
RLHP20
RLHP60
HPETW
ETW
CO2
CO2-RSE
0.0005
0.003
0.032258
3250
204.8645
203.0853
0.0005
0.0065
0.032258
3250
248.2313
247.1682
0.00075
0.004
0.032258
4250
293.0054
294.2893
0.00075
0.006
0.032258
3250
254.2941
252.7991
0.00075
0.006
0.032258
4250
326.5969
327.4422
0.001
0.005
0.032258
3250
252.8225
254.8888
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Figure 2-19: Graphical results.
The validated RSE can now be used by the OMEGA model to generate a CO2 value (or
energy consumption rate, for BEVs) for any vehicle within the range of the controlled set of
ALPHA model simulation results. Utilizing the RSM results in a reduction of simulation and
storage resources by approximately a factor of 100.
2.5 Cost Methodology
EPA has developed several new approaches to estimating technology costs relative to our past
GHG and criteria emission analyses. We describe those new approaches here. Despite our new
approaches, we continue to first estimate direct manufacturing costs and apply to those costs the
well understood learning-by-doing methodology to estimate how those costs are expected to
change going forward (U.S. EPA 2016). We then apply established markups to those direct
manufacturing costs to estimate the indirect costs (e.g., research, development, etc.) associated
with the technology. We provide more detail here in Chapter 2.5 and in Chapter 2.6.
2.5.1 Absolute vs. incremental cost approach
Powertrain costs used in OMEGA are based on a combination of prior GHG and/or criteria air
pollutant rulemaking analyses (e.g., EPA's LD Tier 3 rule), and on new work. However, in
contrast to previous rulemaking analyses, all costs used in this analysis are expressed as cost
curves rather than as discrete costs for specific pieces of technology. More importantly, costs are
now determined as absolute costs rather than incremental costs and geared toward generating full
vehicle costs rather than the incremental costs considered in previous analyses. That is, when the
cost of a new piece of technology or package of technologies is assigned, it is in terms of its
absolute cost instead of the incremental cost relative to the older or less capable piece of
technology or package of technologies that it replaces. This is an important aspect of the
OMEGA technology costs because OMEGA now incorporates a consumer choice element. This
means that the impacts of, for example, a $40,000 BEV versus a $35,000 ICE vehicle of similar
utility (i.e., a 14 percent increase for the BEV) is a much different consideration than a $6,000
incremental BEV cost versus a $1,000 incremental ICE cost (a 500 percent increase for the
BEV).
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2.5.2 Direct manufacturing costs
2.5.2.1 Battery cost modeling methodology
In the 2012 rule, the 2016 Draft TAR, and the 2017 Proposed and Final Determinations, EPA
estimated battery costs by specifying batteries for a large set of modeled BEVs, PHEVs, and
HEVs across a variety of vehicle sizes and driving ranges. This involved first determining the
battery power and gross energy capacity needed by each, and then using ANL BatPaC to
determine the direct manufacturing cost (DMC) for each battery, in dollars per pack. These costs
were assigned to a base year, and costs in future years were estimated by applying a learning
curve to the base year costs.
Later, in the 2021 rule, NHTSA estimated battery costs by means of lookup tables derived
from ANL BatPaC, in which energy capacity (kWh) and battery power (kW) were the primary
variables. Future costs again were estimated by applying a learning curve.
For this proposal, EPA used ANL BatPaC 5.0 to develop base year (MY 2022) battery costs,
expressed as a cost per kWh as a function of battery gross energy capacity (kWh). To assign
costs for future years, we applied a cost reduction due to learning, based on cumulative
Gigawatt-hours (GWh) of battery production necessary to supply the number of BEVs that
OMEGA has placed in the analysis fleet up to that analysis year. Finally, we applied additional
manufacturing cost reductions based on our assessment of the future impact of the Inflation
Reduction Act.
2.5.2.1.1 Battery sizing
The compliance analysis for the current proposal, which uses battery cost as an input, was
performed using the updated version of the OMEGA model. One difference from previous
versions of OMEGA is that the new version directly calculates and assigns the gross battery
capacity of PEVs. When the updated version of OMEGA generates a BEV, it determines the
necessary gross battery capacity (kWh) for that vehicle given its estimate of the vehicle's energy
consumption as a BEV, its target driving range, and other relevant factors. The direct
manufacturing cost for a pack of that capacity is then estimated based on the gross capacity, the
cost is reduced by application of a learning factor, and this cost becomes a term in the calculation
of the total direct manufacturing cost of the vehicle.
Determining the correct gross battery capacity is important because this determines the energy
consumption of the vehicle (which is in turn a result of battery weight) and also battery cost.
Gross battery capacity is generally a function of the desired electric driving range, the fraction of
gross battery capacity that is usable (the SOC swing, in percent), and the on-road DC energy
consumption of the vehicle. The driving range is assumed to be 75 percent of the 55/45 2-cycle
range which is consistent with higher volume BEVs in the market today and which we expect to
be more representative of future BEVs.18 SOC swing for a BEV is assumed to be about 90 to 95
percent. DC energy consumption is the average amount of on-road DC energy required from the
battery per mile driven on the relevant cycles. Note that this is not the same as the energy
consumption reported in the Fuel Economy guide, which includes charging losses incurred
between the grid power outlet and the battery. Charging losses are important for calculating
18 While it varies by model, the current FE label range for BEVs is between 70-75 percent of the 2-cycle range.
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upstream emissions but must be excluded for battery sizing because battery capacity for a given
range is a function of DC energy consumption. DC energy consumption is derived from a
precomputed response surface generated by ALPHA results, using curb weight and other vehicle
attributes as inputs to the response surface. Curb weight is intimately tied to battery capacity, via
the effect of battery weight on total vehicle weight. This requires an iterative process that
OMEGA must perform in order to determine curb weight simultaneously with arriving at the
necessary battery size.
In the first step, OMEGA calculates the on-road DC energy consumption (Wh/mi) as a
function of vehicle parameters including curb weight (which includes a battery weight
determined by later steps). Next it estimates gross battery capacity, using DC energy
consumption, driving range, and usable capacity, according to the formula:
Wh\ driving range in miles
/Wh\
(W^Ocross capacity ~ I - J ^
\ TTLl / nr pnpvnv rnn smnnfi nn
DC energy consumption 0.90
Next it estimates battery weight, using estimated gross capacity and an assumed specific
energy (assumed to be 180-200 Wh/kg):
_ m) Gross Capacity
\kg)Battery Weight Wh/kg
Finally, it returns to the first step with the new battery weight, until the battery weight stops
changing (converges).
2.5.2.1.2 Base year battery cost estimation
To begin estimating cost for a pack in a given year of the analysis, OMEGA first requires
battery cost to be input as a base-year input cost function representing the pack cost per kWh, as
a function of its gross kWh capacity, in the base year (2022).
The base year input cost function was defined as a relationship between the gross capacity of
the battery (kWh) and the cost per kWh. It is generally understood and confirmed via BatPaC
simulation that the cost per kWh for a pack of a given chemistry varies with the gross capacity of
the pack, with packs of larger capacity generally indicating a lower cost per kWh than those of a
smaller capacity. While power can also be a determinant of battery cost, for PEV batteries (as
opposed to HEV batteries) energy capacity is the dominant factor, and through simulation
exercises EPA determined that including power as an input variable in the costing of PEV
battery packs would not meaningfully affect the results. In developing the input cost functions,
PEV batteries were assigned a power-to-energy ratio generally appropriate to the vehicle,
considering typical power requirements and the size of the battery.
To generate the BEV base year input cost function, EPA used Argonne National Laboratory's
BatPaC model version 5.0. A copy of BatPaC version 5.0 was configured to generate a variety of
battery packs that utilize pack topologies and cell sizes that are similar to those seen in emerging
high-production battery platforms, such as for example the GM Ultium battery platform, the VW
MEB vehicle platform, and the Hyundai E-GMP vehicle platform. EPA considers these
platforms to exemplify the trend toward BEV-specific vehicle platforms with battery packs that
can be assembled in several different capacities from various numbers of modules that utilize one
or two standard cell sizes of relatively large capacity, generally forming a flat battery pack
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assembly suitable for residing in the vehicle floor. These platforms utilize pouch cells of
relatively large capacity (78 to 100 Ampere-hours) that are used interchangeably in a range of
pack sizes by varying the number of battery modules and their configuration within the pack.
The modeled battery packs were generated by enumerating all possible combinations of cell
size (from 60 to 90 A-hr in steps of 5 A-hr) and module arrangement, with 24 cells per module.
For battery chemistry, EPA selected an NMC811 cathode with a graphite anode, which among
the chemistries readily modeled by BatPaC, represents in our judgment the most appropriate
representative chemistry found in battery packs of this design being produced today that is also
consistent with trends to balance performance with reduced cobalt content. Default costs
provided in BatPaC for electrode powders and other constituents were used. While iron-
phosphate cathodes are increasingly being used by some manufacturers, their lower specific
energy and energy density may make them less appropriate for the BEV driving range of 300
miles modeled in the analysis.
Of the enumerated pack configurations, those having too high or low a pack voltage (below
300V or above 1000V) were eliminated.19 We thus generated a range of engineering-feasible
pack sizes and configurations, for which costs were determined and plotted as a function of pack
capacity (kWh), for each of four annual production volumes (50,000, 125,000, 250,000, and
450,000), consistent with previous analyses.
Power-law equations were then generated from the plotted points, representing cost as a
function of pack capacity and production volume. The resulting equations are shown below, for
each of the four annual manufacturing volumes. The battery chemistry is the NMC811-G energy
battery defined in BatPaC 5.0.
50k per year-. %/kWh = 284.28 x (gross kWh)~0192
125k per year-. %/kWh = 270.69 x (gross kWh)~0188
250k per year-. %/kWh = 261.61 x (gross kWh)~0184
450k per year-. %/kWh = 254.62 x (gross kWh)~0182
Figure 2-20 below shows these equations plotted on their respective curves.
19 While most BEVs and PHEVs operate at about 350V to 400V, packs approaching 1000V were included in the
plots due to the presence of dual-voltage packs in the market that can be charged at a similar voltage.
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170
160
150
u
^ 140
a
.= 130
5
< 120
1A
110
100
90
Figure 2-
~ S/kwh - SOK
. S/kwh - 125K
> S/kwh - 2S0K
¦ S/kwh - 450K
20 40 60 80 100 120 140
gross kWh
20. Direct manufacturing cost estimates for BEV packs at various annual
production volumes for NMC811-G chemistry, base year 2022.
The equation for a production volume of 250,000 packs was then used as an input to OMEGA
to represent a base year cost, applied to batteries produced in MY 2022. The annual volume of
250,000 is similar to that being produced in the largest plants today, such as those of Tesla, and
also is appropriate for the purpose of causing the BatPaC model to calculate costs applicable to a
production plant of 30 to 40 GWh capacity, which is a common plant capacity among the largest
manufacturers today. The resulting cost for a 75 kWh battery, a commonly encountered size for
BEV batteries in the market today, is about $120/kWh.
HEV battery batteries are much smaller in capacity than BEV batteries but must deliver a
significant amount of power in proportion to their capacity. The higher power-to-energy ratio
means that power plays as strong a role as energy capacity in determining cost. Due to the small
total capacity, these batteries also must be composed of smaller capacity cells in order to have
enough cells in series to achieve the approximately 300V pack voltage that is commonly seen in
HE Vs.
A population of HEV batteries was modeled in BatPaC 5.0, comprised of a single module of
72 cells of NMC811-G (Power) chemistry. These were configured for a range of capacities
between 0.75 and 1.5 kWh, and power ratings between about 18 kW and 48 kW, based on
expected power and energy requirements for a variety of vehicles styles and sizes. This resulted
power-to-energy ratios ranging from about 25 to 35.
For HEV batteries (power split and P2), the following relationship between gross capacity and
total pack cost was developed for NMC811-G (Power) chemistry in BatPaC 5.0. HEV batteries
have a higher power-to-energy ratio than BEV batteries, and cost is therefore higher than for
BEV batteries. Although HEV battery cost is more sensitive to power requirements than BEV
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batteries, it is possible to characterize HEV battery cost in terms of gross capacity as long as the
higher power-to-energy ratio is maintained in the basis.
The plot below shows the derived cost per pack for a range of HEV pack gross capacities. The
plot shows that there is not much variation in HEV total pack cost within the range of capacities
likely to be used in HE Vs.
1200
1000
<7v
800
4~>
£/>
O
u 600
~u
cl 400
200
0
0.7 0.8 0.9 1 1.1 1.2 1.3 1.4 1.5
gross kWh
Figure 2-21. Base year cost per pack for HEV batteries as a function of gross capacity
The equivalent cost per gross kWh for HEV batteries is shown in the chart below. It
demonstrates the fact that battery cost, when expressed on a dollar per kWh basis, is much higher
for HEV batteries than for BEVs due to the difference in their power-to-energy ratio and their
use of smaller cells to reach an appropriate voltage range for an HEV.
1400
„ 1200
¦oy
~ 1000
§ 800
400
O
U 200
0
0.7 0.8 0.9 1 1.1 1.2 1.3 1.4 1.5
gross kWh
Figure 2-22. Base year cost per kWh for HEV batteries as a function of gross capacity
In general, MHEV batteries are even smaller in capacity than strong HEV batteries. These
were given a single specification for all vehicles and costs were determined on that basis.
We also developed costs for PHEV batteries. PHEV battery costs were estimated by
generating a population of packs constructed with cells of varying cell capacities appropriate to
achieve a proper voltage of about 350V and with a sufficient power-to-energy ratio (varying
>...#«••+
y = 173.86x + 760.13
•••...J
V
r = 936.
-0.80
i
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from about 6.5 to 9.5) for the application. The chemistry was NMC811-G (Power formulation)
as defined in BatPaC 5.0. We found that this formulation performed well in both shorter-range
and longer-range PHEVs, with little impact on cost, and so would be suitable for estimating
battery cost in a wide range of PHEV applications. These batteries have sufficient cooling
capacity for applications in which the sustained speed is 70 miles per hour in EV mode (as
defined in BatPaC 5.0), making them suitable for providing substantial all-electric range.
Power law equations were used to characterize the costs as a function of gross capaci ty in
kWh and are depicted on Figure 2-27 below.
340
320
120
100
0 5 10 15 20 25 30 35
gross kWh
Figure 2-23: Direct manufacturing costs derived from BatPaC 5.0 for PFIEV batteries
As described in the Preamble (IV.C.l), EPA has not specifically modeled the adoption of
plug-in hybrid electric vehicle (PHEV) architectures in the analysis for this proposal. However,
the agency recognizes that PFLEVs can provide significant reductions in GHG emissions and that
some vehicle manufacturers may choose to utilize this technology as part of their technology
portfolio. EPA may rely upon these battery cost estimates and other information gathered in
response to this proposal, and on EPA's on-going technical work, for estimating the battery costs
for PHEVs for the final rule.
The BatPaC spreadsheet models used to develop the costs for BEVs, HEVs, and PFEVs are
available in the Docket (US EPA 2023) and fully describe the BatPaC inputs that were used to
generate the cloud of battery pack cost points that are visible in the plots.
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2.5.2.1.3 Development of battery pack cost reduction factors for future years
To estimate battery pack costs for future years extending into the time frame of the rule and
beyond, a dynamically generated learning factor was applied to the base year costs within
OMEGA.
When the OMEGA model generates a compliant fleet in a given future year of the analysis,
battery costs for BEVs in that year are determined dynamically, by calculating a learning cost
reduction factor to apply to the base year cost. The learning factor is calculated based on the
cumulative GWh of battery production necessary to supply the number of BEVs that OMEGA
has thus far placed in the analysis fleet, up to that analysis year. This is consistent with "learning
by doing," a standard basis for representing cost reductions due to learning in which a specific
percentage cost reduction occurs with each doubling of cumulative production over time. This
dynamic method of assigning a cost reduction due to learning means that OMEGA runs that
result in different cumulative battery production levels will have result in somewhat different
battery costs.
For the years 2022 through 2025, we suspended use of the learning factor, to reflect consensus
views that elevated mineral prices are likely to cause battery costs to remain flat for a time (for
more discussion and sources, see the discussion of the reference trajectory, later in this section).
For 2026 and later, we applied a learning factor. The learning factor equation is of the same
form as that used in previous rules, assigning a cost reduction factor due to learning, as a
function of cumulative production up to that point. The battery cost in a given year is calculated
as follows:
1) Calculate the cumulative GWh needed by BEVs placed into the analysis fleet through last
model year.
2) Calculate the cost reduction factor due to learning:20
factor = 4.1917 x (cumulative GWh through last year) ~0,225
3) Calculate battery cost in the base year, as a function of pack kWh, according to the
equation in RIA 2.5.2.1.2:
%/kWh = 261.61 x (gross kWh)~0184
4) Multiply the result of Step 3 by the result of Step 2.
To support comparison of the resultant costs generated by OMEGA to forecasts of future
battery costs found in the literature, we also developed a reference battery cost trajectory derived
from a survey of the most recent forecasts. This trajectory was used only for qualitative
comparison to help understand how well the OMEGA-generated costs compare to consensus
views of future battery costs.
20 The exponent in this equation was calibrated to match a reference cost of $75 per kWh in 2035 under a no action
GWh demand scenario. This cost is part of a reference cost trajectory discussed later in this section.
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In selecting the forecasts to consider, we noted that since the 2021 rule, it has become
increasingly clear that mineral costs have risen sufficiently to interrupt historical trends of
continuous battery cost reduction. For example, the BNEF 2021 battery price survey indicated
that the pace of reduction had slowed considerably and predicted that costs may not reach
$100/kWh (at pack level) until 2024. Elevated prices appear likely to persist for some amount of
time due to speculation and increased demand as manufacturers work to secure long-term
sources for their production needs. Although many forecasts of battery costs exist in the
literature, most were developed prior to the manifestation of these recent mineral cost increases
and may not fully capture their effects. Therefore, we gave particular attention to two sources
that do reflect recent cost increases. Proprietary forecasts produced by Wood Mackenzie in Q3
2022 and provided to EPA as part of a subscription service (Wood Mackenzie 2022) include
recent mineral cost considerations and forecasts. Another recent report by EDF/ERM
(Macintosh, Tolomiczenko and Van Horn 2022) includes a compilation of battery cost
projections from a number of sources. Like most other projections of future battery costs found
in the literature, these studies refer to direct manufacturing cost at the pack level and do not
consider the effect of policy measures that may defray some of this cost from an accounting
perspective, such as the IRA production tax credits, which are discussed in a later section. To
develop the reference trajectory, we began with the base cost of $120 per kWh that was
developed in Section 2.5.2.1.2, representing a 2022 direct manufacturing cost for a battery pack
as described in that section (75 kWh, NMC811-G, at a production rate of 250,000 packs per
year).
We then sought to identify average battery pack costs per kWh expected to occur in future
years by considering estimates from the Wood Mackenzie data and the EDF/ERM report. The
Wood Mackenzie data suggest that battery costs have risen from previous lows and are poised to
remain somewhat elevated until about 2025 to 2026, after which they are expected to resume
their decline as mineral supply and demand balance out. Similarly, the EDF/ERM report's
compilation of battery cost projections (reproduced below in Figure 2-23) shows costs flat from
2022 to 2023, and also references a 2022 BNEF estimate that predicts costs will remain flat to
2024. Considering all of these sources, we chose to keep the base year pack cost per kWh of
$120 unchanged through 2025, which represents a conservative rate of decline relative the
sources.
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600
500
400
300
200
100
¦ BloombergNEF's 2021 Battery Price Survey (Actual Cost)
Projected Average Battery Pack Cost*
• BloombergNEF (2022)
• BloombergNEF (2021)
• IHS Markit (2021)
DOE (2020)
McKinsey (2019)
• NREL's BatPac Software Ahmed et al. (2018)
PIS Wood Mackenzie (range of selected chemistries)
2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030
Figure 2-24 Projected battery pack costs from various sources summarized by EDF/ERM
Looking to 2026, the sources compiled in the EDF/ERM report suggest an average pack cost
of $100/kWh. However, the midrange of Wood Mackenzie estimates for that year suggest a
range of about $100-$130. We selected a point in between, at $110/kWh, giving a slightly larger
weight to the EDF/ERM report due to its basis on multiple studies. Looking to 2029, the sources
compiled in the EDF/ERM report suggest approximately $80/kWh, while the Wood Mackenzie
forecast suggests about $90 to $110. Noting also that the DOE/'ANL high case for battery cost in
2030 is $90/kWh, we selected $90/kWh which is between the Wood Mackenzie and EDF/ERM
estimates.
The potential for cost reductions to reach these levels by 2026 and 2029 is also supported by
our observation that analysts largely expect the price of lithium to stabilize at or near its
historical levels by the mid-2020s,21 suggesting that the elevated battery costs being reported
today will not persist.
Past 2029, fewer pack cost estimates are found in the literature, and such long-term estimates
are by their nature more uncertain than shorter-term estimates. Often, analysts model costs over
the longer term by assuming an annual percentage cost reduction rate. Therefore we adopted this
approach for the years past 2029. We have assumed that by this time, some of the generally
anticipated improvements in battery manufacturing will have already taken place, suggesting a
lower rate of learning than was seen in earlier years. Starting at the $90/kWh selected for 2029,
we applied a 3 percent per year reduction, which results in $75/kWh in 2035. Past 2035, we
applied a 1 percent per year reduction which results in a decline to $65/kWh by 2050.
This results in the reference trajectory for the cost of a representative 75 kWh battery as
shown in Figure 2-24.
21 For example, see Sun et al.. "Surging lithium price will not impede the electric vehicle boom," Joule,
doi:10.1016/j,joule. 2022.06.028 (https://dx.doi.org/10.1016/ijoule.2022.06.028).
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$140
~ $130
$ $120
^ $110
ts $100
8 $90
% $80
« $70
"o $60
ro
$50
$40
2020 2025 2030 2035 2040 2045 2050 2055
Year
Figure 2-25. Reference trajectory of future battery pack manufacturing costs for a 75 kWh
BEV pack
As stated previously, this reference trajectory was used only for qualitative comparison to the
battery costs that are generated by OMEGA, which are dependent on a learning factor that is a
function of the cumulative GWh of battery production in a given run of the model. Since the
reference trajectory was developed using sources that predate the proposal, it is taken to
represent approximate consensus views of where battery costs are considered likely to go in the
absence of additional battery production resulting from the proposed standards.
As an example of how the pack direct manufacturing costs used in the analysis compare with the
reference trajectory, Figure 2-25 shows the sales weighted average cost per kWh generated
by OMEGA for the central case of the proposal, alongside the reference trajectory described
above. Neither include the estimated impact of IRA 45X production tax credits for battery
production, which are applied in a later step.22 The Proposal costs compare quite favorably to
the reference trajectory and vary generally as expected. From 2022 to 2025 they are
somewhat lower, due to the substantially larger average pack size (96 kWh to nearly 100
kWh) compared to the 75 kWh of the reference trajectory. Past 2027, the Proposal costs are
also lower than the reference trajectory, again due in part to the larger pack size, and
increasingly, to the growing cumulative production volume due to the additional BEVs
driven by the proposal.
32 The impact of IRA 45X production tax credits on battery cost to manufacturers as modeled in OMEGA are
developed and discussed in section 2.5.2.1.4.
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160
160
140
(/ft
8 120
4-»
U
m
» 100
_c
5
v>
140
80
60
40
40
2021 2023 2025 2027 2029
Year
2031
2033
2035
Proposal (~100 kWh)
¦Reference (75 kWh)
—Ave kWh
Figure 2-26: Example of pack direct manufacturing cost per kWh and average pack kWh
generated by OMEGA
The 96 kWh to 103 kWh average pack capacity of the BEVs in the proposal is due in part to
their use in relatively large vehicles, such as large SUVs and light trucks, which form a
significant part of the OMEGA modeled compliance fleet and to which OMEGA directs a
significant amount of electrification in its identification of a least cost compliance pathway.
Another factor is the use of a 300-mile driving range for all light-duty BEVs and MDV pickup
truck BEVs in the analysis, which is a longer average range than in some other studies, but
which EPA believes is an appropriate modeling choice to reflect currently prevailing range
expectations by consumers. For medium-duty van BEVs, we assumed a 150-mile range due to
the predominant use of this vehicle type within commercial parcel delivery and comparable to
currently available BEV MDV vans (see Chapter 3.1.2).
More discussion of the OMEGA model and the OMEGA results can be found in Preamble
IV.C and elsewhere in this DRIA. For additional discussion of the battery costing method and
sources considered, and a comparison between the battery costs derived in this analysis and those
of the 2021 final rule analysis, please see Preamble § IV.C.2.
For additional discussion of the battery costing method and sources considered, and a
comparison between the battery costs derived in this analysis and those of the 2021 final rule
analysis, please see Preamble § IV.C.2.
2.5.2.1.4 Battery cost reductions due to Inflation Reduction Act
To reflect the anticipated effect of the Inflation Reduction Act on battery costs to
manufacturers, we applied a further cost reduction based on the Section 45X Advanced
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Manufacturing Production Tax Credit. This provision of the IRA provides a $35 per kWh tax
credit for manufacturers of battery cells, and an additional $10 per kWh for manufacturers of
battery modules, as well as a credit equal to 10 percent of the manufacturing cost of electrode
active materials and another 10 percent for the manufacturing cost of critical minerals (all
applicable to manufacture in the United States). The credits, with the exception of the critical
minerals credit, phase out from 2030 to 2032.
We estimated that, across the PEV industry as a whole, the capability of manufacturers to take
advantage of the $35 cell credit and the $10 module credit would ramp up over time, as new U.S.
battery manufacturing facilities come on line, allowing manufacturing to increasingly take place
in the U.S. We ramped the modeling value of the credit linearly from 60 percent of total cells and
modules in 2023 (a conservative estimate of the current percentage of U.S.-based battery and cell
manufacturing likely to be eligible today for the credit)23 to 100 percent utilization in 2027, and
then ramping down by 25 percent per year as the law phases out the credit from 2030 (75
percent) through 2033 (zero percent). Although a large percentage of 2023 U.S. BEV battery and
cell manufacturing is represented by the production of one OEM, we believe that the many large
U.S. battery production facilities that are being actively developed by suppliers and other OEMs
(as described in IV.C.6 of the Preamble) will allow benefit of the credit to be accessible to all
manufacturers by 2027. We also note that the high value of the credit provides a strong
motivation for manufacturers to utilize it. For the purpose of modeling, the percentages above
represent an average credit amount across the industry as a whole. Although some manufacturers
and vehicles may realize the full value of the credit in any given year, the model requires an
average value across the full market.
Figure 2-26 shows an example of the resulting effect on average pack direct manufacturing
costs (DMC) generated by OMEGA in the central case of the proposal, after application of the
45X credit. The 45X cell and module credits per kWh were applied not to the direct
manufacturing cost per kWh, but to the marked-up cost per kWh (that is, after multiplying the
direct manufacturing cost by the 1.5 retail price equivalent (RPE)). Because RPE is meant to be a
multiplier against the direct manufacturing cost, and the 45X credit does not reduce the actual
direct manufacturing cost at the factory but only compensates the cost after the fact, we felt that
it was most appropriate to apply the 45X credit to the marked-up cost.. The 45X cell and module
credits per kWh were applied by first marking up the direct manufacturing cost by the 1.5 RPE
factor to determine the indirect cost (i.e., 50 percent of the manufacturing cost), then deducting
the credit amount from the marked-up cost to create a post-credit marked-up cost. The post-
credit direct manufacturing cost would then become the post-credit marked-up cost minus the
indirect cost.
23 U.S. Department of Energy, "FOTW #1192, June 28, 2021: Most U.S. Light-Duty Plug-In Electric Vehicle
Battery Cells and Packs Produced Domestically from 2018 to 2020," June 28, 2021.
https://www.energy.gov/eere/vehicles/articles/fotw-1192-june-28-2021-most-us-light-duty-plug-electric-vehicle-
battery
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$180
$160 \
$140 ^
$12° x
$10° N\ \
$80 %
$60
$40
$20
$-
2021 2023 2025 2027 2029 2031 2033 2035
Year
Marked-up Direct
Figure 2-27: Volume weighted average pack direct manufacturing cost and marked-up cost
per kWh after application of 45X credit
EPA did not apply a further cost reduction to represent the 10 percent electrode active
material or critical mineral production credit, which are also available to be utilized by
manufacturers. These credits are likely to have a substantial impact on reducing battery costs,
and their exclusion from the currently modeled cost estimates represents a conservative
assumption. The implementation of battery costs as OMEGA inputs are provided in 2.6.1.3.1.
2.5.2.2 BEV Non-Battery Cost Approach
EPA updated the non-battery powertrain costs that were used to determine the direct
manufacturing cost of electrified powertrains. We referred to a variety of industry and academic
sources, focusing primarily on teardowns of components and vehicles conducted by leading
engineering firms (described in the rest of this section). The equations used in OMEGA for the
non-battery electrified vehicle cost estimates used in the proposal may be found in 2.6.1.3.2.
2.5.2.2.1 Use of teardown studies
While EPA relies on a variety of sources to establish direct manufacturing costs for vehicle
components, we have long considered teardown studies to be the preferred means for doing so.
EPA has previously estimated non-battery costs by commissioning teardown studies of early-
stage EV technologies. For past rulemakings we contracted with FEV North America to conduct
teardowns of electrified vehicle components that were available at the time in order to establish
direct manufacturing costs under high-volume production in a rigorous and transparent way.
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Since then, the ongoing evolution of electrified vehicles has led to the emergence of improved
or entirely new components that benefit from improved manufacturing efficiencies, component
integration, and platform optimization. These developments call for significant updates to the
way we characterized and quantified vehicle costs in the past. In some cases, specific
components that we costed in the past may have become integrated with other components, or
their design has changed so they use less costly materials or can be manufactured in a more
efficient way. The vehicle platforms that incorporate these components may also have changed
to optimize their integration with the rest of the vehicle.
Third-party teardowns of vehicles and components have also become more widely available
from a number of engineering firms. EPA has acquired several of these studies to inform these
changes and to represent the manufacturing cost of today's electrified vehicle components as
accurately as possible. We have also conducted a new full-vehicle teardown of two new vehicles
with FEV North America.
2.5.2.2.2 Munro and Associates teardowns
EPA purchased a set of vehicle and component teardown reports from Munro & Associates
(Munro and Associates 2020a) (Munro and Associates 2021) (Munro and Associates 2020b)
(Munro and Associates 2016) (Munro and Associates 2020c) (Munro and Associates 2018) to
provide a new source of detailed cost data and to become more familiar with recent trends in
component design and integration. EPA worked jointly with CARB to analyze the data in these
reports to help inform our updated costs. The teardowns purchased from Munro are shown in
Table 2-26.
Table 2-25: Munro Teardown Reports Used in the Analysis
Report
12 motor side bv side analysis
6 inverter side by side analysis
Model 3 report
Model Y report
Technologies covered
Model 3 front. Model 3 rear
Model Y front, Model Y rear
BMW i3, Chevy Bolt, Chevy Volt,
Toyota Prius
2019 Jaguar I-PACE
2019 Audi e-tron front, 2019 Audi e-tron
rear
2020 Nissan Leaf
Nissan Leaf
Model 3 rear
2019 Jaguar I-PACE
Audi e-tron
Model Y front. Model Y rear
Entire vehicle: Body and chassis.
Electronics. Interior/Safely. Powertrain.
Battery
Entire vehicle: Body and chassis.
Electronics. Interior/Safely. Powertrain.
Battery
Among these vehicles, we concluded that the components in the Tesla Model 3 and Model Y
were most representative for the cost analysis because they are most likely to represent current
and future capability and have potential for further improvement. Although components from a
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single manufacturer may be unique to that manufacturer's practices and intellectual property, in a
competitive environment it is not likely to prevent other OEMs from achieving similar levels of
integration and optimization. Additionally, the Tesla teardowns were costed by Munro at a
consistent and large annual production volume of 200,000 to 250,000 units, whereas some of the
other studies represented a lower volume that is not likely to represent future trends under the
higher penetration of BEVs anticipated by the proposed rule. Costs were summarized on either a
dollar per kW basis or a fixed cost basis, as applicable, and combined with cost estimates from
other sources.
2.5.2.2.3 EPA-FEV comparative BEV-ICE vehicle teardown
We have also conducted a new full-vehicle teardown of two new vehicles with FEV North
America (FEV Consulting Inc. 2022). We tore down a 2021 Volkswagen ID.4 BEV and a 2021
Volkswagen Tiguan, an ICE vehicle relatively equivalent to the ID.4 in size and function.
This project was initiated in part due to the realization that platform optimization is likely to
affect a variety of cost comparisons between ICE and BEV vehicles. For example, platform
optimization, particularly for BEVs, could potentially lead to differences in indirect costs that are
experienced by the manufacturer (such as assembly cost, certification cost, and calibration cost).
We also considered that the differences between a platform-optimized BEV and a platform-
optimized ICE vehicle might call for an absolute costing approach, instead of assuming that a
BEV can be costed as an ICE vehicle with ICE components removed and BEV components
added.
The study was therefore designed not only to provide an additional source for vehicle
component cost data, but also to inform several issues that are commonly cited with regard to the
difference in cost between conventional and battery electric vehicles. Because ICE vehicles and
BEVs are likely to be built on different dedicated platforms, the study was designed as a ground-
up study for which a complete costed bill of materials would be developed for every component
of each vehicle, including structural and other non-powertrain components. This would support
our intention to move to a costing regime based on absolute vehicle costs instead of relative or
incremental costs (as previously described in 2.5.1), and to allow comparisons to be made on a
vehicle-system basis to identify significant potential cost efficiencies attributable to a dedicated
BEV platform. FEV was also asked to comment on potential differences in indirect costs for
BEV design, certification, and calibration that might become apparent on a close inspection of
the components, their system integration, and their assembly characteristics. We also specified
that a detailed labor assessment be performed for each component, in order to shed light on
differences in amount and type of assembly labor required for production. An additional task
under this work assignment was to evaluate the non-battery HEV and PEV costs EPA has
described under section 2.6.1 of this DRIA, with respect to the cost values used and the method
of scaling these costs across different vehicle performance characteristics and vehicle classes.
Delivery of the teardown study results by FEV was completed in February 2023 and a peer
review is planned to be completed in mid-2023. The FEV review of non-battery costs and scaling
is available in a memo to the Docket entitled "EV Non-Battery Cost Review by FEV." In
developing the costs used in this proposal, we have considered qualitative information gained
thus far in conducting this project, and we expect to incorporate more information from the study
in the final rule analysis.
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2.5.2.2.4 Other teardowns
We also incorporated data from a 2017 UBS teardown of the Chevy Bolt EV (UBS AG 2017),
and a 2018 teardown study of several EV components performed for CARB by Ricardo (Ricardo
Strategic Consulting and Munro and Associates 2017).
UBS is a global financial services company originally from Switzerland and is one of the
largest in the world. UBS contracted Munro & Associates to tear down a Chevy Bolt EV to
better understand the profitability of 200+ mile EVs, particularly the Tesla Model 3. Because the
teardown analysis was contracted to Munro & Associates, this suggests that the costs derived in
the study are comparable to those of the Munro reports that EPA purchased, as well as the
Ricardo teardown studies performed for CARB, which was subcontracted also to Munro &
Associates.
Since it was released, the UBS study has become a widely cited resource across the industry,
as it was one of the first publicly accessible teardowns to provide individual component costs
that were well documented and derived from what was a state-of-the-art vehicle at the time. The
study also provided valuable insight into the breakdown of battery costs for this vehicle and
related its content to the outlook for raw materials markets.
Also in 2017, CARB published a teardown of selected power electronics and thermal systems
in a report titled "Advanced Strong Hybrid and Plug-In Hybrid Engineering Evaluation and Cost
Analysis." (Ricardo Strategic Consulting and Munro and Associates 2017). The selected
components were identified as representing the state-of-the-art for production vehicles at the
time, and included two electric machines (one from the Toyota Prius and one from the Chevy
Volt), two inverter modules (one from the Prius and one from Audi), and one DC-DC converter
from the Ford Fusion. As with the UBS teardown, this teardown was subcontracted by Ricardo
to Munro & Associates.
2.5.2.2.5 Published and other sources
The NAS Phase 3 report was published in 2021 (National Academies of Sciences,
Engineering, and Medicine 2021). This report included cost estimates for various electric vehicle
components including batteries and non-battery components, derived from a survey of a number
of quantitative sources in the literature, combined with a qualitative assessment of their validity.
The sources cited by NAS had significant overlap with the sources EPA used in its analysis,
including reference to a presentation to NAS by Munro and Associates, as well as the 2017 UBS
teardown and other sources. Accordingly, the costs in the NAS report were quite similar to those
that were ultimately used in the EPA analysis. In some cases, the costs we used differ from the
costs cited by NAS, largely because EPA had access to a larger diversity of teardown reports,
some of which were not available to NAS at the time of their research.
In 2021 and 2022, CARB developed ZEV component costs for use in their ACC II program,
and ultimately published two versions of a ZEV costing workbook (California Air Resources
Board 2022) and invited public comment on the costs it reported. When EPA developed its cost
estimates, CARB staff worked jointly with EPA to analyze the teardown data and much of this
work was reflected in CARB's costs. Owing to the differences between specific goals of the
CARB ACC II cost analysis and that of EPA, some costs in the CARB workbook were
developed differently to reflect differences in vehicle configuration or performance expectations
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resulting from the specific regional focus and regulatory environment of the CARB program. In
general, the costs used by EPA and those within the CARB workbook are in good alignment.
In October 2022, ICCT released a report (Slowik, et al. 2022) that included an analysis of
future EV component and battery costs. As with many third-party studies, the ICCT report
differs from the EPA analysis in certain aspects of its approach and assumptions. In general, the
costs EPA developed are not inconsistent with the costs assumed by ICCT.
2.5.3 Approach to cost reduction through manufacturer learning
Within OMEGA, learning factors are applied to technology costs as shown in Table 2-27.
These learning factors were generated with the expectation that learning on ICE body structure
technologies would slow, relative to their traditional rates, in favor of a focus on BEV
technologies.
Importantly, the learning factors shown are multiplicative scaling factors indexed to 2022.
The costs presented below in Chapter 2.6 represent first year costs and the learning factors
shown in Table 2-27 are applied to those first-year costs to arrive at costs for subsequent years.
Learning was applied to BEV and HEV battery costs by dynamic application of a cumulative
GWh-based learning equation that was described in Section 2.5.2.1.3.
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Table 2-26 Learning Factors Applied in OMEGA, Indexed to 2022a
Model Year
ICE Power! rain & Glider Cosls
BEV Non-Ball
2022
1.00
1.00
2023
1.00
0.86
2024
0.99
0.79
2025
0.99
0.74
2026
0.99
0.70
2027
77" 0.9S
0.67
2028
0.98
0.65
2029
0.98
0.63
2030
0.97
0.61
2031 '
0.97
7 0.59
2032
'0.97 '7
0.58
2033
0.96
0.57
2034 ...
'71 0.9(,
77 0.56
2035
0.96
0.55
2036
0.95
0.54
2037
0.95
0.53
2038
0.95
0.52
2039
"" 0.95 7
0.51
2040
0.94
0.51
2041 7
0.94
0.50
2042
0.94
0.50
2043
0.94
7.77 °-49
2044
0.93
7 °-49
2045
0.93
0.48
2046
0.93
0.48
2047
0.92
0.47
2048 '
0.92
7 ..°-47
2049 i
0.92
1 0.46
2050
0.92
[ 0.46
2051
7 o-92
0.45
2052
0.91
0.45
2053
0.91
0.45
20547
0.91
0.44
2055
0.91
0.44
learning factors are indexed to 2022.
2.5.4 Indirect costs
To produce a unit of output, vehicle manufacturers incur direct and indirect costs. Direct costs
include cost of materials and labor costs. Indirect costs are all the costs associated with
producing the unit of output that are not direct costs - for example, they may be related to
production (such as research and development, R&D), corporate operations (such as salaries,
pensions, and health care costs for corporate staff), or selling (such as transportation, dealer
support, and marketing). Indirect costs are generally recovered by allocating a share of the costs
to each unit of good sold. Although it is possible to account for direct costs allocated to each unit
of good sold, it is more challenging to account for indirect costs allocated to a unit of goods sold.
To make a cost analysis process more feasible, markup factors, which relate total indirect costs
to total direct costs, have been developed. These factors are often referred to as a retail price
equivalent (RPE) markup.
EPA has frequently used cost multipliers to predict the resultant impact on costs associated
with manufacturers' responses to regulatory requirements. The best approach, if it were possible,
to determining the impact of changes in direct manufacturing costs on a manufacturer's indirect
costs would be to estimate the cost impact on each indirect cost element. However, doing this
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within the constraints of an agency's time or budget is not always feasible, or the technical,
financial, and accounting information to carry out such an analysis may simply be unavailable.
The RPE multiplier, or RPE markup factor, is based on an examination of historical financial
data contained in 10-K reports filed by manufacturers with the Securities and Exchange
Commission. It represents the ratio between the retail price of motor vehicles and the direct costs
of all activities that manufacturers engage in. The RPE markup provides, at an aggregate level,
the relative shares of revenues (Revenue = Direct Costs + Indirect Costs + Net Income) to direct
manufacturing costs as shown in Table 2-28. Using the RPE markup implicitly assumes that
incremental changes in direct manufacturing costs produce common incremental changes in all
indirect cost contributors as well as net income. However, a concern in using the RPE markup in
cost analysis for new technologies added in response to regulatory requirements is that the
indirect costs of vehicle modifications are not likely to be the same for different technologies.
For example, less complex technologies could require fewer R&D efforts or less warranty
coverage than more complex technologies. In addition, some simple technological adjustments
may, for example, have no effect on the number of corporate personnel and the indirect costs
attributable to those personnel. The use of a single RPE markup, with its assumption that all
technologies have the same proportion of indirect costs, is likely to overestimate the costs of less
complex technologies and underestimate the costs of more complex technologies.
Table 2-27 Retail Price Equivalent Factors in the Heavy-Duty and Light-Duty Industries
(Rogozhin 2009)
Cost Contributor
Contribution to Cost
Direct manufacturing cost
1.0
Warranty
0.03
R&D
0.05
Other (administrative, retirement, health, etc.)
0.36
Profit (cost of capital)
0.06
Retail price equivalent
1.50
To address this concern, modified multipliers were developed by EPA, working with a
contractor, for use in past EPA rulemakings. (Rogozhin 2009) Those modified multipliers were
referred to Indirect Cost Multipliers, or ICMs, and EPA applied low magnitude ICMs (i.e., the 1.5 RPE) to high
complexity technologies. This way, we could analyze the possible pathways toward compliance
with GHG regulations via, for example, application of many low complexity technologies versus
few high complexity technologies. In other words, we could weigh one technology against
another in a more finely tuned way.
The ICM approach served us well when dealing with incremental technology applications and
incremental costs for those technologies as was done in the 2010 and 2012 final rules (75 FR
25324 2010, 77 FR 62624 2012). However, as noted above, we no longer use that approach to
estimating compliance pathways. In contrast, since we now consider the whole vehicle and its
total cost and performance toward compliance, we no longer need the fine tuning of one
technology versus another that the ICM approach provided. As a result, for this analysis, we are
using the full RPE markup as the indirect cost markup as we did in our 2021 final rule (86 FR
74434 2021).
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2.6 Inputs and Assumptions for Compliance Modeling
2.6.1 Powertrain Costs
2.6.1.1 ICE Powertrain Costs
Table 2-28 shows the ICE -specific powertrain costs used as inputs to OMEGA. Note that
hybrid electric vehicles (HEV) and mild HEVs are treated as ICE vehicles when calculating
these powertrain costs.
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Ilcm
Cylinders
Displacement
Gasoline Direct
Injection (GDI)
turbll
turbl2
Cooled EGR
Deac
(Partial, Discrete)
Deac
(Full, Continuous)
atk2
TRX10
TRX11
TRX12
TRX21
TRX22
TRXCV
High efficiency
alternator
Start stop
TWC substrate
TWC washcoat
TWC canning
TWC swept volume ;
TWC Pt grams/liter ;
TWC Pd grams/liter ;
TWC Rh grams/liter ;
GPF
TWC PGM
Table 2-28: ICE Powertrain Cost in OMEGA
Nolc
Troy oz/gram
PT_US D PER OZ
PD USD PER OZ
RH USD PER OZ
Diesel exhaust
aftertreatment
system
LV battery
HVAC
turb scaler
Cosl Curve
(Nolc: Markup = 1.5)
(-28.814 * CYL + 726.27) * CYL * Markup
400 * LITERS * Markup
(43.237 * CYL + 97.35) * Markup
i (-13.149 * CYL2 + 220.34* CYL- 124.73)* Markup !
| (-13.149 * CYL2 + 220.34* CYL- 124.73)* Markup ;
114 * Markup
(-1.0603 * CYL2 + 28.92 * CYL - 8.6935) * Markup
154 * Markup
(4.907 * CYL2 - 29.957 * CYL + 130.18) * Markup
i 1390.20 * Markup j
1431.20 * Markup ;
1653.20 * Markup
1568.20 * Markup
1791.20 * Markup
1000 * Markup
150 * Markup
(0.0149 * CURBWT + 276.82) * Markup i
i(..108 * LITERS * TWC SWEPT YOU Ml. + 1.95456) ;
* Markup
: (5.09 * LITERS * TWCSWEPTVOLUME) * Markup J
;(2.4432 * LITERS * TWC SWl-.l'T YOU Ml.i * Markup;
1.2 multiplier applied to engine displacement
0 j
2
0.11
(14.1940 * LITERS + 39.2867) * Markup ;
(PTGRAMS PER LITER TWC * LITERS *
TWCSWEPTVOLUME * PT USD PER OZ *
OZPERGRAM + PD GRAMS PER LITER TWC * ;
LITERS * TWCSWEPTVOLUME *
PD USD PER OZ * OZ PER GRAM +
RH GRAMS PER LITER TWC * LITERS *
TWCSWEPTVOLUME * RH USD PER OZ *
OZ PER GRAM) * Markup
0.0322
1030
2331
17981
700 * LITERS * Markup
(3 * VEHICLESIZECLAS S + 51) * Markup
(11.5 * VEHICLE SIZE CLASS + 195.5) * Markup
1.2 multiplier applied to (SCylinders + SDisplacement)
Dollar
Basis
2019 :
i 2019 ,
: 2019 :
; 2012
: 2012
j 2012
;2006
; 2017
| 2010
; 2018
; 2018
; 2018
! 2018
: 2018
[ 2019
: 2015
i 2015
2012
2012
2012
2021
CYL=# of cylinders
LITERS =engine
displacement
CYL=# of cylinders
Turbocharging with boost
-18 bar
Turbocharging with boost
~24 bar
Deac=cylinder
deactivation
atk2=Atkinson cycle
engine
TRX=trans mission
TRXCV=HEV
transmission
TWC=3way catalyst;
TWC swept volume=1.2
2020
2019
2019
USD=US dollars;
OZ=Trov ounce
LITERS =engine
displacement
LV=low voltage
Example syslcin kisI
(6CYL, 3L, 4000 ll> CW,
size class 3)
$4,980
$1,800
$535
$1,086
$1,086
$171
$190
$231
$191
$2,085
$2,147
$2,480
$2,352
$2,687
$1,500
$225
$505
$36
$27
$13
GPF=gasoline particulate
filter
PGM=Platinum group
metals (e.g., Pt Pd, Rh)
$123
$1,155
Values are used in the
TWC PGM calculation
$3,150
$90
$345"
1.2 *
($4,980+$l,800)=$8,136
2-63
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Diesel engine cost
scaler
1.5 multiplier applied to 1.2 * (SCylinders +
SDisplacement)
1.5 * 1.2 *
($4,980+$l,800)=$12,204
Markup 1.5 The RPE markup factor to
| account for indirect costs ;
2.6.1.1.1 Cost per cylinder and cost per liter
The most basic piece of ICE powertrain technology is the engine. OMEGA considers the
basic engine cost as a group of cylinders and mass of material (steel, aluminum). As such the
engine costs are estimated based on the number of cylinders and the displacement of the engine
as shown below.
The OMEGA direct manufacturing cost (DMC) per cylinder and DMC per liter curve is based
on the values shown in Table 2-30.
Table 2-29: Cost per Cylinder and Cost per Liter in OMEGA
Item
DMC
Dollar Basis
$/cylinder. 8 cylinder engine
500
2019
$/cylinder. 6 cylinder engine
550
2019
$/cylindcr. 4 cylinder engine
600
2019
$/cylindcr. 3 cylinder engine
650
2019
$/lilcr. ;ill engines
400
2019
Using these values, the following cost curves were generated for use in OMEGA.
CylinderCost = (—28.814 x CYL + 726.27) x CYL x Markup
DisplacementCost = 400 x LITERS x Markup
Where,
CYL = the number of cylinders on the engine
LITERS = the total displacement of the engine
Markup = the markup to cover indirect costs
2.6.1.1.2 Gasoline Direct Injection
The costs for gasoline direct injection (GDI) are based on costs used in past EPA analyses.
Those costs are shown in Table 2-30.
Table 2-30: Gasoline Direct Injection System Cost in OMEGA
Item
DMC
Dollar Basis
GDI. 3 cylinder engine
244
2012
GDI. 4 cylinder engine
244
2012
GDI. 6 cylinder engine
368
2012
GDI. 8 cylinder engine
442
2012
2-64
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Using these values, the following cost curve was generated for use in OMEGA.
GasolineDirectlnjection = (43.237 x CYL + 97.35) x Markup
Where,
CYL = the number of cylinders on the engine
Markup = the markup to cover indirect costs
2.6.1.1.3 Turbocharging
OMEGA estimates two levels of turbocharging, although the costs are identical for both.
These costs are based on past EPA analyses as shown in Table 2-31.
Table 2-31: Turbocharging Costs in OMEGA
TURB
TURB
TURB
TURB
TURB
TURB
TURB
TURB
12.
12.
12..
12.
Item
DMC
Dollar Basis
3 cylinder engine
463
2012
4 cylinder engine
463
2012
6 cylinder engine
780
2012
8 cylinder engine
780
2012
3 cylinder engine
463
2012
4 cylinder engine
463
2012
6 cylinder engine
780
2012
8 cylinder engine
780
2012
Using these values, the following cost curve was generated for use in OMEGA.
TURBll = TURB12 = (-13.149 X CYL2 + 220.34 X CYL - 124.73) X Markup
Where,
CYL = the number of cylinders on the engine
Markup = the markup to cover indirect costs
In addition, any turbocharged engine includes a turbo scaler of 1.2 applied to the cylinder and
displacement costs described above.
TurboEngineCost = 1.2 x (CylinderCost + DisplacementCost) + TURB
Where,
CylinderCost = costs determined by the CylinderCost equation above
DisplacementCost = costs determined by the DisplacementCost equation above
TURB = the cost of TURB 11 or TURB 12 as appropriate
1.2 = the turbo scaler to account for more robustness in the turbocharged engine
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2.6.1.1.4 Cooled Exhaust Gas Recirculation
The cost of cooled exhaust gas recirculation (CEGR) is based on past EPA analysis and is
calculated in OMEGA as below.
CooledEGR = 114 x Markup
Where,
Markup = the markup to cover indirect costs
2.6.1.1.5 Cylinder Deactivation
The costs of cylinder deactivation are based on past EPA analyses as shown in Table 2-32.
Table 2-32: Cylinder Deactivation Costs in OMEGA
Item
DMC
Dollar Basis
Partial discrete. 3 cylinder engine
76
2006
Partial discrete. 4 cylinder engine
76
2006
Partial discrete. 6 cylinder engine
136
2006
Partial discrete. 8 cylinder engine
152
2006
Full continuous, all engines
154
2017
Using these values, the following cost curves were generated for use in OMEGA.
DeacPD = (-1.0603 x CYL2 + 28.92 x CYL - 8.6935) x Markup
DeacFC = 154 x Markup
Where,
CYL = the number of cylinders on the engine
Markup = the markup to cover indirect costs
2.6.1.1.6 Atkinson Cycle Engine
The costs for Atkinson cycle engine (ATK) are based on costs used in past EPA analyses.
Those costs are shown in Table 2-33.
2-66
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Table 2-33: Atkinson Cycle Engine Costs in OMEGA
Item DMC Dollar Basis
ATK. 3 cylinder engine 86 2010
ATK. 4 cylinder engine 86 2010
ATK. 6 cylinder engine 129 2010
ATK. 8 cylinder engine 204 2010
Using these values, the following cost curves were generated for use in OMEGA.
AtkinsonCycleEngine = (4.907 x CYL2 — 29.957 x CYL + 130.18) x Markup
Where,
CYL = the number of cylinders on the engine
Markup = the markup to cover indirect costs
2.6.1.1.7 Transmissions
Transmission costs are based on past EPA analysis, with the addition of an estimated cost for
a base or null transmission, loosely defined as a 5-speed automatic transmission with no
efficiency or shift improvement upgrades. Those costs are shown in Table 2-34. Note that the
null transmission is not shown in Table 2-34 since OMEGA does not apply it, but it is needed
since the past EPA transmission costs were relative to that null transmission. EPA has estimated
that null transmission as costing $800 (direct manufacturing cost in 2012 dollars).
Table 2-34: Transmission Costs in OMEGA
Item
DMC
Dollar Basis
TRX11. front/rear wheel drive
841
2012
TRX12. front/rear wheel drive
1063
2012
TRX21. front/rear wheel drive
978
2012
TRX22. front/rear wheel drive
1201
2012
TRX 11. all/4 wheel drive
1009
2012
TRX 12. all/4 wheel drive
1276
2012
TRX21. all/4 wheel drive
1174
2012
TRX22. all/4 wheel drive
1441
2012
TRXCV. for Powcrsplil HEV
1000
2019
These costs are used as-is in OMEGA other than OMEGA's application of the markup to
account for indirect costs.
2.6.1.1.8 High Efficiency Alternator
OMEGA's high efficiency alternator cost is based on past EPA analyses and is calculated
according to the equation shown below.
HighEfficiencyAlternator = 150 x Markup
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Where,
Markup = the markup to cover indirect costs
2.6.1.1.9 Start-Stop
The costs of start-stop systems are based on past EPA analyses as shown in Table 2-35.
Table 2-35: Start-stop System Costs in OMEGA
Curb Weight DMC Dollar Basis
<=3800 321 2015
3800
-------
TWCwashcoat = (5.09 x 1.2 x LITERS) x Markup
TWCcanning = (2.4432 x 1.2 x LITERS) x Markup
, , TrovOz
TWCpgm = (Ptgpi X Pt$/Troy0z + Pdgpi X Pd$/Troy0z + Rhgpl X Rh$/Troy0z) x 1-2 x LITERS X gram
Where,
LITERS = the engine displacement in liters
7.2 = factor to account for the swept volume of the TWC (i.e., total TWC volume is 1.2x
engine displacement
Ptgpi = Platinum grams/liter, set to 0 in this analysis
Pdgpi = Palladium grams/liter, set to 2 in this analysis
Rhgpi = Rhodium grams/liter, set to 0.11 in this analysis
Pt$/TroyOz = Platinum cost per Troy ounce, set to $1,030 in this analysis
Pd$/TroyOz = Palladium cost per Troy ounce, set to $2,331 in this analysis
Rh$/TroyOz = Rhodium cost per Troy ounce, set to $17,981 in this analysis
TroyOz = Troy ounces
TroyOz/gram = 0.0322, or 31.1 grams per Troy Oz
Markup = the markup to cover indirect costs
2.6.1.1.12 Diesel Exhaust Aftertreatment System
OMEGA's diesel exhaust aftertreatment system (diesel EAS) costs are structured for
consistency with the recent heavy-duty final rule (88 FR 4296 2023). The cost curve is as shown
below.
Diesel EAS = (700 x LITERS) x Markup
Where,
Diesel EAS = diesel exhaust aftertreatment system cost
LITERS = the engine displacement in liters
Markup = the markup to cover indirect costs
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2.6.1.2 HEV-specific and Mild HEV-specific Powertrain Costs
Strong hybrid electric vehicle (HEV) and mild-HEV (MHEV) powertrain costs are broken
into non-battery and battery costs. In addition to the costs presented here, the costs associated
with ICE powertrains presented in 2.6.1.1 would also apply for HEVs and MHEVs. Note that,
throughout this discussion, we use the term "HEV" to refer to a strong hybrid and mild HEV or
MHEV to refer to a mild hybrid.
2.6.1.2.1 HEV and MHEV Non-Battery
HEV and MHEV non-battery costs are shown in Table 2-36.
Table 2-36: HEV & MHEV Non-Battery Costs in OMEGA
Item
; Cost Curve
; Dollar
; Note
; Example system cost
; (Note: Markup = 1.5)
Basis
(10 kW power, vehicle
; size class=3
Single motor
; (6.91 * kW - 8.64) * Markup
; 2019
[ $89
Single Inverter
| (2.4 *kW +231)* Markup
2019
J $383
DC-DC converter kW
] 3.5 * '
2019
Onboard charger & DC-
1 39.754 * DC-DC converter kW *
] 2019
! Onboard charger kW = 0 for
! $209
DC converter
; Markup
| HEV and MHEV
High voltage orange
F (9.5 * VehicleSizeClass + 161.5)
; 2019
: $285
cables
; * Markup
Brake sensors & actuators
i 200 * Markup
: 2019
i $300
Markup
: 1.5 '
The RPE markup to account for
indirect costs
2.6.1.2.2 HEV and MHEV Battery
OMEGA uses the HEV battery cost curve described in Chapter 2.5.2.1.2 and shown below.
OMEGA uses this equation for both mild and strong HEVs.
HEV Battery = (936.1 x kWh~0-802) x kWh x Markup
Where,
kWh = the gross energy capacity of the battery in kilowatt hours
Markup = the markup to account for indirect costs
2.6.1.3 BEV Powertrain Costs
For this analysis, EPA updated the battery costs and non-battery powertrain costs that were
used to determine the cost of electrified vehicles. The sources and methods for deriving these
costs were described in section 2.5.2.
The following sections detail the specific electrification-related costs used in the analysis.
BEV-specific powertrain costs are broken into non-battery and battery costs.
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2.6.1.3.1 BEV Battery
2.6.1.3.1.1 Battery cost estimation curve by kWh
As described previously in Section 2.5.2.1.2, for base year 2022 BEV battery costs, OMEGA
employs the BEV battery cost curve for 250,000 packs per year shown below.
BEV Battery = 261.61 x kWh~0184 x kWh x Markup
Where,
kWh = the gross energy capacity of the battery in kilowatt hours
Markup = the markup to account for indirect costs shown in Table 2-37
2.6.1.3.2 BEV Non-Battery
EPA reviewed several recent teardown reports to develop direct manufacturing costs for
permanent magnet synchronous motors (PMSMs) and for induction motors.
The primary sources we consulted to establish an estimate of base year electric machine costs
included the 2017 UBS teardown of the Chevy Bolt, the CARB teardown performed by Ricardo,
the Munro 12-motor teardown report, and the full-vehicle Munro teardown reports for the 2017
Tesla Model 3 Long Range RWD and the 2020 Tesla Model Y AWD Performance. The Munro
reports are proprietary commercial products describing teardowns performed by Munro &
Associates, copies of which EPA licensed to support its research.
The reports provided cost data points for a variety of PMSM and induction machines of
various power ratings and a range of designs. As shown in Table 2-27, costs for several PMSM
motors were available in these reports.
Table 2-37: PMSM Motors Described in Munro Reports
PMSM motors
kW
Tesla
2017
Model 3 LR RWD
192
Tesla
2020
Model Y AWD Perf- Rear
219
GM
2017
Chevy Boll EV
150
BMW
2015
i3
125
Nissan
2019
Leaf
110
Jaguar
2019
i-Pacc EV400 90 kWh AWD
147
GM
2016
Chevy Volt PHEV
41
Tovola
2016
Prius HEV
53
Of these, we focused on the Tesla components as best representing the state of the art for
optimized, high-volume production of these devices, and compared them to several other
examples. The cost breakdown for electric machines in these reports included only the rotor,
stator, and shaft and did not include supporting parts such as the housing, resolver, and mounts
which the reports costed separately. In order to develop a total motor cost per kW that includes
these components, we took an average of the share of the cost of these parts and determined that
2-71
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the cost of the stator, rotor and shaft should be multiplied by 1.1 (PMSM) and 1.3 (induction) to
represent the total cost.
By averaging the costs from the available sources that we considered to be the best examples
of current technology, we arrived at a cost of $4.29 per kW for a PMSM electric machine, which
represents the mean of the selected rotor/stator/shaft costs multiplied by 1.1 to account for the
added cost of housing, resolver, and mounts.
For induction motors, costs for the following components were available in these reports
(Table 2-38).
Table 2-38: Induction Motors Described in Munro Reports
Induction motors kW
Tcsla 2020 Model Y AWD Pcrf-Front 158
Tcsla 2018 Model 3 PrcmLR AWD - Front 147
Audi 2019 Audi c-tron - Front 141
Audi 2019 Audi c-tron - Rear 172
By the same process we arrived at a cost of $2.40 per kW for an induction machine, which
represents the mean of the selected rotor/stator/shaft costs, multiplied by 1.3 to add the cost of
the housing, resolver, and mounts.
While developing these costs, EPA consulted informally with the Department of Energy
Vehicle Technologies Office (DOE VTO) to compare the developing cost estimates with their
perspectives on current and future cost expectations for EV components and to better interpret
the existing USCAR cost targets for these components. With regard to the emerging cost
estimates, they were seen as being consistent with general expectations between the consulting
staff at the agencies. While some of the emerging costs for electric machines and various power
electronics were somewhat higher than USCAR targets and others were lower, it was noted that
the USCAR targets represent DOE's assessment of an attainable future cost at the time of their
development in about 2017, and the fact that the merging costs were derived from teardown data
from vehicles and components that were not available at the time would be expected to provide a
reliable characterization of current day costs even if they deviated significantly from those
targets.
2.6.1.3.2.1 Power electronics costs
EPA reviewed teardown reports to develop direct manufacturing costs for the major power
electronics components that are found in BEVs and PHEVs. These include inverters used for the
traction motor (two versions, one in silicon IGBT form and another in silicon carbide), onboard
charger, DC fast charging circuit, power management and distribution module, and DCDC
converter. This section also describes the cost assumed for high voltage wiring.
The primary sources for silicon IGBT inverter cost were the Munro teardown reports. We
arrived at an averaged and rounded figure of $2.50 per kW for an IGBT inverter, based on these
sources.
EPA also considered an inverter based on a silicon carbide (SiC) design. A SiC inverter
currently has a higher cost compared to silicon IGBT designs, but offers a higher switching
2-72
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frequency, which allows the inverter to operate at a higher efficiency. This makes it a
particularly good fit for an induction motor, which has lower efficiency in some operating
regions than the more costly PMSM machine. Based on consideration of the information
available in the Munro teardowns, we arrived at an approximate cost of $4 per kW for a SiC
inverter, and selected this design in configurations that employ an induction machine. Based on
verbal conversations with Munro, we expect that the cost could be reduced sufficiently in the
next five years to be comparable with the IGBT design, and so we consider this estimate to be
conservative.
2.6.1.3.2.2 Gearbox costs
The 2017 UBS teardown of the Chevy Bolt established a cost for its single speed gear
reduction at $400. This figure was deemed consistent with the cost estimated by various Munro
teardowns of other BEVs which varied slightly around this figure. We adopted a cost of $410 for
this component.
2.6.1.3.2.3 A WD costs
AWD is typically achieved in a BEV by use of two or more traction motors, with at least one
driving each axle. While a number of configurations are possible, the most common and cost
effective was taken to be a configuration with an induction motor on the front and a PMSM on
the rear, both having its own single-speed gear reduction. The cost difference to add AWD to a
BEV with a PMSM already providing 2WD was thus taken to be the cost of a second motor
(induction machine), a second inverter (SiC based to improve the efficiency of the induction
motor), a second single-speed gear reduction, a second cooling loop to serve the second motor
and inverter, and two additional half-shafts.
2.6.1.3.2.4 Summary of BEV non-battery costs
Costs for other non-battery components and subsystems were derived in a similar manner
from the sources outlined above. The full set of BEV non-battery costs as implemented in
OMEGA is shown in Table 2-40.
2-73
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Table 2-39 BEV Non-battery Powertrain Costs in OMEGA
Item
; Cost Curve
; Dollar
; Note
; Example system cost
; (Note: Markup = 1.5)
Basis
; (80 kWh battery, 150 kW power,
; vehicle size class=3
Single motor
; (4.29 * kW) * Markup
T 2019
T $965
Single Inverter
: (2.5 * kW) * Markup
] 2019
I $563 ....
Dual Motor
: (4.29 * kW/2) * Markup
2019
f $483
Dual Inverter
j (2.5 * kW/2) * Markup J
2019
1"$282
Dual induction motor
r (3.12 * kW/2) * Markup
2019
$351
Dual induction
; (4 * kW/2) * Markup
i 2019
$150
inverter
DC-DC converter kW
i' 3.5
; 2019
Onboard charger &
; 39.754 * (OBC kW+DC-DC ; 2019
! Onboard charger kW:
! 39.754*(11+3.5)* 1.5=$865
DC-DC converter
; converter kW) * Markup
: For battery kWh<70, OBC
kW=7;
For 70100, OBC kW=19
High voltage orange
: (9.5 * VehicleSizeClass +
¦ 2019
: $285
cables
; 161.5) * Markup
Single speed gearbox
: 410 * Markup
2019
) $615
Powertrain cooling
i 300 * Markup
2019
: $450
box
Dual single speed
| 410 * 2 * Markup
2019
| $1,230
gearbox
Dual powertrain
| 300 * 2 * Markup
j 2019
$900
cooling box
Charging cord kit
200 * Markup
: 2019
] $300
DC fast charge
; 160 * Markup
F 2019
$240
circuitry
Power management &
! 720 * Markup
r 2019
; $1,080
distribution
Additional pair of half
; 190 & Markup
j 2019
i $285
shafts
Markup
: 1.5
; The RPE markup to account for
: indirect costs
2.6.1.4 PHEV Powertrain Costs
While EPA has not specifically modeled the adoption of plug-in hybrid electric vehicle
(PHEV) architectures within the analysis for this proposal, the agency recognizes that PHEVs
can provide significant reductions in GHG emissions and that some vehicle manufacturers may
choose to utilize this technology as part of their technology offering portfolio in response to
customer interests and in response to EPA emission standards. Some auto manufacturers are
already doing so today. In order to potentially include an analysis of PHEVs for the final rule,
EPA has developed powertrain costs for PHEV applications based primarily upon the costs
developed for BEVs and HEVs in Chapter 2.6.1.3 and Chapter 2.6.1.2, respectively. PHEV-
specific powertrain costs are subdivided into battery and non-battery costs. PHEV non-battery
costs also include an ICE and a series/parallel hybrid transmission. The costs associated with
ICE powertrains presented in 2.6.1.1 would also apply for PHEVs.
2.6.1.4.1 PHEV Battery Costs
As described in the Preamble (IV.C.l), EPA has not specifically modeled the adoption of
plug-in hybrid electric vehicle (PHEV) architectures in the analysis for this proposal. However,
as described in DRIA 2.5.2.1.2, we did develop battery cost estimates for PHEVs, which are
described fully in that section.
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EPA may rely upon those battery cost estimates and other information gathered in response to
this proposal, and on EPA's on-going technical work, for estimating the battery costs for PHEVs
for the final rule.
2.6.1.4.2 PHEV Non-Battery Costs
As described in the Preamble (IV.C.l), EPA has not specifically modeled the adoption of
plug-in hybrid electric vehicle (PHEV) architectures in the analysis for this proposal. However,
the agency recognizes that PHEVs can provide significant reductions in GHG emissions and that
some vehicle manufacturers may choose to utilize this technology as part of their technology
portfolio. As also described in Preamble IV.C.l, EPA has requested comment on the possibility
of including modeling of PHEVs in the final rule analysis.
In general, EPA anticipates that modeling of PHEVs in the final rule analysis would utilize
power electronic costs, P4 gearbox costs and AWD costs based upon the BEV non-battery costs
presented in Chapter 2.6.1.3.2.
In addition, here we also present costs for a series/parallel hybrid transmission for PHEVs,
consisting of:
• Motor-generator
• Starter-generator
• Clutch-pack to lock the ICE and starter generator to the motor generator for parallel
operation
An example of such a series/parallel hybrid drive system for transverse front-drive
applications is shown in Figure 2-25. Yamagishi and Ishikura provided a detailed description of
application of a similar series/parallel drive system to the Honda Clarity PHEV (Yamagishi and
Ishikura 2018). An application of this type of series/parallel drive to a front-engine/rear-drive
application would require use of a drive shaft and separate, rear-mounted differential.
AWD vehicles include the cost of a series/parallel hybrid transmission with the addition of a
P4 electric machine to either the front or rear depending on the application.
The full set of PHEV non-battery/non-ICE costs that could potentially be implemented in
OMEGA in the final rule analysis is summarized in Table 2-40. An example of potential PHEV
ICE costs is summarized in Table 2-41.
The specific example used within the tables is an LDT4 with OMEGA size-class 7 vehicle
with:
• A combined electric drive system power of 240 kW
• A P4 induction machine for front axle electric-only drive
• A 3.0L Miller Cycle engine coupled to a series/parallel (S/P) drive single-speed
transmission with a drive shaft and rear differential for rear axle drive
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Figure 2-28: An example of a series/parallel hybrid drive system for a transverse/front-
drive application with a portion of the outer casing and stators removed to show internal
details. Adapted from a presentation by Prof. J.D. Kelly, Weber State University (Kelly
2020).
2-76
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Table
Item
MGPM
MGPM IGBT Inverter
SGPM
SGPM IGBT Inverter
P4-MGinduction
P4-MGinduction SiC
Inverter
DC-DC converter kW
Onboard charger &
DC-DC converter
High voltage orange
cables
S/P transmission
Driveshaft and
differential
P4 single speed
gearbox
Dual powertrain
cooling box
Charging cord kit
DC fast charge
circuitry (packs > 20
kWh nominal only)
Power management &
distribution
Additional pair of half
shafts
Markup
2-40: Potential PHEV Non-battery/Non-ICE Powertrain Costs
Cost Curve Dollar Note ; Example system cost:
(Note: Markup = 1.5) ¦ Basis ! ' (LDT4 PHEV, 33 kWh battery, 240 kW
I combined electric power, 3.0L
| Miller/CVVL/CEGR engine, 6200 lb CW,
i size class 7)
(4.29 * 180kW)*
2019 ;
$1,158
Markup
(2*5 * 180kW) * Markup ;
2019 ;
1 $675
(4.29 * 180kW)*
$1,158
Markup
(2.5 * 180kW) * Markup ;
$675
(3.12 *60kW)* Markup ;
2019 ;
$281
(4 * 60kW) * Markup
2019 :
$360
3.5 ; 2019
39.754 * (OBC kW+DC- j 2019
DC converter kW) *
Markup
(9.5 * VehicleSizeClass + ¦ 2019
161.5) * Markup_
600 * Markup [ 2019
200 * Markup 2019
410* Markup i' 2019
300 * 2 * Markup f 2019
200 * Markup 2019
160* Markup 1 2019
Onboard charger kW:
For battery kWh<70, OBC
kW=7;
For 70100, OBC
kW=19
39.754*(7+3.5)*1.5=$626
$342
$900
$300
$615
$900
$300
$240
720 * Markup
190 * Markup
1.5
2019
2019
The RPE markup to
account for indirect costs
$1,080
$285
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Table 2-41: Potential PHEV ICE Costs
Item
Cylinders
Displacement
Gasoline Direct
Injection (GDI)
Cost Curve
(Note: Markup = 1.5)
(-28.814 * CYL + 726.27) * CYL * Markup
400 * LITERS * Markup
(43.237 * CYL + 97.35) * Markup
Dollar
Basis
2019
2019
2019
Note
CYL= 4
I ITI.RS 3.0
CYL= 4
Example system cost
(6CYL, 3.0L
Miller/CVVL/CEGR
engine, 6200 lb CW,
size class 7)
$4,980*
$1,800*
$535
turbl2
Cooled EGR
atk2
TWC substrate
TWC washcoat
TWC canning
TWC swept volume
TWC Pt grams/liter
TWC Pd grams/liter
TWC Rh
grams/liter
GPF
TWC PGM
Troy oz/gram
PT_U SD PER OZ
PD USD PER OZ
RH USD PER OZ
LV battery
HVAC
turbscaler*
Markup
(-13.149 * CYL2 +220.34* CYL- 124.73) * Markup ; 2012
114* Markup ¦ 2012
(4.907 *CYL2- 29.957 *CYL + 130.18) * Markup [ 2010
(6.108* LITERS * TWC SWEPT VOLUME + 1.95456) : 2012
* Markup
(5.09 * LITERS * TWC SWEPT VOLUME) * Markup ; 2012
(2.4432 * LITERS * TWC SWEPT VOLUME) * ! 2012
Markup
1.2 multiplier applied to engine displacement
o *
2
o.ii
(14.1940 * LITERS+ 39.2867) * Markup j 2021
(PTGRAMSPERLITERTWC * LITERS *
TWC SWEPT VOLUME * PT USD PER OZ *
OZPERGRAM + PD GRAMS PER LITER TWC * ;
LITERS * TWC SWEPT VOLUME *
PD USD PER OZ * OZ PER GRAM +
RHGRAMSPERLITERTWC * LITERS *
TWC SWEPT VOLUME * RH USD PER OZ *
OZ PER GRAM) * Markup
0.0322
1030
2331
17981
(3 * VEHICLE SIZE CLASS+ 51) * Markup 2019
(11.5* VEHICLESIZECLASS + 195.5)* Markup i 2019
1.2 multiplier applied to ($Cvlinders + $Displacement)
1.5
Turbocharging
with boost —24
bar
atk2=Atkinson
cycle engine
TWC=3way
catalyst; TWC
swept
volume=1.2
GPF=gasoline
particulate filter
PGM=Platinum
group metals
(e.g., Pt, Pd, Rh)
$819
$171
$133
$36
$27
$13
$123
$1,155
USD=US dollars;
OZ=Trov ounce
Values are used in the
TWC PGM calculation
* Cylinder and displacement costs are used in the turboscaler calculation to determine engine
for additional costs to support turbocharging.
LV=low voltage $108
$414
$6,559*
The RPE markup
to account for
indirect costs
long-block costs. The 1.2 multiplier accounts
2-78
-------
2.6.1.5 Powertrain Costs for All Vehicles
These are additional powertrain costs that apply to all vehicles regardless of fuel used.
2.6.1.5.1 Air Conditioning
Air conditioning (AC) system costs are based on past EPA analyses and are shown in Table
2-40.
Table 2-42 Air Conditioning System Costs in OMEGA
Item DMC Do 1 lu r Basis
AC efficiency improvements 40 * Markup 2010
AC leakage control 63 * Markup 2010
Markup 1.5
OMEGA uses these costs as-is, other than applying the markup to account for indirect costs.
2.6.1.5.2 Low voltage battery
The low voltage battery is estimated using the equation and weight bins shown below.
LowVoltageBattery = (3 x VehicleSizeClass + 51) x Markup
1: CURBWT < 3200,
2: 3000 < CURBWT < 3800,
3: 3800 < CURBWT < 4400,
WeightBins = 4: 4400 < CURBWT < 5000,
5: 5000 < CURBWT < 5600,
6: 5600 < CURBWT < 6200,
^7: 6200 < CURBWT < 14000-
Where,
VehicleSizeClass = the applicable value 1 through 7 depending on the vehicle curb weight, in
pounds, and according to the WeightBins dictionary
WeightBins = the seven curb weight bins into which each vehicle is categorized
CURBWT = the vehicle curb weight, in pounds
Markup = the 1.5 RPE markup to account for indirect costs
2.6.1.5.3 Heating and Ventilation
Heating and ventilation system costs are new and are estimated using the equation shown
below.
HeatingAndVentilation = (11.5 x VehicleSizeClass + 195.5) x Markup
Where,
2-79
-------
VehicleSizeClass = the applicable value 1 through 7 depending on the vehicle curb weight, in
pounds, and according to the WeightBins dictionary shown for low voltage battery costs
Markup = the 1.5 RPE markup to account for indirect costs
2.6.2 Glider Costs
Glider cost curves in OMEGA represent three different body-styles: sedan, CUV/SUV and
pickup; two different structure styles: unibody and ladder frame; two different primary materials:
steel and aluminum; as well as non-structural elements. The relevant curves used in OMEGA are
shown in Table 2-44. Note that "structure mass lbs" term shown in the table is determined
according to the structure mass curves shown in Table 2-45.
Note that, unlike past EPA GHG analyses, OMEGA no longer models mass as a compliance
strategy in discrete percentages of mass reduction. Instead, OMEGA calculates mass based on
the factors shown in Table 2-45, with the compliance strategy decision based primarily on steel
versus aluminum structure. Footprint may also change which would impact the mass of the
vehicle, but the mass associated with potential footprint changes is a secondary effect of the
footprint decision. In other words, footprint does not change as a result of mass reduction
strategies and, instead, mass may change as a result of footprint strategies. The cost of the
resultant mass is estimated using the equations shown in Table 2-44.
2-80
-------
Dollar
Basis
2020
2020
2020
2020
2020
2020
2020
2020
2020
2020
2020
2020
2020
Table 2-43 Glider Costs in OMEGA
Body-style Structure DMC
Material
Sedan Steel (1.5 * slniclurcmasslbs + 1500) * markup
Sedan Aluminum (3.4 * slniclurcmasslbs + 1500) * markup
CUV/SUV Steel (1.5 * struclurc mass lbs + 1700) * markup
CUV/SUV Aluminum (3.4 * stniclurc niass lbs + 1700) * markup
CUV/SUV Slccl ((1.5 * stniclurc niass lbs + 550) + (1.5 * (0.66 *
slruclurcniasslbs) + 2000)) * markup
CUV/SUV Aluminum ((1.5 * stniclurc niass lbs + 550) + (3.4 * (0.66 *
slruclurc niass lbs) + 2000)) * markup
Pickup Slccl ((1.5 * stniclurc niass lbs + 550) + (1.5 * (0.66 *
slruclurc niass lbs) + 2000)) * markup
Pickup Aluminum ((1.5 * slruclurc niass lbs + 550) + (3.4 * (0.66 *
slruclurc niass lbs) + 2000)) * markup
Pickup Slccl (1.5 * stniclurc niass lbs + 1700) * markup
Pickup Aluminum (3.4 * stniclurc niass lbs + 1700) * markup
Sedan Various (24.3 * dcllafoolprinl + 2.4* dcllafoolprinl *
(vcliicle.heightin - vehicle.groundclearancein))
markup
CUV/SUV Various (24.9 * dcllafoolprinl + 2.6 * dcllafoolprinl *
(vcliiclc.liciglilin - vchiclc.groundclcaranccin))
markup
Pickup Various (18.2 * dclla foolprinl + 2.1 * delta footprint *
(vcliiclc.liciglil in - vehicle.ground clearance in))
markup
1.5 RPE markup to account for indirect costs
2-81
-------
Table 2-44 Mass Calculations in OMEGA
Item
Body-style
Structure
Material
Value
Null structure mass
Sedan
Ladder
2.2 * (5.5045 * footprint +
105.4)
Null structure mass
Sedan
U nibodv
2.2 * (5.5045 * footprint +
105.4)
Null structure mass
CUV/SUV
Ladder
2.2 * (7.7955 * footprint +
127.48)
Null structure mass
CUV/SUV
U nibodv
2.2 * (10.077 * footprint -
76.528)
Null structure mass
Pickup
Ladder
2.2 * (7.7955 * footprint +
127.48)
Null structure mass
Pickup
U nibodv
2.2 * (10.077 * footprint -
76.528)
Structure mass lbs
Steel
nullslruclurcmass
Structure mass lbs
Ladder
Aluminum
(0.63 * 0.66 +0.34 )*
nullstruclurcinass
Structure mass lbs
U nibodv
Aluminum
0.65 * null struclurc inass
Delta glider non-structure mass
Sedan
(15.1 * dclla foolprinl + 2.3 *
deltafootprint *
(vehicle.height -
vchiclc.ground_clcarancc)/12)
Delia glider non-slniclurc mass CUV/SUV (17.3 * dcllafoolprinl + 2.5 *
delta footprint *
(vehicle.height -
vchiclc.groundclcarance)/12)
Delta glider non-slruclurc mass Pickup (18.1 * dclla foolprinl + 1.9 *
dclla foolprinl *
(vehicle.height -
vchiclc.groundclcarance)/12)
Note: footprint is in square feet; height and ground clearance are in inches; mass values are in pounds; 2.2
converts kilograms to pounds
2.6.3 Consumer demand assumptions and S-Curves
OMEGA estimates the share of BEVs demanded within each of three body styles as a
function of the relative consumer generalized costs for BEV and ICE vehicles, and a share
weight parameter. The share weight parameter changes over time to account for factors that are
not included in the generalized costs, such as greater access to charging infrastructure or greater
availability and awareness BEVs. The determination of consumer generalized costs and share
weights for ICE and BEVs are described in more detail in Chapter 4.1.
2.6.4 Consideration of constraints in modeling real-world technology adoption
2.6.4.1 Redesign schedules
Consistent with past rulemakings, EPA has included redesign cycles as a constraint to restrict
introduction of new technology for a given vehicle model to the cadence of a typical product
cycle time. As implemented for the proposal, OMEGA may only redesign a vehicle every 5
years for unibody vehicles and every 7 years for body-on-frame vehicles. An example of
behavior for one manufacturer's vehicles can be seen in Figure 2-25.
2-82
-------
2040
i: ,
i_ ^—
2015
2010
2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 2038 2039 2040
Model Year
Figure 2-29. Redesign Years for Select Vehicles
EPA has populated its base year vehicles file with the year of last redesign for each model in
the light duty fleet. Applying OMEGA's redesign constraints above with the distribution of
redesign years across the industry yielded the following distribution redesigns on a sales basis.
Table 2-43 provides a count of the discrete vehicle models and sales in MY 2032, the year in
which they were last redesigned, and the corresponding sales volume that was redesigned in
prior years. As can be seen, there is a fairly even distribution of vehicle model redesign years.
Note that many vehicles which were redesigned in MY 2026 were eligible for another redesign
in MY 2031.
Table 2-45: MY 2032 Vehicles: Year of Last Redesign
Year
# of Models
Total Sales
% of Sales
Redesigned
2026
10
83.818
1%
2027
38
823,932
5%
2028
201
2,601,172
17%
2029
284
2,849,875
19%
2030
284
3,250,190
21%
2031
358
4,136,099
27%
2032
284
1,641,242
11%
Totals
1459
15,386,328
100%
2.6.4.2 Materials and mineral availability
The development of EPA's constraint on BEV production, which is primarily based on a
bound on battery production and lithium availability, can be found in RIA 3.1.3.2.
Table 2-44 shows the limits, in terms of maximum industry GWh (available for production of
U.S. vehicles) that resulted from this assessment and that are applied in OMEGA.
2-83
-------
Table 2-46: Industry Maximum Battery Production Limits (GWh), by Model Year
MY
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
GWh limit
79
150
261
372
483
580
720
860
1000
1100
1200
1300
1400
1500
2.6.5 Manufacturing capacity
In addition to availability of critical minerals, the ability to perform final assembly of vehicles
that use them could also be understood as a potential constraint on increased production of
BEVs. However, EPA notes that major manufacturers are already building a large amount of
assembly plant manufacturing capacity both in the U.S. and abroad to meet future demand for
these vehicles, and these efforts are poised to continue. Unlike critical minerals which have
fundamental constraints on their production due to limited presence of these resources as well as
a relatively long lead time for increasing their extraction, vehicle assembly capacity is a
relatively well understood process that can respond relatively quickly to the necessary
investment commitments. Given the existing activities among automakers in this area, and the
relatively long lead time before MY 2027 when the proposed rule would begin, EPA did not
specifically impose a limit on vehicle assembly capacity. However, as described in DRIA
3.1.3.2, EPA did represent a reasonable rate of battery manufacturing ramp-up by using
information about battery manufacturing facilities announced or in operation, and estimates of
lithium availability, to develop a constraint on annual GWh battery demand for use by OMEGA.
For more discussion of manufacturing capacity and critical minerals, please see DRIA 3.1.3.1,
DRIA 3.1.3.2, and Preamble IV.C.6.
2.6.6 Fuel Prices used in OMEGA
OMEGA uses fuel prices to estimate generalized costs as part of the compliance modeling
algorithm. OMEGA also uses these fuel prices in estimating fuel expenditures and fuel savings
that are included in the benefit-cost analysis results present in Chapter 10 of this draft RIA.
Note that, as discussed in Chapter 5 of this DRIA, EPA has estimates of future retail
electricity prices that include impacts of the Inflation Reduction Act. Those retail electricity
prices are lower than those shown in Table 2-45. The analysis done in OMEGA does not use
those lower electricity prices because EPA did not have analogous liquid fuel prices to use, i.e.,
we did not have liquid fuel price projections that include impacts of the Inflation Reduction Act.
For internal consistency, we have chosen to use AEO 2021 fuel price projections for both liquid
fuels and electricity.
2-84
-------
Table 2-47 AEO2021 Fuel Prices Used in OMEGA (2020 dollars)
Gasoline Diesel Electricity
Calendar
Pre-tax
Retail
Pre-tax
Retail
Pre-tax
Retail
Year
; ($/gallon)
! ($/gallon)
; ($/gallon)
; ($/gallon)
; iSkWIn
; ($/kWh)
2027
2.01
2.56
F 2.52
.ill!
L 0103
; 0.124
2028 1
2.08
...I2-63!.
2.58 "
f 0.103
7 0.125
2029
2.12
2.67
2.62
3.19
: 0.103
i 0.125
" 2030
2.21
2 80
j-":>68;:
J-29 I
! " 0103
! 0.125
2031
2.22
2.81
2.72
3.32
: 0.103
j 0.125
2032 "
r 2.28
2.87
216
3.36 7
! OH'2
i O.I2 1
2033
2.30
2.89 'J
\ 2.79
.US 7
0.103
| 0.125
2034
2.34
2.93
; 2.80
3.40
! 0.103
: 0.125
2035
2.37
2.95
i 2.82
3.41 '7
7 0.103
! 0.125
2036
2.41
2.98
2.84
3.42
; 0.102
: 0.125
2037
' 2.44
3.02
2.88
3.46
| 0.102
0.124
2038
2.48
3.05
2.90
3.49
; 0.101
0.124
2039
2.49
3.06
2.91
3.48
; 0.101
: 0.123
2040
2.55
3.11
2.96
3.54
i 0.101
f 0.123
2041
2.58
3.14
3.00
3.57
: 0.100
; 0.123
2042
[ 2.60
i 3
3.02
3.59 7
r 0100
i 0.123
2043
2.62
; 3.18
3.06
3.62
; 0.099
| 0.122
2044
2.63
3.19
3.07
3.63
r 0.099
: 0.122
2045
2.62
3.17
3.07 7
3.62
; 0.099
, 0.122
' 2046
2.66
3.21
3.11
3.67
; 0.098
r 0.121
2047
2.68
3.22
3.13
3.68
|' 0.098
r 0.121
2048
2.69
3.24
3.14
3.68
0.098
: 0.121
2049
2.69
3.23
3 l(.
3.70 7
P 0.097
i 0.120
2050
2.70
3.23
3.16
3.69
i 0.096
r 0.119
2051
! 2.70
3.23
3.16
3.69
; 0.096
7 0.118
2052
¦ 2-71 ^
' 3.24
J-15 7
3.69
: 0.095
0118
2053
2.71
3.24
3.15
3.68
^ 0.094
r 0.117
2054
f 212
f7 3-24 :
3.15
; 3.68 7
; 0.093
; ' o.l !(•
2055
\ 2.73
3.24
3.15
: 3.67
i 0.093
7 0.115
2056
" 2-73..
..J-25..
3.15
3.67
7 0.092
o.l 1 1
2057
2.74
3.25
3.15
3.67
7 0.091 7
i 0.114
' 2058
2.74
!" 3.25
3.15
3.66
; 0.091
; 0.113
2059
2.75 "7 .
,.3-25I
15 ""
3.66
j 0.090
0.112
2060
2.75
3.26
3.15
3.65
1 0.089
! 0.111
2.6.7 Gross Domestic Product Price Deflators
To adjust all monetary inputs used in OMEGA to a consistent dollar basis, OMEGA uses the
gross domestic product (GDP) implicit price deflators shown in Table 2-46. These deflators were
generated by the Bureau of Economic Analysis, Table 1.1.9, revised on March 25, 2021.
2-85
-------
Table 2-48: Gross domestic product implicit price deflators
Calendar GDP Implicit Price Deflator
Year
2001
79.790
2002
81.052
2003
82.557
2004
84.780
2005
87.421
2006
90.066
2007
92.486
2008
94.285
2009
95.004
2010
96.111
2011
98.118
2012
100.000
2013
101.755
2014
103.638
2015
104.624
2016
105.722
2017
107.710
2018
110.296
2019
112.265
2020
113.625
2.6.8 Inflation Reduction Act
OMEGA explicitly accounts for two elements of the Inflation Reduction Act in compliance
modeling: the battery production tax credit and the BEV purchase incentive.
The IRS Section 45X battery production tax credit is treated within the modeling as a
reduction in direct manufacturing costs, which in turn is assumed to result in a reduction in
purchase price for the consumer after the application of the Retail Price Equivalent (RPE) factor.
The credit phases out by statute from 2030 through 2032. As described previously in section
2.5.2.1.4, we estimated the average amount of the credit in 2023 at 60 percent of the maximum
$45, and ramped the value upward linearly each year until it reaches the maximum $45 in 2027.
For discussion of the justification of this choice, please see Section 2.5.2.1.4 and Preamble
IV.C.2. The resulting value of the credit applied in OMEGA, in terms of dollars per kWh of
gross battery capacity, is shown in Table 2-47. These represent an average credit amount across
the industry as a whole. Although some manufacturers and vehicles may realize the full value of
the credit in any given year, the model requires an average value across the full market.
2-86
-------
Table 2-49: IRA Battery Production Tax Credits in OMEGA
Tax credit value ($/k\Vh)
Year
2023 7
$27
2024
$3 1.50
2025
2IZ $36
2026
$40.50
2027 1
$45
2028
$45
2029
$45
2030
$33.75
2031
$22.50
2032
$ 11.25
2033 1
$0
The IRS 30D and 45W Clean Vehicle Credits are reflected in lower consumer purchase costs,
and therefore have an influence on the shares of BEVs demanded by consumers. The reduction
in costs for the consumer makes BEVs relatively more attractive than ICE alternatives, compared
to the case with no purchase incentives. The purchase incentive is assumed to be realized entirely
by the consumer and does not impact the producer generalized cost value or the manufacturing
cost. While the restrictions imposed by the IRA on the 30D credit (income, MSRP, critical
mineral content, and manufacturing content) limit the vehicles which are eligible for the full
$7,500 incentive under 30D, we believe that manufacturers will work to increase the number of
vehicles that qualify over time due to the high marketing value of the credit. Further, we expect
that the IRS 45W Clean Commercial Vehicle Credit, which is not subject to many of the
restrictions on the 30D credit, will likely impact a significant portion of BEV sales, through fleet
purchases and also through vehicle leasing to consumers. For these reasons, we have
conceptualized the purchase incentive as a combination of 30D and 45W credits. The OMEGA
modeling ramps in the purchase incentive from $3,750 in MY2023 to a maximum of $6,000 in
MY2027, as shown in Table 2-51. See also the discussion in Preamble IV.C.2.
Table 2-50: IRS 30D and 45W Clean Vehicle Credit in OMEGA
Model Combined BEV Purchase
Year
Incentive Value
2022 T
$0
2023
$3750
2024
$4000
2025
$4250
2026
$4500
2027 7
$4750
2028
$5000
2029
$5250
2030
$5500
203 1
$5750
2032 |
$6000
2033 ;
$0
2-87
-------
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420-R-18-006. EPA. https://nepis.epa.gov/Exe/ZyPURL.cgi?Dockey=P 100VK3Z.txt.
Slowik, P., A. Isenstadt, L. Pierce, and S. Searle. 2022. Assessment of Light-Duty Electric
Vehicle Coss and Consumer Benefits in the United States in the 2022-2035 Time Frame.
International Council on Clean Transportation, https://theicct.org/wp-
content/uploads/2022/10/ev-cost-benefits-2035-oct22.pdf.
U.S. EPA. 2023a. Advanced Light-Duty Powertrain and Hybrid Analysis (ALPHA) Tool.
Accessed February 2, 2023. https://www.epa.gov/regulations-emissions-vehicles-and-
engines/advanced-light-duty-powertrain-and-hybrid-analysis-alpha.
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—. 2022c. "ALPHA Documentation 0.2.0 documentation." Accessed 11 17, 2022. https://epa-
alpha-model.readthedocs.io/_/downloads/en/latest/pdf/.
—. 2023. "Battery Cost Estimation Spreadsheets for US EPA LMDV NPRM." Docket EPA-HQ-
OAR-2022-0829.
—. 2023c. Combining Data into Complete Engine ALPHA Maps. Accessed February 2, 2023.
https://www.epa.gov/vehicle-and-fuel-emissions-testing/combining-data-complete-engine-alpha-
maps.
—. 2023b. Complete Electric Motor ALPHA Maps, https://www.epa.gov/vehicle-and-fuel-
emissions-testing/combining-data-complete-emotor-alpha-maps.
U.S. EPA. 2016. "Cost Reduction through Learning in Manufacturing Industries and in the
Manufacture of Mobile Sources, EPA-420-R-16-018."
https://nepis.epa.gov/Exe/ZyPDF.cgi?Dockey=P100PUSX.PDF.
—. 2022e. "Data from Cars used for Testing Fuel Economy." https://www.epa.gov/compliance-
and-fuel-economy-data/data-cars-used-testing-fuel-economy.
—. 2016a. "EPA-420-R-16-020: Proposed Determination on the Appropriateness of the Model
Year 2022-2025 Light-Duty Vehicle Greenhouse Gas Emissions Standards under the Midterm
Evaluation." November. https://nepis.epa.gov/Exe/ZyPDF.cgi?Dockey=P100Q3DO.pdf.
—. 2016b. "EPA-420-R-16-021: 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." November. Accessed November 3, 2022.
https://nepis.epa.gov/Exe/ZyPDF.cgi?Dockey=P100Q3L4.pdf.
—. 2017a. "EPA-420-R-17-001: Final Determination on the Appropriateness of the Model Year
2022-2025 Light-Duty Vehicle Greenhouse Gas Emissions Standards under the Midterm
Evaluation." https://nepis.epa.gov/Exe/ZyPDF.cgi?Dockey=P100QQ91.pdf.
—. 2017b. "EPA-420-R-17-002: 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." January.
https://nepis.epa.gov/Exe/ZyPDF.cgi?Dockey=P100QQ9Y.pdf.
—. 2022b. Technical Publications and Presentations Concerning Benchmarking. Accessed 11 17,
2022. https://www.epa.gov/vehicle-and-fuel-emissions-testing/benchmarking-advanced-low-
emission-light-duty-vehicle-technology.
U.S. EPA; U.S. DOT-NHTSA; CARB. 2016. "EPA-420-D-16-900: 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." July.
https://nepis.epa.gov/Exe/ZyPDF.cgi/P100OXEO.PDF?Dockey=P 100OXEO.PDF.
UBS AG. 2017. "Q-Series: UBS Evidence Lab Electric Car Teardown - Disruption Ahead?"
http s://neo.ubs. com/shared/d 1 wkuDlEb YPj F/.
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W. Zhuanga, S. Li (Eben), X. Zhangc, D. Kum, Z. Song, G. Yin, F. Ju,. 2020. "A survey of
powertrain configuration studies on hybrid electric vehicles." Journal of Applied Energy 262
(114553). doi: 10.1016/j.apenergy.2020.114553.
Wood Mackenzie. 2022. Battery & raw materials - investment horizon outlook to 2032. Wood
Mackenzie.
Yamagishi, Tomoya, and Takashi Ishikura. 2018. "Development of Electric Powertrain for
CLARITY PLUG-IN HYBRID, SAENo. 2018-01-0415." Int. J. Alt. Power. (SAE) 7 (3): 323-
333. doi: 10.4271/2018-01-0415.
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Chapter 3: Analysis of Technologies for Reducing GHG and Criteria
Pollutant Emissions
This chapter summarizes our assessment of the feasibility of the proposed greenhouse gas
(GHG) and criteria pollutant emission standards. It includes a description of the emissions
control technologies considered for criteria pollutant exhaust and evaporative emissions, GHG
emissions control, on-board diagnostics, and specific considerations with regards to plug-in
hybrid electric vehicles (PHEVs).
3.1 Technology Feasibility
The levels of stringency in the proposed standards continue a trend of increased emissions
reductions which have been adopted by prior EPA rules. As with prior rules and as part of the
development of this proposed rulemaking, EPA assessed the feasibility of the proposed standards
in light of current and anticipated progress by automakers in developing and deploying new
emissions-reducing technologies.
Compliance with the EPA GHG standards over the past decade has been achieved
predominantly through the application of advanced technologies to internal combustion engine
(ICE) vehicles. For example, in the analyses performed for the 2012 rule (77 FR 62624 2012),
the Draft Technical Assessment Report (TAR) for the Midterm Evaluation (MTE) of the 2022-
2025 standards (U.S. EPA, CARB, U.S. DOT NHTSA 2016), the 2016 Proposed Determination
(U.S. EPA 2016), and the 2021 rule (86 FR 74434 2021), a significant portion of EPA's analysis
included an assessment of technologies available to manufacturers for achieving compliance
with the standards. Advanced ICE technologies were identified as playing a major role in
manufacturer compliance with the emission reductions required by those rules. Automakers have
also relied to varying degrees on a range of electrification technologies, including hybrid electric
vehicles (HEVs), plug-in hybrid electric vehicles (PHEVs), and battery-electric vehicles (BEVs).
As described in detail in Preamble I.A.2.ii, these technologies have been advancing rapidly over
the past decade. As battery costs have continued to decline, automakers have begun to include
BEVs and PHEVs (together referred to as PEVs or plug-in electric vehicles) as an integral and
growing part of their current and future product lines, leading to increasing penetrations of these
clean vehicles and an increasing diversity of models planned for high-volume production.
Preamble I.A.2.ii also described how PEVs are increasingly popular among a rapidly growing
proportion of consumers who have become familiar with their benefits. Thus, PEVs are already
delivering significant emission reductions through their increasing presence in the fleet and are
poised to deliver greater reductions as their penetration continues to grow.
As described throughout this chapter, EPA has assessed the feasibility of the proposed
standards in light of current and anticipated progress by automakers in developing and deploying
new emissions-reducing technologies. Chapter 3.1 describes our assessment of technology
feasibility in general, by examining recent trends in technology application to light- and medium-
duty vehicles, and also addressing issues specifically related to PEV feasibility. Section 3.1.1
discusses recent trends and feasibility of light-duty vehicle technologies that manufacturers have
available to meet the proposed standards. Similarly, Section 3.1.2 discusses recent trends in
electrification of medium-duty vehicles. Section 3.1.3 describes our assessment of feasibility of
PEV technology.
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3.1.1 Light-duty Vehicle Technologies and Trends
3.1.1.1 Advanced ICE technologies
Innovation in the automobile industry has led to a wide array of technology available to
manufacturers to achieve CO2 emissions, fuel economy, and performance goals (U.S. EPA
2022). Figure 3-1 illustrates manufacturer-specific technology usage for model year 2021, with
larger circles representing higher usage rates (U.S. EPA 2022). These technologies are all being
used by manufacturers to, in part, reduce CO2 emissions and increase fuel economy. Each of the
fourteen largest manufacturers have adopted several of these technologies into their vehicles,
with many manufacturers achieving very high penetrations of several technologies. It is also
clear that manufacturers' strategies to develop and adopt new technologies are unique and vary
significantly. Each manufacturer is choosing technologies that best meet the design requirements
of their vehicles, and in many cases, that technology is changing quickly.
Engine technologies such as turbocharged engines (Turbo) and gasoline direct injection (GDI)
allow for more efficient engine design and operation. Cylinder deactivation (CD) allows for use
of only a portion of the engine when less power is needed, while stop/start systems can turn off
the engine entirely at idle to save fuel. Hybrid vehicles use a larger battery to recapture braking
energy and provide power when necessary, allowing for a smaller, more efficiently operated
engine. The hybrid category includes strong hybrid systems that can temporarily power the
vehicle without engaging the engine and smaller "mild" hybrid systems that cannot propel the
vehicle on their own. Transmissions that have more gear ratios, or speeds, allow the engine to
more frequently operate near peak efficiency. Two categories of advanced transmissions are
shown in Figure 3-1.
In model year 2021, hybrid vehicles reached a new high of 9 percent of all production. This
increase was mostly due to the growth of hybrids in the truck SUV and pickup vehicle types. The
combined category of battery electric vehicles (BEVs), plug-in hybrid vehicles (PHEVs), and
fuel cell electric vehicles (FCEVs) increased to 4 percent of production in model year 2021 and
are projected to reach 8 percent of production in model year 2022, due to expected growth in EV
production across the industry. News media have reported global EV sales reached 10 percent of
all new car sales in 2022 (Boston 2023).
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Tesla -
Subaru
Kia -
Nissan -
Hyundai
Honda -
Mazda -
Toyota
BMW -
VW-
Mercedes -
Ford-
GM -
Stellantis
All Manufacturers -
22% 99%
26% 47%
5% 72%
18% 44%
95%
80%
42% 45% 50% 2%
87% 12%
23% 46% 21% 4%
53% 79% 25% 61% 38% 24% 7%
27% 100% 45%
3% 0%
99% 99%
77% 94% 3%
94% 100% 8%
100%
0%
0%
1%
2%
0%
36% 38% 19% 22% 2%
98% 64% 25% 7%
90% 71% 20% 7%
100% 77% 22%
80% 56% 21% 2% 92% 83% 5% 3%
37% 91% 54% 9% 74% 75% 1%
13% 10% 22% 1% 96% 45% 15% 3%
33% 53% 17% 27% 57% 45% 9% 4%
Turbo GDI CD CVT 7+Gears Non-hybrid Hybrid PHEV/
StopStart EV/FC
Figure 3-1 Manufacturer Use of Key Technologies in Model Year 2021
3.1.1.2 Hybrid Electric Technologies
Hybrid electric vehicles (HEVs) were first introduced in the U.S. marketplace in model year
2000 with the Honda Insight. As more models and options were introduced, hybrid production
increased to 3.8 percent of all vehicles in model year 2010, before declining somewhat over the
next several years. However, in model year 2021 hybrid production reached a new high at 9.3
percent and is projected to reach 10.1 percent in model year 2022, as shown in Figure 3-2 (U.S.
EPA 2022).
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The growth in hybrid vehicles is largely attributable to growth outside of the sedan/wagon
vehicle type. In model year 2020 the production of hybrids in the truck SUV category (largely
mild HEVs) surpassed the production of sedan/wagon hybrids for the first time and did so by
more than 50 percent. Hybrids also began to penetrate the pickup and minivan/van vehicle types.
However, there remain very few hybrid car SUVs. Sedan/wagon hybrids accounted for only 21
percent of all hybrid production in model year 2021.
The growth of hybrids in the pickup vehicle type is largely due to the introduction of "mild"
hybrid systems that are capable of regenerative braking and many of the same functions as other
hybrids but utilize a smaller battery and an electrical motor that cannot directly drive the vehicle.
These mild hybrids account for about a third of hybrid production in model year 2021.
10.0%-
Vehicle Type
Sedan/Wagon
| Car SUV
Truck SUV
Minivan/Van
| Pickup
2000
2005
2015
2020
2010
Model Year
Figure 3-2 Gasoline Hybrid Engine Production Share by Vehicle Type
3.1.1.3 Plug-in Electric Vehicle Technologies
The previously described trend in application of BEV and PHEV technologies to light-duty
vehicles is evidence of a continuing shift toward electrification across the vehicle industry. As
described in detail in the Executive Summary of the Preamble (I. A.2.ii), recent trends in market
penetration of PEVs show that demand for these vehicles in the U.S. is rapidly increasing, as the
production of new PEVs (including both BEVs and PHEVs) is growing rapidly and roughly
doubling every year. As also described at length in that section, manufacturers have increasingly
begun to shift research and development investment away from ICE technologies and are
allocating large amounts of new investment to electrification technology. For more discussion of
these rapidly increasing trends, see Preamble Section I.A.2.ii.
The production of BEVs and PHEVs has increased rapidly in recent years. Prior to model
year 2011, BEVs were available, but generally only in small numbers for lease in California. In
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model year 2011 the first commercially available PHEV, the Chevrolet Volt, was introduced
along with the Nissan Leaf BEV. Many additional models have been introduced since, and in
model year 2021 combined BEV/PHEV production reached 4 percent of all new vehicles.
Combined BEV and PHEV production is projected to reach a new high of 8 percent of all
production in model year 2022. The trend in BEVs, PHEVs, and FCEVs are shown in Figure 3-3
(U.S. EPA 2022).
8% -
6%
-------
VehicleType
Sedan/Wagon
| Car SUV
Truck SUV
B Pickup
0% -
.1
2010
2015
2020
Model Year
Figure 3-4 Electric Vehicle Production Share by Vehicle Type
VehicleType
Sedan/Wagon
¦
Car SUV
Truck SUV
¦
Minivan/Van
2010
2015
2020
Model Year
Figure 3-5 Plug-In Hybrid Vehicle Production Share by Vehicle Type
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Figure 3-6 (U.S. EPA 2022) shows the range and fuel economy trends for EVs and PHEVs.
The average range of new BEVs has climbed substantially. In model year 2021 the average new
BEV range is 298 miles, or about four times the range of an average BEV in 2011. The range
values shown for PHEVs are the charge-depleting range, where the vehicle is operating on
energy in the battery from an external source. This is generally the electric range of the PHEV,
although some vehicles also use the gasoline engine in small amounts during charge depleting
operation. The average charge depleting range for PHEVs has remained largely unchanged since
model year 2011.
Along with improving range, the fuel economy of electric vehicles has also improved as
measured in miles per gallon of gasoline equivalent (mpge). The fuel economy of electric
vehicles increased by about 18 percent between model years 2011 and 2021. The combined fuel
economy of PHEVs has been more variable but is about 30 percent lower in model year 2021
than in model year 2011. This decrease may be attributable to the growth of truck SUV PHEVs.
Range (mi) Fuel Economy (mpge)
Model Year
Figure 3-6 Charge Depleting Range and Fuel Economy for BEVs and PHEVs
Figure 3-7 (U.S. EPA 2022) shows the model year 2021 production volume of BEVs, PHEVs
and FCEVs. More than 600,000 BEVs, PHEVs, and FCVs were produced in the 2021 model
year. Of those vehicles, about 73 percent were BEVs, 27 percent were PHEVs, and less than 1
percent were FCEVs.
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o
o
o
c
o
o
Z5
Tj
O
350-
300
250
200
150
100
50
Electric Vehicle
Plug-In Hybrid Electric Vehicle
Fuel Cell Vehicle
I I
I I I
I
ll.
I I I I I
&
/
Figure 3-7 Model Year 2021 Production of BEVs, PHEVs, and FCEVs
3.1.2 Medium-duty Vehicle Technologies and Trends
The medium-duty sector is also experiencing a shift toward electrification in a similar manner
to the light-duty sector and within several important market segments. As cited in 1.A.2 of the
Preamble, numerous commitments to produce all-electric medium-duty delivery vans have been
announced by large fleet owners including FedEx, Amazon, and Wal-Mart, in partnerships with
various OEMs. This abrupt shift to full electrification from a fleet that is currently predominantly
gasoline- and diesel-powered suggests that the operators of these fleets consider full
electrification as the best available and most cost-effective technology for meeting their mission
objectives, while also reducing the emissions from their business operations. Owing to the large
size of these vehicle fleets, this segment alone is likely to represent a significant portion of the
future electrification of the medium-duty vehicle fleet.
As described in draft RIA Chapter 1.2.2.1 and within § III .A of the Preamble to this proposed
rule, the Agency is proposing to use the term "Medium-duty vehicle" (MDV) for the first time
within its regulations. MDVs are comprised of the following weight categories:
• Class 2b - 8,501 pounds to 10,000 pounds rated gross vehicle weight (GVWR)
• Class 3 - 10,001 to 14,000 pounds GVWR
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For more information, please refer to § III. A. 1 of the Preamble to this proposal. MDVs can
either be "incomplete" chassis cabs onto which customized body work or beds are added after
their original manufacture or are "complete" pickup trucks or vans. Examples of incomplete
vehicles customized for specific applications include motorhomes, ambulances, wreckers, panel
vans, flatbeds, etc. (see Figure 3-8). In model year 2020, less than 5 percent of MDV sales were
incomplete vehicles, with the remainder being complete.
Figure 3-8: Examples of incomplete MDV chassis finished with customized bodies for
specific applications.
MDV pickup trucks are generally built with heavier frames and designed with sufficient brake
and suspension systems to support significantly higher towing capability than found in light-duty
pickup trucks. MDV pickup truck applications have considerable tow capability, which can be in
excess of 20,000 pounds gross combined weight rating (GCWR) pickups with gasoline engines
and can be over 40,000 pounds GCWR for pickups with diesel engines. MDV vans have
comparable payload carrying ability to MDV pickups; however, they typically have significantly
lower tow capability with GCWR comparable to, or less than, many light-duty pickups.
There are both diesel engine and spark-ignition gasoline engine applications in MDV. Their
shares of MDV sales are shown for both pickups, vans, and incomplete vehicles in Table 3-1.
Both gasoline and diesel engines used in van applications and some gasoline engines used in
pickup truck applications are derived from light-duty applications. Examples include the:
• Mercedes Benz OM654 diesel engine in the MY2023 Sprinter Van (engine family
shared with the C-Class and E-Class passenger cars and GLC CUV sold outside the
U.S.)
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• Mercedes Benz M274 turbocharged GDI engine in the MY2023 Sprinter Van (engine
family shared with the C-Class and E-Class passenger cars and GLC light-duty CUV)
• Ford 3.5L EcoBoost in the MY2015-2023 Transit Van (engine family shared with the
2011-2016 Ford F150 light-duty pickup)
• GM LWN diesel engine in the Chevrolet and GMC vans (engine family shared with
Chevrolet Colorado light-duty pickup)
• RAM 6.4L Hemi in the RAM 2500 and 3500 pickups (engine family shared with
RAM light-duty pickups, and Dodge, Jeep and Chrysler passenger cars and light-duty
CUVs)
• GM L8T naturally aspirated GDI engine used in Chevrolet Silverado 2500HD and
3500HD pickups and G3500 vans and sharing the GM "Generation V" V8 engine
family with many GM light-duty trucks, CUVs, SUVs and some passenger cars.
Table 3-1: Percentage of MY2020 sales and sales volumes of pickup, van, and incomplete
MDVs by fuel type
Pickups Vans
Fuel Type* Gasoline Diesel Gasoline Diesel
MY2020 sales share 24.2% 37.1% 30.4% 3.7%
MY2020 sales 213.7% 327.488 269.038 32.351
*Other sources of powertrain energy, including electrification, accounted for <1% of MDV sales in MY2020.
Incomplete Vehicles
Gasoline
4.5%
40.043
Grand
Total
Diesel
0.1% ] 100%
978 883.694
While many gasoline engine families used for pickup truck applications share engine families
and/or key design elements with light-duty applications, in some cases engine block materials
may shift from aluminum in light-duty applications to iron for MDV applications (e.g., GM L8T
engine). In other cases, engine families are solely used in MDV and are also shared with heavier
weight-class trucks above MDV, for example Ford's 7.3 L Super-duty naturally aspirated, port-
fuel-injected, naturally aspirated gasoline engine used in the F250 and F350 MDV pickups,
which is also used in the heavier Ford F450/550/600 and F650/750. Diesel equipped MDV
pickup trucks are equipped with 6L and larger engines, some of which have peak torque ratings
in excess of 1000 ft-lbs. Diesel engines used in MDV pickup trucks have no light-duty
counterparts and most also share engine families with significantly heavier classes of vehicles
(e.g., weight classes 4 through 7) (Title 40 CFR § 86.1803-01 2023).
The use of commercial vans for last mile delivery in the U.S. has grown significantly since
the start of the global COVID-19 pandemic, primarily through the growth of e-commerce24 In
the U.S., 2021 e-commerce sales totaled $870 billion, which represents an increase of over 14
percent from 2020, and over 50.5 percent compared to 2019. U.S. E-commerce represented just
over 13.2 percent of all retail sales in 2021 (U.S. Census Bureau 2022). Globally, the automotive
24 Commercial transactions, including retail sales, conducted electronically on the internet.
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market supporting e-commerce was valued at over $66 billion in 2021 and is expected to grow to
over $75 billion by the end of 2022 and to over $213 billion by 2029. (Fortune Business Insights
2022). Based on the results of a recent pilot study of the electrification of commercial delivery
vans and step vans, the North American Council for Freight Efficiency identified this segment as
"100% electrifiable" (North American Council for Freight Efficiency 2021).
Vans using dedicated battery-electric vehicle (BEV) architectures are beginning to enter the
U.S. market. The first mass-produced models became available for MY2023 and additional
production volume and models have been announced for MY2024. Initial dedicated BEV van
chassis have been predominantly targeted towards parcel delivery and include the GM
BrightDrop Zevo 400 and Zevo 600; and the Rivian EDV 500 and EDV 700 (Figure 3-9). Both
GM and Rivian share key electric powertrain and battery storage components between their
light-duty and/or MDPV BEV products and their dedicated BEV commercial van products,
which provides improved economies of scale for their commercial BEV MDV vans. EPA does
not require manufacturers to the electric range of MDVs, however manufacturers and key
customers (e.g., Amazon and FedEx) appear to be targeting approximately 150 miles of range
based public data battery pack capacity of approximately 135 kWh for the EDV700,
approximately 115-kWh for the Zevo 400, and standard capacity of approximately 115-kWh for
the Zevo 600 with an optional 165-kWh capacity (Seabaugh 2022) (BrightDrop 2022) (Battery
Design 2022).25
Figure 3-9: Rivian EDV 700 (left) and GM BrightDrop ZEVO 600 MDV (right) vans
operated by Amazon and FedEx, respectively.
Although no PHEV pickup truck or MDV applications currently exist nor have they been
explicitly been modeled within the proposed rule, EPA believes the PHEV architecture may lend
itself well to future applications, particularly MDV pickup truck applications at or below 10,000
pounds GVWR and MDV vans used outside of last-mile delivery applications. One major
manufacturer, Stellantis, recently announced at the 2023 Consumer Electronics Show that a
range-extender will be an option on their new full-size electric pickup (Riley 2023). A MDV
PHEV pickup architecture would provide several benefits: some amount of zero emission
electric range (depending on battery size); increased total vehicle range during heavy towing and
25 BrightDrop useable pack capacity calculated from: public data on GM ultium prismatic NC'M A cells at 103 Ah
cell capacity, 3.7 VDC nominal cell voltage; public data on GM Ultium modules at 24 cells per module; and
BrightDrop public data on the availability of 14 module and 20 module Ultium battery packs (Battery Design 2022)
(BrightDrop 2022).
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hauling operations using both charge depleting and charge sustaining modes (depending on ICE-
powertrain sizing); job-site utility with auxiliary power capabilities similar to portable worksite
generators, and the efficiency improvements normally associated with strong hybrids that
provide regenerative braking, extended engine idle-off, and launch assist for high torque demand
applications. Depending on the vehicle architecture, PHEVs used in MDV pickup applications
may also offer additional capabilities, similar to BEV pickups, with respect to torque control
and/or torque vectoring to reduce wheel slip during launch in trailer towing applications. In
addition, PHEVs may help provide a bridge for commercial consumers that may not be ready to
adopt a fully electric MDV pickup.
EPA has initiated contract work to investigate likely technology architectures of both PHEV
and internal combustion engine range-extended electric light-duty and MDV pickup trucks that
we anticipate will provide data in time for the final rule. Costs for potential PHEV designs for
this application are outlined in DRIA 2.6.1.4.
While the agency anticipates that electrification of vans will be a cost-effective compliance
strategy for meeting the proposed GHG and criteria pollutant standards, vehicle manufacturers
may also choose to improve their conventional, ICE-based vehicles. MDV GHG emissions can
be reduced via improving powertrain efficiency or by making improvements to road loads
through improved aerodynamics, reduced tire rolling resistance and reduced vehicle weight. For
a summary of conventional MDV GHG emissions control technology, please refer to Chapter 2.5
of the Heavy-duty Phase 2 GHG Regulatory Impact Analysis. MDV emissions that contribute to
criteria air pollutants can be reduced by improvements to engine management systems, fuel
systems, evaporative emissions control systems, catalyst systems, and via the addition of modern
exhaust filtration systems such as the gasoline particulate filter (GPF). Many of the anticipated
controls for future MDVs share significant design elements with criteria pollutant emissions
controls used for light-duty applications and are discussed in more detail in Chapter 3.4.
3.1.3 PEV Feasibility
3.1.3.1 PEV Technological Feasibility
These trends in light- and medium-duty vehicle technology show that BEV and PHEV
technologies are already being increasingly employed across the fleet in both light-duty and
medium-duty applications. This market shift toward electrification is also evidence that BEVs
and PHEVs are seen not only as an effective and feasible means to comply with emissions
regulations but also as an effective and attractive solution that can serve the functional needs of a
large portion of light- and medium-duty vehicle buyers. This ongoing market shift also
represents an opportunity to accelerate needed reductions in criteria pollutant and GHG
emissions by encouraging and accelerating continued rapid uptake of these technologies in the
U.S. light- and medium-duty vehicle fleet.
As noted previously, zero- and near-zero emissions technologies are more feasible and cost-
effective now than at the time of prior rulemakings. The developments in vehicle electrification
that have brought this about are driven in part by the industry's need to compete in a diverse
market, as zero-emission transportation policies continue to be implemented across the world.
Section I. A.2 of the Preamble provided a comprehensive analysis of recent events in the advance
of electrification of the automotive sector, and established a number of important points, which
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are reviewed briefly below (U.S. EPA 2023). Citations for the content in this section can be
found in the parallel discussions in Section I.A.2 of the Preamble, unless specifically cited here.
One conclusion of that discussion was that advancement of vehicle electrification is likely
being driven in part by automakers' need to compete in a diverse global marketplace in which
many jurisdictions are continuing to implement zero-emission transportation policies.
Specifically:
• At least 20 countries across the world, as well as numerous local jurisdictions, have
announced plans to shift all new passenger car sales to zero-emission vehicles in the
coming years — Norway by 2025; Austria, the Netherlands, Denmark, Iceland, India,
Ireland, Israel, Scotland, Singapore, Sweden, and Slovenia by 2030, Canada, Chile,
Germany, Thailand, and the United Kingdom by 2035, and France, Spain, and Sri
Lanka by 2040.
• Many of these announcements extend to light commercial vehicles as well, and several
also target a shift to 100 percent all-electric medium- and heavy-duty vehicle sales
(Norway targeting 2030, Austria 2035, and Canada and the United Kingdom 2040).
• Together, the countries that had, by the end of 2022, set a target of 100 percent light-
duty zero-emission vehicle sales by 2035, represent at least 25 percent of today's
global light-duty vehicle market.
• Countries of the European Union that were not represented in that total will drive the
total even higher, as the European Parliament approved a measure in 2023 to phase out
sales of ICE passenger vehicles in its 27 member countries by 2035.
• In 2021, BEVs and PHEVs together already comprised about 18 percent of the new
vehicle market in Western Europe, led by Norway which reached almost 80 percent
BEV and 88 percent combined BEVs and PHEVs in 2022.
• In the U.S., an increasing number of U.S. states have taken actions to shift the light-
duty fleet toward zero-emissions technology, including California, New York,
Massachusetts, and Washington state, likely to be followed by Oregon and Vermont.
In addition to spurring industry development of BEV and PHEV technology, developments
such as these suggest a growing global consensus that BEV and PHEV technologies are feasible
candidates for increased use as an emissions-reducing technology. For additional details and
citations regarding these domestic and global developments, please refer to Preamble I.A.2.ii.
The Preamble also established that demand for these vehicles in the U.S. is rapidly
increasing, even under current standards. Major points established by that discussion include
(U.S. EPA 2023):
• The production of new PEVs (including both BEVs and PHEVs) is roughly doubling
every year, projected to be 8.4 percent of U.S. light-duty vehicle production in 2022,
up from 4.4 percent in MY 2021 and 2.2 percent in MY 2020.
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• In California, new light-duty zero-emission vehicle (ZEV) sales in 2022 reached about
19 percent of all new cars, up from 12 percent in 2021 and more than twice the share
from 2020.
• The number of BEV and PHEV models offered for sale in the U.S. more than doubled
between MY 2015 and MY 2021, and is expected to increase to more than 80 models
by MY 2023 and more than 180 by 2025.
• In 2022, BEVs alone accounted for about 807,000 U.S. new car sales, or about 5.8
percent of the new light-duty passenger vehicle market, up from 3.2 percent BEVs the
year before.
Before the Inflation Reduction Act (IRA) became law, analysts were already projecting that
significantly increased penetration of plug-in electric vehicles would occur in the United States
and in global markets. Studies cited in the Preamble established that:
• In 2021, IHS Markit predicted a nearly 40 percent U.S. PEV share by 2030.
• More recent projections by Bloomberg New Energy Finance suggest that under
current policy and market conditions, and prior to the IRA, the U.S. was on pace to
reach 40 to 50 percent PEVs by 2030; when adjusted for the effects of the Inflation
Reduction Act, this estimate increases to 52 percent.
• Another study by the International Council on Clean Transportation (ICCT) and
Energy Innovation that includes the effect of the IRA estimates that the share of BEVs
will increase to 56 to 67 percent by 2032.
• Similarly, Goldman Sachs projects a 50 percent share for BEVs in the U.S. in 2030, 70
percent in 2035 and 85 percent in 2040.
Although the assumptions and other inputs to these forecasts vary, they point to greatly
increased penetration of electrification across the U.S. light-duty fleet in the coming years,
without specifically considering the effect of increased emission standards under this proposed
rule.
A similar trend was seen in forecasts reviewed for the global market, showing that the shift
toward electrification in the U.S. is part of a global phenomenon:
• Global light-duty passenger PEV sales (including BEVs and PHEVs) reached 6.6
million in 2021, bringing the total number of PEVs on the road to more than 16.5
million globally.
• Global sales of fully-electric BEVs rose to 7.8 million in 2022, an increase of about 68
percent from the previous year and representing about 10 percent of the new global
light-duty passenger vehicle market.
• In June 2022, Bloomberg New Energy Finance predicted that global sales will rise to
21 million in 2025 (implying an annual growth rate of about 39 percent from 2022),
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with total global vehicle stock reaching 77 million BEVs by 2025 and 229 million
BEVs by 2030.
We also observed that the year-over-year growth in U.S. BEV sales suggests that an
increasing share of new vehicle buyers are concluding that a PEV is the best vehicle to meet their
needs, for example:
• PEV owners often describe specific advantages of PEVs as key factors motivating
their purchase, such as responsive acceleration, improved performance and handling,
quiet operation, lower cost of ownership, and the ability to charge at home.
• A 2022 survey from Consumer Reports shows that, even at a time when many
consumers are not yet as familiar with BEVs as with ICE vehicles, more than one third
of Americans would either seriously consider or definitely buy or lease a BEV today if
they were in the market for a vehicle.
• According to the U.S. Bureau of Labor Statistics, growth in PEV sales is driven in part
by growing consumer demand and growing automaker commitments to electrification.
• Most PEV owners who purchase a subsequent vehicle choose another PEV, and often
express resistance to returning to an ICE vehicle after experiencing PEV ownership.
• Many analysts believe that as PEVs continue to increase their market share, PEV
ownership will continue to broaden its appeal as consumers gain more exposure and
experience with the technology and with the benefits of PEV ownership, with some
analysts suggesting that a "tipping point" for PEV adoption may then result.
We also noted that, while the purchase price of BEVs is typically higher than for most
comparable ICE vehicles at this time, the price difference is widely expected to narrow or
disappear, particularly for BEVs, as the cost of batteries and other components fall in the coming
years. More specifically, we observed that:
• An emerging consensus suggests that purchase price parity is likely to occur by the
mid-2020s for some vehicle segments and models, and for a broader segment of the
market on a total cost of ownership (TCO) basis.
• By some accounts, a compact car with approximately 150 miles of range may already
be possible to produce and sell for the same price as a compact ICE vehicle.
• Many analysts expect examples of price parity to increasingly appear over the mid- to
late-2020s for larger vehicles and those with a longer range.
• Prospects for price parity improve greatly when considering state and federal purchase
incentives. For example, the Clean Vehicle Credit of up to $7,500 provided under the
Inflation Reduction Act may in many cases exceed the current price premium for some
BEV models.
• Many expect TCO parity to precede price parity by several years, as it accounts for the
reduced cost of operation and maintenance for BEVs; for example, Kelley Blue Book
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already estimates that the lowest TCO for the full-size pickup and luxury car classes of
vehicle are BEVs.
• TCO parity is of particular interest to commercial and fleet operators, for whom lower
TCO is a compelling business consideration.
We also showed that a proliferation of announcements by automakers in the past two years,
signaling a rapidly growing shift in product development focus among automakers away from
internal-combustion technologies and toward electrification, provides further evidence of the
feasibility of BEVs and PHEVs as an emissions-reducing technology. Section I.A.2 of the
Preamble introduces and cites many of these announcements, which are repeated here for
context:
• In January 2021, General Motors announced plans to shift its light-duty vehicles
entirely to zero-emissions by 2035.
• In March 2021, Volvo announced plans to make only electric cars by 2030, and
Volkswagen announced that it expects half of its U.S. sales will be all-electric by
2030.
• In April 2021, Honda announced a full electrification plan to take effect by 2040, with
40 percent of North American sales expected to be fully electric or fuel cell vehicles
by 2030, 80 percent by 2035 and 100 percent by 2040.
• In May 2021, Ford announced that they expect 40 percent of their global sales will be
all-electric by 2030.
• In June 2021, Fiat announced a move to all electric vehicles by 2030, and in July 2021
its parent corporation Stellantis announced an intensified focus on electrification
across all of its brands.
• 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.
• In August 2021, the Alliance for Automotive Innovation expressed continued
commitment to their members' announcements of a shift to electrification and
expressed their support for the goal of achieving 40 to 50 percent sales of zero-
emission vehicles by 2030.
• In December 2021, Toyota announced plans to introduce 30 BEV models by 2030.
• According to a tabulation of these and many other OEM announcements, the sales
collectively implied by such announcements to date would conservatively amount to
about 50 percent new light-duty zero-emission vehicle sales in the U.S. by 2030.
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• In addition, numerous commitments to produce all-electric medium-duty delivery vans
have been announced by large fleet owners including FedEx, Amazon, and Wal-Mart,
in partnerships with various OEMs.
We also noted that these announcements and others like them continue a pattern over the past
several years in which most major manufacturers have taken steps to aggressively invest in zero-
emission technologies and reduce their reliance on the internal-combustion engine in various
markets around the globe:
• One cited analysis indicated that 37 of the world's automakers are planning to invest a
total of almost $1.2 trillion by 2030 toward electrification, a large portion of which
will be used for construction of manufacturing facilities for vehicles, battery cells and
packs, and materials. This would support up to 5.8 terawatt-hours of battery
production and 54 million BEVs per year globally.
• Another cited analysis showed that a significant shift in North American investment is
occurring toward electrification technologies, with more than 90 percent ($36 billion
of about $38 billion) of total automaker manufacturing facility investments announced
in 2021 being slated for electrification-related manufacturing in North America, with a
similar proportion and amount on track for 2022.
• In September 2021, Toyota announced large new investments in battery production
and development to support an increasing focus on electrification, and in December
2021, announced plans to increase this investment.
• In December 2021, Hyundai closed its engine development division at its research and
development center in Namyang, South Korea in order to refocus on BEV
development.
• In summer 2022, Hyundai invested $5.5 billion to fund new battery and electric
vehicle manufacturing facilities in Georgia, and recently announced a $1.9 billion joint
venture with SK to fund additional battery manufacturing in the U.S.
• In September 2022, jointly with the Environmental Defense Fund, General Motors
announced a set of recommendations that "seek to accelerate a zero-emissions, all-
electric future for passenger vehicles in model year 2027 and beyond," including a
recommendation that EPA establish standards to achieve at least a 60 percent
reduction in GHG emissions (compared to MY 2021) and 50 percent zero-emitting
vehicles by MY 2030.
The shift to PEVs is anticipated to accelerate in the United States over the next decade as
provisions of the Inflation Reduction Act of 2022 (IRA) begin to take effect (Public Law 117-
169 2022). The IRA has key provisions that will reduce the cost of PEVs to consumers, reduce
the cost of battery manufacturing in the U.S. for automakers, and foster significant emissions
reductions from the U.S. electric power sector. These include:
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• Vehicle Provisions including the Domestic Manufacturing Conversion Grant Program,
Advanced Technology Vehicle Manufacturing Program, and expanded authorities for
the DOE Loan Programs Office
• Clean Vehicle Tax Credits including 30D, 45W, 25E and 30C
• Advanced Manufacturing Production Credit
• Power Sector Provisions
• Clean Electricity Production and Investment Tax Credits
• Renewable electric generation incentives
• Grid battery storage incentives
• Existing Nuclear Production Tax Credit
• Extends nuclear EGU service life
• Carbon Capture and Storage 45Q Tax Credit
For further discussion of the impacts of the IRA on the electric power sector, please refer to
Chapter 5.2.3 of the DRIA.
Taken together, the developments summarized in this section indicate that proven, zero-
emission PEV technology is an available and feasible way to greatly reduce emissions and is
capable of being implemented across a large portion of the fleet.
In Preamble V.B, we addressed the overall technological feasibility and lead time necessary
for manufacturers to meet the proposed standards using the array of proven, advanced vehicle
technologies that are available to them. There we noted that the technological readiness of the
auto industry to meet the proposed standards for model years 2027-2032 is best understood in the
context of over a decade of light-duty vehicle emissions reduction programs in which the auto
industry has introduced emissions-reducing technologies in a wide lineup of ever more cost
effective, efficient, and high-volume vehicle applications. The developments outlined in this
section further underscore the fact that PEV technology is already poised to enter the fleet in
increasing penetrations.
In considering feasibility of the proposed standards, EPA also considers the impact of
available compliance flexibilities on automakers' compliance options, as well as constraints
posed by the typical cadence of manufacturer redesign cycles. In Preamble V.B we described
how EPA's technical assessment for this proposal accounts for redesign limits.26 Once a redesign
opportunity is encountered, we have assumed limits to the rate at which a manufacturer can ramp
in the transition from an ICE to a BEV vehicle. We have also applied limits to the ramp up of
battery production, considering the time needed to increase the availability of raw materials and
26 In our compliance modeling, we have limited vehicle redesign opportunities in our compliance modeling to every
7 years for pickup trucks, and 5 years for all other vehicles.
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expand battery production facilities. These limits as they are applied in OMEGA are discussed in
DRIA Chapter 2.
Overall, it is our assessment that PEV technology is technologically feasible to play a strong
role in manufacturer compliance with the proposed standards, and that there is sufficient lead
time for the industry to more deploy this technology to successfully comply with the proposed
standards.
Preamble V.B describes the level of PEV penetration indicated by our compliance analysis,
which in the central case of the proposal indicates roughly two-thirds of the light-duty passenger
vehicles sold in 2032 would be BEVs. We believe that the discussion in this section outlining the
rapid growth in BEV penetration that is already occurring, the breadth and significance of
manufacturer plans and investments that underscore this movement, and the overall momentum
evident in the industry, provides strong evidence for the feasibility of BEV technology and
supports our assessment that the projected levels of BEV penetration under the scenarios of the
proposal are feasible and achievable at a reasonable cost. This conclusion is further supported by
our analysis of critical minerals, manufacturing capacity, and mineral security, which is
introduced in Preamble IV.C.6 and further examined in the next section of this DRIA.
For a full discussion of technological feasibility and lead time for compliance with the
proposed standards, please see Preamble V.B.
While EPA has not explicitly modeled the adoption of PHEV architectures within the analysis
for this proposal, the agency recognizes that PHEVs can also provide significant reductions in
GHG emissions and that some vehicle manufacturers may choose to utilize this technology as
part of their technology offering portfolio in response to customer demands/needs and in
response to EPA emission standards (as some firms are already doing today). PHEVs have been
available in the light-duty vehicle market in the U.S. for more than a decade and many models
are now available across a larger breadth of vehicle types, including sedans, such as the Toyota
Prius Prime; and cross-over SUVs, such as the Subaru Crosstrek, Ford Escape PHEV, Kia Niro
Plug-in Hybrid, Kia Sportage Plug-In Hybrid, Hyundai Tucson Plug-In Hybrid, Mitsubishi
Outlander PHEV and Toyota RAV4 Prime. Stellantis currently offers a minivan PHEV, the
Chrysler Pacifica Hybrid; and two large PHEV SUVs are available, the Jeep Grand Cherokee
4xe and Lincoln Corsair Grand Touring. This further confirms that the modeling for the
proposed standards is illustrative of a reasonable path to compliance for automakers, but is not
intended to be prescriptive and may be conservative (i.e., overestimate costs of compliance), as
discussed further in Preamble V.B.
3.1.3.2 Critical Minerals and Manufacturing
In Section IV.C.6 of the Preamble, we provided a comprehensive analysis of recent events in
the growth of U.S. and global battery manufacturing capacity, reviewed the role and importance
of critical minerals, and considered the outlook for critical mineral supply and demand. In that
discussion, we established a number of important points, which are reviewed briefly in this
section. The remainder of this section details how we used this information to develop a
modeling constraint meant to represent a production-based limit on the rate of penetration of
PEV technology into the fleet during the time frame of the proposed standards. Citations for the
content in this section can be found in the parallel discussions in Preamble IV.C.6, unless
specifically cited here.
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The Preamble discussion established a number of key observations about the status of critical
minerals and manufacturing capacity, and the outlook for development of the supply chain in
response to industry investment and government policy (U.S. EPA 2023):
• Although much of the supply chain supporting the manufacture of PEVs is located
outside of the U.S., more than half of battery cells and 84 percent of assembled packs
in PEVs sold in the U.S. from 2010 to 2021 were produced in the U.S.
• This suggests that PEV production in the U.S. need not be heavily reliant on foreign
manufacture of battery cells or packs as PEV penetration increases and domestic
mineral and cell production comes online.
• Many automakers are building battery and cell manufacturing facilities in the U.S. and
are also taking steps to secure domestically sourced minerals and commodities to
supply production for these plants.
• Analysis of constructed and planned plant capacity for assembly of cells and packs
indicates that battery manufacturing capacity does not appear to pose a critical
constraint to expected uptake of PEVs, either globally or domestically.
• Domestically, construction announcements made by the major automakers indicate
that the U.S. will have more than 800 GWh of cell or battery manufacturing capacity
by 2025, and 1000 GWh by 2030, enough to supply from 10 to 13 million BEVs per
year.
We also drew observations regarding which minerals are of greatest concern as a potential
constraint on PEV production during the time frame of the rule:
• Mineral demand for ICE catalyst production is relatively stable and would not be
expected to increase as a result of electrification.
• Rare earths used in permanent magnet motors have potential alternatives in the use of
induction machines or other electric machine technologies that do not require rare-
earth magnets, or in the use of advanced ferrite or other advanced magnets.
• On a sheer quantity basis and probably also on a value basis, battery minerals are
likely to be the most important mineral-related constraint on PEV production during
the time frame of the rule.
• Of these, the most attention is commonly given to lithium, nickel, cobalt, and graphite.
• Currently, most mining and refining of these minerals occurs outside of the U.S. and
they are largely imported as refined products.
• The U.S. does not lack significant deposits of these minerals, and has formerly
produced them, but relatively little mining and refining capacity is currently in
operation or remains undeveloped.
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• The development of mining and refining capacity in the U.S. is a primary focus of
industry toward building a robust domestic supply chain for electrified vehicle
production.
• For example, LG Chem has announced plans for a cathode material production facility
in Tennessee, said to be sufficient to supply 1.2 million high-performance electric
vehicles per year by 2027.
We also noted that further development of a domestic mineral supply chain will be
accelerated by the provisions of the Inflation Reduction Act (IRA) and the Bipartisan
Infrastructure Law (BIL), as well as ongoing efforts by the Executive Branch:
• The IRA offers sizeable tax provisions that incentivize domestic production of
batteries and critical minerals, including a $7,500 Clean Vehicle Credit for vehicles
manufactured in North America that use domestically produced components and
mineral products, and production tax credits that apply to domestically produced cells,
modules, electrode active materials, and critical minerals, that can reduce battery
manufacturing cost by a third or more.
• The BIL provides $7.9 billion to support development of the domestic supply chain
for battery manufacturing, recycling, and critical minerals. Provisions extend across
critical minerals mining and recycling research, USGS energy and minerals research,
rare earth elements extraction and separation research and demonstration, and
expansion of DOE loan programs in critical minerals and recycling.
• Through these provisions DOE is actively working to prioritize points in the domestic
supply chain to target with accelerated development, and rapidly funding those areas
through numerous programs and funding opportunities.
• With BIL funding and matching private investment, more than half of the capital
investment that the Department of Energy's Li-Bridge alliance considers necessary for
supply chain investment to 2030 has already been committed.
We also noted the following observations about forecast global supplies of refined critical
minerals:
• According to analyses by Department of Energy's Li-Bridge, no shortage of cathode
active material or lithium chemical supply is seen globally through 2035 under current
projections of global demand.
• The International Energy Agency reached similar conclusions for cobalt and nickel,
projecting that lithium would be in sufficient supply through at least 2028, before
consideration of new DOE projections of additional capacity that could further boost
lithium supply beyond current IEA and BNEF projections.
• Despite recent short-term fluctuations in price, the price of lithium is expected to
stabilize at or near its historical levels by the mid-2020s, further suggesting that a
critical long-term shortage is not expected to develop.
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In the context of all of the findings reviewed above, EPA recognizes that the global minerals
industry is already anticipating and preparing for accelerated growth in demand for critical
minerals resulting from already-existing expectations of greatly increased global PEV production
and sales in the future, as well as expectations of growing demand for these materials in other
areas of clean energy and decarbonization. Thus, in the context of evaluating the impact of the
proposed standards on demand for critical minerals and development of the domestic supply
chain, EPA recognizes that much of the anticipated growth in global mineral demand stems not
from the incremental effect of the proposed standards but from these ongoing forces that are
already driving the global industry to increase mineral production.
Relatedly, EPA notes that the IRA, the BIL, and ongoing activity on the part of Executive
Branch agencies are actively addressing the need for further development of the domestic supply
chain to supply growing demand for critical minerals. The provisions of the IRA and BIL were
in fact developed with the intent of growing the domestic supply chain for critical minerals and
related products and to achieve mineral security as the industry pursues clean energy technology.
Accordingly, EPA expects that the BIL and IRA will prove instrumental in meeting incremental
needs of the supply chain under the proposed standards.
In modeling potential PEV penetration into the fleet as a result of the proposed standards,
EPA considered how best to represent any limitations that are likely to be imposed by the supply
chain. Potential constraints on availability of minerals that are used in the manufacture of PEVs
are particularly relevant to projecting practical limits on the rate of penetration of PEVs into the
fleet in the future. EPA considered data from industry analysts, including Wood Mackenzie and
Benchmark Minerals Intelligence, to pursue a quantitative and qualitative understanding of the
future availability of these critical battery minerals during the time frame of the rule.
From a modeling perspective, the question of how to constrain the modeled rate of BEV
penetration to remain within limits imposed by the developing supply chain is an important one.
As part of the rulemaking analysis, EPA uses its OMEGA model to identify compliance
pathways (in other words, applications of available technology to the fleet) by which
manufacturers can meet the standards. The OMEGA model selects among available advanced
vehicle technologies and applies them to the fleet in the most cost-effective way, given the cost
of each technology and its effectiveness at achieving manufacturer compliance within the fleet
averaging structure of the program. Although BEV technology has a higher absolute cost than
many other technologies, it is particularly attractive to manufacturers because BEVs achieve zero
tailpipe emissions and are credited with such under the compliance accounting. On the other
hand, there is likely to be a limit to the rate at which BEV technology can phase into the fleet due
to various constraining forces such as growth in consumer acceptance, timing of refresh/redesign
cycles, activation of battery cell and pack manufacturing capacity, and critical mineral
availability, particularly in the early years of the analysis. If these constraints were not
represented, the OMEGA model might select BEV technology at a rate that results in a faster
penetration of BEVs into the fleet than these real-world constraints might practically allow. EPA
implemented several constraints in the OMEGA model to account for these factors.
Consumer acceptance is discussed in more detail in Chapter 4 of this DRIA, and its
representation by means of S-curves in the OMEGA model is discussed in Chapter 2.6.5.
Refresh/redesign cycles are also represented in the OMEGA model and are discussed in more
detail in Chapter 2.6.4.1 of this DRIA as well as IV.C of the Preamble.
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To account for potential limits posed by battery manufacturing capacity and critical minerals,
EPA implemented a constraint in terms of Gigawatt-hours (GWh) of lithium-ion battery
production per year that could be available for BEVs supplying the U.S. new vehicle fleet. As
described below, we developed this constraint by considering estimates of existing and
announced battery production capacity in North America and comparing these forecasts to
estimates of projected lithium supply and demand. To develop a modeling constraint on PEV
production, we considered available data on forecast battery manufacturing capacity, global
lithium demand, and global lithium chemical production. As all such estimates concern
prediction of future events and are by nature uncertain (particularly in the out years), we adopted
a simplified approach that provided what we consider to be a reasonable and conservative view
of future PEV production capacity as constrained by manufacturing and mineral supply.
We selected lithium supply as the primary mineral-based limiting factor for several reasons.
In Preamble IV.C.6 we noted that cobalt, nickel, and manganese are important to today's leading
battery chemistry formulations, but we also note that there is some flexibility in choice of
cathode minerals, and in many cases, opportunity will exist to reduce cobalt and manganese
content or to employ iron-phosphate cathode chemistries that do not utilize nickel, cobalt or
manganese. Graphite is used as the anode of most current and near-term PEV battery
chemistries, and all require lithium in the form of lithium carbonate or lithium hydroxide in the
electrolyte and the cathode. The role of natural graphite in many cases can be served by artificial
graphite or highly refined hard or amorphous carbon. However, lithium has no substitute in
commercially produced automotive applications at this time (however, see the discussion of
alternatives to lithium under development, later in this section). Although the common
chemistries vary in their need for either lithium hydroxide or lithium carbonate, either can
potentially be produced from available lithium sources.
Further, and as described in greater detail in Preamble IV.C.6, we considered the projections
of cobalt, nickel, and lithium supply and demand published in 2022 by the International Energy
Agency (IEA), which concluded that supply of cobalt and nickel should be sufficient to meet
demand between 2020 and 2030 for the two most likely demand scenarios modeled, while
lithium demand may begin to approach available supply after 2025 (for further discussion and
citations see Preamble IV.C.6). By contrast, as also described in Preamble IV.C.6, projections
made by DOE in November 2022 indicate that global supplies of cathode active material (and
incidentally, lithium chemical products) are expected to be sufficient to meet expected global
demand through 2035.
The observations described above, taken together, suggest that critical battery mineral supply
is likely to be adequate to meet anticipated demand, in some cases by a significant margin. This
data also suggests that, among the primary critical minerals needed for battery manufacturing,
growth in demand for lithium would likely be the first to approach available supply, if a battery
mineral shortage were to be encountered. Accordingly, we focused on lithium availability as a
potential limiting factor on the rate of growth of PEV production, and thus the most appropriate
basis for establishing a modeling constraint on PEV penetration into the fleet over the time frame
of the proposed rule.
With regard to battery manufacturing capacity in the U.S., we considered estimates of
announced manufacturer plans and currently installed capacity as reported in mid-2022 by S&P
Global and in late 2022 by Argonne National Laboratory. These sources are discussed in more
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detail in Preamble IV.C.6. S&P Global indicated that U.S. battery manufacturing capacity will
reach 382 GWh by 2025 (S&P Global 2022a), and 580 GWh by 2027. (S&P Global 2022b).
A later and more detailed estimate by Argonne National Laboratory reported 838 GWh of
capacity by 2025, 896 GWh by 2027, and 998 GWh by 2030, the vast majority representing cell
manufacturing capacity, and sufficient to supply final pack assembly for 10 to 13 million BEVs
per year by 2030 (Argonne National Laboratory 2022).While it remains possible that some of
this nameplate capacity may be implemented in stages to match suppliers' expectations of cell
demand, we assumed that the rapidly increasing demand scenario that the industry widely
anticipates will incentivize rapid buildout of the full announced capacity. In such a scenario, the
primary lead time component in meeting new demand is likely to be planning and construction
of the base plant, rather than outfitting production lines once the plant is built.
Although these forecasts suggest that planned manufacturing facility capacity could be a
potential basis for a modeling constraint on battery production, this would not reflect the
possibility that operating capacity could be constrained by mineral availability. We thus sought
to condition these production capacities by comparing them to estimates of global lithium supply
and demand.
Here it is relevant to note that, although the Inflation Reduction Act incentivizes use of
domestically sourced and processed mineral products, it only ties these products to availability of
the related tax incentives (primarily the Clean Vehicle Credit under 30D) and does not prohibit
use of imported mineral products by manufacturers that cannot secure domestic sources. Thus, it
is the global supply for lithium, not only domestically sourced supply, that potentially constrains
battery production.
We then referred to proprietary projections of lithium chemical capacity obtained from Wood
Mackenzie through a service subscription (Wood Mackenzie 2022). Forecast lithium production
in tons per year was reported as lithium carbonate equivalent (LCE) which EPA converted to
GWh of gross battery capacity using a widely accepted conversion factor. As a first-order,
conservative approximation of lithium availability to supply U.S. demand, we first subtracted the
Wood Mackenzie projections of U.S. lithium demand from their projections of global demand, to
estimate a "rest of world" (ROW) lithium demand trajectory. We then calculated the difference
between the ROW demand trajectory and the Wood Mackenzie high and low estimates of global
lithium chemical production. This difference was taken to represent a hypothetical lithium
production capacity that would be available to the U.S. market, assuming that ROW demand was
satisfied first, and growing demand did not generate a demand response among lithium suppliers
beyond what is already represented in the forecast. This is likely a conservative assumption, as
market forces would ultimately play some role in determining distribution, and increased
demand would likely result in higher prices and greater market certainty for investment in
additional supply capacity. We also noted that the resulting availability curve would be most
applicable to earlier years, as the data on which it was based does not represent likely industry
response to increased lithium demand as the market continues to grow. For this reason, we do not
depict the supply curves beyond 2027 due to the lack of modeled demand response in the
underlying data.
Figure 3-10 shows the S&P and Argonne battery plant production capacity estimates plotted
against the calculated lithium production capacity potentially available to the U.S. market (in
estimated battery GWh equivalent).
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1750
1500
1250
X
5
15
1000
750
500
250
A >
! ~ ^ /
• *
• ^
/>'" Jf
¦OMEGA limit
Excess Li, Low
Excess Li, high
US installed (ANL)
US installed (S&P)
2020
2022
2024
2026
2028
Year
2030
2032
2034
2036
Figure 3-10: Limit on battery GWh demand implemented in OMEGA, compared to
projected battery manufacturing capacity and excess lithium supply
We then examined this data to establish a conservative but reasonable limit on GWh battery
supply for use by the OMEGA model. First, we noted that the S&P estimate of U.S. batteiy
manufacturing plant production capacity, which due to its earlier date of origin is likely
conservative and extends only to 2027, is well beneath the low estimate of hypothetical "excess"
lithium supply. This suggests that lithium supply is more than sufficient to sustain the S&P
estimate of U.S. plant operation at full capacity.
The ANL accounting of U.S. plant capacity is larger than the S&P accounting, reflecting the
pace of newer announcements, although it does not distinguish between likely actual production
and nameplate capacity. It exceeds the low estimate of excess lithium supply but is still well
within the upper limit for most of its trajectory.
As a conservative bound on batteiy production capacity for the OMEGA model, we thus
followed the S&P trajectory to 2027 at 580 GWh. This trajectory stays within lower expected
lithium excess capacity for the first several years, when limited time is available for new capacity
to come on-line.
Past 2027, estimates of "excess" lithium as a difference between ROW demand and a current
accounting of global supply become less informative, because a demand response is not built
into the supply data. Therefore, uncertainty about the supply-demand balance against ROW
demand increases rapidly as the time horizon increases. In general, analysts believe that as
demand for a mineral commodity remains strong over time, investment in mining operations and
exploration will consistently increase, which leads to unknown or previously unprofitable
3-25
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geological sources to become available (Sun, Ouyang and Hao 2022). In resonance with this fact,
we noted that it seems very unlikely from an investment point of view that manufacturers and
battery suppliers would plan to construct plant capacity to come online in 2030 if it exceeds their
expectation of availability of mineral products to supply the plant's production. Given the
amount of lead time in the time frame past 2027 and the current level of activity in development
of supply chain capacity across the world, we considered it reasonable to expect that the ANL
estimate for 2030 at 998 GWh should be feasible to supply. We then continued a similar rate of
increase to 2035 at 1500 GWh. Passing through these three defined points results in an almost
linear growth rate that we then adopted as the annual battery GWh production limit for OMEGA
modeling purposes. We flattened the limit at 1500 GWh after 2035 due to lack of data for that
time period.
Here it is important to note, again, that our estimate of "excess" lithium available to the U.S.,
as the difference between currently anticipated global lithium supply and ROW demand, is likely
a conservative estimate because it quantifies only currently known sources of lithium that will
not be subject to demand elsewhere, and does not reflect the development of additional sources
over time, nor the market forces that will ultimately determine where these supplies will be
deployed.
The numeric values for the annual GWh limit input to OMEGA are provided in DRIA 2.6.4.2.
More details on how OMEGA calculates BEV battery capacities that are summed to a fleet GWh
production capacity is provided in DRIA 2.5.2.1.1.
3.1.3.3 Additional Information on Critical Mineral Supply Chain Development
This section provides additional detailed evidence of recent developments in the growth of the
critical mineral supply chain, and other specific topics relevant to this topic. Citations for all of
the examples listed in this section may be found in a Memo to the Docket titled "DOE
Communication to EPA Regarding Critical Mineral Projects."
A number of additional U.S. government efforts are underway to accelerate lithium and
critical minerals production:
• In February 2023, President Biden signed a presidential waiver of some statutory
requirements (Waiver) authorizing the use of the Defense Production Act (DP A) to
allow the Department of Defense (DoD) to more aggressively build the resiliency of
America's defense industrial base and secure its supply chains including for critical
minerals and energy storage. Since many of the investments needed in areas like
mining and processing of critical minerals can be very costly and take several years,
the Waiver permits the DoD to leverage DPA Title III incentives against critical
vulnerabilities, and removes the statutory spending limitation for aggregate action
against a single shortfall exceeding $50 million. This in turn allows the DoD to make
more substantial, longer-term investments.
• In December 2022, the Blue Ribbon Commission on Lithium Extraction in California
issued a report detailing actions to support the further develop geothermal power with
the potential co-benefit lithium recovery from existing and new geothermal facilities
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in the Salton Sea geothermal resource area. The three owners developing projects in
California may produce 600 kt/y LCE from geothermal brines around 2030.
• In June 2022, the United States formed the Minerals Security Partnership, whose goal
is to ensure that critical minerals are produced, processed, and recycled in a manner
that supports the ability of countries to realize the full economic development benefit
of their geological endowments. The MSP will help catalyze investment from
governments and the private sector for strategic opportunities —across the full value
chain —that adhere to the highest environmental, social, and governance standards.
Preamble IV.C.6 mentioned $3.4 billion in DOE Loan Program projects that were recently
awarded to aid in the extraction, processing and recycling of lithium and other critical minerals
to support continued market growth. Details on these projects are provided below.
• A $50M BIL grant to Lilac plans to build out domestic manufacturing capacity for the
company's patented ion-exchange technology to increase production of lithium from
brine resources with minimal environmental impact and streamlined project
development timelines, and develop domestic lithium projects.
• A $141,7M BIL grant to Piedmont Lithium plans to accelerate the construction of the
Tennessee Lithium project in McMinn County as a world-class lithium hydroxide
operation, which is expected to more than double the domestic production of battery-
grade lithium hydroxide. The project is being designed to produce lithium hydroxide
from spodumene concentrate using the innovative Metso:Outotec process flow sheet,
enabling lower emissions and carbon intensity as well as improved capital and
operating costs relative to incumbent operations.
• A $150M BIL grant to Albemarle plans to support a portion of the cost to construct a
new, commercial-scale U.S.-based lithium concentrator facility at Albemarle's Kings
Mountain North Carolina location. Albemarle's "mega-flex" conversion facility would
be capable of accommodating multiple feedstocks, including spodumene from the
proposed reopening of the company's hard rock mine in Kings Mountain; its existing
lithium brine resources in Silver Peak, Nevada, and other global resources; as well as
potential recycled lithium materials from existing batteries. The facility is expected to
eventually produce up to 100,000 metric tons of battery-grade lithium per year to
support domestic manufacturing of up to 1.6 million EVs per year.
• A $700 million DOE loan to Ioneer Rhyolite Ridge LLC plans to help develop
domestic processing capabilities of lithium carbonate for nearly 400,000 EV batteries
from the Rhyolite Ridge Lithium-Boron Project in Esmeralda County, Nevada.
• A $2 billion DOE loan to Redwood Materials plans to construct and expand its battery
materials recycling campus in McCarran, Nevada. It would be the first U.S. facility to
support production of anode copper foil and cathode active materials in a fully closed-
loop lithium-ion battery manufacturing process by recycling end-of-life battery and
production scrap and remanufacturing that feedstock into critical materials, supporting
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EV production of more than 1 million per year. Redwood Materials will use both new
and recycled feedstocks—comprised of critical materials like lithium, nickel, and
cobalt—to produce approximately 36,000 metric tons per year of ultra-thin battery-
grade copper foil for use as the anode current collector, and approximately 100,000
metric tons per year of cathode active materials.
• A $375 million DOE loan to Li-Cycle plans to help finance a high efficiency, low-
emission resource recovery facility for batteries in Rochester, New York. The Li-
Cycle project will use hydrometallurgical recycling to efficiently recover battery-grade
lithium carbonate, cobalt sulfate, nickel sulfate, and other critical materials from
manufacturing scrap materials and used batteries to enable a circular economy.
Although currently there is no alternative to lithium in manufacturing automotive BEV
batteries, several alternatives are under development that may provide an alternative, either in
automotive batteries, or in non-automotive applications whose use of these alternatives would
reduce competition for lithium in automotive applications. Citations for these examples may be
found in a Memo to the Docket titled "DOE Communication to EPA Regarding Critical Mineral
Projects."
• BNEF estimates that sodium-ion batteries are scaling for use in applications that do
not require the high-performance capabilities of large EV batteries, including
stationary energy storage and 2- and 3-wheeled vehicles. Substitution from lithium to
alternative chemistries could alleviate price pressures as soon as 2026.
• A new PNNL molten salt battery design, which uses Earth-abundant and low-cost
materials, has demonstrated superior charge/discharge capabilities at lower operating
temperatures while maintaining high energy storage capacity compared to
conventional sodium batteries.
• NASA's Solid-state Architecture Batteries for Enhanced Rechargeability and Safety
(SABERS) research for aerospace applications will likely have spin-off benefits for
the automotive sector. As lithium-ion based liquid electrolytes are not suitable for
aircraft, the development of a scalable, solid-state battery that is safer, more energy
dense, and capable of faster charging has high commercialization potential in on-road
vehicles applications, and can reduce lithium demand.
Finally, a large amount of research and development is taking place to increase circularity and
effective use of lithium and critical minerals. Beyond commercial technologies, continued
research and development with industry and academia through the US Automotive Battery
Consortium (USABC), Critical Minerals Institute (CMI), and ARPA-E will expand the recycling
and recovery of lithium to help expand the use of unconventional supplies to help pace the
growing demand for EVs:
• A $2M USABC grant to American Battery Technology Company (ABTC) in Fernley,
Nevada will help develop a recycling development program to demonstrate a scaled,
fully-domestic, integrated processing cycle for the universal recycling of large format
Li-ion batteries in coordination with partners in the battery supply chain.
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• The CMI's EC-LEACH project successfully demonstrated a lOx scale-up of
electrochemical leaching for lithium-ion batteries black mass, e-waste comprised of
crushed and shredded battery cells, with a capacity up to 500 g/day, achieving over
96% leaching efficiency for all metals. The scale up demonstrated leaching under
higher voltage while maintaining lower currents and used conventional power
electronics.
• $39 million in ARPA-E funding for the Mining Innovations for Negative Emissions
Resource Recovery (MINER) program will help develop market-ready technologies
that will increase domestic supplies of critical elements, including copper, nickel,
lithium, cobalt, rare earth elements, that are required for the clean energy transition.
The MINER program will fund research that increases the mineral yield while
decreasing the required energy, and subsequent emissions, to mine and extract energy-
relevant minerals.
3.2 Proposed Criteria and Toxic Pollutant Emissions Standards for Model Years 2027-
2032
EPA is proposing changes to criteria pollutant emissions standards for both light-duty
vehicles and medium duty vehicles (MDV). Light-duty vehicles include LDV, LDT, and MDPV.
NMOG+NOx changes for light-duty vehicles include a fleet average that declines from 2027-
2032 in the early compliance program (or steps down in 2030 for GVWR > 6,000 lb. in the
default program), the elimination of higher certification bins, a requirement for the same fleet
average emissions standard to be met across four test cycles (25°C FTP, HFET, US06, SC03), a
change from fleet average NMHC standards to one fleet average NMOG+NOx standard in the -
7°C FTP test, and three NMOG+NOx provisions similar to requirements defined by the CARB
Advanced Clean Cars II program. NMOG+NOx. changes for MDV include a fleet average that
declines from 2027-2032 in the early compliance program (or steps down in 2030 in the default
program), the elimination of higher certification bins, a requirement for the same fleet average
emissions standard to be met across four test cycles (25°C FTP, HFET, US06, SC03), and a new
fleet average NMOG+NOx standard in the -7°C FTP. EPA is proposing a requirement for spark
ignition and compression ignition MDV with GCWR above 22,000 lb to comply with engine-
dynamometer-based criteria pollutant emissions standards under the heavy-duty engine program
instead of the chassis-dynamometer-based criteria pollutant emissions standards (88 FR 4296
2023). EPA is proposing to continue light-duty vehicle and MDV fleet average FTP
NMOG+NOx standards that include both ICE-based and zero emission vehicles in a
manufacturer's compliance calculation. Performance-based standards that include both ICE and
zero emission vehicles are consistent with the existing NMOG+NOx program as well as the GHG
program. EPA has considered the availability of battery electric vehicles as a compliance
strategy in determining the appropriate fleet average standards. Given the cost-effectiveness of
BEVs for compliance with both criteria pollutant and GHG standards, EPA anticipates that most
(if not all) automakers will include BEVs in their compliance strategies. However, the standards
continue to be a performance-based fleet average standard with multiple paths to compliance,
depending on choices manufacturers make about deployment of a variety of emissions control
technologies for ICE as well as electrification and credit trading.
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EPA is proposing a PM standard of 0.5 mg/mi for light-duty vehicles and MDV that must be
met across three test cycles (-7°C FTP, 25°C FTP, US06), a requirement for PM certification
tests at the test group level, and a requirement that every in-use vehicle program (IUVP) test
vehicle is tested for PM. The 0.5 mg/mi standard is a per-vehicle cap, not a fleet average.
EPA is proposing CO and formaldehyde (HCHO) emissions requirement changes for light-
duty vehicles and MDVs including transitioning to emissions caps (as opposed to bin-specific
standards) for all emissions standards, a requirement that CO emissions caps be met across four
test cycles (25°C FTP, HFET, US06, SC03), and a CO emissions cap for the -7°C FTP that is the
same for all light-duty vehicles and MDVs.
EPA is proposing a refueling standards change to require incomplete MDVs to have the same
on-board refueling vapor recovery standards as complete MDVs. EPA is also proposing
eliminating commanded enrichment as an AECD for power and component protection.
The proposal allows light-duty vehicle 25°C FTP NMOG+NOx credits and -7°C FTP NMHC
credits (converting to NMOG+NOx credits) to be carried into the new program. It only allows
MDV 25°C FTP NMOG+NOx credits to be carried into the new program if a manufacturer
selects the early compliance pathway. New credits may be generated, banked and traded within
the new program to provide manufacturers with flexibilities in developing compliance strategies.
The proposed phase-in for criteria pollutant standards, including NMOG+NOx, PM, CO,
HCHO, CARB ACC II NMOG+NOx provisions, and elimination of enrichment, is described in
detail within the Preamble to the proposal in § III.C. 1 and is briefly summarized in Table 3-2
below.
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Table 3-2: All combinations of criteria pollutant phase-in scenarios available to
manufacturers3
Model Year
2027
2028
2029
2030+
Model Year
2027
2028
2029
2030+
Model Year
2027
2028
2029
2030+
Model Year
2027
2028
2029
2030+
: 8,500 lb. GVWR
40%
80%
100%
I 00""
: 8,500 lb. GVWRb
40%
80%
100%""
100%
: 8,500 lb. GVWRb
40%
80%
100%
100%
: 8,500 lb. GVWRb
40%
80%
100%
100%
8,501-14,000 lb. GVWR
Chassis Certificationb
40%
80%
100%
100%
8,501-14,000 lb. GVWR
Chassis Certification
0%
0%
0 %
100%
8,501-14,000 lb. GVWR
Chassis Certificationb
40%
80%
100%
100%
8,501-14,000 lb. GVWR
Chassis Certification
0%
0%
0%""
100%
8,501-14,000 lb. GVWR
Engine Certification3
40%
80%
100%
100%
8,501-14,000 lb. GVWR
Engine Certification3
40%
100%
100%
!,501-14,000 lb. GVWR
Engine Certification
0%
0%
0%
100%
!,501-14,000 lb. GVWR
Engine Certification
0%
°%:
0%
100%
Model Year
2027
2028
2029
2030+
Model Year
2027
2028
2029
2030+
Model Year
2027
2028
2029
2030+
Model Year
2027
2028
2029
2030+
: 6,000 lb. GVWR
40%
80%
100%
100%
: 6,000 lb. GVWR
40%
80%
100%
100%
: 6,000 lb. GVWR
40%
80%
100%
100%
: 6,000 lb. GVWR
40%
80%
100%
100%
6,001-8500 lb. GVWR
0%
0%
0%
100%
6,001-8500 lb. GVWR
0%
0%
0%
100%
6,001-8500 lb. GVWR
0%
0%
0%
100%
6,001-8500 lb. GVWR
0%
0%
0%
100%
8,501-14,000 lb. GVWR
Chassis Certificationb
40%
80%
100"..
100%
8,501-14,000 lb. GVWR
Chassis Certification
0%
0%
0%
100%
8,501-14,000 lb. GVWR
Chassis Certificationb
40%
80%
100%
100%
8,501-14,000 lb. GVWR
Chassis Certification
0%
0%
0%
100%
8,501-14,000 lb. GVWR
Engine Certificationb
40%
80%
100"..
100%
8,501-14,000 lb. GVWR
Engine Certificationb
40%
80%
100%
100%""
8,501-14,000 lb. GVWR
Engine Certification
0%
0%
0%
100%
8,501-14,000 lb. GVWR
Engine Certification
0%
0%
0%
100%
Specific applicable phase-in depends upon a manufacturer's decisions regarding default or early compliance for vehicles above 6,000 pounds
GVWR. See § III.C of the Preamble to the proposed rule
b Early compliance.
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3.2.1 Proposed NMOG+NOx standards
EPA is proposing new NMOG+NOx standards for MY2027 and later. The standards are
structured to take into account this increased vehicle electrification that will be occuring over the
next decade.
The current Tier 3 fleet average NMOG+NOx emissions standards were fully phased-in for
Class 2b and Class 3 (structured together as MDV within this proposal) in 2022 at 178 and 247
mg/mi, respectively. Tier 3 standards for light-duty vehicles, including LDT3 and LDT4 trucks
and medium-duty passenger vehicles (MDPVs), will be fully phased into the Tier 3 30 mg/mi
fleet average NMOG+NOx standard in 2025. Tier 3 standards are feasible without vehicle
electrification. In the absence of our proposed NMOG+NOx standards, as sales of PEVs continue
to increase, there would be an opportunity for the remaining ICE portion of light-duty vehicles
and MDVs to reduce emission control system content (i.e., system costs) and comply with less
stringent NMOG+NOx standard bins under Tier 3. If this were to occur, it would have the effect
of increasing NMOG+NOx emissions from the ICE portion of the light-duty vehicle and MDV
fleet and delay the overall fleet emission reductions of NMOG+NOx that would have occurred
from increased penetration of PEVs into the light-duty vehicle and MDV fleets.
The structure of the proposed NMOG+NOx standards has been designed to cap the
NMOG+NOx contribution of ICE vehicles at approximately Tier 3 levels for light-duty vehicles
and at approximately 100 mg/mi NMOG+NOx for MDV.. The feasibility of ICE MDV meeting
100 mg/mi NMOG+NOx by 2027 is discussed in further detail within Chapter 3.2.1.3. The year-
over-year reductions in 2027 and later light-duty and MDV NMOG+NOx standards from an
average of 30 mg/mi and 100 mg/mi, respectively, thus would occur primarily from increased
year-over-year electrification of new vehicle sales and the resulting averaging of zero emission
vehicles with ICE vehicles within the fleet average light-duty and MDV NMOG+NOx standards.
The Clean Air Act (CAA) requires 4 years of lead time and 3 years of standards stability for
heavy-duty vehicles. There are three categories of vehicles that are currently regulated as light-
duty vehicles but are defined within the CAA as heavy-duty vehicles for purposes of lead time
and standards stability: the heavy-light-duty truck categories (LDT3 and LDT4) and MDPV.27
Furthermore, MDVs are also defined as heavy-duty vehicles under the CAA. EPA is proposing
several alternative pathways for these three categories of vehicles for compliance with the
proposed NMOG+NOx standards. The Agency's early compliance NMOG+NOx program would
apply to all LDV, LDT, MDPV, and MDV vehicles beginning in 2027 in order to coincide with
the timing of increased electrification of these vehicles. However, mandatory regulations
beginning in 2027 would not provide 4 years of lead time as required for vehicles defined as
heavy-duty under the CAA. To address this issue, we are proposing two schedules for
compliance with NMOG+NOx standards for LDT3, LDT4, MDPV, and MDV.
The early compliance pathway (Table 3-3) has LDT3, LDT4 and MDPV meeting identical
and gradually declining fleet average NMOG+NOx emissions standards to those for LDV, LDT1
27 Light-duty truck 3 (LDT3) is defined as any truck with more than 6,000 pounds GVWR and with an ALVW of
5,750 pounds or less. Light-duty truck 4 (LDT4) is defined as any truck is defined as any truck with more than 6,000
pounds GVWR and with an ALVW of more than 5,750 pounds. See 40 CFR 86.1803-01 - Definitions. For current
and proposed MDPV definitions, see § III.D of the Preamble to this proposed rule.
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and LDT2 (see §III.C. 1 .iii in the Preamble for the proposed rule).28 It also includes separate,
gradually declining fleet average NMOG+NOx emissions standards for MDV with less than
22,000 pounds GCWR (see §111.C. 1 .iv in the Preamble for the proposed rule). This pathway for
early compliance with NMOG+NOx emissions standards for LDT3, LDT4, MDPV and/or MDV
includes additional flexibilities (see see §III.C.9 in the Preamble for the proposed rule).
The second, and default, NMOG+NOx compliance path (Table 3-4) has LDV, LDT1, and
LDT2 meeting a gradually declining fleet average NMOG+NOx standards from 2027 through
2032. Vehicles in the LDT3, LDT4, and MDPV categories would continue to meet Tier 3
standards through the end of MY 2029 and then would proceed to meeting a 12 mg/mi
NMOG+NOx standard in a single step in MY 2030 in order to comply with CAA provisions for
4 years of lead time and 3 years of standards stability. Similarly, MDVs would continue to meet
Tier 3 standards through the end of MY 2029 and then MDVs with less than 22,000 lb. GCWR
would proceed to meeting a 60 mg/mi NMOG+NOx standard in a single step in 2030 in order to
comply with CAA provisions for 4 years of lead time and 3 years of standards stability.
We are also proposing a similar choice between early compliance and default compliance
pathways for MDVs with high GCWR, which are defined as being at or above 22,000 lb (see
III.C.2 and III.C.5 in the Preamble to the proposed rule). Under the early compliance pathway,
high GCWR MDVs would comply with MY 2027 and later heavy-duty engine criteria pollutant
emissions standards beginning with MY 2027 (see section III.C.5 in the Preamble for the
proposed rule). Manufacturers with high GCWR MDVs choosing the early compliance pathway
would have additional flexibilities with respect to GHG compliance. They could delay entry into
the MDV GHG work factor-based fleet average standards until the beginning of MY 2030 (see §
III.B.3 in the Preamble for the proposed rule).
Under the default compliance path (Table 3-4), high GCWR MDVs would continue to
comply with Tier 3 standards until the end of MY 2029 and then would comply with MY 2027
and later heavy-duty engine criteria pollutant emissions standards beginning with MY 2030 in
order to comply with CAA provisions for 4 years of lead time. Under this default compliance
path, high GCWR MDVs would comply with fleet average MDV GHG emissions beginning
with MY 2027 (see § III.B.3 in the preamble for the proposed rule).
28 Note that the LDV, LDT1 and LDT2 classifications are defined in 40 CFR 86.1803-01 - Definitions.
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Table 3-3: LDV, LDT, MDPV and MDV fleet average, chassis dynamometer FTP
NMOG+NOx standards under the early compliance pathway
Model Year LDV, LDT1, LDT2, MDVt NMOG+NOx (ing/ini)
LDT3t, LDT4t & Class 2b Class 3
MDPVt NMOG+NOx
(ing/iiii)
2026 30* 178* 247*
2027 22 160
2028 20 140
2029 18 120
2030 16 100
2031 14 80
2032 and later 12 60
* Tier 3 standards provided for reference
| NMOG+NOX credit generated under Tier 3 can be carried forward for 5 years after it is generated. MDV
chassis dynamometer NMOG+NOX standards only apply for vehicles under 22.000 pounds GCWR.
Table 3-4: LDV, LDT, MDPV and MDV fleet average, chassis dynamometer FTP
NMOG+NOx standards under the default compliance pathway
Model Year
LDV, LDT1 &
LDT3, LDT4
MDVt NMOG+NOx
LDT2
& MDPV
(ing/iiii)
NMOG+NOx
NMOG+NOx
Class 2b
Class 3
(ing/ini)
(ing/ini)
2026
30*
30*
178*
247*
2027
22
30*
178*
247*
2028
20
30*
178*
247*
2029
18
30*
178*
247*
2030
16
12
60
203 1
14
12
60
2032 and later
12
12
60
* Tier 3 standards provided for reference
| MDV chassis dynamometer NMOG+NOX standards only apply for vehicles under 22.000 pounds GCWR.
3.2.1.1 Proposed NMOG+NOx bin structure for light-duty and MDVs
The propsoed bin structure being proposed for LDV, LDT, MDPV and MDV below 22,000
pounds GCWR is shown in Table 3-6. The upper two bins are only available to MDV.
For LDV, the revised bin structure removes the highest Tier 3 bins (bin 160 and bin 125) and
adds several new bins (bin 60, bin 40, bin 10). For MDV, the revised bin structure moves away
from separate bins for class 2b and class 3 vehicles, adopting LDV bins with higher bins only
available to MDV.
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Table 3-5: Proposed LDV, LDT, MDPV and MDV1^ NMOG+NOx bin structure
LDV bin
Bin 160*
Bin 125*
Bin 70
Bin 60
Bin 50
Bin 40
Bin 30
Bin 20
Bin 10
BinO
NMOG+NOx (mg/mi)
160
125
70
60
50
40
30
20
10
0
3.2.1.2 Light-duty NMOG+NOx standards and test cycles
EPA is proposing increasingly stringent light-duty vehicle NMOG+NOx standards (Table )
for the sales weighted average inclusive of all LDV, LDT and MDPV (e.g, ICE vehicles, BEVs,
PHEVs, fuel cell, vehicles, etc.). (Table 3-7). For a detailed description of the proposed phase-in
of the standards by vehicle category, please refer to § III.C.l in the Preamble to the proposed
rule.
EPA recognizes that vehicles will differ with respect to their levels of NMOG+NOx emissions
control depending on degree of electrification, choice of fuel, ICE technology, and other
differences. The proposed fleet average standards are feasible in light of anticipated technology
penetration rates commensurate with the GHG technology implementation during this same time
period and increasing electrification of light-duty vehicles. The declining fleet average standards
over the FTP cycle ensure that NMOG+NOx continues to decrease over time for the light-duty
fleet. The elimination of the two highest bins (Table 3-7) caps the maximum NMOG+NOx
emissions from an individual new vehicle model. EPA anticipates that electrified technology,
including BEVs, will play a significant role within the compliance strategies for meeting the fleet
average NMOG+NOx standards for each manufacturer. However, EPA anticipates that
manufacturers may use multiple technology solutions to comply with fleet average NMOG+NOx
standards. For example, a manufacturer may choose to offset any ICE increases with increased
BEV sales, or could alternatively improve engine and exhaust aftertreatment designs to reduce
emissions for ICE vehicles while planning for a more conservative percentage of BEV sales as
part of their compliance with the declining fleet average NMOG+NOx standards (Table 3-8).
Since technologies are available to further reduce NMOG+NOx emissions relative to the
current fleet, and since more than 20 percent of MY 2021 Bin 30 vehicle certifications already
show an FTP certification value under 15 mg/mi NMOG+NOx, achieving reduced NMOG+NOx
emissions through improved ICE technologies is feasible and reasonable (see Chapter 3.2.1.5.
Regardless of the compliance strategy chosen, overall, the fleet will become significantly
cleaner.
EPA is proposing that the same bin-specific numerical standards be applied across four test
cycles: 25°C FTP (40 CFR 1066.801 (c)(l)(i) 2023) (40 CFR 1066.815 2023), HFET (40 CFR
1066.840 2023), US06 (40 CFR 1066.831 2023) and SC03 (40 CFR 1066.835 2023). This means
that a manufacturer certifying a vehicle to comply with Bin 30 NMOG+NOx standards would be
required to meet the Bin 30 emissions standards for all four test cycles. Meeting the same
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NMOG+NOx standards across four cycles is an increase in stringency from Tier 3, which had
one standard for the higher of FTP and HFET, and a less stringent composite based standard for
the SFTP (weighted average of 0.35*FTP + 0.28*US06 + 0.37*SC03).
Present-day engine, transmission, and exhaust aftertreatment control technologies allow
closed-loop air-to-fuel (A/F) ratio control and good exhaust catalyst performance throughout the
US06 and SC03 cycles. As a result, higher emissions standards over these cycles are no longer
necessary. Approximately 60 percent of the test group / vehicle model certifications from MY
2021 have higher NMOG+NOx emissions over the FTP cycle as compared to the US06 cycle,
supporting the conclusion that a single standard is feasible and appropriate.
EPA is proposing to replace the existing -7°C FTP NMHC fleet average standard of 300
mg/mi for passenger cars and LDT1, and 500 mg/mi fleet average standard for LDT2 through
LDT4 and MDPV, with a single NMOG+NOx fleet average standard of 300 mg/mi for LDV,
LDT1 through 4 and MDPVs to harmonize with the combined NMOG+NOx approach adopted in
Tier 3 for all other cycles (i.e., 25°C FTP, HFET, US06, and SC03 cycles). EPA emissions
testing at -7°C FTP showed that a 300 mg/mi standard is feasible with a large compliance margin
for NMOG+NOx. EPA testing of a 2019 F150 5.0L, a 2021 Corolla 2.0L, and a 2021 F150 HEV
at -7°C FTP showed that a 300 mg/mi standard could be met with a large compliance margin for
both NMHC and NMOG+NOx. For example, NMOG+NOx was 189+25, 124+3, and 47+70 for
a 2019 F150 5.0L, a 2021 Corolla 2.0L, and a 2021 F150 HEV, respectively. EPA did not
include EVs in the assessment of the proposed fleet average standard and therefore EVs and
other zero emission vehicles are not included and not averaged into the fleet average -7°C FTP
NMOG+NOx standards. Since -7°C FTP and 25°C FTP are both cold soak tests that include
TWC operation during light-off and at operating temperature, it is appropriate to apply the same
Tier 3 useful life to both standards.
The proposed standards apply equally at high altitude, rather than including compliance relief
provisions from Tier 3 for certification at high altitude. Modern engine management systems can
use idle speed, engine spark timing, valve timing, and other controls to offset the effect of lower
air density on exhaust catalyst performance at high altitudes.
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Table 3-6: LDV, LDT* and MDPV NMOG+NOx NMOG+NOx fleet average FTP
standards
Model Year NMOG+NOx (mg/mi)
2027 22 |
2028 20
2029 18
2030 16
2031 14
2032 12
; * Manufacturers choosing the early compliance pathway
Table 3-7: LDV, LDT* and MDPV* NMOG+NOx fleet average FTP standards
Model Year LDV. LDT1 & LDT2 LDT3. LDT4 & MDPV
NMOG+NOx (mg/mi) NMOG+NOx (mg/mi)
2026 30** 30**
2027 22 30**
2028 20 30**
2029 18 30**
2030 16 12
2031 14 12
2032 and later 12 12
* Manufacturers choosing the default compliance pathway
** Tier 3 standards provided for reference
3.2.1.3 NMOG+NOx Standards for MDV at or below 22,000 lb GCWR
The proposed MDV (medium duty vehicles, 8,501 to 14,000 lb. GVWR) NMOG+NOx
standards for vehicles under 22,000 lb. GCWR are shown in Table 3-8 and Table 3-9 for the
early compliance and default compliance pathways, respectively. Certification data show that for
MY 2022-2023, 75 percent of sales-weighted Class 2b/3 gasoline vehicle certifications were
below 120 mg/mi in FTP and US06 tests. Diesel-powered MDVs designed for high towing
capability (i.e., GCWR over 22,000 lb.) were higher (75 percent were below 180 mg/mi) but they
are not being used to inform the proposed MDV standard because the Agency is proposing the
requirement that MDVs (diesel and gasoline) with GCWR (gross combined weight rating) above
22,000 lb. comply with criteria pollutant emissions standards under the HD engine program.29 As
described in Chapter 3.2.1.5, MDVs with GCWR below 22,000 lb. have comparable emissions
performance to LDVs and LDTs. The year-over-year fleet average FTP standards for MDV
below 22,000 lb. GCWR and the rationale for the manufacturer's choice of early compliance and
default compliance pathways is described in Section III.C.l. For further discussion of MDV
NMOG+NOx feasibility, please refer to Chapter 3.2.1.5.
The proposed MDV NMOG+NOx standards are based on applying existing light-duty vehicle
technologies, including electrification, to MDV. As with the light-duty vehicle categories, EPA
anticipates that there will be multiple compliance pathways, such as increased electrification of
29 See § III.C.5 of the Preamble to this proposed rule.
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vans together with achieving 100 mg/mile NMOG+NOx for ICE-power MDV. Present-day
MDV engine and aftertreatment technology allows fast catalyst light-off after cold-start followed
by closed-loop A/F control and excellent exhaust catalyst emission control on MDV, even at the
adjusted loaded vehicle weight, ALVW [(curb + GVWR)/2] test weight, which is higher than
loaded vehicle weight, LVW (curb + 300 lb.) used for testing light-duty vehicles. The proposed
MDV standards begin to take effect in 2030, consistent with the CAA section 202(a)(3)(C) lead
time requirement for these vehicles.
Table 3-8: MDV fleet average NMOG+NOx standards under the early compliance
pathwayt
Model Year NMOG+NOx (mg/mi)
Class 2b Class 3
2026 178* 247*
2027 160
2028 140
2029 120
2030 100
2031 80
2032 and later 60
| Please refer to § III.C. 1 of the Preamble to the propsed rule for further discussion of the
early compliance and default compliance pathways
* Tier 3 standards provided for reference
Table 3-9: MDV fleet average chassis dynamometer FTP NMOG+NOx standards under
the default compliance pathway*
Model Year MDVt NMOG+NOx (mg/mi)
Class 2b Class 3
2026 178** 247**
2027 178** 247**
2028 178** 247**
2029 178** 247**
2030 60
2031 60
2032 and later 60
I * Please refer to § III.C. 1 of the Preamble to the propsed rule for further discussion of the early
: compliance and default compliance pathways
I ** Tier 3 standards provided for reference
I t MDV chassis dynamometer NMOG+NOx standards only apply for vehicles under 22,000 lb.
| GCWR.
If a manufacturer has a fleet mix with relatively high sales of MDV BEV, that would ease
compliance with MDV NMOG+NOx fleet average standards for MDV ICE-powered vehicles. If
the manufacturer has a fleet mix with relatively low BEV sales, then improvements in
NMOG+NOx emissions control for ICE-powered vehicles would be required to meet the fleet
average standards. Improvements to NMOG+NOx emissions from ICE-powered vehicles are
feasible with available engine, aftertreatment, and sensor technology, and has been shown within
an analysis of MY 2022-2023 MDV certification data (see Chapter 3.2.1.5). Fleet average
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NMOG+NOx will continue to decline to well below the final Tier 3 NMOG+NOx standards of
178 mg/mi and 247 mg/mi for Class 2b and 3 vehicles, respectively.
The proposed standards require the same MDV numerical standards be met across all four test
cycles, the 25°C FTP, HFET, US06 and SC03, consistent with the proposed approach for light-
duty vehicles described in Section IILC.l.ii. This would mean that a manufacturer certifying a
vehicle to bin 60 would be required to meet the bin 60 emissions standards for all four cycles.
Meeting the same NMOG+NOx standard across four cycles is an increase in stringency from
Tier 3, which had one standard over the FTP and less stringent bin standards for the HD-SFTP
(weighted average of 0.35*FTP + 0.28*HDSIM + 0.37*SC03, where HDSIM is the driving
schedule specified in 40 CFR 86.1816-18(b)(l)(ii)). Current MDV control technologies allow
closed-loop A/F control and high exhaust catalyst emissions conversion throughout the US06
and SC03 cycles, so compliance with higher numerical emissions standards over these cycles is
no longer needed. Manufacturer submitted certification data and EPA testing show that Tier 3
MDV typically have similar NMOG+NOx emissions in US06 and 25°C FTP cycles, and
NMOG+NOx from the SC03 is typically much lower. Testing of a 2022 F250 7.3L at EPA
showed average NMOG+NOx emissions of 56 mg/mi in the 25°C FTP and 48 mg/mi in the
US06. Manufacturer-submitted certifications show that MY 2021+2022 gasoline 2b/3 trucks
achieved, on average, 69/87 mg/mi in the FTP, and 75/NA mg/mi in the US06, and 18/25 mg/mi
in the SC03.
Several Tier 3 provisions would end with the elimination of the HD-SFTP and the combining
of bins for Class 2b and class 3 vehicles. First, Class 2b vehicles with power-to-weight ratios at
or below 0.024 hp/lb. could no longer replace the full US06 component of the SFTP with the
second of three sampling bags from the US06. Second, Class 3 vehicles would no longer use the
LA-92 cycle in the HD-SFTP calculation but would rather have to meet the NMOG+NOx
standard in each of four test cycles (25°C FTP, HFET, US06 and SC03). Third, the SC03 could
no longer be replaced with the FTP in the SFTP calculation.
The proposed standards do not include relief provisions for MDV certification at high
altitude. Modern engine systems can use idle speed, engine spark timing, valve timing, and other
controls to offset the effect of lower air density on catalyst light-off at high altitudes.
EPA is also proposing a new -7°C FTP NMOG+NOx fleet average standard of 300 mg/mi for
gasoline and diesel MDV. EPA testing has demonstrated the feasibility of a single fleet average -
7°C FTP NMOG+NOx standard of 300 mg/mi across light-duty vehicles and MDV. EPA did not
include EV's in the assessment of the proposed fleet average standard and therefore EVs and
other zero emission vehicles are not included and not averaged into the fleet average -7°C FTP
NMOG+NOx standards. Since -7°C FTP and 25°C FTP are both cold soak tests that include
TWC operation during light-off and at operating temperature, it is appropriate to apply the same
Tier 3 useful life to both standards. Additional discussion on the feasibility of the proposed
standards can be found in Chapter 3.2.4.
3.2.2 Proposed PM standards for light-duty and MDV at or below 22.000 pounds
GCWR
Details of the proposed PM standards, including test cycles used for compliance, phase-in, the
certification process, demonstration of in-use compliance, and OBD are discussed in further
detail in § III.C.3 of the Preamble to the proposed rule. Details regarding PM emissions control
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feasibility and GPFs are summarized in Chapter 3.2.5. For reference, the proposed light-duty
PM standards are shown in Table 3-10; and PM standards for MDV at or below 22,000 pounds
GVWR are shown in Table 3-11.
Table 3-10: Propsed light-duty PM standards
Test Cycle Tier 3 Standards (mg/mi) Proposed PM Standard (mg/mi)
25°C FTP 3 0.5
US06 6 0.5
-7°C FTP Not applicable 0.5
Table 3-11: Proposed PM standards for MDV at or below 22,000 pounds GCWR
Test Cycle Tier 3 Standards (mg/mi) Proposed PM Standard (mg/mi)
25°C FTP 8 (Class 2b) 0.5
10 (Class 3)
US06 10 (Class 2b) over SFTP 0.5
7 (Class 3) over SFTP
-7°C FTP Not applicable 0.5
3.2.3 Proposed CO and formaldehyde (HCHO) standards
A detailed description of EPA's proposed CO and formaldehyde (HCHO) standards can be
found in § III.C.4 of the Preamble to the proposed rule. For reference, the proposed light-duty
standards are shown in Table 3-12; and standards for MDV at or below 22,000 pounds GVWR
are shown in Table 3-13.
Table 3-12: Light-duty CO and HCHO standards
CO cap for 25°C FTP. HFET. US06. SC03 (g/mi) 1.7
HCHO cap for 25°C FTP (mg/mi) 4
CO cap for -7°C FTP (g/mi) 10.0
Table 3-13: CO and HCHO standards for MDV at or below 22,000 pounds GCWR
CO cap for 25°C FTP. HFET. US06. SC03 (g/mi) 3.2
HCHO cap for 25°C FTP (mg/mi) 6
CO cap for -7°C FTP (g/mi) 10.0
3.2.4 Current ICE-based vehicle NMOG+ NOx emissions
At the time of this proposal Tier 3 emissions standards for light-duty vehicles have not yet
fully phased-in. The current Tier 3 standards will be fully phased-in by MY 2025 and will result
in a fleet average standard for passenger cars and light trucks of 30 mg/mi FTP NMOG+NOx.
This means on average, the light-duty fleet will be certified to Tier 3 Bin 30 in MY 2025. While
the declining FTP NMOG+NOx fleet average in this proposal is fully feasible with the
introduction of zero emission vehicles such as BEV's, continued reductions in ICE-based vehicle
emissions could provide an alternative pathway to compliance or at a minimum offset the
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number of ZEV's a vehicle manufacturer may require to produce to meet the standards. EPA
reviewed the MY 2021 test car data (U.S. EPA 2022b) and identified nineteen vehicles with FTP
emissions performance data currently below 15 mg/mi, two of which are at or below 10 mg/mi
(Table 3-14).
Table 3-14: Examples of NMOG+NOx cert emissions
Manufacturer Model Certified
NMOG
NOX
(g/mi)
Vehicles Certified al 10 mg/mi or less
Audi
Q3
0.008
Hyundai
Sonata Hybrid
0.01
Vehicles Certified al less 15 mg/mi
BMW
X3 xDrive30e
>014
BMW
X5 xDrive45e
0.011
BMW Mini
John Cooper Works Conv
0.014
GMC
Terrain AWD
0.013
Buick
Encore AWD
0.012
Honda
CRVAWD *2
o.o i:
Hyundai
Tuscon
0.012
Jaguar
Range Rover Sport
0.012
Kia
Soul
o.o n
Kia
Forte 5
0.011
Nissan
Sentra SR
0.012
Suharu
Outback
0.014
Lexus
NX 300h AWD ;
0.012
Lexus
UX 200 | j
O.O14
Toyola
Corolla XSE
0.013
Volkswagen
Tiguan AWD
0.014
Volkswagen
JettaGLI
0.012
The Agency also analyzed emissions certification data MY 2022 and MY 2023 MDV
emissions families. The emissions family certification data are graphically represented in Figure
3-11 for gasoline and diesel MDV vans and pickups using a "box-and-whisker" plot (Frigge,
Hoaglin and Iglewicz 1989) (Tukey 1977) (Benjamini 1988). The upper and lower boxes
correspond to the first and third quartiles (the 25th and 75th percentiles), respectively, of the
NMOG+NOx emissions data for each MDV category. The horizontal line between each set of
upper and lower boxes represents median emissions and the "x" represents mean emissions. The
upper vertical line or "whisker" extends from the median to the highest value that is within 1.5X
inner quartile range (IQR) of the median, where IQR is the distance between the first and third
quartiles. The lower "whisker" extends from the median to the lowest value within 1.5X IQR of
the median. A certification emissions data point was considered an outlier if it exceeded a
distance of 1.5 times the IQR below the 1 st quartile or 1.5 times the IQR above the 3rd quartile
and is represented as a "dot" in the "box-and-whisker" plot. The analysis found significant
compliance headroom for MDVs below the current Tier 3 NMOG+NOx emissions standards for
Class 2b and Class 3 MDVs, with median NMOG+NOx emissions of approximately 100 mg/mi
for gasoline pickups, approximately 80 mg/mi for gasoline vans, and approximately 130 mg/mi
for diesel vans. Median emissions for diesel pickups were approximately 170 mg/mi, however all
MY2022 and 2023 diesel pickups were above the 22,000 pound threshold for the proposed MDV
NMOG+NOx standards and would instead need to comply with 2027 and later heavy-duty
emissions standards, with use of engine-dynamometer regulatory cycles for demonstrating
compliance.
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0
Diesel
Gasoline
N V
¦ Gasoline Vans n Gasoline Pickups E Diesel Vans E Diesel Pickups
Figure 3-11: MY2022-2023 MDV box and whisker plot showing the
interquartile range of certification NMOG+NOx data
EPA recognizes that compliance headroom is a concern for vehicle manufacturers. Vehicle
manufacturing variation, test to test variations, and test location variables all contribute to a
manufacturer's desire to have 40 to 50 percent compliance headroom when submitting data and
vehicles to EPA for certification. However, given the low emissions performance demonstrated
by current MY 2021 LD vehicles and MY2022 and MY2023 MDVs, EPA believes that
manufacturers will be able to utilize the lower bins proposed in this NPRM and maintain their
target compliance headroom. Certification of ICE-based vehicles to the lower bins in
combination with the introduction of an increasing number of PEVs into the fleet average
provides a feasible compliance pathway to meet the proposed declining FTP NMOG+NOx fleet
averages for both LD vehicles and MDVs.
3.2.4.1 Current ICE Emissions at -7°C FTP
Table 3-15: Light-Duty Gasoline Vehicles - 7C FTP Emissions (mg/mi)
Engine
Vehicle Class
NMOG
NOx
NMOG+NOx
3.9L Ferrari
LDV
154.1
53.1
207.2
6.3L Ferrari
LDV
220.2
38
258.2
Table 3-16: Light-Duty Diesel Vehicles -7C FTP Emissions
Engine
Vehicle Class
NMOG
NOx
NMOG+NOx
2.8L GM
LDT2/3
45
134
179
3.0L Ram
LDT3/4
58
229
287
3.0L GM
LDT3/4
80
134
214
1.5L Ford
LDT1/2
14
33
47
average
182
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3.2.4.2 Feasibility of a single numerical standard for FTP, HFET, SC03 and US06
Table 3-15 below provides a comparison of FTP, HWFE, SC03 and US06 test results for
several vehicles that represent a broad spectrum of vehicle types and conventional powertrain
technologies. For most of the vehicles identified the FTP results are higher than the HWFE,
SC03 and US06 test results showing that a single standard is feasible and already being met by
some manufacturers. There are several examples where SC03 or US06 results are higher than the
FTP results. The data shows that the FTP and the US06 are the most stringent standards because
the of the FTP cold start and the US06 because of higher power requirements and potential
enrichment. The HWFE and SC03 cycles are less stringent due to the lack of cold start and lower
power demands.
Table 3-17: Comparison of FTP, HFET, SC03, US06 cert test results for LD vehicles
Manufacturer Reported NMOG+NOx Values
Manufacturer
Vehicle
FTP (g/mi)
HWFE (g/mi)
SC03 (g/mi)
US06 (g/mi)
BMW
X4 xDrive 30i
0.02
0.008
0.008
0.014
BMW
13 s REX
0.014
0.02
0.012
0.011
BMW
540i xDrive
0.036
0.02
0.031
0.029
Ford
Corsair
0.035
0.009
0.09
0.03
Ford
Ranger
0.052
0.033
0.05
0.09
Ford
Explorer
0.038
0.025
0.03
0.03
Ford
F150
0.026
0.014
0.017
0.041
GM
Terrain
0.013
0.001
0.011
0.005
GM/Cadillac
XT6
0.026
0.002
0.008
0.005
GMC
K10 Sierra 4WD
0.026
0.005
0.014
0.008
Hyundai
Genesis
0.038
0.014
0.013
0.056
Hyundai
Elantra
0.037
0.015
0.028
0.072
Kia
Sportage
0.036
0.017
0.036
0.024
Kia
Sorento
0.032
0.016
0.03
0.039
Nissan
Altima
0.015
0.006
0.019
0.017
Porsche
Cayenne Turbo
0.072
0.034
0.05
0.05
Volkswagen
Audi Q3
0.008
0.002
0.009
0.012
Volkswagen
Tiguan AWD
0.017
0.002
0.009
0.008
Volkswagen
Jetta GLI
0.017
0.003
0.016
0.009
Average
0.029
0.013
0.025
0.030
As the result of this proposed change, EPA expects light-duty vehicles to have lower
emissions over a broader area of vehicle operation. Present-day engine, transmission, auxiliary
and aftertreatment control technologies allow closed-loop A/F control and good emissions
conversion throughout the HWFE, US06 and SC03 cycles; as a result, higher emissions
standards over these cycles are no longer justified. Overall, approximately 60 percent of the of
test group / vehicle model certifications from MY 2021 have higher NMOG+NOx emissions in
the FTP as compared to the US06, supporting the conclusion that a single standard is feasible
and appropriate.
3.2.4.3 Off-Cycle emission controls
When the agency proposed and subsequently finalized the SFTP standards in 1996, with
phase-in beginning with MY 2000 (61 FR 54852 1996), the agency acknowledged a potential
need for unique operation related to high loads and speeds that would typically result in
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increased emissions from SI engines. This acknowledgement was reflected in both the standard
levels set for the US06 test cycle but also in accompanying AECD language indicating
allowances for control features that deviate from behaviors demonstrated over the test cycles.
These allowances are specific to an operating mode in SI engines called enrichment when the
control system changes the A/F ratio such that more fuel than air is commanded in an attempt to
either make additional power or to lower the exhaust gas temperatures. Unfortunately, during
these enrichment episodes, it is difficult to maintain effective control of HC, CO, PM and NOx.
Engines operate almost like they have no exhaust emission controls, particularly in the case of
HC/NMOG, CO, PM and air toxic emissions, and largely engine out emission levels are
exhausted without the catalyst largely performing any effective reduction in the high engine out
emission levels. In fact, studies suggest that during these enrichment episodes, substantial
increases in PM, ammonia and air toxic emissions have been observed.
At the time of the development of the SFTP FRM, the technology level of vehicle controls
and hardware was very different from today. The operator generally was in full control of the
engine and transmission areas operation because engines possessed very little engine speed and
load controlling or limiting operation, and most transmissions were either hydraulically
controlled automatic transmissions responding to mechanical parameter inputs or manual
transmissions responding to driver decisions on gear selection or clutch engagement. At that
time, throttles still used direct mechanical connection to an accelerator pedal. In MY 1996, most
automatic transmissions had four forward gear ratios.
Since that time, the evolution of powertrain control technology has resulted in full control of
almost every aspect of engine and transmission control via complex and precise electronic
software, feedback sensors and other hardware. Every new vehicle today has incorporated
electronic throttle control that allows the electronic engine management system to control the
throttle with the operator simply "requesting" an engine power level but ultimately the
electronics decide how to safely operate the engine and in the case of automatic transmissions,
which gear to select within the transmission.
These technological advancements have also improved vehicle safety by electronically
limiting acceleration; limiting top speed; and by implementing traction control, antilock braking
systems and vehicle stability control. Electronic powertrain management has also been used
extensively by all auto manufacturers to protect engines and drivelines from excessive torque or
RPM that could potentially damage drivetrain components. Many manufacturers use "torque
limiting" controls to improve durability of various hardware components and systems.
Automatic transmissions have also similarly evolved to allow precise electronic control of
gear selection, shift points, torque converter lockup and other operational parameters. By 2021,
most transmissions have more than seven gear rations (see Chapter 3.1.1) with both a wider
range of ratios and smaller steps between ratios than the previous four and five speed automatic
transmissions of two decades ago. Some of these control improvements are related to
expectations by the driver/customer regarding shift quality, powertrain noise, and other drive-
quality attributes. However, these controls and hardware designs have also resulted in improved
acceleration performance, improved fuel economy and lower GHG emissions made possible via
multi-gear (more than 7 forward gears) transmissions and related complimentary engine and
electronic transmission controls that optimize and capitalize on the synergies of the engine and
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transmission as a system. Additionally, many of the few remaining manual transmissions have
been replaced with electronically controlled, automated dual-clutch transmissions (DCT).
Modern engines have also added technologies such as VVT, cylinder deactivation, turbo
boost control and other technologies that effectively allow the manufacturer designed controls,
when used in conjunction with electronic throttle and transmission control to operate the engine
in nearly any manner they determine to be optimal for the customer and manufacturer drivability
expectations and durability goals.
The agency believes that these same technologies, only available in recent years, could also
be used for limiting operation in areas described previously as requiring enrichment that result in
substantial increases in emission levels in normal operation including high acceleration rates,
high loads. The reasons for the original allowances for enrichment discussed in the SFTP FRM
can easily be addressed in modern engine and transmissions by utilizing existing controls to limit
or avoid operation in areas that require enrichment for any normal vehicle operation. Vehicles
can "drive" through these areas but quickly exit by changing the engine airflow control, ignition
timing, valvetrain settings, speed, or other parameters that would avoid this unnecessary increase
in emissions.
This is consistent with strategies used for other purposes including durability, customer
drivability issues and even performance features such as short durations of overboost for extra
horsepower and gear hold on grades, etc. Manufacturers have also implemented controls that
limit the engine and transmission operating range during initial break-in periods ( (Streeter
2021)) and also during high coolant temperatures or coolant loss. Limiting or controlling areas of
engine operation using electronic powertrain controls is common for many manufacturer goals,
with the exception of limiting criteria pollutant emissions increases unless explicitly required to
by emissions regulations.
The agency has required a similar concept in heavy-duty diesel engines to limit emission
increases. Diesel engines are required to go into modes that restrict engine output when the
operator does not have DEF30 available in the storage tank required for the SCR system to
control emissions. The operator might request more acceleration or power but the controls will
limit the speed and loads allowed to be put on the engine in order to limit emission increases.
Another agency requirement for diesel emission control designs that have occasional but
irregular emission increases, similar to the discussion above regarding enrichment episodes in SI
engines, is the infrequent regeneration adjustment factor (IRAF). Because the design of the
diesel emission control requires occasional increases in emissions, the agency has required
manufacturers to quantify that increase and adjust the emission compliance levels to account for
those design-based increases. The SI engine design decisions for hardware and controls also
directly influence the degree to which emission increases will occur for the purposes of
temperature protection and power. The agency could consider the IRAF approach to also apply
30 Diesel emissions fluid (or DEF) is aqueous urea injected into the exhaust as a reductant for selective catalytic
reduction (SCR) of NOx emissions. SCR is used for NOx emissions control in diesel engines and other engines
using net lean combustion strategies.
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to SI emission increases in real world operation and require a similar adjustment to the
compliance level. A similar discussion was included in the HD2027 rule for SI engines.
It is important to note that with the introduction and expanded use of gas particulate filters,
the agency will propose a similar adjustment as the diesel IRAF for any increase in emissions
related to similar regeneration strategies.
The regulations of 40 CFR §86.1809 prohibit the use of strategies that unnecessarily reduce
emission control effectiveness exhibited during the Federal or Supplemental Federal emissions
test procedures (FTP or SFTP) when the vehicle is operated under conditions which may
reasonably be expected to be encountered in normal operation and use. Unless the need for the
strategy or Auxiliary Emission Control Device (AECD) is justified in terms of protecting the
vehicle against damage or accident (ref.40 CFR §86.1803-01
Most vehicles today incorporate AECDs which utilize enrichment (i.e., commanding air/fuel
ratio less than the stoichiometric air/fuel ratio) for the purpose of protecting components in the
exhaust system from thermal damage during normal operation and use. EPA considers normal
operation and use to include all operation within the vehicles design parameters for example:
driving at sustained high speeds, maximum acceleration at wide open throttle, operating at the
max gross vehicle weight rating, trailer towing within the rated trailer tow limits. Normal
operation and use does not include conditions of component failure or engine overheating
protection mode where the check engine light or other warning systems are active. EPA is also
aware that some vehicles incorporate similar strategies for the purpose of increasing the power
output of the engine and such strategies significantly reduce the effectiveness of three-way
catalytic converters, which require the exhaust gas composition to be precisely controlled via
engine operation near the stoichiometric air-to-fuel ratio.
Technologies exist today which can prevent thermal damage of exhaust system components
without the use of commanded enrichment during normal operation and use, and modern
vehicles have sufficient power without the use of commanded enrichment. The use of
commanded enrichment only has the potential to increase power by approximately 5 percent on a
naturally aspirated engine but significantly reduces the effectiveness of three-way catalytic
converter systems, resulting in increases of NMOG, CO and air toxics, in some cases by orders
of magnitude. Even for particularly challenging operating conditions, for example sustained
high-load conditions that may be encountered by highly loaded vehicles or vehicles towing
heavy trailer loads, measures can be taken via both the engine management system and within
the design of powertrain components to allow operation closer to a stoichiometric air-to-fuel
ratio. Specific examples include reducing torque demand via electronic throttle control, changing
electronic transmission shift control, and improvements to the cooling system , exhaust valve
materials and exhaust system component design. Analyses of the impacts of operating with and
without power enrichment were conducted as part of the Regulatory Impact Analysis (U.S. EPA
2022)31 for the recently finalized 2027 and later heavy-duty vehicle and engine standards (88 FR
4296 2023). As summarized within Chapter 3.2.2.2 of the HD-RIA, EPA conducted testing of a
light-heavy-duty gasoline spark-ignition engine over the Heavy-duty Supplemental Emissions
Test (SET) (Title 40 CFR § 600.311-12 2021), which includes sustained high-load operation.
Power and torque results for this testing are shown in Table 3-15 for the SET A, B and C
31 This will be referred to as the "HD-RIA" to differentiate from the RIA for this proposal.
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setpoints. Sustained operation at near-stoichiometric air-to-fuel ratio conditions during testing
over the SET resulted in power that was approximately 5 percent less and torque that was
approximately 4 percent less when compared to allowing power enrichment.
Table 3-18: SET Operation Mode Power Comparison
Power (kW) Torque (Nm)
SET Set Points* SET Set Points*
A
B
C
A
B
C
Power Enrichment Allowed
211
187
145
546
572
547
Enrichment for Catalyst Protection with
211
182
141
542
554
524
No Pow er Enrichment
Stoichiometric Operation. Catalyst
201
179
137
522
551
526
Protection via Load Reduction
; * The A, B and C engine speeds are setpoints defined within the SET procedures (Title 40 CFR § 600.311-12 2021).
Contract work conducted by Southwest Research Institute for EPA using a modern, 6.4L
heavy-duty gasoline engine32 demonstrated the use of close-coupled exhaust catalysts and a
combination of down-speeding and near-stoichiometric operation that achieved adequate
component protection for the catalyst, low NMHC and NOx emissions, and reduced GHG
emissions (Southwest Research Institute 2022).
EPA is proposing in this rulemaking to eliminate the allowance of the use of commanded
enrichment as an AECD for either power enrichment or component protection during normal
operation and use with exceptions for conditions of imminent component failure or engine
overheating protection modes specifically where the check engine light, MIL, or other warning
systems are triggered.
Additionally, EPA is proposing that vehicle emission control strategies used for both gasoline
and diesel vehicles and demonstrated over the emission test cycles also perform at similar
effectiveness levels over normal vehicle operation. This includes operation at higher legal speeds
observed on public roads but also under loaded conditions that vehicles are designed and
advertised to perform for consumers. If a vehicle is designed to carry high loads or tow trailers
by the manufacturer and such operation does not conflict with manufacturer's recommendations
and/or does not potentially void warranty coverage, that operation is considered normal vehicle
operation for purposes of expectation of similar emission control system design effectiveness.
3.2.5 Particulate Matter Emissions Control
The proposed PM standard and phase-in are presented in Preamble Section III.C.3. An
overview of GPF technology is provided in Chapter 3.2.2.1. GPF benefits are introduced in
Chapter 3.2.2.2. The importance of the three PM certification test cycles is described in Chapter
3.2.2.3. A demonstration of the feasibility of the PM standard for light-duty vehicles and MDVs
is provided in Chapter 3.2.2.4. Finally, GPF cost is discussed in Chapter 3.2.2.5.
32 Note that this 6.4L heavy-duty gasoline engine used in RAM Class 4 applications shares an engine family with
engines used in RAM MDV pickup truck applications.
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3.2.5.1 Overview of GPF technology
Gasoline particulate filter (GPF) technology is not new. It has been used in series production
on all new pure GDI vehicle models (type approvals) in Europe since 2017 (WLTC and RDE test
cycles) and on all pure GDI vehicles in Europe since 2019 (WLTC and RDE test cycles) to meet
a 6x101! #/'km solid particle number (PN) standard. All gasoline vehicles in China have had to
meet the same 6xlOu #/km solid PN limit in the WLTC test since 2020, and in the WLTC and
RDE starting in 2023. In India, BS6 stage 2 requires gasoline vehicles to also meet the 6xlOn
#/km solid PN limit in the MIDC (Indian version of NEDC) and RDE starting in April 2023.
U.S., European, and Asian manufacturers have extensive experience with applying GPF
technology to series production vehicles and several manufacturers assemble vehicles with GPF
in the U.S. for export to other markets.
GPFs being used in Europe and Asia and expected to be used in the U.S. to meet the proposed
0.5 mg/mi PM standard across 25°C FTP, US06, and -7°C FTP cycles, use a ceramic honeycomb
structure with alternating channels plugged at their inlet and outlet ends (Figure 3-12). GPFs use
Cordierite for its low coefficient of thermal expansion and thermal shock tolerance. GPF
substrates typically have 45-65 percent porosity, 10-25 |im median pore size, 6-12 mil (1 mil =
1/1000 inch) wall thickness, and 200-300 cpsi (cells per square inch) cell density. GPF substrates
can be manufactured in various diameters, lengths, and shapes (e.g., round or oval).
Wall flow filters allow exhaust gases to flow through porous filter walls while particulates are
captured in or on the wall (Figure 3-12). Gasoline engine-out particulates (typically from <10 to
300 tim) are smaller than GPF mean pore size (typically 10-25 (im), but particles are captured at
high filtration efficiencies across the engine-out size range by Brownian diffusion (small
particles), interception (intermediate particles), and inertial impaction (large particles).
Diagram courtesy of Coming
Figure 3-12: Wall-flow GPF design.
A clean GPF initially captures particulates within its pore structure (depth filtration mode); at
high levels of soot loading, additional particulates form a soot layer (soot cake) on the top of the
wall (soot cake or surface filtration mode). Filtration efficiency improves rapidly with initial soot
and ash loading (Lambert, et al. 2017), then levels off at high soot loading. GPF backpressure
3-48
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increases with soot and ash loading. Operation at low levels of soot loading are more challenging
for PM filtration because the GPF cannot rely on stored soot to assist with filtration.
Both bare and catalyzed GPFs are used in series production. Catalyzed GPFs typically use a
washcoat containing Pd and Rh for TWC-type activity. Catalyzed GPFs reduce the temperature
needed to oxidize stored soot and convert criteria emissions like a TWC does. A catalyzed GPF
and can replace one of the TWCs on a vehicle, potentially reducing system cost. Optimizing
filtration, backpressure, and gaseous emissions light-off, however, can be more challenging with
a catalyzed GPF.
Accumulated soot in a GPF is oxidized to CO2 and H2O in the presence of sufficient
temperature and oxidants (mostly O2 in gasoline engines). Significant rates of GPF regeneration
are observed above 600°C for a bare GPFs (Borger, et al. 2018) and above 500°C for a catalyzed
GPFs (Saito, et al. 2011). In most applications, normal vehicle operation results in sufficiently
high temperature, and deceleration fuel cut-off (DFCO) supplies the GPF with sufficient O2,
resulting in passive regeneration. If a vehicle is only operated at very low load conditions or is
not allowed to warm up, a differential pressure sensor on the GPF can sense imminent GPF
overloading and initiate an active regeneration in which engine settings are adjusted to increase
GPF temperature and supply it with sufficient O2. Active GPF regeneration strategies are
discussed in (van Nieuwstadt, et al. 2019).
GPFs are sometimes installed close to the engine in a "close-coupled" position, immediately
following the TWC, to promote passive regeneration and fast light-off of a catalyzed GPF. Other
times GPFs are installed farther from the engine, in an "underfloor" location, for packaging
reasons. The lower exhaust gas temperature in underfloor GPFs also reduces backpressure for a
given GPF size and geometry because cooler exhaust has higher density.
GPF size, design, and installation relative to the engine must be considered for the GPF to
have sufficient PM filtration efficiency, sufficiently low backpressure, sufficient ash loading
capacity, fast light-off if the GPFs washcoat is relied upon for gaseous criteria emissions
conversion, and good regeneration characteristics. Unlike soot that is oxidized after being
captured by the GPF, ash accumulates on the GPF, typically for the life of the vehicle. Thus, ash
capacity is one factor that determines GPF size for a given application.
GPFs are like diesel particulate filters (DPF) in certain respects. Both GPFs and DPFs are
wall-flow filters that use a ceramic honeycomb substrate with alternating channels plugged at
their inlet and outlet ends to filter particulates. But GPFs operate at higher exhaust gas
temperatures, lower soot loadings, lower exhaust gas O2 and NO2 concentrations, and only see
elevated exhaust gas O2 concentrations during DFCO events. High exhaust gas temperature tends
to keep GPFs at lower soot loading through frequent passive regeneration, making high filtration
efficiency harder to achieve in GPFs, especially in applications that frequently operate at high
load. Low soot loading of GPFs results in lower backpressure than DPFs. GPFs require low heat
capacity to make use of relatively short bursts of elevated O2 during DFCO events, so Cordierite
has become the GPF substrate material of choice. DPFs require higher heat capacity to
accommodate larger and less frequent regeneration events involving larger amounts of soot and
high flow rates of exhaust O2 making silicon-carbon a popular DPF substrate material.
GPFs have an excellent record with respect to robust operation and durability since their
introduction into mass production in Europe and China. The first GPFs introduced into series
3-49
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production have not experienced the failures that troubled early DPFs introduced into series
production, in part because the higher exhaust gas temperatures seen by GPFs promote frequent
passive regeneration, avoiding larger, less frequent regeneration events seen by DPFs that store
larger amounts of soot and have high exhaust O2 flow under all conditions.
GPF technology has been studied extensively for more than a decade and there exists
extensive literature on GPF. GPF technology review articles include (Saito, et al. 2011), (Joshi
and Johnson 2018), (Boger and Cutler 2019).
3.2.5.2 GPF benefits
GPF technology offers benefits of reduced PM emissions, BC emissions, and PAH
reductions. This section begins by showing measured reductions in PM mass, black carbon (BC),
and polycyclic aromatic hydrocarbon (PAH) using a MY 2011 F150 and a MY 2019 GPF. The
second part of this section presents reductions in PM mass emissions resulting from the addition
of MY 2019 and MY 2022 GPFs to three newer vehicles (MY 2019 F150, MY 2021 F150 HEV,
and MY 2022 F250.
3.2.5.2.1 PM mass. BC. and PAH emissions reductions over a composite drive
cycle
The test vehicle was a MY 2011 F150 and the GPF was an underfloor catalyzed MY 2019
GPF. Additional details of the vehicle, GPF, and test setup are described in Section 3.2.2.5 and in
(Bohac and Ludlam, Characterization of a Lightly Loaded Underfloor Catalyzed Gasoline
Particulate Filter in a Turbocharged Light Duty Truck 2023). Emissions were quantified over a
composite test cycle, comprised of vehicle operation at 60 mph cruise control, 25°C FTP, HFET,
and US06. Results are shown in total emissions mass per total distance of the test cycle. Tailpipe
emissions were quantified a) without a GPF, b) with the GPF in a lightly loaded state with the
GPF predominantly in the depth filtration mode (Konstandopoulos 2008) (0.1-0.6 g/L, grams
soot per liter of GPF substrate volume), and c) with the GPF predominantly in the soot-cake or
surface filtration mode (Konstandopoulos 2008) (1.7-2.0 g/L).
Composite cycle PM emissions are shown in Figure 3-13. PM was reduced by 94 percent with
the GPF in a lightly loaded state and 98 percent with the GPF in a heavily loaded state.
Additional results and discussion, including cycle-specific PM reductions, can be found in
(Bohac, Ludlam and Martin, et al. 2022).
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6
5
4
3
2
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IGPF, 0.1-0.6 g/L soot
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98% reduction
Figure 3-13: Composite cycle PM reduction at low and high GPF soot loading.
EC emissions without a GPF and with the GPF in a lightly loaded state (0.1-0.6 g/L soot
loading) are shown in Figure 3-14. EC was reduced by 100.0 percent in the 60 mph, 25°C FTP,
and HFET cycles, and was reduced by 98.5 percent in the US06 cycle. EC measurements were
performed using 47 mm quartz fiber filters (Pall Tissuquartz 7202) and a Sunset Laboratory
model 5L OCEC Analyzer running National Institute for Occupational Safety and Health
(NIOSH) method 870.
Exhaust elemental carbon (EC) emissions quantified in this study and airborne black carbon
(BC) studied by climate scientists have different operationally defined definitions, but they are
closely related and often used as surrogates (Bond, Doherty and Fahey 2013).
3-51
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8
60mph FTP HWFET US06
Figure 3-14: Cycle-specific EC reduction.
Another significant benefit of GPF technology is the reduction of PAH emissions. To quantify
PAH emissions reductions, filter-collected PAH were sampled onto 47 mm quartz fiber filters
(Pall Tissuquartz 7202) and gas-phase PAH were sampled using sorbent tubes (Carbotrap C+F).
PAHs on filter punches and sorbent tubes were thermally desorbed, cryofocused, and speciated
(Agilent 6890/5973 GCMS operated in selected ion mode). 26 PAHs ranging from naphthalene
to coronene were quantified. Additional sampling and analysis details can be found in (Bohac,
Ludlam and Martin, et al. 2022).
PAH emissions reductions are shown in Figure 3-15. Measurements were performed with the
GPF in lightly loaded state (0.1-0.6 g/L soot loading) and in a heavily loaded state (1.7-2.0 g/L
soot loading). Filter-collected PAH emissions (those collected by the PM sampling filter) were
reduced by over 99 percent and gas-phase PAH emissions (those passing through the PM
sampling filter) were reduced by about 55 percent. The percentage reduction in gas-phase PAH
emissions may be less for bare GPFs.
3-52
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filter collected PAHs shown on the left and sum of 26 gas phase PAHs shown on the right.
As shown in Figure 3-16, filter-collected PAHs ranged from 2-ring naphthalene to 7-ring
coronene for no GPF and GPF test cases. High rates of PAH reduction were seen across all 26
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3.2.5.2.2 Cycle-specific reduction in PM mass emissions from GPF application
to three vehicles
Reductions in cycle-specific PM mass emissions resulting from the adoption of GPF
technology is discussed in this subsection. Three vehicle examples are presented: a MY 2019
F150 5.0L, a MY 2021 F150 HEV 3.5L Powerboost, and MY 2022 F250 7.3L.
The first test vehicle is a MY 2019 F150 5.0L that was tested stock and with a MY 2019
European Ford Mustang 5.0L aftertreatment system. PM emissions are shown in Figure 3-18.
This GPF system reduced PM emissions by 91 percent, 90 percent, and 77 percent in the -7°C
FTP, 25C FTP, and US06 cycles, respectively. The testing was conducted with the GPFs in a
lightly loaded state. The lightly loaded state was achieved by running a sawtooth GPF
regeneration cycle after several tests were completed. Older technology GPFs like the one used
in on this test have lower filtration efficiency at low soot loading than newer GPFs used on the
other two test vehicles described in this subsection. Figure 3-18 shows that filtration efficiency
3-54
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6
5
4
3
2
1
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91%
reduction
| 90%
-7°C FTP 25°C FTP US06
Figure 3-18: PM emissions from a MY 2019 F150, with and without a MY 2019 GPF.
The second test vehicle is a MY 2021 F150 HEV Powerboost that was tested with and
without a MY 2022 bare underfloor GPF. PM emissions are shown in Figure 3-19. The MY 2022
GPF reduced PM emissions by 99 percent, 96 percent, and 96 percent in the -7°C FTP, 25C FTP,
and US06 cycles, respectively. The GPF was fully regenerated immediately before each day of
testing using a sawtooth GPF regeneration cycle. The GPF results shown here are worst case
with respect to PM filtration because testing was preceded by a GPF regeneration, so the GPF
was evaluated with almost no soot. Filtration efficiency of the MY 2022 GPF was significantly
better than what was achieved with the MY 2019 GPF shown in Figure 3-18, especially in the
US06. Additional details of the vehicle and GPFs are provided in Section 3.2.2.4.
3-55
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Figure 3-19: PM emissions from a MY 2021 F150 HEV, with and without a MY 2022 GPF.
The third test vehicle is a MY 2022 F250 7.3L that was retrofit with two MY 2022 GPFs, one
for each engine bank. PM emissions are shown in Figure 3-20. The MY 2022 GPFs reduced PM
emissions by 98 percent, 78 percent, and 98 percent in the -7°C FTP, 25C FTP, and US06 cycles,
respectively. The GPF was fully regenerated immediately before each day of testing using a
sawtooth GPF regeneration cycle. The GPF results shown are worst case with respect to PM
filtration because testing was preceded by a GPF regeneration, so the GPF was tested with
almost no soot.
Filtration efficiency of the MY 2022 GPFs on the MY 2022 F250 was nearly identical to the
filtration efficiency of the MY 2022 GPF on the MY 2021 F150 HEV in the -7°C FTP and US06
cycles. Filtration efficiency in the 25C FTP test was higher on the MY 2021 F150 HEV than on
the MY 2022 F250, but the extremely low GPF-equipped levels of PM, around 0.04 to 0.06
mg/mi makes precise PM mass measurements more challenging.
Tailpipe PM was significantly lower with the MY 2022 GPFs as compared to the MY 2019
GPF, especially in the US06 cycle where passive GPF regeneration occurs. Additional details of
the vehicle and GPFs are provided in Section 3.2.2.4.
3-56
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Figure 3-20: PM emissions from a MY 2022 F250, with and without MY 2022 GPFs.
3.2.5.3 Importance of test cycles
The -7°C FTP test is essential to the proposed PM standard because -7°C33 is an important
real-world temperature with significant uncontrolled PM emissions. Based on EPA testing, PM
emissions in the -7°C FTP are significantly higher than those demonstrated during a 25°C FTP
test (e.g., Figure 3-18, Figure 3-19, Figure 3-20, and Preamble Figure 11 in Chapter 3). In
addition to controlling high cold weather PM emissions that were uncontrolled in Tier 3, the -
7°C FTP test differentiates Tier 3 levels of PM from GPF-level PM.
PM is elevated in the -7°C FTP test because heavy species in gasoline have very low vapor
pressure at cold temperatures, making them difficult to vaporize on cold engine surfaces. For
example, as shown in Figure 3-21, the vapor pressure of toluene and n-decane, two
representative heavy species of gasoline, are reduced by 6.5X and 12X, respectively, as
temperature decreases from 25° to -7°C. Early examples of peer-reviewed literature showing
cold ambient temperature (including -7°C) increases PM mass and solid PN from a GPF-
equipped vehicle include (T. W. Chan 2013) and (T. W. Chan 2014).
33 -7°C is approximately 20°F, a temperature common through much of the United States during winter months.
3-57
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Figure 3-21: Vapor pressure of toluene and n-decane as a function of temperature.
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The 25°C FTP test is retained from prior standards because it ensures that vehicles are
designed and calibrated to operate clean over a range of ambient temperatures. The US06 test is
important because 1) it represents higher load real-world driving, and 2) it ensures low tailpipe
PM during and after a GPF regeneration, when soot loading is low and makes PM filtration more
challenging. The relatively poor filtration of earlier GPF designs, during and immediately after
regeneration, e.g., in a US06 cycle, has been discussed in the literature for some time, e.g., (T.
W. Chan 2016).
In sum, the combination of -7°C FTP, 25°C FTP, and US06 standards ensures that a vehicle
has good PM control over the broadest area of vehicle operation and environmental conditions.
In Tier 3, most Class 2b vehicles used the US06 cycle, while low power to weight Class 2b
vehicles and all class 3 vehicles used the LA92 cycle in the SFTP calculation. The proposed rule
requires all LD vehicles and MDVs to certify using the same cycles: -7°C FTP, 25°C FTP, and
US06. Requiring the US06 for all class 2b/3 vehicles ensures that GPF regeneration occurs
during the test cycle and requires high GPF filtration under all operating conditions, even during
and after a GPF regeneration. Without the US06 test, GPF regeneration may not occur during
any certification test cycle, allowing for high PM emissions during high load operation such as
trailer towing. If a class 2b/3 vehicle cannot follow the US06 trace, it must be run at maximum
effort, and in this case the test will not be voided.
3.2.5.4 Demonstration of the feasibility of the standard
3.2.5.4.1 Setup and Test Procedures
A demonstration of the feasibility of the PM standard for light-duty vehicles and MDVs is
described in this section. Testing was performed using five chassis dynamometer test cells at
three organizations (EPA, ECCC, FEV) and five test vehicles in stock and GPF configurations.
Test vehicles included light-duty vehicles and MDVs powered by naturally aspirated and
3-58
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turbocharged PFDI (port and direct fuel injection), DI (direct injection), and PFI (port fuel
injection) gasoline engines. GPF-equipped vehicles used series-production GPFs from MY 2019
and MY 2022 GPF. GPFs used catalyzed and bare substrates, and they were installed in close-
coupled and underfloor configurations.
The five chassis dynamometer test cells used in the demonstration included three test cells at
EPA National Vehicle and Fuel Emissions Laboratory (NVFEL), one test cell at ECCC, and one
test cell at FEV. -7°C FTP tests were performed at EPA (one test cell), ECCC, and FEV. 25°C
FTP and US06 tests were performed at EPA (three test cells), ECCC, and FEV. Three test
vehicles were tested at all organizations, while two vehicles were only tested at EPA.
All five test cells used in the demonstration were designed to be compliant with 40 CFR Part
1065 and 1066. In each test cell, vehicle exhaust gas is diluted in a constant volume sampler
(CVS) full-flow dilution tunnel system. Heated particulate filter samplers draw dilute exhaust
through a coarse particle separator (-2.5 |im cut at sampling conditions) and 47 mm PTFE
membrane filters [e.g., Measurement Technology Laboratories (MTL) PT47DMCAN],
PM filters were conditioned at 22±1°C, 9.5±1°C dew point for a minimum of 1 hour before
being weighed, before and after being loaded with PM. Filters were weighed using a
microbalance (e.g., Mettler-Toledo XPU2) while being surrounded by strips of Po210 (e.g., 5
strips of 500 |iCi each) for static charge removal. EPA used an MTL A250 robotic autohandler
for filter weighing; ECCC and FEV labs used manual filter weighing.
To increase sample filter loading and increase signal to noise ratio for GPF-equipped tests,
test cell sampling settings were adjusted relative to test settings typically used to measure Tier 3
levels of PM emissions, within boundaries defined by the CFR. For GPF-equipped tests, 1)
Dilution factor (DF) was set to the lower/middle part of the CFR-allowable range of 7-20. 2)
25°C FTP and -7°C FTP tests were mostly run using a single filter, as allowed by
§1066.815(b)(5). 3) In many tests, filter flow was increased from a typical setting of -58 slpm to
-65.25 slpm in phases 1&2 and -87 slpm in phases 3&4 to increase filter loading and maintain
proper phase weighting using flow weighting, while staying below the maximum allowable filter
face velocity (FFV) of 140 cm/s as specified by §1066.110(b)(2)(iii)(C). 4) Many of the 25°C
FTP and -7°C FTP tests were run as 4-phase FTP tests as opposed to 3-phase FTP tests, although
in hindsight, phase 4 didn't add much PM mass to the sampling filter and may not be worth the
extra test time. Additionally, to further increase PM filter loading, some of the GPF-equipped
tests used double sampled US06 tests, which is not included in the CFR. In retrospect, a standard
single sampled US06 would have likely been sufficient. Additional testing is being conducted to
confirm this.
Tier 3 certification fuel was used for 25°C FTP and US06 testing, and Tier 3 winter
certification fuel was used for -7°C FTP testing at all three organizations. Engine oil was
conditioned in each vehicle for a minimum of 600 miles prior to emissions sampling to stabilize
the oil (Christianson, Bardasz and Nahumck 2010).
The newest of the five test vehicles was an MDV Tier 3 bin 200 MY 2022 Ford F250 with a
naturally aspirated 7.3L V8 PFI engine. It was tested at an ETW of 8000 lb. The F250 was tested
in stock and GPF configurations. Vehicle mileage at the start of testing was 2700 miles. For GPF
testing, series-production MY 2022 GPFs, one for each bank, were installed downstream of the
stock TWCs where the resonator is normally mounted. The GPF used bare substrates of c|)6.443"
3-59
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x 6" (3.21 L each), 200 cpsi, 8 mil wall thickness. GPFs were aged through 1500 miles of road
driving prior to emissions sampling. GPF pressure drop and temperatures were recorded.
The second newest test vehicle was a LDT4 Tier 3 bin 70 MY 2021 Ford F150 HEV with a
turbocharged (Ecoboost) 3.5L V6 PFDI engine. It was tested at an ETW of 6000 lb. The F150
HEV was only tested in GPF configuration. Vehicle mileage at the start of testing was 5000
miles. A series-production MY 2022 GPF was installed after the Y-pipe in place of the resonator.
The GPF used a bare substrate of c|)6.443" x 6" (3.21 L), 200 cpsi, 8 mil wall thickness. The GPF
was aged through 1500 miles of road driving prior to emissions sampling. GPF pressure drop and
temperatures were recorded.
The third newest test vehicle was an LDV Tier 3 bin 30 MY 2021 Toyota Corolla with a
naturally aspirated 2.0L 14 PFDI engine. It was tested at an ETW of 3375 lb. The Corolla was
only tested in stock (no GPF) configuration. Vehicle mileage at the start of testing was 5800
miles.
The fourth newest test vehicle was an LDT4 Tier 3 bin 125 MY 2019 Ford F150 with a
naturally aspirated 5.0L V8 PFDI engine. It was tested at an ETW of 5000 lb. The 2019 F150
was tested in stock and GPF configurations. Vehicle mileage at the start of testing was 6700
miles. For GPF testing, a series-production aftertreatment system from a MY 2019 European
Ford Mustang 5.0L replaced the stock aftertreatment system on the F150. The Mustang
aftertreatment system uses a ccl (close-coupled, position 1) TWC and a cc2 catalyzed GPF for
each bank of the engine. The stock aftertreatment system uses a ccl TWC and a cc2 TWC for
each bank. The Mustang GPFs are c()5.2" x 3.3" (1.15 L each), 300 cpsi, 12 mil wall thickness.
The Mustang aftertreatment system was aged through 1500 miles of road driving prior to
emissions sampling. GPF pressure drop and temperatures were recorded.
The oldest test vehicle was an LDT4 Tier 2 bin 4 MY 2011 Ford F150 with a turbocharged
(Powerboost) 3.5L V6 DI engine. It was tested at an ETW of 5500 lb. The 2011 F150 was tested
in stock and GPF configurations. Vehicle mileage at the start of testing was 21,100 miles. For
GPF testing, a series-production MY2019 GPF was installed after the Y-pipe in place of the
resonator. The GPF used a catalyzed substrate of c|)5.66" x 4" (1.65 L), 300 cpsi, 12 mil wall
thickness. The GPF was aged through 600 miles of dynamometer driving prior to emissions
sampling. GPF pressure drop and temperatures were recorded.
GPF operation was characterized over a range of soot loadings, but because GPFs are required
to comply with the proposed PM standard in any state of soot loading, only results from low-
soot-loading tests (which are worst case with respect to tailpipe PM) are included in the
following demonstration of meeting the proposed PM standard. GPFs were regenerated before
each set of tests by using a sawtooth regeneration cycle.
3.2.5.5 GPF cost
A GPF cost model was developed to estimate direct manufacturing cost (DMC) of a bare GPF
and associated hardware in the exhaust system of a gasoline-powered light-duty vehicle or MDV
where the GPF is installed downstream of the TWCs in its own aftertreatment enclosure (can).
The cost model has been incorporated in the OMEGA model.
A bare GPF installed downstream of the TWCs may have higher DMC than a catalyzed GPF
that replaces a TWC because the bare downstream GPF requires an additional substrate,
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substrate matting, and can. However, some or all of the additional DMC of a bare downstream
GPF may be offset by enabling a reduction in total precious metal content because the precious
metal content can all be used on the thinner and lower heat capacity walls of the TWCs that
warm up faster after an engine start. Overall, it is believed that the GPF cost model in OMEGA
estimates a DMC that is either higher or similar to the DMC of a catalyzed GPF that replaces a
TWC.
Indirect costs (IC), including research, development, OBD, and markup, of a bare downstream
GPF are also calculated by OMEGA. OMEGA estimate the IC of a bare downstream GPF in the
same way as it does for other emissions control components, so these IC are not included in the
GPF DMC model discussed below.
The GPF DMC model is based on an ICCT GPF cost analysis for a bare "stand-alone" GPF
(Minjares and Sanchez 2011). The DMC model considers costs for the GPF substrate, housing,
accessories, pressure sensor, labor and 40 percent overhead, machinery, and warranty. Substrate
and housing costs scale with GPF volume. The substrate cost in the ICCT analysis is reduced by
30 percent (from 30 $/literGPF to 21 $/literGPF) based on information from substrate suppliers.
The reduced substrate cost reflects manufacturing learning. Accessories, pressure sensor, labor
and 40 percent overhead, and machinery costs are a fixed dollar amount per vehicle ($39.58).
Warranty costs are 3 percent of all of the above-mentioned costs. A production volume discount
of 20 percent is then applied, and finally, total cost is converted from 2011 to 2021 dollars
(multiplier of 1.2046).
To estimate the GPF size needed by a specific engine, and therefore the DMC, a GPF volume
to engine displacement ratio is used. The ICCT analysis calculated GPF to engine volume ratios
for three vehicles and suggested using the average result of 0.55. EPA compared the ICCT
average to two more recent European GPF-equipped vehicles. The MY 2019 European Mustang
had a volume ratio that is 8 percent lower than the ICCT average, while a MY 2018 European
Wrangler had a volume ratio that is 13 percent higher than the ICCT average.
To provide an overview of the GPF DMC of a bare downstream GPF, the cost model was run
for engines ranging in size from 1.0 to 7.0 liters using GPF to engine volume ratios from the
2018 Wrangler, the ICCT average, and the 2019 Mustang. Figure 3-22 shows the resulting DMC
estimates. DMC for a bare downstream GPF ranges from $51 dollars for a 1.0 liter engine using
the volume ratio of the 2019 Mustang, up to $166 dollars for a 7.0 liter engine using the volume
ratio of the 2019 Wrangler.
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200
175
150
125
¦M
(/)
100
0
u
75
50
25
0
I Pfe
1.0 2.0 3.0 4.0 5.0
engine displacement (L)
Figure 3-22: GPF cost estimate.
6.0
7.0
3.2.5.6 GPF impact on CO2 emissions
Integrating GPF technology into vehicle aftertreatment systems has the potential to increase
CO2 emissions in two ways: during active GPF regeneration, and from increased backpressure.
Active regeneration can increase CO2 emissions while the engine adds more heat to the exhaust
gas. However, based on discussions with vehicle manufacturers and GPF suppliers, and
supported by testing conducted by EPA, most production vehicles will rarely or never need to
use active GPF regeneration because systems with close-coupled GPFs or underfloor GPFs with
insulated exhaust pipes (i.e., double wall) naturally cause sufficiently high GPF temperature for
passive GPF regeneration. CO2 increase due to active regeneration is therefore considered
negligible in this analysis. The following paragraphs address the effect of GPF backpressure on
CO2 emissions.
GPF pressure drop (i.e., backpressure) and CO2 increase were measured on four test vehicles
across three test cycles (-7°C FTP, 25°C FTP, US06). Table 3-16 presents a summary of key
vehicle and GPF specifications. Additional vehicle details are provided in Section 3.2.2.4.
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Table 3-19: Vehicle and GPF specifications.
MY2022
F250 7.3L
MY2021 F150 3.5L
Powerboost HEV
MY2019
F150 5.0L
MY2011
3.5L Ecoboost
GPF
model year
2022
2022
2019
2019
GPF type and
location
bare
underfloor
bare
underfloor
catalyzed
close-coupled
catalyzed
underfloor
GPF size
(L)
6.42 (total for two)
3.21
2.30 (total for two)
1.65
GPF volume / engine
displacement (-)
0.88
0.92
0.46
0.47
GPF volume / ave
US06 power (L/kW)
0.199
0.115
0.107
0.065
GPF c|) x L
(in)
6.443 x 6 (each)
6.443 x 6
5.2 x 3.3 (each)
5.66x4
GPF cell density
(cpsi)
200
200
300
300
GPF wall thickness
(mil)
8
8
12
12
Average GPF pressure drop for each test cycle and vehicle is shown in Figure 3-23. Average
GPF pressure drop is highest in the US06 because this cycle demands the highest average power
and has the highest average exhaust flow rate. Average GPF pressure drop is similar for the -7°C
FTP and 25°C FTP because these cycles use the same drive trace. The 2011 F150 showed
slightly higher GPF pressure drop in the -7°C FTP as compared to the 25°C FTP, presumably
because powertrain friction increases at cold temperatures before the powertrain warms up.
Figure 3-23 does not show GPF pressure drop for the 2019 F150 because the GPF differential
pressure sensor was installed on this vehicle after -7°C FTP testing was conducted.
5
-7°C FTP 25°C FTP US06
Figure 3-23: Cycle-average GPF pressure drop as a function of test cycle.
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Figure 3-24 shows, as expected, GPF pressure drop in the US06 decreases asymptotically as
the ratio of GPF volume to average US06 power increases. Larger GPF volume provides more
GPF wall area for exhaust flow, and lower average US06 power results in reduced exhaust flow
volume (due to reduced exhaust mass flow and lower exhaust temperature).
The results shown in Figure 3-23 show how for each test cycle (-7°C FTP, 25°C FTP, US06),
average GPF pressure drop increases with decreasing ratio of GPF volume to average power in
the US06. Based on a review of several European production vehicles and discussions with GPF
suppliers, GPF volumes of the 2022 F250 and the 2021 F150 HEV are within typical production
ranges for such vehicles, while GPF volumes of the 2019 F150 and 2011 F150 are relatively
small for these vehicles, despite the GPF system on the 2019 F150 test vehicle coming from a
European 2019 series production Mustang that uses the same engine displacement as the 2019
F150 test vehicle.
ro
Q_
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to
o
I/) „
Q- 9
< 2
Ll_
Q_
<3 1
0)
>
0
0.00 0.04 0.08 0.12 0.16 0.20 0.24
ratio of GPF size to US06 power (L/kW)
Figure 3-24: Cycle-average GPF pressure drop as a function of the ratio of GPF size to
average power required to drive US06 cycle.
Higher GPF pressure drop increases the work that an engine must do to expel exhaust gas
through the exhaust system. To maintain commanded power to follow a drive trace, the throttle
is opened more, and this reduces intake pumping loss, partially offsetting the increased exhaust
pumping work. The net effect is expected to be a slight reduction in brake thermal efficiency and
a slight increase in CO2 emissions. A more detailed discussion can be found in (Bohac and
Ludlam, Characterization of a Lightly Loaded Underfloor Catalyzed Gasoline Particulate Filter
in a Turbocharged Light Duty Truck 2023).
Table 3-17 shows the change in measured CO2 emissions for each test cycle when GPFs were
added, when results are averaged across the four test vehicles. Averaging across four test
vehicles results in CO2 increases between 0.0 percent for the 25°C FTP and 0.9 percent for the
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US06. Since two of the test vehicles were equipped with somewhat undersized GPFs, these
average CO2 increases may be higher than for productions vehicles with more typical GPF
volumes.
Table 3-20: Change in measured C02 emissions for each test cycle when GPFs are added,
averaged across four test vehicles (2022 F250, 2021 F150 HEV, 2019 F150, 2011 F150).
Test Cycle
CO2 Increase (%)
-7°C FTP
0.6
25°C FTP
0.0
US06
0.9
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E
o
o
(/>
ro
a
u
c
fM
O
u
4
3
2
1
0
-1
-2
-3
,/
p<0.05
I
-7°C FTP
25°C FT
2022 F250 7.3L
2021 F150 HEV
2019 F150 5.0L
2011 F150 3.5L
.
US06 |
Figure 3-25: CO2 increase caused by added GPF. Only the two light blue bars indicated are
statistically significant to 95% confidence (p<0.05).
Considering the analyses summarized in Table 3-17 and Figure 3-25, it is estimated that
integrating GPFs into vehicle aftertreatment systems likely causes less than 1 percent increase in
CO2 emissions in the -7°C FTP, 25°C FTP and US06 cycles.
3.2.6 Evaporative Emissions Control
The agency is proposing to require that incomplete medium duty vehicles meet the same on-
board refueling vapor recovery (ORVR) standards as currently required for complete vehicles.
Incomplete vehicles have not been required to comply with the ORVR requirements because on
the potential complexity of their fuel systems, primarily the filler neck and fuel tank. Unlike
complete vehicles which have permanent fuel system designs that are fully integrated into the
vehicle structure at time of original construction by manufacturers, it was believed that
incomplete vehicles which are typically finished at an upfitter who adds needed hardware and
accessories, may need to change or modify some of fuel system components during their
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finishing assembly. For this reason, it was determined that ORVR might introduce a complexity
for the upfitters that is unnecessarily burdensome.
In observations by the agency of current ORVR equipped vehicles and their incomplete
versions, the agency believes that the fuel system designs are almost identical with only the
ORVR components removed for the incomplete version. The complete and incomplete vehicles
appear to share the same fuel tanks, lines, and filler tubes. The original thought that extensive
differences between the original manufacturer's designs and the upfitter modifications to the fuel
system would be required have not been observed. Therefore, the agency believes that almost all
incomplete vehicles can comply with the same ORVR standards as complete vehicles with the
addition of the same ORVR components on the incomplete vehicles as the complete version of
the vehicle possesses
The current practice of manufacturers of the original incomplete vehicles is to specify to the
upfitter that modifications of the fuel system are not allowed by the upfitter. This is because the
incomplete vehicle manufacturers are responsible for all current evaporative requirements (2-
day, 3-day, running loss, etc.) and almost any modification could compromise compliance with
those program standards. There is also an aspect of compliance with crash and safety
requirements that prevent upfitters from making changes to the fuel system components. For
these reasons, with rare exception, the fuel system design and installation is completed by the
original vehicle manufacturer. The exception that the agency observed is that some incomplete
vehicles do not have the filler tube permanently mounted to a body structure until the upfitter
adds the finishing body hardware (ie; flatbed, box). In these cases, the upfitter is limited to only
attaching the filler tube to their added structure but must maintain the original manufacturer
designs that are certified to meet existing EPA evaporative emission standards.
3.2.6.1 Technologies to Address Evaporative and Refueling Emissions
As exhaust emissions from gasoline engines continue to decrease, evaporative emissions
become an increasingly significant contribution to overall HC emissions from gasoline-fueled
vehicles. Opportunity exists to extend the usage of the refueling evaporative emission control
technologies already implemented in complete medium-duty gasoline vehicles to the incomplete
gasoline vehicle versions of the same basic vehicle. The primary technology we are considering
is the addition of ORVR, which was first introduced to the chassis-certified light-duty and
medium-duty applications beginning in MY 2000 (65 FR 6698, February 10, 2000). An ORVR
system includes a carbon canister, which is an effective technology designed to capture HC
emissions during refueling events when liquid gasoline displaces HC vapors present in the
vehicle's fuel tank as the tank is filled. Instead of releasing the HC vapors into the ambient air,
ORVR systems recover these HC vapors and store them for later use as fuel to operate the
engine.
The fuel systems on these 8,501 to 14,000 pound GVWR incomplete medium-duty gasoline
vehicles are similar if not almost identical to complete medium-duty vehicles that are already
required to incorporate ORVR. These incomplete vehicles almost always have identical fuel
tanks to the complete medium-duty gasoline vehicles. There may be occasional optional larger
fuel tanks requiring a greater ORVR system storage capacity and possibly some unique
accommodations for dual tanks (e.g., separate fuel filler locations), but we expect they will
maintain a similar design. Figure 3-26 presents a schematic of a standard ORVR system.
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Carbon
Canister
Multi-Function
Control Valves
Filler Pipe
Fill Umrt
Vent Valve
Figure 3-26: Schematic of an ORVR system34
3.2.6.2 Filler Pipe and Seal
In an ORVR system, the design of the filler pipe, the section of line connecting the point at
which the fuel nozzle introduces fuel into the system to the gas tank, is integral to how fuel
vapors displaced during a fuel fill will be handled. The filler pipe is typically sized to handle the
maximum fill rate of liquid fuel allowed by law while also integrating one of two methods to
prevent fuel vapors from exiting through the filler pipe to the atmosphere: a mechanical seal or a
liquid seal approach. A dual fuel tank chassis configuration may require a separate filler pipe and
seal for each fuel tank.
The mechanical seal is typically located at the top of the filler neck at the location where the
fuel nozzle is inserted into fuel neck. The hardware piece forms a seal against the fuel nozzle by
using some form of a flexible material (usually a plastic material) that makes direct contact with
the fuel station fuel-filling nozzle to prevent fuel vapors from exiting the filler pipe as liquid fuel
is pumped into the fuel tank. In the case of capless systems, this seal may be integrated into the
spring-loaded seal door that opens when the nozzle is inserted into the filler pipe receptacle.
There are concerns with a mechanical seal's durability due to wear over time, and its ability to
maintain a proper seal with unknown service station fill nozzle integrity and variations beyond
design tolerances.
31 Slant ORVR System http://stant.com/orvr/orvr-Systems/
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The liquid seal approach uses the size and bends of the filler pipe to cause a condition where
the entire cross-section of the filler pipe is located in the fuel tank or close to the entry into the
fuel tank and is full of the incoming liquid fuel preventing fuel vapors from escaping up and out
through the filler pipe. By creating a solid column of liquid fuel in the filler pipe, the liquid seal
approach does not require a mechanical contact point with the fill nozzle to prevent escape of
vapors. The liquid seal has been the predominant sealing method implemented in the regulated
fleet in response to the ORVR requirements.
3.2.6.3 ORVR Flow Control Valve
As described above, the sealing of the filler pipe prevents the fuel vapors from escaping into
the ambient air; however, the fuel vapors that are displaced by the incoming liquid fuel need to
be routed to the canister. In order to properly manage the large volume of vapors during
refueling that need to be controlled, most ORVR systems have implemented a flow control valve
that senses that the fuel tank is getting filled with fuel and triggers a unique low-restriction flow
path to the canister. This flow path is specifically used only during the refueling operation and is
unique in that it provides the ability to quickly move larger volumes of fuel vapors into the tank
than normally required under other operation outside of refueling events. The flow control valve
will allow this larger flow volume path while refueling but then return to a more restrictive vapor
flow path under all other conditions, including while driving and while parked for overnight
diurnal s.
The flow control valve is generally a fully-mechanical valve system that utilizes connections
to the fuel tank and filler pipe to open and close vapor pathways with check valves and check
balls and pressure switches via diaphragms. The valve may be integrated into the fuel tank and
incorporate other aspects of the fuel handling system ("multi-function control valve" in Figure
3-26) including roll-over valve, fuel and vapor separators to prevent liquid fuel from reaching the
canister, and other fuel tank vapor control hardware. Depending on the design, the filler pipe
may also be integrated with the flow control valve to provide the necessary pressure signals. A
dual fuel tank chassis configuration may require a separate flow control valve for each fuel tank.
3.2.6.4 Canister
The proven technology to capture and store fuel vapors has been activated charcoal. This
technology has been used in vehicles for over 50 years to reduce evaporative emissions from
sources such as fuel tanks and carburetors. When ORVR was originally discussed, existing
activated charcoal technology was determined to be the appropriate technology for the capture
and storage of refueling related fuel vapors. This continues to be the case today, as all known
ORVR-equipped vehicles utilize some type of activated charcoal.
The activated charcoal is contained in a canister, which is made from a durable material that
can withstand the fuel vapor pressures, vibration, and other durability concerns. For vehicles
without ORVR systems, canisters are sized to handle evaporative emissions for the three-day
diurnal test with the canister volume based on the fuel tank capacity. A dual fuel tank chassis
configuration may require a separate canister for each fuel tank.
3.2.6.5 Purge Valve
The purge valve is the electro-mechanical device used to remove fuel vapors from the fuel
tank and canister by routing the vapors to the running engine where they are burnt in the
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combustion chamber. This process displaces some amount of the liquid fuel required from the
fuel tank to operate the engine and results in a small fuel savings. The purge valve is controlled
by the engine or emission control electronics with the goal of removing the necessary amount of
captured fuel vapors from the canister in order to prepare the canister for subsequent fuel vapor
handling needs of either the next refueling event or vapors generated from a diurnal event. All
on-road vehicles equipped with a canister for evaporative emissions control utilize a purge valve.
Depending on the design, a dual fuel tank chassis configuration may require a separate purge
valve for each fuel tank.
3.2.6.6 Design considerations for Unique Fuel Tanks
The commercial truck market gasoline applications may incorporate several fuel tank options
that may require unique ORVR design considerations. While most commercial vehicle fuel tanks
are similar to the already ORVR-compliant complete vehicles in the 8500 to 14,000 GVWR
class, some of the commercial vehicles include larger tank sizes (up to 50 gallons) or may have
a dual tank option. As described above, the canister sizing will be a function of the required
amount of fuel vapor handling during refueling. Larger fuel tanks will require larger canisters
with more activated charcoal than historically found in other gasoline vehicles. Some design
challenges will likely exist in designing the canister system to handle the large vapor volumes
while balancing the restriction to flow through the larger activated charcoal containing canisters.
Dual fuel tank systems, which have very limited availability, may also require some unique
design considerations. Typically, the canister is located in very close proximity to the fuel tank to
properly manage the refueling fuel vapors efficiently with minimal distance between the tank
and canister. Dual fuel tanks may require duplicate ORVR systems to have the necessary
flexibility to manage the refueling vapors, particularly since the fuel tanks are filled
independently through separate filler pipe assemblies.
3.2.6.7 Onboard Refueling Vapor Recovery Anticipated Costs
MDVs certified as incomplete vehicles are not currently required to meet ORVR. There are
four main equipment components and strategies incomplete medium-duty vehicles need to
update to implement ORVR: increased working capacity of the carbon canister to handle
additional vapors volumes during refueling, flow control valves to manage vapor flow pathway
during refueling, filler pipe and seal to prevent vapors from escaping, and the purge system and
management of the additional stored fuel vapors. The associated direct manufacturing costs for
these updates are summarized below. No labor cost was identified so the direct manufacturing
cost is equal to the piece cost plus tooling cost (per piece). ORVR requirements will be extended
to medium-duty gasoline engines in incomplete vehicles starting in model year 2030. For our
cost analysis, we assumed all medium-duty gasoline engines that are identified as incomplete
light-heavy-duty trucks in MOVES will have an average fuel tank capacity of 35-gallons.
Capturing the increased vapor volume from the vapor displaced during a refueling event will
require canisters to increase vapor or "working" capacity approximately 15 to 40 percent
depending on the individual vehicle systems (i.e., fuel tank size). This can be achieved by
increasing the canister volume using conventional carbon, the fundamental material used to store
fuel vapors. A typical Tier 3 canister has approximately 2.6 liters of conventional carbon to
capture overnight diurnal evaporative emissions for a 35-gallon fuel tank. An increase in
required capacity to allow refueling vapors to be captured results in the need for an additional 1
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liters of conventional carbon. A change in canister volume to accommodate additional carbon
includes increased costs for retooling and additional canister plastic material, as well as design
considerations to fit the larger canister on the vehicle. However, because these medium-duty
vehicles almost always have a complete version already required to comply with refueling
standards, the necessary larger canister sizes are already produced and available likely neglecting
the need for any additional tooling investment.
An alternative to retooling for a larger single canister would be to add a second canister for
the extra canister volume to avoid the re-tooling costs. Several smaller volume canisters are
available on the market today. Another approach, based on discussions with canister and carbon
manufacturers, can be achieved by using a higher adsorption carbon along with modifications to
compartmentalization within the existing canister plastic shell that will increase the canister
working capacity without requiring a larger canister size.
Additionally, there are two primary technologies used to prevent vapors from escaping into
the atmosphere through the filler neck and around the fuel nozzle area when the vehicle is
refueling that can affect the canister vapor capacity design requirements: a mechanical seal
which makes direct physical contact with the refueling nozzle to create a nozzle to filler neck
seal; or a liquid seal further down in the filler pipe which uses the liquid fuel mass flowing down
the filler pipe and entering the tank to hydraulically prevent vapors from migrating back up the
fill pipe. There is approximately a 20 percent reduction in carbon volume required if a
mechanical seal is used at the filler neck versus a liquid seal approach. While mechanical seals
are not currently the preferred technology, manufacturers facing the choices available for the
larger volume fuel tanks and the need for a larger matching carbon containing canister to handle
these large quantities of fuel vapors, may opt for more a mechanical seal design to avoid excess
canister carbon requirements and possible retooling charges. We share our assumptions and cost
estimates for both seal options in Table 3-18 and Table 3-19. A dual tank may require two seals
if dual filler necks are used instead of a single filler neck and transfer pump to move fuel
between the two tanks.
The second required equipment update would be to install flow control valves, which may be
integrated into existing roll-over/vapor lines. The flow control valves are needed to manage the
vapors during the refueling event by providing a low restriction pathway for vapors to enter the
canister for adsorption and storage on the carbon materials. We anticipate vehicles would require
on average one valve per vehicle which would be approximately $6.50 per valve. A dual tank
system may require a flow control valve system per tank depending on the design approach.
Thirdly, as mentioned above, a filler pipe and seal system would be needed for each filler
nozzle to keep the vapors contained during refueling. Manufacturers have the option of a
mechanical seal that costs approximately $10.00 per seal, or a liquid seal which in itself costs
nothing but may require hardware modifications to provide enough back pressure to stop the
refueling nozzle fuel flow when tank reaches full capacity if the incomplete version doesn't
already share the same filler tube design with the refueling requirements compliant complete
version.
Lastly, the engine control of the canister purge rates may need to be addressed. This update
would include calibration improvements and potentially additional hardware to ensure adequate
purge volumes are achieved as required to maintain an appropriate canister state to manage
vapors generated during diurnal and subsequent refueling events. However, if the incomplete
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version shares engines and fuel systems with the complete vehicle versions, the development of
calibrations for the required purge volumes has likely already been completed eliminating any
need for further changes or development work. If required for a dual tank system, an extra purge
valve may be needed if the two-tank system maintains independent canisters instead of a single
common cannister as observed in dual-tank, single canister light-duty applications.
Table 3-18 shows our calculations estimating the amount of extra canister size for
conventional carbon for a 35-gallon tank, using Tier 3 core evaporative requirements (i.e. 2-day
and 3-day shed) as a baseline. Currently under Tier 3 requirements the canister and purge
strategy are sized for the diurnal test and designed to meet the Bleed Emissions Test Procedure
(BETP) requirements. During the diurnal test, the canister is loaded with hydrocarbons over two
or three days, allowing the hydrocarbons to load a conventional carbon canister (1500 GWC,
gasoline working capacity) at a 70 g/L effectiveness. During a refueling event, which takes place
over a few minutes, the vapor from the gas tank is quickly loaded onto the carbon in the canister
with an ORVR system, causing the efficiency of the canister loading to drop to 50 g/L
effectiveness mainly because of the high volume of fuel vapors required to be adsorbed in the
short period of a refueling event. Typically, a design safety margin adds an extra 10 percent
carbon to ensure adequate performance over the life of the system. Therefore, even though there
is typically less fuel vapor mass generated and managed during a refueling event than is
generated over a three-day diurnal time period, the amount of carbon that is necessary to contain
the vapor is higher for a refueling event.
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Table 3-21: ORVR Specifications and Assumptions used in the Cost Analysis for
Incomplete MDVs (8501 lbs to 14,000 lb GVWR).
Tier 3
Baseline
Diurnal
Diurnal Heal Build 72-%°F
RVP
Nominal Tank Volume
Fill Volume 40%
Air Ingestion Rale
Mass Vented per heal build, g/d 60
Mass Vented per refueling event
Hot Soak Vapor Load 2.5
Mass vented over 48-hour test 114
Mass vented over 72-hour test 162
1500 GWC. g/La 70
Excess Capacity 10%
Estimated Canister Volume Requirement, liters'1
48-hour Evaporative onh 1.8
72-hour Evaporative onh 2.5
Total of 72-hour + ORVRc
a Efficiency of conventional carbon
b Canister Volume = l.l(mass vented)/ 1500 GWC (Efficiency)
c ()RVR adds .3 liters and 1 liter for Mechanical Seal and Liquid Seal respectively
ORVR Filler Neck Options
Mechanical Seal Liquid Seal
ORVR
80°F
9 psi
35 gallons
10% to 100%
"
128
50
10%
2.8
13.50%
158
50
10%
3.5
Table 3-22: Estimated Direct Manufacturing Costs for ORVR Over Tier 3 as Baseline
Liquid Seal Mechanical Seal
New Canister New Canister
Additional Canister $10 $4
Costs
Additional Tooling (a) $0.50 $0.50
Flow Control Valves $6.50 $6.50
Seal $0 $10
Total (b) $17 $21
a Assumes the retooling costs will be spread over a five-year period
b Possible additional hardware for spitback requirements
3.3 On-board Diagnostics
EPA regulations state that onboard diagnostics (OBD) systems must generally detect
malfunctions in the emission control system, store trouble codes corresponding to detected
malfunctions, and alert operators appropriately. EPA adopted (as a requirement for an EPA
certificate) the 2013 California Air Resources Board (CARB) OBD regulation, with certain
additional provisions, clarifications and exceptions, in the Tier 3 Motor Vehicle Emission and
Fuel Standards final rulemaking (40 CFR 86.1806-17; 79 FR 23414, April 28, 2014). Since that
time, CARB has made several updates to their OBD regulations and continues to consider
changes periodically. In this NPRM, EPA is proposing to update to the latest version of the
CARB OBD regulation (California's 2022 OBD-II requirements are part of (Title 13 § 1968.2
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California Code of Regulations 2022)). This is accomplished by adding a new section for
vehicles built after 2027 model year and only putting in requirements in that section that are not
in the new CARB regulation. EPA is also adding a new monitoring requirement for gasoline
particulate filters (GPFs) since the CARB regulation does not yet have a requirement for a
particulate filter diagnostic for gasoline vehicles and EPA is projecting that manufacturers will
utilize GPFs as a control strategy in meeting our proposed PM standards in the time frame of this
rulemaking.
As mentioned above, CARB has made changes to their regulation since we adopted the 2013
version. Most notably CARB added evaporative reporting and diagnostic language that both adds
functions that we had in our Tier 3 regulation and clarifies which diagnostics are required and
what to report. This makes the evaporative reporting language in our regulation obsolete and
make it necessary to remove the language to prevent conflicts.
EPA has worked closely with CARB on the development of EPA's diagnostic requirements
for GPFs. CARB has reviewed and helped determine the EPA requirements. EPA started with
CARB's requirements for its diesel particulate filter diagnostic. EPA then removed the failure
modes that both EPA and CARB felt weren't germane to the GPF system. This left three
diagnostic requirements along with requirements for tracking and reporting. The required ratio
for tracking and reporting is 0.150 as calculated using procedures in (Title 13 § 1968.2 California
Code of Regulations 2022). The first is a monitor that is required if removing the GPF would
cause PM to go above 10 mg/mi over the FTP. The second is a requirement to detect if frequent
regeneration cycles cause HC, CO, or NOx to exceed 1.5 times the standard for HC, CO, or
NOx. Or, if no number of cycles would cause the 1.5 times exceedance, then the diagnostic must
trigger when the number of regeneration cycles exceed the manufacturers specified limit for
regeneration cycles. The third requirement is for detecting when the GPF is missing from the
system, significantly damaged, or destroyed (further details are available in the regulations). This
third requirement along with checking regeneration cycles (too frequent and cycles not restoring
the filter) is the default diagnostic set if the vehicle never exceeds 10 mg/mi with the GPF
removed.
3.4 PHEV Accounting
3.4.1 Proposed Approach for the Revised PHEV Utility Factor
EPA is proposing to revise the light-duty vehicle PHEV Fleet Utility Factor curve used in
CO2 compliance calculation for PHEVs, beginning in MY 2027. The agency believes the current
LD vehicle PHEV compliance methodology significantly underestimates PHEV CO2 emissions.
The mechanism that is used to apportion the benefit of a PHEV's electric operation for purposes
of determining the PHEVs contribution towards the fleet average GHG requirements is the fleet
utility factor (FUF), further explained below. We have analyzed available data and compiled
literature (Krajinska, Poliscanova, Mathieu, & Ambel, Transport & Environment 2020), (Plotz,
P., Moll, C., Bieker, G., Mock, P., Li, Y. 2020), (Plotz, P., Link, S., Ringelschwendner, H.,
Keller, M., Moll, C., Bieker, G., Dornoff, J., Mock, P. 2022), (Patrick Plotz et al 2021) showing
that the current utility factors are overestimating the operation of PHEVs on electricity, and
therefore would underestimate the CO2 g/mi compliance result. The current and proposed FUF's
are shown in Figure 3-27, shown below.
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Fleet Utility Factors
—
- SAE J2841 FUF
- FUF Proposed
/•
/
/
/"
/" >
/' /
/' /
/
/
/
/'
/ y
/ /
/" /
/ /
//
//
//
//
CD Range [miles]
Figure 3-27: Current and Proposed Fleet Utility Factor for PHEV Compliance
The current FUFs were developed in SAE 2841 (SAE J2841 2010) and are used to estimate
the percentage of operation that is expected to be in charge depleting mode (vehicle operation
that occurs while the battery charge is being depleted, sometimes referred to as electric range.).
The measurement of the charge depleting (CD) range is performed over the EPA city and
highway test cycles, also called the 2-cycle tests. The tested cycle specific charge depleting range
is used as an input to the FUF curves (or lookup tables, as shown in Tables 1 and 2 in 40 CFR
§600.116-12) to determine the specific city and highway FUFs. The resulting FUFs are used to
calculate a composite CO2 value for the city and highway CO2 results, by weighting the charge
depleting CO2 by the FUF and weighting the charge sustaining (CS) CO2 by one minus the FUF.
The FUFs developed in SAE J2841 rely on a few important assumptions and underlying data:
(1) trip data from the 2001 National Household Travel Survey,35 used to establish daily driving
distance assumptions, and (2) the assumption that the vehicle is fully charged before each day's
operation. These assumptions are important because they affect the shape of the utility factor
curves, and therefore affect the weighting of CD (primarily electric operation)36 CO2 and CS
35 We used the latest NHTS data (2017) and executed the utility factor code that is in SAE J2841, Appendix C, and
found that the latest NHTS data did not significantly change the utility factor curves. NHTS data can be found at
U.S. Department of Transportation, Federal Highway Administration, 2017 National Household Travel Survey.
URL: https://nlits.ornl.gov/
36 The complexity of PHEV designs is such that not all PHEVs operate solely on the electric portion of the
propulsion system even when the battery has energy available. Engine operation during these scenarios may be
required because of such design aspects as blended operation when both the electric power and the engine are being
utilized, or during conditions such as when heat or air conditioning is needed for the cabin and can only be obtained
with engine operation
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(primarily internal combustion engine operation)37 CO2 in the compliance value calculation.
SAE J2841 was developed more than ten years ago during the early introduction of light-duty
PHEVs and at the time was a reasonable approach for weighting the CD and CS vehicle
performance for a vehicle manufacturer's compliance calculation given the available information.
The PHEV market has since grown, and there is significantly more real-world data available to
EPA on which to design an appropriate compliance program for PHEVs. The agency believes
that the use of an FUF is still an appropriate and reasonable means of calculating the contribution
of PHEVs to GHG emissions and compliance, but the real-world data available today no longer
supports the FUF established in SAE J2841 more than a decade ago.
Because the tailpipe CO2 produced from PHEVs varies significantly between CD and CS
operation, both the charge depleting range and the utility factor curves play an important role in
determining the magnitude of CO2 that is calculated for compliance. In charge depleting mode
EPA is proposing to maintain a zero gram per mile contribution when the internal combustion
engine is not running. The significant difference noted above is the difference between,
potentially, zero grams per mile in CD mode versus CO2 grams per mile that are likely to be
similar to a hybrid (non-plug-in) vehicle. The charge depleting range for a PHEV is determined
by performing single cycle city and highway charge depleting tests according to SAE Standard
J1711 (SAE J1711 2023), Recommended Practice for Measuring the Exhaust Emissions and
Fuel Economy of Hybrid-Electric Vehicles, Including Plug-In Hybrid Vehicles. The charge
depleting range is determined by arithmetically averaging the city and highway range values
weighted 55 percent and 45 percent, respectively, as noted in §600.3 ll-12(j)(4)(i) (Title 40 CFR
§ 600.311-12 2021).
3.4.1.1 FUF Comparisons with Real World Data
Recent literature and data have identified that the current utility factor curves may
overestimate the fraction of driving that occurs in charge depleting operation (Plotz, P. and
Johrens, J. 2021), (Transport & Environment 2022). This literature also concludes that vehicles
with lower charge depleting ranges have even greater discrepancy in CO2 emissions.
EPA and ICCT (Aaron Isenstadt, Zifei Yang, Stephanie Searle, John German 2022) have also
evaluated recently available California Bureau of Automotive Repair (BAR) OBD data,
(California Air Resource Board [OBD data records] 2022) that has been collected through the
California Bureau of Automotive Repair and found that the data shows that, on average, there is
more charge sustaining operation and more gasoline operation than is predicted by the current
fleet utility factor curves. The BAR OBD data enable the evaluation of real-world PHEV
distances travelled in various operational modes; these include charge-depleting engine-off
distance, charge-sustaining engine-on distance, total distance traveled, odometer readings, total
fuel consumed, and total grid energy inputs and outputs of the battery pack. These fields of data
allow us to use the BAR OBD data to filter the data and calculate 5-cycle comparable real-world
37 Because most CD operation occurs without engine operation, the CO2 value for CD operation is often 0 or near 0
g/mi. This means that a high utility factor results in a CO2 compliance value that is heavily weighted with 0 or near
0 g/mi.
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driving ratios of charge depleting distance to total distance and to then compare to the existing
FUFs, using the 5-cycle range from the fuel economy and environment label.38
There are some limitations to the PHEV data collected through the BAR OBD data. Data
collection occurs through the California Bureau of Automotive Repair and is limited to vehicles
with ownership changes, vehicles entering the state, or vehicles that are at least 8-years old
(California Bureau of Automotive Repair [OBD data records] 2022). In addition, the PHEV
BAR OBD data requirements are recent; they began in model year 2019 and were not fully
phased in until model year 2021 (California Bureau of Automotive Repair [OBD data records]
2022). The dataset also contains some reporting errors and some very low mileage data.
To address some of the data collection issues, the BAR OBD data were filtered to exclude low
mileage vehicles, vehicles with extreme or conflicting data, and vehicles that were missing
critical data such as total distance travelled. Similar to the ICCT data filtering, EPA filtered the
CARB OBD data by removing vehicles that met the following criteria: vehicles with less than
3000 km total distance travelled; vehicles and that have odometer readings that are greater than
20 percent different from the total distance travelled data; and vehicles where the total grid
energy inputs and outputs of the battery pack differed by more than 20 percent.
38 Because the data collected is real-world data, we used the combined city and highway 5-cycle label range as an
input to the FUF curve described in SAE J2841, to create an apples-to-apples comparison. The existing regulatory
FUFs are separate city and highway curves, and the charge depleting ranges that are used with the city and highway
FUF curves are 2-cycle range.
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Minimum VMT [km]
Figure 3-28: FUFs with various data filtering sensitivities
To investigate data sampling sensitivities, we used the minimum VMT values shown in
Figure 3-28 (above) for filtering data using the October 2022 BAR OBD data (California Air
Resource Board [OBD data records] 2022). As shown in Figure 3-28, the relative FUFs over the
SAE J2841 FUFs are not significantly different at various minimum VMT filtering.
As of October 2022, the BAR OBD dataset has around 8,400 PHEV vehicles, and over 233.2
million vehicle miles traveled. The filtered dataset has 30 PHEV models, and 2060 individual
vehicles that travelled 58.9 million miles.
A comparison of the results of EPA's data analysis of the BAR OBD data to the ICCT
analyses is shown below in Figure 3-29. The combined city and highway FUF in SAE J2841
(corresponding to the 55 percent city/45 percent highway weighing of the city and highway
FUFs) in the current regulations is labeled as "SAE J2841 FUF". EPA's data analysis of the
CARB OBD data is labeled as "Linear Regression Fit" and the two ICCT curves are labeled as
"ICCT-BAR" and "ICCT-FUELLY". The EPA "Linear Regression Fit" (where about 78 percent
of the total data points are between 12- to 32-miles for the CD range when fitting samples >= 5)
lies on top of the "ICCT-BAR" curve, showing good agreement between the two separate
analyses of the BAR OBD data. In addition to the BAR OBD data, ICCT also evaluated a dataset
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from Fuelly.com.39; The curve that is fitted from the Fuelly.com data also yields lower utility
factors than the SAE J2841 FUF curve, for the same charge depleting distance; however, the
Fuelly curve is not as low as the BAR OBD curve.
The BAR OBD data is a recent and relatively large dataset that includes the charge depleting
distance (or electric operating distance) and total distance, which makes it a reasonable source
for evaluating the real-world utility factors for recent PHEV usage. However, we recognize that
the curve developed from this data is a departure from the SAE J2841 FUF curves, that the BAR
OBD data has some limitations (described above), and that the original SAE J2841 FUF
methodology was also a reasonable approach at the time it was adopted. Therefore, we created
the proposed curve by averaging the SAE J2841 FUF curve and the ICCT-BAR curve. The
resulting proposed FUF curve lies almost on top of the ICCT-FUELLY curve. Some of the data
suggest that a lower curve might more appropriately reflect current real-world usage, however,
EPA recognizes that PHEV technology has the potential to provide significant GHG reductions
and an overly low FUF curve could disincentive manufacturers to apply this technology. In
addition, anticipated longer all-electric range and greater all-electric performance, partially
driven by CARB's ACC II program, as well as increased consumer technology familiarity and
available infrastructure should result in performance more closely matching our proposed curve.
EPA will continue to monitor real-world data as it becomes available.
The proposed curve (see Figure 3-29, "FUF Proposed") is based on the Equation (3-1), (Title
40 CFR 600.116-12 2022) using the SAE J2841 FUF weighting coefficients, and a new
normalized distance (ND) of five hundred eighty-three (583) miles. Other UF curves shown
include: the current SAE J2841 FUF, which uses the combined city /highway FUF coefficients,
and a ND of 399.9 miles; the label MDIUF40 (SAE J2841 MDIUF), which uses the MDIUF
weighting coefficients and a ND of 400 miles; the ICCT-developed curve for the Fuelly data
(ICCT-FUELLY), which uses the MDIUF coefficients, and a ND of seven hundred (700) miles
and the ICCT-developed curve for the BAR OBD data (ICCT-BAR), which uses the MDIUF
coefficients, and a ND of nine hundred eighty-five (985) (Aaron Isenstadt, Zifei Yang, Stephanie
Searle, John German 2022) miles.
The FUF and the MDIUF weighting coefficients (Cj) of the UF Proposed, ICCT-BAR, and
ICCT-FUELLLY curves are listed in Table 2 of the SAE J2841 standard (SAE J2841 2010).
where:
CD = charge depleting range in miles
ND = normalized distance
Cj = the weighting coefficient for term j
k = number of coefficients (10 for the MDIUF Fit and 6 for the FUF Fit)
39 Fuelly [aggregated user-reported fuel economy data]. 2022. Retrieved from https://www.fuelly.com/car
40 The SAE J2841, the FUF is recommended for fleet vehicle fuel consumption calculations, and the MDIUF is
recommended to estimate of an individual vehicle's fuel economy. EPA has incorporated the FUF for compliance
calculations, and the MDIUF for fuel economy labelling calculations. Among other differences, the MDIUF is a
vehicle-weighted calculation, and the FUFs are VMT distance-weighted calculations..
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30 40 50
CD Range [miles]
Figure 3-29: The Proposed FUF, SAE MDIUF/FUF, and ICCT BAR/FUELLY Curves
SAE J2841 MDIUF
SAE J2841 FUF
ICCT-FUELLY
FUF Proposed
ICCT-BAR
Linear Regression Fit
MDL1: 38
MDL2: 26
MDL3: 56
MDL4: 71
MDL5; 30
MDL6: 43
MDL7: 35
MDL8: 6
MDL9: 66
MDL1Q: 4
MDL11: 30
MDL12:1
MDL13:15
MDL14: 24
MDL15: 4
MDL16: 737
MDL17: 209
MDL18: 4
MDL19:17
MDL20; 13
MDL21:107
MDL22: 7
MDL23: 42
MDL24: 26
MDL25: 7
MDL26: 1
MDL27:12
MDL28: 193
MDL29: 54
MDL30:182
JT 06
5 0.4
Five hundred eighty-three (583) Normalized Distance (ND) was calculated by the
minimization of the sum of the squared residual norm in Equation (3-2) when iterating the
normalized distance constant /.
£3
A
l>
Z71—400 p
0.5 (UFsae y2841 FUF + UFICcT-BAr)
i=0 L
where:
ND\ = normalized distance for term j from 400 to 985
PHEV UF (VMT >
U Fpr0p0secL, ND j
= 3000km)
c
As stated above, the proposed FUF curve in Figure 3-29 is constructed using the averages of
the SAE J2841 FUF curve and the ICCT-BAR curve from the real-world charging data with the
latest BAR OBD open-source data records. This method creates a proposed FUF curve that is
adjusted to better reflect the real-world PHEV data (California Bureau of Automotive Repair
[OBD data records] 2022).
Table 3-20 shows PEDEV vehicles that had sample sizes greater than or equal to 10 in the
CARB OBD dataset (California Air Resource Board [OBD data records] 2022) and also includes
several additional high-volume PFlEVs. The compliance CO2 results range from a 19.5% to
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47.8% (median = 31%) increase in CO2 g/mi, for the example vehicles below, when using the
proposed FUF compared to the existing FUF.
Table 3-23: CO2 Emissions [g/mi] Calculated using Existing FUF and Proposed FUF
Mode
Manufacturer
PHEV Model
Existing:
Proposed:
1 Year
Compliance CO2
using Existing FUF
Estimated Compliance
CO2 using Proposed FUF
2022
AUDI
Q5E
116.3
165.7
2022
BMW
330E
100.2
132.5
2021
BMW
530E
114.4
147.1
2020
BMW
18
126.7
156.8
2021
BMW
X3 xDrive
136.6
168.5
2022
BMW
X5
108.5
154.3
2019
CHEVROLET
VOLT
29.9
44.2
2021
CHRYSLER
PACIFICA
73.0
97.1
2022
FORD
ESCAPE
47.9
63.8
2021
HONDA
CLARITY
33.4
47.9
2022
HYUNDAI
IONIQ
47.3
62.0
2019
HYUNDAI
SONATA PHEV
63.7
86.0
2021
JEEP
WRANGLER 4XE
161.0
202.7
2022
KIA
NIRO
59.4
75.9
2020
KIA
OPTIMA PHEV
59.8
80.1
2022
KIA
SORENTO SX
68.7
90.5
2020
MERCEDES-BENZ
GLC 350E
122.5
160.4
2022
MINI
COOPER
116.7
142.3
2022
SUBARU
CROSSTREK
99.0
118.3
2022
TOYOTA
PRIUS PRIME
57.5
70.6
2022
TOYOTA
RAV4 PRIME
55.1
71.7
2022
VOLVO
S60
111.0
148.3
2022
VOLVO
XC60
155.2
196.8
2022
VOLVO
XC90
149.0
186.4
We believe that it is important for PHEV compliance utility factors to accurately reflect the
apportionment of charge depleting operation, for weighting the 2-cycle CO2 test results;
therefore, we are proposing to update the city and highway fleet utility factor curves with a new,
single curve that is shown in Figure 3-27 above. We are proposing a single curve to better reflect
real world performance where the underlying real-world data is not parsed into city and highway
data. Since the fleet average calculations are based on a combined city and highway CO2 value, a
single FUF curve can be used for these calculations.
3.5 GHG Emissions Control Technologies
3.5.1 Engine Technologies
The following is detailed information about the ALPHA inputs for internal combustion
engines used to create ALPHA Outputs for Response Surface Equations (RSE's) used by
OMEGA. These were first discussed and listed in Table 2-2. Specific details about each engine
are contained in the engine's data package available on EPA's webpage (U.S. EPA 2023b). Each
engine data package is contained in a .zip file identified using the engine name mentioned in the
caption of the associated ALPHA efficiency map shown below.
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3.5.1.1 2013 Chevrolet 2.5L Ecotec LCV Engine Reg E10 Fuel
This naturally aspirated engine features continuously variable valve timing, high-pressure
direct injection, electronic throttle control, coil-on-plugs and has an 11.3:1 compression ratio.
Testing was conducted in a test cell operated by FEV Engine Technologies and purchased to
support the Mid Term Evaluation (MTE) Engine Benchmarking project. (Newman, K., Kargul,
J., and Barba, D. 2015).
2013 Chevrolet 2.5L Ecotec LCV Engine Reg E10 Fuel
Brake Thermal Efficiency { % )
Speed ( RPM )
Figure 3-30 2013 Chevrolet 2.5L Ecotec LCV Engine Reg E10 Fuel (U.S. EPA 2023b)
3.5.1.2 GT Power Baseline 2020 Ford 7.3L Engine from
Argonne Report Tier 3 Fuel41
This medium-duty naturally aspirated engine included port fuel injection, a 2-valve head, and
a 10.5 compression ratio. The engine was modeled in GT-Power® and then calibrated and
validated against test data available at Southwest Research Institute or provided by the Original
Equipment Manufacturers (OEMs). (Thomas E. Reinhart 2021) The provided baseline model
was only configured to simulate wide-open throttle operation with power enrichment and used a
Wiebe function for describing combustion. Once the model achieved satisfactory results, the
41 Not included in the draft but are likely to be added to the analysis.
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engine performance was mapped over the speed and load range. The image and any supporting
data available were digitized by loading the image into MATLAB and manually tracing the
efficiency contours.
GT Power Baseline 2020 Ford 7.3L Engine from Argonne Report Tier 3 Fuel
Brake Thermal Efficiency (%)
375 kW
350 kW
325 kW
300 kW
275 kW
250 kW
225 kW
200 kW
175 kW
150 kW
125 kW
100 kW
75 kW
50 kW
25 kW
12.5 kW
0 0
500 1000 1500 2000 2500 3000 3500 4000 4500 5000
Speed ( RPM )
Figure 3-31 GT Power Baseline 2020 Ford 7.3L Engine
from Argonne Report Tier 3 Fuel (U.S. EPA 2023b)
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3.5.1.3 2014 Chevrolet 4.3L EcoTecS LV3 Engine LEVIII Fuel
Features of this engine include side mount direct-injection, cylinder deacti vation,
continuously variable valve timing, pushrod, single cam, and active fuel management. The
engine uses cylinder deactivation to improve thermal efficiency by reducing pumping losses
during low-load operation. This testing was performed by the EPA at the National Center for
Advanced Technology (NCAT) with the engine installed in a dynamometer test cell tethered as
though the engine were operating in the vehicle. (Mark Stuhldreher 2016) Two methods of
coupling the engine to the dynamometer were needed to gather data where the torque
measurement was very sensitive to the engine's torsional accelerations. Direct drive shaft engine
to dynamometer coupling worked best to gather most of the data but where needed, the engine
was coupled to the dynamometer through its transmission and torque converter.
225 kW
2014 Chevrolet 4.3L EcoTec3 LV3 Engine LEV III Fuel - Cylinder Deac Disabled
Brake Thermal Efficiency ( % )
250 kW
200 kW
175 kW
150 kW
125 kW
100 kW
75 kW
50 kW
25 kW
12.5 kW
1000 1500 2000 2500 3000 3500 4000 4500 5000 5500
speed ( RPM )
Figure 3-32 2014 Chevrolet 4.3L EcoTec3 LV3 Engine LEV III Fuel - Cyl Deac Disabled
(U.S. EPA 2023b)
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3.5.1.4 2013 Ford 1.6L EcoBoost Engine LEV III Fuel42
The selected feature of this turbocharged gasoline engine was the inclusion of spray-guided
direct-injection. The testing was performed by the EPA at the National Center for Advanced
Technology (NCAT) with the engine installed in a dynamometer test cell tethered as though the
engine were operating in the vehicle. (Mark Stuhldreher, Charles Schenk, Jessica Brakora, David
Hawkins, Andrew Moskalik, and Paul DeKraker 2015)
2013 Ford 1.6L EcoBoost Engine LEV III Fuel
Brake Thermal Efficiency ( % )
100
- 0
1000 1500 2000 2500 3000 3500 4000 4500 5000 5500
Speed { RPM )
Figure 3-33 2013 Ford 1.6L EcoBoost Engine LEV III Fuel (U.S. EPA 2023b)
42 Not included in the draft but are likely to be added to the analysis.
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3.5.1.5 2015 Ford 2.7L EcoBoost Engine Tier 3 Fuel
This turbocharged engine features intake and exhaust cam phasing, direct injection, and
integrated exhaust manifolds. The testing was performed by the EPA at the National Center for
Advanced Technology (NCAT) with the engine installed in a dynamometer test cell tethered as
though the engine were operating in the vehicle. This testing provided thorough test data for
constructing the main operating portion of the engine map. There was also subsequent testing in
a heavy-duty test cell to generate additi onal data for the high speed and high load mapping
needed to construct a more complete engine map.
o o
1000 1500 2000 2500 3000 3500 4000 4500 5000 5500
Speed ( RPM )
Figure 3-34 2015 Ford 2.7L EcoBoost V6 Engine Tier 3 Fuel (U.S. EPA 2023b)
2015 Ford 2.7L EcoBoost V6 Engine Tier 3 Fuel
Brake Thermal Efficiency ( % )
CL 15 -
LU
2
CO
275 kW
250 kW
225 kW
200 kW
175 kW
150 kW
125 kW
100 kW
75 kW
50 kW
25 kW
12.5 kW
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3.5.1.6 2016 Honda 1.5L L15B7 Engine Tier 3 Fuel
Features of this engine include direct-injection, single-scroll turbocharger, and dual variable
valve timing control (VTC). Fhe testing was performed by the EPA at the National Center for
Advanced Technology (NCAT) with the engine installed in a dynamometer test cell tethered as
though the engine were operating in the vehicle. (Stuhldreher, Mark; Kargul, John; Barba, Daniel
; McDonald, Joseph; Bohac, Stanislav; Dekraker, Paul; Moskalik, Andrew; 2018) The engine
was coupled to the dynamometer using a modified manual transmission and clutch with a
torsional spring assembly and rubber isolated driveshaft to allow for stable torque measurements.
Both steady-state and transient engine test data were collected during the benchmark testing.
Two different test procedures were needed to appropriately replicate steady-state engine
operation at low/mid loads and transient engine operation at high loads when the engine is
protecting itself.
2016 Honda 1.5L L15B7 Engine Tier 3 Fuel
Speed ( RPM )
Figure 3-35 2016 Honda 1.5L L15B7 Engine Tier 3 Fuel (U.S. EPA 2023b)
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3.5.1.7 Volvo VEP 2.0L LP Gen3 Miller Engine from 2020 Aachen Paper Octane
Modified for Tier3 Fuel
This Miller cycle engine includes an increased compression ratio, a short intake valve opening
duration, an integrated exhaust manifold, a new intake port and piston design together with a
VGT turbo as described in Dahl et al (2020), "The New Volvo Mild Hybrid Miller Engine"
presented in Aachen Colloquium Automobile and Engine Technology. (Daniel Dahl, Ayolt
Helmantel, Fredrik Wemmert, Mats Moren, Staffan Rengmyr, and Ali Sahraeian 2020) The
image provided in this paper was digitized by loading the image into MATLAB and manually
tracing the efficiency contours. NCAT used the peak BSFC and BTE values referenced in the
paper to calculate the lower heating value for the test fuel having a reported RON of 98 and
because the authors did not provide any test data for this engine using Tier 3 fuel, the decision
was made to use ALPHA'S Octane Modifier to also develop an estimated Tier 3 fuel map.
o
1000 1500 2000 2500 3000 3500 4000 4500 5000
Speed ( RPM )
Figure 3-36 Volvo 2.0L VEP LP Gen3 Miller Engine from 2020 Aachen Paper
Octane Modified for Tier 3 Fuel (U.S. EPA 2023b)
Volvo 2.0L VEP LP Gen3 Miller Engine from 2020 Aachen Paper
Octane Modified for Tier 3 Fuel
Brake Thermal Efficiency { % )
250
165 kW
150 kW
135 kW
120 kW
105 kW
90 kW
75 kW
60 kW
45 kW
30 kW
15 kW
7.5 kW
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3.5.1.8 Geely 1.5L Miller GHE from 2020 Aachen Paper Octane Modified for Tier
3 Fuel
Zhang et al (2020), "Geely HybridEngine: World Class Efficiency for Hybrid Vehicles"
presented in the 29th Aachen Colloquium (GuiQiang Zhang, Qian Wang, Guang Chen, et al.
2020) reported this engine has a high efficiency Miller-cycle combustion system with high
tumble and turbulence kinetic energy, low friction, optimized mixture formation using a new 350
bar fuel injection system and a 13:1 compression ratio. These features are then combined with a
fully matched turbocharger with highly cooled low pressure EGR and a water-charge air cooler.
The image provided in this paper was digitized by loading the image into MATLAB and
manually tracing the efficiency contours. Since the fuel used to map this engine had a relatively
high lower heating value there was an assumption of a likely corresponding high RON value of
98 and because the authors did not provide any test data for this engine using Tier 3 fuel, the
decision was made to use ALPHA'S Octane Modifier to also develop an estimated Tier 3 fuel
map.
130 kW
120 kW
110 kW
kW
90 kW
80 kW
70 kW
60 kW
50 kW
40 kW
30 kW
20 kW
10 kW
5 kW
Brake Thermal Efficiency ( % }
—-——2s
V, — 11— 23 23
20 20 -^0 ¦ 20
IV ~1S= __ 15 lT
10 10 10~ 10
I " * I I I - 1 I ' '• I
1000 1500 2000 2500 3000 3500 4000 4500 5000
Speed ( RPM )
Figure 3-37 Geely 3-cyl 1.5L Miller GHE from 2020 Aachen Paper
Octane Modified for Tier 3 Fuel (U.S. EPA 2023b)
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3.5.1.9 2018 Toyota 2.5L A25AFKS Engine Tier 3 Fuel
This 4-cylinder, naturally aspirated, Atkinson Cycle gasoline engine with cooled-EGR also
includes direct & port injection, VVT electric intake & hydraulic exhaust, high induction
turbulence/high speed combustion, high energy ignition, friction reduction, a variable capacity
oil pump, and an electric water pump. The testing was performed by the EPA at the National
Center for Advanced Technology (NCAT) with the engine installed in a dynamometer test cell
tethered as though the engine were operating in the vehicle. The engine was coupled to the
dynamometer using an automatic transmission and torque converter to allow for an accurate
gathering of test data where the torque measurement is very sensitive to the engine's torsional
accelerations. (John Kargul, Mark Stuhldreher, Dan Barba, Charles Schenk, Stani Bohac, Joseph
McDonald, and Paul Dekraker 2019)
2018 Toyota 2.5L A25A-FKS Engine Tier 3 Fuel
Brake Thermal Efficiency ( % )
Speed (RPM )
Figure 3-38 2018 Toyota 2.5L A25A-FKS Engine Tier3 Fuel (U.S. EPA 2023b)
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3.5.1.10 Toyota 2.5L TNGA Prototype Hybrid Engine from 2017 Vienna Paper
Octane Modified for Tier 3 Fuel
This inline 4 cylinder 2.5L gasoline naturally aspirated (NA) engine is thoroughly described
in Tadashi Toda et al (2017), "The New Inline 4 Cylinder 2.5L Gasoline Engine with Toyota New
Global Architecture Concept" presented at Internationales Wiener Motorensymposium. (T.
Toda, M. Sakai, M. Hakariya, and T. Kato 2017) Features include high energy ignition coil
rnotor-driven VVT for Atkinson cycle, a D-4S system (direct and port injection) with new multi
hole injectors, cooled EGR, and a variable oil-pressure pump system. The image provided in this
paper was digitized by loading the image into MATLAB and manually tracing the efficiency
contours. There was no information presented regarding the fuel used for the map, so the
decision was made assuming that data in the paper was based on a Tier 2 fuel and to use
ALPHA'S Octane Modifier to develop an estimated Tier 3 fuel map.
Toyota 2.5L TNGA Prototype Hybrid Engine from 2017 Vienna Paper Octane Modified for Tier 3 Fu(
Brake Thermal Efficiency ( % )
135 kW
oL
1000 1500 2000 2500 3000 3500 4000 4500 5000
Speed ( RPM )
Figure 3-39 Toyota 2.5L TNGA Prototype Hybrid Engine from 2017
Octane Modified for Tier 3 Fuel (U.S. EPA 2023b)
5500
Vienna Paper
10 -
120 kW
105 kW
90 kW
75 kW
60 kW
45 kW
30 kW
15 kW
7.5 kW
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3.5.2 Electrification Technologies
The following is detailed information about the ALPHA inputs for electric inverters, motors,
and generators used to create ALPHA Outputs for Response Surface Equations (RSE's) used by
OMEGA. These were first discussed and listed in Table 2-3. Specific details about each electric
motor are contained in the engine's data package available on EPA's webpage (U.S. EPA 2023a).
Each engine data package is contained in a .zip file identified using the electric motor name
mentioned in the caption of the associated ALPHA efficiency map shown below.
3.5.2.1 2010 Toyota Prius 60kW 650V MG2 EMOT
The 60kW 650V MG2 electric motor paired with an inverter and a 36hp (27kW) nickel-metal
hydride battery pack was combined with a 1.8L 4-cylinder Atkinson cycle engine. The
component benchmarking testing for this program was conducted by Oak Ridge National
Laboratory's (ORNL) Power Electronics and Electric Machinery Research Center (PEEMRC), a
broad-based research center for power electronics and electric machinery (e-motor)
development. (Olszewski, Mitch 2011) The resulting measurements were used to create a
combined efficiency map of the main drive e-motor and inverter without including any gearing,
categorized together as an EMOT.
250 kw
200 kW
150 kW
100 kW
-100 kW
-150 kW
-200 kW
-250 kW
-300 kW
0 2000 4000 6000 8000 10000 12000
Speed ( RPM )
Figure 3-40 2010 Toyota Prius 60kW 650V MG2 EMOT (U.S. EPA 2023a)
2010 Toyota Prius 60kW 650V MG2 EMOT
Efficiency (% )
300 kW
50 kW
25 kW
-25 kW
-50 kW
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3.5.2.2 Est 2010 Toyota Prius 60kW 650V MG1 EMOT
The Toyota Prius uses a secondary electric motor called an MG1, which functions as a
generator to transfer power from the ICE to recharge the battery. Oak Ridge National Laboratory
(ORNL) did not specifically benchmark this electric generator motor, presumably because of its
similarity to the MG2 electric drive motor discussed in the previous section. (Olszewski, Mitch
2011) However, chassis test data provided by Southwest Research Institute (SwRI) indicated the
maximum operating power for the MG1 generator motor is different than the MG2 drive motor.
The maximum power curve for the MG1 is a constant value rather than variable as MG2's power
curve. Consequently, the MG2 ORNL benchmark data was used along with the max power data
provided from the SwRI chassis test data to create a constant power version for the MG1. The
MG1 efficiency map estimates the combined efficiency of the main generator e-motor and its
inverter, categorized together as an EMOT. The "Est" in the front of the e-motor's name indicates
that it is an estimated map.
250 kW
200 kW
150 kW
100 kW
-100 kW
-150 kW
-200 kW
-250 kW
2010 Toyota Prius 60kW 650V MG1 EMOT
Efficiency (% )
300 kW
50 kW
25 kW
-25 kW
-50 kW
-300
0 2000 4000 6000 8000 10000 12000
Speed ( RPM )
Figure 3-41 Est 2010 Toyota Prius 60kW 650V MG1 EMOT (U.S. EPA 2023a)
kW
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3.5.2.3 2011 Hyundai Sonata 30kW 270V EMOT
This 30 kW 270V electric motor was paired with an inverter, categorized together as an
EMOT, and powered by a 270-volt lithium polymer battery. The map was created using
benchmarked data that measured the efficiency of the combination of the main drive e-motor and
its inverter without including any gearing. The component testing for this program was
conducted by Oak Ridge National Laboratory's (ORNL) Power Electronics and Electric
Machinery Research Center (PEEMRC), a broad-based research center for power electronics and
electric machinery (e-motor) development (Rogers, Susan 2012)
2011 Hyundai Sonata 30kW 270V EMOT
Efficiency (% )
) 1000 2000 3000 4000 5000 6000
Speed ( RPM )
Figure 3-42 2011 Hyundai Sonata 30kW 270V EMOT (U.S. EPA 2023a)
100 kW
50 kW
-50 kW
-100 kW
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3.5.2.4 2012 Hyundai Sonata 8.5kW 270V BISG
Hyundai's Hybrid Starter Generator (HSG) electric motor with published specifications listed
as 43 Nm, 8.5kW, and 15,750 rpm was paired with an inverter and powered by a 270-volt
lithium polymer battery. The application of this type of electric motor is normally found in mild
hybrid electric vehicles (MHEV), often called P0 mild hybrids. The goal was to create a map
representing the combined efficiency of the starter/generator motor, its inverter, and the drive
belt, categorized together as a BISG (belt-inverter-starter/generator). The component testing for
this program was conducted by Oak Ridge National Laboratory's (ORNL) Power Electronics
and Electric Machinery Research Center (PEEMRC), a broad-based research center for power
electronics and electric machinery (e-motor) development. (Rogers, Susan 2013)
2012 Hyundai Sonata 8.5kW270V BISG
Efficiency (%)
0 1000 2000 3000 4000 5000 6000
BISG Output Speed ( RPM )
Figure 3-43 2012 Hyundai Sonata 8.5kW 270V BISG (U.S. EPA 2023a)
a.
O
0
w
co
70 kW
60 kW
50 kW
40 kW
30 kW
20 kW
10 kW
5kW
-5 kW
-10 kW
-20 kW
-30 kW
-40 kW
-50 kW
-60 kW
-70 kW
3-94
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3.5.2.5 Generic IPM 150kW EDU
The Generic IPM 150kW 350V Electric Drive Unit (EDU) efficiency map was generated
using confidential benchmarking test data from several state-of-the-art internal permanent
magnet synchronous reluctance (IPMSRM) e-motors used in current production battery electric
vehicles. Transformation functions whose coefficients- represent the averaged power
consumption data were utilized to blend and transform the confidential test data. The final map
was then scaled to 150kW to represent a generic EDU suitable for use in a BEV. The generated
efficiency map represents the combined operating boundaries and electrical power consumption
of the electric motor, inverter, and gearing, categorized together as an EDU. The gear ratio for
this EDU is 9.5:1.
Efficiency ( % )
Speed (RPM )
Figure 3-44 Generic IPM 150kW EDU (U.S. EPA 2023a)
3.5.3 Vehicle Architectures
A summary of the five vehicle architectures used in ALPHA 3.0 is provided in Section 2.4.4.
Figure 2-3 summarizes the five vehicle models used to simulate vehicle efficiency for this
proposal, including the conventional model used in previous versions of ALPHA, the three new
hybrid electric models, and the one new battery electric vehicle model added for ALPHA 3.0.
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3.5.4 Other Vehicle Technologies
Depending on vehicle design, other vehicle technologies such as transmissions, non-hybrid
stop-start, electrified power steering, accessories, secondary axle disconnect, low drag brakes,
and air conditioning may have been used in the creation of ALPHA outputs for the Response
Surface Equations (RSE's) used by OMEGA. These other technologies were first discussed in
the previous version of ALPHA used for the 2017 Final Determination (U.S. EPA 2017) and the
modeling has not changed. While the EPA believes that the proposed standards will be largely
met through electrification, because the proposed standards are performance based,
improvements in all vehicle and powertrain technologies will contribute to a vehicle
manufacturer's compliance.
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https://www.census.gov/retail/index.html.
U.S. EPA. 2023b. Combining Data into Complete Engine ALPHA Maps. Accessed February 2,
2023. https://www.epa.gov/vehicle-and-fuel-emissions-testing/combining-data-complete-engine-
alpha-maps.
—. 2023a. Complete Electric Motor ALPHA Maps, https://www.epa.gov/vehicle-and-fuel-
emissions-testing/combining-data-complete-emotor-alpha-maps.
U.S. EPA. 2022. "Control of Air Pollution from New Motor Vehicles: Heavy-Duty Engine and
Vehicle Standards Regulatory Impact Analysis, EPA-420-R-22-035."
https://nepis.epa.gov/Exe/ZyPDF.cgi?Dockey=P1016A9N.pdf.
—. 2022b. Data on Cars used for Testing Fuel Economy, https://www.epa.gov/compliance-and-
fuel-economy-data/data-cars-used-testing-fuel-economy.
—. 2017. "EPA-420-R-17-001: Final Determination on the Appropriateness of the Model Year
2022-2025 Light-Duty Vehicle Greenhouse Gas Emissions Standards under the Midterm
Evaluation." https://nepis.epa.gov/Exe/ZyPDF.cgi?Dockey=P100QQ91.pdf.
—. 2023. "Preamble to the Light- and Medium-Duty Multipollutant Emissions Standards."
Preamble to the Light- and Medium-Duty Multipollutant Emissions Standards.
U.S. EPA. 2016. "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." https://nepis.epa.gov/Exe/ZyPDF.cgi?Dockey=P 100Q3DO.pdf.
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U.S. EPA. 2022. "The 2022 EPA Automotive Trends Report: Greenhouse Gas Emissions, Fuel
Economy, and Technology since 1975, EPA-420-R-22-029." Accessed December 13, 2022.
https://www.epa.gov/system/files/documents/2022-12/420r22029.pdf.
U.S. EPA, CARB, U.S. DOT NHTSA. 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, Report No. EPA-420-D-16-900."
https://nepis.epa.gov/Exe/ZyPDF.cgi/P100OXEO.PDF?Dockey=P 100OXEO.PDF.
van Nieuwstadt, M., A. Shah, E. Serban, and D. Martin. 2019. "Regeneration Strategies for
Gasoline Particulate Filters." SAE Paper 2019-01-0969. doi: 10.4271/2019-01-0969.
Wood Mackenzie. 2022. Battery & raw materials - investment horizon outlook to 2032. Wood
Mackenzie.
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Chapter 4: Consumer Impacts and Related Economic Considerations
This chapter discusses the impacts of the proposed rule on consumers and related economic
considerations, where "consumer" refers to buyers and lessees of new light-duty vehicles for
personal use. Regarding consumer impacts, we examine the implications of the proposed
standards on consumers from three frames of reference, namely the purchase decision, the
ownership experience, and social benefits and costs. These three perspectives overlap but also
differ in important ways, which we discuss in this chapter. In addition, these three frames of
reference relate to EPA OMEGA modeling (see DRIA Chapter 2) and inform EPA's analysis of
costs and benefits (see DRIA Chapter 10). Furthermore, the impacts of this proposed rule on
consumers affect projections of vehicle sales and consequently inform EPA's employment
analysis, which we also discuss in this chapter.
In our representation of the purchase decision, we include costs that consumers incorporate
into their purchase decision; how consumers respond to costs; and how consumer perceptions of
technologies change or do not change over time. In the discussion of the ownership experience,
we focus on vehicle use and on consumer savings and expenses for BEVs and ICE vehicles
across three body styles. Specifically, we present projected savings and expenses for average
MY 2032 vehicles at the time of purchase and averaged over the first eight years of vehicle life.
In the discussion of consumer-related costs and benefits, we include components of social costs
and benefits that are included in the benefit-cost analysis and that have direct consumer impacts.
In the discussion of vehicle sales, we explain how sales impacts are modeled, as well as show
how total vehicle sales are expected to increase. We conclude with a discussion of employment
impacts in which we discuss potential impacts of the growing prevalence of electric vehicles,
present a quantitative discussion of partial employment impacts on sectors directly impacted by
this proposed rule, and discuss potential employment impacts on other related sectors.
4.1 Modeling the Purchase Decision
In this section, we focus our discussion on our modeling of the consumer purchase decision.
The vehicle purchase decision is a complex process (Jackman, et al. 2023, 12-14) (Taylor and
Fujita 2018). Consumers consider and value a wide array of vehicle attributes and features as
they develop and seek to satisfy their purchase criteria (Fujita, et al. 2022). Body style is a
particularly important consumer criterion. Individuals tend to consider vehicle within a body
style (Fujita, et al. 2022). Thus, we model vehicle choice within body style, namely sedan and
wagons, CUVs and SUVs, and pickups and across other vehicle attributes. Value, as in "value
for the money," is also among the most compelling vehicle attributes that consumers consider
(Fujita, et al. 2022, 748 & 754) (Jackman, et al. 2023). "Value" is a multi-factor consideration; it
includes factors such as purchase, fueling, maintenance, and repair costs, wholly or in part. Thus,
these costs play an important role in consumers' decision processes as does consumer sensitivity
to those costs, which we capture and quantify in our analyses. Also important to the vehicle
purchase decision, but harder to capture and quantify, are consumers' diverse perceptions of
other vehicle attributes. These more subjective assessments pertain to vehicle attributes such as
comfort, design, image, and performance (Fujita, et al. 2022, 748), as well as to technology (e.g.,
ICE vehicle, PEV) where decision rules (e.g., compensatory, non-compensatory), attitudes (e.g.,
technological affinity), and psychological biases (e.g., risk and uncertainty aversion, loss
aversion) may be at play (Taylor and Fujita 2018, 37).
4-1
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In the following discussion of consumers' decision processes, we narrow our focus and
modeling to the following key elements: costs that consumers incorporate into their purchase
decisions (i.e., purchase, fueling, maintenance, repair, and depreciation); how consumers respond
to costs (i.e., logit parameters); and how consumer perceptions of technologies change or do not
change over time (i.e., share weight parameters). In addition, as enablers of consumer acceptance
of PEVs grow and expand rapidly, we expect that electrification of the light-duty vehicle market
will also accelerate dramatically.43 Thus, we specifically attend to the choice consumers will
increasingly make between BEVs and ICE vehicles by estimating the proportions of new vehicle
sales expected to be BEVs and ICE vehicles. In our modeling, methods are the same for all body
styles and powertrains, though the inputs may differ. We address those differences in the
following chapters.
4.1.1 Costs Incorporated in the Purchase Decision
During the vehicle purchase decision process, consumers reference a wide variety of
information during the stages of vehicle purchase. This includes what consumers believe they
already know and what they learn from other parties (e.g., friends, family) and external sources
(e.g., vehicle labels, websites). From one consumer to the next and from one purchase to another,
this information varies in type, quality, and precision. In our representation of the vehicle
purchase decision, consumers incorporate into their purchase decisions reasonably good
estimates of the number of miles they expect to drive per year, fueling expenses and efficiency,
and other ownership expenses in addition to purchase price. In our modeling, consumers assume
that approximate annual VMT is 12,000 miles, annual non-fuel ownership costs for BEVs are
$1,600, and annual non-fuel ownership costs for ICE vehicles are $2,000. In addition, via the
fuel economy and environment label, consumers have implicit information regarding the
"refueling efficiency" of BEVs and ICE vehicles, estimated to be 0.9 and 1 respectively, which
are captured in the consumer purchase decision.44 These purchase price and ownership costs are
translated into total cost per mile, also called consumer generalized cost. This translation allows
the consumer as represented in the model to compare vehicles. It also requires costs to be spread
over time (i.e., annualize) and miles traveled. In our modeling, we annualize purchase price over
5 years using a 10% discount rate. We summarize the above information in Table 4-1.
43 There are numerous indicators of increasing and rapid electrification in the LD vehicle market. In recent years,
BEV sales have grown exponentially with more than 16.5 million PEVs on the road globally in 2021. BEV options
for consumers across body styles and price points have grown by many orders of magnitude. Large public and
private investment in BEV and EVSE technologies and deployment have been made and announced. Currently,
consumer demand for BEVs appears to be unsatiated as evidenced by BEV supply shortages and waiting lists,
suggesting that current market conditions are ripe for enabling rapid growth in PEV adoption. See Preamble I.A.2.ii
for more information. Also see Jackman et al. (2023) for a summary of PEV acceptance enablers and obstacles.
44 The fuel economy and environment label is affixed to every new vehicle sold in the United States. The test
procedures used to determine MPGe and kWh per 100 miles for BEVs take into account charging inefficiencies.
4-2
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Table 4-1: Consumer generalized cost inputs
Powertrain
BEV
ICE
Annual
VMT
(iniles)
12.000
12.000
Annual
Non-Fuel
Ownership
Costs
$1,600
$2,000
Fueling
Efficiency
0.9
With the above inputs and the objective of developing total cost per mile (aka consumer
generalized cost), we represent the consumer purchase decision with a series of related
equations, all of which represent consumer costs as consumers estimate them. We begin the
series of related equations with total cost per mile, which can logically be separated into fuel
costs per mile and non-fueling costs per mile, as in the equation below.
Total cost per mile = fuel costs per mile + nonfueling costs per mile
Fueling cost per mile depends on fuel cost, fuel economy, and refueling efficiency. However,
these measures clearly differ between BEVs and ICE vehicles due to the energy source (i.e.,
electricity and liquid fuel), units (i.e., kilowatt hours and gallons), and fueling efficiency.45
fuel costs per mileBEV = (cost per kWh * kWh per mile) -h fueling efficiency
fuel costs per mileICEV = (cost per gallon * gallons per mile) -h fueling efficiency
Note that because fueling efficiency for BEVs is 0.9, which is less than 1, dividing by fueling
efficiency increases BEV fuel cost per mile. For ICE vehicles, fueling efficiency is 1 and has no
effect on ICE vehicle fuel cost per mile.46
Non-fueling ownership costs include purchase price, often referred to as up-front or capital
costs, and annual non-fueling costs like maintenance and repair. To populate the second term of
the total cost per mile equation, we annualize non-fueling ownership costs, then convert them to
per mile values. To annualize capital costs over a 5-year time period, we first calculate the
annualization factor using a 10% discount rate.
annualization factor = rate *{l+ r-r . . ,)
1 V (1 + rate)time'Perwd-1J
Then, we multiply the annualization factor and capital costs to determine annualized capital
costs.
annualized capital costs = annualization factor * captial costs
The remaining non-fueling ownership costs are given as annual values in Table 4-1. Thus,
total annualized non-fueling costs are the sum of annualized capital costs and annual non-fueling
ownership costs. We then calculate non-fueling costs per mile by dividing that sum by estimated
45 Note that throughout the equations in the chapter, we will be abbreviating ICE vehicle to ICEV.
46 To estimate gallons per mile in OMEGA, we divide estimated onroad grams of CO2 per mile by the estimate fuel
carbon intensity.
4-3
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annual vehicle miles.47 The following equation shows this calculation and provides the last term
of the total cost per mile calculation.
nonfueling costs per mile
annualized capital costs + annual nonfueling ownership costs
annual VMT
With the above equation, we have all of the components needed to determine total cost per
mile given by the first equation in this section. Total cost per mile is the cost component of the
consumer decision process as represented in our modeling, which we also refer to as consumer
generalized cost. It is important to note that consumer generalized costs are not meant to be
perfectly consistent with costs calculated within the effects module of OMEGA. The values here
represent the perceptions and expectations of consumers during the decision process, and are not
reflective of the values used in our benefit cost analysis.
4.1.2 Consumer Response to Costs and Perceptions of Technology
Total sales are determined as described in Chapter 4.4 below. Here we focus on how we
model consumer choice and arrive at the proportions of total sales that are BEVs and ICE
vehicles, which we call market shares.48 We calculate the proportions of BEVs and ICE vehicles
as one calculates weighted averages. Thus, proportions of BEVs and ICE vehicles are given by
the market share equations given below.
weightBEV
TTLCLVkct sflQ,V6ggy —
market shareICEV
weightBEV + weightICEV
w eight ICEV
weightBEV + weightICEV
The weight components of these equations come from a logit formulation that we use to
represent consumer choice and describe below. This representation of consumer choice includes
consumer generalized costs (i.e., total costs per mile) as well as consumer response to costs (i.e.,
logit parameter) and consumer perceptions of technology (i.e., shareweight parameters).
Setting aside the mathematics of the logit formulation for now, we first describe consumer
choice conceptually. Consumers match vehicle attributes to purchase criteria in their purchase
decision (Fujita, et al. 2022). In addition to body style and powertrain, the vehicle attributes we
47 OMEGA also includes a dollar adjustment factor where needed to ensure that costs estimated in OMEGA are in a
consistent dollar year. Specifically, in the calculation of nonfueling costs per mile, the sum of annualized capital
costs and annual nonfueling ownership costs is also divided by a dollar adjustment factor. This ensures that costs are
estimated in 2020 dollars. The dollar adjustment factor is estimated using the GDP Implicit Price Deflator published
by the U.S. Bureau of Economic Analysis (see Chapter 2.6.7 of this DRIA).
48 EPA's OMEGA model estimates total vehicle production and sales separately from BEV and ICE vehicle market
shares. In short, sales are based on EIA sales projections, market conditions/modeling context not included in EIA
sales projections, market-based estimates of demand elasticity for vehicles, and producer decision processes. See
Chapter 2 and Chapter 4.4.
4-4
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incorporate into our modeling of consumer choice are represented by an estimate of generalized
consumer cost, as derived in Chapter 4.1.1. Generalized consumer cost creates a comparable
metric across the variety of vehicle attributes for all vehicles, and the monetization of purchase
price and ownership costs implicitly includes consumer preferences over vehicle attributes,
including powertrain. Thus, generalized consumer cost effectively provides an ordering of
vehicle alternatives within body styles. Typically, when presented with two identical items, a
hypothetical consumer will select the lower priced item. In reality, vehicle attributes and features
differ, as do consumers and their purchase criteria. Consumers purchase comparable vehicles
over a range of prices. Mathematically, we apply a logit exponent of 0.8 to total cost per mile
(aka consumer generalized cost), per Chapter 4.1.1, to achieve this effect.49
weightBEV = shareweightBEV * (total cost per mileBEV)l°ait
weightICEV = shareweightICEV * (total cost per mileICEV)l°ait
Our modeling separately attends to powertrain (i.e., BEVs and ICE vehicles) for several
reasons. BEV technology is "consumer facing," meaning that the technology is clearly apparent
to consumers, in addition to the vehicle attributes that consumers associate with the technology
(e.g., acceleration, noise, efficiency, repair and maintenance costs). Also, historically, new BEVs
sales made up single digit percentages of the new vehicle market. However, BEV sales have
grown rapidly, PEV approval is strong, and PEV acceptance (i.e., awareness, access, approval,
and adoption) is expected to continue to grow in response to ongoing and emerging market
enablers of PEV purchase, such as increasing exposure to and familiarity with PEVs resulting
from more models, greater PEV prevalence, expanding infrastructure, and advertising (Jackman,
et al. 2023). We capture this evolution of consumer acceptance of BEVs using parameters called
shareweights. Conceptually, shareweights represent non-cost elements of the consumer purchase
decision. These elements primarily include internal and external characteristics of individuals
and households (e.g., attitudes, demographics) and also of their physical, social, economic and
governmental systems in which they reside (Jackman, et al. 2023). Shareweights can remain
constant over time if consumer acceptance of the technology is not changing; they can increase if
consumer acceptance of the technology is increasing; or they can decrease if consumer
acceptance waning.
Mathematically, the shareweight is multiplied by the exponential term in the weight equations
above. Effectively, the shareweight mediates the effect of total cost per mile term on the
consumer purchase decision. For ICE vehicles, the shareweight in every year and in every
scenario is equal to 1. This means that consumer acceptance of ICE vehicles does not change
over time, and the ICE vehicle purchase decision, as modeled, depends on the vehicle attributes
that consumer generalized cost implicitly encapsulates. A constant shareweight of 1 reflects the
long-established nature of ICE technology in the light-duty vehicle market and the expectation
that consumer attitudes toward ICE vehicles is stable. For BEVs, shareweights increase over
time, beginning below one and increasing to or toward 1 in the No Action Case and Proposal
scenario. As such, BEV shareweights reflect growth in BEV acceptance over time, from lower
levels in the early years of BEVs, to the higher levels of BEV acceptance that we are currently
49 The Global Change Analysis Model (GCAM) also uses the logit formulation to represent economic choice
(GCAM n.d.) (Taylor 2023 (forthcoming)).
4-5
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observing, and into the future, when BEV attributes will increasingly drive the purchase decision
(Jackman, et al. 2023) as with ICE vehicles. Table 4-2 shows shareweight values for the No
Action case and Proposal by body style for BEVs and for all body styles for ICE vehicles. Figure
illustrates shareweight values for just BEVs by body style; shareweights for ICE vehicles are
always 1 for all body styles.
Table 4-2: Central case shareweight values by body style for light-duty
ICE
All body styles
Calendar Year
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
2036
2037
2038
2039
2040
2041
2042
2043
2044
2045
2046
2047
2048
2049
2050
Sedans /W agons
0.69
0.77
83
88
)2
)4
)6
)1
)9
)9
)9
BEV
CUV/SUV
Pickups
0.17
0.03
0.22
0.04
0.29
0.06
0.37
0.08
0.47
0.12
0.61
0.17
0.77
0.23
0.92
0.3 1
0.98
0.40
1.00
0.50
1.00
0.60
1.00
0.69
1.00
0.77 *
1.00
0.83
1.00
0.88
1.00
0.92
1.00
0.94
1.00
0.96
1.00
0.97
1.00
0.98
1.00
0.99
1.00
0.99
1.00
0.99
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
4-6
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Figure 4-1: Central case shareweight values by body style for LD BEVs.
0.00
2022
2027
2032
Sedan/Wagon
2037 2042
CUV/SUV Pickups
2047
The BEV shareweights shown in Figure 4-1 were developed by EPA as calibrated values using
the generalized logistic form.50 By calibrating shareweight values specifically for this analysis
rather than, for example, using values directly from GCAM or other choice models, we are
ensuring consistency with EPA's other modeling assumptions such as the projected state of ICE
and BEV technologies, production constraints, consumer awareness, charging infrastructure, etc.
Our approach to calibration involved determining the appropriate relative position of
shareweights by body style, determining appropriate value bounds, and finally, appropriate
absolute shareweight values.
We expect that the historical progression of market uptake of BEVs by body style that is
already apparent in the market, will continue in the future. Beginning with sedans, then CUVs
and SUVs, and followed by pickup trucks, we have accounted for this staggered timing of BEV
acceptance by bodystyle by including a time difference between body styles at any given
shareweight value. The resulting progression is seen as the gap between the shareweight curves
moving from left to right along the horizontal axis in Figure 4-1.
511 We use the generalized logistic form in the calibration of shareweights. Specifically, Y(t) = K A . , where t
(C+Qe~Bt)
is time and Y (t) is shareweight at time t.
4-7
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We calibrated absolute shareweight values so that the overall BEV shares produced by the
OMEGA model would align with an external projection of BEV sales published by IHS Markit
(IHS Markit 2021). For calibration purposes, but unlike our Central case analysis, we included
ZEV mandates in ACC2-adopting states consistent with consideration of state-level policies in
the IHS Markit projection. Similarly, the calibration of shareweight values did not include the
IRA BEV incentive provisions because the IHS Markit projections were made prior to the
passage of the IRA. The calibration points (for MYs 2026 and 2030) and OMEGA results for the
calibration case are shown in Figure 4-2.
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Figure 4-3: Comparison of BEV penetrations for No Action - No IRA and No Action - IRA
cases, both without ACC2
Shareweights complete the weight calculations for BEVs and ICE vehicles (i.e., the second
pair of equations in this section), and therefore, the calculation of BEV and ICE vehicle market
shares (i.e., the first pair of equations in this section). Market shares are multiplied by total
vehicle sales, per Chapter 4.4, to arrive at the OMEGA's estimate of BEV and ICE vehicle sales
4.1.3 Sensitivities
The shareweights used in the No Action case, Proposal, and Alternatives reflect the current
state of the art in terms of the scientific literature on consumer acceptance of PEVs (Jackman, et
al. 2023), existing policy-relevant models and modeling paradigms (Taylor 2023 (forthcoming)),
and calibration to third party estimates as well as Congressional investments (e.g., BIL, IRA).
We refer to those above shareweights as the Central case.
We acknowledge, however, that a very rapid transition to electric vehicles may be under way
as appears to be reflected in the popular media. In a Faster BEV Acceptance case, BEV
acceptance could rise very quickly and exceed acceptance of ICE vehicles by orders of
magnitude. For sedans and wagons this could mean that, within just a few years BEV acceptance
will match that of ICE vehicles. In other words, all else equal, a consumer is just as likely to
choose a BEV as an ICE vehicle. In fact, recent evidence from suggests that BEVs may already
be preferred, all else equal (Gillingham, et al. 2023). Specifically, Gillingham et al. (2023)
examined all new LD vehicles sold in the U.S. between 2014 and 2020 and compared existing
electric vehicles to their most similar ICE vehicle counterpart. They found that BEVs are
preferred to the ICE counterpart in some segments. In addition, a survey from Consumer Reports
in 2022 indicates that more than 70 percent of survey respondents felt that BEVs are as good or
better than ICE vehicles, up from about 46 percent in 2017. (Bartlett 2022) Assuming more rapid
BEV acceptance, BEV acceptance continues to rise notably through to 2032, at which time it
tapers off at roughly three times that of ICE vehicles. CUVs, SUVS, and Pickups follow suit,
No
No
Action
Action
case, with IRA
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4-9
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lagging somewhat in timing and not reaching the same level of preference over ICE vehicles that
sedans and wagons reach. Table 4-3 and Figure 4-4 show shareweights for faster BEV
acceptance shareweight values by body style.
Table 4-3: Faster BEV acceptance shareweight values by body style for light-duty
BEV
BEV
BEV
ICE Vehicle
Calendar Year
Sedans/W agons
CUVs/SUVs
Pickups
All body Styles
2022
0.72
0.24
0.05
1.00
2023
0.94
0.35
0.08
1.00
2024
1.19
0.50
0.11
1.00
2025
1.45
0.67
0.17
1.00
2026
1.70
0.88
0.24
1.00
2027
1.93
1.11
0.34
1.00
2028
2.14
1.35
0.46
1.00
2029
2.32
1.59
0.62
1.00
2030
2.47
1.81
0.80
1.00
2031
2.60
2.00
1.00
1.00
2032
2.69
2.17
1.20
1.00
2033
2.77
2.31
1.38
1.00
2034
2.82
2.42
1.54
1.00
2035
2.87
2.51
1.66
1.00
2036
2.90
2.58
1.76
1.00
2037
2.93
2.64
1.83
1.00
2038
2.95
2.68
1.89
1.00
2039
2.96
2.71
1.92
1.00
2040
2.97
2.73
1.95
1.00
2041
2.98
2.75
1.96
1.00
2042
2.98
2.76
1.98
1.00
2043
2.99
2.77
1.98
1.00
2044
2.99
2.78
1.99
1.00
2045
2.99
2.78
1.99
1.00
2046
2.99
2.79
2.00
1.00
2047
3.00
2.79
2.00
1.00
2048
3.00
2.79
2.00
1.00
2049
3.00
2.80
2.00
1.00
2050
3.00
2.80
2.00
1.00
4-10
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2022
2027
2032
2037
2042
2047
BEV Sedans/Wagons
• - BEV Pickups
BEV CUVs/SUVs
ICE Vehicle All body Styles
Figure 4-4: Faster BEV acceptance shareweight values by body style for light-duty
Though we believe it to be very unlikely given the thoroughness of the Central case and
evidence of BEV acceptance discussed throughout this chapter, we acknowledge that BEV
acceptance may be slower than characterized in the Central case. Jackman et al. (2023) discusses
some of the issues new vehicle buyers might have with purchasing a PEV, such as lack of
familiarity with PEVs and uncertainty about charging infrastructure. As we discuss in Chapter
5.3.1, we believe the large investments in charging infrastructure from the private sector and the
U.S. government via the BIL and IRA will counter and resolve these uncertainties over time.
Nevertheless, in characterizing slower acceptance, we assume that CUVs, SUVs, and Pickup
trucks will be less preferred than ICE vehicles for a sizeable subset of the population, perhaps
based on use cases like towing and/or remote locations. We also parametrize shareweights for
sedan and wagons, CUVs and SUVs, and pickups so that acceptance begins to grow less rapidly
in the early to mid-2030's, roughly coincident with the expiration of IRA producer and consumer
incentives. Table 4-4 and Figure 4-5 show slower BEV acceptance shareweight values by body
style for light-duty
4-11
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Table 4-4: Slower BEV acceptance shareweight values by body style for light-duty
BEV
BEV
BEV
ICE Veil
Calendar Year
Sedans /W agons
CUVs/SUVs
Pickups
All body 5
2022
0.13
0.07
0.01
1.00
2023
0.16
0.09
0.02
1.00
2024
0.20
0.12
0.03
1.00
2025
0.24
0.15
0.04
1.00
2026
0.29
0.20
0.05
1.00
2027
0.35
0.25
0.07
1.00
2028
0.41
0.31
0.10
1.00
2029
0.48
0.38
0.13
1.00
2030
0.56
0.47
0.17
1.00
2031
0.63
0.55
0.21
1.00
2032
0.71
0.64
0.25
1.00
2033
0.77
0.71
0.29
1.00
2034
0.83
0.77
0.33
1.00
2035
0.88
0.81
0.37
1.00
2036
0.91
0.84
0.40
1.00
2037
0.94
0.87
0.43
1.00
2038
0.96
0.88
0.45
1.00
2039
0.97
0.89
0.46
1.00
2040
0.98
0.89
0.47
1.00
2041
0.99
0.90
0.48
1.00
2042
0.99
0.90
0.49
1.00
2043
0.99
0.90
0.49
1.00
2044
1.00
0.90
0.49
1.00
2045
1.00
0.90
0.49
1.00
2046
1.00
0.90
0.50
1.00
2047
1.00
0.90
0.50
1.00
2048
1.00
0.90
0.50
1.00
2049
1.00
0.90
0.50
1.00
2050
1.00
0.90
0.50
1.00
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Low Acceptance
BEV Sedans/Wagons BEV CUVs/SUVs
• - BEV Pickups ICE Vehicle All body Styles
Figure 4-5: Slower BEV acceptance shareweight values by body style for light-duty
4.2 Ownership Experience
Having described how we model the consumer purchase decision in Chapter 4.1, we turn to
the estimated effects of the proposed standards on individual consumers. In this section, we focus
specifically on the ownership experience of vehicle consumers, including vehicle miles traveled
and rebound effect, consumer savings and expenses, and other ownership considerations. A
discussion of consumer-related social benefits and costs appears in Chapter 4.3.
4.2.1 Vehicle Miles Traveled and Rebound Effect
Critical to estimating the impacts of emissions standards is the number of vehicle miles
traveled (VMT). In the 2021 rulemaking, as well as in this proposed rule, we acknowledge that
individual vehicle miles vary. (U.S. EPA 2021) However, in our analyses, aggregate vehicle
miles are determined exogenously (see DRIA Chapter 9 for details). While measures and
estimates of VMT for ICE vehicles is well-established in previous EPA LD rules, and described
in DRIA Chapter 9, how much consumers drive their BEVs has been changing as the technology
evolves and BEV become more common. Thus, in the following discussion, we give particular
attention to electric vehicle miles traveled (eVMT).
The rebound effect is the means by which aggregate VMT is influenced by the policy
alternatives. The rebound effect generally refers to the additional energy consumption that may
arise from the introduction of a more efficient, lower cost energy service. Previous rules
incorporated the rebound effect based on changes in fuel cost per mile, without distinguishing
between vehicles with different fuel sources. With the growing number of battery electric
vehicles, we acknowledge that rebound may differ for BEVs and ICE vehicles. To clarify the
following discussion, we define rebound separately for ICE vehicles and for PEVs. We name
them combustion rebound and electric rebound, respectively. Whether a mile is a combustion
mile or electric mile is determined by the energy source that generates the mile, not necessarily
by the vehicle type. PHEVs, for example, produce both combustion and electric miles, BEVs
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produce only electric miles, and ICE vehicles produce only combustion miles.51 Combustion
rebound is defined as above, namely as the additional miles traveled as a result of increased
vehicle fuel efficiency and the consequent lower cost per mile of driving. For combustion
rebound, "fuel efficiency" refers specifically to liquid fuels. Electric rebound is also defined as
the additional miles traveled as a result the lower cost per mile of driving due to reduced energy
intensity (kWh/mi).
Importantly, the rebound effect offsets, to some degree, the energy savings benefits of
efficiency improvements. Because rebound driving consumes fuel and generates emissions, the
magnitude of the rebound effect influences actual fuel savings and emission reductions that will
result from the standards. Furthermore, rebound driving provides value to the consumer if they
choose to drive more. We discuss these costs and benefits in Chapter 4.3, and in Section VIII of
the Preamble. In this chapter, we address miles driven and rebound.
4.2.1.1 Basis for Vehicle Miles Traveled for Battery Electric Vehicles
The eVMT literature consists of a handful of studies, including the very recent studies listed
in Table 4-5. Two of the listed studies are based on California data collected by UC Davis
researchers, and both find that annual VMT for PEVs (eVMT) is similar to annual VMT for ICE
vehicles (Chakraborty et al. 2022; Raghavan and Tal 2021). The three other studies, using New
York, California, and national data, find that annual VMT for PEVs is less than annual VMT for
ICE vehicles (Nehiba 2022) (Burlig, et al. 2021) (Davis 2019).52 These studies offer a similar
summary of the scarce pre-existing data and research related to eVMT in the U.S. Namely,
though lower cost per mile has historically been associated with more driving, this has not been
observed for PEVs, for which the cost of driving per miles is lower than for ICE vehicles.53
Instead, average annual VMT for PEVs has historically been estimated to be lower than for ICE
vehicles. This observation has been attributed to the shorter range of first generation PEVs,
typically less than 100 miles just five or six years ago, as well as to substitution across vehicles
for multiple vehicle households, and to the typical type of households who bought an electric
vehicle in the time frame of the data (Chakraborty, Hardman and Tal 2022) (Davis 2022)
(Raghavan and Tal 2021) (Davis 2019), that is, households with characteristics correlated with
lower VMT regardless of vehicle technology (Chakraborty, Hardman and Tal 2022). This area of
research continues to face several challenges including the relatively low market penetration and
uneven distribution of PEVs; the rapid evolution of PEV technology and the PEV market; and
the relative difficulty in obtaining comprehensive data on how PEVs are driven (Burlig, et al.
2021) (Chakraborty, Hardman and Tal 2022) (Nehiba 2022) (Jackman, et al. 2023). As a result,
the data that are available for empirical analyses are not likely representative of the current and
future general population of car buyers and their driving behavior.
Table 4-5: Recent scientific studies of eVMT
Sludy Average Annual Eli-ctl ie VMT Results Da(a Description
51 Similarly, Raghavan and Tal (2021) define eVMT "as the miles driven by off-board grid electricity."
Relatedly, the fraction of VMT electrified using off-board electricity is the utility factor (UT).
52 (Nehiba 2022) notes that forthcoming work from K. Gillingham, B. Spiller, and M. Talevi finds "similar levels of
BEV mileage and a common trend of increasing mileage across model years" for vehicles in Massachusetts.
53 See Chapter 11.2.3 of this DRIA, which compares fueling costs for PEVs and ICE vehicles within its discussion
of energy security.
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(Chakrabortv. Hardman and Tal 2022)
(Raghavan and Tal 2021)
(Nehiba 2022)
(Burlig. et al. 2021)
(Davis 2019)
"Overall, we observe that factors influencing
PEV VMT are like those observed for
conventional gasoline vehicles. We find that
PEVs drive a similar amount as conventional
vehicles, not less ... as some have
suggested."
Average annual eVMT by vehicle model
ranged from 10,841 for the Nissan Leaf to
17,236 for the Tesla Model S with 80kWh
battery capacity.
"The average BEV in New York is driven
9.060 miles/year, substantially less than the
10.910 miles/year average for all passenger
cars and light truck s in New York in 2019
... However BEV vehicle miles increased
rapidly across vehicle model years,
suggesting that BEV and ... ICEV mileage
may be converging."
"... our estimates [of overall household
electricity load around EV registration
events] indicate that EV load in California is
surprisingly low. ... Given the fleet of EVs
in our sample, and correcting for the share of
out-of-home charging, our estimates
translate to approximately 1,700 electric
vehicle miles traveled (eVMT) per year for
plug-in hybrid EVs (PHEVs) and 6,700
eVMT per year for battery EVs (BEVs).
These eVMT values are substantially less
than internal combustion engine (ICE)
VMT."
"These data show that electric vehicles are
driven considerably less on average than
gasoline- and diesel-powered vehicles. In the
complete sample, electric vehicles are driven
an average of 7,000 miles per year,
compared to 10,200 for gasoline and diesel-
powered vehicles. The difference is highly
statistically significant and holds for both
all-electric and plug-in hybrid vehicles, for
both single- and multiple-vehicle
households, and both inside and outside
California."
Location: California
Years: 2015-2019
Sources: Two surveys with reported
odometer readings and on-board recorders
Number of PEVs: 16.736 (survey) and 369
(on board recorders)
Location: California
Years: 2015 (survey and logger), 2017
(survey only), 2019 (logger only)
Sources: GPS loggers on vehicles in two-car
households and online survey
Number of households: 73
Location: New York
Years: January 2017 - January 2021
Sources: Annual vehicle safety inspection
odometer readings; residential and
residential electricity prices matched by zip
code
Location: California
Year: 2014-2017
Sources: 10 percent of residential electricity
meters in the Pacific Gas and Electric
(PG&E) utility territory (362,945
households) merged with EV registration
records (63,765 vehicles)
Number of PEVs: 57,290
Name: 2017 National Household Travel
Survey
Location: United States
Year: 2017
Source: Survey with reported annual vehicle
miles
Number of PEVs: 862
Based on these study results and the transparency with which they communicate data
limitations, there is no evidence that PEVs are driven more than ICE vehicles, and study results
conflict regarding whether annual eVMT is less for PEVs. EPA concludes that the existing
empirical evidence does not support the conclusion that average annual eVMT differs from
annual VMT for ICE vehicles. Therefore, EPA analyses use the same annual VMT for PEVs and
ICE vehicles in the No Action case.
4.2.1.2 Basis for the Rebound Effect for Internal Combustion Engines
In the 2021 rule, EPA provided a summary of the historical and recent literature on the light-
duty (LD) vehicle rebound effect, the ways it is defined (e.g., direct, indirect, economy-wide,
short- to medium-run, long-run), how it is estimated, and the basis for the rebound effect used for
internal combustion engine vehicles (ICE vehicles). Based on that review and assessment of
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studies of the LD rebound effect, EPA used a single point estimate of 10 percent for the direct,
short- to medium-run rebound effect for ICE vehicles in the 2021 rule. In this current rule, EPA
is again using a value of 10 percent as an input to the agency's analyses for the direct, short- to
medium-run rebound effect for ICE vehicles. We refer the reader to RIA Chapter 3, and
Preamble Section 1 of the 2021 rule for the full discussion of the rebound effect and the point
estimate used. (U.S. EPA 2021)
4.2.1.3 Basis for Rebound Effect for Battery Electric Vehicles
As described briefly above, the rebound effect for BEVs is the additional miles traveled as a
result of increased vehicle fuel efficiency and the consequent lower cost per mile of driving. As
with ICE vehicles, it is estimated via the relationship between VMT and fuel price (i.e., an
elasticity), specifically the response of eVMT to changes in electricity price. If we extrapolate
the ICE VMT rebound literature to PEVs, we expect eVMT to rise (decline) in response to
reductions (increases) in electricity price. EPA identified two current studies that estimate an
eVMT rebound effect in the U.S., which we list in Table 4-6. Using data gathered from
California PEV drivers, Chakraborty, Hardman, and Tal (2022) find no evidence of an eVMT
rebound effect. Nehiba (2022) finds a rebound effect of 10 percent in an analysis of the "entire
BEV population in New York."54 Nehiba (2022) also finds that the responsiveness of eVMT to
electricity prices "falls as public charging stations - where prices are often decoupled from
electricity costs - become available," which may signal that conventional approaches to
estimating rebound for ICE vehicles may not be sufficient for eVMT rebound.55
54 Note that while we include Nehiba (2022) in our revies of the scientific literature regarding eVMT rebound,
Nehiba (2022) is a working paper; by definition, it is a work in progress that to our knowledge has not been subject
to formal peer review.
55 For estimating PEV VMT and eVMT rebound, Davis (2022) and Raghavan and Tal (2021) both note the potential
importance of understanding the substitution across vehicles in households with both an ICE vehicle and a PEV.
They also note that BEV utilization in multi-vehicle household has scarcely been studied even though 89% of
households with an EV also had a non-electric vehicle according to the 2017 National Household Travel Survey.
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Table 4-6: Recent scientific studies of eVMT rebound
Study
(Chakraborty, Hardman and Tal
2022)
(Nehiba 2022)
Electric Rebound Results
"Moreover, while lower electricity
price at home may lead to a higher
share of PEV VMT in total
household VMT, we do not identify
the presence of 'rebound effect'."
"A 10% increase in per mile
[residential] electricity costs
reduces mileage by 1%," but "BEV
drivers become less responsive to
residential prices when public
charging stations ... become
available."
Data
Location: California
Years: 2015-2019
Sources: Two surveys with reported
odometer readings and on-board
recorders
Number of PEVs: 16,736 (survey)
and 369 (onboard recorders)
Location: New York
Years: January 2017 - January
2021
Sources: Annual vehicle safety
inspection odometer readings;
residential and residential
electricity prices matched by zip
code
Given the estimates of eVMT rebound provided by Chakraborty et al. (2022) and Nehiba
(2022), we are left with only two research-based hypotheses: eVMT rebound is 0 percent or
eVMT rebound is 10 percent, the same VMT rebound as for ICE vehicles. Given the historical
evidence that BEVs are not driven more than ICE vehicles, EPA assumes no eVMT rebound in
our analyses.
4.2.2 Consumer Savings and Expenses
Over time, the price of the average new vehicle has risen as producers shift business models
toward larger and more expensive vehicles and as vehicles become safer, more durable, and less
polluting. Based on the proposed standards, we project that on average, vehicle technology costs
will increase by $1,200 (See Preamble Section VI.B and Chapter 10 of the DRIA). This increase
in production costs reflects modest advancements in ICE vehicle technology as well as
substantial increases in BEV market share (See Preamble Section IV.D.).
Specifically, consumer uptake of zero-emission vehicle technology is expected to continue to
grow with increasing market presence, more model choices, expanding infrastructure, and
decreasing costs to consumers. First, annual sales of LD PEVs in the U.S. have grown robustly
and are expected to continue to grow. This history of robust growth, combined with vehicle
manufacturers' plans to expand of PEV production, strongly suggests that PEV market share will
continue to grow rapidly. Second, the number of PEV models available to consumers is
increasingly meeting consumer demand for a variety of body styles and price points.
Specifically, the number of BEV and PHEV models available for sale in the U.S. has more than
doubled from about 24 in MY 2015 to about 60 in MY 2021, with offerings in a growing range
of vehicle segments (U.S. DOE and U.S. EPA 2015) (U.S. DOE and U.S. EPA 2021). Recent
model announcements indicate that this number will increase to more than 80 models by MY
2023 (M.J. Bradley and Assoc. 2021), and more than 180 models by 2025 (ERM 2022). Third,
the expansion of charging infrastructure appears to have kept up with PEV adoption (See DRIA
Chapter 5.3). This trend is expected to continue, particularly in light of very large public and
private investments (See Chapter 5.3). Lastly, as the cost of batteries falls, PEV production rises
(ERM 2022), and purchase incentives, such as the Inflation Reduction Act Clean Vehicle Credit,
become available, PEV purchase prices are dropping. Among the many studies that address cost
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parity, an emerging consensus suggests that purchase price parity is likely to be achievable by
the mid-2020s for some vehicle segments and models, and total cost of ownership (TCO) parity
even sooner for a broader segment of the market (Slowik, et al. (ERM 2022) (Burnham, Gohlke,
etal. 2021).
Given the trends described above, the following provides a summary of estimated consumer
savings and expenses experienced by individual new vehicle owners of BEVs and ICEVs for
three body styles - sedans and wagons, CUVs and SUVs, and pickups. Specifically, we provide
OMEGA estimated national average individual vehicle ownership savings and expenses
associated with new model year 2032 BEVs and ICEVs. We also provide information from other
sources, as indicated in Table 4-7. Consistent with OMEGA and EPA's benefit cost analysis, the
EPA estimated dollar amounts are given in 2020 dollars (2020$) with no discounting. Other
dollar amounts are consistent with original sources and noted. Further, we calculate averages
over the first 8 years of vehicle life. This coincides with the timeframe of EPA OMEGA
modeling and is the current average amount of time the first owner has possession of the vehicle
(Blackley n.d.) (Autolist 2022).56'57
Table 4-7 groups savings and expenses based on whether they occur with vehicle purchase
(i.e., upfront), reoccur (i.e., average annual), or represent an optional, one-time household
investment (i.e., one-time optional). For upfront purchase-related items and one-time household
investments, we present the full amounts. For recurring savings and expenses, we present
average annual, undiscounted amounts over the first 8 years of vehicle life. Many line items are
drawn from EPA OMEGA modeling. Others are drawn from the scientific literature. All sources
are noted.
Importantly, Table 4-7 represents a subset of ownership savings, expenses, incentives, and
investments that meet the following criteria: evidence demonstrates that dollar amounts differ
for BEVs and ICEVs, the dollar amounts can reasonably be expected to be experienced by
buyers of MY 2032 vehicles; data and generally accepted conventions exist to estimate
reasonably precise quantities; and bounds on uncertainty can be established. These criteria
imply the exclusion of savings and expenses that vary substantially across individuals and/or
locations and can be calculated for a specific person or place based on information in the
table and other readily available information. Furthermore, this discussion should not be
56 The average vehicle ages at which original owners sell their cars, SUVs, and pickup trucks were 8.4, 8.3 and 8.7
years, respectively, according to a study conducted by iSeeCars.com. "iSeeCars.com analyzed more than 5 million
5-year-old or older used cars sold by their original owners between Jan. 1, 2014 and Dec. 31, 2018. Models which
were owned for less than 5 years were excluded from the analysis, to eliminate the effect of short lease terms on the
data. Models that were in production for less than 9 of the 10 most recent model years (2010 to 2019), heavy-duty
trucks and vans, and models no longer in production as of the 2019 or 2020 model years were also removed from
further analysis. The average age of each vehicle, defined as nameplate + bodystyle, was mathematically modeled
using the ages of cars when they were first listed for sale" (Blackley n.d.). In contrast, Argonne National
Laboratories (Burnham, Gohlke, et al. 2021, 116) state that the typical period of initial ownership is "approximately
5 years" without citation.
57 According to S&P Global, the average age of vehicles on U.S. roadways is approximately 12 years (S&P Global
Mobility 2021). Argonne National Laboratory "analyzed survivability rates from data published by the National
Highway Traffic Safety Administration (NHTSA) of the U.S. Department of Transportation (Lu 2006) and by the
EPA (EPA 2016), finding the average lifetime of a vehicle in the United States was approximately 14 years in 2006
and just under 16 years in 2016" (Burnham, Gohlke, et al. 2021, 24).
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interpreted as a "total cost of ownership" analysis but as a summary of MY2032 vehicle
expenses and savings across body styles and powertrains under the proposed standards that
fit the above criteria.58 Lastly, these consumer ownership savings and expenses should not be
confused with the societal costs and benefits that appear in Chapter 4.3 and DRIA Chapter
10.
The sources of the expenses and savings that are included in Table 4-7 are listed below:
• Purchase Price - EPA OMEGA modeled average retail price. OMEGA first estimates
the cost to the manufacturer to produce a given vehicle. Then, the model performs an
iterative search where the Producer offers different combinations of ICEV and BEV
shares and levels of cross-subsidization between BEV and ICE vehicles until the
Consumer and Producer are in agreement for vehicle shares and price. The resulting
prices are defined by the sum of the marked-up vehicle production costs and any
internal cross-subsidies applied by the model. This resulting price, representing a retail
price, is the value shown in the table.
• Federal Purchase Incentive - Maximum potential consumer purchase incentive
provided via the Inflation Reduction Act ($7,500). The actual purchase incentive any
given consumer might receive is based on several eligibility requirements for the
consumer and the actual vehicle. This is a savings for consumers and appears as a
negative value in Table 4-7.
• Vehicle Miles - EPA OMEGA estimated national average annual per vehicle miles
traveled.
• Retail Fuel - EPA OMEGA estimated national average annual per vehicle fuel
expense.
• Refueling Time - EPA OMEGA estimated and monetized national average annual per
vehicle refueling time. See Chapter 4.3.5 below for procedure for estimating and
monetizing refueling time.
• Maintenance - EPA OMEGA estimated national average annual maintenance
expenses.
• Repair - EPA OMEGA estimated national average annual repair expenses.
• Registration - National average annual vehicle registration fee according to Burnham,
Gohkle, et al. (2021) is $68 for ICE vehicles. The additional national average fee is
$73 forBEVs, totaling to $141 forBEVs.
58 Argonne National Laboratory provides a comprehensive and recent summary of total cost of ownership (TCO) of
vehicles, which includes a review of other TCO studies (Burnham, Gohlke, et al. 2021). Total costs of ownership
analyses typically aim to provide a full accounting of ownership costs rather than fitting the criteria specified here.
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• Residential Charging Equipment & Installation - National estimated range of
expenses associated with home charging equipment and installation. In Chapter 5.3 of
the DRIA, we provide a description and summary of charging infrastructure
investments, including home charging.
Table 4-7: National per vehicle ownership savings and expenses for new model year 2032
vehicles under the proposed standards (2020 dollars)
Sedan/Wagon
CUV/SUV
Pickup
BEV
(Electric)
ICEV
(Gasoline)
BEV
(Electric)
ICEV
(Gasoline)
BEV
(Electric)
ICEV
(Gasoline)
Upfront Purchase Related Expenses and (Savings)
Purchase Pricea
(2020$)
34,100
28,900
42,100
35,000
46,700
43,200
Maximum Federal
Purchase Incentive
(2020$)
(7,500)
-
(7,500)
-
(7,500)
-
Net Purchase Price
(2020$)
26,600
28,900
34,600
35,000
39,200
43,200
Annual Eight-Year Average Expenses and (Savings)
Vehicle Milesa
(miles/year)
15,700
15,700
16,300
16,300
17,700
17,800
Retail Fuela
(2020$/year)
520
1,350
690
1,720
980
2,250
Refueling Timea
(2020$/year)
110
50
160
70
140
80
Maintenance21
(2020$/year)
550
870
590
940
700
1,100
Repaira
(2020$/year)
400
510
290
390
240
310
Registration13
(2019$/year)
140
70
140
70
140
70
Total Average
Annual Expenses
($/year)
1,720
2,850
1,870
3,190
2,200
3,810
Optional One-Time Investment
Residential Charging
Equipment &
Installation0
(2019$)
0-3,700
-
0-3,700
-
0-3,700
-
a Per OMEGA.
b Per Burnham, Gohlke, et al. (2021).
c Per DRIA Chapter 5.3.
In the above table, when comparing new BEVs and ICE vehicles within body style, we make
several important observations. First, on average, net purchase expenses are lowest across all
body styles for BEVs, assuming the maximum Federal purchase incentive of $7,500 available on
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vehicles at this price point.59 Second, on average, BEV owners save on fuel, maintenance, and
repair when compared to ICE vehicles buyers, roughly $1,100 per year for sedans and wagons,
$1,300 per year for CUVs and SUVs, and $1,600 per year for pickups. In contrast, the average
annual registration fees for BEVs are larger on average than for ICE vehicles, and time spent
fueling a BEV requires a few more hours per year on average than fueling ICE vehicles
(monetized in Table 4-7). However, registration fees and refueling time are small compared to
other ownership expense.
In the above table we also show a range of investments into residential charging equipment
and installation. Importantly, home charging is not required for BEV ownership, and charging at
home is feasible via a standard 120 volt outlet (aka Level 1 which delivers 2 to 5 miles of range
per hour) or 240 volt outlet (Level 2 which delivers 10 to 20 or more miles of range per hour) (
(Borlaug, et al. 2020) citing (U.S. Department of Energy (DOE) 2020)). In some cases,
additional equipment or upgrades for vehicle charging may not be needed.60 Charging at home
does deliver convenience. It very likely reduces time spent actively charging, as well as the time-
associated expense, since charging occurs when the vehicle is parked. In fact, Level 2 charging at
home has been shown to be associated with PEV continuance, that is, purchasing a PEV after
relinquishing a previous PEV (Hardman and Tal 2021). When electrical upgrades are desired,
home charging equipment and installation costs differ from one household to the next based,
primarily on housing type (e.g., detached, attached, apartment) and type of upgrade required
(e.g., none, outlet upgrade, charger upgrade). Thus, the table provides a range described in
Chapter 5 of this DRIA, though national average estimates are available. For example, Nichols
(2019) estimates that Level 1 investments typically range from $400 to $900, and Level 2
investments typically range from $680 to $4,100 (Nicholas 2019, 6). Borlaug et al. (2020)
estimate median capital costs for residential Level 2 charging equipment and installation to be
$1,836 (Borlaug, et al. 2020).61 Bauer et al. (2021) show per electric vehicle estimate for home
charging to be $850.
Consumers who chose to purchase a new MY 2032 BEV instead of an ICE vehicle save
between $1,100 and $1,600 at the time of purchase and between $9,000 and $13,000 on
operating expenses over the first 8 years of vehicle life. Those savings, summarized in Table 4-8,
are substantial and would be experienced by a BEV owner whether or not they considered that
savings at the time of purchase.
Table 4-8: Estimated average savings over the first 8 years of vehicle life when MY 2032
BEV purchased instead of ICE vehicle (2020 dollars)
59 For new vehicles, the maximum Federal purchase incentive of $7,500 is available on cars priced up to $55,000
and on vans, SUVs, and pickups up to $80,000 depending on the buyer's income. For used vehicles, the maximum
Federal purchase incentive of $4,000 is available on vehicles priced up to $25,000 depending on the buyer's income.
60 The ability to charge at home with at most behavior modification (i.e., electrical access with [at most] behavior
modification) varies among individual households with patterns emerging among housing types and between owners
and renters. The National Renewable Energy Laboratory (NREL) estimates home charging is currently feasible
without any upgrades (i.e., no cost) for 28 to 72% of single dwelling structures (i.e., attached and detached single
family and mobile homes) and for 11 - 40% of multiple dwelling structures (i.e., apartments) (Ge, et al. 2021).
61 Using a different metric, the levelized cost of charging (LCOC), Bourlag, Salisbury, Gerdes, and Muratori (2020)
estimate that "an upgrade to [Level 2] for residential charging adds more than $0.04/kWh to the cost to charge when
levelized over a 15 year period (a 37% increase compared to use of [Level 1])"
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Sedan/ CUV/SUV
Pickup
Wagon
Savings on Net Purchase Price
Including Maximum Purchase Incentive
8 -year Operating Savings
Residential Charging Expense
Total Savings with Max Residential Charging
Expense
Total Savings without Residential Charging Expense
9.040
0 - 3,700
7.640
11.340
2.300
10.560
0 - 3,700
7.260
10.960
400
12.880
0 - 3,700
13.180
16.880
4.000
In concluding this summary of consumer savings and expenses for new MY2032, we again
note that this is not a total costs analysis. According to the criteria that we specified above, we
have excluded expenses that consumers customarily incur that are typically included in a total
cost of ownership analysis. For example, we exclude vehicle sales tax and property tax since
these quantities depend on the value of the vehicle and vary across locations. A national average,
though meaningful in a total costs analysis for some audiences,62 is not sufficiently precise to be
useful for a given individual and instead can be calculated for a specific person based on readily
available information. For similar reasons, we acknowledge but exclude cost associated with
financing. While many buyers finance, loan principle, interest rate, and loan period differ
substantially across individuals. We also exclude regional-, state- and local-level monetary
purchase incentives as well as other regional-, state- and local-level monetary and non-monetary,
"perks'Vpolicies associated with PEV ownership. Regional-, state-, and local-level incentives and
policies take many forms across the U.S., differing in source (e.g., governments, utilities),
amount, and eligibility (Wakefield 2023) (Bui, Slowik and Lutsey 2020) (Greschak, Kreider and
Legault 2022), and some may not persist into the timeframe represented in Table 4-7. Lastly, we
exclude insurance and depreciation as recent evidence shows that these are quite similar for
similarly valued BEVs and ICE vehicles.
4.2.3 Other Ownership Considerations
In addition to ownership savings and expenses experienced under the proposed standard
provided above in Chapter 4.2.24.2.2 and impacts of the proposed standards on consumers
quantified in benefit costs analysis below in Chapter 4.3 and in Chapter 10, we also consider the
effects of the proposed standards on low-income households and on consumers of low-priced
new vehicles and used vehicles. These effects depend, in large part, on countervailing elements
of vehicle ownership experience under the proposed standards, namely a) higher up front, net
purchase prices,63 b) net fuel savings,64 and c) maintenance and repair. The net effect varies
across households and as demonstrated above across vehicle types. However, net fuel savings
may be especially relevant for low-income households and consumers in the used and low-priced
new vehicle markets. First, fuel, maintenance, and repair expenditures are a larger portion of
expenses for low-income households compared to higher income households (Hardman,
62 Burnham et al. (2021, 8-14) provide an excellent summary and critique of "literature related to a holistic TCO
calculation" as well as their own comprehensive analysis.
63 Per vehicle compliance costs are $1,400 including IRA producer incentives (See Chapter 13).
64 By net fuel savings, we are referring to fuel costs and time spent refueling.
4-22
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Fleming, et al. 2021).65 Second, lower-priced new vehicles have historically been more fuel
efficient. Third, fuel economy, and therefore fuel savings, do not decline as vehicles age even
though the price paid for vehicles typically declines as vehicles age and are resold. Fourth, low-
income households are more likely to purchase lower-priced new vehicles and used vehicles
(Hutchens, et al. 2021).
Additionally, BEV purchase incentives are available for new and used vehicles. For new
vehicles, the maximum Federal purchase incentive of $7,500 is available on cars priced up to
$55,000 and on vans, SUVs, and pickups up to $80,000 depending on the buyer's income. For
used vehicles, the maximum Federal purchase incentive of $4,000 is available on vehicles priced
up to $25,000 depending on buyer's income. Lower priced new vehicles and many used vehicles
meet the criteria for the maximum incentive and low-income buyers are more likely, by
definition, to qualify for maximum incentives. Furthermore, the IRA purchase incentives for
BEVs not only lowers the net purchase price, in some cases, the net price of some BEVs will be
lower than that of comparable ICE vehicles, as demonstrated in Chapter 4.2.2. Finally, we also
show that maintenance and repair costs for BEVs are lower than that of comparable ICE
vehicles, also demonstrated in Chapter 4.2.2 above and Chapter 4.3 below. (See also DRIA
Chapter 11.2.3.1).
Furthermore, most vehicle consumers finance, making access to credit for vehicle purchases
essential. The ability to finance may be of particular concern for low-income households. As
above, the effects of the standards on access to credit is influenced by the potentially
countervailing forces of vehicle purchase and other ownership costs. However, the degree of
influence and the net effect is not clear (See Chapter 8.4 of the 2021 rule). Increased purchase
price and presumably higher loan principal may, in some cases, discourage lending, while
reduced fuel costs may, in some cases, improve lenders' perceptions of borrowers' repayment
reliability.
Finally, while access to conventional fuels can be assumed for the most part, the number and
density of charging stations varies considerably (U.S. Department of Energy 2022). The
expansion of public and private charging infrastructure has been keeping up with PEV adoption
and is generally expected to continue to grow, particularly in light of very large public and
private investments (See DRIA Chapter 5) and local level priorities (Bui, Slowik and Lutsey
2020) (Greschak, Kreider and Legault 2022). This includes home charging events, which are
likely to continue to grow with PEV adoption but are also expected to represent a declining
proportion charging events as PEV share increases (Ge, et al. 2021). Thus, publicly accessible
charging is an important consideration, especially among renters and residents of multi-family
dwellings and others who charge away from home (Consumer Reports 2022). Households
without access to charging at home or the workplace will likely incur additional charging costs.
Thus, among consumers who rely upon public charging, the higher price of public charging is
especially important. Please see Chapter 5 of this DRIA for a more detailed discussion of public
and private investments in charging infrastructure, and our assessment of infrastructure needs
and costs under this proposal. See also, Chapter 4.2.2 for information on home charging
65 In the U.S., according to (Hardman, Fleming, et al. 2021), the lowest income households spend 11.2 percent of
their annual income on fuel, maintenance, and repairs of vehicles compared to all other households that spend 4.5
percent of their annual income on these expenses.
4-23
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equipment and installation costs as well as Chapter 11.2.3.1 for a discussion of charging and
home charging installation for low-income households.
4.3 Consumer-Related Social Benefits and Costs
4.3.1 Vehicle Technology Cost Impacts
Table 4-9 shows the estimated annual vehicle technology costs of the proposal and each
alternative, estimated in OMEGA, for the indicated calendar years (CY). The table also shows
the present-values (PV) of those costs and the equivalent annualized values (EAV) for the
calendar years 2027-2055 using both 3 percent and 7 percent discount rates.66
Table 4-9: Vehicle technology costs, light-duty and medium-duty (billions of 2020 dollars)
Calendar Year
Proposal
Alternative 1
Alternative 2
Altc
2027
:"7.5
7.9
: 5.5
2.6
2028
6.8
10
i 5
; 2.3
2029
6.6
14
5.8
1.8
2030
8.7
17
6.1
4.9
203 1
13
20
11
12
2032
17
23
15
18
2035
' 22
24
17
24
2040
19
20
15
18
2045
13
13
10
13
2050
12
13
10
12
2055
10
11
8.8
11
PV3
280
330
7 230
) 270
PV7
180
220
140
170
EAV3
15
17
12
14
EAV7
15
18
12
14
We expect the technology costs of the program will result in a rise in the average purchase
prices for consumers, for both new and used vehicles. While we expect that vehicle
manufacturers will strategically price vehicles (e.g., subsidizing a lower price for some vehicles
with a higher price for others), we assume in our modeling that increased vehicle technology
costs will be fully reflected in higher average purchase prices paid by consumers. Note that these
technology cost increases are offset by fuel, maintenance and repair costs, discussed in Chapter
4.3.4 and Chapter 4.3.6.
4.3.2 Value of Rebound Driving
As discussed above, the assumed rebound effect might occur when an increase in vehicle fuel
efficiency leads people to choose to drive more because of the lower cost per mile of driving.
When we estimate fuel expenditures, we multiply the number of miles driven on a given fuel by
its price per unit, i.e., dollars per gallon for liquid fuels and dollars per kWh for electricity.
Therefore, any reductions in fuel expenditures (fuel savings) associated with a policy include
additional fuel expenditures associated with rebound driving. If we ignored those rebound miles,
66 For the estimation of the stream of costs and benefits, we assume that after implementation of the MY 2027 and
later standards, the MY 2032 standards apply to each year thereafter.
4-24
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the fuel savings would be calculated using the same number of miles in both the policy and no-
action cases but with a lower fuel cost per mile in the policy case.
However, drivers would drive those additional rebound miles only if they find value in them.
The increase in travel associated with the rebound effect produces additional benefits to vehicle
drivers, which reflect the value of the added social and economic opportunities that become
accessible with additional travel. This analysis estimates the economic benefits from increased
rebound-effect driving as the sum of the fuel costs paid to drive those miles and the drive
surplus, which is the additional value that drivers derive from those miles.
The value of the rebound miles driven is simply the number of rebound miles multiplied by
the cost per mile of driving them.
Value of Rebound VMT = VMTrebound x | ^ j
^ ' action
The economic value of the increased owner/operator surplus provided by added driving, the
drive surplus, is estimated 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.
\mileJNnAnti.nn \mile)AMn
VMTrebourld
DriveSurplus =
Thus, the economic benefits from increased rebound driving, called Drive Value, is then
calculated as below.
DriveValue = Value of Rebound VMT + DriveSurplus
Drive value depends on the extent of improvement in fuel consumption and fuel prices, which
depend upon vehicle model year, the calendar year, and the standards being analyzed. Thus, the
value of benefits from increased vehicle use also depends upon model year and calendar year,
and it varies among alternative standards.
4.3.3 Fuel Consumption
Overall, the proposed standards are projected to reduce liquid fuel consumption while
simultaneously increasing electricity consumption as shown in Table 4-10 and Table 4-11,
respectively. These values are generated in OMEGA and used in the benefit cost analysis
described in DRIA Chapter 10.
Table 4-10: Liquid-fuel consumption impacts, light-duty and medium-duty (billion gallons)
Calendar
Liquid-Fuel Impacts.
Liquid-Fuel Impacts.
Liquid-Fuel Impacts.
Liquid-Fuel Impacts.
Year
Proposal
Alternative 1
Alternative 2
Alternative 3
2027
-0.89
-0.93
-0.65
-0.53
2028
-2.2
; -2.5
-1.6
-1.3
2029
-4
-4.4
, -3.2
; -2.3
2030
-6.1
f-7'.r"
-4.9
-3.9
203 1
-8.6
-9.8
-1
-6.3
4-25
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2032 -12 -13 -9.6 -9.3
2035 -21 -23 -19 -19
2040 -34 -38 -31 -33
2045 -42 -47 -38 -42
2050 -48 -52 -43 -48
2055 -49 -54 -44 -49
sum -900 -1.000 -810 -870
Table 4-11 Electricity consumption impacts, light-duty and medium-duty (terawatt hours)
Calendar Electricity Impacts. Electricity Impacts. Electricity Impacts. Electricity Impacts.
Year
Proposal
Alternative 1
Alternative 2
Alternative 3
2027
8.9
9.3
6.4
5.4
2028
21
; 23
15
13
2029
38
39
29
T 22
2030
56
61
44
36
2031
" 1 78 7
84
64
58
2032
100
110
86
85
2035
190
200
170
170
2040
300
330
280
290
2045
380
420
350
380
2050
430
470
390
430
2055
440
490
400
440
sum
8.100
8.900
7.400
7.900
4.3.4 Monetized Fuel Savings
Table 4-12 shows the undiscounted annual monetized fuel savings associated with the
proposal and each alternative as well as the present value (PV) of those costs and equivalent
annualized value (EAV) for the calendar years 2027-2055 using both 3 percent and 7 percent
discount rates. In Chapter 10, we present pretax fuel savings which are used in the benefit cost
analysis. In Chapter 10 we also present transfers, or taxes, associated with fuel expenditure
changes and battery and vehicle purchase credit incentives.
4-26
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Table 4-12: Retail fuel expenditure savings, light-duty and medium-duty (billions of 2020
dollars)*
Calendar
Retail Fuel Savings.
Retail Fuel Savings.
Retail Fuel Savings.
Retail Fuel Savings.
Year
Proposal
Alternative 1
Alternative 2
Alternative 3
2027
1.2
1.3
; 0.9
0.7
2028
r 3.2
'3.7
2.4
1.9
2029
6
7
4.8
['3.5
2030
10
12
8.1
6.5
203 1
14
17
12
11
2032
20
23
17
16
2035
39
44
34
: 35
2040
69
77
61
66
2045
89
98
80
[ 87 ^
2050
100
110
93
100
2055
110
120
98
110
PV3
1.100
1.200
950
1.000
PV7
550
610
490
520
EAV3
56
62
50
54
EAV7
45
50
40
42
* Positive
values indicate savings
in fuel expenditures.
4.3.5 Costs Associated with the Time Spent Refueling
More stringent GHG standards have traditionally resulted in lower fuel consumption by liquid
fueled vehicles. Provided fuel tanks on liquid fueled vehicles retain their capacity (i.e., gas tanks
don't change volume), the lower fuel consumption would be expected to reduce the frequency of
refueling events. However, if manufacturers choose to maintain traditional range (i.e., miles
traveled on a full tank of fuel), then the possibility exists that tank capacities would become
smaller and, therefore, the frequency of refueling events would not change, although time spent
at the fuel pump may be reduced. There are indications that both outcomes are happening, with
some vehicles reducing tank sizes while others are maintaining them.
Of course, electric vehicles are not fueled in the same way. Many refueling events for electric
vehicles would be expected to occur either overnight where the vehicle is parked or during the
workday using an employer owned charge point, neither of which require extra time from the
driver, especially compared to refueling a liquid fueled vehicle. However, some recharging
events will undoubtedly occur in public places, especially when drivers are in the midst of an
extended road trip. These mid-trip charging events are the focus of this analysis. For purposes of
this analysis, we have made the simplifying assumption that PHEVs will not make use of mid-
trip charging since the vehicle can continue to operate on gasoline once the battery is depleted.
To estimate the refueling costs associated with liquid-fueled vehicles, we have borrowed
heavily from the approach used by EPA in the December 2021 GHG final rule (U.S. EPA 2021)
(U.S. DOT 2021) with updated inputs developed in support of the 2022 CAFE final rule
(NHTSA 2022). The refueling costs for liquid-fueled vehicles are calculated on a cost per gallon
basis while for BEVs it is calculated on a cost per mile basis. The calculations used are shown in
the equation immediately below for liquid-fueled vehicles and in the subsequent equation for
BEVs with a discussion following.
4-27
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Cost
1
X
Fixed Time+
Tank SizexShared Filled
Fill Rate
x Time Value x 0.6
Gallon Tank SizexShare Filled
60
Where.
Cost/Gallon = the refueling cost per gallon of fuel consumed,
Tank Size = the volume, in gallons, of the liquid fuel tank,
Share Filled = the typical share of the tank volume filled during a refill event,
Fixed Time = the fixed time, in minutes, between deciding to refill and returning to the trip,
Fill Rate = the fuel dispense rate, in gallons per minute, of liquid fuel pumps,
60 converts minutes to hours
Time Value = the value of the time for the occupants of the vehicle,
0.6 = a scalar value to count only 60 percent of refueling events
We have estimated tank sizes the same way it was done in our 2021 GHG final rule, which
was based on a 2016 internal Department of Transportation (DOT) memorandum. (CAFE TSD
2021) (White September 27, 2016) The most recent data reported was for the 2016 model year
and showed that the average tank sizes of some of the most popular vehicles in the United States
were 15.7, 18.7 and 27.3 for cars, vans and SUVS, and pickup trucks, respectively, all in gallons.
We have used those values for all vehicles in each of those categories.
The share filled values are consistent for all vehicles at 0.65, meaning that the typical refill
event includes filling 65 percent of the capacity of the tank.
The fixed time value is also consistent for all vehicles at 3.5 minutes per event, while the fill
rate is held constant at 7.5 gallons per minute reflecting the legal restriction of 10 gallons per
minute and the fact that not all people refill at that maximum rate.
The time value has been extensively analyzed by DOT for use in regulatory analyses. The
values, which account for wage rates, miles driven in urban and rural settings, the different uses
of vehicles whether it be personal or commercial use, and the typical number of occupants over
the age of 5 years for different vehicles. The hourly values ($/hour) derived and which we use
are $25.55 and $30.75 for passenger cars and light-trucks, respectively, both in 2018 dollars
(NHTSA 2022).
As described by NHTSA, the 0.6 scaling factor is meant to capture those drivers whose
primary reason for the refueling trip was due to a low reading on the gas gauge. Such drivers
experience a cost due to added mileage driven to detour to a filling station, as well as added time
to refuel and complete the transaction at the filling station. Drivers who refuel on a regular
schedule or incidental to stops they make primarily for other reasons (e.g., using restrooms or
buying snacks) do not experience the cost associated with detouring to locate a station or paying
for the transaction, because the frequency of refueling for these reasons is unlikely to be affected
by fuel economy improvements. This restriction was imposed to exclude distortionary effects of
4-28
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those who refuel on a fixed (e.g., weekly) schedule and may be unlikely to alter refueling
patterns due to increased driving range (NHTSA 2022).
To estimate the refueling costs associated with BEVs, we calculate cost per mile.
Cost ( Fixed Time 1 Share Charged\
,= — x — H—— x Time Value
Mile \Charge Frequency 60 Charge Rate )
Where,
Cost/Mile = the refueling cost per mile driven
Fixed Time = the fixed time, in minutes, between deciding to refill and returning to the trip,
Charge Frequency = the cumulative number of miles driven before a mid-trip charging event
is triggered,
Share Charged = the share of miles that will be charged mid-trip,
Charge Rate = the typical recharge rate, in miles per hour of charging,
Time Value = the value of the time for the occupants of the vehicle.
The fixed time value is taken to be equal to that for liquid-fueled vehicles, at 3.5 minutes per
event, and the time value is equal to those stated above for liquid-fueled vehicles.
The charge rate reflects the number of miles of driving provided by a one hour charging
session. Different BEVs have different limits on how much energy can be delivered to the
battery pack, and other factors - ambient conditions, the power level of the charging equipment,
on-vehicle accessory loads during charging - impact the energy transfer. For our analysis, we use
the same value of 100 miles of driving added for each hour of charging and use that value for all
BEVs.67
For the charge frequency and share charged parameters, we have used values developed by
NHTSA and presented in the CCEMS input files used in support of their September 2021
proposal. (U.S. DOT 2021) In their analysis, NHTSA estimated the frequency of mid-trip
charging events and the share of miles driven that require mid-trip charging as shown in Table
4-13. As Table 4-13 shows, cars would be expected to require less frequent mid-trip charges and
a smaller share of miles driven with mid-trip charge events. Pickups and vans/SUVs have fairly
similar measures, with vans and SUVs requiring slightly more mid-trip charging than pickups.
67 Charging equipment is available in a variety of power levels (see DRIA Ch. 5.3.1.2), with higher-power
equipment generally able to charge vehicles more quickly. To the extent mid-trip charging occurs at a higher charge
rate, the resulting cost per mile for time spent charging electric vehicles would be lower. To illustrate the lower
potential time needed to recharge mid-trip, vehicles using DC fast charging equipment can add 200 or more miles
per hour.
4-29
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Table 4-13: BEV recharging thresholds by body style and range
Cars
Vans & SUVs
Pickups
Miles to mid-trip charging event, BEV200
2,000
1,500
1,600
Miles to mid-trip charging event, BEV300
5,200
3,500
3,800
Miles to mid-trip charging event, BEV400
10,400
7,000
7,600
Miles to mid-trip charging event, BEV500
20,800
14,000
15,200
Share of miles charged mid-trip, BEV200
0.06
0.09
0.08
Share of miles charged mid-trip, BEV300
0.03
0.04
0.04
Share of miles charged mid-trip, BEV400
0.015
0.02
0.02
Share of miles charged mid-trip, BEV500
0.0075
0.01
0.01
Using the values in Table 4-13, EPA has developed curves for each body style as a function
of range. These curves are second order polynomials as a function of BEV range. These curves
and their coefficient values are shown in Figure 4-6 and Figure 4-7.
Miles to mid-trip charge event
25000
20000
y = 0.18x2 - 64.4x + 7840 0
15000
10000
5000
/y = 0.135x2 - 49.9x + 6290
200
y = 0.125x2 - 46.5x + 5900
"
300
400
500
0 100
• Cars
Poly. (Cars)
Figure 4-6: Curve fits for miles driven to a mid-trip charge event.
600
Vans/SUVs
Poly. (Vans/SUVs)
Pickups
Poly. (Pickups)
4-30
-------
Share of miles charged mid-trip
0.1
0.09
0.08
0.07
0.06
0.05
0.04
0.03
0.02
0.01
0
0 100 200 300 400 500 600
• Cars • Vans/SUVs
• Pickups Poly. (Cars)
Poly. (Vans/SUVs) Poly. (Pickups)
•
«\
\ \
y = lE-06x2-0.001x +0.241
y = 7E-07x2 - 0.0008X + 0.2005 \*
\\
'•••• "\
y = 6E-07x2 - 0.0006X + 0.1504
Sv..
Figure 4-7: Curve fits for the share of miles charged in mid-trip events.
The curve fits shown in these figures are shown in Table 4-. These coefficients are used to
calculate the charge frequency and share charged parameters of the Cost/Mile equation above
using the functional form shown in the equation below.
Charge Frequency; Share Charged = A x Range2 + B x Range + C
Where,
A, B & C are the applicable coefficient values shown in Table 4-14,
Range = the range of the given BEV.
Table 4-14: Curve coefficients used to estimate charge frequency and share charged
A
B
C
Miles to mid-trip charge. Car
0.18
-64.4
7840
Miles to mid-trip charge, Van/SUV
0.125
-45.5
5900
Miles to mid-trip charge. Pickup
0.135
-49.9
6290
Share of miles charged mid-trip. Car
0.0000006
-0.0006
0.1504
Share of miles charged mid-trip, Van/SUV
0.000001
-0.001
0.241
Share of miles charged mid-trip. Pickup
0.0000007
-0.0008
0.2005
4-31
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4.3.6 Maintenance and Repair Costs
Maintenance and repair (M&R) are large components of vehicle cost of ownership for any
vehicle. According to Edmunds, maintenance costs consist of two types of maintenance:
scheduled and unscheduled. Scheduled maintenance is the performance of factory-recommended
actions at periodic mileage or calendar intervals, like oil changes. Unscheduled maintenance
includes wheel alignment and the replacement of items such as the battery, brakes, headlights,
hoses, exhaust system parts, taillight/turn signal bulbs, tires, and wiper blades/inserts. (Edmunds
2023) Repairs, in contrast, are done to fix malfunctioning parts that inhibit the use of the vehicle.
The differentiation between the items that are included in unscheduled maintenance versus
repairs is likely arbitrary, but the items considered repairs seem to follow the systems that are
covered in vehicle comprehensive (i.e., "bumper-to-bumper") warranties offered by automakers,
which exclude common "wear" items like tires, brakes, and starter batteries. (Muller 2017)
To estimate maintenance and repair costs, we have used the data gathered and summarized by
Argonne National Laboratory (ANL) in their look at the total cost of ownership for vehicles of
various sizes and powertrains (Burnham, Gohlke, et al. 2021).
4.3.6.1 Maintenance Costs
Maintenance costs, and differences between more traditional ICE vehicles and HEVs versus
BEVs and PHEVs, are an importance consideration in not only the full accounting of social
benefits and costs, but also the consumer decision making process when comparing ICE/HEV
technology versus BEV/PHEV technology. If BEVs and PHEVs are less costly to maintain, a
consumer might find the potentially higher purchase price of the vehicle to be "worth it" given
the possibly lower fuel and maintenance costs over time. The reverse is also true - more costly
BEV/PHEV maintenance relative to ICE/HEV might make the potentially higher purchase price
even less appealing, even if the fueling costs are lower.
In their study, ANL developed a generic maintenance service schedule for various powertrain
types using owner's manuals from various makes and models including the Toyota Yaris,
Camry, Camry HEV, Prius, and Prius Prime; Chevrolet Cruze, Volt, and Bolt; Nissan Sentra,
Kicks, and Leaf; Kia Optima, Optima HEV, and Optima PHEV; Kia Soul and Soul EV; Tesla
Model 3 and Model S, Ford Focus; Lincoln MKZ; BMW i3; VW Golf and e-Golf; and Fiat 500
and 500e. The analysis assumed that drivers would follow the recommended service intervals.
The authors noted that, in practice, not everyone follows the recommended service intervals but
also noted that owners likely do so at the expense of either future repair costs or the early
scrappage of the vehicle (Burnham, Gohlke, et al. 2021, 81). The authors also noted that
estimates were made for certain "wear items" that might not normally be included in a
recommended maintenance schedule (e.g., brake pads and rotors) for which they estimated
average lifetimes based on guidance from several experts and from automotive websites
(Burnham, Gohlke, et al. 2021, 81).
After developing the maintenance schedules, the authors collected national average costs for
each of the preventative and unscheduled services. The authors noted that service cost varies by
several factors, including the type of mechanic (dealership vs. chain vs. independent), part
quality (OEM vs. aftermarket), and make and model cost characteristics (domestic vs. import and
mass market vs. luxury). The authors did not assume drivers would perform any of their own
maintenance services, stating a lack of data available on how often drivers do so. The authors
4-32
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noted that "do it yourself' maintenance would reduce costs, though depending on the service
would require investment in both tools and skill development (Burnham, Gohlke, et al. 2021,
81).
The authors noted that vehicle type (sedan, SUV, pickup) may influence maintenance costs as
some part sizes and fluid capacities can be larger for bigger vehicles (e.g., larger tires needed for
a pickup). However, when examining the data at their disposal, the authors found no significant
difference over 10 years of ownership. But total maintenance and repair costs of medium-duty
diesel vehicles were about 34% higher than that of their gasoline counterparts. The authors
attributed that difference to repairs rather than maintenance, since the most obvious maintenance
difference between the vehicles is that diesels do not have spark plugs which is a relatively small
cost. The authors acknowledge that their dataset had a very limited number of diesel vehicles and
there appeared to be no clear trend regarding higher or lower maintenance costs for diesel fueled
vehicles.
Specific to tires and tire replacement, an issue often cited with respect to BEVs versus ICE
vehicles, the authors noted that their analysis assumed that tire life and replacement costs are the
same for all powertrains. However, advanced powertrain vehicles often are equipped with
specially designed tires that provide low rolling resistance (LRR) to improve fuel efficiency
(Burnham, Gohlke, et al. 2021, 83). Presumably, the authors are speaking of tires on BEV, and
maybe PHEV, powertrains when speaking of "advanced powertrain vehicles." EPA believes that
most new vehicles are equipped and sold with low rolling resistance tires. That said, some BEVs
are equipped with tires that differ from those on typical ICE vehicles to address tread wear and
the instant torque of BEVs making the issue raised by the authors a valid issue for consideration.
The authors point to several studies looking into the issue with no clear conclusion being drawn
about tire and tire replacement costs for BEVs versus ICE vehicles. The authors did reiterate a
Goodyear claim that traditional tires wear 30 percent faster when installed on BEVs (Burnham,
Gohlke, et al. 2021, 83).
Regarding brake-related maintenance, the authors assumed that brake pad, rotor, and caliper
replacement intervals could be extended by 33% for HEVs and by 50% for PHEVs and BEVs,
relative to ICE vehicles, due to less friction wear that would result from the use of regenerative
braking. Further, they assumed that PHEVs and BEVs would have more regenerative braking
capabilities than HEVs and, therefore, that their service intervals could be extended longer than
HEVs due to their larger battery capacity and electric motor (Burnham, Gohlke, et al. 2021, 84).
Table 4-16 shows the maintenance costs used as inputs to OMEGA.
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Table 4-15: Maintenance service schedule by powertrain
Service
Miles per Event ICE
Miles per Event
Miles per
Miles per
Cost per
HEV
Event PHEV
Event BEV
Event
(2019
dollars)
Engine Oil
7,500
7,500
9,000
n/a
$65
Oil Filler
7,500
7,500
9,000
n/a
$20 ^
Tire Rotation
7,500
7,500
7,500
7,500
$50
Wiper Blades
15.000
15.000
15.000
15.000
$45
Cabin Air Filter
20.000
20.000
20.000
20.000
$50
Multi-Point
20.000
20.000
20.000
20.000
$110
Inspection
Engine Air Filter
30,000
66.667
83,333
n/a
$40
Brake Fluid
37,500
37,500
37,500
37,500
$150
Tires Replaced
50.000
50.000
50.000
50.000
$525
Brake Pads
50.000
66.667
75.000
75.000
$350
Starter Battery
50.000
50.000
50.000
50.000
$175
Spark Plugs
60,000
120.000
120.000
n/a
$225
Oxygen Sensor
80.000
80.000
80.000
n/a
$350
Headlight Bulbs
80.000
80.000
80.000
80.000
$90
Transmission
90,000
1 10.000
1 10.000
n/a
$200
Service
Timing Belt
90,000
1 10.000
1 10.000
n/a
' $750
Acccssorv Drive
90.000
1 10.000
1 10.000
n/a
$165
Belt
HVAC Service
100,000
100,000
100,000
100,000
$50
Brake Rotors
100,000
125.000
150.000
150.000
$500
Shocks and
100,000
100,000
100,000
100,000
$1,000
Struts
Engine Coolant
125.000
125.000
125.000
n/a
$190
EV Battery
n/a
125.000
125.000
125.000
$210
Coolant
Fuel Filter
150.000
150.000
200.000
n/a
$110
Brake Calipers
150.000
187.500
225.000
225.000
$1,000
Using the schedules and costs shown in Table 4-15, OMEGA then calculates the cumulative
maintenance costs from mile zero through mile 225,000. For example, the cumulative costs for
an ICE vehicle at 15,000 miles would be 2 x ($65+$20+$50) + $45, or $315. The cumulative
costs can then be divided by the cumulative miles to determine the average maintenance cost per
mile at any given odometer reading in a vehicle's life. However, that average cost, while
informative, suggests that the first mile incurs the same cost as the last mile. This does not seem
appropriate, especially considering that the cumulative costs for ICE vehicles, $20,050, divided
by 225,000 cumulative miles results in an average cost per mile of $0.09. If that vehicle had a
fuel economy of 35 miles per gallon, assuming $3 per gallon of gasoline, its fuel costs would
also be $0.09 per mile. Over 15,000 first year miles, the fuel costs and maintenance costs would
both be $1,350. Compare this to the $315 estimate of maintenance costs over the first 15,000
miles. Clearly, while the average cost per mile of $0.09 is valid and informative, it is not the best
valuation for our purpose. Instead, we have estimated the cost per mile at a constant slope with
an intercept set to $0 per mile such that the cumulative costs after 225,000 miles would equal the
$20,050 (for an ICE vehicle) included in the suggested maintenance schedule. Following this
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approach, the maintenance cost per mile curves calculated within OMEGA are as shown in
Figure 4-8.
0.30
0.25
o.oo
• PHEV
ICE
HEV
BEV
0
40 80 120 160 200 240 280
Thousand Miles
Figure 4-8: Maintenance cost per mile (2019 dollars) at various odometer readings.
Using these maintenance cost per mile curves, OMEGA then calculates the estimated
maintenance costs in any given year of a vehicle's life based on the miles traveled in that year.
For example, an ICE vehicle having an odometer reading of 120,000 miles would have a
maintenance cost per mile of $0.10 (see Figure 4-8). If that vehicle travels 10,000 miles in the
given year, then its estimated maintenance costs would be $1,000 in that year. If that vehicle
were to instead travel 15,000 miles in that year, its estimated maintenance costs would be
$1,500.
OMEGA uses these maintenance costs for light-duty and for medium-duty vehicles. The
maintenance costs are included in the benefit and cost analysis. Note that these maintenance
costs differ from those presented in Chapter 4.1 and Chapter 4.2. Chapter 4.1 costs are meant to
reflect the thought process of a potential new vehicle purchaser. Chapter 4.2 amounts are
estimated average expenses per vehicle over the first 8 years of vehicle life. Costs presented
here, in Chapter 4.3 are meant to estimate the actual effects of the proposal.
4.3.6.2 Repair Costs
Repairs are done to fix malfunctioning parts that inhibit the use of the vehicle and are
generally considered to address problems associated with parts or systems that are covered under
typical manufacturer bumper-to-bumper type warranties. In the ANL study, the authors were
able to develop a repair cost curve for a gasoline car and a series of scalers that could be applied
to that curve to estimate repair costs for other powertrains and vehicle types. The repair cost
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curve developed in the ANL study is shown in the equation below (Burnham, Gohlke, et al.
2021).
Repairi = vpaiebx,i = 1, ...,15
Where,
Repain = the repair cost per mile at age i,
v = the appropriate vehicle type multiplier (see Car/SUV/Truck entries in Table 4-16),
p = the appropriate powertrain type multiplier (see ICE/HEV/PHEV/BEV/FCV entries in Table
4-16),
ai = gasoline car repair cost coefficient at age i,
b = exponential constant of 0.00002,
x = the MSRP of the car when sold as new.
Table 4-16: Repair cost per mile coefficient values3
Item
Value
Car multiplier
1.0
SUV multiplier
0.91
Truck multiplier
0.7 '
ICE multiplier
1.0
HEV multiplier
0.91
PHEV multiplier
0.86
BEV multiplier
0.67
FCV multiplier
0.67
ao
0
ai
0
a2
0.00333
a:i
0.01
a4
0.0167
^add-on
0.00333
; a These coefficient values come from Burnham, Gohlke, et al.
| (2021)
OMEGA makes use of the equation developed in the ANL study along with the coefficient
values shown in Table 4-16 to estimate repair costs per mile at any age in a given vehicle's life.
In place of the MSRP68 of the new vehicle, OMEGA uses the estimated technology cost for the
vehicle as described above. Further, OMEGA makes use of this equation for all ages of a
vehicle's life (OMEGA estimates a 30/40-year lifetime) using the aadd-on value for every age
beyond the first five years. In other words, the ax value for age 7 would be 0.0167 + 3 x 0.00333
= 0.02669 (note that, in OMEGA, age=7 is the 8th year of a vehicle's life). The resultant repair
68 Manufacturer suggested retail price
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cost per mile values at all ages are shown in Figure 4-9. Note that the new vehicle cost (used in
place of the MSRP value) is held constant at $35,000 in Figure 4-9, regardless of vehicle type
(car, van/SUV, pickup) and powertrain (ICE vehicle, HEV or MHEV, PHEV, BEV) which is not
likely, but is presented here for illustration only.
OMEGA uses these repair costs for both light-duty and medium-duty. Repair costs are
included in the benefit-cost analysis.
Figure 4-9: Repair cost per mile (2019 dollars) for a $35,000 Car, Van/SUV and Pickup
with various powertrains.
4.3.7 Costs Associated with Noise and Congestion
If consumers choose to drive more, they benefit from the utility derived from those additional
miles, as described in Chapter 4.3.2. In contrast to the benefits associated with additional driving,
there are also costs. 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, we account for them
separately as a cost of addition driving associated with a positive rebound effect.
EPA relies on congestion and noise cost estimates developed by the Federal Highway
Administration to estimate the increased external costs caused by added driving due to a positive
rebound effect. EPA employed estimates from this source previously in the analysis
accompanying the light-duty 2010, 2012 and 2021 final rules. 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. EPA
has applied the congestion cost to the overall VMT therefore the results of this analysis
potentially overestimate the congestion costs associated with increased vehicle use, and thus lead
to a conservative estimate of net benefits.
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EPA uses 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 4-17.
These congestion costs are consistent with those used in the 2021 final rule. These values are
used as inputs to OMEGA and adjusted within the model to the dollar basis used in the benefit
and cost analysis.
Table 4-17: Costs associated with congestion and noise (2018 dollars per vehicle mile)
Sedans/Wagons CUVs/SUVs/Vans Pickups
Congestion 0.0634 0.0634 0.0566
Noise 0.0009 0.0009 0.0009
4.4 New Vehicle Sales
The topic of the "energy paradox" or "energy efficiency gap" has been extensively discussed
in previous analyses of vehicle GHG standards. The idea of the energy efficiency gap is that
existing fuel saving technologies were not widely adopted even though they reduced fuel
consumption enough to pay for themselves in short period of time. Conventional economic
principles suggest that because the benefits to vehicle buyers of the new technologies would
outweigh the costs to those buyers, automakers would provide them and people would buy them.
As described in previous EPA GHG vehicle rules (most recently in the 2021 rule),
engineering analyses identified technologies (such as downsized-turbocharged engines, gasoline
direct injection, and improved aerodynamics) where the additional cost of the technology is
quickly covered by the fuel savings it provides, but they were not widely adopted until after the
issuance of EPA vehicle standards. Research also suggests that the presence of fuel-saving
technologies do not lead to adverse effects on other vehicle attributes, such as performance and
noise. Instead, research shows that there are technologies that exist that provide improved fuel
economy without hindering performance, and in some cases, while also improving performance
(Huang, Helfand, et al. 2018) (Watten, Helfand and Anderson 2021). Additionally, research
demonstrates that, in response to the standards, automakers have improved fuel economy without
adversely affecting other vehicle attributes (Helfand and Dorsey-Palmateer 2015). Lastly, while
the availability of more fuel efficient vehicles has increased steadily over time, research has
shown that the attitudes of drivers towards those vehicles with improved fuel economy has not
been affected negatively (Huang, Helfand, et al. 2018) (Huang, Helfand and Bolon 2018a). In
summary, it appears that in the absence of the standards, markets have not led to the adoption of
fuel efficient technologies with short payback periods and no discernible tradeoffs. Thus, an
energy efficiency gap appears to have existed, especially in the absence of the standards, and
may still exist.
There are a number of hypotheses in the literature that attempt to explain the existence of this
apparent market failure, including both consumer and producer side reasons, though the literature
has not settled on a single explanation (National Academies of Science, Engineering, and
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Medicine 2021). In fact, the gap likely exists due to a combination of consumer- and producer-
side characteristics.69
Consumer-side hypotheses include:
• Consumers might lack information, not have a full understanding of this information
when it is presented, not have correct information, not have the ability to process the
information, or not trust the presented information.
• Consumers might weigh the present or present circumstances (e.g., current costs) more
heavily than future opportunities (e.g., long term savings, changing circumstances) in
their purchase decisions due to, for example, uncertainty about the future, a lack of
foresight, an aversion to short term losses relative to longer term gains, or a preference
for the status quo.
• Consumers might prioritize other vehicle attributes over fuel economy in their vehicle
purchase process.
• Consumers might associate higher fuel economy with lower quality vehicles.
In addition to the research discussed above indicating that fuel-saving technologies are not
likely to be associated with adverse effects on other vehicle attributes, EPA has explored
evidence on how consumers evaluate fuel economy in their vehicle purchase decisions. Overall,
the research has not reached a consensus; results and estimates vary across a range of data types
and statistical models. Thus, it is not clear how consumers incorporate fuel economy in their
purchase decision, nor how consumer behavior might contribute to the energy efficiency gap.
Part of the uncertainty surrounding the reasons behind the energy efficiency gap is that most
of the technology applied to existing ICE vehicles may have been "invisible" to the consumer.
This is for a few reasons, including that the technology itself was not something the mainstream
consumer would know about, or because it was applied to a vehicle at the same time as multiple
other changes, therefore making it unclear to the consumer what changes in vehicle attributes, if
any, could be attributed to a specific technology.
Much less research has been conducted to evaluate the producer side of the market, though
three interrelated themes arise: market structure, business strategy, and technological innovation.
The structure of the automobile industry may inefficiently allocate car attributes, fuel economy
among them, which may contribute to the existence of an energy efficiency gap. Specifically,
vehicle production involves significant fixed costs in which automakers strive to differentiate
their products from each other. In that context, fuel economy of a vehicle could be a just another
factor in a company's product differentiation strategy. Product differentiation can lead to an
under-supply of fuel economy relative to what is cost-effective to consumers in some segments,
69 For simplicity, we present consumer- and producer-side hypotheses for the "energy efficiency gap", consistent
with traditional economy theory. Analogously but somewhat differently, we could have presented these hypotheses
organized according to individual and institutional characteristics, behaviors, and biases. Under that organization
structure, some of the hypotheses we present, such as myopia, uncertainty aversion, loss aversion, asymmetric
information, and status quo bias, could apply to both consumer and producers.
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and an over-supply of fuel economy in other sectors (Fischer 2005). Automobile manufacturers
may adopt a "wait and see" strategy regarding the costs associated with investing in and
commercializing new technologies.
In the absence of standards, automakers have seemed willing to invest in small improvements
upon existing technologies (Helfand and Dorsey-Palmateer 2015) and more reluctant to invest in
major innovations in the absence of standards. This may be a result of first-mover disadvantages
to investing in and commercializing new technologies. The "first-mover disadvantage" occurs
when the "first-mover" pays a higher proportion of the costs of developing, implementing, or
marketing a new technology and loses the long-term advantage when other businesses move into
that market. There could also 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. Additionally, there can be research and development synergies when many
companies work on the same technologies at the same time, assuming there's a reason to
innovate at the same time. Standards can create conditions under which companies invest in
major innovations. 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.
A combination of theories may best explain why there was limited adoption of cost-effective
fuel-saving technologies before the implementation of more stringent standards. However, it
does appear that, while addressing externalities like pollution, regulation has appeared to also
help correct such market failures without serious disruption to vehicle markets. We do not reject
the observation that the energy efficiency gap has existed and may still exist. However, the
availability of more fuel efficient vehicles has increased steadily over time, thus narrowing or
closing the energy efficiency gap, and research has shown that the attitudes of drivers towards
those vehicles with improved fuel economy has not been negatively affected (Huang, Helfand, et
al. 2018) (Huang, Helfand and Bolon 2018a). In addition, research has shown that automakers
have improved fuel economy in response to the standards without adverse effects on other
vehicle attributes (Helfand and Dorsey-Palmateer 2015) (Watten, Helfand and Anderson 2021).
Thus, EPA does not model tradeoffs between fuel economy and performance as a path to
achieving the proposed standards.
Though a slight gap in ICE vehicle purchases may still exist due to uncertainty surrounding
new fuel savings technologies, it becomes less of an issue with the increasing prevalence of
BEVs in the market, as the changes in vehicle attributes due to this technology are clearly
evident to consumers. There is uncertainty in the historical literature regarding consumer
acceptance and adoption of electric vehicles, as described in Chapter 4.1 and Jackman et al.
(2023), however recent research suggests that the demand for electric vehicles is robust, and
adoption is constrained, at least in part, by limited supply. Gillingham et al. (2023) examine all
new LD vehicles sold in the U.S. between 2014 and 2020, focusing on comparisons of existing
electric vehicles to their most similar ICE vehicle counterpart, finding that EVs are preferred to
the ICE counterpart in some segments (Gillingham, et al. 2023). In the paper, the authors show
that, compared to ICE counterparts, EVs have seen relative sales shares of over 30%, which
indicates that the share of PEVs in the marketplace is, at least partially, constrained due to the
lack of offerings needed to convert existing demand into market share. In addition, a survey from
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Consumer Reports in early 2022 shows that more than one third of Americans would either
seriously consider or definitely buy or lease a BEV today, if they were in the market for a
vehicle. (Bartlett 2022).
The rest of this chapter will discuss how sales effects were modeled in OMEGA, as well the
total change in sales estimated due to this proposed rule.
4.4.1 How Sales Impacts Were Modeled
EPA has updated its OMEGA model, in part, to increase the model's useability and
transparency. In addition, the model has been updated to allow for interactions in producer and
consumer decisions in estimating total sales and the share of ICE vehicles and BEVs in the
market that both meet the standard being analyzed and will be accepted by consumers. More
about the updated OMEGA model, including detailed information on the structure and
operations, can be found in DRIA Chapter 2. As in previous rulemakings, the sales impacts are
based on a set of assumptions and inputs, including assumptions about the role of fuel
consumption in vehicle purchase decisions described in Chapter 4.1, and assumptions on
consumers' demand elasticity discussed in below.70
At a high level, OMEGA estimates the effects of a policy on new vehicle sales volumes as a
deviation from the sales that would take place in the absence of the standards.71 This calculation
is based on applying a demand elasticity to the change in new vehicle net price, the price that
incorporates the fact that vehicle buyers are expected to take fuel consumption into consideration
in the purchase process. The modeled BEV shares, as described in Chapter 4.1, are then applied
to the estimated total sales volumes to estimate further effects of the rule, including costs,
emissions and benefits.
4.4.1.1 The Role of Fuel Consumption in Vehicle Sales Estimates
In the 2021 rule, as well as in this proposed rule, EPA assumed that producers account for 2.5
years of fuel consumption in their assessment of the consumer's purchase decision. However, as
discussed in detail in the 2021 rule, there is not a consensus around the role of fuel consumption
in vehicle purchase decisions. Greene, et al. 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 only about 30 percent of that value, or about $334. (Greene, et al. 2018)
This $334 is within the large standard deviation in Greene, et al. (2018) for the willingness to
pay to reduce fuel costs, but it is far lower than both the mean of $1,880 (160 percent of that
value) and the median of $990 (85 percent of that value) per one cent per mile in the paper. On
the other hand, the 2021 NAS report, citing the 2015 NAS report, observed that automakers
"perceive that typical consumers would pay upfront for only one to four years of fuel savings"
(pp. 9-10), which is also within the range of values identified in Greene, et al. (2018) for
consumer response, but also well below the median or mean. Based on these results, it appears
70 The demand elasticity is the percent change in quantity associated with a one percent increase in price. For price,
we use net price, where net price is the difference in technology costs less an estimate of the change in fuel costs
over the number of years we assume fuel costs are taken into account. We also reduce BEV prices in all scenarios,
including the No Action case, due to the IRA BEV purchase and battery tax incentives.
71 We calibrate the sales in OMEGA that would take place in the absence of the standards to data from the U.S.
Energy Information Administration.
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possible that automakers operate under a different perception of consumer willingness to pay for
additional fuel economy than how consumers actually behave. In comments on the 2021 rule,
some commenters suggested that new vehicle buyers care more about fuel consumption than the
use of 2.5 years suggests, and that EPA should model automaker adoption of fuel-saving
technologies based on historical actions. EPA notes that the data, methods and ideas discussed
here are based on historical data, and therefore focus on ICE sales. Consumer response to fuel
savings, and the amount of fuel savings considered in the purchase decision, may be different
with electric vehicles and in an era of high BEV sales.
Chapter 4.1 above describes how OMEGA incorporates fuel costs in consumer purchase
decisions. OMEGA also incorporates fuel cost savings in producer assumptions. Specifically, we
assume producers account for 2.5 years of consumer fuel consumption. To do this, OMEGA
calculates a baseline estimate of the fuel consumption over a user-specified number of years (we
assume 2.5), using AEO projections of fuel cost, the expected vehicle miles traveled by year
(VMT), and the vehicle's survival schedule. The same fuel costs and expected VMT are then
used to calculate fuel consumption in the proposal and alternative scenarios for the same user-
specified number of years, using the revised expected fuel consumption.
4.4.1.2 Elasticity of Demand
By definition, a new vehicle demand elasticity relates the percent change in new vehicle price
to the percent change in new vehicle sales:
AQ/Q
71 AP/P
Where r| is the demand elasticity, Q is the quantity of new vehicles sold, P is the price of new
vehicles, and A refers to the change in the value. Rearranging this equation produces the sales
effect:
AQ = tj * Q * AP/P
As described in Chapter 2.6.3, the baseline quantity, Q, comes from EIA's projections of
vehicle sales. For this proposed rule, the EIA projection includes effects of the 2021 rule, but not
the IRA. The price, P, is proxied with the OMEGA estimated technology costs. The change in
price is the difference between new vehicle net price under this EIA projection, and the net price
under the OMEGA projected scenarios, where net price is new vehicle purchase price including
2.5 years of fuel consumption. The OMEGA projected scenarios for this rule, the No Action
scenario, the Proposed alternative, the more stringent alternative (Alternative 1), and the two less
stringent alternatives (Alternative 2 and Alternative 3), all include the effects of the 2021 rule
and the IRA. The Proposed scenario, and all three alternatives are described in Preamble Section
III.B and III.E.
For durable goods, such as vehicles, people are generally expected to have more flexibility
about when they purchase new vehicles than whether they purchase new vehicles; thus, their
behavior is more inflexible (less elastic) in the long run than in the short run. For this reason,
estimates for long-term elasticities for durable goods are expected to be smaller (in absolute
value) than short-run elasticities. At a market level, short-run responses typically focus entirely
on the new-vehicle market; longer time spans allow for adjustments between the new and used
vehicle markets, and even adjustments outside those markets, such as with public transit.
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Because this rule has effects over time and could have effects related to the used vehicle market,
long-run elasticities that account for effects in the used vehicle market are more appropriate for
estimating the impacts of standards in the new vehicle market than short-run elasticities.
Continuing the approach used in the final 2021 rule, EPA is using a demand elasticity of -0.4
for LD vehicles based on an EPA peer reviewed report (U.S. EPA 2021). However, as noted in
EPA's report and by public commenters on the proposed 2021 rule, -0.4 appears to be the largest
estimate (in absolute value) for a long-run new vehicle demand elasticity in recent studies.
EPA's report examining the relationship between new and used vehicle markets shows that, for
plausible values reflecting that interaction, the new vehicle demand elasticity varies from -0.15
to -0.4. A smaller elasticity does not change the direction of sales effects, but it does reduce the
magnitude of the effects. Using the value of -0.4 is conservative, as the larger estimate yields a
larger change in sales.
The literature used to estimate this elasticity measure is focused on light-duty vehicles, which
are primarily purchased and used as personal vehicles by individuals and households. The
medium-duty vehicle market, in contrast, largely serves commercial applications. The
assumptions in our analysis of the LD sales response are specific to that market, and do not
necessarily carry over to the MD vehicle market. Commercial vehicle owners purchase vehicles
based on the needs for their business, and we believe they are less sensitive to changes in vehicle
price than personal vehicle owners. Though there are not many studies focused on what affects
purchase decisions of medium-duty, or commercial, vehicle buyers, especially in the US, there
are many articles discussing the importance of fuel efficiency, warranty considerations,
maintenance cost, and replacement part availability in choosing which commercial vehicle to
buy.72 In addition, a working paper published by Resources for the Future reports that
commercial vehicle buyers are not sensitive to fuel price changes, likely due to specialized
vehicle needs. (Leard, McConnell and Zhou 2017) For this proposal, we are assuming an
elasticity of 0 for the MD vehicle sales impacts estimates and we are not projecting any
differences in the number of MD vehicles sold between the No Action and the Proposal or
Alternative scenarios. This implicitly assumes that the buyers of MD vehicles are not going to
change purchase decisions if the price of the vehicle changes, all else equal. In other words, as
long as the characteristics of the vehicle do not change, commercial buyers will still purchase the
vehicle that fits their needs. The rest of this chapter focuses on the LD vehicle market.
4.4.2 New LD Vehicle Sales Estimates
For this proposed rule, EPA is maintaining the previous assumptions of 2.5 years of fuel
savings and a new LD vehicle demand elasticity of -0.4 for its modeling.
Table 4-18 shows results for total new LD vehicle sales impacts due to the proposed option.
There is a very small change in total new LD vehicle sales projected in the proposed option
compared to the No Action case. Sales fall in the first two years, increase slightly for the next
72 See, for example: https://www.fleetmaintenance.com/equipment/chassis-body-and-
cab/article/21136479/considerations-for-purchasing-new-and-used-trucks ; https://www.automotive-
fleet.com/159336/10-factors-driving-commercial-fleet-vehicle-acquisitions;
https://www.mwsmag.com/commercial-vehicle-demand-is-rising-and-so-are-prices/. These webpages are saved to
the docket for this rule.
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two, and then fall again. The largest sales effect in the Proposal is a very small in magnitude
decrease of less than 0.4% in 2027. The fall in sales in the early and later years is expected given
that 1) supply and demand theory tell us that quantity falls as costs/prices rise, and 2) we have
seen decreased sales due to increased costs in previous EPA LD rule analyses. However, the
increase in sales in 2029 and 2030 may be unexpected at first. Though average per vehicle costs
are increasing, the estimated 2.5 years of fuel savings offset the additional vehicle cost enough to
lead to increasing demand in those years.73 For more information on fuel prices used in
OMEGA, see DRIA Chapter 2.6.6. For more information on the estimated fuel savings in this
rule, see DRIA Chapter 10.2.
Table 4-18: LD sales impacts in the Proposal scenario
Year
No Action
Proposal
Total
Total Sales
Change from No
Sales
Action (%)
2027
15.432.908
-54.919
15.487.827
(-0.35%)
2028
15.616.676
-20.53 1
15.637.207
(-0.13%)
2029
15.781.094
10.834
15.770.260
(0.07%)
2030
15.814.296
7,247
15.807.049
(0.05%)
2031
15.860.358
-24.370
15.884.729
(-0.15%)
. 2032
15.834,010
-46.150
15.880.160
(-0.29%)
Table 4-19 shows results for new LD sales impacts under the three alternative option
scenarios as described in Preamble Section HE. Alternative 1 (-10) is more stringent than the
proposed scenario, and Alternative 2 (+10) and Alternative 3 (Linear Phase-in) are less stringent.
The results under the most stringent alternative, Alternative 1 (-10) project decreasing sales in all
6 years compared to the No Action case. Alternative 2 (+10) shows results directionally similar
to the proposal, above. Alternative 3 (linear) projects one additional year of increasing sales than
is seen in the Proposal. The results under Alternative 1 are the largest in magnitude, with the
largest result projecting a decrease of less than 0.8 percent in 2032. Alternative 3 projects the
smallest change, in magnitude, in the first two years, with Alternative 2 projecting the smallest
change, in magnitude, in the last two years. Results in 2029 through 2032 are very similar for
the Proposal and the two less stringent scenarios.
Table 4-19: LD sales impacts in the alternative scenarios
Year Alternative 1 (-10) Alternative 2 (+10) Alternative 3 (Linear)
Total Sales Change Total Sales Change Total Change
from No from No Sales from No
73 All scenarios, including the No Action scenario, account for purchase and battery production incentives in the
IRA, which further reduce the cost of BEVs. For more information on the BEV purchase and battery production
incentives included in the consumer generalized cost estimates in OMEGA, see Chapter 2.6.8.
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Action
(%)
Action
(%)
Action
(%)
2027
15,429,939
-57,889
(-0.37%)
15,447,829
-39,998
(-0.26%)
15,476,391
-11,436
(-0.07%)
2028
15,582,224
-54,983
(-0.35%)
15,624,158
-13,048
(-0.08%)
15,643,941
6,734
(0.04%)
2029
15,690,100
-80,160
(-0.51%)
15,778,412
8,153
(0.05%)
15,795,393
25,133
(0.16%)
2030
15,732,702
-74,347
(-0.47%)
15,821,919
14,871
(0.09%)
15,823,563
16,514
(0.10%)
2031
15,774,869
-109,860
(-0.69%)
15,864,090
-20,639
(-0.13%)
15,857,727
-27,001
(-0.17%)
2032
15,758,885
-121,275
(-0.76%)
15,834,633
-45,527
(-0.29%)
15,818,292
-61,868
(-0.39%)
As an alternative representation of results, Figure 4-10 shows the percent change in total new
LD vehicle sales compared to the No Action case for all 4 scenarios.
Change in sales from No Action
0.40%
0.20%
0.00%
-0.20%
-0.40%
-0.60%
-0.80%
-1.00%
Proposal —ip -Alt 1 (-10) —•—Alt 2 (+10) •••••• Alt 3 (Linear)
Figure 4-10: Total new LD vehicle sales impacts, percent change from the No Action case.
The results discussed here focus on sales of new LD vehicles, which does affect the total size
and make-up of the onroad fleet over time.74 In addition to new sales, the analysis for the effects
of this proposed rule also include estimates of which vehicles are re-registered. Re-registered
vehicles are used vehicles that remain on the road and are registered for onroad use for that year.
This is the flip side to scrappage, which estimates the vehicles that are taken out of the total
onroad fleet. For information on estimates of vehicles re-registered in our analysis, see Chapter
9.3.
74 The onroad fleet consists of the total count and types of vehicles on the road, and their characteristics including
transmission type and age
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4.5 Employment
This chapter explains the methods and estimates of employment impacts due to this proposal.
The rule primarily affects LD and MD vehicles, suggesting that there may be employment
effects in the motor vehicle and parts sectors due to the effects of the standards on sales. Thus,
we focus our assessment on the motor vehicle manufacturing and the motor vehicle parts
manufacturing sectors, with some assessment of impacts on additional closely related sectors
likely to be most affected by the standards.
When 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. Instead, labor would
primarily be reallocated from one productive use to another, as workers transition away from
jobs that are less environmentally protective and towards jobs that are more environmentally
protective. Affected sectors may nevertheless experience transitory effects as workers change
jobs. Some workers may retrain or relocate in anticipation of new requirements or require time to
search for new jobs, while shortages in some sectors or regions could bid up wages to attract
workers. These adjustment costs can lead to local labor disruptions. Even if the net change in the
national workforce is small, 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 as discussed further below.
Chapter 4.5.1 offers a brief, high-level explanation of employment impacts due to
environmental regulation and discusses a selection of the peer-reviewed literature on this topic.
Chapter 4.5.2 focuses on potential impacts from growing electrification, and Chapter 4.5.3
qualitatively discusses possible employment impacts of this rule on regulated industries. Chapter
4.5.4 presents a quantitative estimate of partial employment impacts that may occur due to this
proposed rule. In previous rules, we have quantitatively estimated a cost effect, which should be
estimated holding vehicle sales constant. However, the cost estimates come from OMEGA,
which estimates the costs of the proposed rule inclusive of the effects of changes in vehicles
sold. Therefore, the quantitative partial employment analysis for this rule is a combined cost and
demand effect. Chapter 4.5.5 qualitatively discuss potential impacts on related sectors.
4.5.1 Background and Literature
Economic theory of labor demand indicates that employers affected by environmental
regulation may change their demand for different types of labor in different ways. They may
increase their demand for some types, decrease demand for other types, or maintain demand for
still other types. The uncertain direction of labor impacts is due to the different channels by
which regulations affect labor demand. A variety of conditions can affect employment impacts of
environmental regulation, including baseline labor market conditions, employer and worker
characteristics, industry, and region. In general, the employment effects of environmental
regulation are difficult to disentangle from other economic changes (especially the state of the
macroeconomy) and business decisions that affect employment, both over time and across
regions and industries. In light of these difficulties, we look to economic theory to provide a
constructive framework for approaching these assessments and for better understanding the
inherent complexities in such assessments.
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In this chapter, we describe three ways employment at the firm level might be affected by
changes in a firm's production costs due to environmental regulation: a demand effect, caused by
higher production costs increasing market prices and decreasing demand; a cost effect, caused by
additional environmental protection costs leading regulated firms to increase their use of inputs,
including labor, to produce the same level of output; and a factor shift effect, in which post-
regulation production technologies may have different labor intensities than their pre-regulation
counterparts. These effects are outlined in a paper by Morgenstern et al., which provides the
theoretical foundation for EPA's analysis of the impacts of this regulation on labor (Morgenstern,
Pizer and Shih 2002). Due to data limitations, EPA is not quantifying the impacts of the final
regulation on firm-level employment for affected companies. Instead, we discuss factor shift,
demand, and cost employment effects for the regulated sector at the industry level.
Additional papers approach employment effects through similar frameworks. Berman and Bui
model two components that drive changes in firm-level labor demand: output effects and
substitution effects (Berman and Bui 2001).75 Deschenes describes environmental regulations as
requiring additional capital equipment for pollution abatement that does not increase labor
productivity (Deschenes 2018). For an overview of the neoclassical theory of production and
factor demand, see Chapter 9 of Layard and Walters' Microeconomic Theory (Layard and
Walters 1978). Ehrenberg and Smith describe how 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
(Ehrenberg and Smith 2000).
Arrow et al. state that, in the long run, environmental regulation is expected to cause a shift of
employment among employers rather than affect the general employment level (Arrow, et al.
1996). Even if they are mitigated by long-run market adjustments to full employment, many
regulatory actions have transitional effects in the short run (Smith 2015) (U.S. OMB 2015).
These movements of workers in and out of jobs in response to environmental regulation are
potentially important distributional impacts of interest to policy makers. Of particular concern
are transitional job losses experienced by workers operating in declining industries, exhibiting
low migration rates, or living in communities or regions where unemployment rates are high.
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. Compliance with
environmental regulation can result in increased demand for the inputs or factors (including
labor) used in the production of environmental protection. However, the regulated sector
generally relies on revenues generated by their other market outputs to cover the costs of
supplying increased environmental quality, which can lead to reduced demand for labor and
other factors of production used to produce the market output. 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 (Curtis 2018) and
Hafstead and Williams (Hafstead and Williams III 2018)).
75 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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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 that occur
in different sectors related to the regulated industry, both upstream and downstream, or in sectors
producing substitute or complimentary products. Consequently, employment impacts are hard to
disentangle from other economic changes and business decisions that affect employment, over
time and across regions and industries.
4.5.2 Potential Employment Impacts from the Increasing Penetration of Electric
Vehicles
In addition to the employment effects we have discussed in previous rules (for example the
2021 rule), the increasing penetration of electric vehicles in the market 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 battery charging infrastructure. Over time, as BEVs become a greater portion of the
new vehicle fleet, the kinds of jobs in auto manufacturing are expected to change: for instance,
there will be no need for engine and exhaust system assembly for BEVs, while many assembly
tasks will involve electrical rather than mechanical fitting. In addition, batteries represent a
significant portion of the manufacturing content of an electrified vehicle, and some automakers
are likely to purchase the cells, if not pre-assembled modules or packs, from suppliers whose
employment will thereby be affected. Employment in building and maintaining battery charging
infrastructure needed to support the ever-increasing number of BEVs on the road is also expected
to affect the nature of employment in automotive and related sectors. For much of these effects,
there is considerable uncertainty in the data to quantitatively assess how employment might
change as a function of the increased electrification expected to result under the proposed
standards. Some suggest that fewer workers will be needed because BEVs have fewer moving
parts (Krisher and Seewer 2021), while others estimate that the labor-hours involved in BEVs is
almost identical to that for ICE vehicles (Kupper, et al. 2020).
Prior analyses of employment in the auto sector conducted outside of EPA have estimated a
range of impacts. Results from California's ACC II program analysis seem to suggest that there
may be a small decrease, not exceeding 0.3 percent of baseline California employment in any
year, in total employment across all industries in CA through 2040 (California Air Resources
Board 2022). A report by the Economic Policy Institute suggests that US employment in the auto
sector could increase if the share of vehicles, or powertrains, sold in the US that are produced in
the US increases. The BlueGreen Alliance also states that though BEVs have fewer parts than
their ICE counterparts, there is potential for job growth in electric vehicle component
manufacturing, including batteries, electric motors, regenerative braking systems and
semiconductors, and manufacturing those components in the US can lead to an increase in jobs
(BlueGreen Alliance 2021). They go on to state that if the US does not become a major producer
for these components, there is risk of job loss.
The UAW states that re-training programs will be needed to support auto workers in a market
with an increasing share of electric vehicles in order to prepare workers that might be displaced
by the shift to the new technology (UAW 2020). Volkswagen states that labor requirements for
ICE vehicles are about 70% higher than their electric counterpart, but these changes in
employment intensities in the manufacturing of the vehicles can be offset by shifting to the
production of new components, for example batteries or battery cells (Herrmenn, et al. 2020).
Research from the Seattle Jobs Initiative indicates that employment in a collection of sectors
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related to both BEV and ICE vehicle manufacturing is expected to grow slightly through 2029
(Seattle Jobs Initiative 2020). Climate Nexus also indicates that transitioning to electric vehicles
will lead to a net increase in jobs, a claim that is partially supported by the rising investment in
batteries, vehicle manufacturing and charging stations (Climate Nexus 2022). This expected
investment is also supported by recent Federal investment which will allow for increased
investment along the vehicle supply chain, including domestic battery manufacturing, charging
infrastructure, and vehicle manufacturing. The BIL was signed in November 2021 and provides
over $24 billion in investment in electric vehicle chargers, critical minerals, and components
needed by domestic manufacturers of EV batteries and for clean transit and school buses.
(Infrastructure Investment and Jobs Act 2021). 76 The CHIPS Act, signed in August, 2022,
invests in expanding America's manufacturing capacity for the semiconductors used in electric
vehicles and chargers (CHIPS Act of 2022 2022). 77 The IRA provides incentives for producers
to expand domestic manufacturing of BEVs and domestic sourcing of components and critical
minerals needed to produce them (117th Cong. 2022). The IRA also provides incentives for
consumers to purchase both new and used BEVs. These pieces of legislation are expected to
create domestic employment opportunities along the full automotive sector supply chain, from
components and equipment manufacturing and processing to final assembly, as well as
incentivize the development of reliable EV battery supply chains.78 The BlueGreen Alliance and
PERI estimate that IRA will create over 9 million jobs over the next decade, with about 400,000
of those jobs being attributed directly to the battery and fuel cell vehicle provisions in the act
(Political Economy Research Institute 2022).
The U.S. Bureau of Labor Statistics (BLS) recently published an article which identifies three
key occupational areas they expect to be affected by growth in the BEV market, as well as
estimates a change in employment in those sectors between 2021 and 2031 (Colato and Ice
2023). This outlook from the BLS indicates that the increasing prevalence of BEVs in the market
can lead to growth in employment in a range of sectors, including sectors beyond those discussed
in this analysis. The authors note that though it is expected that these sectors will be significant
in BEV production and deployment, they include estimates of the total employment change
across all sectors, not just those related to BEV production and deployment. For example, the
estimates for the change in employment of construction laborers is the effect from all
construction sectors, not just those related to the construction of BEV charging infrastructure. In
the report, BLS estimated employment changes related to occupations employed in the design
and development of electric vehicles, including software developers, electrical engineers,
electronics engineers, and chemical engineers; battery manufacturing, including electrical,
electronic and electromechanical assemblers, and miscellaneous assemblers and fabricators; and
76 The Bipartisan Infrastructure Law is officially titled the Infrastructure Investment and Jobs Act. More information
can be found at https://www.fhwa.dot.gov/bipartisan-infrastructure-law/
77 The CHIPS and Science Act was signed by President Biden in August, 2022 to boost investment in, and
manufacturing of, semiconductors in the U.S. The fact sheet can be found at https://www.whitehouse.gov/briefing-
room/statements-releases/2022/08/09/fact-sheet-chips-and-science-act-will-lower-costs-create-jobs-strengthen-
supply-chains-and-counter-china/
78 More information on how these acts are expected to aid employment growth and create opportunities for growth
along the supply chain can be found in the January, 2023 White House publication "Building a Clean Energy
Economy: A Guidebook to the Inflation Reduction Act's Investments in Clean Energy and Climate Action." found
online at https://www.whitehouse.gov/wp-content/uploads/2022/12/Inflation-Reduction-Act-Guidebook.pdf
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charging network development and maintenance, including urban and regional planners,
electrical, electrical power-line installers and repairers, and construction laborers. With the
exception of the sector miscellaneous assemblers and fabricators, BLS is forecasting an increase
in employment across the board, with the smallest increase, in percentages, being 1.6 percent
(electrical engineers), and the largest increase (software developers) being 26 percent.79 BLS
states that though total employment in the miscellaneous assemblers and fabricators sector is
projected to fall, they do expect a number of job openings in the sector each year to replace
workers who transfer to different occupations or exit the work force. Again, it is difficult to
separate out the effect that the increase in BEV production will have on these sectors from the
macroeconomic effects, or the effects from non-BEV related production activity.
4.5.3 Potential Employment Impacts of the Proposed Standards
Because it is challenging to know the state of the macroeconomy when these standards
become effective, the changing nature of auto manufacturing employment due to the transition to
electric vehicles, and the difficulties of modeling impacts on employment in a complex national
economy, we focus our analysis on the direct impacts in closely affected sectors. In the next
sections, we discuss potential impacts of industry-level employment effects of the proposed rule.
We qualitatively describe the employment impacts due to the factor shift, demand effects and
cost effect, following the structure of Morgenstern et al., as described above. Then we present a
quantitative estimate of partial employment effects of the proposed standards, followed by a
discussion of possible employment impacts on related sectors.
4.5.3.1 The Factor Shift Effect
The factor shift effect reflects employment changes due to changes in labor intensity of
production resulting from compliance activities. A factor shift effect of this rule might occur if
this proposed regulation affects the labor intensity of production of ICE vehicles. It may also
occur if a BEV replaces an ICE vehicle (holding total sales constant). We do not have data on
how the regulation might affect labor intensity of production within ICE vehicle production.
There is ongoing research on the different labor intensity of production between ICE and BEV
production, with inconsistent results. Some research indicates that the labor hours needed to
produce a BEV are fewer than those needed to produce an ICE vehicle, while other research
indicates there are no real differences. EPA is currently working with a research group to
produce a peer-reviewed tear-down study of a BEV to its comparable ICE counterpart. For more
information on this study, see Chapter 2.5.2.2.3. For information on the early indications of labor
differences in ICE and BEV production, see Chapter 4.5.4. As part of this study, we will receive
estimates of labor intensity needed to produce each vehicle. We hope to use this information in
additional analytical discussions in the final rule. Given the current lack of data and
inconsistency in the existing literature, we are unable to estimate a factor shift effect in ICE
vehicle production, nor of increasing relative BEV production as a function of this rule.
79 The urban and regional planners sector is forecast to have the smaller increase in number of employees, with an
increase of about 1,600 employees between 2021 and 2031.
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4.5.3.2 The Demand Effect
Demand effects on employment are due to changes in labor that result from changes in total
new vehicle sales. In previous EPA LD regulations, like the 2021 rule, we have used the CAFE
model to estimate effects of a change in ICE vehicle demand on labor. The model uses a method
of estimating a demand effect on employment through the relationship of hours involved in a
new vehicle sale (for the effect on automotive dealers) or average labor hours per vehicle at a
sample of US assembly plants (for the effect on the final assembly industry) to the change in the
number of vehicles sold due to the regulation. This rule, however, uses EPA's OMEGA model.
We currently do not have the data to estimate these effects in OMEGA.
In general, if the proposed regulation causes total sales of new vehicles to decrease, keeping
the share of BEVs in the new vehicle fleet constant, fewer workers will be needed to assemble
vehicles and manufacture their components. If BEVs and ICE vehicles have different labor
intensities of production, the relative change in BEV and ICE sales will impact the demand effect
on employment. If, for example, total new BEV sales increase more than total new ICE sales
falls, a portion of the change in employment, where the new BEVs replace ICE vehicles, would
be attributed to factor shifts. The additional new BEV sales would increase labor needs by the
labor intensity of BEV production. Due to lack of data, as discussed in the Chapter 4.5.3.1, we
are unable to estimate a change in the employment due to a relative shift in BEV and ICE vehicle
demand, or a change in employment due to a change in demand.
4.5.3.3 The Cost Effect
The cost effects on employment are due to changes in labor associated with increases in costs
of production. In general, if a regulation leads firms to invest in lower-emitting vehicles, we
expect an increase in the labor used to implement those technologies. In previous LD and heavy-
duty (HD) rules, we have estimated a partial employment effect due to the change in costs of
production, where the change in costs of production were assumed to be the change in
technology costs estimated as a result of the rule being analyzed. We estimated the cost effect
using the historic share of labor in the cost of production to extrapolate future estimates of
impacts on labor due to new compliance activities in response to the regulations. Specifically, we
multiplied the share of labor in production costs by the production cost increase estimated as an
impact of the rule. This provided a sense of the magnitude of potential impacts on employment.
For this rule, we estimate partial employment effects using this same basic method. However, as
explained further in Chapter 4.5.4, the impacts estimated in this proposed rule are a combined
cost and demand effect due to how costs are estimated in OMEGA.
The use of the ratio of the share of labor in production costs to estimate a cost effect on
employment has both advantages and limitations. It is often possible to estimate these ratios for
detailed sector definitions, for example, the average number of workers in the automobile and
light-duty motor vehicle manufacturing sector per $1 million spent in that sector, rather than
using ratios from more aggregated sectors, such as the motor vehicle manufacturing sector. This
would avoid extrapolating employment ratios from less closely related sectors. On the other
hand, these estimates are averages, covering all the activities in these sectors, and may not be
representative of the labor effects when expenditures are required for specific activities, or when
manufacturing processes change due to compliance activities in such a way that labor intensity
changes. For instance, the ratio of workers to production cost for the motor vehicle body and
trailer manufacturing sector represents this ratio for all motor vehicles body and trailer
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manufacturing activities, and not just for production processes related to emission reductions
compliance activities. In addition, these estimates do not include changes in industries that
supply these sectors, such as steel or electronics producers. The effects estimated with this
method can be viewed as effects on employment in the sectors included in the analysis due to the
changes in expenditure in that sector, rather than as an assessment of all employment changes
due to the standards being analyzed. In addition, labor intensity is held constant in the face of
increased expenditures; this approach does not take changes in labor intensity due to changes in
the nature of production (the factor shift effect) into account, which could either increase or
decrease the employment impacts estimated using this method.
BEVs and ICE vehicles require different inputs and have different costs of production, though
there are interchangeable, common, parts as well. We used a recent report from the Seattle Jobs
Initiative, which identified sectors most strongly associated with ICE and BEV automotive
production, to determine a list of sectors that may be directly affected by our proposed rule
(Seattle Jobs Initiative 2020). Sectors that are mainly associated with BEV production include
electrical equipment and manufacturing and other electrical equipment and component
manufacturing. Sectors that include employment related to both EV and ICE manufacturing
include motor vehicle manufacturing, motor vehicle body and trailer manufacturing, and motor
vehicle parts manufacturing. A sector that is only associated with ICE vehicle manufacturing is
motor vehicle gasoline engine and engine parts manufacturing. The Employment Requirements
Matrix (ERM) provided by the U.S. Bureau of Labor Statistics (BLS) provides direct estimates
of employees per $1 million in expenditures for a total of 202 aggregated sectors that roughly
correspond to the 4-digit NAICS code level, and provides data from 1997 through 2021 (Bureau
of Labor Statistics 2023). Over time, the amount of labor needed in the motor vehicle industry
has changed: automation and improved methods have led to significant productivity increases.
This is supported by this historical data. In Figure 4-11, we can see that the workers per $1
million in sales for all five of these sectors has, generally, decreased over time. For instance, in
1997, about 1.2 workers in the Motor Vehicle Manufacturing sector were needed per $1 million,
but only 0.5 workers by 2021 (in 2020$). The three sectors mainly associated with BEV
manufacturing show an increase in recent years, with the 2020 ratios for electrical equipment
manufacturing and other electrical equipment and component manufacturing surpassing those
estimated in 2005. This indicates that these sectors have become more labor intensive over time.
Figure 4-11 shows the estimates of employment per $1 million of expenditure for each sector,
adjusted to 2020 dollars using the U.S. Bureau of Economic Analysis Gross Domestic Product
Implicit Price Deflator. The values are adjusted to remove effects of imports through the use of a
ratio of domestic production to domestic sales of 0.81.80
80 To estimate the proportion of domestic production affected by the change in sales, we use data from WardsAuto
for total car and truck production in the U.S. compared to total car and truck sales in the U.S. Over the period 2009-
2021, the proportion averages 0.83 percent. From 2016-2021, the proportion average is slightly lower, at 0.81
percent.
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0.000
Year
— Electrical equipment manufacturing
— Other electrical equipment and component manufacturing
— Semiconductor and other electronic component manufacturing
¦ Motor vehicle manufacturing
< Motor vehicle body and trailer manufacturing
• Motor vehicle parts manufacturing
Figure 4-11: Workers per million dollars in sales, adjusted for domestic production.
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4.5.4 Partial Employment Effects of the Proposed Standards
In previous LD rules, EPA has estimated a cost effect on employment holding sales constant,
and assuming labor intensity is held constant in the face of increasing expenditures. However,
the costs in this proposal are estimated in OMEGA, which works iteratively to estimate a vehicle
fleet that will meet the regulatory standards, as well as be accepted by consumers. The model
estimates this by both changing the number of new vehicles sold, as well as changing the
penetration of BEVs in the market between the No Action and Action cases. Therefore, though
the method used, described in this Chapter 4.5.4, is the same as that in previous rules, we are
unable to estimate a cost employment effect due to this rule while holding sales constant.
Therefore, the partial employment analysis presented here is a change in employment due to the
change in costs, allowing sales to change as well. In other words, it is a combined cost and
demand effect.
We estimate the partial employment effect using the historic share of labor in the cost of
production for a set of sectors affected by this rule. We use these historic shares to extrapolate
estimates of future shares of labor in the cost of production for each of those sectors. We then
multiply the estimated share of labor in production costs by the change in production costs
estimated as an impact of this proposed rule. This provides a sense of the magnitude of potential
impacts on employment. The advantages and limitations of this method are described in Chapter
4.8.3.3.
We rely on three different public sources to get a range of estimates of employment per $1
million expenditures: the Economic Census (EC) and the Annual Survey of Manufactures
(ASM), both provided by the U.S. Census Bureau, and the Employment Requirements Matrix
(ERM) provided by the U.S. Bureau of Labor Statistics (BLS). The EC is conducted every 5
years, most recently in 2017.81 The ASM is an annual subset of the EC and is based on a sample
of establishments. The latest set of data from the ASM is from 2021. The EC and ASM have
more sectoral detail than the ERM, providing estimates out to the 6-digit North American
Industry Classification System (NAICS) code level. They provide separate estimates of the
number of employees and the value of shipments, which we convert to a ratio for this
employment analysis.82 The ERM provides direct estimates of employees per $1 million in
expenditures for a total of 202 aggregated sectors that roughly correspond to the 4-digit NAICS
code level, and it provides data through 2020.
We estimate cost effects on employment by separating out costs mainly associated with BEV
production, costs mainly associated with ICE vehicle production, and costs that are common to
ICE and BEV production due to this rule, applying the BEV cost increases to data from sectors
that primarily include BEV production, ICE costs to the sectors that primarily include ICE
production, and common costs to a set of sectors that include both BEV and ICE manufacturing.
We use the sum of the estimated BEV, ICE and common costs for both LD and MD vehicles.
We used a report from the Seattle Jobs Initiative to identify sectors most strongly associated with
81 Though the Economic Census was conducted in 2022, data from 2022 will not begin to be released until March
2024.
82 The total employment across the NAICS code sectors used in this analysis (see Table 10-6) as reported in the
ASM and the EC ranges from about 1,052,500 to about 1,053,800 depending on which data source is used; as noted
above the most recent data for ASM and EC are from 2021 and 2017, respectively.
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ICE or BEV automotive production and sectors that are common between them (Seattle Jobs
Initiative 2020).
Table 4-20 below shows the sector definition, the NAICS code, and the ERM sector number
EPA used to estimate employment effects in this analysis. It also provides the estimates of
employment per $1 million of expenditure for each sector for each data source, adjusted to 2020
dollars using the U.S. Bureau of Economic Analysis Gross Domestic Product Implicit Price. The
values are adjusted to remove effects of imports through the use of a ratio of domestic production
to domestic sales of 0.81.83 While the estimated labor ratios differ across data sources, they are
fairly similar, and mainly exhibit a similar pattern across the ICE and common sectors. Within
the BEV focused sectors, the ASM and EC are very similar, while the order of most intensive to
least intensive as estimated by the ERM differs. This may be due to the inclusion of additional
NAICS sectors within the larger ERM sectors.84 Within the ASM and EC data, Other Electronic
Component Manufacturing seems to be the most labor-intensive sector, while ERM indicates
Motor and Generator Manufacturing is the most labor-intensive. All three data sets agree that
Automobile and Light-Duty Motor Vehicle Manufacturing is the least labor-intensive.
Table 4-20: Sectors and associated workers per million dollars in expenditures used in this
analysis
C/5
>
W
PQ
i >
i w
5 w
I a
U §
C w
3 U
W ©
Scclor
NAICS Code
ERM Scclor
Ratio of Workers pel $1 Million Expenditures'1
ASM (2018)
EC (2017)
ERM (2021)
Other electronic component
334419
73
3.4
3.3
2.2
manufacturing
Motor and generator manufacturing
335312
78
2.2
2.2
2.9
Battery manufacturing
33591
79
2.6
2.5
2.1
All other miscellaneous electrical
335999
79
2.6
2.5
equipment and component
manufacturing
Automobile and light duty motor
33611
80
0.5
0.5
0.5
vehicle manufacturing
Motor vehicle body and trailer
3362
81
2.6
2.3
2.7
manufacturing
Motor vehicle parts manufacturing (not
3363*
82
1.9
1.8
1.8
gasoline engines)
Motor vehicle electrical and electronic
33632
82
2.0
1.9
equipment manufacturing
Motor vehicle gasoline engine and
33631
82
1.3
1.2
engine parts manufacturing
Values are adjusted for domestic vs. foreign production
* In our analysis. 3363 excludes estimates for NAICS code 33631. NAICS code 33631 only includes ICE vehicle manufacturing, so we subtract
those data out from the main sector. NAICS code 3363. and apply ICE costs to that sub-sector.
Because the ERM is available annually for 1997-2021, we use these data to estimate
productivity improvements over time. We regress logged ERM values on a year trend for each
83 To estimate the proportion of domestic production affected by the change in sales, we use data from WardsAuto
for total car and truck production in the U.S. compared to total car and truck sales in the U.S. Over the period 2009-
2021, the proportion averages 83 percent. From 2016-2021, the proportion average is slightly lower, at 81 percent.
84 ERM sectors are based on the 4-digit level for NAICS code sectors. For example, ERM sector 73, consists of
results from manufacturers in NAICS code 3344.
4-55
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sector.85 We use this approach because the coefficient describing the relationship between time
and productivity is a direct measure of the average percent change in productivity per year. The
results show productivity changes in Semiconductor and Other Electronic Component
Manufacturing (ERM sector 73) of almost -6 percent per year, Electrical Equipment
Manufacturing (ERM sector 78) of about -0.5 percent per year, Other Electrical Equipment and
Component Manufacturing (ERM sector 79) of about 0.1 percent per year, Motor Vehicle
Manufacturing (ERM sector 80) of almost -3 percent per year, Motor Vehicle Body and Trailer
Manufacturing (ERM sector 81) or almost -1.5 percent per year, and Motor Vehicle Parts
Manufacturing (ERM sector 82) of about -2.6 percent per year. These figures coincide with the
general fall in workers per million dollars in sales as seen in Figure 4-11.
We then use those estimated percent improvements in productivity to project the number of
workers per $1 million of production expenditures through 2032. The results provided in Table
4-21 below represent an order of magnitude effect, rather than definitive impacts. We calculate
separate sets of projections (adjusted to 2020$) for each set of data (ERM, EC, and ASM) for all
sectors described above. The ERM projections are calculated directly from the fitted regression
equations used to estimate the projected productivity growth, since the regressions themselves
used ERM data. For the ASM and EC projections of the number of workers needed per $1
million of expenditures (in 2020$), we apply ERM's ratio of projected annual productivity
growth to the projected production expenditure value in 2021 for the ASM and 2017 for the EC
(the base years in our data). In other words, we apply the projected productivity growth
estimated using the ERM data to the ASM and EC numbers.
To simplify the results, we compare the projected employment across data sources and report
only the maximum and minimum (in absolute terms) effects in each year across all sectors.86 We
provide a range rather than a point estimate because of the inherent difficulties in estimating
employment impacts as well as the uncertainty over how the costs are expended. The reported
ranges provide an estimate of the expected magnitude of the effect. The employment effect
estimated here includes the costs of this rule for both LD and MD vehicles, as well as the change
in new vehicles sales for LD vehicles due to this rule. There are no estimated changes in MD
vehicle sales. See Chapter 4.4.2 for more information on the estimates of new vehicle sales
effects due to this rule.
Vehicle technology cost estimates for this rule were developed in OMEGA. Chapter 10
provides information on the total and per-vehicle costs estimated. For this analysis, we use
detailed OMEGA results to get estimates of the costs of manufacturing LD and MD vehicles
separated out by costs expected to apply only to BEVs, those expected to only apply to ICE
vehicles, and those expected to apply to both BEV and ICE vehicles. These costs (in $ million)
are multiplied by the estimates of workers per $1 million in costs. The projected estimates of
technology costs and corresponding minimum and maximum estimated employment impacts for
each year are shown in Table 4-21, below. The effects are shown in job-years, where a job-year
85 Details and results are found in the file LMDVNPRMEmploymentlmpactsCalculations.xlsx, which is in the
docket for this rule.
86 To see details, as well as results for all sources, see "LMDV NPRM EmploymentlmpactsCalculations.xlsx" in
the docket.
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is, for example, one year of full-time work for one person or two years of half-time work for two
workers.
Increased technology costs of vehicles and parts is, allowing for the estimated change in sales
due to the proposal, expected to increase employment over the 2027-2032 time frame under the
assumptions of the maximum estimated effects, with the increase coming from the sectors
common to BEV and ICE production. Changes in ICE and BEV focused manufacturing are
expected to lead to a decrease in job-years in the sector included in this analysis. Under the
assumptions of the minimum estimated effects, we are estimating a net negative employment
effect. In addition, though the range of possible net effects includes zero, the net maximum
impact is larger, in absolute value, than the net minimum impact.87
It should be noted that these results are exclusive of any changes in employment in related
sectors, such as charging infrastructure. While we estimate employment impacts, measured in
job-years, beginning with program implementation, some of these employment gains may occur
earlier as vehicle manufacturers and parts suppliers hire staff in anticipation of compliance with
the standards, or in anticipation of ramping up BEV production.
Table 4-21: Estimated partial employment effects in job-years for BEV and ICE sectors,
sectors common to BEV and ICE, and the net minimum and maximum across all sectors
Common to BEV BEV only ICE only Net
and ICE
Year
Min
Max
Min
Max
Min
Max
Min
Max
2027
7.620
54.000
-9.800
-11.700
-10.200
-11.500
-12.380
30.800
2028
8.600
61.600
-9.100
-11.600
-13.900
-15.700
-14.400
34.300
2029
10.300
75,200
-9.000
-12.100
-19.200
-21.600
-17.900
41.500
2030
11.700
86.900
-9.100
-12.800
-21.600
-24.300
-19.000
49.800
203 1
14.600
109.900
-10.100
-15.100
-26.100
-29.300
-21.600
65.500
2032
17.500
133.300
-11.100
-17.500
-30.500
-34.300
-24.100
81.500
EPA contracted with FEV to perform a detailed tear-down study comparing two similar
vehicles, a 2021 Volkswagen ID.4 (BEV) and a 2021 Volkswagen Tiguan (ICE) (see DRIA
Chapter 2.5.2.2.3 for more details on this study). In the process of compiling the detailed
information, FEV estimated the number of labor hours it takes to build each of the two vehicles.
Under a realistic scenario of assembly based on what OEMs are currently doing, their results
suggest that the labor hours needed to assemble the BEV and ICE vehicles are very similar.88
This indicates that changes in employment in the auto manufacturing sectors from increasing
87 Comparing the net results in Table 4-21 to the lower estimate of total employment across all sectors found in
footnote 82 (1,052,500), net employment effects estimated under the Proposal scenario for 2032 range from -2.3%
to 7.7% of total employment across the sectors.
88 In the realistic scenario, FEV assumes that the automakers purchase EV battery modules and assemble the pack.
Under assumptions that the auto manufacturers provide the least amount of added value in assembly, the Tiguan
(ICE) is estimated to require more man hours to assemble than the ID.4 (BEV). Under assumptions that the auto
manufacturers perform most of the sub system manufacturing and assembly, including the engine, transmission and
battery pack modules, the ID.4 (BEV) is estimated to take more man hours per vehicle than the Tiguan (ICE).
4-57
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electrification will not come from the assembling of the vehicles at the auto manufacturer, but
from changing sales.
4.5.4.1 Partial Employment Effects of the Alterative Scenarios
The estimated partial effect on employment for the three alterative scenarios are in Table 4-22
through Table 4-24, below. Results are directionally similar across all scenarios. Similar to the
results for the proposal, job-years in BEV and ICE related sectors are estimated to fall, while job-
years in sectors common to BEV and ICE are expected to increase. Also like the Proposed
scenario, though the range of possible net effects includes zero, the net maximum impact is
larger, in absolute value, than the net minimum impact for all alternative scenarios.
Table 4-22: Estimated partial employment effects in job-years for BEV and ICE sectors,
sectors common to BEV and ICE, and the net minimum and maximum across all sectors
for Alternative 1 (-10)
Common
BEV
ICE
Net
Year
Min
Max
Min
Max
Min
Max
Min
Max
2027
8,000
56,600
-10,000
-12,000
-10,700
-12,100
-12,700
32,500
2028
10,100
73,000
-8,100
-10,300
-16,000
-18,000
-14,000
44,700
2029
11,600
84,500
-7,100
-9,500
-17,400
-19,500
-12,900
55,500
2030
15,000
111,000
-9,000
-12,700
-24,200
-27,200
-18,200
71,100
2031
16,500
124,100
-9,100
-13,500
-25,900
-29,200
-18,500
81,400
2032
19,200
146,400
-10,500
-16,500
-30,600
-34,500
-21,900
95,400
Table 4-23: Estimated partial employment effects in job-years for BEV and ICE sectors,
sectors common to BEV and ICE, and the net minimum and maximum across all sectors
for Alternative 2 (+10)
Common
BEV
ICE
Net
Year
Min
Max
Min
Max
Min
Max
Min
Max
2027
5,500
38,700
-7,500
-9,000
-6,900
-7,800
-8,900
21,900
2028
5,800
42,000
-6,300
-8,000
-9,100
-10,300
-9,600
23,700
2029
9,000
65,800
-8,100
-10,900
-16,700
-18,800
-15,800
36,100
2030
9,200
68,000
-7,800
-11,000
-17,200
-19,300
-15,800
37,700
2031
12,500
94,200
-9,200
-13,800
-22,200
-25,000
-18,900
55,400
2032
15,500
117,900
-10,200
-16,200
-26,600
-29,900
-21,300
71,800
Table 4-24: Estimated partial employment effects in job-years for BEV and ICE sectors,
sectors common to BEV and ICE, and the net minimum and maximum across all sectors
for Alternative 3 (Linear)
Common
BEV
ICE
Net
Year
Min
Max
Min
Max
Min
Max
Min
Max
2027
4,100
29,000
-7,300
-8,800
-5,800
-6,500
-9,000
13,700
2028
4,800
34,700
-7,400
-9,400
-8,100
-9,200
-10,700
16,100
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2029
5,200
37,800
-6,300
-8,500
-10,100
-11,400
-11,200
17,900
2030
8,300
61,700
-7,900
-11,200
-15,700
-17,700
-15,300
32,800
2031
14,000
105,500
-10,800
-16,100
-24,900
-28,000
-21,700
61,400
2032
18,100
137,800
-12,300
-19,300
-30,900
-34,800
-25,100
83,700
4.5.5 Employment Impacts on Related Sectors
Economy-wide impacts on employment are generally driven by broad macroeconomic effects.
However, employment impacts, both positive and negative, in sectors upstream and downstream
from the regulated sector, or in sectors producing substitute or complementary products, may
also occur as a result of this rule.
For example, as described in DRIA Chapter 9.5, we expect the proposed rule to cause a small
decline in liquid fuel consumption and a small increase in electricity generation which may have
consequences for labor demand in those upstream industries, as well as associated industries
such as extracting, refining, transporting, and storing of petroleum fuels. The lower per-mile fuel
costs could lead to increases in demand for 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.
Although gasoline stations will sell less fuel, the fact that many provide other goods, such as
food or car washes, moderates possible losses in this sector. There will also likely be an increase
in demand for labor in sectors that build and maintain charging stations. 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 between them and the regulated firms.
Expected petroleum fuel consumption reductions found in Chapter 9.5 represent fuel savings
for purchasers of fuel, however they also represent a potential loss in value of output for the
petroleum refining industry, fuel distributors, and gasoline stations. The loss of expenditures to
petroleum fuel suppliers throughout the petroleum fuel supply chain, from the petroleum refiners
to the gasoline stations, could result in reduced employment in these sectors. Because the fuel
production sector is material-intensive, the employment effect is not expected to be large. It may
also be difficult to distinguish these effects from other trends, such as increases in petroleum
sector labor productivity that may also lower labor demand. In addition, there is uncertainty
about the impact of reduced domestic demand for petroleum fuels on the petroleum fuel supply
chain. For instance, refineries might export the volumes of gasoline and diesel fuel that would
otherwise have been consumed in light- and medium-duty vehicles, absent this rulemaking. In
that scenario there would be no impact on employment at refineries.
As discussed in Chapter 4.5.2, above, 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. In addition, the type and number of jobs related to
vehicle maintenance are expected to change, though we expect this to happen over a longer time
span due to the nature of fleet turnover. Though we expect the sale of new BEVs to increase over
the time span of this proposed rule, both new and used ICE vehicles will persist in the fleet for
many years. As vehicles age, they generally require greater amounts of maintenance, possibly
mitigating the expected reduction in the number of ICE vehicles in the onroad fleet over time.
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Over this same time span, though we estimate less maintenance needs for BEVs compared to
ICE vehicles, the total employment related to BEV maintenance is expected to increase due to
the increase in number of BEVs in the onroad fleet. Even if the increase in BEV maintenance-
related employment is smaller than the decrease in ICE vehicle maintenance-related employment
over time, we expect opportunities for workers to retrain to other positions, for example within
BEV maintenance, charging station infrastructure, or elsewhere in the economy.
Effects in the supply chain depend on where goods in the supply chain are developed.
Commenters on the 2021 LD rule argue that developing EVs in the U.S. is critical for domestic
employment, and for the global competitiveness of the U.S. in the future auto industry: as other
countries are moving rapidly to develop EVs, the U.S. auto industry risks falling behind. As
discussed in Preamble Section I.A.2.iii and DRIA 4.5.2, there have been several legislative and
administrative efforts enacted several acts since 2021 aimed at improving the domestic supply
chain for electric vehicles, including electric vehicle chargers, critical minerals, and components
needed by domestic manufacturers of EV batteries. These actions are also expected to provide
opportunities for domestic employment in these associated sectors.
The standards may affect employment for auto dealers through a change in vehicles sold, with
increasing sales being associated with an increase in labor demand. However, vehicle sales are
also affected by macroeconomic effects, and it is difficult to separate out the effects of the
standards on sales from effects due to macroeconomic conditions. In addition, auto dealers may
be affected by changes in maintenance and service costs. Increases in those costs are likely to
increase labor demand in dealerships, and reductions are likely to decrease labor demand. Auto
dealers may also be affected by changes in the maintenance needs of the vehicles sold. For
example, reduced maintenance needs of BEVs could lead to reduced demand for maintenance
labor for dealers that sell BEVs.
As a result of these standards, consumers will likely pay higher up-front costs for the vehicles,
but they are expected to recover those costs through reduced fuel, maintenance, and repair costs,
as well as due to the IRA tax incentives for BEV purchase and battery manufacturing leading to
reduced up-front costs for BEVs. As a result, consumers are expected to have additional money
to spend on other goods and services, though the timing of access to that additional money
depends on aspects including whether the consumer borrows money to buy the vehicle. These
increased expenditures could support employment in those sectors where consumers spend their
savings. If the economy is at full employment, any change in consumer expenditures would
primarily represent a shift in employment among sectors. If, on the other hand, the economy has
substantial unemployment, these expenditures would contribute to employment through
increased consumer demand.
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Chapter 5: Electric Infrastructure Impacts
As plug-in electric vehicles (PEVs) are projected to represent a significant share of the future
U.S. light- and medium-duty vehicle fleet, EPA has developed new approaches to estimate the
power sector emission implications (i.e., from electricity generation, transmission, and
distribution system, which typically ends at a service drop; the run of cables from the electric
power utility's distribution power lines to the point of connection to a customer's premises) of
increased PEV charging. EPA combined the use of three analytical tools to incorporate grid-
related emissions from PEV charging demand within the light- and medium-duty vehicle
emissions inventory analysis for the proposal:
1) OMEGA
2) A suite of electric vehicle infrastructure modeling tools (EVI-X) developed by the National
Renewable Energy Laboratory (NREL)
3) The Integrated Planning Model (IPM)
Chapter 5.1 below provides a summary of EVI-X and how these tools were used together with
OMEGA to estimate charge demand inputs for IPM. The IPM modeling results and how the
results were incorporated into the emissions inventory analysis are described in Chapters 5-8 and
Chapter 9. Chapter 5.3 describes our assessment of PEV charging infrastructure. It should be
noted that charging infrastructure is different from the electric power utility distribution system
infrastructure, which is comprised of distribution feeder circuits, switches, protective equipment,
primary circuits, distribution transformers, secondaries, service drops, etc. The electric power
utility distribution system infrastructure typically ends at a service drop (i.e. the run of cables
from the electric power utility's distribution power lines to the point of connection to a
customer's premises).
Finally, the potential impacts on pending changes to the power sector on grid resiliency are
discussed in Chapter 5.4.
5.1 Modeling PEV Charge Demand and Regional Distribution
Under an Interagency Agreement between EPA and the U.S. Department of Energy, NREL
has continued its development of a suite of electric vehicle infrastructure modeling tools (EVI-X)
and methods for simulating PEV charging infrastructure requirements and associated electricity
loads from best available data. EVI-X tools have informed multiple national, state, and local
PEV charging infrastructure planning studies (E. Wood, et al. 2017) (E. Wood, C. Rames, et al.
2018) (Alexander, et al. 2021), including a forthcoming national infrastructure assessment
through 2030 (Wood, Borlaug, et al. 2023). As noted above, this infrastructure differs from that
of electric power utility distribution system infrastructure. Within the emissions inventory
analysis for the proposal, EVI-X models are used to translate scenario-specific forecasts of
national light-duty vehicle stock and annual energy consumption from the OMEGA model into
spatially disaggregated hourly load profiles required for subsequent power sector modeling using
the Integrated Planning Model (IPM) (see Chapter 5.2). The primary components of the process
flow from OMEGA outputs to IPM inputs as shown in Figure 5-1. IPM outputs also flow back
into inventory analyses in OMEGA as PEV emissions factors (see DRIA Chapter 9).
5-1
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OMEGA Compliance Model
' Q) A jy
w >
National-level PEV stock
projections, vehicle attributes, VMT,
electrical energy consumption,
mobile source inventory, program
costs
Integrated Planning Model
Grid impacts (emissions, energy,
costs, build/retirement, etc.)
PEV Likely Adopter Model
":T
-
PEV stock projections at the IPM
regional level
EVI-X National LDV Framework
Q
Hour (EST)
¦¦I Public DCFC
Public L2
S Work L2
¦¦ Home L2
MM Home LI
Hour(EST)
Spatiotemporal PEV charging load
simulation
Figure 5-1: Modeling process flow highlighting the primary components for translating
OMEGA's national PEV stock projections and PEV attributes into hourly load profiles.
5.1.1 PEV Disaggregation and Charging Simulation
As described in further detail in Chapter 2 of the DRIA, the OMEGA model evaluates the cost
of compliance for meeting the standards and options analyzed within the proposed rule. Each
OMEGA run produces scenario-specific projections of national vehicle sales, stock, energy
consumption, and tailpipe emissions. For PEVs, however, tailpipe emissions are zero in the case
of battery electri c vehicles (BEVs) and during the charge-depleting operation of plug-in hybri d
electric (PEGEVs) with resulting emissions occuring upstream at the electricity generation source,
thus expanding the requisite analytical boundaries of the system with respect to determination of
emissions inventory impacts. To produce estimates of the spatiotemporal charging loads needed
for power sector emissions modeling, the national PEV stock from OMEGA must first be
disaggregated regionally.
The framework developed for PEV disaggregation leverages a likely adopter model (LAM)
adapted by NREL (Ge, et al. 2021) to rank vehicles in the private light-duty fleet for their
likelihood to be replaced by a PEV based on publicly available demographic data, including
housing type, income, tenure (rent or own), state policies (ZEV states), and population density.
The model is trained on the revealed preferences of 3,772 survey respondents (228 PEV owners)
across the United States as described in (Ge, et al. 2021). Vehicle registration data from June
2022 (Experian 2022) were used to develop a set of chassis-specific LAMs for disaggregating
PEV sedans, S/CUVs, pickups, and vans based on current regional vehicle type preferences. This
process is outlined in Figure 2.
5-2
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OMEGA
(National PEV stock)
Simplified PEV chassis-
representation specific LAMs
PEV adoption
(IPM region-level)
-—PEV sedan LAM
* PEV pickup LAM
PEV S/CUV LAM
¥
*
-—+1 PEV van LAM
*
National PEV stock
projections & PEV attributes
Geographic distribution of OMEGA PEV
stock to IPM regions
Figure 5-2: Procedure for disaggregating OMEGA national PEV stock projections to IPM
Vehicles modeled within OMEGA were first assigned to a simplified chassis type (i.e., sedan,
S/CUV, pickup, van). Next, the total number of vehicles in the simplified chassis types were
used as inputs to each of the four chassis-specific LAMs to disaggregate PEVs into IPM regions
based on regional vehicle type preferences and the likelihood of PEV adoption.
The OMEGA model generates vehicle adoption projections for thousands of unique PEV
models over time. Conducting detailed charging simulations for each of these models would be
computationally prohibitive and produce results not expected to meaningfully differentiate from
those generated by a reduced set of representative PEV models. Thus, a clustering approach was
used to generate these representative PEV models for simulation from the complete set of
OMEGA vehicles. K-means clustering was performed over each PEV's respective battery
capacity (kWh) and energy consumption rate (kWh/mi.) parameters as specified by OMEGA. A
silhouette analysis was used to determine the appropriate number of clusters (k 6 for BEVs, k=2
for PHEVs) and OMEGA vehicles were assigned to clusters that minimize the Euclidean
distance to the centroids of the two normalized (Z-score) parameters. These assignments were
retained and used to map OMEGA vehicles to the most similar synthetic representative PEV
model. The cluster centroids were used to produce the battery capacity and energy consumption
rate parameters for the eight representative PEVs required for subsequent PEV charging
simulations. An additional parameter, the max DC charge acceptance, was defined as the
maximum effective charging rate over a typical 20percent to 80percent SOC DC fast charge
(DCFC) window. This was required to simulate DCFC for BEVs and was not directly specified
by the OMEGA model. PHEVs were assumed to be incapable of using DCFC equipment. For
modeling BEV DCFC, a simple heuristic was applied such that pre-2030 model years (Gen 1
batteries) would be capable of 1.5C charging while model year 2030 and after BEVs would be
capable of charging at 3C (Gen 2 batteries).89 The key parameters for simulating charging for
each of the representative PEVs are shown in Table 5-1.
Three separate EVI-X models developed by NREL, namely EVI-Pro (for typical daily travel),
EVI-RoadTrip (for long-distance travel), and EVI-OnDemand (for ride-hailing applications)
® C-rate is a measure of the rate at which a battery is charged/discharged relative to its maximum energy storage
capacity. For example, 1.5C indicates that the battery is fully charged in 40 minutes, while 3C indicates a full
charge in 20 minutes
regions.
5-3
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were used to estimate composite PEV charging load profiles under a unified set of assumptions:
PEV fleet composition, regional home charging access (Ge, et al. 2021), regional weather
conditions, public/workplace infrastructure availability, and charging preferences.
Table 5-1: Representative PEV examples for charging simulations.
Sim vehicle
Vehicle type
Battery capacity
[kWh]
Energy cons, rate
[kWh/mi.]
Max AC accept.
[kW]
(Gen 1 / Gen 2)
Max DC accept.
[kW]
(Gen 1 / Gen 2)
BEV1
BEV
89
0.27
9/12
134 / 267
BEV2
BEV
103
0.31
9/12
154 / 308
BEV3
BEV
114
0.34
9112
171 / 342
BEV4
BEV
128
0.38
9 'f 12
191 / 383
BEV5
BEV
141
0.42
9/12
212/424
BEV6
BEV
157
0.47
9/12
236 ,/ 471
PHEV1
PHEV
18
0.29
9/12
-
PHEV2
PHEV
18
0.38
9 i 12
-
Figure 5-3 shows a schematic summary of the EVI-X models. The EVI-X models perform
bottom-up simulations of charging behavior by superimposing the use of a PEV over travel data
from internal combustion engine vehicles. These independent, but coordinated, simulations
produce daily charging demands for typical PEV use, long-distance travel, and ride-hailing
electrification, respectively, which are indexed in time (hourly over a representative 24-hr period
for weekdays and weekends) and space (county). This process is shown in Figure 3 and
described in (Wood, Borlaug, et al. 2023).
Regional PEV Charging Demand
EVI-X National LDV
Simulation Framework
Inputs
PEV fleet
evolution
Weather
¦f Residential
conditions
n EVSE access
EVI-Pro
• Travel behaviors (2017 NHTS)
• Charging preferences
' EVSE availability by location
• PEV models
EVI-RoadTrip
• long-distance travel fFHWA TAT)
• land use data
4 PEV models
EVI-OnDemand
' Urban TNCVMT
• TNCP£V models
• TNC shift behaviors
• TNC driver demographics
~ EVI-Pro
~ EVI-RoadTrip
¦i EVI-OnDemand
Final
Output
Intermediate
Outputs
Figure 5-3: EVI-X National light-duty vehicle framework simulation showing
spatiotemporal energy demands for three separate use cases: typical daily travel (EVI-Pro),
long-distance travel (EVI-RoadTrip), and ride-hailing (EVI-OnDemand).
5-4
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Following the PEV charging simulations, load profiles were aggregated from the county-level
into IPM regions and converted from local time to Eastern Standard Time (EST) for IPM
implementation. A final corrective step was taken to ensure that the annual energy consumption
estimates supplied by OMEGA were reflected in the PEV load profiles.
For a given OMEGA national PEV stock projection file, the modeling framework produces a
typical weekday and weekend 24-hour (EST) load profile for all IPM regions (plus Hawaii,
Alaska, and Puerto Rico) and analysis years (2026, 2028, 2030, 2032, 2035, 2040, 2045, 2050,
2055). Load profiles were analyzed using output from four separate OMEGA analytical cases:
1) No-action Case: Vehicle electrification under the existing 2023 through 2026 light-duty
vehicle GHG standards as represented by the standards finalized by EPA December 30, 2021
(86 FR 74434 2021), with updated OMEGA compliance modeling (see DRIA Chapter 2).
2) Action Case: Proposed light-and medium-duty vehicle standards
3) High BEV Sensitivity Case
4) Low BEV Sensitivity Case
These analytical cases are described in more detail below. Figure 5-4 provides an example of
how specific load profiles may be used to infer annual PEV charging demands for 2030 and 2050
using an example OMEGA analytical scenario (the "Action Case").
5-5
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a) Annual PEV Charging Demand: 2030
b) Annual PEV Charging Demand: 2050
rjL^
Figure 5-4: Annual PEV charging loads (2030 and 2050 are shown) for each IPM region in
the contiguous United States based on OMEGA charge demand for the proposal in 2030
(top) and 2050 (bottom).
5-6
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In addition to the total hourly energy demands for PEV charging, energy demands were also
broken out by the following charger types - home Level 1 (LI), home Level 2 (L2), work L2,
public L2, and public DCFC (Figure 5-5). See section 5.3.1.2. for additional discussion. Note
that these have been converted to EST and reflect an unmanaged charging scenario where drivers
do not prioritize charging at certain times of the day (i.e., charging starts as soon as possible
when vehicles are plugged in without consideration of electricity price or other factors).
In Figure 5-5, there are clear differences in the magnitude, shape, and charger types between
the West Texas (left-ERCWEST, containing mostly rural areas and small cities such as
Midland and Odessa) and East Texas (right-ERCREST, including multiple major population
centers such as Houston, San Antonio, Austin, and Dallas-Ft. Worth) regions. The EVI-X
National light-duty vehicle framework conducts charging simulations that are reflective of the
regional differences in EV adoption, vehicle type preferences, home ownership, weather
conditions, and travel patterns. These demonstrative results reflect how in ERCWEST, EV
adoption is projected to be low (due to limited population and revealed vehicle preferences)
leading to a reduced demand for home-based charging while public DCFC demands for long-
distance travel across the region (e.g., road trips) are amplified. This leads to a disproportionate
share of public DCFC charging demand along highway corridors within the ERC WEST region.
Alternatively, simulated charging demands in the ERC REST are dominated by home and
workplace charging due to the higher EV adoption and urban travel patterns more common to the
region.
The OMEGA national PEV outputs and the resulting regionalized IPM inputs from EVI-X for
each of the four analyzed cases, for each IPM region and all analytical years (2026, 2028, 2030,
2032, 2035, 2040, 2045, 2050, 2055) are summarized within a separate PEV Regionalized
Charge Demand Report (McDonald 2023).
5-7
-------
ERC WEST: MW Demand
Wkday Wknd
ERC REST: MW Demand
Wkday Wknd
2035 150
2040 150 -
2045 150
Public DCFC
Public L2
Work L2
Home L2
Home LI
3,827
::aa
O 1 -I I 1
2050 150 ¦
2055 iso
wjt* :aa
12
Hour(EST)
Hour (EST)
12 24 0 12 24
Hour (EST) Hour (EST)
Figure 5-5: Yearly hourly (in EST) weekday and weekend load profiles for two IPM
regions (ERC_WEST, west Texas; and ERCJREST, east Texas) broken out by charger
type for an example OMEGA analytical scenario.
5.2 Electric Power Sector Modeling
The analyses for the proposal used EPA's Power Sector Modeling Platform, which utilizes the
Integrated Planning Model (IPM). IPM is a multi-regional, dynamic, deterministic linear
programming model of the U.S. electric power sector. It provides projections of least-cost
capacity expansion, electricity dispatch, and emission control strategies for meeting energy
demand and environmental, transmission, dispatch, and reliability constraints. IPM can be used
to evaluate the cost and emissions impacts of proposed policies to limit emissions of sulfur
5-8
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dioxide (SO2), nitrogen oxides (NOx), carbon dioxide (CO2), hydrogen chloride (HC1), and
mercury (Hg) from the electric power sector. Post-processing IPM outputs allows for the
processing of other emissions, such as volatile organic compounds (VOC) and non-CCh GHGs.
The power-sector modeling used for the proposal included power-sector-related provisions of
both the Bipartisan Infrastructure Law (BIL) and the Inflation Reduction Act (IRA). Additional
information regarding power-sector modeling is available via a report submitted to the docket
(U.S. EPA 2023).
5.2.1 Estimating Retail Electricity Prices
The Retail Price Model (RPM) was developed to estimate retail prices of electricity using
wholesale electricity prices generated by the IPM. The RPM provides a first-order estimate of
average retail electricity prices using information from EPA's Power Sector Modeling Platform
v6.21 using the Integrated Planning Model (IPM) and the EIA's Annual Energy Outlook (AEO).
This model was developed by ICF a under contract with EPA (ICF 2019).
IPM includes a wholesale electric power market model that projects wholesale prices paid to
generators. Electricity consumers—industrial, commercial, and residential customers—face a
retail price for electricity that is higher than the wholesale price because it includes the cost of
wholesale power and the costs of transmitting and distributing electricity to end-use consumers.
The RPM was developed to estimate retail prices of electricity based on outputs of EPA's Base
Case using IPM and a range of other assumptions, including the method of regulation and price-
setting in each state. Traditionally, cost-of-service (COS) or Rate-of-Return regulation sets rates
based on the estimated average costs of providing electricity to customers plus a "fair and
equitable return" to the utility's investors. States that impose cost-of-service regulation typically
have one or more investor-owned utilities (IOUs), which own and operate their own generation,
transmission, and distribution assets. They are also the retail service provider for their franchised
service territory in which IOUs operates. Under this regulatory structure, retail power prices are
based on average historical costs and are established for each class of service by state regulators
during periodic rate case proceedings. Additional documentation on the RPM can be found at on
the EPA website.
5.2.2 IPM emissions post-processing
Emissions of non-C02 GHG (methane, nitrous oxide), PM, VOC, CO and NH3 were
calculated via post-processing of IPM power sector data and using EPA-defined emissions
factors. The EPA GHG Emissions Factors Hub was used to determine fuel-specific emissions
factors for methane and nitrous oxide emissions for the electric power sector (U.S. EPA 2022a).
Emissions factors used for post-processing of PM, VOC, CO and NH3 were documented as part
of EPA's Power Sector Modeling Platform v6 - Summer 2021 Reference Case (U.S. EPA 2021).
5.2.3 IPM National-level Demand. Generation. Emissions and Costs
As EPA was in the process of developing this proposal in the fall of 2022, EPA's Clean Air
Markets Division (CAMD) completed an initial power sector modeling analysis of the BIL and
IRA. The IRA provisions modeled within IPM included:
• Clean Electricity Production and Investment Tax Credits
• Existing Nuclear Production Tax Credit
5-9
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• Carbon Capture and Storage 45Q Tax Credit
This initial modeling did not include other power sector impacts, such as demand impacts
from electrification and energy efficiency provisions, however these are likely to be part of
future CAMD power sector analyses.
The initial modeling of the IRA showed a 70percent reduction of power sector related CO2
emissions from current levels by 2055, and that the changes in CO2 emissions would be driven
primarily by increases in renewable generation and enabled by increased use of grid battery
storage capacity (see Figure 5-6).
~60% reduction
from 2005 levels of
Figure 5-6: Power sector modeling comparing results of the Bipartisan Infrastructure Law
(BIL) and the Inflation Reduction Act (IRA)
Similar to CAMD's earlier power sector analysis, the power sector analysis for both the
proposal and a no-action case show significant reductions in CO2 emissions from 2028 through
2050 despite increased generation and largely due to increased use of renewables for generation.
5-10
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1400
200
0
2025 2030 2035 2040 2045 2050
Year
Figure 5-7: 2028 through 2050 power sector CO2 emissions for the proposal (orange line)
and no-action case (dashed line).
7,000
6,01
5,0ffl
g 4,0ffl
5 3,0CK)
2,0ffl
1,0 m
¦ Other
¦ Hydro
Non-Hydro Renewables
Nuclear
Natural Gas
I Coal
Proposal
Figure 5-8: 2028 through 2055 power sector generation and grid mix.
5-11
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450
400
350
c
,o
u
*Z
aj
300
2 250
¦a
c
(C
i/i
= 200
o
O* 150
z
100
50
0 —1
2025
2030
2035
2040
2045
2050
Year
Figure 5-9: 2028 through 2050 power sector NOx emissions for the proposal (orange line)
and no-action case (dashed line).
80
70
60
¦= 50
0J
TJ
§ 40
3
O
ifl 30
r>i
20
10
0
2025
\\
\ \
\
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X\M
—•—Proposal
—~—No Action
—I—I—I—I—
—.—.—.—.—
—.—.—.—.—
—.—.—.—.—
—.—.—.—.—
,—,—,—,—,—
2030
2035
2040
2045
2050
Year
Figure 5-10: 2028 through 2050 power sector PM2.5 emissions for the proposal (orange line)
and no-action case (dashed line).
5-12
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400
0
2025 2030 2035 2040 2045 2050
Year
Figure 5-11: 2028 through 2050 power sector SO2 emissions for the proposal (orange line)
and no-action case (dashed line).
A summary of national electric power sector emissions, demand, generation, and cost for the
no-action case and for the proposal are presented in Table 5-2 and Table 5-3, respectively. Note
that the total costs presented in both tables represent:
• Capital costs for building new power plants as well as retrofits
• Variable and fixed operation and maintenance costs
• Fuel costs
• Cost of transporting and storing CO2
5-13
-------
Table 5-2: National electric power sector emissions, demand, generation and cost for the
no-action case
Emission 2028 2030 2035 2040 2045 2050
SO2 (million metric tons)
0.351
0.265
0.120
0.0828
0.039
6
0.016
5
PM2.3 (million metric
0.067
0.059
0.042
0.0351
0.027
0.024
tons)
2
7
8
5
2
NOx (million metric tons)
0.409
0.339
0.197
0.149
0.104
0.087
5
VOC (million metric
0.033
0.029
0.023
0.0200
0.016
0.015
tons)
0
1
0
9
5
CO2 (million metric tons)
1.218
980
620
485
415
364
CH4 (metric tons)
75,27
59.87
36.18
28.218
17.38
13.90
0
0
5
8
6
N2O (metric tons)
10.33
8.050
4.718
3.659
2.136
1.668
Hg (metric tons)
2.27
1.92
1.46
1.32
1.06
0.962
HCL (million metric tons)
2.36
1.66
0.845
0.646
0.215
0.118
Demand (TWh)
4.400
4.528
4.854
5.188
5,533
5.885
Generation (TWh)
4.498
4.670
5.096
5.538
5.951
6.437
Total Cost (Billion $)
132
127
130
141
143
146
Table 5-3: National electric power sector emissions, demand, generation and cost for the
proposal
Emission 2028 2030 2035 2040 2045 2050
SO2 (million metric Ions) 0.353 0.269 0.131 0.0849 0.0406 0.0173
PM2.5 (million metric 0.0669 0.0602 0.0451 0.0359 0.0283 0.0249
tons)
NOx (million metric tons) 0.405 0.342 0.209 0.153 0.106 0.0888
VOC (million metric 0.0318 0.0292 0.0237 0.0202 0.0173 0.0159
tons)
CO2 (million metric tons)
1.217
989
662
500
435
380
CH4 (metric tons)
75.340
61.455
39.265
29.323
17.913
14.268
N2O (metric tons)
10.324
8.281
5.146
3.812
2.200
1.709
Hg (metric tons)
2.28
1.97
1.53
1.36
1.08
0.979
HCL (million metric tons)
2.38
1.74
0.961
0.681
0.224
0.121
Demand (TWh)
4.403
4.545
4.972
5,372
5,753
6.118
Generation (TWh)
4.500
4.688
5.210
5,733
6.184
6.689
Total Cost (Billion $)
132
128
136
147
150
153
5-14
-------
5.2.4 Retail Price Modeling Results
EPA estimated the change in the retail price of electri city (2020$) using the Retail Price
Model (RPM) and using the same methodology used in recent power-sector rulemakings (U.S.
EPA 2022b). The RPM was developed by ICF for EPA (ICF 2019) and uses the IPM estimates
of changes in the cost of generating electricity to estimate the changes in average retail electricity
prices. The prices are average prices over consumer classes (i.e., consumer, commercial, and
industrial) and regions, weighted by the amount of electricity used by each class and in each
region. The RPM combines the IPM annual cost estimates in each of the 74 IPM regions with
EIA electricity market data for each of the 25 NERC/ISOy0 electricity supply subregions (Table
5-4 and Figure 5-12) in the electricity market module of the National Energy Modeling System
(NEMS) (U.S. Energy Information Administraton 2019). Table 5-4 summarizes the projected
percentage changes in the retail price of electricity for the proposal versus a no-action case,
respectively. Consistent with other projected impacts presented above, average retail electricity
price differences at the national level are projected to be small at less than 1 percent difference in
2030 and 2050. Regional average retail electricity price differences showed small increases or
decreases (less than approximately 1 to 2pereent) with the sole exception of PJMC, which is the
PJM Commonwealth Edison (Metropolitan Chicago) NERC/ISO subregion.
There is a general trend of reduced national average retail electricity prices from 2021 through
2050, which is largely due to reduced fuel costs from increased use of renewables for generation.
¦ 23H
NWPP
ISNE
19
SPPN
[MISE;
NYUP
Kl2
[PJMCl
¦21 ¦
[CANO]
mom
.P-.IM1-!
NYCW
25
BASN
MISC
13
PJMD
22
CASO
PRCC
Figure 5-12: Electricity Market Module Regions (U.S. Energy Information Administraton
2019).
® NERC is the National Electricity Reliability Corporation. ISO is an Independent System Operator, sometimes
referred to as a Regional Transmission Organization.
5-15
-------
Table 5-4: National Energy Modeling System's Electricity Market Module regions (U.S.
Energy Information Administraton 2019)
umber
Abbreviation
NERC/ISO subregion name
Geographic name
1
TRE
Texas Reliability Entity
Texas
2
FRCC
Florida Reliability Coordinating Council
Florida
3
MISW
Midcontincnt ISO/West
Upper Mississippi Valley
4
MISC
M idcont i nc nt ISO/Cc nt ra 1
Middle Mississippi Valley
s
MISE
Midcontincnt ISO/East
Michigan
6
MISS
Midcontincnt ISO/South
Mississippi Delta
7
ISNE
Northeast Power Coordinating Council/ New England
New England
8
NYCW
Northeast Power Coordinating Council/ New York City &
Metropolitan New York
Long Island
9
NYUP
Northeast Power Coordinating Council/Upstate New York
Upstate New York
10
PJME
PJM/East
Mid-Atlantic
11
PJMW
PJM/Wcst
Ohio Valley
12-
PJMC
PJ M/Commonwca Ith Edison
Metropolitan Chicago
13
PJMD
PJM/Dominion
Virginia
14
SRCA
SERC Reliability Corporation/East
Carolinas
15
SRSE
SERC Reliability Corporation/Southeast
Southeast
16
SRCE
SERC Reliability Corporation/Central
Tennessee Valley
17
SPPS
Southwest Power Pool/South
Southern Great Plains
18
SPPC
Southwest Power Pool/North
Central Great Plains
19
SPPN
Southwest Power Pool/North
Northern Great Plains
20
SRSG
Western Electricity Coordinating Council/Southwest
Southwest
21
CANO
Western Electricity Coordinating Council/California North
Northern California
22
CASO
Western Electricity Coordinating Council/California South
Southern California
23
NWPP
Western Electricity Coordinating Council/
Northwest
Northwest Power Pool
24
RMRG
Western Electricity Coordinating Council/Rockies
Rockies
25
BASN
Western Electricity Coordinating Council/Basin
Great Basin
5-16
-------
Table 5-5: Average retail electricity price by region for the proposal and a no-action case in
2030 and 2050 compared to AEO2021
AEO2021
No-action
Proposal
No-
Proposal
Percent
Percent
2020
2030
2030
action
2050
2050
*
Change
2030
*
Change
2050
AEO/NEMS Model Regions
2020 mills/kWh'
TRE
89.4
80.0
80.5
60.9
61.0
o.6% r
0.1%
FRCC
98.2
89.9
90.3
78.2
78.8
0.4%
0.7%
MISW
108.4
81.7
82.1
86.9
87.6
0.4%
0.8%
MISC
96.2
90.3
90.8
72.0
72.2
0.5%
0.3%
MISE
116.4
99.2
98.3
83.7
83.7
-0.9% [
0.1%
MISS
79.0
90.7
91.0
71.3
71.5
0.4%
0.4%
ISNE
178.3
149.1
149.0
152.5
153.4
-0.1% :
0.6%
NYCW
187.5
206.4
201.2
202.2
203.5
-2.5% 1
0.6%
NYUP
117.8
123.5
120.2
114.2
114.7
-2.6%
0.5%
PJME
109.3
109.1
107.2
103.0
103.5
-1.8%
0.5%
PJMW
103.4
95.4
95.6
78.2
78.8
0.3% 'T
0.8%
PJMC
96.0
80.2
84.8
79.7
83.3
5.8%
4.5%
PJMD
85.4
73.4
73.9
71.8
72.1
0.6%
0.4%
SRCA
102.7
98.0
98.1
89.5
89.5
0.2% ]
0.0%
SRSE
101.6
91.6
91.8
74.5
74.6
0.3% !
0.0%
SRCE
85.0
106.3
106.6
71.7
71.9
0.2% ]
0.2%
SPPS
79.2
70.6
70.8
65.2
65.6
0.3% j
0.5%
SPPC
105.0
81.3
81.5
60.3
60.3
0.3%
0.0%
SPPN
71.5
60.5
60.4
58.7
59.0
-0.1% !
0.6%
SRSG
99.2
84.4
84.6
81.3
81.2
0.2% ]
-0.2%
CANO
151.0
158.6
159.0
150.0
150.0
0.2%
0.0%
CASO
179.4
189.3
189.5
168.6
169.2
0.1% ]
0.3%
NWPP
87.1
77.5
78.5
78.4
79.3
1.3% !
1.2%
RMRG
98.1
87.6
88.0
74.9
74.8
0.4%
-0.1%
BASN
91.4
89.7
90.4
76.2
76.9
0.7% T
0.9%
National
105.3
99.6
99.7
87.8
88.3
0.2% T
0.6%
Table Notes:
* Percentage increase in average retail electricity price from the Proposal to a no-action case. Negative percentages reflect a decrease in average retail electricity price for
the proposal.
"("One mill is equal to 1/1,000 U.S. dollar, or 1/10 U.S. cent. 2020 mills per kilowatthour (mills/kWh) are equivalent to 2020 dollars per megawatt-hour ($/MWh)
5-17
-------
5.2.5 New Builds. Retrofits and Retirements of EGUs
The electric power sector emissions modeling undertaken in support of this rulemaking, using
IPM (described at the beginning of Chapter 5.2), also projects the anticipated mix of electric
power plants required to meet the imposed electric power load from vehicle electrification,
subject to various constraints. These power plants are referred to here collectively as Electric
Generating Units (EGU). This definition includes all types of generating facilities (e.g. fossil
fuel-fired combustion, nuclear, hydroelectric, renewable, etc.).
This modeling reveals anticipated EGU retirements, EGU retrofits, and new EGU
construction, which are discussed below. EGUs are retired by IPM when announced by their
owner and for economic reasons. The IRA and BIL resulted in many EGU retirements. As such,
the number and types of EGU retirements associated with the proposed rule when compared to a
no-action case are small in comparison to those retirements that occurred as a result of the IRA
and BIL.
New EGU capacity modelled by IPM for the no-action case is summarized in Table 5-6. New
EGU capacity modelled by IPM for the proposal is summarized in Table 5-7. EGU retirements
modelled by IPM for the no-action and for the proposal are summarized in Table 5-8 and Table
5-9, respectively. Incremental EGU retirements and incremental new modeled EGU capacity are
summarized in Table 5-10 and Table 5-11, respectively.
For the no-action case, the retirement of coal-fired EGUs account for 81.1%, 80.4%, 75.7%,
74.7%, 65.3%, and 57.4% of all EGU retirements for 2028, 2030, 2035, 2040, 2045, and 2050,
respectively (see Table 5-8). For the proposal, the retirement of coal-fired EGUs are very similar
to the no-action case at 81.7%, 81.3%, 76.2%, 75.7%, 66.0%, and 57.8% of all EGU retirements
for 2028, 2030, 2035, 2040, 2045, and 2050, respectively (see Table 5-9).
For the no-action case, cumulative power generation from new solar EGU builds are expected
account for 11.3%, 23.2%, 28.9%, 31.5%, 28.7%, and 29.2% of all new power generation for
2028, 2030, 2035, 2040, 2045, and 2050, respectively. Also, cumulative power generation from
new wind-powered EGU builds are expected account for 27.0%, 36.9%, 45.4%, 42.7%, 42.9%,
and 40.1%) of all new power generation for 2028, 2030, 2035, 2040, 2045, and 2050,
respectively. Likewise, cumulative power generation from new energy storage EGU builds are
expected account for 31.8%, 24.4%, 15.7%, 13.0%, 10.3%, and 9.2% of all new power
generation for 2028, 2030, 2035, 2040, 2045, and 2050, respectively.
New generation for the proposal is similar to the no-action case. For the proposal,
cumulative power generation from new solar EGU builds are expected account for 10.9%,
22.8%, 28.8%, 31.8%, 29.2%, and 29.5% of all new power generation for 2028, 2030, 2035,
2040, 2045, and 2050, respectively. Also, cumulative power generation from new wind-powered
EGU builds are expected account for 213%, 36.1%, 44.1%, 41.6%, 41.6%, and 39.4% of all new
power generation for 2028, 2030, 2035, 2040, 2045, and 2050, respectively. Likewise,
cumulative power generation from new energy storage EGU builds are expected account for
31.0%, 24.7%,15.8%,12.4%, 9.7%, and 8.8% of all new power generation for 2028, 2030, 2035,
2040, 2045, and 2050, respectively.
Solar-power is expected to become the single largest new source of EGU capacity for 2040,
2045, and 2050, accounting for 34.4%, 35.4%, and 34.0%> of overall new EGU capacity,
5-18
-------
respectively. Wind-driven EGUs are expected to comprise the second largest new source of EGU
capacity for 2040 and 2050, accounting for 28.5% and 28.2% of overall new EGU capacity,
respectively.
Table 5-6: Newly modeled EGU capacity for the no-action case.
NEW MODELED CAPACITY (Cumulative GW)
2028
2030
2035
2040
2045
2050
Hydro
1 0.0
1.5
5.8
8.2
8.2
8.2
Non-Hydro Renewables
42.0
; 131.7
404.4
| 622.4
; 831.8 "!
1.050.3
Biomass
0.0
0.0
0.0
0.0
O.o' ;
0.0
Geolliermal
03
7' 0.3 7
0.3
0-3
0.3
o-3 7
Land fill Gas
13
1.3
1.3
1.3
!-3 '
1.3
Solar
11.9
50.2
156.9
263.6
! 333.2 ' ;
441.6
Wind
28.5
; 79.9
245.9
: 357.2
; 497.i ;
607.1
Coal
0.0
7 o.o
0.0
' 0.0
0.0
0.0
Coal willioul Carbon Capture & Sequestration (CCS)
0.0
0.0
0.0
0.0
| 0.0 7 1
0 0 7
Integrated Gasification Combined Cycle willioul CCS
0.0
0.0
0.0
0.0
0.0
0.0
Coal Willi CCS
7 0.0
0.0
0.0
0.0
; 0.0 "j
0.0 '
Energy Siorage
! 33.7
52.8
85.3
! 108.6
! 119.3 1
139.1
Nuclear
: 0.0
0.0
0.0
0.0
0.0 ;
0.0
Natural Gas
r 30.0
30.2
46.7
; 96.5
i 200.7 'i
315.0
Combined Cycle willioul CCS
21.1
21.3
25.6 7
; 26.2
26.7
.io.i
Combustion Turbine
8.9
8.9
21.1
" 70.3
j 174.0 :
"284.7
Oilier
0.0
0.0 77
0.0
o.o 7
r o.o 77
0.0
Grand Tolal
i 105.7
; 216.2
542.2
i 835.7
I.I (.(!.(!
1.512.5
Table 5-7:Newly modeled EGU capacity for the proposal.
NEW MODELED CAPACITY (Cumulative GW)
2028
2030
2035
2040
2045
2050
Hydro
1 0.0
1.5 7.
| 5.8 71
8.7 7
J 77 8.7 7
I 8.7 7
Non-Hydro Renewables
42.2
: 131.9
: 410.9 ¦
670.7
; 889.8
7 1,114.9
Biomass
0.0
0.0
r 0.0
0.0
o.o 7.
0.0
Geolliermal
0.3
0.3
0.3
0.3
0.3
0.3
Land fill Gas
1.3
1.3
1.3
1.3
I..i
7" 1.3
Solar
11.6
; 49.9
161.8 :
290.0
366.5
: ' 477.0
Wind
29.0
80.4
247.5 7
379. i 7
7 521-6
: 636.3
Coal
0.0
0.0
0.0 7'
0.0 7
r o-o 7.7.
7 0.0
Coal willioul CCS
0.0
0.0
0.0
0.0
0.0
: 0.0
Integrated Gasilicalion Combined Cycle willioul CCS
: 0.0
ro.o
o.o 77
0.0
0.0
0.0
Coal Willi CCS
"i 0.0
0.0
f O.o
o.o
0.0
0.0
Energy Siorage
32.9
54.0
00
00
00
113.0
i 1 -1 9
; 142.5"
Nuclear
j 0.0 '
*7 o.o 7
0.0
0.0
0.0
: 0.0
Natural Gas
; 31.2
31.6
56.3
120.0
; 233.8
| 350.4
Combined Cycle willioul CCS
22.0
22.4
: ...29.7 7 i
30.2
i 31-6
35.6
Combustion Turbine
9.2
7 9.2
26.6 i
89.8
1 202.2
7 314.7
Other
0.0 '"J
o.o 7.
: 0.0 7
0.0
0.0
0.0
Grand Tolal
106.2
: 219.0
i 561.8 :
912.4
i 1,2542"'
1,616.4
5-19
-------
Table 5-8: EGU retirements for the no-action case.
RETIREMENTS (GW)
2028
2030
2035
2040
2045
2050
Combined Cycle Retirements
* 1.5 27
1.5 J*
2.6 77
2.6 77
6.2 77,
15.2
Coal Retirements
56.0
80.3
| 102.5 7
112.8
; 126.2 :
139.3
Combustion Turbine Retirements
02
0.8
r , 2.2
2.7 7
7 J3.7 7
19.3
Nuclear Retirements
0.0
2.7'".'.
9.9 j
14.5
28.7 7. ]
48.2
Oil/Gas Steam Retirements
8.1
10.6
f 1 1.0
1 1.0
f " 14.1 77!
16.5
Integrated Gasification Combined Cycle Retirements
0.2
0.6
0.8
0.8
r 0.8
0.8
Biomass Retirements
3.0
3.2 "
3.3
3.3
3.3
3.3
Fuel Cell Retirements
0.0
r 0.0
0.0
0.0
0.0
0.0
Fossil-Other Retirements
0.0
0.0
0.0
0.0
0.0
0.0
Geolliermal Retirements
0.0
0.0
0.0 |
0.0
• 0.0
0.0
Hydro Retirements
0.0
0.0
0.0
0.0
0.0
0.0
Landlill Gas Retirements
0.0
0.1
0.1
0.1 7
ai
0.1
Non-Fossil, Other Retirements
0.0
0.0
: 0.0 7
0.0
; 0.0
0.0
Enei'gy Storage Retirements
0.0
0.0
0.0
0.0
0.0
0.0
Grand Total
' 69.0
99.9
: 135.4 j
150.9
7 193.1 1
242.6
Table 5-9: EGU retirements for the proposal.
RETIREMENTS (GW)
2028
2030
2035
2040
2045
2050
Combined Cycle Retirements
77 15 7
1.5 7
2.6
7 2.6
6.2
7 15.1
Coal Retirements
55.3
79.9
97.9
110.9
7 125.3
; 138.2
Comlxislioii Turbine Retirements
0.2
0.8
1.5 77
1.9
: 13.0
7 18.4
Nuclear Retirements
0.0
"2.7 77
9.9
1(5
: 28.7 7
18.2
Oil/Gas Steam Retirements
7.3
9.6
12.4
7 12.4
7 7L2.5 77
: 11.9
Integrated Gasification Combined Cycle Retirements
0.2'77
0.5
0.8 7"
0.8
0.8
7 0.8
Biomass Retirements
3.177"
7777 3.2 7777
i 3.3
3.3
3.3
3.3
Fuel Cell Retirements
0.0
0.0
0.0
0.0
0.0
0.0
Fossil-Other Retirements
r 0.0
; 0.0
! 0.0
7 0.0
7 0.0
7 0.0
Geolliermal Retirements
0.0
0.0
0.0
0.0
0.0
0.0
Hydro Retirements
0.0
7 0.0
0.0
0.0
f 0.0
; 0.0
Landfill Gas Retirements
"' 0.1'
0.1
0.17777
0.1
7 0.1
0.1
Non-Fossil, Other Retirements
0.0
7 0.0
0.0
0.0
7 0.0
0.0
Energy Storage Retirements
0.0
1 0.0
0.0
0.0
0.0
0.0
Grand Total
67.7
98.2
7 128.4
f 146.6
; 189.9
239.0
Table 5-10: Incremental EGU retirements comparing the proposal to the no-action case.
Incremental Retirements
2028
2030
2035
2040
2045
2050
Biomass [MW]
92.9 7
0.0
0.0
0.0
0.0
0.0
Landfill Gas [MW]
126.0 *
0.0
7 0.0
0.0 :
0.0
0.0 7
Total [MW]
218.9 I
0.0
0.0
0.0 j
0.0
0.0
Table 5-11: Incremented new EGU capacity comparing the proposal to the no-action case
[Cumulative GW].
Iiicremeiital New Capacity
2028
2030
2035
2040
2045
2050
Solar (GW)
0.0
0.0
4.8
26.4
33.4
35.3
Wind (GW
0.4
0.5
1.6
21.9
1' 24.5
29.3
Energy Storage (GW)
0.0
1.2 ""1
3.6
4.4
i 2.6 ' I
3.4
Hydro (GW)
0.0
0.0 '
0.0
0.5
0.5
0.5
Other (Geolliermal. Biomass.
0.0
0.0
0.0
0.0
0.0
0.0
Landfill Gas. GW)
Combined Cycle (GW)
0.8
1.1
4.0
4.0
4.9 1
5.4
Combustion Turbine (GW)
0.3
0.3
5.5
7 19.5
28.2
30.0
Grand Total (GW)
1.6
3.1
19.6
| 76.7
7' 94.2
103.9
5-20
-------
When comparing the proposal to the no-action case, only existing coal-fired EGUs were
found to receive retrofits. The cumulative capacity modeled by IPM totaled to 1,994.4 MW,
1,891.4 MW, 10,554.4 MW, 3,745.3 MW, 848.5 MW and 2,047.3 MW for the model run years
of 2028, 2030, 2035, 2040, 2045, and 2050, respectively.
5.2.6 Interregional Dispatch
IPM results showing international dispatch are summarized for a no-action case and for the
proposal in Table 5-8 and Table 5-9, respectively. International dispatch only occurred between
Canada and the contiguous United States represented by the IPM regions. Net international
dispatch was also very small as a percentage of total U.S. electricity demand, with electricity
imports less than lpercent for all years and trending towards zero by 2050 for both the no-action
case and proposal.
Table 5-12: IPM results for net export of electricity into the contiguous United States for
the no-action case.*^
2028 2030 2035 2040
Net US Exports -28.519 -23.383 -22.661 -7.997
(GWh)*
US Electricity 4.400.402 4.527.705 4.854.351 5.188.357
Demand (Gwii)
Net US Exports as a -0.65% -0.52% -0.47% -0.15%
Percentage ofTotal
Demand (%)
; Table Notes:
: * Negative net exports represent imports of electricity
| International dispatch to the contiguous United States only occurred over the U.S. - Canada border.
Table 5-13: IPM results for net export of electricity into the contiguous United States for
the proposal.*'^
2028 2030 2035 2040 2045 2050
Net US Exports (GWh) -28.312 -23.879 -24.877 -8.809 -4.453 -22
4.403.327 4.545.283 4.971.619 5.371.913 5.753.443 6.117.592
US Elcctricitv Demand
(GWh)
Net US Exports as a -0.64% -0.53% -0.50% -0.16% -0.08% 0.00%
Percentage ofTotal
Demand (%)
; Table Notes:
: * Negative net exports represent imports of electricity
: t International dispatch to the contiguous United States only occurred over the U.S. - Canada border.
International dispatch only occurred between Canada and the contiguous United States
represented by the IPM regions. To estimate interregional dispatch, IPM utilizes Total Transfer
Capabilities (TTCs), a metric that represents the capability of the power system to import or
export power reliably from one region to another.
The amount of energy and capacity transferred on a given transmission line between IPM
regions is modeled on a seasonal basis for all run years in the EPA Platform v6. All the modeled
transmission lines have the same TTCs for all seasons. The maximum values for these metrics
were obtained from public sources such as market reports and regional transmission plans,
2045
-3.987
2050
-501
5.533.316 5.885.168
-0.07% ; -0.01%
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wherever available. Where public sources were not available, the maximum values for TTCs are
based on ICF's expert view. ICF analyzes the operation of the grid under normal and
contingency conditions, using industry-standard methods, and calculates the transfer capabilities
between regions. To calculate the transfer capabilities, ICF uses standard power flow data
developed by the market operators, transmission providers, or utilities, as appropriate. Additional
information regarding power-sector modeling is available via a report submitted to the docket
(U.S. EPA 2023).
5.3 Assessment of PEV Charging Infrastructure
As PEV adoption grows, more charging infrastructure will be needed to support the fleet. This
section summarizes the status and outlook of U.S. PEV charging infrastructure, how much and
what types of charging may be needed to support the level of PEV penetration in the rulemaking,
and how we estimated the associated costs.
5.3.1 Status and Outlook for PEV Charging Infrastructure
5.3.1.1 Definitions
Terminology for charging infrastructure varies in the literature with terms like "charger",
"plug", "outlet", and "port" sometimes being used interchangeably. Throughout this chapter, we
use the following definitions.91 When referring to public charging, a station is the physical
location where charging occurs. Each station may have one or more Electric Vehicle Supply
Equipment (EVSE) ports that provide electricity to a vehicle. The number of vehicles that can
simultaneously charge at the station is equal to the number of EVSE ports. Each port may also
have multiple connectors or plugs, e.g., to accommodate vehicles that use different connector
types, but each port can only charge one vehicle at a time. While it is less common to refer to the
place home charging occurs (e.g., garage or driveway) as a station, we use the term ports in the
same way for residential and non-residential charging.
It must be noted that charging infrastructure is different from the electric power utility
distribution system infrastructure, which is comprised of distribution feeder circuits, switches,
protective equipment, primary circuits, distribution transformers, secondaries, service drops, etc.
The electric power utility distribution system infrastructure typically ends at a service drop (i.e.
the run of cables from the electric power utility's distribution power lines to the point of
connection to a customer's premises).
5.3.1.2 Charging Types
Electric Vehicle Supply Equipment (EVSE) ports can be alternating or direct current (AC or
DC); they also vary by power level. Common AC charging types include LI (up to about 2 kW
power) and L2 (up to 19.2 kW power) (U.S. Department of Energy, Alternative Fuels Data
Center 2023a) (Schey, Chu and Smart 2022). DC fast charging (DCFC) is available in a range of
power levels today, e.g., 50 kW to 350 kW with standards for even higher-powered DCFC such
as the Megawatt Charging System (MCS) currently in development (CharIN e.V. 2022).
91 Definitions are consistent with those used by (U.S. Department of Energy, Alternative Fuels Data Center 2023a).
A diagram is available at: https://afdc.energy.gov/fuels/electricity_infrastructure.html (last accessed March 8, 2023).
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Generally, the use of higher-power EVSE ports corresponds to faster charging92 though the
maximum power that vehicles can accept varies by model.93
Wireless or inductive charging systems have also been demonstrated and sold as aftermarket
add-ons but have not been widely deployed (U.S. Department of Energy, Alternative Fuels Data
Center 2023a). Due to the uncertainty about the timing and uptake of wireless charging, we
consider it outside the scope of this analysis.
5.3.1.2.1 PEV Charging Infrastructure Status and Trends
Charging infrastructure94 has grown rapidly over the last decade (U.S. Department of Energy,
Alternative Fuels Data Center 2023b). As shown in Figure 5-13, there are more than 50,000 non-
residential charging stations in the U.S. today with over 140,000 EVSE ports.95 This is an
increase from just over 85,000 EVSE ports as of the end of 2019. These include public EVSE
ports, as well as some private ports, e.g., at workplaces or for fleet use. About 80 percent of
EVSE ports today are L2, however, DCFC deployments have generally experienced faster
growth than L2 in the past few years (Brown, et al. 2022). Among DCFC, there is a trend toward
higher power levels with more than half of the EVSE ports over 50 kW and 10 percent at 300
kW or more as of the first quarter of 2021 (U.S. Department of Energy 2021).
160,000
EVSE Ports Stations
92 For example, DCFC can add 200 miles or more of range per hour of charging compared to about 25 miles for L2,
depending on power levels (U.S. Department of Energy, Alternative Fuels Data Center 2023a).
93 Table 5-1 shows the maximum DCFC power levels we assumed for BEV models in our infrastructure cost
analysis.
94 As used herein, "charging infrastructure" refers to EVSE, which is not a part of electric utility distribution
infrastructure, which is comprised of distribution feeder circuits, switches, protective equipment, primary circuits,
distribution transformers, secondaries, service drops, etc. The electric power utility distribution system infrastructure
typically ends at a service drop (i.e. the run of cables from the electric power utility's distribution power lines to the
point of connection to a customer's premises).
95 These counts may include a small number of EVSE ports and stations at multifamily housing.
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Figure 5-13: U.S. Non-residential PEV Charging Infrastructure from 2011—2022 (Data
Source: (U.S. Department of Energy, Alternative Fuels Data Center 2023b)
While estimates for future infrastructure needs vary widely in the literature, (Brown, et al.
2022) found that the overall ratio of EVSE ports to the number of PEVs on the road today
generally compares favorably to projected needs in national assessments by NREL (E. Wood, et
al. 2017) and ATLAS (McKenzie and Nigro 2021)96. For example, the NREL study estimated a
need for 1.8 DCFC ports for every thousand PEVs on the road, while Atlas estimated the need
for 4.7 DCFC ports per thousand PEVs. By mid-2022, there were 9.2 DCFC ports per thousand
PEVs,97 well above the projected needs estimated by these studies (Brown, et al. 2022). By mid-
2022, there were also 40 public and workplace L2 ports for every thousand PEVs on the road.
This is similar to the 40.1 NREL estimated will be needed, and significantly higher than the 5.8
L2 ports per thousand PEVs that Atlas estimated (Brown, et al. 2022). Of course, keeping up
with charging needs as PEV adoption grows will require continued expansion of, and investment
in, charging infrastructure.
5.3.1.3 PEV Charging Infrastructure Investments
Investments in PEV charging infrastructure have grown rapidly in recent years and are
expected to continue to climb. According to BloombergNEF, annual global investment was $62
billion in 2022, nearly twice that of the prior year, and while about 10 years was needed for
cumulative global investment to total $100 billion, $200 billion could be reached in just three
more years (BloombergNEF 2023). This growth was also seen in U.S. infrastructure spending.
Combined investments in hardware and installation for U.S. home and public charging ports was
over $1.2 billion in 2021, nearly a three-fold increase from 2017 (BloombergNEF 2022).
The U.S. government is making large investments in infrastructure through the Bipartisan
Infrastructure Law (Public Law 117-58 2021) and the Inflation Reduction Act (Public Law 117-
169 2022). However, we expect that private investments will also play a critical role in meeting
future infrastructure needs. Private charging companies have already attracted billions globally
in venture capital and mergers and acquisitions (Hampleton 2023). In the U.S., there was $200
million or more in mergers and acquisition activity in 2022 according to the capital market data
provider PitchBook (St. John and Naughton 2022), indicating strong interest in the future of the
charging industry. Bain projects that by 2030, the U.S. market for electric vehicle charging will
be "large and profitable" with both revenue and profits estimated to grow by a factor of twenty
relative to 2021 (Zayer, et al. 2022).98 Domestic manufacturing capacity is also increasing with
over $600 million in announced investments to support the production of charging equipment
and components at existing or new U.S. facilities. (Joint Office of Energy and Transportation
2023) (Kempower 2023). These activities along with the large variety of private investments
96 NREL and ATLAS both assessed future charging infrastructure needs, but under different PEV adoption
scenarios. See studies for details. Ratios discussed above are based on projected infrastructure needs in 2030.
97 Estimates for the number of DCFC and L2 ports available in 2022 include Tesla EVSE ports that are not currently
available for use by non-Tesla vehicles.
98 Estimates account for hardware and installation as well as operations and other charging services such as vehicle-
grid integration.
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detailed in Chapter 5.3.1.3.4 below suggest that companies are positioning themselves to meet
the growing demand for PEV charging.
The following sections outline some current and upcoming investments in charging
infrastructure from both public and private sources.
5.3.1.3.1 Bipartisan Infrastructure Law
The Bipartisan Infrastructure Law (BIL)99 (Public Law 117-58 2021) provides up to $7.5
billion over five years to build out a national network of PEV chargers. Two-thirds of this
funding is for the National Electric Vehicle Infrastructure (NEVI) Formula Program (U.S.
Department of Transportation, Federal Highway Administration 2022a). The remaining $2.5
billion is for the Charging and Fueling Infrastructure (CFI) Discretionary Grant Program, which
is evenly divided between funds for charging and fueling infrastructure along corridors and in
communities where fueling infrastructure can include hydrogen, propane, or natural gas (U.S.
Department of Transportation, Federal Highway Administration 2022a). These programs are
administered under the Federal Highway Administration with support from the Joint Office of
Energy and Transportation.
The first phase of NEVI formula funding for states was launched in 2022 and is focused on
building out Alternative Fuel Corridors (AFCs) on highways. Charging stations for AFCs are
required to have at least four DCFC ports, each 150 kW or higher (88 FR 12724 2023). Per
FHWA's guidance to states, stations generally must be located no more than 50 miles apart and
one mile from the Interstate (U.S. Department of Transportation, Federal Highway
Administration 2022a). Initial plans for all 50 states, DC, and Puerto Rico covering FY22 and
FY23 funds were approved in September 2022. Together the $1.5 billion in funding will help
deploy or expand charging infrastructure on about 75,000 miles of highway (U.S. Department of
Transportation, Federal Highway Administration 2022b). In March 2023, the first funding
opportunity was opened under the CFI Program with up to $700 million to deploy PEV charging
and hydrogen, propane, or natural gas fueling infrastructure in communities and along corridors
(Joint Office of Energy and Transportation 2023b).
In addition to NEVI, there are a variety of other Federal programs that could help reduce State
or private costs associated with deploying EVSE. For example, constructing and installing
charging infrastructure is an eligible activity for other U.S. Department of Transportation
formula programs including the Congestion Mitigation & Air Quality Improvement Program,
National Highway Performance Program, and Surface Transportation Block Grant Program,
which have a total of more than $40 billion in FY22 funds authorized under the BIL (U.S.
Department of Transportation, Federal Highway Administration 2022a).100 Discretionary grant
programs include the Rural Surface Transportation Grant Program, Infrastructure for Rebuilding
America Grant Program, and the Discretionary Grant Program for Charging and Refueling
Infrastructure (U.S. Department of Transportation, Federal Highway Administration 2022a).
99 Signed into law as the "Infrastructure Investment and Jobs Act"
100 Only a portion is likely to be used to support PEV charging infrastructure, and limits and restrictions may apply.
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5.3.1.3.2 Inflation Reduction Act
The Inflation Reduction Act (IRA), signed into law on August 16, 2022, can also help reduce
the cost that consumers and businesses pay toward PEV charging infrastructure (Public Law
117-169 2022).
Section 13404 extends the Alternative Fuel Refueling Property Tax Credit through Dec 31,
2032, with modifications. Under the new provisions, consumers in low-income or rural areas
would be eligible for a 30 percent credit for the costs of installing a residential charging
equipment subject to a $1,000 cap. Businesses would also be eligible for up to 30 percent of the
costs associated with purchasing and installing charging equipment in these areas (subject to a
$100,000 cap per item) if they meet prevailing wage and apprenticeship requirements. The Joint
Committee on Taxation estimates the cost of this tax credit from FY2022—2031 to be $1,738
billion, which reflects a significant level of support for charging infrastructure and other eligible
alternative fuel property (Joint Committee on Taxation 2022).
5.3.1.3.3 Equity Considerations in BIL and IRA
The infrastructure funding in the BIL and the IRA tax credit discussed above can help to
address equity challenges for PEV charging infrastructure. One of the stated goals of the $7.5
billion in infrastructure funding under the BIL is to support equitable access to charging across
the country (U.S. Department of Transportation, Federal Highway Administration 2022a).
Accordingly, FHWA instructed states to incorporate public engagement in their planning process
for the NEVI Formula program, including reaching out to Tribes, and rural, underserved, and
disadvantaged communities among other stakeholders. This funding will also support the
Justice40 target that 40 percent of the benefits go to disadvantaged communities (U.S.
Department of Transportation, Federal Highway Administration 2022a). Separately,
modifications to the Alternative Fuel Refueling Property Tax Credit in IRA limit applicability to
charging infrastructure installed in low-income or rural census tracts starting in 2023 (Public
Law 117-169 2022). This can help residents in these communities install home charging and
provide an incentive for businesses to site stations in these areas.
5.3.1.3.4 Other Public and Private Investments
States, utilities, auto manufacturers, charging network providers and others are also investing
in and supporting PEV charging infrastructure deployment. California announced plans in 2021
to invest over $300 million in light-duty charging infrastructure and nearly $700 million in
medium- and heavy-duty ZEV infrastructure (California Energy Commission 2021). Several
states including New Jersey and Utah offer partial rebates for residential, workplace, or public
charging while others such as Georgia and D.C. offer tax credits (U.S. Department of Energy,
Alternative Fuels Data Center 2023c).101 The NC Clean Energy Technology Center identified
more than 200 actions taken across 38 states and D.C. related to providing financial incentives
for electric vehicles and or charging infrastructure in 2022, a four-fold increase over the number
of actions in 2017 (Apadula, et al. 2023).102 The Edison Electric Institute estimates that electric
101 Details on eligibility, qualifying expenses, and rebate or tax credit amounts vary by state.
102 Includes actions by states and investor-owned utilities.
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companies have already invested nearly $3.7 billion (EEI 2023).103 And over 60 electric
companies and cooperatives serving customers in 48 states and the District of Columbia have
joined together to advance fast charging through the National Electric Highway Coalition (EEI
2023).
Auto manufacturers are investing in charging infrastructure by offering consumers help with
costs to install home charging or providing support for public charging. For example, GM will
pay for a standard installation of a Level 2 (240 V) outlet for customers purchasing or leasing a
new Bolt (Chevrolet 2023). GM is also partnering with charging provider EVgo to deploy over
2,700 DCFC ports and charging provider FLO to deploy as many as 40,000 L2 ports (GM 2021)
(Joint Office of Energy and Transportation 2023). Volkswagen, Hyundai, and Kia all offer
customers complimentary charging at Electrify America's public charging stations (subject to
time limits or caps) in conjunction with the purchase of select new electric vehicle models (VW
2023) (Hyundai 2023) (Kia 2023). Ford has agreements with several charging providers to make
it easier for their customers to charge and pay across different networks (Ford 2019) and plans to
install publicly accessible DCFC ports at nearly 2,000 dealerships (Joint Office of Energy and
Transportation 2023). Mercedes-Benz recently announced that it is planning to build 2,500
charging points in North America by 2027 (Reuters 2023). Tesla has its own network with over
17,000 DCFC ports and nearly 10,000 L2 ports in the United States (U.S. Department of Energy,
Alternative Fuels Data Center 2023d). Tesla recently announced that by 2024, 7,500 or more
existing and new ports (including 3,500 DCFC) would be open to all PEVs (The White House
2023).
Other charging networks are also expanding. Francis Energy, which has fewer than 1000
EVSE ports today (U.S. Department of Energy, Alternative Fuels Data Center 2023d), aims to
deploy over 50,000 by the end of the decade (Joint Office of Energy and Transportation 2023).
Electrify America plans to more than double its network size (U.S. Department of Energy,
Alternative Fuels Data Center 2023d) to 10,000 fast charging ports across 1800 U.S. and
Canadian stations by 2026. This is supported in part by a $450 million investment from Siemens
and Volkswagen Group (Joint Office of Energy and Transportation 2023). Blink plans to invest
over $60 million to grow its network over the next decade. Charging companies are also
partnering with major retailers, restaurants, and other businesses to make charging available to
customers and the public. For example, EVgo is deploying DCFC at certain Meijer locations,
CBL properties, and Wawa. Volta is installing DCFC and L2 ports at select Giant Food, Kroger,
and Stop and Shop stores, while ChargePoint and Volvo Cars are partnering with Starbucks to
make charging available at select Starbucks locations (Joint Office of Energy and Transportation
2023). Other efforts will expand charging access along major highways at up to 500 Pilot and
Flying J travel centers (through a partnership between Pilot, GM and EVgo) and 200
TravelCenters of America and Petro locations (through a partnership between TravelCenters of
America and Electrify America). BP plans to invest $1 billion toward charging infrastructure by
the end of the decade, including through a partnership to provide charging at various Hertz
locations across the country that could support rental and ridesharing vehicles, taxis, and the
public (Joint Office of Energy and Transportation 2023).
103 The $3.7 billion total includes infrastructure deployments and other customer programs to advance transportation
electrification.
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5.3.2 PEV Charging Infrastructure Cost Analysis
To assess the infrastructure needs and associated costs for this proposal, we start with
estimates of PEV charging demand generated using the methodology described in Chapter 5.1.
The share of demand we anticipate being met by different charging types (e.g., home L2 or
public DCFC) is then used to project the number and mix of EVSE ports that may be needed
each year in the proposal and no-action case. Finally, we assign costs for each EVSE port type
intended to reflect upfront hardware and installation costs based on values in the literature.
We note that the no-action case referred to as part of the infrastructure cost analysis was based
on earlier work with lower projected PEV penetration rates than the no-action case used for
compliance modeling and described in Preamble Section IV.B. (See discussion in DRIA Chapter
5.3.2.6.)
5.3.2.1 Charging Demand Projections
Regionalized PEV charging demand under our proposal was simulated for select years from
2026—2055 under an Interagency Agreement between EPA and the U.S. Department of Energy,
National Renewable Energy Laboratory (NREL). NREL's EVI-X modeling suite was used,
including the EVI-Pro model to simulate charging demand from typical daily travel, EVI-
RoadTrip to simulate demand from long-distance travel, and EVI-OnDemand to simulate
demand from ride-hailing applications. Eight unique charging types and locations were
considered: home LI, home L2, work L2, public L2, and public DCFC at 50 kW, 150 kW, 250
kW, and 350 kW power levels (DC-50, DC-150, DC-250, and DC-350). The following
assumptions informed the respective charging shares for daily travel modeled with EVI-Pro:
• PEVs with access to residential charging are assumed to prefer home over either work
or public charging when home charging is sufficient to support all travel needs.
• 75 percent of BEVs and 53 percent of PHEVs are assumed to use L2 for home
charging with the remaining share using LI.104
• Workplace L2 is the next most preferred charging type after home charging.
• Remaining charging needs are met with public charging. DCFC is generally preferred
for BEVs, and among DCFC, the highest power that a vehicle can accept (or "as fast
as possible" charging) is preferred.
• Public L2 charging is used by PHEVs, which are assumed not to be DCFC-capable.
It's also used by BEVs in certain long dwell time location types such as schools or
medical facilities where it's assumed that DCFC is not available.
For road trips and travel by ride-hailing vehicles modeled in EVI-RoadTrip and EVI-
OnDemand respectively, all public charging is assumed to be met with DCFC for BEVs.
104 This in part reflects assumptions about the characteristics of PEVs modeled by OMEGA, including a percentage
of low mileage PEVs for which LI meets daily charging needs.
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Additionally, BEVs able to accept higher-power charging (Gen 2) are assumed to be adopted
more quickly for these applications than for daily travel needs modeled in EVI-Pro.105
As shown in Figure 5-14, the share of PEV charging demand by location and type is similar
for the proposal and no-action case. The majority of PEV charging is home L2 across all years
though the share under the proposal declines from over 70 percent in 2028 to just below 60
percent in 2055 as the share of workplace and public charging grow. DCFC has the next highest
share of demand. Due to the modeling assumption that BEVs charge "as fast as possible" when
using DCFC, 350 kW charging dominates. Since simulated BEV models are capable of higher-
power charging, no DC-50 kW charging is found for either the proposal or no-action case.
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PEV for SFHs and 0.5 EVSE ports per PEV for other home types when PEVs make up the entire
light-duty fleet.
Network sizing for public and workplace charging is based on the regional charging load
profiles described in Chapter 5.1. For each DCFC port type (DC-50, DC-150, DC-250, and DC-
350), the total number of ports needed is scaled such that during the peak hour of usage 20
percent of ports in the region are fully utilized. For work and public L2 charging, 43 percent of
ports are assumed to be fully utilized during the peak hour. These percentages are modeled after
highly utilized stations today (Wood, Borlaug, et al. 2023). 106
Figure 5-15 and Figure 5-16 show the growing charging network that may be needed to meet
PEV charging demand in the proposal and no-action case respectively.107 We anticipate that the
highest number of ports will be needed at homes, growing from under 12 million in 2027 to over
75 million in 2055 under the proposal.108 This is followed by workplace charging, estimated at
about 400,000 EVSE ports in 2027 and over 12.7 million in 2055. Finally, public charging needs
grow from just over 110,000 ports to more than 1.9 million in that timeframe. Notably, while
DCFC at 350 kW constitutes a significant fraction of total electricity demand (Figure 13), the
number of ports needed is relatively small compared to the scale shown. This is because far
fewer 350 kW ports are needed to deliver the same amount of electricity as lower-powered
options. Similar patterns are observed in the no-action case—though fewer total ports are needed
than under the proposal due to the lower anticipated PEV demand.
106 The same method and thresholds for sizing the non-residential charging network based on peak hour of usage
was applied for all years in this analysis. If we instead assumed the percentage of L2 or DCFC ports that are fully
utilized at peak grew as a function of time or PEV penetration, we would expect higher average utilizations per port
and fewer total ports needed.
107 Charging simulations were conducted for 2026, 2028, 2030, 2032, 2035, 2040, 2045, 2050, and 2055. Linear
interpolations were used to estimate the network size in intermediate years. Estimates above do not include PEV
charging demand for medium-duty or heavy-duty vehicles.
108 The number of EVSE ports needed to meet a given level of electricity demand will vary based on the mix of
charging ports, charging preferences, and other factors. Estimates shown reflect assumptions specific to this
analysis, but actual needs could vary.
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In order to estimate the costs incurred each year, we calculate how many EVSE ports of each
type would need to be procured and installed to achieve the charging network sizes shown in
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Figure 5-15 and Figure 5-16. There is limited data on the expected lifespan and maintenance
needs of PEV charging infrastructure. We make the simplifying assumption that all EVSE ports
have a 15-year equipment lifetime (Borlaug, Salisbury, et al. 2020). After that, we assume they
must be replaced at full cost. This assumption likely overestimates costs as some EVSE
providers may opt to upgrade existing equipment rather than incur the cost of a full replacement.
Some installation costs such as trenching or electrical upgrades may also not be needed for the
replacement. We do not attempt to estimate EVSE maintenance costs due to uncertainty but note
that maintenance may be able to extend equipment lifetimes. Another simplifying assumption we
make is that EVSE ports are operational and able to meet PEV charging demand the same year
costs are incurred. The actual time to permit and install can vary widely by port type, power
level, region, site conditions and other factors.
5.3.2.3 Hardware & Installation Costs
We assign costs to each of the above infrastructure types intended to reflect the upfront capital
costs associated with procuring and installing the EVSE ports. There are many factors that can
impact equipment costs, including whether ports are wall-mounted or on a pedestal as well as
differences in equipment features and capabilities (Schey, Chu and Smart 2022). For example, an
ICCT paper found that costs more than doubled between networked and non-networked L2
hardware (Nicholas, Estimating electric vehicle charging infrastructure costs across major U.S.
metropolitan areas 2019). Among networked units with one or two ports per pedestal, about a 10
percent difference in per-port hardware costs was found (Nicholas, Estimating electric vehicle
charging infrastructure costs across major U.S. metropolitan areas 2019). The power level of the
EVSE is one of the most significant drivers of cost differences. While estimates for charging
equipment vary across the literature, higher-power charging equipment is typically more
expensive than lower-power units.
Installation costs may include labor, materials (e.g., wire or conduit), permitting, taxes, and
upgrades or modifications to the on-site electrical service. These costs—particularly labor and
permitting—can vary widely by region (Schey, Chu and Smart 2022). They also vary by site. For
example, how much trenching is needed will depend on the distance from where the charging
equipment will be located and the electrical panel. A recent study found that average L2
installation costs at condominiums and commercial locations increased by $16 or $20 for each
extra foot of distance between the EVSE and power source respectively (Schey, Chu and Smart
2022). How many EVSE ports are installed also impacts cost. ICCT estimated that on a per-port
basis, installation costs for 150 kW ports were about 2.5 times higher when only one port is
installed compared to 6—20 per site (Nicholas, Estimating electric vehicle charging infrastructure
costs across major U.S. metropolitan areas 2019). And, as with hardware costs, installation costs
may rise with power levels.
To reflect the diversity of hardware and installation costs, we considered a range of costs for
each charging type as shown in Table 5-10 and detailed below.109
Table 5-14: Cost (hardware and installation) per EVSE port110
Home Work Public
109 All costs shown above and used within the cost analysis are rounded to the nearest hundred.
110 Costs shown are expressed in 2019 dollars, consistent with the original sources from the literature.
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LI
SFH L2
non-SFH L2
L2
L2
DC-50
DC-150
DC-250
DC-350
Low
i $0
$800
$3,300
$5,100
$5,100
$30,000
$94,000
$124,000
$154,000
Mid
j $o
$1,100
$3,700
$5,900
$5,900
$56,000
$121,000
$153,000
$185,000
High
' $0
$1,500
$4,100
$7,300
$7,300
$82,000
$148,000
$182,000
$216,000
5.3.2.3.1 Home Charging Ports
PEVs typically come with a charging cord that can be used for LI charging by plugging it into
a standard 120 VAC111 outlet, and, in some cases, for L2 charging by plugging into a 240 VAC
outlet.112 We include the cost for this cord as part of the vehicle costs described in Chapter 2, and
therefore don't include it here. We make the simplifying assumption that PEV owners opting for
LI home charging already have access to a 120 VAC outlet and therefore do not incur
installation costs.113
For L2 home charging, some PEV owners may opt to simply install or upgrade to a 240 VAC
outlet for use with a provided cord while others may choose to purchase or install a wall-
mounted or other L2 charging unit, which may have additional features and capabilities. In Table
5-10, the "Low" cost assumes outlet installations only, the "High" cost assumes the purchase and
installation of L2 units, and the "Mid" cost assumes a 50%:50% split.
Costs vary by housing type with installation costs for SFHs typically lower than those for
apartments, condos, or mobile homes (non-SFHs). We use costs by housing type from (Nicholas,
Estimating electric vehicle charging infrastructure costs across major U.S. metropolitan areas
2019) for both outlet upgrades and L2 unit installations.114 For SFH costs, we weight costs for
detached and attached houses by 93 percent to 7 percent.115 We use cost estimates for apartments
to represent all non-SFH home types.
5.3.2.3.2 Work and Public Level 2 Charging Ports
We also source our assumed EVSE costs for work and public AC L2 ports from (Nicholas,
Estimating electric vehicle charging infrastructure costs across major U.S. metropolitan areas
2019).116 We select the lowest per port hardware and installation costs presented for networked
EVSE as our "Low" value and the highest combination of hardware and installation costs
presented as our "High" value. Specifically, we use the following combinations for the costs
shown in Table 5-10:
111 Volts, alternating current.
112 Not all charging cords may be capable of Level 2 charging.
113 (Ge, et al. 2021) found that while residential charging access is expected to decline as PEV adoption grows, the
majority of PEVs are projected to have access to an outlet either where they regularly park or at another parking
location at their home even if PEVs reach 100% of the light-duty fleet.
114 We use costs from Table 5 of (Nicholas 2019), specifically "Level 2 outlet upgrade" for outlet only installations
and "Level 2 charger upgrade" for hardware and installation costs associated with a Level 2 charging unit.
115 Weighting reflects the relative share of light-duty vehicles owned by residents of detached versus attached
houses, sourced from Figure 12 of (Ge, et al. 2021).
116 While (Nicholas 2019) notes that it assumed lower installation costs for workplace charging ports than for public
L2 ports, we make the simplifying assumption that both hardware and installation costs are the same.
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• Low: hardware costs for units with two EVSE ports per pedestal, installation costs for
sites with 6+ EVSE ports outside of California
• Mid: hardware costs for units with two networked EVSE ports per pedestal,
installation costs for sites with 3—5 EVSE ports outside of California
• High: hardware costs for units with one EVSE per pedestal, installation costs for sites
with one EVSE port in California
5.3.2.3.3 Public DC Fast Charging Ports
Cost estimates for DCFC ports are from a 2021 study that drew from various data and
literature sources, including the ICCT report discussed above (Borlaug, Muratori, et al. 2021).
We use the lower end of the ranges presented for procurement and installation costs as the "Low"
costs for 50 kW, 150 kW, and 350 kW DCFCs in Table 5-10, and the upper end of the ranges for
the "High" costs. Our "Mid" costs are the average of "Low" and "High". Since no estimate is
provided for 250 kW DCFCs, we take the average of costs for 150 kW and 350 kW DCFCs.117
5.3.2.4 Will Costs Change Over Time?
The infrastructure costs shown above reflect present day costs (expressed in 2019 dollars).
However, both hardware and installation costs could vary over time. For example, hardware
costs could decrease due to manufacturing learning and economies of scale. Recent studies by
ICCT assumed a 3 percent annual reduction in hardware costs (Nicholas, Estimating electric
vehicle charging infrastructure costs across major U.S. metropolitan areas 2019) (Bauer, Hsu, et
al., Charging Up America: Assessing the Growing Need for U.S. Charging Infrastructure
Through 2030 2021). By contrast, installation costs could increase due to growth in labor or
material costs. As noted above, installation costs also depend on site conditions, including
whether sufficient electric capacity exists to add charging infrastructure and how much trenching
is required between the EVSE port and electrical panel. If easier and, therefore, lower cost sites
are selected first, then over time installation costs could rise as charging stations start to be
installed in more challenging locations. (Bauer, Hsu, et al., Charging Up America: Assessing the
Growing Need for U.S. Charging Infrastructure Through 2030 2021) found that these and other
countervailing factors could result in the average cost of a 150 kW EVSE port in 2030 being
similar (~3 percent lower) to that in 2021.
Due to the uncertainty on how costs may change over time, we have made the simplifying
assumption for this analysis to keep combined hardware and installation costs per EVSE port
constant.
5.3.2.5 Other Considerations
EPA acknowledges that there may be additional infrastructure needs and costs beyond those
associated with charging equipment itself. While planning for additional electricity demand is a
standard practice for utilities and not specific to PEV charging, the buildout of public and private
charging stations (particularly those with multiple high-powered DC fast charging units) could in
117 Costs may not scale linearly with power level. We take the average as a simplifying assumption but continue to
monitor the literature for costs associated with this power level.
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some cases require upgrades to local distribution systems. For example, a recent study found
power needs as low as 200 kW could trigger the need to install a distribution transformer while a
load of 5 MW or more could require upgrades to feeder circuits or the addition of a feeder
breaker (Borlaug, Muratori, et al. 2021).
There are a variety of approaches that could reduce the need or scale of such upgrades—
potentially saving both cost and deployment time. For example, distribution system capacity and
interconnection could be factored into the site selection process, and when possible, utilities
could work with station developers to evaluate multiple potential sites before a selection is made
(Hernandez 2022). Another emerging best practice identified by the Interstate Renewable Energy
Council is for utilities to provide hosting capacity maps (HCMs) that identify grid capacity
constraints (Hernandez 2022). Such maps could help developers determine whether area feeders
or substations have additional capacity for charging or other loads. By mid-2022, requirements
for HCMs or related analyses were in place in ten states identified by Lawrence Berkeley
National Laboratory (Schwartz 2022). More broadly, 25 states and the District of Columbia have
ongoing efforts and requirements to support proactive distribution system planning and grid
modernization (Schwartz 2022).
Managing the additional demand from PEV charging is another key strategy. Automated load
management or power control systems are being explored as a way to dynamically limit total
charging load and ensure it doesn't exceed available capacity—potentially reducing the need for
upgrades at some sites (Nuvve and Enel X 2020) (BATRIES 2023). The use of onsite battery
storage and renewables may also be able to reduce demand on the grid, and some station
operators may opt for these technologies to mitigate demand charges associated with peak power
(Alexander, et al. 2021). In addition, managed or smart charging can be used in some cases to
reduce power or shift charging demand to times when it is easier to meet. Charging equipment
funded under the NEVI Formula Program, or as part of publicly-accessible charging projects
funded under Title 23, U.S.C., must be capable of smart charge management (88 FR 12724
2023).118 Finally, we note that an adapter developed by Argonne National Laboratory to retrofit
non-networked L2 EVSE to allow load management and other smart charging capabilities is in
the process of being commercialized (EVmatch, Inc. 2023). (Also see the discussion of managed
charging and vehicle-grid integration in Chapter 5.4 below.)
Innovative charging approaches may also reduce the need for upgrades in certain cases, or
otherwise reduce infrastructure costs. Mobile charging units could be a solution for locations like
parking garage decks in which it is challenging or costly to install EVSE ports (Alexander, et al.
2021), or be used as a temporary solution while stations are being built. These units are available
in a variety of power levels (e.g., the dual-port Mobi EV charger by (FreeWire Technologies
2023) can provide up to 11 kW while the Lightning Mobile unit can be configured to have up to
five 80 kW DCFC ports (Lightning eMotors 2023)), and can be recharged at times and locations
in which there is sufficient electrical capacity. Standalone charging canopies with integrated
118 The National Electric Vehicle Standards and Requirements Final Rule establishes requirements for standardized
communication among vehicles, charging equipment, and networks to ensure interoperability. Specifically, the use
of ISO 15118 is required for communication between vehicles and chargers, Open Charge Point Protocol for
communication between chargers and networks, and Open Charge Point Interface for communication among
charging networks. (See (88 FR 12724 2023) for details on applicable versions and the timing for these
requirements.)
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solar cells and battery storage that don't need to be connected to the grid (Alexander, et al. 2021)
may be useful for remote locations or where construction is costly or difficult.
There is considerable uncertainty associated with future distribution upgrade needs as well as
with the uptake of the technologies and approaches discussed above that could reduce upgrade
costs, and we do not model them directly as part of our infrastructure cost analysis.119
5.3.2.6 PEV Charging Infrastructure Cost Summary
Table 5-11 shows the estimated annual PEV charging infrastructure costs for the indicated
calendar years in the proposal relative to the no action case using the "Low", "Mid", and "High"
per port cost estimates discussed above.120 Annual costs range from $0.6 billion dollars under the
low scenario to $10 billion under the high scenario. The table also shows the present value (PV)
of these costs and the equivalent annualized value (EAV) for the calendar years 2027-2055 using
both 3 percent and 7 percent discount rates. The "Mid" costs are included as social costs in the
net benefits estimates for this proposal, presented in Chapter 10.6.
Table 5-15: EVSE costs for the proposal relative to no-action case (billions of 2020 dollars)
Calendar Year
Low
Mid
High
2027
1.0
1.3
1.6
2028
0.6
0.7
0.8
2029
0.9
1.1
1.4
2030
0.9
1.1
1.4
203 1
6.8
8.3
10.0
2032
6.8
8.3
10.0
' 2035
5.5
6.7
8.1
2040
6.0
7.1
8.6
2045
6.1
7.3
8.8
2050
5.9
7.1
8.6
2055
6.0
7.1
8.5
PV3
96
120
140
PV7
57
68
83
EAV3
5.1
6.2
7.5
EAV7
4.7
5.6
6.8
As previously noted, the no-action case used throughout the PEV charging infrastructure cost
analysis was based on earlier work with lower projected PEV penetration rates than the no-action
case used for compliance modeling. As a result, the number of EVSE ports and associated costs
for the no-action scenario discussed in this section are likely lower than they would be under the
compliance no-action case. Since we estimate costs for the proposal relative to the no-action
119 The per port EVSE costs shown in Table 5-10 may include some distribution system costs. For example,
(Nicholas 2019) notes that public and workplace installation costs include "utility upgrades". We don't add to, or
otherwise adjust, these values to account for transformer upgrades or other potential upstream distribution costs
specific to the projected port counts in this analysis.
120 See spreadsheet "PEV Charging Infrastructure Cost Analysis" in the docket.
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case, the resulting EVSE costs shown in Table 5-11 are likely to be conservative, or higher, than
if we had applied the same no-action case used for compliance modeling.
5.4 Grid Resiliency
How the additional electricity demand from PEVs will impact the grid will depend on many
factors including the time-of-day that charging occurs, and the use of battery storage and vehicle-
to-grid (V2G) or other Vehicle-Grid Integration (VGI) technology. For example, PEVs can be
scheduled to charge at off-peak hours when the electricity demand is easier to meet. Onsite
battery storage, if deployed at charging stations, could also reduce potential grid impacts by
shifting when electricity is drawn from the grid while still providing power to vehicles when
needed. Managed charging and battery storage could also enable increasing renewable use if
charging load is shifted to times with excess solar or wind that might otherwise be curtailed.
V2G technology, which allows electricity to be drawn from vehicles when not in use, could even
allow PEVs to enhance grid reliability.
Electric power system reliability can be determined using a variety of statistical metrics. The
generally accepted metrics by which electric utilities across the U.S. measure and report electric
power system reliability is set by the Institute of Electrical and Electronics Engineers (IEEE)
using the standard IEEE 1366-2022 (IEEE Guide for Electric Power Distribution Reliability
Indices). The formulation of overall electric power system reliability metrics includes electric
power outages associated with what is known as "loss of supply" events; these are events in
which electric power generation and/or electric power transmission is the root cause for a power
outage. As this discussion is limited to electric power distribution system reliability, an electric
power system reliability metric that excludes electric power outages associated with the loss of
supply events (i.e. loss of electric power generation and/or electric power transmission) is
appropriate.
Using this approach, we observed that electric power utilities in 48 U.S. Census Division and
State regions tracked by the U.S. Energy Information Administration (EIA) had overall trends in
distribution grid reliability that were less than the national average for the years 2013 and 2021
(the most-recent years for which data is available) (EIA, 2022). Conversely, 13 U.S. Census
Division and State regions had overall trends in distribution grid reliability for the same years
that were greater than the national average for the years in question. According to the California
Public Utilities Commission, "This data alone does not fully capture the current state of
reliability of the U.S. electric power distribution system..." (Enis 2021). Given the massive size
of the electric power distribution system - with its multitude of regional, climate, and density
variations - interpreting distribution system reliability indices can be challenging to interpret.
Moreover, such reliability statistics focus on outage duration and customer counts, which may
obscure important regional variations. However, as the expected increase in electricity
generation associated with the proposal relative to a no action case is relatively small -
approximately 4.4 percent increase in 2050 - we do not expect the U.S. electric power
distribution system to be adversely affected by the projected additional number of charging
electric vehicles.
Grid reliability is not expected to be adversely affected by the modest increase in electricity
demand associated with electric vehicle charging. As shown in Figure 5-8, we project the
additional generation needed to meet the demand of PEVs in the proposal to be relatively modest
compared to the no-action case, ranging from less than 0.4percent in 2030 to approximately
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4.4percent in 2050. The California Public Utilities Commission (CPUC) (California Public
Utilities Commission 2022) and the California Energy Commission (CEC) (Lipman, Harrington
and Langton 2021) (Chhaya, et al. 2019) have been actively engaged in VGI121 efforts for over a
decade, along with the California Independent System Operator (CAISO) (California
Independent System Operator 2014, California Energy Commission; California Public Utilities
Commission; Governor's Office of Business and Economic Development 2021), large private
and public electrical utilities (SCE, PG&E, SDG&E, etc.), several automakers (Ford, GM, FCA,
BMW, Audi, Nissan, Toyota, Honda, and others), and EV charger companies, the Electric Power
Research Institute (EPRI), and various other research organizations.
These efforts (Lipman, Harrington and Langton 2021) demonstrated the ability to shift up to
20 percent of electric vehicle charging loads in any given hour to other times of the day as well
as the ability to add up to 30 percent of electric vehicle charging loads in a given hour (Lipman,
Harrington and Langton 2021). We anticipate similar strategies could be used to shift PEV
charging loads from peak times as needed to reduce grid impacts across different regions. As the
expected increase in electric power demand resulting from PEV charging in this proposal will be
well-under 20 percent, we do not anticipate it to pose grid reliability issues.
The increasing integration of electric vehicle charging into the electric power grid has also
been found to increase grid reliability (Chhaya, et al. 2019), as the ability to shift and curtail
electric power loads improves grid operations and, therefore, grid reliability. Such integration
has been found to create value for electric vehicle drivers, electric grid operators, and ratepayers.
Management of PEV charging can reduce overall costs to utility ratepayers by delaying electric
utility customer rate increases associated with equipment upgrades and may allow utilities to use
electric vehicle charging as a resource to manage intermittent renewables or provide ancillary
services.
The Electric Power Research Institute (EPRI)122, is undertaking a three year-long research
project to better-understand the scale of commitment and investment in the electric power grid
that is required to meet the anticipated electric power loads. Thus far, the electric power sector
and its regulators have focused on incremental EV load growth and charger utilization (Electric
Power Research Institute 2022). The work of EPRI focuses on grid impacts and associated lead
times required to better-prepare the grid (including transmission, substation, feeder, and
transformer) for vehicle electrification. These efforts are, in part, based upon grid reliability
research conducted by EPRI (Maitra 2013) (Electric Power Research Institute 2012), which
identified grid and charging behavior characteristics associated with grid resiliency. We also
consulted with FERC staff on distribution system reliability and related issues.
State government plays an important role in vehicle electrification (including aspects of grid
resilience), as most electric utilities are regulated by state Public Service Commissions (PSC)
and Public Utility Commissioners (PUC) and since Federal funding for vehicle electrification is
largely distributed through state agencies. The National Association of Regulatory Utility
Commissioners (NARUC), a national association representing the state public service
commissioners who regulate essential utility services, including energy, telecommunications, and
121 VGI is also sometimes referred to as Vehicle-to-Grid or VTG or V2G.
122 EPRI is an independent, nonprofit, U.S.-based organization that conducts research and development related to the
generation, delivery, and use of electricity [https://www.epri.com/].
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water, produced a series of documents aimed at providing vehicle electrification-related guidance
for state regulators (National Association of Regulatory Utility Commissioners 2022a),
facilitating electric vehicle interoperability (National Association of Regulatory Utility
Commisioners 2022b), and fostering vehicle electrification equity (National Association of
Regulatory Utility Commissioners 2022c). NARUC, in conjunction with the National
Association of State Energy Officials (NASEO) and the American Association of State Highway
and Transportation Officials (AASHTO), also produced a guide for public utility commissions,
state energy offices, and departments of transportation discussing the state-level roles and their
interrelations vis-a-vis transportation electrification (National Council on Electricity Policy,
National Association of Regulatory Utility Commissioners 2022).
We also note that DOE is engaged in multiple efforts to modernize the grid and improve
resilience and reliability. For example, in November 2022, DOE announced $13 billion in
funding opportunities under BIL to support transmission and distribution infrastructure. This
includes $3 billion for smart grid grants with a focus on PEV integration among other topics
(U.S. Department of Energy 2022).
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Interoperability Considerations for Public Utility Regulators - An addendum to the NARUC
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1866-DAAC-99FB-70957246AEBE.
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Trends, Issues, and Considerations for State Regulators; Harper, Charles; McAndrews, Gregory;
Sass Byrnett, Danielle;." Accessed December 15, 2022. https://pubs.naruc.org/pub/32857459-
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—. 2022c. "Models for Incorporating Equity in Transportation Electrification Considerations for
Public Utility Regulators - An addendum to the NARUC report, Electric Vehicles: Key Trends,
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Commissioners. 2022. "Mini Guide on Transportation Electrification: State-Level Roles and
Collaboration among Public Utility Commissions, State Energy Offices, and Departments of
Transportation; Dixon, Delaney; Powers, Cassie; McAdams, Jasmine; Stephens, Sam; Sass
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Nicholas, Michael. 2019. Estimating electric vehicle charging infrastructure costs across major
U.S. metropolitan areas. The International Council on Clean Transportation. Accessed January
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11, 2023.
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117publl69.pdf.
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117publ58/pdf/PLAW-l 17publ58.pdf.
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Schwartz, Lisa. 2022. "State Regulatory Approaches for Distribution Planning." National
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—. 2021. FOTW #1210, November 1, 2021: Sixty Percent of Electric Vehicle DC Fast Charging
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—. 2023b. Electric Vehicle Charging Infrastructure Trends. Accessed January 10, 2023.
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—. 2023. " Post-IRA 2022 Reference Case - EPA's Power Sector Modeling Platform Using
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Chapter 6: [RESERVED]
The content from the previous version of this chapter has been streamlined and incorporated
into other chapters in the DRIA.
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Chapter 7: Health and welfare impacts
The proposed rule will impact emissions of GHGs, criteria pollutants, and air toxic pollutants.
There are health and welfare impacts associated with ambient concentrations of GHGs, criteria
pollutants and air toxics which are described in this chapter.
7.1 Climate Change Impacts from GHG Emissions
Elevated concentrations of GHGs have been warming the planet, leading to changes in the
Earth's climate including changes in the frequency and intensity of heat waves, precipitation, and
extreme weather events, rising seas, and retreating snow and ice. The changes taking place in the
atmosphere as a result of the well-documented buildup of GHGs due to human activities are
changing the climate at a pace and in a way that threatens human health, society, and the natural
environment. While EPA is not making any new scientific or factual findings with regard to the
well-documented impact of GHG emissions on public health and welfare in support of this rule,
EPA is providing some scientific background on climate change to offer additional context for
this rulemaking and to increase the public's understanding of the environmental impacts of
GHGs.
Extensive additional information on climate change is available in the scientific assessments
and the EPA documents that are briefly described in this section, as well as in the technical and
scientific information supporting them. One of those documents is EPA's 2009 Endangerment
and Cause or Contribute Findings for Greenhouse Gases Under section 202(a) of the CAA (74
FR 66496, December 15, 2009). In the 2009 Endangerment Finding, the Administrator found
under section 202(a) of the CAA that elevated atmospheric concentrations of six key well-mixed
GHGs - CO2, methane (CH4), nitrous oxide (N2O), HFCs, perfluorocarbons (PFCs), and sulfur
hexafluoride (SF6) - "may reasonably be anticipated to endanger the public health and welfare of
current and future generations" (74 FR 66523). The 2009 Endangerment Finding, together with
the extensive scientific and technical evidence in the supporting record, documented that climate
change caused by human emissions of GHGs threatens the public health of the U.S. population.
It explained that by raising average temperatures, climate change increases the likelihood of heat
waves, which are associated with increased deaths and illnesses (74 FR 66497). While climate
change also increases the likelihood of reductions in cold-related mortality, evidence indicates
that the increases in heat mortality will be larger than the decreases in cold mortality in the U.S.
(74 FR 66525). The 2009 Endangerment Finding further explained that compared with a future
without climate change, climate change is expected to increase tropospheric ozone pollution over
broad areas of the U.S., including in the largest metropolitan areas with the worst tropospheric
ozone problems, and thereby increase the risk of adverse effects on public health (74 FR 66525).
Climate change is also expected to cause more intense hurricanes and more frequent and intense
storms of other types and heavy precipitation, with impacts on other areas of public health, such
as the potential for increased deaths, injuries, infectious and waterborne diseases, and stress-
related disorders (74 FR 66525). Children, the elderly, and the poor are among the most
vulnerable to these climate-related health effects (74 FR 66498).
The 2009 Endangerment Finding also documented, together with the extensive scientific and
technical evidence in the supporting record, that climate change touches nearly every aspect of
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public welfare in the U.S. (42 USC § 7602 (h) 2021)123 with resulting economic costs,
including: changes in water supply and quality due to changes in drought and extreme rainfall
events; increased risk of storm surge and flooding in coastal areas and land loss due to
inundation; increases in peak electricity demand and risks to electricity infrastructure; and the
potential for significant agricultural disruptions and crop failures (though offset to some extent
by carbon fertilization). These impacts are also global and may exacerbate problems outside the
U.S. that raise humanitarian, trade, and national security issues for the U.S. (74 FR 66530).
In 2016, the Administrator issued a similar finding for GHG emissions from aircraft under
section 231(a)(2)(A) of the CAA (81 FR 54422 2016). In the 2016 Endangerment Finding, the
Administrator found that the body of scientific evidence amassed in the record for the 2009
Endangerment Finding compellingly supported a similar endangerment finding under CAA
section 231(a)(2)(A), and also found that the science assessments released between the 2009 and
the 2016 Findings "strengthen and further support the judgment that GHGs in the atmosphere
may reasonably be anticipated to endanger the public health and welfare of current and future
generations" (81 FR 54424).
Since the 2016 Endangerment Finding, the climate has continued to change, with new
observational records being set for several climate indicators such as global average surface
temperatures, GHG concentrations, and sea level rise. Additionally, major scientific assessments
continue to be released that further advance our understanding of the climate system and the
impacts that GHGs have on public health and welfare both for current and future generations.
These updated observations and projections document the rapid rate of current and future climate
change both globally and in the U.S. (Reidmiller, et al. 2018, Roy, et al. 2019, NASEM 2019,
NOAA2021).
7.2 Health Effects Associated with Exposure to Criteria and Air Toxics Pollutants
Emissions sources impacted by this proposal, including vehicles and power plants, emit
pollutants that contribute to ambient concentrations of ozone, PM, NO2, SO2, CO, and air toxics.
This section of the RIA discusses the health effects associated with exposure to these pollutants.
Additionally, because children have increased vulnerability and susceptibility for adverse
health effects related to air pollution exposures, EPA's findings regarding adverse effects for
children related to exposure to pollutants that are impacted by this rule are noted in this section.
The increased vulnerability and susceptibility of children to air pollution exposures may arise
because infants and children generally breathe more relative to their size than adults do, and
consequently may be exposed to relatively higher amounts of air pollution. (US EPA 2009)
Children also tend to breathe through their mouths more than adults and their nasal passages are
less effective at removing pollutants, which leads to greater lung deposition of some pollutants,
such as PM. (US EPA 2019) (Foos, et al. 2008) Furthermore, air pollutants may pose health
123 The CAA states in section 302(h) that "[a]ll language referring to effects on welfare includes, but is not limited
to, effects on soils, water, crops, vegetation, manmade materials, animals, wildlife, weather, visibility, and climate,
damage to and deterioration of property, and hazards to transportation, as well as effects on economic values and on
personal comfort and well-being, whether caused by transformation, conversion, or combination with other air
pollutants." (42 USC § 7602 (h) 2021).
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risks specific to children because children's bodies are still developing (US EPA 2021).124 For
example, during periods of rapid growth such as fetal development, infancy and puberty, their
developing systems and organs may be more easily harmed. (US EPA 2006, US EPA 2005) EPA
produces the report titled "America's Children and the Environment," which presents national
trends on air pollution and other contaminants and environmental health of children. (US EPA
2022)
7.2.1 Ozone
7.2.1.1 Background on Ozone
Ground-level ozone pollution forms in areas with high concentrations of ambient nitrogen
oxides (NOx) and volatile organic compounds (VOCs) when solar radiation is high. Major U.S.
sources of NOx are highway and nonroad motor vehicles and 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 would 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. 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.
124 Children's environmental health includes conception, infancy, early childhood and through adolescence until 21
years of age as described in an EPA Memorandum (US EPA 2021).
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7.2.1.2 Health Effects Associated with Exposure to Ozone
This section provides a summary of the health effects associated with exposure to ambient
concentrations of ozone.125 The information in this section is based on the information and
conclusions in the April 2020 Integrated Science Assessment for Ozone (Ozone ISA). (US EPA
2020) 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.126 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. The evidence is also
suggestive of a causal relationship between short-term exposure to ozone and cardiovascular
effects, central nervous system effects and total mortality.
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
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
125 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.
126 The 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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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 among 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. Additional
children's vulnerability and susceptibility factors are listed in Section IX.G of the Preamble.
7.2.2 Particulate Matter
7.2.2.1 Background on 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. (US
EPA 2020) Atmospheric particles can be grouped into several classes according to their
aerodynamic diameter 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 PM10 (Title 40 CFR Part 50 2023, Title 40 CFR Part 53 2023, Title 40 CFR Part 58
2023).127
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. (US EPA 2019) 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, 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.
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
127 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 PM10 standard
provides protection against effects associated with short-term exposure to thoracic coarse particles (i.e., PM10-2.5).
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atmospheric chemical reactions of gaseous precursors (e.g., sulfur oxides (SOx), NOx and
VOCs).
7.2.2.2 Health Effects Associated with Exposure to 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, which was finalized in December 2019 (PM ISA). (US EPA 2019) In
addition, there is a more targeted evaluation of studies published since the literature cutoff date
of the 2019 PM ISA in the Supplement to the Integrated Science Assessment for PM
(Supplement). (US EPA 2022) 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. (US EPA 2019) Within this characterization,
the PM ISA summarizes the health effects evidence for short-term (i.e., hours up to one month)
and long-term (i.e., one month to years) exposures to PM2.5, PM10-2.5, and ultrafine particles, and
concludes that exposures to ambient PM2.5 are associated with a number of adverse health
effects. The discussion below highlights the PM ISA's conclusions, and summarizes additional
information from the Supplement where appropriate, 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 2022 Policy Assessment for the review of the PM NAAQS. (US EPA 2022)
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. (US EPA 2009)
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 PM2.5 exposure and cancer. Because of remaining uncertainties and limitations in
the evidence base, EPA determined the evidence is "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 and the Supplement, recent studies continue to
support a "causal relationship" between short- and long-term PM2.5 exposures and mortality. (US
EPA 2019) (US EPA 2022) For short-term PM2.5 exposure, 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, including exacerbations of chronic obstructive pulmonary disease
(COPD) and asthma, provide biological plausibility for cause-specific mortality and ultimately
total mortality. Recent epidemiologic studies evaluated in the Supplement, including studies that
employed alternative methods for confounder control, provide additional support to the evidence
base that contributed to the 2019 PM ISA conclusion for short-term PM2.5 exposure and
mortality (US EPA 2022).
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The 2019 PM ISA concluded a "causal relationship" between long-term PM2.5 exposure and
mortality. In addition to reanalyses 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, 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. Recent cohort studies evaluated in the Supplement, as well as epidemiologic
studies that conducted accountability analyses or employed alternative methods for confounder
controls, support and extend the evidence base that contributed to the 2019 PM ISA conclusion
for long-term PM2.5 exposure and mortality.
A large body of studies examining both short- and long-term PM2.5 exposure and
cardiovascular effects builds on the evidence base evaluated in the 2009 PM ISA. The strongest
evidence for cardiovascular effects in response to short-term PM2.5 exposures is for ischemic
heart disease and heart failure. The evidence for short-term PM2.5 exposure and cardiovascular
effects is coherent across scientific disciplines and supports 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 epidemiologic studies
evaluated in the Supplement, as well as studies that conducted accountability analyses or
employed alternative methods for confounder control, support and extend the evidence base that
contributed to the 2019 PM ISA conclusion for both short- and long-term PM2.5 exposure and
cardiovascular effects.
Studies evaluated in the 2019 PM ISA continue to provide evidence of a "likely to be causal
relationship" between both short- and long-term PM2.5 exposure and respiratory effects.
Epidemiologic studies provide consistent evidence of a relationship between short-term PM2.5
exposure and asthma exacerbation in children and COPD exacerbation in adults, as indicated by
increases in emergency department visits and hospital admissions, which is supported by animal
toxicological studies indicating worsening allergic airways disease and subclinical effects related
to COPD. Epidemiologic studies also provide evidence of a relationship between short-term
PM2.5 exposure and respiratory mortality. However, there is inconsistent evidence of respiratory
effects, specifically lung function declines and pulmonary inflammation, in controlled human
exposure studies. With respect to long term PM2.5 exposure, epidemiologic studies conducted in
the U.S. and abroad provide evidence of a relationship with 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,
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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 of a "likely to be causal relationship". The
strongest evidence for effects on the nervous system come from epidemiologic studies that
consistently report cognitive decrements and reductions in brain volume in adults. The effects
observed in epidemiologic studies in adults 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. There is more limited evidence
for neurodevelopmental effects in children with some studies reporting positive associations with
autism spectrum disorder and others providing limited evidence of an association with cognitive
function. While there is some evidence from animal toxicological studies indicating effects on
the brain (i.e., inflammatory and morphological changes) to support a biologically plausible
pathway for neurodevelopmental effects, epidemiologic studies are limited due to their lack of
control for potential confounding by copollutants, the small number of studies conducted, and
uncertainty regarding critical exposure windows.
Building off the decades of research demonstrating mutagenicity, DNA damage, and other
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 with lung cancer incidence
and mortality in analyses limited to never smokers. In addition, experimental and epidemiologic
studies of genotoxicity, epigenetic effects, carcinogenic potential, and that PM2.5 exhibits several
characteristics of carcinogens, provides biological plausibility for cancer development and
resulted in the conclusion of a "likely to be causal relationship."
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 specific 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." (US EPA 2019)
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,
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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
in the 2009 PM ISA persists with respect to the method used to estimate PM10-2.5 concentrations
in epidemiologic studies. 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, which have often been defined as particles <0.1 |im, 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 examined 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" (US EPA
2019). 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 with some evidence of increased risk for
populations of low socioeconomic status. Recent studies evaluated in the Supplement support the
conclusion of the 2019 PM ISA with respect to disparities in both PM2.5 exposure and health risk
by race and ethnicity and provide additional support for disparities for populations of lower
socioeconomic status. 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, and current/former
smokers could be at increased risk for adverse PM2.5-related health effects. The 2022 Policy
Assessment for the review of the PM NAAQS also highlights that factors that may contribute to
increased risk of PM2.5-related health effects include lifestage (children and older adults), pre-
existing diseases (cardiovascular disease and respiratory disease), race/ethnicity, and
socioeconomic status. (US EPA 2022)
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7.2.3 Nitrogen Oxides
7.2.3.1 Background on Nitrogen Oxides
Oxides of nitrogen (NOx) refers to nitric oxide (NO) and nitrogen dioxide (NO2). MostN02
is formed in the air through the oxidation of nitric oxide (NO) that is emitted when fuel is burned
at a high temperature. NOx is a major contributor to secondary PM2.5 formation, and NOx along
with VOCs are the two major precursors of ozone. The health effects of PM and ozone are
discussed in Sections 7.2.1 and 7.2.2 respectively.
7.2.3.2 Heath Effects Associated with Exposure to 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 (ISA
for Oxides of Nitrogen). (US EPA 2016) 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 emergency department 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 copollutant confounding, particularly by other roadway pollutants. The
available evidence for non-respiratory health effects does not adequately address whether NO2
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 N02-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.
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7.2.4 Sulfur Oxides
7.2.4.1 Background on 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
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.
7.2.4.2 Health Effects Associated with Exposure to 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). (US EPA 2017) 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 copollutant 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 copollutants. 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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7.2.5 Carbon Monoxide
7.2.5.1 Background on 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.
7.2.5.2 Health Effects Associated with Exposure to 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). (US EPA 2010) The CO ISA
presents conclusions regarding the presence of causal relationships between CO exposure and
categories of adverse health effects.128 This section provides a summary of the health effects
associated with exposure to ambient concentrations of CO, along with the CO ISA
conclusions.129
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
128 The ISA evaluates the health evidence associated with different 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 definitions of these levels of
evidence, please refer to Section 1.6 of the ISA.
129 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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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
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 that was often observed in copollutant 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.
7.2.6 Diesel Exhaust
7.2.6.1 Background on Diesel Exhaust
Diesel exhaust is a complex mixture composed of particulate matter, carbon dioxide, oxygen,
nitrogen, water vapor, carbon monoxide, nitrogen compounds, sulfur compounds and numerous
low-molecular-weight hydrocarbons. A number of these gaseous hydrocarbon components are
individually known to be toxic, including aldehydes, benzene, and 1,3-butadiene. The diesel
particulate matter present in diesel exhaust consists mostly of fine particles (< 2.5 |im), of which
a significant fraction is ultrafine particles (< 0.1 |im). These particles have a large surface area
which makes them an excellent medium for adsorbing organics and their small size makes them
highly respirable. Many of the organic compounds present in the gases and on the particles, such
as polycyclic organic matter, are individually known to have mutagenic and carcinogenic
properties.
Diesel exhaust varies significantly in chemical composition and particle sizes between
different engine types (heavy-duty, light-duty), engine operating conditions (idle, acceleration,
deceleration), and fuel formulations (high/low sulfur fuel). Also, there are emissions differences
between on-road and nonroad engines because the nonroad engines are generally of older
technology. After being emitted in the engine exhaust, diesel exhaust undergoes dilution as well
as chemical and physical changes in the atmosphere. The lifetimes of the components present in
diesel exhaust range from seconds to days.
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7.2.6.2 Health Effects Associated with Exposure to Diesel Exhaust
In EPA's 2002 Diesel Health Assessment Document (Diesel HAD), exposure to diesel
exhaust was classified as likely to be carcinogenic to humans by inhalation from environmental
exposures, in accordance with the revised draft 1996/1999 EPA cancer guidelines. (US EPA
1999, US EPA 2002) A number of other agencies (National Institute for Occupational Safety and
Health, the International Agency for Research on Cancer, the World Health Organization,
California EPA, and the U.S. Department of Health and Human Services) made similar hazard
classifications prior to 2002. EPA also concluded in the 2002 Diesel HAD that it was not
possible to calculate a cancer unit risk for diesel exhaust due to limitations in the exposure data
for the occupational groups or the absence of a dose-response relationship.
In the absence of a cancer unit risk, the Diesel HAD sought to provide additional insight into
the significance of the diesel exhaust cancer hazard by estimating possible ranges of risk that
might be present in the population. An exploratory analysis was used to characterize a range of
possible lung cancer risk. The outcome was that environmental risks of cancer from long-term
diesel exhaust exposures could plausibly range from as low as 10"5 to as high as 10"3. Because of
uncertainties, the analysis acknowledged that the risks could be lower than 10"5, and a zero risk
from diesel exhaust exposure could not be ruled out.
Noncancer health effects of acute and chronic exposure to diesel exhaust emissions are also of
concern to EPA. EPA derived a diesel exhaust reference concentration (RfC) from consideration
of four well-conducted chronic rat inhalation studies showing adverse pulmonary effects. The
RfC is 5 |ig/m3 for diesel exhaust measured as diesel particulate matter. This RfC does not
consider allergenic effects such as those associated with asthma or immunologic or the potential
for cardiac effects. There was emerging evidence in 2002, discussed in the Diesel HAD, that
exposure to diesel exhaust can exacerbate these effects, but the exposure-response data were
lacking at that time to derive an RfC based on these then-emerging considerations. The Diesel
HAD states, "With [diesel particulate matter] being a ubiquitous component of ambient PM,
there is an uncertainty about the adequacy of the existing [diesel exhaust] noncancer database to
identify all of the pertinent [diesel exhaust]-caused noncancer health hazards." The Diesel HAD
also notes "that acute exposure to [diesel exhaust] has been associated with irritation of the eye,
nose, and throat, respiratory symptoms (cough and phlegm), and neurophysiological symptoms
such as headache, lightheadedness, nausea, vomiting, and numbness or tingling of the
extremities." The Diesel HAD notes that the cancer and noncancer hazard conclusions applied to
the general use of diesel engines then on the market and as cleaner engines replace a substantial
number of existing ones, the applicability of the conclusions would need to be reevaluated.
It is important to note that the Diesel HAD also briefly summarizes health effects associated
with ambient PM and discusses EPA's then-annual PM2.5 NAAQS of 15 |ig/m3,130 There is a
large and extensive body of human data showing a wide spectrum of adverse health effects
associated with exposure to ambient PM, of which diesel exhaust is an important component.
The PM2.5 NAAQS is designed to provide protection from the noncancer health effects and
premature mortality attributed to exposure to PM2.5. The contribution of diesel PM to total
ambient PM varies in different regions of the country and also, within a region, from one area to
130 See Chapter 8.1 for discussion of the current PM2.5 NAAQS standard.
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another. The contribution can be high in near-roadway environments, for example, or in other
locations where diesel engine use is concentrated.
Since 2002, several new studies have been published which continue to report increased lung
cancer risk associated with occupational exposure to diesel exhaust from older engines. Of
particular note since 2011 are three new epidemiology studies that have examined lung cancer in
occupational populations, including, truck drivers, underground nonmetal miners, and other
diesel motor-related occupations. These studies reported increased risk of lung cancer related to
exposure to diesel exhaust, with evidence of positive exposure-response relationships to varying
degrees. (Garshick 2012, Silverman 2012, Olsson 2011) These newer studies (along with others
that have appeared in the scientific literature) add to the evidence EPA evaluated in the 2002
Diesel HAD and further reinforce the concern that diesel exhaust exposure likely poses a lung
cancer hazard. The findings from these newer studies do not necessarily apply to newer
technology diesel engines (i.e., heavy-duty highway engines from 2007 and later model years)
since the newer engines have large reductions in the emission constituents compared to older
technology diesel engines.
In light of the growing body of scientific literature evaluating the health effects of exposure to
diesel exhaust, in June 2012 the World Health Organization's International Agency for Research
on Cancer (IARC), a recognized international authority on the carcinogenic potential of
chemicals and other agents, evaluated the full range of cancer-related health effects data for
diesel engine exhaust. IARC concluded that diesel exhaust should be regarded as "carcinogenic
to humans." (IARC, Diesel and gasoline engine exhausts and some nitroarenes 2013) This
designation was an update from its 1988 evaluation that considered the evidence to be indicative
of a "probable human carcinogen."
7.2.7 Air Toxics
Light- and medium-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, acetaldehyde, acrolein, benzene, 1,3-butadiene,
ethylbenzene, formaldehyde, naphthalene and polycyclic organic matter. These compounds were
identified as national or regional cancer risk drivers or contributors in the 2018 AirToxScreen
Assessment. (US EPA 2022, US EPA 2022)
7.2.7.1 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. (US EPA 2000, IARC 1982, Irons, et
al. 1992) 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
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risk estimate (URE) for benzene.131 (US EPA 2000) The 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. (IARC 2018, NTP, Report on
Carcinogens, Fourteenth Edition 2016)
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. (Aksoy
1989, Goldstein 1988) The most sensitive noncancer effect observed in humans, based on current
data, is the depression of the absolute lymphocyte count in blood. (Rothman 1996, US EPA
2002) 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.
(O. Qu, et al. 2003, Q. Qu, et al. 2002, Lan, et al. 2004, Turtletaub and Mani 2003) 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.132 (ATSDR, Toxicological profile for benzene 2007)
There is limited information from two studies regarding an increased risk of adverse effects to
children whose parents have been occupationally exposed to benzene. (Corti and Snyder 1996,
P.A., et al. 1991) Data from animal studies have shown benzene exposures result in damage to
the hematopoietic (blood cell formation) system during development. (Keller and Snyder 1986,
Keller and Snyder 1988, Corti and Snyder 1996) Also, key changes related to the development of
childhood leukemia occur in the developing fetus. (US EPA 2002) Several studies have reported
that genetic changes related to eventual leukemia development occur before birth. For example,
there is one study of genetic changes in twins who developed T cell leukemia at nine years of
age. (Ford, et al. 1997)
7.2.7.2 Health Effects Associated with Exposure to 1,3-Butadiene
EPA has characterized 1,3-butadiene as carcinogenic to humans by inhalation. (US EPA
2002)' (US EPA 2002) 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. (IARC 1999)
(IARC 2008) (NTP 2016) (IARC 2012) 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, (US EPA 2002) 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
131A unit risk estimate is defined as the increase in the lifetime risk of cancer of an individual who is exposed for a
lifetime to 1 |ig/m3 benzene in air.
132 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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effect was ovarian atrophy observed in a lifetime bioassay of female mice. (Bevan, Stadler and al
1996) 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).
7.2.7.3 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. (US EPA 1990) 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. (NTP, Report on Carcinogens, Fourteenth Edition
2016, IARC 2006, IARC 2012)
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.
(Hauptmann, Lubin, et al. 2003, Hauptmann, Lubin, et al. 2004, Beane Freeman, et al. 2009) 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. (Pinkerton
2004) Extended 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. (Coggon, et al. 2003) Finally, a study of
embalmers reported formaldehyde exposures to be associated with an increased risk of myeloid
leukemia but not brain cancer. (Hauptmann, et al. 2009)
Health effects of formaldehyde in addition to cancer were reviewed by the ATSDR in 1999,
supplemented in 2010, and by the World Health Organization. (ATSDR 1999, ATSDR 2010,
IPCS 2002) 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. (US EPA 2010) 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. (NRC, Review of the Environmental Protection Agency's Draft IRIS
Assessment of Formaldehyde 2011) EPA's draft assessment, which addresses NRC
recommendations, was suspended in 2018 and unsuspended in March 2021. An external review
draft was released in April 2022 and is currently review by the National Academy of Sciences,
Engineering, and Medicine. (US EPA 2021)
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7.2.7.4 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. (US
EPA 1991) The URE in IRIS for acetaldehyde is 2.2 x 10-6 per |ig/m3. (US EPA 1991)
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.
(NTP, Report on Carcinogens, Fourteenth Edition 2016) (IARC 1999)
The primary noncancer effects of exposure to acetaldehyde vapors include irritation of the
eyes, skin, and respiratory tract. (US EPA 1991) In short-term (4 week) rat studies, degeneration
of olfactory epithelium was observed at various concentration levels of acetaldehyde exposure.
(Appleman, Woutersen and Feron 1982) 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. (Myou, et al. 1993) 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. (OEHHA 2014)
7.2.7.5 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. (US
EPA 1998) Chronic (long term) exposure of workers and rodents to naphthalene has been
reported to cause cataracts and retinal damage. (US EPA 1998) Children, especially neonates,
appear to be more susceptible to acute naphthalene poisoning based on the number of reports of
lethal cases in children and infants (hypothesized to be due to immature naphthalene
detoxification pathways). (US EPA 1998) EPA released an external review draft of a
reassessment of the inhalation carcinogenicity of naphthalene based on a number of recent
animal carcinogenicity studies. (US EPA 1998) The draft reassessment completed external peer
review. (Oak Ridge Institute for Science and Education 2004) Based on external peer review
comments received, EPA is developing a revised draft assessment that considers inhalation and
oral routes of exposure, as well as cancer and noncancer effects (US EPA 2023). 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. (NTP, Report on
Carcinogens, Fourteenth Edition 2016) 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. (IARC 2002)
Naphthalene also causes a number of non-cancer effects in animals following chronic and
less-than-chronic exposure, including abnormal cell changes and growth in respiratory and nasal
tissues. (US EPA 1998) 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.
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(US EPA 1998) The ATSDR MRL for acute and intermediate duration oral exposure to
naphthalene is 0.6 mg/kg/day based on maternal toxicity in a developmental toxicology study in
rats. (ATSDR 2005) ATSDR also derived an ad hoc reference value of 6 x 10-2 mg/m3 for acute
(<24-hour) inhalation exposure to naphthalene in a Letter Health Consultation dated March 24,
2014 to address a potential exposure concern in Illinois. (ATSDR 2014) The ATSDR acute
inhalation reference value was based on a qualitative identification of an exposure level
interpreted not to cause pulmonary lesions in mice. More recently, EPA developed acute RfCs
for 1-, 8-, and 24-hour exposure scenarios; the <24-hour reference value is 2 x 10-2 mg/m3. (US
EPA 2022) EPA's acute RfCs are based on a systematic review of the literature, benchmark dose
modeling of naphthalene-induced nasal lesions in rats, and application of a PBPK
(physiologically based pharmacokinetic) model.
7.2.7.6 Health Effects Associated with Exposure to Acrolein
EPA most recently evaluated the toxicological and health effects literature related to acrolein
in 2003 and concluded that the human carcinogenic potential of acrolein could not be determined
because the available data were inadequate. No information was available on the carcinogenic
effects of acrolein in humans and the animal data provided inadequate evidence of
carcinogenicity. (US EPA 2003) In 2021, the IARC classified acrolein as probably carcinogenic
to humans. (IARC 2021)
Lesions to the lungs and upper respiratory tract of rats, rabbits, and hamsters have been
observed after subchronic exposure to acrolein. (US EPA 2003) The agency has developed an
RfC for acrolein of 0.02 |ig/m3 and an RfD of 0.5 |ig/kg-day. (US EPA 2003)
Acrolein is extremely acrid and irritating to humans when inhaled, with acute exposure
resulting in upper respiratory tract irritation, mucus hypersecretion and congestion. The intense
irritancy of this carbonyl has been demonstrated during controlled tests in human subjects, who
suffer intolerable eye and nasal mucosal sensory reactions within minutes of exposure. (US EPA
2003) These data and additional studies regarding acute effects of human exposure to acrolein
are summarized in EPA's 2003 IRIS Human Health Assessment for acrolein. (US EPA 2003)
Studies in humans indicate that levels as low as 0.09 ppm (0.21 mg/m3) for five minutes may
elicit subjective complaints of eye irritation with increasing concentrations leading to more
extensive eye, nose and respiratory symptoms. Acute exposures in animal studies report
bronchial hyper-responsiveness. Based on animal data (more pronounced respiratory irritancy in
mice with allergic airway disease in comparison to non-diseased mice (Morris JB, et al. 2003))
and demonstration of similar effects in humans (e.g., reduction in respiratory rate), individuals
with compromised respiratory function (e.g., emphysema, asthma) are expected to be at
increased risk of developing adverse responses to strong respiratory irritants such as
acrolein. EPA does not currently have an acute reference concentration for acrolein. The
available health effect reference values for acrolein have been summarized by EPA and include
an ATSDR MRL for acute exposure to acrolein of 7 |ig/m3 for 1-14 days exposure; and
Reference Exposure Level (REL) values from the California Office of Environmental Health
Hazard Assessment (OEHHA) for one-hour and 8-hour exposures of 2.5 |ig/m3 and 0.7 |ig/m3,
respectively. (US EPA 2009)
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7.2.7.7 Health Effects Associated with Exposure to Ethylbenzene
EPA's inhalation RfC for ethylbenzene is 1 mg/m3. This conclusion on a weight of evidence
determination and RfC are contained in the 1991 IRIS file for ethylbenzene. (US EPA 1991) The
RfC is based on developmental effects. A study in rabbits found reductions in live rabbit kits per
litter at 1000 ppm. In addition, a study on rats found an increased incidence of supernumerary
and rudimentary ribs at 1000 ppm, and elevated incidence of extra ribs at 100 ppm. In 1988,
EPA concluded that data were inadequate to give a weight of evidence characterization for
carcinogenic effects. EPA released an IRIS Assessment Plan for Ethylbenzene in 2017 (US EPA
2017) and EPA will be releasing the Systematic Review Protocol for ethylbenzene in 2023. (US
EPA 2022)
California EPA completed a cancer risk assessment for ethylbenzene in 2007 and developed
an inhalation unit risk estimate of 2.5xl0"6. (California OEHHA 2007) This value was based on
incidence of kidney cancer in male rats. California EPA also developed a chronic inhalation
noncancer reference exposure level (REL) of 2000 |ig/m3, based on nephrotoxicity and body
weight reduction in rats, liver cellular alterations, necrosis in mice, and hyperplasia of the
pituitary gland in mice. (California OEHHA 2008)
ATSDR developed chronic Minimal Risk Levels (MRLs) for ethylbenzene of 0.06 ppm based
on renal effects, and an acute MRL of 5 ppm based on auditory effects. (ATSDR 2010)
7.2.7.8 Health Effects Associated with Exposure to PAHs/POM
The term polycyclic organic matter (POM) defines a broad class of compounds that includes
the polycyclic aromatic hydrocarbon compounds (PAHs). One of these compounds,
naphthalene, is discussed separately below. POM compounds are formed primarily from
combustion and are present in the atmosphere in gas and particulate form as well as in some fried
and grilled foods. Epidemiologic studies have reported an increase in lung cancer in humans
exposed to diesel exhaust, coke oven emissions, roofing tar emissions, and cigarette smoke; all
of these mixtures contain POM compounds. (ATSDR 1995) (US EPA 2002) In 1991 EPA
classified seven PAHs (benzo[a]pyrene, benz[a]anthracene, chrysene, benzo[b]fluoranthene,
benzo[k]fluoranthene, dibenz[a,h]anthracene, and indeno[l,2,3-cd]pyrene) as Group B2,
probable human carcinogens based on the 1986 EPA Guidelines for Carcinogen Risk
Assessment. (US EPA 1991) Studies in multiple animal species demonstrate that benzo[a]pyrene
is carcinogenic at multiple tumor sites (alimentary tract, liver, kidney, respiratory tract, pharynx,
and skin) by all routes of exposure. An increasing number of occupational studies demonstrate a
positive exposure-response relationship with cumulative benzo[a]pyrene exposure and lung
cancer. The inhalation URE in IRIS for benzo[a]pyrene is 6 x 10"4 per |ig/m3 and the oral slope
factor for cancer is 1 per mg/kg-day. (US EPA 2017)
Animal studies demonstrate that exposure to benzo[a]pyrene is also associated with
developmental (including developmental neurotoxicity), reproductive, and immunological
effects. In addition, epidemiology studies involving exposure to PAH mixtures have reported
associations between internal biomarkers of exposure to benzo[a]pyrene (benzo[a]pyrene diol
epoxide-DNA adducts) and adverse birth outcomes (including reduced birth weight, postnatal
body weight, and head circumference), neurobehavioral effects, and decreased fertility. The
inhalation RfC for benzo[a]pyrene is 2 x 10"6 mg/m3 and the RfD for oral exposure is 3 x 10"4
mg/kg-day. (US EPA 2017)
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7.2.8 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 studies have been published in
peer-reviewed journals, concluding that concentrations of CO, CO2, NO, NO2, benzene,
aldehydes, PM, 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 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, UFPs, metals, elemental carbon (EC), NO, NOx, and several
VOCs. (Karner, Eisinger and Niemeier 2014) 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
reviewing the literature, Karner et al., (2014) reported that results varied based on the method of
statistical analysis used to determine the gradient in pollutant concentration. More recent studies
continue to show significant concentration gradients of traffic-related air pollution around major
roads. (McDonald, et al. 2014, Kimbrough, Baldauf, et al. 2013, Kimbrough, Palma and Baldauf
2014, Kimbrough, Owen, et al. 2017, Hilker, et al. 2019, Grivas, et al. 2019, Apte, et al. 2017,
Dabek-Zlotorzynska, et al. 2019) There is evidence that EPA's regulations for vehicles have
lowered the near-road concentrations and gradients. (Sarnat, et al. 2018) Starting in 2010, EPA
required through the NAAQS process that air quality monitors be placed near high-traffic
roadways for determining concentrations of CO, NO2, and PM2.5 (in addition to those existing
monitors located in neighborhoods and other locations farther away from pollution sources). The
monitoring data for NO2 indicate that in urban areas, monitors near roadways often report the
highest concentrations of NO2. (Gantt, Owen and Watkins 2021) More recent studies of traffic-
related air pollutants continue to report sharp gradients around roadways, particularly within
several hundred meters. (Apte, et al. 2017, Gu, et al. 2018)
For pollutants with relatively high background concentrations relative to near-road
concentrations, detecting concentration gradients can be difficult. For example, many carbonyls
have high background concentrations as a result of photochemical breakdown of precursors from
many different organic compounds. However, several studies have measured carbonyls in
multiple weather conditions and found higher concentrations of many carbonyls downwind of
roadways. (Liu, et al. 2006, Cahill, Charles and Seaman 2010) These findings suggest a
substantial roadway source of these carbonyls.
In the past 30 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.133 In addition,
numerous studies have found adverse health effects associated with spending time in traffic, such
as commuting or walking along high-traffic roadways, including studies among children. (Laden,
133 In the widely used PubMed database of health publications, between January 1, 1990 and December 31, 2021,
1,979 publications contained the keywords "traffic, pollution, epidemiology," with approximately half the studies
published after 2015.
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et al. 2007, Peters, et al. 2004, Zanobetti, et al. 2009, Adar, et al. 2007) 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. In a 2022 final
report, an expert panel of the Health Effects Institute (HEI) employed a systematic review
focusing on selected health endpoints related to exposure to traffic-related air pollution. (HEI
2022)134 The HEI panel concluded that there was a high level of confidence in evidence between
long-term exposure to traffic-related air pollution and health effects in adults, including all-
cause, circulatory, and ischemic heart disease mortality. (Boogaard, et al. 2022) The panel also
found that there is a moderate-to-high level of confidence in evidence of associations with
asthma onset and acute respiratory infections in children and lung cancer and asthma onset in
adults. This report follows on an earlier expert review published by HEI in 2010, where it found
strongest evidence for asthma-related traffic impacts. Other literature reviews have been
published with conclusions generally similar to the HEI panels'. (Boothe and Shendell 2008,
Salam, Islam and Gilliland 2008, Sun, Zhang and Ma 2014, Raaschou-Nielsen and Reynolds
2006) Additionally, 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
between "postnatal" proximity to traffic and leukemia risks, but no such association for
"prenatal" exposures. (Boothe, et al. 2014) The U.S. Department of Health and Human Services'
National Toxicology Program (NTP) published a monograph including a systematic review of
traffic-related air pollution and its impacts on hypertensive disorders of pregnancy. The NTP
concluded that exposure to traffic-related air pollution is "presumed to be a hazard to pregnant
women" for developing hypertensive disorders of pregnancy. (NTP 2019)
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). (Volk, et al. 2011, Franco-Suglia,
et al. 2007, Power, et al. 2011, Wu, et al. 2011, Stenson, et al. 2021)
In addition to health outcomes, particularly cardiopulmonary effects, conclusions of numerous
studies suggest mechanisms by which traffic-related air pollution affects health. For example,
numerous studies indicate that near-roadway exposures may increase systemic inflammation,
affecting organ systems, including blood vessels and lungs. (Riediker, Cardiovascular effects of
fine particulate matter components in highway patrol officers 2007, Alexeef, et al. 2011, S.P., et
al. 2011, Zhang, et al. 2009) Additionally, long-term exposures in near-road environments have
been associated with inflammation-associated conditions, such as atherosclerosis and asthma.
(Adar, Klein, et al. 2010, Kan, et al. 2008, McConnell, et al. 2010)
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. The 2013 U.S.
Census Bureau's American Housing Survey (AHS) was the last AHS that included whether
housing units were within 300 feet of an "airport, railroad, or highway with four or more
134 This more recent review focused on health outcomes related to birth effects, respiratory effects, cardiometabolic
effects, and mortality.
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lanes."135 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 within
300 feet of 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.
Scientific literature suggests that some factors may increase susceptibility to the effects of
traffic-associated air pollution. Several studies have found stronger adverse health associations
in children experiencing chronic social stress, such as in violent neighborhoods or in homes with
low incomes or high family stress. (Islam, et al. 2011, Clougherty, et al. 2007, Chen, et al. 2008,
Long, Lewis and Langpap 2021) Similarly, two studies found some evidence that children
exposed to higher levels of traffic-related air pollution show poorer academic performance than
those exposed to lower levels of traffic-related air pollution. (Stenson, et al. 2021, Gartland, et al.
2022) However, this evidence was judged to be weak due to limitations in the assessment
methods.
EPA conducted a study to estimate the number of people living near truck freight routes in the
United States, which includes many large highways and other routes where light- and medium-
duty vehicles operate. (US EPA 2021) Based on a population analysis using the U.S. Department
of Transportation's (USDOT) Freight Analysis Framework 4 (FAF4) and population data from
the 2010 decennial census, an estimated 72 million people live within 200 meters of these FAF4
roads, which are used by all types of vehicles (US DOT 2023). 136>137 This analysis includes the
population living within twice the distance of major roads compared with the analysis of housing
units near major roads described above in this section. The larger distance and other
methodological differences explain the difference in the two estimates for populations living near
major roads.
In examining schools near major roadways, we used the Common Core of Data from the U.S.
Department of Education, which includes information on all public elementary and secondary
schools and school districts nationwide.138 To determine school proximities to major roadways,
we used a geographic information system (GIS) to map each school and roadways based on the
U.S. Census's TIGER roadway file. (Pedde and Bailey 2011) Ten million students attend public
schools within 200 meters of major roads, about 20 percent of the total number of public school
135 The variable was known as "ETRANS" in the questions about the neighborhood.
136FAF4 is a model from the USDOT's Bureau of Transportation Statistics (BTS) and Federal Highway
Administration (FHWA), which provides data associated with freight movement in the U.S. It includes data from
the 2012 Commodity Flow Survey (CFS), the Census Bureau on international trade, as well as data associated with
construction, agriculture, utilities, warehouses, and other industries. FAF4 estimates the modal choices for moving
goods by trucks, trains, boats, and other types of freight modes. It includes traffic assignments, including truck
flows on a network of truck routes (US DOT 2023).
137 The same analysis estimated the population living within 100 meters of a FAF4 truck route is 41 million.
138 This information is available at: ll"|;, Mces e,l?"v cc'1
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students in the U.S., and about 800,000 students attend public schools within 200 meters of
primary roads. 139
While near-roadway studies focus on residents near roads or others spending considerable
time near major roads, the duration of commuting results in another important contributor to
overall exposure to traffic-related air pollution. Studies of health that address time spent in transit
have found evidence of elevated risk of cardiac impacts. (Riediker, Cascio, et al. 2004, Peters, et
al. 2004, Adar, Gold and Coull 2007) Studies have also found that school bus emissions can
increase student exposures to diesel-related air pollutants, and that programs that reduce school
bus emissions may improve health and reduce school absenteeism. (Sabin, et al. 2005, Li, N and
Ryan 2009, Austin, Heutel and Kreisman 2019, Adar, D.Souza and Sheppard 2015)
In addition, EPA's Exposure Factor Handbook also indicates that, 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. (US EPA 2016) The duration of commuting results in
another important contributor to overall exposure to traffic-related air pollution. Studies of health
that address time spent in transit have found evidence of elevated risk of cardiac impacts.
(Riediker, Cascio, et al. 2004, Peters, et al. 2004, Adar, Gold and Coull 2007)
7.3 Welfare Effects Associated with Exposure to Criteria and Air Toxics Pollutants
This section discusses the environmental effects associated with non-GHG pollutants affected
by this rule, specifically PM, ozone, NOx, SOx, and air toxics.
7.3.1 Visibility Degradation
Visibility can be defined as the degree to which the atmosphere is transparent to visible light.
(NRC 1993) 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. Fine particles with significant light-extinction efficiencies include sulfates,
nitrates, organic carbon, elemental carbon, sea salt, and soil. (J. e. Hand 2011, Sisler 1996)
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. (US EPA 2019)
The extent to which any amount of light extinction affects a person's ability to view a scene
depends on both scene and light characteristics. For example, the appearance of a nearby object
(e.g., a building) is generally less sensitive to a change in light extinction than the appearance of
a similar object at a greater distance. See Figure 7-1 for an illustration of the important factors
affecting visibility. (Malm 2016)
139Here, "major roads" refer to those TIGER classifies as either "Primary" or "Secondary." The Census Bureau
describes primary roads as "generally divided limited-access highways within the Federal interstate system or under
state management." Secondary roads are "main arteries, usually in the U.S. highway, state highway, or county
highway system."
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Characteristics of Observer
Optical Characteristic! of Illumination
• Sunlight (Sun Angle)
• Cloud Cower (Overcast, Puffy, etc.l
• Sky
tlcal Characteristics of
Optical Characteristics of
Atmosphere
• Color
• Contrast Detail (Texture)
• Form
• Brightness
• Light Added to Sight Path by
Particles and Cases
• Image-Forming Light Subtracted
from Sight Path by Scattering
and Absorption
• Detection Thresholds
• Psychological Response to
Incoming Light
• Value Judgements
Figure 7-1 Important Factors Involved in Seeing a Scenic Vista (Malm, 2016)
EPA is working to address visibility impairment. Reductions in air pollution from
implementation of various programs called for in the Clean Air Act Amendments of 1990
(CAAA) have resulted in substantial improvements in visibility and will continue to do so in the
future. Nationally, because trends in haze are closely associated with trends in particulate sulfate
and nitrate emissions 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. (US EPA 2019) However, in the western
part of the country, changes in total light extinction were smaller, and the contribution of
particulate organic matter to atmospheric light extinction was increasing due to increasing
wildfire emissions. (Hand, et al. 2020)
Image-forming
light scattered
out of sight path
Image-forming
light absorbed
Sunlight J0 3?**
scattered Ught reflec£
from ground
scattered Into
Light from clouds
scattered Into
sight path y
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 (42 USC §7491 (a) 2013). In 1999, EPA finalized the
regional haze program (64 FR 35714) to protect the visibility in Mandatory Class I Federal areas.
Fhere are 156 national parks, forests and wilderness areas categorized as Mandatory Class I
Federal areas (62 FR 38680-38681, July 18, 1997). 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 that were in existence on August 7, 1977. Figure 7-2
shows the location of the 156 Mandatory Class I Federal areas.
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Figure 7-2: Mandatory Class I Federal Areas in the U.S.
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). The secondary (welfare-based) PM NAAQS provide
protection against visibility effects. In recent PM NAAQS reviews, EPA evaluated a target level
of protection for visibility impairment that is expected to be met through attainment of the
existing secondary PM standards.
7.3.1.1 Visibility Monitoring
In conjunction with the U.S. National Park Service, the U.S. Forest Service, other Federal
land managers, and State organizations in the U.S., EPA has supported visibility monitoring in
national parks and wilderness areas since 1988. The monitoring network was originally
established at 20 sites, but it has now been expanded to 152 sites that represent all but one of the
156 Mandatory Federal Class I areas across the country (see Figure 7-2). This long-term
visibility monitoring network is known as IMPROVE (Interagency Monitoring of Protected
Visual Environments).
IMPROVE provides direct measurement of particles that contribute to visibility impairment.
The IMPROVE network employs aerosol measurements at all sites, and optical and scene
measurements at some of the sites. Aerosol measurements are taken for PM10 and PM2.5 mass,
and for key constituents of PM2.5, such as sulfate, nitrate, organic and elemental carbon (OC and
EC), and other elements that can be used to estimate soil dust and sea salt contributions.
Measurements for specific aerosol constituents are used to calculate "reconstructed" aerosol light
extinction by multiplying the mass for each constituent by its empirically-derived scattering
and/or absorption efficiency, with adjustment for the relative humidity. The IMPROVE program
utilizes both an "original" and a "revised" reconstruction formula for this purpose, with the latter
explicitly accounting for sea salt concentrations. Knowledge of the main constituents of a site's
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light extinction "budget" is critical for source apportionment and control strategy development.
In addition to this indirect method of assessing light extinction, there are optical measurements
which directly measure light extinction or its components. Such measurements are made
principally with a nephelometer to measure light scattering; some sites also include an
aethalometer for light absorption; and a few sites use a transmissometer, which measures total
light extinction. Scene characteristics are typically recorded using digital or video photography
and are used to determine the quality of visibility conditions (such as effects on color and
contrast) associated with specific levels of light extinction as measured under both direct and
aerosol-related methods. Directly measured light extinction is used under the IMPROVE
protocol to cross check that total light extinction calculated from the IMPROVE reconstruction
formula are consistent with directly measured extinction. Aerosol-derived light extinction from
the IMPROVE equation is used to document spatial and temporal trends and to determine how
changes in atmospheric constituents would affect future visibility conditions.
Annual average visibility conditions (reflecting light extinction due to both anthropogenic and
non-anthropogenic sources) vary regionally across the U.S. Figures 13-1 through 13-14 in the
PM ISA detail the percent contributions to particulate light extinction for ammonium nitrate and
sulfate, EC and OC, and coarse mass and fine soil, by month. (US EPA 2019)
7.3.2 Plant and Ecosystem Effects of Ozone
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. When
ozone effects that begin at small spatial scales, such as the leaf of an individual plant, occur at
sufficient magnitudes (or to a sufficient degree), they 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.
Ozone can produce both acute and chronic injury in sensitive plant species depending on the
concentration level and the duration of the exposure (73 FR 16486 2008). In those sensitive
species140' 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. (US EPA 2020)141 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 (73 FR 16492 2008). These latter impacts include increased susceptibility of plants to
insect attack, disease, harsh weather, interspecies competition, and overall decreased plant vigor.
140 Only a small percentage of all the plant species growing within the U.S. (over 43,000 species have been
catalogued in the USD A PLANTS database) have been studied with respect to ozone sensitivity.
141 The concentration at which ozone levels overwhelm a plant's ability to detoxify or compensate for oxidant
exposure varies. Thus, whether a plant is classified as sensitive or tolerant depends in part on the exposure levels
being considered.
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The adverse effects of ozone on areas with sensitive species could potentially lead to species
shifts and loss from the affected ecosystems142' resulting in a loss or reduction in associated
ecosystem goods and services (73 FR 16493-16494 2008). 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 (73 FR
16490-16497 2008). 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 (US EPA 2020). 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.143 The Ozone 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.
7.3.3 Deposition
Deposited airborne pollutants contribute to adverse effects on ecosystems, and to soiling and
materials damage. These welfare effects result mainly from exposure to excess amounts of
specific chemical species, regardless of their source or predominant form (particle, gas or liquid).
Nitrogen and sulfur tend to comprise a large portion of PM in many locations; however, gas-
phase forms of oxidized nitrogen and sulfur also cause adverse ecological effects. The following
characterizations of the nature of these environmental effects are based on information contained
in the 2019 PM ISA, and the 2020 Integrated Science Assessment for Oxides of Nitrogen,
Oxides of Sulfur, and Particulate Matter - Ecological Criteria. (US EPA 2020, US EPA 2019)
7.3.3.1 Deposition of Nitrogen and Sulfur
Nitrogen and sulfur interactions in the environment are highly complex, as shown in Figure
7-3. (US EPA 2020) Both nitrogen and sulfur are essential, and sometimes limiting, nutrients
needed for growth and productivity of ecosystem components (e.g., algae, plants). In terrestrial
and aquatic ecosystems, excesses of nitrogen or sulfur can lead to acidification and nutrient
enrichment. (US EPA 2020) In addition, in aquatic ecosystems, sulfur deposition can increase
mercury methylation.
142 Per footnote above, ozone impacts could be occurring in areas where plant species sensitive to ozone have not yet
been studied or identified.
143 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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Ambient Air
Concentration
Sunlight
Dissolution
~ 2H* ~SO**"
* H'+NOj"
Oxidation
SOj * h2so«
NO, *• HNOj
Wet Deposition
H*, NH«\ NOj , S042
Dry deposition
NO,. NH„ SO,
Deposition
Acidification of water + Eutrophication
Ecological
Effect
Figure 7-3: Nitrogen and Sulfur Cycling, and Interactions in the Environment
7.3.3.1.1 Ecological Effects of Acidification
Deposition of nitrogen and sulfur can cause acidification, which alters biogeochemistry and
affects animal and plant life in terrestrial and aquatic ecosystems across the U.S. Soil
acidification is a natural process, but is often accelerated by acidifying deposition, which can
decrease concentrations of exchangeable base cations in soils. (US EPA 2020) Biological effects
of acidification in terrestrial ecosystems are generally linked to aluminum toxicity and decreased
ability of plant roots to take up base cations. (US EPA 2020) Decreases in the acid neutralizing
capacity and increases in inorganic aluminum concentration contribute to declines in
zooplankton, macro invertebrates, and fish species richness in aquatic ecosystems. (US EPA
2020)
Geology (particularly surficial geology) is the principal factor governing the sensitivity of
terrestrial and aquatic ecosystems to acidification from nitrogen and sulfur deposition. (US EPA
2020) Geologic formations having low base cation supply generally underlie the watersheds of
acid-sensitive lakes and streams. Other factors contribute to the sensitivity of soils and surface
waters to acidifying deposition, including topography, soil chemistry, land use, and hydrologic
flow path. (US EPA 2020).
7.3.3.1.1.1 Aquatic Acidification
Aquatic effects of acidification have been well studied in the U.S. and elsewhere at various
trophic levels. These studies indicate that aquatic biota have been affected by acidification at
virtually all levels of the food web in acid sensitive aquatic ecosystems. Effects have been most
clearly documented for fish, aquatic insects, other invertebrates, and algae. Biological effects are
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primarily attributable to a combination of low pH and high inorganic aluminum concentrations.
Such conditions occur more frequently during rainfall and snowmelt that cause high flows of
water, and less commonly during low-flow conditions, except where chronic acidity conditions
are severe. Biological effects of episodes include reduced fish condition factor, changes in
species composition and declines in aquatic species richness across multiple taxa, ecosystems
and regions.
Because acidification primarily affects the diversity and abundance of aquatic biota, it also
affects the ecosystem services, e.g., recreational and subsistence fishing, that are derived from
the fish and other aquatic life found in these surface waters. For example, in the northeastern
United States, the surface waters affected by acidification are a source of food for some
recreational and subsistence fishermen and for other consumers with particularly high rates of
self-caught fish consumption, such as the Hmong and Chippewa ethnic groups. (Hutchison 1994,
Peterson, etal. 1994)
7.3.3.1.1.2 Terrestrial Acidification
Acidifying deposition has altered major biogeochemical processes in the U.S. by increasing
the nitrogen and sulfur content of soils, accelerating nitrate and sulfate leaching from soil to
drainage waters, depleting base cations (especially calcium and magnesium) from soils, and
increasing the mobility of aluminum. Inorganic aluminum is toxic to some tree roots. Plants
affected by high levels of aluminum from the soil often have reduced root growth, which restricts
the ability of the plant to take up water and nutrients, especially calcium. (US EPA 2020) These
direct effects can, in turn, influence the response of these plants to climatic stresses such as
droughts and cold temperatures. They can also influence the sensitivity of plants to other
stresses, including insect pests and disease leading to increased mortality of canopy trees. (Joslin
1992) In the U.S., terrestrial effects of acidification are best described for forested ecosystems
(especially red spruce and sugar maple ecosystems) with additional information on other plant
communities, including shrubs and lichen. (US EPA 2020)
Both coniferous and deciduous forests throughout the eastern U.S. are experiencing gradual
losses of base cation nutrients from the soil due to accelerated leaching from acidifying
deposition. This change in nutrient availability may reduce the quality of forest nutrition over
the long term. Evidence suggests that red spruce and sugar maple in some areas in the eastern
U.S. have experienced declining health because of this deposition. For red spruce (Picea
rubens), dieback or decline has been observed across high elevation landscapes of the
northeastern U.S. and, to a lesser extent, the southeastern U.S., and acidifying deposition has
been implicated as a causal factor. (DeHayes, et al. 1999)
7.3.3.1.2 Ecological Effects from Nitrogen Enrichment
7.3.3.1.2.1 Aquatic Enrichment
Eutrophication in estuaries is associated with a range of adverse ecological effects including
low dissolved oxygen (DO), harmful algal blooms (HABs), loss of submerged aquatic vegetation
(SAV), and low water clarity. Low DO disrupts aquatic habitats, causing stress to fish and
shellfish, which, in the short-term, can lead to episodic fish kills and, in the long-term, can
damage overall growth in fish and shellfish populations. Low DO also degrades the aesthetic
qualities of surface water. In addition to often being toxic to fish and shellfish and leading to fish
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kills and aesthetic impairments of estuaries, HABs can, in some instances, also be harmful to
human health. SAV provides critical habitat for many aquatic species in estuaries and, in some
instances, can also protect shorelines by reducing wave strength; therefore, declines in SAV due
to nutrient enrichment are an important source of concern. Low water clarity is in part the result
of accumulations of both algae and sediments in estuarine waters. In addition to contributing to
declines in SAV, high levels of turbidity also degrade the aesthetic qualities of the estuarine
environment.
An assessment of estuaries nationwide by the National Oceanic and Atmospheric
Administration (NOAA) concluded that 64 estuaries (out of 99 with available data) suffered
from moderate or high levels of eutrophication due to excessive inputs of both nitrogen (N) and
phosphorus. (Bricker, et al. 2007) For estuaries in the Mid-Atlantic region, the contribution of
atmospheric deposition to total N loads is estimated to range between 10 percent and 58 percent.
(Valigura, et al. 2001) Estuaries in the eastern United States are an important source of food
production, in particular for fish and shellfish production. The estuaries are capable of supporting
large stocks of resident commercial species, and they serve as the breeding grounds and interim
habitat for several migratory species. Eutrophi cation in estuaries may also affect the demand for
seafood (after well-publicized toxic blooms), water-based recreation, and erosion protection
provided by SAV.
7.3.3.1.2.2 Terrestrial Enrichment
Terrestrial enrichment occurs when terrestrial ecosystems receive N loadings in excess of
natural background levels, through either atmospheric deposition or direct application.
Atmospheric N deposition is associated with changes in the types and number of species and
biodiversity in terrestrial systems. Nitrogen enrichment occurs over a long time period; as a
result, it may take as many as 50 years or more to see changes in ecosystem conditions and
indicators. One of the main provisioning services potentially affected by N deposition is grazing
opportunities offered by grasslands for livestock production in the Central U.S. Although N
deposition on these grasslands can offer supplementary nutritive value and promote overall grass
production, there are concerns that fertilization may favor invasive grasses and shift the species
composition away from native grasses. This process may ultimately reduce the productivity of
grasslands for livestock production.
Terrestrial enrichment also affects habitats, for example the Coastal Sage Scrub (CSS) and
Mixed Conifer Forest (MCF) habitats which are an integral part of the California landscape.
Together the ranges of these habitats include the densely populated and valuable coastline and
the mountain areas. Numerous threatened and endangered species at both the state and federal
levels reside in CSS and MCF. Nutrient enrichment of the CSS and MCF also affects the
regulating service of fire, by encouraging the growth of more flammable grasses and thus
increasing fuel loads and altering the fire cycle.
7.3.3.1.3 Vegetation Effects Associated with Gaseous Sulfur Dioxide, Nitric
Oxide, Nitrogen Dioxide, Peroxyacetyl Nitrate, and Nitric Acid
Uptake of gaseous pollutants in a plant canopy is a complex process involving adsorption to
surfaces (leaves, stems, and soil) and absorption into leaves. These pollutants penetrate into
leaves through the stomata, although there is evidence for limited pathways via the cuticle. (US
EPA 2020) Pollutants must be transported from the bulk air to the leaf boundary layer in order to
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reach the stomata. When the stomata are closed, as occurs under dark or drought conditions,
resistance to gas uptake is very high and the plant has a very low degree of susceptibility to
injury. In contrast, mosses and lichens do not have a protective cuticle barrier to gaseous
pollutants or stomates and are generally more sensitive to gaseous sulfur and nitrogen than
vascular plants. (US EPA 2020)
Acute foliar injury from SO2 usually happens within hours of exposure, involves a rapid
absorption of a toxic dose, and involves collapse or necrosis of plant tissues. Another type of
visible injury is termed chronic injury and is usually a result of variable SO2 exposures over the
growing season. Besides foliar injury, chronic exposure to low SO2 concentrations can result in
reduced photosynthesis, growth, and yield of plants. (US EPA 2022) These effects are
cumulative over the season and are often not associated with visible foliar injury. As with foliar
injury, these effects vary among species and growing environment. SO2 is also considered the
primary factor causing the death of lichens in many urban and industrial areas. (Hutchinson,
Maynard and Geiser 1996)
Similarly, in sufficient concentrations, nitric oxide (NO), nitrogen dioxide (NO2),
peroxyacetyl nitrate (PAN), and nitric acid (HNO3) can have phytotoxic effects on plants such as
decreasing photosynthesis and inducing visible foliar injury. It is also known that these gases can
alter the N cycle in some ecosystems, especially in the western U.S., and contribute to N
saturation. Further, there are several lines of evidence that past and current HNO3 concentrations
may be contributing to the decline in lichen species in the Los Angeles basin. (Riddell, Nash and
Padgett 2008)
7.3.3.1.4 Mercury Methylation
Mercury is a persistent, bioaccumulative toxic metal that is emitted in three forms: gaseous
elemental Hg (HgO), oxidized Hg compounds (Hg+2), and particle-bound Hg (HgP).
Methylmercury (MeHg) is formed by microbial action in the top layers of sediment and soils
after Hg has precipitated from the air and deposited into waterbodies or land. Once formed,
MeHg is taken up by aquatic organisms and bioaccumulates up the aquatic food web. Larger
predatory fish may have MeHg concentrations many times higher, typically on the order of one
million times, than the concentrations in the freshwater body in which they live. The NOx SOx
ISA—Ecological Criteria concluded that evidence is sufficient to infer a causal relationship
between sulfur deposition and increased mercury methylation in wetlands and aquatic
environments. (US EPA 2020) Specifically, there appears to be a relationship between SO42"
deposition and mercury methylation; however, the rate of mercury methylation varies according
to several spatial and biogeochemical factors whose influence has not been fully quantified.
Therefore, the correlation between SO42" deposition and MeHg cannot yet be quantified for the
purpose of interpolating the association across waterbodies or regions. Nevertheless, because
changes in MeHg in ecosystems represent changes in significant human and ecological health
risks, the association between sulfur and mercury cannot be neglected. (US EPA 2020)
7.3.3.2 Deposition of Metallic and Organic Constituents of PM
Several significant ecological effects are associated with the deposition of chemical
constituents of ambient PM such as metals and organics. (US EPA 2020) The trace metal
constituents of PM include cadmium, copper, chromium, mercury, nickel, zinc, and lead. The
organics include persistent organic pollutants (POPs), polyaromatic hydrocarbons (PAHs) and
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polybrominated diphenyl ethers (PBDEs). Direct effect exposures to PM occur via deposition
(e.g., wet, dry or occult) to vegetation surfaces, while indirect effects occur via deposition to
ecosystem soils or surface waters where the deposited constituents of PM then interact with
biological organisms. While both fine and coarse-mode particles may affect plants and other
organisms, more often the chemical constituents drive the ecosystem response to PM. (Grantz,
Garner and Johnson 2003) Ecological effects of PM include direct effects to metabolic processes
of plant foliage; contribution to total metal loading resulting in alteration of soil biogeochemistry
and microbiology, plant and animal growth and reproduction; and contribution to total organics
loading resulting in bioaccumulation and biomagnification.
Particulate matter can adversely impact plants and ecosystem services provided by plants by
deposition to vegetative surfaces. (US EPA 2020) Particulates deposited on the surfaces of
leaves and needles can block light, altering the radiation received by the plant. PM deposition
near sources of heavy deposition can obstruct stomata (limiting gas exchange), damage leaf
cuticles and increase plant temperatures. (US EPA 2020) Plants growing on roadsides exhibit
impact damage from near-road PM deposition, having higher levels of organics and heavy
metals, and accumulating salt from road de-icing during winter months. (US EPA 2020) In
addition, atmospheric PM can convert direct solar radiation to diffuse radiation, which is more
uniformly distributed in a tree canopy, allowing radiation to reach lower leaves. (US EPA 2020)
Decreases in crop yields (a provisioning service) due to reductions in solar radiation have been
attributed to regional scale air pollution in counties with especially severe regional haze.
(Chameides, etal. 1999)
In addition to damage to plant surfaces, deposited PM can be taken up by plants from soil or
foliage. Copper, zinc, and nickel have been shown to be directly toxic to vegetation under field
conditions. (US EPA 2020) The ability of vegetation to take up heavy metals is dependent upon
the amount, solubility and chemical composition of the deposited PM. Uptake of PM by plants
from soils and vegetative surfaces can disrupt photosynthesis, alter pigments and mineral
content, reduce plant vigor, decrease frost hardiness and impair root development.
Particulate matter can also contain organic air toxic pollutants, including PAHs, which are a
class of polycyclic organic matter (POM). PAHs can accumulate in sediments and
bioaccumulate in freshwater, flora and fauna. The uptake of organic air toxic pollutants depends
on the plant species, site of deposition, physical and chemical properties of the organic
compound and prevailing environmental conditions. (US EPA 2020) Different species can have
different uptake rates of PAHs. PAHs can accumulate to high enough concentrations in some
coastal environments to pose an environmental health threat that includes cancer in fish
populations, toxicity to organisms living in the sediment and risks to those (e.g., migratory birds)
that consume these organisms. (Simcik, S.J. and Lioy 1999, Simcik, et al. 1996) Atmospheric
deposition of particles is thought to be the major source of PAHs in the sediments of Lake
Michigan, Chesapeake Bay, Tampa Bay and other coastal areas of the U.S. (Arzavus, Dickhut
and Canuel 2001)
Contamination of plant leaves by heavy metals can lead to elevated concentrations in the soil.
Trace metals absorbed into the plant, frequently by binding to the leaf tissue, and then are shed
when the leaf drops. As the fallen leaves decompose, the heavy metals are transferred into the
soil. (Cotrufo, et al. 1995, Niklinska, Laskowski and Maryanski 1998) Many of the major
indirect plant responses to PM deposition are chiefly soil-mediated and depend on the chemical
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composition of individual components of deposited PM. Upon entering the soil environment,
PM pollutants can alter ecological processes of energy flow and nutrient cycling, inhibit nutrient
uptake to plants, change microbial community structure, and affect biodiversity. Accumulation
of heavy metals in soils depends on factors such as local soil characteristics, geologic origin of
parent soils, and metal bioavailability. Heavy metals such as zinc, copper, and cadmium, and
some pesticides can interfere with microorganisms that are responsible for decomposition of soil
litter, an important regulating ecosystem service that serves as a source of soil nutrients. (US
EPA 2020) Surface litter decomposition is reduced in soils having high metal concentrations.
Soil communities have associated bacteria, fungi, and invertebrates that are essential to soil
nutrient cycling processes. Changes to the relative species abundance and community
composition are associated with deposited PM to soil biota. (US EPA 2020)
Atmospheric deposition can be the primary source of some organics and metals to watersheds.
Deposition of PM to surfaces in urban settings increases the metal and organic component of
storm water runoff. (US EPA 2020) This atmospherically-associated pollutant burden can then
be toxic to aquatic biota. The contribution of atmospherically deposited PAHs to aquatic food
webs was demonstrated in high elevation mountain lakes with no other anthropogenic
contaminant sources. (US EPA 2020) Metals associated with PM deposition limit phytoplankton
growth, affecting aquatic trophic structure. Long-range atmospheric transport of 47 pesticides
and degradation products to the snowpack in seven national parks in the Western U.S. was
recently quantified indicating PM-associated contaminant inputs in receiving waters during
spring snowmelt. The recently completed Western Airborne Contaminants Assessment Project
(WACAP) is the most comprehensive database on contaminant transport and PM depositional
effects on sensitive ecosystems in the Western U.S. (Landers, et al. 2008) In this project, the
transport, fate, and ecological impacts of anthropogenic contaminants from atmospheric sources
were assessed from 2002 to 2007 in seven ecosystem components (air, snow, water, sediment,
lichen, conifer needles and fish) in eight core national parks. The study concluded that
bioaccumulation of semi-volatile organic compounds occurred throughout park ecosystems, an
elevational gradient in PM deposition exists with greater accumulation in higher altitude areas,
and contaminants accumulate in proximity to individual agriculture and industry sources, which
is counter to the original working hypothesis that most of the contaminants would originate from
Eastern Europe and Asia.
7.3.3.3 Materials Damage and Soiling
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, degrading paints and deteriorating building materials such as stone, concrete
and marble. (US EPA 2020) The effects of PM are exacerbated by the presence of acidic gases
and can be additive or synergistic depending on 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 (such as monuments
and building facings), and surface coatings (paints). (Irving 1991) The effects on historic
buildings and outdoor works of art are of particular concern because of the uniqueness and
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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.
7.3.4 Welfare Effects of Air Toxics
Emissions from producing, transporting, and combusting fuel contribute to ambient levels of
pollutants that contribute to adverse effects on vegetation. VOCs, some of which are considered
air toxics, have long been suspected to play a role in vegetation damage. (US EPA 1991) In
laboratory experiments, a wide range of tolerance to VOCs has been observed. (Cape, et al.
2003) 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 including ethanol and toluene on herbaceous plants, significant effects on seed production,
leaf water content and photosynthetic efficiency were reported for some plant species. (Cape, et
al. 2003)
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 NOx. (Viskari 2000, Ugrekhelidze,
Korte and Kvesitadze 1997, Kammerbauer, et al. 1987) 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.
7.4 Criteria Pollutant Human Health Benefits
The light-duty passenger cars and light trucks and medium-duty vehicles subject to the
proposed standards are significant sources of mobile source air pollution, including directly-
emitted PM2.5 as well as NOx and VOC emissions (both precursors to ozone formation and
secondarily-formed PM2.5). The proposed program would reduce exhaust emissions of these
pollutants from the regulated vehicles, which would in turn reduce ambient concentrations of
ozone and PM2.5. Emissions from upstream sources would likely increase in some cases (e.g.,
power plants) and decrease in others (e.g., refineries). We project that in total, the proposed
standards would result in substantial net reductions of emissions of pollutants like PM2.5, NOx
and VOCs and a net increase in emissions of SO2. Emissions changes attributable to the
proposed standards are presented in Section VII of this preamble. Exposures to ambient
pollutants such as PM2.5 and ozone are linked to adverse environmental and human health
impacts, such as premature deaths and non-fatal illnesses (as explained in Section II.C of this
preamble). Reducing human exposure to these pollutants results in significant and measurable
health benefits.
This section discusses the economic benefits from reductions in adverse health and
environmental impacts resulting from criteria pollutant emission reductions that can be expected
to occur as a result of the proposed emission standards. When feasible, EPA conducts full-scale
photochemical air quality modeling to demonstrate how its national mobile source regulatory
actions affect ambient concentrations of regional pollutants throughout the United States. The
estimation of the human health impacts of a regulatory action requires national-scale
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photochemical air quality modeling to conduct a full-scale assessment of PM2.5 and ozone-
related health benefits.
EPA conducted an illustrative air quality modeling analysis of a regulatory scenario involving
light- and medium-duty vehicle emission reductions and corresponding changes in "upstream"
emission sources like EGU (electric generating unit) emissions and refinery emissions (see
DRIA Chapter 8). Decisions about the emissions and other elements used in the air quality
modeling were made early in the analytical process for the proposed rulemaking. Accordingly,
the air quality analysis does not represent the proposal's regulatory scenario, nor does it reflect
the expected impacts of the Inflation Reduction Act (IRA). Based on updated power sector
modeling that incorporated expected generation mix impacts of the IRA, we are projecting the
IRA will lead to a significantly cleaner power grid. Because the air quality analysis does not
account for these impacts on EGU emissions, we instead used the OMEGA-based emissions
analysis (see DRIA Chapter 9) and benefit-per-ton (BPT) values to estimate the criteria pollutant
(PM2.5) health benefits of the proposed and alternative standards.
The BPT approach estimates the monetized economic value of PIVfo.s-related emission
reductions or increases (such as direct PM, NOx and SO2) due to implementation of the proposed
program. Similar to the SC-GHG approach for monetizing reductions in GHGs, the BPT
approach monetizes health benefits of avoiding one ton of PIVfo.s-related emissions from a
particular onroad mobile or upstream source. The value of health benefits from reductions (or
increases) in PM2.5 emissions associated with this proposal were estimated by multiplying PM2.5-
related BPT values by the corresponding annual reduction (or increase) in tons of directly-
emitted PM2.5 and PM2.5 precursor emissions (NOx and SO2).
The BPT approach monetizes avoided premature deaths and illnesses that are expected to
occur as a result of reductions in directly-emitted PM2.5 and PM2.5 precursors. A chief limitation
to using PM2.5-related BPT values is that they do not reflect benefits associated with reducing
ambient concentrations of ozone, direct exposure to NO2, or exposure to mobile source air
toxics, nor do they account for improved ecosystem effects or visibility. The estimated benefits
of this proposal would be larger if we were able to monetize these unquantified benefits at this
time.
Using the BPT approach, we estimate the present value of PIvfc.s-related benefits of the
proposed program to be $97 to $200 billion at a 3% discount rate and $42 to $89 billion at a 7%
discount rate. Benefits are reported in year 2020 dollars and reflect the PIvfc.s-related benefits
associated with reductions in NOx, SO2, and direct PM2.5 emissions. Because premature
mortality typically constitutes the vast majority of monetized benefits in a PM2.5 benefits
assessment, we present a range of PM benefits based on risk estimates reported from two
different long-term exposure studies using different cohorts to account for uncertainty in the
benefits associated with avoiding PM-related premature deaths. Tables of the monetized PM2.5-
related benefits of the proposed standards can be found in draft RIA Chapter 10.
7.4.1 Approach to Estimating Human Health Benefits
This section summarizes EPA's approach to estimating the economic value of the PM2.5-
related benefits for this proposal. We use a BPT approach that is conceptually consistent with
EPA's use of BPT estimates in its regulatory analyses (US EPA 2018) (US EPA 2023). In this
approach, the PIVfo.s-related BPT values are the total monetized human health benefits (the sum
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of the economic value of the reduced risk of premature death and illness) that are expected from
reducing one ton of NOx, SO2 or directly-emitted PM2.5.
The mobile sector BPT estimates used in this proposal were published in 2019, but were
recently updated using the suite of premature mortality and morbidity studies in use by EPA for
the 2023 PM NAAQS Reconsideration Proposal (Wolfe, et al. 2019) (US EPA 2022). The
upstream Refinery and EGU BPT estimates used in this proposal were also recently updated (US
EPA 2023). The health benefits Technical Support Document (Benefits TSD) that accompanied
the 2023 PM NAAQS Proposal details the approach used to estimate the PM2.5-related benefits
reflected in these BPTs (US EPA 2023). We multiply these BPT values by national reductions in
annual emissions in tons to estimate the total monetized human health benefits associated with
the proposal.
Our procedure for calculating BPT values follows three steps:
1. Using source apportionment photochemical modeling, predict annual average ambient
concentrations of NOx, SO2 and primary PM2.5 that are attributable to each source sector
(Onroad Heavy-Duty Diesel, Onroad Heavy Duty Gas, Refineries, and Electricity Generating
Units), for the Continental U.S. (48 states). This yields the estimated ambient pollutant
concentrations to which the U.S. population is exposed.
2. For each sector, estimate the health impacts, and economic value of those impacts,
associated with the attributable ambient concentrations of NOx, SO2 and primary PM2.5 using the
environmental Benefits Mapping and Analysis Program-Community Edition (BenMAP-CE) (US
EPA 2023).144 This yields the estimated total monetized value of health effects associated with
exposure to the relevant pollutants by sector.
3. For each sector, divide the monetary value of health impacts by the inventory of associated
precursor emissions. That is, primary PM2.5 benefits for a given sector are divided by direct
PM2.5 emissions from that same sector, sulfate benefits are divided by SO2 emissions, and nitrate
benefits are divided by NOx emissions. This yields the estimated monetary value of one ton of
sector-specific direct PM2.5, SO2 or NOx emissions.
The quantified and monetized PM2.5 health categories that are included in the BPT values are
summarized in Table 7-1. Table 7-3 in Chapter 7.4.6 lists the ozone, PM2.5, SO2 and NOx health
and welfare categories that are not quantified and monetized by the BPT approach and are
therefore not included in the estimated benefits analysis for this proposal.
144 BenMAP-CE is an open-source computer program developed by the EPA that calculates the number and
economic value of air pollution-related deaths and illnesses. The software incorporates a database that includes
many of the concentration-response relationships, population files, and health and economic data needed to quantify
these impacts. Information on BenMAP is found at: https://www.epa.gov/benmap/benmap-community-edition, and
the source code is available at: https://github.eom/BenMAPCE/BenMAP-CE.
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Table 7-1 Human Health Effects of PM2.5
Effect (age)
Effect
Effect
More
Quantified
Monetized
Information
Adult premature mortality based on cohort study
/
/
PM ISA
estimates (>17 or >64)
Infant mortality (<1)
/
/
PM ISA
Non-fatal heart attacks (>18)
sf
y
PM ISA
Hospital admissions - cardiovascular (all)
/
/
PM ISA
Hospital admissions - respiratory (<19 and >64)
sf
sf
PM ISA
Hospital admissions - Alzheimer's disease (>64)
/
/
PM ISA
Hospital admissions - Parkinson's disease (>64)
sf
sf
PM ISA
Emergency department visits - cardiovascular (all)
/
/
PM ISA
Emergency department visits - respiratory (all)
sf
sf
PM ISA
Emergency hospital admissions (>65)
/
/
PM ISA
Non-fatal lung cancer (>29)
sf
sf
PM ISA
Stroke incidence (50-79)
/
/
PM ISA
New onset asthma (< 12)
sf
sf
PM ISA
Exacerbated asthma - albuterol inhaler use
/
/
PM ISA
(asthmatics. 6-13)
Lost work days (18-64)
PM ISA
Other cardiovascular effects (e.g.. doctor's visits.
—
—
PM ISA1
prescription medication)
Other respiratory effects (e.g.. pulmonary function.
—
—
PM ISA1
other ages)
Other cancer effects (e.g.. mutagenicity.
—
—
PM ISA1
genotoxicity)
Other nervous system effects (e.g.. dementia)
—
—
PM ISA1
Metabolic effects (e.g.. diabetes, metabolic
—
—
PM ISA1
syndrome)
Reproductive and developmental effects (e.g.. low
—
—
PM ISA1
birth weight, pre-term births)
We assess these benefits qualitatively due to epidemiological or economic data limitations.
Of the PM-related health endpoints listed in Table 7-1, EPA estimates the incidence of air
pollution effects for only those classified as either "causal" or "likely-to-be-causal" in the 2019
PM Integrated Science Assessment (ISA) and the 2022 PM ISA update (US EPA 2019) (US
EPA 2022).145 The full complement of human health effects associated with PM remains
unquantified because of current limitations in methods or available data. Thus, our quantified
PM-related benefits omit a number of known or suspected health effects linked with PM, either
because appropriate health impact functions are not available or because outcomes are not easily
interpretable (e.g., changes in heart rate variability).
145 The ISA synthesizes the toxicological, clinical and epidemiological evidence to determine whether each pollutant
is causally related to an array of adverse human health outcomes associated with either acute (i.e., hours- or days-
long) or chronic (i.e. years-long) exposure. For each outcome, the ISA reports this relationship to be causal, likely
to be causal, suggestive of a causal relationship, inadequate to infer a causal relationship, or not likely to be a causal
relationship.
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We anticipate the proposed program will also yield benefits from reduced exposure to
ambient concentrations of ozone. However, the complex, non-linear photochemical processes
that govern ozone formation prevent us from developing reduced-form ozone BPT values for
mobile sources. The BPT approach also omits health effects associated with ambient
concentrations of NO2 as well as criteria pollutant-related welfare effects such as improvements
in visibility, reductions in materials damage, ecological effects from reduced PM deposition,
ecological effects from reduced nitrogen emissions, and vegetation effects from reduced ozone
exposure. A list of these unquantified benefits can be found in Table 7-3 in Section 7.4.6 of this
Chapter.
We also do not provide estimated monetized benefits due to reductions in mobile source air
toxics. This is primarily 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.
7.4.2 Estimating PM2.5-attributable Adult Premature Death
Of the PM2.5-related health endpoints listed in Table 7-1, adult premature deaths typically
account for the majority of total monetized PM benefits and are thus the primary component of
the PM2.5-related BPT values. In this section, we provide more detail on PM mortality effect
coefficients and the concentration-response functions that underlie the BPT values.
A substantial body of published scientific literature documents the association between PM2.5
concentrations and the risk of premature death (US EPA 2019) (US EPA 2022). This body of
literature reflects thousands of epidemiology, toxicology, and clinical studies. The PM ISA,
completed as part of the review of the recently proposed PM standards and reviewed by the
Clean Air Scientific Advisory Committee (CASAC) (Sheppard 2022), concluded that there is a
causal relationship between mortality and both long-term and short-term exposure to PM2.5 based
on the full body of scientific evidence. The size of the mortality effect estimates from
epidemiologic studies, the serious nature of the effect itself, and the high monetary value
ascribed to prolonging life make mortality risk reduction the most significant health endpoint
quantified in this analysis. EPA selects Hazard Ratios from cohort studies to estimate counts of
PM-related premature death, following a systematic approach detailed in the Benefits TSD that
accompanied the 2023 PM NAAQS Proposal.
For adult PM-related mortality, the BPT values are based on the risk estimates from two
alternative long-term exposure mortality studies: the National Health Interview Survey (NHIS)
chohort study (Pope III et al. 2019) and an extended analysis of the Medicare cohort (Wu et al.
2020). In past analyses, EPA has used two alternate estimates of mortality: one from the
American Cancer Society cohort and one from the Medicare cohort (Turner 2016) (Di 2017)
respectively. We use a risk estimate from Pope III et al., 2019 study in place of the risk estimate
from the Turner et al., 2016 analysis, as it: (1) includes a longer follow-up period that includes
more recent (and lower) PM2.5 concentrations; (2) the NHIS cohort is more representative of the
U.S. population than is the ACS cohort with respect to the distribution of individuals by race,
ethnicity, income and education.
Based on the 2022 Supplement to the PM ISA, EPA substituted a risk estimate from Wu et
al., 2020 in place of a risk estimate from Di et al., 2017. These two epidemiologic studies share
many attributes, including the cohort and model used to characterize population exposure to
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PM2.5. As compared to Di et al., 2017, Wu et al., 2020 includes a longer follow-up period and
reflects more recent PM2.5 concentrations.
The PM ISA also concluded that the scientific literature supports the use of a no-threshold
log-linear model to portray the PM-mortality concentration-response relationship while
recognizing potential uncertainty about the exact shape of the concentration-response
relationship. The 2019 PM ISA, which informed the 2023 PM NAAQS proposal, reviewed
available studies that examined the potential for a population-level threshold to exist in the
concentration-response relationship. Based on such studies, the ISA concluded that "evidence
from recent studies reduce uncertainties related to potential co-pollutant confounding and
continues to provide strong support for a linear, no-threshold concentration-response
relationship." Consistent with this evidence, the Agency historically has estimated health impacts
above and below the prevailing NAAQS.
7.4.3 Economic Value of Health Benefits
The BPT values used in this analysis are a reduced-form approach for relating emission
reductions to reductions in ambient concentrations of PM2.5 and associated improvements in
human health. Reductions in ambient concentrations of air pollution generally decrease the risk
of future adverse health effects by a small amount for a large population. To monetize these
benefits, the appropriate economic measure is willingness to pay (WTP) for changes in risk of a
health effect. For some health effects, such as hospital admissions, WTP estimates are generally
not available, so we use the cost of treating or mitigating the effect. These cost-of-illness (COI)
estimates generally (although not necessarily in every case) understate the true value of
reductions in risk of a health effect. They tend to reflect the direct expenditures related to
treatment, but not the value of avoided pain and suffering from the health effect. The WTP and
COI unit values for each endpoint are provided in the Benefits TSD that accompanied the 2023
PM NAAQS Proposal. These unit values were used to monetize the underlying health effects
included in the PM2.5 BPT values.
Avoided premature deaths typically account for the majority of monetized PM2.5-related
benefits. The economics literature concerning the appropriate methodology for valuing
reductions in premature mortality risk is still developing and is the subject of continuing
discussion within the economics and public policy analysis community. Following the advice of
the SAB's Environmental Economics Advisory Committee (SAB-EEAC), EPA currently uses
the value of statistical life (VSL) approach in calculating estimates of mortality benefits. This
calculation provides the most reasonable single estimate of an individual's WTP for reductions
in mortality risk (US EPA-SAB 2000). The VSL approach is a summary measure for the value of
small changes in mortality risk experienced by a large number of people.
EPA consulted several times with the SAB-EEAC on valuing mortality risk reductions and
continues work to update the Agency's guidance on the issue. Until updated guidance is
available, EPA determined that a single, peer-reviewed estimate applied consistently best reflects
the SAB-EEAC advice we have received. Therefore, EPA applies the VSL that was vetted and
endorsed by the SAB in the Agency's Guidelines for Preparing Economic Analyses (US EPA
2016). This VSL value is the mean of the values reported in 26 labor market and contingent
valuation studies published between 1974 and 1991. The mean VSL across these studies is $4.8
million (1990$). We then adjust this VSL to account for the currency year and to account for
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income growth from 1990 to the analysis year. Specifically, the VSL applied in this analysis in
2020 dollars after adjusting for income growth is $9.5 million for 2020.146
EPA is committed to using scientifically sound, appropriately reviewed evidence in valuing
changes in the risk of premature death and continues to engage with the SAB to identify
scientifically sound approaches to update its mortality risk valuation estimates. Most recently,
the Agency proposed new meta-analytic approaches for updating its estimates, which were
subsequently reviewed by the SAB-EEAC (US EPA 2017). EPA is taking the SAB's formal
recommendations under advisement.
7.4.4 Dollar Value per Ton of Directly-Emitted PMa.s and PMa.s Precursors
The value of health benefits from reductions in PM2.5 emissions associated with this proposal
were estimated by multiplying PM2.5-related BPT values by the corresponding annual reduction
in tons of directly-emitted PM2.5 and PM2.5 precursor emissions (NOx and SO2). As explained in
above, the PM2.5 BPT values represent the monetized value of human health benefits, including
reductions in both premature mortality and nonfatal illnesses. Table 7-2 presents the PM2.5 BPT
values estimated from two different PM-related premature mortality cohort studies, Wu et al.,
2020 (the Medicare cohort study) and Pope III et al., 2019 (the NHIS cohort study). The table
reports different values by source and pollutant because different pollutant emissions do not
equally contribute to ambient PM2.5 formation and different emissions sources do not equally
contribute to population exposure and associated health impacts. BPT values are also estimated
using either a 3 percent or 7 percent discount rate to account for a "cessation" lag between the
change in PM exposures and the total realization of changes in mortality effects. The source
sectors include: onroad light-duty gasoline cars, onroad light-duty gasoline trucks, onroad light-
duty diesel cars/trucks, electricity generating units, and refineries. We note that reductions in
medium-duty vehicle emissions are monetized using light-duty BPT values.
Detailed tables of the monetized PM2.5-related benefits of the proposed standards can be
found in draft RIA Chapter 10.
146 In 1990$, when the study was conducted, the base VSL was $4.8 million.
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Table 7-2 PM2.5-related Benefit Per Ton values (2020$) associated with the reduction of
NOx, SO2 and directly emitted PM2.5 emissions for (A) Onroad light-duty gasoline cars, (B)
Onroad light-duty gasoline trucks, (C) Onroad light-duty diesel cars/trucks, (D) Electricity
Generating Units, and (E) Refineries.
A. Onroad Light-Duty Gasoline Cars
S02
NOX
; 3% Discount Rate : 7% Discount Rate ;
Direct PM
3% Discount Rate
7% Discount Rate
3% Discount Rate
7% Discount Rate
2020$
2025
2030
2035
2040
2045
2050
2055
Wu
$7,230
$8,160
$9,200
$10,100
$10,700
$11,200
$11,700
Pope
$15,400
$16,800
$18,500
$19,900
$21,000
$21,600
$22,500
Wu
$6,490
$7,330
$8,260
$9,050
$9,640
$10,000
$10,500
Pope
$13,800 i
$15,100 ;
$16,600 ;
$17,900
$18,900 ;
$19,500 ;
$20,300
NOX
i 3% Discount Rate : 7% Discount Rate ;
Wu Pope Wu Pope
$128,000 : $274,000 ; $115.000 i $246,000
$147,000 ; $303,000 : $132,000 ; $273,000
$169,000 : $341,000 $152,000 $307,000
$191,000 ; $378,000 ! $172,000 $340,000
$211,000 ; $413,000 : $190,000 : $371,000
$229,000 : $443,000 $206,000 ; $398,000
$249,000 ; $477,000 $224,000 ; $429,000
B. Onroad Light-Duty Gasoline Trucks
S02
Wu
$709,000 i
$814,000
$939,000 :
$1,060,000
$1,170,000
$1,270,000
$1,370,000
Pope
$1,520,000
$1,680,000
$1,890,000
$2,100,000
$2,290,000
$2,450,000
$2,630,000
Wu
$637,000
$731,000
$843,000
$953,000
$1,050,000
$1,140,000
$1,240,000
Pope
$1,360,000
$1,510,000
$1,700,000
$1,890,000
$2,060,000
$2,200,000
$2,360,000
Direct PM
3% Discount Rate
7% Discount Rate
3% Discount Rate
7% Discount Rate
2020$
2025
2030
2035
2040
2045
2050
2055
Wu
$6,550
$7,400
$8,360
$9,190
$9,820
$10,300
$10,800
Pope
$13,900 i
$15,200
$16,800
$18,200 ;
$19,200 ;
$19,900 ;
$20,800
Wu
$5,880
$6,640
$7,510
$8,250
$8,820
$9,220
$9,700
Pope
$12,500
$13,700 ;
$15,100
$16,400
$17,300
$17,900 ;
$18,700
Wu
$102,000 i
$117,000 ;
$135,000 :
$152,000 ;
$168,000
$182,000 :
$197,000 :
Pope
$219,000
$243,000
$272,000
$302,000
$329,000
$352,000
$378,000
Wu
$91,700
$105.000:
$121,000
$137,000
i $151,000 ;
$163,000
$177,000 ;
Pope
$197,000
$218,000
$245,000
$271,000
$296,000
$316,000
$340,000
Wu
$597,000 i
$685,000 :
$789,000
$889,000
$979,000 ;
$1.060.000:
$1,140,000
Pope
$1,280,000
$1,420,000
$1,590,000
$1,760,000
$1,910,000
$2,040,000
$2,190,000
Wu
$536,000
$615,000
$708,000
$798,000
$880,000
$950,000
$1,030,000
Pope
$1,150,000
$1,270,000
$1,430,000
$1,580,000
$1,720,000
$1,840,000
$1,970,000
NOX
C. Onroad Light-Duly Diesel Gal's/Trucks
S02
Direct PM
3°o Discount Rate
7% Discount Rate
3% Discount Rate
7% Discount Rate
3% Discount Rate
7% Discount Rate
2020$
Wu
Pope
Wu
Pope
Wu
Pope
Wu
Pope
Wu
Pope
Wu
Pope
2025
: $5,790
$12,300
$5,200
$11,100
$305,000 I
$655,000
: $274,000
$589,000
$489,000
: $1,050,000 :
$439,000 i
$942,000
2030
; $6,550
$13,500
$5,880
; $12,100
$349,000 ;
$725,000
: $314,000
$652,000 i
$560,000
= $1,160,000 ;
$503,000 :
$1,040,000
2035
: $7,400
$14,900
$6,640
$13,400
$402,000 :
$813,000
$361,000
$731,000 ,
$646,000
, $1,300,000
$580,000
$1,170,000
2040
! $8,130
; $16,100 ;
$7,310
$14,500
$453,000 ;
$900,000
i $407,000
$810,000 :
$728,000
i $1,440,000 s
$654,000 i
$1,300,000
2045
i $8,700
$17,000
$7,820
$15,300
$500,000
$980,000
i $449,000
$882,000 i
$803,000
$1,570,000 ;
$721,000 ;
$1,410,000
2050
; $9,100
$17,700
$8,180
$15,900
$541,000 :
$1,050,000
i $486,000
$944,000 i
$868,000
, $1,680,000 i
$780,000
$1,510,000
2055
, $9,570
$18,400
$8,600
: $16,600
$587,000
$1,130,000
: $528,000
$1,010,000
$939,000
. $1,800,000
$844,000 !
$1,620,000
NOX
i 3% Discount Rate : 7% Discount Rate
D. Electricity Generating Units
S02
Direct PM
3% Discount Rate
7% Discount Rate
3% Discount Rate
7% Discount Rate
Wu
Pope
Wu
Pope
Wu
Pope
Wu
Pope
Wu
Pope
Wu
Pope
2025
$7,470
$15,800
$6,710
$14,200
$55,200
$118,000
, $49,700
$106,000
$110,000
$235,000
$98,400 ;
$211,000
2030
: $8,370
$17,100
$7,530
$15,400 ;
$62,300 ;
$129,000
; $56,000
$116,000
; $125,000 :
$258,000
$112,000
$232,000
2035
i $9,370
$18,700
$8,420
$16,900
$69,900
$141,000
i $62,900
$127,000
; $142,000 ;
$287,000
$128,000
$258,000
2040
; $10,200
$20,000
$9,130
; $18,000
$76,400 :
$152,000
< $68,700
$136,000
: $158,000
$314,000
$142,000
$283,000
E. Refineries
NOX
: 3% Discount Rate 7% Discount Rate :
S02
Direct PM
3% Discount Rate
7% Discount Rate
3% Discount Rate
7% Discount Rate
2020$
Wu
Pope
Wu
Pope
Wu
Pope
Wu
Pope
Wu
Pope
Wu
Pope
2025
; $22,500
i $48,300
$20,200
$43,400 i
$49,600 i
$107,000
$44,500
$96,400
$358,000
$776,000 ;
$322,000 i
$698,000
2030
; $24,800
$51,500
$22,300
$46,300 ;
$54,800 :
$114,000
$49,200
$103,000
: $395,000
$826,000
$355,000 :
$743,000
2035
: $28,500
: $57,500
$25,600
$51,800
$62,700 :
$127,000
$56,400
$115,000
: $453,000
$923,000
$407,000
$831,000
2040
! $31,900
: $63,300
$28,700
$56,900 :
$70,100 :
$140,000
i $63,000
$126,000
i $509,000
$1,020,000 !
$458,000 :
$915,000
Notes: All estimates are rounded to three significant figures. The benefit-per-ton estimates presented in this table assume either a 3 percent or 7 percent
discount rate in the valuation of premature mortality to account for a twenty-year segmented cessation lag. Benefit-per-ton values were estimated for
years 2025, 2030, 2035, 2040, 2045, 2050 and 2055 for mobile sources, and for years 2025, 2030, 2035 and 2040 for EGUs and refineries. We hold
values constant for intervening years (e.g., the 2025 values are assumed to apply to years 2021-2024, and so on). We hold 2040 values constant out to
2055 for EGUs and Refineries.
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7.4.5 Characterizing Uncertainty in the Estimated Benefits
There are likely to be sources of uncertainty in any complex analysis using estimated
parameters and inputs from numerous models, including this analysis. The Benefits TSD that
accompanied the 2023 PM NAAQS Proposal details our approach to characterizing uncertainty
in both quantitative and qualitative terms. That TSD describes the sources of uncertainty
associated with key input parameters including emissions inventories, air quality data from
models (with their associated parameters and inputs), population data, population estimates,
health effect estimates from epidemiology studies, economic data for monetizing benefits, and
assumptions regarding the future state of the country (i.e., regulations, technology, and human
behavior). Each of these inputs is uncertain and affects the size and distribution of the estimated
benefits. When the uncertainties from each stage of the analysis are compounded, even small
uncertainties can have large effects on the total quantified benefits.
The BPT approach is a simplified approach that relies on additional assumptions and has its
own limitations, some of which are described in Chapter 7.4.6. Additional uncertainties related
to key assumptions underlying the estimates for PM2.5-related premature mortality described in
Section 7.4.2 of this chapter include the following:
• We assume that all fine particles, regardless of their chemical composition, are equally
potent in causing premature mortality. This is an important assumption because PM2.5
varies considerably in composition across sources, but the scientific evidence is not
yet sufficient to allow differentiation of effect estimates by particle type. The PM ISA,
which was reviewed by CASAC, concluded that "across exposure durations and health
effects categories ... the evidence does not indicate that any one source or component
is consistently more strongly related with health effects than PM2.5 mass." (US EPA
2019)
• We assume that the health impact function for fine particles is log-linear down to the
lowest air quality levels modeled in this analysis. Thus, the estimates include health
benefits from reducing fine particles in areas with varied concentrations of PM2.5,
including both regions that are in attainment with the fine particle standard and those
that do not meet the standard down to the lowest modeled concentrations. The PM ISA
concluded that "the majority of evidence continues to indicate a linear, no-threshold
concentration-response relationship for long-term exposure to PM2.5 and total
(nonaccidental) mortality." (US EPA 2019)
• We 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 SAB-HES,
which affects the valuation of mortality benefits at different discount rates. The above
assumptions are subject to uncertainty (US EPA-SAB 2005). Similarly, we assume
there is a cessation lag between the change in PM exposures and both the development
and diagnosis of lung cancer.
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7.4.6 Benefit-per-Ton Estimate Limitations
All BPT estimates have inherent limitations. One limitation of using the PM2.5-related BPT
approach is an inability to provide estimates of the health and welfare benefits associated with
exposure to ozone, welfare benefits and some unquantified health benefits associated with PM2.5,
as well as health and welfare benefits associated with ambient NO2 and SO2. Table 7-3 presents a
selection of unquantified criteria pollutant health and welfare benefits categories. Another
limitation is that the mobile sector-specific air quality modeling that underlies the PM2.5 BPT
value did not provide estimates of the PIvfc.s-related benefits associated with reducing VOC
emissions, but these unquantified benefits are generally small compared to benefits associated
with other PM2.5 precursors.
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Category
Nonfatal morbidity from exposure to ozone
Reduced incidence of morbidity from
exposure to N02
Reduced visibility impairment
Reduced effects on materials
Reduced effects from PM deposition (metals
and organics)
Reduced vegetation and ecosystem effects
from exposure to ozone
intified Health and Welfare Benefits Categories
Un(|uanlific
-------
Reduced effects from acid deposition
Reduced effects from nutrient enrichment
Recreational fishing
NOX SOX ISAa
Tree mortality and decline
NOX SOX ISAb
Commercial fishing and forestry effects
NOX SOX ISAb
Recreational demand in terrestrial and
aquatic ecosystems
NOX SOX ISAb
Other non-use effects
NOX SOX ISAb
Ecosystem functions (e.g.. biogeochemical
cycles)
NOX SOX ISAb
Species composition and biodiversity in
terrestrial and estuarine ecosystems
NOX SOX ISAb
Coastal eutrophication
NOX SOX ISAb
Recreational demand in terrestrial and
estuarine ecosystems
NOX SOX ISAb
Other non-use effects
NOX SOX ISAb
Ecosystem functions (e.g.. biogeochemical
cycles, fire regulation)
NOX SOX ISAb
Injury to vegetation from S02 exposure
NOX SOX ISAb
Injury to vegetation from NOX exposure
NOX SOX ISAb
Reduced vegetation effects from ambient
exposure to S02 and NOX
a We assess these benefits qualitatively due to data and resource limitations for this RIA.
k We assess these benefits qualitatively because we do not have sufficient confidence in available data or methods.
1 We assess these benefits qualitatively because current evidence is only suggestive of causality or there are other significant concerns over
the strength of the association.
There are also benefits associated with reductions in air toxic pollutant emissions that would
result from the program (see draft RIA Chapter 7.2.7) but that the PIVfo.s-related BPT approach
does not capture. While EPA continues to work to improve its benefits estimation tools, there
remain critical limitations for estimating incidence and assessing benefits of reducing air toxics.
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
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 photochemically-modeled emissions of the onroad mobile- and
upstream sector-attributable PM2.5 concentrations used to derive the BPT values may not match
the change in air quality resulting from the control strategies associated with the proposed
standards. For this reason, the PM-related health benefits reported here may be larger, or smaller,
than those that would be realized through this proposal.
Given the uncertainty that surrounds BPT analysis, EPA systematically compared benefits
estimated using its BPT approach (and other reduced-form approaches) to benefits derived from
full-form photochemical model representation. This work is referred to as the "Reduced Form
Tool Evaluation Project" (Project), which began in 2017, and the initial results were available at
the end of 2018. The Agency's goal was to better understand the suitability of alternative
reduced-form air quality modeling techniques for estimating the health impacts of criteria
pollutant emissions changes in EPA's benefit-cost analysis. The Project analyzed air quality
policies that varied in the magnitude and composition of their emissions changes and in the
emissions source affected (e.g., on-road mobile, industrial point, or electricity generating units).
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The policies also differed in terms of the spatial distribution of emissions and concentration
changes, and in their impacts on directly-emitted PM2.5 and secondary PM2.5 precursor emissions
(NOx and SO2).
For scenarios where the spatial distribution of emissions was similar to the inventories used to
derive the BPT, the Project found that total PM2.5 BPT-derived benefits were within
approximately 10 percent to 30 percent of the health benefits calculated from full-form air
quality modeling, though the discrepancies varied by regulated scenario and PM2.5 species. The
scenario-specific emission inputs developed for the Project, and a final project report, are
available online (US EPA 2019). We note that the BPT values used to monetize the benefits of
the proposed program were not part of the Project, though we believe they are our best estimate
of benefits absent air quality modeling and we have confidence in the BPT approach and the
appropriateness of relying on BPT health estimates for this rulemaking. EPA continues to
research and develop reduced-form approaches for estimating PM2.5 benefits.
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Chapter 8: Illustrative Analysis of Air Quality Impacts of a Light- and
Medium-Duty Vehicles Regulatory Scenario
EPA conducted an illustrative air quality modeling analysis of a regulatory scenario involving
light- and medium-duty "onroad" vehicle emission reductions and corresponding changes in
"upstream" emission sources like EGU (electric generating unit) emissions and refinery
emissions. Decisions about the emissions and other elements used in the air quality modeling
were made early in the analytical process for the proposed rulemaking. Accordingly, the air
quality analysis does not represent the proposal's regulatory scenario, nor does it reflect the
expected impacts of the Inflation Reduction Act (IRA). Based on updated power sector modeling
that incorporated expected generation mix impacts of the IRA (presented in Chapter 5), we are
projecting the IRA will lead to a significantly cleaner power grid; because the air quality analysis
presented here does not account for these impacts on EGU emissions, the location and magnitude
of the changes in pollutant concentrations should be considered illustrative and not viewed as
Agency projections of what we expect will be the total impact of the proposed standards.
Nevertheless, the analysis provides some insights into potential air quality impacts associated
with emissions increases and decreases from these multiple sectors.
This chapter presents a discussion of current air quality in Chapter 8.1, information about the
inventory used in the illustrative air quality modeling analysis in Chapter 8.2, details related to
the methodology used for the illustrative air quality modeling analysis in Chapter 8.3, results of
the illustrative air quality modeling analysis in Chapter 8.4 and quantified and monetized
benefits of the illustrative analysis in Chapter 8.5.
8.1 Current Air Quality
In this section we present information related to current air pollutant concentrations and
deposition amounts. This provides context for the modeled projections of pollutants in the
illustrative air quality analysis.
8.1.1 PM2.5 Concentrations
As described in Chapter 7 of this DRIA, PM causes adverse health effects, and EPA has set
NAAQS to protect against those health effects. There are two primary NAAQS for PM2.5: an
annual standard (12.0 micrograms per cubic meter ([j,g/m3)) and a 24-hour standard (35 (j,g/m3),
and there are two secondary NAAQS for PM2.5: an annual standard (15.0 jag/ m3) and a 24-hour
standard (35 (J,g/m3). The initial PM2.5 standards were set in 1997 and revisions to the standards
were finalized in 2006 and in December 2012, and then retained in 2020. On January 6, 2023,
EPA announced its proposed decision to revise the PM NAAQS. (US EPA 2023)
There are many areas of the country that are currently in nonattainment for the annual and 24-
hour primary PM2.5 NAAQS. As of December 31, 2022, more than 19 million people lived in the
4 areas that are designated as nonattainment for the 1997 annual PM2.5 NAAQS. (US EPA 2022)
Also, as of December 31, 2022, more than 31 million people lived in the 11 areas that are
designated as nonattainment for the 2006 24-hour PM2.5 NAAQS, and more than 20 million
people lived in the 5 areas designated as nonattainment for the 2012 annual PM2.5 NAAQS. (US
EPA 2022) (US EPA 2022) In total, there are currently 13 PM2.5 nonattainment areas with a
8-1
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population of more than 31 million people. (US EPA 2022)14' Nonattainment areas for the PM2.5
NAAQS are pictured in Figure 8-1.
Counties Designated Nonattainment
for PM-2.5 (1997, 2006, and/or 2012 Standards)
12/31/2022
Designated Nonattainment
All three PM-2.5 Standards
rn Both 2006 and 2012 PM-2.5
I I Both 1997 and 2006 PM-2.5
¦¦ 2012 PM-2.5 only
I I 2006 PM-2.5 only
I I 1997 PM-2.5 only
Nonattainment areas are indicated by color
When only a portion of a county is shown in color,
it indicates that only that part of the county is within
a nonattainment area boundary.
Figure 8-1: Counties designated nonattainment for PM2.5 (1997, 2006, and/or 2012
standards).
8.1.2 Ozone Concentrations
As described in Chapter 7 of this DRIA, ozone causes adverse health effects, and EPA has set
national ambient air quality standards (NAAQS) to protect against those health effects. The
primary NAAQS for ozone, established in 2015 and retained in 2020, is an 8-hour standard with
a level of 0.07 ppm. (US EPA 2020) EPA recently announced that it will reconsider the decision
to retain the ozone NAAQS. (US EPA 2022) EPA is also implementing the previous 8-hour
ozone primary standard, set in 2008 at a level of 0.075 ppm. As of December 31, 2022, there
were 34 ozone nonattainment areas for the 2008 primary ozone NAAQS, composed of 133 full
or partial counties, with a population of more than 90 million (see Figure 8-2); there were 49
ozone nonattainment areas for the 2015 primary ozone NAAQS, composed of 203 full or partial
counties, with a population of more than 125 million (see Figure 8-3). (US EPA 2022) (US EPA
147 The population total is calculated by summing, without double counting, the 1997, 2006 and 2012 PM2.5
nonattainment populations contained in the Criteria Pollutant Nonattainment Summary report
(https://www.epa.gov/green-book/green-book-data-download).
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2022) In total, there were, as of December 31, 2022, 56 ozone nonattainment areas with a
population of more than 122 million people. (US EPA 2022).148
Nonattainment areas are indicated by color.
When only a portion of a county is shown in color,
it indicates that only that part of the county is within
a nonattainment area boundary.
8-hour Ozone Classification
~ Extreme
~ Severe 15
j | Serious
[ | Moderate
j | Marginal
Figure 8-2: 8-Hour ozone nonattainment areas (2008 Standard).
Nonattainment areas are indicated by color.
When only a portion of a county is shown in color,
it indicates that only that part of the county is within
a nonattainment area boundary.
I ISevere-17
I I Seveie-15
I I Serious
I I Moderate
~ Marginal
| | Marginal (Rur
For the Ozone-8Hr (2015) Cincinnati, OH-KY nonattainment area, the Ohio portion was redesignated on June 9,2022. The Kentucky portion has not been redesignated
For the Ozone-8Hr (2015) Louisville, KY-IN nonattainment area .the Ohio portion was redesignated on July5,2022. The Kentucky portion hasnotbeen redesignated
The Kentucky portions of the Cincinnati and Louisville areas were each reclassified from Marginal to Moderate on November?, 2022.
The entire area is not considered in maintenance until all states in a multi-state area are redesignated
Figure 8-3: 8-Hour ozone nonattainment areas (2015 Standard).
148 The total population is calculated by summing, without double counting, the 2008 and 2015 ozone nonattaimnent
populations contained in the Criteria Pollutant Nonattaimnent Summary report (https://www.epa.gov/green-
book/green-book-data-do wnload).
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8.1.3 NOz Concentrations
There are two primary NAAQS for NCb: an annual standard (53 ppb) and a 1-hour standard
(100 ppb).149 In 2010, EPA established requirements for monitoring NO2 near roadways
expected to have the highest concentrations of NO2 within large cities. Monitoring within this
near-roadway network began in 2014, with additional sites deployed in the following years. At
present, there are no nonattainment areas for NO2.
8.1.4 SO? Concentrations
The primary N AAQS for SO2 is a 1-hour standard (95 ppb).150 As of Dec 31, 2022, there are
40 counties that make up 30 SO2 nonattainment areas, with a population of over 2 million
people. (US EPA 2022).
Nonattainment areas are indicated by color. ¦ ».
When only a portion of a county is shown in color, ° j i . j
it indicates that only that part of the county iswithiri " d*
a nonattainment area boundary.
Figure 8-4: counties designated nonattainment for SO2 (2010 standard).
8.1.5 CO Concentrations
There are two primary NAAQS for CO: an 8-hour standard (9 ppm) and a 1-hour standard (35
ppm). There are currently no CO nonattainment areas; as of September 27, 2010, all CO
nonattainment areas had been redesignated to attainment.
149 The statistical form of the 1-hour NAAQS for N02 is the 3-year average of the yearly distribution of 1-hour daily
maximum concentrations.
150 The statistical form of the 1-hour NAAQS for SO2 is the 3-year average of the 99th percentile of 1-hour daily
maximum concentrations.
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8.1.6 Air Toxics Concentrations
The most recent available data indicate that millions of Americans live in areas where air
toxics pose potential health concerns. (US EPA 2022) The levels of air toxics to which people are
exposed vary depending on where people live and work and the kinds of activities in which they
engage, as discussed in detail in EPA's 2007 Mobile Source Air Toxics Rule. (US EPA 2007)
According to EPA's Air Toxics Screening Assessment (AirToxScreen) for 2018, mobile sources
were responsible for 40 percent of outdoor anthropogenic toxic emissions and were the largest
contributor to national average cancer and noncancer risk from directly emitted pollutants. (US
EPA 2018)151 Mobile sources are also significant contributors to precursor emissions which react
to form air toxics. (Cook, et al. 2020) Formaldehyde is the largest contributor to cancer risk of all
71 pollutants quantitatively assessed in the 2018 AirToxScreen. Mobile sources were responsible
for 26 percent of primary anthropogenic emissions of this pollutant in 2018 and are significant
contributors to formaldehyde precursor emissions. Benzene is also a large contributor to cancer
risk, and mobile sources account for about 60 percent of average exposure to ambient
concentrations.
8.1.7 Deposition
Over the past two decades, the EPA has undertaken numerous efforts to reduce nitrogen and
sulfur deposition across the U.S. Analyses of monitoring data for the U.S. show that deposition
of nitrogen and sulfur compounds has decreased over the last 25 years. At 34 long-term
monitoring sites in the eastern U.S., where data are most abundant, average total nitrogen
deposition decreased by 43 percent between 1989-1991 and 2014-2016. (US EPA 2022)
Although total nitrogen deposition has decreased over time, many areas continue to be negatively
impacted by deposition.
8.2 Emissions Modeling for Illustrative Air Quality Analysis
Air pollution emission inventories are an important input to air quality modeling (AQM). This
section describes the modeled changes to onroad emissions from light- and medium-duty
vehicles, as well as modeled emission changes from "upstream" sectors like electricity
generating units (EGUs) and refineries. Emission inventories for unchanging sectors are detailed
in the air quality modeling technical support document (AQM TSD). (US EPA 2023)
For this analysis, air quality modeling was performed for a 2016 base case, a 2055 reference
case, and a 2055 light- and medium duty vehicle (LMDV) regulatory case. The "reference"
scenario represents projected 2055 emissions and air quality without any additional LMDV
controls. The proposal scenario had not been determined at the time of the inventory modeling
for the air quality analysis, so the "LMDV regulatory case" is illustrative and does not represent
the specifics of the proposed rule. The illustrative LMDV regulatory case assumes a light- and
medium-duty fleet that phased-in to reach 50 percent of new vehicle sales as BEVs in 2030 and
remained constant at about 50 percent BEVs sales for model years 2030-2055, for a total national
light duty vehicle population of 48% BEVs in 2055. The regulatory case also assumes a phase-in
151 AirToxScreen also includes estimates of risk attributable to background concentrations, which includes
contributions from long-range transport, persistent air toxics, and natural sources; as well as secondary
concentrations, where toxics are formed via secondary formation. Mobile sources substantially contribute to long-
range transport and secondarily formed air toxics.
8-5
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of gasoline particulate filters for gasoline vehicles beginning in model year 2027. The emissions
used for the 2055 LMDV regulatory case were the same as those in the 2055 reference scenario
for all emissions sectors except for onroad mobile source emissions, EGU emissions, and
petroleum sector emissions (specifically refineries, crude oil production well sites and natural
gas production well sites). The net changes in emissions for these sectors is summarized in Table
8-12 below. Air quality modeling was done for the future year 2055 when the LMDV regulatory
scenario would be fully implemented and when most of the regulated fleet would have turned
over.
The CMAQ air quality model requires hourly emissions of specific gas and particle species
for the horizontal and vertical grid cells contained within the modeled region (i.e., modeling
domain). Additional information on projecting air quality model-ready emissions is included in
the AQM TSD.
8.2.1 Onroad Vehicle Emission Estimates with MOVES
8.2.1.1 Overview
EPA's MOtor Vehicle Emission Simulator (MOVES) is a state-of-the-science emissions
modeling system that estimates air pollution emissions for criteria air pollutants, greenhouse
gases and air toxics. MOVES covers light, medium and heavy-duty onroad vehicles such as cars,
trucks, and buses, and other mobile sources. MOVES accounts for the phase-in of federal
emissions standards, vehicle and equipment activity, fuels, temperatures, humidity, and emission
control activities such as inspection and maintenance (I/M) programs (US EPA 2020). Unlike the
OMEGA model described elsewhere in the DRIA, MOVES can be used to estimate emissions
for specific counties as done here to capture geographical and temporal variation in onroad
vehicle emissions.
8.2.1.2 MOVES version used for air quality modeling
To generate the onroad emission inventories used for this illustrative air quality modeling
analysis, we updated the public MOVES3.0 model to create MOVES3.R1, an internal regulatory
version which incorporates the latest vehicle activity data, newer emission rules, and changes
that reflect improvements in our understanding of vehicle emissions and adds features to better
model electric vehicles. In particular, we updated light-duty energy consumption and light- and
heavy-duty national average electric vehicle fractions to reflect EPA's Revised 2023 and Later
Model Year Light-Duty Vehicle Greenhouse Gas Emissions Standards, (86 FR 74434 2021) and
CARB's Advanced Clean Trucks regulation. (Advanced Clean Trucks 2021) We did not consider
implications of the Inflation Reduction Act since our analysis was completed before the Act was
passed.
The changes to MOVES code and defaults from MOVES3 to MOVES3.R1 are detailed in a
docket memo. (Beardsley 2023) MOVES3.R1 updates were peer reviewed under EPA's peer
review policy. (US EPA 2015) (US EPA 2023) Developing onroad inventories for the LMDV
regulatory case required additional revised inputs as described in Section 8.2.1.3.
County-specific age distributions and fuel mix inputs were derived to preserve current
differences between counties, such that counties with newer-than-average vehicle fleets and
more light-duty electric vehicles than average in calendar year 2020 also have newer fleets and
more electric vehicles in calendar year 2055. Additional detail is provided in the AQM TSD.
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8.2.1.3 Modeling the Regulatory Case with MOVES
The regulatory case was modeled with a light- and medium-duty fleet that phased-in new
vehicle BEV sales to reach 50 percent in 2030 and remain constant at about 50 percent sales for
model years 2030-2055. We assumed no net improvement in average CO2 emissions for light-
duty vehicles; for HC and NOx emissions, we modeled an ICEV emission cap at model year
2026 levels. For PM, we modeled reduced LD gasoline vehicle organic carbon and elemental
carbon rates consistent with predicted impact of gasoline particulate filters based on OTAQ
literature review and testing as described in 8.2.1.3.4.1. More details on each of these changes
are provided below.
8.2.1.3.1 EV sales and stock
The regulatory case EV penetrations (fraction of new sales) for light-duty passenger cars and
light-duty trucks were modeled in MOVES based on OMEGA EV outputs for a pre-IRA
scenario. These penetrations are fleet wide BEV penetration by model years separately for light-
duty passenger cars and light-duty trucks. The passenger cars assume a constant BEV penetration
of 48.45% for model year 2030 and beyond, whereas light-trucks have a constant BEV new sales
penetration of 50.28% for model year 2030 and beyond.
For medium-duty class 2b and 3, the regulatory case was modeled assuming 55% of new sales
are BEVs or FCEVs for model year 2035 and beyond.
The distribution of EV sales among counties was similar to the reference case and is discussed
in detail in the AQM TSD.
Note that the impact of EV sales on vehicle age distributions was not modeled using MOVES.
8.2.1.3.2 ICEV Energy Consumption
For the regulatory case modeling, the ICEV energy rates (MY2027-MY2060) were adjusted
to match rates from OMEGA modeling of a scenario where EV sales were mandated as
described above, and ICEV rates were limited by light-duty fleet-wide averages that assume zero
tailpipe C02 g/mi for BEVs and allow averaging, banking, and trading between ICEVs and
electric vehicles. This meant that average light-duty ICEV fuel efficiency decreased from the
reference to the regulatory case. Energy consumption for medium-duty class 2b and 3 was
modeled the same as in the reference case.
8.2.1.3.3 ICEV NMOG and NOx rates
The regulatory case cap on HC and NOx emissions was modeled by modifying MOVES
inputs to indicate no averaging with electric vehicles. This effectively caps the emissions at the
model year 2026 rate.
8.2.1.3.4 ICEV PM rates
PM emissions reductions were modeled for light-duty gasoline vehicles for model years 2027
and later. The modeled reductions were based on present-day gasoline particulate filters (GPFs)
as the best current PM reduction technology. GPFs filter PM in the exhaust, thus directly
reducing PM emissions. The filter effectiveness differs for Elemental Carbon (EC) PM (MOVES
Pollutant ID 112) and for non-EC PM (MOVES pollutant ID 118). To model the addition of
GPFs, we apply a proportional reduction to the relevant start and running exhaust PM emission
8-7
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rates. In this case, the reductions are applied to start and running emissions for light-duty cars
and trucks for gasoline, diesel and E85 fuels (fuelTypelD in 1,2,5).152 For class 2b and 3 trucks,
the reductions were applied for gasoline trucks only. Note, for MY 2010 and later, the rates for
class 2b and 3 diesel trucks already included reductions representing control from diesel
particulate filters (DPFs).
8.2.1.3.4.1 PM emission reduction fractions
The reduction fractions applied to both elemental carbon (EC) and non-EC PM are derived
from laboratory testing of a lightly loaded underfloor catalyzed gasoline particulate filter. (Bohac
and Ludlum 2023) For that study, EC and organic carbon (OC) measurements were made using
the NIOSH 870 method. Here we use the observed reduction in EC to determine the reduction
fraction for the MOVES EC pollutant. We use the observed OC reduction as the reduction
fraction to apply to the MOVES NonECPM pollutant. OC is not identical to NonECPM because
OC measurements do not include information about other elemental components of the
particulate matter such as hydrogen, nitrogen, oxygen, calcium, and metallic ash components.
For modeling purposes, we assume that the other components of non-elemental PM are filtered
by the GPF in the same proportion as the OC part.
The reduction factors for the start operating modes come from the study's 25°C FTP cycle
tests. For running emissions excluding MOVES operating modes 30 and 40, the reduction factors
come from averaging the results of the 60mph and HWFET tests. The reduction fractions for
operating modes 30 and 40 are from the US06 test. Finally, to avoid computational issues that
arise from setting emission factors to zero, reductions originally reported as 100% were adjusted
to 99.9%. The final PM reductions by operating mode are summarized in Table 8-1 below
152 While GPFs are relevant only for gasoline and E85 vehicles, in MOVES, the emission rates for light-duty
gasoline vehicles are replicated to represent light-duty diesel. This has a negligible impact on calendar year 2055
emissions since we model the diesel fraction of the light-duty fleet as less than 0.002% for all model years after
2018."
8-8
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Table 8-1: PM reduction by MOVES operating mode
Operating Modes EC Reduction (%) nonECPM Reduction (%)
0 - 29
99.9
75
30
98.5
80
33 - 39
99.9
75
40
98.5
80
101 - 108
99.9
91
8.2.1.3.4.2 PM reduction phase-in
To model the air quality modeling regulatory case, we applied the PM reduction phase-in
fractions shown in Table 8-2.
Table 8-2: PM control fraction by MOVES reg class and model year
Model Year Ree Class 20 Ree Class 30 Ree Class 41
2026 0 0 0
2027 0.5 0.25 0
2028 0.75 0.375 0
2029 1 0.5 0
2030+ 1 1 1
The phase-in was combined with the reduction factors for each operating mode to create
weighted reduction factors for each model year. Finally, the weighted reduction factors were
applied to the original MOVES base emission rates to create a set of new, lower PM emission
rates.
8.2.1.3.4.3 PM update for LEV rates
The phase-in described above overlaps with the California 1 mg/mile PM standard that is
relevant for California and for other states that have adopted California requirements under
Clean Air Act Section 177. Prior to phasing in GPF-equivalent PM rates, the rates in the
MOVES emissionRateByAgeLEV table were lower than the rates in the MOVES default
emissionRateByAge table. For passenger cars, the default values are lower than the LEV table
values starting in model year 2027, and for light-duty trucks, the default values are lower starting
in 2030. Therefore, for the regulatory case modeling, the emissionRateByAgeLEV table was
updated by dropping the rates for those years where the new default emission rates are lower
than the LEV rates.
8.2.2 Upstream Emission Estimates for AO Modeling
This section describes emission estimates for the following "upstream" emission sources:
EGU emissions (Chapter 8.2.2.1), refinery emissions (Chapter 8.2.2.2), emissions from crude oil
production well sites and pipeline pumps (Chapter 8.2.2.3), and emissions from natural gas
production well sites and pipeline pumps (Chapter 8.2.2.4). These are sources that change
between the reference and LMDV regulatory case and are not onroad. The EGU emissions were
modeled without including impacts from the IRA, and thus are larger than what we would expect
if the IRA generation mix updates were included in the reference case.
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In 2055, total upstream emissions in the LMDV regulatory case would be between 0.3% and
4.4% higher, depending on the pollutant, than the reference. This increase is driven by the
increase in emissions from EGUs, but there are also increased emissions projected from natural
gas production well sites and pipeline pumps, due to a projected increase in natural gas fueled
EGUs. We also project a small decrease in emissions from refineries and crude production wells
and pipeline pumps due to assumed activity decreases at refineries related to a decrease in
demand for liquid fuels for light- and medium-duty vehicles. Table 8-3 presents the net impact of
these emissions increases and decreases.
Table 8-3: Total upstream emissions increases in LMDV regulatory scenario in 2055
LMDV
Regulatory
Scenario
(tons/yr)
94,533 2,174
933,078 ] ] 12.130 1.3%
340,772 14,279 4.4%
2,770,666 I 8,544 0.3%
Reference
Pollutant
Scenario
(tons/yr)
PM2.5
92.358
NOx
920.948
SO2
326.492
voc
2.762.121
Emissions
Increase
(tons/yr)
%
Difference
2.4%
There is uncertainty about the impact of reduced demand for petroleum fuels on refinery
activity and emissions. For instance, refineries might export the volumes of gasoline and diesel
fuel that would otherwise have been consumed in light- and medium-duty vehicles, absent this
rulemaking. The illustrative air quality analysis assumes a decrease in refining activity in
response to the reduced domestic demand for liquid fuels; however, Table 8-4 presents the net
upstream emissions impacts if we had assumed no decrease in refinery activity.
Table 8-4: Total upstream emission increases in 2055 assuming no change in refinery
emissions
LMDV Scenario
No Decrease in
Refinery
Activity (tons/yr)
94,920 2,561 2.8%
934.481 J13,533 "7 = T L5% i
341,342 14,850 4.5%
2,771,736 9,615 I 0.3%
Reference
Pollutant
Scenario
(tons/yr)
PM2.5
92.358
NOx
920.948
SO2
326.492
VOC
2.762.121
Emissions
Increase
(tons/yr)
%
Difference
8.2.2.1 Electricity Generating Units (EGUs)
The EGU emissions inventories used in the illustrative air quality analysis were developed
from output of the 2022 Reference Case run of the Integrated Planning Model (IPM). This
version of IPM included EGU fleet information, and rules and regulations that were final at the
time the IPM version was finalized, but not impacts due to the Inflation Reduction Act (IRA).153
153 https://www.epa.gov/power-sector-modeling
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More detail on the rules and regulations included in this version of IPM, as well as additional
information on the IPM version, can be found in the AQM TSD. The TSD also includes a
description of inputs to IPM used for the AQM analysis, including how electricity demand from
PEVs was distributed by time of day.154
Emissions of select pollutants from EGUs in 2050 (representing 2055 levels) are shown in
Table 8-5. The LMDV regulatory case causes an increase in all pollutants, which is expected as
the regulatory scenario includes an increase in electric vehicles over what is included in the
reference case. The IPM runs used in the proposal's OMEGA analysis were started after the IPM
runs used for the air quality analysis and were able to account for some IRA impacts (see
Chapter 5.2.3 for more detail). The IPM runs used in the OMEGA analysis projected EGU
emissions in 2050 that are much smaller than what was projected for the illustrative air quality
analysis (see Table 5-2 in this DRIA).
Table 8-5: EGU emissions increases in AQM inventories in 2055
155
Reference
Pollutant
Scenario
(tons/yr)
PM2.S
54.589
NOx
232.63 1
SO2
197.668
VOC
32.493
LMDV
Regulatory
Scenario
(tons/yr)
57,033
243.010
212.643
34.065
Emissions
Increase
(tons/yr)
2.444
10.379
14.975
1.572
% Difference
4%
4%
8%
5%
8.2.2.2 Refineries
The refinery emission inventories used in the illustrative air quality analysis were developed
from refinery emissions in the 2016v2 emissions modeling platform that were projected to 2050
using the reference case modeled by EIA in its 2021 Annual Energy Outlook (AEO). (US EIA
2021)156 (US EPA 2022) Pollutant-specific adjustment factors were developed and then applied
to the reference inventory to generate the reference scenario and the LMDV regulatory scenario
inventory. These adjustment factors are presented in Table 8-6 and account for impacts on fuel
demand that were not included in AEO2021. In the reference case the adjustment factors
incorporated EPA's Revised 2023 and Later Model Year Light-Duty Vehicle Greenhouse Gas
Emissions Standards (86 FR 74434, December 30, 2021). In the LMDV regulatory scenario the
adjustment factors incorporated assumptions about decrease in fuel demand due to the reduced
demand for liquid fuel.
The relationship between AEO2021's low economic growth case and reference case was used
to approximate the impact on the petroleum sector of reduced demand for refined products in
2050. The lower refined product demand of the low economic growth case is assumed to reflect
154 The regional charging load profiles described in DRIA Chapter 5 were not available at the time the IPM model
runs were conducted for the AQM analysis. See the AQM TSD for a description of charging profiles used.
155 IPM output for a set of years with the furthest out year being 2055. The 2050 output was used in the air quality
analysis and was assumed to represent 2055 to avoid any "end of timeframe" issues with using the furthest out year
output from the model.
156 https://www.eia.gov/outlooks/aeo/
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the types of changes that could be expected from the lower demand for refined product caused by
the LMDV regulatory scenario. The impact on refinery and upstream crude oil production
activity as modeled by EIA results in decreases in crude oil and refined product imports and is
used to project how the domestic U.S. refining sector would be impacted by reductions in
domestic demand for gasoline and diesel. Additional detail on how the adjustment factors were
calculated is available in the AQM TSD.
Table 8-6: Adjustment factors to apply to 2050 refinery inventory
Pollutant
Reference
LMDV
Scenario
Regulatory
Scenario
PM2.S
0.897
0.877
NOx
0.899
0.879
SO2
0.901
0.881
VOC
0.906
0.887
Emissions decreases of select pollutants from refineries in 2055 are shown in Table 8-7. We
recognize that there is significant uncertainty in the impact on refinery emissions due to
decreased demand. If refineries do not decrease production in response to lower domestic
demand (e.g., they increase exports), total upstream emissions impacts would be higher, as
shown in Table 8-4.
Table 8-7: Refinery emissions decreases in AQM inventories in 2055
Reference
Pollutant
Scenario
(tons/yr)
PM2.3
18.855
NOx
67.470
SO2
28.851
VOC
56.946
LMDV
Regulatory
Scenario
(tons/yr)
18.468
66.067
28.281
55.876
Emissions
Decrease
(tons/yr)
387
1.403
570
1.070
% Difference
2%
2%
2%
2%
8.2.2.3 Crude Production Well Sites and Pipeline Pumps
The emission inventories for crude production well sites and associated pipeline pumps used
in the illustrative air quality analysis were developed from emissions in the 2016v2 emissions
modeling platform that were projected to 2050 using the reference case modeled by EIA in its
2021 Annual Energy Outlook (AEO). (US EIA 2021) 157 (US EPA 2022) Emissions were
decreased (through application of an adjustment factor) to account for lower activity due to lower
domestic demand for liquid fuels. The adjustment factors are presented in Table 8-8. Additional
detail on how the adjustment factors were calculated is available in the AQM TSD.
Table 8-8: Adjustment factors to apply to 2050 crude production well and pipeline pump
inventory
157 https://www.eia.gov/outlooks/aeo/
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Reference LMDV
Scenario Regulatory
Scenario
0.992 0.990
Decreases in emissions of select pollutants from crude production well sites and pipeline
pumps in 2055 are shown in Table 8-9.
Table 8-9: Crude production well site and pipeline pump decreases in AQM inventories in
2055
LMDV Emissions
Regulatory Decrease
•s . u i \ % Difference
Scenario (tons/yr)
(tons/yr)
4.814 10 0.2%
220,800 442 0.2%
93.016 186 0.2%
1.452,639 2,911 0.2%
Reference
Pollutant
Scenario
(tons/yr)
PM2.S
4.824
NOx
221.243
SO2
93.203
VOC
1.455.550
8.2.2.4 Natural Gas Production Well Sites and Pipeline Pumps
The emission inventories for natural gas production well sites and associated pipeline pumps
used in the illustrative air quality analysis were developed from emissions in the 2016v2
emissions modeling platform that were projected to 2050 using the reference case modeled by
EIA in its 2021 Annual Energy Outlook (AEO). (US EIA 2021) 158 (US EPA 2022) Emissions
were increased (through application of an adjustment factor) to account for increased activity at
natural gas production well sites and pipeline pumps consistent with increased demand for
natural gas fueled EGUs. These adjustment factors are presented in Table 8-10. Additional detail
on how the adjustment factors were calculated is available in the AQM TSD.
Table 8-10: Adjustment factors to apply to 2050 natural gas production well site and
pipeline pump inventory
Reference LMDV
Scenario Regulatory
Scenario
1.000 1.009
Increases in emissions of select pollutants from natural gas production well sites and pipeline
pumps in 2055 are shown in Table 8-9.
Table 8-11: Natural gas production well and pipeline pump increases in AQM inventories
in 2055
LMDV Emissions
Regulatory Increase
Scenario (tons/yr)
(tons/yr)
Pollutant
Reference
Scenario
(tons/yr)
% Difference
158 https://www.eia.gov/outlooks/aeo/
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PM2.S
NOx
SO2
VOC
18.855
67.470
28.851
56.946
18.468
66.067
28.281
55.876
127
3.596
61
10.954
2%
2%
2%
2%
8.2.2.5 Limitations of the Upstream Inventory
There is considerable uncertainty with the upstream inventory (and thus the air quality
modeling results) because it does not include impacts of the IRA on generation mix projections,
which would impact the magnitude and location of EGU emissions as well as the projected
natural gas production well site inventories. Incorporating IRA generation mix updates would
decrease emissions from EGUs in the reference case, as turnover to cleaner power generation
would be accelerated. Additionally, emission increases from EGUs and natural gas production
wells in the regulatory case would be smaller due to increased availability of cleaner powered
EGUs. As described in 8.2.2.1, the IPM runs used in the proposal's OMEGA analysis were
started after the IPM runs used for the air quality analysis and were able to account for some IRA
impacts, and projected proposal EGU emissions are much smaller than what was projected for
the illustrative air quality analysis (see Table 5-2 and Table 8-5 in this DRIA).
The illustrative air quality analysis assumes that there is no change in mandated renewable
fuel volumes and percentages, that refineries will decrease activity rather than export additional
fuels, and that the decreased production occurs at the same rate at all refineries. In addition,
projections out to 2055 inherently are less certain than projections that do not go out as far into
the future.
The upstream emissions inventory does not account for all upstream sources related to
vehicles, fuels, and electricity generation, such as charging infrastructure, storage of petroleum
fuels, battery manufacture, etc.
8.2.3 Combined Onroad and Upstream Emission Impacts
Total onroad, upstream, and net emissions of select pollutants in 2055 are shown in Table
8-12. The LMDV regulatory case has less combined onroad and upstream emissions than the
reference case for many pollutants, including PM2.5, NOx, and VOC, and more combined onroad
and upstream emissions of SO2. The net decreased emissions of PM2.5, NOx and VOC are driven
by reductions in the onroad sector, while EGU emissions drive the net increase in SO2 emissions.
We expect that had we been able to include impacts of the IRA provisions that affect generation
mix in the IPM runs used to generate EGU emissions for this air quality analysis, increases in
SO2 would be smaller or on net a decrease. DRIA Chapter 9.6.6 presents emissions impacts of
the proposal that account for the IRA's projected impacts on the power sector, and these do show
a net decrease in all pollutants in 2055.
Table 8-12: Net impacts3 on criteria pollutant emissions from the LMDV regulatory
scenario
2055 AQM Reference Scenario (tons/vr)
2055 AQM LMDV Regulatory Scenario
(tons/vr)
Net Percent
Emissions Change
Impact ; Emissions
(tons/vr) Impact
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Total
Total
Pollutant
Onroad
Upstream
Onroad and
Upstream
Onroad
Upstream
Onroad and
Upstream
PM2.5
35.737
92.358
128.096
26.833
94.533
121.365
-6.730
-6%
NOx
729.707
920.948
1.650.655
683.096
933.078
1.616.174
-34.481
-2%
S02
7.280
326.492
333.772
7.112
340.772
347.884
14.111
4%
VOC
498.495
2.762.121
3.260.616 ;
392.534
2.770.666
; 3.163.200
-97.417
-3%
a Emissions reductions are presented as negative numbers and emissions increases as positive numbers.
8.3 Air Quality Modeling Methodology
In this section we present information related to the methods used in the air quality analysis
for this proposed rule. Additional information is available in the Air Quality Modeling Technical
Support Document (AQM TSD). (US EPA 2023)
8.3.1 Air Quality Model
CMAQ is a non-proprietary computer model that simulates the formation and fate of
photochemical oxidants, primary and secondary PM concentrations, acid deposition, and air
toxics, over regional and urban spatial scales for given inputs of meteorological conditions and
emissions. CMAQ includes numerous science modules that simulate the emission, production,
decay, deposition and transport of organic and inorganic gas-phase and particle pollutants in the
atmosphere. The CMAQ model is a well-known and well-respected tool and has been used in
numerous national and international applications.159 The air quality modeling completed for the
rulemaking proposal used the 2016v2 platform with the most recent multi-pollutant CMAQ code
available at the time of air quality modeling (CMAQ version 5.3.2).160 The 2016 CMAQ runs
utilized the CB6r3 chemical mechanism (Carbon Bond with linearized halogen chemistry) for
gas-phase chemistry, and AER07 (aerosol model with non-volatile primary organic aerosol) for
aerosols. The CMAQ model is regularly peer-reviewed, CMAQ versions 5.2 and 5.3 beta were
most recently peer-reviewed in 2019 for the U.S. EPA. (Versar, Inc 2019)
8.3.2 Model Domain and Configuration
The CMAQ modeling analyses used a domain covering the continental United States, as
shown in Figure 8-5. This single domain covers the entire continental U.S. (CONUS) and large
portions of Canada and Mexico using 12 km x 12 km horizontal grid spacing. The 2016
simulation used a Lambert Conformal map projection centered at (-97, 40) with true latitudes at
33 and 45 degrees north. The model extends vertically from the surface to 50 millibars
(approximately 17,600 meters) using a sigma-pressure coordinate system with 35 vertical layers.
Table 8-13 provides some basic geographic information regarding the CMAQ domains and
Table 8-14 provides the vertical layer structure for the CMAQ domain.
Table 8-13: Geographic elements of domains used in air quality modeling
CMAQ Modeling Configuration
Grid Resolution 12 km National Grid
Map Projection Lambert Conformal Projection
Coordinate Center 97 dcg W. 40 dcg N
159More information available at: https://www.epa.gov/cmaq.
160 Model code for CMAQ v5.3.2 is available from the Community Modeling and Analysis System (CMAS) at:
http://www.cmascenter.org.
8-15
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True Latitudes
Dimensions
Vertical extent
8-16
33 dcg N and 45 dcg N
396 x 246 x 35
35 Layers: Surface to 50 millibar level
(see Table 8-14)
-------
Table 8-14: Vertical layer structure for CMAQ domain
Vertical
Sigma P
Pressure
Approximate
Layers
(mb)
Height (m)
35
0.0000
50.00
17.556
34
0.0500
97.50
14.780
33
0.1000
145.00
12.822
32
0.1500
192.50
11.282
31
0.2000
240.00
10.002
30
0.2500
287.50
8.901
29
0.3000
335.00
7,932
28
0.3500
382.50
7.064
27
0.4000
430.00
6,275
26
0.4500
477.50
5,553
25
0.5000
525.00
4.885
24
0.5500
572.50
4.264
23
0.6000
620.00
3.683
22
0.6500
667.50
3.136
21
0.7000
715.00
2.619
20
0.7400
753.00
2.226
19
0.7700
781.50
1.941
18
0.8000
810.00
1.665
17
0.8200
829.00
1.485
16
0.8400
848.00
1.308
15
0.8600
867.00
1.134
14
0.8800
886.00
964
13
0.9000
905.00
797
12
0.9100
914.50
714
11
0.9200
924.00
632
10
0.9300
933.50
551
9
0.9400
943.00
470
8
0.9500
952.50
390
7
0.9600
962.00
3 11
6
0.9700
971.50
232
5
0.9800
981.00
154
4
0.9850
985.75
115
3
0.9900
990.50
77
2
0.9950
995.25
38
1
0.9975
997.63
19
0
1.0000
1000.00
0
8-17
-------
12US2 domain
x,y origin: -24120001
col: 396 row:246 i
Figure 8-5: Map of the CMAQ 12 km modeling domain (noted by the purple box)
8.3.3 Model Inputs
The key inputs to the CMAQ model include emissions from anthropogenic and biogenic
sources, meteorological data, and initial and boundary conditions. The emissions inputs are
summarized above in Chapter 8.2.
The CMAQ meteorological input files were derived from simulations of the Weather
Research and Forecasting Model (WRF) version 3.8 for the entire 2016 year. (Skamarock 2008)
(US EPA 2019) The WRF Model is a state-of-the-science mesoscale numerical weather
prediction system developed for both operational forecasting and atmospheric research
applications. (National Center for Atmospheric Research 2022) The meteorological outputs from
WRF were processed to create 12 km model-ready inputs for CMAQ using the Meteorology-
Chemistry Interface Processor (MCIP) version 4.3. These inputs included hourly varying
horizontal wind components (i.e., speed and direction), temperature, moisture, vertical diffusion
rates, and rainfall rates for each grid cell in each vertical layer. (Byun, Ching and EPA 1999)
The boundary and initial species concentrations were provided by a northern hemispheric
CMAQ modeling platform for the year 2016. (Henderson 2018) (Mathur 2017) The hemispheric-
scale platform uses a polar stereographic projection at 108 km resolution to completely and
8-18
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continuously cover the northern hemisphere for 2016. Meteorology is provided by WRF v3.8.
Details on the emissions used for hemispheric CMAQ can be found in the 2016 hemispheric
emissions modeling platform TSD. (US EPA 2019) The atmospheric processing (transformation
and fate) was simulated by CMAQ (v5.2.1) using the CB6r3 and the aerosol model with non-
volatile primary organic carbon (AE6nvPOA). The CMAQ model also included the on-line
windblown dust emission sources (excluding agricultural land), which are not always included in
the regional platform but are important for large-scale transport of dust.
8.3.4 Model Evaluation
The CMAQ predictions for ozone, fine particulate matter, sulfate, nitrate, ammonium, organic
carbon, elemental carbon, nitrogen and sulfur deposition, and specific air toxics (acetaldehyde,
benzene, and formaldehyde) from the 2016 base scenario were compared to measured
concentrations in order to evaluate the ability of the modeling platform to replicate observed
concentrations. This evaluation was comprised of statistical and graphical comparisons of paired
modeled and observed data. Details on the model performance evaluation, including a
description of the methodology, the model performance statistics, and results, are provided in the
AQM TSD. (US EPA 2023)
8.3.5 Model Simulation Scenarios
As part of our analysis for this rulemaking, the CMAQ modeling system was used to calculate
annual PM2.5 concentrations, 8-hour maximum average ozone season concentrations, annual
NO2, SO2, and CO concentrations, annual and seasonal (summer and winter) air toxics
concentrations, and annual nitrogen and sulfur deposition for each of the following emissions
scenarios:
• 2016 base year
• 2055 reference
• 2055 light and medium duty regulatory scenario
We use the predictions from the CMAQ model in a relative sense by combining the 2016
base-year predictions with predictions from each future-year scenario and applying these
modeled ratios to ambient air quality observations to estimate 8-hour ozone concentrations
during the ozone season (May - Sept), daily and annual PM2.5 concentrations, and visibility
impairment for each of the 2055 scenarios. The ambient air quality observations are average
conditions, on a site-by-site basis, for a period centered around the model base year (i.e., 2014-
2018).
The projected annual PM2.5 concentrations were calculated using the Speciated Modeled
Attainment Test (SMAT) approach that utilizes a Federal Reference Method (FRM) mass
construction methodology which results in reduced nitrates (relative to the amount measured by
routine speciation networks), higher mass associated with sulfates (reflecting water included in
FRM measurements), and a measure of organic carbonaceous mass that is derived from the
difference between measured PM2.5 and its non-carbon components. This characterization of
PM2.5 mass also reflects crustal material and other minor constituents. The resulting
characterization provides a complete mass balance. It does not have any unknown mass that is
sometimes presented as the difference between measured PM2.5 mass and the characterized
8-19
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chemical components derived from routine speciation measurements. However, the assumption
that all mass difference is organic carbon has not been validated in many areas of the U.S. The
SMAT methodology uses the following PM2.5 species components: sulfates, nitrates, ammonium,
organic carbon mass, elemental carbon, crustal, water, and blank mass (a fixed value of 0.5
|ig/m3). More complete details of the SMAT procedures can be found in the report "Procedures
for Estimating Future PM2.5 Values for the CAIR Final Rule by Application of the (Revised)
Speciated Modeled Attainment Test (SMAT)." (US EPA 2004) For this analysis, several datasets
and techniques were updated. These changes are fully described within the technical support
document for the Final Transport Rule AQM TSD. (US EPA 2011)
Additionally, we conducted an analysis to compare the absolute differences between the
future year reference and regulatory scenario for annual and seasonal acetaldehyde, benzene,
formaldehyde, and naphthalene, as well as annual NO2, SO2, CO, and nitrate/sulfate deposition.
These data were not compared in a relative sense due to the limited observational data available.
8.4 Results of Illustrative Air Quality Analysis
EPA conducted an illustrative air quality modeling analysis of a regulatory scenario involving
light- and medium-duty "onroad" vehicle emission reductions and corresponding changes in
"upstream" emission sources like EGU (electric generating unit) emissions and refinery
emissions. Decisions about the emissions and other elements used in the air quality modeling
were made early in the analytical process for the proposed rulemaking. Accordingly, the air
quality analysis does not represent the proposal's regulatory scenario, nor does it reflect the
expected impacts of the Inflation Reduction Act (IRA). Based on updated power sector modeling
that incorporated expected generation mix impacts of the IRA (presented in Chapter 5), we are
projecting the IRA will lead to a significantly cleaner power grid; because the air quality analysis
presented here does not account for these impacts on EGU emissions, the location and magnitude
of the changes in pollutant concentrations should be considered illustrative and not viewed as
Agency projections of what we expect will be the total impact of the proposed standards.
Nevertheless, the analysis provides some insights into potential air quality impacts associated
with emissions increases and decreases from these multiple sectors.
Given the considerable uncertainty associated with the upstream emissions inventory (see
Chapter 8.2.2.5), we also modeled a sensitivity case that examined only the air quality impacts of
the onroad emissions changes from the LMDV regulatory scenario. This "onroad-only"
sensitivity case assumed no change in emissions from upstream sources and is based on the
onroad emission inventories described in Chapter 8.2.1.
In this section, we summarize the results of our illustrative air quality modeling based on the
projected emission impacts of the LMDV regulatory scenario as well as the onroad-only
sensitivity case. Air quality modeling was done for the future year 2055 when the program would
be fully implemented and when most of the regulated fleet would have turned over. The
"reference" scenario represents projected 2055 air quality without the illustrative regulatory
scenario and the "control" scenario represents projected 2055 emissions with the illustrative
LMDV regulatory scenario. As described in Chapter 8.2, the illustrative LMDV regulatory
scenario assumes a light- and medium-duty fleet that phased-in to reach 50 percent BEV sales in
2030 and remained constant at 50 percent sales for model years 2030-2055 and a phase-in of
gasoline particulate filters beginning in model year 2027.
8-20
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8.4,1 PMa.5
This section summarizes projected changes in PM2.5 concentrations in 2055 from the LMDV
regulatory scenario. As noted in Chapter 8.4, this analysis is illustrative and the location and
magnitude of concentration changes between the reference case and the LMDV regulatory
scenario are uncertain, particularly because the analysis does not account for the cleaner power
grid we expect to result from the IRA. Figure 8-6 presents the absolute changes in annual
average PM2.5 concentrations in 2055 between the reference and LMDV regulatory scenarios and
indicates that there would be widespread decreases, and in some areas there would be increases.
I >0.070
0.050
0.030
0.010 m
0.000
ai
-0.010 3
1-0.030
-0.050
<-0.070
Figure 8-6: Projected illustrative changes in annual average PM2.5 concentrations in 2055
due to LMDV regulatory scenario.
The LMDV regulatory scenario would decrease annual average PM2.5 concentrations by an
average of 0.01 ug/m ! in 2055, with a maximum decrease of 0.16 ug/'nr and a maximum
increase of 0.27 pg/'m3. The population-weighted average change in annual average PM2.5
concentrations would be 0.03 pg/m3 in 2055.
We also modeled an "onroad-only" sensitivity case. Figure 8-7 presents the absolute changes
in annual average PM2.5 concentrations in 2055 between the reference and onroad-only
sensitivity scenarios. This demonstrates that annual average PM2.5 concentrations would decrease
across much of the country when considering only onroad vehicle emissions (i.e., without
including any changes to emissions from upstream sources like EGUs and refineries).
8-21
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l >0.070
*0.050
0.030
0.010 m
0.000 -I
cn
-0.010 =>
-0.030
I —-0.050
<-0.070
Figure 8-7: Projected illustrative changes in annual average PM2.5 concentrations in 2055
from "onroad-only" emissions changes.
When only the onroad emissions impacts of the LMDV regulatory scenario are considered,
annual average PM2.5 concentrations would decrease by an average of 0.01 pg/rn3 in 2055, with a
maximum decrease of 0.14 j.ig/nr\ The population-weighted average change in annual average
PM2.5 concentrations would be 0.04 pg/m3 in 2055.
8.4.2 Ozone
This section summarizes projected changes in ozone concentrations in 2055 from the LMDV
regulatory scenario. As noted in Chapter 8.4, this analysis is illustrative and the location and
magnitude of concentration changes between the reference case and the LMDV regulatory
scenario are uncertain, particularly because the analysis does not account for the cleaner power
grid we expect to result from the IRA. Figure 8-8 presents the absolute changes in 8-hour ozone
maximum average concentrations over the ozone season (April - September) in 2055 between
the reference and LMDV regulatory scenarios and indicates that there would be widespread
decreases, and in some areas there would be increases.
8-22
-------
>0.30
0.20
0.10
0.05
>
0.00 -g_
Q.
-0.05
-0.10
-0.20
<-0.30
Figure 8-8: Projected illustrative changes in 8-hour maximum average ozone
concentrations in 2055 due to LMDV regulatory scenario.
The LMDV regulatory scenario would decrease 8-hour maximum average ozone
concentrations by an average of 0.04 ppb in 2055, with a maximum decrease of 0.90 ppb and a
maximum increase of 1.36 ppb. The population-weighted average change in 8-hour maximum
average ozone concentrations would be 0.10 ppb in 2055.
We also modeled an "onroad-only" sensitivity case. Figure 8-7 presents the absolute changes
in 8-hour maximum average ozone concentrations in 2055 between the reference and onroad-
only sensitivity scenarios. This demonstrates that ozone concentrations would decrease across
much of the country when considering only onroad vehicle emissions (i.e., without including any
changes to emissions from upstream sources like EGUs and refineries).
8-23
-------
Figure 8-9: Projected illustrative changes in 8-hour maximum average ozone
concentrations in 2055 from "onroad-only" emissions changes.
When only the onroad emissions impacts of the LMDV regulatory scenario are considered, 8-
hour maximum average ozone concentrations would decrease by an average of 0.05 ppb in 2055,
with a maximum decrease of 0.91 ppb. The population-weighted average change in 8-hour
maximum average ozone concentrations would be 0.11 ppb in 2055.
8.4.3 NO?
This section summarizes projected changes in NO2 concentrations in 2055 from the LMDV
regulatory scenario. As noted in Chapter 8.4, this analysis is illustrative and the location and
magnitude of concentration changes between the reference case and the LMDV regulatory
scenario are uncertain, particularly because the analysis does not account for the cleaner power
grid we expect to result from the IRA. Figure 8-10 presents the absolute changes in annual
average NO2 concentrations in 2055 and indicates that there would be widespread decreases, and
in some areas there would be increases.
8-24
-------
Figure 8-10: Projected illustrative changes in annual average NO2 concentrations in 2055
due to LMDV regulatory scenario.
We also modeled an "onroad-only" sensitivity case. Figure 8-11 presents the absolute changes
in annual average NO2 concentrations in 2055 between the reference and onroad-only sensitivity
scenarios. This demonstrates that NO2 concentrations would decrease across much of the country
when considering only onroad vehicle emissions (i.e., without including any changes to
emissions from upstream sources like EGUs and refineries).
Figure 8-11: Projected illustrative changes in annual average NO2 concentrations in 2055
from "onroad-only" emissions changes.
8-25
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8.4,4 S02
This section summarizes projected changes in SO2 concentrations in 2055 from the LMDV
regulatory scenario. As noted in Chapter 8.4, this analysis is illustrative and the location and
magnitude of concentration changes between the reference case and the LMDV regulatory
scenario are uncertain, particularly because the analysis does not account for the cleaner power
grid we expect to result from the IRA. Figure 8-12 presents the absolute changes in annual
average SO2 concentrations in 2055 and indicates that in some areas there would be decreases
and in some areas there would be increases.
¦ V|-
\
tJ0.050
0.010
0.005
0.001
0.000
-0.001
-0.005
-0.010
<-0.050
>
JD
Q.
Max: 0.2436 Min:-0.0849 > \ f ,s \
Figure 8-12: Projected illustrative changes in annual average SO2 concentrations in 2055
due to LMDV regulatory scenario.
We also modeled an "onroad-only" sensitivity case. Figure 8-13 presents the absolute changes
in annual average SO2 concentrations in 2055 between the reference and onroad-only sensitivity
scenarios. This demonstrates that SO2 concentrations would decrease in a few areas of the
country when considering only onroad vehicle emissions (i.e., without including any changes to
emissions from upstream sources like EGUs and refineries).
8-26
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if
Max: 0.0019 M
in' -0.0fl3
fjf'
// \
J ¦'
jf! \ jv# \ 'L_
*7*
X—jrfS
f
'L
¦&}
'V>'5
I
f
f
I
W ' £v
I
>0.050
0.010
0.005
0.001
0.000 ]
1
-0.001
-0.005
-0.010
<-0.050
Figure 8-13: Projected illustrative changes in annual average SO2 concentrations in 2055
from "onroad-only" emissions changes.
8.4.5 Air Toxics
This section summarizes projected changes in concentrations of select air toxics in 2055 from
the LMDV regulatory scenario. As noted in Chapter 8.4, this analysis is illustrative and the
location and magnitude of concentration changes between the reference case and the LMDV
regulatory scenario are uncertain, particularly because the analysis does not account for the
cleaner power grid we expect to result from the IRA. Figure 8-14 to Figure 8-17 present the
absolute changes in annual average acetaldehyde, benzene, formaldehyde, and naphthalene
concentrations in 2055 between the reference and LMDV regulatory scenarios and indicates that
there would be widespread decreases, and in some areas there would be increases.
8-27
-------
-0.005
-0.010
<-0.050
Max: 0.0206 Min: -0.0329
Figure 8-14: Projected illustrative changes in annual average acetaldehyde concentrations
in 2055 due to LMDV regulatory scenario.
I
r-_
Max: 0.0131 Min: -0.0153
N ^ \ I
\ I '
/
I
j,
M fj
r
>, W'
W 0
J ) Ji'v'?
'V'1— T \
^ \ j.X
v-
-*r
#
./
f
V. \
) . j'-,
v ¦
-Ml
>0.050
0.010
0.005
0.001
0.000 ¦§_
-0.001
-0.005
0.010
<-0.050
Figure 8-15: Projected illustrative changes in annual average benzene concentrations in
2055 due to LMDV regulatory scenario.
8-28
-------
<-0.050
j\. V\ /I f
1 S3 »« %J i ; "W; I
Max: 0.1275 Min:-0.0209 > \ f < % '1.3. 35 %-VI
Figure 8-16: Projected illustrative changes in annual average formaldehyde concentrations
in 2055 due to LMDV regulatory scenario.
$
—
)
\
Max: 0.0 Min:-0.0015\ > ¦
1 ' ^ \! >i
w
L,--< ¦
l)
mk)
w
xy
"4 &V
- ¦-*' S
I
>3.00e-03
2.00e-03
1.00e-03
1.00e-04
0.00e+00
-1.00e-04
-1.00e-03
-2.00e-03
<-3.00e-03
Figure 8-17: Projected illustrative changes in annual average naphthalene concentrations
in 2055 due to LMDV regulatory scenario.
We also modeled an "onroad-only" sensitivity case. Figure 8-18 through Figure 8-20 present
the absolute changes in annual average air toxic concentrations in 2055 between the reference
and onroad-only sensitivity scenarios. This demonstrates that annual average air toxics
concentrations would decrease across much of the country when considering only onroad vehicle
8-29
-------
emissions (i.e., without including any changes to emissions from upstream sources like EGUs
and refineries).
Max: -0.0 Min: -0.033 \
Figure 8-18: Projected illustrative changes in annual average acetaldehyde concentrations
in 2055 from "onroad-only" emissions changes.
1
/ i-J
:
T- •
x -'A
I
J J
if p
$ I
f \)51 Vi
P
J
f
\i :v
'-•4, - vj
I
>0.050
0.010
0.005
0.001
0.000 -g_
-0.001
-0.005
-0.010
<-0.050
Max:-0.0 Min:-0.0152':, f . -1 ';-\J
Figure 8-19: Projected illustrative changes in annual average benzene concentrations in
2055 from "onroad-only" emissions changes.
8-30
-------
7f
; if
\
{
\
X X \
Max: le-04 Min:-0.0173
-.J
„s%
I
>0.050
0.010
0.005
0.001
0.000 •§_
-0.001
-0.005
-0.010
<-0.050
Figure 8-20: Projected illustrative changes in annual average formaldehyde concentrations
in 2055 from "onroad-only" emissions changes.
M
&
\ ¦ T
\ s
¦>. _ a -; j\
-M
; m
'W
? i
-A
J •' ^
Max: 0.0 Min: -0.0015
f
) - n
I
>3.00e-03
2.00e-03
1.00e-03
rl.00e-04
r
0.00e+00 J
-1.00e-04
. -1.00e-03
I -2.00e-03
l<-3.00e-03
Figure 8-21: Projected illustrative changes in annual average naphthalene concentrations
in 2055 from "onroad-only" emissions changes.
8-31
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8.4.6 Deposition
This section summarizes projected changes in nitrogen (N) and sulfur (S) deposition in 2055
from the LMDV regulatory scenario. As noted in Chapter 8.4, this analysis is illustrative and the
location and magnitude of concentration changes between the reference case and the LMDV
regulatory scenario are uncertain, particularly because the analysis does not account for the
cleaner power grid we expect to result from the IRA.
Figure 8-22 presents the absolute changes in annual N deposition in 2055 and indicates that
there would be widespread decreases, and in some areas there would be increases. Figure 8-23
presents the absolute changes in annual S deposition in 2055 and indicates that in some areas
there would be increases.
Figure 8-22: Projected illustrative changes in annual nitrogen deposition in 2055 due to
LMDV regulatory scenario.
8-32
-------
m-
ff
¦Li
t.
/
i r C
Max: 1.1283 Min: -0.1385 %
ht\
• rm m
6"\)
j
fx
'h
K-
" T— — —
tH
y
a
-y
u
v
•4 ^
-Y
Figure 8-23: Projected illustrative changes in annual sulfur deposition in 2055 due to
LMDV regulatory scenario.
We also modeled an "onroad-only" sensitivity case. Figure 8-24 presents the absolute changes
in annual N deposition in 2055 between the reference and onroad-only sensitivity scenarios and
Figure 8-25 presents the absolute changes in annual S deposition.
m-
, J
\
ri>
\ \ X
I
Max: -0.0002 Min: -0.8272
w » s \
"\ i\f
&
I)
\ i-
vr
' #
i. /
I
•vX
'Nk N
I
>0.25
0.20
0.15
0.05
m
0.00 f
Ol
-0.05
-0.15
-0.20
<-0.25
Figure 8-24: Projected illustrative changes in annual nitrogen deposition in 2055 from
"onroad-only" emissions changes.
8-33
-------
r§
¦ \
\ 89
V i %
Max; 0,0035 Min; -0.03,07 '»
}
' f4:r^
-J i
#
/
>a
-4;
- - —c'
'Vx, \
v
, '~1,
I
>0.25
0.20
0.15
0.05
03
0.00
CD
-0.05
-0.15
-0.20
<-0.25
Figure 8-25: Projected illustrative changes in annual sulfur deposition in 2055 from
"onroad-only" emissions changes.
8.5 Illustrative Ozone and Particulate Matter Health Benefits
The illustrative air quality modeling analysis does not represent the proposal's regulatory
scenario, and it does not account for the impacts of the IRA. In contrast, the OMEGA-based
emissions analysis (see DRIA Chapter 9) does represent the specifics of the proposal and
accounts for IRA provisions that affect the power sector. As a result, we used the OMEGA-based
emissions analysis and benefit-per-ton (BPT) values to estimate the criteria pollutant (PM2.5)
health benefits of the proposed standards. DRIA Chapter 7.4 describes the benefit-per-ton
valuation methodology and DRIA Chapter 10 presents the PM2.5-related health benefits.
Nevertheless, the illustrative air quality modeling analysis provides some useful insights into
potential air quality impacts associated with emissions increases and decreases from multiple
sectors, and it supports the conclusion that in 2055, the proposal would result in widespread
decreases in ozone and PM2.5 that would lead to substantial improvements in public health and
welfare.
Using the illustrative air quality modeling results, we have quantified and monetized health
impacts in 2055, representing the LMDV regulatory scenario described in DRIA Chapter 8.2.
The approach we used to estimate health benefits is consistent with the approach described in the
technical support document (TSD) that was published for the 2023 PM NAAQS Reconsideration
Proposal (US EPA 2023).
Table 8-15 reports the PM2.5- and ozone-attributable effects we quantified and those we did
not quantify in this illustrative benefits analysis. The list of benefit categories not quantified is
not exhaustive. The table below omits welfare effects such as acidification and nutrient
enrichment.
8-34
-------
Category
Premature
mortality
from
exposure
to PM2.5
Nonfatal
morbidity
from
exposure
to PM2.5
Table 8-15: Health effects of ambient ozone and PM2.5
Effect Effect Effect
Quantified Monetized
Adult premature mortality from long-term exposure (age >17 / /
or >64)
Infant mortality (age <1) / /
Mortality
from
exposure
to ozone
Nonfatal
morbidity
from
exposure
to ozone
y
/
/
/
/
ya
ya
y
y
y
y
y
y
y
y
Non-fatal heart attacks (>18) /
Hospital admissions - cardiovascular (all) /
Hospital admissions - respiratory (<19 and >64) /
Hospital admissions - Alzheimer's disease (>64) /
Hospital admissions - Parkinson's disease (>64) /
Emergency department visits - cardiovascular (all) /
Emergency department visits - respiratory (all) /
Emergency hospital admissions (>65) /
Non-fatal lung cancer (>29)2 /
Out-of-hospital cardiac arrest (all) /
Stroke incidence (50-79) /
New onset asthma (<12) /
Exacerbated asthma - albuterol inhaler use (asthmatics, 6-13) /
Lost work days (18-64) /
Minor restricted-activity days (18-64) /
Other cardiovascular effects (e.g., other ages) —
Other respiratory effects (e.g., pulmonary function, non-asthma —
ER visits, non-bronchitis chronic diseases, other ages and
populations)
Other nervous system effects (e.g., autism, cognitive decline, —
dementia)
Metabolic effects (e.g., diabetes) —
Reproductive and developmental effects (e.g., low birth weight, —
pre-term births, etc.)
Cancer, mutagenicity, and genotoxicitv effects —
Premature respiratory mortality from short-term exposure (0- /
99)
Premature respiratory mortality from long-term exposure (age /
30-99)
Hospital admissions—respiratory (ages 65-99) /
Emergency department visits—respiratory (ages 0-99) /
Asthma onset (0-17) /
Asthma symptoms/exacerbation (asthmatics age 5-17) /
Allergic rhinitis (hay fever) symptoms (ages 3-17) /
Minor restricted-activity days (age 18-65) /
School absence days (age 5-17) /
Decreased outdoor worker productivity (age 18-65) —
Metabolic effects (e.g., diabetes) —
Other respiratory effects (e.g., premature aging of lungs) —
Cardiovascular and nervous system effects —
Reproductive and developmental effects —
1 Valuation estimate excludes initial hospital and/or emergency department visits.
' Not quantified due to data availability limitations and/or because current evidence is only suggestive of causality.
y
y
y
y
y
y
y
y
y
More Information
PM ISA
PM ISA
PM ISA
' PM ISA
PM ISA
PM ISA
PM ISA
PM ISA
PM ISA
PM ISA
PM ISA
PM ISA
PM ISA
PM ISA
PM ISA
PM ISA
PM ISA
PM ISAb
PM ISAb
PM ISAb
PM ISAb
PM ISAb
PM ISAb
Ozone ISA
Ozone ISA
Ozone ISA
Ozone ISA
Ozone ISA
Ozone ISA
Ozone ISA
Ozone ISA
Ozone ISA
Ozone ISAb
Ozone ISAb
Ozone ISAb
Ozone ISAb
Ozone ISAb
8-35
-------
Below we report the estimated number and economic value of reduced premature deaths and
illnesses in 2055 attributable to the illustrative regulatory scenario along with the 95 percent
confidence interval. Table 8-16 reports the number of reduced deaths and illnesses associated
with reductions in PM2.5, along with their monetized economic value. Table 8-17 reports the
number of reduced ozone-related deaths and illness, along with their monetized economic value.
Table 8-18 reports total benefits associated with the illustrative regulatory scenario in 2055,
reflecting alternative combinations of the economic value of PM2.5- and ozone-related premature
deaths summed with the economic value of illnesses for each discount rate.
8-36
-------
Table 8-16: Quantified and monetized avoided PM2.5-related premature mortalities and
illnesses of the illustrative scenario in 2055 (95% confidence interval)3
Avoided PM
Outcomes
All-Cause Mortality
ER visits, respiratory
Hospital Admissions
Respiratory Incidence
Additional Morbidity
Effects
(Wu et al. 2020) (65-99)
(Pope Illetal. 2019) (18-99)
(Woodruff 2008) (0-0)
ER visits, All Cardiac Outcomes
ER visits, respiratory
HA, Alzheimers Disease
HA, Cardio-, Cerebro- and
Peripheral Vascular Disease
HA, Parkinsons Disease
HA, Respiratory-2 HA, All
Respiratory
Incidence, Asthma
Incidence, Hay Fever/Rhinitis
Incidence, Lung Cancer
Incidence, Out of Hospital
Cardiac Arrest
Asthma Symptoms, Albuterol use
Acute Myocardial Infarction,
Nonfatal
Incidence, Stroke
Minor Restricted Activity Days
Work Loss Davs
Point Estimate
730
(640 to 810)
1,400
(1,000 to 1,800)
1.0
(-0.65 to 2.7)
220
(-83 to 500)
380
(75 to 800)
360
(270 to 450)
110
(77 to 130)
42
(22 to 63)
64
(22 to 100)
1,500
(1,400 to 1,500)
9.600
(2.300 to 17.000)
52
(16 to 87)
11
(-4.3 to 24)
280.000
(-140.000 to 690.000)
23
(14 to 33)
41
(11 to 71)
480.000
(390.000 to 570.000)
81.000
(68.000 to 93.000)
3%
7%
3%
7%
3%
7%
3%
7%
3%
7%
3%
7%
Valuation (Millions, 2020$)
$8,600
($800 to $23,000)
$7,700
($720 to $20,000)
$17,000
($1,500 to $45,000)
$15,000
($1,400 to $41,000)
$14
($-7.5 to $54)
$0.29
($-0.11 to $0.68)
$0.39
($0,077 to $0.81)
$5.1
($3.8 to $6.3)
$1.9
($1.4 to $2.4)
$0.63
($0.32 to $0.93)
$1.2
($0.26 to $2.1)
$75
($70 to $80)
$47
($43 to $50)
$6.7
($1.6 to $12)
$1.6
($0.49 to $2.7)
$1.2
($0.37 to $2.0)
$0.44
($-0.18 to $1.0)
$0.43
($-0.18 to $0.98)
$0.11
($0,055 to $0.28)
$1.3
($0.77 to $1.9)
$1.3
($0.75 to $1.8)
$1.6
($0.42 to $2.8)
$41
($21 to $62)
$16
($13 to $18)
Values rounded to two significant figures.
' We discount the value of those avoided health outcomes that are expected to accrue over more than a single year.
8-37
-------
Table 8-17: Quantified and monetized avoided ozone-related premature mortalities and
illnesses of the illustrative scenario in 2055 (95% confidence interval)3
Avoided Ozone Outcomes
Avoided
Premature
Respiratory
Mortalities
Morbidity
Effects
Long-term
Exposure
Short-Term
Exposure
Long-term
Exposure
Short-Term
Exposure
(Turner 2016)
(Katsouvanni 2009) and
(Zanobetti 2008), pooled
Avoided Outcomes
330
(230 to 420)
oo/b
J /o
7%
(5.9 to 23)
Valuation
(Millions, 2020$)
$3,800
($350 to $10,000)
$3,500
($310 to $9,400)
$190
($16to $560)
Asthma Onset
2,200
i 3% ;
$110
(1,900 to 2,500)
($95 to $130)
7%
$69
($59 to $78)
Allergic Rhinitis
13.000
$8.9
Symptoms
(6.700 to 19.000)
($4.7 to $13)
Hospital Admissions -
43
$1.8
Respiratory
(-11 to 96)
($-0.48 to $4.1)
ER Visits - Respiratory
720
$0.73
(200 to 1.500)
($0.20 to $1.5)
Asthma Symptoms
400 000
$110
(-50.000 to 840.000)
($-13 to $220)
MRADs
210.000
$18
(84.000 to 330.000)
($6.6 to $33)
School Absences
150.000
$18
(-21,000 to 310,000)
($-2.5 to $37)
Values rounded to two significant figures.
' We discount the value of those avoided health outcomes that are expected to accrue over more than a single year.
8-38
-------
Table 8-18: Total PM2.5 and ozone benefits of the illustrative scenario in 2055 (95%
confidence interval, billions of 2020 dollars)ab
PM2 5 Ozone Total
Benefits using PIVte.s-rclalcd mortality estimate from Pope III cl al.. 2019 and o/.onc-rclalcd mortality estimate from
Turner el al.. 2016
3% Discount Rate $17 $4.4 $21
($1.6-$45) ($0.33-$12) ($1.9-$57)
7% Discount Rate $15 $4.0 $19
($1.4-$41) ($0.26-$11) ($1.7-$52)
Benefits using PIVte.s-rclalcd mortality estimate from W11 cl al.. 2020 and a pooled o/.onc-relatcd mortality estimated
from Kalsouvanni cl al.. 2009 and Zanobclli cl al.. 2008
3% Discount Rale $8.8 $0.75 $9.5
($0.90-$23) ($0.00-$2.0) ($0.90 - $25)
7% Discount Rale $7.9 $0.71 $8.6
($0.80-$21) ($0.00-$1.9) ($0.76-$23)
a Values rounded to two significant figures.
k The benefits associated with the standards presented here do not include the full complement of health and environmental benefits that if
quantified and monetized, would increase the total monetized benefits.
In any complex analysis using estimated parameters and inputs from numerous models, there
are likely to be many sources of uncertainty. The health benefits TSD that was published for the
2023 PM NAAQS Reconsideration Proposal details our approach to characterizing uncertainty in
both quantitative and qualitative terms. That TSD describes the sources of uncertainty associated
with key input parameters including emissions inventories, air quality data from models (with
their associated parameters and inputs), population data, population estimates, health effect
estimates from epidemiology studies, economic data for monetizing benefits, and assumptions
regarding the future state of the country (i.e., regulations, technology, and human behavior).
Each of these inputs is uncertain and affects the size and distribution of the estimated benefits.
When the uncertainties from each stage of the analysis are compounded, even small uncertainties
can have large effects on the total quantified benefits.
8-39
-------
Chapter 8 References
86 FR 74434 .2021.
Advanced Clean Trucks . 2021. 13 Cal Code Regs, S 963 and 1963.1 through 1963.5
Beardsley, Megan. 2023. "Updates to MOVES for the Mult-Pollutant Emissions Standards for
Model Years 2027 and Later Light-Duty and Medium-Duty Vehicles." Memorandum to Docket
EPA-HQ-OAR-2022-0829. February 10.
Bohac, Stanislav V., and Scott Ludlum. 2023. "Characterization of a Lightly Loaded Underfloor
Catalyzed Gasoline Particulate Filter in a Turbocharged Light Duty Truck." Journal of
Engineering fro Gas Turbines and Power 021024-1 - 021024-7.
Byun, D.W., J.K.S. Ching, and US EPA. 1999. "Science Algorithms of EPA Models-3
Community Multiscale Air Quality (CMAQ) Modeling System."
Cook, Rich, Sharon Phillips, Madeleine Strum, Alison Eyth, and James Thurman. 2020.
"Contribution of mobile sources to secondary formation of carbonyl compounds." Journal of the
Air and Waste Management Association.
Henderson, B. et al. 2018. "Hemispheric-CMAQ Application and Evaluation for 2016." 2019
CMAS Conference .
Katsouyanni, K, Samet, JM, Anderson, HR, Atkinson, R, Le Tertre, A, Medina, S, Samoli, E,
Touloumi, G, Burnett, RT, Krewski, D, Ramsay, T, Dominici, F, Peng, RD, Schwartz, J,
Zanobetti, A and Committee, HEIHR (2009). 2009. "Air pollution and health: a European and
North American approach (APHENA)." Res Rep Health Eff Inst 142: 5-90.
Mathur, R. et al. 2017. "Extending the Community Multiscale Air Quality (CMAQ) Modeling
System to Hemispheric Scales: Overview of Process Considerations and Initial Applications."
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National Center for Atmospheric Research. 2022. Weather Research & Forecasting Model
(WRF), v4.42. Accessed 2023. https://www.mmm.ucar.edu/models/wrf.
Pope III et al., CA, Lefler, JS, Ezzati, M, Higbee, JD, Marshall, JD, Kim, S-Y, Bechle, M,
Gilliat, KS, Vernon, SE and Robinson, AL. 2019. "Mortality risk and fine particulate air
pollution in a large, representative cohort of US adults." Environmental Health Perspectives 127
(7): 077007.
Skamarock, W.C. et al. 2008. "A Description of the Advanced Research WRF Version 3."
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). 2016. "Long-term ozone exposure and
mortality in a large prospective study." American Journal of Respiratory and Critical Care
Medicine 193 (10): 1134-1142.
US EIA. 2021. Annual Energy Outlook 2021. February 3. Accessed 2022.
https://www.eia.gov/outlooks/archive/aeo21/.
8-40
-------
US EPA. 2018. 2018 Air Toxics Screening Assessment Results.
https://www.epa.gov/AirToxScreen/2018-airtoxscreen-assessment-results.
—. 2022. 8-Hour Ozone (2008) Nonattainment Area Summary. December 31. Accessed January
20, 2023. https://www3.epa.gov/airquality/greenbook/hnsum.html.
—. 2022. 8-Hour Ozone (2015) Nonattainment Area Summary. December 31. Accessed January
20, 2023. https://www3.epa.gov/airquality/greenbook/jnsum.html.
—. 2022. EPA to Reconsider Previous Administrator's Decision to Retain 2015 Ozone
Standards. October 11. Accessed January 20, 2023. https://www.epa.gov/ground-level-ozone-
pollution/epa-reconsider-previous-administrations-decision-retain-2015-ozone.
US EPA. 2023. "Estimating PM2.5- and Ozone-Attributable Health Benefits. Technical Support
Document (TSD) for the 2023 PM NAAQS Reconsideration Proposal." EPA-HQ-OAR-2019-
0587, .
US EPA. 2011. "Final Cross State Air Pollution Rule Air Quality Modeling TSD."
—. 2022. Green Book Data Download. December 31. Accessed January 20, 2023.
https://www.epa.gov/green-book/green-book-data-download.
—. 2022. Green Book Data Download. 12 31. Accessed January 20, 2023.
https://www.epa.gov/green-book/green-book-data-download.
US EPA. 2019. "Meteorological Model Performance for Annual 2016 Simulation WRF v3.8."
—. 2020. "Motor Vehicle Emission Simulator: MOVES3." Ann Arbor: Office of Transportation
and Air Quality. US Environmental Protection, https://www.epa.gov/moves.
US EPA. 2007. "MSAT RIA."
—. 2023. National Ambient Air Quality Standards (NAAQS) for PM. Accessed January 20,
2023. https://www.epa.gov/pm-pollution/national-ambient-air-quality-standards-naaqs-pm.
—. 2020. Ozone National Ambient Air Quality Standards (NAAQS). December 23. Accessed
January 20, 2023. https://www.epa.gov/ground-level-ozone-pollution/ozone-national-ambient-
air-quality-standards-naaqs.
—. 2022. PM-2.5 (1997) Nonattainment Area Summary. Accessed January 20, 2023.
https://www3.epa.gov/airquality/greenbook/qnsum.html.
—. 2022. PM-2.5 (2006) Nonattainment Area Summary. December 31. Accessed January 20,
2023. https://www3.epa.gov/airquality/greenbook/rnsum.html.
—. 2022. PM-2.5 (2012) Nonattainment Area Summary. December 31. Accessed January 20,
2023. https://www3.epa.gov/airquality/greenbook/knsum.html.
US EPA. 2022. "Preparation of Emissions Inventories for the 2016v2 North American Emissions
Modeling Platform Technical Support Document."
US EPA. 2004. "Procedures for Estimating Future PM2.5 Values for the CAIR Final Rule by
Application of the (Revised) Speciated Modeled Attainment Test (SMAT) Updated 11/8/04."
8-41
-------
—. 2022. Report on the Environment, technical documentation for acid deposition.
https://cfpub.epa.gov/roe/indicator.cfm?i=l.
—. 2023. Science Inventory, https://cfpub.epa.gov/si/index.cfm.
—. 2022. Sulfur Dioxide (2010) Nonattainment Area Summary. December 31. Accessed January
20, 2023. https://www3.epa.gov/airquality/greenbook/tnsum.html.
US EPA. 2022. "Technical Support Document EPA Air Toxics Screening Assessment. 2017
AirToxScreen TSD. ."
US EPA. 2023. "Technical Support Document: Illustrative Air Quality Modeling Analysis for
the Light and Medium Duty 2027 Multipollutant Proposed Rule."
US EPA. 2019. "Technical Support Document: Preparation of Emissions Inventories for the
Version 7.1 2016 hemispheric Emissions Modeling Platform."
—. 2015. "U.S. Environmental Protection Agency Peer Review Handbook." EPA-100-B15-001.
https://www.epa.gov/sites/default/files/2020-
08/documents/epa_peer_review_handbook_4th_edition.pdf.
Versar, Inc. 2019. "The Sixth External Peer Review of the Community Multiscale Air Quality
(CMAQ) Modeling System." https://www.epa.gov/sites/production/files/2019-
08/documents/sixth_cmaq_peer_review_comment_report_6.19.19.pdf.
Woodruff, T.J., J. Grillo, and K.C. Schoendorf. 2008. "Air pollution and postneonatal infant
mortality in the United States, 1999-2002." Environmental Health Perspectives 116(1): 110-
115.
Wu et al., X, Braun, D, Schwartz, J, Kioumourtzoglou, M and Dominici, F. 2020. "Evaluating
the impact of long-term exposure to fine particulate matter on mortality among the elderly."
Science Advances 6 (29): eaba5692.
Zanobetti, A and Schwartz, J. 2008. "Mortality displacement in the association of ozone with
mortality: an analysis of 48 cities in the United States ." Am J Respir Crit Care Med 177 (2):
184-189.
8-42
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Chapter 9: OMEGA Physical Effects of the Proposed Standards and
Alternatives
This chapter describes the methods and approaches used within the OMEGA model to
estimate physical effects of the proposed standards. Physical effects refer to emission
inventories, fuel consumption, oil imports, vehicle miles traveled including effects associated
with the rebound effect, and safety effects. The cost and benefits of the proposal are tied directly
to these physical effects and are discussed in Chapter 10 of this draft RIA.
9.1 The OMEGA "Context"
OMEGA makes use of projections of fleet size, market shares, fuel prices, vehicle miles
travelled (VMT), etc., from the Annual Energy Outlook. Any AEO can be used provided the
input files are made available to OMEGA. For this analysis, EPA has used AEO 2021. (U.S. EIA
2021) AEO 2021 was done assuming that the future fleet would comply with the 2020 SAFE
FRM. Hence, when running OMEGA, the first scenario run is best meant to reflect the SAFE
FRM standards. That way, future fleet VMT and rebound VMT will be calculated relative to that
projected future, as described below.
9.2 The Analysis Fleet and the Legacy Fleet
OMEGA uses as a "base year fleet" a comprehensive list of vehicles sold in a recent model
year. This base year fleet includes all models of vehicles, their sales, and a long list of attributes
such as their curb weights, their footprints and the primary GHG technologies on those vehicles.
For this analysis, EPA is using the MY 2019 fleet as the base year fleet. When OMEGA runs, it
begins with the 2020 calendar year as the first year of the analysis and uses the fleet of vehicles
contained in the base year fleet as the starting point for the analysis. These MY 2020 and later
vehicles are referred to as the "analysis fleet."
Vehicles that exist in the fleet prior to the first year of the analysis (i.e., MY 2020) are
referred to as the "legacy fleet." Those vehicles are "aged out" of the fleet over the course of
running the analysis. The legacy fleet vehicles are not changed in any way within OMEGA other
than being scrapped (aging out) and driving fewer miles per year.
Figure 9-1 shows ICE vehicle stock—liquid-fueled vehicles including HEVs—and Figure 9-2
shows BEV stock. The ICE vehicle stock can be seen to be aging out of the fleet as the BEV
stock grows. Figure 9-3 shows the total vehicle stock with growth representing economic and
population growth going forward.
9-1
-------
300,000,000
250,000,000
200,000,000
150,000,000
100,000,000
50,000,000
No Action
Alternative 2
• Alternative 3
• Proposal
• Alternative 1
2027 2029 2031 2033 2035 2037 2039 2041 2043 2045 2047 2049 2051 2053 2055
Figure 9-1 ICE vehicle stock used in OMEGA effects calculations
200,000,000
180,000,000
160,000,000
140,000,000
120,000,000
100,000,000
80,000,000
60,000,000
40,000,000
20,000,000
• Alternative 1
¦ Proposal
• Alternative 3
Alternative 2
No Action
2027 2029 2031 2033 2035 2037 2039 2041 2043 2045 2047 2049 2051 2053 2055
Figure 9-2 BEV stock used in OMEGA effects calculations
9-2
-------
9.3 Estimating Vehicle, Fleet and Rebound VMT
OMEGA uses a static set of mileage accumulation rates based on body style. OMEGA uses
three self-explanatory body styles: sedan_wagon; cuv_suv_van; and, pickup. All vehicles in both
the analysis and legacy fleets are characterized as being of one of these body styles. The rates at
which each body style is aged-out of the fleet, or re-registered, and the miles driven by age are
shown in Table 9-1 for light-duty and in Table 9-2 for medium-duty. The same values are used in
both the analysis and the legacy fleets based on vehicle age.
Table 9-1 Mileage accumulation and re-registration rates used for light-duty
Mileage Accumulation Re-Registration Rate
Age
; Sedan Wagon
: CUV_SUV_Van
1 Pickup ;
Sedan Wagon
CUV_SUV_V an
Pickup
0
15,922
16,234
r 18,964
100.0%
100.0% ;
100.0%
1 " '
15,379
15,805
r 17,986 =
98.8%
98.8%
97.8%
2
14,864
15,383
!' 17,076 j
97.7%
97.7%
96.3%
3
14,378
14,966
: 16,231 :
96.1%
96.1%
94.3%
4 ,
' 13,917
14,557
; 15.119
94.5%
94.5%
93.1%
' 5
13,481
14,153
: 14,726 ]
93.0%
93.0%
91.5%
6 "
13,068
7 13>756.!
1 l.oco j
91.1 %
;;; 9i-i%
89.3%
7
12,677
13,366
j 13,448 ]
89.1%
89.1%
87.0%
8 ^
i 12,305
12,982
12,886 :
8(>.9"m "
8(>,9"..
84.1%
9
! 11,952
12,605
I 12,372 "j
8 1.0"..
84.0%
79.6%
10
11,615
12,234
| 11,903 ;
80.0%
80.0%
74.2%
11
11.29 1
11,870
: 11,476
75.6% '
; :;75.6%:
69.2%
12
10,986
11,512
j' 11,088
70.6%
70.6%
64.1%
13
10,690
11,161
: 10,737 j
..765.3%;:
:: :.65.3%:
58.3%
14
10,405
10,816
| 10,418 j
59.5%
::759.5%:
53.5%
15
10,129
10,477
10,131 "i
53.1%
53.1%
48.6%
16
| 9.SI.0
10,146 ''
j 9,871 j
45.8%
'77 45.8%;
44.2%
17
1 9,597
9,820
; 9,635 1
38.3%
38.3%
39.8%
IS
9,338
9,501
: 9,421 J
30.8%
30.8%
35.2%
19
9,081
9,189
i" 9,226 .
24.1%::
"; 24.1%:;
30.9%
20
8,826
8,883
f" 9,047 "
18.3%
18.3%
26.7%
21 ....
8,570
.. .... 8>583
: 8.882
139"..
13.9%
22.8%
22
8,313
8,290
f 8,726 "'i
10.7%
10.7%
20.2%
23
j' 8,051
8,004
8,577 1
8.2%
8.2%::
17.5%
24
[ 7,785
7,724
! 8.133
6.3% ;
6.3%
15.8%
25
7,511
7,450
f 8,290
"5.1%:;
5.1%:;
14.5%
26
7,229
7,183
: 8,146 J
"~4.2%"
4.2%:
13.9%
27
6,938
6,923
: 7,998
3.4%
3.4%
12.5%
28
6,635
¦ ' 6,669
f 7,842 !
2.8%
' .2.8%;;;
1 II"..
29
6,319
: 6,421
; 7,676 ;
2.4%
2.4%
10.3%
30
5,988
6,180
: 7,497 =
0.0%
7 0.0%
9.3%
31
5,641 ""
5,946
: 7,302 J
0.0%
0.0%
8.3%
32
5,277
5,718
1 7,089 ^
0.0%
0.0%
7.3%
33 ""
4,893
5,496
: 6>853 J
p.0%;;;;
::; o.o%'
6.2%
34
4,488
5,281
: 6,593
0.0%
0.0%
5.0%
35
4.06! :
5,072
f 6,305 ]
0.0%
0.0%
"3.8%
36
3,610
4,870
: 5,987 ;
0.0%
0.0%
2.7%
37
3,133
4,674
; 5,635 :
0.0%
0.0%
0.0%
9-3
-------
Table 9-2 Mileage accumulation and re-registration rates used for medium-duty
Mileage Accumulation Re-Registration Rate
Age
; CI V Sl Y Van
Pickup :
CU V_SU V_V an
; Pickup
0
15,352
7 15,352 :
100%
7 ioo%
1 " '
'J 14,843
! 14,843 j
7 99% 7
; 99% 7
2 .7
14,264
14,264 ]
98%
7 98%
3
; 13,795
7 13,795 "i
96%
: 96%
4 r
; 13,372
: 13,372 j
95% 7
7 95%
' 5
12,976
1 12,976 ]
" 93%
| 93%
6 "
12,578 7
; 12,578 :
91%
91%
7
12,210
f 12,210 :
88%
; 88%
8 J
11,853
1 11,853 j
86%
i 86%
9 *2'
11,509
; 11,509 j
81%
; 81%
10
11,183
: 11,183 1
76%
: 76%
11
10,866
1 10,866
71% 7
77 71%
12
10,562
10,562
' 66%
7 66%
13
10,276 7
1 10,276 ;
O
©
ox
O
14
r 7 9.9N(> 2
; 9,986 j
55%
: ...?5%
15
J 9,696
7 9,696 :
50%
7 50%
16
9.115
; 9.115 :
7'46% 7
: 46%
17
9,136
i 9,136 1
41%
7 41%
IS
8,862
¦ 8,862 7
37% 7
37%
19
8,594
; 8,594
32%
I 32%
20
8,337
7 8,337 1
28%
i 28%
21
8,084
: 8,084 !
24% 77
24%
22
7,838
i 7,838 7
22%
7 22%
23
7,599
7 7,599
19%
i 19%
24
7,364
[""7,364"]
17%
7 17%
25
7,131
: 7,131 j
16% 7
f 16%
26
6,904
7 6,904 7
15% 77
i 15% 7
27
6,682
r 6,682 5
14%
r 14%
28
6,460
; 6,460 :
7 13% 77
: 13% 7
29
6,241
i 6,241 I
12%
; 12%
30
6,029
i 6,029 7
1%
1% "
9.3.1 OMEGA "Context" VMT
When running OMEGA, the mileage accumulation rates and re-registration rates shown in
Table 9-1 and Table 9-2 are used for all vehicles in both the analysis and legacy fleets at the
indicated ages. To ensure that the "context" VMT (i.e., the total VMT of the analysis and legacy
fleets) travels the number of miles projected by EIA's Annual Energy Outlook, OMEGA adjusts
the VMT of every vehicle such that the total fleet VMT in any calendar year will equal that
projected in AEO. This is done by determining ratio of the AEO projection for a given calendar
year to that given calendar year's total VMT within OMEGA estimated using the static mileage
accumulation rates shown in Table 9-1 and Table 9-2. That ratio is then applied to every
vehicle's "static" VMT to arrive at a "context" VMT. This way, the fleet context VMT within
OMEGA will be equivalent to the fleet VMT projected by AEO. Importantly, this context VMT
does not yet include any rebound VMT, which is discussed below.
FleetVMTAE0
VehicleVMTC0ntext = VehicleVMTstatic x
FleetVMT0MEGA
Where,
VehicleVMTvrte* = miles driven in OMEGA scenario 0 (the SAFE FRM in this case)
VehicleVMTstntk = miles driven using values shown in Table 9-1
9-4
-------
FleetVMTstatic = the projected annual VMT in the AEO report being used
FleetVMTouEGA = the calculated annual VMT within OMEGA using VehicleVMTstatic
values
9.3.2 Context Fuel Costs Per Mile
The VMT rebound effect is discussed in detail in Chapter 4.2 Estimates of "rebound" miles
driven depends, traditionally, on changing fuel prices and their effect on the number of miles
people drive—as fuel prices rise and the cost per mile of driving increases, people drive less.
OMEGA, we estimate the rebound effect not based on changing fuel prices, but rather on the
changing cost per mile of driving for vehicles of different fuel consumption. In other words,
someone that has purchased a new vehicle that consumes less fuel per mile might drive that
vehicle more than if they would have continued to drive their prior vehicle that consumed more
fuel per mile. As such, OMEGA's estimate of rebound VMT does not include any rebound VMT
in the legacy fleet since the fuel consumption characteristics of the legacy fleet are not changing.
For the analysis fleet, OMEGA first determines the fuel cost per mile for each base year fleet
vehicle in every calendar year included in the analysis. This way, the base year fleet vehicle's
fuel consumption characteristics are not changing through the years but its fuel costs per mile are
due to changing fuel prices. These fuel costs per mile for every vehicle in the base year fleet are
then used as the context fleet, or context vehicle, fuel costs per mile.
In subsequent OMEGA scenarios, which include unique GHG standards that can result in
unique fuel consumption characteristics for all vehicles, the fuel costs per mile for those vehicles
are similarly determined. Since each vehicle in OMEGA is "derived" from a base year fleet
vehicle, the fuel costs per mile for every vehicle can be compared to its context fuel cost per
mile.
9.3.3 Rebound VMT
As discussed in Chapter 4.2, rebound VMT depends on the elasticity of demand for more
driving. The input values used in the analysis were -0.1 for ICE vehicles and zero for BEV
vehicles. We have used a value of zero for BEV vehicles since we do not project improvements
to BEV battery and fuel consumption efficiencies in OMEGA. The rebound effect can then be
calculated as:
V ehicleVMTrebound = VehicleVMTC0ntext x Elasticity x (CPM^~ CPMcontext)
L r M context
Where,
VehicleVM'/'rchound = the rebound miles driven
VehicleVM'/'conicxi = the context VMT discussed above
Elasticity = elasticity of demand
CPMpoiicy = the cost per mile in the policy scenario
9-5
-------
CPA/context = the cost per mile in the context scenario (the SAFE in this case)
And to calculate vehicle miles traveled in the policy scenario:
V ehicleVMTpoiicy VehicleVMTC0ntext + V6hicl6VMTrei)0unci
Where,
VehicleVMTpoiky = the policy VMT
VehicleVM'/'conicxi = the context VMT discussed above
VehicleVM'/'rchound = the rebound miles driven
9.3.4 Summary of VMT in the Analysis
The analysis fleet VMT will vary depending on the rebound elasticities used and the level of
GHG standards, the latter of which impact the fuel consumption characteristics of the future
fleet. The OMEGA No Action VMT and the projected fleet VMT under the proposed and
alternative standards are shown in Table 9-3. Table 9-4 shows the rebound VMT.
Table 9-3 VMT summary, light-duty and medium-duty (billion miles)
Calendar
¦ OMEGA
¦ OMEGA
OMEGA
OMEGA
OMEGA
Year
i No Action
; Proposal
Alternative 1
Alternative 2
Alternative 3
2027
3,230
3,230
3,230
3,230
3,230
' 2028
3,251
3,252 '
3,252
3,252
3,251
2029
3,272
3,273 ""
"" 3,273
3,273
3,272
2030
3,295
| ' 3,296
3,297
3,296 "
3,295
2031
3,316
3,317
3,319
3,317
3,317
2032
3,32!8 ;
3,329
3,332
3,329
3.329 "
2033
3,350
3,352
3,354
; 3,351
3,351
2034
3,37i
3,373
3,376
| 3,373
3,373
2035
3,385
3,387
3,389
3,386
3,386
2036
3.108
3,410
3,413
3,409
3,410
2037
f 3,420
3,423
3,425
3,421
: 3,422
2038
' 3,444
| 3,446
3,448
3,445
f' 3,446
2039
3,457 ^
! 3,460 *
3,462
3,458
3.159
2040
3,480
j' 3,483
3,485
3,481
1 3,482
2041
3.193
3,496'
3,497 '
3,494
; !. 195
2042
3.515
f 3,518
3,519
3,516
I 3,517
' 2043
3,538
3,541
3,542
3,539
3,540
2044
f 3,552
3,555
3,556
3,552
3.55 1
2045
3,575
3,578
3,578
3,575
3,577
2046
3.599
f 3,602
3,602
3,599
3.1.01
2047
3,612
3.1.15
3,615
3,(. 12
3,614
2048
3,636
3,639
3,638
3,636
3,638
2049
3,658 " "
3,66 i
3,661
3,659
3,660
2050
3,682
3,685
3,685
3,683
3,684
2051
3,686
3.(.89
3,688
3,687
3,688
2052
3,690
3,693
3,692
3,691
3,692
2053
3.1.9 1 "
r 3,696
3,696
3,694
3,695
2054
3,697
i' 3,700 '
" 3,699
3,698
3,699
2055
3,701
3,704
3,703
3,702
3,703
Table 9-4 Rebound VMT relative to no action, light-duty and medium-duty (billion miles)
j Calendar ¦ OMEGA ; OMEGA : OMEGA OMEGA
Year ; Proposal = Alternative 1 = Alternative 2 Alternative 3 =
!' 2027 V -0.015 ; -0.0018 0.0044 -0.062
9-6
-------
2028
0.15
0.4
0.19
-0.006
2029
0.35
1.2 77
0.33 777
0.08
2030
0.62
1.9
0.49
0.21
203 i "
0.92
2.7'77
0.77
0.40
2032
.... !-2 I. ;
3.5 77'
li 7.7
0.68
2033
1.6 1
4.1
1.3
1.1
2034
1.9 "J
4.4
7 7 11
1.3
2035
2.0
4.6
J 1.4
1.5
2036
2.3 7
4.6
7 l-3 7
1.8
2037
2.7 1
4.6
1.1
1.9
2038
2.8 " '
"7 4.777
0.93
2.0
2039
2.9 f
777 4-7 77
0.80 77777'
2.1
2040
3.1
4.6
0.68
2.1
2041
' "3.0 7
77 4.3 7.7'
0.56
2.0
2042
3.0' "
4.1
0.44
1.9
2043
2.9 7
3.8
0.3377
1.8
2044
1.2-9 71
I 3.5 7.777 7
0.26
1.8
2045
2.9 =
3.3
0.22
1.8
2046
2.9
3.0
77 0.217
1.8
2047
2.9"'
2.8
0.24
1.8
2048
2.9 71;
2.677
0.30
77.1.8
2049
2.9
2.57.7.
0.42
1.8
2050
2.9
2.4
0.51
1.8
205 i
1.2-81
772.277
o.6i 77
.77 i-7
2052
2.8
2.1 ""
0.71
1.7
2053
2.7
2.0
0.80
\.i
2054
' 2.7
1.8
0.93
1.7
2055
2.7
1.8
1.1
1.8
9.4 Estimating Safety Effects
OMEGA estimates safety effects consistent with methods used in past light-duty GHG
analyses and consistent with the methods developed by NHTSA for use in the CAFE
Compliance and Effects Modeling System (CCEMS). In fact, the inputs used in OMEGA are
identical to inputs used by NHTSA in CCEMS and used by EPA in the 2021 light-duty GHG
final rule. (86 FR 74434 2021) NHTSA is the government entity tasked with regulating vehicle
safety and, as such, NHTSA has the foremost experts in the field. EPA has worked closely with
NTHSA through the years of joint GHG/CAFE regulatory development and has weighed in
extensively on the statistical analyses used in estimating vehicle safety effects. That said, EPA
has always used modeling parameters in OMEGA that are identical to those used by NHTSA in
the CCEMS.
As noted, OMEGA uses vehicle travel fatality rates and safety values associated with mass
reduction that have been generated by NHTSA. These fatality rates and safety values and how
they are generated are described at length in the regulatory documents supporting NHTSA's
2022 CAFE final rule. (2022 CAFE FRIA 2022) (2022 CAFE TSD 2022) The discussion here
does not attempt to provide that same level of detail and is meant only to summarize the NHTSA
analysis to help in understanding the input values used in OMEGA.
The safety analysis is meant to capture effects associated with three factors:
• Changes in vehicle mass or weight;
• Changes associated with fleet composition including car, CUV, SUV, pickup shares
and fleet turnover; and,
9-7
-------
• The potential for additional safety impacts associated with additional driving (i.e., the
"rebound effect" as mentioned in Chapter 9.3.3) that might arise from lower fuel costs
resulting from more stringent GHG standards.
In the following, we first cover the base fatality rates of vehicles in the legacy fleet.. We then
cover the changes to those fatality rates associated with changes in vehicle mass and changes in
the analysis fleet composition. We then summarize the calculation approach to estimating
fatalities within OMEGA and present results.
9.4.1 Fatality Rates used in OMEGA
To estimate the impact of the standards on safety, NHTSA uses statistical models that
explicitly incorporate variation in the safety performance of individual vehicle model years.
They use a model for fatalities that tracks vehicles from when they are produced and sold, enter
the fleet, gradually age and are ultimately retired from service. NHTSA also considers how
newer technologies are likely to affect the safety of both individual vehicles and the combined
fleet. The overall safety of the light-duty vehicle fleet during any future calendar year is
determined by the safety performance of the individual model year cohorts comprising it at the
ages they will have reached during that year, the representation of each model year cohort in that
(calendar) year's fleet, and a host of external factors that fluctuate over time, such as driver
demographics and behavior, economic conditions, traffic levels, and emergency response and
medical care. Combining forecasts of future crash rates for individual model year cohorts at
different ages with the composition of the vehicle fleet produces baseline forecasts of fatalities.
Regulatory alternatives that establish new standards for future model years can change these
forecasts by altering the representation of different model year cohorts making up the future
light-duty fleet (2022 CAFE FRIA 2022). NHTSA's work produces estimates of fatality rates for
each model year making up the fleet during each future calendar year, and the process is
continued until calendar year 2050. Multiplying these rates by the estimated number of miles
driven by vehicles of each model year in use during a future calendar year produces baseline
estimates of total fatalities. As an example, Figure 9-3 illustrates the recent history and baseline
forecast of the overall fatality rate for occupants of cars and light trucks. According to NHTSA,
the sharp rise in the fatality rate for 2020 coincided with the steep drop in car and light truck
VMT during that year due to the COVID-19 pandemic and accompanying restrictions on
activity, combined with an increased number of fatalities in 2020. These rates are also used as
the basis for estimating future fatalities and for estimating changes in safety resulting from
reductions in the mass of new vehicles, additional rebound-effect driving, and changes in the
numbers of cars and light trucks from different model years making up each calendar year's
fleet. The underlying causes and methods for estimating each of those three sources of changes
in safety are discussed in detail in various sub-sections of Chapter 7 of the Technical Support
Document (TSD) accompanying NHTSA's 2022 final rule (2022 CAFE TSD 2022).
9-8
-------
18
0
1990 1995 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050
Calendar Year
Figure 9-3 Recent and projected future fatality rates for cars and light trucks (2022 CAFE
FRIA 2022,109)
9.4.2 Calculating Safety Effects tied to Vehicle Weight Changes
To calculate the safety effects associated with changes to vehicle weight, OMEGA makes use
of fatality rate changes per billion miles of vehicle travel associated with vehicles of different
body styles—as with base fatality rates these are developed by NHTSA—and weight changes
determined within OMEGA as vehicles change to meet future GHG standards. The first of these
factors are, as noted, developed by NHTSA through an analytical process that is detailed in their
2022 final RIA and TSD (2022 CAFE FRIA 2022) (2022 CAFE TSD 2022). OMEGA makes
use of the input parameters used by NHTSA in CCEMS model runs supporting their 2022 final
rule. Those values are shown in Table 9-4.
Table 9-5 Safety values used in OMEGA (2022 CAFE FRIA 2022)
Body style
NHTSA Safety Class
Threshold
(lbs)
Change per 100 lbs below threshold
Change per 100 lbs at or above threshold
sedan
PC
3201
0.012
0.0042
pickup
LT/SUV
5014
0.0031
-0.0061
CUV SUV
CUV/Minivan
3872
-0.0025
-0.0025
For example, the base fatality rate for a pickup would change by -0.0061 for every 100
pounds of weight reduced over 5,014 pounds. However, if that vehicle had a starting weight of
5,064 pounds and its weight was reduced by 100 pounds, then the first 50 of those pounds would
reduce the base fatality rate by -0.00305 (-0.0061 per 100 pounds but for only 50 pounds) and
9-9
-------
the next 50 pounds would increase the base fatality rate by 0.00155 (0.0031 per 100 pounds but
for only 50 pounds). In other words, reducing pickup weight above 5,014 pounds reduces
fatalities while reducing pickup weight below 5,104 increases fatalities. In contrast, increasing
pickup weight above 5,014 pounds increases fatalities while increasing pickup weight below
5,014 pounds reduces fatalities.
Therefore, OMEGA first determines the weight change of the given vehicle. This is calculated
as the curb weight of the vehicle in the policy scenario (i.e., the final weight) relative to the curb
weight of the vehicle in the base year fleet.
DeltaWeight = FinalWeight — BaseYearW eight
Where,
DeltaWeight = the change in weight, where a weight reduction will be a negative value
FinalWeight = the weight of the vehicle in the policy scenario
BaseYearWeight = the weight of the vehicle in the base year fleet
Knowing the delta weight, OMEGA then determines the weight change above and below the
threshold for the body style of the given vehicle. Importantly, because OMEGA sometimes
increases the curb weight of vehicles (e.g., due to conversion to BEV), whether the weight
change is positive (increased weight) or negative (decreased weight) is important given the
safety values and their signs as shown in Table 9-4.
To determine the pounds changed below the threshold, and whether they involve increased or
decreased weight, OMEGA uses the logic shown below:
If: Threshold < Base Weight and Threshold < FinalWeight'.
Then: DeltaPoundsbelow = 0
Else if: BaseWeight < Threshold and FinalWeight < Threshold.
Then: DeltaPoundsbelow = FinalWeight - BaseWeight
Else if: BaseWeight < Threshold < FinalWeight.
Then: DeltaPoundsbelow = Threshold - BaseWeight
Else if: FinalWeight < Threshold< BaseWeight.
Then: DeltaPoundsbeiow = FinalWeight - Threshold
To determine the pounds changed above the threshold, and whether they involve increased or
decreased weight, OMEGA uses the logic shown below:
If: BaseWeight < Threshold and FinalWeight < Threshold
Then: DeltaPoundsnbove = 0
9-10
-------
Else if: Threshold <= BaseWeight and Threshold<= FinalWeight
Then: DeltaPoundsnbove = FinalWeight - BaseWeight
Else if: BaseWeight <= Threshold<= FinalWeight.
Then: DeltaPoundsnbove = FinalWeight - Threshold
Else if: FinalWeight <= Threshold <= BaseWeight.
Then: DeltaPoundsnbove = Threshold - BaseWeight
With the weight change above and below the threshold, OMEGA calculates the fatality rate
changes as shown below:
RateChangebei0W = ChangePerlOOPoundsbeiow x (—DeltaPoundsbeiow)
RateChangeabove = ChangePerlOOPoundsabove x (—DeltaPoundsabove)
Where,
RateChange = the change in fatality rate below/above the weight threshold for the given body
style as shown in Table 9-4; the base fatality rate that is changed by this rate change is discussed
in the next section.
ChangePerlOOPounds = the applicable value for the given body style as shown in Table 9-4
DeltaPounds = the applicable value according to the logic described above.
9.4.3 Calculating Fatalities
OMEGA first calculates the fatality rate of a given vehicle in the given policy scenario. This
is done using the equation below.
P (xtoXityPatGpQiiQy
= FatalityRatebase x (1 + RateChangebei0W) x (1 + RateChangeabove)
Where,
FatalityRatePo\icy = the fatality rate per billion miles traveled in the policy scenario
Fatality Ratebase = the fatality rate per billion miles traveled in the base case (Chapter 9.4.1)
RateChange = the appliable result for the calculations described above (Chapter 9.4.2)
The number of fatalities in the given policy scenario are then calculated as:
9-11
-------
FatalitiespoUcy — Fatality Rat6p0nCy x VMTp0nCy j 10^
Where,
FatalitiesVo\icy = the number of fatalities in the policy scenario
FatalityRatePo\icy = the fatality rate in the policy, as described above
VMTpoiicy = the vehicle miles traveled in the policy, as described in Chapter 9.3
9.4.4 Summary of Safety Effects in the Analysis
Table 9-6 shows the number of fatalities estimated in the No Action case (i.e., the EPA 2021
FRM remains in place) and the Proposal and Alternatives. Table 9-7 shows fatality rate impacts
per billion miles of vehicle travel.
Table 9-6 Fatalities per year, light-duty and medium-duty
Calendar :
No
; Proposal ;
Alternative
Alternative
Alternative
; Proposal
Alternative
Alternative
Alternative
Year
Action
1
2
3
j %
1
2
3
Change
% Change
% Change
% Change
2027
20,432
: 20,438 :
20,438
20,436
20,434
0.03%
0.03%
0.02%
0.01%
2028
19,857
; 19,865 J
19.81.9
19,863
19.859
i 0.04%
1 0.06%
! ' 0.03%
~ 0.01%
2029
19,334
1 19,344 1
19,356
19,341
19,336
0.05%
f 0.11%
0.04%
0.01%
2030 1
18,887
1 18,898
18.91c
18.89 1
i 18,890
0.06%
0.16%
; 0.04%
j" 0.02%
203 i J
18,470
: 18,486 ;
18,508
18,482
18,479
! ' 0.09%
0.21%
0.06%
0.05%
2032
18,056
18,079 ]
18,105
18,074
18,074
0.12%
0.27%
0.10%
0.10%
2033 1
17,732
] 17,766 |
17,793
i7,760
""'17,762']
; '" 0.19%
: 0.35%
0.16%
r 0.17%
2034
17,451
" 17,494 i
17,521
17,485
17,492
!"" 0.25%
;' 0.40%
0.20%
I 0.23%
2035
17,177
j 17,226
17,251
i 1 ~-21 f.
17,225
';' 0.29%
0.43%
0.23%
i " 0.28%
2036 T
17,005
f 17,060 j
17,082
| 17,047
' 17,058
" 0.32%
0.45%
0.24%
0.31%
2037 T
16,835
] 16,896
16,916
16,879
16,893
0.36%
0.48%
0.26%
0.34%
2038 |
16,775
; 16,839 j
16,859
1 ('.822
j 16,835
0.38%
; 0.50%
0.28%
1 0.36%
2039
16,717
16,783 !
16,803
16,767
16,779
1 0.39%
' 0.51%
0.30%
0.37%
2040 j
16,751
f 16,818 7
16,837
i 16,802
16,814
0.40%
0.52%
i 0.31%
0.38%
2041
16,764
| 16,831 :
16,850
! 16,816
16,829
: 0.40%
0.51%
!" 0.31%
0.39%
2042
16,832
; 16,898
1.6,91.6
| 16,884
16,896
0.40%
f" 0.50%
0.31%
.... 0.38%
2043 j
16,918
; 16,985 J
17,001
16,971
16,983
r 0.40%
1 ~ 0.49% '
; 0.31%
0.39%
2044
16,967
1 17,034 !
17,049
17,020
17,033
* 0.40%
0.49%
1 0.32%
0.39%
2045
17,056
1 17,124 j
1". 138
: 17,1 io
17,123
= " 0.39%
0.48%
0.32%
0.39%
2046
17,153
f 17,221 |
17,233
17,207
17,220
0.40%
0.47%
0.32%
| 0.39%
2047 J
1198
! 17,266 ;
17,278
r : 17,252""
17,266
0.40%
[""" 0.46% I
0.31%
0.40%
2048 7
17,296
1 17,366 J
17,377
17,351
17,367
: " 0.40%
| 0.46%
f 0.32%
0.41%
2049
17,404
' 17,476 1
17,486
17,460
17,476
0.41%
0.47%
0.32%
j' 0.41%
2050
17,525
' 17,599 j
17,608
17,582
17,599
j 0.42%
0.47%
0.32%
0.42%
2051
17,568
I 17,643
17,653
17,626
17,644
0.43%
0.48%
! 0.33%
0.43%
2052 T
17,599
I 17,676 '
17,686
17,658
1 17,678
•' 0.43%
1 0.49%
: 0.33%
0.45% 7
2053 [
17,633
17,711 J
17,723
17,693
17,714
; 0.45%
0.51 %
0.35%
0.46%:
2054 |
17,668
: 17,750
17,762
17,731
17,753
0.46%
0.53%
! 0.36%
. 0.48%
2055
17,706
i 17,790 ;
17,803
17,771
i 17,794
0.47%
0.55%
; ' 0.37%
; 0.50%
Table 9-7 Fatality rate impacts, light-duty and medium-duty (fatalities per billion miles)
Calendar :
No
Proposal :
Alternative
Alternative
Alternative
Proposal
Alternative
Alternative
Alternative
Year
Action
1
2
3
%
1
2
3
Change
% Change
% Change
% Change
9-12
-------
2027
6.33
6.33
6.33
6.33
6.33
0.03%
0.03%
0.02%
0.01%
2028 1
6.11
6.11
6.11 71
6.11
6.11
1 0.04% 7
0.05%
0.02% 7
0.01%
2029
5.91
5.91
5.91
5.91
5.91
*0.04%
0.08%
0.03%
0.01%
2030 I
5.73
I5-73.
5.74"*
5.73" *
1 5.73" '
7 *0.04% **}
O.IO""
0.03%
0.0 i%
203 i
I5-57 *
5.57 7
5.58 1
5.57 "71
5.57"'
J '1**0.06% "11
0.13%
1 °-04% 1
0.04""
2032
5.43
"*5.43
5.43
5.43
5.43
1 0.09%
0.16%
0.07%
0.07%
2033 ¦
'5.29 7
5.30 7 j
.... . 3(j
5.30
5.30
T 0.14%
0.23 %
7 °-12%
0.14%
2034
5.18
5.19
5.19
5.18
5.19
; 0.19%
0.27%
0.15%
0.19%
2035
5.07
5.09 71
5.09
5.08
1 5-09
***! 0.23%* j
0.30%
0.19%
: 0.23%
2036
4.99
5.00 ;
5.01
5.00
5.00
*0.25%
0.32%
*0.21%
0.26%
2037 7
1.92
1.9 1
4.94
4.931
1.9 1
7 0.28% [
0.34"..
0.23%
0.29%
2038
4.87 "
4.89
17-89 7
4.88 *
1.89
1 0.30%******]*
0.36%
17.0.25% 'I""
0.30%
2039
4.84
4.85
4.85
4.85
4.85
0.31%
0.37%
0.27%
0.31%
2040 i
1.81
1.83
4.83
4.83
i 4.83
J 0.31%** *[
0.39%
0.29%
0.32%
2041 1
4.80
i.8i i
4.82
4.81
: 4.81
0.31%
0.39%
0.30%
0.33%
2042
4.79
4.80 "71
4.81
1.80
1 4.80
" 0.31% 1
0.38%
1 0.30%
0.33%
2043
4.78
; 4.80
1.80
4.80 "77
4.80
0.32% 7
0.39%
0.30%
0.33%
2044 ;
4.78
4.79
4.79
4.79
4.79
0.32%
0.39%
0.31%
0.34%
2045 j
4.77
4.79 " *i
4.79
l."9
l."9
i *0.31% *!*
0.38%
7 0.31%
0.34%
2046
4.77
4.78
4.78
4.78
4.78
0.32%
0.39%
7 0.31%
; 0.34%
2047
4.76
1114-78...
478 1
4.78
4.78
j* 0.32% 1
0.39%
0.31%
1 0.35%
2048
4.76
: 4.77""
4.78 1
I4-77!
..I4-77...
*1 0.32% 7 i
0.39%
0.31%
1 0.36%
2049
" 4.76
4.77"
4.78
4.77
4.77
T 0.33% ;
0.40%
0.31%
0.36%
2050 ]
4.76
7' 4.78 j*
4.78
4.77
4.78
T 0.34% i
Oil"..
7 0.31%
0.38%
2051
4.77
4.78
4.79
4.78
4.78
0.35%
0.42%
7 0.31%
0.39%
2052 ]
4.77
4.79 ;
479
4.78
4.79"""
1 0.36% ]
0.44% *
| 0.32%
0.40%
2053
4.77
4.79
4.80
4.79
4.79
j "0.37% ;
0.46%
| 0.32%
0.42%
2054 ]
4.78
i 4.80 7
4.80
4.79 7
7 4.80
j 0.39% 7*
0.48%
0.33%
0.43%
2055 *
4.78
4.80
4.81
4.80
4.81
0.40%
0.50%
0.34%
"* 0.45%
9.5 Estimating Fuel Consumption in OMEGA
9.5.1 Drive Cycles for Onroad Fuel Consumption
To develop a best mix of regulatory cycles representing typical onroad vehicle operation,
EPA used two sources: the MOVES light-duty drive cycles and associated weights, and
aggregate vehicle behavior gleaned from California Real Emissions Assessment Logging
(REAL) data.
The MOVES model uses 18 representative cycles. For each cycle, the time, distance, and
energy expenditure at each speed was calculated, then binned in 0.5 mph increments.
Additionally, the average speed and positive kinetic energy ("PKE;" a measure of driver
aggressiveness) was calculated. The energy expenditure was calculated using the equivalent test
weight (ETW) and road load of a nominal vehicle. Nominal vehicle characteristics were
determined using the MOVES average passenger car and light truck parameters, weighted by
vehicle miles traveled (VMT). The statistics for all cycles were combined and weighted based on
the VMT associated with each cycle. The end result indicated an average speed of 36.6 mph and
a PKE of 3700 km/hr2.
From the California REAL data, the average vehicle speed and positive kinetic energy was
determined across a range of vehicles. These data indicated an average speed higher than that
from the MOVES model data (41.1 mph), but a similar PKE (3900 km/hr2).
To represent onroad behavior, EPA began with the energy expenditure distribution from the
MOVES data, as shown in Figure 9-8. The MOVES energy expenditure distribution is shown
compared to the energy expenditure distribution of the "city" (FTP) and highway (HW) cycles,
9-13
-------
weighted 55%/45%. As can be seen, the 55/45 FTP/HW cycle has peak energy expenditure at a
noticeably lower MJ/mile value, leading to a substantially lower cumulative energy expenditure.
(The small peaks in the 55/45 FTP/HW cycle correspond to accelerations of positive and
negative 3.3 mph/sec, accelerations at which these cycles are truncated.)
9-14
-------
Energy Distribution (10,000 miles)
0.025 MJ/mile bins
Cumulative Energy Use per 10,000 miles
0.025 MJ/mile bins
Figure 9-4 Energy distribution (top) and cumulative energy use (bottom) over 10,000 miles
for the MOVES onroad data, compared to FTP/HW regulatory cycles, weighted 55%/45%.
9-15
-------
To develop a better mix of cycles to represent onroad operation, EPA looked primarily at the
energy distribution, but also factored in the distribution of speeds and the PKE. At the end, a mix
of cycles was chosen that best matched these multiple optimization criteria.
EPA evaluated reweighting the bags of the FTP, and incorporating portions of the US06
cycle. Reweighting the FTP did not improve the energy distribution match between vehicle
operation across cycles and representative onroad operation used to estimate energy use and fuel
consumption. However, incorporating the high acceleration and high-speed portions of the US06
did improve the energy distribution match with the MOVES data. Moreover, with the inclusion
of the US06 cycle, incorporating the HW cycle conferred no benefit, and this cycle was dropped.
After considering the effects of various cycle mixes, EPA selected a mix of cycles where the
weighting was 27% FTP, 6% US06 bag 1 (a high acceleration "city" bag), and 67% US06 bag 2
(a high speed "highway" bag). The energy expenditure distribution for this new cycle mix is
shown in Figure 9-9, again compared to the MOVES data. As can be seen, the energy
distribution of this cycle mix is much better aligned with the MOVES data, and the total positive
energy expended is nearly identical.
9-16
-------
Energy Distribution (10,000 miles)
250
200
i 150
bo
c 100
>>
no
£ 50
-50
MOVES Energy
New Cycle Mix
V M
Braking
~
Accel
-2
0 1
0.025 MJ/mile bins
4000
3500
3000
—
2500
I
2000
Clfl
i—
_ro
500
E
3
0
u
-500
-1000
Cumulative Energy Use per 10,000 miles
MOVES Energy
•New Cycle Mix
-2
-10 1
0.025 MJ/mile bins
Figure 9-5 Energy distribution (top) and cumulative energy use (bottom) over 10,000 miles
for the new cycle mix (27% FTP, 6% US06 bag 1, 67% US06 bag 2) compared to the
MOVES onroad data.
9-17
-------
In choosing this new cycle mix, EPA also considered the speed distribution of the mix and the
PKE. This mix of cycles had a PKE of 4300 km/hr2 (slightly higher than the MOVES or REAL
data) and an average speed of 40.6 mph. This average speed is higher than that of the MOVES
data, and closer to (but lower than) the REAL data. The speed distribution for this mix is shown
in Figure 9-10.
As can be inferred from Figure 9-10, the FTP and US06 cycles have substantial periods of
operation within a small speed window, giving the speed distribution the clear double-humped
shape. However, the overall speed profile remains similar that from the MOVES data.
9-18
-------
Speed Distribution
C
^ 20%
JZ
u
CO
QJ
-O 15%
QJ
>
CD
CJ
£
!£?
Q
<4—
o
c
o
*4—'
u
ro
10%
5%
0%
—•—MOVES data
—•—NewCycle Mix
20
40
5mph bins
60
80
Cumulative Speed Distribution
"O
QJ
QJ
>
CD
o
£
¥2
O
s—
O
c
o
4—'
u
ro
*+-
(U
>
V-»
_ro
13
E
=3
u
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
MOVES data
•New Cycle Mix
20
40
5mph bins
60
80
Figure 9-6 Speed distribution for the new cycle mix (27% FTP, 6% US06 bag 1, 67% US06
bag 2) compared to the MOVES onroad data.
9-19
-------
To estimate fuel consumption impacts, OMEGA considers both the fuel(s) used by a given
vehicle and the share of miles driven by the vehicle on that fuel or fuels. For a fossil fuel-only
vehicle (including HEVs) or a BEV, the share of miles driven on the primary fuel would be 100
percent. For a PHEV, the share of miles driven on each fuel, the primary fuel (presumably liquid
fuel), and the secondary fuel (presumably electricity), are considered.
First, the vehicle miles traveled for the given vehicle on each fuel is calculated as below.
Where,
VMTvshicie;fuei = the VMT of the vehicle on a given fuel
VMTvehide = the VMT of the vehicle
FuelShare = the share of miles driven on a given fuel
9.5.2 Electricity Consumption
For BEVs, the fuel share value will be 1, or 100 percent of VMT using electricity. To estimate
fuel consumption, the VMT is multiplied by the rate of energy consumption, or kWh/mile during
onroad operation. The rate of energy consumption during onroad operation is calculated using
the 2-cycle certification rate of energy consumption and a traditional 2-cycle to onroad gap
value, as below.
(kWh /wVt^vehicieionroad = rate of energy consumption during onroad operation
(kWh /wYc,)vehicle:2-cycie = the rate of energy consumption during the certification 2-cycle test
0.7 = the factor to account for losses associated with roadway and environmental factors not
captured on the 2-cycle certification test
Electricity consumption is then calculated as:
VMTVehicle-,fUel VM7vehicle
x FuelShare
Where,
vehicle-, onroad
Where,
FuelConsumptiomehide; electricity = the electricity consumption of the given vehicle
VMTvehide; electricity = the vehicle miles traveled on electricity
(kWh/mile)vehide; onroad = the vehicle rate of energy consumption onroad
9-20
-------
9.5.3 Liquid-Fuel Consumption
For liquid fuel consumption, OMEGA calculates the onroad fuel consumption rate making
use of the onroad C02/mile and the C02 content of a gallon of gasoline, as below.
(C02\
/Gallons\ = \mile) vehicle,2_cycle ^ /_C02_\
V mile )vehicle,onroad 0.8 dllOTL/ ve^jcje. 2-cycle
Where,
(Gallons/mile)vsbic\s-omo&d = the fuel consumption rate of the given vehicle onroad
(C()2 milejychide: 2-cycie = the C02/mile of the given vehicle on the
(C02/gallon)Vehkie; 2-cycie = the C02 emitted from combustion of a gallon of fuel (8,887 for
gasoline, 10,180 for diesel)
0.8 = the factor to account for losses associated with roadway and environmental factors not
captured on the 2-cycle certification test
Liquid-fuel consumption is then calculated as below.
(Gallons\
FuelC onsumptionvehicie.Uquid — VMTvejlicie.iiqUici X I -7 j
\ TrllLe / vehicle; onroad
Where,
FuelConsumptiomehide; liquid = the liquid-fuel consumption of the given vehicle
VMTvshide; liquid = the vehicle miles traveled on liquid-fuel
(Gallons/mile)vsbic\s- onroad = the vehicle rate of liquid-fuel consumption onroad
9-21
-------
9.5.4 Summary of Fuel Consumption in the Analysis
Table 9-8 Fuel consumption impacts, proposed standards, light-duty and medium-duty
Calendar
Liquid Fuel
Electricity
; Liquid Fuel
; Electricity
Year
i (billion gallons)
(TWh)
% Change
! % Change
2027
-0.89
8.9
-0.62%
12%
' 2028
-2.2
21 "
! -1.6%
21%'"
2029
-4
38
; -3.0%
29%
2030
'J -6.1
.......56'"'
i -4.8%
1 35%
2031
-8.6
78
-7.1%
42%
2032
....I:12 I.
100
-9.9%
49%
2033
-15
130
-13%
] 57%
2034
-IS
160
r' -i6% "
64%
2035
-21
190
-20%
70%
2036
-23
210 '
; -23%""
73%
2037
-26
230
! -26%
78%
2038
-29
260
-29%
00
2039
-31
280
-32%
00
2040
-34
300
I -35%
92%
204 i
f -36
320
; -37%
"96% "
2042
-3 8 "
340
! .1 -39%"""
r 1.98%""
' 2043
-40
360
-41%
102%
2044
-41
370
-43%
10.!",,
2045
-42
380
'" -44%
104%
2046
7 -44
390
-46%'
; 107%
2047
-45
400
-47%
I107%
2048
i -46 "" '
410
-47%
; 109%
2049
-47
420
; -48%
; 110%
2050
-48
430
I -49%
110%
2051 ....
-48
430
; -49%
111%
2052
-48
430
! -49%
111%
2053
-49
440 " "
-50%
111%
2054
-19
111!
: -50%
r; m%
2055
-49
440
i -50%
110%
Sum
-900
8100
One Terawatt hour (TWh) is equal to 1 billion kilowatt hours (kWh)
9-22
-------
Table 9-9 Fuel consumption impacts, Alternative 1 standards, light-duty and medium-duty
Calendar
Liquid Fuel
Electricity
; Liquid Fuel
; Electricity
Year
i (billion gallons)
(TWh)
% Change
! % Change
2027
-0.93
9.3
-0.65%
13%
2028
-2.5
23 '
-18"..
| 23%
2029
1. "4-4I
39
-3.4%
r 31%
2030
-7
61
: -5.6%
; 39%
203 i
; -9.8
84
-8.1%
45% '
2032
: -13
110
-11.1%
52%
2033
-17
140 '
-15%
i 6i% ~
2034
'J -20 '
170
-18%
69%
' 2035
-23
200
-22%
76%
2036
r -26
230
-25%
' 79%
2037
-29
260
-29%
00
L/l
2038
-32 3
280
*7-32%I
91%
2039
-35
310
| -36%
96%
2040
-38
330
; -39%
" 101%
2041
-10
360
; -41%
106%
2042
-42
370
| -44%
109%
2043 "" ""
-44
400
-46%
1 13"..
2044
-46
410
-48%
115%
2045
-47
420
-49%
116%
2046
-49 ^ '
440
! -51%
119%
2047 '
-49
450
-51%
119%
2048
-51
460
-52%'
121%
2049
-52
470
-53%
122%
2050
-52
470
-54%
Ll123%
2051
'-53
480
'-54%
123%
2052
-53
480
i -54%
123%
2053
-53^
490
; "55% 1
J 123%
2054
-54
490
i -55%
123%
2055
-54
190
-55%
123%
Sum
-1000
8900
One Terawatt hour (TWh) is equal to 1 billion kilowatt hours (kWh)
9-23
-------
Table 9-10 Fuel consumption impacts, Alternative 2 standards, light-duty and medium-
duty
Calendar
Liquid Fuel
Electricity
; Liquid Fuel
j Electricity
Year
i (billion gallons)
(TWh)
% Change
! % Change
2027
-0.65
6.4
-0.45%
9%
2028
-1.6 * **
15 77.
77 7-1.2% 77
| 15%
2029
"l -3.27
....29.77
-2.4% 7
r '23% 77
2030
-4.9
44
7' -3.8%
28%
203 i
; -7
6477
7-5.8% 77"
34%
2032
: -9.6
86
-8.3%
7 40%
2033
-13
110
-n%77
: 77 49% 77
2034
-16
140
-14%
! 56%
2035
-19
170
-17% 77
62% 777
2036
.7-2 i"'
190
77 -20%77
... 65% 77
2037
-23""
210
-23%
| 71%
2038
7. -26
230
-26% 7
7 76% 7
2039
-28
260
| -29%
80%
2040
-31 7
280 7
-31% 77
7" 84%
2041
-33
300 '
-34% 77.
00
2042
-34
310
i -36%
! 91%
2043
-36 7
330
! -38% 77
; 9i"„
2044
-37
340
-39%
r' 95%
2045
-38
350
-40%
7 7 96% 777
2046
7-40
360
-41% 777 7
: 99% 7
2047
-40
370
-42%
99%
2048
-42 7
7 7 380
-43% 77
100",,
2049
-42
390
-44%
101%
2050
-43 77
390
77:44% 77
102%
2051
-43
400
j -44%
102%
2052
-44
400
; -45% 77
102%
2053
-44
400
-45% 77
102%
2054
-44
400
-45%
101%
2055
-44
400
; -45%
101%
Sum
-810
7400
One Terawatt hour (TWh) is equal to 1 billion kilowatt hours (kWh)
9-24
-------
Table 9-11 Fuel consumption impacts, Alternative 3 standards, light-duty and medium-
duty
Calendar
Liquid Fuel
; Electricity
; Liquid Fuel
; Electricity
Year
i (billion gallons)
(TWh)
% Change
! % Change
2027
-0.53
5.4
-0.37%
7%
2028
-1.3
13 7
777-1.0% 7
| 13%'"
2029
1. "2-3 I.
22
-1.8% 7
r 17%
2030
-3.9
36
7' -3.1%
'" 23%
203 i
; -6.3
58 7
' -5.2% 77
31%
2032
: -9.3
85
-8.1%
40%
2033
-13 "
7 110
-11%.7
i 77490/01
2034
-16
140
-15%
58%
2035
-19 * *
"7i7°7
-18% 7
7 65%
2036
.7-22'"""
190
-21%
00
2037
-25
220
-25%
74%
2038
f .77 ....-28 77' .
250
-28%
80%
2039
-30
270
-31%
00
L/i
2040
-33 7
"290
"34%
i 90%
2041
-35 7
320 '"
-36% "7
95%
2042
-37
330
f -39%
97%
2043
-39
350
]"7" -41%"
101%
2044
-41
; 370
" -42%
103%
2045
-42'7
: 7 380'7
¦ " -44% 7
104%
2046
7-44 7
390
7 ~-45%
107%
2047
-44
400
I -46%
107%
2048
-46 "7
111!
i .7747% 7.7
109%
2049
-47
420
-48%
111%
2050
77.-48 7
430
-49%
112% 7
2051
-48
440
-49%
112%
2052
-18
7 440
-49%'
7 i !2%
2053
-19
111!
77 -50% 7"
112%
2054
-49
440
-50%
112%
2055
-19
440
; -50%
111%""
Sum
-870
7900
One Terawatt hour (TWh) is equal to 1 billion kilowatt hours (kWh)
9.6 Estimating Emission Inventories in OMEGA
To estimate emission inventory effects due to a potential policy, OMEGA uses, as inputs, a
set of vehicle and electricity generating unit (EGU) emission rates. In a circular process, we first
generate emission inventories using very detailed emissions models that estimate inventories
from vehicles (EPA's MOVES model) and EGUs (EPA's Power Sector Modeling Platform,
v.6.21 ). The generation of those inventories is described in Chapter 8 and Chapter 5,
respectively. However, upstream inventories (EGUs) made use of a set of bounding runs that 1
ooked at two possible futures—one with a low level of fleet electrification and another with a
higher level of electrification. These bounding runs represented our best estimate of these two
possible futures—the continuation of the 2021 FRM (lower) and our proposal (upper)—at the time
that those model runs were conducted. With those bounded sets of inventories, and the associated
fuel demands within them (i.e., electricity demands for EGUs), we can calculate emission rates
for the two ends of these bounds. Using those rates, we can interpolate, using the given OMEGA
policy scenario's fuel demands, to generate a unique set of emission rates for that OMEGA
policy scenario. Using those unique rates, OMEGA then generates emission inventories for any
future OMEGA policy scenario depending on the liquid fuel and electricity demands of that
specific policy.
9-25
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For vehicle emissions, EPA made use of two sets of MOVES emission inventory runs—one
assuming no future use of gasoline particulate filters and one assuming such use. Using the miles
traveled (for tailpipe, tire wear and brake wear emissions) and liquid fuel consumed (for
evaporative and fuel spillage emissions), we can then generate sets of emission rates for use in
OMEGA. Using those rates, which are specific to fuel types and vehicle types (car vs. truck,
etc.), we can then generate unique emission inventories for the given OMEGA policy scenario.
This is important given the changing nature of the transportation fleet (BEV vs ICE, car vs CUV
vs pickup) and the way those change for any possible policy scenario and the many factors
within that impact the future fleet composition and the very different vehicle emission rates for
BEV vs ICE vehicles. This is especially true given the consumer choice elements within
OMEGA and the wide variety of input parameters that can have significant impacts on the
projected future fleet.
9.6.1 Calculating EGU Emission Rates in OMEGA
As described in Chapter 5 and presented in Chapter 5.2.3, EPA has generated EGU
inventories for the no-action case and the proposal. Those inventories are presented in Tables 5-2
and 5-3 and are shown graphically in the accompanying charts. To generate those inventories,
EPA first ran OMEGA to estimate PEV energy demands into the future. Those energy demands
were used in the modeling of EGU inventories presented in Chapter 5. EPA then uses the
resultant inventories along with the associated "Generation" values shown in Tables 5-2 and 5-3,
appropriately, and the estimated PEV energy demands from OMEGA used in generating the
EGU inventory results, to generate a set of curves as a function of years from 2020. The set of
curves consist of US generation, US PEV consumption and EGU emission rate curves for each
of the pollutants presented in Chapter 5. The resultant curves for select pollutants are shown in
Table 9-12.
Table 9-12 Select EGU emission rate curves used in OMEGA
case
Pollutant
Emission Rate (g/kWh)
2021 FRM
2021 FRM
2021 FRM
2021 FRM
2021 FRM
2021 FRM
2021 FRM
proposal
proposal
proposal
Proposal
proposal
proposal
proposal
PM2.5
NOx
SOx
VOC
C()2
CH4
N2C)
PM2.5
NOx
SOx
VOC
C()2
CH4
N2C)
-0.00044234 * (CY - 2020) + 0.01622
-0.0030907 * (CY - 2020) + 0.097841
-0.0029835 * (CY - 2020) + 0.083245
-0.00019251 * (CY - 2020) + 0.0078643
-8.50323 * (CY - 2020) + 286.645
-0.000575 * (CY - 2020) + 0.017952
-8.0208e-05 * (CY - 2020) + 0.0024539
-0.00044425 * (CY - 2020) + 0.016266
-0.0030975 * (CY - 2020) + 0.09796
-0.0029965 * (CY - 2020) + 0.083798
-0.00019149 * (CY - 2020) + 0.0077913
-8.50171 * (CY - 2020) + 287.643
-0.00057841 * (CY - 2020) + 0.018106
-8.0707e-05 * (CY - 2020) + 0.0024761
Note: CY = calendar year; g/kWh = grams per kilowatt hour; all values use 6 significant digits.
US Electricity GenerationiowPEV
= 82,260,700,000 * (CY - 2020) + 3,903,690,000,000
US Electricity GenerationhighPEV
= 92,384,200,000 * (CY - 2020) + 3,861,790,000,000
9-26
-------
US PEV ConsumptioriiowpEV = 13,975,600,000 * (CY — 2020) + 27,523,300,000
Where,
lowPEV= low PEV penetration, i.e., the 2021 FRM
highPEV= high PEV penetration, i.e., the proposal
CY = calendar year
Using these curves, OMEGA can calculate the US electricity generation in any year of the
analysis as well as the PEV consumption used in estimating the EGU inventories presented in
Chapter 5.
To estimate the unique EGU emission rates for any given OMEGA scenario, OMEGA first
determines the PEV consumption estimate for a given year, which is driven by the level of the
standards and the expected PEV penetration rate, among other impacts (consumer acceptance,
critical materials, etc.). OMEGA then subtracts from the estimated US generation value for that
year, calculated using the above US Electricity Generation curve, the PEV consumption estimate
used in generating the inventories, calculated using the above US PEV Consumption curve, then
adds to that result the OMEGA estimated PEV consumption for the given year in the given
OMEGA scenario. That result is then used as a new US generation value, unique to the given
OMEGA scenario.
G 6Y16T QtiOYl scenar i0
= GeneratioriiowpEV — US PEV ConsumptioniowPEV
+ US PEV Consumptionscenario
OMEGA then calculates the EGU emission rate for each pollutant in the low PEV (2021
FRM) case and the high PEV (proposal) scenarios, both of which are calculated using the rate
curves shown in Table 9-12. OMEGA then interpolates a set of EGU emission rates unique to
the given scenario as below.
Rdt6scenari0 RdtGiowpEy
(GeneratioriiowpEV G GTiGvatioTt^Q^YiciTio^
(RatelowPEV ~ RatehighPEV)
{GenerationiowpEV G GixcvcitioTtj^igj^p^y^
Where, for a given pollutant in a given year of a given OMEGA scenario,
RdtCsccmiho = the EGU emission rate in the scenario
Rateiowpev = the EGU emission rate calculated using the 2021 FRM rate curves in Table 9-12
itoehighPEv = the EGU emission rate calculated using the proposal rate curves in Table 9-12
GenerationiowFEv = US electricity generation using the low PEV curve
9-27
-------
GenerationbighYYN = US electricity generation using the high PEV curve
(jeneratiorhccnMM = US electricity generation in the scenario using the equation above
9.6.2 Calculating Refinery Emission Rates in OMEGA
As presented and discussed in Chapter 8.2.2 of this DRIA, the illustrative AQM done by EPA
showed refinery emission inventories as shown in Table 9-13.
Table 9-13 Refinery emissions in AQM inventories in 2055
: Pollutant 2016 Reference Scenario LMDV Regulatory Scenario ;
i (tons/vear) (tons/vr) (tons/vr)
PM2.5 78.332 18.855 18.468
NOX 19.958 67.470 66.067
S02 30.065 28.851 28.281
VOC 67.853 56.946 55.876
Using AEO 2021, Table 11, we estimated that the U.S. produced 194 billion gallons of
gasoline and diesel fuel in calendar year 2021 which represented 64 percent of the refined
products produced by U.S. refineries, the rest being liquified petroleum gas, jet fuel, home
heating oil and other. Using these 2021 gallons and attributing them to the 2016 inventories (in
the absence of 2016 gallons or 2021 inventories), we arrived at 2016 refinery emission rates as
shown in Table 9-14 (e.g., for PM2.5, 78,332 tons/year x 907185 grams/ton divided by 194 billion
gallons divided by 0.64 gasoline and diesel share = 0.578 grams/gallon, where rounding might
result in slight differences). We followed the same procedure to estimate refinery emission rates
in the LMDV regulatory scenario by dividing the tons/year shown in Table 9-13 by the estimated
gallons of gasoline and diesel fuel associated with those inventories, or 131 billion gallons (see
Chapter 5.1 of the Air Quality Analysis TSD). Those refinery emission rates are also shown in
Table 9-14. Using the refinery emission rates shown in Table 9-14, we then calculated linear
curves between the years 2016 and 2055, with years from 2016 as the independent variable, for
use as inputs to OMEGA. Those refinery emission rate curves are shown in Table 9-15.
Table 9-14 Refinery emission rates estimated using AQM results
Pollutant
2016
LMDV Regulatory Scenario
; (grams/gallon)
(grams/gallon)
NOx
0.578
0.456
PM2.5
0.147
0.128
SOx
0.222
0.195
VOC
0.500
0.386
Table 9-15 Refinery emission rate curves used in OMEGA
: Pollutant : Emission Rate (grams/gallon)
NOx ; -0.00311 * (CY-2016)+ 0.578
i pM25 : -0.00050 *(CY-2016)+ 0.147 :
SOx ; -0.00068 *(CY- 2016) + 0.222
VOC ; -0.00294* (CY-2016)+ 0.500
Importantly, the AQM for refineries as presented in Chapter 8 of this DRIA includes only the
pollutants discussed there and briefly here. This means that we do not estimate GHG-related
refinery emission impacts in OMEGA at this time. Note also that OMEGA applies a 93 percent
9-28
-------
factor to reduced liquid-fuel demand to account for the share of reduced demand resulting in
reduced domestic refining of liquid fuel. In other words, 93 percent of the reduced liquid fuel
demand results in reduced domestic refining. We also ran a sensitivity that assumes that reduced
liquid fuel demand would have no impact on domestic refining. In that sensitivity, we would be
assuming that the excess liquid fuel would be exported for use elsewhere.
9.6.3 Vehicle Emission Rates in OMEGA
As detailed in a memo to the docket, EPA developed an updated version of MOVES3,
MOVES3.R1, for use in estimating vehicle emissions for this proposal. (Beardsley 2023) To
create inputs for OMEGA, EPA ran MOVES3.R1 model for two scenarios: gasoline engines
with and without gasoline particulate filters (GPFs). The emission rates for these scenarios
differed in that in the scenario with GPFs, the emission rates for exhaust PM were calculated by
applying the GPF reduction factors described in Chapter 8 for MY 2030 and later. We ran
MOVES in inventory mode to create inventory and activity output by calendar year, model year,
fuel type, source type and regulatory class for brake wear, tire wear, start, running and
evaporative emissions for criteria emission precursors and air toxics. In these runs, the only air
toxics affected by GPFs were particle-phase PAHs, which are chained to exhaust PM in
MOVES. We consolidated the PAH output to separately report emissions for naphthalene and
for a potency-weighted (U.S. EPA 2021) sum of the 15 other PAHs estimated by MOVES.
These two sets of MOVES output were then used to generate vehicle emission rate curves for
use as OMEGA inputs. Since MOVES generates emission inventories, and the applicable miles
traveled or gallons consumed attributes associated with those inventories, we can calculate
nationwide vehicle emission rates from them. This is done by calculating a linear relationship
between the vehicle miles traveled, or gallons consumed, and the inventory attribute. This
process generates over 1,400 vehicle emission rate curves including curves for each of the
pollutants and start years shown in Table 9-16 with rates for exhaust PM2.5 shown in Table 9-16
through Table 9-20 for cars, light-duty trucks, medium-duty vans and medium-duty pickups,
respectively. A start year refers to the model year for which a certain set of emission rate curves
would apply, until a subsequent start year becomes more appropriate and is, therefore, used
instead.
9-29
-------
Table 9-16 Pollutants for which vehicle emission rate curves were generated for use in
Start Years
1995,2000,
2005.
2010, 2015,
2017.
2020, 2025,
2030
RegClass:
SourceTvpe
Car:passenger car
Truck:passenger truck
Mediumdutv:passenger
truck
M e diumdutv: light
Commercial truck
Mediumdutv: short-haul
single unit class3
Mediumdutv:long-haul
single unit class3
Mediumdutv:motor home
OMEGA
Vehicle attribute
Miles traveled
Fuel
Gasoline; Diesel
Gallons
consumed
Gasoline; Diesel;
Electricity
Gasoline; Diesel
Gasoline
Gasoline; Diesel
Gasoline
Gasoline; Diesel
Diesel
Gasoline; Diesel
Pollutant
Exhaust CO
Exhaust NMOG
Exhaust NOx
Exhaust CH4
Exhaust N20
Exhaust PM2.5
Brakewear PM2.5
Tirewear PM2.5
Exhaust Acetaldehvde
Exhaust Acrolein
Exhaust Benzene
Exhaust 1.3 Butadiene
Exhaust Ethvlbenzene
Exhaust Formaldehyde
Exhaust Naphthalene
Exhaust 15 PAH
Evaporative permeation NMOG
Evaporative fuel vapor venting NMOG
Evaporative fuel leaks NMOG
Refueling displacement NMOG
Refueling spillage NMOG
Evaporative permeation Benzene
Evaporative fuel vapor venting Benzene
Evaporative fuel leaks Benzene
Refueling displacement Benzene
Refueling spillage Benzene
Evaporative permeation Ethvlbenzene
Evaporative fuel vapor venting
Ethvlbenzene
Evaporative fuel leaks Ethvlbenzene
Refueling displacement Ethvlbenzene
Refueling spillage Ethvlbenzene
Refueling spillage Naphthalene
Exhaust SOx
9-30
-------
Table 9-17 Exhaust
MYs starting in
Fuel
1995
; pump gasoline
1995
pump diesel
2000
pump gasoline
2000
pump diesel
2005
pump gasoline
2005
pump diesel
2010
pump gasoline
2010
pump diesel
2015
; pump gasoline
2015
pump diesel
2017
: pump gasoline
2017
pump diesel
2025
pump gasoline
2025
pump diesel
2030
pump gasoline
2030
pump diesel
PM2.5 emission rates
No GPF (No Action)
2.0575e-05 * age+ 0.02556
1.5354e-05 * age + 0.024823
0.00039934 * age + 0.0036308
0.00037089 * age + 0.0033971
0.00011892 * age + 0.00082091
9.7804e-05 * age + 0.00067925
0.00016215 * age + 0.00090948
0.00012123 * age + 0.00067543
0.00028219 * age + 0.0017636
0.00021421 * age+ 0.001328
0.00020321 * age + 0.0017372
0.00015369 * age + 0.0013093
0.0001862 * age + 0.0019928
0.00014096 * age + 0.0014835
0.00018462 * age + 0.0019789
0.0001397 * age + 0.0014724
, cars, grams/mile
With GPF
(Proposal)
Same
as
No GPF
Same
as
No GPF
Same
as
No GPF
Same
as
No GPF
Same
as
No GPF
Same
as
No GPF
Same
as
No GPF
Same
as
No GPF
Same
as
No GPF
Same
as
No GPF
Same
as
No GPF
Same
as
No GPF
Same
as
No GPF
Same
as
No GPF
9.67e-06 * age + 9.8351e-05
7.2475e-06 * age + 7.259e-05
Table 9-18 Exhaust PM2.5 emission rates, light-duty trucks, grams/mile
MYs starting in
Fuel
No GPF (No Action)
With GPF (Proposal)
1995
pump gasoline
= ((-1.6181e-05 * age)+ 0.025071) ;
Same as No GPF
1995
pump diesel
: ((-3.6539e-06 * age) + 0.023303) :
Same as No GPF
2000
pump gasoline
; ((0.00035199 * age)+ 0.0083178) *
Same as No GPF
2000
pump diesel
: ((0.00034587* age)+ 0.0079013)
Same as No GPF
2005
pump gasoline
: ((0.00013083 * age)+ 0.0021268) ;
Same as No GPF
2005
pump diesel
s ((0.0001082 * age)+ 0.0017689) :
Same as No GPF
2010
pump gasoline
i ((0.00017035 * age)+ 0.0021839)
Same as No GPF
2010
pump diesel
i ((0.00012752 * age)+ 0.0016461) :
Same as No GPF
2015
pump gasoline
((0.00030918 * age)+ 0.0030238) i
Same as No GPF
2015
pump diesel
i ((0.00023035 * age)+ 0.0022633) !
Same as No GPF
2017
pump gasoline
((0.00020713 * age) + 0.0030495) :
Same as No GPF
2017
pump diesel
! ((0.00015328 * age)+ 0.0022712) ¦
Same as No GPF
2025
pump gasoline
i ((0.0001385 * age) + 0.0025349) ;
Same as No GPF
2025
pump diesel
= ((0.00010184* age)+ 0.0018502)
Same as No GPF
2030
pump gasoline
((0.0001346 * age)+ 0.0025428) i
((6.675e-06 * age) + 0.00012137)
2030
pump diesel
; ((9.8859e-05 * age)+ 0.0018471)
((4.8951e-06 * age) + 8.7975e-05)
Table 9-19 Exhaust PM2.5 emission rates, medium-duty vans, grams/mile
MYs starting in
Fuel
No GPF (No Action)
With GPF (Proposal)
1995
pump gasoline
((-4.0895e-05 * age) + 0.071214)
Same as No GPF
1995
pump diesel
((-2.8641e-06 * age) + 0.8262)
Same as No GPF
2000
pump gasoline
((0.0010187 * age) + 0.02384)
Same as No GPF
2000
pump diesel
((-3.1844e-06 * age) + 0.40352)
Same as No GPF
2005
pump gasoline
((0.00075617 * age) + 0.012341)
Same as No GPF
2005
pump diesel
((-1.6069e-06 * age) + 0.27546)
Same as No GPF
2010
pump gasoline
((0.00012855 * age) + 0.010032)
Same as No GPF
2010
pump diesel
((-1.433 le-07 * age) + 0.0071294)
Same as No GPF
2015
pump gasoline
((0.00022315 * age) + 0.0074172)
Same as No GPF
2015
pump diesel
((1.712e-05 * age) + 0.0015567)
Same as No GPF
2017
pump gasoline
((0.00022617 * age) + 0.0074237)
Same as No GPF
2017
pump diesel
((1.7119e-05 * age)+ 0.001591)
Same as No GPF
2025
; pump gasoline
((0.00019164 * age) + 0.0081221)
Same as No GPF
2025
pump diesel
((1.6182e-05 * age) + 0.0015786)
Same as No GPF
2030
pump gasoline
((0.00018797 * age) + 0.0081354)
((2.1309e-05 * age) + 0.0015281)
2030
pump diesel
((1.5947e-05 * age) + 0.0015789)
((1.5947e-05 * age) + 0.0015789)
9-31
-------
Table 9-20 Exhaust PM2.5 emission rates, medium-duty pickups, grams/mile
MYs starting in
Fuel
No GPF (No Action)
With GPF (Proposal)
1995
= pump gasoline
((-4.0885e-05 * age) + 0.072197)
Same as No GPF
1995
pump diesel
((-2.7119e-06 * age) + 0.8453)
Same as No GPF
2000
pump gasoline
((0.001036 * age)+ 0.02352)
Same as No GPF
2000
pump diesel
((3.9354e-06 * age) + 0.41363)
Same as No GPF
2005
pump gasoline
((0.0007791 * age) + 0.012255)
Same as No GPF
2005
pump diesel
((6.295 le-06 * age) + 0.28231)
Same as No GPF
2010
pump gasoline
((0.00014385 * age) + 0.010083)
Same as No GPF
2010
pump diesel
((6.9872e-07 * age) + 0.0072549)
Same as No GPF
2015
pump gasoline
((0.00023683 * age) + 0.0075087)
Same as No GPF
2015
pump diesel
((1.8142e-05 * age) + 0.001583)
Same as No GPF
2017
; pump gasoline
((0.00023969 * age) + 0.0075258)
Same as No GPF
2017
pump diesel
((1.8142e-05 * age) + 0.0016193)
Same as No GPF
2025
i pump gasoline
((0.00020168 * age) + 0.0083007)
Same as No GPF
2025
pump diesel
((1.6845e-05 * age) + 0.0016144)
Same as No GPF
2030
i pump gasoline
((0.00019659 * age) + 0.0083401)
((2.1961e-05 * age) + 0.0015587)
2030
pump diesel
((1.6298e-05 * age) + 0.0016213)
((1.6298e-05 * age) + 0.0016213)
As shown in Table 9-16, rates were also generated for all pollutants, regulatory classes and
fuels. Those other rates are not shown here.
9.6.4 Calculating Upstream Emission Inventories
To calculate upstream emission inventories, OMEGA operates on individual vehicles making
use of the VMTpoiicy on each applicable fuel in the given OMEGA scenario.
For upstream emissions from EGUs, OMEGA first calculates the given vehicle's fuel
consumption according to the FuelConsumptionVehicie;eiectricity equation shown above. OMEGA
then estimates the required EGU generation by accounting for grid losses as below.
FuelConsumptionvehicle.electricity
b\l6l(j6Yl6YQ,tiOYlvp}1jrip.piprtTjrjtv — ~
vehicle,electricity transmission efficiency
Where,
FuelGeneratiorivehicie;eiectricity = the estimated EGU generation requirement to satisfy the fuel
consumption of the vehicle
FuelConsumptiomehicie;eiectricity = the electricity consumption of the given vehicle (described
above)
transmission efficiency = the estimated efficiency of grid transmission (0.935 in this case)
The estimated generation value is then multiplied by the EGU emission rates as described
above to estimate the upstream emissions according to the equation below.
rp _ j-, 1r, w R®tepoiiutant-,scenario
1 OWSvejlicie.p0iiU{-an£ ruCLuCTlCTuLlOTlpgfoicig.gigcj-yicij-y X -
CLTY\S pCT*
Where,
7bmvehicie;Poiiutant = The inventory tons (US or metric) of the given pollutant
FuelGenerationvehicie;eiectricity = the estimated EGU generation requirement to satisfy the fuel
consumption for the vehicle (see above)
9-32
-------
/&7tepollutant;scneario the EGU emission rate for the given pollutant in the given scenario
grams per ton = 1,000,000 for metric tons (GHGs) or 907,185 for US (short) tons (criteria air
pollutants)
A similar process is used for refinery emissions associated with liquid-fuel consumption
although the transmission efficiency is 1 for liquid-fuels making fuel generation value equivalent
to the fuel consumption value described in Chapter 9.5.3. Additionally, a factor to account for
the portion of fuel savings (reduced liquid fuel consumption) leading to reduced refining is also
applied as discussed in Chapter 9.6.2.
9.6.5 Calculating Vehicle Emission Inventories
A similar process to that described above for upstream emissions is used for vehicle emission
with the exception that exhaust emission rates and both brake wear and tire wear emission rates
are multiplied by the VMTpoiicy value while evaporative, spillage and leakage emission rates are
multiplied by the liquid-fuel consumption values described in Chapter 9.5.3. Exhaust emission
inventories are then added to evaporative, spillage and leakage emission inventories to arrive at
vehicle emission inventories.
9-33
-------
9.6.6 Summary of Inventories and Inventory Impacts
9.6.6.1 Greenhouse Gas Inventory Impacts
Table 9-21 Greenhouse gas emission inventory impacts, Proposed standards, light-duty and
medium-duty (million metric tons) *
Calendar
Vehicle
EGU
Year
C()2
CH4
N2C)
cm
CH4
N2C)
2027
-8
: -0.00016 ;
-0.00015
2.2
: 0.00013 "i
0.000018
2028 j
-20
; -0.00038 :
-0.00033
! 4.9
i 0.00030 T
0.000041
2029
-36
; -0.00069 j
-0.00059
f Z9.1
1 0.00052
0.00007 i
2030
-54
: -0.00100
-0.00088
; 12
f 0.00075 :
0.000100
2031 1
'"" -77
j -0.00140 j
-0.00130
I(. '
' 0.00100
0.000140
. 2032' "' 1
-100 '
-0.00190 J
-0.00170
1 21""'
! 0.00130 |
0.000170
2033
-130
; -0.00240 |
-0.00220
! 25
! 0.00150 j
0.000210
2034
-160
j "-0.00290 |
-0.00260
30
f 0.00180 J
0.000240
2035
-190
-0.00350 ;
-0.00310
:' 33
: 0.00200 !
0.000260
2036
-210
I -0.00390
-0.00350
34 '
f 0.00200 j
0.000270
2037
-230
-0.00440 j
-0.00390
36
" 0.00210 :
0.000280
2038
-260
; -0.00490 |
-0.00430
38
I 0.00220 ;
0.000290
2039
-280
; -0.00530 ;
-0.00470
;' 38
0.00220
0.000290
2040
-300
i -0.00570
-0.00510
39
! 0.00220 ;
0.000290
2041
"32°
-0.00620 J
-0.00540
38-0
;' 0.00210
0.000280
2042 ;
-340 "
:' -0.00650
-0.00570
r 37.0
0.00200 I
0.000260
2043 J
-360
; -0.00690 1
-0.00600
; 36.0
; 0.00190
0.000240
2044
-370
! -0.00710 J
-0.00620
3 1.0
i 0.00170 J
0.000220
2045
-380
i -0.00740
-0.00650
31.0
: 0.00150 j
0.000190
2046
-390
; -0.00770
-0.00670
j" 29-0
f 0.00130 !
0.000170
2047 [
-400
: -0.00780 i
-0.00680
f 26.0
!" o.ooiio :
0.000130
2048
-Ho
-0.00810
-0.00700
: "22.0 '
0.00087 :
0.000100
2049
-420
f -0.00830 J
-0.00720
: 19.0
i " 0.00063 J
0.000065
2050
-420
-0.008 10
-0.00730
j" 15.0
[ 0.00037 i'
0.000029
2051
-130
[ -0.00850 |
-0.00740
f 16.0
! 0.00037 '
0.000029
2052
-430
| -0.00860 ""j
-0.00750
16.0
[ 0.00038
0.000029
2053 Hi
:430
i" -0.00870 J
-0.00760
K..0
f 0.00038
0.000029
2054
-440
! -0.00880
-0.00770
1 16.0
0.00038
0.000030
2055 J
-110
-0.00880 J
-0.00770
f" 16.0
i o.ooo.is
0.000030
Sum
-8,000
i -0.16000
-0.14000
r 710
! 0.03500 :
0.004500
*GHG emission rates were not available for calculating GHG inventories from refineries.
9-34
-------
Table 9-22 Greenhouse gas emission inventory impacts, Alternative 1 standards, light-duty
and medium-duty (million metric tons) *
Calendar
Vehicle
EGU
Year
C()2
CH4
N2()
C()2
CH4
N2()
2027
-8
-0.00017
-0.00015
2.3
0.00014 i
0.000019
2028
-22
-0.00041
-0.00036
5.4
0.00033
0.000045
2029
-40
-0.00070
-0.00061
9
0.00055
0.000075
2030
-63
-0.00110
-0.00095
13
0.00082 ;
0.000110
2031
-87
-0.00150
-0.00130
18
0.00110 J
0.000150
2032
-120
-0.00200
-0.00180
22 ""
0.00130
0.000180
2033
-150
-0.00250
-0.00230
27
0.00170 |
0.000220
2034
-180
-0.00310
-0.00280
32
0.00190 I
0.000260
2035
-210
-0.00370
-0.00330
35
0.00210
0.000280
2036
-230
-0.00420
-0.00370
38
0.00220 J
0.000300
2037
-260
-0.00470
-0.00420
40
0.00230 1
0.000310
2038
-290
-0.00520
-0.00470
11
0.00240 |
0.000320
2039
-310
-0.00570
-0.00510
42
0.00240 !
0.000320
2040 '
-3 10
-0.00620
-0.00550
43
0.00240 1
0.000320
2041
-360
-0.00670
-0.00590
42
0.00230
0.000310
2042
-380
-0.00710
-0.00620
41
0.00220
0.000290
2013
-loo
-0.00750
-0.00650
40
0.00210
0.000270
2044
-410
-0.00780
-0.00680
37
0.00190
0.000240
2045
-420
-0.00810
-0.00700
35 ""
o.oo ro
0.000220
2046
-430
-0.00840
-0.00730
32
0.00150
0.000180
2047
-440
-0.00860
-0.00740
29
0.00120 j
0.000150
2048
-450
-0.00880
-0.00760
25 ....
0.00098 j
0.000110
2049
-460
-0.00900
-0.00780
21
0.00070 1
0.000073
2050
-470
-0.00920
-0.00800
17
0.00042 j
0.000033
2051
-470
-0.00930
-0.00810
17
0.00042 ]
0.000033
2052
-480
-0.00940
-0.00820
IS
O.OOOI2
0.000033
2053
-ISO
-0.00950
-0.00820
IS
0.00043 ' J
0.000033
2054
-480
-0.00960
-0.00830
18
0.00043 ;
0.000034
2055
-ISO
-0.00960
-0.00840
IS
0.00043 ;
0.000034
Sum
-8,900
-0.17000
-0.15000
780
0.03900 :
0.005000
*GHG emission rates were not available for calculating GHG inventories from refineries.
9-35
-------
Table 9-23 Greenhouse gas emission inventory impacts, Alternative 2 standards, light-duty
and medium-duty (million metric tons) *
Calendar
Vehicle
EGU
Year
C()2
CH4
\2()
C()2
CH4
N2()
2027
-6
: -0.00012
-0.00011
1.6
: 0.00010 1
0.000013
2028
-14
! -0.00027 :
-0.00024
: 3.5
: 0.00021
0.000029
2029
-28
i -0.00055 |
-0.00047
7
| 0.00041 i
0.000055
2030 I
-43 77
j -0.00082 1
-0.00072
7 io
7 0.00059 7
0.000080
2031
-6317
1 -0.00120 7
-0.00110
13 77
r 0.0008177
0.000110
2032
-86
; -0.00160 i
-0.00140
17
* 0.00100
0.000140
2033
-110
; -0.00210 7
-0.00190
1 I22 1
0.00130
0.000180
2034
-140
1 -0.00260 ;
-0.00240
26
i 0.00150 :
0.000210
2035
-170
; -0.0031 o
-0.00280
...2917
; 0.00170 7
0.000230
2036
-180
1 -0.00350 :
-0.00320
317"
; 0.00180 7
0.000240
2037
-210
; -0.00400
-0.00360
33
0.00190 |
0.000260
2038 I
-230
-0.00440 I
-0.00400
34
; 0.00200 7;
0.000260
2039
-250
7 -0.00480 7
-0.00440
35" '
; 0.00200
0.000270
2040 "".1
-270
! -0.00530 !
-0.00470
35 1
! 0.00200 77
0.000260
2041
-290
7 -0.00570 1 j
-0.00500
35 7
1 0.00190
0000260
2042 ;
-310
| -0.00600
-0.00530
34
7 0.00180
0.000240
2013
-320
: -0.00630 I
-0.00560
33 77
*0.00170 7
0.000220
2044
-330
-0.00660 7
-0.00580
31
! 0.00160 7
0.000200
2045 j
-340
:-0.00680 7
-0.00600
1 29 7
0.001 10
0.000180
2046 j
-360
; -0.00710 :
-0.00620
26
7 0.00120 i
0.000150
2047
-360
f -0.00730
-0.00640
24 77
r 0.00100 7
0.000120
2048
-370
f -0.00750 7
-0.00660
7 ..21.7
1 O.OOOSO
0.000092
2049
-380
i -0.00770 J
-0.00670
17
7 0.00058 1
0.000060
2050
-390
7 -0.00780 7
-0.00680
: 14 7
' 0.00034 7j
0.000026
2051
-390
f -0.00790 :
-0.00690
14
1 0.00034 :
0.000026
2052
-390
1 -0.00800 j
-0.00700
11
!' 0.00034 ;
0.000026
2053
-390
; -0.00800 1 |
-0.00710
11
7 0.00034 f
0.000026
2054
-390
i -0.00810
-0.00710
1 11
1 0.00035
0.000027
2055 7
-400
; -0.00810 7
-0.00720
14 111
i 0.00035 j
0.000027
Sum
-7,200
7 -0.14000 :
-0.13000
i 630
0.03200
0.004000
*GHG emission rates were not available for calculating GHG inventories from refineries.
9-36
-------
Table 9-24 Greenhouse gas emission inventory impacts, Alternative 3 standards, light-duty
and medium-duty (million metric tons) *
Calendar
Vehicle
EGU
Year
C()2
CH4
N2()
C()2
CH4
\2()
2027
-5
: -0.00010
-0.00010
1.3
: 0.00008 1
0.000011
2028
-12
: -0.00025 :
-0.00022
; 3.0
! 0.00019
0.000025
2029
-21
; -0.00043 j
-0.00038
5
: 0.00031 |
0.000042
2030 I
-357
f -0.00068 7
-0.00061
! 1118 7
; 0.00048 7
0.000065
2031
-56 "
7 -0.00110 j
-0.00098
1 "12
! 0.00074 j
0.000100
2032
-84
-0.00150 f
-0.00140
17'
: 0.00100
0.000140
2033
-110
j -0.00210
-0.00190
* 221
: 0.00130 7
0.000180
2034
-140
-0.00260
-0.00240
26
1 0.00160 ;
0.000210
2035
-170
; -0.00320 I
-0.00290
30
j 0.00180 1
0.000240
2036
-190
; -0.00360 j
-0.00330
7 ..32 7"
7 0.001901 1
0.000250
2037
-220
: -0.00410 !
-0.00370
"34
1 0.00200
0.000270
2038 I
-250
r -0.00460 j
-0.00420
36
i 0.00210
0.000280
2039
-270
7 -0.00510 1
-0.00460
1 37
1 0.00210 |
0.000280
2040 "".1
-290
F -0.00560 7
-0.00500
77.38 7
F 0.00210 7
0.000280
2041
-310
I -0.00600 ;
-0.00530
38 7"
: 0.0021o7
0.000270
2042 ;
-330
| -0.00640 I
-0.00560
37
; 0.00200 i
0.000260
2013
-350
: -0.00680 7
-0.00600
1 36 7
[ 0.00190 7
0.000240
2044
-360
-0.00710 7
-0.00620
34
1 0.00170
0.000220
2045 j
-370
:-0.00730 1
-0.00640
31
0.00150 j
0.000190
2046 j
-390
; -0.00760 :
-0.00670
29
: "0.00130
0.000170
2047
-400
1-0.00780
-0.00680
26 7"
i0.00110 7
0.000130
2048
-410
1 -0.00810 7
-0.00700
237.
1 0.00088 7
0.000100
2049
-420
i -0.00830 7
-0.00720
19
; 0.00063 7
0.000066
2050
-420
1" -0.00840 1
-0.00740
16
: 0.00038 7
0.000029
2051
-430
f -0.00850 :
-0.00750
16
! 0.00038 j
0.000029
2052
-430
1 -0.00860 j
-0.00760
r i61
: 0.00038 j
0.000030
2053
-130
; -0.00870 7
-0.00760
' l16 7
: 0.00038 7
0.000030
2054
-440
i -0.00880
-0.00770
16
; 0.00038 5
0.000030
2055 7
-110
i -0.00880 7
-0.00780
161
: 0.00039 7
0.000030
Sum
-7,800
f -0.15000 :
-0.13000
i 670
1 0.03300 !
0.004200
*GHG emission rates were not available for calculating GHG inventories from refineries.
9-37
-------
Table 9-25 Net Greenhouse gas emission inventory impacts, Proposed standards, light-duty
and medium-duty *
Calendar Vehicle, EGU % Change
Year (Million metric tons per year)
C()2
CH4
N2C)
C()2
; CH4 :
N2C)
2027
-5.8 'H
-0.000025
; -0.00013 !
-0.4%
"-0.1% ]
""-0.6%
2028
r ...-15 r
-0.000076
¦ -0.00029 J
-1.2%
l" -0.2% J
-1.3%
2029
-27
-0.00017
! -0.00052 :
-2.3%
i -0.4% ;
-2.4%
2030
[ -42 '
-0.00028
: -0.00078 J
-3.6%
r -0.8%
-3.8%
2031
r -60
-0.00043
; -0.0011
-5.4%
! -1.2% '
-5.7%
2032 "
.82:
-0.00062
" -0.0015 J
-7.6%
i "!-9% /
-7.9%
2033
-110
-0.00087
! -0.002
-10.1%
! "-2.9% '
-10.4%
2034
-130 ;
-0.0012
-0.0024 ""j
-13%
: -4.1% J
-13%
2035
-150 |
-0.0015
r' -0.0028
7 "16% 7.
-5.6%"":
-16%
2036
-170 |
-0.0018
-0.0032
-18%
! -7.1% i
-18%
2037
: -200
-0.0022
-0.0036 ^
-21%
f "9-0% J
"-20%
2038
-220
-0.0027
-0.004
-24%
" -11% !
-23%
2039
-240 1
-0.0031
! -0.0044
-26%
; -14% I
-25%
2040
: -260 1
-0.0036
-0.0048 !
-29%
j" -16%" j
-27%
2041
-280
-0.0041
-0.0052 J
-31%
; -19% *
-29%
2042
: -300 1
-0.0045
-0.0055 1
-34%
; -21%" J
-31%
2043
T -320 :
-0.005
r -0.0058
-36%
-24%
-33%
2044
-330'
-0.0054
; -0.006 I
-38%
[ -27% j
.734%
2045
-350
-0.0059
r -0.0063 1
-39%
i -30% J
-35%
2046
V -360 ;
-0.0063
: -0.0065
-41%
\ "-32% |
-37%
2047
-370 |
-0.0067
: -0.0067 I
J-42% ~
; -35%
-38"..
2048
-390 J
-0.0072
-0.0069 i
-44%
j' -38% |
-39%
2049
7-400 I
-0.0076
; -0.0071 /
" "45%
: .. "40% I:
-39%
2050
]"" -410
-0.008
\ -0.0073
-46%
""'-43% |
-40%
205 i
: -410 I
-0.0081
; -0.0074
'. .-46%
: "44%
-40%
2052
j' -420 j
-0.0082
f -0.0075 :
. .."47% ....
r -44% ^
-41%
2053
-420 ;
-0.0083
f -0.0076
-47%
-45% :
-41%
2054
-420 " ]
-0.0084
-0.0077
...:47% .
-45% '""I
-41%
2055
-420 :
-0.0084
-0.0077
-47%
-45% i
-41%
Sum
-7,300 ;
-0.12
-0.13
-26%
: -17% f
-25%
*GHG emission rates were not available for calculating GHG inventories from refineries.
9-38
-------
Table 9-26 Net Greenhouse gas emission inventory impacts, Alternative 1 standards, light-
duty and medium-duty *
Calendar Vehicle, EGU, Refinery % Change
Year (Million metric tons per year)
C()2
CH4
N2C)
C()2
; CH4 :
N2C)
2027
-6.1 * 1
' -0.000027
-0.00014 |
-0.5%
''-0.1%" \
-0.6%
2028
f ...-17 T.
-0.000073
-0.00031
-1.3%
r -0.2% j
-1.4%
2029
-31
-0.00015
-0.00053 :
-2.5%
; -0.4%
-2.5%
2030
[ -49
-0.00026
-0.00084 j
-4.2%
: "0.7% J
-4.1%
2031
7 -69
-0.00042
-0.0012 :
-6.2%
1 "-1.2% I
-6.0%
2032 "
-93
-0.00062
-0.0016 1
-8.6%
j -1.9%
-8.3%
2033
-120
-0.00089
-0.0021
-11.5%
-2.9% ;
-11.0%
2034
-150 "
-0.00 1 2
-0.0026 ]
"14%
:"-4.2%j
-II"..
2035
-170 1
-0.0016
-0.003 77
-17%
-5.8%
-17%
2036
-200
-0.002
-0.0034
-20%
-7.5%
-19%
2037
-220
-0.0024
-0.0039 j
-23%
! -9.6% J
-22%
2038
-250
-0.0028
-0.0043
-26%
j -12% -
' -24%
2039
-270
-0.0033
-0.0048 1
-29%
j -14%
-27%
2040
; -290 1
-0.0038
-0.0052 1
-32%
! -17% j
-29%
2041
-320
-0.0043
-0.0056
-35%
f -20% j
-32%
2042
: -330 :
-0.0048
-0.0059 1
-37%
i "23% J
7-33%
2043
T -360
-0.0054
-0.0062
-40%
[ -26% ;
-35%
2044
-370' '
-0.0059
7 -0.0065 ]
-42%
¦""-29%'j
" -37%
2045
-390
-0.0064
-0.0068 1
-43%
r32%ll
-38%
2046
V -400 ;
-0.0069
-0.0071
-45%
i -35%
-40%
2047
-III! I
-0.0073
7 -0-0073 ¦
-47%
-38% i
-4i%
2048
-430 J
-0.0078
-0.0075
-48%
| -41% i
-42%
2049
7-440 |
-0.0083
" -0.0077 J
-50%
f :44%
-43%
2050
] "' -450
-0.0088
-0.0079
-51%
\ -47% !
-43%
205 i
r -450' J
-0.0089
-0.008 |
r -5i%
; -48% "
-44%
2052
j' -460 j
-0.009
-0.0081 |
-51%
f -48% 1
7-44%;
2053
-460 ;
-0.0091
-0.0082
-52%
r" -49% ;
-44%
2054
-460 j
-0.0091
-0.0083 j
-52% ~ '
: -49% :
" -44%
2055
-460 ;
-0.0092
-0.0083 j
-52%
r -49% !
-44%
Sum
7 -8,100 i
-0.13
-0.14 i
-29%
' -18"..
-27%
*GHG emission rates were not available for calculating GHG inventories from refineries.
9-39
-------
Table 9-27 Net Greenhouse gas emission inventory impacts, Alternative 2 standards, light-
duty and medium-duty *
Calendar Vehicle, EGU, Refinery % Change
Year (Million metric tons per year)
C()2
CH4
N2C)
C()2
CH4
N2C)
2027
-4.2 '* J
-0.000021
-0.0001
: "0.3%
: "0.0%""
-0.4%
2028
-11.1;
-0.000058
-0.00021
! -09"..
" -0.1%
-1.0%
2029
-22
-0.00014
-0.00042
j -1.8%
i -0.4% "
-2.0%
2030
] -34 1
-0.00023
-0.00064
-2.9%
-0.6%
-3.1%
2031
-49 !
-0.00036
-0.00094
i -4.4%
| -1.0%
-4.8%
2032
] -69 "1
-0.00054
-0.0013
-6.4%
; -1.7%
-6.8%
2033
T -92
-0.00077
-0.0017
; -8.8%
: -2.5%
-9.2%
2034
T -120 7 J
-0.0011
-0.0022
f -11%.I
-3.7%
-12%
2035
7. "14° 11
-0.0014
-0.0026
; -14% "
; -5.0% ""
-14%
2036
-150
-0.0017
-0.0029
: -i6%
-6.4%
-16%
2037
-180
-0.002 J
-0.0033
; -19%
| -8.2%"
-19"..
2038
-200
-0.0024
-0.0037
; -21%
| -10%
-21%
2039
-220 '<
-0.0028
-0.00 II
¦-24%
¦ .:12%
-23%
2040
-240
-0.0033
-0.0044
1 -26%
; -15%
-25%
2041
-260
-0.0037
-0.0048
r -28%
f -17%
-27%
2042
-270
-0.0041
-0.0051
j -30%
1" -20%"
-29%
2043
-290
-0.0046
-0.0054
i -32%
¦ -22%
-31%
2044
: -300 ;
-0.005
-0.0056
-34% ~
f -25% ~
-32%
2045
-310
-0.0054
-0.0058
1 "'-35%'"
V "27% ....
-33%
2046
-330
-0.0059
-0.0061
-37%
-30%
-34%
2047
]' -340 "j
-0.0063
-0.0063
f " -38%
: -32%
" -35%
2048
-350 *
-0.0067
-0.0065
f -40%
r -35%
-36%
2049
-360
-0.0071
-0.0066
:' -4i%""
-38%
-37%
2050
-370
-0.0075
-0.0068
j -42%'"'
j" -40%
-37%
205 i
¦ -370
-0.0076
-0.0069
-42%
""-40 %
-38%
2052
j -380 ";
7 -0.0076
-0.007
! -42%
-41% ... "
-38%
2053
-380
-0.0077
-0.0071
-42%
:"" -41%
-38%
2054 """"
; " -380
-0.0077
-0.0071
i "43%
i -41%"
-38%
2055
-380
-0.0078
-0.0072
"-43%
-42%
-38%
Sum
-6,600 ;
-0.11
-0.12
; -23%
r -15%
-23%
*GHG emission rates were not available for calculating GHG inventories from refineries.
9-40
-------
Table 9-28 Net Greenhouse gas emission inventory impacts, Alternative 3 standards, light-
duty and medium-duty *
Calendar Vehicle, EGU, Refinery % Change
Year (Million metric tons per year)
C()2
CH4
N2C)
CC)2
CH4
N2C)
2027
-3.4 '* J
-0.000023
; -0.00009
: "0.3%
: "-0.1%
-0.4%
2028
-8 9
-0.000062
7 -0.00019
1 -°-7%
" -0.1%
-0.9%
2029
-16
-0.00012
! -0.00033
j -1.3%
i -0.3%
-1.6%
2030
j"' .j"27
"" -0.0002 J
1 -0.00054
: :23%
-0.5%
-2.6%
2031
-44
-0.00033
f -0.00088
\ -4.0%
f -1.0%
-4.4%
2032
J" -66
-0.00051
-0.0013
; -6.2%
"-1.6%
-6.7%
2033
1 -91 ]
-0.00075
7 -0.0017
j -8.7%
! -2.5%
-9.2%
2034
T -120 ;
-0.001
-0.0022
f -1 I""
r -3.7% 7
-12%
2035
-i io ;
-0.0014
-0.0027
"14%"
1 ,-5.1%
-15%
2036
-160
-0.0017
-0.003
i -17%
: -6.6%
-17%
2037
7 -190
-0.0021
-0.0035
: -20%
7'-8.5%'7
-19%
2038
-210
-0.0026
-0.0039
! -22%
-11%
"" -22%
2039
-230
-0.003
-0.0043
| -25% ~
I -13 %
-24%
2040
-250
-0.0035
; -0.0047
-28%
' -15%77
-27%
2041
-280
-0.0039
* -0.0051
-31%
: -18%
-29%
2042
-290
-0.0044
-0.0054
7-33%
f -21%
-31"..
2043
-310
-0.0049
-0.0057 '
' -35%
r -24%
-32%
2044
T -330 7 j
-0.0053
-0.006
f -37%
i -26%
-34%
2045
-340
-0.0058
= -0.0062
i -39%
r "29% 7
-35%
2046
-360
-0.0063
-0.0065
i -41%
-32%
-37%
2047
"7 ...-370 7 7
-0.0067
-0.0067
: '-42%7
r -35% 7
-38"..
2048
-390 *
-0.0072
-0.0069
; -43%
; -38%
-39%
2049
-400
-0.0076
-0.0071
f -45% '
r -4o% 77
-39"..
2050
-410
-0.0081
-0.0073
1 " -46%
"-43%
-40%
205 i
¦ -410
-0.0082
: -0.0074
-46%
f """-44% 77.
-41%
2052
J -420 7
-0.0083
-0-0075
f -47%
7-44% 77
-41%
2053
-420
-0.0083
-0.0076
i -47%
! -45%
-41%
2054 ""
r -42°
-0.0084
7 " -0.0077
; -47%
i -45% 77"
-41%
2055
-420
-0.0084
f -0.0077
: -47%
7" -45%
-41%
Sum
7-7,100;
-0.12
-0.13
| -25%
7 -16%
-24%
*GHG emission rates were not available for calculating GHG inventories from refineries.
9-41
-------
9.6.6.2 Criteria Air Pollutant Inventory Impacts
Table 9-29 Criteria air pollutant impacts from vehicles, Proposed standards, light-duty and
medium-duty
(US tons per year)
Calendar
PM2.5
NOx
NMOG
SOx
CO
Year
2027
-68
-720
-1.100
-50
-24.000
2028
i -170
-1.700
-3.400
-130
-61.000
2029
< -310
-3.200
-7.200
-230
-110.000
2030
: -790
-4.800
-12.000
-350
-180.000
2031
i -1.300
-6.800
-18.000
-490
-250.000
2032
; -1.800
-9.100
-25.000
-650
-330.000
2033
; -2.300
-12.000
-33.000
-830
-430.000
2034
* -2.900
-14.000
-42.000
-1.000
-530.000
2035
, -3.400
-17.000
-52.000
-1.200
-640.000
2036
i -4.000
-19.000
-62.000
-1.300
-720.000
2037
i -4.500
-21.000
-73.000
-1.500
-820.000
2038
; -5.100
-24.000
-85.000
-1.600
-930.000
2039
* -5.600
-26.000
-96.000
-1.800
-1.000.000
2040
i -6.100
-28.000
-110.000
-1.900
-1.100.000
2041
; -6.600
-30.000
-120.000
-2.000
-1.200.000
2042
: -7.000
-32.000
-130.000
-2.100
-1.300.000
2043
-7.500
-33.000
-140.000
-2.300
-1.400.000
2044
; -7.900
-35.000
-150.000
-2.300
-1.400.000
2045
-8.200
-36.000
-160.000
-2.400
-1.500.000
2046
-8.500
-37.000
-170.000
-2.500
-1.600.000
2047
; -8.800
-38.000
-180.000
-2.500
-1.600.000
2048
; -9.000
-39.000
-180.000
-2.600
-1.700.000
2049
: -9.200
-40.000
-190.000
-2.600
-1.700.000
2050
; -9.400
-41.000
-190.000
-2.700
-1.700.000
2051
: -9.500
-42.000
-200.000
-2.700
-1.800.000
2052
-9.600
-43.000
-200.000
-2.700
-1.800.000
2053
! -9.700
-43.000
-200.000
-2.700
-1.800.000
2054
; -9.800
-44.000
-200.000
-2.800
-1.800.000
2055
-9.800
-44.000
-200.000
-2.800
-1.800.000
9-42
-------
Table 9-30 Criteria air pollutant impacts from vehicles, Alternative 1 standards, light-duty
and medium-duty
(US tons per year)
Calendar
; PM2.5
NOx
NMOG
SOx
CO
Year
2027
-70
-750
-1.200
-53
-25.000
2028
i -180
-1.800
-3.600
-140
-65.000
2029
, -320
-3.100
-7.200
-250 =
-110.000
2030
-790
-4.900
-12.000
-400
-180.000
2031
-1.300
-6.900
-19.000
-550
-260.000
2032
-1.800
-9.300
-26.000
-730
-350.000
2033
i -2.300
-12.000
-35.000
-940
-450.000
2034
; -2.900
-15.000
-46.000
-1.100
-570.000
2035
i -3.400
-18.000
-57.000
-1.300
-680.000
2036
-4.000
-20.000
-69.000
-1.500
-780.000
2037
i -4.500
-23.000
-81.000
-1.700
-900.000
2038
-5.100
-25.000
-94.000
-1.800
-1.000.000
2039
: -5.600
-27.000
-110.000
-2.000
-1.100.000
2040
* -6.100
-30.000
-120.000
-2.100
-1.200.000
2041
: -6.600
-32.000
-130.000
-2.300
-1.300.000
2042
-7.100
-34.000
-140.000
-2.400
-1.400.000
2043
! -7.500
-36.000
-160.000
-2.500
-1.500.000
2044
; -7.900
-37.000
-170.000
-2.600
-1.600.000
2045
-8.200
-39.000
-180.000
-2.700
-1.700.000
2046
: -8.600
-40.000
-190.000
-2.800
-1.700.000
2047
i -8.800
-41.000
-190.000
-2.800
-1.800.000
2048
i -9.100
-42.000
-200.000
-2.900
-1.800.000
2049
-9.300
-43.000
-210.000
-2.900
-1.900.000
2050
i -9.500
-44.000
-210.000
-3.000
-1.900.000
2051
; -9.600
-45.000
-220.000
-3.000
-1.900.000
2052
: -9.700
-46.000
-220.000
-3.000
-2.000.000
2053
-9.700
-46.000
-220.000
-3.000
-2.000.000
2054
: -9.800
-47.000
-220.000
-3.000
-2.000.000
2055
i -9.800
-47.000
-230.000
-3.000
-2.000.000
9-43
-------
Table 9-31 Criteria air pollutant impacts from vehicles, Alternative 2 standards, light-duty
and medium-duty
(US tons per year)
Calendar
; PM2.5
NOx
NMOG
SOx
CO
Year
2027
-49
-570
-810
-36
-17.000
2028
i -120
-1.300
-2.400
-91
-42.000
2029
, -250
-2.600
-5.600
-180
-88.000
2030
-730
-3.900
-9.400
-280
-140.000
2031
-1.200
-5.800
-14.000
-400
-200.000
2032
-1.700
-7.900
-20.000
-540
-270.000
2033
i -2.300
-10.000
-28.000
-720
-360.000
2034
; -2.800
-13.000
-36.000
-890
-460.000
2035
i -3.400
-15.000
-45.000
-1.000
-560.000
2036
-3.900
-17.000
-54.000
-1.200
-640.000
2037
i -4.500
-20.000
-64.000
-1.300
-730.000
2038
-5.000
-22.000
-74.000
-1.500
-830.000
2039
: -5.500
-24.000
-85.000
-1.600
-920.000
2040
* -6.100
-26.000
-96.000
-1.700
-1.000.000
2041
: -6.500
-28.000
-110.000
-1.800
-1.100.000
2042
-7.000
-29.000
-120.000
-1.900
-1.200.000
2043
! -7.400
-31.000
-130.000
-2.000
-1.300.000
2044
; -7.800
-32.000
-130.000
-2.100
-1.300.000
2045
-8.200
-34.000
-140.000
-2.200
-1.400.000
2046
: -8.500
-35.000
-150.000
-2.200
-1.400.000
2047
i -8.800
-36.000
-160.000
-2.300
-1.500.000
2048
i -9.000
-37.000
-160.000
-2.300
-1.500.000
2049
-9.200
-38.000
-170.000
-2.400
-1.600.000
2050
i -9.400
-39.000
-170.000
-2.400
-1.600.000
2051
; -9.500
-39.000
-180.000
-2.500
-1.600.000
2052
: -9.600
-40.000
-180.000
-2.500
-1.600.000
2053
-9.700
-40.000
-180.000
-2.500
-1.600.000
2054
: -9.700
-41.000
-180.000
-2.500
-1.600.000
2055
i -9.800
-41.000
-190.000
-2.500
-1.600.000
9-44
-------
Table 9-32 Criteria air pollutant impacts from vehicles, Alternative 3 standards, light-duty
and medium-duty
(US tons per year)
Calendar
; PM2.5
NOx
NMOG
SOx
CO
Year
2027
-43
-550
-800
-30
-15.000
2028
i -110
-1.200
-2.300
-75
-39.000
2029
, -190
-2.100
-4.500
-130
-68.000
2030
-670
-3.400
-7.800
-220
-110.000
2031
-1.200
-5.400
-12.000
-360
-180.000
2032
-1.600
-7.700
-19.000
-530
-260.000
2033
i -2.200
-10.000
-26.000
-710
-360.000
2034
; -2.800
-13.000
-35.000
-910
-470.000
2035
i -3.300
-16.000
-44.000
-1.100
-570.000
2036
-3.800
-18.000
-54.000
-1.200
-660.000
2037
-4.400
-20.000
-65.000
-1.400
-770.000
2038
-5.000
-23.000
-76.000
-1.600
-870.000
2039
: -5.500
-25.000
-88.000
-1.700
-980.000
2040
* -6.000
-27.000
-100.000
-1.900
-1.100.000
2041
: -6.500
-29.000
-110.000
-2.000
-1.200.000
2042
-7.000
-31.000
-120.000
-2.100
-1.300.000
2043
! -7.400
-33.000
-130.000
-2.200
-1.400.000
2044
; -7.800
-34.000
-140.000
-2.300
-1.400.000
2045
-8.100
-36.000
-150.000
-2.400
-1.500.000
2046
: -8.500
-37.000
-160.000
-2.500
-1.600.000
2047
i -8.700
-38.000
-170.000
-2.500
-1.600.000
2048
i -9.000
-39.000
-180.000
-2.600
-1.700.000
2049
-9.200
-40.000
-190.000
-2.600
-1.700.000
2050
i -9.400
-41.000
-190.000
-2.700
-1.700.000
2051
; -9.500
-42.000
-200.000
-2.700
-1.800.000
2052
: -9.600
-43.000
-200.000
-2.700
-1.800.000
2053
-9.700
-43.000
-200.000
-2.700
-1.800.000
2054
: -9.800
-44.000
-200.000
-2.800
-1.800.000
2055
i -9.800
-44.000
-200.000
-2.800
-1.800.000
9-45
-------
Table 9-33 Criteria air pollutant impacts from EGUs and refineries, Proposed standards,
light-duty and medium-duty
(US tons per year)*
Calendar
EGU
Refinery
Year
PM2.5
NOx
NMOG
SOx
PM2.5
NOx
NMOG I
SOx
2027
140
800
68
660 :
-130
-510
-440
-200
2028
310
1.800
150
1.500
-330
-1.200
-1.100 i
-490
2029
540
3.100
260
2.500 :
-590
-2.300
-1.900
-890
2030
790
; 4.400
380
3.600
-900
-3.400
-2.900
-1.400
2031
1.100
5.900 i
510
4.800
-1.300
-4.800
-4.100 !
-1.900
2032
1.300
7.500
660
6.000 ,
-1.700
-6.400
-5.500 !
-2.600
2033
1.600
9.000
800
: 7.100
-2.100
-8.100
-7.000
-3.300
2034
1.900
10.000
940
8.100
-2.600
-9.900
-8.500 ;
-4.000
2035
2.100
11.000
1.100
; 8.800
-3.100
-12.000
-9.900 1
-4.700
2036
2.300
12.000
1.100
9.000
-3.400
-13.000
-11.000
-5.200
2037
2.400
12.000 i
1.200
9.300
-3.800
-14.000
-12.000 ;
-5.800
2038
2.500
13.000
1.300
9.300
-4.200
-16.000
-13.000 ,
-6.400
2039
2.600
: i3.ooo i
1.300
9.100 ,
-4.500
-17.000
-14.000 !
-6.900
2040
2.600
13.000
1.400
8.700
-4.900
-18.000
-16.000 !
-7.400
2041
2.600
12.000
1.400
8.100
-5.200
-19.000
-16.000 i
-7.900
2042
2.600
12.000
1.400
7.300
-5.500
-20.000
-17.000 i
-8.300
2043
2.600
11.000
1.400
6.500
-5.700
-21.000
-18.000 ;
-8.700
2044
2.400
10.000 ;
1.400
5.400 ,
-5.900
-22.000
-19.000 ;
-9.000
2045
2.300
9.200 i
1.300
4.200 1
-6.100
-22.000
-19.000
-9.300
2046
2.200
8.100 ;
1.300
2.900
-6.300
-23.000
-20.000 i
-9.600
2047
2.000
1 6.700
1.200
: 1.500
-6.400
-23.000
-20.000 :
-9.700
2048
1.900
5.400
1.100
1.500
-6.500
-24.000
-20.000
-10.000
2049
1.700
4.000
1.100
1.600 :
-6.600
-24.000
-21.000 ;
-10.000
2050
1.500
2.500
1.000
: 1.600 ,
-6.700
-24.000
-21.000 :
-10.000
2051
1.500
2.500
1.000
1.600
-6.800
-25.000
-21.000 :
-10.000
2052
1.500
: 2.500
1.000
1.600
-6.800
-25.000
-21.000 ,
-10.000
2053
1.500
2.600
1.000
1.600 ,
-6.900
-25.000
-21.000 !
-10.000
2054
1.500
2.600
1.000
1.600 i
-6.900
-25.000
-21.000 ,
-11.000
2055
1.500
2.600 ;
1.000
; 1.600
-6.900
-25.000
-21.000 ,
-11.000
*CO emission rates
were not available for
calculating
CO inventories from EGUs or refineries.
9-46
-------
Table 9-34 Criteria air pollutant impacts from EGUs and refineries, Alternative 1
standards, light-duty and medium-duty
(US tons per year)*
Calendar
EGU
Refinery
Year
PM2.5
NOx
NMOG
SOx
PM2.5
NOx
NMOG
SOx
2027
140
830
71
680
-140
-530
-450
-210
2028
350
2.000
170
1.600
-370
-1.400
-1.200
-560
2029
570
3.300
280
2.700
-660
-2.500
-2.200
-990
2030
860
4.900
420
4.000
-1.000
-3.900
-3.400
-1.600
2031
1.100
6.300
550
5.100
-1.400
-5.400
-4.700
-2.200
2032
1.400
7.900
700
6.300
-1.900
-7.200
-6.200
-2.900
2033
1.800
9.700
860
7.700
-2.400
-9.200
-7.900
-3.700
2034
2.100
11.000
1.000
8.800
-2.900
-11.000
-9.500
-4.500
2035
2.300
12.000
1.100
9.500
-3.400
-13.000
-11.000
-5.200
2036
2.500
13.000
1.200
9.900
-3.800
-14.000
-12.000
-5.800
2037
2.600
14.000
1.300
10.000
-4.300
-16.000
-14.000
-6.500
2038
2.800
14.000
1.400
10.000
-4.700
-17.000
-15.000
-7.100
2039
2.800
14.000
1.500
10.000
-5.100
-19.000
-16.000
-7.700
2040
2.900
14.000
1.500
9.600
-5.400
-20.000
-17.000
-8.300
2041
2.900
14.000
1.500
9.000
-5.800
-21.000
-18.000
-8.800
2042
2.900
13.000
1.500
8.100
-6.100
-22.000
-19.000
-9.200
2043
2.800
12.000
1.500
7.200
-6.400
-23.000
-20.000
-9.700
2044
2.700
11.000
1.500
6.000
-6.600
-24.000
-21.000
-10.000
2045
2.600
10.000
1.500
4.600
-6.700
-25.000
-21.000
-10.000
2046
2.400
8.900
1.400
3.200
-7.000
-25.000
-22.000
-11.000
2047
2.200
7.500
1.300
1.700
-7.100
-26.000
-22.000
-11.000
2048
2.100
6.000
1.300
1.700
-7.200
-26.000
-22.000
-11.000
2049
1.900
4.400
1.200
1.800
-7.300
-27.000
-23.000
-11.000
2050
1.600
2.800
1.100
1.800
-7.400
-27.000
-23.000
-11.000
2051
1.700
2.800
1.100
1.800
-7.500
-27.000
-23.000
-11.000
2052
1.700
2.800
1.100
1.800
-7.500
-27.000
-23.000
-12.000
2053
1.700
2.800
1.100
1.800
-7.500
-27.000
-23.000
-12.000
2054
1.700
2.800
1.100
1.800
-7.600
-27.000
-23.000
-12.000
2055
1.700
2.800
1.100
1.900
-7.600
-27.000
-23.000
-12.000
*CO emission rates were not available for ca
culating CO inventories from EGUs or refineries.
9-47
-------
Table 9-35 Criteria air pollutant impacts from EGUs and refineries, Alternative 2
standards, light-duty and medium-duty
(US tons per year)*
Calendar
EGU
Refinery
Year
I'M 2.5
; NOx j
NMOG
; SOx ]
PM2.5
NOx
: NMOG
SOx
2027
100
T 580 :
49
; 470 I
-96
-370
i -320
-150
2028
220
" 1,300 :
110
1.000
-240
-900
y -780
-360
2029
420
T 2,400 1
210
; 2,000 T
-470
-1,800
: -1,500
-710
2030
620
: 3,500 7
300
T 2,800 V
-7i0
"-2,700
-2.300
-1,100
203 i
860
; 4,800 J
420
j 3,900 J
-1,000
-3,900
-3.100
-1,600
' 2032
1,100
6,200 :
540
: 4,900 :
-1,400
-5,300
-4,600
-2,100
2033
1,400
J 7,800 J
700
6,100 7
-1.900
-".100
: -6,100
-2,800
2034
1,700
i 9,100 ;
830
f 7,100 ;
-2,300
-8,700
f -7,500
-3,500
2035
1900
J 10,000
940
I 7,80° j
-2,700 y
-10,000
-8,700
-4,100
2036
2,000
J 11,000 =
1,000
y 8,ooo y
-3,000
-11,000
i -9,700
-4,600
' 2037
2,200
r 11,000 I
1,100
8.100
-3,400
-13,000
i -11,000
-5,200
2038
2,300
: .12,°°° j
1,200
r 8,400
-3,800
-1 1.000
! -12,000
-5,700
2039
2,400
i 12,000 ]
1,200
; 8.300
-4,100
-15,000
-13,000
-6,200
2040
2,400
J 12,000
1.300
j ' 8,000 i
-4,400
-16,000
-14,000
-6,700
2041
2,400
*' 12,000 ;
1,300
y 7,500 y
-4,700
-17,000
r-15,000
-7,200
2042
2,400
1 1.000
1,300
j 6,800 1
-4,900
-18,000
s -16,000
-7,500
2043
2,400
J 10,000 ]
1,300
6,000 ]
-5,200
-19,000
i"-16,000
-7,900
2044
2,300
9,500
1,300
1 1.900
-5,300
-20,000
;-17,000
-8,100
2045
2,100
: 8,500 ;
1,200
y 3,800 j
-5,500
-20,000
-r.ooo
-8,400
2046
2,000
1 7,400 1
1,200
| 2,700 ;
-5,700
-21,000
I -18,000
-8,700
2047
1,900
J 6,200 1
I.I 00
y i,400 y
-5,800
-21,000
-18,000
-8,800
2048
1,700
J 5>000 J
1,100
I 1-loo
-5,900
"22,000
r -18,000
-9,000
2049
1,500
" 3,700 '
1,000
y 1,4001
-6,000
-22,000
-19,000
-9,200
2050
1,400
2,300 j
930
J !,500 y
-6,100
-22,000
! -19,000
-9,300
2051
1,400
2.300
940
1,500 y
-6,200
-22,000
i -19,000
-9,400
2052
1,400;
J 2,300 J
940
FUOO :
-6,200
-22,000
! -19.000
-9,500
2053
1,400
r 2.3oo
950
y 1,500 y
-6,200
-22,000
' -19,000
-9,500
2054 "2
1,400 ¦
T 2,400 J
950
' i,500 :
-6,200
-22,000
y -19,000
-9,500
2055
1,400
J 2,400 J
950
I yL,5oo yy
-6,200
-22,000
5 -19,000
-9,500
*CO emission rates
were not available for
calculating
CO inventories from EGUs or refineries.
9-48
-------
Table 9-36 Criteria air pollutant impacts from EGUs and refineries, Alternative 3
standards, light-duty and medium-duty
(US tons per year)*
Calendar
EGU
Refinery
Year
PM2.5
NOx
NMOG
SOx
PM2.5
NOx
NMOG I
SOx
2027
84
490
42
400
-78
-300
-260
-120
2028
190
1.100
95
910
-200
-750
-650
-300
2029
320
1.800
160
1.500 ;
-350
-1.300
-1.100 ;
-520
2030
500
, 2.900
250
2.300 :
-570
-2.200 :
-1.900 :
-870
2031
780
4.400
380
3.500 5
-930
-3.500
-3.000
-1.400
2032
1.100
6.100
540
4.900
-1.400
-5.200
-4.500 1
-2.100
2033
1.400
i 7.700
690
6.100
-1.800
-7.000
-6.000
-2.800
2034
1.700
i 9.300
850
7.300
-2.400
-8.900
-7.600
-3.600
2035
2.000
: 10.000
970
; 8.100
-2.800
-11.000
-9.100 ;
-4.300
2036
2.100
i 11.000
1.100
; 8.400
-3.200
-12.000
-10.000 i
-4.800
2037
2.300
12.000
1.200
8.800
-3.600
-13.000
-12.000 ,
-5.500
2038
2.400
12.000 i
1.200
8.900 ,
-4.000
-15.000
-13.000 ;
-6.100
2039
2.500
; 12.000
1.300
8.800
-4.400
-16.000
-14.000 ,
-6.600
2040
2.600
! 12.000
1.300
8.500 i
-4.700
-18.000
-15.000 ,
-7.200
2041
2.600
12.000
1.400
i 8.000
-5.100
-19.000
-16.000 ,
-7.700
2042
2.600
12.000 i
1.400
7.200
-5.300
-20.000
-17.000 !
-8.100
2043
2.500
! 11.000
1.400
; 6.400
-5.600
-21.000
-18.000 i
-8.600
2044
2.400
10.000 i
1.300
5.300 ;
-5.800
-21.000
-18.000 :
-8.900
2045
2.300
i 9.200
1.300
4.100 ,
-6.000
-22.000
-19.000
-9.200
2046
2.200
8.100 :
1.300
2.900 ,
-6.200
-23.000
-19.000 i
-9.500
2047
2.000
6.800
1.200
1.500
-6.300
-23.000
-20.000 :
-9.700
2048
1.900
5.400
1.200
1.600
-6.500
-24.000
-20.000 ;
-9.900
2049
1.700
4.000
1.100
1.600
-6.600
-24.000
-20.000 ;
-10.000
2050
1.500
2.500
1.000
* 1.600 ,
-6.700
-24.000
-21.000 :
-10.000
2051
1.500
2.500
1.000
; 1.600 ;
-6.800
-25.000
-21.000
-10.000
2052
1.500
2.600
1.000
1.600
-6.800
-25.000
-21.000
-10.000
2053
1.500
2.600
1.000
1.600 i
-6.900
-25.000
-21.000 i
-10.000
2054
1.500
2.600
1.000
1.700
-6.900
-25.000
-21.000 :
-11.000
2055
1.500
2.600 ;
1.000
: 1.700 .
-6.900
-25.000
-21.000 ,
-11.000
*CO emission rates
were not available for
calculating
CO inventories from EGUs or refineries.
9-49
-------
Table 9-37 Net criteria air pollutant impacts from vehicles, EGUs and refineries, Proposed
standards, light-duty and medium-duty *
Calendar
Vehicle. EGU. Refinery
% Change
Year
(US tons per year)
PM2.5
; NOx
NMOG
SOx
CO*
PM2.5
NOx
NMOG
SOx
CO
2027
-62
: -430
-1.500 1
410
-24.000
-0.11%
-0.070%
-0.13%
0.89%
-0.22%
2028
i -180
i -1.100
1 -4.300
860
-61.000
-0.33%
-0.21%
-0.42%
1.9%
-0.60%
2029
: -360
; -2.300
-8.900
1.400
-110.000 i
-0.68%
-0.49%
-0.91%
3.1%
-1.2%
2030
-900
: -3.700
-15.000
1.900
-180.000
-1.8%
-0.9%
-1.6%
4.2%
-2.0%
2031
-1.500
: -5.700
: -21.000 i
2.400
-250.000 1
-3.0%
-1.5%
-2.5%
5.3%
-3.1%
2032
; -2.100
-8.100
-30.000
2.800
-330.000 1
-4.4%
-2.4%
-3.6%
6.3%
-4.5%
2033
! -2.800
i -11.000
-39.000
3.000
-430.000 i
-6.0%
-3.5%
-5.1%
7.0%
-6.2%
2034
i -3.600
-14.000
; -50.000 ;
3.100
-530.000 :
-7.7%
-4.9%
-6.9%
7.3%
-8.3%
2035
-4.400
: -17.000
-61.000
3.000
-640.000
-9.5%
-6.5%
-8.9%
7.2%
-11%
2036
. -5.100
i -20.000
i -72.000
2.600
-720.000 i
-11%
-8.2%
-11%
6.3%
-13%
2037
! -5.900
: -23.000
: -84.000 1
2.000
-820.000 i
-13%
-10%
-14%
5.1%
-16%
2038
-6.700
: -26.000
-97.000
1.300
-930.000 i
-15%
-13%
-17%
3.4%
-19%
2039
-7.500
i -30.000
-110.000
400
-i.ooo.ooo :
-17%
-15%
-20%
1.1%
-22%
2040
! -8.400
* -33.000
: -120.000
-650
-1.100.000 :
-19%
-17%
-23%
-1.8%
-25%
2041
i -9.200
-37.000
-130.000
-1.800
-1.200.000
-21%
-20%
-26%
-5.2%
-28%
2042
-9.900
-40.000
, -150.000 i
-3.100
-1.300.000 i
-23%
-22%
-29%
-9%
-31%
2043
: -11.000
: -43.000
: -160.000
-4.500
-1.400.000
-25%
-25%
-32%
-14%
-34%
2044
* -11.000
: -46.000
; -170.000 :
-6.000
-1.400.000 ,
-26%
-27%
-35%
-19%
-37%
2045
; -12.000
: -49.000
; -180.000 :
-7.500
-1.500.000
-28%
-29%
-37%
-25%
-39%
2046
; -i3.ooo
-52.000
; -190.000 i
-9.200
-1.600.000 ;
-30%
-31%
-40%
-32%
-41%
2047
! -13.000
: -55.000
-190.000
-11.000
-1.600.000
-31%
-34%
-42%
-39%
-43%
2048
-14.000
! -58.000
: -200.000 i
-11.000
-1.700.000
-32%
-36%
-44%
-40%
-44%
2049
-14.000
: -6I.000
-210.000 :
-11.000
-1.700.000 :
-33%
-38%
-45%
-40%
-46%
2050
i -15.000
: -63.000
: -210.000 :
-11.000
-1.700.000 :
-34%
-40%
-46%
-41%
-47%
2051
: -15.000
: -64.000
: -220.000 :
-11.000
-1.800.000 :
-35%
-40%
-47%
-41%
-47%
2052
-15.000
-65.000
, -220.000
-12.000
-1.800.000
-35%
-40%
-48%
-41%
-48%
2053
! -15.000
; -65.000
; -220.000 :
-12.000
-1.800.000 ,
-35%
-41%
-49%
-42%
-49%
2054
-15.000
: -66.000
-220.000
-12.000
-1.800.000
-35%
-41%
-49%
-42%
-49%
2055
-15.000
-66.000
-220.000
-12.000
-1.800.000 ;
-35%
-41%
-50%
-42%
-49%
*CO emission rates were not available for calculating CO inventories from EGUs or refineries.
9-50
-------
Table 9-38 Net criteria air pollutant impacts from vehicles, EGUs and refineries,
Alternative 1 standards, light-duty and medium-duty *
Calendar
Year
2027
2028
2029
2030
2031
2032
2033
2034
2035
2036
2037
2038
2039
2040
2041
2042
2043
2044
2045
2046
2047
2048
2049
2050
2051
2052
2053
2054
2055
Vehicle, EGU, Refinery
(US tons per year)
PM2.5
-65
-200
-400
-970
-1.600
-2.200
-3.000
-3.800
-4.500
-5.300
-6.100
-7.000
-7.800
-8.700
-9.500
-10.000
-11.000
-12.000
-12.000
-13.000
-14.000
-14.000
-15.000
-15.000
-15.000
-16.000
-16.000
-16.000
-16.000
NOx
-440
-1.200
-2.400
-3.900
-6.000
-8.600
-12.000
-15.000
-18.000
-21.000
-25.000
-29.000
-32.000
-36.000
-40.000
-43.000
-47.000
-50.000
-53.000
-57.000
-59.000
-63.000
-66.000
-69.000
-69.000
-70.000
-71.000
-71.000
-71.000
NMOG
-1.500
-4.600
-9.000
-15.000
-23.000
-32.000
-42.000
-54.000
-67.000
-80.000
-93.000
-110.000
-120.000
-140.000
-150.000
-160.000
-180.000
-190.000
-200.000
-210.000
-210.000
-220.000
-230.000
-230.000
-240.000
-240.000
-240.000
-250.000
-250.000
SOx
420
940
1.400
2.000
2.400
2.700
3.100
3.100
3.000
2.600
2.100
1.300
340
-780
-2.100
-3.500
-5.000
-6.600
-8.300
-10.000
-12.000
-12.000
-12.000
-13.000
-13.000
-13.000
-13.000
-13.000
-13.000
*CO emission rates were not available for
CO*
-25.000
-65.000
-110.000 i
-180.000
-260.000 I
-350.000 I
-450.000 i
-570.000 :
-680.000
-780.000 :
-900.000 i
-1.000.000 *
-1.100.000
-1.200.000 :
-1.300.000 :
-1.400.000 !
-1.500.000 i
-1.600.000 :
-1.700.000 :
-1.700.000
-i.80o.ooo ;
-1.800.000 !
-1.900.000 i
-1.900.000 ;
-1.900.000 i
-2.000.000 !
-2.000.000 i
-2.000.000
-2.000.000 :
calculating CO
PM2.5
-0.11%
-0.37%
-0.76%
-1.9%
-3.2%
-4.6%
-6.2%
-8.0%
-9.9%
-12%
-14%
-16%
-18%
-20%
-22%
-24%
-26%
-27%
-29%
-31%
-32%
-33%
-35%
-36%
-36%
-36%
-37%
-37%
-37%
inventories
NOx
¦0.072%
-0.22%
-0.49%
-0.9%
-1.6%
-2.5%
-3.8%
-5.3%
-7.0%
-8.9%
-11%
-14%
-16%
-19%
-21%
-24%
-27%
-29%
-32%
-34%
-36%
-39%
-41%
-43%
-43%
-44%
-44%
-44%
-44%
% Change
: NMOG
-0.14%
-0.45%
-0.93%
-I
-2.6%
-3.9%
-5.5%
-7.5%
-9.8%
-12%
-15"..
-18%
-22%
"25° o ....
-29%
-32%
-35%
-38"..
-41%
-44%
-46° o
-48%
-50"..
-51%
-52%
-53"..
-54%
-5 1"..
-55%
SOx
0.92%
2.1%
3.1%
4.4%
5.3%
6.2%
7.0%
7.4%
7.2%
6.4%
5.2%
3.4%
0.9%
-2.2%
-5.9%
-10%
-15%
-21%
-28%
-35%
-43%
-44%
-45%
-45%
-45%
-45%
-46%
-46%
-46%
CO
-0.23%
-0.65%
-1.2%
-2.1%
-3.2%
-4.7%
-6.6%
-8.8%
-11%
-14%
-17%
-20%
-24%
-27%
-31%
-34%
-37%
-40%
-43%
-45%
-47%
-49%
-50%
-52%
-52%
-53%
-54%
-54%
-55%
from EGUs or refineries.
9-51
-------
Table 9-39 Net criteria air pollutant impacts from vehicles, EGUs and refineries,
Alternative 2 standards, light-duty and medium-duty *
alendar
Vehicle, EGU, Refinery
% Change
Year
(US tons per year)
i PM2.5
; NOx
NMOG
SOx
CO*
PM2.5 :
NOx
: NMOG :
SOX :
CO
2027
-45
: -360
-1.100 1
290
i -17.000
-0.08% ;
-0.058%
: -0.10% i
0.64% :
-0.16%
2028
; -130
i -910
J -3,100 .
600
, -42,000
-0.25% J
-0.17%
: -0.30% :
1.3% j
-0.42%
2029
: -290 '
-2,000
: -6,900 i
1,100
j -88,000
-0.55% ;
-0.41%
1-0.71%
" 2.4% ;
-0.9%
2030
-820
7 -3,100
J -11,000 !
1,500 7
-140,000 ;
-1.6% 7
' -0.7%
7 -L2% 77
3.3% 77
-i.6%
2031
-1,400
f -4,900
-17,000 7
1,900
f -200,000 :
-2.8% j
-1.3%
7-2.0% 1
"4.2% I
-2.5%
2032 "2
: -2,000
7 -7,000
1 -24,000 ]
2,200
1 -270,000 j
-II""
-2.1%
7; -3.o%77
5-1% J
-3.7%
2033
-2,700
: -9,600
-33,000 1
2,600
7 -360,000
-5.7% !
-3.2%
7 -4.3% '
' 5.9%""
-5.3%
2034
! "3'400
-12,000
[ "-43,000 j
2,700
r -460,000 77
-7.4%
-4.5%
7 -5.9% /
6.3% 77
-7.2%
2035
; -4,200
i' -15,000
] -53,000 j
2,600
-560,000 J
7"-9.i%j
-5.9%
7 -7.7% 777
""6.3% ""7
-9%
2036
7 -4,900
7-18,000
j -63,000
2,300
-640,000
-11% :
-7.5%
7 -io% i
5.6% ;
-11%
2037
J -5,700
" -21,000
: -74,000
1.900
7 -730,000
-i3% 71
-9% 77
7 -12% 77
4.8% 771
-II"..
2038
: -6,500
7 -24,000
-85,000 1
1,300
: -830,000
"-15% !
-11%
-15% ;
3.3% 7
-17%
2039
-7,300
f-27,000
1 -97,000 J
500 7
7 -920,000 77
-i7% 77
-14%
71 -i7% 7 77
7 1.3% j
-20%
2040
I -8,000
i -31,000
-110,000 1
-430
" -1,000,000 1
-i8% 77
-16%
71 -20% 777
""-1.2%7'j
-23%
2041
-8,800
-34,000
"j -120,000
-1,500
; -1,100,000 :
-20%
-18%
7 -23%
-4.3% 1
-25%
2042
; -9,500
-37,000
; -130,000 j
-2,700
r-1,200,000 7
" -22% 77
:2i%
7 -26% i
-8% 77:
"-28%
2043
| -10,000
-40,000
7 -140,000 '
-4,000
; -1,300,000 1
"" -24% ^
-23%
-29%
"-12% ;
""-31%
2044
f-11,000
; -43,000
j -150,000 j
-5,300
-1,300,000 ;
" -25% j
-25% 77
"7 -31% 77;
-170/0 77;
-33% 7
2045
; -12,000
i' -45,000
J -160,000 1
-6,700
-1,400,000 j
7 -27% 77
-27%
7 -33% 777
-22% 77
-35%
2046
7 -12,000
-48,000
7 -170,000 ~,
-8,300
i' -1,400,000 ]
-28%
-29%
-36% :
-29%" "j
-37%
2047
| -13,000
f -51,000
: -170,000 :
-9,700
-1,500,000 j
-30% J
-31%
77 7-38% 77
""-35% 77
-39%
2048
-13,000
7 -54,000
7 -180,000 "i
-10,000
: -1,500,000
-31% "7
-33%
'7 -39% 7
-36%
"-40%
2049
-14,000
; -56,000
j -190,000 j
-10,000
: -1,600,000 j
-32% 77*
""-35% 77
77 -4i% 777
. -37% 77
-42%
2050
f -14,000
7 -59,000
7 -190,000 ]
-10,000
: -1,600,000 7
-33% ;
" -37%
-42%'"':
-37%1
-43%
205 i
! -14,000
!' -59,000
] '-200,000 ]
-10,000
: -1,600,000 7
7-34% 77
-37% 7 7
"i -43% 77)
7 -37%7
-43% 77
2052
7 -14,000
7 -60,000
7 -200,000 j
-10,000
i -1,600,000 j
"34% 77
-37%
"7 -44% 77
-38% ""7
7 -44%
2053
: -15,000
! -60,000
7 -200,000 ]
-11,000
7-1,600,000 :
"-34% :
-38%
T -44% :
"-38%
-44%
2054
: -15,000
f-61,000
7 -200,000 7
-11,000
f-1,600,000 1
-34% 77 j
-38%
7 7-45%'71
-38% 7
7. -45% 7
2055
: -15,000
-61,000
7 -200,000 1
-11,000
| -1,600,000 7
-34%
-38%
"'-45% ;
-38% 7
-45%
*CO emission rates were not available for calculating CO inventories from EGUs or refineries.
9-52
-------
Table 9-40 Net criteria air pollutant impacts from vehicles, EGUs and refineries,
Alternative 3 standards, light-duty and medium-duty *
Calendar
Vehicle, EGU, Refinery
% Change
Year
(US tons per year)
i PM2.5
; NOx
NMOG
SOx
CO*
PM2.5 :
NOx
: NMOG :
SOx ;
CO
2027
-37
: -360
-1.000 1
250
-15.000
-0.07% ;
-0.058%
: -0.09% i
0.55% :
-0.14%
2028
; -110
i -870
1 "2>900 :
530
-39,000
-0.21% J
-0.16%
: -0.28% j
1.2% j
-0.39%
2029
: -220 "
; -1,600
: -5,500
830
-68,000
-0.42%"
-0.34%
T -0.56% "
1.8% |
-0.7%
2030
"74°
!' -2,700
: -9.100
1,200
-110,000 i
-1.4% :
-0.6%
-Mi".. J
2.7% J
-13"..
2031
-1,300
i -4,500
: -15,000 j
1,700
-180,000
-2.6% ;
-1.2%
"i""-i.7% ;
3.9% ""l
"-2.2%
2032 "2
: -1,900
-6,800
-23,000 J
2,300
-260,000
-4.0% j
-2.0%
..-2.8%' J
5. i% |
-3.6%
2033
f -2,600
-9,500
-31,000
2,600
-360,000
-5.5% |
-3.1%
'"-4.1% " "'
6.0% j
-5.2%
2034
! "3>400
-13,000
V -41,000 '
2,800
-470,000 j
-7.2% J
-4.5%
-5.7%" !
6.5% J
-7.3%
2035
; -4,200
f -16,000
J -52,000 J
2,700
-570,000 j
-9.0% J
-6.1%
-7.7% J
6.5% J
-10%
2036
.' -4,900
-19,000
1 -63,000
2,400
-660,000 !
""-11% ;
-7.8%
i -10% ^
5.9%
-12%
2037
1 "5,700
; -22,000
-75,000
1,900
-770,000
,-13%I'
-10%
1 . .-12%: ;
"" 4.9%'J
-15"..
2038
: -6,500
i -25,000
| -88,000 1
1,300
-870,000 !
" -15% -
-12%
1 -15%
3.3%
-18%
2039
-7,300
i -29,000
: -100,000 1
440 2
-980,000 J
"17% J
-14%
T"-i8%;;:
1.2% J
-2i%
2040
I "-8,200
f -32,000
: -110,000 j
-550
-I.I 00.000
-!9%
"-17%
J -21% J
-1.5% J
-24%
' 2041
-9,000
-36,000
"j -130,000 :
-1,700
-1,200,000 ;
-21%
-19%
'" -24% 1
-4.9% "i
-27%
2042
-9,700
-39,000
j -140,000 j
-3,000
-1,300,000 ;
-23% j
-22%
T -27%
-9% "]
"30%
2043
| -11,000
-43,000
7 -150,000 1
-4,400
-1,400,000 ;
-24% j
-24%
-31% !
-13% :
-33%
2044
f-11,000
| -46,000
j -160,000 j
-5,800
-1,400,000 j
-26% 1
-27%
i .."33% ~ ;
""-i9% j
-36%
2045
; -12,000
f -49,000
T -170,000 =
-7,400
-1,500,000 j
-28% 1
-29%
-36% "j
-25%"";
-38%
' 2046
f -13,000
: -52,000
T-180,000 !
-9,100
-1,600,000 :
-29% :
""-31%
T'" -39% i
"" -31% I
"'-41%
2047
| -13,000
' -55,000
: -190,000 j
-11,000
-1,600,000 j
J"31% j
-33%
.i ..-4i%: i
-39% J
-42%
2048
-14,000
f -58,000
I -200,000
-11,000
-1,700,000 :
-32% .
-36%
". '"-43% !
-40% [
-44%
2049
-14,000
f -60,000
j -210,000 :
-1 1.000
-1,700,000 :
"33% J
-38%
1 "45%! J
-40% ,
-45%
2050
f -15,000
1 -63,000
-210,000 ]
-11,000
-1,700,000 ;
-34%'"' j
-40%
']" -46% !
-41% i
-47%
2051
! -15,000
: -64,000
j -210,000 :
-11,000
-1,800,000 ;
' -35%' J
-40%
-47% ~ ;
"41% J
-47%
2052
-15,000
= -65,000
j -220,000 j
-12,000
-1,800,000 '
-35% J
-40%
i _48% ]
-II"..
-48%
2053
: -15,000
! -65,000
'• -220,000 "1
-12,000
-1.800.000
"~ -35% |
-41%
-19"..
"'-42%'"]
-49%
2054
: -15,000
f -66,000
f-220,000 ]
-12,000
-1,800,000 j
"35% Z
-41%
] -49% J
-42% j
-49%
2055
: -15,000
-66,000
V -220,000 1
-12,000
-1,800,000 "i
-35% -
-41%
-50% !
-42%
-50%
*CO emission rates were not available for calculating CO inventories from EGUs or refineries.
9.7 Estimating Energy Security Effects
The energy security premia (the energy security savings, in dollars, per barrel of reduced
imported oil) and the process used to estimate those values are described in Chapter 12. The
discussion here focuses on how OMEGA estimates the oil consumption impacts to which the
energy security premia can be multiplied to estimate monetized benefits.
9.7.1 Calculating Oil Consumption from Fuel Consumption
Chapter 9.5.3 describes how OMEGA estimates liquid-fuel consumption. This is done for
every vehicle that operates any miles on a liquid-fuel, whether that fuel be gasoline or diesel.
Chapter 0 presents the estimated impacts of the proposal on overall fuel consumption.
9-53
-------
9.7.2 Calculating Oil Imports from Oil Consumption
To estimate energy security benefits, OMEGA converts fuel consumption impacts to oil
import impacts. This is done using the values shown in Table 9-41 Parameters used in Estimating
Oil Import Impacts.
Table 9-41 Parameters used in estimating oil import impacts
Item Value
Share of pure gasoline in retail gasoline 0.9
Share of pure diesel in retail diesel 1.0
Energy density ratio of pure gasoline to crude oil 0.881
Energy density ratio of diesel to crude oil 0.998
Gallons per barrel of crude oil 42
: Oil import reduction as percent of total oil demand reduction 0.907
The barrels of oil consumed in a given scenario are estimated as shown below.
Energy Density Ratio
Barrels = FuelConsumptionvehicle.liquid x Share x GallonsPerBarrel
Where,
Barrels = the barrels of oil associated with the fuel consumption value
FuelConsumptiorivzhicie;iiquid = the liquid-fuel consumption of the given vehicle (see Chapter
9.5.3)
Share = the applicable "pure share" shown in Table 9-41
EnergyDensityRatio = the applicable energy density ratio shown in Table 9-41
GallonsPerBarrel = 42 as shown in Table 9-41
The barrels of imported oil are then calculated as shown below.
Barrelsimported
= Barrels
x (Oil import reduction as percent of total oil demand reduction)
9-54
-------
9.7.3 Summary of Energy Security Effects
Table 9-42 Impacts on oil consumption and oil imports, Proposed standards, light-duty and
medium-duty (millions)
Calendar :
Barrels
; Barrels Imported
; Barrels Imported per Day
Year
2027
-17
-15
-0.042
' 2028
-42
-38
-0.1
2029
-76 "2
: -69
-0.19
2030
-120
! -loo
-0.29
203 i ]
-160
j -150 ' "
"°-41 .1.
2032
-220
{ "-200
-0.54
2033
-280
j -250
-0.69
2034
-340
-310
: -0.85
2035
-400
-360
| -0.99
2036
-450 '
-400
-1.1 'J'
2037 !
-500
-450
-1.2
' 2038
-550
-500
-1.4
2039
-600
-540
| -1.5
2040
-640
-580
-1.6
2041 11
-690
-620
-1.7
2042
-720
-650
-1.8
2043
-760
-690
-1.9
2044
-780
1 -710
-1.9
2045
-810
} -730
-2
2046
-840
-760
-2.1
2047 !
-850
-770
-2.1
2048
-870
-790
-2.2
2049
-890
-810
-2.2
2050
-910
-820
-2.3
205 i "i
-910
' -830
-2.3'
2052
-920
-840
-2.3
2053 j
-930
i -840
1 -2.3 "
2054
-930
j -840
-2.3 "" ...I
2055 "" ;
-930
-850
-2.3
Sum
-17,000
-16,000
9-55
-------
Table 9-43 Impacts on oil consumption and oil imports, Alternative 1 standards, light-duty
and medium-duty (millions)
Calendar :
Barrels
; Barrels Imported
; Barrels Imported per Day
Year
2027
-18
-16
-0.044
2028 2
... "47
-43
-0.12 ^
2029 ]
-8 1 7
-76
-0.21
2030
-130
-120
-0.33
203 i 1
-190
-170
-0.46
2032
-240
-220
-0.61
2033 1
-320
-290
-0."8
2034
-380
-350
-0.95
2035 ]
-440
" " -400
-1.1
2036 |
-500
-450
-1.2
2037
-560
-500
-1.4
2038 1
-610
-560
.JI-i.5"
2039
-670
-600
-1.7
2040
-720
-650
-1.8
2041 * i
-760
7] -690
i 'J -1.9 ' "
2042
-800
-730
-2
2043 1
-8 10
-760
' -2.1
2044
-870
-790
-2.2
2045
-900
-810
7" "2-2 I
2046 j
-930
7 -840
-2.3
2047
-940
-850
-2.3
2018
-960
; -870
-2.4
2049
-980
-890
-2.4
2050 j
-looo
-910
-2.5
2051
-1000
-910
-2.5
2052
-looo
-920
: -2.5 7
2053
-1000
-920
! -2.5
2054
-1000
-930
-2.5
2055
-looo
-930
-2.5
Sum
-19,000
-17,000 '
9-56
-------
Table 9-44 Impacts on oil consumption and oil imports, Alternative 2 standards, light-duty
and medium-duty (millions)
Calendar
Barrels
Barrels Imported
Barrels Imported per Day
Year
2027
-12
-11
-0.031
2028
-30
-28
-0.076
2029
-60
-55 " '
-0.15
2030
-92
-84
-0.23
203 i
-130
'2 -120
-0.33
2032
-180
-170
-0.45
2033
-240
-220
-0.6 'J
2034
-300
-270
-0.75
2035
-350
-320
-0.88 'J
2036
-390
J -360
-0.98
2037
-440
-400
-1.1
2038
-490
-450
I -1-2 7
2039
-540
-490
-1.3
2040
-580
...1.7-53°
-1.4
2041
-620
-560
-1.5
2042
-650
-590
-1.6
2043
-690
-620
-1.7
2044
-710
-640
-1.8
2045
-730
-660
'7 -i.8
2046
-760
-690
' -1.9
2047
-770
-700
-1.9
2048
-790
-720
" -2 7
2049
-810
-730
-2
2050
-820
."-75°""
-2
2051
-830
-750
-2.1
2052
-830
-760
-2.i 7
2053
-840
-760 "
-2.1
2054
-840
-760
-2.1
2055
-840
-770
-2.1
Sum
-15,000
-14,000
9-57
-------
Table 9-45 Impacts on oil consumption and oil imports, Alternative 3 standards, light-duty
and medium-duty (millions)
Calendar :
Barrels
; Barrels Imported
; Barrels Imported per Day
Year
2027
-10
-9.2
-0.025
2028 2
... "25 "
-23
-0.063
2029 ]
-45 ^
-11
-O.I 1
2030
-74
-67
-0.18
203 i 1
-120
-iio
-03 '
2032
-180
-160
-0.44
2033 1
-240
-220
-0.6
2034
-310
-280
-0.76
2035 ]
-370
-330
-0.91
2036 |
-HI!
-380
r 7 -i 7
2037
-470
-430
-1.2
2038 1
-520
-480
-1.3
2039
-570
| -520 '
-1.4
2040
-620
-570
-1.6
2041 * i
-670
] -f'lo
-1,7 .1
2042
-710
-640
-1.8
2043 1
-750
-680
: -1.9
2044
-770
i -700
i -1.9
2045
-800
i -720
1 -2 7
2046 j
-830
J-750 ;
! 7"
2047
-850
-770
-2.1
2018
-870
j 7 -790
-2.2
2049
-890
-810
-2.2
2050 j
-910
II"820
-2.2
2051
-910
-830
-2.3
2052
-920
F -Slo
-2.3"
2053
-930
-840
-2.3']
2054
-930
-840
-2.3
2055
-930
-850
-2.3
Sum
-17,000
-15,000
9-58
-------
Chapter 9 References
2022. "2022 CAFE FRIA." Final Regulatory Impact Analysis: Final Rulemaking for Model
Years 2024-2026 Light-Duty Vehicle Corporate Average Fuel Economy Standards. U.S.
Department of Transportation, National Highway Traffic Safety Administration, March.
2022. "2022 CAFE TSD." Technical Support Document: Final Rulemaking for Model Years
2024-2026 Light-Duty Vehicle Corporate Average Fuel Economy Standards. U.S. Department
Of Transportation, National Highway Traffic Safety Administration, March.
86 FR 74434. 2021.
Beardsley, Megan. 2023. "Updates to MOVES for the Multi-Pollutant Emissions Standards for
Model Years 2027 and Later Light-Duty and Medium-Duty Vehicles." January.
U.S. EIA. 2021. Annual Energy Outlook. Energy Information Administration.
U.S. EPA. 2021. Dose-Response Assessment for Assessing Health Risks Associated With
Exposure to Hazardous Air Pollutants. September 29. Accessed November 15, 2022.
https://www.epa.gov/fera/dose-response-assessment-assessing-health-risks-associated-exposure-
hazardous-air-pollutants.
9-59
-------
Chapter 10: Costs and Benefits of the Proposed Standards in OMEGA
This chapter presents the costs and benefits calculated within OMEGA. The results presented
here show the estimated annual costs, fuel savings and benefits of the program for the indicated
calendar years (CY). The results also show the present-values (PV) of those costs and the
equivalent annualized values (EAV) for the calendar years 2027-2055 using both 3 percent and 7
percent discount rates. For the estimation of the stream of costs and benefits, we assume that
after implementation of the MY 2027 and later standards, the MY 2032 standards apply to each
year thereafter.
10.1 Costs
Vehicle technology costs are estimated in OMEGA using the technology cost inputs presented
in Chapter 2 of this DRIA. Repair, maintenance, congestion, and noise costs are estimated in
OMEGA using the approaches described in Chapter 4 of this DRIA. The resultant costs
associated with the proposed standards are presented in Table 10-1.
Table 10-1 Costs associated with the Proposed standards, light-duty and medium-duty
(billions of 2020 dollars)
Calendar
Vehicle
Repair Costs
Maintenance
Congestion
Noise Costs
Sum
Year
Technology
Costs
Costs
Costs
2027
7.5
0.057
-0.048
-0.00023
-0.000014
7.5
2028
6.8
0.078
-0.34
0.01
0.00014
6.6
2029
6.6
0.017
-0.91
0.022
0.00033
5.8
2030
8.7
-0.15
-1.7
0.038
0.00059
6.9
203 1
13
-0.43
-2.7
0.055
0.00087
9.8
2032
17
-0.84
-4
0.074
0.0012
12
2035
22
-2.8
-9.7
0.12
0.0019
10
2040
19
-9
-23
0.19
0.0029
-13
2045
13
-16
-37
0.17
0.0027
-40
2050
12
-21
-47
0.17
0.0027
-56
2055
10
-24
-51
0.16
0.0025
-65
PV3
280
-170
-410
2.3
0.037
-290
PV7
180
-79
-200
1.3
0.021
-96
EAV3
15
-8.9
-21
0.12
0.0019
-15
EAV7
15
-6.5
-16
0.11
0.0017
-7.8
As shown, estimated repair and maintenance costs, or reductions in those costs, are
significant. BEVs have considerably less maintenance needs than do ICE vehicles (see Chapter
4.3 of this draft RIA which shows BEVs having 30 to 40 percent less maintenance than ICE
vehicles).
Table 10-2, Table 10-3 and Table 10-4 show costs associated with Alternatives 1, 2 and 3,
respectively.
10-1
-------
Table 10-2 Costs associated with Alternative 1, light-duty and medium-duty (billions of
2020 dollars)
Calendar
Vehicle
Repair Costs
Maintenance
Congestion
Noise Costs
Sum
Year
Technology
Costs
Costs
Costs
2027
7.9
0.06
-0.048
0.00063
-0.0000017
7.9
2028
10
0.11
-0.32
0.025
0.00037
9.9
2029
14
0.13
-0.8
0.071
0.0011
13
2030
17
0.032
-1.6
0.11
0.0018
15
203 1
20
-0.17
-2.7
0.17
0.0026
17
2032
23
-0.51
-4.1
0.21
0.0033
19
2035
24
-2.4
-10
0.28
0.0043
12
2040
20
-9
-26
0.27
0.0043
-14
2045
13
-17
-42
0.2
0.003 1
-46
2050
13
-23
-52
0.14
0.0022
-63
2055
11
-26
" -57
0.11
0.0017
-71
PV3
330
-180
-450
3.5
0.055
-300
PV7
220
-82
-220
2 2.2 J
0.034
-82
EAV3
17
-9.3
-24
0.18
0.0028
-15
EAV7
18
-6.7
-18
0.18
0.0027
-6.7
Table 10-3 Costs associated with Alternative 2, light-duty and medium-duty (billions of
2020 dollars)
Calendar
Vehicle
Repair Costs
Maintenance
Congestion
Noise Costs
Sum
Year
Technology
Costs
Costs
Costs
2027
5.5
0.043
-0.032
0.00072
0.0000041
5.6
2028
5
0.058
-0.24
0.012
0.00018
4.8
2029
5.8
0.0065
-0.68
0.02
0.0003 1
5.2
2030
6.1
-0.13
-1.3
0.03
0.00047
4.7
203 1
11
-0.36
-2.1
0.046
0.00073
8.3
2032
15
-0.7
-3.2
0.065
0.001
11
2035
17
-2.5
-8.2
0.082
0.0013
6.6
2040
15
-8.4
-21
0.037
0.00064
-14
2045
10
-15
-34
0.0096
0.00021
-39
2050
10
-20
-43
0.028
0.00048
-53
2055
8.8
-22
-47
0.064
0.001
-60
PV3
230
-160
-370
0.74
0.012
-300
PV7
140
-74
-180
0.48
0.0078
-110
EAV3
12
-8.3
-19
0.039
0.00064
-16
EAV7
12
-6
-14
0.039
0.00064
-8.7
10-2
-------
Table 10-4 Costs associated with Alternative 3, light-duty and medium-duty (billions of
2020 dollars)
Calendar
Vehicle
Repair Costs
Maintenance
Congestion
Noise Costs
Sum
Year
Technology
Costs
Costs
Costs
2027
2.6
0.016
-0.044
-0.0039
-0.000059
2.6
2028
2.3
0.012
-0.22
-0.00089
-0.000006
2.1
2029
1.8
-0.049
-0.54
0.0042
0.000076 'V
1.3
2030
4.9
-0.19
-1
0.012
0.0002
3.7 '
203 1
12
-0.39
-1.7
0.023
0.00038
9.7
2032
18
-0.66
-2.7
0.039
0.00064
15
2035
24
~ ' -2.3 7
-7.7
0.088
0.0015
14
2040
18
-8.5
-21
0.12
0.002
-12
2045
13
-16
-36
0.11
0.0017
-39
2050
12
-21
-47
0.11
0.0017
-56
2055
11
-24
-51
0.11
0.0016
-64
PV3
270
-170
-390
1.5
0.024
-290
PV7
170
-77
-190
0.82
0.013
-95
EAV3
14
-8.6
-20
0.078
0.0012
-15
EAV7
14
-6.3
-15
0.066
0.0011
-7.8
10.2 Fuel Savings
The proposed standards are projected to reduce liquid fuel consumption (e.g., gasoline) while
simultaneously increasing electricity consumption. The estimated impacts on fuel consumption
are shown in Chapter 9.5 of this DRIA.
The net effect of these changes in consumption for consumers is decreased liquid-fuel
expenditures or fuel savings and increased electricity expenditures. For more information of fuel
consumption, including other considerations like rebound, see DRIA Chapter 4. Table 10-2
shows the undiscounted annual monetized fuel savings associated with the proposed standards as
well as the present value (PV) of those costs and equivalent annualized value (EAV) for the
calendar years 2027-2055 using both 3 percent and 7 percent discount rates. We include here the
social costs associated with EVSE ports, as discussed in detail in Chapter 5.3. These reflect the
upfront costs associated with procuring and installing PEV charging infrastructure needed to
meet the anticipated electricity demand in the proposal relative to the no action case. We include
these EVSE port costs in the net benefits presented in Chapter 10.6. Net benefits are determined
using pre-tax fuel savings since fuel taxes do not contribute to the value of the fuel and the EVSE
port costs. We present fuel taxes and other transfers below in Chapter 10.7.
10-3
-------
Table 10-5 Pretax fuel savings and EVSE port costs associated with the Proposed
standards, light-duty and medium-duty (billions of 2020 dollars) *
iilcndar Year
Gasoline
Diesel
Electricity
EVSE Port Costs
Sum
2027
1.7
0.074
-0.92
-1.3
-0.4
2028
4.5
0.12
-2.2
-0.66
1.8
2029
8.3
0.19
-3.9
-1.1
r 3.5
2030
13
0.34
-5.8
-1.1
6.6
2031
19
0.55
-8.1
-8.3
2.8
2032
25
0.9
-11
-8.3
7.4
2035
48
1.9
-19
-6.7
24
2040
83
3.3
-30
-7.1
49
2045
110
4.3
-38
-7.3
67
2050
120
5.2 "
-41
-7.1
81
2055
130
5.8
-41
-7.1
86
PV3
1300
52
-460
-120
[ 77°
PV7
670
27
-240
-68
380
EAV3
68
2.7
-24
-6.2
40
EAV7
54
2.2
-20
-5.6
31
* Positive values represent savings, negative values represent increased costs.
Table 10-6 Pretax fuel savings and EVSE port costs associated with Alternative 1, light-
duty and medium-duty (billions of 2020 dollars) *
iilcndar Year
Gasoline
Diesel
Electricity
EVSE Port Costs
Sum
202"7
1.8
0.0-74
-0.96
-1.3
-0.35
2028
5.1
0.12
-2.4
-0.66
" 2.2
2029
9.3
0.2
-4.1
-1.1
4.3
2030
15
0.34
-6.3
-1.1
8.1
2031
21
0.55
-8.6
-8.3
4.8
2032
29
0.9
-11
-8.3
9.9
2035
54
1.9
-21
-6.7
28
2040
93
3.3
-33
-7.1
56
2045
120
44
-42
-73
75
2050
140
5.3
-46
-7.1
89
2055
140
5.9
-45
-7.1
95
PV3
1400
53
-510
-120
8"70
PV7
"750
27
-270
-68
440
EAV3
75
2.8
-26
-6.2
45
EAV7
61
2.2
-22
-5.6
36
* Positive values represent savings, negative values represent increased costs.
10-4
-------
Table 10-7 Pretax fuel savings and EVSE port costs associated with Alternative 2, light-
duty and medium-duty (billions of 2020 dollars) *
iilcndar Year
Gasoline
Diesel
Electricity
EVSE Port Costs
Sum
2027
1.2
0.074
-0.66
-1.3
-0.63
2028
3.2
0.11
-1.5
-0.66
1.2
2029
6.6
0.19
-3
-1.1
2.6
2030
10
0.34
-4.6
-1.1
5.1
2031
15
0.55
-6.5
-8.3
0.86
2032
21
0.9
-8.8
-8.3
4.9
2035
42
1.9
-17
-6.7
20
2040
75
3.3
-28
-7.1
43
2045
97
4.4
-35
-7.3
59
2050
110
5.3
-38
-7.1
72
2055
120
5.8
-37
-7.1
.177 .
PV3
1200
53
-420
-120
680
PV7
590
27
-220
-68
330
EAV3
60
2.7
-22
-6.2
35
EAV7
48
2.2
-18
-5.6
27
* Positive values represent savings, negative values represent increased costs.
Table 10-8 Pretax fuel savings and EVSE port costs associated with Alternative 3, light-
duty and medium-duty (billions of 2020 dollars) *
iilcndar Year
Gasoline
Diesel
Electricity
EVSE Port Costs
Sum
2027
1
0.072
-0.56
-1.3
-0.77
2028
2.7
0.11
-1.3
-0.66
0.81
2029
4.8
0.18
-2.3
-1.1
1.6
2030
8.3
0.33
-3.7
-1.1
3.8
2031
14
0.54
-6
-8.3
-0.13
2032
21
0.89
-8.7
-8.3
4.4
2035
44
1.9
-18
-6.7
21
2040
81
3.3
-30
-7.1
47
2045
110
4.4
-38
-7.3
66
2050
120
5.3
-42
-7.1
80
2055
130
5.9
-41
-7.1
86
PV3
1200
53
-450
-120
740
PV7
630
" 27
-230
-68
360
EAV3
65
2.1
-23
-6.2
38
EAV7
52
2.2
-19
-5.6
29
* Positive values represent savings, negative values represent increased costs.
10.3 Non-Emission Benefits
Non-emission benefits are shown in Table 10-9 through Table 10-12 for the Proposed
standards, Alternative 1, Alternative 2 and Alternative 3, respectively. The drive value represents
the value that consumers place on the additional driving they may do resulting from the rebound
effect. The value is positive here which represents a benefit to consumers because we have
estimated a small amount of rebound driving relative to the no action case. The value of time
spent refueling is shown as a negative benefit, or disbenefit, because we estimate additional time
spent refueling relative to the no-action scenario. This is due to the additional BEV stock in the
10-5
-------
fleet and the additional time required, using current estimates, to refuel a BEV relative to the
refueling time involved for an ICE vehicle. Energy security benefits are shown as positive
because we estimate reductions in liquid-fuel consumption and corresponding reductions in
imported oil.
Table 10-9 Non-emission benefits associated with the Proposed standards, light-duty and
medium-duty (billions of 2020 dollars) *
Calendar Year Drive Value Value of Time Spent Refueling Energy Security Total
2027
0.001 1
-0.14
0.052
-0.089
2028
0.024
-0.36
0.13
-0.21
2029
0.049
-0.67
0.24
-0.38
2030
0.086
-1
0.37
-0.59
203 1
0.12
-1.5
0.54
-0.8
2032
0.16
-1.9
0.73
-0.99
2035
0.26
-3.4
1.4
-1.7
2040
0.37
-5.5
2.6
; -2.5 "
2045
0.34
-6.9
3.5 ""
-3.1
2050
0.34
-7.9
4.2
-3.3
2055
0.3 1
-8.2
4.4
-3.6
PV3
4.8
-85
41
-39
PV7
2.7
-45
21
-21
EAV3
0.25
-4.4
2.2
-2 ""
EAV7
0.22
-3.6
1.7
-1.7
* Positive values represent benefits while negative values represent disbcncfits.
Table 10-10 Non-emission benefits associated with Alternative 1, light-duty and medium-
duty (billions of 2020 dollars) *
Calendar Year Drive Value Value of Time Spent Refueling Energy Security Total
2027
0.0019
-0.15
0.055
-0.091
2028
0.045
-0.38
0.15
-0.19
2029
0.12
-0.67
0.27
-0.29
2030
0.2
-1.1
0.43
-0.45
203 1
0.28
-1.5
0.61
-0.6
2032
0.37
-1.9
0.82
I -0.75
2035
0.5
-3.5
1.6
-1.4
2040
0.51
-5.8
2.9
-2.4
2045
0.37
-7.4
3.8
P -3.2'
2050
0.29
-8.4
4.7
-3.4
2055
0.22
-8.8
4.8
-3.8
PV3
6.5
-90
46
-38
PV7
3.9
-47
23
-20
EAV3
0.34
-4.7
2.4
-2 '
EAV7
0.32
-3.8
1.9
-1.6
* Positive values represent benefits while negative values represent disbcncfits.
10-6
-------
Table 10-11 Non-emission benefits associated with Alternative 2, light-duty and medium-
duty (billions of 2020 dollars) *
Eilcndar Year
Drive Value
Value of Time Spent Refueling
Energy Security
Total
2027
0.0026
-0.1
0.038
-0.063
2028
0.028
-0.27
0.095
-0.15
2029
0.049
-0.55
0.19
-0.3 1
2030
0.077
-0.88
0.3
-0.5
203 1
0.11
-1.2
0.44
-0.69
2032
0.16
-1.6
0.61
-0.88
2035
0.22
-3.1
1.3
-1.6
2040
0.15
-5.1
2.3
-2.6
2045
0.087
-6.5
3.1
['7-3.2 '
2050
0.11
-7.3
3.8
-3.3
2055
0.17
-7.6
3.9
| -3.5
PV3
2.4
-79
37
-39
PV7
1.5
-41
19
-21
EAV3
0.12
-4.1
1.9
-2 '"
EAV7
0.12
-3.3
1.5
-1.7
* Positive values represent benefits while negative values represent disbcncfils.
Table 10-12 Non-emission benefits associated with Alternative 3, light-duty and medium-
duty (billions of 2020 dollars) *
Calendar Year Drive Value Value of Time Spent Refueling Energy Security Total
2027
-0.0036
-0.093
0.031
-0.065
2028
0.0068
-0.25
0.08
-0.17
2029
0.02
-0.47
0.14
-0.3
2030
0.041
-0.78
0.24
-0.5
203 1
0.063
-1.2
0.4
* -0.72
2032
0.1
-1.6
0.6
-0.93
2035
0.21
-3.2
1.3
-1.7
2040
0.26
-5.4
2.5
-2.6
2045
0.22
-6.9
3.4
1 -3.2 '
2050
0.21
-7.8
4.2
-3.4
2055
0.21
-8.2
4.4
-3.6
PV3
3.2
-83
40
-39
PV7
1.8
-43
20
-21
EAV3
0.17
-4.3
2.1
-2.1
EAV7
0.15
-3.5
1.6
-1.7
* Positive values represent benefits while negative values represent disbcncfils.
10-7
-------
10.4 Climate Benefits
We estimate the social benefits of GHG reductions expected to occur as a result of the
proposed and alternative standards using estimates of the social cost of greenhouse gases (SC-
GHG),161 The SC-GHG is the monetary value of the net harm to society associated with a
marginal increase in emissions of that GHG in a given year. In principle, the SC-GHG includes
the value of all climate change impacts (both negative and positive), 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 and is the
theoretically appropriate value to use in conducting benefit-cost analyses of policies that affect
GHG emissions. In practice, data and modeling limitations naturally restrain the ability of SC-
GHG estimates to include all the important physical, ecological, and economic impacts of
climate change, such that the estimates are a partial accounting of climate change impacts and
will therefore, tend to be underestimates of the marginal benefits of abatement. EPA and other
Federal agencies began regularly incorporating SC-GHG estimates in their benefit-cost analyses
conducted under Executive Order (E.O.) 12866162 since 2008, following a Ninth Circuit Court of
Appeals remand of a rule for failing to monetize the benefits of reducing C02 emissions in a
rulemaking process.
In 2017, the National Academies of Sciences, Engineering, and Medicine published a report
that provides a roadmap for how to update SC-GHG estimates used in Federal analyses going
forward to ensure that they reflect advances in the scientific literature (National Academies
2017). The National Academies' report recommended specific criteria for future SC-GHG
updates, a modeling framework to satisfy the specified criteria, and both near-term updates and
longer-term research needs pertaining to various components of the estimation process. The
research community has made considerable progress in developing new data and methods that
help to advance various components of the SC-GHG estimation process in response to the
National Academies' recommendations.
In a first-day executive order (E.O. 13990), Protecting Public Health and the Environment and
Restoring Science To Tackle the Climate Crisis, President Biden called for a renewed focus on
updating estimates of the social cost of greenhouse gases (SC-GHG) to reflect the latest science,
noting that "it is essential that agencies capture the full benefits of reducing greenhouse gas
emissions as accurately as possible." Important steps have been taken to begin to fulfill this
directive of E.O. 13990. In February 2021, the Interagency Working Group on the SC-GHG
(IWG) released a technical support document (hereinafter the "February 2021 TSD") that
161 Estimates of the social cost of greenhouse gases are gas- specific (e.g., social cost of carbon (SC-C02), social
cost of methane (SC-CH4), social cost of nitrous oxide (SC-N20)), but collectively they are referenced as the social
cost of greenhouse gases (SC-GHG).
162 Benefit-cost analyses have been an integral part of executive branch rulemaking for decades. Presidents since the
1970s have issued executive orders requiring agencies to conduct analysis of the economic consequences of
regulations as part of the rulemaking development process. E.O. 12866, released in 1993 and still in effect today,
requires that for all regulatory actions that are significant under 3(f)(1), an agency provide an assessment of the
potential costs and benefits of the regulatory action, and that this assessment include a quantification of benefits and
costs to the extent feasible.
10-8
-------
provided a set of IWG recommended SC-GHG estimates while work on a more comprehensive
update is underway to reflect recent scientific advances relevant to SC-GHG estimation (IWG
2021). In addition, as discussed further below, EPA has developed a draft updated SC-GHG
methodology within a sensitivity analysis in the regulatory impact analysis of EPA's November
2022 supplemental proposal for oil and gas standards that is currently undergoing external peer
review and a public comment process (U.S. EPA 2022).
The EPA has applied the IWG's recommended interim SC-GHG estimates in the Agency's
regulatory benefit-cost analyses published since the release of the February 2021 TSD and is
likewise using them in this draft RIA. We have evaluated the SC-GHG estimates in the February
2021 TSD and have determined that these estimates are appropriate for use in estimating the
social benefits of GHG reductions expected to occur as a result of the proposed and alternative
standards. These SC-GHG estimates are interim values developed 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. After considering the TSD, and the issues and studies
discussed therein, EPA finds that these estimates, while likely an underestimate, are the best
currently available SC-GHG estimates until revised estimates have been developed reflecting the
latest, peer-reviewed science.
The SC-GHG estimates presented in the February 2021 SC-GHG TSD and used in this draft
RIA were developed over many years, using a 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 develop estimates relying on the
best available science for agencies to use. 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 IAM.163 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. The modeling approach that extends the IWG SC-CO2 methodology to non-
CO2 GHGs has undergone multiple stages of peer review. The SC-CH4 and SC-N2O estimates
were developed by Marten, Kopits, Griffiths, Newbold, and Wolverton (2015) and underwent a
standard double-blind peer review process prior to journal publication. These estimates were
applied in regulatory impact analyses of EPA proposed rulemakings with CH4 and N2O
emissions impacts (U.S. EPA 2015a).164 The EPA also sought additional external peer review of
163 Dynamic Integrated Climate and Economy (DICE) (Nordhaus 2010), Climate Framework for Uncertainty,
Negotiation, and Distribution (FUND) 3.8 (Anthoff 2013b) (Anthoff 2013a), and Policy Analysis of the Greenhouse
Gas Effect (PAGE) 2009 (Hope 2013).
164 The SC-CH4 and SC-N20 estimates were first used in sensitivity analysis for the Proposed Rulemaking for
Greenhouse Gas Emissions and Fuel Efficiency Standards for Medium- and Heavy-Duty Engines and Vehicles-
Phase 2.
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technical issues associated with its application to regulatory analysis. Following the completion
of the independent external peer review of the application of the Marten et al. (2015) estimates,
the EPA began using the estimates in the primary benefit-cost analysis calculations and tables for
a number of proposed rulemakings in 2015 (U.S. EPA 2015b) (U.S. EPA 2015c). The EPA
considered and responded to public comments received for the proposed rulemakings before
using the estimates in final regulatory analyses in 2016. The IWG TSD (2016b) provides
discussion of the SC-CEU and SC-N2O and the peer review and public comment processes
accompanying their development. 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-GHG estimates, a
modeling framework to satisfy the specified criteria, and both near-term updates and longer-term
research needs pertaining to various components of the estimation process. 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-GHG 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 used SC-GHG estimates that attempted to focus on the specific share of
climate change damages in the U.S. as captured by the models (which did not reflect many
pathways by which climate impacts affect the welfare of U.S. citizens and residents) and were
calculated using two default discount rates recommended by Circular A-4, 3 percent and 7
percent.165 All other methodological decisions and model versions used in SC-GHG 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 established an
IWG and directed it to develop an update of the SC-GHG estimates that reflect the best available
science and the recommendations of the National Academies. In February 2021, the IWG
recommended the interim use of the most recent SC-GHG estimates developed by the IWG prior
to the group being disbanded in 2017, adjusted for inflation (IWG 2021). As discussed in the
February 2021 TSD, the IWG's selection of these interim estimates reflected the immediate need
to have SC-GHG estimates available for agencies to use in regulatory benefit-cost analyses and
other applications that were developed using a transparent process, peer reviewed
methodologies, and the science available at the time of that process.
165 EPA regulatory analyses under E.O. 13783 included sensitivity analyses based on global SC-GHG values and
using a lower discount rate of 2.5%. OMB Circular A-4 (OMB, 2003) recognizes that special considerations arise
when applying discount rates if intergenerational effects are important. In the IWG's 2015 Response to Comments,
OMB—as a co-chair of the IWG—made clear that "Circular A-4 is a living document," that "the use of 7 percent is
not considered appropriate for intergenerational discounting," and that "[t]here is wide support for this view in the
academic literature, and it is recognized in Circular A-4 itself." OMB, as part of the IWG, similarly repeatedly
confirmed that "a focus on global SCC estimates in [regulatory impact analyses] is appropriate" (IWG 2015).
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As noted above, EPA participated in the IWG but has also independently evaluated the
interim SC-GHG estimates published in the February 2021 TSD and determined they are
appropriate to use here to estimate climate benefits. EPA and other agencies intend to undertake
a fuller update of the SC-GHG estimates that takes into consideration the advice of the National
Academies (2017) and other recent scientific literature. The EPA has also evaluated the
supporting rationale of the February 2021 TSD, including the studies and methodological issues
discussed therein, and concludes that it agrees with the rationale for these estimates presented in
the TSD and summarized below.
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 concluded that those
estimates fail to capture many climate impacts that can affect the welfare of U.S. citizens and
residents. 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 that can lead to adverse impacts on U.S. national security,
public health, and humanitarian concerns. Those impacts are better captured within global
measures of the social cost of greenhouse gases.
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. A wide range of scientific and economic experts
have emphasized the issue of reciprocity as support for considering global damages of GHG
emissions. Using a global estimate of damages in U.S. analyses of regulatory actions allows the
U.S. to continue to actively encourage other nations, including emerging major economies, to
take significant steps to reduce emissions. The only way to achieve an efficient allocation of
resources for emissions reduction on a global basis—and so benefit the U.S. and its citizens—is
for all countries to base their policies on global estimates of damages.
As a member of the IWG involved in the development of the February 2021 SC-GHG TSD,
EPA agrees with this assessment and, therefore, in this proposed rule the 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. A robust estimate of climate damages only to U.S. citizens and
residents that accounts for the myriad of ways that global climate change reduces the net welfare
of U.S. populations does not currently exist in the literature. As explained in the February 2021
TSD, existing estimates are both incomplete and an underestimate of total damages that accrue to
the citizens and residents of the U.S. because they do not fully capture the regional interactions
and spillovers discussed above, nor do they include all of the important physical, ecological, and
economic impacts of climate change recognized in the climate change literature, as discussed
further below. EPA, as a member of the IWG, will continue to review developments in the
literature, including more robust methodologies for estimating the magnitude of the various
damages to U.S. populations from climate impacts and reciprocal international mitigation
activities, and explore ways to better inform the public of the full range of carbon impacts.
Second, the IWG concluded 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 (2017) and the economic
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literature, the IWG continued to conclude that the consumption rate of interest is the theoretically
appropriate discount rate in an intergenerational context, and recommended that discount rate
uncertainty and relevant aspects of intergenerational ethical considerations be accounted for in
selecting future discount rates (IWG 2010) (IWG 2013) (IWG 2016a) (IWG 2016b).166
Furthermore, the damage estimates developed for use in the SC-GHG are estimated in
consumption-equivalent terms, and so an application of OMB Circular A-4's guidance for
regulatory analysis would then use the consumption discount rate to calculate the SC-GHG. EPA
agrees with this assessment and will continue to follow developments in the literature pertaining
to this issue. EPA also notes that while OMB Circular A-4, as published in 2003, recommends
using 3 percent and 7 percent discount rates as "default" values, Circular A-4 also reminds
agencies that "different regulations may call for different emphases in the analysis, depending on
the nature and complexity of the regulatory issues and the sensitivity of the benefit and cost
estimates to the key assumptions." On discounting, Circular A-4 recognizes that "special ethical
considerations arise when comparing benefits and costs across generations," and Circular A-4
acknowledges that analyses may appropriately "discount future costs and consumption
benefits.. .at a lower rate than for intragenerational analysis." In the 2015 Response to Comments
on the Social Cost of Carbon for Regulatory Impact Analysis, OMB, EPA, and the other IWG
members recognized that "Circular A-4 is a living document" and "the use of 7 percent is not
considered appropriate for intergenerational discounting. There is wide support for this view in
the academic literature, and it is recognized in Circular A-4 itself." Thus, EPA concludes that a 7
percent discount rate is not appropriate to apply to value the social cost of greenhouse gases in
the analysis presented in this analysis. In this analysis, to calculate the present and annualized
values of climate benefits, EPA uses the same discount rate as the rate used to discount the value
of damages from future GHG emissions, for internal consistency. That approach to discounting
follows the same approach that the February 2021 TSD recommends "to ensure internal
consistency—i.e., future damages from climate change using the SC-GHG at 2.5 percent should
be discounted to the base year of the analysis using the same 2.5 percent rate." EPA has also
consulted the National Academies' 2017 recommendations on how SC-GHG estimates can "be
combined in RIAs with other cost and benefits estimates that may use different discount rates."
The National Academies reviewed "several options," including "presenting all discount rate
combinations of other costs and benefits with [SC-GHG] estimates."
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 recommended 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 concluded that it is
appropriate for agencies to revert to the same set of four values drawn from the SC-GHG
166 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.
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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 agency 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
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 10-13, Table 10-14, and Table 10-15 summarize the interim SC-CO2, SC-CFU, and SC-
N2O estimates for the years 2027-2055167. These estimates are reported in 2020 dollars in the
IWG's 2021 TSD but are otherwise identical to those presented in the IWG's 2016 TSD (IWG
2021). For purposes of capturing uncertainty around the SC-CO2 estimates in analyses, the
February 2021 TSD emphasizes the importance of considering all four of the SC-CO2 values.
The SC-CO2 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.
167 The February 2021 TSD provides SC-GHG estimates through emissions year 2050. Estimates were extended for
the period 2051 to 2055 using the IWG methods, assumptions, and parameters identical to the 2020-2050 estimates.
Specifically, 2051-2055 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.
10-13
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Table 10-13 Interim Social Cost of Carbon Values, 2027-2055 (2020$/Metric Ton CO2)
Calendar Year Discount Rate and Statistic
5% Average
1 3% Average
2.5% Average
3%, 95th percentile
2027
$18
$59
$86
$176
2028
T 'J' $18
$60
..././ $8?
SI so 7
2029
J ' $19
$61
7 $88 :
$183
2030
I $19
$62
$89 1
$187
203 i
: $20
; $63
7. $91 .1
$191 7.
2032
$21
! $64
$92
$194
2033
: $21
I 3'" $65
' $94 '7 1
$198
2034
; $22
| $66
$95 1
$202
2035
$22
$67
r $96
$206
2036
$23 "
f $69
$98
$210
2037
$23
1 $70
| $99
$213
2038
$24
; $71 ""
$100"
$217
2039
$25
; $72
$102^
$221
2040
; $25 7
: $73
$103 J
7" $225';
2041
' $26
$74""
$104
$228
2042
$26
$75
$106
$232
2043
$27
$77"*
$ 107
7 $235
2044
"$28
$78
$108
$239
2045
*$28
; $79'J'...7.
[ $110
7$2427
2046
j $29
$80
! $111
$246
2047
*] 'J $30
SSI
$112 :
$249";
2048
T $30
$82 7
$114 :
7* $2537
2049
$31
$84
$115
$256
2050
$32
f $85 ~
$116
$260
2051
i $33
$85
$118
$261
2052
$33'""
$86
$119 *
$262
2053
J" " $34'""
$87
; $120
$263 7
2054
$34
$88
: $121
$263
2055
7 $35
$89
$122
$266
Note: The 2027-2055 SC-C02 values are identical to those reported in the 2016 TSD (IWG 2016a) adjusted to 2017 dollars using the annual
GDP Implicit Price Deflator values in the U.S. Bureau of Economic Analysis' (BE A) NIP A Table 1.1.9 (U.S. BEA 2022). This table displays
the values rounded to the nearest dollar; the annual unrounded values used in the calculations in this analysis are available on OMB's website:
https://www.whitehouse.gOv/omb/information-regulatory-affairs/regulatory-matters/#scghgs.
The estimates were extended for the period 2051 to 2055 using methods, assumptions, and parameters identical to the 2020-2050 estimates.
The values are stated in $/metric ton C02 and vary depending on the year of C02 emissions.
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Table 10-14 Interim Social Cost of Carbon Values, 2027-2055 (2020$/Metric Ton CH4)
Calendar Year
Discount Rale and Statistic
5% Average
3% Average
2.5% Average
3%. 95th percentile
2027
$860
$1,800
$2,300
$4,800
2028
$880
$1,900
$2,400
$4,900
2029
$910
$1,900
$2,500
$5,100
2030
$940
$2,000
$2,500
$5,200
203 1
$970
$2,000
$2,600
$5,300
2032
$1,000
$2,100
$2,600
$5,500
2033
$1,000
$2,100
$2,700
3 $5,700
2034
$1,100
$2,200
$2,800
$5,800
2035
$1,100
$2,200
$2,800
$6,000
2036
$1,100
$2,300
$2,900
$6,100
2037
$1,200
$2,300
$3,000
$6,300
2038
$1,200
$2,400
$3,000
$6,400
2039
$1,200
$2,500
$3,100
$6,600
2040
$1,300
$2,500
$3,100
$6,700
2041
$1,300
$2,600
$3,200
$6,900
2042
$1,400
$2,600
$3,300
$7,000
2043
$1,400
$2,700
$3,300
$7,200
2044
$1,400
$2,700
$3,400
$7,300
2045
$1,500
$2,800
$3,500
$7,500
2046
$1,500
$2,800
$3,500
$7,600
2047
$1,500
$2,900
$3,600
$7,700
2048
$1,600
$3,000
$3,700
$7,900
2049
$1,600
$3,000
$3,700
$8,000
2050
$1,700
$3,100
$3,800
$8,200
2051
$1,700
$3,100
$3,800
$8,200
2052
$1,700
$3,100
$3,900
$8,300
2053
$1,700
$3,200
$3,900
$8,300
2054
$1,800
$3,200
$3,900
$8,300
2055
$1,800
$3,200
$4,000
$8,400
Note: The 2027-2055 SC-CH4 values are identical to those reported in the 2016 TSD (IWG 2016a) adjusted to 2017 dollars using 1
GDP Implicit Price Deflator values in the U.S. Bureau of Economic Analysis' (BE A) NIP A Table 1.1.9 (U.S. BEA 2022). This table displays
the values rounded to the nearest dollar; the annual unrounded values used in the calculations in this analysis are available on OMB's website:
https://www.whitehouse.gOv/omb/information-regulatory-affairs/resulatory-matters/#scghgs.
The estimates were extended for the period 2051 to 2054 us U ds. assumptions, and parameters identical to the 2020-2050 estimates.
The values are stated in $/metric ton CH4 and vary depending on the vear of CH4 emissions.
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Table 10-15 Interim Social Cost of Carbon Values, 2027-2055 (2020$/Metric Ton N2O)
Calendar Year
Discount Rale and Statistic
5% Average
3% Average
2.5% Average
3%, 95(li percentile
2027
$7,200
$21,000
$31,000
$57,000
2028
$7,400'
$22,000
$32,000
$58,000
2029
7 .7. $7,600:7
$22,000
$32,000 7
$59,000
2030
; $7,800
: $23,000
$33,000
$60,000
203 i
$8,000
i $23,000
$33,000
$62,000
2032
$8,300
$24,000
$34,000
$63,000
2033
$8,500
$24,000
$35,000 7
$64,000
2034
$8,800
$25,000
$35,000
$66,000
2035
] $9,0007 '7
! $25>000
$36,000
$67,000
2036
7 $9,300
i $26,000
$36,000 !
$68,000
2037
$9,500
$26,000
$37,000
$70,000
2038
|' $9,800
$27,000
77 $38,000
$71,000
2039
$10,000
$27,000
$38,000
$73,000
2040
$10,000 77
$28,000
$39,000
$74,000
2041
$11,000
$28,000
$39,000
$75,000
2042
$11,000
$29,000
$40,000
$77,000
2043 "" "
$11,000
7. $29,000
$41,000
7 $78,000
2044
$11,000
$30,000
$41,000
$80,000
201?
$12,000
$30,000
$42,000
$81,000
2046
$12,000
$31,000
$43,000
$82,000
"7" .2047"
$12,000
$31,000
77 $43,000
$84,000
7 72048
$13,000
$32,000
$44,000
$85,000
2049
$13,000
$32,000
$45,000
$87,000
2050
$13,000
$33,000
" $45,000"'
$88,000
2051
$14,000
$34,000
$46,000
$89,000
2052
SI l.ooo
$34,000 ^
$47,000 '
$90,000
2053
$14,000
$35,000
$47,000 ' J
$92,000
2054
$14,000
$35,000
$48,000 1
$93,000
2055
$15,000
$36,000
$48,000
$94,000
I Note: The 2027-2055 SC-N20 values are identical to those reported in the 2016 TSD (IWG 2016a) adjusted to 2017 dollars using the annual
j GDP Implicit Price Deflator values in the U.S. Bureau of Economic Analysis' (BE A) NIP A Table 1.1.9 (U.S. BEA 2022). This table displays
I the values rounded to the nearest dollar; the annual unrounded values used in the calculations in this analysis are available on OMB's website:
; https://www.whitehouse.gOv/omb/information-regulatory-affairs/regulatory-matters/#scghgs.
I The estimates were extended for the period 2051 to 2054 using methods, assumptions, and parameters identical to the 2020-2050 estimates.
; The values are stated in $/metric ton N20 and vary depending on the year of N20 emissions.
There are a number of limitations and uncertainties associated with the SC-GHG estimates
presented in Table 10-13, Table 10-14, and Table 10-15. Some uncertainties are captured within
the analysis, while other areas of uncertainty have not yet been quantified in a way that can be
modeled. Figure 10-1, Figure 10-2, and Figure 10-3 present the quantified sources of uncertainty
in the form of frequency distributions for the SC-CO2, SC-CFU, and SC-N2O estimates for
emissions in 2030 (in 2020$). The distribution of the SC-CO2 estimate reflects 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-CO2
estimates for each discount rate. As illustrated by the figure, the assumed discount rate plays a
critical role in the ultimate estimate of the SC-CO2. This is because CO2 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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1 J J of Simulations
II I I I I I I I I I II I > Mill! I I I I II I I I I I I I i
0 20 40 60 80 100 120 140 160 180 200 220 240 260 280
Social Cost of Carbon in 2020 [2020$ / metric ton CO?]
Figure 10-1: Frequency Distribution of SC-CO2 Estimates for 2030168
O
CM
O
5% Average = $670
E
-------
ii ii iiii I I h ' i 11 I'Ii'iI i I i i i I I I I;I I I Ii I I I I I I I i I I II I i I i I II 111 I 11 I> I 11!IM I I I II I ) I
0 8000 16000 24000 32000 40000 48000 56000 64000 72000 80000 88000
Social Cost of Nitrous Oxide in 2020 [2020$ / metric ton N:J0]
Figure 10-3: Frequency Distribution of SC-N20 Estimates for 2030168
The interim SC-GHG estimates presented in Table 10-13 through Table 10-15 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 (1PCC 2007). 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 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-GFIG 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 final rule likely underestimate the damages from GFIG emissions. EPA concurs that the
values used in this rulemaking conservatively underestimate the rule's climate benefits. 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. Since then, the peer-reviewed literature has continued to
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support this conclusion, as noted in the IPCC's Fifth Assessment report and other recent
scientific assessments (IPCC 2014) (IPCC 2018) (IPCC 2019a) (IPCC 2019b) (USGCRP 2016)
(USGCRP 2018) (National Academies 2016b) (National Academies 2019). 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 (IPCC 2007). 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 (USGCRP 2018).
EPA has reviewed and considered the limitations of the models used to estimate the interim SC-
GHG estimates, and concurs with the February 2021 SC-GHG TSD's assessment that, taken
together, the limitations suggest that the interim SC-GHG estimates likely underestimate the
damages from GHG emissions.
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 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 (National Academies 2017). While that
process continues, EPA is continuously reviewing developments in the scientific literature on the
SC-GHG, including more robust methodologies for estimating damages from emissions, and
looking for opportunities to further improve SC-GHG estimation going forward. Most recently,
EPA presented a draft set of updated SC-GHG estimates within a sensitivity analysis in the
regulatory impact analysis of EPA's November 2022 supplemental proposal for oil and gas
standards that that aims to incorporate recent advances in the climate science and economics
literature. Specifically, the draft updated methodology incorporates new literature and research
consistent with the National Academies near-term recommendations on socioeconomic and
emissions inputs, climate modeling components, discounting approaches, and treatment of
uncertainty, and an enhanced representation of how physical impacts of climate change translate
to economic damages in the modeling framework based on the best and readily adaptable
damage functions available in the peer reviewed literature. EPA solicited public comment on the
sensitivity analysis and the accompanying draft technical report, which explains the methodology
underlying the new set of estimates, in the docket for the proposed Oil and Gas rule. EPA is also
embarking on an external peer review of this technical report. More information about this
process and public comment opportunities is available on EPA's website (U.S. EPA 2022).
EPA's draft technical report will be among the many technical inputs available to the IWG as it
continues its work.
EPA estimated the dollar value of the GHG-related effects for each analysis year between
2027 through 2055 by applying the SC-GHG estimates, shown in Table 10-13 through Table
10-15, to the net inventory impacts calculated within OMEGA (vehicle and EGU). EPA then
calculated the present value and annualized benefits from the perspective of 2027 by discounting
each year-specific value to the year 2027 using the same discount rate used to calculate the SC-
10-19
-------
GHG. Climate benefits are shown in Table 10-16 through Table 10-19 for the proposed
standards and each of the alternatives.169
169 According to OMB's Circular A-4 (OMB 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, but separately. Circular A-4
also reminds analysts that "[different regulations may call for different emphases in the analysis, depending on the
nature and complexity of the regulatory issues." To correctly assess the total climate damages to U.S. citizens and
residents, an analysis should account for all the ways climate impacts affect the welfare of U.S. citizens and
residents, including how U.S. GHG mitigation activities affect mitigation activities by other countries, and spillover
effects from climate action elsewhere. The SC-GHG estimates used in regulatory analysis under revoked EO 13783
were a limited approximation of some of the U.S. specific climate damages from GHG emissions. These estimates
range from $8 per metric ton C02, $231 per metric ton CH4, $2,649 per metric ton N20 (2020 dollars) using a 3
percent discount rate for emissions occurring in 2027 to $12 per metric ton C02, $382 per metric ton CH4, $4,281
per metric ton N20 using a 3 percent discount rate for emissions occurring in 2055. Applying these estimates (based
on a 3 percent discount rate) to the C02, CH4, and N20 emissions reduction expected under the proposed rule
would yield benefits from climate impacts of $57 million in 2027, increasing to $5.2 billion in 2055. However, as
discussed at length in the IWG's February 2021 SC-GHG TSD, these estimates are an underestimate of the benefits
of GHG 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. In particular, as discussed in this analysis, EPA concurs
with the assessment in the February 2021 SC-GHG TSD that the estimates developed under revoked E.O. 13783 did
not capture significant regional interactions, spillovers, and other effects and so are incomplete underestimates. As
the U.S. Government Accountability Office (GAO) concluded in a June 2020 report examining the SC-GHG
estimates developed under E.O. 13783, the models "were not premised or calibrated to provide estimates of the
social cost of carbon based on domestic damages" p.29 (U.S. GAO 2020). Further, the report noted that the National
Academies found that country-specific social costs of carbon estimates were "limited by existing methodologies,
which focus primarily on global estimates and do not model all relevant interactions among regions" p.26 (U.S.
GAO 2020). It is also important to note that the SC-GHG estimates developed under E.O. 13783 were never peer
reviewed, and when their use in a specific regulatory action was challenged, the U.S. District Court for the Northern
District of California determined that use of those values had been "soundly rejected by economists as improper and
unsupported by science," and that the values themselves omitted key damages to U.S. citizens and residents
including to supply chains, U.S. assets and companies, and geopolitical security. The Court found that by omitting
such impacts, those estimates "fail[ed] to consider.. .important aspect[s] of the problem" and departed from the "best
science available" as reflected in the global estimates. California v. Bernhardt, 472 F. Supp. 3d 573, 613-14 (N.D.
Cal. 2020). EPA continues to center attention in this analysis on the global measures of the SC-GHG as the
appropriate estimates given the flaws in the U.S. specific estimates, and as necessary for all countries to use to
achieve an efficient allocation of resources for emissions reduction on a global basis, and so benefit the U.S. and its
citizens.
10-20
-------
Table 10-16 Climate benefits from reductions in GHG emissions associated with the
Proposed standards, light-duty and medium-duty (billions of 2020 dollars)
:ndar Year
5% Average SC-
3% Average SC-
2.5% Average SC-
3%. 95th pcrcc
GHG
GHG
GHG
GHG
2027
0.1
0.34
0.5
1
2028
0.27
0.88
1.3
= *2.7
2029
0.52
1.7
2.4
5
2030
0.82
2.6
3.8
7.9
203 1
1.2
3.8
5.5
12
2032
1.7
5.3
7.6
16
2033
2.3
6.9
10
21
2034
2.9
00
00
13
27
2035
3.5
11
15
'32"
2036
4
12
17
37*'"
2037
4.7
14
20
42
2038
5.3
16
22
48
2039
6
18
25
54
2040
6.7
19
27
60
2041
7.4
21
30
65
2042
8
23
32
70
2043
8.8
25
35
76
2044
9.4
26
37
80
2045
10
28
38
85
2046
11
29
41
90
2047
11
31
42
94
2048
12
32
44
98
2049
12
34
46
100
2050
13
35
48
110
2051
14
35
49
110
2052
14
36
50
110
2053
14
37
51
110
2054
15
37
51
110
2055
15
38
52
110
PV
82
330
500
1000
EAV
5.4
17
25
52
Notes: Climate benefits are based on changes (reductions) in C02, CH4, and N20 emissions and are calculated using four different estimates
of the SC-C02, SC-CH4, and SC-N20 CH4 (model average at 2.5 percent, 3 percent, and 5 percent discount rates; and 95th percentile at 3
percent discount rate). The IWG emphasized the importance and value of considering the benefits calculated using all four estimates. As
discussed in the Technical Support Document: Social Cost of Carbon, Methane, and Nitrous Oxide Interim Estimates under EO 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.
10-21
-------
Table 10-17 Climate benefits from reductions in GHG emissions associated with
Alternative 1, light-duty and medium-duty (billions of 2020 dollars)
:ndar Year
5% Average SC-
3% Average SC-
2.5% Average SC-
3%. 95th percentile SC
GHG
GHG
GHG
GHG
2027
0.11
0.36
0.52
1.1
2028
0.31
1
1.5
3
2029
0.58
1.9
2.7
5.6
2030
0.96
3.1
4.4
9.2
203 1
1.4
4.4
6.3
13
2032
1.9
6
8.6
18
2033
2.6
7.9
11
24
2034
3.2
9.9
14
30
2035
3.9
12
17
36
2036
4.5
14
19
41
2037
5.2
16
22
48
2038
6
18
25
54
2039
6.7
20
28
60
2040
7.5
22
30
66
2041
8.2
24
33
73
2042
8.9
25
36
78
2043
9.7
27
38
84
2044
10
29
40
89
2045
11
31
43
94
2046
12
32
45
100
2047
12
34
47
100
2048
13
35
49
110
2049
14
' 37
51
110
2050
14
38
53
120
2051
15
39
54
120
2052
15
40
55
120
2053
16
40
56
120
2054
16
41
56
120
2055
16
41
57
120
PV
91
360
560
1100
EAV
6
19
27
58
Notes: Climate benefits are based on changes (reductions) in C02, CH4, and N20 emissions and are calculated using four different estimates
of the SC-C02, SC-CH4, and SC-N20 CH4 (model average at 2.5 percent, 3 percent, and 5 percent discount rates; and 95th percentile at 3
percent discount rate). The IWG emphasized the importance and value of considering the benefits calculated using all four estimates. As
discussed in the Technical Support Document: Social Cost of Carbon, Methane, and Nitrous Oxide Interim Estimates under EO 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.
10-22
-------
Table 10-18 Climate benefits from reductions in GHG emissions associated with
Alternative 2, light-duty and medium-duty (billions of 2020 dollars)
Calendar Year
5% Average SC-
3% Average SC-
2.5% Average SC-
3%. 95th percentile SC-
GHG
GHG
GHG
GHG
2027
0.076
0.25
0.36
0.75
2028
0.2
0.65
0.95
' 2
2029
0.41
1.3
1.9
4
2030
0.66
2.1
3
6.3
203 1
0.99
3.1
4.5
9.5
2032
1.4
4.4
6.4
13
2033
2
6
8.7
18
2034
2.5
: 7.7
11
23
2035
3.1
9.3
13
28
2036
3.6
11
15
33
2037 7.
4.2
12
18
38
2038
4.8
14
20
43
2039
5.4
16
22
48
2040
6
17
25
54
2041
6.7
19
27
59
2042
7.3
21
29
63
2043
7.9
22
31
69
2044
8.5
24
33
73
2045
9
25
35
77
2046
9.6
| 27
! 37
81
2047
10
28
38
85
2048
11
29
40
89
2049
11
30
42
93
2050
12
32
44
97
2051
12
32
45
98
2052
13
33
45
99
2053
13
33
46
100
2054
13
34
47
100
2055
13
34
47
100
PV
74
290
450
900
EAV
4.9
15
! 22
47
Notes: Climate benefits are based on changes (reductions) in C02, CH4, and N20 emissions and are calculated using four different estimates
of the SC-CQ2, SC-CH4, and SC-N2Q CH4 (model average at 2.5 percent, 3 percent, and 5 percent discount rates; and 95th percentile at 3
percent discount rate). The IWG emphasized the importance and value of considering the benefits calculated using all four estimates. As
discussed in the Technical Support Document: Social Cost of Carbon, Methane, and Nitrous Oxide Interim Estimates under EO 13990 (IWG,
2021), a consideration of climate benefits calculated using discount rates below 3 percent, including 2 percent and lower, are also warranted
\\ hen 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.
10-23
-------
Table 10-19 Climate benefits from reductions in GHG emissions associated with
Alternative 3, light-duty and medium-duty (billions of 2020 dollars)
Calendar Year
5% Average SC-
3% Average SC-
2.5% Average SC-
3%. 95th percentile SC
GHG
GHG
GHG
GHG
2027
0.062
0.2
0.3
0.61
2028
0.17
0.54
0.78
1.6
2029
0.3
0.98
1.4
2.9
2030
0.53
1.7
2.4
5.1
203 1
0.89
2.8
4.1
8.5
2032
1.4
4.3
6.2
13
2033
1.9
6
8.6
18
2034
2.6
7.8
11
24
2035
3.2
9.7
14
29
2036
3.7
11
16
34
2037 7.
4.4
13
19
40
2038
5.1
15
21
46
2039
5.8
17
24
52 3
2040
6.5
19
26
58
2041
7.2
21
29
63
2042
7.9
22
31
69
2043
8.6
24
34
74
2044
9.2
26
36
79
2045
9.9
27
38
84
2046
11
29
40
89
2047
11
30
42
93
2048
12
32
44
98
2049
12
33
46
100
2050
13
35
48
110
2051
14
1 35
49
110
2052
14
36
50
110
2053
14
| 37
51
110
2054
15
i 37
51
110
2055
15
38
52
110
PV
80
320
490
970
EAV
5.3
17
24
51
: Notes: Climate benefits are based on changes (reductions) in C02, CH4, and N20 emissions and are calculated using four different estimates
: of the SC-CQ2, SC-CH4, and SC-N2Q CH4 (model average at 2.5 percent, 3 percent, and 5 percent discount rates; and 95th percentile at 3
percent discount rate). The IWG emphasized the importance and value of considering the benefits calculated using all four estimates. As
: discussed in the Technical Support Document: Social Cost of Carbon, Methane, and Nitrous Oxide Interim Estimates under EO 13990 (IWG,
; 2021), a consideration of climate benefits calculated using discount rates below 3 percent, including 2 percent and lower, are also warranted
\\ hen 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.
10.5 Criteria Air Pollutant Benefits
For the analysis of the proposed standards, we use a reduced-form "benefit-per-ton" (BPT)
approach to estimate the monetized PIvfc.s-related health benefits of this proposal. As described
in draft RIA Chapter 7.4, the BPT approach monetizes avoided premature deaths and illnesses
that are expected to occur as a result of reductions in directly-emitted PM2.5 and PM2.5 precursors
attributable to the proposed standards. A chief limitation to using PIVfo.s-related BPT values is
that they do not reflect benefits associated with reducing ambient concentrations of ozone, direct
exposure to NO2, or exposure to mobile source air toxics, nor do they account for improved
10-24
-------
ecosystem effects or visibility. The estimated benefits of this proposal would be larger if we were
able to monetize these unquantified benefits at this time.
Using the BPT approach, we estimate the present value of PM2.5-related benefits of the
proposed program to be $140 to $280 billion at a 3% discount rate and $63 to $130 billion at a
7% discount rate. Benefits are reported in year 2020 dollars and reflect the PIVfo.s-related benefits
associated with reductions in NOx, SO2, and direct PM2.5 emissions. Because premature
mortality typically constitutes the vast majority of monetized benefits in a PM2.5 benefits
assessment, we present a range of PM benefits based on risk estimates reported from two
different long-term exposure studies using different cohorts to account for uncertainty in the
benefits associated with avoiding PM-related premature deaths: the National Health Interview
Survey (NHIS) cohort study (Pope III et al. 2019) and an extended analysis of the Medicare
cohort (Wu et al. 2020).
Table 10-20 presents the annual, undiscounted PM2.5-related health benefits estimated for the
stream of years beginning with the first year of rule implementation, 2027, through 2055 for the
proposed standards. Benefits are presented by source (onroad and upstream) and are estimated
using either a 3 percent or 7 percent discount rate to account for a "cessation" lag between the
change in PM exposures and the total realization of changes in mortality effects. Table 10-20
also shows the present and annualized values of PM2.5-related benefits for the proposed program
between 2027 and 2055 (discounted back to 2027). Table 10-21 through Table 10-23 present the
results for each of the alternatives.
10-25
-------
Table 10-20 Monetized PM2.5 health benefits of onroad and upstream emissions reductions
associated with the Proposed standards (billions of 2020 dollars)
Total Onroad Total Upstream Total Benefits
3% Discount Rate 7% Discount 3% Discount Rate ; 7% Discount Rate j 3% Discount Rate ; 7% Discount Rate
Rate
2027
0.053 - 0.11
0.048-0.1
0.011-0.026
0.01-0.023
0.064-0.14
0.058 - 0.13
2028
0.13-0.28
f" 0.12-0.25
0.039-0.088
0.035 -0.08
0.17-0.37
0.15 - 0.33
2029
0.24-0.52
j 0.22-0.47
0.083 - 0.19
0.075 -0.17
0.33-0.71
0.29 - 0.63
2030
0.65 - 1.3
0.58- 1.2
0.15-0.33
0.14-0.29
0.8- 1.7
0.72-1.5
2031
1-2.1
0.93-1.9
0.24-0.52
0.22-0.47
1.3 - 2.7
1.2-2.4
2032
1.4-3
1.3 - 2.7
0.36-0.77
0.33-0.69
1.8-3.7
1.6-3.4
2033
1.9-3.9
1.7-3.5
0.51-1.1
0.45-0.96
2.4-4.9
2.1 - 4.4
2034
2.3-4.8
2.1-4.3
0.67 - 1.4
0.6- 1.3
3-6.2
2.7 - 5.6
2035
3.2-6.4
2.9-5.8
0.98 - 2
0.88-1.8
4.2 - 8.4
3.7-7.6
2036
3.7-7.4
3.3-6.6
1.2-2.4
1-2.2
4.8 - 9.8
4.3 - 8.8
2037
4.2-8.4
i 3.7-7.5
1.4-2.8
1.2-2.6
5.6-11
5-10
2038 '
4.7-9.4
4.2-8.5
1.6-3.3
1.5-3
6.3 - 13
5.6 -11
2039
5.1-10
4.6 - 9.3
1.9-3.8
1.7-3.4
7-14
6.3 - 13
2040
6.3 - 13
5.7-11
2.4-4.8
2.1 - 4.3
8.7 - 17
7.8 - 16
2041 V
6.8- 14
6.1-12
2.7-5.3
2.4-4.8
9.5 - 19
8.5 - 17
2042
7.3-14
6.6- 13
2.9-5.8
2.6 - 5.2
10-20
9.2 - 18
2043
7.8- 15
7-14
3.2-6.4
2.9-5.8
11-22
9.8 - 20
2044
8.1 - 16
7.3 - 14
3.4-6.9
3.1 - 6.2
12-23
10-21
2045
9.3 - 18
8.4-16
3.7-7.4
3.3-6.6
13-26
12-23
2046
9.7- 19
8.7- 17
4-7.9
3.6-7.1
14-27
12-24
2047
10-20
9-18
4.2-8.3
3.8-7.5
14-28
13 - 25
2048
10-20
9.2- 18
4.3 - 8.6
3.9-7.7
15 - 29
13 - 26
2049
11-21
9.4- 18
4.4-8.9
4-8
15-29
13-26
2050
12 - 22
10-20
4.6-9.1
4.1-8.2
16-31
14-28
2051
12-23
11 - 20
4.6-9.2
4.1 - 8.2
16-32
15-29
2052
12-23
11-21
4.6-9.2
4.1-8.3
16-32
15-29
2053
12-23
11-21
4.6-9.3
4.2-8.3
17-32
15-29
2054
12-23
11-21
4.6-9.3
4.2-8.3
17-32
15-29
2055
13-25
12-22
4.6-9.3
4.2-8.3
18-34
16-31
Present Value
100-200
46-91
39 - 79
17 - 35
140- 280
63 - 130
Equivalent 5.4- 11 3.7-7.4 2.1 -4.1 1.4- 2.8 7.5 - 15 5.1 - 10
Annualized
Value
Notes: The range of benefits in this table reflect the range of premature mortality estimates derived from the Medicare study (Wu et al.. 2020) and the
NHIS study (Pope et al., 2019). All benefits estimates are rounded to two significant figures. Annual benefit values presented here are not discounted.
The present value of benefits is the total aggregated value of the series of discounted annual benefits that occur between 2027-2055 (in 2020 dollars)
using either a 3% or 7% discount rate. The benefits associated with the standards presented here do not include the full complement of health and
environmental benefits that if quantified and monetized, would increase the total monetized benefits.
10-26
-------
Table 10-21 Monetized PM2.5 health benefits of onroad and upstream emissions reductions
associated with Alternative 1 (billions of 2020 dollars)
Total Onroad Total Upstream Total Benefits
3% Discount Rate
7% Discount Rate
3% Discount Rate
7% Discount Rate
3% Discount Rate
7% Discount Rate
2027
0.055 - 0.12
0.05-0.11
0.012-0.027
0.011 - 0.025
0.067-0.15
0.06-0.13
2028
0.14-0.3
0.13-0.27
0.048-0.11
0.044 - 0.098
0.19-0.41
0.17-0.37
2029
0.25-0.53
0.22-0.48
0.11-0.23
0.095 -0.21
0.35-0.76
0.32-0.69
2030
0.66 - 1.4
0.59-1.2
0.2-0.42
0.18-0.38
0.85 - 1.8
0.77-1.6
2031
1-2.2
0.93 - 1.9
031-0.65
0.28-0.59
1.3-2.8
1.2-2.5
2032
1.4-3
1.3-2.7
0.44-0.94
0.4-0.84
1.9-3.9
1.7-3.5
2033
1.9-3.9
1.7-3.5
0.61 - 1.3
0.55 - 1.2
2.5-5.2
2.2-4.6
2034
23 - 4.8
2.1-4.3
0.78- 1.7
0.71 - 1.5
3.1 - 6.5
2.8-5.8
2035
j 3.2-6.5
2.9-5.8
1.1-2.3
1-2.1
4.3 - 8.8
3.9-7.9
2036
3.7 - 7.4
3.3-6.7
1.3-2.7
1.2 - 2.5
5-10
4.5-9.1
2037
4.2 - 8.5
3.8-7.6
1.6-3.2
1.4-2.9
5.8-12
5.2- 11
2038
4.7 - 9.5
4.2-8.6
1.8-3.7
1.6-3.4
6.5 - 13
5.9 - 12
2039
< 5.2-10
4.7 - 9.4
2.1-4.2
1.9-3.8
7.3 - 15
6.5-13
2040
6.4- 13
5.7-11
2.7 - 5.3
2.4-4.8
9.1-18
8.1 - 16
2041
6.9 - 14
6.2 - 12
3 - 5.9
2.7 - 5.3
9.9-20
8.9 - 18
2042
7.4-15
6.6- 13
3.2-6.5
2.9-5.8
11-21
9.5 - 19
2043
7.8-15
7 - 14
3.5-7.1
3.2-6.4
11 - 23
10-20
2044
8.2-16
7.4-15
3.8-7.6
3.4-6.8
12-24
11-21
2045
9.4- 18
8.5- 17
4.1 - 8.2
3.7-7.3
14-27
12-24
2046
9.8-19
8.8 - 17
4.4-8.8
3.9-7.9
14-28
13-25
2047
10 - 20
9.1 - 18
4.6-9.2
4.1 - 8.3
15-29
13-26
2048
10 - 20
9.3 - 18
4.8 - 9.5
4.3 - 8.6
15-30
14 - 27
2049
11-21
9.5 - 19
4.9-9.8
4.4 - 8.8
16-31
14-27
2050
12-23
11-20
5-10
4.5-9
17-33
15-29
2051
12-23
11-21
5 - 10
1.5 - 9.1
17-33
15-30
2052
: 12-23
11-21
5.1-10
4.6-9.1
17-33
15-30
2053
12-23
11-21
5.1 - 10
4.6-9.1
17-33
15-30
2054
12-23
11-21
5.1 - 10
4.6-9.1
17-34
15-30
2055
13-25
12-23
5.1 - 10
4.6-9.1
18-35
16-32
Present Value
100-210
46-92
44-88
19-39
150- 290
66- 130
Equivalent
Annualized
Value
5.4-11
3.8-7.5
2.3 - 4.6
1.6-3.2
7.7 - 15
5.3-11
Notes: The range of benefits in this table reflect the range of premature mortality estimates derived from the Medicare study (Wu et al.. 2020) and the
NHIS study (Pope et al., 2019). All benefits estimates are rounded to two significant figures. Annual benefit values presented here are not discounted.
The present value of benefits is the total aggregated value of the series of discounted annual benefits that occur between 2027-2055 (in 2020 dollars)
using either a 3% or 7% discount rate. The benefits associated with the standards presented here do not include the full complement of health and
environmental benefits that if quantified and monetized, would increase the total monetized benefits.
10-27
-------
Table 10-22 Monetized PM2.5 health benefits of onroad and upstream emissions reductions
associated with Alternative 2 (billions of 2020 dollars)
Total Onroad Total Upstream Total Benefits
2027
3% Discount Rate ;
0.039 - 0.083
7% Discount Rate
0.035 - 0.075
3% Discount Rate
0.0083 - 0.019
; 7% Discount Rate
|' 0.0075 -0.017
j 3% Discount Rate
0.047-0.1
7% Discount Rate
0.0 12 - 0.092
2028
0.094- 0.2
0.084-0.18
0.031 - 0.07
0.028 - O.IK.3
0.13 - 0.27
0.11-0.24
2029
0.19-0.41 j
0.17-0.37
0.069- 0.15
0.062-0.14
0.26-0.56
0.23-0.51
2030
0.59-1.2
0.53-1.1
0.12-0.27
0.11 - 0.24
0.71-1.5
0.64-1.3
2031
0.97 - 2
0.87- 1.8
0.2 - 0.43
0.18-0.39
1.2-2.4
1.1-2.2
2032
L4-2.8
1.2-2.5
031-0.65
; 0.28-0.59
1.7-3.5
1.5 - 3.1
2033
1.8-3.7
1.6-3.3
0.44-0.94
0.4-0.85
2.2-4.6
2-4.2
2034
2.2-4.6
2-4.2
0.59-1.2
0.53 - 1.1
| 2.8 - 5.9
2.5 - 5.3
2035
3.1-6.2
2.8-5.6
0.87- 1.8
0.78- 1.6
4-8
3.6-7.2
2036
3.6-7.2
3.2-6.5
1 - 2.1
0.92 - 1.9
4.6 - 9.3
4.1 - 8.4
2037
4.1 - 8.2
3.7-7.4
1.2-2.5
1.1-2.3
5.3-11
4.8-9.6
2038
4.6-9.2
4.1-8.3
1.4-2.9
1.3-2.6
6 - 12
5.4-11
2039
5.1-10
4.5-9.2
1.6 - 3.4
1.5-3
6.7- 14
6-12
2040
6.2 - 12
5.6-11
2.1-4.3
1.9-3.8
8.4 - 17
7.5 - 15
2041
6.7- 13
6.1-12
2.4-4.8
2.1 - 4.3
9.1 - 18
8.2 - 16
2042
7.2 - 14
6.5 - 13
2.6-5.2
2.4-4.7
9.8 - 19
8.8 - 18
2043
7.7-15
6.9- 14
2.9-5.8
2.6 - 5.2
11-21
9.5 - 19
2044
8- 16
7.2 - 14
3.1-6.2
2.8 - 5.6
11 - 22
10-20
2045
9.2-18
8.3-16
3.3 - 6.6
3-6
13-25
11-22
2046
9.6 - 19
8.6- 17
3.6-7.1
3.2-6.4
13 - 26
12-23
2047
9.9-19
8.9- 17
3.8 -7.5
3.4 - 6.8
14-27
12-24
2048
10 - 20
9.1-18
3.9-7.8
3.5-7
14 - 28
13-25
2049
10-20 :
9.4- 18
4-8
; 3.6 - 7.2
14-28
13 - 26
2050
11 - 22
10-20
4.1-8.3
3.7-7.4
16-30
14-27
2051
12-22
10-20
4.2-8.3
3.7-7.5
16-31
14-28
2052
12-23
11-20
4.2-8.3
3.8-7.5
16-31
14-28
2053
12-23
11-20
4.2-8.4
r 3.8-7.5
16-31
14-28
2054
12-23
11-21
4.2-8.4
3.8-7.5
16-31
14-28
2055
13-25
12-22
4.2-8.4
3.8-7.5
17-33
15-30
Present
Value
Equivalent
Annualized
Value
100 - 200
5.3-10
45-89
3.7-7.3
35-71
1.8-3.7
15 - 31
13-2.5
140 - 270
7.2-14
61 - 120
4.9-9.8
Notes: The range of benefits in this table reflect the range of premature mortality estimates derived from the Medicare study (Wu et al., 2020) and the
NHIS study (Pope et al., 2019). All benefits estimates are rounded to two significant figures. Annual benefit values presented here are not discounted.
The present value of benefits is the total aggregated value of the series of discounted annual benefits that occur between 2027-2055 (in 2020 dollars)
using either a 3% or 7°/o discount rate. The benefits associated with the standards presented here do not include the full complement of health and
environmental benefits that if quantified and monetized, would increase the total monetized benefits.
Table 10-23 Monetized PM2.5 health benefits of onroad and upstream emissions reductions
associated with Alternative 3 (billions of 2020 dollars)
10-28
-------
Total Onroad Total Upstream Total Benefits
2027
3% Discount Rate
0.034-0.073
; 7% Discount Rate
; 0.031 -0.066
3% Discount Rate
0.0057- 0.013
: 7% Discount Rate
r 0.0051 -0.012
3% Discount Rate
0.04-0.086
7% Discount Rate
0.036 - 0.078
2028
0.085 - 0.18
0.076-0.16 j
0.023 - 0.052
0.021 - 0.047
0.11-0.23
0.097-0.21
2029
0.15-0.32
0.14-0.29 1
0.049- 0.11
0.044 - 0.098
0.2-0.43
0.18-0.39
2030
0.54-1.1
0.48 - 1
0.098- 0.21
0.088-0.19
0.63 - 1.3
0.57-1.2
2031 ;
0.92- 1.9
0.83 - 1.7
0.18-0.38
0.16-0.34
i 1.1-2.3
0.99-2.1
2032
1.3-2.7
1.2-2.4 :
0.29-0.62
0.26-0.56
1.6-3.3
1.4-3
" 2033
1.7-3.6
1.6-3.3
0.43-0.92
¦ 0.39-0.83
2.2-4.5
2-4.1
2034
2.2-4.6
2-4.1 f
0.6-1.3
0.54-1.1
2.8 - 5.8
2.5 - 5.2
2035
3-6.1
2.7-5.5
0.9- 1.8
0.81 - 1.7
3.9-8
3.5-7.2
2036
3*5-7.1
| 3.2 - 6.4
1.1-2.2
0.97-2
4.6 - 9.3
4.1 - 8.4
2037
4-8.1
3.6-7.3
1.3-2.7
1.2-2.4
5.3-11
4.8-9.7
2038
4.6 - 9.2
4.1-8.3
1.5-3.1
1.4-2.8
6.1-12
5.5-11
2039 ¦
5-10
| 4.5-9.1
1.8-3.6
! 1.6-3.3
6.8-14
6.1 - 12
2040 '
6.2 - 12
5.6-11
2.3-4.6
2.1 - 4.1
8.5 - 17
7.7 - 15
2041
6.7- 13
6 - 12
2.6-5.2
2.3-4.6
9.3 - 18
8.4 - 17
2042
7.2- 14
6.5-13
2.8-5.7
2.6-5.1
10 - 20
9-18
2043 ;
7.7- 15
i 6.9- 14
3.1-6.3
2.8-5.6
11-21
9.7 - 19
2044 "
8-16
7.2- 14
3.4-6.8
3-6.1
11-23
10-20
2045
9.3-18
8.3 - 16
3.6-7.3
3.3 - 6.5
13-25
12-23
2046 !
9.7 - 19
8.7- 17
3.9 - 7.8
3.5-7
14-27
12-24
2047
9.9- 19
8.9 - 17
4.1-8.3
3.7 - 7.4
14-28
13-25
2048
10-20
9.2- 18
4.3 - 8.6
3.9-7.7
15-29
13-26
2049
10-20
9.4-18
4.4-8.9
4-8
15-29
13 - 26
2050
12-22
' 10-20
4.6-9.1
: 4.1-8.2
16-31
14-28
2051
12-23
10-20
4.6-9.2
4.1-8.2
16-32
15-29
2052
12-23
; 11-21
4.6-9.2
4.1-8.3
16-32
15-29
2053
12-23
11-21
4.6-9.2
4.2-8.3
16-32
15-29
2054
12-23
11 - 21
4.6-9.3
4.2-8.3
17-32
15-29
2055
13-25
12-22
4.6-9.3
4.2 - 8.3
18-34
16-31
Present Value
100-200
45 - 89
38-77
17-33
140 - 280
62 - 120
Equivalent
Annualized
Value
5.3 - 10
3.7-7.3
2-4
1.4-2.7
7.3 - 14
5 - 10
Notes: The range of benefits in this table reflect the range of premature mortality estimates derived from the Medicare study (Wu et al., 2020) and the
NHIS study (Pope et al.. 2019). All benefits estimates are rounded to two significant figures. Annual benefit values presented here are not discounted.
The present value of benefits is the total aggregated value of the series of discounted annual benefits that occur between 2027-2055 (in 2020 dollars)
using either a 3% or 7% discount rate. The benefits associated with the standards presented here do not include the full complement of health and
environmental benefits that if quantified and monetized, would increase the total monetized benefits.
10-29
-------
10.6 Summary and Net Benefits
The above costs, savings and benefits are summarized for the proposed standards in Table
10-24 along with net benefits. Table 10-25, Table 10-26 and Table 10-27 present this
information for Alternatives 1, 2 and 3, respectively.
Table 10-24 Summary of costs, fuel savings and benefits of the Proposal standards, light-
duty and medium-duty (billions of 2020 dollars)ab c
j CY 2055 PV, 3%
PV, 7%
EAV, 3%
EAV, 7%
Non-Emission Costs
Vehicle Technology Costs
10
280
7 J 80
7715
7 is
Repair Costs
-24
-170 j
-79
-8.9
-6.5
Maintenance Costs
-51
-410 ;
-200
-21
77 -16 77
Congestion Costs
7 0.16
2.3 7
...7L3 .r
0.12
0.11
Noise Costs
0.0025
0.037
0.021
0.0019
0.0017
Sum of Non-Emission Costs
-65 ""
-290
"7 -96 77J
7
-7.8
Fueling Impacts
Pre-tax Fuel Savings
! 93
890
77 450 '
7 46 "
37
EVSE Port Costs
7.1
120
68 ;
7 6.2
f 7 77 5.6
Sum of Fuel Savings less EVSE Port Costs
86
770
380 j
40 7
77" 3177
Non-Emission Benefits
Drive Value Benefits
0.31
4.8
2.7
0.25
0.22
Refueling Time
-S.2
-85"""
-45 7.
-4.4 7
* -3.6
Energy Security
4.4
41
21
2.2
1.7
Sum of Non-Emission Benefits
-3.6
-39
-21
-2 7
-1.7 77
Climate Benefits
5% Average
15
82
82 ;
5.4
5.4
3% Average
! 38 7 ;
330 ...777
330 7 ;
7717 7
17 7
2.5% Average
52 ;
' 500
500
25
25
3% 95th Percentile
7 II"
1,000
1.000 ' 7
52
52 7
Criteria Air Pollutant
Benefits
PM2.5 Health Benefits - Wu et al., 2020
16-18
140
63
7*5
5.1
PM2.5 Health Benefits - Pope III et al., 2019
31-34
280
130
15
10
Net Benefits
With Climate 5% Average
180-200 ;
1,400
610
74
77 48
With Climate 3% Average
200-220 !
1,600
850
85
60
With Climate 2.5% Average
210-230
1,800
1,000
93
77.7 67 7
With Climate 3% 95th Percentile
280-290
2,300
1,500
120
95 ' *
a 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 and equivalent annualized values of SC-GHGs for internal consistency, while all other costs and benefits are discounted at either 3
percent or 7 percent.
k PM2.5-related health benefits are presented based on two different long-term exposure studies of mortality risk: a Medicare study (Wu et
al., 2020) and a National Health Interview Survey study (Pope III et al., 2019). The criteria pollutant benefits associated with the standards
presented here do not include the full complement of health and environmental benefits that, if quantified and monetized, would increase the
total monetized benefits.
For net benefits, the range in 2055 uses the low end of the Wu range and the high end of the Pope III et al. range. The present and equivalent
annualized value of net benefits for a 3 percent discount rate reflect benefits based on the Pope III et al. study while the present and equivalent
annualized value of net benefits for a 7 percent discount rate reflect benefits based on the Wu et al. study.
10-30
-------
Table 10-25 Summary of costs, fuel savings and benefits of Alternative 1, light-duty and
medium-duty (billions of 2020
dollars)abc
CY 2055 PV, 3%
PV, 7%
EAV, 3%
EAV, 7%
Non-Emission Costs
Vehicle Technology Costs
11
330
: '220 :
17
18
Repair Costs
-26
-180
; -82 1 r
-9.3 11
-6.7 1
Maintenance Costs
-57
-450
-220
-24
-18
Congestion Costs
0.11
3.5 1
2.2
0.18
0.18
Noise Costs
0.0017 ;
0.055
.0.034
0.0028
0.0027
Sum of Non-Emission Costs
-71 |
-300
-82
111 -15
-6.7
Fueling Impacts
Pre-tax Fuel Savings
100
990
510
51
41
EVSE Port Costs
r- ...7.1.1
120
68 1 r
6.2 1
5.6 1
Sum of Fuel Savings less EVSE Port Costs
95'!
870
[11 440 1
45 1
36 "1
Non-Emission Benefits
Drive Value Benefits
0.22 :
6.5
j 3.9 '
0.34
0.32
Refueling Time
-8.8 :
-90
-47
-4.7
-3.8
Energy Security
J 1 '4.8 1"
46 11
23 1
2.4 1
1 1.9 1
Sum of Non-Emission Benefits
-3.8
-38
' -20
-21"
-1.6
Climate Benefits
5% Average
16
91
| 91 1
1 6
: 6
3% Average
1 41
360
360 '
19
19
2.5% Average
1 57
560
560
27
1' 271
3% 95th Percentile
11 120
1,100
1,100
58 1
58 1"
Criteria Air Pollutant
Benefits
PM2.5 Health Benefits - Wu et al., 2020
16-18
150
i 66
7.7
5.3
PM2.5 Health Benefits - Pope III et al., 2019
^32-35
290
130
15
11
Net Benefits
With Climate 5% Average
200-210
1,500
'1 660 j"
80
52
With Climate 3% Average
With Climate 2.5% Average
With Climate 3% 95th Percentile
220 - 240
240 - 260
300-320
1,800
2,000
2,500
930
1,100
1,700
93
100
130
65
73
100
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 and equivalent annualized values of SC-GHGs for internal consistency, while all other costs and benefits are discounted at either 3
percent or 7 percent.
k PM2.5-related health benefits are presented based on two different long-term exposure studies of mortality risk: a Medicare study (Wu et
al., 2020) and a National Health Interview Survey study (Pope III et al., 2019). The criteria pollutant benefits associated with the standards
presented here do not include the full complement of health and environmental benefits that, if quantified and monetized, would increase the
total monetized benefits.
L For net benefits, the range in 2055 uses the low end of the Wu range and the high end of the Pope III et al. range. The present and equivalent
annualized value of net benefits for a 3 percent discount rate reflect benefits based on the Pope III et al. study while the present and equivalent
annualized value of net benefits for a 7 percent discount rate reflect benefits based on the Wu et al. study.
10-31
-------
Table 10-26 Summary of costs, fuel savings and benefits of Alternative 2, light-duty and
medium-duty (billions of 2020 dollars)a b c
CY 2055
PV, 3%
PV, 7%
EAV, 3%
EAV, 7%
Non-Emission Costs
Vehicle Technology Costs
8.8
230
140
12
12
Repair Costs
-22
-160
-74
-8.3 '
-6 7 7
Maintenance Costs
-47
-370
-iso
-19
-14
Congestion Costs
0.064
0.74
0.48
0*039
0.039
Noise Costs
0.001 ~
0.012
0.0078
0.00064
0.00064
Sum of Non-Emission Costs
-60
-300
7 -no "2
-16 1* =
...... "8-7....
Fueling Impacts
Pre-tax Fuel Savings
84
790
400
41
33
EVSE Port Costs
7.1 77
120 77.
| 68 77
' 6.2 7 7
7 J-6 7.,
Sum of Fuel Savings less EVSE Port Costs
77
680
330
35 77 ' ... 1...
2i 77.
Non-Emission Benefits
Drive Value Benefits
0.17
2.4
1.5
777 0-12
0.12 '
Refueling Time
-7.6
-79
-41
-4.i
-3.3
Energy Security
3.9 "
37 ~
19
1.9 777
1.5 77
Sum of Non-Emission Benefits
-3.5
-39
-21
7 -2 77.77 ¦'.
-1.7
Climate Benefits
5% Average
i 13
74
i 74
4.9
4.9
3% Average
! 34
290
290
15
15 "
2.5% Average
1 ' 47
450
'777 450
227
*7'2217
3% 95th Percentile
! ioo
900
i '900
47 7
7*47 7
Criteria Air Pollutant Benefits
PM2.5 Health Benefits - Wu et al., 2020
15-17
140
61
7.2
49
PM2.5 Health Benefits - Pope III et al., 2019
30-33
270
! 120
14
10
Net Benefits
With Climate 5% Average
160 - 180
1,300
; 7 550 "
68 '
44
With Climate 3% Average
180 - 200
1,500
780
78
54
With Climate 2.5% Average
200- 210
1,700
930
85
61
With Climate 3% 95th Percentile
; 250- 270
2,100
1,400
110
86
"he 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 and equivalent annualized values of SC-GHGs for internal consistency, while all other costs and benefits are discounted at either 3
percent or 7 percent.
k PM2.5-related health benefits are presented based on two different long-term exposure studies of mortality risk: a Medicare study (Wu et
al., 2020) and a National Health Interview Survey study (Pope III et al., 2019). The criteria pollutant benefits associated with the standards
presented here do not include the full complement of health and environmental benefits that, if quantified and monetized, would increase the
total monetized benefits.
For net benefits, the range in 2055 uses the low end of the Wu range and the high end of the Pope III et al. range. The present and equivalent
annualized value of net benefits for a 3 percent discount rate reflect benefits based on the Pope III et al. study while the present and equivalent
annualized value of net benefits for a 7 percent discount rate reflect benefits based on the Wu et al. study.
10-32
-------
Table 10-27 Summary of costs, fuel savings and benefits of Alternative 3, light-duty and
medium-duty (billions of 2020 dollars)a b c
CY 2055 PV, 3%
PV, 7%
EAV, 3%
EAV, 7%
Non-Emission Costs
Vehicle Technology Costs
11
270 :
170
14
14
Repair Costs
-24
-170 [
-77 _
-8.6
-6.3 1
Maintenance Costs
-51 '
-390
-190
-20
-15
Congestion Costs
o.ii
1.5 1
0.82
0.078
0.066
Noise Costs
0.0016 :
0.024
0.013
0.0012 I
0.0011
Sum of Non-Emission Costs
-64 :
-290
1.-95 1"!
-15 1
11 "7-8 1
Fueling Impacts
Pre-tax Fuel Savings
93
850
430
45
35
EVSE Port Costs
! 7.1
120
T 68 .... ...
6.2 1
5.6 1'
Sum of Fuel Savings less EVSE Port Costs
86
740
.... ... 36(j
' 38
29 I'
Non-Emission Benefits
Drive Value Benefits
0.21
3.2 :
1.8 J
OAl ' "" "
0.15
Refueling Time
-8.2 |
' -83
-43 j
-4.3
-3.5 ' '
Energy Security
4.4 ""
40 ..." J
20
2.1 11
1 1.6 1'
Sum of Non-Emission Benefits
-3.6
-39
.1-21..'.''
"1-2.'l"""'
1111 -1.7"""
Climate Benefits
5% Average
15
80
80
"p 3 1"
'5.3'"
3% Average
1 38 1
320
320
17
17 '
2.5% Average
' 52
490 j
490
24
24 ....
3% 95th Percentile
no
970
970
51
1 511"
Criteria Air Pollutant
Benefits
PM2.5 Health Benefits - Wu et al., 2020
16-18
140
62
7.3
5^0
PM2.5 Health Benefits - Pope III et al., 2019
31-34 |
280
120
14
10
Net Benefits
With Climate 5% Average
180- 190 :
1,300
580
71
46 "
With Climate 3% Average
I 200-220 ;
1,600
820
.82 I 1
57 "
With Climate 2.5% Average
1 210-230
1,800
990
90
64'"
With Climate 3% 95th Percentile
270-290
2,200
1,500
120
91
; a 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 and equivalent annualized values of SC-GHGs for internal consistency, while all other costs and benefits are discounted at either 3
percent or 7 percent.
k PM2.5-related health benefits are presented based on two different long-term exposure studies of mortality risk: a Medicare study (Wu et
: al.. 2020) and a National Health Interview Survey study (Pope III et al., 2019). The criteria pollutant benefits associated with the standards
presented here do not include the full complement of health and environmental benefits that, if quantified and monetized, would increase the
; total monetized benefits.
; L For net benefits, the range in 2055 uses the low end of the Wu range and the high end of the Pope III et al. range. The present and equivalent
: annualized value of net benefits for a 3 percent discount rate reflect benefits based on the Pope III et al. study while the present and equivalent
annualized value of net benefits for a 7 percent discount rate reflect benefits based on the Wu et al. study.
10.7 Transfers
There are three types of transfers included in our analysis. Two of these transfers come in the
form of tax credits arising from the Inflation Reduction Act to encourage investment in battery
technology and the purchase of electrified vehicles. These are transfers from the government to
producers of vehicles (the battery tax credit) or purchasers of vehicles (the vehicle purchase tax
credit). The third is fuel taxes which are transfers from purchasers of fuel to the government. The
proposal results in less liquid-fuel consumed and, therefore, less money transferred from
purchasers of fuel to the government.
Table 10-28 presents transfers associated with the proposed standards. Table 10-29, Table
10-30 and Table 10-31 present transfers associated with Alternatives 1, 2 and 3, respectively.
10-33
-------
Table 10-28 Transfers associated with the Proposed standards, light-duty and medium-
duty (billions of 2020 dollars)
Calendar
; Battery Tax
Purchase
Fuel Taxes
Sum
Year
Credits
Tax Credits ;
2027
i 68
6.7
0.31
14
2028
1 9.2""
9.9 !
0.77
20
2029
13
14
1.1
29
2030
1 1
is
2.4""
31
2031
9
22
3.3
34
2032
]'5.3 ~
27""" .
I4-5. 7":
37
2035
; o
0 ""j
8 *1".. i
8
2040
1 o
o j
12
12
2045
] o
3 o
15 "T"
15
2050
: 0
o :
16
16
2055
0
o
15
~15
PV3
49
86
180
320
PV7
7 7 43
74
97 " . f
210
I.AY.i
' 2.6""".....
".4.5
9.5
I 'l7 '
EAV7
3.5
6
7.9
17
Table 10-29 Transfers associated with Alternative 1, light-duty and medium-duty (billions
of 2020 dollars)
Calendar
; Battery Tax
Purchase
Fuel Taxes
Sum
Year
Credits
Tax Credits ;
2027
7.1
7
0.32
14
" 2028 "
....". 11 7.
1 1
0.88
22"
2029
13
11
J-6 .....
2 8
2030
13
20 1
2.8
36
2031
9.3
23
3.9 i"
36
2032
5.5
29"'
5.2
39
2035
] 0 7
0 ~ !
9 *r
9 "
2040
0;:
0""
11
11
2045
1 0
0
16
16
2050
0""
O'""
17"" J
17
2055
0
0
17 r
17
PV3
; 52
92
200
350
PV7
* 46
79
no
230
I.AY.i
2.7"
4.8
11
IS
EAV7
3.8
6.4
00
00
19
10-34
-------
Table 10-30 Transfers associated with Alternative 2, light-duty and medium-duty (billions
of 2020 dollars)
Calendar
; Battery Tax
Purchase
Fuel Taxes
Sum
Year
Credits
Tax Credits ;
2027
i 4-8
48
0.22
9.8
2028
1 6.3 ""
6.7 '1
0.57
11
2029
11
13
1.1
25
2030
8.7
1141
1.9 3 11
24'
2031
7.6
19
2.7
29
2032
]' 4.6
24
I3-8 7
32
2035
; o
0
7 j
7 ^
2040
1 o
o
1 1
11
2045
] o
7 0.1 " :
13
' 13"
2050
1 o
0 ''
14
14"
2055
0
.... p'l" "~'j
14
11
PV3
i 39
71 ;
160
270
PV7
J 7 34
60
85 ~
180
I.AY.i
' 2 ] '
J.7
"...8.4""".
14
EAV7
: 2.8
4.9
7
15
Table 10-31 Transfers associated with Alternative 3, light-duty and medium-duty (billions
of 2020 dollars)
Calendar
; Battery Tax
Purchase
Fuel Taxes
Sum
Year
Credits
Tax Credits ;
2027
I.I
4
0.18
8.3
7 2028
5.6^
6.f
0.46
12
2029
71. 6.9 7
7.7
0 8 1
15
2030
7.9
13 "
1.5
22
2031
78-4 7
2ill ;
11 2-4 ....
31
2032
5.4
27 *
3.6 ¦
36
2035
] 0 7
0 11 i
7.3 1 '
11 7-3
2040
011
oil
12 1
12'
2045
1 0
0 |
14
14
2050
o'l
0" :
16
1116
2055
0
"0 !
15
15 '
PV3
; 34
68
1 170 7
280
PV7
* 30
58 |
91
180
I.AY.i
1.8
3.6
9 17
14
EAV7
2.4
4.7
7.4
15
10-35
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Chapter 10 References
Alex L. Marten, et al. 2015. "Incremental CH4 and N20 mitigation benefits consistent with the
US Government's SC-C02 estimates." Climate Policy 15 (2): 272-298.
doi: 10.1080/14693062.201.
IPCC. 2014. Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and
III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Edited by
Core Writing Team, R.K. Pachauri and L. A. Meyer. Geneva.
—. 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. Edited by P.R. Shukla, J. Skea, E. Calvo Buendia, V. Masson-Delmot, H-
O. Portner, D. C. Roberts, P. Zhai, et al.
—. 2007. Core WritingTeam; Pachauri, R.K; and Reisinger, A. (ed.) Climate Change 2007:
Synthesis Report, Contribution of Working Groups I, II, and II to the Fourth Assessment Report
of the Intergovernmental Panel on Climate Change. Vols. ISBN 92-9169-122-4. IPCC.
—. 2018. Global Warming of 1.5°C. An IPCC Special Report on the impacts of global warming
of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways, in the
context of strengthening the global response to the threat of climate change,. Edited by Masson-
Delmotte, V., P. Zhai, H.-O Portner, D. Roberts, J. Skea, P.R. Shukla, et al.
—. 2019b. IPCC Special Report on the Ocean and Cryosphere in a Changing Climate. Edited by
H.-O. Portner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K.
Mintenbeck, et al.
IWG. 2016a. Addendum to Technical Support Document on Social Cost of Carbon for
Regulatory Impact Analysis under Executive Order 12866: Application of the Methodology to
Estimate the Social Cost of Methane and the Social Cost of Nitrous Oxide.
https://www.epa.gov/sites/default/files/2016-12/documents/addendum_to_sc-
ghg_tsd_august_2016. pdf.
—. 2010. Technical Support Document: Social Cost of Carbon for Regulatory Impact Analysis
under Executive Order 12866. Accessed 2023. https://www.epa.gov/sites/default/files/2016-
12/documents/scc_tsd_2010 .pdf.
—. 2021. Technical Support Document: Social Cost of Carbon, Methane, and Nitrous Oxide
Interim Estimates under Executive Order 13990. Accessed 2023.
https://www.whitehouse.gov/wp-
content/uploads/2021/02/TechnicalSupportDocument_SocialCostofCarbonMethaneNitrousOxide
.pdf.
—. 2016b. Technical Support Document: Technical Update of the Social Cost of Carbon for
Regulatory Impact Analysis Under Executive Order 12866. Accessed 2023.
https://www.epa.gov/sites/default/files/2016-12/documents/sc_co2_tsd_august_2016.pdf.
10-36
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—. 2013. Technical Support Document: Technical Update of the Social Cost of Carbon for
Regulatory Impact Analysis Under Executive Order 12866.
https://www.ourenergypolicy.org/wp-
content/uploads/2013/06/social_cost_of_carbon_for_ria_2013_update.pdf.
National Academies. 2016b. Attribution of Extreme Weather Events in the Context of Climate
Change. Washington, D.C.: The National Academies Press, doi: 10.17226/21852.
—. 2019. Climate Change and Ecosystems. Washington, D.C.: The National Academies Press,
doi: 10.17226/25504.
—. 2017. Valuing Climate Damages: Updating Estimation of the Social Cost of Carbon Dioxide.
Washington, D.C.: The National Academies Press.
Pope III et al., CA, Lefler, JS, Ezzati, M, Higbee, JD, Marshall, JD, Kim, S-Y, Bechle, M,
Gilliat, KS, Vernon, SE and Robinson, AL. 2019. "Mortality risk and fine particulate air
pollution in a large, representative cohort of US adults." Environmental Health Perspectives 127
(7): 077007.
U.S. EPA. 2022. "EPA's Social Cost of Greenhouse Gases Web Area." Accessed January 10,
2022. https://www.epa.gov/environmental-economics/scghg.
—. 2015a. Proposed Rulemaking for Greenhouse Gas Emissions and Fuel Efficiency Standards
for Medium - and Heavy-Duty Engines and Vehicles-Phase 2. Vols. (EPA-420-D-15-900).
Washington, D.C.: Office of Transportation and Air Quaility, Assessment and Standards
Division. Accessed 2023.
https://nepis.epa.gov/Exe/ZyPDF.cgi/P100MKYR.PDF?Dockey=P 100MKYR.PDF.
—. 2015b. Regulatory Impact Analysis for the Proposed Revisions to the Emission Guidelines
for Existing Sources and Supplemental Proposed New Source Performance Standards in the
Municipal Solid Waste Landfills Sector. Accessed January 2023.
https://www.regulations.gov/document?D=EPA-HQ-OAR-2014-0451-0086.
—. 2015c. Regulatory Impact Analysis of the Proposed Emission Standards for New and
Modified Sources in the Oil and Natural Gas Sector. Accessed January 2023.
https://www.regulations.gOv/document/EPA-HQ-OAR-2010-0505-5258.
USGCRP. 2018. Impacts, Risks, and Adaptation in the United States: Fourth National Climate
Assessment, Volume II. Edited by D.R. Reidmiller, C.W. Avery, D.R. Easterling, K.E. Kunkel,
K.L.M. Lewis, T.K. Maycock and B.C. Stewart. Washington, D.C.: U.S. Global Change
Research Program. doi:10.7930/NCA4.2018.
—. 2016. The Impacts of Climate Change on Human Health in the United States: A Scientific
Assessment. Edited by A. Crimmins, J. Balbus, J.L. Gamble, C.B. Beard, J.E. Bell, D. Dodgen,
R.J. Eisen, et al. U.S. Global Change Research Program.
Wu et al., X, Braun, D, Schwartz, J, Kioumourtzoglou, M and Dominici, F. 2020. "Evaluating
the impact of long-term exposure to fine particulate matter on mortality among the elderly."
Science Advances 6 (29): eaba5692.
10-37
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Chapter 11: Energy Security Impacts
In this section of the DRIA, we evaluate the energy security impacts of this proposed light-
and medium-duty vehicle (LMDV) (2027-2032) rule. Energy security is broadly defined as the
uninterrupted availability of energy sources at affordable prices (IEA 2019). Most discussions of
U.S. energy security revolve around the topic of the economic costs of U.S. dependence on oil
imports.170 Energy independence and energy security are distinct but related concepts, and an
analysis of energy independence informs our assessment of energy security. The goal of U.S.
energy independence is generally the elimination of all U.S. imports of petroleum and other
foreign sources of energy, or more broadly, reducing the sensitivity of the U.S. economy to
energy imports and foreign energy markets (Greene 2010).
The U.S.'s oil consumption had been gradually increasing in recent years (2015-2019) before
the COVID-19 pandemic in 2020 dramatically decreased U.S. and global oil consumption (EIA
2022). By July 2021, U.S. oil consumption had returned to pre-pandemic levels and has
remained fairly stable since then (EIA 2022). The U.S. has increased its production of oil,
particularly "tight" (i.e., shale) oil, over the last decade (EIA 2022). As a result of the recent
increase in U.S. oil production, the U.S. became a net exporter of crude oil and refined petroleum
products in 2020 and is projected to be a net exporter of crude oil and refined petroleum products
for the foreseeable future (EIA 2022). 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 refined petroleum
products starting in the early 1950s (EIA 2022).
Oil is a commodity that is globally traded and, as a result, an oil price shock is transmitted
globally. Given that the U.S. is projected to be a modest net exporter of crude oil and refined
petroleum products for the time frame of this analysis (2027-2055), one could reason that the
U.S. no longer has a significant energy security problem. However, U.S. refineries still rely on
significant imports of heavy crude oil which could be subject to supply disruptions. Also, oil
exporters with a large share of global production have the ability to raise or lower the price of oil
by exerting the market power associated with the Organization of Petroleum Exporting Countries
(OPEC) to alter 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 refined petroleum products. Reducing U.S. net oil
imports and use reduces the U.S.'s exposure to oil price volatility.
EPA estimates that U.S. consumption and net imports of petroleum will be reduced as a result
of this proposed rule, both from an increase in fuel efficiency of LMDVs using petroleum-based
fuels and from the greater use of plug-in electric vehicles (PEVs), which are fueled with
electricity. A reduction of U.S. net petroleum imports reduces both financial and strategic risks
caused by potential sudden disruptions in the supply of petroleum to the U.S. and global market,
thus increasing U.S. energy security. In other words, reduced U.S. oil imports act as a "shock
absorber" when there is a supply disruption in world oil markets.
170 The issue of cyberattacks is another energy security issue that could grow in significance over time. For example,
in 2021, one of the U.S.'s largest pipeline operators, Colonial Pipeline, was forced to shut down after being hit by a
ransomware attack. The pipeline carries refined gasoline and jet fuel from Texas to New York (Sanger, Krauss and
Perlroth2021).
11-1
-------
It is anticipated that manufacturers will choose to comply with this proposed standard with
significant increases in PEVs in the LMDV fleet. The wider use of electricity to power vehicles
in the U.S. will likely result in the use of a generally more affordable fuel that has less price
volatility compared to the current widespread use of gasoline in LMDVs. Furthermore, the U.S.
supply and demand of electricity is almost entirely domestic, and largely independent of
electricity markets outside of North America. Over time, the wider penetration of PEVs into the
U.S. vehicle fleet will likely provide significant energy security benefits, principally by reducing
the overall U.S. demand for oil. As new PEVs enter the vehicle market and the stock of PEVs
becomes an increasingly larger fraction of the total stock of vehicles on the road, high oil prices
and oil price shocks will have a diminishing impact on the overall U.S. economy, leading to
greater energy security. The wider use of electricity to power LMDVs will also move the U.S.
towards energy independence, that is independence of foreign markets, since the electricity to
power PEVs will almost exclusively be produced in the U.S.
This Chapter of the DRIA first reviews the historical and recent energy security literature
relevant in the context of this proposed LMDV rule. This review provides a discussion of recent
oil security literature, recent studies on tight oil and recent electricity security studies on the
wider use of PEVs. Second, this Chapter also provides an assessment of the electricity security
implications of this LMDV proposed rule. Third, in the last section of this Chapter, the agency's
estimates of U.S. oil import reductions of the proposed LMDV GHG standards for model years
2027-2032 are presented. The military cost impacts of this proposed rule are discussed as well.
However, due to methodological issues, we do not quantify the military costs savings from
reduced U.S. oil imports.
11.1 Review of Historical Energy Security Literature
Energy security discussions are typically based around the concept of the oil import premium,
sometimes also labeled the oil security premium. The oil import premium is the extra
cost/impacts of importing oil beyond the price of the oil itself as a result of: (1) potential
macroeconomic disruption and increased oil import costs to the economy from oil price spikes or
"shocks"; and (2) monopsony impacts. Monopsony impacts stem from changes in the demand
for imported oil, which changes the price of all imported oil.
The so-called oil import premium gained attention as a guiding concept for energy policy in
the aftermath of the oil shocks of the 1970s. (Bohi and Montgomery 1982), (EMF 1982), and
(Plummer, et al. 1982) provided valuable discussion of many of the key issues related to the oil
import premium as well as the analogous oil stockpiling premium. (Bohi and Montgomery 1982)
detailed the theoretical foundations of the oil import premium and established many of the
critical analytic relationships. Broadman and Hogan revised and extended the established
analytical framework to estimate optimal oil import premia with a more detailed accounting of
macroeconomic effects (Broadman and Hogan 1988) (Broadman 1986) (Hogan 1981). Since the
original work on energy security was undertaken in the 1980s, there have been a couple of
reviews on this topic: (Leiby, Jones, et al. 1997), (Parry and Darmstadter 2003).
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, (National Research Council
2010) argued that the non-environmental externalities associated with dependence on foreign oil
are small, and potentially trivial. (Nordhaus 2007) and (Blanchard and Gall 2010) question the
11-2
-------
impact of oil price shocks on the economy in the early 2000s time frame. They were motivated
by attempts to explain why the economy actually expanded during the oil shock in the early-
2000s 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) reviews the empirical literature on oil shocks and suggests that the results
are mixed, noting that some work (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. For
example, (Baumeister and Peersman 2013) 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
meant that a given physical disruption had a bigger effect on price and turned out to have a
similar effect on output as in the earlier data." Hamilton observes that "a negative effect of oil
prices on real output has also been reported for a number of other countries, particularly when
non-linear functional forms have been employed" (citing as examples (Kim 2012) and
(Engemann, Kliesen and Owyang 2011)). 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."
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 Van Robays 2010). (Kilian and Vigfusson 2014),
for example, assigns a more prominent role to the effects of price increases that are unusual, in
the sense of being beyond the range of recent experience. Kilian and Vigfusson also conclude
that the difference in response to oil shocks may well stem from the different effects of demand-
and supply-based price increases: "One explanation is that oil price shocks are associated with a
range of oil demand and oil supply shocks, some of which stimulate the U.S. economy in the
short-run and some of which slow down U.S. growth (see Kilian 2009)" (Kilian 2009).
The general conclusion that oil supply-driven shocks reduce economic output is also reached
in (Cashin, et al. 2014), which focused on 38 countries from 1979 to 2011. They state: "The
results indicate that the economic consequences of a supply-driven oil-price shock are very
different from those of an oil-demand shock driven by global economic activity and vary for oil-
importing countries compared to energy exporters." Cashin et al. continues ".. .oil importers
(including the U.S.) typically face a long-lived fall in economic activity in response to a supply-
driven surge in oil prices." But almost all countries see an increase in real output caused by an
oil-demand disturbance.
Considering all of the recent energy security literature, EPA's assessment concludes that there
are benefits to the U.S. from reductions in its oil imports. There is some debate as to the
magnitude, and even the existence, of energy security benefits from U.S. oil import reductions.
11-3
-------
However, differences in economic impacts from oil demand and oil supply shocks have been
distinguished, with oil supply shocks resulting in economic losses in oil importing countries. The
oil import premium calculations in this analysis (described in Chapter 11.4.2) are based on price
shocks from potential future supply events. 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 1970s.
11.2 Review of Recent Energy Security Literature
There have also been a handful of recent studies that are relevant for the issue of oil security:
one by Resources for the Future (RFF), a study by Brown, two studies by Oak Ridge National
Laboratory (ORNL), and three studies by Newell and Prest, Bj0rnland et al. and Walls and
Zheng, on the responsiveness of U.S. tight oil to world oil price changes. We provide a review
and high-level summary of each of these studies below. In addition, we review the recent
literature on electricity security in the context of the wider use of PEVs.
11.2.1 Recent Oil Security Studies
The first studies on the energy security impacts of oil that we review are by Resources for the
Future (RFF), a study by Brown and two studies by Oak Ridge National Laboratory (ORNL).
The RFF study (Krupnick, et al. 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. In a follow-on study, (Brown 2018) summarized the RFF study results as
well. The RFF work argues that there have been major changes that have occurred in recent
years that 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 such as ethanol
and biodiesel. In addition, RFF argues that the U.S. economy is more resilient to oil shocks than
in the earlier 2000s timeframe. 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 2018). The second set of modeling frameworks use alternative
structural vector autoregressive models of the global crude oil market (Kilian 2009), (Kilian and
Murphy 2014), (Baumeister and Hamilton 2019). The last of the models utilized is the National
Energy Modeling System (NEMS).
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.
11-4
-------
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.
There have only been two recent situations where events have led to a potential significant
supply-side oil shock in the last several years. The first event was the attack on the Saudi
Aramco Abqaiq oil processing facility and the Khurais oil field. On September 14th, 2019, a
drone and cruise missile attack damaged the Saudi Aramco Abqaiq oil processing facility and the
Khurais oil field in eastern Saudi Arabia. The Abqaiq oil processing facility is the largest crude
oil processing and stabilization plant in the world, with a capacity of roughly 7 MMBD or about
7 percent of global crude oil production capacity (EIA 2019). On September 16th, the first full
day of commodity trading after the attack, both Brent and WTI crude oil prices surged by
$7.17/barrel and $8.34/barrel, respectively, in response to the attack, the largest price increase in
roughly a decade.
However, by September 17th, Saudi Aramco reported that the Abqaiq plant was producing 2
MMBD, and they expected its entire output capacity to be fully restored by the end of September
(EIA 2019). Tanker loading estimates from third-party data sources indicated that loadings at
two Saudi Arabian export facilities were restored to the pre-attack levels (EIA 2019). As a result,
both Brent and WTI crude oil prices fell on September 17th, but not back to their original levels.
The oil price spike from the attack on the Abqaiq plant and Khurais oil field was prominent and
unusual, as Kilian and Vigfusson (2014) describe. While pointing to possible risks to world oil
supply, the oil shock was short-lived, and generally viewed by market participants as being
transitory, so it did not influence oil markets over a sustained time period.
The second situation is the set of events leading to the recent world oil price spike
experienced in 2022. World oil prices rose fairly rapidly at the beginning of 2022. For example,
as of January 3rd, 2022, the WTI crude oil price was roughly $76 per barrel (EIA 2022). The
WTI oil price increased to roughly $123 per barrel on March 8th, 2022, a 62 percent increase
(EIA 2022). High and volatile oil prices in the first part of 2022 were a result of supply concerns
with Russia's invasion of Ukraine on February 24th contributing to crude oil price increases
(EIA 2023). Russia's invasion of Ukraine came after eight consecutive quarters of global crude
oil inventory decreases. The lower inventory of crude oil stocks were the result of rising
economic activity after COVID-19 pandemic restrictions were eased. Oil prices have drifted
downwards throughout the second half of 2022 and in the early part of 2023. It is not clear to
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what extent the current oil price volatility will continue, or even increase, or be transitory. Since
both significant demand and supply factors are influencing world oil prices in 2022, it is not
clear how to evaluate unfolding oil market price trends from an energy security standpoint. Thus,
the attack of the Abqaiq oil processing facility in Saudi Arabia and the unfolding events in the
world oil market in 2022 do not currently provide enough empirical evidence to undertake an
updated estimate of the response of the U.S. economy to an oil supply shock of a significant
magnitude.171
A second set of recent studies related to energy security are from ORNL. In the first study,
(Una-Martinez, et al. 2018) undertake a quantitative meta-analysis of world oil demand
elasticities based upon the recent economics literature. The ORNL study estimates oil demand
elasticities for two sectors (transportation and non-transportation) and by world regions (OECD
and Non-OECD) by meta-regression. To establish the dataset for the meta-analysis, the authors
undertake 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
elasticity estimates from 75 published studies. The study finds a short-run price elasticity of
world oil demand of-0.07 and a long-run price elasticity of world oil demand of-0.26.
The second relevant ORNL 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 (Oladosu, et al. 2018). Nineteen studies after 2000 were identified that
contain quantitative/accessible estimates of the economic impacts of oil price shocks. Almost all
studies included in the review were published since 2008. The key result that the study 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.
11.2.2 Recent Tight (i.e.. Shale) Oil Studies
The discovery and development of U.S. tight (i.e., shale) oil reserves that started in the mid-
20008 could affect U.S. energy security in at least a couple of ways.172 First, the increased
availability of domestic supplies has resulted in a reduction of U.S. oil imports and an increasing
role of the U.S. as exporter of crude oil and petroleum-based products. In December 2015, the
40-year ban on the export of domestically produced crude oil was lifted as part of the
Consolidated Appropriations Act, 2016. Pub. L. 114-113 (Dec. 18th, 2015). According to the
GAO, the ban was lifted in part due to increases in tight (i.e., shale) oil (GAO 2020).173 Second,
171 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 refined petroleum products
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.
172 The Union of Concerned Scientist define tight oil as follows: "Tight oil is a type of oil found in impermeable
shale and limestone rock deposits. Also known as "shale oil", tight oil is processed into gasoline, diesel, and jet
fuels-just like conventional oil-but is extracted using hydraulic fracturing, or 'Tracking." (Union of Concerned
Scientists 2016).
173 According to the GAO, "Between 1975 and the end of 2015, the Energy Policy and Conservation Act directed
a ban on nearly all exports of U.S. crude oil. This ban was not considered a significant policy issue when U.S. oil
production was declining and import volumes were increasing. However, U.S. crude oil production roughly
doubled from 2009 to 2015, due in part to a boom in shale oil production made possible by advancements in
drilling technologies. In December 2015, Congress effectively repealed the ban, allowing the free export of U.S.
crude oil worldwide".
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due to differences in development cycle characteristics and average well productivity, tight oil
producers could be more price responsive than most other oil producers. However, the oil price
level that triggers a substantial increase in tight oil production appears to be higher in 2021-2022
relative to the 2010s as tight oil producers seek higher profit margins per barrel in order to
reduce the debt burden accumulated in previous cycles of production growth (Kemp 2021).
Other factors such as cost inflation and supply chain constraints have contributed to the slow
pace of tight oil production growth in the early 2020s, despite high world oil prices. Although
some of those factors may be transitory, the muted production response of 2021-2022 suggests
that tight oil producers (and their investors) are not likely to increase drilling in a quick,
coordinated manner in response to future potential world oil price spikes. For that reason, the
short-run price responsiveness assumed for U.S. tight oil for the estimation of the oil security
benefits of this proposed rule is the same as for other non-OPEC oil supplies.
U.S. crude oil production increased from 5.0 Million Barrels a Day (MMBD) in 2008 to an
all-time peak of 12.3 MMBD in 2019 and tight oil wells have been responsible for most of the
increase (EIA 2022). Figure 11-1 below shows tight oil production changes from various tight oil
producing regions (i.e., Eagle Ford, Bakken etc.) in the U.S. and the West Texas Intermediate
(WTI) crude oil spot price. As illustrated in Figure 11-1, the annual average U.S. tight oil
production grew from 0.6 MMBD in 2008 to 7.8 MMBD in 2019 (EIA 2022). Growth in U.S.
tight oil production during this period was only interrupted in 2015-2016 following the world oil
price downturn which began in mid-2014. The second growth phase started in late 2016 and
continued until 2020. The sharp decrease in demand that followed the onset of the COVID-19
pandemic resulted in a 25 percent decrease in tight oil production in the period from December
2019 to May 2020. U.S. tight oil production in 2020 and 2021 averaged 7.4 MMBD and 7.2
MMBD, respectively. U.S. tight oil production represents a relatively modest share (less than 10
percent in 2019) of global liquid fuel supply.174
Importantly, U.S. tight oil is considered the most price-elastic component of non-OPEC
supply due to differences between its development and production cycle and that of conventional
oil wells. Unlike conventional wells where oil starts flowing naturally after drilling, tight oil
wells require the additional step of fracking to complete the well and release the oil.175 Tight oil
producers keep a stock of drilled but uncompleted wells and can optimize the timing of the
completion operation depending on oil price expectations. Combining this decoupling between
drilling and production with the "front-loaded" production profile of tight oil-the fraction of total
output from a well that is extracted in the first year of production is higher for tight oil wells than
conventional oil wells-tight oil producers have a clear incentive to be responsive to prices in
order to maximize their revenues (Bj0rnland, Nordvik and Rohrer 2020).
174 The 2019 global crude oil production value used to compute the U.S. tight oil share is from (EIA 2022).
175 Hydraulic fracturing ("fracking") involves injecting water, chemicals, and sand at high pressure to open fractures
in low-permeability rock formations and release the oil that is trapped in them.
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10-
r 150
m
"5 6
- 4-i
v> 2 -
co
0-'
90 ~
60 c
CO
30 ¦=
2008 2010 2012 2014 2016 2018 2020 2022
Producing Regions
Bakken Niobrara-Codell
(ND&MT) ¦ (CO&WY)
¦ Spraberry Wolfcamp
(TX Permian) (TX & NM Permian)
Price
Bonespring
(TX & NM Permian)
Rest of US
Eagle Ford
(TX)
WTI
Figure 11-1. U.S. tight oil production by producing regions (in MMBD) and West Texas
Intermediate (WTI) crude oil spot price (in U.S. Dollars per Barrel) Source: (EIA 2022)
(EIA 2022)
Only in recent years have the implications of the "tight/shale oil revolution" been felt in the
international market where U.S. production of oil is rising to be roughly on par with Saudi
Arabia and Russia. Recent economics literature of the tight oil expansion in the U.S. has a
bearing on the issue of energy security as well. It could be that the large expansion in tight oil
has eroded the ability of OPEC to set world oil prices to some degree, since OPEC cannot
directly influence tight oil production decisions. Also, by affecting the percentage of global oil
supply controlled by OPEC, the growth in U.S. oil production may be influencing OPEC's
degree of market power. But given that the tight oil expansion is a relatively recent trend, it is
difficult to know how much of an impact the increase in tight oil is having, or will have, on
OPEC behavior.
Three recent studies have examined the characteristics of tight oil supply that have relevance
for the topic of energy security. In the context of energy security, the question that arises is: can
tight oil respond to an oil price shock more quickly and substantially than conventional oil? 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 of conventional versus
tight oil wells, using a detailed dataset of 150,000 oil wells, during the time frame of 2005-2017
in five major oil-producing states: Texas, North Dakota, California, Oklahoma, and Colorado.
For both conventional oil wells and tight oil wells, Newell and Prest estimate the elasticities of
drilling operations and well completion operations with respect to expected revenues and the
elasticity of supply from wells already in operation with respect to spot prices. Combining the
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three elasticities and accounting for the increased share of tight oil in total U.S. oil production
during the period of analysis, they conclude that U.S. oil supply responsiveness to prices
increased more than tenfold from 2006 to 2017. They find that tight oil wells are more price
responsive than conventional oil wells, mostly due to their much higher productivity, but the
estimated oil supply elasticity is still relatively small. Newell and Prest note that the tight oil
supply response still takes more time to arise than is typically considered for a "swing producer,"
referring to a supplier able to increase production quickly, within 30-90 days. In the past, only
Saudi Arabia and possibly one or two other oil producers in the Middle East have been able to
ramp up oil production in such a short period of time.
Another study, (Bj0rnland, Nordvik and Rohrer 2020), uses a well-level monthly production
data set covering more than 16,000 crude oil wells in North Dakota from February 1990 to June
2017 to examine differences in supply responses between conventional and tight oil. They find a
short-run (i.e., one-month) supply elasticity with respect to oil price for tight oil wells of 0.71,
whereas the one-month response of conventional oil supply is not statistically different from
zero. It should be noted that the elasticity value estimated by Bj0rnland et al. combines the
supply response to changes in the spot price of oil as well as changes in the spread between the
spot price and the 3-month futures price. (Walls and Zheng 2022) explore the change in U.S. oil
supply elasticity that resulted from the tight oil revolution using monthly, state-level data on oil
production and crude oil prices from January 1986 to February 2019 for North Dakota, Texas,
New Mexico, and Colorado. They conduct statistical tests that reveal an increase in the supply
price elasticities starting between 2008 and 2011 coinciding with the times in which tight oil
production increased sharply in each of these states. Walls and Zheng also find that supply
responsiveness in the tight oil era is greater with respect to price increases than price decreases.
The short-run (one-month) supply elasticity with respect to price increases during the tight oil
area ranges from zero in Colorado to 0.076 in New Mexico; pre-tight oil, it ranged from zero to
0.021.
The results from (Newell and Prest 2019), (Bj0rnland, Nordvik and Rohrer 2020), and (Walls
and Zheng 2022) all suggest that tight oil may have a larger supply response to oil prices in the
short-run than conventional oil, although the estimated short-run elasticity is still relatively
small. The three studies use datasets that end in 2019 or earlier. The responsiveness of U.S. tight
oil production to recent price increases does not appear to be consistent with that observed
during the episodes of crude oil price increases in the 2010s captured in these three studies.
Despite an 80 percent increase in the WTI crude oil spot price from October 2020 to the end of
2021, Figure 11-1 shows that U.S. tight oil production has increased by only 8 percent in the
same period. It is a somewhat challenging period in which to examine the supply response of
tight oil to its price to some degree, given that the 2020-2021 time period coincided with the
COVID-19 pandemic. Previous tight oil production growth cycles were financed predominantly
with debt, at very low interest rates (McLean 2018). Most U.S. tight oil producers did not
generate positive cashflow (McLean 2018). As of 2021, U.S. tight oil producers have pledged to
repay their debt and reward shareholders through dividends and stock buybacks (Crowley and
Wethe 2021). These pledges translate into higher prices that need to be reached (or sustained for
a longer period) than in the past decade to trigger large increases in drilling activity.
In its first quarter 2022 energy survey, the Dallas Fed (Federal Reserve Bank of Dallas 2022)
asked oil exploration and production (E&P) firms about the WTI price levels needed to cover
operating expenses for existing wells or to profitably drill a new well. The average breakeven
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price to continue operating existing wells in the tight oil regions ranged from $23/barrel (bbl) to
$35/bbl. To profitably drill new wells, the required average WTI prices ranged from $48/bbl to
$69/bbl. For both types of breakeven prices, there was substantial variation across companies,
even within the same region. The actual WTI price level observed in the first quarter of 2022 was
roughly $95/bbl, substantially larger than the breakeven price to drill new wells. However, the
median production growth expected by the respondents to the Dallas Fed Energy Survey from
the fourth quarter of 2021 to the fourth quarter of 2022 is modest (6 percent among large firms
and 15 percent among small firms). Investor pressure to maintain capital discipline was cited by
59 percent of respondents as the primary reason why publicly traded oil producers are restraining
growth despite high oil prices. The other reasons cited included supply chain constraints,
difficulty in hiring workers, environmental, social, and governance concerns, lack of access to
financing, and government regulations. Given the recent behavior of tight oil producers, we do
not believe that tight oil will provide additional significant energy security benefits in the time
frame of this analysis, 2027-2055, due to its muted price responsiveness. The ORNL model still
accounts for the effect of U.S. tight oil production increases on U.S. oil imports and, in turn, the
U.S.'s energy security position.
Finally, despite continuing uncertainty about oil market behavior and outcomes and the
sensitivity of the U.S. economy to oil shocks, it is generally agreed that it is beneficial to reduce
petroleum fuel consumption from an energy security standpoint. The relative significance of
petroleum consumption and import levels for the macroeconomic disturbances that follow from
oil price shocks is not fully understood. Recognizing that changing petroleum consumption will
change U.S. imports, our quantitative assessment of the oil security costs of this rule focuses on
those incremental social costs that follow from the resulting changes in net imports, employing
the usual oil import premium measure used in the energy security literature.
11.2.3 Recent Electricity Security Studies
The International Energy Agency (IEA) defines energy security as the uninterrupted
availability of energy sources at affordable prices (IEA 2019). The energy security literature, first
developed in response to the oil shocks of the 1970s, is extensive. This literature mainly focuses
on the energy security benefits of reduced oil use, particularly oil imports. However, even though
there is likely to be a substantial increase in the use of electricity from PEVs in the U.S., the
literature on the topic of the energy security implications of wider use of PEVs is somewhat
limited. We have not been able to identify any study that systematically quantifies the
differential energy security risks of using electricity versus petroleum-based fuels to power
vehicles in the U.S. Nonetheless, a review of existing, published studies provides information to
help assess the implications of the use of electricity as transportation fuel in LMDVs in the U.S.
across multiple dimensions of energy security-affordability, price stability, and
resilience/reliability-as well as energy independence.176
Since the energy security literature has largely focused on the economic and national security
risks associated with oil imports, early studies considering the energy security benefits of PEVs
focus on the reduction in oil imports that result from widespread PEV adoption. (Michalek, et al.
176 Our discussion of "affordability" in this Chapter only considers fuel costs, including gasoline prices and charging
costs for PEVs. Vehicle purchase costs are not considered within the scope of our evaluation of energy security.
More discussion of consumer impacts in the context of PEVs is presented in Chapter 4 of this DRIA.
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2011) quantifies this aspect of the energy security impacts of PEVs. The study focuses on the
benefits associated with a reduction in U.S. oil imports from the wider use of PEVs and provide
a direct estimate of the energy security benefits of using PEVs in the U.S. based on the amount
of oil PEV's displace over the lifetime of a typical PEV. They use a $0.34/gal (2010 dollars)
estimate of the avoided macroeconomic disruption costs/ monopsony/ military cost savings for
oil to calculate an energy security benefit of roughly $1,000 over the lifetime of a PEV.
(Michalek, et al. 2011) is similar to the approach used by EPA in past vehicle rulemakings:
estimate the displaced petroleum use and apply a security cost premium that draws on some of
the same studies that EPA uses. But EPA does not include monopsony impacts or quantify
military cost savings as benefits. The Michalek et al. study also does not account for electricity
supply stability.
11.2.3.1 Fuel Costs
Most of the cost comparisons of PEVs versus gasoline-powered vehicles in the literature are
total cost of ownership (TCO) studies, which compare the total cost of purchasing, owning, and
operating each type of vehicle for a specified number of years. They include the vehicle purchase
costs as well as annual operation (fees, fuel, and insurance) and maintenance costs.
Vehicles are refueled fairly frequently and increased fueling costs due to energy price spikes
are felt almost immediately by consumers, whereas the impact of price changes in components
and materials used to produce vehicles (e.g., alloys, batteries, etc.), which are also considered in
a TCO analysis, only impact consumers when purchasing a vehicle. Our focus in this Chapter is
on energy markets. Critical materials and the supply chains necessary for PEV production are,
therefore, outside of our intended scope in this discussion of energy security. See Preamble
IV.C.6 and Chapter 3.1.3 of the DRIA for a discussion of critical materials and PEV supply
chains.
TCO studies of vehicles in the U.S. find that fuel costs are lower for PEVs than internal
combustion engine (ICE) vehicles. See, for examples, (P. Slowik, A. Isenstadt, et al., Assessment
of Light-duty Electric Vehicle Costs and Consumer Benefits in the United States in the 2022-
2035 Time Frame 2022), (Liu, et al. 2021), (Lutsey and Nicholas 2019), and (Breetz and Salon
2018). TCO studies tend to not explore in great detail the heterogeneity in fuel costs for PEV
owners depending on geography and charging location or strategy, but other studies focus on the
sources of PEV fuel cost variability. For example, a 2017 brief by the Union of Concerned
Scientists examines the rates offered by electric utilities in the 50 largest U.S. cities and finds
that all of them offered at least one electricity rate that results in fuel savings for PEV owners
compared to a gasoline-powered vehicle, with median annual savings of $770 (Union of
Concerned Scientists 2017). Clearly these savings depend on the prevailing price of petroleum
fuels, which varies widely over location and time, and the assumed efficiency of the comparable
gasoline vehicle.
(Borlaug, Salisbury, et al., Levelized Cost of Charging Electric Vehicles in the United States
2020) perform a detailed analysis of PEV charging costs in the U.S. that takes into consideration
the type of charging equipment, a range of real-world electricity rates, and frequency of charging
at home versus workplace or public stations. They find that PEV fuel cost savings over a 15-year
period ranged from $3,000 to $10,500 (2019 dollars) for average U.S. electricity and gasoline
price projections, with additional variability across states and depending on PEV lifetimes. The
percentage of battery charging done at home versus using public chargers is an important source
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of variability in the fuel costs of individual PEV owners. Extracting charging rate information
from a commercial database that includes records for more than 30,000 U.S. public chargers,
(Trinko, et al. 2021) reports mean rates of 28 cents/kWh for Level 2 chargers and 32 cents/kWh
for faster Direct Current Fast Charging chargers; in contrast, the study reports a lower mean
residential electric rate of 13 cents/kWh as of March 2021.
To date, residential charging access has been prevalent among PEV owners. (Y. Ge, C.
Simeone, et al., There's No Place Like Home: Residential Parking, Electrical Access, and
Implications for the Future of Electric Vehicle Charging Infrastructure 2021) find that the
percentage of PEVs with residential charging access is likely to become more uncertain as the
PEV market share of light-duty (LD) vehicles increases. They conduct a survey to gather
detailed information on residential parking availability, parking behavior, and electrical access
by parking location. Combining public data on housing stock and LD vehicle stock
characteristics with the survey results, the authors develop estimates of residential charging
access percentages for each housing type and a PEV adoption likelihood model using housing
type, housing tenure (owning versus renting), income, population density, and presence/absence
of zero emission vehicle incentives in the state of residence as explanatory variables. For PEV
shares no greater than 10 percent of total LD vehicles, residential charging access is estimated to
range from 78 percent to 98 percent. For a 90 percent PEV share, the estimated residential
charging access percentage ranges from 35 percent to 75 percent. The higher end of the ranges
represents a scenario that requires modifications in parking behavior (e.g., parking in garage
rather than driveway) and installation of electrical access whenever possible, if not already
available at the residential parking location.
In a study for the California Public Utility Commission, (Sieren-Smith, et al. 2021) projects
future fuel costs of PEVs in California for the 2020-2030 time frame in comparison to gasoline-
powered vehicles. This study finds that there is wide spatial variability in fuel costs for PEVs and
there are substantial differences across individual electric utilities within California alone. The
study also finds that for customers with Time of Use (TOU) tariffs, charging a PEV regularly at
the off-peak rates (i.e., "managed charging" as opposed to "unmanaged charging") results in
significant fuel cost savings. With TOU tariffs or Time Variable Pricing, electricity prices
depend on the time of use, and change at set times and amounts through the day-generally with
higher prices in an afternoon peak period and lower prices in overnight off-peak hours (DOE
2022). The study also finds that PEV fuel costs are likely to be lower than gasoline-powered
vehicles' fuel costs across a variety of assumptions about projected gasoline and electricity
prices and managed/unmanaged PEV charging rates in California over the time frame of the
analysis.
In the U.S., according to (Hardman, et al. 2021), the lowest income households spend 11.2
percent of their annual income on fuel, maintenance, and repairs of vehicles compared to all
other households that spend 4.5 percent of their annual income on these expenses. For the most
common use case in terms of PEV charging equipment (i.e., at-home charging), fuel costs in the
U.S. are lower for PEVs than gasoline-powered vehicles. Therefore, owning a PEV results in a
lower percentage of household income going toward that expense category. However,
(Hardman, et al. 2021) find that lower income households are less able to afford installation of
residential charging equipment and more likely to live in multi-unit dwellings without a
designated parking space and charging equipment. Thus, low-income households that purchase a
PEV and have no residential charging and, thus, rely primarily on public chargers, could face
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higher fuel costs and a larger overall energy burden (i.e., fraction of household income directed
toward energy costs) with a PEV than a gasoline-powered vehicle. The Inflation Reduction Act
(IRA) signed into law on August 16, 2022, can help reduce the costs for deploying charging
infrastructure (Inflation Reduction Act 2022). The IRA extends the Alternative Fuel Refueling
Property Tax Credit (Section 13404) through Dec 31, 2032, with modifications. Under the new
provisions, residents in low-income and rural areas would be eligible for a 30 percent credit for
the cost of installing residential charging equipment up to a $1,000 cap.
11.2.3.2 Fuel Price Stability/Volatility
(Melodia and Karlsson 2022) show that the rate of inflation and volatility of U.S. retail
electricity prices have been historically much lower than for gasoline. Using consumer price data
from the Bureau of Labor Statistics from 1968 to 2022, the authors report that gasoline was
almost four times more volatile than electricity during that period. The diversity of the fuel mix
used to produce electricity and the stronger regulatory oversight of the U.S. electricity sector,
where residential electricity rates must meet a "just and reasonable" standard, are among the
reasons for the lower volatility of electricity prices versus gasoline prices. The authors also
discuss how renewable electricity generation can contribute to electricity price stability. First, the
cost profile of renewable resources such as wind and solar involves an initial large fixed-capital
investment but have no fuel costs once they are in operation, removing a key source of the price
volatility experienced by electricity generation plants that use fossil fuels. Moreover, wind and
solar resources are available much more widely across the globe than oil and gas resulting in
lower geopolitical supply risk-although some risk is still present through the critical materials
needed to produce renewable energy infrastructure components such as wind turbines, solar
panels, and electric batteries (Melodia and Karlsson 2022).
While (Melodia and Karlsson 2022) discuss the positive contribution that increased use of
renewables can make to electricity price stability, other authors consider how the process of
decarbonization in the energy sector might affect oil price stability. (Bordoff and O'Sullivan
2022) suggest that a smooth transition to clean energy in response to climate change may be
challenging and may result in more price volatility in oil markets. In other words, they suggest
that the transition to clean energy may be "jagged". According to the authors, the combination of
pressure on investors to divest from fossil fuels and uncertainty about the future of oil demand
may raise concerns that oil investment levels may decrease in the future, leading to oil supplies
declining at a faster rate than oil demand falls-or declining even as oil demand continues to rise.
This outcome could produce more volatile oil prices. Also, in the early stages of the transition to
clean energy before oil demand declines significantly, the power of OPEC and other non-
competitive suppliers could be boosted by increasing their revenues, while giving OPEC extra
clout as a "swing producer" when world oil markets are tight.
11.2.3.3 Electricity Reliability/Resiliency
Reliability and resilience of electricity service are needed to ensure the "continuous
availability" of service that is required for a fuel to be considered secure. (DOE 2017) defines the
two terms as follows. Reliability is "the ability of the electric power sector to provide a stable
source of electricity to consumers, both households and businesses, under normal operating
conditions". Resilience is "the ability of the electric power sector to withstand and recover from
any disruptions created by extreme weather, cyberattack, terrorism, or other unanticipated
events." A reliable and resilient electricity sector is crucial for the U.S.'s national security. The
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Department of Defense is the largest customer of the electricity grid in the U.S. (DOE 2017).
Also, the electricity sector is interconnected with many other types of critical infrastructure-
water systems, oil, natural gas, communications, information technology, and financial services-
crucial for the U.S. economy to function (DOE 2017). Standards and metrics to track reliability
are better established than those for resilience, which is concerned with lower probability, high-
consequence events (DOE 2017).
Electricity, while generally reliably provided in the U.S., is subject to periodic supply
disruptions (i.e., "electricity outages") due to a variety of factors including (but not limited to):
weather-related events such as hurricanes, heat waves/storms, wildfires; cybersecurity risks and
system/equipment failures. On average, U.S. electricity customers experienced 8 hours of power
outages in 2020, the most since the DOE's Energy Information Administration (EIA) began
collecting electricity reliability data in 2013 (EIA 2021). The Fourth National Climate
Assessment, released in 2018, concludes that "climate change will increasingly threaten the U.S.
energy supply via more frequent and long-lasting power outages that will broadly affect critical
energy infrastructure" (Zamuda, et al. 2018). It also states that extreme weather is already the
most frequent cause of electricity grid outages in the U.S. Electricity in the U.S. is provided by a
set of local and regional interconnected electric grids. Thus, electricity supply disruptions are
likely to result in electricity outages that are more local or regional in their nature in comparison
to petroleum disruptions, which commonly have national or, oftentimes, global impacts.
U.S. electric utilities follow long-term plans to ensure electricity reliability. These plans,
typically known as integrated resource plans, set out an investment roadmap to ensure sufficient
regional generation capacity and power purchases to meet the projected demand in their
electricity service areas. According to (Bistline 2021), although these long-term plans contribute
to electricity supply reliability, both resource planning and electric grid operation are becoming
more difficult due to overlapping layers of increased variability in electricity supply and demand.
For example, climate change is leading to an increase in the frequency and severity of extreme
weather events which affects both supply (e.g., droughts reducing hydropower generation) and
demand (e.g., record peak loads due to heat waves). Increased penetration of wind and solar also
results in significant fluctuations in electricity production at different time scales that need to be
managed by electric grid operators and planners. Maintaining reliability of supply and price
stability under this new set of evolving conditions requires a range of technology, analysis, and
policy solutions (Bistline 2021).
As auto manufacturers respond to this proposed rule with increased sales of PEVs, U.S.
electricity demand is anticipated to increase. Overall, U.S. electricity demand is projected to
increase by 2 Terawatt-hours (TWh) in 2028 (a 0.04 percent increase), 18 TWh in 2030 (a 0.39
percent increase), 114 TWh in 2035 (a 2.25 percent increase), 195 TWh in 2040 (a 3.52 percent
increase) and 252 TWh in 2050 (a 3.92 percent increase). See Chapter 5 of this DRIA for more
discussion of these estimates. Projections of PEV uptake will need to be accounted for by U.S.
electric utilities and transmission system operators in their resource planning processes. It is
difficult to assess the combined effects of higher demand for electricity from PEVs, increasing
extreme weather events in the context of climate change, and the greater use of variable supply
technologies, such as wind/solar power, on electricity grid reliability and resiliency issues in the
U.S. In part, this is because there is little experience to assess the impacts of significant PEV use
on U.S. electric grid reliability and resiliency.
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At early levels of PEV adoption, the investments needed to shore up electric grid reliability
might first appear at the local distribution level. Early PEV sales to date have often happened in
clusters such that some neighborhoods have achieved large PEV penetrations even as PEV
market share remained lower at the regional or national level. The extent of distribution level
reliability impacts will depend on multiple factors: number of PEVs, PEV mix (BEVs/PHEVs),
type of charger used (Level 1, Level 2), and most importantly, whether charging is managed or
unmanaged. (Muratori 2018) evaluates the effect of uncoordinated PEV charging on residential
demand. The author finds that uncoordinated PEV charging leads to more pronounced and abrupt
load (i.e., electricity demand) peaks which shorten the life of distribution transformers. Using
detailed datasets of charging events at homes and public chargers in California to simulate future
PEV charging behavior (timing of charging and duration), (Jenn and Highleyman 2022)
conclude that in a scenario with 6 million PEVs in California (compared to approximately 1
million in 2021), more than 20 percent of distribution feeder circuits would experience loads
greater than their capacity, resulting in accelerated degradation of the distribution network
equipment and requiring upgrades to maintain adequate electricity grid reliability.
(Powell, et al. 2022) explore electric grid impacts in the U.S. portion of the Western
Interconnection grid in 2035 under scenarios with high penetration (greater or equal to 50
percent adoption) of LD PEVs. They find that the timing of the extra electricity demand brought
about by PEVs depends on charging behavior and is crucial to the magnitude of the electric grid
impacts. The authors develop a detailed model of charging behavior where drivers are assigned
to clusters based on combinations of the battery capacity of their PEVs, number of miles driven
per year, and access to charging infrastructure. The aggregated PEV charging demand is then
used as an input in a generation dispatch model that represents the Western Interconnection 2035
grid by accounting for planned generation unit additions/retirements, increasing baseline demand
to reflect electrification of other sectors, and multiplying solar generation by a factor of 3.5 and
wind generation by a factor of 3 relative to 2019 levels.
The authors calculate the electric grid impacts for various scenarios regarding charging
controls and access to home and workplace charging infrastructure. All charging scenarios
assume unidirectional charging (i.e., no vehicle-to-grid flows). For the Western Interconnection,
given the high level of penetration of solar generation expected by 2035, daytime charging leads
to lower costs and emissions because it aligns better with the solar generation profile. Investing
in widespread access to workplace charging leads to lower peak net demand (i.e., peak demand
net of solar and wind generation), lower electricity grid storage capacity investment needs, less
ramping-related costs from the operation of fossil fuel generators, and lower CO2 emissions per
mile driven by PEVs. Since the U.S. electricity grid is composed of a set of regional electricity
grids with different fuel mixes, the charging infrastructure and charging schedules that will best
match and balance the extra electricity demand from PEVs with electricity supply will vary on a
region-by-region basis.
Large and abrupt electricity demand peaks due to PEV charging deserve special attention
when they are linked to extreme weather events that can also disrupt the demand and supply of
electricity. (Feng, et al. 2020) explore the mobility implications of vehicle fleets with high PEV
penetration rates during extreme weather events triggering evacuation orders. They simulate the
evacuation traffic flow during Hurricane Irma and compare electricity demand if all evacuating
vehicles were PEVs with the transmission capacity in the Florida electric grid. They conclude
that up to a fleet-wide PEV penetration rate of 45 percent could have been supported by the
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existing transmission network during that evacuation scenario. The more general insights from
the analysis include: 1) fleetwide PEV penetration levels of up to 45 percent can be helpful
during an evacuation scenario to alleviate gasoline shortages, 2) PHEVs are especially valuable
during those events as drivers can start the evacuation trip using their battery and fill their
gasoline tanks away from the population centers that experience gasoline shortages when an
evacuation order is announced, and, 3) development of disaster-optimized charging schedules
would be crucial to avoid surges of power during an extreme event such as a hurricane as PEV
1 77
penetration increases.
With PEVs becoming an increasingly significant portion of vehicles on the road in the U.S.,
some losses in overall U.S. output, measured in terms of a loss in U.S. gross domestic product
(GDP), will likely result from electricity supply disruptions. The losses in U.S. output will be
determined by the extent and duration of the future electricity supply disruptions, the flexibility
of the additional electricity demand from PEVs, and whether PEVs can help avoid or ameliorate
electricity supply disruptions. Given the local and regional nature of electricity supply
disruptions and noting that the U.S. is projected to produce almost all of its own electricity (see
discussion below), the losses in U.S. output from future electricity supply disruptions will likely
be lower than output losses that have resulted from world oil supply disruptions with the
widespread use of gasoline-powered vehicles. Higher electricity payments in the event of a U.S.
electricity supply disruption will be transferred to other electricity producers in the U.S., not to
foreign suppliers, as was the case in past oil supply disruptions, which will reduce the effective
cost to the U.S. economy. However, more analysis is needed to make a definitive statement
about the net effect of this proposed rule on expected GDP losses from future electricity and oil
supply disruptions or price spikes. Estimates of disruption probabilities and associated U.S.
macroeconomic disruption costs are available for oil but not for electricity. Without an estimate
of electricity disruption probabilities and expected U.S. output losses, it is difficult to conduct
assessments of the size and types of potential investments, or initiatives in the U.S. electricity
sector, that could mitigate or adapt to those losses.
Although PEVs can pose challenges for electricity supply reliability if PEV charging is not
coordinated, PEVs can also potentially provide an important source of electricity storage, which
could help to improve the overall functioning of the U.S. electricity grid in terms of the
reliability and availability of electricity over time. See Chapter 5.4 of the DRIA for more
discussion on this topic. With a bidirectional connection to the electricity grid that enables
vehicle-to-grid (V2G) flows, PEVs can act as a storage resource that provides energy during
electric peak demand hours by discharging their batteries while parked. PEVs can also provide
services to the electrical grid such as frequency and voltage regulation or act as electricity
reserves, ready to supply energy in response to an outage at an electricity generation facility. In
addition, PEVs can be used to provide electricity to home residences in the event of an electricity
supply disruption. Managed bidirectional flows of energy from a large PEV fleet could also be
particularly valuable to integrate higher levels of variable renewables (wind and solar) into the
electricity generation mix (Yilmaz and Krein 2013).
177 Under the proposed standards, the penetration rates of PEVs in the stock of U.S. light- and medium-duty vehicles
are projected to remain below a 45 percent rate until the late 2030's. By the late 2030's, there should be sufficient
lead time for the U.S. electricity grid to expand and accommodate increasingly higher penetration rates of PEVs.
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The wider use of electricity in U.S. vehicles also provides both short- and long-run fuel
substitution opportunities for vehicle owners facing high and volatile world oil prices. For
example, drivers of PHEVs can switch to using more electricity during an oil price shock
(Lemoine 2010). Also, during an oil shock, a wider penetration of PEVs will allow for a short-
run reduction in oil use by multi-vehicle households that can drive their PEVs more, rather than
using their gasoline-powered vehicles. Flexibility is achieved when drivers have options to shift
to electricity, and the responsiveness of oil demand to the oil price (i.e., the elasticity of demand
for oil) increases. These benefits occur because there is more substitutability between electricity
and oil in end-use fuel use. With electricity supply disruptions, on the other hand, multi-vehicle
households could also switch to driving their gasoline-powered vehicles more. Households with
only one vehicle, dedicated to gasoline or electricity, are likely be the most affected by volatile
oil prices and electricity outages, since they cannot substitute among vehicles or fuels in
response to changing oil prices and the availability of electricity, as multi-vehicle households or
owners of PHEVs can.
11.2.3.4 Energy Independence
The goal of U.S. energy independence is generally equated with the elimination of all U.S.
imports of petroleum and other foreign sources of energy, but more broadly, it is the elimination
of U.S. sensitivity to the variations in the price and supply of foreign sources of energy (Greene
2010). (Grove 2008) and (Stein 2013) promote the idea that the wider use of PEVs can bring
about U.S. energy independence by substituting electricity for oil to power vehicles in the U.S.
As Grove/Stein note, the physical characteristics of oil and electricity can have very different
consequences for energy independence. Oil is a commodity that is globally traded. In
comparison, Grove labels electricity as "sticky": in other words, "it stays in the continent where
it is produced." As a result, global electricity markets are not nearly as linked or interconnected
as global oil markets. The interconnectedness of the oil market means that price shocks are
transmitted globally but it also contributes to its resilience. Oil tankers can be redirected to those
destinations where price signals reveal that their value is highest. In contrast, the volume of
electricity that can be rerouted across regions in response to an emergency is strictly limited by
the number and configuration of electricity transmission interconnections.
The wider use of PEVs in U.S. LMDVs will likely result in the substitution of one fuel, oil,
with significant imports and which is subject to global price shocks, for another fuel, electricity,
which is almost exclusively produced in the U.S. and has different and an independent set of
local and regional factors influencing its reliability and resiliency. As (Bordoff and O'Sullivan
2022) point out, electricity is much more likely to be produced locally and regionally; less than
three percent of global electricity was traded across international borders in 2018, compared with
two-thirds of global oil supplies in 2014. As a result, the greater use of electricity as
transportation fuel will move the U.S. towards the goal of energy independence.
U.S. energy security analysis has traditionally focused on the benefits of reduction of U.S. oil
imports. However, even when oil imports get close to zero, energy security concerns remain for
oil because of the global, integrated nature of the oil market. Unless the U.S. entirely disengages
from international oil trade, oil price shocks starting anywhere in the world will continue to be
transmitted to oil prices in the U.S. and those price shocks still will have adverse impacts on U.S.
households. An increased movement towards electrification does not eliminate energy security
concerns. Supply shocks for electricity also happen, but they are typically of a different nature
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than oil shocks: they are local or regional instead of global, and they may involve a combination
of electricity outages and/or retail electricity price increases.
Recent geopolitical events are an example of how the energy price and energy price stability
attributes in U.S. energy security remain an important concern even after the U.S. has become a
net exporter of crude oil and petroleum products. (Bordoff and O'Sullivan 2022) suggest that
energy security will join climate change as a top concern for policymakers as a result of the
Russian-Ukrainian war, which has disrupted energy supplies and increased global energy prices.
They argue that these dual priorities-energy security and climate change-are poised to reshape
national energy planning, energy trade flows, and the broader global economy. One consequence
of the Russian-Ukrainian War, according to Bordoff and O'Sullivan, is that countries across the
world will increasingly be looking inward, prioritizing domestic energy production and regional
cooperation even as they transition to net-zero carbon emissions. These changes will likely be
defined by greater, not less, government intervention in the world's energy sector.
11.3 Electricity Security Impacts
Addressing the issue of U.S. energy security, this section offers comparisons of electricity and
gasoline as transportation fuels in terms of cost per mile driven and their relative price stability
and volatility. In the U.S. during the past decade, the cost per mile driven for a new PEV
charging at home has been consistently lower than that for a new gasoline-powered vehicle using
regular gasoline. This result is robust to the spatial variation in relative electricity and gasoline
prices in different U.S. states. The impact of fuel costs on consumers is not only about average
fuel cost levels but also fuel cost stability. On the metric of fuel cost stability, retail electricity
also has fared better than gasoline because retail electricity prices have been more predictable
and less volatile for vehicle owners than gasoline prices. The predictability is partly a result of
the electricity rate setting process-most consumers pay a set tariff (i.e., electricity price) that
only changes at monthly or annual intervals. The section also presents data to support the idea
that an increased use of electricity as a transportation fuel in U.S. LMDVs moves the U.S.
towards greater energy independence.
11.3.1 Recent Fuel Costs for Gasoline-Powered Vehicles Compared to PEVs in the
U.S.
11.3.1.1 National (i.e., U.S.) Analysis
To compare fuel costs of PEVs versus gasoline-powered vehicles, the relevant units are
dollars per mile instead of dollars per gallon of gasoline equivalent (or other energy content unit)
because of the higher end-use efficiency of the electric motor relative to the internal combustion
engine (ICE). This is a central feature of the comparison between PEVs and gasoline-powered
vehicles. The relative cost of gasoline and electric fuel in the U.S. will depend on three main
factors: the efficiency of the vehicle; the prevailing prices of gasoline and electricity (electricity
prices being more stable over time), and the market in which the PEV is recharged (electricity
costs tend to vary significantly across states to a greater degree than gasoline prices, and
commercial recharging costs are higher than residential charging costs).
Most PEV charging to date in the U.S. uses at-home chargers, and thus EPA's analysis of fuel
costs hinges on prices observed by U.S. households: retail, regular gasoline prices (in dollars per
gallon) and retail residential electricity rates (in cents per kilowatt-hour). As PEV adoption
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extends to drivers without at-home charging capabilities in the future, commercial charging rates
will play a larger role in a national analysis of PEV fuel costs, but home recharging is expected
to continue to play a dominant role.
Comparing fuel costs for PEVs and gasoline LD vehicles requires converting retail prices into
a common unit (U.S. cents per mile driven) that accounts for the differences in energy content
between gasoline and electricity as well as the higher efficiency of electric drivetrains relative to
internal combustion engines, expressed as fuel economies (miles per gallon of gasoline
equivalent (gge)).178 The fuel economy data used to compute fuel costs per mile driven are on-
road new vehicle values by model year (i.e., the average fuel economy across all sold new
gasoline LD vehicles or PEVs of a same model year). The data for PEVs includes only battery
electric vehicles (BEVs), but also applies to plug-in hybrid electric vehicles (PHEVs) for the
miles driven in electric vehicle mode, and the data for gasoline vehicles includes conventional
hybrids.17'' On-road fuel economy increased from 22.2 miles per gge in 2011 to 24.6 miles per
gge in 2021 for new gasoline LD vehicles and from 97 miles per gge to 112.8 miles per gge for
new PEVs.
Figure 11-2 shows the average U.S. fuel cost per mile driven for two vehicle-fuel
combinations, gasoline-powered LD vehicles using regular gasoline and PEVs charging at-home
at the residential retail rate, and Figure 11-3 presents the same information for a subset of
individual states in the U.S.
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Figure 11-2. Average U.S. fuel cost per vehicle mile driven of gasoline-powered vehicles and
PEVs from 2011 to 2021 Sources: Electricity prices: (EIA 2022); Gasoline prices: (EIA
2022); Fuel economies: (EPA 2022)
Monthly fuel cost per mile driven has been consistently and substantially lower for new PEVs
than new gasoline LD vehicles. The average fuel cost per mile driven from January 2011 to
December 2021 was 13.7 cents per mile for new gasoline vehicles using regular gasoline and 4.6
cents per mile for a new PEV charged at-home 100 percent of the time at the average residential
retail rate. The average annual fuel savings of new PEVs in comparison to a new gasoline
vehicle using regular gasoline over the ten-year time frame of 2011 to 2021 was $1,260. We
recognize that, to date, the bulk of PEVs sold tend to be in the small or mid-size car segments
and, thus, more energy efficient. This is evolving as more PEV models are offered. For Model
Year 2022, an analysis of fuel costs for every LD vehicle model shows that most PEV models
have lower fuel costs than most gasoline-powered models regardless of vehicle class and size
(DOE 2022).
While vehicle size and prevailing oil prices matter, the lower fuel cost per mile driven for
PEVs is largely a result of the much higher efficiency of electric drivetrains relative to internal
combustion engines. Comparing U.S. electricity and gasoline prices on a dollar per unit-energy
basis, residential electricity has actually been somewhat more expensive than retail gasoline over
the last decade: the 2011-2021 averages were 2.6 cents per megajoule (MJ) for regular gasoline
and 4.0 cents per MJ for residential retail electricity.180
11.3.1.2 State-Level Analysis
The fuel cost per mile driven for new PEVs was lower than the fuel cost for new gasoline LD
vehicles in all the states shown in Figure 11-3 (see below) and in every month from 2011 to the
end of 2021. However, as stated above, the fuel cost savings do vary significantly across states.
The average savings in fuel cost per mile driven for a new PEV versus a new gasoline vehicle
ranged from 6.7 cents in Massachusetts to 10.2 cents in California and 11.9 cents in Washington.
For the other three states depicted in Figure 11-3, Texas, Ohio and Florida, the fuel savings
averaged 8-9 cents per mile. Both California and Massachusetts have some of the highest
electricity residential retail rates in the U.S. The large savings afforded by PEVs in California
result from that state having higher retail gasoline prices than the rest of the states in Figure 11-3.
The savings are even larger for Washington because of a combination of high gasoline prices and
low electricity rates due to Washington's relative abundance of hydroelectric power resources
(EIA 2022). Assuming that new gasoline-powered cars and new PEVs are both driven -14,000
miles per year, the annual average fuel cost savings in the first year of vehicle operation during
this period would have ranged from $933 in Massachusetts to $1,643 in Washington (Davis and
Boundy 2022). While vehicle use typically declines with age, the decline is slow, and 15 years
later the average car would still provide 62 percent of these annual savings (Davis and Boundy
2022).
1801 kWh equals 3.6 MJ, and a typical gallon of gasoline contains 120,280 Btu or 126.8 MJ.
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Figure 11-3. Fuel cost per mile driven by gasoline-powered vehicles and PEVs for six states
from 2011 to 2021 Sources: Electricity prices: (EIA 2022); Gasoline prices: (EIA 2022);
Fuel economies: (EPA 2022)
11.3.2 Fuel Price Stability/Volatility
Absolute differences in fuel costs between PEVs and ICE vehicles, discussed above, are an
important aspect of the "affordability" component of IEA's definition of energy security, but fuel
price stability is another important consideration from the consumer's perspective.181 While U.S.
retail electricity prices vary widely with location, charging equipment, and charging behavior,
they are generally more stable over time than U.S. gasoline prices. Figure 11-4 displays the
monthly percentage price changes for U.S. retail gasoline and residential electricity. The monthly
change in U.S. average residential electricity prices was less than 5 percent (in absolute value) in
every month during the 2011-2021 period. For regular gasoline, prices changed up or down by
more than 5 percent in 30 percent of months over that period. The volatility of monthly U.S.
retail prices from January 2011 to December 2021 was 21 percent for residential electricity
prices and 60 percent for regular gasoline prices.182
181 The International Energy Agency (IEA) defines energy security as the uninterrupted availability of energy
sources at affordable prices. (IEA 2019)
182 Volatility is calculated as the standard deviation of the monthly price returns multiplied by the squared root of the
number of periods (months).
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Figure 11-4. Monthly percentage changes in U.S. retail electricity and gasoline prices from
2011 to 2021 Source: (EIA 2022)
Another desirable attribute of PEVs for fuel cost stability is that PEVs diversify and, thereby,
help stabilize total road-vehicle fuel costs. Diversification benefits are gained when the prices of
the two fuels do not move together. In fact, historically when oil prices increased, electricity
prices have tended to decrease, and vice-versa. Looking at fuel price trends over roughly the last
decade, from January 2011 to December 2021, monthly U.S. residential electricity prices have
been negatively correlated, -0.37, with monthly U.S. average gasoline prices.183 A negative
correlation helps plug-in hybrid electric vehicle (PHEV) owners and multi-vehicle households
with access to gasoline LD vehicles and PEVs, and the nation as a whole, by diversifying
transportation fuel cost risk. During all of the 2011-2021 period, the cost of at-home PEV
charging resulted in lower fuel costs than gasoline refilling. The value of a household being able
to switch between PEVs and gasoline-powered vehicles depending upon prevailing fuel prices
(or between electricity and gasoline for a PHEV), is sometime labeled the "real option value".
Real option value could increase if the residential electricity costs of PEVs increase, or
commercial recharging costs decrease, and the relative ranking of home or commercial PEV
; The estimated correlation coefficient is a Pearson correlation coefficient with a p-value of 0.0069.
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charging versus gasoline refueling costs changes more frequently in the future as oil prices
fluctuate.184
11.3.3 Energy Independence
The substitution of electricity for oil for powering U.S. vehicles will reduce U.S reliance on
fuel imports. Although the U.S. has become a net exporter of crude oil and petroleum liquids, it
still imports significant volumes of crude oil to meet the preferred barrel specifications of
domestic refineries. See Table 11-1 below for estimates of U.S. oil import reductions from this
proposed LMDV (2027-2032) rule. Figure 11-5 shows that the U.S. has been a very small net
importer of electricity over the most recent decade: net U.S. imports accounted for an average of
only 1.2 percent of total U.S. electricity use from 2011 to 2020. The EIA projects net U.S.
imports of electricity to decrease further from that average percentage in the next decades across
all the Annual Energy Outlook (AEO) 2022 scenarios. By 2050, the AEO scenarios project net
U.S. electricity imports to range from 0.7 percent in the Low Renewables Cost scenario to 0.9
percent in the High Renewables Cost scenario. However, all the AEO 2022 scenarios model a
significantly lower level of PEV penetration-U.S. PEV sales account for 9 percent to 24 percent
of U.S. LD vehicle sales in 2050-compared to higher projected PEV penetration rates in EPA's
proposed LMDV (2027-2032) rule.
184 "Real option value" analysis applies the concepts used to value the financial assets called "options" to
investments in certain real/physical assets. Unlike traditional discounted cashflow analysis which states that
investment in a project/asset should only happen if its expected net present value is greater than zero, real option
analysis takes into account the extra value that can be realized when cashflows are uncertain and the asset holder can
choose between the different options. In the LMDV case considered here with PEVs and gasoline-powered vehicles,
real option value results when households can switch between the PEVs and gasoline-powered vehicles when fuel
costs fluctuate. Vehicle switching in this case, allows households to purchase the least costly fuel.
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1.8
Historical
Projections
2010 2015 2020 2025 2030 2035 2040 20*45 2050
Figure 11-5. U.S. electricity net imports as percentage of total electricity use from 2011 to 2020
and projected U.S. electricity net imports from 2021 to 2050. Source: (EIA 2022), (EIA 2022),
(EIA 2022), (EIA 2022)
In the past decade, the U.S. has traded electricity with only two countries: Canada and
Mexico, both in North America. The U.S. imports more electricity than it exports from both
countries. On average, from 2011 to 2020, the volume of electricity imported from Canada was
equal to 1.4 percent of U.S. electricity use and the volume exported to Canada was 0.23 percent
of U.S. electricity use. Average traded electricity volumes with Mexico were lower; imports
from Mexico were equivalent to 0.13 percent of U.S. electricity use and export volumes to
Mexico were 0.07 percent of U.S. electricity use. Although net U.S. imports represent a very
small fraction of total electricity use at the national level, they can play a larger role in some
regional electricity grids in the U.S. For example, ISO-NE-the electricity transmission grid
operator in New England-reported that 16 percent of the net energy for load in their system in
2021 originated in Canadian electricity imports (ISO New England 2022).
In addition, EPA uses ICF's Integrated Planning Model (IPM) to estimate the impacts of this
proposed rule on U.S. electricity markets and also international electricity dispatches. Only
Canadian electricity dispatches are estimated as electricity dispatched from Mexico is de
minimis. The IPM results show that net U.S. electricity international dispatch is very small as an
overall percentage of total U.S. electricity demand. U.S. net electricity imports are less than I
percent for all years and trending towards zero by 2050 for both the "no action" and "proposal"
case of this proposed rule. See Tables 5—12 and 5—13 of Chapter 5 of the DRIA for more detail
on the impacts of this proposed rule on net U.S. electricity international dispatch impacts.
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11.4 Oil Security Impacts
11.4.1 U.S. Oil Import Reductions
Over the time frame of analysis of this proposed rule, 2027-2055, 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
oil185 (EIA 2021). 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 relatively stable, between 3.0 and 3.4 MMBD, from 2027 through 2050. See
Table 11-1 below. U.S. crude oil imports, meanwhile, are projected to range between 6.7 and 7.6
MMBD between 2027 and 2050. The AEO 2021 also projects that U.S. net oil refined product
exports will remain relatively stable from 2027 (5.6 MMBD) through 2035 (5.5 MMBD) before
dropping off to 4.4 MMBD by 2050.
U.S. oil consumption is estimated to have decreased from 19.8 MMBD in 2019 to 17.5
MMBD in 2020 and 19.1 MMBD in 2021 as a result of social distancing and quarantines that
limited personal mobility as a result of the COVID-19 pandemic (EIA 2022)186. AEO 2021
projects that U.S. oil consumptions will continue to increase from 19.1 MMBD in 2027 to 20.3
MMBD in 2050 (EIA 2021). 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. During the 2027-2055 time frame, the U.S.
is projected to continue to consume significant quantities of oil and to rely on significant
quantities of crude oil imports. As a result, U.S. oil markets are expected to remain tightly linked
to trends in the world crude oil market.
In Chapter 9, EPA estimates changes in U.S. petroleum consumption as a result of this
proposed rule. For this energy security analysis, we undertake a detailed analysis of differences
in U.S. fuel consumption, crude oil imports/exports, and exports of petroleum products for the
time frame 2027-2050 using the AEO 2021 (Reference Case) in comparison with an alternative
AEO 2021 sensitivity case, Low Economic Growth. The Low Economic Growth Case is used
since oil demand decreases in comparison to the Reference Case. EPA estimates that
approximately 90.7 percent of the change in fuel consumption resulting from these proposed
standards is likely to be reflected in reduced U.S. imports of crude oil over the time frame of
analysis of this proposed rule.187 The 90.7 percent oil import reduction factor is calculated by
taking the ratio of the changes in U.S. net crude oil and refined petroleum product imports
185 We are using AEO 2021, as opposed to the more recent AEO 2022, for the quantitative analysis of this proposed
rule to maintain consistency with other parts of the analysis (i.e., air quality modeling) of this proposed rule. The
AEO 2021 projects oil market trends through 2050. The time frame for EPA's analysis of this proposed rule is from
2027 to 2055. Thus, we report oil market trends to 2050 based upon AEO 2021 in Table 11-1. We also report U.S.
oil import reductions through 2055 in Table 11-1 as well.
186 Calculated using series "Petroleum Consumption (Excluding Biofuels) Annual" (Table 1.3) and "Petroleum
Consumption Total Heat Content Annual" (Table A3).
187 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 an alternative case, the Low
Economic Growth Case. See the spreadsheet in the Docket, "AEO2021 Change in product demand on imports".
11-25
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divided by the change in U.S. oil consumption in the two different AEO cases considered. Thus,
on balance, each gallon of petroleum reduced as a result of this proposed LMDV rule is
anticipated to reduce total U.S. imports of petroleum by 0.907 gallons.
Based upon the changes in oil consumption estimated by EPA and the 90.7 percent oil import
reduction factor, the reductions in U.S. oil imports as a result of this proposed rule are estimated
in Table 11-1 below for the 2027-2055 time frame.188 Included in Table 11-1 are estimates of
U.S. crude oil exports and imports, net oil refined product exports, net crude oil and refined
petroleum product exports and U.S. oil consumption for the years 2027-2050 based on the AEO
2021 (EIA2021).
Table 11-1 Projected trends in U.S. crude oil exports/imports, net refined oil product
exports, net crude oil and refined petroleum product imports, oil consumption and U.S. oil
import reductions resulting from the proposed LMDV rule from 2027 to 2050 (MMBD)a
2027
2030
2032
2035
2040
2045
2050
U.S. Crude Oil Exports
3.3
3.1
3.1
3.3
3.2
3.1
3.1
U.S. Crude Oil Imports
7.2
6.9
6.9
7.0
7.5
7.3
7.6
U.S. Net Refined Petroleum Product
5.6
5.7
5.7
5.5
5.3
5.0
4.4
Exports'1
U.S. Net Crude Oil and Petroleum Product
1.8
2.0
2.0
1.9
1.2
0.9
0.1
Exports
U.S. Oil Consumption0
19.1
19.1
19.1
19.3
19.5
19.9
20.3
Reduction in U.S. Oil Imports from the
0.0
0.3
0.5
1.0
1.6
2.0
" 2.3
Proposed Standards'
Table Notes:
a The AEO 2021 Reference Case, Table All. Values have been rounded off from the AE
-------
account for updated projections of future energy market and economic trends reported in the U.S.
EIA's AEO. For this proposed rule, EPA updated the ORNL methodology using the AEO 2021.
The ORNL methodology is used to compute the oil import premium (concept defined in
Chapter 11.1) per barrel of imported oil. The values of U.S. oil import premium components
(macroeconomic disruption/adjustment costs and monopsony components) are numerically
estimated with a compact model of the oil market by performing simulations of market outcomes
using probabilistic distributions for the occurrence of oil supply shocks, calculating marginal
changes in economic welfare with respect to changes in U.S. oil import levels in each of the
simulations, and summarizing the results from the individual simulations into a mean and 90
percent confidence intervals for the import premium estimates. The macroeconomic
disruption/adjustment import cost component is the sum of two parts: the marginal change in
expected import costs during disruption events and the marginal change in gross domestic
product due to the disruption. The monopsony component is the long-run change in U.S. oil
import costs as the level of oil import changes.
For this proposed rule, EPA is using oil import premiums that incorporate the oil price
projections and energy market and economic trends, particularly global regional oil supplies and
demands (i.e., the U.S./OPEC/rest of the world), from the AEO 2021 into its model.189 EPA only
considers the avoided macroeconomic disruption/adjustment oil import premiums (i.e., labeled
macroeconomic oil security premiums below) as costs, since we consider the monopsony
impacts stemming from changes in U.S. oil imports, transfer payments. In previous EPA rules
when the U.S. was projected by EIA to be a net importer of crude oil and petroleum-based
refined products, monopsony impacts represented reduced payments by U.S. consumers to oil
producers outside of the U.S. There was some debate among economists as to whether the U.S.
exercise of its monopsony power in oil markets, for example from the implementation of EPA's
rules, was a "transfer payment" or a "benefit". Given the redistributive nature of this monopsony
impact from a global perspective, and since there are no changes in resource costs when the U.S.
exercises its monopsony power, some economists argued that it is a transfer payment. Other
economists argued that monopsony impacts were a benefit since they partially address, and
partially offset, the market power of OPEC. In previous EPA rules, after weighing both
countervailing arguments, EPA concluded that the U.S.'s exercise of its monopsony power was a
transfer payment, and not a benefit (EPA 2016).
In the time frame covered by this proposed LMDV rule, the U.S.'s oil trade balance is
projected to be quite a bit different than during the time periods covered in many previous EPA
rules. Starting in 2020, the U.S. became a net exporter of crude oil and refined oil products and
the U.S. is projected to continue to be a net exporter of crude oil and refined petroleum products
189 The oil market projection data used for the calculation of the oil import premiums came from AEO 2021,
supplemented by the latest EIA international projections from the Annual Energy Outlook (AEO)/International
Energy Outlook (IEO) 2019. Global oil prices and all variables describing U.S. supply and disposition of petroleum
liquids (domestic supply, tight oil supply fraction, imports, demands) as well as U.S. non-petroleum liquids supply
and demand are from AEO 2021. Global and OECD Europe supply/demand projections as well as OPEC oil
production share are from IEO 2019. The need to combine AEO 2021 and IEO 2019 data arises due to two reasons:
(a) EIA stopped including Table 21 "International Petroleum and Other Liquids Supply, Disposition, and Prices" in
the U.S.-focused Annual Energy Outlook after 2019, (b) EIA does not publish complete updates of the IEO every
year.
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in the time frame covered by the proposed LMDV standards, 2027-2032. As a result, reductions
in U.S. oil consumption and, in turn, U.S. oil imports, still lower the world oil price modestly.
But the net effect of the lower world oil price is now a decrease in revenue for U.S. exporters of
crude oil and refined petroleum products, instead of a decrease in payments to foreign oil
producers. The argument that monopsony impacts address the market power of OPEC is no
longer appropriate. Thus, we continue to consider the U.S. exercise of monopsony power to be
transfer payments. We also do not consider the effect of this proposed rule on the costs
associated with existing energy security policies (e.g., maintaining the Strategic Petroleum
Reserve or strategic military deployments), which are discussed below.
In addition, EPA and ORNL have worked together to revise the oil import premiums based
upon recent energy security literature. Based upon EPA and ORNL's review of the recent energy
security literature, EPA is assessing its macroeconomic oil security premiums for this proposed
rule. The recent economics literature (discussed in Chapter 11.2.1) focuses on three factors that
can influence the macroeconomic oil security premiums: the price elasticity of oil demand, the
GDP elasticity in response to oil price shocks, and the impacts of the U.S. tight oil boom. We
discuss each factor below and provide a rationale for how we are developing estimates for the
first two factors for the macroeconomic oil security premiums being used in this proposal. We
are not accounting for how U.S. tight oil is influencing the macroeconomic oil security premiums
in this proposed rule, other than how tight oil significantly reduces the need for U.S. oil imports.
First, we assess the price elasticity of demand for oil. In previous EPA light-duty vehicle
rulemakings (i.e., Model Year 2012-2016, Model Year 2017-2025) EPA used a short-run
elasticity of demand for oil of -0.045 (EPA 2010) (EPA 2016). In the most recent EPA rule
setting GHG emissions standards for passenger cars and light trucks in model years 2023 through
2026, we used a short-run elasticity of demand for oil of -0.07, an update of previously used
elasticities based on the below considerations (EPA 2021). For this rule, we continue to use the
elasticity value of -0.07.
From the 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 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 2019 (IEA 2022). The next largest sector by oil consumption, and an area of recent
growth, is petrochemicals. There are limited alternatives to oil use in this sector, particularly in
the time frame of this proposed rule. Thus, we believe it would be surprising if short-run oil
demand responsiveness has changed in a dramatic fashion.
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, we believe using a short-run price elasticity of demand
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for oil of -0.07 is more appropriate.190 This increase has the effect of lowering the
macroeconomic oil security premium estimates undertaken by ORNL for EPA.
Second, we consider the elasticity of GDP to an oil price shock. In previous EPA Vehicle
rulemakings (i.e., Model Year 2012-2016, Model Year 2017-2025), EPA used an elasticity of
GDP to an oil shock of-0.032 (EPA 2010) (EPA 2016). In the most recent EPA rule setting
GHG emissions standards for passenger cars and light trucks through model years 2023 through
2026, we used an elasticity of GDP of-0.021, an update of previously used elasticities based on
the below considerations (EPA 2021). For this rule, we continue to use the elasticity value of-
0.021.
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-0.021, a 34 percent reduction from the GDP elasticity
used previously (i.e., the -0.032 value). This GDP elasticity is within the range of RFF's "new
literature" elasticity, -0.018, and the elasticity EPA has used in previous rulemakings, -0.032,
but lower than RFF's "blended" GDP elasticity, -0.028. This decrease has the effect of lowering
the macroeconomic oil security premium estimates. For U.S. tight oil, EPA has not made any
adjustments to the ORNL model, given the limited tight oil production response to rising world
oil prices in the recent 2021-2022 time frame.191 Increased tight oil production still results in
energy security benefits though, through its impact of reducing U.S. oil imports in the ORNL
model.
Table 11-2 below provides estimates of EPA's macroeconomic oil security premium
estimates for 2027-2055. The macroeconomic oil security premiums are relatively steady over
the time period of this proposed rule at $3.41/barrel (8 cents/gallon) in 2027 and $3.55/barrel in
2030 (8 cents/gallon), $3.91/barrel in 2035 (9 cents per gallon), $4.39/barrel 10 cents per gallon)
in 2040 and $5.15/barrel (12 cents/gallon) in 2050 and 2055 (in 2020 U.S. dollars).
190 EPA and ORNL have worked together to develop an updated estimate of the short-run elasticity of demand for
oil for use in the ORNL model.
191 The short-run oil supply elasticity assumed in the ORNL model is 0.06 and is applied to production from both
conventional and tight (i.e., shale) oil wells.
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Table 11-2 Macroeconomic oil security premiums for 2027-2055 (2020$/barrel)a b
Year
Avoided Macroeconomic Disruption/Adjustment Costs
(Range)
2027
$3.41
($0.74 - $6.36)
2030
$3.55
($0.65 - $6.68)
2032
$3.70
($0.68-$6.94)
2035
$3.91
($0.73-$7.34)
2040
$4.39
($1.08-$8.09)
2045
$4.73
($1.23-$8.64)
2050
$5.15
($1.52-$9.28
2055
$5.15
($1.52-$9.28)
I Table Notes:
= a The top values in each cell are mean values. Values in parentheses are 90 percent confidence intervals.
I b The AEO 2021 only provides oil market trend estimates to 2050. We use the same macroeconomic oil security premium for 2055 as the
I value for 2050.
11.4.3 Cost of Existing U.S. Oil 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
(Energy Policy and Conservation Act 1975). Emergency SPR drawdowns have taken place in
1991 (Operation Desert Storm), 2005 (Hurricane Katrina), 2011 (Libyan Civil War), and 2022
(War in Ukraine) (DOE 2022). All of these releases have been in coordination with releases of
strategic stocks from other International Energy Agency (IEA) member countries. In the first
four months of 2022, using the statutory authority under Section 161 of the Energy Policy and
Conservation Act, the U.S. President directed the U.S. DOE to conduct two emergency SPR
drawdowns in response to ongoing oil supply disruptions. The first drawdown resulted in a sale
of 30 million barrels in March 2022 (DOE 2022). The second drawdown, announced in April,
authorized a total release of approximately one MMBD from May to October 2022 (DOE 2022).
For 2023, the DOE has announced plans to sell 26 million barrels of oil between April and June
(DOE 2023). 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.
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We have also considered the possibility of quantifying the military benefits components of
energy security but have not done so here for several reasons. The literature on the military
components of energy security has described four broad categories of oil-related military and
national security costs, all of which are 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
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 and because the military budget is presented along regional accounts rather than by
mission, according to (Crane, et al. 2009), the allocation to particular missions is not always
clear. 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).
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, Behrens and Blodgett 1997).
(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. 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. 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-$74 billion
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annually. Delucchi and Murphy assume that military costs from U.S. oil import reductions can
be scaled proportionally, attempting to address the incremental issue.
(Crane, et al. 2009) 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 Future Energy (SAFE) (2018) found a
conservative estimate of approximately $81 billion per year spent by the U.S. military protecting
global oil supplies (SAFE 2018). 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.
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 likely 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. We are unaware of a robust
methodology for assessing the effect on military costs of a partial reduction in U.S. oil use.
Therefore, we are unable to quantify this effect resulting from the projected reduction in U.S. oil
use attributable to this proposed rule.
11.4.4 Oil Security Benefits of Proposed Rule
Estimates of the total annual oil security benefits of the proposed standards are based on the
ORNL oil import premium methodology with updated oil import premium estimates reflecting
the recent energy security literature and using the AEO 2021. Annual per-gallon benefits are
applied to the reductions in U.S. crude oil and refined petroleum product imports. We do not
consider military cost impacts or the monopsony effect of U.S. crude oil and refined petroleum
product import changes on the energy security benefits of this proposed rule. The energy security
benefits of this proposal are presented in Table 10-9 of Chapter 10, Non-Emissions Benefits of
the Proposal, Light-Duty and Medium-Duty.
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Chapter 12: 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 (Title
13 CFR 121.201 2023), (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.
There are three types of small entities that could potentially be impacted by the proposed
GHG standards: 1) small entity vehicle manufacturers; 2) alternative fuel converters, which are
companies that take a vehicle for which an OEM has already accounted for GHG compliance
and convert it to operate on a cleaner fuel such as natural gas or propone; and 3) -independent
commercial importers (ICIs), which are firms that import vehicles from other countries for
individual vehicle purchasers.
EPA initiated the Small Business Advocacy Review Panel process and had a pre panel
meeting with small businesses representing the small entity manufacturers, the alternative fuel
converters, and the ICIs. EPA presented the areas it expected to make changes in this NPRM at a
high level and heard from the small businesses their initial concerns if any on the potential
changes based on this rulemaking. EPA also learned in more detail how these entities conduct
their business to help assess the impact the standards proposed in this NPRM may have and
enable EPA to mitigate any impacts.
EPA is certifying that this rule will have no significant economic impact on a substantial
number of small entities (No SISNOSE). EPA has focused its assessment of potential small
business impacts on three key aspects of the proposed standards, including GHG emissions
standards, criteria pollutants (NMOG+NOx fleet-average standards) and PM emissions
standards), and EV battery warranty and durability.
Under the current light-duty GHG program, small entities are exempt from the GHG
standards. EPA is proposing to continue the current exemption for all three types of small
entities, including small entity manufacturers, Alternate Fuel Converters, and Independent
Commercial Importers (ICIs). However, EPA is proposing to add some environmental
protections for imported vehicles, as described below. EPA is also proposing to continue the
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current provision allowing small entity manufacturers to opt into the GHG program to earn
credits to sell in the credit market. The only small entity vehicle manufacturers in the market at
this time produce only electric vehicles. EPA is requesting comment on the potential need for
small entity manufacturers to have an annual vehicle production cap (e.g., 200-500 vehicles per
year) on vehicles eligible for the exemption. On average, historical production data indicates that
small entities' annual sales have been well below this range as shown in Table 1. EPA believes
that capping the number of vehicles exempted could be an appropriate protection for GHG
emissions, while still allowing small entities to produce vehicles consistent with typical past
annual sales.
Table 3 Small Entity Production from 2017 to 2021
Karma
RUF
Koenigsegg
Pagani
Rimac
2017
0
0
0
46
1
2018
295
2
10
10
0
2019
83
1
12
0
1
2020
153
6
4
0
0
2021
78
7
11
0
0
While ICI's imported vehicles have not been accounted for in a manufacturer's GHG average
there are typically only a small number of vehicles imported each year. Since 2014, none of the
current ICIs have imported more than 15 vehicles each year. Under existing EPA regulations,
each ICI' is currently limited to importing 50 vehicles per year. EPA is proposing to reduce the
limit to 25 non-ZEV vehicles per year, as a means of limiting the potential environmental impact
of importing vehicles with potentially high GHG emissions. Importing of ZEVs will not count
against the 25 vehicles limit and EPA will put in language to clarify this fact. Table 2 below
shows the number of vehicles imported by each of the current ICIs. EPA believes this lower
vehicle limit is important for capping the potential for high-emitting imported vehicles, because,
unlike with criteria pollutant emissions as discussed below, there are very limited add-on
emissions control options for reducing the GHG emissions of an imported vehicle. This action
will have no financial impact on the ICI businesses, as it still far above the average number of
vehicles imported by ICIs in recent years.
Table 4 ICI Import Records
2014
2015
2016
2017
2018
2019
2020
2021
2022
Current ICIs
G&K
7
7
6
6
8
12
6
10
8
JK Technologies
13
15
8
10
10
9
3
4
5
Wallace Labs
0
0
15
1
7
5
4
4
10
EPA also has evaluated the potential impacts on small businesses for the proposed criteria
pollutant emissions standards, including both the NMOG+ NOx standard and the PM standard.
EPA's proposed NMOG+NOx standards should have no impact on the existing small entity
manufacturers which produce only electric vehicles. The proposed standards are expected to
have minimal impact on both the alternate fuel converters and ICIs. Alternate fuel converters are
getting vehicles that would already meet the standard on gasoline or diesel fuel and have the
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ability to make changes, such as calibration, so the vehicles continue to meet the standard on an
alternate fuel such as propane or natural gas. ICIs take vehicles that were certified in a foreign
country and make the vehicle meet the EPA standard for the year the vehicle was built. This may
require catalyst and calibration changes depending on the vehicle's original requirements. Based
on the pre panel meeting, EPA believes changes to the NMOG+ NOx standard will require a
similar amount of effort for both alternative fuel converters and ICIs to meet the new standard
when compared to the previous (Tier 3) emissions standard.
The proposed PM standard could potentially have a unique impact on each type of small
entity. The current small entity manufacturers all produce only EVs which have no tailpipe
emissions and therefore would automatically comply with the PM standard. Alternative fuel
converters buy OEM vehicles that already would need to be compliant for PM but must test the
vehicle on the converted fuel and show that it still meets the standard. There would be an
increased testing burden to measure PM on the cold temperature test (as discussed further in
Preamble Section III.C.2), but alternative fuel vehicles are already exempted from doing any
cold testing under existing EPA regulations. EPA is proposing to continue this exemption for
cold temperature testing, and thus there would be no impact on alternative fuel converters. ICI's
must do a complete set of emissions tests for an imported vehicle that do not already have an
existing certificate (referred to as non-conforming vehicles). ICI's currently only have to test
non-methane hydrocarbons (NMHC) on the cold test; to minimize the testing burden on ICIs
EPA is proposing to exempt ICI from measuring PM during cold testing. ICIs will only need to
comply with the new PM levels on the FTP75 and US06. The stringency of the proposed PM
standard may lead to OEMs choosing to comply by the use of gasoline particulate filters (GPFs).
Most of the ICE vehicles since 2014 have been imported from Europe where GPFs are
mandatory, so EPA estimates that there will be no financial impact to ICIs based on additional
testing or ensuring imported vehicles are compliant with emissions standards.
The final aspect of the NPRM that could have potential impacts on small entities is battery
durability and warranty (Preamble Section III.F.2 and Preamble Section III.F.3). The current
small entity manufacturers all have warranties that meet or exceed our proposed requirements.
EPA is proposing to exempt small entities form meeting the proposed battery durability and
warranty requirements since the reporting requirements would be an added financial burden that
is not necessary given their current warranties.
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Chapter 12 References
Title 13 CFR 121.201. 2023.
12-4
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Chapter 13: Compliance Effects
This chapter summarizes the outputs from OMEGA2 related to the proposed GHG standards
and the three alternatives which were presented in III.E of the preamble.
In the following sections we provide detailed modeling results of GHG targets, projected
achieved compliance GHG rates, as well as vehicle costs and technology penetrations. These
projections are grouped by car and truck regulatory classes, and in select tables, using EPA's
classification of body style in its OMEGA model.
13.1 Light-Duty Vehicles
13.1.1 GHG Targets and Compliance Levels
13.1.1.1 C02 g/mi
Shown below are the projected average GHG targets for each manufacturer, as well as their
corresponding average achieved compliance, in g/mi, for cars and trucks. A combined fleet g/mi
comparison is not shown, because a fleet g/mi value, even with a sales-weighted average of car
and truck values, would not accurately represent the differences in lifetime VMT for the car and
truck fleets used in the compliance calculations for each OEM.
13.1.1.1.1 Proposed standards
OEM-specific GHG emissions targets for the proposed standards are shown in Table 13-1 and
Table 13-2 for cars and trucks, respectively192. Similarly, projected achieved GHG emissions
levels are given for cars and trucks in Table 13-3 and Table 13-4.
192 Only manufacturers with annual sales exceeding 25,000 units are provided in the tables in this chapter. However,
the industry sales-weighted averages include all vehicles and manufacturers (even those not shown).
13-1
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Table 13-1: Projected GHG Targets, Proposed Standards - Cars
Manufacturer
2027
2028
2029
2030
203 1
2032
BMW
135
117
99
91
83
74
Ford
135
117
99
91
82
i 73 '
General Motors
134
116
98
90
82
72
Honda
133
116
98
91
82
' 73 '
Hyundai
134
116
98
91
82
73 '
JLR
136
118
100
92
83
74
Kia
134
116
98
90
82
73 '
Ma/da
133
116
98
90
82
73
Mercedes Ben/.
135
117
99
91
83
74
Mitsubishi
131
114
97
90
81
72
Nissan
133
116
98
90
82
73 '
Stellantis
135
118
100
92
83
73
Subaru
134
116
98
90
82
72
Tcsla
137
119
101
92
84
74
Toyota
133
116
98
91
82
: 73 '
Volvo
137
119
101
92
84
74
VW
133
116
98
90
82
73 '
TOTAL
134
116
99
91
82
73
Table 13-2: Projected GHG Targets, Proposed Standards - Trucks
Manufacturer
2027
2028
2029
2030
203 1
2032
BMW
158
139
117
107
98
87
Ford
179
154
130
119
108
96
General Motors
175
150
128
117
106
94
Honda
152
133
113
104
95
84
Hyundai
149
130
111
102
93
82
JLR
161
140
118
109
99
86
Kia
155
131
111
103
93
83
Ma/da
146
128
109
101
91
81
Mercedes Ben/.
156
136
115
107
97
86
Mitsubishi
137
121
104
96
87 ""
77
Nissan
157
136
115
107
98
87 '
Stellantis
166
144
122
113
102
91
Subaru
145
126
107
99
89
79
Tcsla
173
150
126
116
105
93
Toyota
157
136
116
107
97
86
Volvo
155
135
115
106
96
85
VW
155
135
113
106
96
85
TOTAL
163
142
120
110
100
89
13-2
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Table 13-3: Achieved GHG Levels, Proposed Standards - Cars
Manufacturer
2027
2028
2029
2030
203 1
2032
BMW
137
129
96
61
45
43
Ford
106
102
85
92
81
96
General Motors
106
77
72
74
70
63
Honda
119
115
100
70
78
59
Hyundai
130
111
95
74
69
65
JLR
141
101
127
88
84
73
Kia
121
110
96
74
60
51
Ma/da
128
112
97
75
79
72
Mercedes Ben/.
124
108
75
61
67
62
Mitsubishi
106
94
77
55
56
53
Nissan
117
103
86
69
78 ""
66
Stellantis
114
89
74
81
80
76
Subaru
75 ]
56
94
81
11 '
76
Tcsla
0
0
0
0
o '
0
Toyota
122
110
91
81
55
45
Volvo
105
81
54
47
65
67
VW
112
73
39
48
54
29
TOTAL
115
100
84
72
68
60
Table
13-4:
Achieved GHG Levels, Proposed Standards
- Trucks
Manufacturer
2027
2028
2029
2030
203 1
2032
BMW
170
128
136
133
135
117
Ford
158
155
127
125
111
81
General Motors
216
175
138
134
123
105
Honda
147
122
104
99
76
82
Hyundai
159
135
115
104
123
112
JLR
156
136
105
88
104
114
Kia
152
125
109
84
80
80
Ma/da
136
118
104
85
90
90
Mercedes Ben/.
185
155
154
119
101
102
Mitsubishi
136
114
93
82
92
92
Nissan
144
140
126
107
73 '
74
Stellantis
218
158
126
119
113
98
Subaru
137
113
83
74
90
90
Tcsla
0
0
o
o
o
0
Toyota
151
145
121
108
115
107
Volvo
169
143
114
97
109
109
VW
158
151
147
113
102
108
TOTAL
176
149
123
113
106
95
13-3
-------
13.1.1.1.2 Alternative 1
Table 13-5 and Table 13-6 show the OEM-specific targets for Alternative 1. Achieved levels
are presented, by manufacturer, in Table 13-7 and Table 13-8.
Table 13-5: Projected GHG Targets, Alternative 1 - Cars
Manufacturer
2027
2028
2029
2030
203 1
2032
BMW
125
107
89
81
73
63
Ford
125
107
89
81
73
63
General Motors
124
106
89
80
72
63
Honda
124
106
88
81
72
63
Hyundai
124
106
89
81
72
63
JLR
126
108
90
82
74
64
Kia
124
106
88
81
72
63
Ma/da
123
106
88
80
72
63
Mercedes Ben/.
125
107
89
81
73
63
Mitsubishi
122
105
87
80
71
62
Nissan
123
106
88
81
72
63
Stellantis
125
108
90
82
73
63
Subaru
124
106
88
80
72
62
Tcsla
127
109
91
82
74
64
Toyota
124
106
89
81
73
63
Volvo
127
109
91
82
74
64
VW
124
106
88
80
72
62
TOTAL
124
106
89
81
72
63
Table 13-6: Projected GHG Targets, Alternative 1 - Trucks
Manufacturer
2027
2028
2029
2030
203 1
2032
BMW
147
128
108
98
87
75
Ford
167
143
119
108
97
84
General Motors
163
139
117
106
95
83
Honda
142
123
105
96
86
74
Hyundai
139
120
101
93
83
72 "
JLR
150
129
108
99
88
76
Kia
145
120
101
93
83
72 "
Ma/da
136
118
99
91
82
71
Mercedes Ben/.
149
128
107
98
88
76
Mitsubishi
128
112
94
87
78
68
Nissan
146
126
106
97
87
76
Stellantis
155
133
112
102
92
80
Subaru
135
117
98
90
80
69
Tcsla
161
138
115
105
94
82
Toyota
147
126
105
96
86
75
Volvo
145
125
105
96
86
75
VW
143
125
106
97
86
75 "
TOTAL
153
131
110
100
90
78
13-4
-------
2032
53
66
28
69
63
25
63
46
84
51
40
49
62
0
28
54
78
51
2032
138
68
92
75
103
90
74
81
78
65
95
73
77
0
106
86
87
84
Table 13-7: Achieved GHG Levels, Alternative 1 - Cars
2027
2028
2029
2030
203
137
131
109
77
50
106
98
88
63
52
108
81
67
44
39
119
116
85
74
72
129
104
82
62
64
139
68
38
22
23
124
108
82
66
68
128
104
77
^ 57
54
128
109
83
85
3 78
105
86
69
52 ""
55
117
92
74
59
49
113
11
71
74
46
70
55
93
75
52
0
0 '
0
0
0
122
100
80
51
34
105
79
64
47
57
74
102
104
91
71
113
97
80
62
53
Table 13-8: Achieved GHG Levels, Alternative 1 - Trucks
2027
2028
2029
2030
203 1
169
131
95
96
141
158
139
124
103
100
211
149
128
111
1 1 1
147
122
90
71
77
159
136
109
91
107
156
132
103
84
90
143
125
96
78
69
136
114
89
78
81
167
141
113
68
69
134
101
82
68
69
140
130
105
87
91
217
145
122
94
92
137
113
84
' 75 *7
86
0
0
o
0
0
151
139
118
104
115
169
132
107
86
94
179
135
91
76
93
175
137
113
94
97
13-5
-------
13.1.1.1.3 Alternative 2
Table 13-9 and Table 13-10 show the OEM-specific targets for Alternative 2. Achieved levels
are presented, by manufacturer, in Table 13-11 and Table 13-12.
Table 13-9: Projected GHG Targets, Alternative 2 - Cars
Manufacturer
2027
2028
2029
2030
203 1
2032
BMW
145
127
109
101
93
83
Ford
145
127
109
101
92
83
General Motors
143
126
108
100
92
82
Honda
143
126
108
100
92
83
Hyundai
144
126
108
100
92
83
JLR
146
128
109
101
93
84
Kia
144
126
108
100
92
83
Ma/da
142
125
108
100
92
82
Mercedes Ben/.
145
127
109
101
93
83
Mitsubishi
141
124
107
99
91
82
Nissan
143
126
108
100
92
82
Stellantis
145
127
109
102
93
83
Subaru
143
126
107
100
91
82
Tcsla
147
129
111
103
94
84
Toyota
143
126
108
100
92
83
Volvo
148
129
111
102
93
84
VW
143
126
108
100
92
82
TOTAL
144
126
108
100
92
83
Table 13-10: Projected GHG Targets, Alternative 2 - Trucks
Manufacturer
2027
2028
2029
2030
203 1
2032
BMW
168
147
127
118
108
96
Ford
190
166
142
130
119
107
General Motors
186
161
139
128
117
105
Honda
162
142
122
113
104
94
Hyundai
158
139
120
111
102
92
JLR
171
150
128
119
109
95
Kia
165
140
121
112
103
92
Ma/da
154
136
117
109
100
90
Mercedes Ben/.
165
146
126
117
107
96
Mitsubishi
145
129
112
105
96
86
Nissan
166
145
126
116
107
97
Stellantis
176
154
133
123
113
101
Subaru
154
135
116
108
98
88
Tcsla
184
161
138
127
116
104
Toyota
167
146
126
117
107
96
Volvo
164
144
124
115
105
95
VW
163
144
124
115
106
95
TOTAL
173
152
130
121
111
99
13-6
-------
2032
50
102
59
70
75
137
60
77
87
78
77
84
94
0
43
102
64
68
2032
129
92
118
95
99
130
88
109
102
97
80
109
101
0
122
118
103
106
Table 13-11: Achieved GHG Levels, Alternative 2 - Cars
2027
2028
2029
2030
203 1
135
128
93
108
71
106
91
74
73
95
128
95
70
82
83
121
118
96
85
76
134
128
101
98
83
144
172
182
169
161
122
124
95
91
72
137
118
105
103
82
136
155
123
114
95
115
109
92
93
88
120
108
84
80
70
132
110
89
86
86
82
95
99
108
105
0
0
0
0
0
125
113
91
87
65
112
92
84
98
111
74
94
77
85
74
119
110
87
87
77
Table 13-12: Achieved GHG Levels, Alternative 2 - Trucks
2027
2028
2029
2030
203 1
170
161
138
115
124
158
167
136
116
109
223
169
144
133
125
148
143
119
125
108
162
147
118
128
122
188
168
143
145
144
141
148
114
110
91
159
152
136
129
130
200
152
120
121
106
154
143
121
118
110
151
150
134
129
112
229
160
130
122
119
145
130
112
119
115
0
0
0
0
0
159
162
132
126
124
192
172
148
142
134
184
161
134
129
113
183
158
132
124
117
13-7
-------
13.1.1.1.4 Alternative 3
Table 13-13 and Table 13-14 show the OEM-specific targets for Alternative 3. Achieved
levels are presented, by manufacturer, in Table 13-15 and Table 13-16.
Table 13-13: Projected GHG Targets, Alternative 3 - Cars
Manufacturer
2027
2028
2029
2030
203 1
2032
BMW
140
127
113
100
87
73
Ford
140
126
113
100
87
73 '
General Motors
138
126
112
99
86
72
Honda
138
126
112
99
86
73
Hyundai
139
126
112
99
86
73 '
JLR
141
128
114
100
87
74
Kia
139
126
112
99
86
73
Ma/da
138
125
112
99
86
73
Mercedes Ben/.
140
127
114
100
87
74
Mitsubishi
136
124
111
98
85
72
Nissan
138
126
112
99
86
73 '
Stellantis
140
127
113
100
87
73
Subaru
138
126
112
99
86
72'
Tcsla
142
129
115
101
88
74
Toyota
138
126
112
99
86
73 '
Volvo
143
129
115
101
88
74
VW
138
126
112
99
86
73
TOTAL
139
126
112
99
86
73
Table 13-14: Projected GHG Targets, Alternative 3 - Trucks
Manufacturer
2027
2028
2029
2030
203 1
2032
BMW
178
159
140
122
104
87
Ford
195
174
154
135
114
95
General Motors
197
174
154
134
113
94
Honda
171
154
136
120
101
84
Hyundai
168
152
134
117
99
83
JLR
181
162
143
125
105
86
Kia
176
152
135
118
100
83
Ma/da
164
147
130
115
97
81
Mercedes Ben/.
175
157
139
122
104
86
Mitsubishi
154
140
125
110
93
77
Nissan
176
158
140
123
104
86
Stellantis
187
166
147
129
110
91
Subaru
163
146
130
114
95
79
Tcsla
195
174
153
134
113
93
Toyota
176
158
140
123
104
86
Volvo
174
156
138
120
102
85
VW
173
156
138
121
103
86
TOTAL
183
163
144
126
107
89
13-8
-------
2032
56
85
65
60
67
117
57
74
64
55
54
68
78
0
58
55
55
62
2032
104
84
101
87
98
107
76
92
101
96
88
99
90
0
99
110
97
94
Table 13-15: Achieved GHG Levels, Alternative 3 - Cars
2027
2028
2029
2030
203 1
136
97
85
62
60
117
135
115
88
63
129
120
99
85
71
120
108
108
96
80
133
112
106
93
80
144
173
185
170
153
121
110
106
89
66
135
107
100
98
89
138
101
88
88
81
114
106
97
90
73
123
105
106
89
73
131
125
119
94
74
82
90
102
107
97
0
0
0
0
0
133
115
104
96
78
111
88
70
64
67
80
68
86
82
70
122
110
103
89
73
Table 13-16: Achieved GHG Levels, Alternative 3 - Trucks
2027
2028
2029
2030
203 1
168
157
164
163
129
175
181
155
130
110
225
191
167
145
124
152
132
120
109
94
161
134
126
125
125
188
166
145
146
133
140
120
101
91
80
159
150
149
130
114
196
183
181
147
117
152
139
127
121
112
160
137
129
114
95
228
185
159
138
118
144
125
114
118
110
0
0
0
0
0
169
164
146
126
111
191
172
158
151
135
183
153
137
135
113
188
167
147
131
112
13-9
-------
13.1.1.2 C02 Mg
Shown below are the projected average GHG targets for each manufacturer, as well as their
corresponding average achieved compliance, in Mg, for cars, trucks, and the combined fleet.
Total emissions are calculated by multiplying the relevant CO2 emission rate, the production
volume of applicable vehicles, and the expected lifetime vehicle miles traveled (VMT) of those
vehicles. The equation to calculate total Mg (for either total emissions, or credits based on the
difference between target g/mi and achieved g/mi) is:
CO2 (Mg) = (CO2 (g/mi) x VMT x Production) / 1,000,000
In the above equation, "VMT" is in miles, and specified in the regulations as 195,264 miles
for cars and 225,865 for trucks. When using these equations to calculate values for cars and
trucks in aggregate, we use a production weighted average of the car and truck VMT values.
13.1.1.2.1 Proposed standards
OEM-specific GHG emissions targets for the proposed standards (in Mg) are shown in Table
13-17, Table 13-18, and Table 13-19 for cars, trucks, and the combined fleet, respectively.
Similarly, projected achieved GHG emissions (in Mg) are given for cars, trucks, and the
combined fleet in Table 13-20, Table 13-21, and Table 13-22. Finally, overall credits or debits
earned are provided for the combined fleet on a manufacturer-specific basis, in Table 13-23.
Table 13-17: Projected GHG Targets (Mg), Proposed Standards - Cars
Manufacturer
2027
2028
2029
2030
203 1
2032
BMW
5.091.076
4.482.814
3.834.746
3,576,737 ^
3,278,332 ;
2.906.949
Ford
12.921.363
11.456.020
9.809.548
9.111.663
8,333,236 :
7.406.773
General Motors
19.880.592
17.643.576
15.131.637
14.044.454
12.774.852
11.348.172
Honda
20.487.741
18.056.558
15.544.825
14.431.748
13.228.332
11.715.789
Hyundai
15.131.453
13.377.798
11.481.251
10.657.511
9.731.226
8.623.810
JLR
142.373
124.988
107.245
99.111
91.716
81.023
Kia
7,777,133 :
6.829.572
5.884.835
5.460.408
5.048.145
4.448.328
Ma/da
3.138.485
2.788.394
2.401.375
2.248.925
2.046.205
1.818.581
Mercedes Ben/.
4.467.668
3.945.841
3.387.156
3.159.587
2.874.472
2,557,223
Mitsubishi
1.415.160
1.249.288
1.078.460
1.006.639
912.826
812.341
Nissan
17.778.679
15.730.647
13.535.632
12.590.751
11.537.354
10.227.611
Stellantis
9.353.558
8.264.397
7.106.572
6.588.895
6.011.507
5.312.072
Subaru
2,837,220 :
2.520.83 1
2.175.765
2.031.656
1.871.219
1.664.402
Tcsla
2.216.521
1.937.768
1.679.457
1.537.450
1.425.760
1.253.596
Toyota
21.992.964
19.429.686
16.718.150
15.492.249
14.242.094
12.605.690
Volvo
583.538
514.381
440.917
406.432
370.516
328,807
VW
7.888.276
6.997.575
6.014.856
5.603.817
5.103.534
4.540.678
TOTAL
153,473,059
135,675,676
116,611,884
108,309,703
99,122,439
87,866,548
13-10
-------
Table 13-18: Projected GHG Targets (Mg), Proposed Standards - Trucks
Manufacturer
2027
2028
2029
2030
203 1
2032
BMW
4.585.917
4.069.028
3.433.605
3,175,730 ;
2.870.067
2.546.508
Ford
51.565.944
45.145.105
38.266.619
34.833.245
31.508.729
27.924.230
General Motors
62.254.967
53.949.63 1
46.138.573
41.986.088
38.150.861
33.655.269
Honda
24.788.279
21.874.742
18.689.609
17.185.259
15.683.277
13.780.819
Hyundai
390.487
343.903
293.675
267.065
243.128
213.533
JLR
3.640.854
3.186.197
2.716.766
2.486.827
2.255.786
1.948.220
Kia
6.710.436
5.694.380
4.860.482
4.483.838
4.086.903
3.582.678
Ma/da
4.338.956
3.858.754
3.309.762
3.089.838
2,787,793 ;
2.465.273
Mercedes Ben/.
4.525.924
4.015.467
3.400.141
3.184.025
2.870.676
2.543.195
Mitsubishi
2.034.619
1.824.916
1.578.988
1.475.375
1.324.649
1.179.383
Nissan
16.912.134
14.849.698
12.666.297
11.690.662
10.685.309
9.412.617
Stellantis
63.656.884
55.278.306
47.390.264
43.224.338
39.366.294
34.617.037
Subaru
20.375.971
18.096.047
15.628.506
14.526.483
13.015.614
11.551.607
Tcsla
433.602
377,300 i
322.947
293.310
267.304
234.225
Toyota
42.141.432
36.924.639
31.560.895
28.960.844
26.240.750
23.125.665
Volvo
2.968.623
2.616.517
2.245.552
2,080,273 :
1.876.706
1.659.992
VW
14.427.239
12.726.343
10.785.567
10.099.673
9.075.062
8.027.615
TOTAL
326,089,198
285,127,535
243,539,630
223,274,057
202,520,153
178,654,821
Table 13
-19: Projected GHG Targets (Mg), Proposed Standards - Combined
Manufacturer
2027
2028
2029
2030
203 1
2032
BMW
9.676.993
8.551.841
7.268.350
6.752.467
6.148.399
5.453.457
Ford
64.487.307
56.601.125
48.076.167
43.944.908
39.841.965
35.331.003
General Motors
82.135.559
71.593.208
61.270.210
56.030.542
50.925.713
45.003.442
Honda
45.276.019
39.931.300
34.234.435
31.617.007
28.911.609
25.496.608
Hyundai
15.521.940
13.721.702
11.774.926
10.924.576
9.974.354
8.837.343
JLR
3,783,227 i
3.311.184
2.824.010
2.585.938
2.347.501
2.029.243
Kia
14.487.569
12.523.953
10.745.317
9.944.246
9.135.048
8.031.006
Ma/da
7.477.441
6.647.148
5.711.137
5,338,763 :
4.833.998
4.283.854
Mercedes Ben/.
8.993.592
7.961.308
6,787,297 =
6.343.611
5.745.148
5.100.418
Mitsubishi
3.449.779
3.074.204
2.657.449
2.482.014
2,237,475
1.991.724
Nissan
34.690.813
30.580.345
26.201.929
24.281.413
22.222.662
19.640.228
Stellantis
73.010.441
63.542.703
54.496.836
49.813.233
45.377.801
39.929.109
Subaru
23.213.192
20.616.878
17.804.271
16.558.139
14.886.833
13.216.008
Tcsla
2.650.122
2.315.069
2.002.404
1.830.760
1.693.065
1.487.821
Toyota
64.134.396
56.354.325
48.279.045
44.453.093
40.482.844
35.731.354
Volvo
3.552.160
3.130.897
2.686.470
2.486.705
2,247,222
1.988.799
VW
22.315.514
19.723.918
16.800.422
15.703.490
14.178.596
12.568.293
TOTAL
479,562,257
420,803,211
360,151,514
331,583,760
301,642,592
266,521,369
13-11
-------
Table 13-20: Achieved GHG Levels (Mg), Proposed Standards - Cars
Manufacturer
2027
2028
2029
2030
203 1
2032
BMW
5.184.002
4.940.134
3,707,063 :
2.405.553
1.762.767
1.718.999
Ford
10.201.303
9.938.462
8.459.397
9.183.562
8.208.390
9.696.628
General Motors
15.710.377
11.683.229
11.009.187
11.533.201
10.866.052
9.824.717
Honda
18.224.481
17.981.721
15.777.062
11.143.242
12.602.021
9.462.690
Hyundai
14.688.431
12.772.168
11.052.595
8.703.179
8.178.183
7.702.989
JLR
147.940
106.799
136.883
95.333
91.885
80.326
Kia
7.031.023
6.490.078
5.750.388
4.471.761
3.695.053
3.112.022
Ma/da
3.020.258
2.695.925
2,376,712 :
1.856.574
1.969.680
1.799.619
Mercedes Ben/.
4.088.748
3.644.510
2.551.652
2.098.035
2.334.299
2.151.849
Mitsubishi
1.138.709
1.031.710
851.708
621.242
628.541
598.267
Nissan
15.546.157
14.008.977
11.832.099
9.627.341
10.989.653
9.233.292
Stellantis
7.875.138
6.236.802
5.240.860
5.824.713
5,778,423
5.536.104
Subaru
1.590.438
1.205.798
2.083.948
1.838.377
1.766.658
1.759.293
Tcsla
0
0 i
0 I
0
0
0
Toyota
20.031.917
18.383.178
15.496.475
13.781.883
9.471.044
7.789.008
Volvo
445.468
346.723
234,737
207.161
290.394
296.466
VW
6.616.698
4.393.966
2.382.044
3.004.519
3.346.113
1.800.241
TOTAL
131,933,221
116,214,446
99,233,987
86,595,798
82,118,708
72,715,073
Table 13-21: Achieved GHG Levels (Mg), Proposed Standards - Trucks
Manufacturer
2027
2028
2029
2030
203 1
2032
BMW
4.933.415
3.758.808
3.995.566
3.923.128
3.967.083
3.451.368
Ford
45.621.962
45.443.335
37.374.958
36.653.276
32.512.389
23.644.934
General Motors
76.759.011
62.804.517
49.903.778
48.132.447
44.461.267
37,700,767
Honda
23.961.309
19.992.389
17.285.982
16.342.784
12.589.264
13.454.573
Hyundai
417.488
355.211
304.444
272.159
323.080
289.316
JLR
3,535,202 i
3.095.691
2.404.230
2.001.884
2,378,567 ;
2,587,222
Kia
6.583.708
5.434.657
4.803.662
3.670.916
3.525.620
3.485.653
Ma/da
4.040.644
3,550,230
3.168.207
2.605.941
2.756.712
2.756.208
Mercedes Ben/.
5.390.165
4.568.891
4.570.989
3,536,227 :
2.982.035
3.022.149
Mitsubishi
2.018.519
1.719.915
1.415.167
1.266.103
1.404.174
1.415.397
Nissan
15.595.848
15.318.234
13.805.975
11.747.252
8.009.637
8.071.035
Stellantis
83,272,673
60.765.551
48.776.713
45.837.855
43.286.368
37,305,858
Subaru
19.221.507
16.130.549
12.085.194
10.791.109
13.148.828
13.098.622
Tcsla
0 ;
0 ;
0 :
0
0
0
Toyota
40.607.266
39.383.249
32.982.740
29.204.468
31.307.422
28.737.015
Volvo
3,237,235 i
2.766.920
2,234,733
1.903.298
2.134.105
2.125.143
VW
14.691.218
14.263.541
13.955.782
10.773.613
9.573.33 1
10.188.530
TOTAL
350,286,970
299,684,909
249,405,966
228,964,961
214,646,091
191,586,773
13-12
-------
Table 13-22: Achieved GHG Levels (Mg), Proposed Standards - Combined
Manufacturer
2027
2028
2029
2030
203 1
2032
BMW
10.117.416
8.698.942
7,702,628 ;
6.328.681
5.729.850
5.170.368
Ford
55.823.265
55.381.798
45.834.355
45.836.838
40.720.779
33.341.562
General Motors
92.469.387
74.487.746
60.912.964
59.665.648
55.327.318
47.525.484
Honda
42.185.790
37.974.110
33.063.044
27.486.026
25.191.285
22.917.263
Hyundai
15.105.919
13.127.380
11.357.039
8.975.339
8.501.263
7.992.305
JLR
3.683.142
3.202.489
2.541.113
2.097.217
2.470.452
2.667.548
Kia
13.614.731
11.924.735
10.554.050
8.142.677
7.220.674
6.597.675
Ma/da
7.060.902
6.246.154
5.544.919
4.462.514
4.726.391
4.555.827
Mercedes Ben/.
9.478.913
8.213.402
7.122.640
5.634.262
5.316.334
5.173.998
Mitsubishi
3,157,227 ;
2.751.625
2.266.875
1.887.345
2.032.714
2.013.663
Nissan
31.142.004
29.327.211
25.638.074
21.374.593
18.999.290
17.304.327
Stellantis
91.147.811
67,002,353 :
54.017.572
51.662.568
49.064.791
42.841.962
Subaru
20.811.946
17.336.347
14.169.142
12.629.485
14.915.486
14.857.915
Tcsla
0 :
0 ;
0 ;
0 :
0 :
0
Toyota
60.639.184
57.766.427
48.479.216
42.986.352
40.778.466
36.526.023
Volvo
3,682,703 :
3.113.643
2.469.470
2.110.458
2.424.499
2.421.609
VW
21.307.916
18.657.507
16.337.825
13.778.132
12.919.444
11.988.770
TOTAL
482,220,191
415,899,355
348,639,953
315,560,759
296,764,799
264,301,846
Table 13-23: GHG Credits/Debits Earned (Mg), Proposed Standards - Combined
Manufacturer
2027
2028
2029
2030
203 1
2032
BMW
(440.423)
(147.101)
(434.278)
423.786
418.549
283.089
Ford
8.664.042
1.219.327
2.241.812
(1.891.930)
(878.815)
1.989.441
General Motors
(10.333.828)
(2.894.538)
357,245
(3.635.106)
(4.401.605)
(2.522.042)
Honda
3.090.229
1.957.190
1.171.391
4.130.981
3,720,324 :
2.579.345
Hyundai
416.021
594.322
417.887
1.949.237
1.473.091
845.037
JLR
100.085
108.695
282.897
488.721
(122.951)
(638.305)
Kia
872.839
599.217
191.267
1.801.569
1.914.374
1.433.331
Ma/da
416.539
400.994
166.218
876.248
107.606
(271.973)
Mercedes Ben/.
(485.322)
(252.093)
(335.343)
709.349
428.814
(73,580)
Mitsubishi
292.552
322,579 :
390.574
594.669
204.760
(21.939)
Nissan
3.548.808
1.253.133
563.855
2.906.821
3,223,372 :
2.335.901
Stellantis
(18.137.370)
(3.459.650)
479.264
(1.849.335)
(3.686.990)
(2.912.853)
Subaru
2.401.246
3.280.53 1
3.635.128
3.928.654
(28.653)
(1.641.906)
Tcsla
2.650.122
2.315.069
2.002.404
1.830.760
1.693.065
1.487.821
Toyota
3.495.212
(1.412.102)
(200.171)
1.466.741
(295.622)
(794.669)
Volvo
(130.543)
17.254
217.000
376.246
(177,277) :
(432.810)
VW
1.007.598
1.066.411
462.597
1.925.358
1.259.153
579,523
TOTAL
(2,657,934)
4,903,857
11,511,560
16,023,002
4,877,793
2,219,523
13-13
-------
13.1.1.2.2 Alternative 1
OEM-specific GHG emissions targets for Alternative 1 (in Mg) are shown in Table 13-24,
Table 13-25, and Table 13-26 for cars, trucks, and the combined fleet, respectively. Projected
achieved GHG emissions (in Mg) are given for cars, trucks, and the combined fleet in Table
13-27, Table 13-28, and Table 13-29. Overall credits or debits earned are provided for the
combined fleet on a manufacturer-specific basis, in Table 13-30.
Table 13-24: Projected GHG Targets (Mg), Alternative 1 - Cars
Manufacturer
2027
2028
2029
2030
203 1
2032
BMW
4.715.912
4.095.468
3.433.113
3.175.010
2.883.059
2,500,753
Ford
11.969.168
10.457.110
8.765.952
8.100.730
7.306.502
6,357,537
General Motors
18.412.440
16.095.390
13.535.584
12.473.309
11.217.636
9,763,073
Honda
18.980.636
16.502.990
13.788.858
12.708.885
11.527.714
9.992.768
Hyundai
14.017.005
12.077.115
10.220.210
9.378.705
8.464.206
7.350.186
JLR
131.675
114.328
96.882
88.590
80.982
69.927
Kia
7.201.545
6.232.130
5.278.3 11
4.854.242
4.430.374
3.822.839
Ma/da
2.907.636
2.544.664
2.150.211
1.997.187
1.788.227
1.558.117
Mercedes Ben/.
4.129.372
3.588.788
3.022.014
2.792.219
2.512.022
2.186.809
Mitsubishi
1.310.555
1.136.117
965.725
893.549
800.451
698.344
Nissan
16.464.577
14.260.660
12.068.886
11.136.075
10.054.007
8,737,265
Stellantis
8.666.061
7.548.045
6,350,572
5.829.098
5,255,007 ;
4.553.709
Subaru
2.629.183
2,303,538
1.957.087
1.810.204
1.648.154
1.434.304
Tcsla
2.053.603
1.770.482
1.510.277
1.370.436
1.256.202
1.080.586
Toyota
20.374.547
17,733,633
14.987.767
13.773.540
12.529.396
10.863.132
Volvo
540.625
469.215
395.668
361.053
325.384
282,552
VW
7.329.904
6.379.518
5.354.184
4.958.638
4.473.053
3.891.425
TOTAL
142,176,558
123,605,917
104,131,070
95,933,187
86,763,010
75,326,848
Table 13-25: Projected GHG Targets (Mg), Alternative
1 - Trucks
Manufacturer
2027
2028
2029
2030
203 1
2032
BMW
4.283.186
3.753.461
3.158.040
2.884.357
2.545.602
2.214.539
Ford
48.158.011
41.767.919
34.805.720
31.511.863
28.132.374
24.517.570
General Motors
58.156.445
49.916.061
41.961.928
37.920.978
34.018.125
29.590.834
Honda
23.151.909
20.190.094
17.108.580
15.595.575
13.954.226
12.092.369
Hyundai
364.669
313.578
265.345
238.835
215.661
186.009
JLR
3.394.080
2.937.324
2.476.632
2.241.456
2.013.434
1.713.434
Kia
6.269.075
5.234.989
4.420.811
4.028.778
3.639.954
3.132.079
Ma/da
4.052.085
3,552,072 :
3.003.494
2.772.081
2.470.689
2.149.478
Mercedes Ben/.
4.316.153
3,762,339 !
3.155.592
2.917.500
2.586.201
2,235,305
Mitsubishi
1.900.510
1.675.776
1.432.531
1.327.023
1.178.954
1.032.051
Nissan
15.814.318
13.680.241
11.522.088
10.528.917
9.369.463
8.125.355
Stellantis
59.455.718
51.086.139
43.097.966
39.035.115
34.994.456
30.316.915
Subaru
19.028.769
16.704.018
14.243.570
13.096.079
11.601.424
10.134.543
Tcsla
404.934
348.314
294.368
264.379
238.266
205,273
Toyota
39.359.239
34.035.131
28.652.921
26.008.151
23.293.634
20.213.770
Volvo
2,772,505
2.416.814
2.045.598
1.870.129
1.669.957
1.455.840
VW
13.377.891
11.811.549
9.999.3 16
9.192.266
8.111.056
7.081.382
TOTAL
304,574,272
263,460,949
221,883,111
201,649,528 180,228,213
156,565,782
13-14
-------
Table 13-26: Projected GHG Targets (Mg), Alternative 1 - Combined
Manufacturer
2027
2028
2029
2030
203 1
2032
BMW
8.999.098
7.848.929
6.591.153
6.059.368
5.428.661
4.715.291
Ford
60.127.179
52.225.029
43.571.672
39.612.592
35.438.877
30.875.106
General Motors
76.568.885
66.011.451
55.497.512
50.394.287
45.235.761
39.353.907
Honda
42.132.545
36.693.084
30.897.438
28.304.459
25.481.940
22.085.138
Hyundai
14.381.674
12.390.694
10.485.554
9.617.540
8.679.867
7.536.195
JLR
3,525,755
3.051.651
2.573.514
2.330.046
2.094.416
1.783.361
Kia
13.470.620
11.467.119
9.699.122
8.883.020
8,070,327 i
6.954.919
Ma/da
6.959.721
6.096.736
5.153.705
4.769.268
4.258.916
3.707.594
Mercedes Ben/.
8.445.525
7.351.126
6.177.606
5.709.719
5.098.223
4.422.114
Mitsubishi
3.211.065
2.811.893
2.398.256
2,220,572
1.979.405
1.730.395
Nissan
32.278.895
27.940.901
23.590.974
21.664.991
19.423.470
16.862.620
Stellantis
68.121.778
58.634.184
49.448.538
44.864.213
40.249.464
34.870.624
Subaru
21.657.952
19.007.556
16.200.657
14.906.282
13.249.578
11.568.847
Tcsla
2.458.537
2.118.797
1.804.645
1.634.815
1.494.468
1.285.859
Toyota
59.733.786
51.768.764
43.640.688
39.781.691
35,823,030 ;
31.076.902
Volvo
3.313.130
2.886.029
2.441.266
2.231.182
1.995.341
1.738.392
VW
20.707.794
18.191.067
15.353.501
14.150.904
12.584.109
10.972.807
TOTAL
446,750,829
387,066,866
326,014,181
297,582,715
266,991,223
231,892,631
Table 13-27:
Achieved GHG Levels (Mg), Alternative 1 - Cars
Manufacturer
2027
2028
2029
2030
203 1
2032
BMW
5.168.300
5.021.911
4.213.081
3.008.943
1.959.781
2.076.664
Ford
10.199.352
9.568.639
8.676.175
6.264.726
5.212.193
6.646.973
General Motors
16.000.502
12.313.027
10.299.662
6.895.993
6.133.611
4.371.704
Honda
18.255.203
18.113.465
13.242.767
11.712.532
11.487.093
11.016.306
Hyundai
14.586.338
11.771.265
9.461.300
7,173,378 :
7.436.478
7.371.139
JLR
145.601
71.876
41.087
23,832 :
24.740
27.442
Kia
7.170.213
6.353.416
4.900.143
3.946.259
4.190.206
3.865.367
Ma/da
3.020.258
2.511.850
1.891.281
1.408.923
1.338.506
1.153.691
Mercedes Ben/.
4.218.661
3.631.637
2.803.422
2.926.901
2,687,376 :
2.902.370
Mitsubishi
1.128.811
933.738
765.592
577,902 :
615.196
576,357
Nissan
15.556.381
12.407.646
10.149.390
8.131.565
6.822.287
5.586.299
Stellantis
7.799.560
5.422.028
5.012.615
5.254.356
3.276.001
3.534.147
Subaru
1.488.842
1.200.961
2.067.695
1.683.921
1.185.604
1.416.150
Tcsla
0
0 ;
0 !
0 ;
o ;
0
Toyota
20.078.276
16.654.648
13.517.173
8.701.353
5.853.174
4.768.886
Volvo
444.717
341.270
277,674 :
205.484
252.289
238.511
VW
4.377.868
6.156.898
6.3 15.660
5.631.838
4.398.316
4.828.647
TOTAL
130,028,568
112,876,738
93,996,496
73,839,628
63,168,254
60,733,098
13-15
-------
Table 13-28: Achieved GHG Levels (Mg), Alternative 1 - Trucks
Manufacturer
2027
2028
2029
2030
203 1
2032
BMW
4.921.893
3.844.455
2.797.483
2.843.867
4.150.263
4.049.708
Ford
45.622.660
40.666.205
36,336,738 :
30.197.856
29.205.091
19.903.575
General Motors
75,125,737 ;
53.579.583
46.121.186
39.881.370
39.736.426
32,875,225
Honda
23.913.800
20.137.969
14.755.813
11.634.127
12.546.655
12.104.986
Hyundai
416.319
353,653 i
287,533 ;
233,852 i
278.214
263.117
JLR
3.512.983
2.997.842
2.342.875
1.900.124
2.036.885
2.047.225
Kia
6.190.392
5.420.217
4.188.824
3.405.613
3,007,276 !
3.189.539
Ma/da
4.040.644
3.427.540
2.694.651
2.395.456
2.439.562
2.458.476
Mercedes Ben/.
4.832.578
4.132.026
3.320.636
2.007.296
2.014.283
2.292.319
Mitsubishi
1.992.670
1.513.234
1.237.934
1.037.106
1.046.692
990.505
Nissan
15,077,767
14.067.232
11.450.233
9.407.873
9.822.839
10.173.409
Stellantis
83.002.652
55.802.220
47.072.302
35.826.862
34.897.455
27.403.108
Subaru
19.208.578
16.239.724
12.171.445
10.917.840
12.524.702
11.263.190
Tcsla
0 :
0 i
0 i
0 i
0 i
0
Toyota
40.475.942
37.559.085
32.182.064
28.014.264
31.066.566
28.542.354
Volvo
3.232.699
2,553,715 ;
2.092.843
1.675.236
1.825.270
1.669.826
VW
16.652.730
12.728.632
8.627.118
7.228.190
8.735.162
8.163.602
TOTAL
348,616,595
275,314,233
227,843,395
188,749,021
195,464,505
167,502,242
Table 13-29: Achieved GHG Levels (Mg), Alternative 1 - Combined
Manufacturer
2027
2028
2029
2030
203 1
2032
BMW
10.090.193
8.866.366
7.010.564
5.852.810
6.110.044
6.126.371
Ford
55.822.012
50.234.844
45.012.914
36.462.582
34.417.284
26.550.548
General Motors
91.126.239
65.892.610
56.420.848
46.777.363
45.870.037
37.246.928
Honda
42.169.003
38.251.433
27.998.579
23.346.659
24.033.748
23.121.292
Hyundai
15.002.657
12.124.918
9.748.834
7,407,230 :
7.714.692
7.634.256
JLR
3.658.584
3.069.719
2.383.963
1.923.956
2.061.625
2.074.667
Kia
13.360.605
11.773.633
9.088.967
7,351,872 :
7.197.482
7.054.906
Ma/da
7.060.902
5.939.390
4.585.933
3.804.379
3.778.068
3.612.168
Mercedes Ben/.
9.051.239
7,763,663 ;
6.124.058
4.934.197
4.701.659
5.194.689
Mitsubishi
3.121.482
2.446.972
2.003.526
1.615.008
1.661.888
1.566.862
Nissan
30.634.147
26.474.879
21.599.623
17.539.438
16.645.126
15.759.708
Stellantis
90.802.211
61.224.248
52.084.916
41.081.218
38.173.456
30.937.254
Subaru
20.697.420
17.440.685
14.239.140
12.601.762
13.710.305
12.679.340
Tcsla
0 i
0 i
0 :
0 :
0
0
Toyota
60.554.218
54.213.733
45.699.238
36.715.617
36.919.740
33.311.240
Volvo
3.677.416
2.894.986
2,370,517 i
1.880.720
2,077,559 :
1.908.337
VW
21.030.598
18.885.531
14.942.778
12.860.028
13.133.478
12.992.248
TOTAL
478,645,162
388,190,970
321,839,892
262,588,649
258,632,759
228,235,340
13-16
-------
Table 13-30: GHG Credits/Debits Earned (Mg), Alternative 1 - Combined
Manufacturer
2027
2028
2029
2030
203 1
2032
BMW
(1.091.094)
(1.017.437)
(419.411)
206.558
(681.383)
(1.411.080)
Ford
4.305.167
1.990.185
(1.441.242)
3.150.011
1.021.593
4.324.558
General Motors
(14.557.354)
118.841
(923,335) :
3.616.924
(634.276)
2.106.979
Honda
(36.458)
(1.558.349)
2.898.858
4.957.801
1.448.192
(1.036.154)
Hyundai
(620.983)
265,776 :
736.721
2.210.310
965.175
(98.061)
JLR
(132.829)
(18.067)
189.551
406.090
32.791
(291.306)
Kia
110.015
(306.514)
610.155
1.531.149
872.846
(99.987)
Ma/da
(101.181)
157.346
567,772 :
964.889
480.848
95.427
Mercedes Ben/.
(605.714)
(412.537)
53.548
775,522 :
396.564
(772,575)
Mitsubishi
89.583
364.921
394.730
605.564
317.518
163.534
Nissan
1.644.747
1.466.022
1.991.351
4.125.554
2,778,345
1.102.912
Stellantis
(22.680.433)
(2.590.064)
(2.636.378)
3.782.995
2,076,007 ;
3,933,370
Subaru
960.532
1.566.871
1.961.517
2.304.521
(460.727)
(1.110.493)
Tcsla
2.458.537
2.118.797
1.804.645
1.634.815
1.494.468
1.285.859
Toyota
(820.432)
(2.444.969)
(2.058.550)
3.066.074
(1.096.710)
(2.234.338)
Volvo
(364.286)
(8.957)
70.749
350.462
(82.218)
(169.945)
VW
(322.804)
(694.464)
410.723
1.290.876
(549.369)
(2.019.441)
TOTAL
(31,894,333)
(1,124,105)
4,174,289
34,994,066
8,358,463
3,657,291
13.1.1.2.3 Alternative 2
OEM-specific GHG emissions targets for Alternative 2 (in Mg) are shown in Table 13-31,
Table 13-32, and Table 13-33 for cars, trucks, and the combined fleet, respectively. Projected
achieved GHG emissions (in Mg) are given for cars, trucks, and the combined fleet in Table
13-34, Table 13-35 and Table 13-36. Overall credits or debits earned are provided for the
combined fleet on a manufacturer-specific basis, in Table 13-37.
Table 13-31: Projected GHG Targets (Mg), Alternative 2 - Cars
Manufacturer
2027
2028
2029
2030
203 1
2032
BMW
5.469.214
4.861.680
4.226.802
3.956.713
3.669.385
3.294.201
Ford
13.871.182
12.402.818
10.787.026
10.135.481
9.328.046
8.374.326
General Motors
21.333.140
19.075.838
16.632.981
15.562.481
14.305.565
12.869.654
Honda
21.998.539
19.576.358
17.101.232
16.006.107
14.842.440
13.278.261
Hyundai
16.253.251
14.510.923
12.616.228
11.823.749
10.902.857
9.765.83 1
JLR
152.964
134.837
117.552
109.272
102.293
91.468
Kia
8.342.190
7.423.897
6.483.402
6.068.799
5.665.711
5.047.575
Ma/da
3.376.620
3.024.590
2.641.427
2.495.675
2.293.339
2.060.074
Mercedes Ben/.
4.823.808
4.280.028
3.717.676
3.498.692
3.225.946
2.890.813
Mitsubishi
1.524.614
1.360.804
1.189.443
1.123.928
1.028.481
925.687
Nissan
19.097.856
17.042.314
14.883.071
13.966.983
12.942.559
11.592.418
Stellantis
10.042.227
8.933.164
7.800.186
7.304.187
6.735.133
6.026.082
Subaru
3.048.551
2.726.466
2.392.097
2,255,571 :
2.099.327
1.887.960
Tcsla
2,379,572 :
2.097.547
1.845.519
1.705.034
1.597.886
1.419.537
Toyota
23.625.937
21.053.675
18.378.670
17.173.140
15.951.385
14.294.321
Volvo
627.487
557,253 :
484.184
449.93 1
413.594
371.058
VW
8.497.476
7.575.93 1
6.596.562
6.208.138
5.717.304
5.138.473
TOTAL
164,862,483
146,991,280
128,202,208
120,134,023
111,091,704
99,570,976
13-17
-------
Table 13-32: Projected GHG Targets (Mg), Alternative 2 - Trucks
Manufacturer
2027
2028
2029
2030
203 1
2032
BMW
4.874.383
4.323.076
3.739.493
3.491.806
3.171.797
2,833,303
Ford
54.871.678
48.399.568
41.629.787
38.260.278
34.872.121
3 1.243.376
General Motors
66.200.586
57.866.675
50.116.651
45.983.512
42.132.028
37.584.080
Honda
26.375.695
23.326.611
20.292.367
18.717.165
17.184.933
15.359.871
Hyundai
415.423
368.504
319.480
290.374
268.246
239.035
JLR
3.861.907
3.405.301
2.941.875
2.699.890
2.478.157
2.162.785
Kia
7.141.549
6.102.570
5.303.658
4.889.893
4.512.997
4.004.130
Ma/da
4.591.920
4.100.316
3,572,062 i
3.351.388
3.047.256
2,739,577
Mercedes Ben/.
4.827.059
4.338.822
3.753.949
3.493.219
3.177.163
2.844.767
Mitsubishi
2.160.085
1.952.476
1.714.869
1.621.689
1.469.880
1.323.791
Nissan
17.970.831
15.893.993
13.793.655
12.756.596
11.691.650
10.517.203
Stellantis
67.632.164
59.309.189
51.548.583
47.284.393
43.414.325
38.602.504
Subaru
21.669.854
19.356.597
16.934.798
15.851.167
14.316.098
12.880.045
Tcsla
461.393
404.799
351.674
321.016
295.114
261.607
Toyota
44.778.219
39.531.499
34.318.914
31.597.339
28.927.634
25.764.212
Volvo
3.142.189
2.783.951
2.418.273
2.244.197
2.054.404
1.847.310
VW
15,222,252 :
13.622.357
11.816.580
11.020.457
10.006.324
8.996.970
TOTAL
346,554,702
305,402,956
264,840,896
244,126,712
223,252,650
199,412,853
Table 13-33: Projected GHG Targets (Mg), Alternative 2 - Combined
Manufacturer
2027
2028
2029
2030
203 1
2032
BMW
10.343.596
9.184.756
7.966.295
7.448.519
6.841.182
6.127.504
Ford
68.742.859
60.802.385
52.416.813
48.395.760
44.200.167
39.617.702
General Motors
87,533,726 :
76.942.513
66.749.63 1
61.545.993
56.437.593
50.453.735
Honda
48.374.234
42.902.969
37.393.600
34,723,272
32,027,373 !
28.638.132
Hyundai
16.668.674
14.879.427
12.935.708
12.114.123
11.171.103
10.004.866
JLR
4.014.871
3.540.138
3.059.427
2.809.163
2.580.450
2.254.252
Kia
15.483.739
13.526.467
11.787.060
10.958.692
10.178.709
9.051.705
Ma/da
7.968.540
7.124.906
6.213.489
5.847.063
5.340.594
4.799.651
Mercedes Ben/.
9.650.867
8.618.850
7.471.625
6.991.911
6.403.110
5,735,580
Mitsubishi
3.684.700
3.313.280
2.904.312
2.745.617
2.498.361
2.249.478
Nissan
37.068.687
32,936,307 :
28.676.726
26,723,579 ;
24.634.209
22.109.621
Stellantis
77.674.391
68.242.352
59.348.769
54.588.579
50.149.458
44.628.587
Subaru
24.718.406
22.083.063
19.326.896
18.106.738
16.415.425
14.768.004
Tcsla
2.840.965
2.502.346
2.197.192
2.026.050
1.893.000
1.681.145
Toyota
68.404.155
60.585.174
52.697.584
48.770.479
44.879.019
40.058.533
Volvo
3.769.676
3.341.204
2.902.457
2.694.127
2.467.998
2.218.368
VW
23,719,727 1
21.198.288
18.413.142
17.228.595
15.723.628
14.135.443
TOTAL
511,417,185
452,394,235
393,043,104
364,260,735
334,344,354
298,983,829
13-18
-------
Table 13-34: Achieved GHG Levels (Mg), Alternative 2 - Cars
Manufacturer
2027
2028
2029
2030
203 1
2032
BMW
5.105.676
4.889.603
3.613.103
4.216.531
2.795.835
1.971.022
Ford
10.201.303
8.883.099
7,297,873 :
7.332.114
9.613.798
10.297.965
General Motors
19.024.845
14.318.141
10.790.055
12.801.056
12.975.006
9.205.109
Honda
18.560.569
18.347.050
15.116.493
13,577,307 :
12.179.055
11.217.735
Hyundai
15.123.166
14.757.149
11.782.992
11.547.657
9.846.254
8.850.667
JLR
150.540
182.172
195.713
182.025
177.289
149.788
Kia
7.066.638
7.3 15.345
5.684.232
5,527,206 !
4.423.598
3.660.527
Ma/da
3.252.491
2.847.751
2.575.269
2.569.765
2.058.780
1.937.338
Mercedes Ben/.
4.527.069
5.229.330
4.199.782
3.932.887
3.290.900
3.004.550
Mitsubishi
1.242.324
1.192.627
1.024.298
1.053.640
994.208
888.119
Nissan
16.029.938
14.716.991
11.535.299
11.160.615
9.789.372
10.837.908
Stellantis
9.144.814
7,708,507 :
6.351.058
6.159.876
6.214.591
6.083.049
Subaru
1.753.093
2.059.809
2.196.640
2.434.815
2.412.697
2.158.722
Tcsla
0 :
0
0 i
0 i
0 ;
0
Toyota
20.695.975
18.905.940
15.517.881
14.970.541
11.190.632
7.363.661
Volvo
475,527
397.413
365.880
429.802
492.265
452.220
VW
4.413.531
5.666.221
4.715.916
5,307,779 1
4.631.177
4.009.794
TOTAL
137,119,018
127,790,627
103,203,495
103,449,119
93,243,983
82,241,566
Table 13-35: Achieved GHG Levels (Mg), Alternative 2 - Trucks
Manufacturer
2027
2028
2029
2030
203 1
2032
BMW
4.930.657
4.718.005
4.063.130
3.398.022
3.641.464
3.803.259
Ford
45.621.962
48.792.361
39.809.794
34.058.157
31.871.792
26.978.835
General Motors
79.423.067
60.645.517
52.050.844
47.779.581
44.898.956
42.319.581
Honda
24.219.750
23.585.493
19.725.493
20.683.808
17.876.638
15.601.972
Hyundai
425.591
389.138
314.286
335,737 :
318.939
257,545
JLR
4.246.111
3.817.254
3.275.656
3,303,225 ;
3.274.016
2.958.815
Kia
6.087.125
6.479.380
5.012.786
4.815.374
3.984.315
3.842.545
Ma/da
4.721.984
4.593.587
4.161.409
3.959.667
3.974.306
3.325.501
Mercedes Ben/.
5.835.654
4.497.741
3.553.066
3.608.339
3.119.039
3.018.782
Mitsubishi
2.289.157
2.163.514
1.848.162
1.819.988
1.683.466
1.487.890
Nissan
16.337.378
16.385.552
14.744.799
14.102.141
12.194.390
8.720.402
Stellantis
87.942.987
61.310.675
50.404.593
46.931.345
45.756.650
41.454.125
Subaru
20.379.483
18.633.599
16.240.229
17.421.692
16,777,726 !
14.802.405
Tcsla
0 :
0 ;
0
0 :
0
0
Toyota
42.794.233
43.856.734
35.897.234
34.107.009
33.712.264
32.784.704
Volvo
3.687.748
3.326.090
2.885.865
2.780.807
2.625.992
2.301.162
VW
17.122.920
15.291.343
12.730.842
12.321.264
10.609.378
9.692.149
TOTAL
366,511,198
318,879,007
267,101,854
251,794,899
236,676,089
213,659,213
13-19
-------
Table 13-36: Achieved GHG Levels (Mg), Alternative 2
- Combined
Manufacturer
2027
2028
2029
2030
203 1
2032
BMW
10.036.333
9.607.608
7,676,233 :
7.614.553
6.437.299
5,774,282
Ford
55.823.265
57.675.460
47.107.667
41.390.271
41.485.590
37.276.799
General Motors
98.447.913
74.963.658
62.840.899
60.580.637
57.873.962
51.524.690
Honda
42.780.319
41.932.543
34.841.986
34.261.115
30.055.693
26.819.707
Hyundai
15.548.756
15.146.287
12.097.278
11.883.394
10.165.193
9.108.211
JLR
4.396.651
3.999.426
3.471.369
3.485.249
3.451.305
3.108.603
Kia
13.153.763
13.794.726
10.697.018
10.342.580
8.407.914
7,503,072
Ma/da
7.974.475
7.441.338
6.736.678
6.529.432
6.033.086
5.262.840
Mercedes Ben/.
10.362.723
9.727.071
7,752,847
7.541.225
6.409.940
6,023,332
Mitsubishi
3.531.480
3.356.142
2.872.460
2,873,627 1
2.677.674
2.376.010
Nissan
32.367.316
31.102.544
26.280.098
25,262,757
21.983.762
19.558.310
Stellantis
97.087.801
69.019.182
56.755.651
53.091.221
51.971.241
47,537,175
Subaru
22.132.576
20.693.408
18.436.869
19.856.507
19.190.423
16.961.127
Tcsla
0 i
o ;
0 ;
0 I
0 ;
0
Toyota
63.490.208
62.762.675
51.415.114
49.077.550
44.902.896
40.148.365
Volvo
4.163.275
3,723,503 :
3.251.744
3.210.609
3.118.257
2,753,383
VW
21.536.451
20.957.564
17.446.758
17.629.043
15.240.555
13.701.943
TOTAL
503,630,216
446,669,634
370,305,349
355,244,019
329,920,072
295,900,779
Table
13-37: GHG
Credits/Debits
Earned (Mg), Alternative 2 - Combined
Manufacturer
2027
2028
2029
2030
203 1
2032
BMW
307,263 ;
(422.852)
290.062
(166.034)
403.883
353,223
Ford
12.919.594
3.126.926
5.309.146
7.005.489
2,714,577
2.340.903
General Motors
(10.914.187)
1.978.855
3,908,733 i
965.356
(1.436.369)
(1.070.955)
Honda
5.593.915
970.426
2.551.614
462.157
1.971.680
1.818.425
Hyundai
1.119.918
(266.860)
838.43 1
230.729
1.005.910
896.655
JLR
(381.780)
(459.287)
(411.942)
(676.087)
(870.855)
(854.350)
Kia
2.329.976
(268.259)
1.090.042
616.112
1.770.795
1.548.634
Ma/da
(5,935)
(316.432)
(523.189)
(682.369)
(692.491)
(463.189)
Mercedes Ben/.
(711.856)
(1.108.221)
(281.222)
(549.314)
(6.830)
(287,752)
Mitsubishi
153.220
(42.862)
31.853
(128.011)
(179.313)
(126.532)
Nissan
4.701.371
1.833.763
2.396.628
1.460.822
2.650.446
2.551.311
Stellantis
(19.413.410)
(776.830)
2.593.118
1.497.358
(1.821.783)
(2.908.588)
Subaru
2.585.830
1.389.655
890.027
(1.749.769)
(2.774.999)
(2.193.123)
Tcsla
2.840.965
2.502.346
2.197.192
2.026.050
1.893.000
1.681.145
Toyota
4.913.947
(2.177.501)
1.282.470
(307.071)
(23,877) :
(89.832)
Volvo
(393.599)
(382.299)
(349.288)
(516.482)
(650.259)
(535.015)
VW
2,183,277
240.724
966.384
(400.448)
483.073
433.500
TOTAL
7,786,969
5,724,601
22,737,755
9,016,716
4,424,282
3,083,050
13-20
-------
13.1.1.2.4 Alternative 3
OEM-specific GHG emissions targets for Alternative 3 (in Mg) are shown in Table 13-38,
Table 13-39 and Table 13-40 for cars, trucks, and the combined fleet, respectively. Projected
achieved GHG emissions (in Mg) are given for cars, trucks, and the combined fleet in Table
13-41, Table 13-42 and Table 13-43. Overall credits or debits earned are provided for the
combined fleet on a manufacturer-specific basis, in Table 13-44.
Table 13-38: Projected GHG Targets (Mg), Alternative 3 - Cars
Manufacturer
2027
2028
2029
2030
203 1
2032
BMW
5.285.122
4.873.075
4.394.626
3.924.647
3.439.604
2.902.799
Ford
13.570.387
12.523.449
11.270.690
10.064.519
8,802,573 :
7.423.180
General Motors
20.610.721
19.043.434
17.221.033
15.373.039
13.398.569
11.325.060
Honda
21.256.643
19.559.218
17.732.590
15.788.422
13.883.775
11.701.172
Hyundai
15.697.796
14.481.561
13.107.855
11.678.319
10.207.497
8.607.379
JLR
147.763
134.746
122.022
107.920
95.872
80.571
Kia
8,057,872 :
7.394.385
6.714.875
5.981.034
5.295.728
4.441.962
Ma/da
3,262,202
3.022.696
2,743,717 :
2.466.23 1
2.148.623
1.816.485
Mercedes Ben/.
4.658.444
4.301.034
3.879.546
3.466.062
3.026.495
2.556.302
Mitsubishi
1.473.228
1.359.847
1.234.767
1.110.263
960.873
811.180
Nissan
18.465.597
17.021.980
15.437.350
13.786.526
12.108.685
10.210.221
Stellantis
9.702.444
8.936.272
8.082.953
7.219.455
6.323.642
5.3 14.438
Subaru
2.944.461
2.724.126
2.482.328
2.228.405
1.965.933
1.662.317
Tcsla
2.298.366
2.095.565
1.916.197
1.682.907
1.495.627
1.252.096
Toyota
22.838.022
21.040.319
19.070.186
16.963.826
14.925.402
12.579.420
Volvo
605.925
557.011
503.168
444.670
388.627
328.521
VW
8.206.320
7.576.471
6.843.940
6.135.908
5.354.149
4.528.598
TOTAL
159,465,556
146,998,697
133,077,194
118,708,400
104,074,783
87,756,322
Table 13-39: Projected GHG Targets (Mg), Alternative 3 - Trucks
Manufacturer
2027
2028
2029
2030
203 1
2032
BMW
5.174.534
4.688.733
4.133.007
3.617.479
3.072.390
2.555.018
Ford
57.112.878
51.351.024
45.633.939
39.786.295
33.542.401
27.797.449
General Motors
70,220,235 :
62.589.986
55,703,757 !
48.152.229
40.762.472
33.602.105
Honda
27.954.739
25.317.246
22.608.833
19.755.164
16.706.467
13.755.324
Hyundai
440.748
399.761
355,233 !
305.250
260.705
214.548
JLR
4.097.075
3,687,553 ;
3,275,655 :
2.836.819
2.403.802
1.940.308
Kia
7.575.890
6,627,070 :
5.915.498
5.149.420
4.371.237
3.584.246
Ma/da
4.870.021
4.439.460
3.962.927
3.517.865
2.965.832
2.460.570
Mercedes Ben/.
5.120.628
4.665.184
4.118.753
3.649.433
3.071.236
2.546.343
Mitsubishi
2.292.191
2.118.885
1.912.824
1.700.873
1.419.273
1.177.642
Nissan
19.024.809
17.265.184
15.400.716
13.456.643
11.369.667
9.355.901
Stellantis
71.760.813
63.925.152
57.028.741
49.518.358
42.079.205
34.570.064
Subaru
22.991.091
20.968.641
18.856.756
16.645.752
13.880.967
11.530.262
Tcsla
489.496
438.136
391.588
337.153
285.740
233.945
Toyota
47.429.987
42.759.734
38.071.393
33.194.434
28.133.968
23.176.680
Volvo
3.334.762
3.018.117
2.688.958
2.354.484
1.990.161
1.655.698
VW
16.156.068
14.772.853
13.149.076
11.592.204
9.705.859
8.066.856
TOTAL
366,425,327
329,377,366
293,514,438
255,837,178
216,247,848
178,410,301
13-21
-------
Table 13-40: Projected GHG Targets (Mg), Alternative 3 - Combined
Manufacturer
2027
2028
2029
2030
203 1
2032
BMW
10.459.656
9.561.808
8,527,633 :
7.542.126
6.511.994
5.457.818
Ford
70.683.265
63.874.473
56.904.628
49.850.814
42.344.974
35.220.629
General Motors
90.830.956
81.633.419
72.924.790
63.525.268
54.161.041
44.927.165
Honda
49.211.382
44.876.464
40.341.423
35.543.586
30.590.242
25.456.496
Hyundai
16.138.544
14.881.322
13.463.088
11.983.569
10.468.202
8.821.926
JLR
4.244.837
3.822.299
3,397,677 i
2.944.739
2.499.675
2.020.879
Kia
15.633.762
14.021.455
12.630.373
11.130.454
9.666.964
8.026.208
Ma/da
8.132.223
7.462.155
6.706.644
5.984.096
5.114.455
4,277,055
Mercedes Ben/.
9.779.072
8.966.218
7.998.299
7.115.495
6.097.73 1
5.102.645
Mitsubishi
3.765.418
3,478,732
3.147.590
2.811.136
2.380.146
1.988.821
Nissan
37.490.406
34.287.164
30.838.066
27.243.170
23.478.352
19.566.122
Stellantis
81.463.256
72.861.424
65.111.693
56.737.813
48.402.847
39.884.502
Subaru
25.935.552
23.692.768
21.339.084
18.874.157
15.846.900
13.192.580
Tcsla
2,787,862
2.533.701
2.307.784
2.020.059
1.781.366
1.486.041
Toyota
70.268.009
63.800.053
57.141.579
50.158.261
43.059.369
35.756.100
Volvo
3.940.688
3,575,127
3.192.125
2.799.154
2,378,788 !
1.984.219
VW
24.362.388
22.349.324
19.993.016
17.728.112
15.060.008
12.595.455
TOTAL
525,890,883
476,376,063
426,591,633
374,545,578
320,322,631
266,166,623
Table 13-41:
Achieved GHG Levels (Mg), Alternative 3 - Cars
Manufacturer
2027
2028
2029
2030
203 1
2032
BMW
5.122.608
3.723.696
3.316.706
2,427,785
2.388.634
2.193.402
Ford
11.428.596
13.374.378
11.505.693
8.870.808
6.405.551
8.646.457
General Motors
19.261.866
18.139.978
15.231.906
13.195.226
11.057.378
10.154.968
Honda
18.508.923
16.833.777
17.151.781
15.281.144
12.807.677
9.592.063
Hyundai
15.023.488
12.913.431
12.389.462
10.932.578
9.458.589
7.907.517
JLR
150.541
182.477
199.136
183.465
168.051
127,522
Kia
7,027,555
6.476.946
6.364.800
5,377,368 i
4.052.836
3.502.198
Ma/da
3.191.379
2.588.172
2.446.509
2.449.434
2.214.032
1.857.681
Mercedes Ben/.
4.582.955
3.399.151
2.993.679
3.063.475
2.798.163
2,228,552
Mitsubishi
1.238.034
1.166.389
1.087.688
1.012.968
824.452
623.961
Nissan
16.406.441
14.274.531
14.582.847
12.367.993
10.254.480
7.630.987
Stellantis
9.046.443
8.755.000
8.489.537
6,787,025 :
5.401.155
4.898.269
Subaru
1.752.861
1.961.205
2.278.345
2.427.423
2.218.472
1.803.250
Tcsla
0
0 ;
0
0 :
0 :
0
Toyota
22.054.465
19.265.683
17.676.074
16.404.593
13.417.064
10.052.010
Volvo
469.995
378.865
303.790
282.409
295.842
241.801
VW
4.736.115
4.114.700
5.234.029
5.092.103
4.332.437
3.450.659
TOTAL
140,351,610
127,846,774
121,566,816
106,420,718
88,267,387
75,064,295
13-22
-------
Table 13-42: Achieved GHG Levels (Mg), Alternative 3 - Trucks
Manufacturer
2027
2028
2029
2030
203 1
2032
BMW
4.889.445
4.631.937
4.844.028
4.839.472
3.781.585
3.042.950
Ford
51.044.089
53,623,367 :
45.817.073
38.420.852
32.209.490
24.496.734
General Motors
80.024.637
68.449.896
60.204.636
52.318.739
44.681.980
36.230.079
Honda
24.811.719
21.785.837
19.902.785
17.948.203
15.516.836
14.273.001
Hyundai
423,733
353.406
334.239
327.890
327,557 :
252.844
JLR
4.249.270
3.773.918
3.323.123
3.313.087
3.034.390
2.423.729
Kia
6.052.204
5,232,723 :
4.454.594
3.979.499
3,507,331 :
3.276.886
Ma/da
4.732.642
4.516.972
4.556.364
3.993.427
3.469.286
2.802.417
Mercedes Ben/.
5.732.844
5.439.695
5.368.625
4.407.064
3.461.554
2.985.645
Mitsubishi
2.267.508
2.098.074
1.940.013
1.873.009
1.706.556
1.462.008
Nissan
17.359.113
14.987.762
14.164.169
12.453.475
10.368.235
9.563.360
Stellantis
87.581.743
71.424.347
61.552.271
52,758,827 =
45.300.693
37.540.415
Subaru
20.261.349
17.948.795
16.583.953
17.303.578
16.071.044
13.100.879
Tcsla
0 i
0 :
0 :
0
0 i
0
Toyota
45.504.3 16
44.368.723
39.779.548
34.208.098
30.024.708
26.516.260
Volvo
3.659.143
3.334.636
3.081.382
2.956.352
2.642.680
2.151.899
VW
17.049.204
14.423.842
12.992.355
12.918.564
10.613.742
9.185.490
TOTAL
376,086,754
336,747,041
299,243,957
264,320,009
227,027,890
189,566,862
Table 13-43: Achieved GHG Levels (Mg), Alternative 3 - Combined
Manufacturer
2027
2028
2029
2030
203 1
2032
BMW
10.012.053
8.355.633
8.160.734
7,267,256 i
6.170.219
5,236,352
Ford
62.472.685
66.997.745
57,322,766 :
47.291.660
38.615.041
33.143.191
General Motors
99.286.504
86.589.874
75.436.543
65.513.965
55.739.359
46.385.047
Honda
43.320.642
38.619.613
37.054.565
33.229.347
28.324.513
23.865.063
Hyundai
15.447.221
13.266.837
12.723.701
11.260.468
9.786.146
8.160.361
JLR
4.399.811
3.956.395
3.522.259
3.496.552
3.202.441
2.551.251
Kia
13.079.760
11.709.669
10.819.395
9.356.867
7.560.167
6.779.084
Ma/da
7.924.021
7.105.145
7,002,872 i
6.442.861
5.683.317
4.660.098
Mercedes Ben/.
10.315.799
8.838.845
8.362.304
7.470.539
6.259.717
5.214.198
Mitsubishi
3.505.542
3.264.463
3.027.701
2.885.977
2.531.008
2.085.969
Nissan
33.765.554
29.262.292
28.747.015
24.821.468
20.622.716
17.194.347
Stellantis
96.628.186
80.179.348
70.041.808
59.545.853
50.701.847
42.438.684
Subaru
22.014.209
19.909.999
18.862.298
19.731.001
18.289.517
14.904.129
Tcsla
0
0 :
0
0 :
0 ;
0
Toyota
67.558.781
63.634.406
57.455.622
50.612.691
43.441.772
36.568.269
Volvo
4.129.138
3.713.501
3.385.171
3,238,760
2.938.522
2.393.700
VW
21.785.319
18.538.542
18.226.384
18.010.668
14.946.179
12.636.149
TOTAL
516,438,364
464,593,815
420,810,773
370,740,727
315,295,277
264,631,157
13-23
-------
Table 13-44: GHG Credits/Debits Earned (Mg), Alternative 3 - Combined
Manufacturer
2027
2028
2029
2030
203 1
2032
BMW
447.603
1.206.175
366.899
274.870
341.775
221.466
Ford
8.210.580
(3.123.272)
(418.137)
2.559.154
3.729.933
2.077.438
General Motors
(8.455.548)
(4.956.455)
(2.511.753)
(1.988.697)
(1.578.318)
(1.457.882)
Honda
5.890.740
6.256.851
3.286.858
2.314.239
2,265,730 :
1.591.433
Hyundai
691.324
1.614.485
739,387
723.102
682.056
661.566
JLR
(154.973)
(134.096)
(124.582)
(551.813)
(702,767) :
(530.371)
Kia
2.554.002
2.311.786
1.810.979
1,773,587 :
2.106.797
1.247.123
Ma/da
208.202
357.011
(296.229)
(458.764)
(568.862)
(383.043)
Mercedes Ben/.
(536.726)
127,373 ;
(364.005)
(355.044)
(161.987)
(111.553)
Mitsubishi
259,877 ;
214.269
119.889
(74.841)
(150.862)
(97.148)
Nissan
3.724.852
5.024.872
2.091.050
2.421.702
2.855.636
2,371,776
Stellantis
(15.164.930)
(7.317.924)
(4.930.115)
(2.808.040)
(2.299.000)
(2.554.181)
Subaru
3.921.342
3,782,768 :
2.476.786
(856.844)
(2.442.617)
(1.711.549)
Tcsla
2,787,862 !
2.533.701
2.307.784
2.020.059
1.781.366
1.486.041
Toyota
2.709.229
165.647
(314.043)
(454.430)
(382.403)
(812.169)
Volvo
(188.450)
(138.374)
(193.046)
(439.606)
(559.735)
(409.481)
VW
2.577.069
3.810.783
1.766.632
(282.556)
113.829
(40.695)
TOTAL
9,452,518
11,782,248
5,780,860
3,804,851
5,027,354
1,535,466
13.1.2 Projected Manufacturing Costs per Vehicle
EPA has performed an assessment of the estimated per-vehicle production costs for
manufacturers to meet the proposed MY 2027-2032 standards, relative to the No Action case.
The fleet average costs per vehicle have been grouped, as in past rules, by regulatory class.
EPA's OMEGA model also tracks vehicles by body style (sedans, crossovers/SUVs and
pickups). We have included summary tables in this format. The costs in this section represent
compliance costs to the industry and are not necessarily the same as the costs experienced by the
consumer when purchasing a new vehicle. For example, the costs presented here do not include
any state and Federal purchase incentives that are available to consumers. Also, the manufacturer
decisions for the pricing of individual vehicles may not align exactly with the production cost
impacts for that particular vehicle. EPA's OMEGA model assumes that manufacturers distribute
compliance costs through limited cross-subsidization of prices between vehicles in order to
maintain an appropriate mix of debit- and credit-generating vehicles that achieves compliance in
a cost-minimizing fashion.
13-24
-------
13.1.2.1 Proposed GHG Standards
Incremental costs per vehicle for the proposed standards (compared to the No Action case) are
summarized by regulatory class in Table 13-45 and by body style in Table 13-46.
Table 13-45: Projected Manufacturing Costs Per Vehicle, Proposed Standards
2027
2028
2029
2030
203 1
2032
Cars
$249
$102
$32
$100
$527
$844
T nicks
$891
$767
$653
$821
$1,100
$1,385
Total
$633
$497
$401
$526
$866
$1,164
Table 13-46: Projected Manufacturing Costs Per Vehicle, Proposed Standards (by Body
Style)
2027
2028
2029
2030
2031
2032
Sedans
$181
$79
$51
$194
$625
$1,015
Crossovcrs/SUVs
$657
$448
$332
$487
$804
$962
Pickups
$1,374
$1,478
$1,333
$1,324
$1,574
$2,266
Total
$633
$497
$401
$526
$866
$1,164
Incremental costs per vehicle for the proposed standards, compared to the No Action case, are
shown for each OEM in Table 13-47, Table 13-48, and Table 13-49 for cars, trucks, and the
combined fleet, respectively.193'194
193 Only manufacturers with annual sales exceeding 25,000 units are provided in these tables. However, the industry
sales-weighted average includes all vehicles and manufacturers (even those not shown).
194 Some manufacturers in these tables show manufacturing costs for the proposed standards are projected to be
lower than in the No Action case. This reflects the combined effects of cost learning with the higher accumulated
battery production under the proposed standards, and more ICE technology applied by some manufacturers in the
No Action case.
13-25
-------
Table 13-47: Projected Manufacturing Costs Per Vehicle, Proposed Standards - Cars
Manufacturer
2027
2028
2029
2030
203 1
2032
BMW
$5%
$367 ""
$202
$449
$609
$847
Ford
$972
$940
$649
$764
$772
$508
General Motors
$1,206
$1,131
$854
$196
$72
$363
Honda
-$1,146
-$1,314
-$1,232
-$694
$465
$962
Hyundai
$962
$461
$335
$462
$762 '
$983
JLR
$268
-$1,791
-$1,716
-$1,501
-$1,604
-$1,668
Kia
$895
$416
$317
$608
$948
$1,120
Ma/da
-$666
-$723
-$646
-$373
$455
$740
Mercedes Ben/.
$1,984
$1,388
$864
$919
$1,250
$880
Mitsubishi
$988
$524
' $437
$892
$977
$1,138
Nissan
$378
$211
$171
$308
$336
$785
Stellantis
-$501
-$536
-$700
-$278
-$392
$617
Subaru
$8 J
-$168
-$12
$168
$418
$633
Tcsla
-$50
-$316
-$444
-$528
-$580
-$630
Toyota
-$612
-$539
-$469
-$455
$924
$1,269
Volvo
$3,185
$2,475
$1,207
-$306
$567
$686
VW
$246
$578
$1,071
$713
$749
$1,325
TOTAL
$249
$102
$32
$100
$527
$844
Table 13-48: Projected Manufacturing Costs Per Vehicle, Proposed Standards - Trucks
Manufacturer
2027
2028
2029
2030
203 1
2032
BMW
$650
$798
$457
$468
$656
$895
Ford
$2,840
$2,539
$2,199
$1,953
$2,102
$2,220
General Motors
$265
$115
$196
$643
$948
$1,383
Honda
-$913
-$862
-$523
-$452
$1,191
$1,217
Hyundai
$1,892
$1,448
$1,095
$1,022
$450
$721
JLR
$2,421
$1,919
$1,481
$1,522
$1,573
$1,053
Kia
$1,835
$853
$638
$975
$1,136
$1,276
Ma/da
$1,600
$1,169
$861
$1,054
$1,086
$1,200
Mercedes Ben/.
" $754
$914
$365
$697
$1,018
$1,091
Mitsubishi
-$1,092
$588
$541
$782
$656
$767
Nissan
$1,055
$642
$542
$706
$1,394
$1,560
Stellantis
$293
$607
$537
$605
$815
$1,339
Subaru
$1,420
$1.117
$1,019
$1,328
$856
$1,009
Tcsla
-$62
-$392
-$550
-$654
-$719
-$780
Toyota
$1,130
' ] $875
$669
$816
$777
$1,203
Volvo
$527
$608
$455
$820
$837
$1,015
VW
$758
$225
-$288
$366
$787
$785
TOTAL
$891
$767
$653
$821
$1,100
$1,385
13-26
-------
Table 13-49: Projected Manufacturing Costs Per Vehicle, Proposed Standards - Combined
Manufacturer
2027
2028
2029
2030
203 1
2032
BMW
$618
$539
$303
$456
$628
$866
Ford
$2,321
$2,093
$1,764
$1,616
$1,722
$1,730
General Motors
$572
$448
$412
$494
$655
$1,040
Honda
-$1,035
-$1,098
-$895
-$580
$805
$1,081
Hyundai
$981
$480
$350
$472
$756
$978
JLR
$2,312
$1,729
$1,316
$1,365
$1,405
$909
Kia
$1,263
$587
$441
$749
$1,019
$1,180
Ma/da
$514
$260
$134
$362
$779 ;
$976
Mercedes Ben/.
$1,452
$1,183
$650
$824
$1,152
$969
Mitsubishi
-$143
$559
$493
$833 ;
$804
$938
Nissan
$657
$388
$322
$469
$761
$1,095
Stellantis
$156
$408
$320
$447
$598
$1,209
Subaru
$1,210
$925
$864
$1,152
$789
$951
Tcsla
-$52
-$325 1
-$457
-$542 "
-$596
-$647
Toyota
$407
$286
$192
$280
$839
$1,231
Volvo
$1,069
$990
$609
$587
$781
$947
VW
$541
$375
$292
$515
$771
$1,019
TOTAL
$633
$497
$401
$526
$866
$1,164
13.1.2.2 Alternative 1
Incremental costs per vehicle for Alternative 1 (compared to the No Action case) are
summarized by regulatory class in Table 13-50 and by body style in Table 13-51.
Table 13-50: Projected Manufacturing Costs Per Vehicle, Alternative 1
2027
2028
2029
2030
203 1
2032
Cars
$290
$382
$649
$752
$1,290
$1,461
T nicks
$922
$1,085
$1,436
$1,609
$1,751
$1,989
Total
$668
$804
$1,120
$1,262
$1,565
$1,775
Table 13-51: Projected Manufacturing Costs Per Vehicle, Alternative 1 (by Body Style)
2027
2028
2029
2030
2031
2032
Sedans
$204
$276
$480
$601
$1,143
$1,301
Crossovcrs/SUVs
$704
$740
$1,228
$1,422
$1,788
$2,056
Pickups
$1,382
$2,033
$1,871
$1,866
$1,469
$1,544
Total
$668
$804
$1,120
$1,262
$1,565
$1,775
Incremental costs per vehicle for Alternative 1, compared to the No Action case, are shown
for each OEM in Table 13-52, Table 13-53, and Table 13-54 for cars, trucks, and the combined
fleet, respectively.195
195 Only manufacturers with annual sales exceeding 25,000 units are provided in these tables. However, the industry
sales-weighted average includes all vehicles and manufacturers (even those not shown).
13-27
-------
Table 13-52: Projected Manufacturing Costs Per Vehicle, Alternative 1 - Cars
Manufacturer
2027
2028
2029
2030
203 1
2032
BMW
$592
J J
$753
$814
$731
$970
Ford
$960
$1,033
$1,437
$1,825
$1,661
$1,480
General Motors
$1,311
$1,195
$1,330
$743
$831
$1,123
Honda
-$1,152
-$1,331
-$347
-$232
$1,065
$1,217
Hyundai
$982
$1,466
$1,297
$1,587
$1,738
$1,843
JLR
$690
$400
' -$177
-$479
-$540
-$650
Kia
$836
$462
$621
$840
$946
$1,034
Ma/da
-$666
-$472
-$16
$148
$1,400
$1,561
Mercedes Ben/.
$1,385
$1,668
$1,743
$1,667
$2,200
$1,608
Mitsubishi
$1,018
$878
$720
$932
$951
$1,134
Nissan
$374
$1,115
$1,008
$1,077
$1,964
$2,186
Stellantis
-$477
-$306
-$639
$186
$760
$1,595
Subaru
$51
-$166
-$15
$312
$867
$919
Tcsla
-$50
-$314
-$470
-$544
-$619
-$665
Toyota
-$619
-$233
$229
$478
$1,452
$1,717
Volvo
$3,183
$2,438
$1,582
-$55
$784
$987
VW
$1,146
$141
$832
$680
$893
$904
TOTAL
$290
$382
$649
$752
$1,290
$1,461
Table 13-53: Projected Manufacturing Costs Per Vehicle, Alternative 1 - Trucks
Manufacturer
2027
2028
2029
2030
203 1
2032
BMW
$660
$748
$932
$971
$567
$636
Ford
$2,840
$2,803
$2,756
$2,519
$2,657
$2,747
General Motors
$334
$593
$932
$1,387
$1,476
$1,741
Honda
-$906
-$880
$1,784
$1,618
$2,745
$2,872
Hyundai
$1,902
$1,432
$1,132
$1,161
$741
$933
JLR
$2,795
$2,257
$2,413
$2,309
$2,426
$2,061
Kia
$2.03 1
$1,113
$1,184
$1,331
$1,560
$1,632
Ma/da
$1,600
$1,340
$1,461
$1,457
$2,088
$2,176
Mercedes Ben/.
$2,512
$2,329
$1,923
$2,073
$2,156
$1,618
Mitsubishi
-$1,035
$1,237
$1,061
$1,373
$1,449
$1,687
Nissan
$1,187
$1,380
$1,515
$1,582
$1,631
$1,758
Stellantis
$311
$936
$1,458
$1,749
$1,964
$2,379
Subaru
$1,422
$1,105
$995
$1,302
$912
$1,262
Tcsla
-$62
-$389
-$582
-$673
-$767
-$824
Toyota
$1,142
$1,143
$828
$1,018
$901
$1,215
Volvo
$532
$982
$724
$1,474
$1,574
$1,858
VW
$176
$579
$1,111
$1,446
$1,445
$1,707
TOTAL
$922
$1,085
$1,436
$1,609
$1,751
$1,989
13-28
-------
Table 13-54: Projected Manufacturing Costs Per Vehicle, Alternative 1 - Combined
Manufacturer
2027
2028
2029
2030
203 1
2032
BMW
$619
$498
$824
$876
$667
$840
Ford
$2,317
$2,309
$2,385
$2,322
$2,372
$2,384
General Motors
$652
$791
$1,063
$1,172
$1,260
$1,533
Honda
-$1,034
-$1.116
$665
$642
$1,853
$1,992
Hyundai
$1,000
$1,466
$1,294
$1,579
$1,719
$1,826
JLR
$2,688
$2,163
$2,280
$2,164
$2,270
$1,918
Kia
$1,304
$716
$840
$1,029
$1,180
$1,262
Ma/da
$514
$470
$749
$823
$1,753
$1,876
Mercedes Ben/.
$1,872
$1,953
$1,820
$1,840
$2,182
$1,612
Mitsubishi
-$98
$1,073
$905
$1,171
$1,220
$1,432
Nissan
$709
$1,224
$1,215
$1,281
$1,830
$2,015
Stellantis
$175
$719
$1,089
$1,470
$1,748
$2,237
Subaru
$1,218
$915
$843
$1,152
$905
$1,210
Tcsla
-$52
-$323
-$483
-$559
-$636
-$684
Toyota
$411
$570
^ $577 2
$790
$1,135
$1,429
Volvo
$1,074
$1,280
$900
$1,158
$1,410
$1,677
VW
$587
$393
$992
$1,117
$1,205
$1,359
TOTAL
$668
$804
$1,120
$1,262
$1,565
$1,775
13.1.2.3 Alternative 2
Incremental costs per vehicle for Alternative 2 (compared to the No Action case) are
summarized by regulatory class in Table 13-55 and by body style in Table 13-56.
Table 13-55: Projected Manufacturing Costs Per Vehicle, Alternative 2
2027
2028
2029
2030
203 1
2032
Cars
$129
-$77
-$6
-$95
$417
$745
T nicks
$686
$651
$599
$637
$927
$1,246
Total
$462
$355
$353
$337
$718
$1,041
Table 13-56: Projected Manufacturing Costs Per Vehicle, Alternative 2 (by Body Style
2027
2028
2029
2030
203 1
2032
Sedans
$106
-$74
$16
$8
$556
$827
Crossovers/SUVs
$391
$233
$263
$250
$599
$1,029
Pickups
$1,406
$1,656
$1,353
$1,328
$1.511
$1,503
Total
$462
$355
$353
$337
$718
$1,041
Incremental costs per vehicle for Alternative 2, compared to the No Action case, are shown
for each OEM in Table 13-57, Table 13-58, and Table 13-59 for cars, trucks, and the combined
fleet, respectively.
13-29
-------
Table 13-57: Projected Manufacturing Costs Per Vehicle, Alternative 2 - Cars
Manufacturer
2027
2028
2029
2030
203 1
2032
BMW
$498
$201
-$143
-$87 ^
$340
$758
Ford
$972
$1,093
$794
$949
$569
$343
General Motors
$903
$1,048
$892
$184
-$115
$474
Honda
-$1,230
-$1,367
-$1,148
-$916
$548
$808
Hyundai
$857
$51
$285
$58
$576
$896
JLR
$220 ]
-$3,168
-$2,820
-$2,743
-$2.861
-$2,784
Kia
$883
-$25
$226
$203
$680
$924
Ma/da
-$952
-$877
-$787
-$838
$614
$950
Mercedes Ben/.
' $536
-$349
-$140
-$109
$314
$552
Mitsubishi
$678
$245
$214
-$75
$106
$399
Nissan
$279
$123
$253
$149
$576 ]
$584
Stellantis
-$910
-$768
-$766
-$265
-$389
$558
Subaru
-$60
-$183
-$47
-$223
-$30 7
$352
Tcsla
-$50
-$257
-$361
-$451
-$488
-$534
Toyota
-$717
-$573 "
-$423
-$469
$837 '
$1,385
Volvo
$3.3 11
$2,790
$1,863
$75
$111
$317
VW
$1,131
$227
$573
$241
$499
$776
TOTAL
$129
-$77
-$6
-$95
$417
$745
Table 13-58: Projected Manufacturing Costs Per Vehicle
, Alternative 2
- Trucks
Manufacturer
2027
2028
2029
2030
203 1
2032
BMW
$476
$84
$332
$643
$772
$725
Ford
$2,840
$2,609
$2,268
$1,897
$2,043
$2,273
General Motors
$131
$286
$174
$693
$984
$1,178
Honda
-$956
-$1,290
-$727
-$866
$643
$999
Hyundai
$1,821
$1,272
$1,107
$794
$553 ""
$1,027
JLR
$2,015
$1,761
$1,510
$1,428
$1,459
$1,031
Kia
$2,082
$289
$545
$483
$956
$1,141
Ma/da
$918
$403
$281
$221
$280
$815
Mercedes Ben/.
$383
$991
$876
$703
$1.011
$1,190
Mitsubishi
-$1,679
-$152
-$15
$37
$284
$714
Nissan
$854
$461
$432 ;
$358
$700
$1,534
Stellantis
-$22
$608
$531
$566
$725
$1,148
Subaru
$1,218
$838
$731
$687
$391
$798
Tcsla
-$62
-$318
-$447
-$559
-$604
-$662
Toyota
$915
$615
$572
$590
$721
$929
Volvo
-$52
$92
$33
$196
$477
$900
VW
$23
$28
-$38 |
$136
$660
$1,015
TOTAL
$686
$651
$599
$637
$927
$1,246
13-30
-------
Table 13-59: Projected Manufacturing Costs Per Vehicle, Alternative 2 - Combined
Manufacturer
2027
2028
2029
2030
203 1
2032
BMW
$489
$155
$46
$202
$509
$745 '
Ford
$2,321
$2,186
$1,854
$1,628
$1,622
$1,720
General Motors
$383
$536
$411
$523 '
$617
$942
Honda
-$1,099
-$1,330
-$948
-$892
$593
$898
Hyundai
$876
$75
$300
$72
$575
$898
JLR
$1,924
$1,510
$1,287
$1,211
$1,231
$830
Kia
$1,352
$98
$349
$311
$786
$1,006
Ma/da
$22
-$212
-$234
-$292
$442
$881
Mercedes Ben/.
$470
$230
$296
$238
$609
$822
Mitsubishi
-$603
$29
$90
-$15
$202
$569
Nissan
$516
$262
$326
$233
$626
$965
Stellantis
-$175
$368
$303
$418
$525
$1,041
Subaru
$1,028
$686
$614
$549
$326
$729
Tcsla
-$52
-$264
-$371
-$464
-$501
-$549
Toyota
$237
$120
$155
$143
$770
$1,123
Volvo
$635
$644
$409
$171
$401
$779
VW
$493
$113
$223
$181
$590
$911
TOTAL
$462
$355
$353
$337
$718
$1,041
13.1.2.4 Alternative 3
Incremental costs per vehicle for Alternative 3 (compared to the No Action case) are
summarized by regulatory class in Table 13-60 and by body style in Table 13-61.
Table 13-60: Projected Manufacturing Costs Per Vehicle, Alternative 3
2027
2028
2029
2030
203 1
2032
Cars
$27
-$42
-$194
-$84
$539
$945
T nicks
$296
$238
$208
$481
$980
$1,471
Total
$189
$125
$45
$250
$800
$1,256
Table 13-61: Projected Manufacturing Costs Per Vehicle, Alternative 3 (by Body Style
2027
2028
2029
2030
203 1
2032
Sedans
-$21
-$28
-$208
-$65
$562
$1,030
Crossovcrs/SUVs
$251
$122
$58
$288
$786
$1,142
Pickups
$320
$421
$467
$698
$1.311
$2,148
Total
$189
$125
$45
$250
$800
$1,256
Incremental costs per vehicle for Alternative 3, compared to the No Action case, are shown
for each OEM in Table 13-62, Table 13-63, and Table 13-64 for cars, trucks, and the combined
fleet, respectively.
13-31
-------
Table 13-62: Projected Manufacturing Costs Per Vehicle, Alternative 3 - Cars
Manufacturer
2027
2028
2029
2030
203 1
2032
BMW
$385
$233
-$206
$288
$433
$716
Ford
$339
$138
$136
$728
$1,095
$858
General Motors
$866
$813
$768
$297
$233
$481
Honda
-$1,221
-$1,138
-$1,304
-$999
$559
$1,080
Hyundai
$881
$456
$165
$197
$693
$1,091
JLR
$220 *
-$3,151
-$2,827
-$2,703
-$2,698
-$2,439
Kia
$901
$372
$113
$373
$909
$1,060
Ma/da
-$882
-$644
-$701
-$696
$375
$806
Mercedes Ben/.
$500
$433
$180
$202
$538 ""
$987
Mitsubishi
$692
$287
$106
$97
$603
$1,198
Nissan
$197
'$215 ;
-$126
$54
$568
$1,153
Stellantis
-$879
-$1,015
-$1,052
-$338
-S245
$861
Subaru
-$60
-$147
-$58
-$133
$199
$723
Tcsla
-$50
-$206
-$285
-$323
-$383
-$470
Toyota
-$933
-$593
-$579 '
-$599
$647
$1,165
Volvo
$3,296
$2,746
$1,989
-$199
$954
$1,223
VW
$1,002
$730
$493
$347
$629
$972
TOTAL
$27
$42
$194
$84
$539
$945
Table 13-63: Projected Manufacturing Costs Per Vehicle
, Alternative 3
- Trucks
Manufacturer
2027
2028
2029
2030
203 1
2032
BMW
$527
$186
$25
$1
$788
$1,295
Ford
$483
$237
$386
$576
$1,193
$1,767
General Motors
$98
$86
$132
$721
$1,197
$1,701
Honda
-$1,056
-$1,034
-$700
-$482
$1,002
$1,232
Hyundai
$1,838
$1,515
$1,069
$900
$629
$1,191
JLR
$2,013
$1,800
$1,557
$1,514
$1,590
$1,920
Kia
$2,099
$1,028
$895
$1,001
$1,307
$1,520
Ma/da
$906
$479
$62
$268
$703
$1,274
Mercedes Ben/.
$469
$111
-$282
$112
$685
$1,075
Mitsubishi
-$1,632
-$4
2 -$122
$67
'$343
$829
Nissan
$597
$762
$565
$723
$1,128
$1,536
Stellantis
" $3
$93
$103
$380
$793
$1,368
Subaru
$1,239
$943
$739 ;
$767
$530
$1,094
Tcsla
-$62
-$255
-$353
-$401
-$474
-$582
Toyota
$647
$594
$396
$671
$1,033
$1,491
Volvo
-$15
$78
-$70
$163
$550
$1,120
VW
$42
$242
-$37
$127
$770
1 $1,227
TOTAL
$296
$238
$208
$481
$980
$1,471
13-32
-------
Table 13-64: Projected Manufacturing Costs Per Vehicle, Alternative 3 - Combined
Manufacturer
2027
2028
2029
2030
203 1
2032
BMW
$441
$214
-$115
$175
$572
$942
Ford
$443
$209
$316
$619
$1,165
$1,506
General Motors
$348
* $325 ""
$341
$580
$875
$1,291
Honda
-$1,142
-$1,088
-$1,017
-$754
$767
$1,151
Hyundai
$900
$476
$182
$210
$691
$1,093
JLR
$1,922
$1,548
$1,331
$1,295
$1,364
$1,689
Kia
$1,370
$628
$417
$615
$1,061
$1,235
Ma/da
$49
-$60
-$306
-$199
$543
$1,046
Mercedes Ben/.
$487
$294
-$18
$164
$601
$1,024
Mitsubishi
-$571
$129
-$17
$81
$462
$999
Nissan
$362
$440
$156
$325
$793
$1,307
Stellantis
-$150
-$100
-$100
$252
$607
$1,277
Subaru
$1,045
$781
$620
$63 1
$479
$1,037
Tcsla
-$52
-$211
-$293
-$332
-S3 93
-$482
Toyota
-$9
$99
-$12
$135
$869
$1,352
Volvo
$661
$624
$353
$88
$634
$1,141
VW
$449
$449
$189
$221
$709
$1.116
TOTAL
$189
$125
$45
$250
$800
$1,256
13.1.3 Technology Penetration Rates
Presented below are the projected technology penetration rates, by manufacturer, for cars and
trucks, for the No Action case, and the proposed standards and alternatives.
Tables are provided by manufacturer and regulatory class for BEV penetrations. Summary
tables for strong HEV penetrations and a few key ICE technology groupings (TURB12 and
Atkinson engines) are also provided.
13.1.3.1 No Action Case
Table 13-65 through Table 13-67 give BEV penetrations for the No Action case, by
manufacturer.
13-33
-------
Table 13-65: Projected BEV Penetrations, No Action - Cars
Manufacturer
2027
2028
2029
2030
203 1
2032
BMW
32%
37%
48%
48%
45%
44%
Ford
38%
31%
36%
37%
38%
37%
General Motors
30%
34%
39%
45%
43%
41%
Honda
T 37%
39% ~
44%
44%
43%
42%
Hyundai
29%
37% ;
42%
44%
43%
42%
JLR
39%
58%
78%
86%
86%
85%
Kia
33%
38%
43%
45%
44%
43%
Ma/da
37%
40%
44%
45%
43%
41%
Mercedes Ben/.
36%
40%
44%
45%
43%
42%
Mitsubishi
30%
34%
39%
42%
40%
39%
Nissan
31%
37%
42%
43%
42%
41%
Stellantis
30%
36%
39%
40%
39%
38%
Subaru
51%
45%
48%
47%
44%
42%
Tcsla
100%
100%
100%
100%
100%
100%
Toyota
36%
38%
42%
43%
41%
40%
Volvo
37%
48%
49%
48%
45%
45%
VW
39%
42%
37%
42%
41%
41%
TOTAL
35%
38%
42%
44%
43%
42%
Table 13-66: Projected BEV Penetrations,
No Action -
Trucks
Manufacturer
2027
2028
2029
2030
203 1
2032
BMW
26%
33%
32%
36%
38%
38%
Ford
26%
25%
31%
34%
35%
35%
General Motors
18%
26%
30%
34%
35%
36%
Honda
22%
29%
36%
39%
39%
39%
Hyundai
16%
27%
34%
38%
39%
39%
JLR
26%
31%
35%
36%
36%
35%
Kia
27%
33%
40%
42%
41%
41%
Ma/da
; 20%
28%
35%
39%
39%
39%
Mercedes Ben/.
20%
29%
36%
39%
39%
39%
Mitsubishi
23%
32%
38%
41%
40%
39%
Nissan
26%
31%
37%
39%
40%
39%
Stellantis
17%
26%
33%""
36%
36% i
36%
Subaru
21%
31%
37%
40%
40%
39%
Tcsla
100%
100%
100%
100%
100%
100%
Toyota
25%
28%
35%
38%
38%
38%
Volvo
23% 1
29%
36%
39% '
39%
37%
VW
25%
32%
44%
43%
43%
42%
TOTAL
22%
28%
34%
37%
37%
37%
13-34
-------
Table 13-67: Projected BEV Penetrations, No Action - Combined
Manufacturer
2027
2028
2029
2030
203 1
2032
BMW
30%
35%
42%
43%
42%
42%
Ford
29%
26%
32%
35%
36%
36%
General Motors
22%
29%
33%
38%
38%
37%
Honda
30%
35%
40%
42%
41%
40%
Hyundai
29%
36%
42%
43%
43%
42%
JLR
26%
32%
37%
38%
38%
38%
Kia
30%
36%
42%
43%
43%
42%
Ma/da
28%
34%
40%
42%
41%
40%
Mercedes Ben/.
29%
"3^0/
JJ/O
41%
42%
42%
41%
Mitsubishi
26%
33%
39% :
41%
40%
39%
Nissan
29%
34%
40%
42%
41%
41%
Stellantis
20%
28%
34%
37%
37%
37%
Subaru
25%
33%
39%
41%
40%
39%
Tcsla
100%
100%
100%
100%
100%
100%
Toyota
29%
32%
38%
40%
39%
39%
Volvo
26%
33%
39%
41%
40%
39%
VW
31%
36%
41%
43%
42%
42%
TOTAL
27%
32%
37%
40%
40%
39%
The tables below provide summary technology penetrations for the proposed standards for
strong hybrids, TURB12 and ATK. While strong hybrids may include turbocharged engines or
Atkinson engines, The TURB12 and ATK penetrations shown are only for non-hybrid versions
of those vehicles.
Table 13-68: Projected Strong HEV Penetrations, No Action
2027
2028
2029
2030
203 1
2032
(a is
6%
6%
5%
4%
0%
0%
T nicks
2%
1%
1%
0%
0%
0%
Total
4%
3%
3%
2%
0%
0%
Table 13-69: Projected TURB12 Penetrations
, No Action
2027
2028
2029
2030
203 1
2032
(a is
21%
22%
23%
25%
33%
34%
T nicks
2%
1%
2%
6%
8%
8%
Total
10%
9%
11%
14%
18%
19%
Table 13-70: Projected ATK
Penetrations, No Action
2027
2028
2029
2030
203 1
2032
(a is
37%
34%
28%
24%
24%
24%
T nicks
63%
70%
63%
57%
55%
55%
Total
53%
55%
49%
44%
42%
42%
13-35
-------
13.1.3.2 Proposal
Table 13-71 through Table 13-73 give BEV penetrations for the proposed standards, by
manufacturer.
Table 13-71: Projected BEV Penetrations, Proposed Standards - Cars
Manufacturer
2027
2028
2029
2030
2031
2032
BMW
39%
41%
56%
73%
81%
82%
Ford
49%
51%
61%
57%
64%
58%
General Motors
46%
61%
63%
64%
68%
71%
Honda
40%
41%
50%
66%
63%
73%
Hyundai
38%
47%
55%
65%
69%
71%
JLR
46%
52%
40%
58%
61%
65%
Kia
39%
44%
52%
63%
70%
74%
Ma/da
37% ;
45%
55%
66%
65%
68%
Mercedes Ben/.
44%
51%
66%
73%
69%
74%
Mitsubishi
37%
47%
57%
69%
71%
14%
Nissan
39%
46%
56%
65%
62%
68%
Stellantis
46%
58%
65%
62%
65%
70%
Subaru
57%
68%
56%
65%
66%
67%
Tcsla
100%
100%
100%
100%
100%
100%
Toyota
40%
46%
55%
62%
75%
80%
Volvo
43%
56%
72%
80%
75%
75%
VW
43%
63%
81%
77%
75%
86%
TOTAL
43%
51%
59%
65%
69%
73%
Table
13-72: Projected BEV Penetrations
, Proposed
Standards - Trucks
Manufacturer
2027
2028
2029
2030
203 1
2032
BMW
34%
50%
47%
49%
51%
58%
Ford
35%
36%
48%
49%
56%
68%
General Motors
24%
36%
51%
53%
57%
62%
Honda
37%
48%
55%
58%
69%
67%
Hyundai
27%
39%
48%
53%
53%
58%
JLR
39%
47%
59%
68%
62%
61%
Kia
32%
47%
54%
64%
66%
66%
Ma/da
36%
47%
57%
65%
63%
63%
Mercedes Ben/.
29%
39%
39%
54%
60%
61%
Mitsubishi
34%
47%
57%
62%
58%
57%
Nissan
38%
41%
50%
57%
71%
71%
Stellantis
25%
40%
52%
55%
58%
65%
Subaru
30%
43%
58%
67%
62%
63%
Tcsla
100%
100%
100%
100%
100%
100%
Toyota
41%
43%
52%
58%
56%
59%
Volvo
38%
43%
55%
63%
59%
59%
VW
34%
37%
39%
56%
61%
58%
TOTAL
32%
41%
51%
56%
60%
64%
13-36
-------
Table 13-73: Projected BEV Penetrations, Proposed Standards - Combined
Manufacturer
2027
2028
2029
2030
203 1
2032
BMW
37%
44%
52%
63%
70%
72%
Ford
39%
40%
52%
51%
58%
65%
General Motors
31%
44%
55%
57%
60%
65%
Honda
38%
44%
53%
62%
66%
70%
Hyundai
38%
47%
55%
65%
69%
70%
JLR
39%
47%
58%
67%
62%
61%
Kia
37%
45%
52%
63%
68%
71%
Ma/da
36%
46%
56%
65%
64%
66%
Mercedes Ben/.
37%
46%
55%
64%
65%
68%
Mitsubishi
36%
47%
57%
65%
64%
65%
Nissan
39%
44%
54%
62%
66%
69%
Stellantis
29%
43%
55%
56%
60%
66%
Subaru
34%
47%
58%
66%
63%
63%
Tcsla
100%
100%
100%
100%
100%
100%
Toyota
40%
44%
54%
60%
64%
68%
Volvo
39%
46%
58%
66%
62%
62%
VW
38%
48%
57%
65%
67%
70%
TOTAL
36%
45%
55%
60%
63%
67%
The tables below provide summary technology penetrations for the proposed standards for
strong hybrids, TURB12 and ATK. While strong hybrids may include turbocharged engines or
Atkinson engines, The TURB12 and ATK penetrations shown are only for non-hybrid versions
of those vehicles.
Table 13-74: Projected Strong HEV Penetrations, Proposed Standards
2027 2028
2029
2030
203 1
2032
Cars
3% 2%
2%
"2%
1%
0%
T nicks
4% 2%
2%
1%
1%
1%
Total
3% 2%
2%
1%
1%
0%
Table 13-75: Projected TURB12 Penetrations, Proposed Standards
2027 2028
2029
2030
203 1
2032
Cars
18% 16%
17%
16%
19%
16%
T nicks
2% 1%
1%
2%
4%
4%
Total
8% 7%
7%
8%
10%
9%
Table 13-76: Projected ATK Penetrations,
Proposed Standards
2027 2028
2029
2030
203 1
2032
Cars
36% 30%
22%
17%
12%
11%
T nicks
51% 56%
45%
41%
36%
32%
Total
45% 46%
36%
31%
26%
23%
13-37
-------
13.1.3.3 Alternative 1
Table 13-77 through Table 13-79 give BEV penetrations for Alternative 1, by manufacturer.
Table 13-77: Projected BEV Penetrations, Alternative 1 - Cars
Manufacturer
2027
2028
2029
2030
203 1
2032
BMW
39%
40%
49%
64%
77%
76%
Ford
49%
53%
56%
67%
73%
68%
General Motors
44%
58%
62%
76%
79%
86%
Honda
40%
41%
54%
60%
63%
65%
Hyundai
38%
47%
58%
66%
65%
66%
JLR
46%
65%
80%
89%
89%
88%
Kia
38%
45%
57%
65%
62%
64%
Ma/da
37%
47%
58%
70%
70%
75%
Mercedes Ben/.
42%
49%
59%
58%
59%
60%
Mitsubishi
38%
47%
57%
69%
66%
69%
Nissan
39%
47%
57%
65%
69%
75%
Stellantis
46%
63% !
66%
64%
75%
78%
Subaru
60%
68%
56%
65%
75%
71%
Tcsla
100%
100%
100%
100%
100%
100%
Toyota
40%
50%
57%
72%
83%
86%
Volvo
43%
57%
66%
78%
77%
78%
VW
62%
48%
46%
54%
65%
61%
TOTAL
44%
50%
58%
67%
72%
74%
Table 13-78: Projected BEV Penetrations, Alternative 1 - Trucks
Manufacturer
2027
2028
2029
2030
203 1
2032
BMW
34%
49%
63%
62%
47%
48%
Ford
35%
42%
48%
58%
59%
72%
General Motors
26%
41%
49%
57%
58%
66%
Honda
37%
47%
56%
67%
65%
66%
Hyundai
27%
38%
50%
59%
63%
64%
JLR
39%
48%
58%
66%
64%
65%
Kia
36%
44%
55%
64%
68%
66%
Ma/da
36%
46%
57%
62%
60%
60%
Mercedes Ben/.
32%
42%
53%
71%
71%
69%
Mitsubishi
35%
47%
58%
65%
64%
66%
Nissan
40%
44%
54%
62%
61%
59%
Stellantis
26%
44%
50%
61%
62%
71%
Subaru
30%
42%
58%
66%
62%
66%
Tcsla
100%
100%
100%
100%
100%
100%
Toyota
41%
43%
52%
57%
54%
58%
Volvo
38%
46%
56%
63%
61%
64%
VW
25%
44%
61%
70%
64%
66%
TOTAL
32%
44%
52%
61%
60%
66%
13-38
-------
Table 13-79: Projected BEV Penetrations, Alternative 1 - Combined
Manufacturer
2027
2028
2029
2030
203 1
2032
BMW
37%
43%
54%
64%
65%
65%
Ford
39%
45%
50%
60%
63%
71%
General Motors
32%
47%
53%
63%
65%
73%
Honda
38%
44%
55%
63%
64%
65%
Hyundai
38%
47%
58%
66%
65%
66%
JLR
39%
49%
59%
68%
65%
66%
Kia
37%
45%
56%
65%
64%
65%
Ma/da
36%
47%
58%
66%
65%
67%
Mercedes Ben/.
38%
46%
56%
64%
64%
64%
Mitsubishi
36%
47%
57%
67%
65%
67%
Nissan
40%
46%
56%
64%
66%
68%
Stellantis
29%
47%
53%
62%
64%
72%
Subaru
35%
46%
57%
66%
64%
67%
Tcsla
100%
100%
100%
100%
100%
100%
Toyota
40%
46%
54%
63%
66%
70%
Volvo
39%
48%
58%
66%
64%
67%
VW
41%
46%
55%
63%
64%
64%
TOTAL
37%
46%
54%
63%
65%
69%
The tables below provide summary technology penetrations for Alternative 1 for strong
hybrids, TURB12 and ATK. While strong hybrids may include turbocharged engines or
Atkinson engines, The TURB12 and ATK penetrations shown are only for non-hybrid versions
of those vehicles.
Table 13-80: Projected Strong HEV Penetrations
, Alternative 1
2027 2028
2029
2030
203 1
2032
Cars
3% 6%
11%
9%
9%
7%
T nicks
4% 3%
7%
6%
6%
5%
Total
3% 4%
9%
7%
7%
6%
Table 13-81: Projected TURB12 Penetrations, Alternative 1
2027 2028
2029
2030
203 1
2032
Cars
17% 13%
9%
7%
8%
7%
T nicks
2% 1%
1%
1%
2%
2%
Total
8% 6%
4%
4%
4%
4%
Table 13-82: Projected ATK Penetrations, Alternative 1
2027 2028
2029
2030
203 1
2032
Cars
36% 29%
23%
16%
11%
11%
T nicks
51% 53%
39%
32%
31%
27%
Total
45% 43%
33%
26%
23%
20%
13-39
-------
13.1.3.4 Alternative 2
Table 13-83 through Table 13-85 give BEV penetrations for Alternative 2, by manufacturer.
Table 13-83: Projected BEV Penetrations, Alternative 2 - Cars
Manufacturer
2027
2028
2029
2030
203 1
2032
BMW
40%
42%
60%
54%
71%
80%
Ford
49%
56%
67% |
67%
58%
57%
General Motors
35%
52%
64%
61%
63%
74%
Honda
39%
41%
53%
59%
65%
68%
Hyundai
36%
40%
j 53%
55%
62%
65%
JLR
46%
35%
32%
36%
39%
49%
Kia
39%
41%
55%
57% 1
66%
72%
Ma/da
33%
43%
52%
53%
63%
64%
Mercedes Ben/.
43%
36%
49%
53%
61%
64%
Mitsubishi
33% |
41%
50%
51%
53%
59%
Nissan
38%
44%
58%
60%
67%
62%
Stellantis
37%
48%
58%
60%
62%
66%
Subaru
53%
46%
53% :
53%
54%
59%
Tcsla
100%
100%
100%
100%
100%
100%
Toyota
38%
45%
55%
58%
70%
81%
Volvo
39%
50%
56%
54%
54%
58%
VW
62%
52%
63%
59%
65%
69%
TOTAL
41%
46%
58%
59%
65%
69%
Table
13-84: Projected
BEV Penetrations, Alternative 2
- Trucks
Manufacturer
2027
2028
2029
2030
203 1
2032
BMW
34%
38%
47%
56%
54%
52%
Ford
35%
32%
45%
54%
58%
65%
General Motors
21%
35%
47%
53%
56%
58%
Honda
36%
38%
51%
49%
56%
62%
Hyundai
26%
110/
JJ/O
46%
42%
52%
60%
JLR
27%
35%
45%
47%
47%
54%
Kia
38%
40%
54%
56%
63%
64%
Ma/da
25%
32%
43%
46%
46%
55%
Mercedes Ben/.
25%
42%
54%
54%
60%
62%
Mitsubishi
26%
33%
44%
47%
50%
56%
Nissan
35%
37%
50%
52%
58%
70%
Stellantis
21%
40%
51%
54%
56%
61%
Subaru
26%
34%
44%
46%
49%
55%
Tcsla
100%
100%
100%
100%
100%
100%
Toyota
37%
39%
50%
52%
53%
55%
Volvo
30%
32%
41%
44%
47%
53%
VW
23%
33%
46%
51%
58%
62%
TOTAL
29%
36%
48%
52%
55%
60%
13-40
-------
Table 13-85: Projected BEV Penetrations, Alternative 2 - Combined
Manufacturer
2027
2028
2029
2030
2031
2032
BMW
38%
40%
55%
55%
64%
69%
Ford
39%
38%
51%
58%
58%
63%
General Motors
25%
41%
53%
56%
58%
63%
Honda
38%
39%
52%
54%
61%
65%
Hyundai
36% ;
40%
52%
54%
62%
65%
JLR
28%
35%
44%
46%
47%
53%
Kia
38%
41%
55%
56%
65%
69%
Ma/da
29%
37%
48%
50%
54%
59%
Mercedes Ben/.
35%
39%
51%
53%
60%
63%
Mitsubishi
29%
37%
46%
49%
52%
57%
Nissan
37%
41%
55%
57%
63%
66%
Stcllantis
24%
41%
52%
55%
57%
62%
Subaru
30%
36% i
45%
47%
50%
56%
Tcsla
100%
100%
100%
100%
100%
100%
Toyota
38%
41%
52%
55%
60%
66%
Volvo
32%
36%
45%
46%
48%
54%
VW
40%
41%
53%
54%
61%
65%
TOTAL
33%
40%
52%
55%
59%
64%
The tables below provide summary technology penetrations for Alternative 2 for strong
hybrids, TURB12 and ATK. While strong hybrids may include turbocharged engines or
Atkinson engines, The TURB12 and ATK penetrations shown are only for non-hybrid versions
of those vehicles.
Table 13-86: Projected Strong HEV Penetrations, Alternative 2
2027 2028 2029 2030 2031 2032
Cars 3% 2% 1% 1% 1% 0%
Trucks 4% 3% 2% 1% 1% 1%
Total 3% 2% 2% 1% 1% 0%
Table 13-87: Projected TURB12 Penetrations, Alternative 2
2027 2028 2029 2030 2031 2032
Cars 18% 18% 18% 19% 21% 20%
Trucks 2% 1% 1% 4% 5% 4%
Total 8% 8% 8% 10% 12% 11%
Table 13-88: Projected ATK Penetrations, Alternative 2
2027
2028
2029
2030
203 1
2032
Cars
38%
33%
22%
19%
13%
10%
T nicks
55%
60%
48%
43%
38%
35%
Total
48%
49%
38%
33%
28%
25%
13-41
-------
13.1.3.5 Alternative 3
Table 13-89 through Table 13-91 give BEV penetrations for Alternative 3, by manufacturer.
Table 13-89: Projected BEV Penetrations, Alternative 3 - Cars
Manufacturer
2027
2028
2029
2030
2031
2032
BMW
40%
58%
63%
74%
75%
77%
Ford
46%
38%
48%
61%
72%
63%
General Motors
34%
39%
48%
57%
66%
69%
Honda
39%
45%
46%
53%
63%
72%
Hyundai
36%
48%
50%
57%
64%
69%
JLR
46%
35%
30%
36%
42%
56%
Kia
39%
46%
47%
56%
67%
71%
Ma/da
34%
48%
56%
55%
61%
67%
Mercedes Ben/.
43%
59%
64%
64%
67%
74%
Mitsubishi
33% |
41%
46%
54%
63%
73%
Nissan
36%
45%
47%
55%
65%
14%
Stellantis
38%
41%
44%
56%
67%
72%
Subaru
53%
49%
51%
53%
58%
66%
Tcsla
100%
100%
100%
100%
100%
100%
Toyota
34%
44%
49%
56%
65%
14%
Volvo
40%
53%
63%
72%
71%
77%
VW
59%
65%
58%
61%
67%
14%
TOTAL
40%
46%
50%
58%
66%
72%
Table
13-90: Projected
BEV Penetrations, Alternative 3
- Trucks
Manufacturer
2027
2028
2029
2030
203 1
2032
BMW
35%
39%
36%
37%
54%
63%
Ford
33%
30%
42%
52%
61%
70%
General Motors
20%
31%
41%
50%
57%
64%
Honda
34%
43%
51%
57%
63%
66%
Hyundai
26%
39%
43%
43%
57%
66%
JLR
27%
36%
44%
46%
51%
60%
Kia
38%
49%
57%
61%
66%
68%
Ma/da
25%
33%
38%
46%
53%
62%
Mercedes Ben/.
25%
32%
33% i
45%
56%
63%
Mitsubishi
26%
38%
44%
46%
50%
57%
Nissan
32%
42%
51%
57%
65%
66%
Stellantis
21%
31%
40%
47%
55%
64%
Subaru
27%
36%
42%
46%
52%
61%
Tcsla
100%
100%
100%
100%
100%
100%
Toyota
33%
38%
45%
52%
59%
64%
Volvo
30%
33%
39%
41%
47%
57%
VW
24%
37%
45%
52%
60%
66%
TOTAL
27%
35%
43%
51%
58%
65%
13-42
-------
Table 13-91: Projected BEV Penetrations, Alternative 3 - Combined
Manufacturer
2027
2028
2029
2030
203 1
2032
BMW
38%
50%
52%
59%
67%
72%
Ford
37%
32%
43%
54%
64%
68%
General Motors
25%
34%
44%
52%
60%
65%
Honda
37%
44%
48%
54%
63%
69%
Hyundai
36% ;
47%
50%
57%
64%
69%
JLR
28%
35%
43%
46%
51%
60%
Kia
39%
47%
51%
58%
67%
70%
Ma/da
29%
40%
46%
51%
57%
65%
Mercedes Ben/.
35%
47%
51%
56%
62%
69%
Mitsubishi
29%
39%
45%
49%
56%
64%
Nissan
34%
44%
49%
56%
65%
71%
Stellantis
24%
33%
41%
49%
58%
65%
Subaru
31%
38%
44%
47%
53%
62%
Tcsla
100%
100%
100%
100%
100%
100%
Toyota
34%
40%
47%
54%
62%
68%
Volvo
32%
37%
44%
48%
52% ;
61%
VW
39%
49%
51%
56%
63%
69%
TOTAL
32%
39%
46%
54%
62%
68%
The tables below provide summary technology penetrations for Alternative 3 for strong
hybrids, TURB12 and ATK. While strong hybrids may include turbocharged engines or
Atkinson engines, The TURB12 and ATK penetrations shown are only for non-hybrid versions
of those vehicles.
Table 13-92: Projected Strong HEV Penetrations, Alternative 3
2027 2028 2029 2030 2031 2032
Cars 2% 2% 1% 1% 0% 0%
Trucks 2% 1% 1% 0% 0% 0%
Total 2% 2% 1% 0% 0% 0%
Table 13-93: Projected TURB12 Penetrations, Alternative 3
2027 2028 2029 2030 2031 2032
Cars 19% 17% 19% 19% 20% 16%
T nicks 2% 1% 2% 4% 4% 4%
Total 9% 8% 9% 10% 11% 9%
Table 13-94: Projected ATK Penetrations, Alternative 3
2027
2028
2029
2030
2031
2032
Cars
39%
34%
27%
22%
13%
12%
T nicks
57%
63%
54%
45%
37%
31%
Total
49%
51%
43%
35%
28%
23%
13-43
-------
13.1.4 Light-Duty Vehicle Sensitivities
Light-duty sensitivities are described in IV.E of the preamble. This section provides the
analytical results for the proposed standards and the three alternative sets of standards across the
various sensitivities.
13.1.4.1 State-level ZEV Policies (ACC II)
Table 13-95: Projected targets with ACC II, for No Action case, proposed and alternatives
(CO2 grams/mile) - cars and trucks combined
Table 13-96
2027
2028
2029
2030
2031
2032
No Action
164
164
165
165
164
164
Proposed
151
131
111
102
93
82
Alternative 1
141
121
102
92
83
72
Alternative 2
161
141
121
112
103
92
Alternative 3
166
149
132
115
99
82
Table 13-97: Projected achieved levels with ACC II, for No Action case, proposed and
alternatives (CO2 grams/mile) - cars and trucks combined
2027
2028
2029
2030
2031
2032
No Action
146
123
104
100
103
99
Proposed
149
129
107
96
90
81
Alternative 1
145
122
99
83
73
66
Alternative 2
153
132
119
110
100
90
Alternative 3
154
133
122
113
96
81
Table 13-98: BEV penetrations with ACC II, for No Action case, proposed and alternatives
- cars and trucks combined
2027
2028
2029
2030
2031
2032
No Action
32%
42%
49%
52%
52%
54%
Proposed
37%
45%
55%
61%
64%
68%
Alternative 1
38%
47%
55%
63%
68%
72%
Alternative 2
37%
46%
51%
57%
61%
65%
Alternative 3
36%
45%
50%
55%
62%
68%
Table 13-99: Average incremented vehicle cost vs. No Action case with ACC II, proposed
and alternatives - cars and trucks combined
2027
2028
2029
2030
2031
2032
6-vr avg
Proposed
$172
$56
$11
$57
$268
$423
$164
Alternative 1
$454
$639
$1,130
$1,050
$1,212
$1,186
$945
Alternative 2
$106
-$29
-$184
-$188
$73
$235
$2
Alternative 3
$85
-$43
-$221
-$182
$214
$483
$56
13-44
-------
13.1.4.2 Battery Costs
13.1.4.2.1 Low Battery Costs
Table 13-100. Projected targets with Low Battery Costs for No Action case, proposed and
alternatives (CO2 grams/mile) - cars and trucks combined
2027
2028
2029
2030
2031
2032
No Action
162
162
164
164
164
163
Proposed
152
132
111
102
93
82
Alternative 1
141
122
102
93
83
72
Alternative 2
161
141
121
113
103
92
Alternative 3
165
148
131
115
99
82
Table 13-101. Projected achieved levels with Low Battery Costs, for No Action case,
proposed and alternatives (CO2 grams/mile) - cars and trucks combined
2027
2028
2029
2030
2031
2032
No Action
152
138
108
106
99
111
Proposed
154
130
110
100
83
80
Alternative 1
154
125
102
83
70
65
Alternative 2
157
136
119
96
98
90
Alternative 3
161
141
124
109
95
80
Table 13-102. BEV penetrations with Low Battery Costs, for No Action case, proposed and
alternatives - cars and trucks combined
2027
2028
2029
2030
2031
2032
No Action
34%
39%
51%
52%
55%
51%
Proposed
38%
46%
54%
59%
66%
68%
Alternative 1
38%
46%
54%
63%
68%
71%
Alternative 2
37%
46%
53%
63%
62%
66%
Alternative 3
36%
44%
51%
58%
63%
69%
Table 13-103. Average incremental vehicle cost vs. No Action case for Low Battery Costs,
proposed and alternatives - cars and trucks combined
2027
2028
2029
2030
2031
2032
6-yr avg
Proposed
$623
$553
$303
$313
$365
$490
$441
Alternative 1
$623
$1,441
$1,690
$1,568
$1,392
$1,443
$1,360
Alternative 2
$319
$213
-$13
$112
$7
$286
$154
Alternative 3
$161
$128
-$81
-$22
$64
$446
$116
13-45
-------
13.1.4.2.2 High Battery Costs
Table 13-104. Projected targets with High Battery Costs for No Action case, proposed and
alternatives (CO2 grams/mile) - cars and trucks combined
2027
2028
2029
2030
2031
2032
No Action
166
165
164
163
161
161
Proposed
153
132
112
102
93
82
Alternative 1
143
122
102
92
83
72
Alternative 2
163
142
122
112
103
92
Alternative 3
167
150
133
116
99
82
Table 13-105. Projected achieved levels with High Battery Costs, for No Action case,
proposed and alternatives (CO2 grams/mile) - cars and trucks combined
2027
2028
2029
2030
2031
2032
No Action
162
153
152
155
160
159
Proposed
151
130
110
100
92
81
Alternative 1
144
121
100
90
82
71
Alternative 2
159
139
119
110
101
92
Alternative 3
164
147
131
115
98
83
Table 13-106. BEV penetrations with High Battery Costs, for No Action case, proposed and
alternatives - cars and trucks combined
2027
2028
2029
2030
2031
2032
No Action
21%
26%
28%
29%
29%
29%
Proposed
33%
41%
51%
55%
60%
65%
Alternative 1
36%
44%
54%
60%
63%
69%
Alternative 2
29%
36%
47%
52%
56%
60%
Alternative 3
27%
33%
42%
50%
58%
64%
Table 13-107. Average incremental vehicle cost vs. No Action case for High Battery Costs,
proposed and alternatives - cars and trucks combined
2027
2028
2029
2030
2031
2032
6-yr avg
Proposed
$1,246
$1,057
$1,329
$1,553
$2,103
$2,505
$1,632
Alternative 1
$1,884
$1,676
$1,768
$1,885
$2,430
$2,750
$2,066
Alternative 2
$888
$874
$1,227
$1,347
$1,938
$2,340
$1,436
Alternative 3
$820
$785
$1,138
$1,484
$2,242
$2,803
$1,545
13-46
-------
13.1.4.3 Consumer Acceptance
13.1.4.3.1 Faster BEV Acceptance
Table 13-108. Projected targets with Faster BEV Acceptance for No Action case, proposed
and alternatives (CO2 grams/mile) - cars and trucks combined
2027
2028
2029
2030
2031
2032
No Action
163
163
164
165
165
166
Proposed
151
132
112
103
93
83
Alternative 1
141
122
102
93
83
72
Alternative 2
161
141
121
113
103
93
Alternative 3
165
148
132
116
99
82
Table 13-109. Projected achieved levels with Faster BEV Acceptance, for No Action case,
proposed and alternatives (CO2 grams/mile) - cars and trucks combined
2027
2028
2029
2030
2031
2032
No Action
147
131
100
76
79
71
Proposed
157
129
107
86
73
59
Alternative 1
156
128
104
80
66
53
Alternative 2
157
136
116
100
80
71
Alternative 3
159
140
118
96
90
76
Table 13-110. BEV penetrations with Faster BEV Acceptance, for No Action case,
proposed and alternatives - cars and trucks combined
2027
2028
2029
2030
2031
2032
No Action
36%
42%
54%
63%
63%
66%
Proposed
38%
46%
55%
63%
69%
75%
Alternative 1
38%
46%
55%
63%
69%
76%
Alternative 2
38%
46%
54%
61%
69%
73%
Alternative 3
38%
46%
54%
63%
66%
71%
Table 13-111. Average incremented vehicle cost vs. No Action case for Faster BEV
Acceptance, proposed and alternatives - cars and trucks combined
2027
2028
2029
2030
2031
2032
6-yr avg
Proposed
$287
$982
$809
$602
$746
$712
$211
Alternative 1
$317
$1,001
$1,209
$1,533
$1,675
$1,445
$783
Alternative 2
$212
$214
-$34
-$194
$179
$163
-$10
Alternative 3
$54
$33
-$176
-$235
-$66
$53
-$20
13-47
-------
13.1.4.3.2 Slower BEV Acceptance
Table 13-112. Projected targets with Slower BEV Acceptance for No Action case, proposed
and alternatives (CO2 grams/mile) - cars and trucks combined
2027
2028
2029
2030
2031
2032
No Action
164
162
162
161
161
160
Proposed
153
133
112
103
93
82
Alternative 1
143
122
102
92
83
72
Alternative 2
163
142
122
112
103
92
Alternative 3
167
149
132
115
99
82
Table 13-113. Projected achieved levels with Slower BEV Acceptance, for No Action case,
proposed and alternatives
CO2 grams/mile
2027
2028
2029
2030
2031
2032
No Action
161
160
154
159
152
158
Proposed
150
131
110
101
92
82
Alternative 1
144
118
99
90
81
74
Alternative 2
160
140
119
111
101
90
Alternative 3
164
148
128
113
97
80
- cars and trucks combined
Table 13-114. BEV penetrations with Slower BEV Acceptance, for No Action case,
proposed and alternatives - cars and trucks combined
2027
2028
2029
2030
2031
2032
No Action
22%
23%
28%
27%
33%
31%
Proposed
34%
42%
53%
59%
63%
68%
Alternative 1
36%
47%
55%
61%
66%
69%
Alternative 2
29%
39%
50%
55%
59%
64%
Alternative 3
28%
35%
45%
53%
61%
68%
Table 13-115. Average incremental vehicle cost vs. No Action case for Slower BEV
Acceptance, proposed and alternatives - cars and trucks combined
2027
2028
2029
2030
2031
2032
6-yr avg
Proposed
$877
$1,135
$755
$898
$995
$1,498
$1,026
Alternative 1
$1,336
$1,470
$1,143
$1,244
$1,393
$1,731
$1,386
Alternative 2
$695
$853
$560
$689
$888
$1,344
$838
Alternative 3
$508
$734
$473
$702
$1,005
$1,621
$841
13-48
-------
13.2 Medium-Duty Vehicles
13.2.1 GHG Targets and Compliance Levels
13.2.1.1 C02 g/mi
Shown below are the projected average GHG targets for each manufacturer, as well as their
corresponding average achieved compliance, in g/mi, for vans and pickups. A combined fleet
g/mi comparison is not shown, because a fleet g/mi value, even with a sales-weighted average of
van and pickup values, would not accurately represent the differences in lifetime VMT for the
van and pickup fleets used in the compliance calculations for each OEM.
13.2.1.1.1 Proposed GHG standards
OEM-specific GHG emissions targets for the proposed standards are shown in Table 13-116
and Table 13-117 for vans and pickups, respectively. Similarly, projected achieved GHG
emissions levels are given for vans and pickups in Table 13-118 and Table 13-119.
Table 13-116: Projected GHG Targets, Proposed Standards - Medium Duty Vans
Manufacturer
2027
2028
2029
2030
203 1
2032
Ford
383
371
337
303
270
238
General Motors
391
377
342
306
273
241
Mercedes Ben/.
426
412
375
337
302
266
Nissan
391
378
344
309
' 276
243
Slellanlis
399
384
347
310
276
243
TOTAL
393
379
345
309
276
243
Table 13-117: Projected GHG Targets, Proposed Standards - Medium Duty Pickups
Manufacturer
2027
2028
2029
2030
203 1
2032
Ford
458
448
410
370
328
288
General Motors
464
454
415
378 ^
335
294
Mercedes Ben/.
-
-
-
-
-
-
Nissan
424
416
373
336
301
265
Slellanlis
464
454
415
376
329
295
TOTAL
462
452
413
374
331
292
Table 13-118: Achieved GHG Levels, Proposed Standards - Medium Duty Vans
Manufacturer
2027
2028
2029
2030
203 1
2032
Ford
280
192
114
26
6
5
General Motors
316
218
129
30
o
0 ""
Mercedes Ben/.
288
198
104
45
45
45
Nissan
282
194
116
42
38
37 '
Slellanlis
295
208
131
72
20
5
TOTAL
292
202
119
36
12
10
13-49
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Table 13-119: Achieved GHG Levels, Proposed Standards - Medium Duty Pickups
Manufacturer
2027
2028
2029
2030
203 1
2032
Ford
523
555
542
526
478
427
General Motors
518
549
538
512
464
404
Mercedes Ben/.
-
-
-
-
-
-
Nissan
416
443
363
453
460
458
Slellanlis
4%
526
516
490
450
391
TOTAL
515
546
534
512
466
410
13.2.1.2 C02 Mg
Shown below are the projected average GHG targets for each manufacturer, as well as their
corresponding average achieved compliance, in Mg, for cars, trucks, and the combined fleet.
Total emissions are calculated by multiplying the relevant CO2 emission rate, the production
volume of applicable vehicles, and the expected lifetime vehicle miles traveled (VMT) of those
vehicles. The equation to calculate total Mg (for either total emissions, or credits based on the
difference between target g/mi and achieved g/mi) is:
CO2 (Mg) = (CO2 (g/mi) x VMT x Production) / 1,000,000
In the above equation, "VMT" is in miles, and specified in the regulations as 150,000 miles.
When using these equations to calculate values for cars and trucks in aggregate, we use a
production weighted average of the car and truck VMT values.
13.2.1.2.1 Proposed standards
OEM-specific GHG emissions targets for the proposed standards (in Mg) are shown in Table
13-120, Table 13-121, and Table 13-122 for vans, pickups, and the combined fleet, respectively.
Similarly, projected achieved GHG emissions (in Mg) are given for vans, pickups, and the
combined fleet in Table 13-123, Table 13-124, and Table 13-125. Finally, overall credits or
debits earned are provided for the combined fleet on a manufacturer-specific basis, in Table
13-126.
Table 13-120: Projected GHG Targets (Mg), Proposed Standards - Medium Duty Vans
Manufacturer
2027
2028
2029
2030
203 1
2032
Ford
6.522.743
6.311.012
5.754.284
5.186.343
4.668.259
4.150.724
General Motors
3.910.642
3,775,242
3.435.120
3.088.929
2,777,305
2.469.539
Mercedes Ben/.
1.862.021
1.801.793
1.642.836
1.482.940
1.337.297
1.189.101
Nissan
685.541
662.963
604.198
544.945
491.168
436.688
Slellanlis
1.912.932
1.840.237
1.669.031
1.495.278
1.341.697
1.191.470
TOTAL
14,893,879
14,391,247
13,105,469
11,798,434
10,615,726
9,437,522
Table 13-121:
Projected GHG Targets
(Mg), Proposed Standards
- Medium Duty Pickups
Manufacturer
2027
2028
2029
2030
203 1
2032
Ford
12.438.978
12.235.086
11.231.346
10.215.915
9.194.759
8.164.130
General Motors
12.566.840
12.354.816
11.346.578
10.417.371
9.365.128
8.318.738
Mercedes Ben/.
Nissan
51.194
50.43 1
45.506
41.311
37.496
33.442
Slellanlis
7,738,363
7.611.465
6.988.697
6.382.832
5.660.795
5.140.250
TOTAL
32,795,375
32,251,799
29,612,126
27,057,429
24,258,178
21,656,560
13-50
-------
Table 13-122: Projected GHG Targets (Mg), Proposed Standards - Medium Duty
Combined
Manufacturer 2027 2028 2029 2030 2031 2032
Ford 18.961.722 18.546.098 16.985.631 15.402.258 13.863.018 12.314.853
General Motors 16.477.482 16.130.058 14.781.698 13.506.300 12.142.433 10.788.277
Mercedes Ben/. 1.862.021 1.801.793 1.642.836 1.482.940 1.337.297 1.189.101
Nissan 736.735 713.394 649.703 586.255 528.664 470.130
Slcllanlis 9.651.295 9.451.702 8.657.728 7.878.111 7.002.492 6.331.720
TOTAL 47,689,254 46,643,046 42,717,596 38,855,863 34,873,904 31,094,082
Table 13-123: Achieved
GHG Levels
(Mg), Proposed Standards
- Medium Duty Vans
Manufacturer
2027
2028
2029
2030
203 1
2032
Ford
4.758.966
3.272.901
1.938.968
450.683
98.193
81.273
General Motors
3.168.452
2.178.961
1.290.834
299.867
2.554
11
Mercedes Ben/.
1.256.139
863.880
453.787
197.712
199.287
201.181
Nissan
494.750
341.090
203.176
74.654
68.336
66.021
Slcllanlis
1.411.730
998.450
627.699
348.859
96.066
26.512
TOTAL
11,090,038
7,655,283
4,514,464
1,371,776
464,435
374,999
Table 13-124: Achieved GHG Levels (Mg), Proposed Standards - Medium Duty Pickups
Manufacturer 2027 2028 2029 2030 2031 2032
Ford 14.204.434 15.138.319 14.865.910 14.527.658 13.392.450 12.114.910
General Motors 14.037.520 14.960.172 14.713.293 14.129.554 12.977.998 11.447.587
Mercedes Ben/. ......
Nissan 50.208 53.672 44.193 55.605 57.198 57.787
Slcllanlis 8.281.215 8.820.766 8.689.493 8.318.697 7.745.694 6.808.215
TOTAL 36,573,377 38,972,929 38,312,889 37,031,514 34,173,340 30,428,499
Table 13-125: Achieved GHG Levels (Mg), Proposed Standards - Medium Duty Combined
Manufacturer 2027 2028 2029 2030 2031 2032
Ford 18.963.400 18.411.220 16.804.878 14.978.341 13.490.643 12.196.183
General Motors 17.205.973 17.139.133 16.004.127 14.429.421 12.980.551 11.447.598
Mercedes Ben/. 1.256.139 863.880 453.787 197.712 199.287 201.181
Nissan 544.958 394.762 247.369 130.260 125.534 123.808
Slcllanlis 9.692.945 9.819.216 9.317.192 8.667.555 7.841.761 6.834.727
TOTAL 47,663,415 46,628,211 42,827,353 38,403,289 34,637,776 30,803,498
Table 13-126: GHG Credits/Debits Earned (Mg), Proposed Standards - Medium Duty
Combined
Manufacturer
2027
2028
2029
2030
203 1
2032
Ford
(764.771)
(753.048)
(188.764)
(260.367)
(338.464)
(296.675)
General Motors
(575.509)
(353.113)
(821.195)
(143.791)
(155.080)
(357,366)
Mercedes Ben/.
(29.210)
(119.383)
(63.927)
(28.799)
100.537
210.887
Nissan
1.368.231
1.305.289
1.044.626
125.015
(55.848)
(270.510)
Slcllanlis
1.406
(79.812)
29.188
307,876
448.783
625.199
TOTAL
148
(67)
(72)
(66)
(71)
(88,465)
13-51
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13.2.2 Projected Manufacturing Costs per Vehicle
EPA has performed an assessment of the estimated per-vehicle costs for manufacturers to
meet the proposed MY 2027-2032 standards, relative to the No Action case. The fleet average
costs per vehicle are grouped by vans and pickups. We have included summary tables in this
format. The costs in this section represent compliance costs to the industry and are not
necessarily the same as the costs experienced by the consumer when purchasing a new vehicle.
For example, the costs presented here do not include any state and Federal purchase incentives
that are available to consumers. Also, the manufacturer decisions for the pricing of individual
vehicles may not align exactly with the production cost impacts for that particular vehicle. EPA's
OMEGA model assumes that manufacturers distribute compliance costs through limited cross-
subsidization of prices between vehicles in order to maintain an appropriate mix of debit- and
credit-generating vehicles that achieves compliance in a cost-minimizing fashion.
13.2.2.1 Proposed Standards
Incremental costs per vehicle for the proposed standards (compared to the No Action case) are
summarized by van and truck in Table 13-127.
Table 13-127: Projected Manufacturing Costs Per Vehicle, Proposed Standards - Medium
Duty Vehicles
2027
2028
2029
2030
203 1
2032
Vans
$322
$658
$711
$1,184
$1,592
$1,932
Pickups
$386
$31
$67
$374
$603
$1,706
Total
$364
$249
$290
$654
$944
$1,784
Incremental costs per vehicle for the proposed standards, compared to the No Action case, are
shown for each OEM in Table 13-128, Table 13-129, and Table 13-130 for vans, pickups, and
the medium duty combined fleet, respectively.
Table 13-128: Projected Manufacturing Costs Per Vehicle, Proposed Standards - Medium
Duty Vans
Manufacturer
2027
2028
2029
2030
203 1
2032
Ford
$392
$785
$873
$1,478
$1,836
$2,166
General Motors
$236
$607
$584
$1,053
$1,576
$1,903
Mercedes Ben/.
$344
$413
$602
$663
$911
$1,260
Nissan
$342
$497
$431
$856
$1,032
$1,325
Slellanlis
$230
$599
$602
$1,009
$1,586
$2,000
TOTAL
$322
$658
$711
$1,184
$1,592
$1,932
13-52
-------
Table 13-129: Projected Manufacturing Costs Per Vehicle, Proposed Standards - Medium
Duty Pickups
Manufacturer
2027
2028
2029
2030
203 1
2032
Ford
$387
$23
$66
$137
$579
$1,408
General Motors
$353
$19
$47
$250
7" $523 Z
$1,652
Mercedes Ben/.
-
-
-
-
-
-
Nissan
$592
-$178
$963
-$226
-$317
-$348
Slellanlis
$435
$67
$95
$964
$778
$2,296
TOTAL
$386
$31
$67
$374
$603
$1,706
Table 13-130: Projected Manufacturing Costs Per Vehicle, Proposed Standards - Medium
Duty Combined
Manufacturer
2027
2028
2029
2030
203 1
2032
Ford
$389
$316
$376
$650
$1,058
$1,696
General Motors
$322
$177
$191
$465
$804
$1,719
Mercedes Ben/.
$344
$413
$602
$663
$911
$1,260
Nissan
$358
$453
$465
$786
$944
$1,216
Slellanlis
$389
$185
$208
$974
$956
$2,231
TOTAL
$364
$249
$290
$654
$944
$1,784
13.2.3 Technology Penetration Rates
Presented below are the projected technology penetration rates, by manufacturer, for vans and
pickups, for the No Action case and the proposed standards. Tables are summarized by body
style for BEV penetrations, with the remainder of the fleet being ICE vehicles.
13.2.3.1 No Action Case
Table 13-131 summarizes medium duty vehicle BEV penetrations for the No Action case.
Table 13-131: Projected BEV Penetrations, No Action - Medium Duty Vehicles
2027
2028
2029
2030
203 1
2032
Vans
25%
24%
24%
22%
21%
22%
Pickups
0%
0%
0%
0%
0%
0%
Total
9%
8%
8%
8%
7%
8%
13.2.3.2 Proposal
Table 13-132 summarizes medium duty vehicle BEV penetrations for the proposed standards.
Table 13-132: Projected BEV Penetrations, Proposed Standards - Medium Duty Vehicles
2027
2028
2029
2030
203 1
2032
Vans
35%
55%
73%
92%
97%
98%
Pickups
7%
1%
3%
4%
15%
19%
Total
17%
20%
28%
13-53
34%
43%
46%
-------
13.2.4 Medium-Duty Vehicle Sensitivities
The tables below summarize the projected average GHG targets and average achieved
compliance, in g/mi, BEV penetrations, and incremental vehicle cost vs the No Action case, for
medium duty vehicles. They are prepared for both the Low Battery Cost and the High Battery
Cost sensitivities.
13.2.4.1 Low Battery Costs
Table 13-133. Projected targets with Low Battery Costs for No Action case and proposed
standards (CO2 grams/mile) - Medium Duty Combined
2027
2028
2029
2030
2031
2032
No Action
479
478
478
480
481
481
Proposed
437
423
386
349
312
275
Table 13-134. Projected achieved levels with Low Battery Costs for No Action case and
proposed standards (CO2 grams/mile) - Medium Duty Combined
2027
2028
2029
2030
2031
2032
No Action
478
478
478
480
480
480
Proposed
436
423
385
350
307
273
Table 13-135. BEV penetrations with Low Battery Costs for No Action case and proposed
standards - Medium Duty Combined
2027
2028
2029
2030
2031
2032
No Action
9%
8%
7%
7%
8%
7%
Proposed
17%
18%
26%
33%
38%
44%
Table 13-136. Average incremental vehicle manufacturing cost vs. No Action case for Low
Battery Costs, proposed standards - Medium Duty Combined
2027
2028
2029
2030
2031
2032
6-yr avg
Proposed
$118
$4
-$142
$5
$564
$1,094
$274
13.2.4.2 High Battery Costs
Table 13-137. Projected targets with High Battery Costs for No Action case and proposed
standards
CO2 grams/mile) - Medium Duty Combined
2027
2028
2029
2030
2031
2032
No Action
482
482
482
482
483
483
Proposed
439
428
390
355
316
276
13-54
-------
Table 13-138. Projected achieved levels with High Battery Costs for No Action case and
proposed standards (CO2 grams/mile) - Medium Duty Combined
2027
2028
2029
2030
2031
2032
No Action
482
482
482
481
482
483
Proposed
439
428
389
352
313
273
Table 13-139. BEV penetrations with High Battery Costs for No Action case and proposed
standards - Medium Duty Combined
2027
2028
2029
2030
2031
2032
No Action
5%
5%
5%
5%
3%
3%
Proposed
14%
17%
25%
27%
36%
43%
Table 13-140. Average incremented vehicle manufacturing cost vs. No Action case for High
Battery Costs, proposed standards - Medium Duty Combined
2027
2028
2029
2030
2031
2032
6-yr avg
Proposed
$810
$640
$919
$1,648
$2,191
$3,072
$1,547
13-55
------- |