Regulatory Impact Analysis for the Proposed 

Emission Guidelines for Greenhouse Gas 

Emissions from Existing Electric Utility 

Generating Units; Revisions to Emission 

Guideline Implementing Regulations; Revisions 

to New Source Review Program
ii
iii

EPA-452/R-18-006

August 2018

Regulatory Impact Analysis for the Proposed Emission Guidelines for Greenhouse Gas 

Emissions from Existing Electric Utility Generating Units; Revisions to Emission Guideline 

Implementing Regulations; Revisions to New Source Review Program

U.S. Environmental Protection Agency

Office of Air Quality Planning and Standards

Health and Environmental Impact Division

Research Triangle Park, NC
iv

CONTACT INFORMATION

This document has been prepared by staff from the Office of Air Quality Planning and 

Standards, the Office of Atmospheric Programs, and the Office of Policy of the U.S. 

Environmental Protection Agency. Questions related to this document should be addressed to 

Brian Keaveny, U.S. Environmental Protection Agency, Office of Air Quality Planning and 

Standards, Research Triangle Park, North Carolina 27711 (email: keaveny.brian@epa.gov).
v

TABLE OF CONTENTS

LIST OF TABLES .................................................................................................................... VIII

LIST OF FIGURES ....................................................................................................................XII

EXECUTIVE SUMMARY.....................................................................................................ES-1

ES.1 INTRODUCTION......................................................................................................................................ES-1

ES.2 ANALYSIS..............................................................................................................................................ES-1

ES.3 COMPLIANCE COSTS..............................................................................................................................ES-6

ES.4 EMISSIONS CHANGES ............................................................................................................................ES-7

ES.5 CLIMATE AND HEALTH CO-BENEFITS .................................................................................................ES-10

ES.6 NET BENEFITS .....................................................................................................................................ES-13

ES.7 ECONOMIC AND EMPLOYMENT IMPACTS.............................................................................................ES-19

ES.8 LIMITATIONS AND UNCERTAINTY .......................................................................................................ES-21

ES.9 REFERENCES .......................................................................................................................................ES-24

CHAPTER 1: INTRODUCTION AND BACKGROUND ..................................................... 1-1

1.1 INTRODUCTION ............................................................................................................................................ 1-1

1.2 LEGAL AND ECONOMIC BASIS FOR THIS RULEMAKING ................................................................................ 1-1

1.2.1 Statutory Requirement ....................................................................................................................... 1-1

1.2.2 Market Failure ................................................................................................................................... 1-3

1.3 BACKGROUND.............................................................................................................................................. 1-3

1.3.1 Emission Guidelines and Revisions to New Source Review ............................................................. 1-3

1.3.2 Regulated Pollutant............................................................................................................................ 1-4

1.3.3 Definition of Affected Sources.......................................................................................................... 1-4

1.4 OVERVIEW OF REGULATORY IMPACT ANALYSIS ......................................................................................... 1-5

1.4.1 Base Case........................................................................................................................................... 1-6

1.4.2 BSER and Policy Scenarios............................................................................................................... 1-7

1.4.3 Years of Analysis............................................................................................................................... 1-9

1.5 BSER TECHNOLOGIES ................................................................................................................................. 1-9

1.5.1 Neural Network/Intelligent Sootblower............................................................................................. 1-9

1.5.2 Boiler Feed Pumps........................................................................................................................... 1-10

1.5.3 Air Heater and Duct Leakage Control ............................................................................................. 1-10

1.5.4 Variable Frequency Drives (VFDs)................................................................................................. 1-11

1.5.5 Blade Path Upgrade (Steam Turbine) .............................................................................................. 1-12

1.5.6 Redesign/Replace Economizer ........................................................................................................ 1-12

1.5.7 Additional Documentation............................................................................................................... 1-12

1.6 DEVELOPMENT OF ILLUSTRATIVE POLICY SCENARIOS............................................................................... 1-13

1.6.1 Technical Basis................................................................................................................................ 1-13

1.6.2 How HRI are Represented in the Policy Scenarios.......................................................................... 1-18

1.7 ORGANIZATION OF THE REGULATORY IMPACT ANALYSIS ......................................................................... 1-19

1.8 REFERENCES .............................................................................................................................................. 1-20

CHAPTER 2: ELECTRIC POWER SECTOR INDUSTRY PROFILE............................... 2-1

2.1 INTRODUCTION ............................................................................................................................................ 2-1

2.2 POWER SECTOR OVERVIEW ......................................................................................................................... 2-1

2.2.1 Generation.......................................................................................................................................... 2-1

2.2.2 Transmission.................................................................................................................................... 2-10

2.2.3 Distribution...................................................................................................................................... 2-11

2.3 SALES, EXPENSES AND PRICES................................................................................................................... 2-12

2.3.1 Electricity Prices.............................................................................................................................. 2-12

2.3.2 Prices of Fossil Fuels Used for Generating Electricity .................................................................... 2-18

2.3.3 Changes in Electricity Intensity of the U.S. Economy..................................................................... 2-18

2.4 DEREGULATION AND RESTRUCTURING ...................................................................................................... 2-20
vi

2.5 EMISSIONS OF GREENHOUSE GASES FROM ELECTRIC UTILITIES ................................................................ 2-26

2.6 REVENUES AND EXPENSES......................................................................................................................... 2-29

2.7 NATURAL GAS MARKET ............................................................................................................................ 2-30

2.8 REFERENCES .............................................................................................................................................. 2-34

CHAPTER 3: COST, EMISSIONS, ECONOMIC, AND ENERGY IMPACTS.................. 3-1

3.1 INTRODUCTION ............................................................................................................................................ 3-1

3.2 OVERVIEW ................................................................................................................................................... 3-1

3.3 POWER SECTOR MODELING FRAMEWORK ................................................................................................... 3-2

3.4 RECENT UPDATES TO EPA’S POWER SECTOR MODELING PLATFORM V6 USING IPM.................................. 3-4

3.5 SCENARIOS ANALYZED ................................................................................................................................ 3-7

3.6 MONITORING, REPORTING, AND RECORDKEEPING COSTS.......................................................................... 3-10

3.7 PROJECTED POWER SECTOR IMPACTS ........................................................................................................ 3-14

3.7.1 Projected Emissions......................................................................................................................... 3-14

3.7.2 Projected Compliance Costs............................................................................................................ 3-17

3.7.3 Projected Compliance Actions for Emissions Reductions............................................................... 3-18

3.7.4 Projected Generation Mix................................................................................................................ 3-22

3.7.5 Projected Changes to Generating Capacity...................................................................................... 3-26

3.7.6 Projected Coal Production and Natural Gas Use for the Electric Power Sector .............................. 3-31

3.7.7 Projected Fuel Price, Market, and Infrastructure Impacts................................................................ 3-33

3.7.8 Projected Retail Electricity Prices ................................................................................................... 3-34

3.8 DEMAND-SIDE ENERGY EFFICIENCY SENSITIVITY TO THE BASE CASE (CPP)............................................ 3-35

3.8.1 Demand-side Energy Efficiency Revised Electric Demand Projection ........................................... 3-35

3.8.2 Demand-side Energy Efficiency Costs............................................................................................ 3-36

3.8.3 Demand-side Energy Efficiency Sensitivity to the Base Case: Projected EE Benefits and Compliance 

Costs 3-37

3.8.4 Demand-side Energy Efficiency Sensitivity to the Base Case: Projected Emissions ...................... 3-40

3.9 LIMITATIONS OF ANALYSIS........................................................................................................................ 3-42

3.10 REFERENCES ......................................................................................................................................... 3-46

CHAPTER 4: ESTIMATED FORGONE CLIMATE BENEFITS AND FORGONE

HUMAN HEALTH CO-BENEFITS ........................................................................................ 4-1

4.1 INTRODUCTION ............................................................................................................................................ 4-1

4.2 CLIMATE CHANGE IMPACTS......................................................................................................................... 4-1

4.3 APPROACH TO ESTIMATING FORGONE CLIMATE BENEFITS FROM CO2 ........................................................ 4-2

4.4 APPROACH TO ESTIMATING FORGONE HUMAN HEALTH ANCILLARY CO-BENEFITS.................................... 4-7

4.4.1 Air Quality Modeling Methodology ................................................................................................ 4-10

4.4.2 Estimating PM2.5 and Ozone Related Health Impacts...................................................................... 4-16

4.4.3 Economic Value of Forgone Ancillary Health Co-benefits............................................................. 4-22

4.4.4 Characterizing Uncertainty in the Estimated Forgone Benefits....................................................... 4-24

4.5 AIR QUALITY AND HEALTH IMPACT RESULTS ........................................................................................... 4-29

4.5.1 Air Quality Results .......................................................................................................................... 4-29

4.5.2 Estimated Number and Economic Value of Forgone Ancillary Health Co-Benefits....................... 4-31

4.6 TOTAL FORGONE CLIMATE AND HEALTH BENEFITS .................................................................................. 4-41

4.7 FORGONE ANCILLARY CO-BENEFITS NOT QUANTIFIED............................................................................. 4-45

4.7.1 Hazardous Air Pollutant Impacts..................................................................................................... 4-47

4.7.2 Forgone NO2 Health Co-Benefits.................................................................................................... 4-51

4.7.3 Forgone SO2 Health Co-Benefits..................................................................................................... 4-51

4.7.4 NO2 and SO2 Forgone Welfare Co-Benefits.................................................................................... 4-52

4.7.5 Forgone Ozone Welfare Co-Benefits............................................................................................... 4-53

4.7.6 Forgone Carbon Monoxide Co-Benefits.......................................................................................... 4-54

4.7.7 Forgone Visibility Impairment Co-Benefits.................................................................................... 4-54

4.8 REFERENCES .............................................................................................................................................. 4-56

CHAPTER 5: ECONOMIC AND EMPLOYMENT IMPACTS........................................... 5-1
vii

5.1 ECONOMIC IMPACTS .................................................................................................................................... 5-1

5.1.1 Market Impacts.................................................................................................................................. 5-1

5.1.2 Distributional Impacts........................................................................................................................ 5-4

5.1.3 Impacts on Small Entities .................................................................................................................. 5-8

5.2 EMPLOYMENT IMPACTS ............................................................................................................................... 5-8

5.3 REFERENCES .............................................................................................................................................. 5-15

CHAPTER 6: COMPARISON OF BENEFITS AND COSTS............................................... 6-1

6.1 INTRODUCTION ............................................................................................................................................ 6-1

6.2 METHODS..................................................................................................................................................... 6-1

6.3 RESULTS ...................................................................................................................................................... 6-3

6.3.1 Analysis of 2023-2037 for E.O. 13771, Reducing Regulation and Controlling Regulatory Costs.... 6-3

6.3.2 Net Benefits Analysis ........................................................................................................................ 6-5

6.4 REFERENCES .............................................................................................................................................. 6-18

CHAPTER 7: APPENDIX – UNCERTAINTY ASSOCIATED WITH ESTIMATING THE

SOCIAL COST OF CARBON.................................................................................................. 7-1

7.1 OVERVIEW OF METHODOLOGY USED TO DEVELOP INTERIM DOMESTIC SC-CO2 ESTIMATES ..................... 7-1

7.2 TREATMENT OF UNCERTAINTY IN INTERIM DOMESTIC SC-CO2 ESTIMATES................................................ 7-2

7.3 FORGONE GLOBAL CLIMATE BENEFITS ....................................................................................................... 7-7

7.4 REFERENCES ................................................................................................................................................ 7-9

CHAPTER 8: APPENDIX – AIR QUALITY MODELING .................................................. 8-1

8.1 AIR QUALITY MODELING PLATFORM........................................................................................................... 8-1

8.1.1 Air Quality Model, Meteorology and Boundary Conditions ............................................................. 8-1

8.1.2 2011 and 2023 Emissions .................................................................................................................. 8-3

8.1.3 2011 Model Evaluation for Ozone and PM2.5 .................................................................................... 8-6

8.2 SOURCE APPORTIONMENT TAGS ................................................................................................................ 8-10

8.3 APPLYING SOURCE APPORTIONMENT CONTRIBUTIONS TO CREATE AIR QUALITY FIELDS FOR THE BASE CASE 

AND FOUR ILLUSTRATIVE SCENARIOS ................................................................................................................. 8-18

8.3.1 Estimation methods for Emissions that Represent the Base Case and Four Illustrative Scenarios.. 8-18

8.3.2 Scaling Ratio Applied to Source Apportionment Tags.................................................................... 8-21

8.4 CREATING FUSED FIELDS BASED ON OBSERVATIONS AND MODEL SURFACES .......................................... 8-42

8.5 REFERENCES .............................................................................................................................................. 8-45
viii

LIST OF TABLES

Table ES-1 Present Value and Equivalent Annualized Value of Compliance Costs, Climate Benefits, and Net 

Benefits Associated with Targeted Pollutant (CO2), Relative to Base Case (CPP), 3 and 7 Percent 

Discount Rates, 2023-2037 (billions of 2016$)................................................................................ES-5

Table ES-2 Present Value and Equivalent Annualized Value of Compliance Costs, Climate Benefits, and Net 

Benefits Associated with Targeted Pollutant (CO2), Relative to the No CPP Alternative Baseline, 3 

and 7 Percent Discount Rates, 2023-2037 (billions of 2016$).........................................................ES-6

Table ES-3 Compliance Costs, Relative to Base Case (CPP) (billions of 2016$)...............................................ES-7

Table ES-4 Compliance Costs, Relative to No CPP Alternative Baseline (billions of 2016$) ...........................ES-7

Table ES-5 Projected CO2 Emission Impacts, Relative to Base Case (CPP) Scenario .......................................ES-8

Table ES-6 Projected CO2 Emission Impacts, Relative to No CPP Alternative Baseline ...................................ES-8

Table ES-7 Projected CO2, SO2, and NOX Electricity Sector Emission Increases, Relative to the Base Case (CPP) 

(2025-2035)......................................................................................................................................ES-9

Table ES-8 Projected CO2, SO2, and NOX Electricity Sector Emission Changes, Relative to the No CPP 

Alternative Baseline (2025-2035) ..................................................................................................ES-10

Table ES-9 Monetized Benefits, Relative to Base Case (CPP) (billions of 2016$) ..........................................ES-13

Table ES-10 Present Value and Equivalent Annualized Value of Compliance Costs, Climate Benefits, and Net 

Benefits Associated with Targeted Pollutant (CO2), Relative to Base Case (CPP), 3 and 7 Percent 

Discount Rates, 2023-2037 (billions of 2016$)..............................................................................ES-14

Table ES-11 Compliance Costs, Climate Benefits, and Net Benefits Associated with Targeted Pollutant (CO2), 

Relative to Base Case (CPP), 3 and 7 Percent Discount Rates, 2025, 2030, and 2035 (billions of 

2016$) ............................................................................................................................................ES-15

Table ES-12 Present Value and Equivalent Annualized Value of Compliance Costs, Total Benefits, and Net 

Benefits, Relative to Base Case (CPP), 3 and 7 Percent Discount Rates, 2023-2037 (billions of 2016$)

........................................................................................................................................................ES-16

Table ES-13 Compliance Costs, Total Benefits, and Net Benefits, Relative to Base Case (CPP), 3 and 7 Percent 

Discount Rates, 2025, 2030, and 2035 (billions of 2016$) ............................................................ES-17

Table ES-14 Present Value and Equivalent Annualized Value of Compliance Costs, Climate Benefits, and Net 

Benefits Associated with Targeted Pollutant (CO2), Relative to the No CPP Alternative Baseline, 3 

and 7 Percent Discount Rates, 2023-2037 (billions of 2016$).......................................................ES-18

Table ES-15 Present Value and Equivalent Annualized Value of Compliance Costs, Total Benefits, and Net 

Benefits, Relative to the No CPP Alternative Baseline, 3 and 7 Percent Discount Rates, 2023-2037 

(billions of 2016$)..........................................................................................................................ES-19

Table ES-16 Summary of Certain Energy Market Impacts, Relative to Base Case (CPP) (Percent Change).....ES-20

Table 1-1 Availability of Heat Rate Improvement Candidate Technologies (“No NSR Reform” Case) ......... 1-15

Table 1-2 Availability of Heat Rate Improvement Candidate Technologies (“NSR Reform” Case) ............... 1-15

Table 1-3 Heat Rate Improvement Potential (%).............................................................................................. 1-16

Table 1-4 Heat Rate Improvement Cost ($2016/kW)....................................................................................... 1-16

Table 1-5 Fleet-Wide Capacity Weighted Average Improvement and Costs (“No NSR Reform” Case) ........ 1-17

Table 1-6 Fleet-Wide Capacity Weighted Average Improvement and Costs (“NSR Reform” Case) .............. 1-17

Table 2-1 Existing Electricity Net Summer Generating Capacity by Energy Source, 2006 and 2016 ............... 2-3

Table 2-2 Net Generation in 2006 and 2016 (Trillion kWh = TWh).................................................................. 2-5

Table 2-3 Coal and Natural Gas Generating Units, by Size, Age, Capacity, and Thermal Efficiency (Heat Rate)

2-6

Table 2-4 Total U.S. Electric Power Industry Retail Sales in 2006 and 2016 (billion kWh) ........................... 2-12

Table 2-5 Domestic Emissions of Greenhouse Gases, by Economic Sector (million tons of CO2 equivalent) 2-27

Table 2-6 Greenhouse Gas Emissions from the Electricity Sector (Generation, Transmission and Distribution), 

2006 and 2015 (million tons of CO2 equivalent).............................................................................. 2-28

Table 2-7 Revenue and Expense Statistics for Major U.S. Investor-Owned Electric Utilities for 2006, 2011 and 

2016 (nominal $millions) ................................................................................................................. 2-30

Table 3-1 Coal and Natural Gas Generating Units, by Size, Age, Capacity, and Thermal Efficiency (Heat Rate)

3-6

Table 3-2 Years 2023, 2025, 2030, and 2035: Summary of State and Industry Annual Respondent Burden and 

Cost of Reporting and Recordkeeping Requirements (Million 2016$)............................................ 3-12
ix

Table 3-3 Years 2025, 2030, and 2035: Total State and Industry Annual Cost of Reporting and Recordkeeping 

Requirements, Relative to the Base Case (Million 2016$)............................................................... 3-13

Table 3-4 Projected CO2 Emission Impacts, Relative to Base Case (CPP) Scenario ....................................... 3-14

Table 3-5 Projected CO2 Emission Impacts, Relative to No CPP Scenario...................................................... 3-15

Table 3-6 Projected CO2 Emission Impacts, Relative to 2005 ......................................................................... 3-15

Table 3-7 Projected Emissions of SO2, NOX, and Hg....................................................................................... 3-16

Table 3-8 Percent Change in Projected SO2, NOX, and Hg Emissions, Relative to Base Case (CPP) Scenario3-16

Table 3-9 Percent Difference in Projected SO2, NOX, and Hg Emissions, Relative to No CPP Scenario ........ 3-17

Table 3-10 Total Projected Power Sector System Costs (billions of 2016$)...................................................... 3-18

Table 3-11 Annualized Compliance Costs, Relative to Base Case (CPP) Scenario (billions of 2016$) ............ 3-18

Table 3-12 Annualized Compliance Costs, Relative to No CPP Scenario (billions of 2016$)........................... 3-18

Table 3-13 Projected CO2 Emissions by Generation Source (MM short tons)................................................... 3-19

Table 3-14 Projected SO2 Emissions by Generation Source (thousand short tons)............................................ 3-20

Table 3-15 Projected NOX Emissions by Generation Source (thousand short tons)........................................... 3-20

Table 3-16 Projected Mercury Emissions by Generation Source (short tons).................................................... 3-21

Table 3-17 Projected Generation Mix (thousand GWh)..................................................................................... 3-23

Table 3-18 Percent Change in Projected Generation Mix, Relative to Base Case (CPP) Scenario .................... 3-24

Table 3-19 Percent Change in Projected Generation Mix, Relative to No CPP Scenario .................................. 3-25

Table 3-20 Total Generation Capacity by 2025-2035 (GW) .............................................................................. 3-27

Table 3-21 Percent Change in Total Generation Capacity by 2025-2035, Relative to Base Case Scenario (CPP) 3-

28

Table 3-22 Percent Change in Total Generation Capacity by 2025-2035, Relative to No CPP Scenario .......... 3-29

Table 3-23 Projected Natural Gas Combined Cycle Capacity Additions and Changes Relative to Base Case (CPP)

3-30

Table 3-24 Projected Renewable Capacity Additions and Changes Relative to Base Case (CPP)..................... 3-30

Table 3-25 Projected Natural Gas Combined Cycle Capacity Additions and Changes Relative to No CPP 

Scenario............................................................................................................................................ 3-30

Table 3-26 Projected Renewable Capacity Additions and Changes Relative to No CPP Scenario .................... 3-31

Table 3-27 2025 Projected Coal Production for the Electric Power Sector (million short tons) ........................ 3-31

Table 3-28 2030 Projected Coal Production for the Electric Power Sector (million short tons) ........................ 3-32

Table 3-29 2035 Projected Coal Production for the Electric Power Sector (million short tons) ........................ 3-32

Table 3-30 Projected Power Sector Gas Use ...................................................................................................... 3-32

Table 3-31 Projected Average Minemouth and Delivered Coal Prices (2016$/MMBtu)................................... 3-33

Table 3-32 Projected Average Henry Hub (spot) and Delivered Natural Gas Prices (2016$/MMBtu).............. 3-33

Table 3-33 Percent Change in Projected Average Henry Hub (spot) and Delivered Natural Gas Prices, Relative to 

Base Case (CPP)............................................................................................................................... 3-33

Table 3-34 Percent Change in Projected Average Henry Hub (spot) and Delivered Natural Gas Prices, Relative to 

No CPP Scenario.............................................................................................................................. 3-34

Table 3-35 Projected Contiguous U.S. Retail Electricity Prices (cents/kWh), 2025-2035................................. 3-34

Table 3-36 Percent Change in Projected Contiguous U.S. Retail Electricity Prices, Relative to Base Case (CPP), 

2025-2035......................................................................................................................................... 3-35

Table 3-37 Percent Change in Projected Contiguous U.S. Retail Electricity Prices, Relative to No CPP Scenario, 

2025-2035......................................................................................................................................... 3-35

Table 3-38 Change in Electricity Demand Due to Demand-side Energy Efficiency, CPP Scenario vs. No CPP 

Scenario in AEO2017....................................................................................................................... 3-36

Table 3-39 Costs of Demand-side Energy Efficiency (billions of 2016$).......................................................... 3-37

Table 3-40 Annualized Compliance Costs of the No CPP Scenario (billions of 2016$).................................... 3-39

Table 3-41 Projected CO2 Emission Impacts, Relative to Illustrative No CPP Scenario ................................... 3-40

Table 3-42 Projected SO2, NOX, and Mercury Emissions.................................................................................. 3-41

Table 3-43 Projected SO2, NOX, and Mercury Emission Impacts, Relative to Illustrative No CPP Scenario.... 3-41

Table 4-1 Interim Domestic Social Cost of CO2, 2015-2050 (in 2016$ per metric ton)*.................................. 4-4

Table 4-2 Estimated Forgone Domestic Climate Benefits, Relative to Base Case (CPP) (billions 2016$)*...... 4-5

Table 4-3 Projected EGU Emissions of SO2, NOX, and PM2.5* ......................................................................... 4-8

Table 4-4 Human Health Effects of Ambient PM2.5 and Ozone ....................................................................... 4-18

Table 4-5 Estimated Incremental PM2.5 and Ozone-Related Premature Deaths and Illnesses in 2025*........... 4-32

Table 4-6 Estimated Incremental PM2.5 and Ozone-Related Premature Deaths and Illnesses in 2030*........... 4-33
x

Table 4-7 Estimated Incremental PM2.5 and Ozone-Related Premature Deaths and Illnesses in 2035*........... 4-34

Table 4-8 PM-Related Premature Deaths Estimated Using Alternative Approaches to Evaluate Uncertainty at 

Low-Concentations (95% Confidence Interval), Relative to Base Case (CPP)*.............................. 4-35

Table 4-9 Estimated Economic Value of Incremental PM2.5 and Ozone-Attributable Deaths and Illnesses for 

Illustrative Scenarios & Three Alternative Approaches to Representing PM Effects in 2025, Relative 

to Base Case (CPP) (95% Confidence Interval; Billions of 2016$)A ............................................... 4-36

Table 4-10 Estimated Economic Value of Forgone PM2.5 and Ozone-Attributable Deaths and Illnesses for 

Illustrative Scenarios & Three Alternative Approaches to Representing PM Effects in 2030, Relative 

to Base Case (CPP) (95% Confidence Interval; Billions of 2016$)A ............................................... 4-37

Table 4-11 Estimated Economic Value of Forgone PM2.5 and Ozone-Attributable Deaths and Illnesses for 

Illustrative Scenarios & Three Alternative Approaches to Representing PM Effects in 2035, Relative 

to Base Case (CPP) (95% Confidence Interval; Billions of 2016$)A ............................................... 4-38

Table 4-12 Estimated Percent of PM2.5-related Premature Deaths Above and Below PM2.5 Concentration Cut 

Points................................................................................................................................................ 4-40

Table 4-13 Forgone Climate Benefits and Ancillary Health Co-Benefits, Relative to Base Case (CPP) (billion 

2016$) .............................................................................................................................................. 4-42

Table 4-14 Forgone Climate Benefits and Ancillary Health Co-Benefits, showing only PM2.5 Related Benefits 

above the Lowest Measured Level of Each Long-Term PM2.5 Mortality Study, Relative to Base Case 

(CPP) (billion 2016$)....................................................................................................................... 4-43

Table 4-15 Forgone Climate Benefits and Ancillary Health Co-Benefits, showing only PM2.5 Related Benefits 

above PM2.5 National Ambient Air Quality Standard (billion 2016$).............................................. 4-44

Table 4-16 Forgone Climate Benefits and Ancillary Health Co-Benefits using Alternate Method for Representing 

PM2.5 Benefits at Low Levels, Relative to Base Case (CPP) (billion 2016$)................................... 4-45

Table 4-17 Unquantified Forgone Ancillary Health and Welfare Co-Benefits Categories ................................ 4-46

Table 5-1 Summary of Certain Energy Market Impacts, Relative to Base Case (CPP) (Percent Change)......... 5-2

Table 6-1 Compliance Costs for the Illustrative Scenarios, Relative to Base Case (CPP), 2023-2037 (billion 

2016$) ................................................................................................................................................ 6-4

Table 6-2 Present Value of Compliance Costs for the Illustrative Scenario, Relative to Base Case (CPP), 3 and 7 

Percent Discount Rates, 2023-2037 (billion 2016$)........................................................................... 6-5

Table 6-3 Present Value of Compliance Costs, Benefits, and Net Benefits Associated with Targeted Pollutant 

(CO2), Illustrative No CPP Scenario, Relative to Base Case (CPP), 3 and 7 Percent Discount Rates, 

2023-2037 (billion 2016$).................................................................................................................. 6-7

Table 6-4 Present Value of Compliance Costs, Benefits, and Net Benefits Associated with Targeted Pollutant 

(CO2), Illustrative 2 Percent HRI at $50/kW Scenario, Relative to Base Case (CPP), 3 and 7 Percent 

Discount Rates, 2023-2037 (billion 2016$) ....................................................................................... 6-8

Table 6-5 Present Value of Compliance Costs, Benefits, and Net Benefits Associated with Targeted Pollutant 

(CO2), Illustrative 4.5 Percent HRI at $50/kW Scenario, Relative to Base Case (CPP), 3 and 7 Percent 

Discount Rates, 2023-2037 (billion 2016$) ....................................................................................... 6-9

Table 6-6 Present Value of Compliance Costs, Benefits, and Net Benefits Associated with Targeted Pollutant 

(CO2), Illustrative 4.5 Percent HRI at $100/kW Scenario, Relative to Base Case (CPP), 3 and 7 

Percent Discount Rates, 2023-2037 (billion 2016$)......................................................................... 6-10

Table 6-7 Present Value of Compliance Costs, Benefits, and Net Benefits Associated with Targeted Pollutant 

(CO2), Relative to Base Case (CPP), 3 and 7 Percent Discount Rates, 2023-2037 (billion 2016$). 6-11

Table 6-8 Illustrative No CPP Scenario: Present Value of Compliance Costs, Benefits (Inclusive of Health CoBenefits), and Net Benefits, Relative to Base Case (CPP), 20230-2037 (billion 2016$)................. 6-12

Table 6-9 Illustrative 2 Percent HRI at $50/kW Scenario: Present Value of Compliance Costs, Benefits 

(Inclusive of Health Co-Benefits), and Net Benefits, Relative to Base Case (CPP), 2023-2037 (billion 

2016$) .............................................................................................................................................. 6-13

Table 6-10 Illustrative 4.5 Percent HRI at $50/kW Scenario: Present Value of Compliance Costs, Benefits 

(Inclusive of Health Co-Benefits), and Net Benefits, Relative to Base Case (CPP), 2023-2037 (billion 

2016$) .............................................................................................................................................. 6-13

Table 6-11 Illustrative 4.5 Percent HRI at $100/kW Scenario: Present Value of Compliance Costs, Benefits 

(Inclusive of Health Co-Benefits), and Net Benefits, Relative to Base Case (CPP), 2023-2037 (billion 

2016$) .............................................................................................................................................. 6-14

Table 6-12 Present Value of Compliance Costs, Benefits (Inclusive of Health Co-Benefits), and Net Benefits, 

Relative to Base Case (CPP), 3 and 7 Percent Discount Rates, 2023-2037 (billion 2016$)............. 6-14
xi

Table 6-13 Present Value of Compliance Costs, Benefits, and Net Benefits assuming that PM2.5 Related Benefits 

Fall to Zero Below the Lowest Measured Level of Each Long-Term PM2.5 Mortality Study, Relative 

to Base Case (CPP), 3 and 7 Percent Discount Rates, 2023-2037 (billion 2016$)........................... 6-16

Table 6-14 Present Value of Compliance Costs, Benefits, and Net Benefits assuming that PM2.5 Related Benefits 

Fall to Zero Below the PM2.5 National Ambient Air Quality Standard, Relative to Base Case (CPP), 3 

and 7 Percent Discount Rates, 2023-2037 (billion 2016$)............................................................... 6-16

Table 6-15 Present Value of Compliance Costs, Benefits, and Net Benefits assuming Alternate Method for 

Calculating PM2.5 Benefits at Low Levels, Relative to Base Case (CPP), 3 and 7 Percent Discount 

Rates, 2023-2037 (billion 2016$)..................................................................................................... 6-17

Table 8-1 Model Performance Statistics by Region for PM2.5............................................................................ 8-9

Table 8-2 Model Performance Statistics by Region for Ozone on Days Above 60 ppb................................... 8-10

Table 8-3 Table of Source Apportionment Tags .............................................................................................. 8-11

Table 8-4 Tribal Fractions by State in the 2023 Emissions .............................................................................. 8-21

Table 8-5 Scaling Ratios for Primary PM2.5 for Coal EGUs............................................................................. 8-26

Table 8-6 Scaling Ratios for Primary PM2.5 for Non-Coal EGUs .................................................................... 8-28

Table 8-7 Scaling Ratios for Sulfate for Coal EGUs........................................................................................ 8-30

Table 8-8 Scaling Ratios for Sulfate for Non-Coal EGUs................................................................................ 8-32

Table 8-9 Scaling Ratios for Nitrate for Coal EGUs ........................................................................................ 8-34

Table 8-10 Scaling Ratios for Nitrate for Non-Coal EGUs................................................................................ 8-36

Table 8-11 Scaling Ratios for Ozone for Coal EGUs......................................................................................... 8-38

Table 8-12 Scaling Ratios for Ozone for Non-Coal EGUs................................................................................. 8-40
xii

LIST OF FIGURES

Figure 2-1 New Build and Retired Capacity (MW) by Technology, 2006-2016................................................. 2-4

Figure 2-2 Cumulative Distribution in 2025 of Coal and Natural Gas Electricity Capacity by Age ................... 2-7

Figure 2-3 2016 Annual Average Capacity Factor for Coal Steam Generators, by Capacity .............................. 2-8

Figure 2-4 2016 Annual Average Capacity Factor for Coal Steam Generators, by Age in 2016 ........................ 2-9

Figure 2-5 Electricity Generating Facilities, by Size and Type ......................................................................... 2-10

Figure 2-6 Average Retail Electricity Price by State (cents/kWh), 2016........................................................... 2-14

Figure 2-7 Nominal National Average Electricity Prices for Three Major End-Use Categories....................... 2-15

Figure 2-8 Relative Increases in Nominal National Average Electricity Prices for Major End-Use Categories, 

With Inflation Indices....................................................................................................................... 2-16

Figure 2-9 Real National Average Electricity Prices (2016$) for Three Major End-Use Categories................ 2-17

Figure 2-10 Relative Change in Real National Average Electricity Prices (2016) for Three Major End-Use 

Categories......................................................................................................................................... 2-17

Figure 2-11 Change in National Annual Average Cost of Real Fossil Fuel Receipts at EGUs per MMBtu ....... 2-18

Figure 2-12 Relative Growth of Electricity Generation, Population and Real GDP Since 2006 ......................... 2-19

Figure 2-13 Relative Change of Real GDP, Population and Electricity Generation Intensity Since 2006 .......... 2-20

Figure 2-14 Status of State Electricity Industry Restructuring Activities............................................................ 2-22

Figure 2-15 Capacity Mix by Ownership Type, 2006 & 2016............................................................................. 2-24

Figure 2-16 Generation Mix by Ownership Type, 2006 & 2016 ......................................................................... 2-24

Figure 2-17 Generation Capacity Built between 2006 and 2016 by Ownership Type......................................... 2-25

Figure 2-18 Generation Capacity Retired between 2006 and 2016 by Ownership Type ..................................... 2-26

Figure 2-19 Domestic Emissions of Greenhouse Gases from Major Sectors, 2006 and 2015 (million tons of CO2

equivalent)........................................................................................................................................ 2-27

Figure 2-20 Relative Change Nominal and Real (2016$) Prices of Natural Gas Delivered to the Power Sector 

($/MMBtu) ....................................................................................................................................... 2-31

Figure 2-21 Relative Change in Real (2016$) Prices of Fossil Fuels Delivered to the Power Sector ($/MMBtu) ......

.......................................................................................................................................................... 2-32

Figure 3-1 Generation Mix (thousand GWh)..................................................................................................... 3-26

Figure 4-1 Relationship between the PM2.5 Concentrations Considered in Epidemiology Studies and our 

Confidence in the Estimated PM-related Premature Deaths ............................................................ 4-26

Figure 4-2 Number of Individuals Exposed According to Annual Mean PM2.5 Concentration in 2030............ 4-28

Figure 4-3 Number of PM2.5-Related Premature Deaths According to PM2.5 Concentration in 2030 ............... 4-29

Figure 4-4 Change in Annual Mean PM2.5 (μg/m3) and Summer Season Average Daily 8hr Maximum Ozone 

(ppb) in 2025 (Difference Calculated as Illustrative Scenario - Base Case) .................................... 4-30

Figure 4-5 Estimated Forgone Avoided PM2.5 and Ozone Deaths for Each Illustrative Scenario in 2025, Relative 

to Base Case (CPP) (Deaths per 100k People)................................................................................. 4-39

Figure 7-1 Frequency Distribution of Interim Domestic SC-CO2 Estimates for 2030 (in 2016$ per metric ton 

CO2).................................................................................................................................................... 7-5

Figure 8-1 Air Quality Modeling Domain ........................................................................................................... 8-2

Figure 8-2 NOAA Climate Regions.................................................................................................................... 8-8

Figure 8-3 Map of Pennsylvania Coal EGU Tag Contribution to Seasonal Average MDA8 Ozone................. 8-12

Figure 8-4 Map of Pennsylvania Non-Coal EGU Tag Contribution to Seasonal Average MDA8 Ozone......... 8-13

Figure 8-5 Map of Texas Coal EGU Tag Contribution to Seasonal Average MDA8 Ozone ............................ 8-13

Figure 8-6 Map of Texas Non-Coal EGU Tag Contribution to Seasonal Average MDA8 Ozone .................... 8-14

Figure 8-7 Map of Indiana Coal EGU Tag Contributions to Wintertime Average (January-March) Nitrate .... 8-15

Figure 8-8 Map of Indiana Coal EGU Tag Contributions to Summertime Average (July-September) Nitrate . 8-15

Figure 8-9 Map of Indiana Coal EGU Tag Contributions to Wintertime Average (January-March) Sulfate .... 8-16

Figure 8-10 Map of Indiana Coal EGU Tag Contributions to Summertime Average (July-September) Sulfate. 8-16

Figure 8-11 Map of Indiana Coal EGU Tag Contributions to Wintertime Average (January-March) Primary PM2.5

8-17

Figure 8-12 Map of Indiana Coal EGU Tag Contributions to Summertime Average (July-September) Primary 

PM2.5................................................................................................................................................. 8-17
ES-1

EXECUTIVE SUMMARY

ES.1 Introduction

With this notice, the Environmental Protection Agency (EPA) is proposing three distinct 

actions, including Emission Guidelines for Greenhouse Gas Emissions from Existing Electric 

Utility Generating Units (EGUs). First, EPA is proposing to replace the Clean Power Plan (CPP) 

with revised emissions guidelines (the Affordable Clean Energy (ACE) rule) for states to follow 

in developing implementation plans to reduce greenhouse gas emission from certain EGUs. In 

the proposed emissions guidelines (UUUUa), consistent with the interpretation described in the 

proposed repeal of the CPP, the Agency is proposing to determine that heat rate improvement 

(HRI) measures are the best system of emission reduction (BSER) for existing coal-fired EGUs. 

Second, EPA is proposing new regulations that provide direction to both EPA and the states on 

the implementation of emission guidelines. The new proposed implementing regulations would 

apply to this action and any future emission guideline issued under section 111(d) of the Clean 

Air Act (CAA). Third, the Agency is proposing revisions to the New Source Review (NSR) 

program that will help prevent NSR from being a barrier to the implementation of efficiency 

projects at EGUs. 

This proposed action is an economically significant regulatory action that was submitted 

to the Office for Management and Budget (OMB) for interagency review. Any changes made in 

response to interagency review have been documented in the docket. This regulatory impact 

analysis (RIA) presents an assessment of the regulatory compliance costs and benefits associated 

with this action and is consistent with Executive Orders 12866, 13563, and 13771.

ES.2 Analysis

In this RIA, the Agency provides a full benefit cost analysis of four illustrative scenarios. 

The four illustrative scenarios include a scenario modeling the full repeal of the CPP, which we 

term a No CPP case, and three replacement policy scenarios modeling heat rate improvements 

(HRI) at coal-fired EGUs. Throughout this RIA, these three illustrative policy scenarios are 

compared against a base case, which includes the CPP. By analyzing against the existing CPP, 

the reader can understand the combined impact of a repeal and replacement. Inclusion of a No 

CPP case allows for an understanding of the repeal alone and allows the reader to evaluate the 
ES-2

impact of the policy cases against a No CPP scenario. This RIA assumes a mass-based 

implementation of the CPP for existing affected sources, and does not assume interstate trading. 

The three illustrative policy scenarios represent potential outcomes of state determinations of 

standards of performance, and compliance with those standards by affected coal-fired EGUs. 

The analysis relies on EPA’s Power Sector Modeling Platform v6 using the Integrated 

Planning Model (IPM). This accounts for changes in the power sector since promulgation of the 

CPP in 2015, and projects our best understanding of important technological and economic 

trends into the future. This RIA also updates the analysis in the October 2017 RIA for the 

proposed repeal of the CPP, by updating, among other elements of the analysis, the expected 

future economic conditions affecting the electricity sector in both the base case, which includes 

the CPP, and the No CPP scenario.

Three of the illustrative scenarios model different levels and costs of HRIs applied 

uniformly at all affected coal-fired EGUs in the contiguous U.S. beginning in 2025. EPA has 

identified the BSER to be HRI. In final Emission Guidelines, EPA will provide states with a list 

of candidate HRI technologies that must be evaluated when establishing standards of 

performance. Each of these illustrative scenarios assumes that the affected sources are no longer 

subject to the state plan requirements of the CPP (i.e., the mass-based requirements assumed for 

CPP implementation in the base case for this RIA). The cost, suitability, and potential 

improvement for any of these HRI technologies is dependent on a range of unit-specific factors 

such as the size, age, fuel use, and the operating and maintenance history of the unit. As such, the 

HRI potential can vary significantly from unit to unit. EPA does not have sufficient information 

to assess HRI potential on a unit-by-unit basis. CAA 111(d) also provides States with the 

responsibility to establish standards of performance and provides considerable flexibility in 

applying those emission standards. States may take many factors into consideration – including 

the remaining useful life of the affected source – when applying the standards of performance. 

Therefore, any analysis of the proposed rule must be highly illustrative. However, EPA believes 

that such illustrative analyses can provide important insights at the national level and can inform 

the public on a range of potential outcomes. To avoid the impression that EPA can sufficiently 

distinguish likely standards of performance across individual affected units and their compliance 
ES-3

strategies, this analysis assumes different HRI levels and costs are applied uniformly to affected 

coal-fired EGUs under each of three illustrative policy scenarios:

• 2 Percent HRI at $50/kW: This illustrative scenario represents a policy case that reflects 

modest improvements in HRI absent any revisions to NSR requirements. For many years, 

industry has indicated to the Agency that many sources have not implemented certain 

HRI projects because the burdensome costs of NSR cause such projects to not be viable. 

Thus, absent NSR reform, HRI at affected units might be expected to be modest. Based 

on numerous studies and statistical analysis, the Agency believes that the HRI potential 

for coal-fired EGUs will, on average, range from one to three percent at a cost of $30 to 

$60 per kilowatt (kW) of EGU generating capacity. The Agency believes that this 

scenario (2 percent HRI at $50/kW) reasonably represents that range of HRI and cost. 

• 4.5 Percent HRI at $50/kW: This illustrative scenario represents a policy case that 

includes benefits from the proposed revisions to NSR, with the HRI modeled at a low 

cost. As mentioned earlier, the Agency is proposing revisions to the NSR program that 

will provide owners and operators of existing EGUs greater ability to make efficiency 

improvements without triggering provisions of NSR. This scenario is informative in that 

it represents the ability of all coal-fired EGUs to obtain greater improvements in heat rate 

because of NSR reform at the $50/kW cost identified earlier. EPA believes this higher 

heat rate improvement potential is possible because without NSR a greater number of 

units may have the opportunity to make cost effective heat rate improvements such as 

turbine upgrades that have the potential to offer greater heat rate improvement 

opportunities.

• 4.5 Percent HRI at $100/kW: This illustrative scenario represents a policy case that 

includes the benefits from the proposed revisions to NSR, with the HRI modeled at a 

higher cost. This scenario is informative in that it represents the ability of a typical coalfired EGUs to obtain greater improvements in heat rate because of NSR reform but at a 

much higher cost ($100/kW) than the $50/kW cost identified earlier. Particularly for 

lower capacity units or those with limited remaining useful life, this could ultimately 

translate into HRI projects with higher costs.

Combined, the 4.5 percent HRI at $50/kW scenario and the 4.5 percent HRI at $100/kW 

scenario represent a range of potential costs for the proposed policy option that couples HRI with 

NSR reform. Modeling this at $50/kW and $100/kW provides a sensitivity analysis on the cost of 

the proposed policy including NSR reform. The $50/kW cost represents an optimistic bounding 

where NSR reform unleashes significant new opportunity for low-cost heat rate improvements. 

The $100/kW cost scenario represents higher costs. Additional information describing these 

illustrative scenarios is located in Chapter 1.

The Agency understands that there may be interest in comparing the three illustrative 

policy scenarios against an alternative baseline that does not include the CPP. For those 
ES-4

interested in comparing the potential impacts of policy scenarios in a world without the CPP, 

results from the three illustrative policy scenarios may be compared against an alternative 

baseline results from the illustrative No CPP case, which we term the No CPP alternative 

baseline. The presentation of an alternative baseline is consistent with Circular A-4, which states, 

“When more than one baseline is reasonable and the choice of baseline will significantly affect 

estimated benefits and costs, you should consider measuring benefits and costs against 

alternative baselines”

1 While these comparisons are not presented throughout the RIA, we 

provide information in this Executive Summary comparing the three illustrative policy scenarios 

to the No CPP alternative baseline. In addition, the full suite of model outputs and additional

comparison tables are available in the docket.

We evaluate the potential regulatory impacts of the illustrative No CPP scenario and the 

three illustrative policy scenarios using the present value (PV) of costs, benefits, and net benefits, 

calculated for the years 2023-2037 from the perspective of 2016, using both a three percent and 

seven percent beginning-of-period discount rate. In addition, the Agency presents the assessment 

of costs, benefits, and net benefits for specific snapshot years, consistent with historic practice. In 

this RIA, the regulatory impacts are evaluated for the specific years of 2025, 2030, and 2035. 

The Agency believes that these specific years are each representative of several 

surrounding years, which enables the analysis of costs and benefits over the timeframe of 2025-

2037. The year 2025 is an approximation for when the standards of performance under the 

proposed rule might be implemented, and the Agency estimates that monitoring, reporting, and 

recordkeeping (MR&R) costs may begin in 2023. Therefore, MR&R costs analysis is presented 

beginning in the year 2023, and full benefit cost analysis is presented beginning in the year 2025. 

The analytical timeframe concludes in 2037, as this is the last year that may be represented with 

the analysis conducted for the specific year of 2035.

This RIA builds upon the methodological changes contained in the Regulatory Impact 

Analysis for the Review of the Clean Power Plan: Proposal. In addition, EPA is currently 

seeking comment, through its Advanced Notice of Proposed Rulemaking on Increasing 

 

1 Office of Management and Budget (OMB), 2003, Circular A-4,

https://www.whitehouse.gov/sites/whitehouse.gov/files/omb/circulars/A4/a-4.pdf
ES-5

Consistency and Transparency in Considering Costs and Benefits in the Rulemaking Process (83 

FR 27524), on a variety of related matters, including possible approaches for increasing 

consistency and transparency in considering costs and benefits in the rulemaking process.

While the results are described and presented in more detail later in this executive 

summary and throughout the RIA, we present the high-level results of the analysis here, for both 

baselines. Table ES-1 provides the present value (PV) and equivalent annualized value (EAV) of 

costs, benefits, and net benefits relative to the base case (which includes CPP) associated with 

the targeted pollutant, CO2, over the timeframe of 2023-2037. Table ES-2 presents the same set 

of information, but relative to the No CPP alternative. In these two tables, negative costs indicate 

avoided costs, negative benefits indicate forgone benefits, and negative net benefits indicate 

forgone net benefits.

Table ES-1 Present Value and Equivalent Annualized Value of Compliance Costs, 

Climate Benefits, and Net Benefits Associated with Targeted Pollutant (CO2), 

Relative to Base Case (CPP), 3 and 7 Percent Discount Rates, 2023-2037 

(billions of 2016$)

Costs Domestic 

Climate Benefits

Net Benefits 

associated with the 

Targeted Pollutant (CO2)

3% 7% 3% 7% 3% 7%

Present Value

No CPP (5.2) (3.1) (3.9) (0.4) 1.2 2.7 

2% HRI at $50/kW (0.4) (0.3) (3.2) (0.3) (2.8) (0.1)

4.5% HRI at $50/kW (6.4) (3.7) (3.2) (0.3) 3.2 3.4 

4.5% HRI at $100/kW 3.0 1.7 (2.4) (0.2) (5.4) (2.0)

Equivalent Annualized Value

No CPP (0.4) (0.3) (0.3) (0.0) 0.1 0.3 

2% HRI at $50/kW (0.0) (0.0) (0.3) (0.0) (0.2) (0.0)

4.5% HRI at $50/kW (0.5) (0.4) (0.3) (0.0) 0.3 0.4 

4.5% HRI at $100/kW 0.3 0.2 (0.2) (0.0) (0.5) (0.2)

Notes: Negative costs indicate avoided costs, negative benefits indicate forgone benefits, and negative net benefits 

indicate forgone net benefits. All estimates are rounded to one decimal point, so figures may not sum due to 

independent rounding. Climate benefits reflect the value of domestic impacts from CO2 emissions changes. This 

table does not include estimates of ancillary health co-benefits from changes in electricity sector SO2 and NOX

emissions.
ES-6

Table ES-2 Present Value and Equivalent Annualized Value of Compliance Costs, 

Climate Benefits, and Net Benefits Associated with Targeted Pollutant (CO2), 

Relative to the No CPP Alternative Baseline, 3 and 7 Percent Discount Rates, 

2023-2037 (billions of 2016$)

Costs Domestic 

Climate Benefits

Net Benefits 

associated with 

the Targeted 

Pollutant (CO2)

3% 7% 3% 7% 3% 7%

Present Value

2% HRI at $50/kW 4.8 2.8 0.8 0.1 (4.1) (2.8)

4.5% HRI at $50/kW (1.2) (0.6) 0.7 0.1 2.0 0.7 

4.5% HRI at $100/kW 8.2 4.8 1.6 0.2 (6.6) (4.7)

Equivalent Annualized Value

2% HRI at $50/kW 0.4 0.3 0.1 0.0 (0.3) (0.3)

4.5% HRI at $50/kW (0.1) (0.1) 0.1 0.0 0.2 0.1 

4.5% HRI at $100/kW 0.7 0.5 0.1 0.0 (0.6) (0.5)

Notes: Negative costs indicate avoided costs, negative benefits indicate forgone benefits, and negative net benefits 

indicate forgone net benefits. All estimates are rounded to one decimal point, so figures may not sum due to 

independent rounding. Climate benefits reflect the value of domestic impacts from CO2 emissions changes. This 

table does not include estimates of ancillary health co-benefits from changes in electricity sector SO2 and NOX

emissions.

ES.3 Compliance Costs

The power industry’s “compliance costs” are represented in this analysis as the change in 

electric power generation costs between the base case and illustrative scenarios, including the 

cost of monitoring, reporting, and recordkeeping (MR&R). In simple terms, these costs are an 

estimate of the increased power industry expenditures required to implement the HRI required by 

the proposed rule, minus the sectoral cost of complying with the CPP assumed in the base case. 

Table ES-3 presents the annualized compliance costs of the three illustrative policy scenarios and 

the illustrative No CPP scenario.2In this table, and throughout the RIA, negative costs indicate 

avoided costs relative to the base case (which includes the CPP), and positive costs indicate an 

increase in projected compliance costs, relative to the base case. As shown in Table ES-3, the 

Agency estimates that there are avoided costs under three out of the four illustrative scenarios

base case (which includes the CPP). EPA uses the projection of private compliance costs as an 

 

2 This RIA does not identify who ultimately bears the compliance costs, such as owners of generating assets through 

changes in their profits or electricity consumers through changes in their bills.
ES-7

estimate of the social cost, which is the appropriate metric for formal economic welfare analysis, 

of this proposal.

Table ES-3 Compliance Costs, Relative to Base Case (CPP) (billions of 2016$)

No CPP 2% HRI at 

$50/kW

4.5% HRI at 

$50/kW

4.5% HRI at 

$100/kW

2025 (0.7) 0.0 (0.6) 0.5

2030 (0.7) (0.2) (1.0) 0.2

2035 (0.4) 0.1 (0.6) 0.5

Notes: Negative costs indicate that, on net, the illustrative scenario avoids costs relative to the base case with the 

CPP. Compliance costs equal the projected change in total power sector generating costs, plus the costs of 

monitoring, reporting, and recordkeeping.

As shown in Table ES-4, EPA estimates that there are avoided costs under one of the three 

illustrative scenarios relative to the No CPP alternative baseline.

Table ES-4 Compliance Costs, Relative to No CPP Alternative Baseline (billions of 

2016$)

2% HRI at $50/kW 4.5% HRI at $50/kW 4.5% HRI at $100/kW

2025 0.7 0.1 1.3

2030 0.5 (0.2) 0.9

2035 0.5 (0.2) 0.8

Notes: Negative costs indicate that, on net, the illustrative scenario reduces costs relative to the No CPP alternative 

baseline. Compliance costs equal the projected change in total power sector generating costs, plus the costs of 

monitoring, reporting, and recordkeeping.

Due to a number of changes in the electricity sector since the CPP was finalized, as 

documented in the October 2017 RIA conducted for the proposed CPP repeal and Chapter 3 of 

this RIA, the sector has become less carbon intensive over the past several years, and the trend is 

projected to continue. These changes and trends are reflected in the modeling used for this 

analysis. As such, achieving the emissions levels required under CPP requires less effort and 

expense, relative to a scenario without the CPP, and the estimated compliance costs are 

significantly lower than what was estimated in the final CPP RIA (U.S. EPA, 2015).

ES.4 Emissions Changes

Emissions are projected to be higher under the three illustrative policy scenarios and the 

illustrative No CPP scenario than under the base case, as the base case includes the CPP. Table 

ES-5 shows the projected CO2 emissions impacts of each scenario, relative to the base case

including the CPP.
ES-8

Table ES-5 Projected CO2 Emission Impacts, Relative to Base Case (CPP) Scenario

CO2 Emissions

(MM Short Tons)

CO2 Emissions Change 

(MM Short Tons)

CO2 Emissions Change 

Percent Change

2025 2030 2035 2025 2030 2035 2025 2030 2035

No CPP 1,829 1,811 1,794 50 74 66 3% 4% 4%

Base Case (CPP) 1,780 1,737 1,728 -- -- -- -- -- --

2% HRI at $50/kW 1,816 1,798 1,783 37 61 55 2% 3% 3%

4.5% HRI at $50/kW 1,812 1,797 1,787 32 60 59 2% 3% 3%

4.5% HRI at $100/kW 1,799 1,785 1,772 20 47 44 1% 3% 3%

Table ES-6 shows the projected CO2 emissions impacts of each scenario, relative to the No CPP 

alternative baseline.

Table ES-6 Projected CO2 Emission Impacts, Relative to No CPP Alternative Baseline

CO2 Emissions

(MM Short Tons)

CO2 Emissions Change 

(MM Short Tons)

CO2 Emissions Change 

Percent Change

2025 2030 2035 2025 2030 2035 2025 2030 2035

No CPP 1,829 1,811 1,794 -- -- -- -- -- --

2% HRI at $50/kW 1,816 1,798 1,783 -13 -13 -11 -1% -1% -1%

4.5% HRI at $50/kW 1,812 1,797 1,787 -18 -14 -7 -1% -1% 0%

4.5% HRI at $100/kW 1,799 1,785 1,772 -30 -27 -22 -2% -1% -1%

Table ES-7 shows projected emission increases relative to the base case for carbon dioxide 

(CO2), sulfur dioxide (SO2) and nitrogen oxides (NOX) from the electricity sector.
ES-9

Table ES-7 Projected CO2, SO2, and NOX Electricity Sector Emission Increases, Relative 

to the Base Case (CPP) (2025-2035)

CO2

(million short tons)

SO2

(thousand short tons)

NOX

(thousand short tons)

No CPP

2025 50 36 32

2030 74 60 47

2035 66 44 43

2% HRI at $50/kW

2025 37 35 24

2030 61 53 39

2035 55 34 39

4.5% HRI at $50/kW

2025 32 40 21

2030 60 53 39

2035 59 43 43

4.5% HRI at $100/kW

2025 20 32 14

2030 47 45 32

2035 44 29 33

Source: Integrated Planning Model, 2018. 

Notes: CO2 emission increases are used to estimate forgone domestic climate benefits. SO2, and NOX increases are 

used for estimating the forgone health benefits from reduced particulate matter and ozone exposures.

Table ES-8 shows projected emission changes relative to the No CPP alternative baseline.
ES-10

Table ES-8 Projected CO2, SO2, and NOX Electricity Sector Emission Changes, Relative 

to the No CPP Alternative Baseline (2025-2035)

CO2

(million short 

tons)

SO2

(thousand short 

tons)

NOX

(thousand short 

tons)

Base Case (CPP)

2025 -50 -36 -32

2030 -74 -60 -47

2035 -66 -44 -43

2% HRI Scenario at $50/kW

2025 -13 0 -8

2030 -13 -7 -8

2035 -11 -11 -5

4.5% HRI Scenario at $50/kW

2025 -18 4 -11

2030 -14 -7 -8

2035 -7 -1 -1

4.5% HRI Scenario at $100/kW

2025 -30 -3 -18

2030 -27 -15 -15

2035 -22 -16 -11

Source: Integrated Planning Model, 2018. 

ES.5 Climate and Health Co-Benefits

We estimated climate-related impacts from changes in CO2 and the air quality-related 

impacts from changes in SO2 and NOX. We refer to climate benefits as “targeted pollutant 

benefits” because these are the direct benefits of reducing CO2. We refer to air pollution health 

benefits as ancillary “co-benefits” because they result from policies affecting CO2, but are not 

the goal of this policy. To estimate the climate benefits associated with changes in CO2

emissions, we apply a measure of the domestic social cost of carbon (SC-CO2). The SC-CO2 is a 

metric that estimates the monetary value of impacts associated with marginal changes in CO2

emissions in each year. The SC-CO2 estimates used in this RIA account for the direct impacts of 

climate change that are anticipated to occur within the contiguous 48 states. 

We performed gridded photochemical air quality modeling to support the air quality 

benefits assessment of this proposal, and quantified the health benefits attributable to changes in 

fine particles 2.5 microns and smaller (PM2.5) and ground-level ozone. This modeling accounted 
ES-11

for the current suite of local, state and federal policies expected to reduce PM2.5 and PM2.5

precursor emissions in future years.3Table ES-9 reports the combined domestic climate benefits 

and ancillary health co-benefits attributable to changes in SO2 and NOX emissions, discounted at

3 percent and 7 percent and presented in 2016 dollars, in the years 2025, 2030 and 2035. This 

table reports the air pollution effects calculated using PM2.5 log-linear concentration-response 

functions that quantify risk associated with the full range of PM2.5 exposures experienced by the 

population (U.S. EPA, 2009; U.S. EPA, 2011; NRC, 2002).4 Nearly all the PM2.5-related forgone 

benefits reported for each year and for each scenario occur in locations where the annual mean 

PM2.5 concentrations are projected to be below the annual PM2.5 standard of 12 μg/m3. We 

estimate that only about 1 percent of the PM2.5-related premature deaths will occur in locations 

exceeding the annual PM standard in 2025, 2030 and 2035 (see Chapter 4).

When setting the 2012 PM NAAQS, the Administrator acknowledged greater uncertainty 

in specifying the “magnitude and significance” of PM-related health risks at PM concentrations 

below the NAAQS. As noted in the preamble to the 2012 PM NAAQS final rule, in the context 

of selecting and alternative NAAQS, “EPA concludes that it is not appropriate to place as much 

confidence in the magnitude and significance of the associations over the lower percentiles of the 

distribution in each study as at and around the long-term mean concentration.” (78 FR 3154, 15 

January 2013).

In general, we are more confident in the size of the risks we estimate from simulated 

PM2.5 concentrations that coincide with the bulk of the observed PM concentrations in the 

epidemiological studies that are used to estimate the benefits. Likewise, we are less confident in 

the risk we estimate from simulated PM2.5 concentrations that fall below the bulk of the observed 

data in these studies.5 To give readers insight to the uncertainty in the estimated forgone PM2.5

 

3 Policies expected to impact EGU sector emissions are accounted for out to 2025, 2030, and 2035 future years, but 

policies expected to impact other emissions source sectors are only accounted for out to 2023.

4 This approach is consistent with employing a no-threshold assumption for estimating PM2.5-related health effects. 

The preamble to the 2012 PM NAAQS noted that “[a]s both the EPA and CASAC recognize, in the absence of a 

discernible threshold, health effects may occur over the full range of concentrations observed in the 

epidemiological studies.” (78 FR 3149, 15 January 2013). This log-linear, no-threshold approach to calculating 

and reporting the risk of PM2.5-attributable premature deaths is consistent with recent RIAs (U.S. EPA 2009b, 

2010c, 2010d, 2011a, 2011b, 2011c, 2012, 2013, 2014, 2015a, 2016).

5 The Federal Register Notice for the 2012 PM NAAQS indicates that “[i]n considering this additional population 

level information, the Administrator recognizes that, in general, the confidence in the magnitude and significance of 
ES-12

mortality benefits occurring at lower ambient levels, we also report the PM benefits according to 

alternative concentration cut-points and concentration-response parameters. The percentage of 

estimated PM2.5-related deaths occurring below the lowest measured levels (LML) of the two 

long-term epidemiological studies we use to estimate risk varies between 16 percent (Krewski et 

al. 2009) and 79 percent (Lepeule et al. 2012). The percentage of estimated premature deaths 

occurring above the LML and below the NAAQS ranges between 84 percent (Krewski et al. 

2009) and 21 percent (Lepeule et al. 2012). Less than 1% of the estimated premature deaths 

occur above the annual mean PM2.5 NAAQS of 12 μg/m3. 

Below we report the benefits forgone to society for each of the policy scenarios. In these 

tables, negative values represent forgone benefits and positive benefits represent realized 

benefits. 

In Table ES-9 negative benefits indicate benefits forgone to society. All estimated 

benefits reported in Table ES-9 are negative, indicating that each of the four illustrative scenarios 

yield forgone climate benefits and forgone ancillary health co-benefits relative to the base case, 

which includes the CPP.

 

an association identified in a study is strongest at and around the long-term mean concentration for the air quality 

distribution, as this represents the part of the distribution in which the data in any given study are generally most 

concentrated. She also recognizes that the degree of confidence decreases as one moves towards the lower part of 

the distribution.”
ES-13

Table ES-9 Monetized Benefits, Relative to Base Case (CPP) (billions of 2016$)

Values Calculated using 3% Discount Rate Values Calculated using 7% Discount Rate

Domestic 

Climate 

Benefits

Ancillary

Health 

Co-Benefits

Total 

Benefits

Domestic 

Climate 

Benefits

Ancillary

Health 

Co-Benefits

Total 

Benefits

No CPP

2025 (0.3) (2.8) to (6.6) (3.2) to (7.0) (0.1) (2.6) to (6.1) (2.7) to (6.1)

2030 (0.5) (4.9) to (11.4) (5.4) to (11.9) (0.1) (4.5) to (10.5) (4.6) to (10.6)

2035 (0.5) (3.8) to (8.8) (4.3) to (9.3) (0.1) (3.5) to (8.1) (3.6) to (8.2)

2% HRI at $50/kW 

2025 (0.2) (2.6) to (5.9) (2.8) to (6.2) (0.0) (2.4) to (5.4) (2.4) to (5.5)

2030 (0.4) (4.5) to (10.6) (4.9) to (11.0) (0.1) (4.1) to (9.8) (4.2) to (9.9)

2035 (0.4) (3.0) to (7.0) (3.4) to (7.4) (0.1) (2.7) to (6.5) (2.8) to (6.6)

4.5% HRI at $50/kW 

2025 (0.2) (2.7) to (6.2) (2.9) to (6.4) (0.0) (2.5) to (5.7) (2.5) to (5.7)

2030 (0.4) (4.2) to (9.8) (4.6) to (10.2) (0.1) (3.9) to (9.0) (3.9) to (9.1)

2035 (0.5) (4.0) to (9.3) (4.4) to (9.8) (0.1) (3.7) to (8.6) (3.7) to (8.7)

4.5% HRI at $100/kW 

2025 (0.1) (2.1) to (4.9) (2.3) to (5.0) (0.0) (2.0) to (4.4) (2.0) to (4.4)

2030 (0.3) (3.6) to (8.2) (3.9) to (8.6) (0.1) (3.3) to (7.6) (3.3) to (7.6)

2035 (0.3) (2.6) to (6.0) (2.9) to (6.3) (0.1) (2.4) to (5.5) (2.4) to (5.6)

Notes: Negative benefit values indicate forgone benefits relative to the base case, which includes the CPP. All 

estimates are rounded to one decimal point, so figures may not sum due to independent rounding. Climate benefits 

reflect the value of domestic impacts from CO2 emissions changes. The ancillary health co-benefits reflect the sum 

of the PM2.5 and ozone benefits from changes in electricity sector SO2 and NOX emissions and reflect the range 

based on adult mortality functions (e.g., from Krewski et al. (2009) with Smith et al. (2009) to Lepeule et al. (2012) 

with Jerrett et al. (2009)). 

ES.6 Net Benefits

In the decision-making process it is useful to consider the change in benefits due to the 

targeted pollutant relative to the costs. Therefore, in Chapter 6 we present a comparison of the 

benefits from the targeted pollutant – CO2 – with the compliance costs. Excluded from this 

comparison are the benefits from changes in PM2.5 and ozone concentrations from changes in 

SO2 and NOX, emissions that are projected to accompany changes in CO2 emissions.

Table ES-10 presents the present value (PV) and equivalent annualized value (EAV) of 

the estimated costs, benefits, and net benefits associated with the targeted pollutant, CO2, for the 

timeframe of 2023-2037, relative to the base case, which includes the CPP. The EAV represents 

an even-flow of figures over the timeframe of 2023-2037 that would yield an equivalent present 
ES-14

value. The EAV is identical for each year of the analysis, in contrast to the year-specific 

estimates presented earlier for the snapshot years of 2025, 2030, and 2035. 

In Table ES-10, and all net benefit tables, negative costs indicate avoided costs, negative 

benefits indicate forgone benefits, and negative net benefits indicate forgone net benefits.

Table ES-10 Present Value and Equivalent Annualized Value of Compliance Costs, 

Climate Benefits, and Net Benefits Associated with Targeted Pollutant (CO2), 

Relative to Base Case (CPP), 3 and 7 Percent Discount Rates, 2023-2037 

(billions of 2016$)

Costs Domestic 

Climate Benefits

Net Benefits 

associated with the 

Targeted Pollutant (CO2)

3% 7% 3% 7% 3% 7%

Present Value

No CPP (5.2) (3.1) (3.9) (0.4) 1.2 2.7 

2% HRI at $50/kW (0.4) (0.3) (3.2) (0.3) (2.8) (0.1)

4.5% HRI at $50/kW (6.4) (3.7) (3.2) (0.3) 3.2 3.4 

4.5% HRI at $100/kW 3.0 1.7 (2.4) (0.2) (5.4) (2.0)

Equivalent Annualized Value

No CPP (0.4) (0.3) (0.3) (0.0) 0.1 0.3 

2% HRI at $50/kW (0.0) (0.0) (0.3) (0.0) (0.2) (0.0)

4.5% HRI at $50/kW (0.5) (0.4) (0.3) (0.0) 0.3 0.4 

4.5% HRI at $100/kW 0.3 0.2 (0.2) (0.0) (0.5) (0.2)

Notes: Negative costs indicate avoided costs, negative benefits indicate forgone benefits, and negative net benefits 

indicate forgone net benefits. All estimates are rounded to one decimal point, so figures may not sum due to 

independent rounding. Climate benefits reflect the value of domestic impacts from CO2 emissions changes. This 

table does not include estimates of ancillary health co-benefits from changes in electricity sector SO2 and NOX

emissions.

Table ES-11 presents the costs, benefits, and net benefits associated with the targeted 

pollutant for specific years, rather than as a PV or EAV as found in Table ES-10.
ES-15

Table ES-11 Compliance Costs, Climate Benefits, and Net Benefits Associated with 

Targeted Pollutant (CO2), Relative to Base Case (CPP), 3 and 7 Percent 

Discount Rates, 2025, 2030, and 2035 (billions of 2016$)

Costs Domestic 

Climate Benefits 

Net Benefits 

associated with the 

Targeted Pollutant (CO2)

3% 7% 3% 7% 3% 7%

No CPP

2025 (0.7) (0.7) (0.3) (0.1) 0.4 0.7

2030 (0.7) (0.7) (0.5) (0.1) 0.2 0.6

2035 (0.4) (0.4) (0.5) (0.1) (0.1) 0.3

2% HRI at $50/kW

2025 0.0 0.0 (0.2) (0.0) (0.3) (0.1)

2030 (0.2) (0.2) (0.4) (0.1) (0.2) 0.2

2035 0.1 0.1 (0.4) (0.1) (0.6) (0.2)

4.5% HRI at $50/kW

2025 (0.6) (0.6) (0.2) (0.0) 0.4 0.6

2030 (1.0) (1.0) (0.4) (0.1) 0.5 0.9

2035 (0.6) (0.6) (0.5) (0.1) 0.2 0.5

4.5% HRI at $100/kW

2025 0.5 0.5 (0.1) (0.0) (0.7) (0.5)

2030 0.2 0.2 (0.3) (0.1) (0.5) (0.2)

2035 0.5 0.5 (0.3) (0.1) (0.8) (0.5)

Notes: Negative costs indicate avoided costs, negative benefits indicate forgone benefits, and negative net benefits 

indicate forgone net benefits. All estimates are rounded to one decimal point, so figures may not sum due to 

independent rounding. Climate benefits reflect the value of domestic impacts from CO2 emissions changes. This 

table does not include estimates of ancillary health co-benefits from changes in electricity sector SO2 and NOX

emissions.

Table ES-12 and Table ES-13 provide the estimated costs, benefits, and net benefits, 

inclusive of the ancillary health-co benefits. Table ES-12 presents the PV and EAV estimates, 

and Table ES-13 presents the estimates for the specific years of 2025, 2030, and 2035.
ES-16

Table ES-12 Present Value and Equivalent Annualized Value of Compliance Costs, Total 

Benefits, and Net Benefits, Relative to Base Case (CPP), 3 and 7 Percent 

Discount Rates, 2023-2037 (billions of 2016$)

Costs Benefits Net Benefits

3% 7% 3% 7% 3% 7%

Present Value

No CPP (5.2) (3.1) (37.2) to (81.5) (17.9) to (41.3) (32.0) to (76.3) (14.8) to (38.2)

2% HRI at $50/kW (0.4) (0.3) (32.7) to (72.4) (15.9) to (36.9) (32.3) to (72.0) (15.7) to (36.7)

4.5% HRI at $50/kW (6.4) (3.7) (34.3) to (75.2) (16.6) to (39.4) (27.9) to (68.8) (12.8) to (35.6)

4.5% HRI at $100/kW 3.0 1.7 (27.2) to (60.2) (13.9) to (31.9) (30.2) to (63.2) (15.6) to (33.7)

Equivalent Annualized Value

No CPP (0.4) (0.3) (3.1) to (6.8) (2.0) to (4.5) (2.7) to (6.4) (1.6) to (4.2)

2% HRI at $50/kW (0.0) (0.0) (2.7) to (6.1) (1.7) to (4.1) (2.7) to (6.0) (1.7) to (4.0)

4.5% HRI at $50/kW (0.5) (0.4) (2.9) to (6.3) (1.8) to (4.3) (2.3) to (5.8) (1.4) to (3.9)

4.5% HRI at $100/kW 0.3 0.2 (2.3) to (5.0) (1.5) to (3.5) (2.5) to (5.3) (1.7) to (3.7)

Notes: Negative costs indicate avoided costs, negative benefits indicate forgone benefits, and negative net benefits 

indicate forgone net benefits. All estimates are rounded to one decimal point, so figures may not sum due to 

independent rounding. Total benefits include both climate benefits and ancillary health co-benefits. Climate benefits 

reflect the value of domestic impacts from CO2 emissions changes. The ancillary health co-benefits reflect the sum 

of the PM2.5 and ozone benefits from changes in electricity sector SO2 and NOX emissions and reflect the range

based on adult mortality functions (e.g., from Krewski et al. (2009) with Smith et al. (2009) to Lepeule et al. (2012) 

with Jerrett et al. (2009)).
ES-17

Table ES-13 Compliance Costs, Total Benefits, and Net Benefits, Relative to Base Case 

(CPP), 3 and 7 Percent Discount Rates, 2025, 2030, and 2035 (billions of

2016$)

Costs Benefits Net Benefits

3% 7% 3% 7% 3% 7%

No CPP

2025 (0.7) (0.7) (3.2) to (7.0) (2.7) to (6.1) (2.4) to (6.2) (1.9) to (5.4)

2030 (0.7) (0.7) (5.4) to (11.9) (4.6) to (10.6) (4.7) to (11.2) (3.8) to (9.8)

2035 (0.4) (0.4) (4.3) to (9.3) (3.6) to (8.2) (3.9) to (8.9) (3.2) to (7.8)

2% HRI at $50/kW

2025 0.0 0.0 (2.8) to (6.2) (2.4) to (5.5) (2.8) to (6.2) (2.4) to (5.5)

2030 (0.2) (0.2) (4.9) to (11.0) (4.2) to (9.9) (4.7) to (10.8) (3.9) to (9.7)

2035 0.1 0.1 (3.4) to (7.4) (2.8) to (6.6) (3.5) to (7.6) (3.0) to (6.7)

4.5% HRI at $50/kW

2025 (0.6) (0.6) (2.9) to (6.4) (2.5) to (5.7) (2.3) to (5.8) (1.9) to (5.1)

2030 (1.0) (1.0) (4.6) to (10.2) (3.9) to (9.1) (3.7) to (9.2) (3.0) to (8.1)

2035 (0.6) (0.6) (4.4) to (9.8) (3.7) to (8.7) (3.8) to (9.2) (3.1) to (8.1)

4.5% HRI at $100/kW

2025 0.5 0.5 (2.3) to (5.0) (2.0) to (4.4) (2.8) to (5.5) (2.5) to (5.0)

2030 0.2 0.2 (3.9) to (8.6) (3.3) to (7.6) (4.1) to (8.7) (3.5) to (7.8)

2035 0.5 0.5 (2.9) to (6.3) (2.4) to (5.6) (3.4) to (6.8) (2.9) to (6.0)

Notes: Negative costs indicate avoided costs, negative benefits indicate forgone benefits, and negative net benefits 

indicate forgone net benefits. All estimates are rounded to one decimal point, so figures may not sum due to 

independent rounding. Total benefits include both climate benefits and ancillary health co-benefits. Climate benefits 

reflect the value of domestic impacts from CO2 emissions changes. The ancillary health co-benefits reflect the sum 

of the PM2.5 and ozone benefits from changes in electricity sector SO2 and NOX emissions and reflect the range 

based on adult mortality functions (e.g., from Krewski et al. (2009) with Smith et al. (2009) to Lepeule et al. (2012) 

with Zanobetti & Schwartz. (2008)).
ES-18

Table ES-14 provides the PV and EAV of costs, benefits, and net benefits relative to the 

No CPP alternative baseline associated with the targeted pollutant, CO2.

Table ES-14 Present Value and Equivalent Annualized Value of Compliance Costs, 

Climate Benefits, and Net Benefits Associated with Targeted Pollutant (CO2), 

Relative to the No CPP Alternative Baseline, 3 and 7 Percent Discount Rates, 

2023-2037 (billions of 2016$)

Costs Domestic 

Climate Benefits

Net Benefits 

associated with 

the Targeted 

Pollutant (CO2)

3% 7% 3% 7% 3% 7%

Present Value

2% HRI at $50/kW 4.8 2.8 0.8 0.1 (4.1) (2.8)

4.5% HRI at $50/kW (1.2) (0.6) 0.7 0.1 2.0 0.7 

4.5% HRI at $100/kW 8.2 4.8 1.6 0.2 (6.6) (4.7)

Equivalent Annualized Value

2% HRI at $50/kW 0.4 0.3 0.1 0.0 (0.3) (0.3)

4.5% HRI at $50/kW (0.1) (0.1) 0.1 0.0 0.2 0.1 

4.5% HRI at $100/kW 0.7 0.5 0.1 0.0 (0.6) (0.5)

Notes: Negative costs indicate avoided costs, negative benefits indicate forgone benefits, and negative net benefits 

indicate forgone net benefits. All estimates are rounded to one decimal point, so figures may not sum due to 

independent rounding. Climate benefits reflect the value of domestic impacts from CO2 emissions changes. This 

table does not include estimates of ancillary health co-benefits from changes in electricity sector SO2 and NOX

emissions.
ES-19

Table ES-15 provides the estimated costs, benefits, and net benefits, inclusive of the 

ancillary health-co benefits for the No CPP alternative baseline in PV and EAV forms. 

Table ES-15 Present Value and Equivalent Annualized Value of Compliance Costs, Total 

Benefits, and Net Benefits, Relative to the No CPP Alternative Baseline, 3 

and 7 Percent Discount Rates, 2023-2037 (billions of 2016$)

Costs Benefits Net Benefits 

3% 7% 3% 7% 3% 7%

Present Value

2% HRI at $50/kW 4.8 2.8 4.5 to 9.2 2.0 to 4.3 (0.3) to 4.3 (0.9) to 1.5

4.5% HRI at $50/kW (1.2) (0.6) 2.9 to 6.3 1.4 to 1.9 4.1 to 7.5 2.0 to 2.6

4.5% HRI at $100/kW 8.2 4.8 10.0 to 21.3 4.1 to 9.4 1.8 to 13.2 (0.8) to 4.5

Equivalent Annualized Value

2% HRI at $50/kW 0.4 0.3 0.4 to 0.8 0.2 to 0.5 (0.0) to 0.4 (0.1) to 0.2

4.5% HRI at $50/kW (0.1) (0.1) 0.2 to 0.5 0.1 to 0.2 0.3 to 0.6 0.2 to 0.3

4.5% HRI at $100/kW 0.7 0.5 0.8 to 1.8 0.4 to 1.0 0.1 to 1.1 (0.1) to 0.5

Notes: Negative costs indicate avoided costs, negative benefits indicate forgone benefits, and negative net benefits 

indicate forgone net benefits. All estimates are rounded to one decimal point, so figures may not sum due to 

independent rounding. Total benefits include both climate benefits and ancillary health co-benefits. Climate benefits 

reflect the value of domestic impacts from CO2 emissions changes. The ancillary health co-benefits reflect the sum 

of the PM2.5 and ozone benefits from changes in electricity sector SO2 and NOX emissions and reflect the range 

based on adult mortality functions (e.g., from Krewski et al. (2009) with Smith et al. (2009) to Lepeule et al. (2012) 

with Jerrett et al. (2009)).

ES.7 Economic and Employment Impacts

The proposed actions have energy market implications. Table ES-16 presents a variety of 

energy market impacts for 2025, 2030, and 2035 for the four illustrative scenarios, relative to the 

base case.
ES-20

Table ES-16 Summary of Certain Energy Market Impacts, Relative to Base Case (CPP) 

(Percent Change)

2025 2030 2035

No CPP

Retail electricity prices -0.5% -0.4% -0.1%

Average price of coal delivered to the power sector -0.1% -0.2% -0.4%

Coal production for power sector use 6.1% 9.2% 9.5%

Price of natural gas delivered to power sector -1.1% -0.3% 0.1%

Price of average Henry Hub (spot) -1.4% -0.8% -0.2%

Natural gas use for electricity generation -1.5% -1.5% -0.9%

2% HRI at $50/kW

Retail electricity prices -0.3% -0.2% -0.1%

Average price of coal delivered to the power sector 0.2% -0.1% -0.4%

Coal production for power sector use 5.5% 8.0% 8.4%

Price of natural gas delivered to power sector -1.1% -0.9% -0.4%

Price of average Henry Hub (spot) -1.4% -1.3% -0.6%

Natural gas use for electricity generation -2.5% -1.7% -1.1%

4.5% HRI at $50/kW

Retail electricity prices -0.5% -0.4% -0.2%

Average price of coal delivered to the power sector 0.7% 0.6% 0.3%

Coal production for power sector use 5.8% 8.6% 9.5%

Price of natural gas delivered to power sector -1.4% -1.1% -0.7%

Price of average Henry Hub (spot) -1.7% -1.6% -1.0%

Natural gas use for electricity generation -3.4% -2.5% -1.9%

4.5% HRI at $100/kW

Retail electricity prices -0.2% 0.0% 0.0%

Average price of coal delivered to the power sector 0.5% 0.3% -0.1%

Coal production for power sector use 4.5% 7.1% 7.4%

Price of natural gas delivered to power sector -1.3% -1.1% -0.7%

Price of average Henry Hub (spot) -1.6% -1.6% -1.0%

Natural gas use for electricity generation -3.4% -2.3% -1.6%

Note: Positive values indicate increases relative to the base case, which includes the CPP.

Environmental regulation may affect groups of workers differently, as changes in 

abatement and other compliance activities cause labor and other resources to shift. An 

employment impact analysis describes the characteristics of groups of workers potentially 

affected by a regulation, as well as labor market conditions in affected occupations, industries, 

and geographic areas. Market and employment impacts of this proposed action are discussed 

more extensively in Chapter 5 of this RIA.
ES-21

ES.8 Limitations and Uncertainty

The OMB circular Regulatory Analysis (Circular A-4) provides guidance on the 

preparation of regulatory analyses required under E.O. 12866. Circular A-4 requires a formal 

quantitative uncertainty analysis for rules with annual economic effects of $1 billion or more.6

This proposed rulemaking potentially surpasses this $1 billion threshold for both compliance 

costs and benefits. Throughout this RIA we consider a number of sources of uncertainty, both 

quantitatively and qualitatively, on benefits and costs. Some of these elements are evaluated 

using probabilistic techniques. For other elements, where the underlying likelihoods of certain 

outcomes are unknown, we use scenario analysis to evaluate their potential effect on the benefits 

and costs of this rulemaking. We summarize key elements of our analysis of uncertainty here:

• The extent to which all coal-fired EGUs will improve heat rates under this proposal, on 

average; 

• The cost to improve heat rates at all affected coal-fired EGUs nationally;

• Uncertainty in monetizing climate-related benefits; and,

• Uncertainty in the estimated health impacts attributable to changes in particulate matter.

We also summarize other potential sources of benefits and costs that may result from this 

proposed rule that have not been quantified or monetized. We did not account for certain benefits 

and costs that may affect the size of the estimated net-benefits; these include certain omitted 

benefits and costs from changes in CO2, SO2, and NOX from the electricity sector, from changes 

in other pollutants within and outside the electricity sector, and effects outside of the electricity 

market. These limitations, including where possible how they directly may affect estimated 

benefits and costs, are summarized below and discussed in more detail throughout the RIA.

There are important impacts that EPA could not monetize. Due to current data and 

modeling limitations, our estimates of the benefit impacts from reducing CO2 emissions do not 

include important impacts like ocean acidification or potential tipping points in natural or 

managed ecosystems. Ancillary benefits from changing direct exposure to SO2, NOX, as well as 

ecosystem changes and visibility impairment, from changes in these pollutants are also omitted. 

 

6 Office of Management and Budget (OMB), 2003, Circular A-4,

https://www.whitehouse.gov/sites/whitehouse.gov/files/omb/circulars/A4/a-4.pdf
ES-22

Changes in the health and ecosystems from changes in mercury from the electricity sector 

are not monetized, although increases in mercury emissions are reported in Chapter 3. Potential 

changes in other air and water emissions from the electricity sector, including hazardous air 

pollutants (e.g., hydrochloric acid) and their associated effects on heath, ecosystems, and 

visibility are not quantified. Potential changes in emissions from producing fuels, such as 

methane from coal and gas production, are also unaccounted for.

The avoided compliance costs reported in this RIA are not social costs, although elements 

of the compliance costs are social costs. Changes in costs and benefits due to changes in 

economic welfare of suppliers to the electricity market, including workers in the electricity 

market and in related markets, and non-electricity consumers from those suppliers (net of 

transfers), such as industrial consumers of fossil fuels, are not accounted for. Furthermore, costs 

due to interactions with pre-existing market distortions outside the electricity sector are omitted.

Key uncertainties that affect the estimates of benefits and costs of the proposed regulation 

include those that affect costs and emissions from the electricity sector. As described above, 

there is uncertainty in the availability of HRI technologies at all affected coal-fired EGUs 

nationally and their associated costs. In addition, there is uncertainty in future economic 

conditions that could affect fuel supplies, technology costs, and electricity demand in the 

electricity sector. Furthermore, changes in the assumed state plan approach for CPP compliance 

or compliance methods may affect the estimated benefits and costs.

The estimated health benefits from changes in PM2.5 and ozone concentrations are subject 

to uncertainties related to: (1) the projected future PM2.5 and ozone concentrations; and, (2) the 

relationship between air quality changes and health outcomes. For the first uncertainty, which is 

discussed in more detail in Chapter 8, we are more confident in the estimated change in annual 

mean PM2.5 concentrations than we are in the estimated absolute PM2.5 levels. Consequently, we 

are more confident in the estimated total benefits than in sensitivity estimates of benefits over 

specific concentration ranges as described in Chapter 4. We address the second uncertainty in 

part by quantifying benefits using a range of adult mortality concentration-response relationships 

(e.g., from Krewski et al. (2009) with Smith et al. (2009) to Lepeule et al. (2012) with Jerrett et 

al. (2009)). The PM2.5 concentration-response models assume that all fine particles, regardless of 
ES-23

their chemical composition, are equally potent in causing premature mortality because the 

scientific evidence is not yet sufficient to allow differentiation of effect estimates by particle 

type.7 Furthermore, as discussed above, there is greater uncertainty in the effects of exposure at 

low PM2.5 levels.

This rule will affect future levels of PM2.5 and ozone both within and beyond current and 

projected NAAQS non-attainment areas. This RIA does not project changes in attainment status. 

The U.S. has experienced significant improvement in PM2.5 and ozone concentrations. Between 

2000 and 2016, PM2.5 levels fell by more than 40 percent.8 Only nine areas in four states were 

designated nonattainment for the 2012 annual PM2.5 NAAQS.9 The extent to which the health cobenefits and costs are overestimated or underestimated partially depends on a variety of federal 

and state decisions with respect to NAAQS implementation and compliance, including 

Prevention of Significant Deterioration (PSD) requirements.

 

7 More information on potential uncertainties and assumptions for PM2.5 benefits is available in OMB’s 2017 Draft 

Report to Congress on the Benefits and Costs of Federal Regulations and Agency Compliance with the Unfunded 

Mandates Reform Act, pg. 13 – 18

8 https://www.epa.gov/air-trends/air-quality-national-summary#air-quality-trends

9 https://www.epa.gov/particle-pollution-designations/additional-final-area-designations-and-technical-amendment2012
ES-24

ES.9 References

Jerrett, M., Burnett, R.T., Pope, C.A., Ito, K., Thurston, G., Krewski, D., Shi, Y., Calle, E., Thun, 

M., 2009. Long-term ozone exposure and mortality. N. Engl. J. Med. 360, 1085–95. 

https://doi.org/10.1056/NEJMoa0803894

Krewski, D., Jerrett, M., Burnett, R.T., Ma, R., Hughes, E., Shi, Y., Turner, M.C., Pope, C.A., 

Thurston, G., Calle, E.E., Thun, M.J., Beckerman, B., DeLuca, P., Finkelstein, N., Ito, K., 

Moore, D.K., Newbold, K.B., Ramsay, T., Ross, Z., Shin, H., Tempalski, B., 2009. 

Extended follow-up and spatial analysis of the American Cancer Society study linking 

particulate air pollution and mortality. Res. Rep. Health. Eff. Inst. 5-114–36.

Lepeule, J., Laden, F., Dockery, D., Schwartz, J., 2012. Chronic exposure to fine particles and 

mortality: an extended follow-up of the Harvard Six Cities study from 1974 to 2009. 

Environ. Health Perspect. https://doi.org/10.1289/ehp.1104660

NRC, 2002. Estimating the Public Health Benefits of Proposed Air Pollution Regulations. 

Washington, D.C.

Smith, R.L., Xu, B., Switzer, P., 2009. Reassessing the relationship between ozone and shortterm mortality in U.S. urban communities. Inhal. Toxicol. 21 Suppl 2, 37–61. 

https://doi.org/10.1080/08958370903161612

U.S. EPA, 2009. Integrated Science Assessment for Particulate Matter. U.S. Environmental 

Protection Agency, National Center for Environmental Assessment, Research Triangle 

Park, NC.

U.S. EPA, 2011. Policy Assessment for the Review of the Particulate Matter National Ambient 

Air Quality Standards. Research Triangle Park, NC.

U.S. EPA, 2015. Regulatory Impact Analysis for the Clean Power Plan Final Rule. EPA-452/R15-003. Office of Air Quality Planning and Standards, Health and Environmental Impacts 

Division, Research Triangle Park, NC.

U.S. EPA, 2017. Regulatory Impact Analysis for the Review of the Clean Power Plan: Proposal. 

EPA-452/R-17-004. Office of Air Quality Planning and Standards, Health and 

Environmental Impacts Division, Research Triangle Park, NC.
1-1

CHAPTER 1: INTRODUCTION AND BACKGROUND

1.1 Introduction 

With this notice, the Environmental Protection Agency (EPA) is proposing three distinct 

actions, including Emission Guidelines for Greenhouse Gas Emissions from Existing Electric 

Utility Generating Units (EGUs). First, EPA is proposing to replace the Clean Power Plan (CPP) 

with revised emissions guidelines (the Affordable Clean Energy (ACE) rule) for states to follow 

in developing implementation plans to reduce greenhouse gas emission from certain EGUs. In 

the proposed emissions guidelines (UUUUa), consistent with the interpretation described in the 

proposed repeal of the CPP, the Agency is proposing to determine that heat rate improvement 

(HRI) measures are the best system of emission reduction (BSER) for existing coal-fired EGUs. 

Second, EPA is proposing new regulations that provide direction to both EPA and the states on 

the implementation of emission guidelines. The new proposed implementing regulations would 

apply to this action and any future emission guideline issued under section 111(d) of the Clean 

Air Act (CAA). Third, the Agency is proposing revisions to the New Source Review (NSR) 

program that will help prevent NSR from being a barrier to the implementation of efficiency 

projects at EGUs. 

This report presents the expected costs, benefits and economic impacts of illustrative 

scenarios representing approaches that states may implement to comply with this proposed rule. 

This chapter contains background information on this rule, an overview of the regulatory impact 

analysis conducted and scenarios analyzed, as well as an outline of the chapters in this report. 

1.2 Legal and Economic Basis for this Rulemaking

1.2.1 Statutory Requirement

Clean Air Act section 111, which Congress enacted as part of the 1970 Clean Air Act 

Amendments, establishes mechanisms for controlling emissions of air pollutants from stationary 

sources. This provision requires EPA to promulgate a list of categories of stationary sources that 

the Administrator, in his or her judgment, finds “causes, or contributes significantly to, air 
1-2

pollution which may reasonably be anticipated to endanger public health or welfare.”1 EPA has 

listed more than 60 stationary source categories under this provision.2 Once EPA lists a source 

category, EPA must, under CAA section 111(b)(1)(B), establish “standards of performance” for 

emissions of air pollutants from new sources in the source categories.3 These standards are 

known as new source performance standards (NSPS), and they are national requirements that 

apply directly to the sources subject to them. 

When EPA establishes NSPS for sources in a source category under CAA section 111(b), 

EPA is also required, under CAA section 111(d)(1), to prescribe regulations for states to submit 

plans regulating existing sources in that source category for any air pollutant that, in general, is 

not regulated under the CAA section 109 requirements for the NAAQS or regulated under the 

CAA section 112 requirements for hazardous air pollutants (HAP). CAA section 111(d)’s 

mechanism for regulating existing sources differs from the one that CAA section 111(b) provides 

for new sources because CAA section 111(d) contemplates states submitting plans that establish 

“standards of performance” for the affected sources and that contain other measures to 

implement and enforce those standards. 

“Standards of performance” are defined under CAA section 111(a)(1) as standards for 

emissions that reflect the emission limitation achievable from the “best system of emission 

reduction,” considering costs and other factors, that “the Administrator determines has been 

adequately demonstrated.” CAA section 111(d)(1) grants states the authority, in applying a 

standard of performance, to take into account the source’s remaining useful life and other factors. 

Under CAA section 111(d), a state must submit its plan to EPA for approval, and EPA 

must approve the state plan if it is “satisfactory.”4If a state does not submit a plan, or if EPA

does not approve a state’s plan, then EPA must establish a plan for that state.5 Once a state 

receives EPA’s approval of its plan, the provisions in the plan become federally enforceable 

 

1 CAA §111(b)(1)(A).

2 See 40 CFR 60 subparts Cb – OOOO.

3 CAA §111(b)(1)(B), 111(a)(1).

4 CAA section 111(d)(2)(A).

5 CAA section 111(d)(2)(A).
1-3

against the entity responsible for noncompliance, in the same manner as the provisions of an 

approved State Implementation Plan (SIP) under the Act.

1.2.2 Market Failure

Many regulations are promulgated to correct market failures, which otherwise lead to a 

suboptimal allocation of resources within the free market. Air quality and pollution control 

regulations address “negative externalities” whereby the market does not internalize the full 

opportunity cost of production borne by society as public goods such as air quality are unpriced.

While recognizing that optimal social level of pollution may not be zero, GHG emissions 

impose costs on society, such as negative health and welfare impacts, that are not reflected in the 

market price of the goods produced through the polluting process. For this regulatory action the 

good produced is electricity. If a fossil fuel-fired electricity producer pollutes the atmosphere 

when it generates electricity, this cost will be borne not by the polluting firm but by society as a 

whole, thus the producer is imposing a negative externality, or a social cost of emissions. The 

equilibrium market price of electricity may fail to incorporate the full opportunity cost to society 

of generating electricity. Consequently, absent a regulation on emissions, the EGUs will not 

internalize the social cost of emissions and social costs will be higher as a result. This regulation 

will regulation will work towards addressing this market failure by causing affected EGUs to 

begin to internalize the negative externality associated with CO2 emissions. 

1.3 Background 

1.3.1 Emission Guidelines and Revisions to New Source Review

This analysis is intended to be an illustrative representation and analysis of the proposed 

rule to replace the Clean Power Plan.6 The proposed rule presents a framework for states to 

develop state plans that will establish standards of performance for existing affected sources of 

GHG emissions. The proposed rule does not itself specify any standard of performance, but 

rather establishes the “best system of emission reduction”7(BSER), i.e. technology options for 

heat rate improvements (HRI), that States may choose to rely upon as they develop standards of 

 

6 For more details on legal authority and justification of this action, see rule preamble.

7 The best system of emission reduction (BSER) is outlined in the CAA 111(d), see preamble for further discussion. 
1-4

performance and State plans. The specific technology options that might be used to establish a 

standard of performance for individual affected sources are unknown. Affected sources may not 

be able to apply the technology options because they have already adopted these technologies, 

they are not applicable to the source, or for other reasons. The rule also re-proposes reforms to 

New Source Review (NSR) that may facilitate the application of HRI technologies from the 

BSER to sources that the States otherwise may have deemed inapplicable to those sources as part 

of their state plans.

1.3.2 Regulated Pollutant

The purpose of this CAA section 111(d) rule is to address CO2 emissions from fossil 

fuel-fired power plants in the U.S. because they are the largest domestic stationary source of 

emissions of carbon dioxide (CO2). CO2 is the most prevalent of the greenhouse gases (GHG), 

which are air pollutants that EPA has determined endangers public health and welfare through 

their contribution to climate change.

1.3.3 Definition of Affected Sources

EPA is proposing that an affected EGU subject to regulation upon finalization of this 

proposal is any fossil fuel-fired electric utility steam generating unit (i.e., utility boilers) that is 

not an integrated gasification combined cycle (IGCC) unit (i.e., utility boilers, but not IGCC 

units) that was in operation or had commenced construction as of the publication date of this 

proposal and that meets the following criteria. To be an affected EGU, a fossil fuel-fired electric 

utility steam generating unit must serve a generator capable of selling greater than 25 MW to a 

utility power distribution system and have a base load rating greater than 260 GJ/h (250 

MMBtu/h) heat input of fossil fuel (either alone or in combination with any other fuel). 

EPA is proposing different applicability criteria than in the CPP to reflect EPA’s 

determination of the BSER for only fossil fuel-fired electric utility steam generating units. In this 

proposal, EPA does not identify a BSER for stationary combustion turbines and IGCC units and, 

thus, such units are not affected EGUs for purposes of this action (see discussion below). EPA 

notes that under the CPP certain EGUs were not considered to be affected EGUs, and therefore 

were exempt from inclusion in a state plan. Similarly, EPA is proposing that certain EGUs 
1-5

should be excluded from a state’s plan based on specific criteria. For specifics on these criteria, 

see section IV of the preamble.

1.4 Overview of Regulatory Impact Analysis

In accordance with Executive Order 12866, Executive Order 13563, OMB Circular A-4, 

and EPA’s Guidelines for Preparing Economic Analyses, EPA prepared this RIA for this 

“significant regulatory action.” This action is an economically significant regulatory action 

because it may have an annual effect on the economy of $100 million or more or adversely affect 

in a material way the economy, a sector of the economy, productivity, competition, jobs, the 

environment, public health or safety, or state, local, or tribal governments or communities.8

In this RIA, the Agency provides a full benefit cost analysis of four illustrative scenarios. 

The four illustrative scenarios include a scenario modeling the full repeal of the CPP and three 

replacement policy scenarios modeling heat rate improvements (HRI) at coal-fired EGUs. 

Throughout this RIA, these four illustrative policy scenarios are compared against a base case

scenario, which represents baseline conditions. The base case scenario includes promulgated 

regulations, including the CPP. By analyzing against the existing CPP, the reader can understand 

the combined impact of a repeal and replacement. Inclusion of a no CPP case allows for an 

understanding of the repeal alone and allows the reader to evaluate the impact of the policy cases 

against a no CPP scenario. This RIA assumes a mass-based implementation of the CPP for 

existing affected sources, and does not assume interstate trading. The three illustrative policy 

scenarios represent potential outcomes of state determinations of standards of performance, and 

compliance with those standards by affected coal-fired EGUs. This RIA also updates the analysis 

in the October 2017 RIA for the proposed repeal of the CPP, by updating, among other elements 

of the analysis, the expected future economic conditions affecting the electricity sector in both 

the base case, which includes the CPP, and the full repeal scenario. This RIA also reports the

impact of climate benefits from changes in CO2 and the impact on ancillary health benefits 

attributable to changes in SO2 and NOX emissions.

 

8 The analysis in this proposal RIA constitutes the economic assessment required by CAA section 317. In EPA’s 

judgment, the assessment is as extensive as practicable taking into account EPA’s time, resources, and other duties 

and authorities.
1-6

Additionally, this RIA includes information about potential impacts of the proposed rule 

on electricity markets, employment, and markets outside the electricity sector. The RIA also 

presents discussion of the uncertainties and limitations of the analysis.

1.4.1 Base Case

The analysis relies on EPA’s Power Sector Modeling Platform v6 using the Integrated 

Planning Model (IPM). This accounts for changes in the power sector since promulgation of the 

CPP in 2015, and projects our best understanding of important technological and economic 

trends into the future. Due to a number of changes in the electricity sector since the CPP was 

finalized, as documented in the October 2017 RIA for the proposal to repeal the CPP and 

Chapter 3 of this RIA, the sector has become less carbon intensive over the past several years, 

and this trend is projected to continue in the future. These changes and trends are reflected in the 

modeling used for this analysis. 

Because air quality modeling was used to determine health co-benefits, the base case 

included emissions from all sources. Consequently, in addition to rules included in the IPM base 

case, the base case for this analysis included emissions from, and rules for, non-EGU point 

sources, on-road vehicles, non-road mobile equipment and marine vessels.9 Additional 

information on what is included in the air quality modeling inventory is detailed in Chapter 4 and 

Chapter 8.

This analysis reflects the best data available to EPA at the time the modeling was 

conducted. As with any modeling of future projections, many of the inputs are uncertain. In this 

context, notable uncertainties include the cost of fuels, the cost to operate existing power plants, 

the cost to construct and operate new power plants, infrastructure, demand, and policies affecting 

the electric power sector. The modeling conducted for this RIA is based on estimates of these 

variables, which were derived from the data currently available to EPA. However, future 

realizations of these characteristics may deviate from expectations. The results of counterfactual 

simulations presented in this RIA are not a prediction of what will happen, but rather projections 

 

9 Using the air quality modeling techniques in this RIA, the impacts of these non-EGU rules are determined as of 

2023, so any implementation or effects expected to occur after 2023 are not accounted for in this RIA. However, the 

effect on non-EGU emissions on changes in pollution concentrations between the base case and illustrative scenarios 

is likely small. 
1-7

of plausible scenarios describing how this proposed regulatory action may affect electricity 

sector outcomes in the absence of unexpected shocks. The results of this RIA should be viewed 

in that context.

1.4.2 BSER and Policy Scenarios

Three of the illustrative scenarios model different levels and costs of HRIs applied 

uniformly at all affected coal-fired EGUs in the contiguous U.S. beginning in 2025. EPA has 

identified the BSER to be HRI. In the proposed Emission Guidelines, EPA proposes to provide

states with a list of candidate HRI technologies that must be evaluated when establishing 

standards of performance. Each of these illustrative scenarios assumes that the affected sources 

are no longer subject to the state plan requirements of the CPP (e.g., the mass-based 

requirements assumed for CPP implementation in the base case for this RIA). The cost, 

suitability, and potential improvement for any of these HRI technologies is dependent on a range 

of unit-specific factors such as the size, age, fuel use, and the operating and maintenance history 

of the unit. As such, the HRI potential can vary significantly from unit to unit. EPA does not 

have sufficient information to assess HRI potential on a unit-by-unit basis. CAA 111(d) also 

provides States with the responsibility to establish standards of performance and provides 

considerable flexibility in applying those emission standards. States may take many factors into 

consideration – including among other factors, the remaining useful life of the affected source –

when applying the standards of performance.10 Therefore, any analysis of the proposed rule must 

be highly illustrative. However, EPA believes that such illustrative analyses can provide 

important insights at the national level and can inform the public on a range of potential 

outcomes. To avoid the impression that EPA can sufficiently distinguish likely standards of 

performance across individual affected units and their compliance strategies, this analysis 

assumes different HRI levels and costs are applied uniformly to affected coal-fired EGUs under 

each of three illustrative policy scenarios:

• 2 Percent HRI at $50/kW: This illustrative scenario represents a policy case that reflects 

modest improvements in HRI absent any revisions to NSR requirements. For many years, 

industry has indicated to the Agency that many sources have not implemented certain 

 

10 See Section VI of the preamble for a discussion of factors that EPA is proposing to allow states to consider in 

establishing a standard of performance for state plans in response to this emission guideline.
1-8

HRI improvement projects because the burdensome costs of NSR cause such projects to 

not be viable.11 Thus, absent NSR reform, HRI at affected units might be expected to be 

modest. Based on numerous studies and statistical analysis, the Agency believes that the 

HRI potential for coal-fired EGUs will, on average, range from one to three percent at a 

cost of $30 to $60 per kilowatt (kW) of EGU generating capacity. The Agency believes 

that this scenario (2 percent HRI at $50/kW) reasonable represents that range of HRI and 

cost. 

• 4.5 Percent HRI at $50/kW: This illustrative scenario represents a policy case that 

includes benefits from the proposed revisions to NSR, with the HRI modeled at a low 

cost. As mentioned earlier, the Agency is proposing revisions to the NSR program that 

will provide owners and operators of existing EGUs greater ability to make efficiency 

improvements without triggering provisions of NSR. This scenario is informative in that 

it represents the ability of all coal-fired EGUs to obtain greater improvements in heat rate 

because of NSR reform at the $50/kW cost identified earlier. EPA believes this higher 

heat rate improvement potential is possible because without NSR a greater number of 

units may have the opportunity to make cost effective heat rate improvements such as 

turbine upgrades that have the potential to offer greater heat rate improvement 

opportunities.

• 4.5 Percent HRI at $100/kW: This illustrative scenario represents a policy case that 

includes the benefits from the proposed revisions to NSR, with the HRI modeled at a 

higher cost. This scenario is informative in that it represents the ability of a typical coalfired EGUs to obtain greater improvements in heat rate because of NSR reform but at a 

much higher cost ($100/kW) than the $50/kW cost identified earlier. Particularly for 

lower capacity units or those with limited remaining useful life, this could ultimately 

translate into HRI projects with higher costs.

Combined, the 4.5 percent HRI at $50/kW scenario and the 4.5 percent HRI at $100/kW 

scenario represent a range of potential costs for the proposed policy option that couples HRI with 

NSR reform. Modeling this at $50/kW and $100/kW provides a sensitivity analysis on the cost of 

the proposed policy including NSR reform. The $50/kW cost represents an optimistic bounding 

where NSR reform unleashes significant new opportunity for low-cost heat rate improvements. 

The $100/kW cost scenario, while informative, represents a higher cost scenario, particularly for 

 

11 As expressed by one industry representative, “EGUs engaging in HRI projects can face NSR pre-construction 

permitting requirements consisting of, at a minimum, costly, detailed analyses and permitting delays. In some cases, 

this has resulted in costly and protracted litigation, and expensive new emission control requirements, both of which 

result in substantial time delays for these projects. These concerns remain should unit operators pursue HRI 

upgrades—many of which EPA has mentioned in the ANPR—that could trigger NSR in an effort to comply with 

emissions standards designed to comply with revised CAA section 111(d) GHG emissions guidelines.” See Edison 

Electric Institute comments on the U.S. Environmental Protection Agency's Advanced Notice of Proposed 

Rulemaking entitled, "State Guidelines for Greenhouse Gas Emissions from Existing Electric Utility Generating 

Units," 82 FR 61507 (Dec. 28, 2017) at 22 (EPA-HQ-OAR-2017-0545-0221). 
1-9

lower capacity factor units and those with limited remaining useful life. Additional information 

describing the analytical basis for these illustrative scenarios is provide in Section 1.6.

1.4.3 Years of Analysis

We evaluate the potential regulatory impacts of the illustrative No CPP scenario and the 

three illustrative policy scenarios using the present value (PV) of costs, benefits, and net benefits, 

calculated for the years 2023-2037 from the perspective of 2016, using both a three percent and 

seven percent beginning-of-period discount rate. In addition, the Agency presents the assessment 

of costs, benefits, and net benefits for specific snapshot years, consistent with historic practice. In 

this RIA, the regulatory impacts are evaluated for the specific years of 2025, 2030, and 2035.

The Agency believes that these specific years are each representative of several 

surrounding years, which enables the analysis of costs and benefits over the timeframe of 2025-

2037. The year 2025 is an approximation for when the standards of performance under the 

proposed rule might be implemented, and the Agency estimates that monitoring, reporting, and 

recordkeeping (MR&R) costs may begin in 2023. Therefore, MR&R costs analysis is presented 

beginning in the year 2023, and full benefit cost analysis is presented beginning in the year 2025. 

The analytical timeframe concludes in 2037, as this is the last year that may be represented with 

the analysis conducted for the specific year of 2035.

1.5 BSER Technologies

The list of candidate technologies that EPA is proposing to constitute the BSER are 

summarized below, and are described in greater detail in Section V of the preamble.

1.5.1 Neural Network/Intelligent Sootblower

1.5.1.1 Neural Networks

Computer models, known as neural networks, can be used to simulate the performance of 

the power plant at various operating loads. Typically, the neural network system ties into the 

plant’s distributed control system for data input (process monitoring) and process control. The 

system uses plant specific modeling and control modules to optimize the unit’s operation and 

minimize the emissions. This model predictive control can be particularly effective at improving 

the plants performance and minimizing emissions during periods of rapid load changes. The 
1-10

neural network can be used to optimize combustion conditions, steam temperatures, and air 

pollution control equipment. 

1.5.1.2 Intelligent Sootblowers 

During operations at a coal-fired power plant, particulate matter (ash or soot) builds up 

on heat transfer surfaces. This build-up degrades the performance of the heat transfer equipment 

and negatively affects the efficiency of the plant. Power plant operators use steam injection 

“sootblowers” to clean the heat transfer surfaces by removing the ash build-up. This is often 

done on a routine basis or as needed based on monitored operating characteristics. Intelligent 

sootblowers (ISB) are automated systems that use process measurements to monitor the heat 

transfer performance and strategically allocate steam to specific areas to remove ash buildup. 

The cost to implement an ISB system is relatively inexpensive if the necessary hardware 

is already installed. The ISB software/control system is often incorporated into the neural 

network software package mentioned above. As such, the HRIs obtained via installation of 

neural network and ISB systems are not necessarily cumulative. 

1.5.2 Boiler Feed Pumps

A boiler feed pump (or boiler feedwater pump) is a device used to pump feedwater into a 

boiler. The water may be either freshly supplied or returning condensate produced from 

condensing steam produced by the boiler. The boiler feed pumps consume a large fraction of the 

auxiliary power used internally within a power plant. Boiler feed pumps can require power in 

excess of 10 MW on a 500-MW power plant. Therefore, the maintenance on these pumps should 

be rigorous to ensure both reliability and high-efficiency operation. Boiler feed pumps wear over 

time and subsequently operate below the original design efficiency. The most pragmatic remedy 

is to rebuild a boiler feed pump in an overhaul or upgrade. 

1.5.3 Air Heater and Duct Leakage Control

The air pre-heater is a device that recovers heat from the flue gas for use in pre-heating 

the incoming combustion air, and potentially for other uses such as coal drying. Properly 

operating air pre-heaters play a significant role in the overall efficiency of a coal-fired EGU. A 

major difficulty associated with the use of regenerative air pre-heaters is air leakage from the 
1-11

combustion air side to the flue gas side. Air leakage affects boiler efficiency due to lost heat 

recovery and affects the axillary load since any leakage requires additional fan capacity. The 

amount of air leaking past the seals tends to increase as the unit ages. Improvements to seals on 

regenerative air pre-heaters have enabled the reduction of air leakage. 

1.5.4 Variable Frequency Drives (VFDs)

1.5.4.1 VFD on ID Fans

The increased pressure required to maintain proper flue gas flow through add-on air 

pollutant control equipment may require additional fan power, which can be achieved by an 

induced draft (ID) fan upgrade/replacement or an added booster fan. Generally, older power 

plant facilities were designed and built with centrifugal fans. 

The most precise and energy-efficient method of flue gas flow control is use of VFD. The 

VFD controls fan speed electrically by using a static controllable rectifier (thyristor) to control 

frequency and voltage and, thereby, the fan speed. The VFD enables very precise and accurate 

speed control with an almost instantaneous response to control signals. The VFD controller 

enables highly efficient fan performance at almost all percentages of flow turndown. Due to 

current electricity market conditions, many units no longer operate at baseload capacity and, 

therefore, VFDs, also known as variable-speed drives on fans can greatly enhance plant 

performance at off-peak loads. 

1.5.4.2 VFD on Boiler Feed Pumps 

VFDs can also be used on boiler feed water pumps as mentioned previously. Generally, if 

a unit with an older steam turbine is rated below 350 MW the use of motor-driven boiler 

feedwater pumps as the main drivers may be considered practical from an efficiency standpoint. 

If a unit cycles frequently then operation of the pumps with VFDs will offer the best results on 

heat rate reductions, followed by fluid couplings. The use of VFDs for boiler feed pumps is 

becoming more common in the industry for larger units. And with the advancements in low 

pressure steam turbines, a motor-driven feed pump can improve the thermal performance of a 

system up to the 600-MW range, as compared to the performance associated with the use of 

turbine drive pumps. Smaller and older units will generally not upgrade to a VFD boiler feed 

pump drive due to high capital costs.
1-12

1.5.5 Blade Path Upgrade (Steam Turbine)

Upgrades or overhauls of steam turbines offer the greatest opportunity for HRI on many 

units. Significant increases in performance can be gained from turbine upgrades when plants 

experience problems such as steam leakages or blade erosion. The typical turbine upgrade 

depends on the history of the turbine itself and its overall performance. The upgrade can entail 

myriad improvements, all of which affect the performance and associated costs. The availability 

of advanced design tools, such as computational fluid dynamics (CFD), coupled with improved 

materials of construction and machining and fabrication capabilities have significantly enhanced 

the efficiency of modern turbines. These improvements in new turbines can also be utilized to 

improve the efficiency of older steam turbines whose efficiency has degraded over time. 

1.5.6 Redesign/Replace Economizer

In steam power plants, economizers are heat exchange devices used to capture waste heat 

from boiler flue gas which is then used to heat the boiler feedwater. This use of waste heat 

reduces the need to use extracted energy from the system and, therefore, improves the overall 

efficiency or heat rate of the unit. As with most other heat transfer devices, the performance of 

the economizer will degrade with time and use, but replacements are often delayed or avoided 

due to concerns about triggering NSR. In some cases, economizer replacement projects have 

been undertaken concurrently with retrofit installation of selective catalytic reduction (SCR) 

systems because the entrance temperature for the SCR unit must be controlled to a specific 

range. 

1.5.7 Additional Documentation

Government agencies and laboratories, industry research organizations, engineering 

firms, equipment suppliers, and environmental organizations have conducted studies examining 

the potential for improving heat rate in the U.S. EGU fleet or a subset of the fleet. Section V of 

the preamble provides a list of some reports, case studies, and analyses about heat rate 

improvement opportunities in the U.S.
1-13

1.6 Development of Illustrative Policy Scenarios

1.6.1 Technical Basis

The illustrative scenarios modeled are based on a bottom-up analyses of fleet-wide HRI 

potential by identifying HRI technologies that may be available to certain categories of coal-fired 

EGUs.12 In the analyses, EPA considered how the available HRI measures that are included in 

the BSER list of candidate technologies (see Table 1-1) may apply to these categories. This 

initial analysis uses the HRI percentages from the findings of the EPA-sponsored 2009 Sargent 

& Lundy (S&L) study13 and applies S&L study costs that were updated to 2016 dollars. EPA 

evaluated a case that assumed current NSR requirements that industry claims to be a major 

deterrent to coal-fired EGU efficiency improvements and a case that reflects implementation of 

the proposed NSR reforms that will provide the regulatory clarity needed for industry to make 

HRI improvements without triggering NSR (i.e., the NSR reform scenarios). These cases are 

referred to respectively as the “no NSR reform” and “NSR reform” cases. 

The analysis uses the population of existing coal-fired steam EGUs that are included in 

the NEEDS v6 database that have a capacity greater than or equal to 25 MW and for which the 

database does not reflect a planned retirement date prior to 2024.14 These coal-fired steam EGUs 

were binned by capacity in the following three size categories (1) those ≤ 200 MW, (2) those that 

are > 200 MW to those that are ≤ 500 MW, and (3) those that are > 500 MW. This breakdown by 

size allows use of the breakdown of HRI and cost by size (200 MW, 500 MW, and 900 MW) in 

the S&L study and allowed the analyses to capture differences based on unit size. 

 

12 This methodology is similar to the bottom-up approach used by the Energy Information Administration (EIA, 

2015) to identify the possible HRI available at different categories of coal-fired units. However, the costs and HRI 

potentials used here are not those used in the EIA study. Furthermore, as described below, given uncertainty in the 

applicability of HRI to individual units, the analysis in this RIA applies a given HRI levels and costs uniformly to 

affected coal-fired EGUs in each of the three illustrative policy scenarios. 

13 “Coal-Fired Power Plant Heat Rate Reductions” Sargent & Lundy report SL-009597 (2009) 

https://www.epa.gov/sites/production/files/2015-08/documents/coalfired.pdf

14 NEEDS v6 is the database of generating units and their characteristics that is used in the modeling described in 

Chapter 3. The database includes information on the primary fuel type, nameplate capacity, on-line year, and heat 

rate for each generating unit used in the analysis described in this section. For additional information, see: 

https://www.epa.gov/airmarkets/national-electric-energy-data-system-needs-v6
1-14

Within each capacity bin, the units were divided into heat rate quartiles based on reported 

heat rates used in NEEDS, with the first quartile (Q1) representing the most efficient 25 percent 

of units in the bin and the fourth quartile (Q4) representing the least efficient 25 percent of units 

in the bin. 3. EPA assumed that better performing units (i.e., those in Q1 and Q2) will have fewer 

opportunities for heat rate improvements than the lesser performing units. The units in each case 

were then separated by their on-line year because the EIA study determined that units that came 

online after 1990 generally offered the smallest HRI potentials. In the EIA study fewer HRI were 

applied to units that came on line after 1990 relative to the remainder of the fleet because on 

average the units that came on-line after 1990 had lower heat rates.15 The availability of HRI 

candidate technologies that were assumed to be available to units in different quartiles and online years are based on the assumptions identified in Table 1-1 for the “No NSR Reform” case 

and in Table 1-2 for the “NSR Reform” case. The actual applicability of these technologies to 

each of the capacity bins and quartile ranges is unknown given limited information on the 

availability of further HRI opportunities coal-fired EGUs. For the purposes of this initial step in 

identifying illustrative scenarios for this RIA, the assumed applicability of these HRI 

technologies to the different bins is based on EPA’s expert judgement, which is based in part on 

a review of existing technical studies identified in the preamble.

For the “No NSR Reform” case, the analysis assumed that the “steam turbine upgrade” 

and the “redesign/replace the economizer” HRI options would not be available as those are 

among the efficiency improvements that industry believes will trigger NSR. EPA solicits 

comment on that assumption. In this analysis, those HRI are assumed to not be available for any 

units. For the “NSR Reform” case, the analysis assumed that the “steam turbine upgrade” and the 

“redesign/replace the economizer” HRI options would be available for some units. 

 

15 The EIA study used a different inventory of existing coal-fired EGUs to divide EGUs into heat rate quartiles than 

the inventory that EPA uses in this analysis. In the EIA study, 70% of the units that came online after 1990 fell into 

EIA’s first and second quartile bins, so fewer HRI measures were attached to them.
1-15

Table 1-1 Availability of Heat Rate Improvement Candidate Technologies (“No NSR 

Reform” Case)

On-Line Year

Heat Rate Improvement Technology <1990 >=1990

Neural Network/Intelligent Sootblowers Q2, Q3, Q4 Q2, Q3, Q4

Boiler Feed Pumps Q2, Q3, Q4 Q4

Air Heater & Duct Leakage Control Q2, Q3, Q4 Q4

Variable Frequency Drives Q2, Q3, Q4 Q3, Q4

Blade path upgrade (steam turbine) - -

Redesign and replace economizer - -

Table 1-2 Availability of Heat Rate Improvement Candidate Technologies (“NSR 

Reform” Case)

On-Line Year

Heat Rate Improvement Technology <1990 ≥1990

Neural Network/Intelligent Sootblowers Q2, Q3, Q4 Q2, Q3, Q4

Boiler Feed Pumps Q2, Q3, Q4 Q4

Air Heater & Duct Leakage Control Q2, Q3, Q4 Q4

Variable Frequency Drives Q2, Q3, Q4 Q3, Q4

Blade path upgrade (steam turbine) Q2, Q3, Q4 Q3, Q4

Redesign and replace economizer Q2, Q3, Q4 Q3, Q4

All of the performance of the available HRI measures in this analysis are based on the 

range of HRI potentials and costs reported in S&L study with costs adjusted to $2016.16 The 

performance and costs are in Table 1-3 and Table 1-4. Note that these costs reflect the range 

reported by S&L using their methodology and do not reflect the full range in the cost of these 

technologies for all potential applications at coal-fired EGUs. All operating costs for HRI 

measures, which are capital-intensive, were converted to a capital cost equivalent assuming a 

typical size and capacity factor in each capacity bin. 

 

16 EIA (2015) used different HRI costs and potentials than those used here. 
1-16

Table 1-3 Heat Rate Improvement Potential (%)

<200 MW 200 to 500 MW >500 MW

Heat Rate Improvement Candidate Technologies Min Max Min Max Min Max 

Neural Network/Intelligent Sootblowers 0.5 1.4 0.3 1.0 0.3 0.9

Boiler Feed Pumps 0.2 0.5 0.2 0.5 0.2 0.5

Air Heater & Duct Leakage Control 0.1 0.4 0.1 0.4 0.1 0.4

Variable Frequency Drives 0.2 0.9 0.2 1.0 0.2 1.0

Blade path upgrade (steam turbine) 0.9 2.7 1.0 2.9 1.0 2.9

Redesign and replace economizer 0.5 0.9 0.5 1.0 0.5 1.0

Table 1-4 Heat Rate Improvement Cost ($2016/kW)

<200 MW 200 to 500 MW >500 MW

Heat Rate Improvement Candidate Technologies Min Max Min Max Min Max 

Neural Network/Intelligent Sootblowers $4.7 $4.7 $2.5 $2.5 $1.4 $1.4 

Boiler Feed Pumps $1.4 $2.0 $1.1 $1.3 $0.9 $1.0 

Air Heater & Duct Leakage Control $3.6 $4.7 $2.5 $2.7 $2.1 $2.4 

Variable Frequency Drives $9.1 $11.9 $7.2 $9.4 $6.6 $7.9 

Blade path upgrade (steam turbine) $11.2 $66.9 $8.9 $44.6 $6.2 $31.0 

Redesign and replace economizer $13.1 $18.7 $10.5 $12.7 $10.0 $11.2 

After applying each relevant available technology to the universe of coal steam units for 

each of the policy cases (i.e., “No NSR Reform” and “NSR Reform”), the fleet-wide capacity 

weighted average HRI (%) and cost ($/kW) were calculated for both the minimum and maximum 

of the ranges presented in the S&L study and are shown in Tables 1-5 and 1-6. We assume that 

the minimum range of performance corresponds to the minimum range of cost, while the 

maximum performance corresponds with the maximum cost. 
1-17

Table 1-5 Fleet-Wide Capacity Weighted Average Improvement and Costs (“No NSR 

Reform” Case)

Min Max

Heat Rate Improvement (%) 0.6% 2.0%

Costs ($/kW) $8 $10

Table 1-6 Fleet-Wide Capacity Weighted Average Improvement and Costs (“NSR 

Reform” Case)

Min Max

Heat Rate Improvement (%) 1.6% 4.7%

Costs ($/kW) $21 $44

Again, to avoid the impression that EPA can sufficiently distinguish likely standards of 

performance across individual affected units and their compliance strategies, this analysis 

assumes different HRI levels and costs are applied uniformly to affected coal-fired EGUs under 

each of three illustrative policy scenarios. The illustrative scenario that assumes no revisions to 

NSR applies an HRI of 2.0 percent, which is consistent with the high end of HRI percentage 

determined from the “No NSR Reform” case. We choose the high end of the HRI percentage 

reduction on heat rates to evaluate the possible influence of a system-wide rebound effect, which 

is the increase in system-wide generation that may accompany an improvement in fuel 

efficiency, on the benefits and costs of the proposed rule. For the cost of HRI in this illustrative 

scenario, EPA uses higher costs relative to the average cost shown in Table 1-5 ($8 to $10/kW). 

EPA selected a cost per kW of HRI from the low-end of the range of HRI costs identified in the 

BSER Building Block 1 analysis for the Final CPP (USEPA 2015). 

For the two illustrative scenarios that assume revisions to NSR apply an HRI of 4.5 

percent which is consistent with the high end of HRI (%) determined from the “NSR Reform” 

case. Again, we choose the high end of the HRI percentage reduction to evaluate the potential 

rebound effect. For the scenario that assumes a fleet-wide HRI of 4.5 percent at cost of $50/kW 

we round up the cost from the high end of the cost to $50/kW. This cost is also consistent with 

the range of costs identified in the BSER Building Block 1 analysis for the Final CPP (Ibid.). For 

the scenario that assumes a fleet-wide 4.5 percent HRI at a cost of $100/kW we apply a higher 

cost because 1) those EGUs for which these technologies are costlier may be installing them 

when the technologies are widely adopted, 2) other studies identify blade path upgrades and 
1-18

economizer redesign and replacements as more expensive HRI technologies than the S&L study, 

and 3) $100/kW is at the upper-end of the range of the BSER Building Block 1 analysis for the 

Final CPP (Ibid.). 

The quantitative results of the scenarios with and without NSR revisions, because of the 

analytical approach informing the different illustrative scenarios, cannot be directly compared to 

each other. The analytical basis supporting the performance and cost of HRI differs across the 

scenarios for reasons other than the whether there are or are not revisions to NSR are 

represented, and therefore the incremental differences between the illustrative scenarios cannot 

be fully attributed to differences in NSR. However, the directional differences between the 

illustrative scenarios across time are nonetheless generally informative.

As described in the preamble for this proposal, EPA is taking comment on the costs, 

performance, and availability of the HRI technologies that constitute BSER for fossil steam 

EGUs. Based on any new information EPA receives, and considering existing studies evaluating 

HRI cost and potential, EPA will consider changes to its analysis to reflect a different 

representation of the potential heterogeneity in HRI potential across different types of fossilsteam EGUs and the average HRI improvement expected across the fleet. These might include, 

but are not limited to, the heat rate, age, remaining useful life, utilization, and size of EGUs.

While it may be possible to generalize differences in HRI availability across different types of 

units, the circumstances of individual units will still be unknown, and for this reason such 

generalizations may not necessarily apply to specific EGUs.

1.6.2 How HRI are Represented in the Policy Scenarios

As discussed above, the proposed regulation requires states to develop standards of 

performance based on the BSER, which EPA has determined to be HRI at existing EGUs. 

Conceptually, the illustrative policy scenarios presume required standards of performance that 

are established by the states and assume an approach for how each affected source complies with 

its standard of performance (and associated cost of that approach per kW of installed capacity). 

For example, the illustrative scenarios with a greater percentage HRI presume a numerically 

more stringent standard of performance than the scenario with a lower percentage HRI. 

However, the standards of performance are not represented in the model directly and, as 
1-19

discussed above, are uncertain because the applicability of these HRI technologies across the 

fleet and the standards of performance the states will require are uncertain.17 In practice, affected 

sources may have certain flexibilities in how they comply with the standards of performance that 

differ from the technologies used to determine the sources’ standards of performance, but this 

possibility is not captured in the modeling for this RIA. For ease of modeling, in the illustrative 

policy scenarios, sources may adopt the assumed HRI level or may retire in the model, based on 

prevailing economics. However, it is possible that States may use opportunities afforded to them 

in the proposed rule when applying BSER to avoid implementing HRI and retirement of affected 

sources, and the scenarios do not capture this possibility. 

The three illustrative policy scenarios reflect a range of technology improvements applied 

uniformly across the fleet. Again, it is important to note that current data limitations hinder our 

ability to apply more customized HRI and cost functions to specific units. Due to these 

limitations, as described above EPA used the best available information, research, and analysis to 

arrive at the assumptions used in these three scenarios. 

1.7 Organization of the Regulatory Impact Analysis

This report presents EPA’s analysis of the potential costs, benefits, and other economic 

effects of the proposed rule to fulfill the requirements of an RIA. This RIA includes the 

following chapters:

• Chapter 2, Electric Power Sector Industry Profile

• Chapter 3, Costs, Emissions, Economic, and Energy Impacts

• Chapter 4, Estimated Forgone Climate Benefits and Forgone Human Health Co-Benefits

• Chapter 5, Economic and Employment Impacts

• Chapter 6, Comparison of Benefits and Costs

• Chapter 7, Appendix – Uncertainty Associated with Estimating the Social Cost of Carbon

• Chapter 8, Appendix – Air Quality Modeling

 

17 Note that, in the modeling, the total cost of the HRI is reflected as a capital cost. However, for some HRI 

technologies, a small share of the total cost may be variable, and thus might have a small effect on dispatch 

decisions.
1-20

1.8 References 

40 CFR Chapter I [EPA–HQ–OAR–2009–0171; FRL–9091–8] RIN 2060–ZA14, 

“Endangerment and Cause or Contribute Findings for Greenhouse Gases under Section 

202(a) of the Clean Air Act,” Federal Register / Vol. 74, No. 239 / Tuesday, December 

15, 2009 / Rules and Regulations.

U.S. Energy Information Administration (EIA), “Analysis of Heat Rate Improvement Potential at 

Coal-Fired Power Plants”, May 2015.

National Research Council. Climate Stabilization Targets: Emissions, Concentrations, and 

Impacts over Decades to Millennia. Washington, DC: The National Academies Press, 

2011.

USEPA, 2015. Greenhouse Gas Mitigation Measures. Technical Support Document (TSD) for 

Carbon Pollution Guidelines for Existing Power Plants: Emission Guidelines for 

Greenhouse Gas Emissions from Existing Stationary Sources: Electric Utility Generating 

Units. Docket ID No. EPA-HQ-OAR-2013-0602
2-1

CHAPTER 2: ELECTRIC POWER SECTOR INDUSTRY PROFILE

2.1 Introduction 

This chapter discusses important aspects of the power sector that relate to this proposed 

rulemaking, including the types of power-sector sources affected by the regulation, and provides 

background on the power sector and EGUs. In addition, this chapter provides some historical 

background on trends in the past decade in the power sector, as well as about existing EPA 

regulation of the power sector.

In the past decade there have been significant structural changes in both the mix of 

generating capacity and in the share of electricity generation supplied by different types of 

generation. These changes are the result of multiple factors in the power sector, including normal 

replacements of older generating units with new units, changes in the electricity intensity of the 

US economy, growth and regional changes in the US population, technological improvements in 

electricity generation from both existing and new units, changes in the prices and availability of 

different fuels, and substantial growth in electricity generation by renewable and unconventional 

methods. Many of these trends will continue to contribute to the evolution of the power sector. 

The evolving economics of the power sector, in particular the increased natural gas supply and 

subsequent relatively low natural gas prices, have resulted in more gas being utilized as baseload 

energy in addition to supplying electricity during peak load. This chapter presents data on the 

evolution of the power sector from 2006 through 2016. Projections of new capacity and the 

impact of this rule on these new sources are discussed in more detail in Chapter 3 of this RIA.

2.2 Power Sector Overview 

The production and delivery of electricity to customers consists of three distinct 

segments: generation, transmission, and distribution.

2.2.1 Generation

Electricity generation is the first process in the delivery of electricity to consumers. There 

are two important aspects of electricity generation; capacity and net generation. Generating 

Capacity refers to the maximum amount of production from an EGU in a typical hour, typically 
2-2

measured in megawatts (MW) or gigawatts (1 GW = 1000 MW). Electricity Generation refers to 

the amount of electricity actually produced by EGUs, measured in kilowatt-hours (kWh) or 

gigawatt-hours (GWh = 1 million kWh). Net Generation is the amount of electricity that is 

available to the grid from the EGU (i.e., excluding the amount of electricity generated but used 

within the generating station for operations). In addition to producing electricity for sale to the 

grid, generators perform other services important to reliable electricity supply, such as providing 

backup generating capacity in the event of unexpected changes in demand or unexpected 

changes in the availability of other generators. Other important services provided by generators 

include facilitating the regulation of the voltage of supplied generation.

Individual EGUs are not used to generate electricity 100 percent of the time. Individual 

EGUs are periodically not needed to meet the regular daily and seasonal fluctuations of 

electricity demand. Furthermore, EGUs relying on renewable resources such as wind, sunlight 

and surface water to generate electricity are routinely constrained by the availability of adequate 

wind, sunlight or water at different times of the day and season. Units are also unavailable during 

routine and unanticipated outages for maintenance. These factors result in the mix of generating 

capacity types available (e.g., the share of capacity of each type of EGU) being substantially 

different than the mix of the share of total electricity produced by each type of EGU in a given 

season or year. 

Most of the existing capacity generates electricity by creating heat to create high pressure 

steam that is released to rotate turbines which, in turn, create electricity. Natural gas combined 

cycle (NGCC) units have two generating components operating from a single source of heat. The 

first cycle is a gas-fired turbine, which generates electricity directly from the heat of burning 

natural gas. The second cycle reuses the waste heat from the first cycle to generate steam, which 

is then used to generate electricity from a steam turbine. Other EGUs generate electricity by 

using water or wind to rotate turbines, and a variety of other methods including direct 

photovoltaic generation also make up a small, but growing, share of the overall electricity 

supply. The generating capacity includes fossil-fuel-fired units, nuclear units, and hydroelectric 

and other renewable sources (see Table 2-1). Table 2-1 also shows the comparison between the 

generating capacity in 2006 and 2016.
2-3

In 2016, the power sector consisted of over 19,000 generating units with a total capacity1

of 1,177 GW, an increase of 102 GW (or 9 percent) from the capacity in 2006 (1,076 GW). The 

101 GW increase consisted primarily of natural gas fired EGUs (70 GW) and wind generators 

(71 GW), other renewables (26 GW), and miscellaneous (4 GW) with substantially smaller net 

increases and decreases in other types of generating units. 

Table 2-1 Existing Electricity Net Summer Generating Capacity by Energy Source, 

2006 and 2016

2006 2016 Change Between '06 and '16

Energy Source

Generator 

Net 

Summer 

Capacity 

(MW)

% Total 

Capacity

Generator 

Net 

Summer 

Capacity 

(MW)

% Total 

Capacity

% 

Change

Net 

Summer 

Capacity 

Change 

(MW)

% of 

Total 

Capacity 

Change

Coal 312,956 32% 266,620 25% -15% -46,336 -53%

Natural Gas1 388,294 39% 446,823 42% 15% 58,529 66%

Nuclear 100,334 10% 99,565 9% -1% -769 -1%

Hydro 99,282 10% 102,692 10% 3% 3,410 4%

Petroleum 58,097 6% 34,382 3% -41% -23,715 -27%

Wind 11,329 1% 81,287 8% 618% 69,958 79%

Solar -- -- 21,951 2% -- -- --

Other Renewable 12,784 1% 16,542 2% 29% 3,757 4%

Misc. 3,139 0% 4,472 0% 42% 1,333 2%

Total 986,215 100% 1,074,333 100% 9% 88,118 100%

Source: U.S. EIA Electric Power Annual, Tables 4.2.A, 4.2.B

Note: This table presents generation capacity. Actual net generation is presented in Table 2 2. 2006 solar data is not 

reported in the U.S. EIA Electric Power Annual.

1 Natural Gas information in this chapter (unless otherwise stated) reflects data for all generating units using natural 

gas as the primary fossil heat source. This includes Natural Gas Fired Combined Cycle (59 percent of 2016 natural 

gas fired capacity), Natural Gas Fired Combustion Turbine (35 percent of 2016 natural gas fired capacity), Natural 

Gas Internal Combustion (5 percent of 2016 natural gas fired capacity), and Other Natural Gas (< 1 percent).

 

1 As with all data presented in this section, this includes generating capacity not only at EGUs primarily operated to 

supply electricity to the grid, but also generating capacity at commercial and industrial facilities that produce both 

electricity used onsite as well as dispatched to the grid. Unless otherwise indicated, capacity data presented in this 

RIA is installed nameplate capacity (also known as nominal capacity), defined by EIA as “The maximum rated 

output of a generator, prime mover, or other electric power production equipment under specific conditions 

designated by the manufacturer.” Nameplate capacity is consistently reported to regulatory authorities with a 

common definition, where alternate measures of capacity (e.g., net summer capacity and net winter capacity) can 

use a variety of definitions and specified conditions.
2-4

The nine percent increase in generating capacity is the net impact of newly built generating 

units, retirements of generating units, and a variety of increases and decreases to the nameplate 

capacity of individual existing units due to changes in operating equipment, changes in emission 

controls, etc. During the period 2006 to 2016, a total of 228 GW of new generating capacity was 

built and brought online, while 111 GW of electric generating capacity was retired. The changes 

in capacity are shown in Figure 2-1. 

Figure 2-1 New Build and Retired Capacity (MW) by Technology, 2006-2016

Source: EIA Form 860 (2016)

In 2016, electric generating sources produced a net 4,077 trillion kWh to meet electricity 

demand, a 0.3 percent increase from 2006 (11 trillion kWh). As presented in Table 2-2, 65 

percent of electricity in 2016 was produced through the combustion of fossil fuels, primarily coal 

and natural gas, with natural gas accounting for the largest single share. Although the share of 

the total generation from fossil fuels in 2016 (65 percent) was only modestly smaller than the 
2-5

total fossil share in 2006 (71 percent), the mix of fossil fuel generation changed substantially 

during that period. Coal generation declined by 38 percent and petroleum generation by 62 

percent, while natural gas generation increased by 69 percent. This reflects both the increase in 

natural gas capacity during that period as well as an increase in the utilization of new and 

existing gas EGUs during that period. Wind generation also grew from a very small portion of 

the overall total in 2006 to 6 percent of the 2016 total.

Table 2-2 Net Generation in 2006 and 2016 (Trillion kWh = TWh)

2006 2016 Change Between '06 and '16

Energy Source

Net 

Generation 

(TWh)

Fuel 

Source 

Share

Net 

Generation 

(TWh)

Fuel 

Source 

Share

Net Generation 

Change (TWh)

% Change in 

Net 

Generation

Coal 1,991 49% 1,239 30% -751 -38%

Natural Gas 816 20% 1,378 34% 562 69%

Nuclear 787 19% 806 20% 18 2%

Hydro 283 7% 261 6% -22 -8%

Petroleum 64 2% 24 1% -40 -62%

Wind 27 1% 227 6% 200 754%

Solar 1 0% 36 1% 36 6997%

Other Renewable 69 2% 79 2% 9 13%

Misc. 27 1% 27 1% -1 -2%

Total 4,065 100% 4,077 100% 12 0.3%

Source: U.S. EIA Electric Power Annual, Tables 3.1.A, 3.1.B

Coal-fired and nuclear generating units have historically supplied baseload electricity, the 

portion of electricity loads which are continually present, and typically operate throughout all 

hours of the year. There can be notable differences across various facilities (see Table 2-3). For 

example, coal-fired units less than 100 megawatts (MW) in size compose 37 percent of the total 

number of coal-fired units, but only 6 percent of total coal-fired capacity. Gas-fired generation is 

better able to vary output and is the primary option used to meet the variable portion of the 

electricity load and has historically supplied “peak” and “intermediate” power, when there is 

increased demand for electricity (for example, when businesses operate throughout the day or 

when people return home from work and run appliances and heating/air-conditioning), versus 

late at night or very early in the morning, when demand for electricity is reduced. 
2-6

Table 2-3 also shows comparable data for the capacity and age distribution of natural gas 

units. Compared with the fleet of coal EGUs, the natural gas fleet of EGUs is generally smaller 

and newer. Many of the largest gas units are gas-fired steam-generating EGUs.

Table 2-3 Coal and Natural Gas Generating Units, by Size, Age, Capacity, and 

Thermal Efficiency (Heat Rate)

Unit Size 

Grouping 

(MW)

No. 

Units

% of All 

Units

Avg. 

Age

Avg. Net 

Summer 

Capacity 

(MW)

Total Net 

Summer 

Capacity 

(MW)

% Total 

Capacity

Avg. Heat 

Rate 

(Btu/kWh)

COAL

0 – 24 33 6% 57 11 375 0% 12,362

25 – 49 41 7% 41 37 1,499 1% 12,050

50 – 99 39 7% 47 74 2,894 1% 11,929

100 – 149 45 8% 57 122 5,485 2% 11,266

150 – 249 88 15% 54 193 17,013 8% 10,899

250 – 499 140 23% 46 368 51,468 23% 10,605

500 – 749 143 24% 45 609 87,055 39% 10,301

750 – 999 56 9% 42 827 46,293 20% 10,069

1000 - 1500 11 2% 47 1257 13,831 6% 9,802

Total Coal 596 100% 48 379 225,913 100% 10,843

NATURAL GAS

0 – 24 3950 53% 38 5 20,425 5% 14,144

25 – 49 910 12% 31 41 37,065 9% 11,968

50 – 99 983 13% 30 71 69,749 17% 12,274

100 – 149 371 5% 25 127 47,248 11% 9,116

150 – 249 991 13% 20 178 176,610 43% 8,034

250 – 499 178 2% 16 319 56,727 14% 7,017

500 – 749 7 0.1% 11 549 3,840 1% 6,881

Total Gas 7,390 100% 33 56 411,663 100% 12,377

Source: National Electric Energy Data System (NEEDS) v.6.

Note: Natural gas includes combustion turbines and combined cycles. The average heat rate reported is the mean of 

the heat rate of each unit. A lower heat rate indicates a higher level of fuel efficiency. Table is limited to coal-steam 

units in operation in 2016, and excludes units with planned retirements prior to 2025. Age is estimated for the year 

2025. 

In terms of the age of the generating units, by 2025, over 50 percent of the total existing 

coal generating capacity will have been in service for more than 47 years, while about 50 percent 

of the existing natural gas capacity will have been in service for 22 years. Figure 2-2 presents the 

cumulative age distributions of the coal and gas fleets, highlighting the pronounced differences 

in the ages of the fleets of these two types of fossil-fuel generating capacity.
2-7

Figure 2-2 Cumulative Distribution in 2025 of Coal and Natural Gas Electricity 

Capacity by Age

Source: National Electric Energy Data System (NEEDS) v6

Note: Natural gas includes combustion turbines and combined cycles. Table is limited to coal-steam units in 

operation in 2016 or earlier, and excludes those units in NEEDS with planned retirements prior to 2025. Age is 

estimated in the year 2025.

Capacity factors measure the amount of electricity produced relative to the maximum 

potential production for a given generator. The 2016 average capacity factors for coal steam 

generators in the contiguous U.S. are depicted in Figure 2-3 and Figure 2-4. These figures 

demonstrate that, in 2016, domestic coal generators operated over a wide range of capacity 

factors. While many of these generators were designed to operate at annual average capacity 

factors of 80 to 85 percent, most of these generators were operating at considerably lower 

capacity factors in 2016, regardless of age or capacity. 

0%

20%

40%

60%

80%

100%

0 20 40 60 80 100

Cumulative Share of Capacity

Age in 2025 (years)

Coal

Gas
2-8

Figure 2-3 2016 Annual Average Capacity Factor for Coal Steam Generators, by 

Capacity

Source: EIA Forms 860 and 923

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 200 400 600 800 1000 1200 1400 1600

Capacity Factor in 2016

Capacity (MW)

2016 Annual Average Capacity Factor, Coal Steam by Capacity
2-9

Figure 2-4 2016 Annual Average Capacity Factor for Coal Steam Generators, by Age in 

2016

Source: EIA Forms 860 and 923

The locations of generating capacity in EPA’s National Electric Energy Data System 

(NEEDS) v.6 are shown in Figure 2-5.

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 10 20 30 40 50 60 70 80 90

Capacoty Factor in 2016

Age in 2016 (years)

2016 Annual Average Capacity Factor, Coal Steam by Age
2-10

Figure 2-5 Electricity Generating Facilities, by Size and Type

Source: National Electric Energy Data System (NEEDS) v.6

Note: This map displays generating capacity at facilities in the NEEDS v.6 database. This database reflects available 

capacity online by the end of 2021 and includes planned new builds already under construction and planned 

retirements. In areas with a dense concentration of facilities, some facilities may be obscured.

2.2.2 Transmission

Transmission is the term used to describe the bulk transfer of electricity over a network 

of high voltage lines, from electric generators to substations where power is stepped down for 

local distribution. In the U.S. and Canada, there are three separate interconnected networks of 

high voltage transmission lines,2each operating synchronously. Within each of these 

 

2 These three network interconnections are the Western Interconnection, comprising the western parts of both the US 

and Canada (approximately the area to the west of the Rocky Mountains), the Eastern Interconnection, comprising 
2-11

transmission networks, there are multiple areas where the operation of power plants is monitored 

and controlled by regional organizations to ensure that electricity generation and load are kept in 

balance. In some areas, the operation of the transmission system is under the control of a single 

regional operator3; in others, individual utilities4 coordinate the operations of their generation, 

transmission, and distribution systems to balance the system across their respective service 

territories.

2.2.3 Distribution

Distribution of electricity involves networks of lower voltage lines and substations that 

take the higher voltage power from the transmission system and step it down to lower voltage 

levels to match the needs of customers. The transmission and distribution system is the classic 

example of a natural monopoly, in part because it is not practical to have more than one set of 

lines running from the electricity generating sources to substations or from substations to 

residences and businesses.

Over the last few decades, several jurisdictions in the United States began restructuring 

the power industry to separate transmission and distribution from generation, ownership, and 

operation. Historically, the transmission system had been developed by vertically integrated 

utilities, establishing much of the existing transmission infrastructure. However, as parts of the 

country have restructured the industry, transmission infrastructure has also been developed by 

transmission utilities, electric cooperatives, and merchant transmission companies, among others. 

Distribution, also historically developed by vertically integrated utilities, is now often managed 

by a number of utilities that purchase and sell electricity, but do not generate it. As discussed 

below, electricity restructuring has focused primarily on efforts to reorganize the industry to 

encourage competition in the generation segment of the industry, including ensuring open access 

of generation to the transmission and distribution services needed to deliver power to consumers. 

In many states, such efforts have also included separating generation assets from transmission 

 

the eastern parts of both the US and Canada (except those part of eastern Canada that are in the Quebec 

Interconnection), and the Texas Interconnection (which encompasses the portion of the Texas electricity system 

commonly known as the Electric Reliability Council of Texas (ERCOT)). See map of all NERC interconnections at 

http://www.nerc.com/AboutNERC/keyplayers/Documents/NERC_Interconnections_Color_072512.jpg

3 E.g., PMJ Interconnection, LLC, Western Area Power Administration (which comprises 4 sub-regions).

4 E.g., Los Angeles Department of Power and Water, Florida Power and Light.
2-12

and distribution assets to form distinct economic entities. Transmission and distribution remain 

price-regulated throughout the country based on the cost of service. 

2.3 Sales, Expenses and Prices

These electric generating sources provide electricity for commercial, industrial and 

residential ultimate customers. Each of the three major ultimate categories consume roughly a 

quarter to a third of the total electricity produced5(see Table 2-4). Some of these uses are highly 

variable, such as heating and air conditioning in residential and commercial buildings, while 

others are relatively constant, such as industrial processes that operate 24 hours a day. The 

distribution between the end use categories changed very little between 2006 and 2016. 

Table 2-4 Total U.S. Electric Power Industry Retail Sales in 2006 and 2016 (billion 

kWh)

2006 2016

Sales/Direct 

Use (Billion 

kWh)

Share of Total 

End Use

Sales/Direct 

Use (Billion 

kWh)

Share of Total End 

Use

Sales

Residential 1,352 35% 1,411 36.2%

Commercial 1,300 34% 1,367 35.0%

Industrial 1,011 26% 977 25.0%

Transportation 7 7 0.2%

Other N/A 0.2% NA

Total 3,670 96% 3,762 96%

Direct Use 147 4% 140 4%

Total End Use 3,817 100% 3,902 100%

Source: Table 3.2, EIA Electric Power Annual, 2016

Notes: Retail sales are not equal to net generation (Table 2-2) because net generation includes net exported 

electricity and loss of electricity that occurs through transmission and distribution.

Direct Use represents commercial and industrial facility use of onsite net electricity generation; and 

electricity sales or transfers to adjacent or co-located facilities for which revenue information is not 

available.

2.3.1 Electricity Prices

Electricity prices vary substantially across the United States, differing both between the 

ultimate customer categories and also by state and region of the country. Electricity prices are 

typically highest for residential and commercial customers because of the relatively high costs of 

 

5 Transportation (primarily urban and regional electrical trains) is a fourth ultimate customer category which 

accounts less than one percent of electricity consumption.
2-13

distributing electricity to individual homes and commercial establishments. The high prices for 

residential and commercial customers are the result of the extensive distribution network 

reaching to virtually every part of the country and every building, and the fact that generating 

stations are increasingly located relatively far from population centers (which increases 

transmission costs). Industrial customers generally pay the lowest average prices, reflecting both 

their proximity to generating stations and the fact that industrial customers receive electricity at 

higher voltages (which makes transmission more efficient and less expensive). Industrial 

customers frequently pay variable prices for electricity, varying by the season and time of day, 

while residential and commercial prices historically have been less variable. Overall industrial 

customer prices are usually considerably closer to the wholesale marginal cost of generating 

electricity than residential and commercial prices.

On a state-by-state basis, all retail electricity prices vary considerably. In 2016, the 

national average retail electricity price (all sectors) was 10.41 cents/kWh, with a range from 7.46 

cents (Louisiana) to 23.87 (Hawaii). The Northeast, California and Alaska have average retail 

prices that can be as much as double those of other states (see Figure 2-6), and Hawaii has the 

most expensive retail price of electricity in the country. 
2-14

Figure 2-6 Average Retail Electricity Price by State (cents/kWh), 2016

Source: EIA State Electricity Profiles 2016 (https://www.eia.gov/electricity/state/) Accessed March 2018.

Average national overall retail electricity prices increased between 2006 and 2016 by 15.4 

percent in nominal (current year $) terms. The amount of increase differed for the three major 

end use categories (i.e., residential, commercial and industrial). As seen in Figure 2-8, national 

average residential prices increased the most (20.7 percent), and commercial prices increased the 

least (10.3 percent). The nominal year prices for 2006 through 2016 are shown in Figure 2-7. 
2-15

Figure 2-7 Nominal National Average Electricity Prices for Three Major End-Use 

Categories

Source: EIA 861, Table 3.4

Electricity prices for the commercial and industrial end-use categories did not increase 

more than overall inflation through this period, measured by either the GDP implicit price 

deflator (17.5 percent) or the consumer price index (CPI-U, which increased by 19.1 percent).6

The increase in nominal electricity prices for the major end use categories, as well as increases in 

the GDP price and CPI-U indices for comparison, are shown in Figure 2-8.

 

6 Source: Federal Reserve Economic Data, FRB St. Louis. Available online at: http://research.stlouisfed.org/fred2/.

0

2

4

6

8

10

12

14

2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016

Electricity Price (Cents/kWh, nominal $)

Residential Commercial Industrial Total
2-16

Figure 2-8 Relative Increases in Nominal National Average Electricity Prices for Major 

End-Use Categories, With Inflation Indices

Source: EIA 861, Table 3.4

The real (inflation-adjusted) change in average national electricity prices can be calculated 

using the GDP implicit price deflator. Figure 2-9 shows real7(2016$) electricity prices for the 

three major customer categories from 1960 to 2016, and Figure 2-10 shows the relative change in 

real electricity prices relative to the prices in 1960. As can be seen in the figures, the price for 

industrial customers has always been lower than for either residential or commercial customers, 

but the industrial price has been more volatile. While the industrial real price of electricity in 

2016 was relatively unchanged from 1960, residential and commercial real prices are 24 percent 

and 32 percent lower respectively than in 1960.

 

7 All prices in this section are estimated as real 2016 prices adjusted using the GDP implicit price deflator unless 

otherwise indicated.

0%

5%

10%

15%

20%

25%

2006 2008 2010 2012 2014 2016

% Change in Nominal Electricity Prices 

and Inflation Indices since 2006

Residential Commercial Industrial CPI-U GDP Price
2-17

Figure 2-9 Real National Average Electricity Prices (2016$) for Three Major End-Use 

Categories

Source: EIA Monthly Energy Review, Dec. 2017, Table 9.8

Notes: Price data is five-year averages for 1960 through 2000, and annual from 2016 through 2016.

Figure 2-10 Relative Change in Real National Average Electricity Prices (2016) for Three 

Major End-Use Categories

Source: EIA Monthly Energy Review, Dec. 2017, Table 9.8

Notes: Price data is five-year averages for 1960 through 2000, and annual from 2016 through 2016.

0

2

4

6

8

10

12

14

16

18

1960 1970 1980 1990 2000 2010

Real Electricity Price, 1960-2016 (including taxes)

Residential Commercial Industrial Total

-40%

-30%

-20%

-10%

0%

10%

20%

30%

40%

50%

1960 1970 1980 1990 2000 2010

Change in Real Electricity Prices

Relative Change in Electricity Prices, 1960 -

2016 (including taxes)

Residential Commercial Industrial Total
2-18

2.3.2 Prices of Fossil Fuels Used for Generating Electricity

Another important factor in the changes in electricity prices are the changes in fuel prices 

for the three major fossil fuels used in electricity generation; coal, natural gas and oil. Relative to 

real prices in 2006, the national average real price (in 2016$) of coal delivered to EGUs in 2016 

had increased by 6.2 percent, while the real price of natural gas decreased by 65 percent. The real 

price of oil decreased by 28 percent. The combined real delivered price of all fossil fuels in 2016 

decreased by 30 percent over 2006 prices. Figure 2-11 shows the relative changes in real price of 

all 3 fossil fuels between 2006 and 2016. 

Figure 2-11 Change in National Annual Average Cost of Real Fossil Fuel Receipts at 

EGUs per MMBtu 

Source: EIA Monthly Energy Review Dec. 2017, Table 9.9

Note: Costs include taxes.

2.3.3 Changes in Electricity Intensity of the U.S. Economy

An important aspect of the changes in electricity generation (i.e., electricity demand) 

between 2006 and 2016 is that while total net generation increased by less than 1 percent over 

that period, the demand growth for generation has been low, and in fact was lower than both the 

population growth (8.4 percent) and real GDP growth (14 percent). Figure 2-12 shows the 

growth of electricity generation, population and real GDP during this period. 

-100%

-50%

0%

50%

100%

2006 2008 2010 2012 2014 2016

Coal Oil Gas Average
2-19

Figure 2-12 Relative Growth of Electricity Generation, Population and Real GDP Since 

2006

Sources: U.S. EIA Monthly Energy Review, March 2018. Table 7.2a Electricity Net Generation: Total (All Sectors). 

U.S. Census.

Because demand for electricity generation grew more slowly than both the population 

and GDP, the relative electric intensity of the U.S. economy improved (i.e., less electricity used 

per person and per real dollar of output) during 2006 to 2016. On a per capita basis, real GDP per 

capita grew by 5.53 percent, increasing from $48,977 (in 2016$) per person in 2006 to 

$51,688/person in 2016. At the same time electricity generation per capita decreased by 7.14 

percent, declining from 0.014 MWh/person in 2006 to 0.013 MWh/person in 2016. The 

combined effect of these two changes improved the overall electricity efficiency of the U.S. 

market economy. Electricity generation per dollar of real GDP decreased 12.3 percent, declining 

from 278 MWh per $1 million of GDP to 244 MWh/$1 million GDP. These relative changes are 

shown in Figure 2-13. Figure 2-12 and Figure 2-13 clearly show the effects of the 2007 – 2009 

recession on both GDP and electricity generation, as well as the effects of the subsequent 

economic recovery.

-5%

0%

5%

10%

15%

20%

2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016

Real GDP Population Generation
2-20

Figure 2-13 Relative Change of Real GDP, Population and Electricity Generation 

Intensity Since 2006

Sources: U.S. EIA Monthly Energy Review, March 2018. Table 7.2a Electricity Net Generation: Total (All Sectors). 

U.S. Census

2.4 Deregulation and Restructuring 

The process of restructuring and deregulation of wholesale and retail electric markets has 

changed the structure of the electric power industry. In addition to reorganizing asset 

management between companies, restructuring sought a functional unbundling of the generation, 

transmission, distribution, and ancillary services the power sector has historically provided, with 

the aim of enhancing competition in the generation segment of the industry.

Beginning in the 1970s, government policy shifted against traditional regulatory 

approaches and in favor of deregulation for many important industries, including transportation 

(notably commercial airlines), communications, and energy, which were all thought to be natural 

monopolies (prior to 1970) that warranted governmental control of pricing. However, 

deregulation efforts in the power sector were most active during the 1990s. Some of the primary 

drivers for deregulation of electric power included the desire for more efficient investment 

choices, the economic incentive to provide least-cost electric rates through market competition, 

reduced costs of combustion turbine technology that opened the door for more companies to sell 

power with smaller investments, and complexity of monitoring utilities’ cost of service and 

-15%

-10%

-5%

0%

5%

10%

2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016

Real GDP/Capita Generation/Capita Generation/Real GDP
2-21

establishing cost based rates for various customer classes. Deregulation and market restructuring 

in the power sector involved the divestiture of generation from utilities, the formation of 

organized wholesale spot energy markets with economic mechanisms for the rationing of scarce 

transmission resources during periods of peak demand, the introduction of retail choice 

programs, and the establishment of new forms of market oversight and coordination.

The pace of restructuring in the electric power industry slowed significantly in response 

to market volatility in California and financial turmoil associated with bankruptcy filings of key 

energy companies. Currently, restructuring has been suspended in California, (shown as 

“Suspended” in Figure 2-14). Twenty-six other states are not considering restructuring at this 

time (Figure 2-14). Currently, there are 13 states plus the District of Columbia that allow retail 

access (Figure 2-14). Power sector restructuring is more or less at a standstill; by 2010 there 

were no active proposals under review by the Federal Energy Regulatory Commission (FERC) 

for actions aimed at wider restructuring, and no additional states have begun retail deregulation 

activity since that time.
2-22

Figure 2-14 Status of State Electricity Industry Restructuring Activities

Source: Map adapted from Rose, Ken. 2017. Retail Electricity Markets. 59th Annual Regulatory Studies Program 

Institute of Public Utilities, Michigan State University.

One major effect of the restructuring and deregulation of the power sector was a 

significant change in type of ownership of electricity generating units in the states that 

deregulated prices. Throughout most of the 20th century, electricity was supplied by vertically 

integrated regulated utilities. The traditional integrated utilities generation, transmission and 

distribution in their designated areas, and prices were set by cost of service regulations set by 

state government agencies (e.g., Public Utility Commissions). Deregulation and restructuring 

resulted in unbundling of the vertical integration structure. Transmission and distribution 

continued to operate as monopolies with cost of service regulation, while generation shifted to a 

mix of ownership affiliates of traditional utility ownership and some generation owned and 

operated by competitive companies known as Independent Power Producers (IPP). The resulting 

generating sector differed by state or region, as the power sector adapted to the restructuring and 

deregulation requirements in each state. 
2-23

By 2006 the major impacts of adapting to changes brought about by deregulation and 

restructuring during the 1990s were largely in place. The resulting ownership mix of generating 

capacity (MW) in 2006 was about 58 percent of the generating capacity owned by traditional 

utilities, 39 percent owned by IPPs8, and 3 percent owned by commercial and industrial 

producers. The mix of electricity generated (MWh) was more heavily weighted towards the 

utilities, with a distribution in 2006 of 66 percent, 30 percent and 4 percent for utilities, IPPs and 

commercial/industrial, respectively.

Since 2006 IPPs have expanded faster than traditional utilities, substantially increasing 

their share by 2016 of both capacity (56 percent utility, 41 percent IPPs, and 3 percent 

commercial/industrial) and generation (58 percent utility, 38 percent IPPs, and 4 percent 

commercial/industrial). 

The mix of capacity and generation for each of the ownership types is shown in Figure 

2-15 (capacity) and Figure 2-16 (generation). The capacity and generation data for commercial 

and industrial owners are not shown on these figures due to the small magnitude of those 

ownership types. Figure 2-15 and Figure 2-16 present the mixes in 2006 and 2016. Traditional 

utilities have expanded capacity in gas, and IPPs have increased their capacity in wind. Both 

traditional utilities and IPPs have markedly reduced their generation from coal and increased 

their generation from wind between 2006 and 2016.

 

8

IPP data presented in this section include both combined and non-combined heat and power plants.
2-24

Figure 2-15 Capacity Mix by Ownership Type, 2006 & 2016

Source: Electric Power Annual 2016 (Released December 7, 2017) (https://www.eia.gov/electricity/annual/)

Figure 2-16 Generation Mix by Ownership Type, 2006 & 2016

Source: Electric Power Annual 2016 (Released December 7, 2017) (https://www.eia.gov/electricity/annual/)

The mix of capacity by fuel types that have been built and retired between 2006 and 2016 

also varies significantly by type of ownership. Figure 2-17 presents the new capacity built during 

that period, showing that IPPs built the majority of both new wind and solar generating capacity, 

0

100,000

200,000

300,000

400,000

500,000

600,000

700,000

2006 -

Utility

2016 -

Utility

2006 -

IPP

2016 -

IPP

Capacity (MW)

Nuclear Coal Gas Hydro Wind All Other

0

500,000

1,000,000

1,500,000

2,000,000

2,500,000

3,000,000

Utilities

2006

Utilities

2016

IPP 2006 IPP 2016

Generation (GWh)

Nuclear Coal Gas Hydro Wind All Other
2-25

but significantly less natural gas capacity than the traditional utilities built. Figure 2-18 presents 

comparable data for the retired capacity, showing that utilities retired more coal and “other” 

capacity (mostly residual fuel oil) than IPPs retired, while the IPPs retired more natural gas 

capacity than the utilities retired.

Figure 2-17 Generation Capacity Built between 2006 and 2016 by Ownership Type

Sources: EIA Form 860 (2016)

0

20,000

40,000

60,000

80,000

100,000

120,000

140,000

Utility IPP

New Capacity (MW)

Coal Gas Wind & Solar Other
2-26

Figure 2-18 Generation Capacity Retired between 2006 and 2016 by Ownership Type

Sources: EIA Form 860 (2016)

2.5 Emissions of Greenhouse Gases from Electric Utilities 

The burning of fossil fuels, which generates about 65 percent of our electricity 

nationwide, results in emissions of greenhouse gases. The power sector is a major contributor of 

CO2 in particular, but also contributes to emissions of sulfur hexafluoride (SF6), CH4, and N2O. 

Including both generation and transmission (a source of SF6), the power sector accounted for 29 

percent of total nationwide greenhouse gas emissions, measured in CO2 equivalent. Table 2-5

and Figure 2-19 show the GHG emissions9from the power sector relative to other major

economic sectors. Table 2-6 shows the contributions of CO2 and other GHGs towards total 

power sector GHG emissions. 

 

9 CO2 equivalent data in this section are calculated with the IPCC SAR (Second Assessment Report) GWP potential 

factors.

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

Utility IPP

Retired Capacity (MW) Coal Natural Gas Wind & Solar Other
2-27

Table 2-5 Domestic Emissions of Greenhouse Gases, by Economic Sector (million tons 

of CO2 equivalent)

2006 2015 Change Between '06 and '15

Sector/Source GHG 

Emissions

% Total 

GHG 

Emissions

GHG 

Emissions

% Total 

GHG 

Emissions

Change 

in 

Emissions

% 

Change in 

Emissions

% of 

Total 

Change 

in 

Emissions

Electric Power Industry 2,629 33% 2,140 29% -489 -19% 67%

Transportation 2,199 28% 1,991 27% -207 -9% 29%

Industry 1,652 21% 1,556 21% -96 -6% 13%

Agriculture 646 8% 629 9% -18 -3% 2%

Commercial 427 5% 482 7% 55 13% -8%

Residential 370 5% 411 6% 41 11% -6%

US Territories 65 1% 51 1% -14 -21% 2%

Total GHG Emissions 7,988 100% 7,260 100% -727 -9% 100%

Sinks and Reductions -817 -837 -20 2%

Net GHG Emissions 7,171 6,424 -747 -10%

Source: EPA, 2017 “Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015”, Table 3-10. Includes 

CO2, CH4, N2O and SF6 emissions.

Figure 2-19 Domestic Emissions of Greenhouse Gases from Major Sectors, 2006 and 2015 

(million tons of CO2 equivalent)

Source: EPA, 2017 “Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015”, Table 3-10.

Not Shown: CO2e emissions from US Territories

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

9,000

2006 2015

GHG Emissions (Million Tons CO2e)

Electric Power Industry Transportation

Industry Agriculture

Commercial Residential
2-28

The amount of CO2 emitted during the combustion of fossil fuels varies according to the 

carbon content and heating value of the fuel used. Coal has higher carbon content than oil or 

natural gas, and thus releases more CO2 during combustion. Coal emits around 1.7 times as much 

carbon per unit of energy when burned as natural gas (EPA 2013).

Table 2-6 Greenhouse Gas Emissions from the Electricity Sector (Generation, 

Transmission and Distribution), 2006 and 2015 (million tons of CO2

equivalent)

2006 2015 Change Between '06 

and '15

Gas/Fuel Type or Source GHG 

Emissions

% of 

Total 

GHG 

Emissions 

from 

Power 

Sector

GHG 

Emissions

% of Total 

GHG 

Emissions 

from Power 

Sector

Change in 

GHG 

Emissions

% Change 

in 

Emissions

CO2 2,603 99.0% 2,113 98.7% -490 -19%

Fossil Fuel 

Combustion 2,585 98.32% 2,095 97.9% -490 -19%

Coal 2,154 81.9% 1,489 69.6% -665 -31%

Natural Gas 373 14.17% 580 27.10% 207 56%

Petroleum 58.6 2.23% 26.1 1.22% -32.5 -55%

Geothermal 0.4 0.02% 0.4 0.02% 0.0 0%

Incineration of 

Waste 13.8 0.52% 11.8 0.55% -2.0 -14%

Other Process Uses 

of Carbonates 4.0 0.15% 6.2 0.29% 2.2 56%

CH4 0.6 0.02% 0.4 0.02% -0.1 -20%

Stationary 

Combustion* 0.6 0.02% 0.4 0.02% -0.1 -20%

Incineration of 

Waste + + 

N2O 18.3 0.70% 21.8 1.02% 3.5 19%

Stationary

Combustion* 17.9 0.68% 21.5 1.00% 3.6 20%

Incineration of 

Waste 0.4 0.02% 0.3 0.02% -0.1 -25%

SF6 7.6 0.29% 4.6 0.22% -3.0 -39%

Electrical 

Transmission and 

Distribution

7.6 0.29% 4.6 0.22% -3.0 -39%

Total GHG Emissions 2,630 2,140 -490

Source: EPA, 2017 “Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015”, Table 3-11

* Includes only stationary combustion emissions related to the generation of electricity.

** SF6 is not covered by this rule, which specifically regulates GHG emissions from combustion.

+ Does not exceed 0.05 Tg CO2 Eq. or 0.05 percent.
2-29

2.6 Revenues and Expenses 

Total utility operating revenues have moderately increased from about $275 billion in 

2006 to about $282 billion in 2016. With revenues increasing between 2006 and 2016, operating 

expenses decreased from 2006 to 2016 and as a result, net income increased by over $13 billion 

between these years (see Table 2-7). While real electricity prices have generally declined and net 

generation has remained relatively stable over the last several years, utilities’ net income has 

increased as operating expenses have decreased over the same time period due to declines in 

fossil fuel prices to utilities.

Table 2-7 shows that investor-owned utilities (IOUs) earned income of about 15.4 

percent compared to total revenues in 2016. The 2016 return on revenue was the highest year for 

the period 2006 to 2016 (average: 12.4 percent range: 10.6 percent to 15.4 percent). 
2-30

Table 2-7 Revenue and Expense Statistics for Major U.S. Investor-Owned Electric 

Utilities for 2006, 2011 and 2016 (nominal $millions)

2006 2011 2016

Utility Operating Revenues 275,501 280,520 282,499

Electric Utility 246,736 255,573 261,047

Other Utility 28,765 24,946 21,451

Utility Operating Expenses 245,589 247,118 239,037

Electric Utility 218,445 228,873 226,457

Operation 158,893 161,460 145,077

Production 127,494 122,520 100,852

Cost of Fuel 37,945 42,779 32,621

Purchased Power 79,205 61,447 49,962

Other 10,371 18,294 18,269

Transmission 6,179 6,876 10,447

Distribution 3,640 4,044 4,734

Customer Accounts 4,409 5,180 5,077

Customer Service 2,536 5,311 6,187

Sales 240 185 205

Admin. and 

General

14,580 17,343 17,575

Maintenance 12,838 15,772 16,982

Depreciation 17,373 22,555 30,097

Taxes and Other 28,149 29,086 34,301

 Other Utility 27,143 18,245 12,579

Net Utility Operating Income 29,912 33,402 43,462

Source: Table 8.3, EIA Electric Power Annual, 2016

Note: This data does not include information for public utilities, nor for Independent Power Producers (IPPs).

2.7 Natural Gas Market 

The natural gas market in the United States has historically experienced significant price 

volatility from year to year, between seasons within a year, can undergo major price swings 

during short-lived weather events (such as cold snaps leading to short-run spikes in heating 

demand), and has seen a dramatic shift since 2008 due to increased production from shale 

formations. Over the last decade, the annual average nominal price of gas delivered to the power 

sector peaked in 2008 at $9.02/MMBtu and has since fallen dramatically to a low of 
2-31

$2.87/MMBtu in 2016. During that time, the daily price10 of natural gas reached as high as 

$18.48/MMBtu and as low as $1.05. Adjusting for inflation using the GDP implicit price deflator 

in 2016$, the annual average price of natural gas delivered to the power sector peaked at 

$10.13/MMBtu in 2008 and has fallen dramatically to a low of $2.87 in 2016. The annual natural 

gas prices in both nominal and real (2016$) terms are in Figure 2-20 and Figure 2-21. A 

comparison of the trends in the real price of natural gas with the real prices of delivered coal and 

oil are shown in Figure 2-21. Figure 2-21 shows that while the real price of coal increased by 6 

percent and oil decreased by 28 percent from 2006 to 2016, the real price of natural gas declined 

by 65 percent in the same period. Most of the decline in real natural gas prices occurred between 

2008 (the peak price year) and 2012, during which real gas prices declined by 64 percent while 

coal and oil prices both increased by slightly over 8 percent. The sharp decline in natural gas 

prices from 2008 to 2012 was primarily caused by the rapid increase in natural gas production 

from shale formations.

Figure 2-20 Relative Change Nominal and Real (2016$) Prices of Natural Gas Delivered 

to the Power Sector ($/MMBtu)

Source: EIA, Electric Power Annual, 2016, Table 7.4

 

10 Henry Hub daily prices. Henry Hub is a major gas distribution hub in Louisiana; Henry Hub prices are generally 

seen as the primary metric for national gas prices for all end uses. The price of natural gas delivered to electricity 

generation differs substantially in different regions of the country, and can be higher or lower than the Henry Hub 

national benchmark price.

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

8.00

9.00

10.00

11.00

1 3 5 7 9 11

Natural Gas Prices ($/MMBtu)

Series1 Series2
2-32

Figure 2-21 Relative Change in Real (2016$) Prices of Fossil Fuels Delivered to the Power 

Sector ($/MMBtu)

Source: EIA, Electric Power Annual, 2016, Table 7.4

Current and projected natural gas prices are considerably lower than the prices observed 

over the past decade, largely due to advances in hydraulic fracturing and horizontal drilling 

techniques that have opened up new shale gas resources and substantially increased the supply of 

economically recoverable natural gas. 

The U.S. Energy Information Administration's (EIA) Annual Energy Outlook (AEO) 

2018 estimates that the United States possessed 2,462 trillion cubic feet (Tcf) of technically 

recoverable dry natural gas resources as of January 1, 2016. At the 2017 rate of U.S. 

consumption (about 27.5 Tcf per year), the 2,462 Tcf of technically recoverable dry natural gas 

reserves would be enough to supply more than 90 years of use.

Technically recoverable reserves include proved reserves and unproved resources. Proved 

reserves of crude oil and natural gas are the estimated volumes expected to be produced, with 

reasonable certainty, under existing economic and operating conditions. Unproved resources of 

crude oil and natural gas are additional volumes estimated to be technically recoverable without 

consideration of economics or operating conditions, based on the application of current 

-80%

-60%

-40%

-20%

0%

20%

40%

60%

80%

100%

2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016

Coal Oil Gas Average
2-33

technology. Proven reserves make up 12 percent of the technically recoverable total estimate, 

with the remaining 88 percent from unproven reserves.
2-34

2.8 References

Federal Reserve Bank of St. Louis. Available online at: 

https://fred.stlouisfed.org/series/GDPDEF#0. Accessed March 2018.

Rose, Ken. 2017. Retail Electricity Markets. 59th Annual Regulatory Studies Program Institute of 

Public Utilities, Michigan State University.

U.S. Census Bureau American Fact Finder. Available online 

at:https://factfinder.census.gov/bkmk/table/1.0/en/PEP/2017/PEPANNRES/0100000US|0

100000US.04000|0200000US3|0200000US4 and 

https://www.census.gov/data/tables/time-series/demo/popest/intercensal-2000-2010-

national.html Accessed March 2018.

U.S. Energy Information Administration (U.S. EIA). 2016. Electric Power Annual 2016.

Available online at: http://www.eia.gov/electricity/annual/. Accessed March 2018.

U.S. Energy Information Administration (U.S. EIA). 2017. Annual Energy Outlook 2017. 

Available online at: https://www.eia.gov/outlooks/archive/aeo17/. Accessed March 2018.

U.S. Energy Information Administration (U.S. EIA). 2018. Annual Energy Outlook 2018. 

Available online at: https://www.eia.gov/outlooks/aeo/. Accessed March 2018.

U.S. Energy Information Administration (U.S. EIA). 2017 and 2018. Monthly Energy Review, 

Dec. 2017 and March 2018. Available online at: 

http://www.eia.gov/totalenergy/data/monthly/. Accessed March 2018.

U.S. Environmental Protection Agency (U.S. EPA). 2017. Inventory of U.S. Greenhouse Gas 

Emissions and Sinks: 1990–2015. Available online at: 

https://www.epa.gov/ghgemissions/inventory-us-greenhouse-gas-emissions-and-sinks1990-2015-Main-Text.pdf. Accessed March 2018.

U.S. Energy Information Administration. State Electricity Profiles. 2016. Available online at: 

https://www.eia.gov/electricity/state/. Accessed March 2018.
2-35

U.S. Energy Information Administration. 2016 Form 860. Available online at: 

http://eia.gov/electricity/annual/html/epa_02_02.html & 

http://www.eia.gov/electricity/data.cfm#gencapacity "3_1_Generator_Y2016" Excel, 

"Operable" Sheet and "LayoutY2016" Excel, "Reference Table 28" sheet. Accessed 

March 2018.

U.S. Energy Information Administration. Form EIA-861 Annual Electric Power Industry Report. 

Available online at: https://www.eia.gov/electricity/annual/html/epa_02_04.html. 

Accessed March 2018.
3-1

CHAPTER 3: COST, EMISSIONS, ECONOMIC, AND ENERGY IMPACTS

3.1 Introduction 

This chapter presents the compliance cost, emissions, economic, and energy impact 

analysis for the power sector, in support of this proposed rulemaking. The results are generated 

from a detailed power sector model called the Integrated Planning Model (IPM),1 a version of 

which is developed and used by EPA to support regulatory efforts. The model can be used to 

examine air pollution control policies for a variety of pollutants throughout the contiguous 

United States for the entire power system.

3.2 Overview 

This analysis is intended to be an illustrative representation and analysis of the proposed 

rule to replace the Clean Power Plan.2 The proposed rule presents a framework for states to 

develop state plans that will establish standards of performance for existing affected sources of 

GHG emissions. The proposed rule does not itself specify any standard of performance, but 

rather establishes the “best system of emission reduction”3(BSER), i.e. technology options for 

heat rate improvements (HRI), that States may choose to rely upon as they develop standards of 

performance and State plans. The specific technology options that might be used to establish a 

standard of performance for individual affected sources are unknown. Affected sources may not 

be able to apply the technology options because they have already adopted these technologies, 

they are not applicable to the source, or for other reasons. The rule also re-proposes reforms to 

New Source Review (NSR) that may facilitate the application of HRI technologies from the 

BSER to sources that the States otherwise may have deemed inapplicable to those sources as part

of their state plans.

For these reasons, there is considerable uncertainty regarding the specific technology 

measures that might be applied by States from the BSER across the universe of affected sources, 

 

1 The specific version model used in this RIA is operated by ICF International, at EPA’s direction.

2 For more details on legal authority and justification of this action, see rule preamble.

3 The best system of emission reduction (BSER) is outlined in the CAA 111(d), see preamble for further discussion. 
3-2

and the subsequent standard of performance that will result from that process. Hence, this 

analysis presents illustrative scenarios that are intended to broadly reflect how States might apply 

BSER and develop state plans, and are intended to inform and present the potential impacts of 

the proposed rule. Each illustrative scenario assigns the same average percentage HRI and 

associated average capital cost to each affected coal steam unit in the contiguous U.S.4 The 

analysis is not meant to reflect what EPA believes can be undertaken at each affected source, but 

rather to estimate potential national impacts by applying controls measures that EPA believes are 

reasonable, on an average basis. Given the unique nature of each individual generating unit and 

the lack of data and information on specific individual unit-level actions with regards to the 

BSER technologies, in addition to uncertainty about how BSER might ultimately be 

implemented by States through a performance standard, EPA believes that this illustrative 

modeling approach is suitable to inform the potential impacts of the rule from a national 

perspective.

3.3 Power Sector Modeling Framework 

IPM is a state-of-the-art, peer-reviewed, dynamic linear programming model that can be 

used to project power sector behavior and examine prospective air pollution control policies 

throughout the contiguous United States for the entire electric power system.5It provides 

forecasts of least cost capacity expansion, electricity dispatch, and emission control strategies 

while meeting energy demand and environmental, transmission, dispatch, and reliability 

constraints. EPA has used IPM for over two decades to better understand power sector behavior 

into the future and to evaluate the economic and emission impacts of prospective environmental 

policies. EPA uses the best available information from utilities, industry experts, gas and coal 

market experts, financial institutions, and government statistics as the basis for the detailed 

power sector modeling in IPM. The model documentation provides additional information on the 

assumptions summarized here as well as all other model assumptions and inputs.6 The model 

 

4 This is consistent with past modeling approaches for CPP and the 316(b) rule regarding Cooling Water Intake, 

where generic assumptions were used to inform the RIA where more specific unit-level data was lacking.

5 For more detail on IPM, see model documentation available at https://www.epa.gov/airmarkets/clean-air-marketspower-sector-modeling

6 For documentation, see https://www.epa.gov/airmarkets/clean-air-markets-power-sector-modeling
3-3

also incorporates a detailed representation of the fossil-fuel supply system that is used to forecast 

equilibrium fuel prices for natural gas and coal.

The costs presented in this RIA reflect the IPM-projected annualized estimates of private 

compliance costs.7 The IPM-projected annualized estimates of private compliance costs provided 

in this analysis are meant to show the change in production (generating) costs to the power sector 

in response to various regulatory changes. The private compliance costs equal the difference 

between capital, operating, and fuel expenditures net of taxes and subsidies in the electricity 

sector between a baseline and policy scenario. This RIA does not identify who ultimately bears 

these compliance costs, such as owners of generating assets through changes in their profits or 

electricity consumers through changes in their bills, although the potential impacts on consumers 

and producers are described in Chapter 5.8 Furthermore, EPA uses the projection of private 

compliance costs as an estimate of the social cost of the proposed requirements, as the social cost 

is the appropriate metric for formal economic welfare analysis.9 Section 3.9 describes the 

limitations with using this estimate of private compliance costs as an estimate of the social cost. 

To estimate these annualized capital costs, EPA uses a conventional and widely accepted 

approach that applies a capital recovery factor (CRF) multiplier to capital investments and adds 

that to the annual incremental operating expenses. The CRF is derived from estimates of the cost 

of capital (private discount rate), the amount of insurance coverage required, local property 

taxes, and the life of capital. It is important to note that there is no single CRF factor applied in 

the model; rather, the CRF varies across technologies in the model to better simulate power 

sector decision-making.

While the CRF is used to annualize costs within IPM, a discount rate is used to estimate 

the net present value of the intertemporal flow of the annualized capital and operating costs. The 

optimization model then identifies power sector investment decisions that minimize the net 

present value of all private costs over the full planning horizon while satisfying a wide range of 

 

7 Where relevant, cost estimates for demand-side energy efficiency improvements are included.

8 As discussed in further detail in Chapter 5, the ultimate incidence of this proposed action will depend on the 

distribution of both the costs and the health and welfare impacts presented in Chapter 4 across households.

9 See, Tietenberg and Lewis, 2008; Freeman, 2003, and USEPA, 2010.
3-4

demand, capacity, reliability, emissions, and other constraints. As explained in model 

documentation, the discount rate is derived as a weighted average cost of capital that is a 

function of capital structure, post-tax cost of debt, and post-tax cost of equity. It is important to 

note that this discount rate is selected for the purposes of best simulating power sector behavior, 

and not for the purposes of discounting social costs or benefits.

EPA has used IPM extensively over the past two decades to analyze options for reducing 

power sector emissions. Previously, the model has been used to forecast the costs, emission 

changes, and power sector impacts for the Clean Air Interstate Rule (CAIR), Cross-State Air 

Pollution Rule (CSAPR), the Mercury and Air Toxics Standards (MATS), and the Clean Power 

Plan (CPP). IPM has also been used to estimate the air pollution reductions and power sector 

impacts of water and waste regulations affecting EGUs, including Cooling Water Intakes 

(316(b)) Rule, Disposal of Coal Combustion Residuals from Electric Utilities (CCR) and Steam 

Electric Effluent Limitation Guidelines (ELG).

The model and EPA's input assumptions undergo periodic formal peer review. The 

rulemaking process also provides opportunity for expert review and comment by a variety of 

stakeholders, including owners and operators of capacity in the electricity sector that is 

represented by the model, public interest groups, and other developers of U.S. electricity sector 

models. The feedback that the Agency receives provides a highly-detailed review of key input 

assumptions, model representation, and modeling results. 

3.4 Recent Updates to EPA’s Power Sector Modeling Platform v6 using IPM

In June 2018 EPA updated its application of IPM to version 6. This update incorporates 

important structural improvements, as well as routine data updates, and reflects a robust 

representation of electricity generation and related fuel markets. Important 

improvements/updates include:

• Use of the Energy Information Agency’s (EIA) Annual Energy Outlook (AEO) 2017 

demand projections

• Adjustment and increase of model region boundaries to reflect current state of power 

markets

• Incorporation of three seasons, with additional load segments
3-5

• Routine updates to key power sector inputs based on recent data from EIA, NERC, 

FERC, etc.

• Updated inventory of State and Federal power sector regulations

• Updated transmission representation and regional reserve margins from ISO/RTO NERC 

Reports

• Updates to EPA’s National Electric Energy Data System, the database of existing and 

planned-committed units and their emission control configurations

• Adjustments and updates to nuclear life extension costs 

• Updated cost and performance characteristics for potential (new) conventional, renewable 

and nuclear generating units

• Updated wind and solar cost and resource base estimates, capacity credit calculation 

methodology, hourly generation profiles and time of day based load segments

• Update of cost and performance assumptions for SO2, NOX, Hg, HCl and CO2 emission 

controls using the most current data available

• Inclusion of cost and performance assumptions for coal-to-gas and HRI technologies

• Update of coal supply curves and transportation matrix

• Natural gas assumptions modeled through annual gas supply curves and IPM region level 

seasonal basis differentials 

More information on these updates is available in the comprehensive model 

documentation, which is available on EPA’s website.10

The updated modeling platform incorporates updated data to reflect an evolving power 

system. Since analysis was conducted in 2015, notable changes have occurred in the industry 

based on a variety of factors. As a starting point, IPM reflects a detailed snapshot of the current 

universe of electric generating units throughout the contiguous United States that supply 

electricity to the grid. This database, called the National Electric Energy Data System (NEEDS), 

contains an inventory of every unit represented in IPM, summarized below for coal and natural 

gas-fired sources, including average heat rate data across categories of units. Over the past few 

years, the power sector has changed notably, with a considerable number of coal-steam power

plants coming offline, and increases in natural gas and renewable sources of electricity. For 

example, the previous iteration of NEEDS that was used during the Final CPP RIA conducted in 

 

10 See Documentation for EPA's Power Sector Modeling Platform v6 Using the Integrated Planning Model, 

available at: https://www.epa.gov/airmarkets/epas-power-sector-modeling-platform-v6-using-ipm
3-6

2015 showed the total coal-steam capacity to be 269 GW (subtracting known planned 

retirements from the database at the time of analysis), compared to 226 GW in this updated 

modeling. This reflects the market trends and changes that have occurred in the power sector 

over the past few years, where abundant natural gas supplies and low prices, large increases in 

renewable energy deployment, and flat overall electric demand have all contributed to shifts 

away from existing coal-fired capacity in the marketplace (See Chapter 2, Industry Profile, for 

more information on various changes in the power sector over the last decade).

Table 3-1 Coal and Natural Gas Generating Units, by Size, Age, Capacity, and 

Thermal Efficiency (Heat Rate)

Unit Size 

Grouping 

(MW)

No. 

Units

% of All 

Units

Avg. 

Age

Avg. Net 

Summer 

Capacity 

(MW)

Total Net 

Summer 

Capacity 

(MW)

% Total 

Capacity

Avg. Heat 

Rate 

(Btu/kWh)

COAL

0 – 24 33 6% 57 11 375 0% 12,362

25 – 49 41 7% 41 37 1,499 1% 12,050

50 – 99 39 7% 47 74 2,894 1% 11,929

100 – 149 45 8% 57 122 5,485 2% 11,266

150 – 249 88 15% 54 193 17,013 8% 10,899

250 – 499 140 23% 46 368 51,468 23% 10,605

500 – 749 143 24% 45 609 87,055 39% 10,301

750 – 999 56 9% 42 827 46,293 20% 10,069

1000 - 1500 11 2% 47 1257 13,831 6% 9,802

Total Coal 596 100% 48 379 225,913 100% 10,843

NATURAL GAS

0 – 24 3950 53% 38 5 20,425 5% 14,144

25 – 49 910 12% 31 41 37,065 9% 11,968

50 – 99 983 13% 30 71 69,749 17% 12,274

100 – 149 371 5% 25 127 47,248 11% 9,116

150 – 249 991 13% 20 178 176,610 43% 8,034

250 – 499 178 2% 16 319 56,727 14% 7,017

500 – 749 7 0.1% 11 549 3,840 1% 6,881

Total Gas 7,390 100% 33 56 411,663 100% 12,377

Source: National Electric Energy Data System (NEEDS) v.6.

Note: Natural gas includes combustion turbines and combined cycles. The average heat rate reported is the mean of 

the heat rate of each unit. A lower heat rate indicates a higher level of fuel efficiency. Table is limited to coal-steam 

units in operation in 2016, and subtracts units with planned retirements prior to 2025. Age is estimated for the year 

2025, the first year of analysis.

This analysis reflects the best data available to EPA at the time the modeling was 

conducted. As with any modeling of future projections, many of the inputs are uncertain. In this 

context, notable uncertainties include the cost of fuels, the cost to operate existing power plants, 
3-7

the cost to construct and operate new power plants, infrastructure, demand, and policies affecting 

the electric power sector. The modeling conducted for this RIA is based on estimates of these 

variables, which were derived from the data currently available to EPA. However, future 

realizations of these characteristics may deviate from expectations. The results of counterfactual 

simulations presented in this RIA are not a prediction of what will happen, but rather projections 

of plausible scenarios describing how this proposed regulatory action may affect electricity 

sector outcomes in the absence of unexpected shocks. The results of this RIA should be viewed 

in that context.

3.5 Scenarios Analyzed

Several illustrative scenarios were analyzed to estimate potential costs and benefits of the 

proposed rule. These scenarios represent the CPP, replacement to CPP (this proposal), and No 

CPP (no Federal regulatory action under CAA Section 111). The following scenarios incorporate 

the updates discussed in Section 3.4:

• Base Case Scenario (CPP): This scenario includes the Clean Power Plan (CPP) 

modeled in a similar fashion to previous EPA analytical efforts.11 More 

specifically, this scenario utilizes a mass-based implementation of CPP at the state 

level, with intra-state trading covering existing sources only, and no incremental 

demand-side energy efficiency investments. In this scenario, coal steam units may 

choose to adopt technologies that achieve a 2.1 percent to 4.3 percent HRI at a 

capital cost of $100/kW based on prevailing economics, but are not required to do 

so given the flexible compliance afforded under CPP. The improvement and cost 

level of HRI in this scenario is consistent with the assumptions regarding the 

availability and cost of HRI used to develop building block 1 in the final CPP, and 

is the same level applied in the final RIA for the CPP (U.S. EPA, 2015a). In this 

RIA, this scenario represents the primary electricity and related fuel market 

baseline for comparison with the three illustrative policy scenarios below.

• Illustrative 2 percent HRI at $50/kW Scenario: This illustrative scenario 

represents a policy case that reflects modest improvements in HRI absent any 

revisions to NSR requirements. This scenario assumes a uniform potential HRI is 

available for all affected sources. In the model, this scenario requires a source to 

improve its heat rate by 2 percent, at a capital cost of $50/kW. The source can 

 

11 It is the same general framework and analysis that was used when the CPP was proposed and finalized. The 

differences include no modeled assumptions to address leakage (such as the New Source Complement or renewable 

set-aside), allowing banking across modeled years due to new model year configuration, and application of regional 

heat rate levels at different levels/cost. Heat rate improvements are not applied in an additive manner across any 

scenario. 
3-8

either adopt the improvement or retire, based upon the prevailing economics in 

the model.

• Illustrative 4.5 percent HRI at $50/kW Scenario: This illustrative scenario 

represents a policy case that includes benefits from the proposed revisions to 

NSR, with the HRI modeled at a low cost. This scenario assumes a uniform 

potential HRI is available for all affected sources. In the model, this scenario 

requires a source to improve its heat rate by 4.5 percent, at a capital cost of 

$50/kW. The source can either adopt the improvement or retire, based upon the 

prevailing economics in the model.

• Illustrative 4.5 percent HRI at $100/kW Scenario: This illustrative scenario 

represents a policy case that includes the benefits from the proposed revisions to 

NSR, with the HRI modeled at a higher cost. This scenario assumes a uniform 

potential HRI is available for all affected sources. In the model, this scenario 

requires a source to improve its heat rate by 4.5 percent, at a capital cost of 

$100/kW. The source can either adopt the improvement or retire, based upon the 

prevailing economics in the model.

• Illustrative No CPP Scenario: This illustrative scenario does not apply any

standards of performance under section 111(d) of the CAA for CO2 emissions 

from existing sources. Furthermore, in this scenario, it is assumed that no source 

adopts any heat rate improvements.

The year of implementation for the illustrative policy scenarios is 2025, as an 

approximation for when the standards for performance under the proposed rule might be 

implemented. The requirements do not change over time. For CPP, the year of implementation is 

2023 (the IPM model year that most closely represents the 2022 implementation year of CPP) 

through 2030 (the CPP emissions requirements hold constant thereafter). 

Due to a number of changes in the electricity sector since the CPP was finalized, as 

documented in the October 2017 RIA proposing to repeal the CPP and Chapter 2 of this RIA, the 

sector has become less carbon intensive over the past several years, and this trend is projected to 

continue in the future. These changes and trends are reflected in the modeling used for this 

analysis. As a result of these changes, the projected compliance costs of achieving the emissions 

levels required under CPP is now projected to be significantly lower than the estimates presented 

in the final CPP RIA (U.S. EPA, 2015a).

As discussed above, the proposed regulation requires states to develop standards of 

performance based on EPA's determination of BSER, which are methods of heat rate 

improvement that reduce CO2 emissions. The standards of performance are not represented in the 
3-9

model directly and, as discussed above, are uncertain because the applicability of these 

technologies across the fleet and the standards of performance the states will require are 

uncertain.12 In practice, affected sources may have certain flexibilities in how they comply with 

the standards of performance that differs from the technologies used to determine the sources’ 

standards of performance, but this possibility is not captured in the modeling for this RIA.

For ease of modeling, in the scenarios representing the proposed rule, sources may adopt 

the assumed HRI level or may retire in the model, based on prevailing economics. However, it is 

possible that States may use opportunities afforded to them in the proposed rule when applying 

BSER to avoid retirement of affected sources, and the scenarios do not capture this possibility. 

However, as discussed in Section 3.7.5, there are relatively few retirements modeled in these 

scenarios. 

The three HRI improvement scenarios reflect a range of technology improvements across 

the fleet, applied uniformly. Again, it is important to note that current data limits our ability to 

apply more customized HRI and cost functions to specific units. Due to these limitations, EPA 

used the best available information, research, and analysis to arrive at the assumptions used in 

these three scenarios. 

The primary driver for the difference in HRI level across the scenarios is an assumption 

pertaining to proposed changes to the New Source Review (NSR) program. In the past, the NSR 

program has often been cited as an inherent limit on the potential activities, upgrades, and 

changes that would otherwise be undertaken cost-effectively at particular units, which could 

result in improved performance and reduced CO2 emission rates. In this action, EPA proposes 

certain changes and reforms to the NSR program that are expected to remove regulatory barriers 

to HRI.13 This proposed change is the primary driver for including two different levels of HRI to 

better understand the potential impacts, with the lower level of HRI representing a replacement 

rule without the NSR regulatory changes, and the higher HRI scenario reflecting a replacement 

rule that also reflects NSR reform.

 

12 Note that, in the modeling, the total cost of the HRI is reflected as a capital cost. However, for some HRI 

technologies, a small share of the total cost may be variable, and thus the cost of the HRI might have a small effect 

on dispatch decisions.

13 See Chapter 1 for additional information.
3-10

The quantity of electricity demanded in each region and model year is assumed to be the 

same across the base case scenario, which includes the CPP, and the illustrative scenarios 

described above. An additional scenario was conducted that included CPP with a revised electric 

demand projection, reflecting end-use energy efficiency measures that were allowed as a 

compliance option under CPP. This alternative base case scenario is discussed in section 3.8 and 

compared to the other scenarios that are the focus of this RIA.

3.6 Monitoring, Reporting, and Recordkeeping Costs 

EPA projected monitoring, reporting and recordkeeping costs for both state entities and 

affected EGUs for the years 2023, 2025, 2030, and 2035. The MR&R cost estimates presented 

below apply to the three illustrative policy scenarios. EPA estimates that would be no

incremental MR&R costs under the illustrative No CPP scenario.

In calculating the costs for state entities, EPA estimated personnel costs to oversee 

compliance, and review and report annually to EPA on program progress relative to meeting the 

state’s reduction goal. To calculate the national costs, EPA estimated that 49 states and 277 

facilities would be affected. EPA estimated that the majority of the cost to EGUs would be in 

calculating net energy output. Since the majority of EGUs do have some energy usage meters or 

other equipment available to them, EPA believes a new system for calculating net energy output 

is not needed. 

EPA has made it a priority to streamline reporting and monitoring requirements. In this 

rule, EPA is making implementation as efficient as possible for both the states and the affected 

EGUs by allowing state plans to utilize the current monitoring and recordkeeping requirements 

and pathways that have already been well established in other EPA rulemakings. For example, 

under the Acid Rain Program’s continuous emissions monitoring, 40 CFR Part 75, EPA has 

established requirements for the majority of the EGUs that would be affected by a 111(d) state 

plan to monitor CO2 emissions and report that data using the Emissions Collection and 

Monitoring Plan System (ECMPS). Additionally, since the CO2 hourly data is already reported 

to EPA’s ECMPS there is no additional burden associated with the reporting of that data. Since 

the ECMPS pathway is already in place, EPA will allow for states to utilize the ECMPS system 

to facilitate the data reporting of the additional net energy output data required under the 
3-11

emission guidelines. However, because the Acid Rain Program does not require net energy 

output to be reported, there is some additional burden (Shown in Table 3-2) in updating an 

affected EGUs monitoring system to be able to report the associated net energy output of an 

affected EGU.

EPA estimates that it would take three working months for a technician to retrofit any 

existing energy meters to meet the requirements set in the state plan. Additionally EPA believes 

that 50 hours will be needed for each EGU operator to read the rule and understand how the 

facility will comply with the rule, based on an average reading rate of 100 words per minute and 

a projected rule word count of 300,000 words.14 Also, after all modifications are made at a 

facility to measure net energy output, each EGU’s Data Acquisition System (DAS) would need 

to be upgraded to supply the rate-based emissions value to either the state or EPA’s Emissions 

Collection and Monitoring Plan System (ECMPS). Note the costs to develop net energy output 

monitoring and to upgrade each facility’s DAS system are one-time costs incurred in 2023. 

Recordkeeping and reporting costs substantially decrease after 2023. The projected costs for 

2023, 2025, 2030, and 2035 are summarized below.

In calculating the cost for states to comply, EPA estimates that each state will rely on the 

equivalent of two full time staff to oversee program implementation, assess progress, develop 

possible contingency measures, perform state plan revisions and host the subsequent public 

meetings if revisions are indeed needed, download data from the ECMPS for their annual 

reporting and develop their annual EPA report. The burden estimate was based on an analysis of 

similar tasks performed under the Regional Haze Program, whereby states were required to 

develop their list of eligible sources, draft implementation plans, revise initial drafts, identify 

baseline controls, identify data gaps, identify initial strategies, conduct various reviews, and 

manage their programs. A total estimate of 78,000 hours of labor performed by seven states over 

a three-year period resulted in 3,714 hours per year, per entity. Due to the nature of this proposed 

rule whereby we believe the air office and the energy office will both be involved in performing 

 

14 According to one source, the average person can proofread at about 200 words per minute on paper and 180 words 

per minute on a monitor. (Source: Ziefle, M. 1988. “Effects of Display Resolution on Visual Performance.” Human 

Factors 40(4):554-68). Due to the highly technical nature of the rule requirements in subpart UUUUa, a more 

conservative estimate of 100 words per minute was used to determine the burden estimate for reading and 

understanding rule requirements.
3-12

the above-mentioned tasks, we rounded up to the equivalent of two full time staff, which totaled 

4,160 hours per year.15 Table 3-2 presents the estimates of the annual state and industry 

respondent burden and costs of reporting and recordkeeping for the three illustrative policy 

scenarios in 2023, 2025, 2030, and 2035.

Table 3-2 Years 2023, 2025, 2030, and 2035: Summary of State and Industry Annual 

Respondent Burden and Cost of Reporting and Recordkeeping 

Requirements (Million 2016$)

Totals

Total 

Annual 

Labor 

Burden 

(Hours)

Total

Annual

Labor Costs

Total 

Annualized

Capital 

Costs

Total

Annual

O&M Costs

Total 

Annualized 

Costs

Total 

Annual 

Respondent 

Costs

States

2023 200,237 16.3 0.0 0.04 0.04 16.4

2025 215,639 17.6 0.0 0.02 0.02 17.6

2030 215,639 17.6 0.0 0.02 0.02 17.6

2035 215,639 17.6 0.0 0.02 0.02 17.6

Industry

2023 154,010 14.1 0.0 0.28 0.28 14.4

2025 0 0.0 0.0 0.00 0.00 0

2030 0 0.0 0.0 0.00 0.00 0

2035 0 0.0 0.0 0.00 0.00 0

Total

2023 354,247 30.5 0.0 0.31 0.31 30.8

2025 215,639 17.6 0.0 0.02 0.02 17.6

2030 215,639 17.6 0.0 0.02 0.02 17.6

2035 215,639 17.6 0.0 0.02 0.02 17.6

The labor costs associated with MR&R activities represent part of the total costs of the 

rule. Other categories of labor that may be impacted by the rule are described in Section 5.2 

“Employment Impacts”. Estimates in Table 3-2 of the total annual labor burden in hours, for 

MR&R activities, can be converted to estimates of full-time equivalent (FTE) jobs using the 

above estimate of 4,160 hours per year for two full time staff, i.e. 2,080 hours per year for one 

FTE job. Within this category of MR&R labor, as shown in Table 3-2, amounts of labor needed 

range from approximately 74 FTE for industry in illustrative policy scenario year 2023, to 

 

15 Renewal of the ICR for the Regional Haze Rule, Section 6(a) Tables 1 through 4 based on 7 states’ burden. EPAHQ-OAR-2003-0162-0001.
3-13

approximately 170 FTE for both states and industry in the illustrative policy scenario year 2023, 

and approximately 104 FTE in policy scenario years 2025, 2030, and 2035.

In the 2015 CPP RIA, EPA projected monitoring, reporting, and recordkeeping costs for

both state entities and affected EGUs for the compliance years 2020, 2025, and 2030 (U.S. EPA, 

2015a). These estimated costs are applied to the base case for this action, which includes the 

CPP. As we evaluate MR&R costs for the illustrative policy scenarios in 2023, 2025, 2030, and 

2035, it is necessary to compare them against applicable base case MR&R costs in these years. 

We assume that 2020 MR&R costs from the CPP RIA are applicable to 2023 in the base case for 

this action. Similarly, we assume that 2030 MR&R costs from the CPP RIA are applicable to 

2035 in the base case for this action. 

Table 3-3 presents the MR&R costs associated with this action, which is the difference 

between the cost estimates for the illustrative policy scenario and the MR&R cost estimates for 

the base case (CPP).

Table 3-3 Years 2025, 2030, and 2035: Total State and Industry Annual Cost of 

Reporting and Recordkeeping Requirements, Relative to the Base Case 

(Million 2016$)

No CPP 2% HRI at $50/kW 4.5% HRI at 

$50/kW

4.5% HRI at 

$100/kW

2023 (70.5) (39.7) (39.7) (39.7)

2025 (15.9) 1.7 1.7 1.7 

2030 (15.9) 1.7 1.7 1.7 

2035 (15.9) 1.7 1.7 1.7 

As shown in Table 3-2, almost all MR&R costs are labor costs. Within this category of 

MR&R, relative to the baseline scenario, Table 3-2 indicates that incremental labor impacts due 

to monitoring, reporting, and record keeping may range from small negative impacts, e.g. in 

illustrative policy year 2023 for all four policy options shown in Table 3-2 or all four illustrative 

years for the “No CPP” option, to small positive impacts in later illustrative policy years (2025, 

2030, 2035), for the HRI policy options. In the context of other categories of labor potentially 

impacted by the rule, such as labor associated with heat rate improvements, labor needed for 

production of electricity by type of fuel, or labor needed for coal or natural gas fuel production, 
3-14

which are all described in Section 5.2 “Employment Impacts”, MR&R labor is a smaller 

category. See Section 5.2 for a discussion of the current U.S. economic climate with low 

unemployment and full employment conditions, which indicates that while affected workers may 

experience potential impacts due to the rule, overall, such impacts would most likely be 

temporary and aggregate employment would be unchanged.

3.7 Projected Power Sector Impacts 

The following sections report the results from the power sector modeling, comparing the 

illustrative policy scenarios (replacement) to the CPP as the primary comparison point of 

reference.16 Other useful comparison points are added, where appropriate.

3.7.1 Projected Emissions

Under the illustrative policy scenarios, EPA projects an annual CO2 emissions increase in 

the contiguous U.S. of about 1-2 percent above the base case (CPP) annually in 2025, and 3 

percent above the base case (CPP) in 2030 and 2035. For comparison, EPA projects that a full 

repeal of the CPP would result in an annual CO2 emissions increase of about 3 percent above the 

base case (CPP) annually in 2025, and 4 percent above the base case (CPP) in 2030 and 2035. 

Relative to a projected future without CPP, the illustrative policy scenarios are projected to result 

in an annual CO2 emissions decrease of, at most, 2 percent in 2025-2035. Additionally, EPA 

projects a 2030 CO2 emissions decrease of 35 percent below 2005 levels for the base case (CPP), 

and 33 percent below 2005 levels for each of the illustrative policy scenarios.

Table 3-4 Projected CO2 Emission Impacts, Relative to Base Case (CPP) Scenario

CO2 Emissions

(MM Short Tons)

CO2 Emissions Change 

(MM Short Tons)

CO2 Emissions Change 

Percent Change

2025 2030 2035 2025 2030 2035 2025 2030 2035

No CPP 1,829 1,811 1,794 50 74 66 3% 4% 4%

Base Case (CPP) 1,780 1,737 1,728 -- -- -- -- -- --

2% HRI at $50/kW 1,816 1,798 1,783 37 61 55 2% 3% 3%

4.5% HRI at $50/kW 1,812 1,797 1,787 32 60 59 2% 3% 3%

4.5% HRI at $100/kW 1,799 1,785 1,772 20 47 44 1% 3% 3%

 

16 The detailed modeling output files for all of the scenarios described in this chapter are available in the docket and 

on EPA’s website, which include additional data and information, including results from additional model run years. 
3-15

Table 3-5 Projected CO2 Emission Impacts, Relative to No CPP Scenario

CO2 Emissions

(MM Short Tons)

CO2 Emissions Change 

(MM Short Tons)

CO2 Emissions Change 

Percent Change

2025 2030 2035 2025 2030 2035 2025 2030 2035

No CPP 1,829 1,811 1,794 -- -- -- -- -- --

Base Case (CPP) 1,780 1,737 1,728 -50 -74 -66 -3% -4% -4%

2% HRI at $50/kW 1,816 1,798 1,783 -13 -13 -11 -1% -1% -1%

4.5% HRI at $50/kW 1,812 1,797 1,787 -18 -14 -7 -1% -1% 0%

4.5% HRI at $100/kW 1,799 1,785 1,772 -30 -27 -22 -2% -1% -1%

Table 3-6 Projected CO2 Emission Impacts, Relative to 2005

CO2 Emissions

(MM Short Tons)

CO2 Emissions:

Change from 2005

(MM Short Tons)

CO2 Emissions:

Percent Change from 

2005

2020 2025 2030 2020 2025 2030 2020 2025 2030

No CPP 1,829 1,811 1,794 -854 -872 -889 -32% -32% -33%

Base Case (CPP) 1,780 1,737 1,728 -903 -946 -955 -34% -35% -36%

2% HRI at $50/kW 1,816 1,798 1,783 -867 -885 -900 -32% -33% -34%

4.5% HRI at $50/kW 1,812 1,797 1,787 -871 -886 -896 -32% -33% -33%

4.5% HRI at $100/kW 1,799 1,785 1,772 -884 -898 -911 -33% -33% -34%

Under the illustrative policy scenarios, EPA projects a 4 percent increase in SO2

emissions in 2025, a 5-6 percent increase in SO2 emissions in 2030, and a 4-5 percent increase in 

SO2 emissions in 2035, relative to the base case (CPP). Additionally, EPA projects a 2-3 percent 

increase in NOX emissions in 2025, a 4-5 percent increase in 2030, and a 4-6 percent increase in 

2035, relative to the base case (CPP). In addition to increases in SO2 and NOX emissions, EPA 

also projects an increase in mercury emissions relative to the base case (CPP): a 2-3 percent 

increase in 2025, a 4-5 percent increase in 2030, and a 3-4 percent increase in 2035.
3-16

Table 3-7 Projected Emissions of SO2, NOX, and Hg

SO2

(thousand tons)

NOX

(thousand tons)

Hg

(tons)

2025

No CPP 959 874 4.9

Base Case (CPP) 923 842 4.7

2% HRI at $50/kW 959 866 4.9

4.5% HRI at $50/kW 963 863 4.9

4.5% HRI at $100/kW 956 856 4.8

2030

No CPP 950 833 4.7

Base Case (CPP) 891 786 4.4

2% HRI at $50/kW 943 825 4.6

4.5% HRI at $50/kW 943 825 4.7

4.5% HRI at $100/kW 935 818 4.6

2035

No CPP 865 783 4.3

Base Case (CPP) 821 740 4.1

2% HRI at $50/kW 855 778 4.2

4.5% HRI at $50/kW 864 782 4.3

4.5% HRI at $100/kW 849 772 4.2

Table 3-8 Percent Change in Projected SO2, NOX, and Hg Emissions, Relative to Base 

Case (CPP) Scenario

SO2

(thousand tons)

NOX

(thousand tons)

Hg

(tons)

2025

No CPP 3.9% 3.8% 3.6%

Base Case (CPP) -- -- --

2% HRI at $50/kW 3.8% 2.8% 3.2%

4.5% HRI at $50/kW 4.3% 2.5% 3.1%

4.5% HRI at $100/kW 3.5% 1.6% 2.3%

2030

No CPP 6.7% 6.0% 5.4%

Base Case (CPP) -- -- --

2% HRI at $50/kW 5.9% 5.0% 4.6%

4.5% HRI at $50/kW 5.9% 5.0% 4.9%

4.5% HRI at $100/kW 5.0% 4.1% 4.0%

2035

No CPP 5.4% 5.9% 4.4%

Base Case (CPP) -- -- --

2% HRI at $50/kW 4.1% 5.2% 3.5%

4.5% HRI at $50/kW 5.3% 5.8% 4.3%

4.5% HRI at $100/kW 3.5% 4.4% 3.0%
3-17

Table 3-9 Percent Difference in Projected SO2, NOX, and Hg Emissions, Relative to No 

CPP Scenario

SO2

(thousand tons)

NOX

(thousand tons)

Hg

(tons)

2025

No CPP -- -- --

Base Case (CPP) -3.7% -3.7% -3.5%

2% HRI at $50/kW 0.0% -0.9% -0.4%

4.5% HRI at $50/kW 0.4% -1.3% -0.5%

4.5% HRI at $100/kW -0.3% -2.1% -1.3%

2030

No CPP -- -- --

Base Case (CPP) -6.3% -5.7% -5.1%

2% HRI at $50/kW -0.7% -1.0% -0.8%

4.5% HRI at $50/kW -0.7% -1.0% -0.5%

4.5% HRI at $100/kW -1.6% -1.8% -1.4%

2035

No CPP -- -- --

Base Case (CPP) -5.1% -5.5% -4.2%

2% HRI at $50/kW -1.2% -0.6% -0.9%

4.5% HRI at $50/kW -0.1% -0.1% -0.1%

4.5% HRI at $100/kW -1.8% -1.4% -1.3%

3.7.2 Projected Compliance Costs

The power industry’s ‘compliance costs’ are represented in this analysis as the change in 

total electric power generation costs, also known as the system costs, between the base case 

(CPP) and the three illustrative policy scenarios, including the cost of monitoring, reporting, and 

recordkeeping (MR&R) costs. The system costs include projected power industry expenditures 

on capital, operating and fuels, and reflect the least cost power system outcome in response to 

assumed market and regulatory requirements. In simple terms, the compliance costs are an 

estimate of the change in projected system costs between two scenarios. This RIA does not 

identify who ultimately bears these compliance costs, such as owners of generating assets 

through changes in their profits or electricity consumers through changes in their bills, although 

the potential impacts on consumers and producers are described in Chapter 5.17

As shown in Table 3-11, EPA projects that the annual compliance costs of the illustrative 

2 percent HRI at $50/kW scenario range from roughly no change in 2025, to a $200 million 

 

17 Note that the projected compliance costs in this RIA reflect changes in total system costs and do not reflect 

potential projected changes in electricity consumer expenditures (e.g., expenditures on net imports, which are a 

very small percentage of total system costs). 
3-18

avoided cost in 2030, to a $100 million cost in 2035. Under the illustrative 4.5 percent HRI at 

$50/kW scenario, EPA projects that the annual compliance costs are a $700 million dollar 

savings in 2025, a $1 billion avoided cost in 2030, and a $600 million avoided cost in 2035, 

relative to the base case (CPP). Under the illustrative 4.5 percent HRI at $100/kW scenario, EPA 

projects that the annual compliance costs are $500 million 2025, $200 million in 2030, and $500 

million in 2035, relative to the base case (CPP). Results comparing the illustrative scenarios to 

the No CPP case are shown in Table 3-12. Table 3-10 reports the total generation cost projected 

by IPM in these five scenarios. 

Table 3-10 Total Projected Power Sector System Costs (billions of 2016$)

2025 2030 2035

No CPP $144.5 $156.1 $165.2

Base Case (CPP) $145.2 $156.8 $165.6

2% HRI at $50/kW $145.3 $156.6 $165.7

4.5% HRI at $50/kW $144.6 $155.8 $164.9

4.5% HRI at $100/kW $145.8 $157.0 $166.0

Table 3-11 Annualized Compliance Costs, Relative to Base Case (CPP) Scenario (billions 

of 2016$)

2025 2030 2035

No CPP -$0.7 -$0.7 -$0.4

2% HRI at $50/kW $0.0 -$0.2 $0.1

4.5% HRI at $50/kW -$0.6 -$1.0 -$0.6

4.5% HRI at $100/kW $0.5 $0.2 $0.5

Note: Includes MR&R costs (see 3.6)

Table 3-12 Annualized Compliance Costs, Relative to No CPP Scenario (billions of 

2016$)

2025 2030 2035

Base Case (CPP) $0.7 $0.7 $0.4

2% HRI at $50/kW $0.7 $0.5 $0.5

4.5% HRI at $50/kW $0.1 -$0.2 -$0.2

4.5% HRI at $100/kW $1.3 $0.9 $0.8

Note: Includes MR&R costs (see 3.6)

3.7.3 Projected Compliance Actions for Emissions Reductions

As discussed above, the illustrative policy scenarios require that all affected sources 

invest in measures that improve the heat rate performance of each source in order to continue 
3-19

operation or otherwise retire. Some affected sources are projected to retire while all others are 

assumed to adopt the HRI, which reduces the amount of fuel necessary to generate electricity, 

and thus decreases the CO2 emissions rate (per unit output) of affected sources. In the modeling 

of the illustrative policy scenarios, the sources that are projected to operate are projected to, on 

average, increase generation as a result of the HRI. This increase in generation, coupled with a 

decrease in the CO2 emissions rate, largely results in an overall decrease in CO2 emissions from 

the affected sources, relative to the repeal scenario.18 See Table 3-13 below for a summary of 

projected CO2 emissions by generation sources under each scenario. 

Table 3-13 Projected CO2 Emissions by Generation Source (MM short tons)

Coal >

25 MW All Other Total

2025

No CPP 1,054 776 1,829

Base Case (CPP) 992 788 1,780

2% HRI at $50/kW 1,048 768 1,816

4.5% HRI at $50/kW 1,051 760 1,812

4.5% HRI at $100/kW 1,039 761 1,799

2030

No CPP 1,026 786 1,811

Base Case (CPP) 940 797 1,737

2% HRI at $50/kW 1,015 783 1,798

4.5% HRI at $50/kW 1,020 777 1,797

4.5% HRI at $100/kW 1,006 778 1,785

2035

No CPP 920 874 1,794

Base Case (CPP) 847 882 1,728

2% HRI at $50/kW 912 872 1,783

4.5% HRI at $50/kW 923 864 1,787

4.5% HRI at $100/kW 905 867 1,772

 

18 Note that emissions might increase at some generators. 
3-20

Table 3-14 Projected SO2 Emissions by Generation Source (thousand short tons)

Coal >

25 MW All Other Total

2025

No CPP 919 40 959

Base Case (CPP) 880 43 923

2% HRI at $50/kW 919 40 959

4.5% HRI at $50/kW 923 40 963

4.5% HRI at $100/kW 916 40 956

2030

No CPP 914 37 950

Base Case (CPP) 852 39 891

2% HRI at $50/kW 907 37 943

4.5% HRI at $50/kW 906 38 943

4.5% HRI at $100/kW 898 38 935

2035

No CPP 837 28 865

Base Case (CPP) 793 28 821

2% HRI at $50/kW 826 28 855

4.5% HRI at $50/kW 836 28 864

4.5% HRI at $100/kW 821 28 849

Table 3-15 Projected NOX Emissions by Generation Source (thousand short tons)

Coal >

25 MW All Other Total

2025

No CPP 611 263 874

Base Case (CPP) 569 273 842

2% HRI at $50/kW 606 260 866

4.5% HRI at $50/kW 606 257 863

4.5% HRI at $100/kW 600 256 856

2030

No CPP 587 246 833

Base Case (CPP) 535 251 786

2% HRI at $50/kW 580 244 825

4.5% HRI at $50/kW 582 243 825

4.5% HRI at $100/kW 575 243 818

2035

No CPP 523 260 783

Base Case (CPP) 478 262 740

2% HRI at $50/kW 519 259 778

4.5% HRI at $50/kW 524 258 782

4.5% HRI at $100/kW 515 257 772
3-21

Table 3-16 Projected Mercury Emissions by Generation Source (short tons)

Coal >

25 MW All Other Total

2025

No CPP 3.5 1.4 4.9

Base Case (CPP) 3.3 1.4 4.7

2% HRI at $50/kW 3.4 1.4 4.9

4.5% HRI at $50/kW 3.4 1.4 4.9

4.5% HRI at $100/kW 3.4 1.4 4.8

2030

No CPP 3.3 1.4 4.7

Base Case (CPP) 3.0 1.4 4.4

2% HRI at $50/kW 3.2 1.4 4.6

4.5% HRI at $50/kW 3.3 1.4 4.7

4.5% HRI at $100/kW 3.2 1.4 4.6

2035

No CPP 2.9 1.4 4.3

Base Case (CPP) 2.7 1.4 4.1

2% HRI at $50/kW 2.9 1.4 4.2

4.5% HRI at $50/kW 2.9 1.4 4.3

4.5% HRI at $100/kW 2.8 1.4 4.2
3-22

3.7.4 Projected Generation Mix

Generation by generator type for each of the scenarios is reported in Table 3-17. As can 

be seen in Table 3-18, the illustrative policy scenarios show an overall increase in generation 

from the coal steam units covered by this proposed rule. Table 3-19 shows how generation in 

each of the scenarios differs from the No CPP scenario. Relative to the No CPP scenario, 

national coal generation is projected to increase between approximately 1 and 5 percent over the 

time horizon analyzed in this RIA, depending on the assumptions regarding HRI level and cost in 

each illustrative policy scenario. Figure 3-1 summarizes the information in the tables. 
3-23

Table 3-17 Projected Generation Mix (thousand GWh)

No 

CPP

Base Case 

(CPP)

2% HRI at 

$50/kW

4.5% HRI at 

$50/kW

4.5% HRI at 

$100/kW

2025

Coal 962 908 976 1,004 992

NG Combined Cycle (existing) 1,564 1,574 1,552 1,536 1,537

NG Combined Cycle (new) 11 16 12 12 14

Combustion Turbine 38 44 36 34 34

Oil/Gas Steam 63 66 62 65 63

Non-Hydro Renewables 570 575 572 572 574

Hydro 318 320 318 319 319

Nuclear 685 704 682 670 678

Other 37 37 37 37 37

Total 4,248 4,245 4,248 4,248 4,249

2030

Coal 936 861 944 974 961

NG Combined Cycle (existing) 1,548 1,550 1,542 1,536 1,534

NG Combined Cycle (new) 70 91 72 65 71

Combustion Turbine 40 41 40 38 38

Oil/Gas Steam 60 63 60 62 62

Non-Hydro Renewables 699 722 697 694 695

Hydro 324 325 324 324 324

Nuclear 660 683 658 646 654

Other 36 36 36 36 36

Total 4,374 4,372 4,375 4,375 4,376

2035

Coal 837 774 846 878 861

NG Combined Cycle (existing) 1,560 1,549 1,554 1,551 1,550

NG Combined Cycle (new) 263 296 266 253 264

Combustion Turbine 57 57 56 54 55

Oil/Gas Steam 66 66 67 69 67

Non-Hydro Renewables 705 728 702 699 700

Hydro 327 327 327 327 327

Nuclear 660 674 658 646 653

Other 37 37 37 37 37

Total 4,512 4,509 4,513 4,514 4,514
3-24

Table 3-18 Percent Change in Projected Generation Mix, Relative to Base Case (CPP) 

Scenario

No 

CPP

Base Case 

(CPP)

2% HRI at 

$50/kW

4.5% HRI at 

$50/kW

4.5% HRI at 

$100/kW

2025

Coal 5.9% -- 7.4% 10.5% 9.2%

NG Combined Cycle (existing) -0.6% -- -1.4% -2.4% -2.3%

NG Combined Cycle (new) -33.1% -- -24.1% -29.4% -11.7%

Combustion Turbine -13.7% -- -18.8% -22.4% -23.9%

Oil/Gas Steam -3.8% -- -5.3% -1.8% -4.0%

Non-Hydro Renewables -0.8% -- -0.4% -0.5% -0.1%

Hydro -0.7% -- -0.6% -0.5% -0.4%

Nuclear -2.8% -- -3.1% -4.8% -3.7%

Other 0.0% -- 0.0% 0.0% 0.0%

Total 0.1% -- 0.1% 0.1% 0.1%

2030

Coal 8.7% -- 9.7% 13.1% 11.6%

NG Combined Cycle (existing) -0.1% -- -0.5% -0.9% -1.0%

NG Combined Cycle (new) -22.6% -- -20.2% -28.3% -21.3%

Combustion Turbine -2.7% -- -2.6% -6.4% -6.7%

Oil/Gas Steam -4.8% -- -4.4% -1.7% -2.0%

Non-Hydro Renewables -3.2% -- -3.4% -3.9% -3.8%

Hydro -0.3% -- -0.2% -0.2% -0.2%

Nuclear -3.4% -- -3.7% -5.5% -4.3%

Other 0.1% -- 0.1% 0.1% 0.1%

Total 0.0% -- 0.1% 0.1% 0.1%

2035

Coal 8.1% -- 9.2% 13.4% 11.2%

NG Combined Cycle (existing) 0.7% -- 0.3% 0.1% 0.1%

NG Combined Cycle (new) -11.2% -- -10.2% -14.7% -10.9%

Combustion Turbine -0.1% -- -2.5% -5.6% -4.4%

Oil/Gas Steam -0.6% -- 0.6% 4.0% 0.7%

Non-Hydro Renewables -3.2% -- -3.5% -4.0% -3.9%

Hydro 0.1% -- 0.1% 0.1% 0.1%

Nuclear -2.0% -- -2.4% -4.1% -3.0%

Other 0.1% -- 0.2% 0.1% 0.1%

Total 0.1% -- 0.1% 0.1% 0.1%
3-25

Table 3-19 Percent Change in Projected Generation Mix, Relative to No CPP Scenario

No 

CPP

Base Case 

(CPP)

2% HRI at 

$50/kW

4.5% HRI at 

$50/kW

4.5% HRI at 

$100/kW

2025

Coal -- -5.6% 1.5% 4.4% 3.2%

NG Combined Cycle (existing) -- 0.6% -0.8% -1.8% -1.7%

NG Combined Cycle (new) -- 49.6% 13.5% 5.6% 32.1%

Combustion Turbine -- 15.9% -5.9% -10.0% -11.9%

Oil/Gas Steam -- 4.0% -1.5% 2.1% -0.2%

Non-Hydro Renewables -- 0.9% 0.4% 0.3% 0.8%

Hydro -- 0.7% 0.2% 0.2% 0.3%

Nuclear -- 2.9% -0.3% -2.1% -0.9%

Other -- 0.0% 0.0% 0.0% 0.0%

Total -- -0.1% 0.0% 0.0% 0.0%

2030

Coal -- -8.0% 0.9% 4.0% 2.6%

NG Combined Cycle (existing) -- 0.1% -0.4% -0.8% -0.9%

NG Combined Cycle (new) -- 29.2% 3.1% -7.4% 1.7%

Combustion Turbine -- 2.8% 0.1% -3.8% -4.2%

Oil/Gas Steam -- 5.0% 0.4% 3.2% 2.9%

Non-Hydro Renewables -- 3.3% -0.3% -0.7% -0.6%

Hydro -- 0.3% 0.0% 0.0% 0.0%

Nuclear -- 3.5% -0.3% -2.1% -1.0%

Other -- -0.1% 0.0% 0.0% 0.0%

Total -- 0.0% 0.0% 0.0% 0.0%

2035

Coal -- -7.5% 1.0% 4.9% 2.9%

NG Combined Cycle (existing) -- -0.7% -0.4% -0.6% -0.6%

NG Combined Cycle (new) -- 12.7% 1.1% -3.8% 0.4%

Combustion Turbine -- 0.1% -2.4% -5.6% -4.3%

Oil/Gas Steam -- 0.6% 1.2% 4.7% 1.3%

Non-Hydro Renewables -- 3.3% -0.3% -0.8% -0.7%

Hydro -- -0.1% 0.1% 0.0% 0.0%

Nuclear -- 2.1% -0.3% -2.1% -1.0%

Other -- -0.1% 0.0% 0.0% 0.0%

Total -- -0.1% 0.0% 0.0% 0.0%
3-26

Figure 3-1 Generation Mix (thousand GWh)

3.7.5 Projected Changes to Generating Capacity

Capacity by generator type for each of the scenarios is reported in Table 3-20. As shown 

in Table 3-21, relative to the base case (CPP), projections of coal capacity are 1-3 percent higher, 

depending on the illustrative policy scenario. Commensurately, new natural gas combined cycle 

(NGCC) capacity, new renewable capacity, and existing nuclear capacity are lower in the 

illustrative scenarios relative to the base case (CPP). Relative to the base case (CPP), EPA 

projects up to a 30 percent decrease in new NGCC capacity, and a up to a 4 percent decrease in 

new renewable capacity. The illustrative policy scenarios also result in a projection of additional 

nuclear capacity retirements, which are projected to result in up to a 6 percent reduction of total 

nuclear capacity, depending on the illustrative policy scenario. Capacity changes of the various 

scenarios relative to the No CPP case are reported in Table 3-22. Generally, they show that coal 

and nuclear capacity is lower in the policy scenarios relative to the repeal scenarios. Table 3-23

through Table 3-26 show the incremental capacity additions over time in the illustrative policy 

scenarios relative to the base case (CPP) and No CPP scenarios for natural gas combined cycle 

capacity and renewable technologies, which were highlighted in the 2015 CPP RIA. These tables 

966 913 980 1,008 996 940 865 949 978 965 841 778 850 882 865

1,677 1,700 1,662 1,647 1,649 1,718 1,745 1,715 1,701 1,706 1,946 1,969 1,943 1,927 1,936

685 704 682 670 678 660 683 658 646 654

660 674 658 646 653

584 589 586 586 588 713 736 711 708 708

718 742 716 713 713

319 321 319 319 320

325 326 325 325 325

328 327 328 328 328

0

500

1,000

1,500

2,000

2,500

3,000

3,500

4,000

4,500

5,000

CPP Repeal Base Case (CPP) 2% HRI at $50/kW 4.5% HRI at $50/kW 4.5% HRI at $100/kWCPP Repeal Base Case (CPP) 2% HRI at $50/kW 4.5% HRI at $50/kW 4.5% HRI at $100/kWCPP Repeal Base Case (CPP) 2% HRI at $50/kW 4.5% HRI at $50/kW 4.5% HRI at $100/kW

Coal Natural Gas Nuclear Non-hydro Renewables Hydroelectric Other

2025 2030 2035
3-27

more readily reveal how the temporal flows of these capacity increases differ across the 

scenarios than the preceding tables.

Table 3-20 Total Generation Capacity by 2025-2035 (GW)

No 

CPP

Base Case 

(CPP)

2% HRI at 

$50/kW

4.5% HRI at 

$50/kW

4.5% HRI at 

$100/kW

2025

Coal 183 177 181 182 179

NG Combined Cycle (existing) 264 264 264 264 264

NG Combined Cycle (new) 1 2 2 2 2

Combustion Turbine 152 151 152 152 152

Oil/Gas Steam 78 79 79 79 80

Non-Hydro Renewables 203 206 205 204 206

Hydro 110 110 110 110 110

Nuclear 87 89 86 85 86

Other 8 8 8 8 8

Total 1,085 1,086 1,086 1,086 1,086

2030

Coal 182 176 180 181 177

NG Combined Cycle (existing) 264 264 264 264 264

NG Combined Cycle (new) 9 12 9 9 9

Combustion Turbine 156 153 156 157 157

Oil/Gas Steam 78 79 79 79 80

Non-Hydro Renewables 255 260 255 254 255

Hydro 110 111 110 110 110

Nuclear 84 87 83 82 83

Other 8 8 8 8 8

Total 1,145 1,149 1,145 1,144 1,144

2035

Coal 177 173 175 177 171

NG Combined Cycle (existing) 264 264 264 264 264

NG Combined Cycle (new) 35 39 35 33 35

Combustion Turbine 170 167 171 172 174

Oil/Gas Steam 78 79 79 79 80

Non-Hydro Renewables 257 263 258 257 257

Hydro 111 111 111 111 111

Nuclear 84 85 83 82 83

Other 8 8 8 8 8

Total 1,184 1,188 1,184 1,183 1,183
3-28

Table 3-21 Percent Change in Total Generation Capacity by 2025-2035, Relative to Base 

Case Scenario (CPP)

No 

CPP

Base Case 

(CPP)

2% HRI at 

$50/kW

4.5% HRI at 

$50/kW

4.5% HRI at 

$100/kW

2025

Coal 3.2% -- 2.1% 2.9% 0.7%

NG Combined Cycle (existing) 0.0% -- 0.0% 0.0% 0.0%

NG Combined Cycle (new) -33.4% -- -24.4% -29.6% -12.0%

Combustion Turbine 0.3% -- 0.3% 0.3% 0.4%

Oil/Gas Steam -0.4% -- 0.2% 1.1% 2.2%

Non-Hydro Renewables -1.4% -- -0.6% -0.8% 0.0%

Hydro -0.3% -- -0.3% -0.3% -0.3%

Nuclear -2.9% -- -3.2% -4.9% -3.8%

Other 0.0% -- 0.0% 0.0% 0.0%

Total -0.1% -- -0.1% 0.0% 0.0%

2030

Coal 3.2% -- 2.1% 2.9% 0.8%

NG Combined Cycle (existing) 0.0% -- 0.0% 0.0% 0.0%

NG Combined Cycle (new) -22.6% -- -20.2% -28.4% -21.3%

Combustion Turbine 1.5% -- 2.1% 2.3% 2.6%

Oil/Gas Steam -0.4% -- 0.2% 1.0% 2.1%

Non-Hydro Renewables -2.0% -- -2.0% -2.2% -2.0%

Hydro -0.1% -- -0.1% -0.1% -0.1%

Nuclear -3.4% -- -3.7% -5.5% -4.4%

Other 0.0% -- 0.0% 0.0% 0.0%

Total -0.3% -- -0.3% -0.4% -0.4%

2035

Coal 2.7% -- 1.5% 2.6% -0.9%

NG Combined Cycle (existing) 0.0% -- 0.0% 0.0% 0.0%

NG Combined Cycle (new) -11.3% -- -10.3% -14.7% -10.9%

Combustion Turbine 2.3% -- 3.0% 3.3% 4.7%

Oil/Gas Steam -0.4% -- 0.2% 1.0% 2.1%

Non-Hydro Renewables -2.1% -- -2.1% -2.4% -2.2%

Hydro 0.0% -- 0.0% 0.0% 0.0%

Nuclear -2.0% -- -2.4% -4.1% -3.0%

Other 0.0% -- 0.0% 0.0% 0.0%

Total -0.3% -- -0.3% -0.4% -0.4%
3-29

Table 3-22 Percent Change in Total Generation Capacity by 2025-2035, Relative to No 

CPP Scenario

No 

CPP

Base Case 

(CPP)

2% HRI at 

$50/kW

4.5% HRI at 

$50/kW

4.5% HRI at 

$100/kW

2025

Coal -- -3.1% -1.1% -0.3% -2.4%

NG Combined Cycle (existing) -- 0.0% 0.0% 0.0% 0.0%

NG Combined Cycle (new) -- 50.1% 13.5% 5.6% 32.1%

Combustion Turbine -- -0.3% 0.0% 0.0% 0.0%

Oil/Gas Steam -- 0.4% 0.7% 1.5% 2.6%

Non-Hydro Renewables -- 1.5% 0.8% 0.7% 1.4%

Hydro -- 0.3% 0.0% 0.0% 0.0%

Nuclear -- 3.0% -0.3% -2.0% -0.9%

Other -- 0.0% 0.0% 0.0% 0.0%

Total -- 0.1% 0.0% 0.0% 0.0%

2030

Coal -- -3.1% -1.0% -0.3% -2.3%

NG Combined Cycle (existing) -- 0.0% 0.0% 0.0% 0.0%

NG Combined Cycle (new) -- 29.3% 3.1% -7.4% 1.7%

Combustion Turbine -- -1.5% 0.5% 0.7% 1.1%

Oil/Gas Steam -- 0.4% 0.6% 1.5% 2.5%

Non-Hydro Renewables -- 2.1% 0.1% -0.2% 0.0%

Hydro -- 0.1% 0.0% 0.0% 0.0%

Nuclear -- 3.6% -0.3% -2.1% -0.9%

Other -- 0.0% 0.0% 0.0% 0.0%

Total -- 0.3% 0.0% -0.1% -0.1%

2035

Coal -- -2.7% -1.2% -0.2% -3.5%

NG Combined Cycle (existing) -- 0.0% 0.0% 0.0% 0.0%

NG Combined Cycle (new) -- 12.7% 1.1% -3.8% 0.4%

Combustion Turbine -- -2.3% 0.6% 1.0% 2.3%

Oil/Gas Steam -- 0.4% 0.6% 1.5% 2.5%

Non-Hydro Renewables -- 2.2% 0.0% -0.3% -0.1%

Hydro -- 0.0% 0.0% 0.0% 0.0%

Nuclear -- 2.1% -0.3% -2.1% -1.0%

Other -- 0.0% 0.0% 0.0% 0.0%

Total -- 0.3% 0.0% -0.1% -0.1%
3-30

Table 3-23 Projected Natural Gas Combined Cycle Capacity Additions and Changes 

Relative to Base Case (CPP)

Cumulative Capacity 

Additions: NGCC (GW)

Incremental Cumulative 

Capacity Additions, 

Relative to the Base Case 

(CPP)

Percent Change in 

Incremental Cumulative 

Capacity Additions, 

Relative to the Base Case 

(CPP)

2025 2030 2035 2025 2030 2035 2025 2030 2035

No CPP 1.4 9.2 34.5 -0.7 -2.7 -4.4 -33.4% -22.6% -11.3%

Base Case (CPP) 2.2 11.9 38.9 -- -- -- -- -- --

2% HRI at $50/kW 1.6 9.5 34.9 -0.5 -2.4 -4.0 -24.4% -20.2% -10.3%

4.5% HRI at $50/kW 1.5 8.5 33.2 -0.6 -3.4 -5.7 -29.6% -28.4% -14.7%

4.5% HRI at $100/kW 1.9 9.4 34.7 -0.3 -2.5 -4.2 -12.0% -21.3% -10.9%

Table 3-24 Projected Renewable Capacity Additions and Changes Relative to Base Case 

(CPP)

Cumulative Capacity 

Additions: Renewables 

(GW)

Incremental Cumulative 

Capacity Additions, 

Relative to the Base Case 

(CPP)

Percent Change in 

Incremental Cumulative 

Capacity Additions, 

Relative to the Base Case 

(CPP)

2025 2030 2035 2025 2030 2035 2025 2030 2035

No CPP 89.5 142.2 145.4 -3.3 -5.5 -5.6 -3.6% -3.7% -3.7%

Base Case (CPP) 92.8 147.7 151.0 -- -- -- -- -- --

2% HRI at $50/kW 91.2 142.4 145.4 -1.7 -5.3 -5.6 -1.8% -3.6% -3.7%

4.5% HRI at $50/kW 90.9 141.7 144.7 -1.9 -6.0 -6.3 -2.1% -4.0% -4.2%

4.5% HRI at $100/kW 92.5 142.3 145.1 -0.4 -5.4 -5.8 -0.4% -3.6% -3.9%

Table 3-25 Projected Natural Gas Combined Cycle Capacity Additions and Changes 

Relative to No CPP Scenario

Cumulative Capacity 

Additions: NGCC (GW)

Incremental Cumulative 

Capacity Additions, Relative 

to No CPP Scenario

Percent Change in 

Incremental Cumulative 

Capacity Additions, Relative 

to No CPP Scenario

2025 2030 2035 2025 2030 2035 2025 2030 2035

No CPP 1.4 9.2 34.5 -- -- -- -- -- --

Base Case (CPP) 2.2 11.9 38.9 0.7 2.7 4.4 50.1% 29.3% 12.7%

2% HRI at $50/kW 1.6 9.5 34.9 0.2 0.3 0.4 13.5% 3.1% 1.1%

4.5% HRI at $50/kW 1.5 8.5 33.2 0.1 -0.7 -1.3 5.6% -7.4% -3.8%

4.5% HRI at $100/kW 1.9 9.4 34.7 0.5 0.2 0.1 32.1% 1.7% 0.4%
3-31

Table 3-26 Projected Renewable Capacity Additions and Changes Relative to No CPP

Scenario

Cumulative Capacity 

Additions: Renewables 

(GW)

Incremental Cumulative 

Capacity Additions, Relative 

to No CPP Scenario

Percent Chane in 

Incremental Cumulative 

Capacity Additions, Relative 

to No CPP Scenario

2025 2030 2035 2025 2030 2035 2025 2030 2035

No CPP 89.5 142.2 145.4 -- -- -- -- -- --

Base Case (CPP) 92.8 147.7 151.0 3.3 5.5 5.6 3.7% 3.8% 3.9%

2% HRI at $50/kW 91.2 142.4 145.4 1.6 0.2 0.0 1.8% 0.1% 0.0%

4.5% HRI at $50/kW 90.9 141.7 144.7 1.4 -0.5 -0.7 1.5% -0.4% -0.5%

4.5% HRI at $100/kW 92.5 142.3 145.1 2.9 0.1 -0.2 3.3% 0.1% -0.2%

3.7.6 Projected Coal Production and Natural Gas Use for the Electric Power Sector

Relative to the base case (CPP), EPA projects a 7 to 9 percent increase in overall coal 

production for use by the electric power sector in the illustrative policy scenarios in 2030. Most 

of this increase is projected to occur in production of western subbituminous coals. Relative to 

the No CPP scenario, under the illustrative policy scenarios coal use is projected to decrease, 

with most of the reduction in 2030 and 2035 occurring in the west, followed by production in 

Appalachia. 

Table 3-27 2025 Projected Coal Production for the Electric Power Sector (million short 

tons)

No CPP Base Case (CPP) 2% HRI at 

$50/kW

4.5% HRI at 

$50/kW

4.5% HRI at 

$100/kW

Appalachia 74 70 75 73 73

Interior 126 123 124 123 122

West 323 300 322 325 320

Waste Coal 2 2 2 2 2

Total 525 495 522 524 517
3-32

Table 3-28 2030 Projected Coal Production for the Electric Power Sector (million short 

tons)

No CPP Base Case (CPP) 2% HRI at 

$50/kW

4.5% HRI at 

$50/kW

4.5% HRI at 

$100/kW

Appalachia 62 58 61 61 60

Interior 124 123 124 124 124

West 324 287 320 322 316

Waste Coal 2 2 2 2 2

Total 513 470 507 510 503

Table 3-29 2035 Projected Coal Production for the Electric Power Sector (million short 

tons)

No CPP Base Case (CPP) 2% HRI at 

$50/kW

4.5% HRI at 

$50/kW

4.5% HRI at 

$100/kW

Appalachia 43 47 43 41 41

Interior 114 112 113 117 115

West 306 264 302 304 298

Waste Coal 2 2 2 2 2

Total 465 424 460 465 456

Relative to the base case (CPP) EPA projects a 1 to 3 percent reduction in total gas use in 

the electric power sector, depending on the illustrative compliance scenario and year. 

Table 3-30 Projected Power Sector Gas Use

Power Sector Gas Use (TCF)

Percent Change in Power 

Sector Gas Use, Relative to 

Base Case (CPP)

Percent Change in Power 

Sector Gas Use, Relative to 

No CPP

2025 2030 2035 2025 2030 2035 2025 2030 2035

No CPP 11.96 12.18 13.65 -1.5% -1.5% -0.9% -- -- --

Base Case (CPP) 12.14 12.36 13.78 -- -- -- 1.5% 1.5% 0.9%

2% HRI at $50/kW 11.84 12.15 13.62 -2.5% -1.7% -1.1% -1.0% -0.3% -0.2%

4.5% HRI at $50/kW 11.72 12.05 13.52 -3.4% -2.5% -1.9% -1.9% -1.0% -1.0%

4.5% HRI at $100/kW 11.73 12.08 13.56 -3.4% -2.3% -1.6% -1.9% -0.8% -0.7%
3-33

3.7.7 Projected Fuel Price, Market, and Infrastructure Impacts

Relative to the base case (CPP), the illustrative policy scenarios result in small changes in 

electric power sector delivered coal and natural gas prices, on a Btu-weighted average basis. 

Depending on the illustrative policy scenario and year, the model projects changes to delivered 

coal prices that range from a small increase to a small decrease, less than one percent. 

Additionally, depending on the illustrative policy scenario and year, EPA projects a reduction in 

delivered natural gas prices on the order of about 1 percent. 

Table 3-31 Projected Average Minemouth and Delivered Coal Prices (2016$/MMBtu)

Minemouth Delivered - Electric Power Sector

2025 2030 2035 2025 2030 2035

No CPP 1.29 1.35 1.42 2.03 2.09 2.15

Base Case (CPP) 1.29 1.36 1.45 2.03 2.09 2.15

2% HRI at $50/kW 1.29 1.35 1.42 2.03 2.09 2.14

4.5% HRI at $50/kW 1.29 1.35 1.42 2.04 2.11 2.16

4.5% HRI at $100/kW 1.29 1.35 1.42 2.04 2.10 2.15

Table 3-32 Projected Average Henry Hub (spot) and Delivered Natural Gas Prices 

(2016$/MMBtu)

Henry Hub Delivered - Electric Power Sector

2025 2030 2035 2025 2030 2035

No CPP 3.56 3.67 3.74 3.58 3.64 3.56

Base Case (CPP) 3.61 3.70 3.75 3.62 3.65 3.56

2% HRI at $50/kW 3.56 3.65 3.73 3.58 3.62 3.54

4.5% HRI at $50/kW 3.54 3.64 3.71 3.57 3.61 3.53

4.5% HRI at $100/kW 3.55 3.64 3.71 3.57 3.61 3.53

Table 3-33 Percent Change in Projected Average Henry Hub (spot) and Delivered 

Natural Gas Prices, Relative to Base Case (CPP)

Henry Hub Delivered - Electric Power Sector

2025 2030 2035 2025 2030 2035

No CPP -1.4% -0.8% -0.2% -1.1% -0.3% 0.1%

Base Case (CPP) -- -- -- -- -- --

2% HRI at $50/kW -1.4% -1.3% -0.6% -1.1% -0.9% -0.4%

4.5% HRI at $50/kW -1.7% -1.6% -1.0% -1.4% -1.1% -0.7%

4.5% HRI at $100/kW -1.6% -1.6% -1.0% -1.3% -1.1% -0.7%
3-34

Table 3-34 Percent Change in Projected Average Henry Hub (spot) and Delivered 

Natural Gas Prices, Relative to No CPP Scenario

Henry Hub Delivered - Electric Power Sector

2025 2030 2035 2025 2030 2035

No CPP -- -- -- -- -- --

Base Case (CPP) 1.4% 0.8% 0.2% 1.2% 0.3% -0.1%

2% HRI at $50/kW 0.0% -0.5% -0.4% 0.1% -0.5% -0.4%

4.5% HRI at $50/kW -0.4% -0.8% -0.8% -0.3% -0.8% -0.8%

4.5% HRI at $100/kW -0.2% -0.8% -0.8% -0.1% -0.8% -0.8%

3.7.8 Projected Retail Electricity Prices

Relative to the base case (which includes CPP), EPA estimates the impact of the 

illustrative policy scenarios on retail electricity prices to be very small, on average.19 See Table 

3-35.

20 Given the limitations of this analysis, including the uncertainty regarding state 

implementation (see section 3.9), the RIA presents retail price projections at a national level. 

Under the illustrative policy scenarios, EPA projects changes in average retail electricity prices 

across the contiguous U.S. ranging from a one half of one percent decrease to no change, relative 

to the base case (CPP). See Table 3-36. Relative to the repeal scenario, EPA projects national 

changes in average retail electricity prices to be similarly small, ranging from a one half of one 

percent increase to a one tenth of one percent decrease. See Table 3-37.

Table 3-35 Projected Contiguous U.S. Retail Electricity Prices (cents/kWh), 2025-2035

2025 2030 2035

No CPP 10.1 10.2 10.3

Base Case (CPP) 10.2 10.3 10.3

2% HRI at $50/kW 10.1 10.3 10.3

4.5% HRI at $50/kW 10.1 10.2 10.2

4.5% HRI at $100/kW 10.1 10.3 10.3

 

19 The electricity price impacts are estimated using the Retail Price Model (RPM) and IPM model outputs. 

Documentation for the RPM is available at: https://www.epa.gov/airmarkets/epas-power-sector-modelingplatform-v6-using-ipm

20 The base case electricity prices assume that no allowance value from the CPP is used to lower electricity prices. If 

allowance value was used to reduce electricity prices, the difference in electricity prices from the base case 

reported in Table 3-29 would be smaller.
3-35

Table 3-36 Percent Change in Projected Contiguous U.S. Retail Electricity Prices, 

Relative to Base Case (CPP), 2025-2035

2025 2030 2035

No CPP -0.5% -0.4% -0.1%

2% HRI at $50/kW -0.3% -0.2% -0.1%

4.5% HRI at $50/kW -0.5% -0.4% -0.2%

4.5% HRI at $100/kW -0.2% 0.0% 0.0%

Table 3-37 Percent Change in Projected Contiguous U.S. Retail Electricity Prices, 

Relative to No CPP Scenario, 2025-2035

2025 2030 2035

Base Case (CPP) 0.5% 0.4% 0.1%

2% HRI at $50/kW 0.2% 0.2% 0.0%

4.5% HRI at $50/kW 0.0% 0.0% -0.1%

4.5% HRI at $100/kW 0.3% 0.4% 0.1%

3.8 Demand-side Energy Efficiency Sensitivity to the Base Case (CPP)

An additional scenario was conducted that included CPP with a revised electric demand 

projection reflecting demand-side energy efficiency measures that were allowed as a compliance 

option under CPP. This scenario is provided as a sensitivity analysis to the base case for this 

RIA, and shows the potential effects of CPP with and without demand-side energy efficiency, 

and in relation to a Repeal Scenario. These scenarios are compared to each other to provide 

information on CPP, alternative electric demand considering demand-side EE with CPP, and 

repeal of CPP.

3.8.1 Demand-side Energy Efficiency Revised Electric Demand Projection

For this EE illustrative scenario, the level of reduced electricity consumption (i.e., 

electricity savings) due to the adoption of demand-side energy efficiency for compliance with the 

CPP is derived from the AEO 2017 reference case and “no CPP” side case (EIA, 2017), which 

were from the most recent version of the AEO available when the modeling for this proposal 

RIA was commenced. The difference between projected electricity demand in the two cases is 

used as an estimate of reduced electricity demand due to demand-side energy efficiency used for 

compliance with the CPP in the alternative base case. Regional demand reductions from the 
3-36

AEO 2017 analysis are applied to each region in the IPM model. The national savings are 

summarized in Table 3-38. In 2025, 2030, and 2035, these savings represent 2.6 percent, 4.0 

percent, and 4.2 percent of electricity sales to end users, respectively. Note that these levels are 

lower than (approximately half of) the levels assumed for CPP compliance in the illustrative 

mass-based and rate-based scenarios in the 2015 CPP RIA (U.S. EPA, 2015a).

Table 3-38 Change in Electricity Demand Due to Demand-side Energy Efficiency, CPP 

Scenario vs. No CPP Scenario in AEO2017

2025 2030 2035

Change in Electricity Demand (TWh) 101 159 171

Percent Change in Electricity Demand 2.6% 4.0% 4.2%

3.8.2 Demand-side Energy Efficiency Costs

To estimate the cost of achieving these electricity savings, the cost to save a MWh 

(2016$/MWh) were multiplied by the electricity savings in each year. These levelized costs are 

referred to as “levelized costs of saved energy” (LCSE). The LCSE value used is taken from an 

extensive database of utility energy efficiency program costs and savings as publicly reported to 

their state utility commissions and compiled and analyzed by Lawrence Berkeley National 

Laboratory (LBNL) (Hoffman et al., 2017). The database represents more than 70 percent of 

total utility program savings from the years 2009 through 2011 and was collected from more 

than 100 energy efficiency program administrators across more than 30 states. Data were 

collected from over 1,700 individual programs covering more than 4,000 individual programyears of data points. This LCSE value is $46/MWh (2012$). The value is the cost per gross 

MWh saved and is calculated using a discount rate of 6 percent. This value represents the total 

costs including both the costs to the program administrator and the participants in the program. 

To account for free ridership, spillover, etc., this value is grossed up by a net-to-gross (NTG) 

factor of 0.85.21 This NTG value is based upon an EPA compilation and analysis of NTG factors 

used by utilities across the country (Synapse, 2015). The resulting value is then inflated to 2016$ 

and the result is $57.3 2016$/MWh (the levelized cost per net MWh saved) in each year of the 

 

21 See Synapse, 2015, for a detailed discussion of NTG definitions and how factors are derived.
3-37

analysis and is applied nationally.22 See Table 3-39 for a summary of total demand side-energy 

efficiency (DS-EE) costs by year.

The LCSE value used in this analysis differs from the one used in the RIA for the final 

CPP in 2015 (U.S. EPA, 2015a). In that analysis, at lower levels of savings from energy 

efficiency, costs were assumed to be double the value found in the literature and then to decline 

by 20 percent and then 40 percent as higher levels of savings were achieved. This approach was 

based on the assumption that LCSE starts much higher than the average costs and declines as 

savings increase, due to factors such as economies of scale. This resulted in significantly 

conservative (higher values for LCSE by year and level of savings. As noted in the Demand-side 

Energy Efficiency TSD for the 2015 RIA (U.S. EPA, 2015b), the literature is inconclusive on 

whether costs increase or decrease at higher levels of savings, with preliminary analyses falling 

on both sides of the issue. An analysis by LBNL based on the same data set as used for their 

2015 cost of saved energy analysis (cited above) shows increasing costs at higher levels of 

savings LBNL (Hoffman et al., 2015). The current analysis assumes neither increasing or 

decreasing costs but rather a level LCSE value, in constant dollars, across all years of the 

analysis and regardless of the level of savings.

Table 3-39 Costs of Demand-side Energy Efficiency (billions of 2016$)

2025 2030 2035

Demand-Side EE Costs (2016$) $5.8 $9.1 $9.8

3.8.3 Demand-side Energy Efficiency Sensitivity to the Base Case: Projected EE 

Benefits and Compliance Costs

The compliance costs of a repeal scenario relative to a base case (CPP) with EE are 

estimated in Table 3-40 below, in a manner consistent with the accounting conventions specified 

by the OMB Guidance for Implementing E.O. 13771.23 This OMB guidance states that 

 

22 The calculation is (($46 2012$/MWh gross) / (0.85 net MWh/gross MWh)) x (1.059 2016$/2012$) = $57.3 

2016$/net MWh.

23 U.S. Office of Management and Budget. 2017. “Guidance Implementing Executive Order 13771, Titled 

‘Reducing Regulation and Controlling Regulatory Costs’” [Memorandum]. Available at: < 

https://www.whitehouse.gov/sites/whitehouse.gov/files/omb/memoranda/2017/M-17-21-OMB.pdf> Accessed July

19, 2018.
3-38

accounting for “savings, such as fuel savings associated with energy efficiency investments, as 

benefits is a common accounting convention followed in the OMB Office of Information and 

Regulatory Affairs’ reports to Congress on the benefits and costs of Federal regulations.” 

Under the accounting convention consistent with OMB guidance related to EO 13771, 

EPA projects that the gross compliance costs of the repeal relative to the base case (CPP) with 

energy efficiency, excluding the forgone energy cost savings attributable to EE, range from -$6.5 

billion in 2025 to -$10.2 billion in 2035 (Table 3-40). These projected compliance costs do not 

include the forgone benefit of energy cost savings,24 which range from $5.6 billion in 2025 to 

$10.5 billion in 2035 (Table 3-40). These excluded energy cost savings reflect both forgone

variable cost savings (e.g., fuel and variable O&M) as well as forgone fixed cost savings (e.g., 

new power plants, and fixed O&M).

 

24 For the purposes of this document, “energy cost savings” is the value of the reduced costs of producing electricity 

that is attributable to the demand-side energy efficiency programs. We estimate this value by calculating the 

difference in projected system costs between the two CPP modeling scenarios, with and without EE. The term 

“energy savings” is also commonly used to describe the amount of energy saved as a result of demand-side energy 

efficiency measures, usually expressed in terms of megawatt-hours, but in this document, it will refer to the financial 

value of those savings unless otherwise noted. 
3-39

Table 3-40 Annualized Compliance Costs of the No CPP Scenario (billions of 2016$)

2025 2030 2035

Total Power Sector Generating Costs

Repeal Scenario (A) 144.5 156.1 165.2

CPP without EE (B) 145.3 156.8 165.6

CPP with EE (C) 139.7 147.4 155.1

Demand-Side EE Costs

CPP without EE 0.0 0.0 0.0

CPP with EE (D) 5.8 9.1 9.8

Compliance Costs of CPP Repeal that Include the Benefit of 

Forgone Energy Cost Savings for CPP with EE

CPP without EE (A-B) -0.7 -0.7 -0.4

CPP with EE (E = A-C-D) -0.9 -0.5 0.3

Forgone Benefit of Energy Cost Savings

(Not Included in Compliance Cost of CPP Repeal)

CPP without EE 0.0 0.0 0.0

CPP with EE (F = B-C) 5.6 9.4 10.5

Compliance Costs of CPP Repeal

(Consistent with OMB EO 13771)

CPP without EE (A-B) -0.7 -0.7 -0.4

CPP with EE (E-F)* -6.5 -9.8 -10.2

Note: Includes MR&R costs (see 3.6)

* The full equation is (A-C-D) - (B-C), which simplifies to A-B-D.

Note that estimates of the forgone benefit of energy cost savings presented in Table 3-40

cannot be added to the forgone monetized benefits presented in Chapter 4 of this RIA. The 

benefit estimates presented in the main analysis of the RIA are based upon a base case with CPP 

without demand-side energy efficiency. The trajectory of the emissions changes estimated in the 

main analysis for this proposed action are different than they would be assuming demand-side 

energy efficiency was used as CPP compliance strategy. Also, for this reason, the cost of the 

repeal estimated using this different base case should not be compared to, or otherwise presented 

alongside, the forgone environmental benefits reported elsewhere in this RIA.
3-40

3.8.4 Demand-side Energy Efficiency Sensitivity to the Base Case: Projected 

Emissions

Under the base case (CPP) with energy efficiency, EPA projects a 6 percent reduction in 

CO2 emissions in 2025, a 7 percent reduction in CO2 emissions in 2030, and a 6 percent 

reduction in CO2 emissions in 2035, relative to the illustrative repeal scenario. Additionally, 

relative to a repeal scenario, EPA projects that the base case (CPP) with energy efficiency would 

result in reductions of SO2 (about 8 percent annually across 2025-2035), reductions in NOX

(about 7 percent annually across 2025-2035), and reduction in mercury (about 7-8 percent 

annually over 2025-2035). See the following tables. These emissions impacts have not been 

quantified in Chapter 4 for the CPP with EE scenario and thus net benefits for this scenario are 

not presented in Chapter 6.

Table 3-41 Projected CO2 Emission Impacts, Relative to Illustrative No CPP Scenario

CO2 Emissions

(MM Short Tons)

CO2 Emissions Change 

(MM Short Tons)

CO2 Emissions 

Percent Change Relative to 

Illustrative Repeal 

Scenario

2025 2030 2035 2025 2030 2035 2025 2030 2035

Repeal Scenario 1,829 1,811 1,794 -- -- -- -- -- --

Base Case (CPP) 1,780 1,737 1,728 -50 -74 -66 -3% -4% -4%

Base Case (CPP) with EE 1,725 1,695 1,682 -104 -117 -112 -6% -7% -6%
3-41

Table 3-42 Projected SO2, NOX, and Mercury Emissions

SO2

(thousand tons)

NOX

(thousand tons)

Mercury

(tons)

2025

No CPP 959 874 4.9

Base Case (CPP) 923 842 4.7

Base Case (CPP) with EE 883 814 4.5

2030

No CPP 950 833 4.7

Base Case (CPP) 891 786 4.4

Base Case (CPP) with EE 871 772 4.3

2035

No CPP 865 783 4.3

Base Case (CPP) 821 740 4.1

Base Case (CPP) with EE 799 731 4.0

Table 3-43 Projected SO2, NOX, and Mercury Emission Impacts, Relative to Illustrative 

No CPP Scenario

SO2

(thousand tons)

NOX

(thousand tons)

Mercury

(tons)

2025

No CPP -- -- --

Base Case (CPP) -3.7% -3.7% -3.5%

Base Case (CPP) with EE -7.9% -6.8% -7.2%

2030

No CPP -- -- --

Base Case (CPP) -6.3% -5.7% -5.1%

Base Case (CPP) with EE -8.3% -7.3% -7.6%

2035

No CPP -- -- --

Base Case (CPP) -5.1% -5.5% -4.2%

Base Case (CPP) with EE -7.6% -6.6% -6.9%
3-42

3.9 Limitations of Analysis 

Cost estimates for the proposal scenarios are based on rigorous power sector modeling 

using ICF’s Integrated Planning Model. IPM assumes “perfect foresight” of market conditions 

over the time horizon modeled; to the extent that utilities and/or energy regulators have different 

judgments about future conditions affecting the economics of operation or pollution control, 

proposed costs may be understated or overstated.

The modeling reported in this chapter is based on expert judgment of various input 

assumptions for variables whose outcomes are in fact uncertain, including fuel supplies, 

technology costs, and electricity demand. As a general matter, the Agency reviews the best 

available information regarding these and other variables to support a reasonable modeling 

framework for analyzing the cost, emission changes, and other impacts of regulatory actions.

Regarding fuel supply, EPA observes that future long-term natural gas price projections used in 

this analysis are somewhat higher than current observed short-term market rates (e.g., Henry Hub 

prices). EPA is exploring doing additional work regarding these data, and is soliciting comment 

on alternative data, assumptions, and sensitivity analyses that EPA might consider for the final 

rule analysis.

As previously stated, this analysis is intended to be illustrative, and not intended to 

evaluate the many specific approaches that individual states might choose as they implement 

BSER, or how sources might have responded to those specific policy signals or requirements. It 

is important to note, that EPA has not analyzed or modeled a specific standard of performance, 

given that this proposal establishes BSER, and it is up to states to determine appropriate 

standards of performance for sources. It is important to note that there is inadequate and 

incomplete information regarding how states might specifically implement this rule, and the 

estimated range of costs and impacts presented in this chapter is based on the assumptions 

described above.

EPA assumed different heat rate assumptions in the base case for this RIA than the 

illustrative policy scenarios. In the base case (CPP), EPA assumed that HRI was available at 

assumptions consistent with the final CPP ($100/kW) and about 2 to 4 percent improvement 

depending on the region, consistent with how HRI was modeled in the final CPP), and in this 
3-43

scenario EPA modeling estimates an installation of less than 5 GW of capacity. The No CPP

scenario does not allow any heat rate improvement.

The base case assumes the states adopt a specific approach for implementing the CPP. 

States have flexibility under the CPP to comply using other approaches, including using a ratebased approach, allowing for interstate trading, and participating in the Clean Energy Incentive 

Program (CEIP). States adopting mass-based plans must address leakage, which is not modeled 

in the illustrative CPP approach in this RIA. Changes in the assumed state plan approach for CPP 

compliance or compliance methods may affect the estimated benefits and costs.

The analysis in this chapter is limited to the effects of the proposed regulation in the 

contiguous U.S. The analysis in this RIA excludes the potential costs and emission changes 

incurred in non-contiguous states and territories from the proposed rule (as well as the benefits 

from changes in emissions from and in those areas).25

IPM assumes a fixed quantity of electricity demand over the modeling timeframe, which 

does not change in response to changes in retail electricity prices. In reality, the quantity of 

electricity demanded may change either through consumer response or the adoption of demandside energy efficiency programs. Changes in the demand for electricity affect both compliance 

and social costs. Generally, an assumption that the quantity of electricity demanded does not 

change with changes in electricity prices leads to higher partial equilibrium estimates of the cost 

of policy, but this is not always the case. As noted above, the estimated impact on average retail 

electricity prices is small.

Potential changes in emissions other than emissions of CO2, SO2, NOX and Hg from the 

electricity sector are not estimated directly using IPM and are not reported in this chapter. This 

includes hazardous air pollutants and direct particulate matter (PM2.5) emissions and water 

emissions. Similarly, the potential changes in emissions from producing fuels, such as methane 

from coal and gas production, are not estimated in this Chapter. Therefore, the associated effects 

 

25 The limited exception to this is MR&R costs, as MR&R costs are estimated for 49 states, including Alaska and 

Hawaii. One contiguous state is estimated to have no MR&R costs, as it is expected to submit a negative 

declaration.
3-44

on heath, ecosystems, and visibility from these potential changes in other pollutants from the 

electricity and other sectors are not quantified in subsequent chapters.

As discussed in EPA’s Guidelines for Preparing Economic Analyses, social costs are the 

total economic burden of a regulatory action. This burden is the sum of all opportunity costs 

incurred due to the regulatory action, where an opportunity cost is the value lost to society of any 

goods and services that will not be produced and consumed as a result of reallocating some 

resources related to changes in pollution levels. Estimates of social costs may be compared to the 

social benefits expected as a result of a regulation to assess its net impact on society. The social 

costs of a regulatory action will not necessarily be equivalent to the expenditures associated with 

compliance. Nonetheless, here we use compliance costs as a proxy for social costs. Differences 

between estimates of social cost include the treatment of tax payments and subsidy receipts, the 

changes in which are accounted for in compliance costs but would be excluded from the estimate 

of social costs as they are a transfer. Social costs also include the effect of the regulation on 

profitability of suppliers to the electricity sector.26 Also, a social cost estimate would account for 

how the regulation would affect preexisting distortions in the economy that reduce economic 

efficiency. Chapter 5 discusses these other potential effects of the regulation and how they may 

affect the estimates of social costs and benefits.

The demand-side energy efficiency sensitivity analysis is an illustrative scenario that 

provides information on the potential effects had demand-side energy efficiency been used for 

compliance with the CPP. As described in section 3.8, the level of reduced electricity 

consumption through energy efficiency are derived from the AEO 2017 reference and “no CPP” 

side case and is therefore subject to all the assumptions that underlie those scenarios. These are 

summarized at length in U.S. EIA, 2017.

In addition to the level of reduced electricity consumption, the assumed cost of saved 

energy, as discussed in section 3.8.2, is another key component of the demand-side EE 

sensitivity analysis. The values used in this analysis are based on a review of energy efficiency 

 

26 Much of the social cost borne by electricity consumers is accounted for in the compliance cost estimate as they 

ultimately will bear part of this cost through changes in electricity prices. Note that this analysis does not identify 

who ultimately bears the compliance costs, which also include owners of generating assets through changes in their 

profits.
3-45

data and studies, and expert judgment. As noted, the levelized cost saved energy used in our 

analysis is $57.3 2016$/MWh. This LCSE value is the total levelized cost, including both 

program and participant costs. Analysis of energy efficiency costs is limited and the results vary 

significantly. Studies are of two types: bottom-up engineering-based analyses and top-down 

analyses employing econometric techniques. Bottom-up engineering-based analyses are much 

more prevalent. They are carried out by third-party evaluators and reviewed in regulatory 

proceedings by oversight entities such as state utility commissions that serve to protect the 

interests of utility customers while allowing a fair return to utility investors. The value chosen for 

the current analysis is based upon the most extensive national database of bottom-up, 

engineering-based analyses of energy efficiency costs. The value falls within, but at the lower 

end, of the range of results from top-down econometric analyses. See U.S. EPA, 2015a and 

2017, for more extensive discussions of the limitations of the analysis of energy efficiency costs.

Demand-side energy-efficiency in response to the CPP is applied in every state in the 

Base Case (CPP) with EE scenario. However, as observed in the Base Case (CPP), the total 

emissions from affected sources is projected to be less than the mass-based goals for existing 

sources, suggesting that many states do not need to undertake compliance activities under the 

CPP, including demand-side EE. Changing the analysis so that demand-side energy efficiency is 

only applied in those states where it could be utilized for compliance would affect the projected 

emissions changes and costs between the alternative base case and the policy scenarios.
3-46

3.10 References

Freeman, Myrick. 2003. The Measurement of Environmental and Resource Values. 2nd Edition. 

Resources for the Future: Washington D.C.

Hoffman, IM., Rybka, G., Leventis, G., Goldman, CA., Schwartz, L., Billingsley, M., Schiller, S. 

2015. The Total Cost of Saving Electricity through Utility Customer-Funded Energy 

Efficiency Programs: Estimates at the National, State, Sector and Program Level. 

Berkeley, CA. Available at http://eta-publications.lbl.gov/sites/default/files/total-cost-ofsaved-energy.pdf.

Hoffman, IM., Goldman, CA., Rybka, G., Leventis, G., Schwartz, L., Sanstad, AH, Schiller S., 

2017. Estimating the cost of savings electricity through U.S. utility customer-funded 

energy efficiency programs. Energy Policy 104 (2017) 1-12.

Synapse Energy Economics, Inc. (Synapse), 2015. State Net-to-Gross Ratios: Research Results 

and Analysis for Average State Net-to-Gross Ratios Used in Energy Efficiency Program 

Estimates. Boston, MA. Prepared for the United States Environmental Protection 

Agency. Available at http://www.synapse-energy.com/sites/default/files/NTG-Research14-053.pdf.

Tietenberg, Tom and Lynne Lewis. 2009. Environmental & Natural Resource Economics. 10th 

Edition. Routledge: New York. 

U.S. Energy Information Administration (U.S. EIA), 2017. Assumptions to the Annual Energy 

Outlook 2017. Available at 

https://www.eia.gov/outlooks/aeo/assumptions/pdf/0554(2017).pdf.

U.S. Environmental Protection Agency (EPA), 2010. Guidelines for Preparing Economic 

Analyses. Available at: https://www.epa.gov/environmental-economics/guidelinespreparing-economic-analyses 

U.S. Environmental Protection Agency (EPA). 2015a. Regulatory Impact Analysis for the Clean 

Power Plan Final Rule. EPA-452/R-15-003. Office of Air Quality Planning and 

Standards, Health and Environmental Impacts Division, Research Triangle Park, NC.

U.S. Environmental Protection Agency (EPA), 2015b. Technical Support Document (TSD) for 

the Final Carbon Pollution Emission Guidelines for Existing Stationary Sources: Electric 

Utility Generating Units. Demand-side Energy Efficiency. Available at 

https://archive.epa.gov/epa/sites/production/files/2015-11/documents/tsd-cpp-demandside-ee.pdf.

U.S. Environmental Protection Agency (EPA), 2017. Regulatory Impact Analysis for the Review 

of the Clean Power Plan. EPA-452/R-17-004. Office of Air Quality Planning and 

Standards, Health and Environmental Impacts Division. Research Triangle Park, NC. 

Available at https://www.epa.gov/sites/production/files/2017-

10/documents/ria_proposed-cpp-repeal_2017-10.pdf.
4-1

CHAPTER 4: ESTIMATED FORGONE CLIMATE BENEFITS AND FORGONE

HUMAN HEALTH CO-BENEFITS

4.1 Introduction 

As compared to the standards of performance that it replaces (i.e., the 2015 Clean Power 

Plan) and as documented in Chapter 3, implementing the proposed rule is expected to increase 

emissions of carbon dioxide (CO2) and increase the level of emissions of certain pollutants in the 

atmosphere that adversely affect human health. These emissions include directly emitted fine 

particles sized 2.5 microns and smaller (PM2.5), sulfur dioxide (SO2), nitrogen dioxide (NOX), 

and mercury (Hg). SO2 and NOX are each a precursor to ambient PM2.5, and NOX emissions are 

also a precursor in the formation of ambient ground-level ozone.

This chapter describes the methods used to estimate the forgone domestic climate 

benefits associated with the increase in CO2 emissions and forgone domestic health benefits 

associated with the increase in PM2.5 and ground-level ozone. We refer to the health benefits as 

“co-benefits” (or, “ancillary co-benefits”) in this RIA because they occur as a result of 

implementing the policy but are not necessarily the intended outcome of the standards of 

performance. By contrast, reducing CO2 is a goal of this policy, and so we treat CO2 as the 

“targeted pollutant”. Data, resource, and methodological limitations prevent EPA from 

estimating all forgone domestic climate benefits and forgone health and environmental cobenefits, including those from health effects from direct exposure to SO2, NO2, and hazardous air 

pollutants (HAP) including Hg, and ecosystem effects and visibility impairment. We discuss 

these unquantified effects in section 4.7.

Elsewhere in the RIA, including the Executive Summary, estimates of forgone benefits 

are presented as negative benefit values to make it easier for readers to compare costs and 

benefits. In this chapter, which only presents estimated forgone benefits, these figures are not 

presented as negative values, but are shown as positive values. 

4.2 Climate Change Impacts

In 2009, EPA Administrator found that elevated concentrations of greenhouse gases in the 

atmosphere may reasonably be anticipated both to endanger public health and to endanger public 
4-2

welfare.1It is these adverse impacts that necessitate EPA regulation of GHGs from EGU sources. 

Since 2009, other science assessments suggest accelerating trends2.

4.3 Approach to Estimating Forgone Climate Benefits from CO2

We estimate the forgone climate benefits from this proposed rulemaking using a measure 

of the domestic social cost of carbon (SC-CO2). The SC-CO2 is a metric that estimates the 

monetary value of projected impacts associated with marginal changes in CO2 emissions in a 

given year. It includes a wide range of anticipated climate impacts, such as net changes in 

agricultural productivity and human health, property damage from increased flood risk, and 

changes in energy system costs, such as reduced costs for heating and increased costs for air 

conditioning. It is typically used to assess the avoided damages as a result of regulatory actions 

(i.e., benefits of rulemakings that lead to an incremental reduction in cumulative global CO2

emissions). The SC-CO2 estimates used in this RIA focus on the projected impacts of climate 

change that are anticipated to directly occur within U.S. borders.

The SC-CO2 estimates presented in this RIA are interim values developed under E.O. 

13783 for use in regulatory analyses until an improved estimate of the impacts of climate change 

to the U.S. can be developed based on the best available science and economics. E.O. 13783 

directed agencies to ensure that estimates of the social cost of greenhouse gases used in 

regulatory analyses “are based on the best available science and economics” and are consistent 

with the guidance contained in OMB 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)). In addition, E.O. 13783 withdrew the technical support documents 

(TSDs) used in the 2015 CPP RIA for describing the global social cost of greenhouse gas 

 

1

“Endangerment and Cause or Contribute Findings for Greenhouse Gases Under Section 202(a) of the Clean Air 

Act,” 74 Fed. Reg. 66,496 (Dec. 15, 2009) (“Endangerment Finding”).

2 Melillo, Jerry M., Terese (T.C.) Richmond, and Gary W. Yohe, Eds., 2014: Climate Change Impacts in the United 

States: The Third National Climate Assessment. U.S. Global Change Research Program, 841 pp. 

doi:10.7930/J0Z31WJ2; and USGCRP, 2017: Climate Science Special Report: Fourth National Climate 

Assessment, Volume I [Wuebbles, D.J., D.W. Fahey, K.A. Hibbard, D.J. Dokken, B.C. Stewart, and T.K. 

Maycock (eds.)]. U.S. Global Change Research Program, Washington, DC, USA, 470 pp., doi: 

10.7930/J0J964J6.
4-3

estimates developed under the prior Administration as no longer representative of government 

policy. 

Regarding the two analytical considerations highlighted in E.O. 13783 – how best to 

consider domestic versus international impacts and appropriate discount rates – current guidance 

in OMB Circular A-4 is as follows. Circular A-4 states that analysis of economically significant 

proposed and final regulations “should focus on benefits and costs that accrue to citizens and 

residents of the United States.” We follow this guidance by adopting a domestic perspective in 

our central analysis. Regarding discount rates, Circular A-4 states that regulatory analyses 

“should provide estimates of net benefits using both 3 percent and 7 percent.” The 7 percent rate 

is intended to represent the average before-tax rate of return to private capital in the U.S. 

economy. The 3 percent rate is intended to reflect the rate at which society discounts future 

consumption, which is particularly relevant if a regulation is expected to affect private 

consumption directly. EPA follows this guidance below by presenting estimates based on both 3 

and 7 percent discount rates in the main analysis. See Chapter 7 for a discussion the modeling 

steps involved in estimating the domestic SC-CO2 estimates based on these discount rates.

In January 2017, the Academies released their final report, Assessing Approaches to 

Updating the Social Cost of Carbon, and recommended specific criteria for future updates to the 

SC-CO2 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 (National Academies 2017). 3 These SC-CO2 estimates developed under E.O. 13783 

presented below will be used in regulatory analysis until more comprehensive domestic estimates 

can be developed, which would take into consideration the recent recommendations from the 

National Academies of Sciences, Engineering, and Medicine to further update to the current 

methodology to ensure that the SC-CO2 estimates reflect the best available science. 

Table 4-1 presents the average domestic SC-CO2 estimate across all the model runs for 

each discount rate for the years 2015 to 2050. As with the global SC-CO2 estimates, the domestic 

SC-CO2 increases over time because future emissions are expected to produce larger incremental 

damages as physical and economic systems become more stressed in response to greater climatic 

 

3 See National Academies of Sciences, Engineering, and Medicine, Valuing Climate Damages: Updating Estimation 

of the Social Cost of Carbon Dioxide, Washington, D.C., January 2017. http://www.nap.edu/catalog/24651/valuingclimate-changes-updating-estimation-of-the-social-cost-of
4-4

change, and because GDP is growing over time and many damage categories are modeled as 

proportional to gross GDP. For emissions occurring in the year 2030, the two domestic SC-CO2

estimates are $1 and $8 per metric ton of CO2 emissions (2016$), using a 7 and 3 percent 

discount rate, respectively. 

Table 4-1 Interim Domestic Social Cost of CO2, 2015-2050 (in 2016$ per metric ton)*

Year

Discount Rate and Statistic

3% Average 7% Average

2015 $6 $1

2020 7 1

2025 7 1

2030 8 1

2035 9 2

2040 9 2

2045 10 2

2050 11 2

* These SC-CO2 values are stated in $/metric ton CO2 and rounded the nearest dollar. These values may be 

converted to $/short ton using the conversion factor 0.90718474 metric tons in a short ton for application to the short 

ton CO2 emission impacts provided in this rulemaking. Such a conversion does not change the underlying 

methodology nor does it change the meaning of the SC-CO2 estimates. For both metric and short tons denominated 

SC-CO2 estimates, the estimates vary depending on the year of CO2 emissions and are defined in real terms, i.e., 

adjusted for inflation using the GDP implicit price deflator. 

Table 4-2 reports the forgone domestic climate benefits in the three analysis years (2025, 

2030, 2035) for the four illustrative scenarios, compared to the base case.
4-5

Table 4-2 Estimated Forgone Domestic Climate Benefits, Relative to Base Case (CPP) 

(billions 2016$)*

3% Discount Rate 7% Discount Rate

No CPP

2025 0.32 0.053 

2030 0.53 0.092 

2035 0.51 0.10 

2% HRI at $50/kW

2025 0.24 0.039 

2030 0.43 0.075 

2035 0.43 0.080 

4.5% HRI at $50/kW

2025 0.21 0.034 

2030 0.43 0.074 

2035 0.46 0.086 

4.5% HRI at $100/kW

2025 0.13 0.021 

2030 0.34 0.059 

2035 0.34 0.064

* Values rounded to two significant figures. The SC-CO2 values are dollar-year and 

emissions-year specific. SC-CO2 values represent only a partial accounting of climate impacts.

The limitations and uncertainties associated with the SC-CO2 analysis, which were 

discussed at length in the 2015 CPP RIA, likewise apply to the domestic SC-CO2 estimates 

presented in this RIA. Some uncertainties are captured within the analysis, as discussed in detail 

in Chapter 7, while other areas of uncertainty have not yet been quantified in a way that can be 

modeled. For example, limitations include the incomplete way in which the integrated 

assessment models capture catastrophic and non-catastrophic impacts, 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. The science incorporated into these models 

understandably does not reflect all of the recent recommendations of the National Academy’s

2017 report or the most recent research, and the limited amount of research linking climate 

impacts to economic damages makes the modeling exercise even more difficult. These individual 

limitations and uncertainties do not all work in the same direction in terms of their influence on 

the SC-CO2 estimates. In accordance with guidance in OMB Circular A-4 on the treatment of 

uncertainty, Chapter 7 provides a detailed discussion of the ways in which the modeling 
4-6

underlying the development of the SC-CO2 estimates used in this RIA addressed quantified 

sources of uncertainty, and presents a sensitivity analysis to show consideration of the 

uncertainty surrounding discount rates over long time horizons. 

Recognizing the limitations and uncertainties associated with estimating the social cost of 

carbon, the research community has continued to explore opportunities to improve SC-CO2

estimates. Notably, the National Academies of Sciences, Engineering, and Medicine conducted a 

multi-discipline, multi-year assessment to examine potential approaches, along with their relative 

merits and challenges, for a comprehensive update to the current methodology. The task was to 

ensure that the SC-CO2 estimates that are used in Federal analyses reflect the best available 

science, focusing on issues related to the choice of models and damage functions, climate science 

modeling assumptions, socioeconomic and emissions scenarios, presentation of uncertainty, and 

discounting. 

The National Academies’ 2017 report also discussed the challenges in developing 

domestic SC-CO2 estimates, noting that current integrated assessment models (IAMs) do not 

model all relevant regional interactions – i.e., how climate change impacts in other regions of the 

world could affect the United States, through pathways such as global migration, economic 

destabilization, and political destabilization. The Academies concluded that it “is important to 

consider what constitutes a domestic impact in the case of a global pollutant that could have 

international implications that impact the United States. More thoroughly estimating a domestic 

SC-CO2 would therefore need to consider the potential implications of climate impacts on, and 

actions by, other countries, which also have impacts on the United States.” (National Academies 

2017, pg. 12-13).

In addition to requiring reporting of impacts at a domestic level, Circular A-4 states that 

when an agency “evaluate[s] a regulation that is likely to have effects beyond the borders of the 

United States, these effects should be reported separately” (page 15). This guidance is relevant to 

the valuation of damages from CO2 and other GHGs, given that GHGs contribute to damages 

around the world independent of the country in which they are emitted. Therefore, in accordance 

with this guidance in OMB Circular A-4, Chapter 7 presents the forgone global climate benefits 

from this proposed rulemaking using global SC-CO2 estimates based on both 3 and 7 percent 
4-7

discount rates. Note EPA did not quantitatively project the full impact of the CPP on 

international trade and the location of production, so it is not possible to present analogous 

estimates of international costs resulting from the proposed action. However, to the extent that 

the IPM analysis endogenously models international electricity and natural gas trade, and to the 

extent that affected firms have some foreign ownership, some of the costs accruing to entities 

outside U.S. borders is captured in the compliance costs presented in this RIA. See Chapter 5 for 

more discussion of challenges involved in estimating the ultimate distribution of avoided 

compliance costs.

4.4 Approach to Estimating Forgone Human Health Ancillary Co-Benefits 

As noted above, this proposed rule is designed to affect emissions of CO2 from the EGU 

sector, but will also influence the level of other pollutants emitted in the atmosphere that 

adversely affect human health; these include directly emitted PM2.5 as well as SO2 and NOX, 

which are both precursors to ambient PM2.5. NOX emissions are also a precursor to forming 

ambient ground-level ozone. The EGU emissions associated with the base case and each of the 

four illustrative scenarios are shown in Table 4-3. The change in emissions between the base 

case and each illustrative scenario will in turn alter the ambient levels, population exposure and 

human health impacts associated with PM2.5 and ozone. Finally, ambient levels of both SO2 and 

NOX pose health risks independent of PM2.5 and ozone, though we do not quantify these impacts 

in this analysis (U.S. EPA 2016b, 2017). 
4-8

Table 4-3 Projected EGU Emissions of SO2, NOX, and PM2.5*

SO2

(thousand tons)

NOX

(thousand tons)

PM2.5

(thousand tons)

2025

No CPP 959 874 111

Base Case (CPP) 923 842 109

2% HRI at $50/kW 959 866 110

4.5% HRI at $50/kW 963 863 109

4.5% HRI at $100/kW 956 856 109

2030

No CPP 950 833 112

Base Case (CPP) 891 786 110

2% HRI at $50/kW 943 825 111

4.5% HRI at $50/kW 943 825 111

4.5% HRI at $100/kW 935 818 110

2035

No CPP 865 783 114

Base Case (CPP) 821 740 113

2% HRI at $50/kW 855 778 113

4.5% HRI at $50/kW 864 782 113

4.5% HRI at $100/kW 849 772 113

* The SO2 and NOX emissions are direct outputs from the IPM simulations as reported in Chapter 3; however, the 

PM2.5 emissions were derived based on IPM-predicted heat rate and other factors as described in chapter 8.

This section is a summary of our approach to estimating the incidence and economic 

value of the forgone PM2.5 and ozone-related ancillary co-benefits estimated for this proposed 

rule relative to a baseline that includes the 2015 CPP RIA. The Regulatory Impact Analysis 

(RIA) for the Particulate Matter (PM) National Ambient Air Quality Standards (NAAQS) (U.S. 

EPA 2012b) the RIA for the Ozone NAAQS (U.S. EPA 2015e) and the BenMAP-CE user 

manual (U.S. EPA 2018a) provides a full discussion of the Agency’s approach for quantifying 

the number and value of estimated air pollution-related impacts. In these documents the reader 

can find the rationale for selecting health endpoints to quantify; the demographic, health and 

economic data we apply within the environmental Benefits Mapping and Analysis Program—

Community Edition (BenMAP-CE); modeling assumptions; and our techniques for quantifying 

uncertainty.

These estimated forgone ancillary health co-benefits do not account for the influence of 

future changes in the climate on ambient levels of pollutants (USGCRP 2016). For example, 

recent research suggests that future changes to climate may create conditions more conducive to 

forming ozone; the influence of changes in the climate on PM2.5 levels are less clear (Fann et al. 
4-9

2015). The estimated ancillary health co-benefits also do not consider the potential for climateinduced changes in temperature to modify the relationship between ozone and the risk of 

premature death (Jhun et al. 2014; Ren et al. 2008b, 2008a). 

Implementing the proposed guidelines will affect the level and distribution of PM2.5 and 

ozone concentrations throughout the U.S.; this includes locations both meeting and exceeding the 

NAAQS for PM and ozone. This RIA estimates foregone PM2.5- and ozone-related health 

impacts that are distinct from those reported in the RIAs for both NAAQS (U.S. EPA 2012b, 

2015e). The PM2.5 and ozone NAAQS RIAs hypothesize, but do not predict, the benefits and 

costs of strategies that States may choose to enact when implementing a revised NAAQS; these 

costs and benefits are illustrative and cannot be added to the costs and benefits of policies that 

prescribe specific emission control measures.

Some portion of the foregone air quality and health benefits estimated for this rule will 

occur in areas not attaining the PM2.5 or Ozone NAAQS. This RIA predicts increased levels of 

PM2.5 and ozone in some locations compared to the base case that includes the 2015 CPP. In 

these instances, States would identify additional opportunities to reduce emissions from local 

sources relative to the base case. States may meet the NAAQS using other approaches, thus 

negating the increased PM2.5 and ozone concentrations we predicted in this RIA. In this case, the 

forgone benefits would be lower than we estimated here and States would incur the costs of these 

alternative approaches. We did not separately estimate these costs and did not separately report 

the change in PM2.5 or ozone in areas projected to not attain either standard. The base case, 

which includes the CPP, projected reduced EGU emissions in areas already attaining the 

NAAQS. And, in some cases, the CPP would have created “room” for new and expanding 

sources in these areas to increase pollutant emissions. In these instances, the forgone health cobenefits we estimated could be overestimated. The extent to which we over-estimated foregone 

health co-benefits will depend on how States and the U.S. EPA choose to implement the 

NAAQS and address Prevention of Significant Deterioration (PSD) requirements. Conversely, 

the policy cases may inhibit the ability of sources to expand, yielding foregone benefits.
4-10

4.4.1 Air Quality Modeling Methodology

We performed nationwide photochemical modeling and related analyses to develop 

spatial fields of air quality across the U.S. for input to BenMAP-CE, which was used to quantify 

the forgone benefits from this proposed rule. Spatial fields of air quality were prepared for each 

of the following health-impact metrics: annual mean PM2.5, May through September seasonal 

average 8-hour daily maximum (MDA8) ozone, April through October seasonal average 1-hour 

daily maximum (MDA1) ozone for scenarios that reflect EGU emissions analyzed in this 

proposed rule RIA. The EGU emissions for each of the scenarios were obtained from the outputs 

of the corresponding IPM runs, as described in Chapter 3. 

All of the air quality model simulations (i.e., model runs) were performed using the 

Comprehensive Air Quality Model with Extensions (CAMx)4(Ramboll Environ, 2016). Our 

CAMx nationwide modeling domain (i.e., the geographic area included in the modeling) covers 

all lower 48 states plus adjacent portions of Canada and Mexico using a horizontal grid 

resolution of 12 x 12 km. In this section we provide an overview of the air quality modeling and 

the methodologies we used to develop spatial fields of annual PM2.5 and seasonal average ozone 

concentrations. More information on the air quality modeling platform (inputs and set-up), model 

performance evaluation for ozone and PM2.5, emissions processing for this analysis, and 

additional details and numerical examples of the methodologies for developing PM2.5 and ozone 

spatial fields are provided in Chapter 8.

Several types of photochemical model runs were performed as part of this analysis. The 

modeling included annual model runs for a 2011 base year and a 2023 future year to provide 

hourly concentrations of ozone as well as primary and secondarily formed PM2.5 component 

species (e.g., sulfate, nitrate, ammonium, elemental carbon, organic matter, and crustal material) 

for both years nationwide. The year 2023 was used as the future year because emissions from all 

anthropogenic source types in the modeling domain for 2023 represent EPA’s most up to date 

future year projections that are available for the analysis of this proposed rule. As described 

below, the photochemical modeling results for 2011 and 2023 were part of the inputs used to 

construct the air quality spatial fields that reflect the influence of EGU emissions in 2025, 2030, 

 

4 CAMx version 6.40 was used for the modeling to support the proposal RIA. This version of CAMx is the latest 

public release version of the model at the time of proposal.
4-11

and 2035 for the base case which includes the 2015 CPP final rule and each of the four 

illustrative scenarios we analyzed for this proposal. Due to timing constraints we did not perform 

explicit air quality modeling for each of the 2025/2030/2035 base case and the illustrative 

scenarios. Rather, we used emissions data and the results of the 2011 and 2023 modeling in 

conjunction with source apportionment modeling for 2023 to estimate the ozone and PM2.5

concentrations for each year of the base case which includes the 2015 CPP final rule and the 

illustrative scenarios. In general, source apportionment modeling quantifies the air quality 

concentrations formed from individual, user-defined groups of emissions sources or “tags”. 

These source tags are tracked through the transport, dispersion, chemical transformation, and 

deposition processes in the model to obtain hourly gridded5 contributions from the emissions in 

each individual tag to hourly modeled concentrations of ozone and PM2.5.

6 For this analysis we 

performed source apportionment modeling for ozone and PM2.5 based on 2023 emissions using 

the tools in CAMx7to obtain the contributions from EGU emissions as well as other sources to 

ozone and to PM2.5 component species concentrations.8

The source apportionment modeling was used to quantify the contributions from EGU 

emissions on a state-by-state or, in some cases, on a multi-state basis. For ozone, we modeled the 

contributions from the 2023 EGU NOX and VOC emissions to hourly ozone concentrations for 

the period April through October to provide data for developing spatial fields for the two 

seasonal ozone benefits metrics identified above. For PM2.5, we modeled the contributions from 

the 2023 EGU sector emissions of SO2, NOX, and directly emitted PM2.5 for the entire year to 

inform the development of spatial fields of annual mean PM2.5. For each state or multi-state 

group we separately tagged EGU emissions depending on whether the emissions were from coal-

 

5 Hourly contribution information is provided for each grid cell to provide spatial patterns of the contributions from 

each tag.

6 Note that the sum of the contributions in a model grid cell from each tag for a particular pollutant equals the total 

concentration of that pollutant in the grid cell.

7 Ozone contributions were modeled using the Ozone Source Apportionment Technique/Anthropogenic Precursor 

Culpability Assessment (OSAT/APCA) tool and PM2.5 component species contributions were modeled using the 

Particulate Source Apportionment Technique (PSAT) tool.

8

In the source apportionment modeling for PM2.5 we tracked the source contributions from primary, but not 

secondary organic aerosols (SOA). The method for treating SOA concentrations is described later in this section.
4-12

fired units or non-coal units.9In addition to tagging EGU emissions we also tracked the ozone 

and PM2.5 contributions from the following “domain-wide” tags (i.e., tags that are not 

geographically grouped by state or multi-state area):

• two tags for emissions from those EGUs that were operating in the 2023 case, but are now 

expected to retire before 203010; one EGU retirement tag includes emissions from sources 

that have announced retirements before 2025, and a second tag for EGUs with announced 

retirements between 2025 and 2030;11

• one tag for all U.S. anthropogenic emissions from source sectors other than EGUs;

• one tag for international emissions that are located within the modeling domain, including 

anthropogenic emissions in Canada, Mexico, as well as offshore marine vessels and 

drilling platforms;

• one tag that includes emissions from wildfires and prescribed fires;

• one tag for biogenic source emissions; and

• one tag to provide the contributions from concentrations along the outer boundary of the 

modeling domain. 

The development of the EGU tags and the other tags listed above is described in more 

detail in Chapter 8.

The following data were used to create the spatial fields of ozone and PM2.5 for the base 

case and illustrative scenarios case in 2025, 2030, and 2035. The following data were used to 

create the spatial fields of ozone and PM2.5 for the base case and the four illustrative scenarios 

case in 2025, 2030, and 2035:

(1) 2023 annual EGU SO2, NOX, and directly emitted PM2.5 emissions and 2023 ozone 

season12 EGU NOx emissions for each EGU tag as described in Chapter 8;

(2) 2025, 2030, and 2035 annual EGU emissions of SO2, NOX, and directly emitted PM2.5 

and EGU ozone season NOX emissions for the base case including CPP and illustrative 

 

9 For the purposes of this analysis non-coal fuels include emissions from natural gas, oil, biomass, municipal waste 

combustion and waste coal EGUs.

10 Note that emissions associated with units in the two EGU retirements tags are not included in the state-level EGU 

tags (i.e. there is no double-counting of emissions contributions).

11 At the time of this analysis, there were no announced EGU retirements after 2030.

12 “ozone season NOX emissions” refer to total NOX (ton) emitted during the period of May-September.
4-13

scenarios that correspond to each of the 2023 EGU tags defined for the 2023 source 

apportionment modeling;

(3) Daily 2011 and 2023 modeling-based concentrations of 24-hour average PM2.5 

component species and MDA1 and MDA8 ozone; 

(4) 2023 daily contributions to 24-hour average PM2.5 component species and MDA1 and 

MDA8 ozone from each of the various source tags; and

(5) Base period “fused surfaces” of measured and modeled air quality13 representing 

quarterly average PM2.5 component species concentrations and ozone concentrations for 

the two seasonal average ozone metrics. These “fused surfaces” use the ambient data to 

adjust modeled fields to match observed data at locations of monitoring sites. Details on 

the methods for creating fused surfaces are provided in Chapter 8.

Next, we identify the general process for developing the spatial fields for PM2.5 using the 

2025 base case including CPP as an example to illustrate the procedure. The steps in this process 

are as follows:

(1) We use the EGU annual SO2, NOX, and directly emitted PM2.5 emissions14 for the 2025 

base case including CPP and the corresponding 2023 SO2, NOX, and directly emitted 

PM2.5 emissions to calculate the ratio of 2025 base case emissions to 2023 emissions for 

each of these pollutant for each EGU tag (i.e. a scaling ratio for each pollutant and each 

tag).

(2) The tag-specific 2025 to 2023 EGU emissions-based scaling ratios from step (1) are 

multiplied by the corresponding 365 daily 24-hour average PM2.5 component species 

contributions from the 2023 contribution modeling. The emissions ratios for SO2 are 

applied to sulfate contributions; ratios for annual NOX are applied to nitrate contributions; 

and ratios for directly emitted PM2.5 are applied to the EGU contributions to primary 

organic matter, elemental carbon and crustal material. This step results in 365 adjusted 

daily PM2.5 component species contributions for each EGUs tag that reflects the 

emissions in the 2025 base case including CPP.

(3) For each individual PM2.5 component species, the adjusted contributions for each EGU 

tag from step (2) are added together to produce a daily EGU tag total. Then the 24-hour 

average contributions, if any, from units that will retire by 2030 (i.e., the 2025-2030 

retirements tag) are included by adding their contribution from the corresponding daily 

EGU tag total.15

 

13 In this analysis, a “fused surface” represents a spatial field of concentrations of a particular pollutant that was 

derived by applying the Enhanced Voronoi Neighbor Averaging with adjustment using modeled and measured air 

quality data (i.e., eVNA) technique (Ding et al. 2016).

14 The 2025, 2030, and 2035 EGU SO2 and NOX emissions for the base case and the four illustrative scenarios were 

obtained from IPM outputs for these scenarios. EGU emissions of directly emitted PM2.5 were derived based on heat 

rate data from the IPM outputs, using a methodology described in Chapter 8.

15 Note that contributions from units that will retire before 2025 (i.e. the 2025 retirements tag) are not added to the 

EGU surface since those sources are not expected to have any contributions to PM2.5 in 2025.
4-14

(4) The daily total EGU contributions for each PM2.5 component species from step (3) are 

then combined with the species contributions from each of the other source tags, as 

identified above. As part of this step we also add the total secondary organic aerosol 

concentrations from the 2023 modeling to the net EGU contributions of primary organic 

matter. Note that the secondary organic aerosol concentration does not change between 

scenarios. This step results in 24-hour average PM2.5 component species concentrations 

for the 2025 base case including CPP in each model grid cell, nationwide for each day in 

the year.

(5) For each PM2.5 component species, we average the daily concentrations from step (4) for 

each quarter of the year.

(6) The quarterly average PM2.5 component species concentrations from step (5)16 are divided 

by the corresponding quarterly average species concentrations from the 2011 model run. 

This step results in a Relative Response Factor (i.e., RRF) between 2011 and the 2025 

base case for each species in each model grid cell.

(7) The species-specific quarterly RRFs from step (6) are then multiplied by the 

corresponding species-specific quarterly average concentrations from the base period 

fused surfaces to produce quarterly average species concentrations for the 2025 base 

case.

(8) The 2025 base case quarterly average species concentrations from step (7) are summed 

over the species to produce total PM2.5 concentrations for each quarter. Finally, total 

PM2.5 concentrations for the four quarters of the year are averaged to produce the spatial 

field of annual average PM2.5 concentrations for the 2025 base case that are input to 

BenMAP-CE.

The steps above are repeated for each of the four illustrative scenarios and each of the 3 analysis 

years.17

For generating the spatial fields for each of the two ozone metrics we follow steps similar 

to those above for PM2.5. Again, we use the 2025 base case to illustrate the steps for producing 

ozone spatial fields for each of the cases we analyzed. We use the EGU May through September 

(i.e., Ozone Season - OS) NOX for the 2025 base case and the corresponding 2023 OS NOX

emissions to calculate the ratio of 2025 base case emissions to 2023 emissions for each EGU tag 

(i.e. an ozone-season scaling factor for each tag).

 

16 Ammonium concentrations are calculated assuming that the degree of neutralization of sulfate ions remains at 

2011 levels (see Chapter 8 for details).

17 For 2030 and 2035 analysis years, the 2025-2030 retirements tag is not added to the state-level EGU emissions 

since those sources are not expected to impact PM2.5 in those year.
4-15

(1) The source apportionment modeling provided separate ozone contributions for ozone 

formed in VOC-limited chemical regimes (O3V) and ozone formed in NOX-limited 

chemical regimes (O3N).18 The tag-specific 2025 to 2023 EGU NOX emissions-based 

scaling ratios from step (1) are multiplied by the corresponding O3N daily contributions 

to MDA1 and MDA8 concentrations from the 2023 contribution modeling. This step 

results in adjusted gridded daily MDA1 and MDA8 contributions due to NOX changes for 

each EGUs tag that reflect the emissions in the 2025 base case.

(2) For MDA1 and MDA8, the adjusted contributions for each EGU tag from step (2) are 

added together to produce a daily EGU tag total. Since IPM does not output VOC from 

EGUs, there are no predicted changes in VOC emissions in these scenarios so the O3V 

contributions remain unchanged. The contributions from the unaltered 2023 O3V tags are 

added to the summed adjusted O3N EGU tags. Finally, the contributions, if any, to 

MDA1 and MDA8 concentrations from units that will retire by 2030 (i.e., the 2025-2030 

retirements tag) are included by adding their contribution from the corresponding daily 

EGU tag total.19

(3) The daily total EGU contributions for MDA1 and MDA8 from step (3) are then 

combined with the contributions to MDA1 and MDA8 from each of the other source tags. 

This step results in MDA1 and MDA8 concentrations for the base case EGU emissions in 

each model grid cell, nationwide for each day in the ozone season.

(4) For MDA1, we average the daily concentrations from step (4) across all the days in the 

period April 1 through October 31. For MDA8, we average the daily concentrations 

across all days in the period May 1 through September 30.

(5) The seasonal mean concentrations from step (5) are divided by the corresponding 

seasonal mean concentrations from the 2011 model run. This step results in a Relative 

Response Factor (i.e., RRF) between 2011 and the 2025 base case for MDA1 and MDA8 

in each model grid cell.

(6) Finally, the RRFs for the seasonal mean metrics from step (6) are then multiplied by the 

corresponding seasonal mean concentrations from the base period MDA1 and MDA8 

fused surfaces to produce seasonal mean concentrations for MDA1 and MDA8 for the 

2025 base case that are input to BenMAP-CE.

As with PM2.5, the steps outlined NOX for ozone are repeated for each of the four illustrative 

scenarios and each of the 3 analysis years.20

 

18 Information on the treatment of ozone contributions under NOx-limited and VOC-limited chemical regimes in the 

CAMx APCA source apportionment technique can be found in the CAMx v6.40 User’s Guide (Ramboll, 2016).

19 Note that contributions from units that will retire before 2025 (i.e. the 2025 retirements tag) are not added to the

EGU surface since those sources are not expected to have any contributions to PM2.5 in 2025.

20 For 2030 and 2035 analysis years, the contributions from 2025-2030 retirements tag is not added to the state-level 

EGU emissions since those sources are not expected to impact PM2.5 in those years.
4-16

As noted above, additional information on the emissions data and analytic steps 

summarized in this section can be found in Chapter 8. Select maps showing changes in air 

quality concentrations between the illustrative scenarios and the base case are provided later in 

this chapter. 

4.4.2 Estimating PM2.5 and Ozone Related Health Impacts

We estimate the quantity and economic value of air pollution-related effects using a 

“damage-function” This approach quantifies counts of air pollution-attributable cases of adverse 

health outcomes and assigns a dollar values to those counts, while assuming that each outcome is 

independent of one another. We construct this damage function by adapting primary research—

specifically, air pollution epidemiology studies and economic value studies—from similar 

contexts. This approach is sometimes referred to as “benefits transfer.” Below we describe the 

procedure we follow for: (1) selecting air pollution health endpoints to quantify; (2) calculating 

counts of air pollution effects using a health impact function; (3) specifying the health impact 

function with concentration-response parameters drawn from the epidemiological literature. 

4.4.2.1 Selecting air pollution health endpoints to quantify

As a first step in quantifying PM2.5 and ozone-related human health impacts, the Agency 

consults the Integrated Science Assessment for Particulate Matter (PM ISA) (U.S. EPA 2009)

and the Integrated Science Assessment for Ozone and Related Photochemical Oxidants (Ozone 

ISA) (U.S. EPA 2013a). These two documents synthesize 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 causal. 

In brief, the ISA for PM2.5 found acute exposure to PM2.5 to be causally related to 

cardiovascular effects and mortality (i.e., premature death), and respiratory effects as likely-tobe-causally related. The ISA identified cardiovascular effects and total mortality as being 

causally related to long-term exposure to PM2.5 and respiratory effects as likely-to-be-causal; the 

ISA indicated reproductive, developmental, cancer, mutagenicity and genotoxicity outcomes as 
4-17

being suggestive. The ISA for ozone found acute exposure to ozone to be causally related to 

respiratory effects, a likely-to-be-causal relationship with cardiovascular effects and total 

mortality and a suggestive relationship for neurological outcomes. Among chronic effects, the 

ISA reported a likely-to-be-causal relationship for respiratory outcomes and respiratory 

mortality, and suggestive relationship for cardiovascular outcomes and reproductive effects. The 

ISA reported a suggestive relationship for reproductive and neurological effects, and inadequate 

evidence to determine a relationship for cancer. 

The Agency estimates counts of air pollution effects for those endpoints above classified 

as either causal or likely-to-be-causal. Table 4-4 reports the effects we quantified and those we 

did not quantify in this RIA. The list of benefit categories not quantified is not exhaustive; 

effects identified as being quantified may not have been quantified completely. The table below 

omits health effects associated with SO2, NO2, and mercury, and any welfare effects such as 

acidification and nutrient enrichment; these effects are described in Chapters 5 and 6 of the PM 

NAAQS RIA (U.S. EPA 2012b) and summarized later in this chapter. 
4-18

Table 4-4 Human Health Effects of Ambient PM2.5 and Ozone

Category Effect Effect 

Quantified

Effect 

Monetized

More 

Information

Premature mortality 

from exposure to 

PM2.5

Adult premature mortality based on cohort study 

estimates and expert elicitation estimates (age >25 

or age >30)

✓ ✓ PM ISA

Infant mortality (age <1) ✓ ✓ PM ISA

Morbidity from 

exposure to PM2.5

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

Hospital admissions—respiratory (all ages) ✓ ✓ PM ISA

Hospital admissions—cardiovascular (age >20) ✓ ✓ PM ISA

Emergency room visits for asthma (all ages) ✓ ✓ PM ISA

Acute bronchitis (age 8-12) ✓ ✓ PM ISA

Lower respiratory symptoms (age 7-14) ✓ ✓ PM ISA

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

Exacerbated asthma (asthmatics age 6-18) ✓ ✓ PM ISA

Lost work days (age 18-65) ✓ ✓ PM ISA

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

Chronic Bronchitis (age >26) — — PM ISA1

Emergency room visits for cardiovascular effects 

(all ages) — — PM ISA1

Strokes and cerebrovascular disease (age 50-79) — — PM ISA1

Other cardiovascular effects (e.g., other ages) — — PM ISA2

Other respiratory effects (e.g., pulmonary function, 

non-asthma ER visits, non-bronchitis chronic 

diseases, other ages and populations)

— — PM ISA2

Reproductive and developmental effects (e.g., low 

birth weight, pre-term births, etc.) — — PM ISA2,3

Cancer, mutagenicity, and genotoxicity effects — — PM ISA2,3

Mortality from 

exposure to ozone

Premature mortality based on short-term study 

estimates (all ages) ✓ ✓ Ozone ISA

Premature mortality based on long-term study 

estimates (age 30–99)

✓ ✓ Ozone ISA1

Morbidity from 

exposure to ozone

Hospital admissions—respiratory causes (age > 65) ✓ ✓ Ozone ISA

Emergency department visits for asthma (all ages) ✓ ✓ Ozone ISA

Exacerbated asthma (asthmatics age 6-18) ✓ ✓ Ozone ISA

Minor restricted-activity days (age 18–65) ✓ ✓ Ozone ISA

School absence days (age 5–17) ✓ ✓ Ozone ISA

Decreased outdoor worker productivity (age 18–65) — — Ozone ISA1

Other respiratory effects (e.g., premature aging of 

lungs) — — Ozone ISA2

Cardiovascular and nervous system effects — — Ozone ISA2

Reproductive and developmental effects — — Ozone ISA2,3

1 We assess these co-benefits qualitatively due to data and resource limitations for this analysis. In other analyses we quantified 

these effects as a sensitivity analysis.

2 We assess these co-benefits qualitatively because we do not have sufficient confidence in available data or methods.

3 We assess these co-benefits qualitatively because current evidence is only suggestive of causality or there are other significant 

concerns over the strength of the association.

4.4.2.2 Calculating counts of air pollution effects using the health impact function

We use the environmental Benefits Mapping and Analysis Program—Community 

Edition (BenMAP-CE) software program to quantify counts of premature deaths and illnesses 
4-19

attributable to photochemical modeled changes in annual mean PM2.5 and summer season 

average ozone concentrations for the years 2025, 2030 and 2035 using a health impact function 

(Fann et al. 2017; Hubbell et al. 2005). A health impact function combines information regarding 

the: concentration-response relationship between air quality changes and the risk of a given 

adverse outcome; population exposed to the air quality change; baseline rate of death or disease 

in that population; and, level of air pollution to which the population is exposed. 

In the example below, we estimate counts of PM2.5-related total deaths (yij) during each 

year i (i=2025) among adults aged 30 and older (a) in each county in the contiguous U.S. j 

(j=1,...,J where J is the total number of counties) as

yij= Σa yija

yija = moija ×(eβ∙C

ij-1) × Pija, Eq[1]

where moija is the baseline all-cause mortality rate for adults aged a=30-99 in county j in year i 

stratified in 10-year age groups, β is the risk coefficient for all-cause mortality for adults 

associated with PM2.5 exposure, Cij is annual mean PM2.5 concentration in county j in year i, and 

Pija is the number of county adult residents aged a=30-99 in county j in year i stratified into 5-

year age groups.21

The BenMAP-CE tool is pre-loaded with: projected population; projected death rates; 

recent-year baseline rates of hospital admissions, emergency department visits and other 

morbidity outcomes; concentration-response parameters; and, economic unit values for each 

endpoint. PM2.5 (and ozone) concentrations are taken from the air pollution spatial surfaces 

described above in section 4.4.1. Beginning with this RIA, the Agency updated a number of 

population and baseline incidence input parameters with more recent data. For example, we 

replaced the baseline rates of age- and cause-stratified death from the Centers for Disease 

Control and Prevention (CDC) with more recent CDC-supplied data. These data are documented 

in the appendices to the BenMAP-CE user manual (U.S. EPA 2018c). A memo detailing the 

 

21 In this illustrative example, the air quality is resolved at the county level. In this analysis, the air quality model 

predicts air pollutant concentrations at a 12km by 12km grid. The BenMAP-CE tool assigns the rates of baseline 

death and disease stored at the county level to the 12km by 12km grid cells using an area-weighted algorithm. This 

approach is described in greater detail in the appendices to the BenMAP-CE user manual appendices. 
4-20

Agency’s quality assurance procedures for evaluating these new data and the results of an 

analysis characterizing the sensitivity of estimated health impacts to using these new input 

parameters may be found on the BenMAP-CE website (U.S. EPA 2018b).

This health impact assessment quantifies outcomes using a suite of concentrationresponse parameters described in the PM NAAQS RIA (U.S. EPA 2012b) and Ozone NAAQS 

RIA (U.S. EPA 2015e). These two RIAs describe in detail our rationale for selecting air 

pollution-related health endpoints, the source of the epidemiological evidence, the specific 

concentration-response parameters applied, and our approach for pooling evidence across 

epidemiological studies. Given both the severity of air pollution-related mortality and its large 

economic value, below we describe the source of the concentration-response parameters.

4.4.2.3 Quantifying Cases of PM2.5-Attributable Premature Death

For adult PM-related mortality, we use the effect coefficients from the most recent 

epidemiology studies examining two large population cohorts: the American Cancer Society 

cohort (Krewski et al. 2009) and the Harvard Six Cities cohort (Lepeule et al. 2012). The 

Integrated Science Assessment for Particulate Matter (PM ISA) (U.S. EPA 2009) concluded that 

the ACS and Six Cities cohorts produce the strongest evidence of the association between longterm PM2.5 exposure and premature mortality with support from additional cohort studies. The 

SAB's Health Effects Subcommittee (SAB-HES) also supported using these two cohorts for 

analyses of the benefits of PM reductions (U.S. EPA-SAB 2010). As both the ACS and Six 

Cities cohort studies exhibit both strengths and weaknesses, we present PM2.5 related effects 

derived using relative risk estimates from both cohorts.

The PM ISA, which was twice reviewed by the Clean Air Scientific Advisory Committee 

of EPA’s Science Advisory Board (SAB-CASAC) (EPA-SAB 2008a, 2009), concluded that 

there is a causal relationship between mortality and both long-term and short-term exposure to 

PM2.5 based on the entire body of scientific evidence. The PM ISA also concluded that the 

scientific literature supports the use of a no-threshold log-linear model to portray the PMmortality concentration-response relationship while recognizing potential uncertainty about the 

exact shape of the concentration-response function. The PM ISA, which informed the setting of 

the 2012 PM NAAQS, reviewed available studies that examined the potential for a population-
4-21

level threshold to exist in the concentration-response relationship. Based on such studies, the ISA 

concluded that the evidence supports the use of a “no-threshold” model and that “little evidence 

was observed to suggest that a threshold exists” (U.S. EPA 2009) (pp. 2-25 to 2-26). Consistent 

with this evidence, the Agency historically has estimated health impacts above and below the 

prevailing NAAQS (U.S. EPA 2010b, 2010c, 2015c, 2015a, 2015d, 2015b, 2016c, 2011c, 2011b, 

2012a, 2013b, 2014a, 2014c, 2014b, 2015e).

22

Following this approach, we report the forgone PM2.5 and ozone-related benefits (in 

terms of both health impacts and monetized values) for the four illustrative scenarios and for the 

years 2025, 2030 and 2035, where the PM2.5-related forgone benefits are calculated using a loglinear concentration-response function that quantifies risk from the full range of PM2.5 exposures 

(NRC 2002; U.S. EPA 2009). When setting the 2012 PM NAAQS, the Administrator also 

acknowledged greater uncertainty in specifying the “magnitude and significance” of PM-related 

health risks at PM concentrations below the NAAQS. As noted in the preamble to the 2012 PM 

NAAQS final rule, “EPA concludes that it is not appropriate to place as much confidence in the 

magnitude and significance of the associations over the lower percentiles of the distribution in 

each study as at and around the long-term mean concentration.” (78 FR 3154, 15 January 2013).

The preamble separately noted that “[a]s both the EPA and CASAC recognize, in the absence of 

a discernible threshold, health effects may occur over the full range of concentrations observed 

in the epidemiological studies.” (78 FR 3149, 15 January 2013). In general, we are more 

confident in the size of the risks we estimate from simulated PM2.5 concentrations that coincide 

with the bulk of the observed PM concentrations in the epidemiological studies that are used to 

estimate the benefits. Likewise, we are less confident in the risk we estimate from simulated 

PM2.5 concentrations that fall below the bulk of the observed data in these studies.23 To give 

 

22 The Federal Reference Notice for the 2012 PM NAAQS notes that “[i]n reaching her final decision on the

appropriate annual standard level to set, the Administrator is mindful that the CAA does not require that primary

standards be set at a zero-risk level, but rather at a level that reduces risk sufficiently so as to protect public health,

including the health of at-risk populations, with an adequate margin of safety. On balance, the Administrator

concludes that an annual standard level of 12 mg/m3 would be requisite to protect the public health with an

adequate margin of safety from effects associated with long- and short-term PM2.5 exposures, while still

recognizing that uncertainties remain in the scientific information.”

23 The Federal Register Notice for the 2012 PM NAAQS indicates that “[i]n considering this additional population 

level information, the Administrator recognizes that, in general, the confidence in the magnitude and significance of 

an association identified in a study is strongest at and around the long-term mean concentration for the air quality 
4-22

insight to the level of uncertainty in the estimated forgone PM2.5 mortality benefits at lower 

ambient levels, we report the PM benefits according to alternative concentration cut-points. 

Below we further describe our rationale for selecting these cut-points and report a suite of 

sensitivity analyses. In addition to adult mortality discussed in above, we use effect coefficients 

from a multi-city study to estimate PM-related infant mortality (Woodruff et al. 1997).

4.4.2.4 Quantifying Cases of Ozone-Attributable Premature Death

In 2008, the National Academies of Science (NRC 2008) issued a series of 

recommendations to EPA regarding the procedure for quantifying and valuing ozone-related 

short-term mortality. Chief among these was that “...short-term exposure to ambient ozone is 

likely to contribute to premature deaths” and the committee recommended that “ozone-related 

mortality be included in future estimates of the health benefits of reducing ozone exposures...” 

The NAS also recommended that “...the greatest emphasis be placed on the multicity and 

[National Mortality and Morbidity Air Pollution Studies (NMMAPS)] ...studies without 

exclusion of the meta-analyses” (NRC 2008). Prior to the 2015 Ozone NAAQS RIA, the Agency 

estimated ozone-attributable premature deaths using an NMMAPS-based analysis (Bell et al. 

2004), two multi-city studies (Huang et al. 2004; Schwartz 2005) and effect estimates from the 

three meta-analyses (Bell et al. 2005; Ito et al. 2005; Levy et al. 2005). Beginning with the 2015 

Ozone NAAQS RIA, the Agency began quantifying ozone-attributable premature deaths using 

two newer multi-city studies (Smith et al. 2009; Zanobetti and Schwartz 2008) and one longterm cohort study (Jerrett et al. 2009). We report the ozone-attributable deaths in this RIA as a 

range reflecting the concentration-response parameters from these two studies. 

4.4.3 Economic Value of Forgone Ancillary Health Co-benefits

We next quantify the economic value of the PM2.5 and ozone-related deaths and illnesses 

estimated above. Changing ambient concentrations of air pollution generally yields a small 

change in the risk of future adverse health effects for a large number of people. Therefore, 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 not generally available, 

 

distribution, as this represents the part of the distribution in which the data in any given study are generally most 

concentrated. She also recognizes that the degree of confidence decreases as one moves towards the lower part of 

the distribution” 
4-23

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 unit values applied in this 

analysis are provided in Table 5-9 of the PM NAAQS RIA for each health endpoint (U.S. EPA 

2012b).

The value of avoided premature deaths account for 98 percent of ancillary monetized 

PM-related co-benefits and over 90 percent of monetized ozone-related co-benefits. The 

economics literature concerning the appropriate method for valuing reductions in premature 

mortality risk is still developing. The value for the projected reduction in the risk of premature 

mortality 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, because we believe this calculation provides the most reasonable 

single estimate of an individual’s willingness to trade off money for changes in the risk of death 

(U.S. EPA-SAB 2000). The VSL approach is a summary measure for the value of small changes 

in the risk of death experienced by a large number of people.

EPA continues work to update its guidance on valuing mortality risk reductions, and the 

Agency consulted several times with the SAB-EEAC on this issue. Until updated guidance is 

available, the Agency determined that a single, peer-reviewed estimate applied consistently, best 

reflects the SAB-EEAC advice it has received. Therefore, EPA applies the VSL that was vetted 

and endorsed by the SAB in the Guidelines for Preparing Economic Analyses (U.S. EPA 2016a)

while the Agency continues its efforts to update its guidance on this issue. This approach 

calculates a mean value across VSL estimates derived from 26 labor market and contingent 

valuation studies published between 1974 and 1991. The mean VSL across these studies is $6.3 

million (2000$).24 We then adjust this VSL to account for the currency year and to account for 

income growth from 1990 to the analysis year. Specifically, the VSLs applied in this analysis in 

2016$ after adjusting for income growth is $10.5 million for 2025.

 

24 In 1990$, this base VSL is $4.8 million.
4-24

The Agency 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” (U.S. 

EPA 2010d), which were subsequently reviewed by the SAB-EEAC. EPA is taking the SAB’s 

formal recommendations under advisement (U.S. EPA 2017).

In valuing PM2.5-related premature mortality, we discount the value of premature 

mortality occurring in future years using rates of 3 percent and 7 percent (U.S. Office of 

Management and Budget 2003). We assume that there is a multi-year “cessation” lag between 

changes in PM exposures and the total realization of changes in health effects. Although the 

structure of the lag is uncertain, EPA follows the advice of the SAB-HES to use a segmented lag 

structure that assumes 30 percent of premature deaths are reduced in the first year, 50 percent 

over years 2 to 5, and 20 percent over the years 6 to 20 after the reduction in PM2.5 (U.S. EPASAB 2004). Changes in the cessation lag assumptions do not change the total number of 

estimated deaths but rather the timing of those deaths. Because short-term ozone-related 

premature mortality occurs within the analysis year, the estimated ozone-related co-benefits are 

identical for all discount rates.

4.4.4 Characterizing Uncertainty in the Estimated Forgone Benefits

This analysis includes many data sources as inputs that are each subject to uncertainty. 

Input parameters include emission 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 co-benefits, and assumptions 

regarding the future state of the world (i.e., regulations, technology, and human behavior). When 

compounded, even small uncertainties can greatly influence the size of the total quantified 

benefits. 

Our estimate of the total monetized co-benefits is based on EPA’s interpretation of the 

best available scientific literature and methods and supported by the SAB-HES and the National 

Academies of Science (NRC 2002). Below are key assumptions underlying the estimates for 

PM2.5-related premature mortality. 
4-25

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 concluded that “many 

constituents of PM2.5 can be linked with multiple health effects, and the evidence is not yet 

sufficient to allow differentiation of those constituents or sources that are more closely related to 

specific outcomes” (U.S. EPA 2009)

We assume that the health impact function for fine particles is log-linear without a 

threshold. Thus, the estimates include health co-benefits from reducing fine particles in areas 

with varied concentrations of PM2.5, including both areas that do not meet the fine particle 

standard and those areas that are in attainment, down to the lowest modeled concentrations. 

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 (U.S. EPA-SAB 2004), 

which affects the valuation of mortality co-benefits at different discount rates. Each of the above 

assumptions are subject to uncertainty. 

In general, we are more confident in the magnitude of the risks we estimate from 

simulated PM2.5 concentrations that coincide with the bulk of the observed PM concentrations in 

the epidemiological studies that are used to estimate the benefits. Likewise, we are less confident 

in the risk we estimate from simulated PM2.5 concentrations that fall below the bulk of the 

observed data in these studies. There are uncertainties inherent in identifying any particular point 

at which our confidence in reported associations decreases appreciably, and the scientific 

evidence provides no clear dividing line. This relationship between the air quality data and our 

confidence in the estimated risk is represented below (Figure 4-1).
4-26

More confident

Mean of PM2.5 data in epidemiology study

1 standard deviation below the mean PM2.5

data in epidemiology study

Below LML of PM2.5 data in epidemiology 

study (extrapolation)

Less confident

Figure 4-1 Relationship between the PM2.5 Concentrations Considered in Epidemiology 

Studies and our Confidence in the Estimated PM-related Premature Deaths

In this analysis, we build upon the concentration benchmark approach (also referred to as 

the Lowest Measured Level analysis) that has been featured in recent RIAs and EPA’s Policy 

Assessment for Particulate Matter (U.S. EPA 2011a) by reporting the estimated PM-related 

deaths according to alternative concentration cutpoints.

Concentration benchmark analyses allow readers to determine the portion of population 

exposed to annual mean PM2.5 levels at or above different concentrations, which provides some 

insight into the level of uncertainty in the estimated PM2.5 mortality benefits., EPA does not view 

these concentration benchmarks as concentration thresholds below which we would not quantify 

health co-benefits of air quality improvements.25 Rather, the co-benefits estimates reported in this 

RIA are the most appropriate estimates because they reflect the full range of air quality 

concentrations associated with the emission reduction strategies being evaluated in this proposal. 

The PM ISA concluded that the scientific evidence collectively is sufficient to conclude that the 

relationship between long-term PM2.5 exposures and mortality is causal and that overall the 

 

25 For a summary of the scientific review statements regarding the lack of a threshold in the PM2.5-mortality 

relationship, see the TSD entitled Summary of Expert Opinions on the Existence of a Threshold in the 

Concentration-Response Function for PM2.5-related Mortality (U.S. EPA, 2010b).
4-27

studies support the use of a no-threshold log-linear model to estimate PM-related long-term 

mortality (U.S. EPA 2009). 

Figure 4-2 and Figure 4-3 report the percentage of the population, and number of PMrelated deaths, both above and below concentration benchmarks in the proposed policy modeling 

for the year 2030. Both figures identify the LML for each of the major cohort studies in orange 

and the annual mean PM2.5 NAAQS of 12 μg/m3 in red. For Krewski, the LML is 5.8 μg/m3 and

for Lepeule et al., the LML is 8 μg/m3. These results are sensitive to the annual mean PM2.5

concentration the air quality model predicted in each 12km by 12km grid cell (see section 4.4.1). 

The air quality modeling predicts PM2.5 concentrations to be at or below 12 μg/m3in nearly all 

locations. The photochemical modeling we employ accounts for the suite of local, state and 

federal policies expected to reduce PM2.5 and PM2.5 precursor emissions in future years, such that 

we project a very small number of locations exceeding the annual standard. After presenting the 

full suite of results below (Table 4-5; Table 4-6;Table 4-7) we stratify these estimated PM 

mortality deaths according to the concentration at which they occurred: below the LML, between 

the LML and the NAAQS and above the NAAQS in future years across different policy 

scenarios (Table 4-12). The results above should be viewed in the context of the air quality 

modeling technique we used to estimate PM2.5 concentrations. As described in Chapter 8, we are 

more confident in our ability to use the air quality modeling technique described above to 

estimate changes in annual mean PM2.5 concentrations than we are in our ability to estimate 

absolute PM2.5 levels.
4-28

Figure 4-2 Number of Individuals Exposed According to Annual Mean PM2.5

Concentration in 2030
4-29

Figure 4-3 Number of PM2.5-Related Premature Deaths According to PM2.5

Concentration in 2030

4.5 Air Quality and Health Impact Results

4.5.1 Air Quality Results

Below we present the model-predicted change in annual mean PM2.5 concentrations and 

summer-season average daily 8hr maximum concentrations for the 2 percent HRI at $50/kW, 4.5 

percent HRI at $50/kW, 4.5 percent HRI at $100/kW, and No CPP illustrative scenarios (Figure 

4-4). All maps display the change in air pollution calculated as the policy case minus the base 

case. These spatial fields serve as an input to the benefits analysis, the results of which are 

described further below.
4-30

Annual Mean PM2.5 Summer Season Average Ozone

2% HRI at $50/kW 

Illustrative Policy Scenaro

4.5% HRI at $50/kW 

Illustrative Policy 

Scenario

4.5% HRI at $100/kW 

Illustrative Policy Scenario

No CPP

Figure 4-4 Change in Annual Mean PM2.5 (μg/m3) and Summer Season Average Daily 

8hr Maximum Ozone (ppb) in 2025 (Difference Calculated as Illustrative 

Scenario - Base Case)
4-31

4.5.2 Estimated Number and Economic Value of Forgone Ancillary Health CoBenefits 

Below we report the estimated number of forgone PM2.5 and ozone-related premature 

deaths and illnesses in each year and for each year and illustrative scenario (Table 4-5, Table 4-6, 

Table 4-7), relative to the base case, which include the CPP. These tables are followed by the 

estimated number of forgone PM2.5-related premature deaths calculated using different 

approaches to evaluate uncertainty of the effect of PM2.5 concentrations at lower ambient levels 

(Table 4-8). We summarize the dollar value of these impacts for each policy scenario across all 

PM2.5 and ozone-related premature deaths and illnesses, using four alternative approaches to 

representing and quantifying PM mortality risk effects (Table 4-9, Table 4-10, and Table 4-11). 

The alternative approaches to quantifying and presenting mortality risk effects include both 

different means for quantifying expected impacts using concentration-response functions over 

the entire domain of exposure (i.e., the no-threshold model) along with different means of 

presenting impacts by limiting consideration to only those impacts at exposures above the LML 

or above the NAAQS. Finally, we display the spatial distribution of the sum of the estimated 

forgone PM2.5 and ozone-attributable deaths in each year Figure 4-5.
4-32

Table 4-5 Estimated Incremental PM2.5 and Ozone-Related Premature Deaths and Illnesses in 2025*

No CPP 2% HRI at $50/kW 4.5% HRI at $50/kW 4.5% HRI at $100/kW

Changes in premature death among adults PM2.5

Krewski et al. (2009) 280

(190 to 370)

260

(170 to 340)

280

(19 to 370)

220

(150 to 300)

Lepeule et al. (2012) 640

(320 to 960)

590

(290 to 890)

630

(310 to 950)

510

(250 to 760)

Ozone

Smith et al. (2009) 11

(6 to 17)

5

(3 to 8)

3

(2 to 5)

-2

(-1 to -3)

Jerrett et al. (2009) 41

(14 to 68)

20

(7 to 33)

12

(4 to 20)

-6.5

(-2 to -11)

PM2.5- related non-fatal heart attacks among adults

Peters et al. (2001) 290

(71 to 510)

270

(66 to 470)

290

(71 to 510)

230

(57 to 410)

Pooled estimate 32

(12 to 85)

29

(11 to 78)

31

(12 to 84)

25

(9 to 68)

All other morbidity effects 

Hospital admissions—

cardiovascular (PM2.5)

72

(32 to 130)

67

(29 to 120)

72

(31 to 130)

58

(25 to 110)

Hospital admissions—

respiratory (PM2.5 & O3)

90

(27 to 170)

75

(27 to 140)

77

(30 to 140)

55

(26 to 100)

ED visits for asthma 

(PM2.5 & O3)

190

(-44 to 440)

150

(-44 to 330)

140

(-48 to 310)

84

(-97 to 220)

Exacerbated asthma 

(PM2.5 & O3)

23,000

(-15,000 to 56,000)

12,000

(-5,600 to 29,000)

8,600

(-2,200 to 20,000)

-2,200

(-18,000 to 18,000)

Minor restricted-activity 

days (PM2.5 & O3)

210,000

(150,000 to 260,000)

170,000

(130,000 to 210,000)

170,000

(140,000 to 210,000)

120,000

(100,000 to 130,000)

Acute bronchitis 

(PM2.5)

310

(-73 to 700)

290

(-68 to 650)

320

(-75 to 710)

250

(-59 to 560)

Upper resp. symptoms 

(PM2.5)

5,600

(1,000 to 10,000)

5,300

(960 to 9,600)

5,700

(1,000 to 10,000)

4,600

(830 to 8,300)

Lower resp. symptoms 

(PM2.5)

4,000

(1,500 to 6,400)

3,700

(1,400 to 6,000)

4,000

(1,500 to 6,500)

3,200

(1,200 to 5,200)

Lost work days 

(PM2.5)

28,000

(24,000 to 33,000)

26,000

(22,000 to 30,000)

29,000

(24,000 to 33,000)

23,000

(19,000 to 26,000)

School absence days 

(O3)

12,000

(4,400 to 28,000)

4,500

(1,600 to 10,000)

1,500

(540 to 3,400)

-5,400

(-12,000 to -1,900)

* Values rounded to two significant figures 
4-33

Table 4-6 Estimated Incremental PM2.5 and Ozone-Related Premature Deaths and Illnesses in 2030*

No CPP 2% HRI at $50/kW 4.5% HRI at $50/kW 4.5% HRI at $100/kW

Changes in premature death among adults PM2.5

Krewski et al. (2009) 470

(320 to 630)

410

(280 to 550)

410

(280 to 550)

350

(240 to 470)

Lepeule et al. (2012)

1,100

(540 to 1,600)

940

(470 to 1,400)

940

(470 to 1,400)

800

(400 to 1,200)

Ozone

Smith et al. (2009) 24

(12 to 36)

38

(19 to 57)

16

(8 to 25)

12

(6 to 18)

Jerrett et al. (2009) 86

(29 to 140)

140

(47 to 230)

59

(20 to 98)

43

(14 to 71)

PM2.5- related non-fatal heart attacks among adults

Peters et al. (2001) 490

(120 to 860)

430

(100 to 750)

430

(110 to 760)

360

(89 to 640)

Pooled estimate 53

(20 to 140)

46

(17 to 120)

47

(17 to 120)

39

(15 to 110)

All other morbidity effects

Hospital admissions—

cardiovascular (PM2.5)

120

(53 to 230)

110

(46 to 200)

110

(47 to 200)

91

(40 to 170)

Hospital admissions—

respiratory (PM2.5 & O3)

130

(210 to 250)

110

(26 to 210)

140

(35 to 280)

87

(24 to 170)

ED visits for asthma 

(PM2.5 & O3)

250

(-50 to 620)

210

(-37 to 530)

280

(-51 to 690)

170

(-34 to 410)

Exacerbated asthma 

(PM2.5 & O3)

44,000

(-31,000 to 110,000)

40,000

(-29,000 to 96,000)

48,000

(-34,000 to 120,000)

29,000

(-20,000 to 69,000)

Minor restricted-activity 

days (PM2.5 & O3)

290,000

(200,000 to 370,000)

230,000

(160,000 to 310,000)

300,000

(210,000 to 390,000)

190,000

(140,000 to 250,000)

Acute bronchitis 

(PM2.5)

570

(-130 to 1,300)

500

(-120 to 1,100)

500

(-120 to 1,100)

420

(-99 to 940)

Upper resp. symptoms 

(PM2.5)

10,000

(1,900 to 19,000)

9,000

(1,600 to 16,000)

9,000

(1,600 to 16,000)

7,700

(1,400 to 14,000)

Lower resp. symptoms 

(PM2.5)

7,200

(2,800 to 12,000)

6,300

(2,400 to 10,000)

6,300

(2,400 to 10,000)

5,400

(2,000 to 8,700)

Lost work days 

(PM2.5)

48,000

(40,000 to 55,000)

42,000

(35,000 to 48,000)

42,000

(35,000 to 48,000)

35,000

(30,000 to 41,000)

School absence days 

(O3)

31,000

(11,000 to 71,000)

60,000

(22,000 to 140,000)

21,000

(7,700 to 48,000)

16,000

(5,600 to 35,000)

* Values rounded to two significant figures 
4-34

Table 4-7 Estimated Incremental PM2.5 and Ozone-Related Premature Deaths and Illnesses in 2035*

No CPP 2% HRI at $50/kW 4.5% HRI at $50/kW 4.5% HRI at $100/kW

Changes in premature death among adults PM2.5

Krewski et al. (2009) 370

(250 to 490)

290

(190 to 380)

380

(260 to 510)

250

(170 to 330)

Lepeule et al. (2012) 830

(420 to 1,200)

650

(330 to 980)

870

(440 to 1,300)

570

(280 to 850)

Ozone

Smith et al. (2009) 19

(9 to 28)

18

(9 to 27)

22

(11 to 33)

12

(6 to 19)

Jerrett et al. (2009) 68

(23 to 110)

65

(22 to 110)

81

(27 to 130)

45

(15 to 75)

Non-fatal heart attacks among adults

Peters et al. (2001) 390

(95 to 680)

310

(75 to 540)

410

(100 to 720)

270

(65 to 470)

Pooled estimate 42

(15 to 110)

33

(12 to 89)

44

(16 to 120)

29

(11 to 77)

All other morbidity effects

Hospital admissions—

cardiovascular (PM2.5)

97

(42 to 180)

77

(33 to 140)

100

(44 to 190)

67

(29 to 120)

Hospital admissions—

respiratory (PM2.5 & O3)

130

(-8 to 250)

110

(26 to 210)

140

(35 to 280)

87

(24 to 170)

ED visits for asthma 

(PM2.5 & O3)

250

(-50 to 620)

210

(-37 to 530)

280

(-51 to 690)

170

(-34 to 410)

Exacerbated asthma 

(PM2.5 & O3)

44,000

(-31,000 to 110,000)

40,000

(-29,000 to 96,000)

48,000

(-34,000 to 120,000)

29,000

(-20,000 to 69,000)

Minor restricted-activity 

days (PM2.5 & O3)

290,000

(200,000 to 370,000)

230,000

(160,000 to 310,000)

300,000

(210,000 to 390,000)

190,000

(140,000 to 250,000)

Acute bronchitis 

(PM2.5)

430

(-100 to 960)

330

(-78 to 740)

440

(-100 to 980)

290

(-69 to 650)

Upper resp. symptoms 

(PM2.5)

7,800

(1,400 to 14,000)

6,100

(1,100 to 11,000)

8,000

(1,500 to 15,000)

5,300

(960 to 9,700)

Lower resp. symptoms 

(PM2.5)

5,500

(2,100 to 8,900)

4,200

(1,600 to 6,900)

5,600

(2,100 to 9,100)

3,700

(1,400 to 6,000)

Lost work days 

(PM2.5)

36,000

(31,000 to 42,000)

28,000

(24,000 to 32,000)

37,000

(31,000 to 43,000)

24,000

(21,000 to 28,000)

School absence days 

(O3)

16,000

(5,900 to 37,000)

24,000

(8,600 to 54,000)

29,000

(10,000 to 64,000)

16,000

(5,900 to 37,000)

* Values rounded to two significant figures
4-35

Table 4-8 PM-Related Premature Deaths Estimated Using Alternative Approaches to Evaluate Uncertainty at LowConcentations (95% Confidence Interval), Relative to Base Case (CPP)*

No CPP 2% HRI

at $50/kW

4.5% HRI

at $50/kW

4.5% HRI

at $100/kW

2025

Log-Linear no-threshold model

Krewski et al. (2009) 280

(190 to 370)

260

(170 to 340)

280

(190 to 370)

220

(150 to 300)

Lepeule et al. (2012) 640

(320 to 960)

590

(290 to 890)

630

(310 to 950)

510

(250 to 760)

Assuming PM effects below the LML of each study fall to zero 

Krewski et al. (2009) 

(LML= 5.8 μg/m3)

240

(160 to 310)

220

(150 to 290)

230

(160 to 310)

190

(130 to 250)

Lepeule et al. (2012) 

(LML=8μg/m3)

140

(67 to 200)

130

(64 to 190)

150

(74 to 220)

110

(57 to 170)

2030

Log-Linear no-threshold model

Krewski et al. (2009) 470

(320 to 630)

410

(280 to 550)

410

(280 to 550)

350

(240 to 470)

Lepeule et al. (2012) 1,100

(540 to 1,600)

940

(470 to 1,400)

940

(470 to 1,400)

800

(400 to 1,200)

Assuming PM effects below the LML of each study fall to zero 

Krewski et al. (2009) 

(LML= 5.8 μg/m3)

400

(270 to 530)

350

(240 to 460)

350

(240 to 460)

300

(200 to 390)

Lepeule et al. (2012) 

(LML=8μg/m3)

260

(130 to 380)

220

(110 to 330)

220

(110 to 330)

180

(92 to 280)

2035

Log-Linear no-threshold model

Krewski et al. (2009) 370

(250 to 490)

290

(190 to 380)

380

(260 to 510)

250

(170 to 330)

Lepeule et al. (2012) 830

(420 to 1,200)

650

(330 to 980)

870

(440 to 1,300)

570

(280 to 850)

Assuming PM effects below the LML of each study fall to zero 

Krewski et al. (2009) 

(LML= 5.8 μg/m3)

320

(220 to 420)

250

(170 to 330)

330

(220 to 440)

220

(150 to 290)

Lepeule et al. (2012) 

(LML=8μg/m3)

220

(110 to 330)

170

(83 to 250)

210

(110 to 320)

150

(73 to 220)

* Values rounded to two significant figures
4-36

Table 4-9 Estimated Economic Value of Incremental PM2.5 and Ozone-Attributable Deaths and Illnesses for Illustrative 

Scenarios & Three Alternative Approaches to Representing PM Effects in 2025, Relative to Base Case (CPP)

(95% Confidence Interval; Billions of 2016$)A

No CPP 2% HRI at $50/kW 4.5% HRI at $50/kW 4.5% HRI at $100/kW

Ozone benefits summed with PM benefits: 3% Discount Rate

No-threshold 

modelB

$2.8

($0.3 to 

$7.7)

to

$6.6

($0.6 to 

$19)

$2.6

($0.3 to 

$7)

to

$5.9

($0.5 to 

$17)

$2.7

($0.3 to 

$7.4)

to

$6.2

($0.6 to 

$18)

$2.1

($0.2 to 

$5.9)

to

$4.9

($0.2 to 

$14)

Limited to above 

LMLC

$1.8

($0.1 to 

$5.2)

to

$2.4

($0.1 to 

$7)

$1.5

($0.1 to 

$4)

to

$2.2

($0.2 to 

$6)

$1.6

($0.2 to 

$4.6)

to

$2.3

($0.2 to 

$6)

$1.1

(-$.1 to 

$3.3)

to

$1.8

($0.1 to 

$5)

Effects above 

NAAQSD

$0.12

($0 to 

$0.4)

to

$0.4

($0 to 

$1.3)

$0.06

($0 to 

$0.2)

to

$0.21

($0 to 

$0.6)

$0.04

($0 to 

$0.1)

to

$0.12

($0 to 

$0.4)

-$0.07

(-$0.2 to -

$0)

to

-$0.02

(-$0.1 to 

$0)

Ozone benefits summed with PM benefits: 7% Discount Rate

No-threshold 

modelB

$2.6

($0.3 to 

$7)

to

$6.1

($0.6 to 

$17)

$2.4

($0.2 to 

$6.4)

to

$5.4

($0.5 to 

$15)

$2.5

($0.2 to 

$6.7)

to

$5.7

($0.5 to 

$16)

$2

($0.1 to 

$5.4)

to

$4.4

($0.2 to 

$13)

LML modelC $1.7

($0 to $5) to

$2.2

($0.1 to 

$6)

$1.4

($0.1 to 

$4)

to

$2

($0.2 to 

$5)

$1.5

($0.1 to 

$4.2)

to

$2.1

($0.2 to 

$6)

$0.99

(-$0.1 to 

$3)

to

$1.7

($0 to 

$4.6)

Effects above 

NAAQSD

$0.12

($0 to 

$0.4)

to

$0.43

($0 to 

$1.3)

$0.06

($0 to 

$0.2)

to

$0.21

($0 to 

$0.6)

$0.04

($0 to 

$0.1)

to

$0.12

($0 to 

$0.4)

-$0.07

(-$0.2 to -

$0)

to

-$0.02

(-$0.1 to 

$0)

A Values rounded to two significant figures

B PM effects quantified using a no-threshold model. Low end of range reflects dollar value of effects quantified using concentration-response parameter from Krewski et al. 

(2009) and Smith et al. (2008) studies; upper end quantified using parameters from Lepeule et al. (2012) and Jerrett et al. (2009). 

C PM effects quantified at or above the Lowest Measured Level of each long-term epidemiological study. Low end of range reflects dollar value of effects quantified down to 

LML of Lepeule et al. (2012) study (8 μg/m3); high end of range reflects dollar value of effects quantified down to LML of Krewski et al. (2009) study (5.8 μg/m3). 

D PM effects only quantified at or above the annual mean of 12 to provide insight regarding the fraction of benefits occurring above the NAAQS. Range reflects effects 

quantified using concentration-response parameters from Smith et al. (2008) study at the low end and Jerrett et al. (2009) at the high end.
4-37

Table 4-10 Estimated Economic Value of Forgone PM2.5 and Ozone-Attributable Deaths and Illnesses for Illustrative 

Scenarios & Three Alternative Approaches to Representing PM Effects in 2030, Relative to Base Case 

(CPP) (95% Confidence Interval; Billions of 2016$)

A

No CPP 2% HRI at $50/kW 4.5% HRI at $50/kW 4.5% HRI at $100/kW

Ozone benefits summed with PM benefits: 3% Discount Rate

No-threshold 

modelB

$4.9

($0.47 to 

$13)

to

$11

($1 to 

$33)

$4.5

($0.4 to 

$12)

to

$11

($1 to 

$30)

$4.2

($0.4 to 

$11)

to

$9.8

($0.9 to 

$28)

$3.6

($0.34 to 

$9.7)

to

$8.2

($0.8 to 

$24)

LML modelC

$3.5

($0.33 to 

$10)

to

$4.2

($0.4 to 

$11)

$3.7

($0.3 to 

$11)

to

$3.8

($0.4 to 

$10)

$2.9

($0.3 to 

$8.3)

to

$3.6

($0.4 to 

$10)

$2.3

($0.22 to 

$6.7)

to

$3

($0.3 to 

$8)

Effects above 

NAAQSD

$0.26

($0 to 

$0.75)

to

$0.92

($0 to 

$2.7)

$0.43

($0 to 

$1.2)

to

$1.5

($0.1 to 

$4)

$0.18

($0 to $0.5) to

$0.63

($0.1 to 

$1.9)

$0.13

($0 to $0.4) to

$0.46

($0 to 

$1.4)

Ozone benefits summed with PM benefits: 7% Discount Rate

No-threshold 

modelB

$4.5

($0.43 to 

$12)

to

$10

($1 to 

$30)

$4.1

($0.4 to 

$11)

to

$9.8

($0.9 to 

$28)

$3.9

($0.4 to 

$11)

to

$9

($0.8 to 

$26)

$3.3

($0.3 to 

$8.9)

to

$7.6

($0.7 to 

$22)

LML modelC

$3.3

($0.3 to 

$9.4)

to

$3.8

($0.4 to 

$10)

$3.5

($0.3 to 

$9.7)

to

$3.5

($0.3 to 

$10)

$3.3

($0.32 to 

$9)

to

$2.7

($0.3 to 

$7.7)

$2.2

($0.2 to 

$6.2)

to

$2.8

($0.3 to 

$8)

Effects above 

NAAQSD

$0.26

($0 to $0.8) to

$0.92

($0.1 to 

$3)

$0.43

($.04 to 

$1.2)

to

$1.5

($0.1 to 

$4)

$0.18

($0 to $0.5) to

$0.63

($0.1 to 

$2)

$0.13

($0 to $0.4) to

$0.46

($0 to 

$1.4)

A Values rounded to two significant figures

B PM effects quantified using a no-threshold model. Low end of range reflects dollar value of effects quantified using concentration-response parameter from Krewski et al. 

(2009) and Smith et al. (2008) studies; upper end quantified using parameters from Lepeule et al. (2012) and Jerrett et al. (2009). 

C PM effects quantified at or above the Lowest Measured Level of each long-term epidemiological study. Low end of range reflects dollar value of effects quantified down to 

LML of Lepeule et al. (2012) study (8 μg/m3); high end of range reflects dollar value of effects quantified down to LML of Krewski et al. (2009) study (5.8 μg/m3). 

D PM effects only quantified at or above the annual mean of 12 to provide insight regarding the fraction of benefits occurring above the NAAQS. Range reflects effects 

quantified using concentration-response parameters from Smith et al. (2008) study at the low end and Jerrett et al. (2009) at the high end.
4-38

Table 4-11 Estimated Economic Value of Forgone PM2.5 and Ozone-Attributable Deaths and Illnesses for Illustrative 

Scenarios & Three Alternative Approaches to Representing PM Effects in 2035, Relative to Base Case (CPP)

(95% Confidence Interval; Billions of 2016$)A

No CPP 2% HRI at $50/kW 4.5% HRI at $50/kW 4.5% HRI at $100/kW

Ozone benefits summed with PM benefits: 3% Discount Rate

No-threshold 

modelB

$3.8

($0.4 to $10) to

$8.8

($1 to $25)

$3

($0.29 to 

$8.1)

to

$7

($0.6 to 

$20)

$4

($0.4 to 

$11)

to

$9.3

($1 to $27)

$2.6

($0.25 to 

$7)

to

$6

($1 to $17)

LML modelC

$2.9

($0.3 to 

$8.4)

to

$3.3

($0.3 to 

$9)

$2.4

($0.2 to 

$7)

to

$2.6

($0.3 to 

$7)

$3

($0.3 to 

$8.6)

to

$3.5

($0.3 to 

$9)

$2

($0.2 to $6) to

$2.3

($0.2 to 

$6)

Effects above 

NAAQSD

$0.21

($0 to $0.6) to

$0.73

($0.1 to 

$2)

$0.2

($0 to 

$0.6)

to

$0.69

($0.1 to 

$2)

$0.24

($0 to $0.7) to

$0.86

($0.1 to 

$3)

$0.14

($0 to $0.4) to

$0.48

($0 to $1)

Ozone benefits summed with PM benefits: 7% Discount Rate

No-threshold 

modelB

$3.5

($0.3 to $9) to

$8.1

($1 to $23)

$2.7

($0.3 to 

$8)

to

$6.5

($1 to $19)

$3.7

($0.4 to 

$10)

to

$8.6

($1 to $25)

$2.4

($0.2 to $6) to

$5.5

($1 to $16)

LML modelC $2.7

($0.3 to $8) to

$3

($0.3 to 

$8)

$2.2

($0.2 to 

$6.4)

to

$2.4

($0.2 to 

$7)

$2.8

($0.26 to 

$8)

to

$3.2

($0.3 to 

$9)

$1.8

($0.17 to 

$5.3)

to

$2.1

($0.2 to 

$6)

Effects above 

NAAQSD

$0.21

($0 to $0.6) to

$0.73

($0.1 to 

$2)

$0.2

($0 to 

$0.6)

to

$0.69

($0.1 to 

$2)

$0.24

($0 to $0.7) to

$0.86

($0.1 to 

$3)

$0.14

($0 to $0.4) to

$0.48

($0 to $1)

A Values rounded to two significant figures

B PM effects quantified using a no-threshold model. Low end of range reflects dollar value of effects quantified using concentration-response parameter from Krewski et al. 

(2009) and Smith et al. (2008) studies; upper end quantified using parameters from Lepeule et al. (2012) and Jerrett et al. (2009). 

C PM effects quantified at or above the Lowest Measured Level of each long-term epidemiological study. Low end of range reflects dollar value of effects quantified down to 

LML of Lepeule et al. (2012) study (8 μg/m3); high end of range reflects dollar value of effects quantified down to LML of Krewski et al. (2009) study (5.8 μg/m3). 

D PM effects only quantified at or above the annual mean of 12 to provide insight regarding the fraction of benefits occurring above the NAAQS. Range reflects effects 

quantified using concentration-response parameters from Smith et al. (2008) study at the low end and Jerrett et al. (2009) at the high end.
4-39

2% HRI at $50/kW 

Illustrative Policy 

Scenario

4.5% HRI at $100/kW 

Illustrative Policy 

Scenario

4.5% HRI at $50/kW 

Illustrative Policy 

Scenario

No CPP

Figure 4-5 Estimated Forgone Avoided PM2.5 and Ozone Deaths for Each Illustrative 

Scenario in 2025, Relative to Base Case (CPP) (Deaths per 100k People)

-0.56 - -0.21

-0.20 - 0.01

0.02 - 0.06

0.07 - 0.19

0.20 - 0.30

0.31 - 0.42

0.43 - 0.58

0.59 - 0.85

0.86 - 1.39

1.40 - 2.40
4-40

Table 4-12 Estimated Percent of PM2.5-related Premature Deaths Above and Below 

PM2.5 Concentration Cut Points

PM2.5-related premature deaths reported by 

air quality cutpoint

Year Policy option

Epidemiological 

study

Total 

mortality

Above 

NAAQS

Below NAAQS 

and Above LML A Below LML A

2025

No CPP

Krewski 280 <1

(<1%)

240

(84%)

44

(16%)

Lepeule 640 <1

(<1%)

140

(21%)

510

(79%)

2% HRI @ 

$50/kW

Krewski 260 <1

(<1%)

220

(84%)

41

(16%)

Lepeule 590 <1

(<1%)

130

(22%)

460

(78%)

4.5% HRI @ 

$50/kW

Krewski 280 <1

(<1%)

230

(85%)

42

(15%)

Lepeule 630 <1

(<1%)

150

(23%)

480

(77%)

4.5% HRI @ 

$100/kW

Krewski 220 <1

(<1%)

190

(85%)

34

(15%)

Lepeule 510 <1

(<1%)

110

(23%)

390

(77%)

2030

No CPP

Krewski 470 <1

(<1%)

400

(85%)

71

(15%)

Lepeule 1,100 2

(<1%)

240

(22%)

840

(78%)

2% HRI @ 

$50/kW

Krewski 410 0.7

(<1%)

350

(84%)

64

(16%)

Lepeule 940 1.6

(<1%)

210

(22%)

730

(78%)

4.5% HRI @ 

$50/kW

Krewski 410 0.79

(<1%)

350

(84%)

66

(16%)

Lepeule 940 1.8

(<1%)

210

(22%)

740

(78%)

4.5% HRI @ 

$100/kW

Krewski 350 0.5

(<1%)

290

(84%)

57

(16%)

Lepeule 800 1.2

(<1%)

180

(22%)

630

(78%)

2030

No CPP

Krewski 370 1

(<1%)

320

(87%)

48

(13%)

Lepeule 830 2.1

(<1%)

180

(22%)

650

(78%)

2% HRI @ 

$50/kW

Krewski 290 0.8

(<1%)

250

(87%)

37

(13%)

Lepeule 650 2

(<1%)

140

(22%)

510

(78%)

4.5% HRI @ 

$50/kW

Krewski 380 1

(<1%)

330

(86%)

54

(14%)

Lepeule 870 2.3

(<1%)

190

(22%)

680

(78%)

4.5% HRI @ 

$100/kW

Krewski 250 0.8

(<1%)

220

(87%)

32

(13%)

Lepeule 570 1.8

(<1%)

130

(22%)

440

(78%)

A The LML of the Krewski study is 5.8 μg/m3

and 8 μg/m3for Lepeule et al study.
4-41

The estimated number of deaths above and below the LML varies considerably according 

to the epidemiology study used to estimate risk. Thus, for any year analyzed, we estimate a 

substantially larger fraction of PM-related deaths above the LML of the Krewski et al. (2009) 

study than we do the Lepeule et al. (2012) study. Likewise, we estimate a greater percentage of 

PM-related deaths below the LML of the Lepeule et al. (2012) study than we do the Krewski et 

al. (2009) study. We estimate a very small percentage of PM-related premature deaths occurring 

above the NAAQS in any future year using either of these two studies.

4.6 Total Forgone Climate and Health Benefits 

In this analysis, we estimated the dollar value of changes in CO2 emissions and the 

ancillary co-benefits of changes in exposure to PM2.5 and ozone, but were unable to quantify the 

economic value of changes in exposure to mercury, carbon monoxide, SO2, and NO2, ecosystem 

effects or visibility impairment. Table 4-13 through Table 4-16 report the combined forgone 

domestic climate benefits, and forgone health co-benefits discounted at rates of 3 percent and 7 

percent for the four illustrative scenarios evaluated for each analysis year: 2025, 2030, and 2035.
4-42

Table 4-13 Forgone Climate Benefits and Ancillary Health Co-Benefits, Relative to Base 

Case (CPP) (billion 2016$)

Values Calculated using 3% Discount Rate Values Calculated using 7% Discount Rate

Forgone 

Domestic 

Climate 

Benefits

Forgone 

Health 

Co-Benefits

Total 

Forgone 

Benefits

Forgone 

Domestic 

Climate 

Benefits

Forgone 

Health 

Co-Benefits

Total 

Forgone 

Benefits

No CPP

2025 0.3 2.8 to 6.6 3.2 to 7.0 0.1 2.6 to 6.1 2.7 to 6.1 

2030 0.5 4.9 to 11.4 5.4 to 11.9 0.1 4.5 to 10.5 4.6 to 10.6 

2035 0.5 3.8 to 8.8 4.3 to 9.3 0.1 3.5 to 8.1 3.6 to 8.2 

2% HRI at $50/kW

2025 0.2 2.6 to 5.9 2.8 to 6.2 0.0 2.4 to 5.4 2.4 to 5.5 

2030 0.4 4.5 to 10.6 4.9 to 11.0 0.1 4.1 to 9.8 4.2 to 9.9 

2035 0.4 3.0 to 7.0 3.4 to 7.4 0.1 2.7 to 6.5 2.8 to 6.6 

4.5% HRI at $50/kW 

2025 0.2 2.7 to 6.2 2.9 to 6.4 0.0 2.5 to 5.7 2.5 to 5.7 

2030 0.4 4.2 to 9.8 4.6 to 10.2 0.1 3.9 to 9.0 3.9 to 9.1 

2035 0.5 4.0 to 9.3 4.4 to 9.8 0.1 3.7 to 8.6 3.7 to 8.7 

4.5% HRI at $100/kW 

2025 0.1 2.1 to 4.9 2.3 to 5.0 0.0 2.0 to 4.4 2.0 to 4.4 

2030 0.3 3.6 to 8.2 3.9 to 8.6 0.1 3.3 to 7.6 3.3 to 7.6 

2035 0.3 2.6 to 6.0 2.9 to 6.3 0.1 2.4 to 5.5 2.4 to 5.6 

Notes: Estimates rounded to one decimal point, so figures may not sum due to independent rounding. The forgone 

climate benefit estimates in this table reflect the value of domestic impacts from CO2 emission changes and do not 

account for changes in non-CO2 GHG emissions. Forgone ozone co-benefits occur in analysis year, so they are the 

same for all discount rates. The health co-benefits reflect the sum of the PM2.5 and ozone co-benefits and reflect the 

range based on adult mortality functions (e.g., from Krewski et al. (2009) with Smith et al. (2009) to Lepeule et al.

(2012) with Zanobetti & Schwartz. (2008)). The forgone health co-benefits do not account for direct exposure to 

NO2, SO2, and HAP; ecosystem effects; or, visibility impairment.
4-43

Table 4-14 Forgone Climate Benefits and Ancillary Health Co-Benefits, showing only 

PM2.5 Related Benefits above the Lowest Measured Level of Each LongTerm PM2.5 Mortality Study, Relative to Base Case (CPP) (billion 2016$)

Values Calculated using 3% Discount Rate Values Calculated using 7% Discount Rate

Forgone 

Domestic 

Climate 

Benefits

Forgone 

Health 

Co-Benefits

Total 

Forgone 

Benefits

Forgone 

Domestic 

Climate 

Benefits

Forgone 

Health 

Co-Benefits

Total 

Forgone 

Benefits

No CPP

2025 0.3 2.4 to 1.8 2.8 to 2.1 0.1 2.2 to 1.7 2.3 to 1.7 

2030 0.5 4.2 to 3.5 4.7 to 4.0 0.1 3.8 to 3.3 3.9 to 3.4 

2035 0.5 3.3 to 2.9 3.8 to 3.4 0.1 3.0 to 2.7 3.1 to 2.8 

2% HRI at $50/kW 

2025 0.2 2.2 to 1.5 2.4 to 1.7 0.0 2.0 to 1.4 2.0 to 1.4 

2030 0.4 3.8 to 3.7 4.3 to 4.1 0.1 3.5 to 3.5 3.6 to 3.6 

2035 0.4 2.6 to 2.4 3.1 to 2.8 0.1 2.4 to 2.2 2.5 to 2.3 

4.5% HRI at $50/kW 

2025 0.2 2.3 to 1.6 2.5 to 1.8 0.0 2.1 to 1.5 2.1 to 1.5 

2030 0.4 3.6 to 2.9 4.0 to 3.3 0.1 3.3 to 2.7 3.4 to 2.8 

2035 0.5 3.5 to 3.0 3.9 to 3.5 0.1 3.2 to 2.8 3.3 to 2.9 

4.5% HRI at $100/kW 

2025 0.1 1.8 to 1.1 1.9 to 1.2 0.0 1.7 to 1.0 1.7 to 1.0 

2030 0.3 3.0 to 2.3 3.4 to 2.7 0.1 2.8 to 2.2 2.8 to 2.2 

2035 0.3 2.3 to 2.0 2.6 to 2.3 0.1 2.1 to 1.8 2.1 to 1.9 

Notes: Estimates rounded to one decimal point, so figures may not sum due to independent rounding. The forgone 

climate benefit estimates in this table reflect the value of domestic impacts from CO2 emission changes and do not 

account for changes in non-CO2 GHG emissions. Forgone ozone co-benefits occur in analysis year, so they are the 

same for all discount rates. The health co-benefits reflect the sum of the PM2.5 and ozone co-benefits and reflect the 

range based on adult mortality functions (e.g., from Krewski et al. (2009) with Smith et al. (2009) to Lepeule et al.

(2012) with Zanobetti & Schwartz. (2008)). The forgone health co-benefits do not account for direct exposure to 

NO2, SO2, and HAP; ecosystem effects; or, visibility impairment. 
4-44

Table 4-15 Forgone Climate Benefits and Ancillary Health Co-Benefits, showing only

PM2.5 Related Benefits above PM2.5 National Ambient Air Quality Standard 

(billion 2016$)

Values Calculated using 3% Discount Rate Values Calculated using 7% Discount Rate

Forgone 

Domestic 

Climate 

Benefits

Forgone Health 

Co-Benefits

Total 

Forgone 

Benefits

Forgone 

Domestic 

Climate 

Benefits

Forgone Health 

Co-Benefits

Total 

Forgone 

Benefits

No CPP

2025 0.3 0.1 to 0.4 0.4 to 0.8 0.1 0.1 to 0.4 0.2 to 0.5 

2030 0.5 0.3 to 0.9 0.8 to 1.4 0.1 0.3 to 0.9 0.4 to 1.0 

2035 0.5 0.2 to 0.7 0.7 to 1.2 0.1 0.2 to 0.7 0.3 to 0.8 

2% HRI at $50/kW 

2025 0.2 0.1 to 0.2 0.3 to 0.4 0.0 0.1 to 0.2 0.1 to 0.3 

2030 0.4 0.4 to 1.5 0.9 to 1.9 0.1 0.4 to 1.5 0.5 to 1.6 

2035 0.4 0.2 to 0.7 0.6 to 1.1 0.1 0.2 to 0.7 0.3 to 0.8 

4.5% HRI at $50/kW 

2025 0.2 0.0 to 0.1 0.2 to 0.3 0.0 0.0 to 0.1 0.1 to 0.2 

2030 0.4 0.2 to 0.6 0.6 to 1.1 0.1 0.2 to 0.6 0.3 to 0.7 

2035 0.5 0.2 to 0.9 0.7 to 1.3 0.1 0.2 to 0.9 0.3 to 0.9 

4.5% HRI at $100/kW 

2025 0.1 (0.0) to (0.1) 0.1 to 0.1 0.0 (0.0) to (0.1) (0.0) to (0.0)

2030 0.3 0.1 to 0.5 0.5 to 0.8 0.1 0.1 to 0.5 0.2 to 0.5 

2035 0.3 0.1 to 0.5 0.5 to 0.8 0.1 0.1 to 0.5 0.2 to 0.5 

Notes: Estimates rounded to one decimal point, so figures may not sum due to independent rounding. The forgone 

climate benefit estimates in this table reflect the value of domestic impacts from CO2 emission changes and do not 

account for changes in non-CO2 GHG emissions. Forgone ozone co-benefits occur in analysis year, so they are the 

same for all discount rates. The health co-benefits reflect the sum of the PM2.5 and ozone co-benefits and reflect the 

range based on adult mortality functions (e.g., from Krewski et al. (2009) with Smith et al. (2009) to Lepeule et al.

(2012) with Zanobetti & Schwartz. (2008)). The forgone health co-benefits do not account for direct exposure to 

NO2, SO2, and HAP; ecosystem effects; or, visibility impairment. 
4-45

Table 4-16 Forgone Climate Benefits and Ancillary Health Co-Benefits using Alternate 

Method for Representing PM2.5 Benefits at Low Levels, Relative to Base Case 

(CPP) (billion 2016$)

Values Calculated using 3% Discount Rate Values Calculated using 7% Discount Rate

Forgone 

Domestic 

Climate 

Benefits

Forgone 

Health 

Co-Benefits

Total 

Forgone 

Benefits

Forgone 

Domestic 

Climate 

Benefits

Forgone 

Health 

Co-Benefits

Total 

Forgone 

Benefits

No CPP

2025 0.3 5.5 5.8 0.1 5.0 5.1 

2030 0.5 9.3 9.8 0.1 8.5 8.6 

2035 0.5 7.2 7.8 0.1 6.6 6.7 

2% HRI at $50/kW 

2025 0.2 5.0 5.2 0.0 4.6 4.6 

2030 0.4 8.3 8.7 0.1 7.6 7.7 

2035 0.4 5.7 6.2 0.1 5.2 5.3 

4.5% HRI at $50/kW 

2025 0.2 5.3 5.5 0.0 4.8 4.9 

2030 0.4 8.1 8.5 0.1 7.4 7.5 

2035 0.5 7.6 8.1 0.1 7.0 7.0 

4.5% HRI at $100/kW 

2025 0.1 4.2 4.4 0.0 3.9 3.9 

2030 0.3 6.8 7.2 0.1 6.3 6.3 

2035 0.3 4.9 5.3 0.1 4.5 4.6 

Notes: Estimates rounded to two one decimal point, so figures may not sum due to independent rounding. The 

forgone climate benefit estimates in this table reflect the value of domestic impacts from CO2 emission changes and 

do not account for changes in non-CO2 GHG emissions. Forgone ozone co-benefits occur in analysis year, so they 

are the same for all discount rates. The health co-benefits reflect the sum of the PM2.5 and ozone co-benefits and 

reflect the range based on adult mortality functions (e.g., from Krewski et al. (2009) with Smith et al. (2009) to 

Lepeule et al. (2012) with Zanobetti & Schwartz. (2008)). The forgone health co-benefits do not account for direct 

exposure to NO2, SO2, and HAP; ecosystem effects; or, visibility impairment. 

4.7 Forgone Ancillary Co-Benefits Not Quantified

The forgone monetized co-benefits estimated above are a subset of those we expect to 

occur. Data, time, and resource limitations prevented EPA from quantifying the impacts to, or 

monetizing the co-benefits from, several important benefit categories; these include forgone cobenefits associated with exposure to several HAPs (including mercury and hydrogen chloride), 

SO2 and NO2, as well as ecosystem effects, and visibility impairment. Below is a qualitative 

description of these benefits (Table 4-17).
4-46

Table 4-17 Unquantified Forgone Ancillary Health and Welfare Co-Benefits Categories

Category Effect Effect 

Quantified

Effect 

Monetized

More 

Information

Improved Human Health

Reduced incidence of 

morbidity from 

exposure to NO2

Asthma hospital admissions (all ages) — — NO2 ISA1

Chronic lung disease hospital admissions (age > 

65) — — NO2 ISA1

Respiratory emergency department visits (all 

ages) — — NO2 ISA1

Asthma exacerbation (asthmatics age 4–18) — — NO2 ISA1

Acute respiratory symptoms (age 7–14) — — NO2 ISA1

Premature mortality — — NO2 ISA1,2,3

Other respiratory effects (e.g., airway 

hyperresponsiveness and inflammation, lung 

function, other ages and populations)

— — NO2 ISA2,3

Reduced incidence of 

morbidity from 

exposure to SO2

Respiratory hospital admissions (age > 65) — — SO2 ISA1

Asthma emergency department visits (all ages) — — SO2 ISA1

Asthma exacerbation (asthmatics age 4–12) — — SO2 ISA1

Acute respiratory symptoms (age 7–14) — — SO2 ISA1

Premature mortality — — SO2 ISA1,2,3

Other respiratory effects (e.g., airway 

hyperresponsiveness and inflammation, lung 

function, other ages and populations)

— — SO2 ISA1,2

Reduced incidence of 

morbidity from 

exposure to CO

Cardiovascular effects — — CO ISA 1,2

Respiratory effects — — CO ISA 1,2,3

Central nervous system effects — — CO ISA 1,2,3

Premature mortality — — CO ISA 1,2,3

Reduced incidence of 

morbidity from 

exposure to 

methylmercury

Neurologic effects—IQ loss — —

IRIS; NRC, 

20001

Other neurologic effects (e.g., developmental 

delays, memory, behavior) — —

IRIS; NRC, 

20002

Cardiovascular effects — —

IRIS; NRC, 

20002,3

Genotoxic, immunologic, and other toxic effects — —

IRIS; NRC, 

20002,3

Improved Environment

Reduced visibility 

impairment

Visibility in Class 1 areas — — PM ISA1

Visibility in residential areas — — PM ISA1

Reduced effects on 

materials

Household soiling — — PM ISA1,2

Materials damage (e.g., corrosion, increased 

wear) — — PM ISA2

Reduced effects from 

PM deposition (metals 

and organics)

Effects on Individual organisms and ecosystems — — PM ISA2

Reduced vegetation 

and ecosystem effects 

from exposure to 

ozone

Visible foliar injury on vegetation — — Ozone ISA1

Reduced vegetation growth and reproduction — — Ozone ISA1

Yield and quality of commercial forest products 

and crops — — Ozone ISA1

Damage to urban ornamental plants — — Ozone ISA2

Carbon sequestration in terrestrial ecosystems — — Ozone ISA1

Recreational demand associated with forest 

aesthetics — — Ozone ISA2

Other non-use effects Ozone ISA2
4-47

Category Effect Effect 

Quantified

Effect 

Monetized

More 

Information

Ecosystem functions (e.g., water cycling, 

biogeochemical cycles, net primary productivity, 

leaf-gas exchange, community composition)

— — Ozone ISA2

Reduced effects from 

acid deposition

Recreational fishing — — NOx SOx ISA1

Tree mortality and decline — — NOx SOx ISA2

Commercial fishing and forestry effects — — NOx SOx ISA2

Recreational demand in terrestrial and aquatic 

ecosystems — — NOx SOx ISA2

Other non-use effects NOx SOx ISA2

Ecosystem functions (e.g., biogeochemical 

cycles) — — NOx SOx ISA2

Reduced effects from 

nutrient enrichment

Species composition and biodiversity in terrestrial 

and estuarine ecosystems — — NOx SOx ISA2

Coastal eutrophication — — NOx SOx ISA2

Recreational demand in terrestrial and estuarine 

ecosystems — — NOx SOx ISA2

Other non-use effects NOx SOx ISA2

Ecosystem functions (e.g., biogeochemical 

cycles, fire regulation) — — NOx SOx ISA2

Reduced vegetation 

effects from ambient 

exposure to SO2 and 

NOx

Injury to vegetation from SO2 exposure — — NOx SOx ISA2

Injury to vegetation from NOx exposure — — NOx SOx ISA2

Reduced ecosystem 

effects from exposure 

to methylmercury

Effects on fish, birds, and mammals (e.g., 

reproductive effects) — —

Mercury Study 

RTC2

Commercial, subsistence and recreational fishing — —

Mercury Study 

RTC1

1 We assess these co-benefits qualitatively due to data and resource limitations.

2 We assess these co-benefits qualitatively because we do not have sufficient confidence in available data or methods.

3 We assess these co-benefits qualitatively because current evidence is only suggestive of causality or there are other significant 

concerns over the strength of the association.

4.7.1 Hazardous Air Pollutant Impacts

Due to methodology and resource limitations, we were unable to estimate the impacts 

associated with changes in emissions of the hazardous air pollutants in this analysis. EPA’s 

SAB-HES concluded that “the challenges for assessing progress in health improvement as a 

result of reductions in emissions of HAPs are daunting...due to a lack of exposure-response 

functions, uncertainties in emissions inventories and background levels, the difficulty of 

extrapolating risk estimates to low doses and the challenges of tracking health progress for 

diseases, such as cancer, that have long latency periods” (EPA-SAB 2008b). In 2009, EPA

convened a workshop to address the inherent complexities, limitations, and uncertainties in 

current methods to quantify the benefits of reducing HAP. Recommendations from this 

workshop included identifying research priorities, focusing on susceptible and vulnerable 

populations, and improving dose-response relationships (Gwinn et al. 2011). 
4-48

4.7.1.1 Mercury

Mercury in the environment is transformed into a more toxic form, methylmercury 

(MeHg). Because Hg is a persistent pollutant, MeHg accumulates in the food chain, especially 

the tissue of fish. When people consume these fish, they consume MeHg. In 2000, the NAS 

Study was issued which provides a thorough review of the effects of MeHg on human health 

(NRC 2000).

1 Many of the peer-reviewed articles cited in this section are publications originally 

cited in the Mercury Study.2In addition, EPA has conducted literature searches to obtain other 

related and more recent publications to complement the material summarized by the NRC in 

2000.

In its review of the literature, the NAS found neurodevelopmental effects to be the most 

sensitive and best documented endpoints and appropriate for establishing a reference dose (RfD) 

(NRC 2000); in particular NAS supported the use of results from neurobehavioral or 

neuropsychological tests. The NAS report noted that studies on animals reported sensory effects 

as well as effects on brain development and memory functions and supported the conclusions 

based on epidemiology studies. The NAS noted that their recommended endpoints for a RfD are 

associated with the ability of children to learn and to succeed in school. They concluded the 

following: “The population at highest risk is the children of women who consumed large 

amounts of fish and seafood during pregnancy. The committee concludes that the risk to that 

population is likely to be sufficient to result in an increase in the number of children who have to 

struggle to keep up in school.”

The NAS summarized data on cardiovascular effects available up to 2000. Based on these 

and other studies, the NRC concluded that “Although the data base is not as extensive for 

cardiovascular effects as it is for other end points (i.e., neurologic effects), the cardiovascular 

system appears to be a target for MeHg toxicity in humans and animals.” The NRC also stated 

that “additional studies are needed to better characterize the effect of methylmercury exposure on 

blood pressure and cardiovascular function at various stages of life.”

 

1 National Research Council (NRC). 2000. Toxicological Effects of Methylmercury. Washington, DC: National 

Academies Press.

2 U.S. Environmental Protection Agency (U.S. EPA). 1997. Mercury Study Report to Congress, EPA–HQ–OAR–

2009–0234–3054. December. Available on the Internet at <http://www.epa.gov/hg/report.htm>.
4-49

Additional cardiovascular studies have been published since 2000. EPA did not develop a 

quantitative dose-response assessment for cardiovascular effects associated with MeHg 

exposures, as there is no consensus among scientists on the dose-response functions for these 

effects. In addition, there is inconsistency among available studies as to the association between 

MeHg exposure and various cardiovascular system effects. The pharmacokinetics of some of the 

exposure measures (such as toenail Hg levels) are not well understood. The studies have not yet 

received the review and scrutiny of the more well-established neurotoxicity data base. 

The Mercury Study noted that MeHg is not a potent mutagen but is capable of causing 

chromosomal damage in a number of experimental systems. The NAS concluded that evidence 

that human exposure to MeHg caused genetic damage is inconclusive; they note that some earlier 

studies showing chromosomal damage in lymphocytes may not have controlled sufficiently for 

potential confounders. One study of adults living in the Tapajós River region in Brazil (Amorim 

et al. 2000) reported a direct relationship between MeHg concentration in hair and DNA damage 

in lymphocytes, as well as effects on chromosomes.3 Long-term MeHg exposures in this 

population were believed to occur through consumption of fish, suggesting that genotoxic effects 

(largely chromosomal aberrations) may result from dietary and chronic MeHg exposures similar 

to and above those seen in the Faroes and Seychelles populations.

Although exposure to some forms of Hg can result in a decrease in immune activity or an 

autoimmune response (ATSDR 1999), evidence for immunotoxic effects of MeHg is limited 

(NRC 2000).

4 Based on limited human and animal data, MeHg is classified as a “possible” 

human carcinogen by the International Agency for Research on Cancer (IARC 1994)5 and in 

IRIS (U.S. EPA 2002).

6 The existing evidence supporting the possibility of carcinogenic effects 

 

3 Amorim, M.I.M., D. Mergler, M.O. Bahia, H. Dubeau, D. Miranda, J. Lebel, R.R. Burbano, and M. Lucotte. 2000. 

Cytogenetic damage related to low levels of methyl mercury contamination in the Brazilian Amazon. An. Acad. 

Bras. Ciênc. 72(4): 497-507.

4 Agency for Toxic Substances and Disease Registry (ATSDR). 1999. Toxicological Profile for Mercury. U.S. 

Department of Health and Human Services, Public Health Service, Atlanta, GA.

5

International Agency for Research on Cancer (IARC). 1994. IARC Monographs on the Evaluation of Carcinogenic 

Risks to Humans and their Supplements: Beryllium, Cadmium, Mercury, and Exposures in the Glass Manufacturing 

Industry. Vol. 58. Jalili, H.A., and A.H. Abbasi. 1961. Poisoning by ethyl mercury toluene sulphonanilide. Br. J. 

Indust. Med. 18(Oct.):303-308 (as cited in NRC, 2000).
4-50

in humans from low-dose chronic exposures is tenuous. Multiple human epidemiological studies 

have found no significant association between Hg exposure and overall cancer incidence, 

although a few studies have shown an association between Hg exposure and specific types of 

cancer incidence (e.g., acute leukemia and liver cancer) (NRC 2000).

There is also some evidence of reproductive and renal toxicity in humans from MeHg 

exposure. However, overall, human data regarding reproductive, renal, and hematological 

toxicity from MeHg are very limited and are based on either studies of the two high-dose 

poisoning episodes in Iraq and Japan or animal data, rather than epidemiological studies of 

chronic exposures at the levels of interest in this analysis.

4.7.1.2 Hydrogen Chloride

Hydrogen chloride (HCl) is a corrosive gas that can cause irritation of the mucous 

membranes of the nose, throat, and respiratory tract. Brief exposure to 35 ppm causes throat 

irritation, and levels of 50 to 100 ppm are barely tolerable for 1 hour.7 Concentrations in typical 

human exposure environments are much lower than these levels and rarely exceed the reference 

concentration.8The greatest impact is on the upper respiratory tract; exposure to high 

concentrations can rapidly lead to swelling and spasm of the throat and suffocation. Most 

seriously exposed persons have immediate onset of rapid breathing, blue coloring of the skin, 

and narrowing of the bronchioles. Exposure to HCl can lead to Reactive Airways Dysfunction 

Syndrome (RADS), a chemically, or irritant-induced type of asthma. Children may be more 

vulnerable to corrosive agents than adults because of the relatively smaller diameter of their 

airways. Children may also be more vulnerable to gas exposure because of increased minute

 

6 U.S. Environmental Protection Agency (EPA). 2002. Integrated Risk Information System (IRIS) on 

Methylmercury. National Center for Environmental Assessment. Office of Research and Development. Available at 

http://www.epa.gov/iris/subst/0073.htm.

7 Agency for Toxic Substances and Disease Registry (ATSDR). Medical Management Guidelines for Hydrogen 

Chloride. Atlanta, GA: U.S. Department of Health and Human Services. Available at 

http://www.atsdr.cdc.gov/mmg/mmg.asp?id=758&tid=147#bookmark02.

8 Table of Prioritized Chronic Dose-Response Values: http://www2.epa.gov/sites/production/files/2014-

05/documents/table1.pdf
4-51

ventilation per kg and failure to evacuate an area promptly when exposed. Hydrogen chloride has 

not been classified for carcinogenic effects.9

4.7.2 Forgone NO2 Health Co-Benefits

In addition to being a precursor to PM2.5 and ozone, NOX emissions are also linked to a 

variety of adverse health effects associated with direct exposure. We were unable to estimate the 

health co-benefits associated with reduced NO2 exposure in this analysis. Therefore, this analysis 

only quantified and monetized the PM2.5 and ozone co-benefits associated with the reductions in 

NO2 emissions. Following a comprehensive review of health evidence from epidemiologic and 

laboratory studies, the Integrated Science Assessment for Oxides of Nitrogen —Health Criteria

(NOX ISA) (U.S. EPA 2008a) concluded that there is a likely causal relationship between 

respiratory health effects and short-term exposure to NO2. These epidemiologic and experimental 

studies encompass a number of endpoints including emergency department visits and 

hospitalizations, respiratory symptoms, airway hyperresponsiveness, airway inflammation, and 

lung function. The NOX ISA also concluded that the relationship between short-term NO2

exposure and premature mortality was “suggestive but not sufficient to infer a causal 

relationship,” because it is difficult to attribute the mortality risk effects to NO2 alone. Although 

the NOX ISA stated that studies consistently reported a relationship between NO2 exposure and 

mortality, the effect was generally smaller than that for other pollutants such as PM. 

4.7.3 Forgone SO2 Health Co-Benefits

In addition to being a precursor to PM2.5, SO2 emissions are also linked to a variety of 

adverse health effects associated with direct exposure. We were unable to estimate the health cobenefits associated with reduced SO2 in this analysis. Therefore, this analysis only quantifies and 

monetizes the PM2.5 co-benefits associated with the reductions in SO2 emissions. 

Following an extensive evaluation of health evidence from epidemiologic and laboratory 

studies, the Integrated Science Assessment for Oxides of Sulfur —Health Criteria (SO2 ISA) 

concluded that there is a causal relationship between respiratory health effects and short-term 

exposure to SO2 (U.S. EPA 2008c). The immediate effect of SO2 on the respiratory system in 

 

9 U.S. Environmental Protection Agency (U.S. EPA). 1995. “Integrated Risk Information System File of Hydrogen 

Chloride.” Washington, DC: Research and Development, National Center for Environmental Assessment. This 

material is available at http://www.epa.gov/iris/subst/0396.htm.
4-52

humans is bronchoconstriction. Asthmatics are more sensitive to the effects of SO2 likely 

resulting from preexisting inflammation associated with this disease. A clear concentrationresponse relationship has been demonstrated in laboratory studies following exposures to SO2 at 

concentrations between 20 and 100 ppb, both in terms of increasing severity of effect and 

percentage of asthmatics adversely affected. Based on our review of this information, we 

identified three short-term morbidity endpoints that the SO2 ISA identified as a “causal 

relationship”: asthma exacerbation, respiratory-related emergency department visits, and 

respiratory-related hospitalizations. The differing evidence and associated strength of the 

evidence for these different effects is described in detail in the SO2 ISA. The SO2 ISA also 

concluded that the relationship between short-term SO2 exposure and premature mortality was 

“suggestive of a causal relationship” because it is difficult to attribute the mortality risk effects to 

SO2 alone. Although the SO2 ISA stated that studies are generally consistent in reporting a 

relationship between SO2 exposure and mortality, there was a lack of robustness of the observed 

associations to adjustment for other pollutants. We did not quantify these co-benefits due to data 

constraints.

4.7.4 NO2 and SO2 Forgone Welfare Co-Benefits

As described in the Integrated Science Assessment for Oxides of Nitrogen and Sulfur —

Ecological Criteria (NOX/SOX ISA) (U.S. EPA 2008b), SO2 and NOX emissions also contribute 

to a variety of adverse welfare effects, including those associated with acidic deposition, 

visibility impairment, and nutrient enrichment. Deposition of nitrogen causes acidification, 

which can cause a loss of biodiversity of fishes, zooplankton, and macro invertebrates in aquatic 

ecosystems, as well as a decline in sensitive tree species, such as red spruce (Picea rubens) and 

sugar maple (Acer saccharum) in terrestrial ecosystems. In the northeastern U.S., the surface 

waters affected by acidification are a source of food for some recreational and subsistence 

fishermen and for other consumers and support several cultural services, including aesthetic and 

educational services and recreational fishing. Biological effects of acidification in terrestrial 

ecosystems are generally linked to aluminum toxicity, which can cause reduced root growth, 

restricting the ability of the plant to take up water and nutrients. These direct effects can, in turn, 

increase the sensitivity of these plants to stresses, such as droughts, cold temperatures, insect 

pests, and disease leading to increased mortality of canopy trees. Terrestrial acidification affects 
4-53

several important ecological services, including declines in habitat for threatened and endangered 

species (cultural), declines in forest aesthetics (cultural), declines in forest productivity 

(provisioning), and increases in forest soil erosion and reductions in water retention (cultural and 

regulating) (U.S. EPA 2008b).

Deposition of nitrogen is also associated with aquatic and terrestrial nutrient enrichment. 

In estuarine waters, excess nutrient enrichment can lead to eutrophication. Eutrophication of 

estuaries can disrupt an important source of food production, particularly fish and shellfish 

production, and a variety of cultural ecosystem services, including water-based recreational and 

aesthetic services. Terrestrial nutrient enrichment is associated with changes in the types and 

number of species and biodiversity in terrestrial systems. Excessive nitrogen deposition upsets 

the balance between native and nonnative plants, changing the ability of an area to support 

biodiversity. When the composition of species changes, then fire frequency and intensity can 

also change, as nonnative grasses fuel more frequent and more intense wildfires (U.S. EPA 

2008b). 

Reductions in emissions of NO2 and SO2 will improve the level of visibility throughout 

the United States because these gases (and the particles of nitrate and sulfate formed from these 

gases) impair visibility by scattering and absorbing light (U.S. EPA 2009). Visibility is also 

referred to as visual air quality (VAQ), and it directly affects people’s enjoyment of a variety of 

daily activities (U.S. EPA 2009). Good visibility increases quality of life where individuals live 

and work, and where they travel for recreational activities, including sites of unique public value, 

such as the Great Smoky Mountains National Park (U.S. EPA 2009).

4.7.5 Forgone Ozone Welfare Co-Benefits

Exposure to ozone has been associated with a wide array of vegetation and ecosystem 

effects in the published literature (U.S. EPA 2013a). Sensitivity to ozone is highly variable 

across species, with over 65 plant species identified as “ozone-sensitive”, many of which occur 

in state and national parks and forests. These effects include those that damage or impair the 

intended use of the plant or ecosystem. Such effects can include reduced growth and/or biomass 

production in sensitive plant species, including forest trees, reduced yield and quality of crops, 

visible foliar injury, species composition shift, and changes in ecosystems and associated 
4-54

ecosystem services. 

4.7.6 Forgone Carbon Monoxide Co-Benefits

CO in ambient air is formed primarily by the incomplete combustion of carboncontaining fuels and photochemical reactions in the atmosphere. The amount of CO emitted from 

these reactions, relative to carbon dioxide (CO2), is sensitive to conditions in the combustion 

zone, such as fuel oxygen content, burn temperature, or mixing time. Upon inhalation, CO 

diffuses through the respiratory system to the blood, which can cause hypoxia (reduced oxygen 

availability). Carbon monoxide can elicit a broad range of effects in multiple tissues and organ 

systems that depend on concentration and duration of exposure. The Integrated Science 

Assessment for Carbon Monoxide (U.S. EPA 2010a) concluded that short-term exposure to CO 

is “likely to have a causal relationship” with cardiovascular morbidity, particularly in individuals 

with coronary heart disease. Epidemiologic studies associate short-term CO exposure with 

increased risk of emergency department visits and hospital admissions. Coronary heart disease 

includes those who have angina pectoris (cardiac chest pain), as well as those who have 

experienced a heart attack. Other subpopulations potentially at risk include individuals with 

diseases such as chronic obstructive pulmonary disease (COPD), anemia, or diabetes, and 

individuals in very early or late life stages, such as older adults or the developing young. The 

evidence is suggestive of a causal relationship between short-term exposure to CO and 

respiratory morbidity and mortality. The evidence is also suggestive of a causal relationship for 

birth outcomes and developmental effects following long-term exposure to CO, and for central 

nervous system effects linked to short- and long-term exposure to CO.

4.7.7 Forgone Visibility Impairment Co-Benefits

Reducing secondary formation of PM2.5 would improve levels visibility in the U.S. because 

suspended particles and gases degrade visibility by scattering and absorbing light (U.S. EPA 

2009). Fine particles with significant light-extinction efficiencies include sulfates, nitrates, 

organic carbon, elemental carbon, and soil (Sisler 1996). Visibility has direct significance to 

people’s enjoyment of daily activities and their overall sense of wellbeing. Good visibility 

increases the quality of life where individuals live and work, and where they engage in 

recreational activities. Particulate sulfate is the dominant source of regional haze in the eastern 
4-55

U.S. and particulate nitrate is an important contributor to light extinction in California and the 

upper Midwestern U.S., particularly during winter (U.S. EPA 2009). Previous analyses show that 

visibility co-benefits can be a significant welfare benefit category(U.S. EPA 2011d). Without air 

quality modeling, we are unable to estimate visibility related benefits, and we are also unable to 

determine whether the emission reductions associated with the final emission guidelines would 

be likely to have a significant impact on visibility in urban areas or Class I areas.
4-56

4.8 References 

Amorim MIM, MERGLER D, BAHIA MO, DUBEAU H, MIRANDA D, LEBEL J, et al. 2000. 

Cytogenetic damage related to low levels of methyl mercury contamination in the Brazilian 

Amazon. An Acad Bras Cienc 72:497–507; doi:10.1590/S0001-37652000000400004.

ATSDR. 1999. ATSDR - Toxicological Profile: Mercury.

Bell ML, Dominici F, Samet JM. 2005. A meta-analysis of time-series studies of ozone and 

mortality with comparison to the national morbidity, mortality, and air pollution study. 

Epidemiology 16: 436–45.

Bell ML, McDermott A, Zeger SL, Samet JM, Dominici F. 2004. Ozone and short-term 

mortality in 95 US urban communities, 1987-2000. JAMA 292:2372–8; 

doi:10.1001/jama.292.19.2372.

Ding D, Zhu Y, Jang C, Lin C-J, Wang S, Fu J, et al. 2016. Evaluation of health benefit using 

BenMAP-CE with an integrated scheme of model and monitor data during Guangzhou 

Asian Games. J Environ Sci 42:9–18; doi:10.1016/J.JES.2015.06.003.

EPA-SAB US. 2008a. Review of EPA’s Integrated Science Assessment for Particulate Matter 

(First External Review Draft, December 2008).

EPA-SAB US. 2009. Review of Integrated Science Assessment for Particulate Matter (Second 

External Review Draft, July 2009).

EPA-SAB US. 2008b. Subject: Benefits of Reducing Benzene Emissions in Houston, 1990-

2020.

Fann N, Kim S-Y, Olives C, Sheppard L. 2017. Estimated Changes in Life Expectancy and 

Adult Mortality Resulting from Declining PM2.5 Exposures in the Contiguous United 

States: 1980-2010. Environ Health Perspect 125; doi:10.1289/EHP507.

Fann N, Nolte CG, Dolwick P, Spero TL, Brown AC, Phillips S, et al. 2015. The geographic 

distribution and economic value of climate change-related ozone health impacts in the 

United States in 2030. J Air Waste Manag Assoc 65; doi:10.1080/10962247.2014.996270.

Gwinn MR, Craig J, Axelrad D a, Cook R, Dockins C, Fann N, et al. 2011. Meeting report: 

Estimating the benefits of reducing hazardous air pollutants--summary of 2009 workshop 

and future considerations. Environ Health Perspect 119:125–30; doi:10.1289/ehp.1002468.

Huang Y, Dominici F, Bell M. 2004. Bayesian Hierarchical Distributed Lag Models for Summer 

Ozone Exposure and Cardio-Respiratory Mortality. Johns Hopkins Univ Dept Biostat Work 

Pap Ser.

Hubbell BJ, Hallberg A, McCubbin DR, Post E. 2005. Health-related benefits of attaining the 8-

hr ozone standard. Environ Health Perspect 113: 73–82.
4-57

IARC. 1994. Mercury Carcinogenicity.

Ito K, De Leon SF, Lippmann M. 2005. Associations Between Ozone and Daily Mortality. 

Epidemiology 16:446–457; doi:10.1097/01.ede.0000165821.90114.7f.

Jerrett M, Burnett RT, Pope CA, Ito K, Thurston G, Krewski D, et al. 2009. Long-term ozone 

exposure and mortality. N Engl J Med 360:1085–95; doi:10.1056/NEJMoa0803894.

Jhun I, Fann N, Zanobetti A, Hubbell B. 2014. Effect modification of ozone-related mortality 

risks by temperature in 97 US cities. Environ Int 73C:128–134; 

doi:10.1016/j.envint.2014.07.009.

Krewski D, Jerrett M, Burnett RT, Ma R, Hughes E, Shi Y, et al. 2009. Extended follow-up and 

spatial analysis of the American Cancer Society study linking particulate air pollution and 

mortality. Res Rep Health Eff Inst 5-114; discussion 115–36.

Lepeule J, Laden F, Dockery D, Schwartz J. 2012. Chronic exposure to fine particles and 

mortality: an extended follow-up of the Harvard Six Cities study from 1974 to 2009. 

Environ Health Perspect 120:965–970; doi:10.1289/ehp.1104660.

Levy JI, Chemerynski SM, Sarnat JA. 2005. Ozone exposure and mortality: an empiric bayes 

metaregression analysis. Epidemiology 16: 458–68.

Melillo, Jerry M., Terese (T.C.) Richmond, and Gary W. Yohe, Eds., 2014: Climate Change 

Impacts in the United States: The Third National Climate Assessment. U.S. Global Change 

Research Program, 841 pp. doi:10.7930/J0Z31WJ2.

NRC. 2008. Estimating Mortality Risk Reduction and Economic Benefits from Controlling 

Ozone Air Pollution. National Academies Press:Washington, D.C.

NRC. 2002. Estimating the Public Health Benefits of Proposed Air Pollution Regulations. 

Washington, D.C.

NRC. 2000. Toxicological Effects of Methylmercury. National Academies Press:Washington, 

D.C.

Ramboll Environ International Corporation. User’s Guide: Comprehensive Air Quality Model 

with Extensions version 6.40.

Ren C, Williams GM, Mengersen K, Morawska L, Tong S. 2008a. Does temperature modify 

short-term effects of ozone on total mortality in 60 large eastern US communities? An 

assessment using the NMMAPS data. Environ Int 34:451–8; 

doi:10.1016/j.envint.2007.10.001.

Ren C, Williams GM, Morawska L, Mengersen K, Tong S. 2008b. Ozone modifies associations 

between temperature and cardiovascular mortality: analysis of the NMMAPS data. Occup 

Environ Med 65:255–60; doi:10.1136/oem.2007.033878.
4-58

Schwartz J. 2005. How sensitive is the association between ozone and daily deaths to control for 

temperature? Am J Respir Crit Care Med 171:627–31; doi:10.1164/rccm.200407-933OC.

Schwartz J, Coull B, Laden F, Ryan L. 2008. The effect of dose and timing of dose on the 

association between airborne particles and survival. Environ Health Perspect 116:64–9; 

doi:10.1289/ehp.9955.

Sisler JF. 1996. Spatial and Seasonal P atterns and Long - Term Variability of the Composition 

of the Haze in the United States: An analysis of data from the IMPROVE network.; 

doi:ISSN 0737 - 5352 - 32.

Smith RL, Xu B, Switzer P. 2009. Reassessing the relationship between ozone and short-term 

mortality in U.S. urban communities. Inhal Toxicol 21 Suppl 2:37–61; 

doi:10.1080/08958370903161612.

U.S. EPA-SAB. 2004. Advisory Council on Clean Air Compliance Analysis Response to 

Agency Request on Cessation Lag.

U.S. EPA-SAB. 2000. An SAB Report on EPA’s White Paper Valuing the Benefits of Fatal 

Cancer Risk Reduction.

U.S. EPA-SAB. 2011. Review of: Valuing Mortality Risk REductions for Environmental Policy: 

A White Paper.

U.S. EPA-SAB. 2010. Review of EPA’s Draft Health Benefits of the Second Section 812 

Prospective Study of the CAA.

U.S. EPA. 2018a. Environmental Benefits Mapping and Analysis Program--Community Edition 

(BenMAP-CE).

U.S. EPA. 2016a. Guidelines for Preparing Economic Analyses.

U.S. EPA. 2010a. Integrated Science Assessment (ISA) for Carbon Monoxide (Final Report).

U.S. EPA. 2008a. Integrated Science Assessment (ISA) for Nitrogen Dioxide (Health Criteria).

U.S. EPA. 2008b. Integrated Science Assessment (ISA) for Oxides of Nitrogen and Sulfur -

Ecological Criteria (Final Report, Dec 2008).

U.S. EPA. 2008c. Integrated Science Assessment (ISA) for Sulfur Oxides (Health Criteria).

U.S. EPA. 2016b. Integrated Science Assessment for Oxides of Nitrogen: Final Report.

U.S. EPA. 2009. Integrated Science Assessment for Particulate Matter. EPA/600/R-.

U.S. EPA. 2017. Integrated Science Assessment for Sulfur Oxides: Final Report.

U.S. EPA. 2013a. Integrated Science Assessment of Ozone and Related Photochemical Oxidants 

(Final Report).; doi:EPA/600/R-10/076F.
4-59

U.S. EPA. 2002. Methylmercury (MeHg) CASRN 22967-92-6 | IRIS | US EPA, ORD.

U.S. EPA. 2011a. Policy Assessment for the Review of the Particulate Matter National Ambient 

Air Quality Standards.

U.S. EPA. 2018b. Quality Assuring BenMAP-CE Demographic and Economic Input Data.

U.S. EPA. 2010b. Regulatory Impact Analysis (RIA) for Existing Stationary Compression 

Ignition Engines NESHAP Final Draft.

U.S. EPA. 2014a. Regulatory Impact Analysis (RIA) for Proposed Residential Wood Heaters 

NSPS Revision.

U.S. EPA. 2015a. Regulatory Impact Analysis (RIA) for Residential Wood Heaters NSPS 

Revision: Final Report.

U.S. EPA. 2015b. Regulatory Impact Analysis for the Clean Power Plan Final Rule.

U.S. EPA. 2011b. Regulatory Impact Analysis for the Federal Implementation Plans to Reduce 

Interstate Transport of Fine Particulate Matter and Ozone in 27 States; Correction of SIP 

Approvals for 22 States.

U.S. EPA. 2011c. Regulatory Impact Analysis for the Final Mercury and Air Toxics Standards.

U.S. EPA. 2013b. Regulatory Impact Analysis for the Final Revisions to the National Ambient 

Air Quality Standards for Particulate Matter.

U.S. EPA. 2014b. Regulatory Impact Analysis for the Proposed Carbon Pollution Guidelines for 

Existing Power Plants and Emission Standards for Modified and Reconstructed Power 

Plants.

U.S. EPA. 2015c. Regulatory Impact Analysis for the Proposed Cross-State Air Pollution Rule 

(CSAPR) Update for the 2008 Ozone National Ambient Air Quality Standards (NAAQS).

U.S. EPA. 2015d. Regulatory Impact Analysis for the Proposed Federal Plan Requirements for 

Greenhouse Gas Emissions from Electric Utility Generating Units Constructed on or Before 

January 8, 2014; Model Trading Rules; Amendments to Framework Regulations.

U.S. EPA. 2010c. Regulatory Impact Analysis for the Proposed Federal Transport Rule.

U.S. EPA. 2012a. Regulatory Impact Analysis for the Proposed Revisions to the National 

Ambient Air Quality Standards for Particulate Matter.

U.S. EPA. 2016c. Regulatory Impact Analysis of the Cross-State Air Pollution Rule (CSAPR) 

Update for the 2008 National Ambient Air Quality Standards for Ground-Level Ozone.

U.S. EPA. 2014c. Regulatory Impact Analysis of the Proposed Revisions to the National 

Ambient Air Quality Standards for Ground-Level Ozone.
4-60

U.S. EPA. 2015e. Regulatory Impact Analysis of the Revisions to the National Ambient Air 

Quality Standards for Ground-Level Ozone.

U.S. EPA. 2011d. Regulatory Impact Assessment for the Mercury and Air Toxics Standards.

U.S. EPA. 2012b. Regulatory Impact Assessment for the Particulate Matter National Ambient 

Air Quality Standards.

U.S. EPA. 2018c. User Manual for Environmental Benefits Mapping and Analysis Program 

(BenMAP). User Man.

U.S. EPA. 2010d. Valuing Mortality Risk Reductions for Environmental Policy: A White Paper.

U.S. Office of Management and Budget. 2003. Circular A-4: Regulatory Analysis.

USGCRP. 2016. The Impacts of Climate Change on Human Health in the United States: A 

Scientific Assessment.; doi:http://dx.doi.org/10.7930/J0R49NQX.

USGCRP, 2017: Climate Science Special Report: Fourth National Climate Assessment, Volume 

I [Wuebbles, D.J., D.W. Fahey, K.A. Hibbard, D.J. Dokken, B.C. Stewart, and T.K. 

Maycock (eds.)]. U.S. Global Change Research Program, Washington, DC, USA, 470 

pp., doi: 10.7930/J0J964J6.

Woodruff TJ, Grillo J, Schoendorf KC. 1997. The relationship between selected causes of 

postneonatal infant mortality and particulate air pollution in the United States. Environ 

Health Perspect 105: 608–12.

Zanobetti A, 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 177:184–

9; doi:10.1164/rccm.200706-823OC.
5-1

CHAPTER 5: ECONOMIC AND EMPLOYMENT IMPACTS

5.1 Economic Impacts 

5.1.1 Market Impacts

The energy sector impacts presented in Chapter 3 of this RIA include potential changes in 

the prices for electricity, natural gas, and coal resulting from this proposal. This chapter 

addresses the impact of these potential changes on other markets and discusses some of the 

determinants of the magnitude of these potential impacts. We refer to these changes as secondary 

market impacts.

Under the emission guidelines of either the 2015 CPP or this proposal, states are not 

required to use any of the measures that EPA determines constitute BSER, or use those measures 

to the same degree of stringency that EPA determines is achievable at reasonable cost. Rather, 

CAA section 111(d) allows each state to determine the appropriate combination of, and the 

extent of its reliance on, measures for its state plan. Given the flexibilities afforded states in 

complying with the emission guidelines under 111(d), the benefits, cost and economic impacts 

reported in this RIA are illustrative of actions that states may take. The implementation 

approaches adopted by the states, and the strategies adopted by affected EGUs, will ultimately 

drive the magnitude and timing of secondary impacts from changes in the price of electricity, and 

the demand for inputs by the electricity sector, on other markets that use and produce these 

inputs.

To estimate the costs, benefits, and impacts of implementing the proposed guidelines, 

EPA modeled illustrative policy scenarios. Chapter 1 and Chapter 3 describe the illustrative 

policy scenarios analyzed. This chapter provides a quantitative assessment of the energy price 

impacts for these illustrative policy scenarios and a qualitative assessment of the factors that will 

in part determine the timing and magnitude of potential effects in other markets. Table 5-1

summarizes projected changes in energy prices resulting from the illustrative policy scenarios.
5-2

Table 5-1 Summary of Certain Energy Market Impacts, Relative to Base Case (CPP) 

(Percent Change)

2025 2030 2035

No CPP

Retail electricity prices -0.5% -0.4% -0.1%

Average price of coal delivered to the power sector -0.1% -0.2% -0.4%

Coal production for power sector use 6.1% 9.2% 9.5%

Price of natural gas delivered to power sector -1.1% -0.3% 0.1%

Price of average Henry Hub (spot) -1.4% -0.8% -0.2%

Natural gas use for electricity generation -1.5% -1.5% -0.9%

2% HRI at $50/kW

Retail electricity prices -0.3% -0.2% -0.1%

Average price of coal delivered to the power sector 0.2% -0.1% -0.4%

Coal production for power sector use 5.5% 8.0% 8.4%

Price of natural gas delivered to power sector -1.1% -0.9% -0.4%

Price of average Henry Hub (spot) -1.4% -1.3% -0.6%

Natural gas use for electricity generation -2.5% -1.7% -1.1%

4.5% HRI at $50/kW

Retail electricity prices -0.5% -0.4% -0.2%

Average price of coal delivered to the power sector 0.7% 0.6% 0.3%

Coal production for power sector use 5.8% 8.6% 9.5%

Price of natural gas delivered to power sector -1.4% -1.1% -0.7%

Price of average Henry Hub (spot) -1.7% -1.6% -1.0%

Natural gas use for electricity generation -3.4% -2.5% -1.9%

4.5% HRI at $100/kW

Retail electricity prices -0.2% 0.0% 0.0%

Average price of coal delivered to the power sector 0.5% 0.3% -0.1%

Coal production for power sector use 4.5% 7.1% 7.4%

Price of natural gas delivered to power sector -1.3% -1.1% -0.7%

Price of average Henry Hub (spot) -1.6% -1.6% -1.0%

Natural gas use for electricity generation -3.4% -2.3% -1.6%

The projected energy market and electricity retail rate impacts of this proposal are 

discussed more extensively in Chapter 3, which also presents projections of power sector 

generation and capacity changes by technology and fuel type. The change in wholesale energy 

prices and the changes in power generation were forecasted using IPM. The change in retail 

electricity prices reported in Chapter 3 is a national average across residential, commercial, and 

industrial consumers. The change in electricity retail prices and bills were forecasted using 

outputs of IPM.
5-3

Changes in supply or demand for electricity, natural gas, and coal can impact markets for 

goods and services produced by sectors that use these energy inputs in the production process or 

that supply those sectors. Changes in cost of production may result in changes in price and/or 

quantity produced by these sectors and these market changes may affect the profitability of firms 

and the economic welfare of their consumers and owners. Any potential changes in the operation 

of the electric power sector due to the proposed rule may also have an effect on upstream 

industries that supply goods and services to the sector. For example, losses for owners and 

workers at firms that supply new generation technologies and gains for firms that supply the 

parts and labor necessary to improve heat rates at existing coal steam generators. The magnitude

and direction of these potential effects outside the electricity sector and related fuel markets are 

not analyzed in this RIA. 

One potential approach to evaluating whether there are important secondary market 

impacts is to use an economy-wide model. Economy-wide models - and, more specifically, 

computable general equilibrium (CGE) models - are analytical tools that can be used to evaluate 

the impacts of a regulatory action beyond the directly-regulated sector. CGE models provide 

aggregated representations of the entire economy in equilibrium in the baseline and under a 

regulatory or policy scenario. CGE models are designed to capture substitution possibilities 

between production, consumption and trade; interactions between economic sectors; and 

interactions between a policy shock and pre-existing distortions, such as taxes. They can provide 

information on changes outside of the directly-regulated sector attributable to a regulation. For 

example, CGE studies of air pollution regulations for the power sector have found that the social 

costs and benefits may be greater or lower than partial equilibrium estimates when these 

secondary market impacts are taken into account, and that the direction of the estimates may 

depend on the form of the regulation (e.g. Goulder et al. 1999, Williams 2002, Goulder et al. 

2016). 

In March 2015, EPA established a Science Advisory Board (SAB) panel to consider the 

technical merits and challenges of using economy-wide models to evaluate costs, benefits, and 

economic impacts in regulatory development.1In September 2017, the SAB issued its final 

 

1 Science Advisory Board, USEPA. Economy-wide Modeling of the Benefits and Costs of Environmental
5-4

report, which provided recommendations on how EPA can use CGE models to offer a more 

comprehensive assessment of the benefits, costs, and economic impacts of regulatory actions.2

The report noted that the case for using CGE models to evaluate a regulation’s effects is 

strongest when the costs of abatement are expected to be large in magnitude and the sector has 

strong linkages to the rest of the economy, although the CGE models may also be useful to 

evaluate impacts of smaller regulations in some situations. The report also noted that the extent 

to which CGE models add value to the analysis depends on data availability, in that data 

limitations are a significant obstacle to achieving the granularity needed to adequately represent 

a regulation in the model to estimate its effects. In response to these and other SAB 

recommendations, EPA is in the process of building capacity to allow for the use of CGE models 

in the analysis of future regulatory actions when warranted, developing guidance for analysts on 

when CGE analysis is most likely to add value, and pursuing research priorities highlighted by 

the SAB in its report. 

5.1.2 Distributional Impacts

Any potential costs or benefits of this proposed action are not expected to be experienced 

uniformly across the population, and may not accrue to the same individuals or communities. 

OMB recommends including a description of distributional effects, as part of a regulatory 

analysis, “so that decision makers can properly consider them along with the effects on economic 

efficiency [i.e., net benefits]. Executive Order 12866 authorizes this approach.” (U.S. Office of 

Management and Budget 2003). Understanding the distribution of the compliance costs and 

benefits can aid in understanding community-level impacts associated with this proposed action.3

This section discusses the general expectations regarding how compliance costs, and health 

benefits might be distributed across the population, relying on a review of recent literature. For 

 

Regulation.

2 Science Advisory Board, USEPA. SAB Advice on the Use of Economy-Wide Models in Evaluating the Social 

Costs, Benefits, and Economic Impacts of Air Regulations. 

https://yosemite.epa.gov/sab/SABPRODUCT.NSF/0/4B3BAF6C9EA6F503852581AA0057D565/$File/EPA-SAB17-012.pdf

3 Executive Order 12898, Federal Actions to Address Environmental Justice in Minority Populations and LowIncome Populations, directs agencies to address impacts on minority and low-income populations, particularly those 

that may be considered disproportionate. EPA developed guidance, both in its Guidelines for Preparing Economic 

Analyses (U.S. EPA 2010) and Technical Guidance for Assessing Environmental Justice in Regulatory Analyses 

(U.S. EPA 2016) to provide recommendations for how to consider distributional impacts of rules on vulnerable 

populations.
5-5

example, Fullerton (2011) discussed six potential distributional impacts related to environmental 

policy using a carbon permit system: impacts on consumers (e.g. higher energy prices); impacts 

on producers or factors (e.g., lower returns to capital); scarcity rents (e.g. value of emissions 

permits); benefits associated with pollution reduction; and transition costs (e.g., from changes in 

employment or capital mix). EPA did not conduct a quantitative assessment of these 

distributional impacts for this proposal, but the qualitative discussion in this section provides a 

general overview of the types of impacts that could result from this action. We begin each subsection below with a general discussion of the incidence from the literature, followed by a brief 

discussion of the distributional consequences we might expect from this proposed action.

5.1.2.1 Distributional Aspects of Compliance Costs

The compliance costs associated with a regulatory action can impact households by 

raising the prices of goods and services; the extent of the price increase depends on if and how 

producers pass-through those costs to consumers.4 The literature evaluating the distributional 

effects of introducing a new regulation can shed light into the potential distributional impacts of 

this proposed action; as the literature relates to this action these effects can be interpreted in 

reverse, in so much as it reduces the burden on regulated entities. Expenditures on energy are 

usually a larger share of low-income household income than that of other households, and this 

share falls as income increases. Therefore, policies that increase energy prices have been found 

to be regressive, placing a greater burden on lower income households (e.g., Burtraw et al., 2009; 

Hassett et al., 2009; Williams et al. 2015). However, compliance costs will not be solely passed 

on in the form of higher energy prices, but also through lower labor earnings and returns to 

capital in the sector. Changes in employment associated with lower labor earnings can have 

distributional consequences depending on several factors (Section 5.2 discusses employment 

effects further). Capital income tends to make up a greater proportion of overall income for high 

income households. As result, the costs passed through to households via lower returns to capital 

tend to be progressive, placing a greater share of the burden on higher income households in 

these instances (Rausch et al., 2011; Fullerton et al., 2011). 

 

4 For simplicity of exposition, this discussion focuses on the incidence of compliance costs. If a deregulatory action 

reduces expected compliance costs then the distribution of cost savings would follow the incidence of the initial 

compliance costs.
5-6

The ultimate distributional outcome will depend on how changes in electricity and other 

fuel and input prices and lower returns to labor and capital propagate through the economy and 

interact with existing government transfer programs. Some studies using an economy-wide 

framework find that the overall distribution of compliance costs is progressive due to the changes 

in capital payments and the expectation that existing government transfer indexed to inflation 

will offset the burden to lower income households5(Fullerton et al., 2011; Blonz et al., 2012). 

However, others have found the distribution of compliance costs to be regressive due to a 

dominating effect of changes in energy prices to consumers (Fullerton 2011; Burtraw, et. al., 

2009; Williams, et al., 2015). Depending on the design of the policy, conclusions regarding the 

overall distributional impact can also depend on how the value of allowances are distributed or 

any revenue raised from a carbon policy is used (e.g., lowering other taxes) (Burtraw, et al., 

2009). There may also be significant heterogeneity in the costs borne by individuals within 

income deciles (Rausch et al., 2011; Cronin et al., 2017). Different classifications of households, 

for example based on lifetime income rather than contemporaneous annual income, may provide 

notably different results (Fullerton and Metcalf, 2002; Fullerton et al., 2011). 

Furthermore, there may be important regional differences in the incidence of regulations. 

There are differences in the composition of goods consumed, regional production methods (e.g., 

the composition of the generation fleet), the stringency of a rule, as well as the location of 

affected labor and capital ownership (the latter of which may be foreign-owned) (e.g. Caron et. al 

2017; Hassett et al. 2009).

Given the complexity of problem, understanding the full distributional impacts of 

compliance costs requires an economy-wide analysis (Rausch and Mowers, 2014). While such an 

analysis was not conducted for this proposal, we can attempt to understand the distributional 

impacts of a policy by examining its various components in their relevant partial equilibrium 

settings (Fullerton 2011). For example, using partial-equilibrium modeling, studies that have 

focused on the incidence of electricity sector regulations have generally found that consumers 

bear more of the compliance cost of a regulation than producers because demand for electricity is 

 

5 The incidence of government transfer payments (e.g., Social Security) is generally progressive because these 

payments represent a significant source of income for lower income deciles and only a small source for high income

deciles. Government transfer programs are often, implicitly or explicitly, indexed to inflation. For example, Social 

Security payments and veterans’ benefits are adjusted every year to account for changes in prices (i.e., inflation).
5-7

relatively inelastic and, in cost-of-service regions, increased production costs may be passed 

through electricity prices (e.g. Burtraw and Palmer 2008). Even in these studies, the details of the 

form of the regulation matters.

While the aforementioned components are important for understanding the ultimate 

distribution of compliance costs in this context, it is not clear the degree to which the specific 

results may be transferred to the current context. For example, much of the previous literature 

has focused on the distributional impacts of first best policies, such as an economy-wide 

emissions fee or permit trading program.6 Subsequent research focusing on second best policy 

designs such as economy-wide clean or renewable energy standards or power sector only permit 

trading programs have found the net distribution of costs to be relatively regressive even when 

accounting for the impacts on consumers and factors of production, as well as the indexing of 

transfer payments to inflation (Rausch and Mowers, 2014). This suggests that moving from a 

more flexible to a less flexible regulatory design, will in and of itself, affect the distribution of 

regulatory burden.

Examination of the distributional consequences of this action are further complicated by 

uncertainty regarding the compliance options that might have ultimately been adopted under the 

CPP absent of this proposed action. For example, in cases where mass-based trading programs 

were adopted, the distributional impacts would also depend on how allowances would have been 

distributed. Ultimately, the distribution of compliance costs may be regressive or progressive, 

depending on the factors indicated above as well as other implementation choices. 

5.1.2.2 Distributional Aspects of the Health Benefits 

This proposed rule would affect the level and distribution of air pollutants in the 

atmosphere. A distributional, or Environmental Justice, analysis characterizes the change in air 

pollution exposure and risk among population subgroups (see U.S. EPA 2016). Often the 

baseline incidence of health outcomes is greater among low-income or minority population 

subgroups due to a variety of factors, including a greater number of pollution sources located 

where low-income and minority populations live, work and play (Bullard, et al. 2007; United 

Church of Christ 1987); greater susceptibility to a given exposure due to physiology or other 

 

6 The directional results previously discussed are prior to any recycling of revenue from emissions fees or auctioned 

permits.
5-8

triggers (Akinbami et al. 2012); and pre-existing conditions (Schwartz et al. 2011). For these 

reasons, an EJ analysis can characterize the change in the estimated distribution of risk occurring 

as a result of implementing the policy. While the Agency did not perform a quantitative 

distributional analysis for this proposed policy, the Agency anticipates doing so in the 

Regulatory Impact Analysis for the final promulgated policy.

5.1.3 Impacts on Small Entities

Emission guidelines established under CAA section 111(d) do not impose any 

requirements on regulated entities and, thus, will not have direct impacts on these entities. After 

emission guidelines are promulgated, states establish emission standards on existing sources, and 

it is those requirements that could potentially impact small entities. As a result, this action will 

not have a significant economic impact on a substantial number of small entities under the RFA. 

Our analysis here is consistent with the analysis of the analogous situation arising when 

EPA establishes NAAQS, which do not impose any requirements on regulated entities. As here, 

any impact of a NAAQS on small entities would only arise when states take subsequent action to 

maintain and/or achieve the NAAQS through their state implementation plans. See American 

Trucking Assoc. v. EPA, 175 F.3d 1029, 1043-45 (D.C. Cir. 1999) (NAAQS do not have 

significant impacts upon small entities because NAAQS themselves impose no regulations upon 

small entities).

5.2 Employment Impacts 

Environmental regulation may affect groups of workers differently, as changes in 

abatement and other compliance activities cause labor and other resources to shift. An 

employment impact analysis describes the characteristics of groups of workers potentially 

affected by a regulation, as well as labor market conditions in affected occupations, industries, 

and geographic areas. Standard benefit-cost analyses have not typically included a separate 

analysis of regulation-induced employment impacts.7In this section we discuss the potential 

employment impacts of this proposed rule.

 

7 Labor expenses do, however, contribute toward total costs in EPA’s standard benefit-cost analyses. See Section 3.6 

of this RIA, for a discussion of labor expenses required for monitoring, reporting, and record keeping. 
5-9

Employment impacts of environmental regulations are composed of a mix of potential 

declines and gains in different sectors of the economy over time. Impacts on employment can 

vary according to labor market conditions and may differ across occupations, industries, and 

regions. Isolating employment impacts of regulation is a challenge, as they are difficult to 

disentangle from employment impacts caused by a wide variety of ongoing concurrent economic 

changes.

Environmental regulation “typically affects the distribution of employment among 

industries rather than 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 (U.S. Office of Management and Budget 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. 

If the U.S. economy is at full employment, as current economic conditions indicate is 

likely, even a large-scale environmental regulation is unlikely to have a noticeable impact on 

aggregate net employment.8Instead, labor in affected sectors would primarily be reallocated from 

one productive use to another (e.g., from producing electricity or steel to producing high 

efficiency equipment), and net national employment effects from environmental regulation 

would be small and transitory (e.g., as workers move from one job to another). There may still be 

employment effects, negative and positive, for groups of affected workers, even if the overall net 

effect is small or zero. 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. Although 

the net change in the national workforce is expected to be small under conditions of full 

employment, localized reductions in employment may adversely impact individuals and 

communities just as localized increases may have positive impacts.

 

8 Full employment is a conceptual target for the economy where everyone who wants to work and is available to

do so at prevailing wages is actively employed. The unemployment rate at full employment is not zero.
5-10

An environmental regulation affecting the power sector is expected to have a variety of 

transitional employment impacts, including reduced employment at retiring coal-fired facilities, 

as well as increased employment for the manufacture, installation, and operation of pollution 

control equipment and construction of new generation sources to replace retiring units 

(Schmalensee and Stavins (2011)). For this regulatory proposal, EPA expects potential for 

changes in the amount of labor needed in different parts of the utility power sector.9 These 

employment impacts, both negative and positive, are likely to be smaller in magnitude than those 

described in the 2015 CPP RIA (U.S. EPA 2015a), given the difference between total costs in the 

proposed option of this rule as compared to the 2015 CPP.

Illustrative compliance cost projections for the electric power sector and for the fuel 

production sector (coal and natural gas) are described in more detail in Chapter 3 of this RIA, 

and may include effects attributable to heat rate improvements (HRIs), changes in construction 

of new EGUs, generation shifts, and changes in fuel use and type. Considering first the electric 

power sector, transitional employment impacts may occur in the short-run, where we project a 

decrease in construction of new capacity, and during plant installation or modification of 

equipment and buildings, and training of new processes. Over a longer time frame, transitional 

employment impacts are replaced by ongoing operation and maintenance labor requirements. 

An important impact of the proposed rule is the implementation of measures that improve 

heat rate at existing coal-fired generators which are associated with two main categories of 

employment. In the short-run, there will be construction-related work; e.g., engineering, design, 

and installation of boilermakers and associated materials and equipment. In the long-run, there 

may be operation and maintenance employment to ensure the heat rate improvement is 

maintained in future years. (Staudt 2014). Likewise, there are similar categories of employment 

for the other shifts caused by the proposed rule such as a decrease in the construction and 

operation of new EGUs, shifts in generation, and for the fuel production sectors - coal and 

natural gas - employment impacts may occur with changes in projected coal extraction and 

natural gas extraction.

 

9 The employment analysis in this RIA is part of EPA’s ongoing effort to “conduct continuing evaluations of 

potential loss or shifts of employment which may result from the administration or enforcement of [the Act]” 

pursuant to CAA section 321(a).
5-11

Given the range of approaches to heat rate improvements that may be used to meet the 

requirements of the proposed rule, and the flexibility for States to determine these requirements, 

it is challenging to quantify the associated employment impacts. For this regulatory proposal, 

based on the illustrative scenarios modeled in IPM which are described in more detail in chapter 

3 of this RIA, EPA expects there may be potential for changes in the amount of labor needed in 

different parts of the utility power sector, but overall employment impacts are expected to be 

relatively small. The pattern of how these impacts may be distributed, across projected changes 

in electricity generation, by fuel type, indicates that coal-fired power sector employment and coal 

mining employment may be unaffected or positively impacted by this rule, whereas natural gas 

generation and fuels, nuclear, and renewable generation employment may be unaffected or 

negatively impacted by the rule. 

The U.S. Department of Energy, in cooperation with BLS, gathered and published 

detailed information on energy employment (U.S. DOE (2017a & 2017b)).10 Detailed 

information on characteristics of workers, by job tasks, is available for the electricity sector and 

related sectors, and by state. To shed light on who will be affected by any potential employment 

changes associated with the proposed rule, we review the characteristics of potentially affected 

workers.

For workers in coal-fired utilities, there are notable differences in the characteristics of 

average groups of workers relative to national workforce averages. At coal-fired utilities, there 

are more men than women in the workforce (63 percent versus 53 percent), and they are, on 

average, younger (13 percent are 55 and over, versus 22 percent nationally) (U.S. DOE 2017a). 

These characteristics for workers in natural gas electricity generation are similar, in that there are 

more men than women in the workforce (60 percent versus 53 percent), and they are, on average, 

younger (17 percent are 55 and over, versus 22 percent nationally) (U.S. DOE 2017a). For 

 

10 Main website: https://energy.gov/downloads/2017-us-energy-and-employment-report, with links to the 2017 

report (https://energy.gov/sites/prod/files/2017/01/f34/2017%20US%20Energy%20and%20Jobs%20Report_0.pdf) 

and associated state charts 

(https://energy.gov/sites/prod/files/2017/01/f34/2017%20US%20Energy%20and%20Jobs%20Report%20State%20C

harts%202_0.pdf). U.S. DOE produced the U.S. Energy and Employment Report in 2016 and 2017, and did not 

produce a report in 2018. In 2018, Energy Futures Initiative (EFI) with support from the National Association of 

State Energy Officials (NASEO) drafted a report on employment in the energy sector, available here: 

https://www.usenergyjobs.org/. 
5-12

hydroelectric and nuclear generation, there are more men in the workforce (66 percent for 

traditional hydroelectric, 62 percent for nuclear), and they are as a group, younger (14 percent 

are 55 and over, in traditional hydroelectric generation, and 12 percent in nuclear). Finally, for 

renewables, there are more male than female workers in solar electric generation (67 percent), 

also in wind (68 percent male), and in bioenergy for electricity generation (66 percent male). 

These workforces are also, on average, younger: in solar generation 13 percent of workers are 55 

and over, versus 22 percent nationally, in wind 14 percent are 55 and over, and for workers in 

bioenergy for electric generation; 11 percent are 55 and over. Electric utilities and their 

workforce are distributed widely across the country. This lessens concerns that they are 

regionally concentrated in a high unemployment location.

The demographic differences of employees in coal mining and natural gas fuels, relative 

to national workforce averages, are more notable than for electric utility workers. Men compose 

most of the coal mining workforce (76 percent versus national average 53 percent), and they are, 

on average, older, with 28 percent of the coal mining workforce age 55 and over, versus 22 

percent nationally (U.S. DOE 2017a). Similarly, men compose most of the natural gas fuels 

workforce (76 percent), and they are, on average, older, with 24 percent of the natural gas fuels 

workforce age 55 and over (U.S. DOE 2017a). Coal mines are necessarily located on coal seams, 

and natural gas fuels are extracted from basins; these and are not distributed evenly throughout 

the U.S. As such, coal and natural gas fuels workers may be more tied to local labor markets and 

economies in terms of available employment opportunities. This raises a concern discussed 

further below.

The location of energy generation and fuel extraction activities is an important issue for 

considering distributional effects. Department of Energy (2017a) observes: “But within this 

overall story of [energy employment] growth is also an uneven trajectory where some states 

experience new jobs and others grapple with decline. States such as California and Texas, which 

have abundant solar, wind, and fossil fuel resources, have shown dramatic employment gains, 

despite some losses linked to low fossil fuel prices. Coal-dependent states, such as West Virginia 

and Wyoming, have seen declines in employment since 2015.” (U.S. DOE, 2017a). In addition to 

the main report, Department of Energy has published similarly detailed information on energy 

employment, by state (DOE 2017a, 2017b).
5-13

The extent to which workers in declining industries will be significantly affected by the 

proposed action, depends on such factors as the transferability of affected workers’ skills with 

shifting labor demand in different sectors due to the action, the availability of local employment 

opportunities for affected workers in communities or industries with high unemployment, and 

the extent to which migration costs serve as barriers to job search. This latter factor is a bigger 

concern in areas with historically low migration rates. 

On the other hand, dislocated workers operating in tight labor markets may have 

experienced relatively brief periods of transitional unemployment. Some job seekers may find 

new employment opportunities due to this proposed rule; for example, if their skill set qualified 

them for new jobs implementing heat rate improvements.

Speaking more generally, localized reductions in employment may adversely affect 

individuals and communities, just as localized increases may have positive effects (U.S. EPA 

2015a p. 6-5). If potentially dislocated workers are vulnerable, for example as those in 

Appalachia likely are, besides experiencing persistent job loss as already mentioned, earnings 

can be permanently lowered, and the wider community may be negatively affected. Communitywide effects can include effects on the local tax base, the provision and quality of local public 

goods, and changes in demand for local goods and services. Neighborhood effects, when people 

influence neighbors’ behaviors, may be possible. As an example, consider the influence that 

social networks can have on job acquisition. Many job vacancies are filled by people who know 

an employee at the firm with the vacancy. This type of networking is weakened by high 

unemployment rates (Durlauf 2004). 

The distributional effects of workforce disruptions may extend beyond impacts on 

employment. Sociological studies examine different effects than those that are typically 

examined in economic studies. Workers experiencing unemployment may also experience 

negative health impacts. The unemployed population is observed to be less healthy than those 

who are employed, and the differences in health across these groups can be significant (see, for 

example, Roelfs, et al. 2011) including different rates of substance abuse (Compton, et al. 2014). 

The literature describes difficulties in identifying the cause of poorer health for the unemployed 

population. Associations between unemployment and poorer health may be partially driven by 
5-14

the possibility that workers in poorer health may be more likely to become unemployed. 

Estimates of the magnitude of the association may be biased, in part, by factors not easily 

observed or addressed by researchers that contribute both to unemployment risk as well as poorer 

health (Jin 1995, Sullivan and von Wachtner 2009). Several recent papers have attempted to 

identify a causal relationship between unemployment and health. These papers examined the 

health effects of involuntary job loss by focusing on workers who have lost their jobs due to 

layoffs or other firm-level employment reductions. For example, Sullivan and von Wachtner 

(2009) found increased mortality rates among displaced workers in Pennsylvania; and in a study 

of displaced Austrian workers, Kuhn, et al. (2007) found that job loss negatively affected men’s 

mental health.
5-15

5.3 References 

Akinbami, L.J., J.E. Mooreman, C. Bailey, H. Zahran, M. King, C. Johnson, and X. Liu. 2012. 

Trends in Asthma Prevalence, Health Care Use, and Mortality in the United States, 2001-

2010. NCHS data brief no. 94. Hyattsville, MD: National Center for Health Statistics. 

Retrieved from http://www.cdc.gov/nchs/data/databriefs/db94.htm.

Arrow, K. J.; M. L. Cropper; G. C. Eads; R. W. Hahn; L. B. Lave; R. G. Noll; Paul R. Portney;

M. Russell; R. Schmalensee; V. K. Smith and R. N. Stavins. 1996. “Benefit-Cost 

Analysis in Environmental, Health, and Safety Regulation: A Statement of Principles.” 

American Enterprise Institute, the Annapolis Center, and Resources for the Future; AEI 

Press. Available at 

<https://scholar.harvard.edu/files/stavins/files/benefit_cost_analysis_in_environmental.ae

i_.1996.pdf>. Accessed September 25, 2017.

Bullard, R.D., P. Mohai, R. Saha, and B. Wright. 2007. Toxic Wastes and Race at Twenty: 1987-

2007 Grassroots Struggles to Dismantle Environmental Racism in the United States.

Cleveland, OH: United Church of Christ Justice and Witness Ministries.

Compton, Wilson M., Joe Gfroerer, Kevin P. Conway, and Matthew S. Finger. 2014.

“Unemployment and substance outcomes in the United States 2002-2010.” Drug and 

Alcohol Dependence, 142: 350-353. 

Durlauf, S. 2004. “Neighborhood Effects.” Handbook of Regional and Urban Economics vol. 4,

J.V. Henderson and J.F. Thisse, eds. Amsterdam: North Holland.

Executive Order 13777. 2017. Presidential Executive Order on Enforcing the Regulatory Reform

Agenda. February 24.

Fullerton, D. 2011. Six Distributional Effects of Environmental Policy. Risk Analysis, 31(6):

923-929.

Goulder, L., Parry, I., Williams, R., and Burtraw, D. 1999. “The Cost-Effectiveness of

Alternative Instruments for Environmental Protection in a Second-Best Setting.” Journal 

of Public Economics, 72(3): 329-360. 144

Goulder, Lawrence H., Marc A. C. Hafstead, and Roberton C. Williams III. 2016. "General

Equilibrium Impacts of a Federal Clean Energy Standard." American Economic Journal:

Economic Policy, 8(2): 186-218.

Hassett, K., A. Mathur, G. Metcalf. 2009. “The Incidence of a U.S. Carbon Tax: A Lifetime and 

Regional Analysis”. Energy Journal, 30(2): 155-177. 

Jin, Robert L., Chandrakant P. Shah, and Tomislav J. Svoboda. 1995. “The Impact of

Unemployment on Health: A Review of the Evidence.” Canadian Medical Association
5-16

Journal, 153(5): 529-40. 

Kuhn, Andreas, Rafael Lalive, and Josef Zweimuller. 2007. “The Public Health Costs of

Unemployment,” Cahiers de Recherches Economiques du D ́epartement d’Econom ́etrie 

et d’Economie politique (DEEP) 07.08, Universit ́e de Lausanne, Facult ́e des HEC, 

DEEP. 

Roelfs, David J., Eran Shor, Karina W. Davidson, and Joseph E. Schwartz. 2011. Losing Life

and Livelihood: A Systematic Review and Meta-Analysis of Unemployment and AllCause Mortality. Social Science & Medicine, 72(6): 840-54. 

Schmalansee, R. and R. Stavins (2011). “A Guide to Economic and Policy Analysis for the

Transport Rule.” White Paper. Boston, MA. Exelon Corp.

Schwartz, J., D. Bellinger, and T. Glass. 2011a. Exploring Potential Sources of Differential

Vulnerability and Susceptibility in Risk from Environmental Hazards to Expand the 

Scope of Risk Assessment. American Journal of Public Health, 101 Suppl 1, S94-101.

Sieg, H., Smith, V.K., Banzhaf, S., and Walsh, R. 2004 “Estimating the General Equilibrium

Benefits of Large Changes in Spatially Delineated Public Goods.” International 

Economic Review, 45(4): 1047-1077.

Staudt, James E. 2014. “Estimating Labor Effects of Heat Rate Improvements. Report prepared 

for the Clean Power Plan Proposed Rule, March 6, 2014.”

Sullivan, D. and T. von Wachter. 2009. “Job Displacement and Mortality: An Analysis Using

Administrative Data.” The Quarterly Journal of Economics, 124(3): 1265-1306. 

United Church of Christ. 1987. Toxic Waste and Race in the United States: A National Report on

the Racial and Socio-Economic Characteristics of Communities with Hazardous Waste 

Sites. United Christ Church, Commission for Racial Justice.

U.S. Environmental Protection Agency (U.S. EPA). 2015a. Regulatory Impact Analysis for the

Clean Power Plan Final Rule. EPA-452/R-15-003. Office of Air Quality Planning and

Standards, Health and Environmental Impacts Division, Research Triangle Park, NC.

U.S. Environmental Protection Agency (U.S. EPA). 2016. Technical Guidance for Assessing

Environmental Justice in Regulatory Analysis. June, 2016. Available at:

<https://www.epa.gov/sites/production/files/2016-06/documents/ejtg_5_6_16_v5.1.pdf>.

U.S. Department of Energy (U.S. DOE). 2017a. U.S. Energy and Employment Report. January. 

Available at: < 

https://www.energy.gov/sites/prod/files/2017/01/f34/2017%20US%20Energy%20and%2

0Jobs%20Report_0.pdf>. Accessed 5/14/2018.

U.S. Department of Energy (U.S. DOE). 2017b. U.S. Energy and Employment Report – State 

Charts. January. Available at: < 
5-17

https://www.energy.gov/sites/prod/files/2017/01/f34/2017%20US%20Energy%20and%2

0Jobs%20Report%20State%20Charts%202_0.pdf >. Accessed 5/14/2018.

U.S. Office of Management and Budget. 2003. “Circular A-4, Regulatory Analysis”. Available

at: <https://obamawhitehouse.archives.gov/omb/circulars_a004_a-4/>. Accessed May 23,

2017.

U.S. Office of Management and Budget. 2015. 2015 Report to Congress on the Benefits and

Costs of Federal Regulations and Agency Compliance with the Unfunded Mandates 

Reform Act. Available at: 

<https://obamawhitehouse.archives.gov/sites/default/files/omb/inforeg/2015_cb/2015-

costbenefit-report.pdf>. Accessed Sept. 15, 2017.

Williams, R. 2002. “Environmental Tax Interactions when Pollution Affects Health or

Productivity.” Journal of Environmental Economics and Management, 44(2): 261-270.
6-1

CHAPTER 6: COMPARISON OF BENEFITS AND COSTS

6.1 Introduction 

This chapter presents the estimates of the climate benefits, ancillary health co-benefits, 

compliance costs and net benefits associated with this action, relative to the base case, which 

includes the CPP. We evaluate the potential regulatory impacts of the illustrative No CPP

scenario and the three illustrative policy scenarios using the present value (PV) of costs, benefits, 

and net benefits, calculated for the years 2023-2037 from the perspective of 2016, using both a 

three percent and seven percent beginning-of-period discount rate. All benefit analysis, and most 

cost analysis, begins in the year 2025. The only cost analysis for a year prior to 2025 is that for 

monitoring, reporting, and recordkeeping (MR&R), as MR&R costs are estimated to begin in 

2023. In addition, the Agency presents the assessment of costs, benefits, and net benefits for 

specific snapshot years, consistent with historic practice. In this RIA, the regulatory impacts are 

evaluated for the specific years of 2025, 2030, and 2035. 

There are potential sources of benefits and costs that may result from this proposed rule 

that have not been quantified or monetized. Due to current data and modeling limitations, our 

estimates of the benefits from reducing CO2 emissions do not include important impacts like 

ocean acidification or potential tipping points in natural or managed ecosystems. Unquantified 

benefits also include climate benefits from reducing emissions of non-CO2 greenhouse gases and 

benefits from reducing exposure to SO2, NOX, and hazardous air pollutants (e.g., mercury), as 

well as ecosystem effects and visibility impairment. The avoided compliance costs reported in 

this RIA are not social costs, although elements of the compliance costs are social costs. We do 

not account for changes in costs and benefits due to changes in economic welfare of suppliers to 

the electricity market, or to non-electricity consumers from those suppliers. Furthermore, costs 

due to interactions with pre-existing market distortions outside the electricity sector are omitted.

6.2 Methods 

EPA calculated the present value of costs, as well as the benefits and net benefits, for the 

years 2023 through 2037, using both a three percent and seven percent beginning-of-period 

discount rate from the perspective of 2016. This calculation of a present value requires an annual 
6-2

stream of costs for each year of the 2023-2037 timeframe. EPA used IPM to estimate cost and 

emission changes for the projection years 2025, 2030, and 2035. The Agency believes that these 

specific years are each representative of several surrounding years, which enables the analysis of 

costs and benefits over the timeframe of 2025-2037. The year 2025 is an approximation for when 

the standards of performance under the proposed rule might be implemented, and the Agency 

estimates that monitoring, reporting, and recordkeeping (MR&R) costs may begin in 2023. 

Therefore, MR&R costs analysis is presented beginning in the year 2023, and full benefit cost 

analysis is presented beginning in the year 2025. The analytical timeframe concludes in 2037, as 

this is the last year that may be represented with the analysis conducted for the specific year of 

2035.

Estimates of costs and emission changes in other years are determined from the mapping 

of projection years to the calendar years that they represent. In the IPM modeling for this RIA, 

the 2025 projection year represents 2025-2027, 2030 represents 2028-2032, and 2035 represents 

2033-2037.1 Consequently, the cost and emission estimates from IPM in each projection year are 

applied to the years which it represents.2 Climate benefits estimates are based on these projection 

year emission estimates, and also account for year-specific interim domestic SC-CO2 values.3

Ancillary health co-benefits are based on projection year emission estimates, and also account 

for year-specific variables that influence the size and distribution of the benefits; these include 

population growth, income growth and the baseline rate of death.4 EPA has estimated MR&R 

costs for 2023, and applies these costs only to 2023 in the present value analysis. MR&R costs 

for 2025 are applied to 2024, and all subsequent MR&R costs are applied to the 2025-2037 

timeframe in the same fashion as other cost estimates.

EPA calculated the present value and equivalent annualized value of costs, benefits, and 

net benefits over the 2023-2037 timeframe for the four illustrative scenarios under different 

methodologies for calculating benefits. In this chapter, negative cost values indicate cost savings, 

 

1 For more information regarding the mapping of projection years to calendar years, see Documentation for EPA’s 

Power Sector Modeling Platform v6 Using the Integrated Planning Model (2018), available at: 

https://www.epa.gov/airmarkets/clean-air-markets-power-sector-modeling

2 MR&R costs estimates are not based on IPM. For information on MR&R costs, see Chapter 3. 

3 As the interim domestic SC-CO2 value varies by year, the climate benefit estimates vary by year, even when 

different years are based on the same IPM projection year emission estimate.

4 As these variables differ by year, the ancillary health co-benefit estimates vary by year, even when different years 

are based on the same IPM projection year emission estimate.
6-3

negative benefit values indicate forgone benefits, and negative net benefits indicate forgone net 

benefits. 

6.3 Results

6.3.1 Analysis of 2023-2037 for E.O. 13771, Reducing Regulation and Controlling 

Regulatory Costs 

This proposed action, when finalized, would be considered a deregulatory action under 

E.O. 13771, Reducing Regulation and Controlling Regulatory Costs. Three out of the four 

illustrative scenarios analyzed have total costs less than zero. An E.O. 13771 deregulatory action 

qualifies as both: (1) one of the actions used to satisfy the provision to repeal or revise at least 

two existing regulations for each regulation issued, and (2) a cost savings for purposes of the 

total incremental cost allowance. 

Table 6-1 presents the undiscounted compliance costs for the four illustrative scenarios, 

relative to the base case, which includes the CPP. As noted earlier, the avoided compliance cost 

estimates from each IPM model year are applied to the appropriate surrounding years.
6-4

Table 6-1 Compliance Costs for the Illustrative Scenarios, Relative to Base Case (CPP), 

2023-2037 (billion 2016$)

No CPP 2% HRI 

at $50/kW

4.5% HRI 

at $50/kW

4.5% HRI 

at $100/kW

2023 (0.1) (0.0) (0.0) (0.0)

2024 (0.0) 0.0 0.0 0.0

2025 (0.7) 0.0 (0.6) 0.5

2026 (0.7) 0.0 (0.6) 0.5

2027 (0.7) 0.0 (0.6) 0.5

2028 (0.7) (0.2) (1.0) 0.2

2029 (0.7) (0.2) (1.0) 0.2

2030 (0.7) (0.2) (1.0) 0.2

2031 (0.7) (0.2) (1.0) 0.2

2032 (0.7) (0.2) (1.0) 0.2

2033 (0.4) 0.1 (0.6) 0.5

2034 (0.4) 0.1 (0.6) 0.5

2035 (0.4) 0.1 (0.6) 0.5

2036 (0.4) 0.1 (0.6) 0.5

2037 (0.4) 0.1 (0.6) 0.5

a All estimates are rounded to one decimal point, so figures may not sum due to independent rounding.

b Compliance costs included avoided compliance costs and avoided MR&R costs.

c Negative costs indicate avoided costs.

EPA calculated the present value of costs using both a three percent and seven percent 

discount rate. Whereas beginning-of-period discount rates are used elsewhere in the RIA, EPA 

used an end-of-period discount rate for E.O. 13771 analysis. These estimates for the four 

illustrative scenarios are presented in Table 6-2 and are from the perspective of 2016. 

Table 6-2 shows that that three out of the four illustrative scenarios provide cost savings. 

Under the illustrative No CPP scenario, the present value of the stream of cost savings is $5.0 

billion when discounted at 3 percent, and $2.9 billion when discounted at 7 percent. Under the 

illustrative 2 percent HRI at $50/kW scenario, the present value of the stream of cost savings is 

$0.4 billion when discounted at 3 percent, and $0.2 billion when discounted at 7 percent. Under 

the illustrative 4.5 percent HRI at $50/kW scenario, the present value of the stream of cost 

savings is $6.2 billion when discounted at 3 percent, and $3.5 billion when discounted at 7 

percent. Under the illustrative 4.5 percent HRI at $100/kW scenario, the present value of the 

stream of costs is $2.9 billion when discounted at 3 percent, and $1.6 billion when discounted at 

7 percent. These avoided compliance cost estimates represent the regulatory cost savings related 

to the regulatory allowance under E.O. 13771. Table 6-2 also presents the equivalent annualized 
6-5

value, which is a calculation that yields and even-flow of figures that would yield an equivalent 

present value.

Table 6-2 Present Value of Compliance Costs for the Illustrative Scenario, Relative to 

Base Case (CPP), 3 and 7 Percent Discount Rates, 2023-2037 (billion 2016$)

No CPP 2% HRI at 

$50/kW

4.5% HRI at 

$50/kW

4.5% HRI at 

$100/kW

3% 7% 3% 7% 3% 7% 3% 7%

2023 (0.1) (0.0) (0.0) (0.0) (0.0) (0.0) (0.0) (0.0)

2024 (0.0) (0.0) 0.0 0.0 0.0 0.0 0.0 0.0 

2025 (0.5) (0.4) 0.0 0.0 (0.5) (0.3) 0.4 0.3 

2026 (0.5) (0.3) 0.0 0.0 (0.5) (0.3) 0.4 0.2 

2027 (0.5) (0.3) 0.0 0.0 (0.5) (0.3) 0.4 0.2 

2028 (0.5) (0.3) (0.2) (0.1) (0.7) (0.4) 0.1 0.1 

2029 (0.5) (0.3) (0.2) (0.1) (0.6) (0.4) 0.1 0.1 

2030 (0.5) (0.3) (0.1) (0.1) (0.6) (0.3) 0.1 0.1 

2031 (0.4) (0.2) (0.1) (0.1) (0.6) (0.3) 0.1 0.1 

2032 (0.4) (0.2) (0.1) (0.1) (0.6) (0.3) 0.1 0.1 

2033 (0.2) (0.1) 0.1 0.0 (0.4) (0.2) 0.3 0.1 

2034 (0.2) (0.1) 0.1 0.0 (0.4) (0.2) 0.3 0.1 

2035 (0.2) (0.1) 0.1 0.0 (0.3) (0.2) 0.3 0.1 

2036 (0.2) (0.1) 0.1 0.0 (0.3) (0.1) 0.2 0.1 

2037 (0.2) (0.1) 0.1 0.0 (0.3) (0.1) 0.2 0.1 

Present Value (5.0) (2.9) (0.4) (0.2) (6.2) (3.5) 2.9 1.6 

Equivalent Annualized 

Value (0.4) (0.3) (0.0) (0.0) (0.5) (0.4) 0.2 0.2 

Notes: Negative costs indicate avoided costs. Compliance costs included avoided compliance costs and avoided 

MR&R costs. All estimates are rounded to one decimal point, so figures may not sum due to independent rounding. 

This table reflects end-of-period discount rates. 

6.3.2 Net Benefits Analysis

Net benefits analysis is presented in terms of present value and equivalent annualized 

value from the perspective of 2016, calculated using both a three percent and seven percent 

beginning-of-period discount rate. As noted earlier, negative cost values indicate cost savings, 

negative benefit values indicate forgone benefits, and negative net benefits indicate forgone net 

benefits.

6.3.2.1 Target Pollutant

Regulating pollutants jointly can promote a more efficient outcome in pollution control 

management (Tietenberg, 1973). However, in practice regulations are promulgated sequentially 

and therefore, the benefit-cost analyses supporting those regulations are also performed 
6-6

sequentially. The potential for interaction between regulations suggests that their sequencing 

may affect the realized efficiency of their design and the estimated net benefits for each 

regulation. For the 2015 Final CPP rulemaking, EPA did not consider alternative regulatory 

approaches to jointly control CO2, SO2, and NOX emission from existing power plants. This 

leaves open the possibility that an option which jointly regulates CO2, SO2, and NOX emissions 

from power plants could have achieved these reductions more efficiently than through a single 

regulation targeting CO2 emissions, conditional on statutory authority to promulgate such a 

regulation. 

In Table 6-3 through Table 6-7 we offer one perspective on the costs and benefits of this 

rule by presenting a comparison of the benefit impact associated with the targeted pollutant –

CO2 – with the compliance cost impact. Excluded from this comparison are the benefit impacts 

from SO2 and NOX emission changes that are projected to accompany the CO2 changes.5 Table 

6-3 presents results for the illustrative No CPP scenario, Table 6-4 presents results for the 

illustrative 2 percent HRI at $50/kW scenario, Table 6-5 presents results for the illustrative 4.5 

percent at $50/kW scenario, and Table 6-6 presents results for the illustrative 4.5 percent at 

$100/kW scenario. All values in Table 6-3 through Table 6-7 are present value estimates.

 

5 When considering whether a regulatory action is a potential welfare improvement (i.e., potential Pareto 

improvement) it is necessary to consider all impacts of the action. 
6-7

Table 6-3 Present Value of Compliance Costs, Benefits, and Net Benefits Associated 

with Targeted Pollutant (CO2), Illustrative No CPP Scenario, Relative to 

Base Case (CPP), 3 and 7 Percent Discount Rates, 2023-2037 (billion 2016$)

Costs Domestic 

Climate Benefits

Net Benefits 

associated with the 

Targeted Pollutant (CO2)

3% 7% 3% 7% 3% 7%

2023 (0.1) (0.0) 0.0 0.0 0.1 0.0 

2024 (0.0) (0.0) 0.0 0.0 0.0 0.0 

2025 (0.6) (0.4) (0.2) (0.0) 0.3 0.4 

2026 (0.5) (0.4) (0.2) (0.0) 0.3 0.3 

2027 (0.5) (0.3) (0.2) (0.0) 0.3 0.3 

2028 (0.5) (0.3) (0.4) (0.0) 0.1 0.3 

2029 (0.5) (0.3) (0.4) (0.0) 0.1 0.3 

2030 (0.5) (0.3) (0.3) (0.0) 0.1 0.2 

2031 (0.5) (0.3) (0.3) (0.0) 0.1 0.2 

2032 (0.4) (0.2) (0.3) (0.0) 0.1 0.2 

2033 (0.2) (0.1) (0.3) (0.0) (0.1) 0.1 

2034 (0.2) (0.1) (0.3) (0.0) (0.1) 0.1 

2035 (0.2) (0.1) (0.3) (0.0) (0.1) 0.1 

2036 (0.2) (0.1) (0.3) (0.0) (0.1) 0.1 

2037 (0.2) (0.1) (0.3) (0.0) (0.1) 0.1 

Note: Negative costs indicate avoided costs, negative benefits indicate forgone benefits, and negative net benefits 

indicate forgone net benefits.
6-8

Table 6-4 Present Value of Compliance Costs, Benefits, and Net Benefits Associated 

with Targeted Pollutant (CO2), Illustrative 2 Percent HRI at $50/kW 

Scenario, Relative to Base Case (CPP), 3 and 7 Percent Discount Rates, 2023-

2037 (billion 2016$)

Costs Domestic 

Climate Benefits

Net Benefits 

associated with the 

Targeted Pollutant (CO2)

3% 7% 3% 7% 3% 7%

2023 (0.0) (0.0) 0.0 0.0 0.0 0.0 

2024 0.0 0.0 0.0 0.0 (0.0) (0.0)

2025 0.0 0.0 (0.2) (0.0) (0.2) (0.0)

2026 0.0 0.0 (0.2) (0.0) (0.2) (0.0)

2027 0.0 0.0 (0.2) (0.0) (0.2) (0.0)

2028 (0.2) (0.1) (0.3) (0.0) (0.1) 0.1 

2029 (0.2) (0.1) (0.3) (0.0) (0.1) 0.1 

2030 (0.2) (0.1) (0.3) (0.0) (0.1) 0.1 

2031 (0.1) (0.1) (0.3) (0.0) (0.1) 0.1 

2032 (0.1) (0.1) (0.3) (0.0) (0.1) 0.1 

2033 0.1 0.0 (0.2) (0.0) (0.3) (0.1)

2034 0.1 0.0 (0.2) (0.0) (0.3) (0.1)

2035 0.1 0.0 (0.2) (0.0) (0.3) (0.1)

2036 0.1 0.0 (0.2) (0.0) (0.3) (0.1)

2037 0.1 0.0 (0.2) (0.0) (0.3) (0.1)

Note: Negative costs indicate avoided costs, negative benefits indicate forgone benefits, and negative net benefits 

indicate forgone net benefits.
6-9

Table 6-5 Present Value of Compliance Costs, Benefits, and Net Benefits Associated 

with Targeted Pollutant (CO2), Illustrative 4.5 Percent HRI at $50/kW 

Scenario, Relative to Base Case (CPP), 3 and 7 Percent Discount Rates, 2023-

2037 (billion 2016$)

Costs Domestic 

Climate Benefits

Net Benefits 

associated with the 

Targeted Pollutant (CO2)

3% 7% 3% 7% 3% 7%

2023 (0.0) (0.0) 0.0 0.0 0.0 0.0 

2024 0.0 0.0 0.0 0.0 (0.0) (0.0)

2025 (0.5) (0.4) (0.2) (0.0) 0.3 0.3 

2026 (0.5) (0.3) (0.2) (0.0) 0.3 0.3 

2027 (0.5) (0.3) (0.2) (0.0) 0.3 0.3 

2028 (0.7) (0.4) (0.3) (0.0) 0.4 0.4 

2029 (0.7) (0.4) (0.3) (0.0) 0.4 0.4 

2030 (0.6) (0.4) (0.3) (0.0) 0.4 0.3 

2031 (0.6) (0.3) (0.3) (0.0) 0.3 0.3 

2032 (0.6) (0.3) (0.3) (0.0) 0.3 0.3 

2033 (0.4) (0.2) (0.3) (0.0) 0.1 0.2 

2034 (0.4) (0.2) (0.3) (0.0) 0.1 0.2 

2035 (0.4) (0.2) (0.3) (0.0) 0.1 0.1 

2036 (0.3) (0.2) (0.3) (0.0) 0.1 0.1 

2037 (0.3) (0.1) (0.3) (0.0) 0.1 0.1 

Note: Negative costs indicate avoided costs, negative benefits indicate forgone benefits, and negative net benefits 

indicate forgone net benefits.
6-10

Table 6-6 Present Value of Compliance Costs, Benefits, and Net Benefits Associated 

with Targeted Pollutant (CO2), Illustrative 4.5 Percent HRI at $100/kW 

Scenario, Relative to Base Case (CPP), 3 and 7 Percent Discount Rates, 2023-

2037 (billion 2016$)

Costs Domestic 

Climate Benefits

Net Benefits 

associated with the 

Targeted Pollutant (CO2)

3% 7% 3% 7% 3% 7%

2023 (0.0) (0.0) 0.0 0.0 0.0 0.0 

2024 0.0 0.0 0.0 0.0 (0.0) (0.0)

2025 0.4 0.3 (0.1) (0.0) (0.5) (0.3)

2026 0.4 0.3 (0.1) (0.0) (0.5) (0.3)

2027 0.4 0.2 (0.1) (0.0) (0.5) (0.3)

2028 0.1 0.1 (0.2) (0.0) (0.4) (0.1)

2029 0.1 0.1 (0.2) (0.0) (0.3) (0.1)

2030 0.1 0.1 (0.2) (0.0) (0.3) (0.1)

2031 0.1 0.1 (0.2) (0.0) (0.3) (0.1)

2032 0.1 0.1 (0.2) (0.0) (0.3) (0.1)

2033 0.3 0.1 (0.2) (0.0) (0.5) (0.2)

2034 0.3 0.1 (0.2) (0.0) (0.5) (0.2)

2035 0.3 0.1 (0.2) (0.0) (0.5) (0.1)

2036 0.3 0.1 (0.2) (0.0) (0.4) (0.1)

2037 0.2 0.1 (0.2) (0.0) (0.4) (0.1)

Note: Negative costs indicate avoided costs, negative benefits indicate forgone benefits, and negative net benefits 

indicate forgone net benefits.

Table 6-7 presents a summary of the present value and equivalent annualized value of 

cost, benefits, and net benefits associated with the four illustrative scenarios, relative to the base 

case including CPP.
6-11

Table 6-7 Present Value of Compliance Costs, Benefits, and Net Benefits Associated 

with Targeted Pollutant (CO2), Relative to Base Case (CPP), 3 and 7 Percent 

Discount Rates, 2023-2037 (billion 2016$)

Costs Domestic 

Climate Benefits

Net Benefits 

associated with the 

Targeted Pollutant 

(CO2)

3% 7% 3% 7% 3% 7%

Present Value

No CPP (5.2) (3.1) (3.9) (0.4) 1.2 2.7 

2% HRI at $50/kW (0.4) (0.3) (3.2) (0.3) (2.8) (0.1)

4.5% HRI at $50/kW (6.4) (3.7) (3.2) (0.3) 3.2 3.4 

4.5% HRI at $100/kW 3.0 1.7 (2.4) (0.2) (5.4) (2.0)

Equivalent Annualized Value

No CPP (0.4) (0.3) (0.3) (0.0) 0.1 0.3 

2% HRI at $50/kW (0.0) (0.0) (0.3) (0.0) (0.2) (0.0)

4.5% HRI at $50/kW (0.5) (0.4) (0.3) (0.0) 0.3 0.4 

4.5% HRI at $100/kW 0.3 0.2 (0.2) (0.0) (0.5) (0.2)

Note: Negative costs indicate avoided costs, negative benefits indicate forgone benefits, and negative net benefits 

indicate forgone net benefits.

6.3.2.2 Net Benefits Including Forgone Air Pollutant Co-Benefits

When considering whether a regulatory action is a potential welfare improvement (i.e., 

potential Pareto improvement) it is necessary to consider all impacts of the action. Therefore, 

tables in this section provide the estimates of the benefits, costs, and net benefits of the 

illustrative scenarios, inclusive of the benefit impacts from the SO2 and NOX emission changes 

that are projected to accompany the CO2 changes. In these tables, the estimates for the ancillary 

health co-benefits are derived using PM2.5 log-linear concentration-response functions that 

quantify risk associated with the full range of PM2.5 exposures experienced by the population.

There are additional important benefit impacts that EPA could not monetize. Due to 

current data and modeling limitations, our estimates of the benefit impacts from changing CO2

emissions do not include important impacts like ocean acidification or potential tipping points in 

natural or managed ecosystems. Unquantified benefits also include climate benefits from 

changing emissions of non-CO2 greenhouse gases and co-benefits from changes in exposure to 

SO2, NOX, and hazardous air pollutants (e.g., mercury), as well as ecosystem effects and 

visibility impairment.
6-12

Table 6-8 through Table 6-12 contain present value estimates of compliance costs, 

benefits, and net benefits inclusive of ancillary health co-benefits for the four illustrative 

scenarios.

Table 6-8 Illustrative No CPP Scenario: Present Value of Compliance Costs, Benefits 

(Inclusive of Health Co-Benefits), and Net Benefits, Relative to Base Case 

(CPP), 20230-2037 (billion 2016$)

Costs Benefits Net Benefits

3% 7% 3% 7% 3% 7%

2023 (0.1) (0.0) 0.0 to 0.0 0.0 to 0.0 0.1 to 0.1 0.0 to 0.0 

2024 (0.0) (0.0) 0.0 to 0.0 0.0 to 0.0 0.0 to 0.0 0.0 to 0.0 

2025 (0.6) (0.4) (2.4) to (5.3) (1.4) to (3.3) (1.9) to (4.8) (1.1) to (2.9)

2026 (0.5) (0.4) (2.4) to (5.3) (1.4) to (3.2) (1.9) to (4.8) (1.0) to (2.8)

2027 (0.5) (0.3) (2.4) to (5.2) (1.3) to (3.1) (1.9) to (4.7) (1.0) to (2.7)

2028 (0.5) (0.3) (3.6) to (7.9) (1.9) to (4.4) (3.1) to (7.3) (1.6) to (4.1)

2029 (0.5) (0.3) (3.6) to (7.8) (1.8) to (4.2) (3.1) to (7.3) (1.5) to (3.9)

2030 (0.5) (0.3) (3.6) to (7.9) (1.8) to (4.1) (3.1) to (7.4) (1.5) to (3.8)

2031 (0.5) (0.3) (3.5) to (7.8) (1.7) to (3.9) (3.1) to (7.3) (1.4) to (3.6)

2032 (0.4) (0.2) (3.5) to (7.8) (1.6) to (3.8) (3.1) to (7.3) (1.4) to (3.5)

2033 (0.2) (0.1) (2.5) to (5.4) (1.1) to (2.5) (2.3) to (5.2) (1.0) to (2.4)

2034 (0.2) (0.1) (2.5) to (5.4) (1.0) to (2.4) (2.2) to (5.1) (0.9) to (2.3)

2035 (0.2) (0.1) (2.4) to (5.3) (1.0) to (2.3) (2.2) to (5.1) (0.9) to (2.2)

2036 (0.2) (0.1) (2.4) to (5.3) (0.9) to (2.2) (2.2) to (5.0) (0.8) to (2.1)

2037 (0.2) (0.1) (2.4) to (5.2) (0.9) to (2.1) (2.2) to (5.0) (0.8) to (2.0)

Note: Negative costs indicate avoided costs, negative benefits indicate forgone benefits, and negative net benefits 

indicate forgone net benefits.
6-13

Table 6-9 Illustrative 2 Percent HRI at $50/kW Scenario: Present Value of Compliance 

Costs, Benefits (Inclusive of Health Co-Benefits), and Net Benefits, Relative 

to Base Case (CPP), 2023-2037 (billion 2016$)

Costs Benefits Net Benefits

3% 7% 3% 7% 3% 7%

2023 (0.0) (0.0) 0.0 to 0.0 0.0 to 0.0 0.0 to 0.0 0.0 to 0.0 

2024 0.0 0.0 0.0 to 0.0 0.0 to 0.0 (0.0) to (0.0) (0.0) to (0.0)

2025 0.0 0.0 (2.2) to (4.7) (1.3) to (3.0) (2.2) to (4.7) (1.3) to (3.0)

2026 0.0 0.0 (2.1) to (4.7) (1.2) to (2.8) (2.2) to (4.7) (1.3) to (2.9)

2027 0.0 0.0 (2.1) to (4.6) (1.2) to (2.7) (2.1) to (4.7) (1.2) to (2.7)

2028 (0.2) (0.1) (3.2) to (7.0) (1.7) to (3.9) (3.0) to (6.8) (1.6) to (3.8)

2029 (0.2) (0.1) (3.2) to (6.9) (1.6) to (3.7) (3.0) to (6.8) (1.5) to (3.6)

2030 (0.2) (0.1) (3.2) to (7.3) (1.6) to (3.8) (3.1) to (7.2) (1.5) to (3.7)

2031 (0.1) (0.1) (3.2) to (7.2) (1.5) to (3.7) (3.0) to (7.1) (1.5) to (3.6)

2032 (0.1) (0.1) (3.2) to (7.2) (1.5) to (3.5) (3.1) to (7.1) (1.4) to (3.4)

2033 0.1 0.0 (2.3) to (5.0) (1.0) to (2.3) (2.3) to (5.1) (1.0) to (2.4)

2034 0.1 0.0 (2.2) to (5.0) (0.9) to (2.2) (2.3) to (5.1) (1.0) to (2.3)

2035 0.1 0.0 (1.9) to (4.2) (0.8) to (1.8) (2.0) to (4.3) (0.8) to (1.8)

2036 0.1 0.0 (1.9) to (4.2) (0.7) to (1.7) (2.0) to (4.3) (0.8) to (1.8)

2037 0.1 0.0 (1.9) to (4.1) (0.7) to (1.6) (2.0) to (4.2) (0.7) to (1.7)

Note: Negative costs indicate avoided costs, negative benefits indicate forgone benefits, and negative net benefits 

indicate forgone net benefits.

Table 6-10 Illustrative 4.5 Percent HRI at $50/kW Scenario: Present Value of 

Compliance Costs, Benefits (Inclusive of Health Co-Benefits), and Net 

Benefits, Relative to Base Case (CPP), 2023-2037 (billion 2016$)

Costs Benefits Net Benefits

3% 7% 3% 7% 3% 7%

2023 (0.0) (0.0) 0.0 to 0.0 0.0 to 0.0 0.0 to 0.0 0.0 to 0.0 

2024 0.0 0.0 0.0 to 0.0 0.0 to 0.0 (0.0) to (0.0) (0.0) to (0.0)

2025 (0.5) (0.4) (2.2) to (4.9) (1.4) to (3.1) (1.7) to (4.4) (1.0) to (2.8)

2026 (0.5) (0.3) (2.2) to (4.9) (1.3) to (3.3) (1.7) to (4.4) (1.0) to (2.9)

2027 (0.5) (0.3) (2.2) to (4.9) (1.2) to (3.1) (1.7) to (4.4) (0.9) to (2.8)

2028 (0.7) (0.4) (3.4) to (7.3) (1.8) to (4.5) (2.7) to (6.6) (1.4) to (4.1)

2029 (0.7) (0.4) (3.3) to (7.3) (1.7) to (4.3) (2.7) to (6.6) (1.3) to (3.9)

2030 (0.6) (0.4) (3.1) to (6.7) (1.5) to (3.5) (2.4) to (6.1) (1.2) to (3.1)

2031 (0.6) (0.3) (3.0) to (6.7) (1.5) to (3.3) (2.4) to (6.1) (1.1) to (3.0)

2032 (0.6) (0.3) (3.0) to (6.7) (1.4) to (3.2) (2.4) to (6.1) (1.1) to (2.9)

2033 (0.4) (0.2) (2.2) to (4.7) (0.9) to (2.1) (1.8) to (4.3) (0.7) to (1.9)

2034 (0.4) (0.2) (2.1) to (4.6) (0.9) to (2.0) (1.8) to (4.3) (0.7) to (1.9)

2035 (0.4) (0.2) (2.5) to (5.6) (1.0) to (2.4) (2.2) to (5.2) (0.9) to (2.2)

2036 (0.3) (0.2) (2.5) to (5.5) (1.0) to (2.3) (2.2) to (5.2) (0.8) to (2.1)

2037 (0.3) (0.1) (2.5) to (5.4) (0.9) to (2.2) (2.1) to (5.1) (0.8) to (2.0)

Note: Negative costs indicate avoided costs, negative benefits indicate forgone benefits, and negative net benefits 

indicate forgone net benefits.
6-14

Table 6-11 Illustrative 4.5 Percent HRI at $100/kW Scenario: Present Value of 

Compliance Costs, Benefits (Inclusive of Health Co-Benefits), and Net 

Benefits, Relative to Base Case (CPP), 2023-2037 (billion 2016$)

Costs Benefits Net Benefits

3% 7% 3% 7% 3% 7%

2023 (0.0) (0.0) 0.0 to 0.0 0.0 to 0.0 0.0 to 0.0 0.0 to 0.0 

2024 0.0 0.0 0.0 to 0.0 0.0 to 0.0 (0.0) to (0.0) (0.0) to (0.0)

2025 0.4 0.3 (1.7) to (3.8) (1.1) to (2.4) (2.1) to (4.2) (1.4) to (2.7)

2026 0.4 0.3 (1.9) to (4.2) (1.2) to (2.8) (2.2) to (4.6) (1.5) to (3.1)

2027 0.4 0.2 (1.8) to (4.2) (1.2) to (2.7) (2.2) to (4.5) (1.4) to (2.9)

2028 0.1 0.1 (2.8) to (6.3) (1.7) to (3.9) (2.9) to (6.4) (1.7) to (4.0)

2029 0.1 0.1 (2.8) to (6.3) (1.6) to (3.7) (2.9) to (6.4) (1.7) to (3.8)

2030 0.1 0.1 (2.6) to (5.7) (1.3) to (3.0) (2.7) to (5.8) (1.4) to (3.0)

2031 0.1 0.1 (2.6) to (5.6) (1.2) to (2.8) (2.7) to (5.7) (1.3) to (2.9)

2032 0.1 0.1 (2.5) to (5.6) (1.2) to (2.7) (2.7) to (5.7) (1.2) to (2.8)

2033 0.3 0.1 (1.8) to (3.9) (0.8) to (1.8) (2.1) to (4.2) (0.9) to (1.9)

2034 0.3 0.1 (1.8) to (3.9) (0.8) to (1.7) (2.1) to (4.1) (0.9) to (1.9)

2035 0.3 0.1 (1.7) to (3.6) (0.7) to (1.5) (1.9) to (3.9) (0.8) to (1.7)

2036 0.3 0.1 (1.6) to (3.6) (0.6) to (1.5) (1.9) to (3.8) (0.8) to (1.6)

2037 0.2 0.1 (1.6) to (3.5) (0.6) to (1.4) (1.9) to (3.8) (0.7) to (1.5)

Note: Negative costs indicate avoided costs, negative benefits indicate forgone benefits, and negative net benefits 

indicate forgone net benefits.

Table 6-12 presents a summary of the present value and equivalent annualized value of 

these four illustrative scenarios, inclusive of ancillary health co-benefits.

Table 6-12 Present Value of Compliance Costs, Benefits (Inclusive of Health CoBenefits), and Net Benefits, Relative to Base Case (CPP), 3 and 7 Percent 

Discount Rates, 2023-2037 (billion 2016$)

Costs Benefits Net Benefits

3% 7% 3% 7% 3% 7%

Present Value

No CPP (5.2) (3.1) (37.2) to (81.5) (17.9) to (41.3) (32.0) to (76.3) (14.8) to (38.2)

2% HRI at $50/kW (0.4) (0.3) (32.7) to (72.4) (15.9) to (36.9) (32.3) to (72.0) (15.7) to (36.7)

4.5% HRI at $50/kW (6.4) (3.7) (34.3) to (75.2) (16.6) to (39.4) (27.9) to (68.8) (12.8) to (35.6)

4.5% HRI at $100/kW 3.0 1.7 (27.2) to (60.2) (13.9) to (31.9) (30.2) to (63.2) (15.6) to (33.7)

Equivalent Annualized Value

No CPP (0.4) (0.3) (3.1) to (6.8) (2.0) to (4.5) (2.7) to (6.4) (1.6) to (4.2)

2% HRI at $50/kW (0.0) (0.0) (2.7) to (6.1) (1.7) to (4.1) (2.7) to (6.0) (1.7) to (4.0)

4.5% HRI at $50/kW (0.5) (0.4) (2.9) to (6.3) (1.8) to (4.3) (2.3) to (5.8) (1.4) to (3.9)

4.5% HRI at $100/kW 0.3 0.2 (2.3) to (5.0) (1.5) to (3.5) (2.5) to (5.3) (1.7) to (3.7)

Note: Negative costs indicate avoided costs, negative benefits indicate forgone benefits, and negative net benefits 

indicate forgone net benefits.
6-15

6.3.2.3 Net Benefits Including Forgone Air Pollution Co-Benefits Calculated According to 

Sensitivity Analysis Assumptions

Table 6-13 through Table 6-15 report the estimated benefits, costs, and net benefits of the 

illustrative scenarios according to different sensitivity analysis assumptions. These results reflect 

different assumptions regarding the relationship between PM2.5 exposure and the risk of 

premature death, as detailed in Chapter 4. In Table 6-12, we report the net benefits calculated 

using the sum of the estimated ozone and PM2.5-related forgone benefits using a no-threshold 

concentration-response parameter for PM2.5. In Table 6-13, we report the net benefits calculated 

using the sum of the estimated ozone and PM2.5-related forgone benefits assuming that the PM2.5-

attributable risks fall to zero below the lowest measured levels of the two long-term PM2.5

mortality studies used to quantify risk. In Table 6-14, we report the net benefits calculated using 

the sum of the estimated ozone and PM2.5-related forgone benefits assuming that PM2.5 related 

benefits fall to zero below the PM2.5 National Ambient Air Quality Standard. Finally, we report 

the net benefits calculated using the sum of the estimated ozone and PM2.5-related forgone 

benefits using an alternative concentration-response parameter to quantify PM2.5-attributable 

risks at low levels (Table 6-15). These are present value and equivalent annualized value 

estimates, similar to the presentation of results in Table 6-12.

EPA has generally expressed a greater confidence in the effects observed around the 

mean PM2.5 concentrations in the long-term epidemiological studies; this does not necessarily 

imply a concentration threshold below which there are no effects. As such, these analyses are 

designed to increase transparency rather than imply a specific lower bound on the size of the 

ancillary health co-benefits. As noted in the preceding section, there are additional important 

benefit impacts that EPA could not monetize.
6-16

Table 6-13 Present Value of Compliance Costs, Benefits, and Net Benefits assuming that 

PM2.5 Related Benefits Fall to Zero Below the Lowest Measured Level of 

Each Long-Term PM2.5 Mortality Study, Relative to Base Case (CPP), 3 and 

7 Percent Discount Rates, 2023-2037 (billion 2016$)

Costs Benefits Excluding Benefits 

below LML

Net Benefits Excluding Benefits 

below LML

3% 7% 3% 7% 3% 7%

Present Value

No CPP (5.2) (3.1) (32.6) to (27.1) (15.5) to (12.7) (27.4) to (21.9) (12.3) to (9.6)

2% HRI at $50/kW (0.4) (0.3) (28.6) to (24.7) (13.7) to (11.8) (28.2) to (24.3) (13.5) to (11.5)

4.5% HRI at $50/kW (6.4) (3.7) (29.8) to (23.9) (14.2) to (11.2) (23.4) to (17.5) (10.4) to (7.5)

4.5% HRI at $100/kW 3.0 1.7 (23.7) to (19.3) (11.5) to (9.3) (26.7) to (22.3) (13.3) to (11.1)

Equivalent Annualized Value

No CPP (0.4) (0.3) (2.7) to (2.3) (1.7) to (1.4) (2.3) to (1.8) (1.4) to (1.1)

2% HRI at $50/kW (0.0) (0.0) (2.4) to (2.1) (1.5) to (1.3) (2.4) to (2.0) (1.5) to (1.3)

4.5% HRI at $50/kW (0.5) (0.4) (2.5) to (2.0) (1.6) to (1.2) (2.0) to (1.5) (1.1) to (0.8)

4.5% HRI at $100/kW 0.3 0.2 (2.0) to (1.6) (1.3) to (1.0) (2.2) to (1.9) (1.5) to (1.2)

Note: Negative costs indicate avoided costs, negative benefits indicate forgone benefits, and negative net benefits 

indicate forgone net benefits.

Table 6-14 Present Value of Compliance Costs, Benefits, and Net Benefits assuming that 

PM2.5 Related Benefits Fall to Zero Below the PM2.5 National Ambient Air 

Quality Standard, Relative to Base Case (CPP), 3 and 7 Percent Discount 

Rates, 2023-2037 (billion 2016$)

Costs Benefits Excluding Benefits 

below NAAQS

Net Benefits Excluding Benefits 

below NAAQS

3% 7% 3% 7% 3% 7%

Present Value

No CPP (5.2) (3.1) (5.6) to (9.8) (1.3) to (3.7) (0.4) to (4.6) 1.8 to (0.6)

2% HRI at $50/kW (0.4) (0.3) (5.0) to (9.6) (1.3) to (3.8) (4.7) to (9.2) (1.1) to (3.6)

4.5% HRI at $50/kW (6.4) (3.7) (4.3) to (7.0) (0.9) to (2.4) 2.1 to (0.6) 2.8 to 1.4

4.5% HRI at $100/kW 3.0 1.7 (3.5) to (6.2) (0.9) to (2.4) (6.5) to (9.2) (2.6) to (4.2)

Equivalent Annualized Value

No CPP (0.4) (0.3) (0.5) to (0.8) (0.1) to (0.4) (0.0) to (0.4) 0.2 to (0.1)

2% HRI at $50/kW (0.0) (0.0) (0.4) to (0.8) (0.1) to (0.4) (0.4) to (0.8) (0.1) to (0.4)

4.5% HRI at $50/kW (0.5) (0.4) (0.4) to (0.6) (0.1) to (0.3) 0.2 to (0.1) 0.3 to 0.2

4.5% HRI at $100/kW 0.3 0.2 (0.3) to (0.5) (0.1) to (0.3) (0.5) to (0.8) (0.3) to (0.5)

Note: Negative costs indicate avoided costs, negative benefits indicate forgone benefits, and negative net benefits 

indicate forgone net benefits.
6-17

Table 6-15 Present Value of Compliance Costs, Benefits, and Net Benefits assuming 

Alternate Method for Calculating PM2.5 Benefits at Low Levels, Relative to 

Base Case (CPP), 3 and 7 Percent Discount Rates, 2023-2037 (billion 2016$)

Costs

Benefits

Assuming Alternate 

Concentration-Response

Net Benefits 

Assuming Alternate 

Concentration-Response

3% 7% 3% 7% 3% 7%

Present Value

No CPP (5.2) (3.1) (67.7) (33.8) (62.5) (30.7)

2% HRI at $50/kW (0.4) (0.3) (59.3) (29.9) (58.9) (29.7)

4.5% HRI at $50/kW (6.4) (3.7) (63.2) (31.8) (56.8) (28.0)

4.5% HRI at $100/kW 3.0 1.7 (50.2) (25.5) (53.2) (27.2)

Equivalent Annualized Value

No CPP (0.4) (0.3) (5.7) (3.7) (5.2) (3.4)

2% HRI at $50/kW (0.0) (0.0) (5.0) (3.3) (4.9) (3.3)

4.5% HRI at $50/kW (0.5) (0.4) (5.3) (3.5) (4.8) (3.1)

4.5% HRI at $100/kW 0.3 0.2 (4.2) (2.8) (4.5) (3.0)

Note: Negative costs indicate avoided costs, negative benefits indicate forgone benefits, and negative net benefits 

indicate forgone net benefits.
6-18

6.4 References 

Tieteneberg, T. 1973. “Specific Taxes and the Control of Pollution: A General Equilibrium 

Analysis.” The Quarterly Journal of Economics, 86:503-522.
7-1

CHAPTER 7: APPENDIX – UNCERTAINTY ASSOCIATED WITH ESTIMATING THE

SOCIAL COST OF CARBON

7.1 Overview of Methodology Used to Develop Interim Domestic SC-CO2 Estimates

The domestic SC-CO2 estimates rely on the same ensemble of three integrated 

assessment models (IAMs) that were used to develop the IWG global SC-CO2 estimates (DICE 

2010, FUND 3.8, and PAGE 2009).1 The three IAMs translate emissions into changes in 

atmospheric greenhouse concentrations, atmospheric concentrations into changes in temperature, 

and changes in temperature into economic damages. The emissions projections used in the 

models are based on specified socio-economic (GDP and population) pathways. These emissions 

are translated into atmospheric concentrations, and concentrations are translated into warming 

based on each model’s simplified representation of the climate and a key parameter, equilibrium 

climate sensitivity. The effect of the changes in estimated in terms of consumption-equivalent 

economic damages. As in the IWG exercise, three key inputs were harmonized across the three 

models: a probability distribution for equilibrium climate sensitivity; five scenarios for 

economic, population, and emissions growth; and discount rates.2 All other model features were 

left unchanged. Future damages are discounted using constant discount rates of both 3 and 7 

percent, as recommended by OMB Circular A-4. The domestic share of the global SC-CO2 – i.e., 

an approximation of the climate change impacts that occur within U.S. borders – are calculated 

directly in both FUND and PAGE. However, DICE 2010 generates only global SC-CO2

estimates. Therefore, EPA approximated U.S. damages as 10 percent of the global values from 

the DICE model runs, based on the results from a regionalized version of the model (RICE 2010) 

reported in Table 2 of Nordhaus (2017).3

 

1 The full models names are as follows: Dynamic Integrated Climate and Economy (DICE); Climate Framework for 

Uncertainty, Negotiation, and Distribution (FUND); and Policy Analysis of the Greenhouse Gas Effect (PAGE).

2 See the IWG’s summary of its methodology in the 2015 Clean Power Plan docket, document ID number EPA-HQOAR-2013-0602-37033, “Technical Update of the Social Cost of Carbon for Regulatory Impact Analysis Under 

Executive Order 12866, Interagency Working Group on Social Cost of Carbon (May 2013, Revised July 2015)”. See 

also National Academies (2017) for a detailed discussion of each of these modeling assumptions.

3 Nordhaus, William D. 2017. Revisiting the social cost of carbon. Proceedings of the National Academy of 

Sciences of the United States, 114(7): 1518-1523.
7-2

The steps involved in estimating the social cost of CO2 are as follows. The three 

integrated assessment models (FUND, DICE, and PAGE) are run using the harmonized 

equilibrium climate sensitivity distribution, five socioeconomic and emissions scenarios, 

constant discount rates described above. Because the climate sensitivity parameter is modeled 

probabilistically, and because PAGE and FUND incorporate uncertainty in other model 

parameters, the final output from each model run is a distribution over the SC-CO2 in year t 

based on a Monte Carlo simulation of 10,000 runs. For each of the IAMs, the basic 

computational steps for calculating the social cost estimate in a particular year t is 1.) calculate 

the temperature effects and (consumption-equivalent) damages in each year resulting from the 

baseline path of emissions; 2.) adjust the model to reflect an additional unit of emissions in year 

t; 3.) recalculate the temperature effects and damages expected in all years beyond t resulting 

from this adjusted path of emissions, as in step 1; and 4.) subtract the damages computed in step 

1 from those in step 3 in each model period and discount the resulting path of marginal damages 

back to the year of emissions. In PAGE and FUND step 4 focuses on the damages attributed to 

the US region in the models. As noted above, DICE does not explicitly include a separate US 

region in the model and therefore, EPA approximates U.S. damages in step 4 as 10 percent of the 

global values based on the results of Nordhaus (2017). This exercise produces 30 separate 

distributions of the SC-CO2 for a given year, the product of 3 models, 2 discount rates, and 5 

socioeconomic scenarios. Following the approach used by the IWG, the estimates are equally 

weighted across models and socioeconomic scenarios in order to reduce the dimensionality of 

the results down to two separate distributions, one for each discount rate.

7.2 Treatment of Uncertainty in Interim Domestic SC-CO2 Estimates

There are various sources of uncertainty in the SC-CO2 estimates used in this RIA. Some 

uncertainties pertain to aspects of the natural world, such as quantifying the physical effects of 

greenhouse gas emissions on Earth systems. Other sources of uncertainty are associated with 

current and future human behavior and well-being, such as population and economic growth, 

GHG emissions, the translation of Earth system changes to economic damages, and the role of 

adaptation. It is important to note that even in the presence of uncertainty, scientific and 

economic analysis can provide valuable information to the public and decision makers, though 

the uncertainty should be acknowledged and when possible taken into account in the analysis 
7-3

(National Academies 2013).4 OMB Circular A-4 also requires a thorough discussion of key 

sources of uncertainty in the calculation of benefits and costs, including more rigorous 

quantitative approaches for higher consequence rules. This section summarizes the sources of 

uncertainty considered in a quantitative manner in the domestic SC-CO2 estimates. 

The domestic SC-CO2 estimates consider various sources of uncertainty through a 

combination of a multi-model ensemble, probabilistic analysis, and scenario analysis. We 

provide a summary of this analysis here; more detailed discussion of each model and the 

harmonized input assumptions can be found in the 2017 National Academies report. For 

example, the three IAMs used collectively span a wide range of Earth system and economic 

outcomes to help reflect the uncertainty in the literature and in the underlying dynamics being 

modeled. The use of an ensemble of three different models at least partially addresses the fact 

that no single model includes all of the quantified economic damages. It also helps to reflect 

structural uncertainty across the models, which is uncertainty in the underlying relationships 

between GHG emissions, Earth systems, and economic damages that are included in the models. 

Bearing in mind the different limitations of each model and lacking an objective basis upon 

which to differentially weight the models, the three integrated assessment models are given equal 

weight in the analysis.

Monte Carlo techniques were used to run the IAMs a large number of times. In each 

simulation the uncertain parameters are represented by random draws from their defined 

probability distributions. In all three models the equilibrium climate sensitivity is treated 

probabilistically based on the probability distribution from Roe and Baker (2007) calibrated to 

the IPCC AR4 consensus statement about this key parameter.5 The equilibrium climate 

sensitivity is a key parameter in this analysis because it helps define the strength of the climate 

response to increasing GHG concentrations in the atmosphere. In addition, the FUND and PAGE 

models define many of their parameters with probability distributions instead of point estimates. 

For these two models, the model developers’ default probability distributions are maintained for 

 

4

Institute of Medicine of the National Academies. 2013. Environmental Decisions in the Face of Uncertainty. The 

National Academies Press.

5 Specifically, the Roe and Baker distribution for the climate sensitivity parameter was bounded between 0 and 10 

with a median of 3 °C and a cumulative probability between 2 and 4.5 °C of two-thirds.
7-4

all parameters other than those superseded by the harmonized inputs (i.e., equilibrium climate 

sensitivity, socioeconomic and emissions scenarios, and discount rates). More information on the 

uncertain parameters in PAGE and FUND is available upon request.

For the socioeconomic and emissions scenarios, uncertainty is included in the analysis by 

considering a range of scenarios selected from the Stanford Energy Modeling Forum exercise, 

EMF-22. Given the dearth of information on the likelihood of a full range of future 

socioeconomic pathways at the time the original modeling was conducted, and without a basis 

for assigning differential weights to scenarios, the range of uncertainty was reflected by simply 

weighting each of the five scenarios equally for the consolidated estimates. To better understand 

how the results vary across scenarios, results of each model run are available in the docket.

The outcome of accounting for various sources of uncertainty using the approaches 

described above is a frequency distribution of the SC-CO2 estimates for emissions occurring in a 

given year for each discount rate. Unlike the approach taken for consolidating results across 

models and socioeconomic and emissions scenarios, the SC-CO2 estimates are not pooled across 

different discount rates because the range of discount rates reflects both uncertainty and, at least 

in part, different policy or value judgements; uncertainty regarding this key assumption is 

discussed in more detail below. The frequency distributions reflect the uncertainty around the 

input parameters for which probability distributions were defined, as well as from the multimodel ensemble and socioeconomic and emissions scenarios where probabilities were implied 

by the equal weighting assumption. It is important to note that the set of SC-CO2 estimates 

obtained from this analysis does not yield a probability distribution that fully characterizes 

uncertainty about the SC-CO2 due to impact categories omitted from the models and sources of 

uncertainty that have not been fully characterized due to data limitations.

Figure 7-1 presents the frequency distribution of the domestic SC-CO2 estimates for 

emissions in 2030 for each discount rate. Each distribution represents 150,000 estimates based 

on 10,000 simulations for each combination of the three models and five socioeconomic and 

emissions scenarios. In general, the distributions are skewed to the right and have long right tails, 

which tend to be longer for lower discount rates. To highlight the difference between the impact 

of the discount rate on the SC-CO2 and other quantified sources of uncertainty, the bars below 
7-5

the frequency distributions provide a symmetric representation of quantified variability in the 

SC-CO2 estimates conditioned on each discount rate. The full set of SC-CO2 results through 

2050 is available in the docket.

Figure 7-1 Frequency Distribution of Interim Domestic SC-CO2 Estimates for 2030 (in 

2016$ per metric ton CO2)

As illustrated by the frequency distributions in Figure 7-1, the assumed discount rate 

plays a critical role in the ultimate estimate of the social cost of carbon. 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. Circular 

A-4 recommends that costs and benefits be discounted using the rates of 3 percent and 7 percent 

to reflect the opportunity cost of consumption and capital, respectively. Circular A-4 also 

recommends quantitative sensitivity analysis of key assumptions6, and offers guidance on what 

sensitivity analysis can be conducted in cases where a rule will have important intergenerational 

benefits or costs. To account for ethical considerations of future generations and potential 

 

6

“If benefit or cost estimates depend heavily on certain assumptions, you should make those assumptions explicit 

and carry out sensitivity analyses using plausible alternative assumptions.” (OMB 2003, page 42).
7-6

uncertainty in the discount rate over long time horizons, Circular A-4 suggests “further 

sensitivity analysis using a lower but positive discount rate in addition to calculating net benefit 

using discount rates of 3 and 7 percent” (page 36) and notes that research from the 1990s 

suggests intergenerational rates “from 1 to 3 percent per annum” (OMB 2003). We consider the 

uncertainty in this key assumption by calculating the domestic SC-CO2 based on a 2.5 percent 

discount rate, in addition to the 3 and 7 percent used in the main analysis. Using a 2.5 percent 

discount rate, the average domestic SC-CO2 estimate across all the model runs for emissions 

occurring over 2025-2035 ranges from $10 to $12 per metric ton of CO2 (2016$). In this case the 

forgone domestic climate benefits in 2025 are $340, $300, $180 and $460 million under the 

illustrative 2 percent HRI at $50/kW scenario, 4.5 percent HRI at $50/kW scenario, 4.5 percent 

HRI at $100/kW scenario, and No CPP scenario, respectively; by 2035, the estimated forgone 

benefits increase to $590, $640, $470and $710 million under the illustrative 2 percent HRI at 

$50/kW scenario, 4.5 percent HRI at $50/kW scenario, 4.5 percent HRI at $100/kW scenario, 

and No CPP scenario, respectively.

In addition to the approach to accounting for the quantifiable uncertainty described 

above, the scientific and economics literature has further explored known sources of uncertainty 

related to estimates of the SC-CO2. For example, researchers have published papers that explore 

the sensitivity of IAMs and the resulting SC-CO2 estimates to different assumptions embedded in 

the models (see, e.g., Hope (2013), Anthoff and Tol (2013), and Nordhaus (2014)). However, 

there remain additional sources of uncertainty that have not been fully characterized and 

explored due to remaining data limitations. Additional research is needed in order to expand the 

quantification of various sources of uncertainty in estimates of the SC-CO2 (e.g., developing 

explicit probability distributions for more inputs pertaining to climate impacts and their 

valuation). On the issue of intergenerational discounting, some experts have argued that a 

declining discount rate would be appropriate to analyze impacts that occur far into the future 

(Arrow et al., 2013). However, additional research and analysis is still needed to develop a 

methodology for implementing a declining discount rate and to understand the implications of 

applying these theoretical lessons in practice. The 2017 National Academies report also provides 

recommendations pertaining to discounting, emphasizing the need to more explicitly model the 

uncertainty surrounding discount rates over long time horizons, its connection to uncertainty in 

economic growth, and, in turn, to climate damages using a Ramsey-like formula (National 
7-7

Academies 2017). These and other research needs are discussed in detail in the 2017 National 

Academies’ recommendations for a comprehensive update to the current methodology, including 

a more robust incorporation of uncertainty. 

7.3 Forgone Global Climate Benefits

In addition to requiring reporting of impacts at a domestic level, OMB Circular A-4 

states that when an agency “evaluate[s] a regulation that is likely to have effects beyond the 

borders of the United States, these effects should be reported separately” (page 15).7 This 

guidance is relevant to the valuation of damages from CO2 and other GHGs, given that GHGs 

contribute to damages around the world independent of the country in which they are emitted. 

Therefore, in this section we present the forgone global climate benefits in 2030 from this 

proposed rulemaking using the global SC-CO2 estimates corresponding to the model runs that 

generated the domestic SC-CO2 estimates used in the main analysis. The average global SC-CO2

estimate across all the model runs for emissions occurring over 2025-2035 range from $6 to $9 

per metric ton of CO2 emissions (in 2016 dollars) using a 7 percent discount rate, and $53 to $63 

per metric ton of CO2 emissions (2016$) using a 3 percent discount rate. The domestic SC-CO2

estimates presented above are approximately 19 percent and 14 percent of these global SC-CO2

estimates for the 7 percent and 3 percent discount rates, respectively. 

Applying these estimates to the forgone CO2 emission reductions results in estimated 

forgone global climate benefits in 2025 of $210, $180, $110, and $280 million (2016$) under the 

illustrative 2 percent HRI at $50/kW scenario, 4.5 percent HRI at $50/kW scenario, 4.5 percent 

HRI at $100/kW scenario, and No CPP scenario, respectively, using a 7 percent discount rate; 

this increases to $1.7 billion, $1.5 billion, $950 million, and $2.4 billion (2016$) under the 

illustrative 2 percent HRI at $50/kW scenario, 4.5 percent HRI at $50/kW scenario, 4.5 percent 

 

7 While Circular A-4 does not elaborate on this guidance, the basic argument for adopting a domestic only 

perspective for the central benefit-cost analysis of domestic policies is based on the fact that the authority to regulate 

only extends to a nation’s own residents who have consented to adhere to the same set of rules and values for 

collective decision-making, as well as the assumption that most domestic policies will have negligible effects on the 

welfare of other countries’ residents (EPA 2010; Kopp et al. 1997; Whittington et al. 1986). In the context of 

policies that are expected to result in substantial effects outside of U.S. borders, an active literature has emerged 

discussing how to appropriately treat these impacts for purposes of domestic policymaking (e.g., Gayer and Viscusi 

2016, 2017; Anthoff and Tol, 2010; Fraas et al. 2016; Revesz et al. 2017). This discourse has been primarily focused 

on the regulation of greenhouse gases (GHGs), for which domestic policies may result in impacts outside of U.S. 

borders due to the global nature of the pollutants.
7-8

HRI at $100/kW scenario, and No CPP scenario, respectively, using a 3 percent discount rate. By 

2035, the forgone global climate benefits are estimated to be $460, $490, $360 and $550 million 

(2016$) under the illustrative 2 percent HRI at $50/kW scenario, 4.5 percent HRI at $50/kW 

scenario, 4.5 percent HRI at $100/kW scenario, and No CPP scenario, respectively, using a 7 

percent discount rate. Using a 3 percent discount rate, this increases to $3.1, $3.4, $2.5, and $3.8 

billion under the illustrative 2 percent HRI at $50/kW scenario, 4.5 percent HRI at $50/kW 

scenario, 4.5 percent HRI at $100/kW scenario, and No CPP scenario, respectively.

Under the sensitivity analysis considered above using a 2.5 percent discount rate, the 

average global SC-CO2 estimate across all the model runs for emissions occurring over 2025-

2035 ranges from $77 to $90 per metric ton of CO2 (2016$); in this case the forgone global 

climate benefits in 2025 are $2.6, $2.3, $1.4, and $3.5 billion (2016$) under the illustrative 2 

percent HRI at $50/kW scenario, 4.5 percent HRI at $50/kW scenario, 4.5 percent HRI at 

$100/kW scenario, and No CPP scenario, respectively; by 2035, the forgone global benefits in 

this sensitivity case increase to $4.5, $4.8, $3.6, and $5.4 billion (2016$) under the illustrative 2 

percent HRI at $50/kW scenario, 4.5 percent HRI at $50/kW scenario, 4.5 percent HRI at 

$100/kW scenario, and No CPP scenario, respectively.
7-9

7.4 References

Anthoff, D., and R. J. Tol. 2010. On international equity weights and national decision making 

on climate change. Journal of Environmental Economics and Management, 60(1): 14-20.

Anthoff, D. and Tol, R.S.J. 2013. “The uncertainty about the social cost of carbon: a 

decomposition analysis using FUND.” Climatic Change, 117: 515-530.

Arrow, K., M. Cropper, C. Gollier, B. Groom, G. Heal, R. Newell, W. Nordhaus, R. Pindyck, W. 

Pizer, P. Portney, T. Sterner, R.S.J. Tol, and M. Weitzman. 2013. “Determining Benefits 

and Costs for Future Generations.” Science, 341: 349-350.

Fraas, A., R. Lutter, S. Dudley, T. Gayer, J. Graham, J.F. Shogren, and W.K. Viscusi. 2016. 

Social Cost of Carbon: Domestic Duty. Science, 351(6273): 569.

Gayer, T., and K. Viscusi. 2016. Determining the Proper Scope of Climate Change Policy 

Benefits in U.S. Regulatory Analyses: Domestic versus Global Approaches. Review of 

Environmental Economics and Policy, 10(2): 245-63.

Hope, Chris. 2013. “Critical issues for the calculation of the social cost of CO2: why the 

estimates from PAGE09 are higher than those from PAGE2002.” Climatic Change, 117: 

531-543.

Kopp, R.J., A.J. Krupnick, and M. Toman. 1997. Cost-Benefit Analysis and Regulatory Reform: 

An Assessment of the Science and the Art. Report to the Commission on Risk 

Assessment and Risk Management.

National Academies of Sciences, Engineering, and Medicine. 2017. Valuing Climate Damages: 

Updating Estimation of the Social Cost of Carbon Dioxide. National Academies Press. 

Washington, DC Available at <https://www.nap.edu/catalog/24651/valuing-climatedamages-updating-estimation-of-the-social-cost-of> Accessed May 30, 2017.

Nordhaus, W. 2014. “Estimates of the Social Cost of Carbon: Concepts and Results from the 

DICE-2013R Model and Alternative Approaches.” Journal of the Association of 

Environmental and Resource Economists, 1(1/2): 273-312.

Nordhaus, William D. 2017. “Revisiting the social cost of carbon.” Proceedings of the National 

Academy of Sciences of the United States, 114 (7): 1518-1523.

Revesz R.L., J.A. Schwartz., P.H. Howard Peter H., K. Arrow, M.A. Livermore, M. 

Oppenheimer, and T. Sterner Thomas. 2017. The social cost of carbon: A global 

imperative. Review of Environmental Economics and Policy, 11(1):172–173.

Roe, G., and M. Baker. 2007. “Why is climate sensitivity so unpredictable?” Science, 318:629-

632.

U.S. Environmental Protection Agency (U.S. EPA). 2010. Guidelines for Preparing Economic 

Analyses. Office of the Administrator. EPA 240-R-10-001 December 2010. Available at: 
7-10

<https://www.epa.gov/environmental-economics/guidelines-preparing-economicanalyses>.

Whittington, D., & MacRae, D. (1986). The Issue of Standing in Cost-Benefit Analysis. Journal 

of Policy Analysis and Management, 5(4): 665-682.
8-1

CHAPTER 8: APPENDIX – AIR QUALITY MODELING

8.1 Air Quality Modeling Platform

In this section we describe the air quality modeling platform that was used to support the 

benefits analysis for the proposed rule. As part of this assessment we used existing air quality 

modeling for 2011 and 2023 to estimate PM2.5 and ozone concentrations in 2025, 2030, and 2035 

for each of the base case and four illustrative scenarios identified in Chapter 4. The modeling 

platform consists of several components including the air quality model, meteorology, estimates 

of international transport, and base year and future year emissions from anthropogenic and 

natural sources. An overview of each of these platform comments is provided in the subsections 

below.

8.1.1 Air Quality Model, Meteorology and Boundary Conditions

We used the Comprehensive Air Quality Model with Extensions (CAMx version 6.40) 

with the Carbon Bond chemical mechanism CB6r4 for modeling base year and future year ozone 

and PM2.5 concentrations (Ramboll, 2016). CAMx is a three-dimensional grid-based 

photochemical air quality model designed to simulate the formation and fate of oxidant 

precursors, primary and secondary particulate matter concentrations, and deposition over 

national, regional and urban spatial scales. Consideration of the different processes (e.g., 

transport and deposition) that affect primary (directly emitted) and secondary (formed by 

atmospheric processes) pollutants in different locations is fundamental to understanding and 

assessing the effects of emissions on air quality concentrations.

The geographic extent of the modeling domain covers the 48 contiguous states along with 

the southern portions of Canada and the northern portions of Mexico as shown in 8-1. This 

modeling domain contains 25 vertical layers with a top at about 17,550 meters and horizontal 

grid resolution of 12 km x 12 km. The model simulations produce hourly air quality 

concentrations for each 12-km grid cell across the modeling domain. 
8-2

Figure 8-1 Air Quality Modeling Domain

The 2011 meteorological data for air quality modeling were derived from running 

Version 3.4 of the Weather Research Forecasting Model (WRF) (Skamarock, et al., 2008). The 

meteorological outputs from WRF include hourly-varying horizontal wind components (i.e., 

speed and direction), temperature, moisture, vertical diffusion rates, and rainfall rates for each 

vertical layer in each grid cell. The 2011 meteorology was used for both the 2011 base year and 

2023 future year air quality modeling. Details of the annual 2011 meteorological model 

simulation and evaluation are provided in a separate technical support document (US EPA, 

2014a) which can be obtained at: 

http://www.epa.gov/ttn/scram/reports/MET_TSD_2011_final_11-26-14.pdf 

The lateral boundary and initial species condition concentrations are provided by a threedimensional global atmospheric chemistry model, GEOS-Chem (Yantosca, 2004) standard 

version 8-03-02 with 8-02-01 chemistry. The global GEOS-Chem model simulates atmospheric 

chemical and physical processes driven by assimilated meteorological observations from the 

NASA’s Goddard Earth Observing System (GEOS-5).1 GEOS-Chem was run for 2011 with a 

grid resolution of 2.0 degrees x 2.5 degrees (latitude-longitude). The predictions were used to 

provide one-way dynamic boundary condition concentrations at three-hour intervals and an 

initial concentration field for the CAMx simulations. The 2011 boundary concentrations from 

 

1 Additional information available at: 

http://gmao.gsfc.nasa.gov/GEOS/ and http://wiki.seas.harvard.edu/geos-chem/index.php/GEOS-5).
8-3

GEOS-Chem were used for both the 2011 and 2023 model simulations. The procedures for 

translating GEOS-Chem predictions to initial and boundary concentrations are described 

elsewhere (Henderson, 2014). More information about the GEOS-Chem model and other 

applications using this tool is available at: http://www-as.harvard.edu/chemistry/trop/geos

8.1.2 2011 and 2023 Emissions

The purpose of the 2011 base case is to represent the year 2011 in a manner consistent 

with the methods used in corresponding future-year cases, including the 2023 future year base 

case. The emissions data in this platform are primarily based on the 2011NEIv2 for point 

sources, nonpoint sources, commercial marine vessels (CMV), nonroad mobile sources and fires. 

The onroad mobile source emissions are similar to those in the 2011NEIv2, but were generated 

using the 2014a version of the Motor Vehicle Emissions Simulator (MOVES2014a) 

(http://www.epa.gov/otaq/models/moves/). The 2011 and 2023 emission inventories incorporate 

revisions implemented based on comments received on the Notice of Data Availability (NODA) 

issued in January 2017 “Preliminary Interstate Ozone Transport Modeling Data for the 2015 

Ozone National Ambient Air Quality Standard” (82 FR 1733), along with revisions made from 

prior notices and rulemakings on earlier versions of the 2011 platform. The preparation of the 

emission inventories for air quality modeling is described in the Technical Support Document 

(TSD) Additional Updates to Emissions Inventories for the Version 6.3, 2011 Emissions 

Modeling Platform for the Year 2023 (US EPA, 2017a). Electronic copies of the emission 

inventories and ancillary data used to produce the emissions inputs to the air quality model are 

available from the 2011en and 2023en section of the EPA Air Emissions Modeling website for 

the 2011v6.3 emissions modeling platform: https://www.epa.gov/air-emissions-modeling/2011-

version-63-platform

The emission inventories for the future year of 2023 were developed using projection 

methods that are specific to the type of emission source. Future emissions are projected from the 

2011 base case either by running models to estimate future year emissions from specific types of 

emission sources (e.g., EGUs, and onroad and nonroad mobile sources), or for other types of 

sources by adjusting the base year emissions according to the best estimate of changes expected 

to occur in the intervening years. For sectors which depend strongly on meteorology (such as 

biogenic and fires), the same emissions are used in the base and future years to be consistent with 
8-4

the 2011 meteorology used when modeling 2023. For the remaining sectors, rules and specific 

legal obligations that go into effect in the intervening years, along with changes in activity for 

the sector, are considered when possible. Emissions inventories for neighboring countries used in 

our modeling are included in this platform, specifically 2011 and 2023 emissions inventories for 

Mexico, and 2013 and 2025 emissions inventories for Canada. The meteorological data used to 

create and temporalize emissions for the future year cases is held constant and represents the 

year 2011. The same ancillary data files2 are used to prepare the future year emissions 

inventories for air quality modeling as were used to prepare the 2011 base year inventories with 

the exception of speciation profiles for mobile sources and temporal profiles for EGUs.

The projected EGU emissions reflect the emissions reductions in the Final Mercury and 

Air Toxics (MATS) rule announced on December 21, 2011, the Cross-State Air Pollution Rule 

(CSAPR) issued July 6, 2011, and the CSAPR Update issued October 26, 2016. The 2023 EGU 

projected inventory was developed using an engineering analysis approach. EPA started with 

2016 reported, seasonal, historical emissions for each unit. The emissions data for NOX and SO2

for units that report data under either the Acid Rain Program (ARP) and/or the CSAPR were 

aggregated to the summer/ozone season period (May-September) and winter/non-ozone period 

(January-April and October-December).3 Adjustments to 2016 levels were made to account for 

retirements, coal to gas conversion, retrofits, state-of-the-art combustion controls, along with 

other unit-specific adjustments. Details and these adjustments, and information about handling 

for units not reporting under Part 75 and pollutants other than NOX and SO2 are described in the 

emissions modeling TSD (US EPA, 2017a). 

The 2023 non-EGU stationary source emissions inventory includes enforceable national 

rules and programs including the Reciprocating Internal Combustion Engines (RICE) and 

cement manufacturing National Emissions Standards for Hazardous Air Pollutants (NESHAPs) 

and Boiler Maximum Achievable Control Technology (MACT) reconsideration reductions. 

Projection factors and percent reductions for non-EGU point sources reflect comments received 

 

2 Ancillary data files include temporal, spatial, and VOC/PM2.5 speciation surrogates.

3 EPA notes that historical state-level ozone season EGU NOX emission rates are publicly available and quality 

assured data. They are monitored using continuous emissions monitors (CEMs) data and are reported to EPA

directly by power sector sources. They are reported under Part 75 of the CAA.
8-5

by EPA in response to the January 2017 NODA, along with emissions reductions due to national 

and local rules, control programs, plant closures, consent decrees and settlements. Growth and 

control factors provided by states and by regional organizations on behalf of states were applied. 

Reductions to criteria air pollutant (CAP) emissions from stationary engines resulting as cobenefits to the Reciprocating Internal Combustion Engines (RICE) National Emission Standard 

for Hazardous Air Pollutants (NESHAP) are included. Reductions due to the New Source 

Performance Standards (NSPS) VOC controls for oil and gas sources, and the NSPS for process 

heaters, internal combustion engines, and natural gas turbines were also included. 

For point and nonpoint oil and gas sources, state projection factors were generated using 

historical oil and gas production data available for 2011 to 2015 from EIA and information from 

AEO 2017 projections to year 2023. Co-benefits of stationary engines CAP reductions (RICE 

NESHAP) and controls from New Source Performance Standards (NSPS) are reflected for select 

source categories. Mid-Atlantic Regional Air Management Association (MARAMA) factors for 

the year 2023 were used where applicable. Projection factors for other nonpoint sources such as 

stationary source fuel combustion, industrial processes, solvent utilization, and waste disposal, 

reflect emissions reductions due to control programs along with comments on the growth and 

control of these sources as a result of the January 2017 NODA and information gathered from 

prior rulemakings and outreach to states on emission inventories. 

The MOVES2014a-based 2023 onroad mobile source emissions account for changes in 

activity data and the impact of on-the-books national rules including: the Tier 3 Vehicle 

Emission and Fuel Standards Program, the 2017 and Later Model Year Light-Duty Vehicle 

Greenhouse Gas Emissions and Corporate Average Fuel Economy Standards (LD GHG), the 

Renewable Fuel Standard (RFS2), the Mobile Source Air Toxics Rule, the Light Duty Green 

House Gas/Corporate Average Fuel Efficiency (CAFE) standards for 2012-2016, the Greenhouse 

Gas Emissions Standards and Fuel Efficiency Standards for Medium- and Heavy-Duty Engines 

and Vehicles, the Light-Duty Vehicle Tier 2 Rule, and the Heavy-Duty Diesel Rule. The 

MOVES-based emissions also include state rules related to the adoption of LEV standards, 

inspection and maintenance programs, Stage II refueling controls, and local fuel restrictions. 
8-6

The nonroad mobile 2023 emissions, including railroads and commercial marine vessel 

emissions also include all national control programs. These control programs include the Clean 

Air Nonroad Diesel Rule – Tier 4, the Nonroad Spark Ignition rules, and the Locomotive-Marine 

Engine rule. For ocean-going vessels (Class 3 marine), the emissions data reflect the 2005 

voluntary Vessel Speed Reduction (VSR) within 20 nautical miles, the 2007 and 2008 auxiliary 

engine rules, the 40 nautical mile VSR program, the 2009 Low Sulfur Fuel regulation, the 2009-

2018 cold ironing regulation, the use of 1 percent sulfur fuel in the Emissions Control Area 

(ECA) zone, the 2012-2015 Tier 2 NOX controls, the 2016 0.1 percent sulfur fuel regulation in 

ECA zone, and the 2016 International Marine Organization (IMO) Tier 3 NOX controls. NonU.S. and U.S. category 3 commercial marine emissions were projected to 2025 using consistent 

methods that incorporated controls based on ECA and IMO global NOX and SO2 controls. 

8.1.3 2011 Model Evaluation for Ozone and PM2.5

An operational model performance evaluation was conducted to examine the ability of 

the 2011 base year model run to simulate the corresponding 2011 measured ozone and PM2.5

concentrations. This evaluation focused on four statistical metrics comparing model predictions 

to the corresponding observations. The performance statistics include mean bias, mean error, 

normalized mean bias, and normalized mean error. Mean bias (MB) is the sum of the difference 

(predicted – observed) divided by the total number of replicates (n). Mean bias is given in units 

of ppb and is defined as:

MB = 1

n

∑ (P − O)

n

1

(Eq-1)

Where:

• P is the model-predicted concentration;

• O is the observed concentrations; and

• n is the total number of observation

Mean error (ME) calculates the sum of the absolute value of the difference (predicted -

observed) divided by the total number of replicates (n). Mean error is given in units of ppb and is 

defined as:
8-7

ME = 1

n

∑ |P − O|

n

1

(Eq-2)

Normalized mean bias (NMB) is the sum of the difference (predicted - observed) over the 

sum of observed values. NMB is a useful model performance indicator because it avoids over 

inflating the observed range of values, especially at low concentrations. Normalized mean bias is 

given in percentage units and is defined as:

NMB = ∑ (P−O)

n

1

∑ (O)

n

1

∗ 100 (Eq-3)

Normalized mean error (NME) is the sum of the absolute value of the difference 

(predicted - observed) divided by the sum of observed values. Normalized mean error is given in 

percentage units and is defined as:

NME = ∑ |P−O|

n

1

∑ (O)

n

1

∗ 100 (Eq-4)

For PM2.5, performance statistics were calculated for modeled and observed 24-hour 

average concentrations paired by day and location for the entire year. Performance statistics were 

calculated for monitoring data in the Chemical Speciation Network (CSN)4 and, separately, for 

monitoring data in the Interagency Monitoring of Protected Visual Environments (IMPROVE)5

network. For ozone, performance statistics were calculated for modeled concentrations with 

observed 8-hour daily maximum (MDA8) ozone concentrations at or above 60 ppb6 over the 

period May through September for monitoring sites in the Air Quality System (AQS)7,8 network. 

 

4 Additional information on the measurements made at CSN monitoring sites can be found at the following web link: 

https://www.epa.gov/amtic/chemical-speciation-network-csn. 

5 Additional information on the measurements made at IMPROVE monitoring sites can be found at the following 

web link: https://www3.epa.gov/ttnamti1/visdata.html. 

6 Performance statistics are calculated for days with measured values at or above 60 ppb in order to focus the 

evaluation on days with high rather than low concentrations.

7 Additional information on the measurements made at AQS monitoring sites can be found at the following web 

link: https://www.epa.gov/aqs. 
8-8

For both PM2.5 and ozone, the modeled and predicted pairs of data were aggregated by 9 regions 

across the U.S. for the calculation of model performance statistics. These 9 regions are shown in 

Figure 8-2.

9

Figure 8-2 NOAA Climate Regions

Model performance statistics for PM2.5 for each region are provided in Table 8-1. These 

data indicate that over the year as a whole, PM2.5 is over predicted in the Northeast, Ohio Valley, 

Upper Midwest, Southeast, and Northwest regions and under predicted in the South and 

Southwest regions. Normalized mean bias is within ±30 percent in all regions except the 

Northwest which has somewhat larger model over-predictions. Model performance for PM2.5 for 

the 2011 modeling platform is similar to the model performance results for other contemporary, 

state of the science photochemical model applications (Simon et al., 2012). Additional details on 

PM2.5 model performance for the 2011 base year model run can be found in the Technical 

Support Document for EPA’s preliminary regional haze modeling (US EPA, 2017b). 

 

8 Note that the AQS data base also includes measurements made at monitoring sites in the Clean Air Status and 

Trends Network (CASTNet).

9 Source: http://www.ncdc.noaa.gov/monitoring-references/maps/us-climate-regions.php#references. 
8-9

Table 8-1 Model Performance Statistics by Region for PM2.5

Region Network No. of 

Obs

MB

(μg/m3)

ME

(μg/m3)

NMB

(%)

NME

(%)

Northeast

IMPROVE 1577 0.87 2.21 17.70 44.90

CSN 2788 0.97 4.04 9.70 40.40

Ohio Valley

IMPROVE 680 0.10 2.96 1.20 35.50

CSN 2475 0.13 3.85 1.10 32.80

Upper Midwest

IMPROVE 700 0.83 2.37 14.20 40.40

CSN 1343 1.37 3.66 13.60 36.30

Southeast

IMPROVE 1172 0.52 3.54 6.30 43.20

CSN 1813 0.19 3.92 1.70 34.20

South

IMPROVE 933 -0.47 2.69 -6.50 37.40

CSN 962 -0.08 4.48 -0.75 39.50

Southwest

IMPROVE 3695 -1.12 1.86 -28.00 46.30

CSN 746 -0.08 3.93 -1.00 47.10

N. Rockies/

Plains

IMPROVE 1952 0.07 1.39 2.40 44.90

CSN 275 -2.07 4.18 -21.80 43.90

Northwest

IMPROVE 1901 1.19 2.28 43.20 82.90

CSN 668 5.77 7.25 69.90 87.90

West

IMPROVE 1782 -1.08 2.08 -25.30 48.50

CSN 936 -2.92 5.08 -23.10 40.30

Model performance statistics for May through September modeled and MDA8 ozone 

concentrationsfor each region are provided in Table 8-2. Overall, measured ozone is under 

predicted in most regions, except for the Northeast and Southeast where over prediction is found. 

Normalized mean bias is within ±15 percent in all regions. Model performance for ozone for the 

2011 modeling platform is similar to the model performance results for other contemporary, state 

of the science photochemical model applications (Simon et al., 2012). Additional details on 

ozone model performance for the 2011 base year model run can be found in the Air Quality 

Technical Support Document for EPA’s preliminary interstate ozone transport modeling for the 

2015 ozone National Ambient Air Quality Standard (US EPA, 2017c). 
8-10

Table 8-2 Model Performance Statistics by Region for Ozone on Days Above 60 ppb

Region No. of Obs MB

(ppb)

ME

(ppb)

NMB

(%)

NME

(%)

Northeast 4085 1.20 7.30 1.80 10.70

Ohio Valley 6325 -0.60 7.50 -0.90 11.10

Upper Midwest 1162 -4.00 7.60 -5.90 11.10

Southeast 4840 2.30 6.80 3.40 10.20

South 5694 -5.30 8.40 -7.60 12.20

Southwest 6033 -6.20 8.50 -9.40 12.90

N. Rockies/Plains 380 -7.20 8.40 -11.40 13.40

Northwest 79 -5.60 9.00 -8.70 14.00

West 8655 -8.60 10.30 -12.20 14.50

Thus, the model performance results demonstrate the scientific credibility of our 2011 

modeling platform for predicting PM2.5 and ozone concentrations. These results provide 

confidence in the ability of the modeling platform to provide a reasonable projection of expected 

future year ozone concentrations and contributions.

8.2 Source Apportionment Tags

As described in Chapter 4, CAMx source apportionment modeling was used to track 

ozone and PM2.5 component species impacts from pre-defined groups of emissions sources 

(source tags). Separate tags were created for state-level EGUs split by fuel type (coal units versus 

non-coal units10). For some states with low EGU emissions, EGUs are grouped with nearby states 

that also have low EGU emissions. In addition, there are no coal EGUs operating in the 2023 

emissions case for the following states: Idaho, Oregon, and Washington. Therefore, there is no 

coal EGU tag for those states. Similarly, there were no EGUs (coal or non-coal) in Washington 

D.C. in the 2023 emissions scenario, so there were no EGU tags for Washington D.C. There 

were also several domain-wide tags for sources other than EGUs. Table 9-3 provides a full list of 

the emissions group tags that were tracked in the source apportionment modeling.

 

10 For the purposes of this analysis non-coal fuels include emissions from natural gas, oil, biomass, and waste coalfired EGUs.
8-11

Table 8-3 Table of Source Apportionment Tags

Coal-fired EGU tags Non-coal EGU tags Domain-wide tags

• Alabama

• Arizona

• Arkansas

• California

• Colorado

• Connecticut + Rhode Island

• Delaware + New Jersey

• Florida

• Georgia

• Illinois

• Indiana

• Iowa

• Kansas

• Kentucky

• Louisiana

• Maine + Mass. + New Hamp. + 

Vermont

• Maryland

• Michigan

• Minnesota

• Mississippi

• Missouri

• Montana

• Nebraska

• Nevada

• New Mexico

• New York

• North Carolina

• North Dakota + South Dakota

• Ohio

• Oklahoma

• Pennsylvania

• South Carolina

• Tennessee

• Texas

• Utah

• Virginia

• West Virginia

• Wisconsin

• Wyoming

• Tribal Data*

• Alabama

• Arizona

• Arkansas

• California

• Colorado

• Connecticut + Rhode Island

• Delaware + New Jersey

• Florida

• Georgia

• Idaho + Oregon + Washington

• Illinois

• Indiana

• Iowa

• Kansas

• Kentucky

• Louisiana

• Maine + Mass. + New Hamp. + 

Vermont

• Maryland

• Michigan

• Minnesota

• Mississippi

• Missouri

• Montana

• Nebraska

• Nevada

• New Mexico

• New York

• North Carolina

• North Dakota + South Dakota

• Ohio

• Oklahoma

• Pennsylvania

• South Carolina

• Tennessee

• Texas

• Utah

• Virginia

• West Virginia

• Wisconsin

• Wyoming

• Tribal Data11

• EGU retirements 

through 2025

• EGU retirements 

2026-2030

• All U.S. 

anthropogenic 

emissions from 

source sectors 

other than EGUs

• International 

within-domain 

emissions 

(sources 

occurring in 

Canada, Mexico, 

and from 

offshore marine 

vessels and 

drilling 

platforms)

• Fires (wildfires 

and prescribed 

fires)

• Biogenic sources

• Boundary 

conditions

 

11 EGUs operating on tribal lands were tracked together in a single tag. There are EGUs on tribal land in the 

following states: Utah (coal), New Mexico (coal), Arizona (coal and non-coal), Idaho (non-coal). EGU emissions 

occurring on tribal lands were not included in the state-level EGU source tags.
8-12

Examples of the magnitude and spatial extent of ozone tagged contributions are provided 

in Figure 8-3 through Figure 8-6 for coal and non-coal EGUs in Pennsylvania and Texas. These 

figures show how both the magnitude and the spatial patterns of contributions can differ between 

coal and non-coal EGU units within a state and downwind. In addition, the figures demonstrate 

that the spatial extent of contributions can vary substantially from state to state depending on the 

location of sources, the magnitude of their emissions, and meteorology. Moreover, day to day 

variations in meteorology can have a substantial impact on day to day patterns in contributions, 

which we capture in our analysis. While we used the daily contributions in our calculations, 

seasonal average contributions are presented here to provide a general illustration of the 

differential spatial patterns of contribution. 

Figure 8-3 Map of Pennsylvania Coal EGU Tag Contribution to Seasonal Average 

MDA8 Ozone
8-13

Figure 8-4 Map of Pennsylvania Non-Coal EGU Tag Contribution to Seasonal Average 

MDA8 Ozone

Figure 8-5 Map of Texas Coal EGU Tag Contribution to Seasonal Average MDA8 

Ozone
8-14

Figure 8-6 Map of Texas Non-Coal EGU Tag Contribution to Seasonal Average MDA8 

Ozone

Examples of the magnitude and spatial extent of tagged contributions for PM2.5

component species are provided in Figure 8-7 through Figure 8-12. Examples are provided for 

coal-fired EGUs in Indiana. These figures show how both the magnitude and the spatial patterns 

of contributions can differ by season and by PM2.5 component species. The species which are 

formed through chemical reactions in the atmosphere (sulfate and nitrate) have a more regional 

signal than directly emitted primary PM2.5 (organic aerosol (OA), elemental carbon (EC), and 

crustal material12) whose impact is more local in nature. In addition, the chemistry and transport 

can vary by season with nitrate contributions being higher in the winter than in the summer and 

sulfate contributions being higher in the summer than in the winter.

 

12 Crustal material refers to metals that are commonly found in the earth’s crust such as Aluminum, Calcium, Iron, 

Magnesium, Manganese, Potassium, Silicon, Titanium and the associated oxygen atoms.
8-15

Figure 8-7 Map of Indiana Coal EGU Tag Contributions to Wintertime Average 

(January-March) Nitrate

Figure 8-8 Map of Indiana Coal EGU Tag Contributions to Summertime Average (JulySeptember) Nitrate
8-16

Figure 8-9 Map of Indiana Coal EGU Tag Contributions to Wintertime Average 

(January-March) Sulfate

Figure 8-10 Map of Indiana Coal EGU Tag Contributions to Summertime Average (JulySeptember) Sulfate
8-17

Figure 8-11 Map of Indiana Coal EGU Tag Contributions to Wintertime Average 

(January-March) Primary PM2.5

Figure 8-12 Map of Indiana Coal EGU Tag Contributions to Summertime Average (JulySeptember) Primary PM2.5
8-18

The contributions represent the spatial and temporal distribution of the emissions within 

each source tag. Thus, the contribution modeling results do not allow us to represent any changes 

to any “within tag” spatial distributions. For example, the location of coal-fired EGUs in 

Michigan are held in place based on locations in the 2023 emissions. Additionally, the relative 

magnitude of sources within a source tag do not change from what was modeled with the 2023 

emissions inventory. 

8.3 Applying Source Apportionment Contributions to Create Air Quality Fields for the 

Base Case and Four Illustrative Scenarios

As explained in Chapter 4, we created air quality surfaces for the base case and 

illustrative scenarios by scaling the EGU sector tagged contributions from the 2023 modeling 

based on relative changes in EGU emissions associated with each tagged category between the 

2023 emissions case and the scenario of interest. The following subsections describe in more 

detail the emissions used to represent each scenario and provide equations used to apply these 

scaling ratios along with tables of the ratios.

8.3.1 Estimation methods for Emissions that Represent the Base Case and Four 

Illustrative Scenarios

Annual NOX, SO2, and heat input by state and fuel (coal and noncoal) as well as ozone 

season13 NOX by state and fuel were obtained for the base case and illustrative scenarios 

described in Chapter 3. In addition to NOX and SO2, emissions, PM2.5 emissions were also 

needed for the base case and illustrative scenarios. Since these were not generated by IPM, we 

estimated PM2.5 emissions by using the ratio of 2023 heat input for combustion-based EGUs14 to 

the heat input of each scenario from combustion-based EGUs to scale the 2023 PM2.5. However, 

2023 heat input totals were only available for units with Continuous Emissions Monitoring 

Systems (CEMS) data so an additional scalar was used to adjust the CEMS heat value before the 

PM2.5 emissions are calculated as follows. First, the following data was obtained:

 

13 For the purpose of this analysis the ozone season is defined as the months of May-September

14 Heat input for nuclear units and other non-combustion based EGUs that do not emit PM2.5 were not included in 

any heat input numbers described in this chapter.
8-19

• Projected 2023 CEMS heat input values (MMBtu) by ORIS facility and unit ID15 along 

with Carbon Monoxide (CO) and PM2.5 emissions (tons/yr) for each CEMS unit

• 2023 EGU total CO and PM2.5 emissions (tons/yr) by state and fuel type 

• Base case and illustrative scenario heat input values (MMBtu) by state and fuel type

Next, the CEMS EGU unit-level emissions values for CO and PM2.5 were aggregated to 

state and fuel type. Since CO emissions correlate with heat input, the ratio of CO from all EGUs 

to CO from CEMS units in each state-fuel category was used to scale CEMS heat inputs to 

represent total EGU heath input for combustion units as shown in Equation (4) and Equation (5).

2023 Heat Scalarstate,fuel =

Total 2023 EGU COstate,fuel

2023 CEMS COstate,fuel (Eq-5)

Total 2023 Heatstate,fuel = 2023 Heat Scalarstate,fuel × 2023 CEMS Heatstate,fuel (Eq-6)

Finally, using Equation (6) and Equation (7), the 2023 PM2.5 emissions were scaled to 

represent PM2.5 emissions for the base case and illustrative scenarios based on relative changes in 

heat input from 2023 (as obtained by Equation 2). 

PM2.5 Scalarscenario,state,fuel =

IPM Heatscenario,state,fuel

Total 2023 Heatstate,fuel (Eq-7)

PM2.5 scenario,state,fuel = PM2.5 Scalarscenario,state,fuel ∗ 2023 EGU PM2.5 state,fuel (Eq-8)

For states and fuels without CEMS CO data or where 2023 CO emissions equal zero, the 

2023 EGU PM2.5 value was passed through to the base case or illustrative scenario unchanged. 

This was the case for North Dakota non-coal and California coal only.

One limitation of this methodology was identified after emissions scaling was complete. 

Waste coal units were included in the non-coal EGU tags. There are 3 states in which some 

EGUs are fueled by waste coal: Montana, Pennsylvania and West Virginia. Only in West 

Virginia do the majority of non-coal primary PM2.5 emissions come from waste coal. The base 

 

15 Data obtained from files available at: 

https://www.cmascenter.org/smoke/documentation/4.5/html/ch02s09s19.html
8-20

case and illustrative scenarios predict substantial growth compared to 2023 in non-coal heat 

input in West Virginia from natural gas units which have low PM2.5 emissions rates. The 

methodology described above scales PM2.5 emissions from relatively high emitting waste coal 

EGUs in West Virginia to predict new heat input from lower emitting natural gas EGUs. 

Therefore, this methodology likely overestimates the direct PM2.5 emissions associated with noncoal EGUs in West Virginia for the base case and illustrative scenarios. This was not as 

problematic for the two other states, Pennsylvania and Montana, with waste coal EGUs. In 

Pennsylvania, waste coal makes up a relatively small fraction of PM2.5 emissions within the noncoal EGU tag. In Montana, non-coal EGU heat input is predicted to decrease substantially from 

2023 levels in the base case and illustrative scenarios and therefore PM2.5 emissions are predicted 

to be quite small.

As discussed above, EGU emissions occurring on tribal lands were tagged separately 

from state-level emissions in the 2023 source apportionment tracking. Since the IPM summaries 

included tribal emissions within the state (i.e. tribal emissions were not split-out from state 

emissions), we estimated tribal emissions by reallocating a portion of EGU emissions from 

Arizona, Idaho, New Mexico and Utah using the fraction of tribal emissions within each state 

from the 2023 emissions. For instance, emissions occurring on tribal lands accounted for 23

percent of total EGU NOX from Utah, 17 percent of EGU NOX from New Mexico, 36 percent of 

EGU NOX from Arizona and 7 percent of EGU NOX from Idaho in 2023. We use these 

percentages to estimate total EGU tribal NOX emissions for the base case and illustrative 

scenarios for both coal and non-coal fuel types. We also adjust the state-level emissions to 

exclude those emissions from state totals so that our IPM break-outs match the definitions of the 

source apportionment tags. Table 8-4 provides fractions of EGU emissions coming from tribal 

lands for all pollutants and states. The relatively high scaling ratios for tribal non-coal EGU 

emissions shown in Table 8-6, Table 8-8, Table 8-10, are the result of not breaking out the statefractions by fuel type to calculate tribal emissions combined with the fact that tribal non-coal 

EGU emissions in 2023 were much smaller than tribal coal EGU emissions. However, since the 

ozone and PM2.5 contributions from 2023 non-coal EGU units were extremely small, these large 

scaling factors did not have a noticeable impact on the final air quality surfaces.
8-21

Table 8-4 Tribal Fractions by State in the 2023 Emissions

State NOX SO2 PM2.5

Arizona 0.36 0.20 0.38

Idaho 0.07 0.11 0.14

New Mexico 0.17 0.62 0.69

Utah 0.23 0.12 0.23

8.3.2 Scaling Ratio Applied to Source Apportionment Tags

Scaling ratios for PM2.5 components that are emitted directly from the source (OA, EC, 

crustal) were based on relative changes in annual primary PM2.5 emissions between the 2023 

emissions case and the base case and each of the four illustrative scenarios. Scaling ratios for 

components that are formed through chemical reactions in the atmosphere were created as 

follows: scaling ratios for sulfate were based on relative changes in annual SO2 emissions; 

scaling ratios for nitrate were based on relative changes annual NOX emissions; and scaling 

ratios for ozone formed in NOX-limited regimes16 (“O3N”) were based on relative changes in 

ozone season (May-September) NOX emissions. The scaling ratios that were applied to each 

species and scenario are provided in Table 8-5 through Table 8-12.

17

Scaling ratios were applied to create air quality surfaces for ozone using equation (9):

 

16 The CAMx model internally determines whether the ozone formation regime is NOX-limited or VOC-limited 

depending on predicted ratios of indicator chemical species.

17 Note that while there were no EGU emissions from Washington D.C. in the 2023 source apportionment 

simulations, there were extremely small emissions predicted in the base case and four illustrative scenarios (~1 ton 

per year of NOX and 0 tons per year of SO2,). Since the emissions were negligible and there was no associated 

source apportionment tag to scale to, we did not include any impact of Washington D.C. EGU emissions in the air 

quality surfaces.
8-22

Ozonem,g,d,i,y = Cm,g,d,BC + Cm,g,d,int + Cm,g,d,bio + Cm,g,d,fires

+ Cm,g,d,USanthro + Cm,g,d,y,EGUret + ∑CVOC,m,g,d,t

T

t=1

+ ∑CNOx,m,g,d,tSt,i,y

T

t=1

(Eq-9)

where: 

• Ozonem,g,d,i,y is the estimated ozone for metric, “m” (MDA8 or MDA1), gridcell, “g”, day, “d”, scenario, “i”, and year, “y”; 

• Cm,g,d,BC is the total ozone contribution from the modeled boundary inflow; 

Cm,g,d,int is the total ozone contribution from international emissions within the 

model domain; 

• Cm,g,d,bio is the total ozone contribution from biogenic emissions;

• Cm,g,d,fires is the total ozone contribution from fires; 

• Cm,g,d,USanthro is the total ozone contribution from U.S. anthropogenic sources 

other than EGUs; 

• Cm,g,d,y,EGUret is the total ozone contribution from retiring EGUs after year, “y” 

(this term is equal to 0 in 2030 and 2035);

• CVOC,m,g,d,tis the ozone contribution from EGU emissions of VOCs from tag, “t”; 

• CNOx,m,g,d,tis the ozone contribution from EGU emissions of NOX from tag, “t”; 

and 

• St,i,y is the ozone scaling ratio for tag, “t”, scenario, “i”, and year, “y”.

Scaling ratios were applied to create air quality surfaces for PM2.5 species using equation 

(10) (for sulfate, nitrate, EC or crustal material) or using equation (11) (for OA):
8-23

PMs,g,d,i,y = Cs,g,d,BC + Cs,g,d,int + Cs,g,d,bio + Cs,g,d,fires

+ Cs,g,d,USanthro + Cs,g,d,y,EGUret + ∑Cs,g,d,tSs,t,i,y

T

t=1

(Eq-10)

OAg,d,i,y = CPOA,g,d,BC + CPOA,g,d,int + CPOA,g,d,bio + CPOA,g,d,fires

+ CPOA,g,d,USanthro + CPOA,g,d,y,EGUret + SOAg,d

+ ∑CPOA,g,d,tSpri,t,i,y

T

t=1

(Eq-11)

where: 

• PMs,g,d,i,y is the estimated concentration for species, “s” (sulfate, nitrate, EC, or crustal 

material), grid-cell, “g”, day, “d”, scenario, “i”, and year, “y”; 

• Cs,g,d,BC is the species contribution from the modeled boundary inflow; 

• Cs,g,d,int is the species contribution from international emissions within the model 

domain;

• Cs,g,d,bio is the species contribution from biogenic emissions; 

• Cs,g,d,fires is the species contribution from fires;

• Cs,g,d,USanthro is the species contribution from U.S. anthropogenic sources other than 

EGUs;

• Cs,g,d,y,EGUret is the species contribution from retiring EGUs after year, “y” (this term is 

equal to 0 in 2030 and 2035);

• Cs,g,d,tis the species contribution from EGU emissions from tag, “t”; and 

• Ss,t,i,y is the scaling ratio for species, “s”, tag, “t”, scenario, “i”, and year, “y”. 
8-24

Similarly, for Equation (11):

• OAg,d,i,y is the estimated OA concentration for grid-cell, “g”, day, “d”, scenario, “i”, 

and year, “y”;

• Each of the contribution terms refers to the contribution to primary OA (POA); and

• SOAg,d represents the modeled secondary organic aerosol concentration for girdcell, “g”, and day, “d”, which does not change among scenarios

The scaling methodology described above treats air quality changes from the tagged 

sources as linear and additive. It therefore does not account for nonlinear atmospheric chemistry 

and also doesn’t account for non-linear interactions between emissions of different pollutants and 

between emissions from different tagged sources. This is consistent with how air quality 

estimations have been treated in past regulatory analyses (EPA, 2015). We note that air quality is 

calculated in the same manner for the base case and each of the four illustrative scenarios so any 

uncertainty associated with these assumptions is carried through all scenarios in the same manner 

and is thus not expected to impact the air quality differences between scenarios. In addition, 

emissions changes between scenarios are relatively small compared to 2023 totals. Previous 

studies have shown that air pollutant concentrations generally respond linearly to small 

emissions changes of up to 30 percent (Dunker et al., 2002; Cohan et al., 2005; Napelenok et al., 

2006; Koo et al., 2007; Zavala et al., 2009; Cohan and Napelenok, 2011) and therefore it is 

reasonable to expect that the differences between the base case and illustrative scenarios can be 

adequately represented using this methodology. We note that there is somewhat larger 

uncertainty in the estimations of absolute PM2.5 and ozone concentrations associated with each of 

the scenarios due to fact that the emissions in the scenarios are quite different from the 2023 

emissions for some tagged source categories as shown in Table 8-5 through Table 8-12. For 

example, in Table 8-7 the scaling ratio for sulfate impacts of coal EGU’s in Louisiana for the 

2035 base case is 0.30 indicating that emissions of SO2 for this source category decreased by 70

percent compared to the 2023 modeled year, although the net change in emissions when 

accounting for all sources will be lower. The assumption of linearity in sulfate impacts to this 

relatively large change in emissions adds uncertainty to the total predicted sulfate concentrations.

However, the 2035 No CPP case and the 3 illustrative scenarios had scaling ratios ranging from 
8-25

0.27-0.29 which are relatively close to the base case. Consequently, the linear response 

assumption should not drastically impact the estimates of changes in sulfate concentrations due 

to emissions changes from Louisiana coal EGU’s between scenarios. In addition, the absolute 

concentrations do not represent a single year of predicted air pollution but rather a combination 

of emissions expected in 2023 for all source other than EGUs and emissions expected in 2025, 

2030, or 2035 from EGU sources. This adds uncertainty to what is represented by the absolute 

air pollution predictions but not to the differences in air quality between the base case and 

illustrative scenarios within a single year.
8-26

Table 8-5 Scaling Ratios for Primary PM2.5 for Coal EGUs

Base Case (CPP) No CPP 2% HRI at $50/kW 4.5% HRI at $50/kW 4.5% HRI at $100/kW

State 2025 2030 2035 2025 2030 2035 2025 2030 2035 2025 2030 2035 2025 2030 2035

AL 0.58 0.64 0.58 0.47 0.61 0.54 0.47 0.59 0.53 0.53 0.63 0.55 0.50 0.59 0.53

AZ 0.48 0.46 0.40 0.48 0.46 0.40 0.48 0.45 0.39 0.47 0.44 0.38 0.47 0.44 0.38

AR 0.38 0.45 0.38 0.45 0.52 0.51 0.48 0.53 0.51 0.51 0.54 0.53 0.51 0.53 0.52

CA 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00

CO 0.88 0.78 0.73 1.10 1.10 1.01 1.08 1.08 0.99 1.06 1.05 0.98 1.06 1.05 0.98

CT+RI 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

DE+NJ 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

FL 0.44 0.42 0.51 0.39 0.40 0.50 0.41 0.41 0.50 0.42 0.43 0.50 0.41 0.43 0.50

GA 0.42 0.49 0.43 0.50 0.51 0.45 0.50 0.51 0.48 0.50 0.51 0.48 0.49 0.51 0.48

IL 0.85 0.82 0.75 0.82 0.81 0.74 0.82 0.80 0.74 0.81 0.79 0.74 0.79 0.76 0.71

IN 0.67 0.68 0.58 0.66 0.67 0.57 0.65 0.66 0.56 0.64 0.65 0.56 0.64 0.65 0.56

IA 0.74 0.70 0.64 0.93 0.89 0.88 0.92 0.88 0.87 0.91 0.88 0.86 0.90 0.87 0.86

KS 0.68 0.59 0.59 0.91 0.86 0.79 0.90 0.87 0.80 0.90 0.86 0.80 0.90 0.86 0.80

KY 0.36 0.34 0.25 0.35 0.31 0.24 0.35 0.31 0.24 0.34 0.32 0.25 0.34 0.31 0.24

LA 0.16 0.19 0.22 0.15 0.19 0.23 0.15 0.16 0.20 0.15 0.21 0.21 0.15 0.19 0.19

ME+MA+NH+

VT 0.22 0.22 0.22 0.17 0.17 0.17 0.06 0.06 0.06 0.13 0.13 0.13 0.00 0.00 0.00

MD 0.10 0.00 0.00 0.07 0.00 0.00 0.10 0.01 0.00 0.10 0.05 0.00 0.10 0.05 0.00

MI 0.79 0.73 0.67 0.95 0.96 0.87 0.94 0.95 0.87 0.92 0.93 0.89 0.92 0.92 0.87

MN 1.11 0.91 0.87 1.27 0.96 0.95 1.23 0.95 0.93 1.16 0.97 0.91 1.16 0.97 0.91

MS 0.28 0.30 0.30 0.23 0.27 0.28 0.22 0.27 0.29 0.23 0.30 0.30 0.27 0.30 0.30

MO 0.81 0.77 0.72 0.95 0.93 0.90 0.93 0.90 0.88 0.94 0.91 0.89 0.92 0.89 0.86

MT 0.94 0.94 0.88 1.04 1.04 1.04 1.02 1.02 1.02 0.99 0.99 0.99 0.99 0.99 0.99

NE 0.75 0.67 0.67 1.01 1.00 0.99 0.99 0.98 0.97 0.96 0.96 0.95 0.96 0.96 0.95

NV 0.59 0.50 0.54 0.45 0.45 0.44 0.55 0.47 0.43 0.54 0.46 0.42 0.54 0.46 0.42

NM 0.51 0.48 0.46 0.50 0.47 0.46 0.50 0.47 0.46 0.50 0.47 0.46 0.49 0.46 0.46

NY 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

NC 0.51 0.40 0.30 0.48 0.38 0.27 0.48 0.39 0.29 0.49 0.40 0.31 0.49 0.40 0.31
8-27

Base Case (CPP) No CPP 2% HRI at $50/kW 4.5% HRI at $50/kW 4.5% HRI at $100/kW

State 2025 2030 2035 2025 2030 2035 2025 2030 2035 2025 2030 2035 2025 2030 2035

ND+SD 0.69 0.67 0.63 0.87 0.91 0.90 0.86 0.89 0.88 0.85 0.86 0.86 0.85 0.86 0.86

OH 0.93 0.93 0.72 0.91 0.87 0.67 0.90 0.85 0.65 0.89 0.86 0.68 0.89 0.85 0.66

OK 0.58 0.47 0.37 0.45 0.40 0.35 0.47 0.39 0.35 0.47 0.39 0.34 0.47 0.39 0.34

PA 0.63 0.61 0.42 0.57 0.55 0.39 0.58 0.55 0.38 0.57 0.53 0.38 0.57 0.53 0.37

SC 0.68 0.59 0.48 0.69 0.58 0.48 0.69 0.59 0.47 0.69 0.60 0.49 0.68 0.60 0.49

TN 0.69 0.65 0.52 0.68 0.60 0.52 0.63 0.59 0.52 0.67 0.63 0.57 0.60 0.56 0.49

TX 1.17 1.10 1.04 1.09 1.06 1.02 1.12 1.08 1.02 1.14 1.10 1.03 1.14 1.10 1.03

UT 0.63 0.63 0.59 0.65 0.65 0.60 0.64 0.64 0.60 0.64 0.64 0.59 0.62 0.62 0.59

VA 0.24 0.14 0.12 0.15 0.13 0.11 0.13 0.10 0.08 0.18 0.16 0.11 0.16 0.13 0.10

WV 0.54 0.49 0.49 0.80 0.76 0.54 0.79 0.74 0.53 0.77 0.73 0.55 0.77 0.73 0.52

WI 0.54 0.46 0.43 0.65 0.65 0.62 0.64 0.63 0.62 0.68 0.67 0.66 0.66 0.66 0.63

WY 0.99 0.97 0.95 0.97 0.94 0.91 0.96 0.92 0.89 0.93 0.91 0.87 0.90 0.89 0.85

Tribal 0.33 0.32 0.29 0.33 0.32 0.29 0.33 0.31 0.29 0.32 0.31 0.28 0.32 0.31 0.28
8-28

Table 8-6 Scaling Ratios for Primary PM2.5 for Non-Coal EGUs

Base Case (CPP) No CPP 2% HRI at $50/kW 4.5% HRI at $50/kW 4.5% HRI at $100/kW

2025 2030 2035 2025 2030 2035 2025 2030 2035 2025 2030 2035 2025 2030 2035

AL 0.54 0.54 0.57 0.54 0.53 0.57 0.53 0.54 0.56 0.53 0.54 0.56 0.53 0.53 0.56

AZ 0.59 0.75 0.82 0.55 0.73 0.80 0.55 0.73 0.79 0.53 0.72 0.79 0.53 0.72 0.79

AR 1.91 1.95 1.97 1.89 1.86 1.86 1.89 1.83 1.81 1.85 1.83 1.81 1.88 1.83 1.79

CA 0.36 0.23 0.23 0.37 0.23 0.23 0.37 0.23 0.23 0.37 0.23 0.24 0.37 0.23 0.24

CO 0.53 0.75 0.85 0.40 0.54 0.70 0.40 0.54 0.70 0.40 0.53 0.69 0.41 0.54 0.69

CT+RI 0.32 0.30 0.31 0.33 0.30 0.31 0.33 0.31 0.32 0.33 0.30 0.31 0.33 0.31 0.32

DE+NJ 0.62 0.65 0.62 0.69 0.76 0.77 0.69 0.75 0.77 0.68 0.75 0.76 0.67 0.75 0.76

FL 0.44 0.45 0.47 0.44 0.45 0.47 0.44 0.45 0.47 0.44 0.45 0.47 0.43 0.45 0.47

GA 1.56 1.58 1.72 1.56 1.59 1.76 1.55 1.58 1.74 1.55 1.60 1.73 1.51 1.59 1.70

ID+OR+WA 0.59 0.63 0.65 0.58 0.63 0.65 0.59 0.63 0.65 0.59 0.63 0.65 0.59 0.63 0.65

IL 0.98 1.12 1.22 0.86 1.11 1.16 0.86 1.07 1.16 0.85 1.04 1.16 0.86 1.01 1.17

IN 1.18 1.25 1.86 1.17 1.24 1.77 1.14 1.23 1.74 1.14 1.24 1.71 1.13 1.27 1.76

IA 1.23 1.19 1.44 1.08 1.22 1.54 1.07 1.22 1.55 1.04 1.20 1.52 1.07 1.22 1.55

KS 0.51 0.41 0.58 0.48 0.47 0.64 0.49 0.45 0.65 0.46 0.46 0.61 0.48 0.46 0.62

KY 2.57 4.34 5.35 2.46 4.45 5.37 2.57 4.33 5.38 2.45 4.29 5.14 2.74 4.30 5.44

LA 0.84 0.83 0.89 0.84 0.83 0.87 0.83 0.83 0.88 0.83 0.81 0.87 0.83 0.82 0.88

ME+MA+NH+

VT 0.15 0.02 0.02 0.03 0.02 0.02 0.03 0.02 0.02 0.03 0.02 0.02 0.03 0.02 0.02

MD 3.04 3.13 3.25 2.95 2.99 3.05 2.98 2.97 3.06 2.89 2.90 3.04 2.90 2.89 3.14

MI 1.11 1.16 1.68 1.13 1.19 1.49 1.11 1.18 1.48 1.11 1.18 1.40 1.11 1.19 1.47

MN 1.71 1.90 2.19 1.40 1.88 2.25 1.29 1.82 2.20 1.21 1.80 2.17 1.23 1.80 2.19

MS 0.92 0.94 0.98 0.92 0.94 0.98 0.92 0.94 0.94 0.92 0.94 0.94 0.92 0.94 0.94

MO 1.15 1.30 1.58 1.31 1.46 1.76 1.29 1.40 1.72 1.18 1.36 1.68 1.17 1.36 1.65

MT 0.04 0.04 0.04 0.03 0.03 0.04 0.03 0.03 0.04 0.03 0.03 0.04 0.03 0.04 0.04

NE 0.56 0.58 0.58 0.62 0.68 0.92 0.61 0.70 0.90 0.71 0.67 0.90 0.69 0.67 0.90

NV 0.86 1.02 1.11 0.87 1.01 1.09 0.86 1.00 1.09 0.86 1.01 1.09 0.86 1.00 1.10

NM 0.31 0.28 0.27 0.27 0.26 0.27 0.27 0.26 0.27 0.27 0.26 0.27 0.27 0.26 0.27

NY 0.73 0.70 0.71 0.74 0.71 0.72 0.74 0.72 0.72 0.74 0.71 0.72 0.74 0.71 0.71
8-29

Base Case (CPP) No CPP 2% HRI at $50/kW 4.5% HRI at $50/kW 4.5% HRI at $100/kW

2025 2030 2035 2025 2030 2035 2025 2030 2035 2025 2030 2035 2025 2030 2035

NC 1.77 2.05 2.33 1.78 2.12 2.33 1.76 2.11 2.34 1.77 2.09 2.33 1.76 2.08 2.31

ND+SD 2.37 2.41 2.32 1.54 1.81 2.37 1.54 1.85 2.37 1.54 1.86 2.37 1.55 1.77 2.37

OH 2.10 2.17 2.74 2.03 2.19 2.73 2.02 2.13 2.74 1.99 2.13 2.61 1.99 2.16 2.74

OK 1.14 1.15 1.53 1.04 1.06 1.34 0.99 1.06 1.33 0.95 1.05 1.32 0.95 1.04 1.31

PA 1.55 1.54 1.59 1.45 1.44 1.58 1.43 1.44 1.58 1.43 1.44 1.58 1.41 1.44 1.57

SC 0.97 1.26 1.45 0.97 1.29 1.49 0.97 1.25 1.47 0.94 1.20 1.43 0.94 1.20 1.43

TN 2.33 2.37 3.24 2.31 2.44 3.26 2.33 2.51 3.34 2.30 2.37 3.25 2.31 2.48 3.33

TX 0.81 0.79 0.88 0.81 0.80 0.88 0.79 0.79 0.89 0.78 0.78 0.88 0.78 0.78 0.88

UT 0.49 0.63 0.66 0.42 0.54 0.62 0.40 0.54 0.62 0.37 0.49 0.61 0.40 0.51 0.63

VA 1.06 1.19 1.32 1.01 1.11 1.29 1.00 1.11 1.26 1.00 1.10 1.24 1.00 1.09 1.22

WV 1.47 9.66 23.30 1.01 3.74 20.54 0.93 5.34 21.54 0.93 4.02 21.94 0.91 4.75 21.90

WI 2.29 2.30 2.37 2.25 2.26 2.34 2.25 2.26 2.35 2.20 2.23 2.27 2.24 2.26 2.30

WY 0.20 4.37 4.53 0.09 4.37 4.53 0.28 4.37 4.53 0.22 4.37 4.53 0.37 4.37 4.43

Tribal 14.78 16.83 17.94 13.58 16.09 17.63 13.39 16.08 17.50 13.12 15.91 17.33 13.23 15.99 17.39
8-30

Table 8-7 Scaling Ratios for Sulfate for Coal EGUs

Base Case (CPP) No CPP 2% HRI at $50/kW 4.5% HRI at $50/kW 4.5% HRI at $100/kW

State 2025 2030 2035 2025 2030 2035 2025 2030 2035 2025 2030 2035 2025 2030 2035

AL 0.91 1.02 0.85 0.71 0.91 0.79 0.71 0.89 0.77 0.76 0.94 0.79 0.74 0.89 0.77

AZ 1.02 0.99 0.81 1.02 0.99 0.81 1.00 0.97 0.80 0.98 0.94 0.78 0.98 0.94 0.78

AR 1.47 1.97 1.69 1.92 2.45 2.35 2.10 2.50 2.35 2.28 2.57 2.40 2.25 2.55 2.37

CA 0.98 0.00 0.00 0.98 0.00 0.00 0.96 0.00 0.00 0.93 0.00 0.00 0.93 0.00 0.00

CO 0.78 0.71 0.66 0.98 1.00 0.90 0.96 0.98 0.88 0.95 0.95 0.86 0.95 0.95 0.86

CT+RI 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

DE+NJ 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

FL 0.77 0.74 0.88 0.67 0.69 0.86 0.72 0.72 0.86 0.74 0.76 0.87 0.73 0.76 0.86

GA 1.72 1.65 1.45 2.11 1.74 1.53 2.04 1.72 1.63 2.06 1.71 1.64 2.01 1.71 1.62

IL 0.86 0.83 0.77 0.85 0.83 0.75 0.84 0.83 0.74 0.82 0.81 0.75 0.81 0.79 0.73

IN 0.96 0.91 0.81 0.95 0.90 0.80 0.94 0.88 0.80 0.93 0.87 0.79 0.93 0.87 0.79

IA 0.40 0.37 0.35 0.50 0.47 0.46 0.49 0.47 0.46 0.48 0.47 0.46 0.48 0.46 0.45

KS 1.96 1.70 1.69 2.53 2.45 2.26 2.50 2.44 2.26 2.50 2.41 2.27 2.51 2.40 2.26

KY 0.34 0.34 0.24 0.33 0.30 0.23 0.32 0.30 0.23 0.32 0.30 0.24 0.31 0.30 0.23

LA 0.23 0.26 0.30 0.22 0.24 0.29 0.22 0.23 0.28 0.21 0.28 0.28 0.21 0.28 0.27

ME+MA+NH+VT 0.67 0.67 0.67 0.52 0.52 0.52 0.17 0.17 0.17 0.41 0.41 0.41 0.01 0.01 0.01

MD 0.05 0.00 0.00 0.04 0.00 0.00 0.05 0.01 0.00 0.05 0.03 0.00 0.06 0.03 0.00

MI 0.77 0.64 0.61 0.96 0.98 0.74 0.95 0.97 0.77 0.93 0.95 0.87 0.94 0.94 0.80

MN 1.33 1.31 1.29 1.38 1.31 1.30 1.36 1.30 1.29 1.31 1.30 1.26 1.32 1.29 1.26

MS 0.86 0.91 0.91 0.70 0.83 0.86 0.68 0.82 0.89 0.71 0.91 0.91 0.84 0.90 0.91

MO 1.10 1.12 1.09 1.20 1.27 1.24 1.18 1.24 1.22 1.20 1.22 1.20 1.20 1.21 1.18

MT 0.46 0.55 0.51 0.52 0.61 0.61 0.51 0.60 0.60 0.50 0.58 0.58 0.50 0.58 0.58

NE 0.72 0.69 0.87 0.78 0.78 0.96 0.76 0.76 0.94 0.74 0.74 0.92 0.74 0.74 0.92

NV 5.04 4.47 4.67 2.89 2.93 2.85 3.54 3.03 2.79 3.45 2.96 2.72 3.49 2.99 2.76

NM 1.40 1.30 1.26 1.39 1.30 1.27 1.38 1.29 1.28 1.37 1.28 1.27 1.36 1.28 1.27

NY 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.01 0.01 0.01 0.01 0.01 0.01 0.01

NC 0.57 0.46 0.37 0.53 0.44 0.35 0.54 0.45 0.36 0.54 0.46 0.39 0.54 0.46 0.39

ND+SD 0.56 0.55 0.49 0.61 0.64 0.63 0.60 0.62 0.62 0.59 0.61 0.61 0.59 0.61 0.61
8-31

Base Case (CPP) No CPP 2% HRI at $50/kW 4.5% HRI at $50/kW 4.5% HRI at $100/kW

State 2025 2030 2035 2025 2030 2035 2025 2030 2035 2025 2030 2035 2025 2030 2035

OH 0.83 0.83 0.58 0.69 0.76 0.58 0.69 0.75 0.56 0.69 0.76 0.60 0.68 0.76 0.57

OK 1.49 1.14 0.98 1.24 0.97 0.88 1.26 0.95 0.87 1.24 0.93 0.85 1.24 0.93 0.85

PA 0.50 0.48 0.34 0.45 0.45 0.32 0.46 0.44 0.31 0.45 0.43 0.31 0.44 0.42 0.30

SC 2.51 2.08 1.65 2.52 2.12 1.67 2.51 2.16 1.62 2.49 2.19 1.66 2.49 2.20 1.67

TN 0.66 0.59 0.45 0.66 0.56 0.45 0.62 0.54 0.46 0.64 0.58 0.49 0.59 0.53 0.44

TX 0.97 0.91 0.87 0.88 0.88 0.84 0.91 0.90 0.82 0.94 0.90 0.83 0.94 0.89 0.84

UT 1.12 1.26 1.42 1.14 1.27 1.39 1.12 1.25 1.36 1.12 1.25 1.38 1.07 1.21 1.32

VA 0.79 0.55 0.45 0.52 0.51 0.44 0.40 0.39 0.31 0.57 0.57 0.43 0.49 0.49 0.37

WV 0.77 0.76 0.81 1.47 1.34 0.81 1.44 1.31 0.79 1.39 1.29 0.82 1.39 1.29 0.78

WI 0.82 0.66 0.63 0.98 0.98 0.94 0.98 0.97 0.94 1.05 1.02 1.01 1.01 1.01 0.96

WY 0.79 0.59 0.58 0.79 0.58 0.64 0.77 0.56 0.63 0.75 0.55 0.61 0.74 0.54 0.60

Tribal 1.22 1.18 1.14 1.22 1.18 1.13 1.21 1.17 1.13 1.20 1.16 1.12 1.18 1.15 1.11
8-32

Table 8-8 Scaling Ratios for Sulfate for Non-Coal EGUs

Base Case (CPP) No CPP 2% HRI at $50/kW 4.5% HRI at $50/kW 4.5% HRI at $100/kW

2025 2030 2035 2025 2030 2035 2025 2030 2035 2025 2030 2035 2025 2030 2035

AL 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

AZ 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

AR 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

CA 0.19 0.01 0.01 0.19 0.01 0.02 0.19 0.01 0.02 0.19 0.01 0.02 0.19 0.01 0.02

CO 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

CT+RI 1.96 1.96 1.96 1.96 1.96 1.96 1.96 1.96 1.96 1.96 1.96 1.96 1.96 1.96 1.96

DE+NJ 2.67 2.67 2.67 2.67 2.67 2.67 2.67 2.67 2.67 2.67 2.67 2.67 2.67 2.67 2.67

FL 0.68 0.68 0.67 0.68 0.68 0.67 0.68 0.68 0.67 0.68 0.68 0.67 0.68 0.68 0.67

GA 0.05 0.06 0.09 0.05 0.05 0.09 0.04 0.05 0.09 0.04 0.05 0.05 0.04 0.05 0.05

ID+OR+WA 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07

IL 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

IN 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.20

IA 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

KS 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

KY 0.03 0.03 0.02 0.03 0.03 0.02 0.03 0.03 0.02 0.03 0.03 0.02 0.03 0.03 0.02

LA 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06

ME+MA+NH+VT 0.59 0.54 0.59 0.59 0.54 0.59 0.59 0.54 0.59 0.59 0.54 0.59 0.58 0.54 0.59

MD 0.45 0.45 0.45 0.45 0.45 0.45 0.45 0.45 0.45 0.45 0.45 0.45 0.45 0.45 0.45

MI 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06

MN 0.36 0.36 0.36 0.36 0.36 0.36 0.36 0.36 0.36 0.36 0.36 0.36 0.36 0.36 0.36

MS 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

MO 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

MT 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

NE 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

NV 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

NM 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

NY 0.69 0.64 0.64 0.69 0.64 0.64 0.69 0.64 0.64 0.69 0.64 0.64 0.69 0.64 0.64

NC 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01
8-33

Base Case (CPP) No CPP 2% HRI at $50/kW 4.5% HRI at $50/kW 4.5% HRI at $100/kW

2025 2030 2035 2025 2030 2035 2025 2030 2035 2025 2030 2035 2025 2030 2035

ND+SD 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

OH 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08

OK 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

PA 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04

SC 0.01 0.01 0.01 0.01 0.01 0.00 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01

TN 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

TX 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04

UT 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

VA 0.21 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.21 0.20 0.20 0.21 0.20 0.20

WV 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

WI 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

WY 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Tribal 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
8-34

Table 8-9 Scaling Ratios for Nitrate for Coal EGUs

Base Case (CPP) No CPP 2% HRI at $50/kW 4.5% HRI at $50/kW 4.5% HRI at $100/kW

State 2025 2030 2035 2025 2030 2035 2025 2030 2035 2025 2030 2035 2025 2030 2035

AL 0.43 0.48 0.44 0.34 0.45 0.40 0.35 0.44 0.39 0.39 0.48 0.42 0.37 0.44 0.39

AZ 0.77 0.77 0.68 0.77 0.77 0.68 0.76 0.75 0.66 0.74 0.73 0.65 0.74 0.73 0.65

AR 0.87 0.98 0.82 1.02 1.14 1.10 1.08 1.15 1.11 1.13 1.17 1.13 1.13 1.16 1.12

CA 0.18 0.00 0.00 0.18 0.00 0.00 0.17 0.00 0.00 0.17 0.00 0.00 0.17 0.00 0.00

CO 0.92 0.81 0.77 0.89 0.92 0.83 0.88 0.90 0.82 0.86 0.88 0.80 0.86 0.88 0.80

CT+RI 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

DE+NJ 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

FL 0.52 0.54 0.62 0.47 0.50 0.60 0.49 0.53 0.60 0.51 0.55 0.60 0.49 0.54 0.60

GA 0.37 0.43 0.39 0.44 0.45 0.41 0.43 0.46 0.44 0.44 0.46 0.44 0.43 0.46 0.44

IL 1.10 1.05 0.97 1.06 1.04 0.95 1.05 1.03 0.94 1.05 1.02 0.95 1.02 0.98 0.91

IN 0.79 0.80 0.69 0.78 0.78 0.68 0.77 0.77 0.67 0.76 0.76 0.67 0.76 0.76 0.66

IA 0.98 0.91 0.84 1.25 1.19 1.17 1.23 1.18 1.17 1.22 1.18 1.15 1.22 1.16 1.15

KS 0.85 0.71 0.73 1.08 1.03 0.93 1.08 1.03 0.94 1.07 1.02 0.94 1.07 1.02 0.94

KY 0.53 0.49 0.36 0.52 0.45 0.35 0.51 0.44 0.35 0.50 0.45 0.36 0.50 0.45 0.35

LA 0.22 0.25 0.29 0.21 0.23 0.28 0.21 0.22 0.27 0.21 0.27 0.27 0.20 0.26 0.26

ME+MA+NH+VT 0.23 0.23 0.23 0.18 0.18 0.18 0.06 0.06 0.06 0.14 0.14 0.14 0.00 0.00 0.00

MD 0.07 0.00 0.00 0.05 0.00 0.00 0.07 0.01 0.00 0.07 0.03 0.00 0.07 0.04 0.00

MI 0.91 0.81 0.75 1.12 1.14 0.95 1.10 1.13 0.97 1.08 1.10 1.04 1.08 1.10 0.99

MN 1.17 0.94 0.92 1.30 0.98 0.97 1.25 0.96 0.96 1.18 0.98 0.93 1.18 0.98 0.93

MS 0.29 0.50 0.50 0.23 0.41 0.49 0.23 0.41 0.49 0.24 0.53 0.53 0.36 0.52 0.52

MO 0.84 0.76 0.71 1.07 1.03 0.97 1.04 0.99 0.94 1.07 1.01 0.96 1.03 0.98 0.92

MT 0.91 0.91 0.85 0.97 0.97 0.97 0.95 0.95 0.95 0.92 0.92 0.92 0.92 0.92 0.92

NE 0.82 0.75 0.76 1.14 1.14 1.13 1.12 1.12 1.11 1.09 1.09 1.08 1.09 1.09 1.08

NV 1.75 1.55 1.62 1.01 1.02 0.99 1.24 1.06 0.97 1.20 1.03 0.95 1.22 1.04 0.96

NM 0.75 0.67 0.63 0.74 0.66 0.62 0.73 0.65 0.62 0.71 0.64 0.61 0.71 0.63 0.61

NY 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

NC 0.74 0.60 0.42 0.71 0.57 0.37 0.71 0.58 0.39 0.71 0.59 0.44 0.71 0.59 0.44

ND+SD 0.58 0.56 0.53 0.81 0.84 0.83 0.80 0.83 0.82 0.79 0.80 0.80 0.79 0.80 0.80
8-35

Base Case (CPP) No CPP 2% HRI at $50/kW 4.5% HRI at $50/kW 4.5% HRI at $100/kW

State 2025 2030 2035 2025 2030 2035 2025 2030 2035 2025 2030 2035 2025 2030 2035

OH 1.14 1.13 0.86 1.11 1.04 0.79 1.10 1.03 0.78 1.10 1.06 0.81 1.09 1.04 0.79

OK 1.70 1.40 1.10 1.34 1.20 1.06 1.40 1.18 1.05 1.40 1.16 1.03 1.39 1.16 1.03

PA 0.93 0.87 0.55 0.82 0.72 0.52 0.83 0.72 0.51 0.82 0.70 0.51 0.82 0.70 0.50

SC 0.99 0.87 0.72 1.02 0.86 0.72 1.02 0.87 0.70 1.00 0.88 0.72 1.00 0.88 0.72

TN 0.61 0.58 0.47 0.60 0.54 0.48 0.56 0.53 0.47 0.59 0.57 0.51 0.53 0.51 0.44

TX 1.09 1.02 0.94 1.02 0.99 0.92 1.04 0.99 0.91 1.06 1.00 0.93 1.05 1.00 0.93

UT 1.08 1.07 1.01 1.15 1.15 1.06 1.13 1.13 1.05 1.11 1.11 1.02 1.10 1.10 1.04

VA 0.27 0.16 0.11 0.17 0.15 0.11 0.14 0.11 0.07 0.20 0.17 0.10 0.18 0.14 0.09

WV 0.63 0.57 0.56 0.95 0.88 0.62 0.93 0.86 0.61 0.91 0.85 0.62 0.91 0.85 0.60

WI 0.56 0.47 0.44 0.67 0.67 0.64 0.67 0.66 0.64 0.71 0.70 0.69 0.69 0.68 0.65

WY 1.05 0.86 0.85 1.04 0.84 0.83 1.02 0.83 0.81 1.00 0.81 0.79 0.98 0.80 0.78

Tribal 0.73 0.71 0.66 0.75 0.74 0.67 0.74 0.73 0.67 0.73 0.71 0.65 0.72 0.71 0.66
8-36

Table 8-10 Scaling Ratios for Nitrate for Non-Coal EGUs

Base Case (CPP) No CPP 2% HRI at $50/kW 4.5% HRI at $50/kW 4.5% HRI at $100/kW

State 2025 2030 2035 2025 2030 2035 2025 2030 2035 2025 2030 2035 2025 2030 2035

AL 0.79 0.90 1.12 0.80 0.89 1.12 0.77 0.88 1.09 0.78 0.90 1.08 0.77 0.87 1.07

AZ 0.23 0.30 0.34 0.22 0.30 0.33 0.22 0.30 0.33 0.21 0.30 0.33 0.21 0.30 0.33

AR 0.79 0.79 0.82 0.79 0.79 0.81 0.79 0.78 0.80 0.78 0.78 0.80 0.78 0.79 0.79

CA 3.42 0.57 0.66 2.99 0.57 0.66 2.98 0.57 0.66 2.98 0.58 0.67 2.97 0.58 0.67

CO 0.39 0.56 0.62 0.23 0.35 0.45 0.23 0.35 0.45 0.23 0.35 0.45 0.24 0.36 0.44

CT+RI 1.19 1.18 1.19 1.20 1.18 1.19 1.20 1.19 1.20 1.20 1.19 1.19 1.21 1.19 1.20

DE+NJ 1.25 1.30 1.29 1.33 1.42 1.45 1.32 1.40 1.45 1.32 1.39 1.44 1.31 1.38 1.42

FL 1.00 1.02 1.01 1.01 1.03 1.01 1.00 1.02 1.02 0.99 1.01 1.02 0.98 1.01 1.02

GA 1.19 1.32 1.33 1.18 1.33 1.31 1.14 1.32 1.28 1.21 1.36 1.25 1.10 1.35 1.24

ID+OR+WA 0.58 0.66 0.69 0.57 0.65 0.67 0.57 0.65 0.67 0.58 0.65 0.67 0.58 0.65 0.68

IL 0.87 0.94 1.00 0.80 0.93 0.98 0.80 0.93 0.98 0.79 0.92 0.98 0.80 0.91 0.99

IN 1.00 1.03 1.24 1.01 1.05 1.23 0.97 1.03 1.24 0.97 1.03 1.24 0.96 1.03 1.24

IA 1.01 1.01 1.17 0.91 0.99 1.24 0.90 1.03 1.25 0.90 1.01 1.22 0.91 1.04 1.26

KS 1.14 0.86 1.04 1.14 1.08 1.18 1.14 1.07 1.23 1.12 1.11 1.21 1.14 1.11 1.21

KY 1.23 1.47 1.54 1.26 1.41 1.54 1.23 1.37 1.55 1.14 1.36 1.51 1.12 1.31 1.54

LA 0.48 0.41 0.43 0.48 0.41 0.42 0.48 0.41 0.42 0.48 0.40 0.42 0.48 0.41 0.41

ME+MA+NH+VT 1.07 0.64 0.71 0.73 0.65 0.71 0.73 0.64 0.71 0.73 0.64 0.71 0.72 0.64 0.71

MD 1.26 1.25 1.22 1.26 1.28 1.21 1.25 1.25 1.21 1.24 1.24 1.19 1.24 1.23 1.19

MI 1.15 1.17 1.23 1.17 1.20 1.23 1.16 1.20 1.23 1.16 1.21 1.23 1.16 1.20 1.21

MN 0.71 0.72 0.79 0.68 0.73 0.80 0.67 0.73 0.80 0.66 0.73 0.80 0.66 0.74 0.81

MS 0.44 0.51 0.51 0.42 0.48 0.49 0.42 0.48 0.50 0.43 0.48 0.49 0.41 0.49 0.50

MO 0.48 0.49 0.59 0.48 0.52 0.62 0.47 0.51 0.63 0.45 0.51 0.62 0.46 0.51 0.62

MT 0.01 0.01 0.02 0.01 0.01 0.02 0.01 0.01 0.02 0.01 0.01 0.02 0.01 0.01 0.02

NE 0.78 0.92 0.92 0.88 0.94 0.97 0.87 0.95 0.97 0.93 0.94 0.97 0.90 0.94 0.97

NV 0.84 1.10 1.32 0.90 1.02 1.16 0.87 1.03 1.16 0.88 1.03 1.17 0.87 1.03 1.16

NM 0.41 0.23 0.20 0.41 0.22 0.19 0.41 0.22 0.19 0.41 0.22 0.18 0.41 0.22 0.18

NY 1.00 0.98 0.98 1.01 0.99 0.99 1.02 0.99 0.99 1.01 0.99 1.00 1.02 0.99 0.99

NC 0.88 0.86 0.90 0.89 0.86 0.89 0.88 0.87 0.91 0.90 0.89 0.97 0.89 0.89 0.95
8-37

Base Case (CPP) No CPP 2% HRI at $50/kW 4.5% HRI at $50/kW 4.5% HRI at $100/kW

State 2025 2030 2035 2025 2030 2035 2025 2030 2035 2025 2030 2035 2025 2030 2035

ND+SD 0.42 0.42 0.42 0.31 0.34 0.46 0.31 0.35 0.46 0.31 0.35 0.46 0.29 0.33 0.46

OH 1.59 1.66 1.80 1.57 1.72 1.75 1.55 1.69 1.73 1.50 1.65 1.66 1.50 1.67 1.70

OK 0.80 0.78 1.00 0.74 0.75 0.93 0.70 0.74 0.94 0.68 0.74 0.94 0.69 0.74 0.94

PA 1.40 1.32 1.30 1.24 1.18 1.29 1.20 1.17 1.29 1.20 1.17 1.27 1.18 1.16 1.23

SC 0.76 0.78 0.80 0.77 0.76 0.79 0.76 0.75 0.78 0.73 0.72 0.76 0.69 0.70 0.76

TN 0.78 0.93 1.05 0.77 0.92 1.04 0.78 0.93 1.06 0.75 0.88 1.02 0.77 0.97 1.03

TX 0.85 0.84 0.91 0.85 0.84 0.90 0.84 0.84 0.91 0.82 0.83 0.90 0.82 0.83 0.90

UT 0.39 0.47 0.49 0.36 0.41 0.47 0.35 0.41 0.47 0.34 0.39 0.47 0.35 0.40 0.48

VA 1.04 1.17 1.22 0.98 1.11 1.19 0.97 1.10 1.16 0.97 1.09 1.15 0.97 1.07 1.13

WV 0.20 0.53 1.04 0.18 0.33 0.95 0.13 0.36 0.99 0.13 0.28 1.00 0.13 0.31 1.00

WI 1.00 1.01 1.06 0.96 0.97 1.03 0.95 0.97 1.04 0.93 0.97 1.01 0.94 0.99 1.04

WY 0.03 0.62 0.64 0.01 0.62 0.64 0.04 0.62 0.64 0.03 0.62 0.64 0.05 0.62 0.63

Tribal 8.17 9.05 9.83 7.85 8.81 9.63 7.79 8.84 9.60 7.67 8.76 9.56 7.72 8.82 9.49
8-38

Table 8-11 Scaling Ratios for Ozone for Coal EGUs

Base Case (CPP) No CPP 2% HRI at $50/kW 4.5% HRI at $50/kW 4.5% HRI at $100/kW

State 2025 2030 2035 2025 2030 2035 2025 2030 2035 2025 2030 2035 2025 2030 2035

AL 0.53 0.61 0.65 0.43 0.57 0.61 0.44 0.55 0.59 0.50 0.60 0.64 0.47 0.53 0.57

AZ 0.75 0.78 0.80 0.75 0.78 0.80 0.73 0.76 0.78 0.71 0.74 0.76 0.71 0.74 0.76

AR 0.94 1.18 1.20 1.04 1.42 1.46 1.14 1.45 1.47 1.17 1.42 1.45 1.17 1.42 1.45

CA 0.17 0.00 0.00 0.17 0.00 0.00 0.17 0.00 0.00 0.17 0.00 0.00 0.17 0.00 0.00

CO 0.93 0.94 0.92 0.91 0.94 0.92 0.90 0.93 0.90 0.88 0.89 0.88 0.88 0.91 0.88

CT+RI 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

DE+NJ 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

FL 0.66 0.79 1.05 0.65 0.73 1.03 0.65 0.77 1.03 0.64 0.78 1.04 0.63 0.77 1.03

GA 0.61 0.79 0.89 0.72 0.81 0.93 0.71 0.80 1.02 0.70 0.80 0.99 0.69 0.80 0.99

IL 1.00 1.02 0.96 0.99 1.01 0.94 0.98 1.00 0.95 0.95 0.98 0.95 0.92 0.93 0.92

IN 0.80 0.82 0.68 0.79 0.79 0.68 0.77 0.78 0.67 0.76 0.77 0.65 0.76 0.77 0.65

IA 0.85 0.84 0.82 1.10 1.11 1.09 1.07 1.09 1.09 1.07 1.08 1.08 1.07 1.08 1.08

KS 0.86 0.90 0.90 1.07 1.12 1.11 1.04 1.09 1.09 1.02 1.06 1.08 1.02 1.06 1.09

KY 0.60 0.56 0.52 0.60 0.54 0.49 0.59 0.54 0.50 0.58 0.53 0.53 0.57 0.55 0.50

LA 0.32 0.33 0.44 0.31 0.34 0.44 0.30 0.32 0.42 0.30 0.38 0.42 0.29 0.36 0.41

ME+MA+NH+VT 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

MD 0.09 0.00 0.00 0.04 0.00 0.00 0.08 0.00 0.00 0.09 0.03 0.00 0.09 0.03 0.00

MI 0.81 0.82 0.77 1.02 1.05 0.94 1.00 1.02 0.95 0.99 1.00 0.98 0.99 1.00 0.93

MN 1.20 0.96 0.94 1.28 0.95 0.95 1.23 0.95 0.94 1.16 1.03 0.92 1.16 1.01 0.92

MS 0.31 0.66 0.66 0.23 0.66 0.66 0.23 0.65 0.65 0.26 0.63 0.63 0.50 0.63 0.63

MO 0.93 0.94 0.93 1.08 1.10 1.10 1.04 1.06 1.06 1.05 1.06 1.08 1.02 1.03 1.04

MT 1.02 1.02 0.95 1.08 1.08 1.08 1.06 1.06 1.06 1.03 1.03 1.03 1.03 1.03 1.03

NE 0.92 0.89 0.91 1.15 1.15 1.15 1.13 1.13 1.13 1.10 1.10 1.10 1.10 1.10 1.10

NV 0.71 0.88 1.05 0.71 0.83 0.78 0.69 0.83 0.76 0.68 0.81 0.74 0.68 0.81 0.74

NM 0.84 0.84 0.84 0.82 0.82 0.82 0.81 0.81 0.81 0.79 0.79 0.79 0.78 0.78 0.78

NY 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

NC 0.79 0.69 0.57 0.78 0.65 0.50 0.79 0.65 0.55 0.77 0.67 0.63 0.77 0.67 0.63

ND+SD 0.66 0.67 0.66 0.84 0.91 0.89 0.84 0.89 0.88 0.81 0.87 0.86 0.81 0.87 0.86
8-39

Base Case (CPP) No CPP 2% HRI at $50/kW 4.5% HRI at $50/kW 4.5% HRI at $100/kW

State 2025 2030 2035 2025 2030 2035 2025 2030 2035 2025 2030 2035 2025 2030 2035

OH 1.09 1.09 0.99 1.06 1.08 0.91 1.06 1.06 0.90 1.04 1.03 0.95 1.04 1.03 0.92

OK 1.80 1.66 1.57 1.53 1.57 1.58 1.58 1.54 1.54 1.57 1.51 1.51 1.55 1.51 1.51

PA 0.72 0.72 0.59 0.65 0.66 0.57 0.67 0.68 0.56 0.66 0.66 0.55 0.66 0.66 0.55

SC 0.99 0.95 0.94 1.00 0.95 0.95 0.97 0.95 0.92 0.97 0.95 0.97 0.97 0.95 0.97

TN 0.62 0.65 0.57 0.60 0.59 0.61 0.55 0.59 0.61 0.60 0.65 0.65 0.52 0.56 0.57

TX 1.02 1.11 1.14 0.97 1.11 1.14 0.98 1.09 1.12 0.99 1.07 1.09 0.98 1.07 1.09

UT 1.12 1.12 1.12 1.19 1.19 1.19 1.17 1.17 1.17 1.15 1.15 1.15 1.14 1.14 1.14

VA 0.25 0.18 0.14 0.18 0.18 0.14 0.16 0.13 0.10 0.23 0.17 0.13 0.21 0.15 0.11

WV 0.75 0.75 0.81 1.06 1.06 0.85 1.04 1.04 0.85 1.02 1.02 0.85 1.03 1.02 0.84

WI 0.45 0.44 0.41 0.57 0.64 0.62 0.56 0.61 0.60 0.61 0.65 0.64 0.59 0.63 0.61

WY 1.01 0.83 0.83 1.00 0.81 0.81 0.99 0.80 0.80 0.96 0.79 0.78 0.95 0.79 0.79

Tribal 0.76 0.77 0.78 0.78 0.79 0.80 0.77 0.78 0.79 0.75 0.76 0.77 0.75 0.76 0.76
8-40

Table 8-12 Scaling Ratios for Ozone for Non-Coal EGUs

Base Case (CPP) No CPP 2% HRI at $50/kW 4.5% HRI at $50/kW 4.5% HRI at $100/kW

2025 2030 2035 2025 2030 2035 2025 2030 2035 2025 2030 2035 2025 2030 2035

AL 1.19 1.22 1.16 1.20 1.21 1.15 1.18 1.21 1.14 1.17 1.21 1.14 1.17 1.21 1.13

AZ 0.25 0.31 0.32 0.24 0.32 0.32 0.24 0.32 0.32 0.24 0.32 0.33 0.24 0.32 0.32

AR 0.66 0.63 0.58 0.67 0.59 0.53 0.67 0.58 0.52 0.65 0.59 0.54 0.66 0.60 0.53

CA 2.82 0.55 0.73 2.39 0.55 0.73 2.39 0.55 0.72 2.39 0.56 0.74 2.39 0.56 0.74

CO 0.46 0.57 0.49 0.31 0.37 0.42 0.31 0.37 0.42 0.31 0.37 0.42 0.31 0.37 0.42

CT+RI 1.15 1.16 1.19 1.16 1.16 1.19 1.16 1.17 1.20 1.16 1.17 1.20 1.17 1.17 1.21

DE+NJ 1.15 1.21 1.12 1.19 1.26 1.25 1.19 1.25 1.24 1.19 1.25 1.24 1.19 1.24 1.22

FL 0.97 0.97 0.90 0.97 0.97 0.90 0.96 0.96 0.93 0.96 0.96 0.93 0.95 0.96 0.93

GA 1.22 1.35 1.15 1.20 1.41 1.14 1.14 1.39 1.10 1.29 1.47 1.03 1.10 1.46 1.02

ID+OR+WA 0.46 0.52 0.57 0.45 0.52 0.55 0.45 0.52 0.55 0.45 0.52 0.55 0.46 0.52 0.56

IL 1.01 1.00 1.09 0.95 0.99 1.06 0.95 1.00 1.08 0.95 1.01 1.08 0.96 1.02 1.09

IN 1.19 1.14 1.36 1.20 1.17 1.33 1.16 1.18 1.34 1.17 1.18 1.36 1.15 1.18 1.36

IA 1.15 1.10 1.27 1.03 1.03 1.40 1.02 1.10 1.43 1.02 1.10 1.39 1.04 1.14 1.45

KS 1.58 1.10 1.41 1.57 1.41 1.61 1.57 1.42 1.69 1.55 1.47 1.65 1.57 1.48 1.66

KY 1.28 1.17 1.14 1.37 1.28 1.15 1.29 1.21 1.16 1.13 1.20 1.12 1.09 1.10 1.13

LA 0.57 0.50 0.39 0.57 0.49 0.39 0.57 0.50 0.39 0.57 0.49 0.38 0.56 0.50 0.38

ME+MA+NH+VT 1.10 0.66 0.79 0.75 0.66 0.79 0.75 0.66 0.79 0.75 0.66 0.79 0.73 0.66 0.79

MD 1.13 1.08 1.01 1.12 1.12 1.02 1.12 1.11 1.02 1.11 1.11 1.00 1.11 1.08 1.00

MI 1.15 1.14 1.18 1.19 1.20 1.18 1.17 1.20 1.17 1.17 1.21 1.20 1.18 1.20 1.16

MN 0.75 0.73 0.87 0.72 0.75 0.89 0.71 0.75 0.88 0.70 0.75 0.87 0.71 0.76 0.87

MS 0.41 0.47 0.38 0.39 0.46 0.36 0.39 0.47 0.37 0.39 0.46 0.34 0.37 0.47 0.36

MO 0.59 0.50 0.57 0.54 0.54 0.61 0.54 0.54 0.65 0.54 0.55 0.63 0.56 0.55 0.64

MT 0.03 0.03 0.04 0.02 0.02 0.05 0.02 0.02 0.05 0.02 0.02 0.05 0.02 0.03 0.05

NE 0.71 0.88 0.88 0.83 0.89 0.93 0.82 0.90 0.92 0.88 0.89 0.92 0.84 0.89 0.92

NV 0.67 0.78 1.05 0.68 0.77 0.98 0.68 0.77 0.98 0.68 0.76 0.98 0.68 0.77 0.98

NM 0.50 0.38 0.26 0.52 0.38 0.28 0.51 0.38 0.28 0.51 0.38 0.27 0.51 0.38 0.27

NY 0.92 0.90 0.88 0.93 0.91 0.90 0.94 0.91 0.90 0.94 0.91 0.90 0.94 0.91 0.89

NC 1.10 0.95 0.89 1.11 0.98 0.88 1.11 1.02 0.88 1.10 1.05 0.95 1.09 1.05 0.94
8-41

Base Case (CPP) No CPP 2% HRI at $50/kW 4.5% HRI at $50/kW 4.5% HRI at $100/kW

2025 2030 2035 2025 2030 2035 2025 2030 2035 2025 2030 2035 2025 2030 2035

ND+SD 0.52 0.52 0.54 0.48 0.51 0.60 0.48 0.51 0.60 0.48 0.51 0.60 0.44 0.51 0.60

OH 1.56 1.51 1.56 1.53 1.56 1.66 1.50 1.51 1.62 1.49 1.52 1.52 1.48 1.53 1.54

OK 1.01 0.93 0.91 0.94 0.90 0.94 0.92 0.91 0.96 0.91 0.91 0.97 0.92 0.90 0.97

PA 1.09 1.00 0.97 1.01 0.94 0.96 0.97 0.94 0.96 0.98 0.94 0.96 0.95 0.94 0.93

SC 0.96 0.87 0.82 0.97 0.84 0.81 0.96 0.83 0.79 0.91 0.78 0.77 0.83 0.76 0.77

TN 0.64 0.67 0.75 0.63 0.65 0.75 0.64 0.66 0.76 0.63 0.63 0.74 0.63 0.65 0.73

TX 0.93 0.87 0.86 0.94 0.88 0.86 0.92 0.88 0.87 0.90 0.88 0.88 0.91 0.89 0.88

UT 0.26 0.27 0.28 0.23 0.25 0.25 0.22 0.25 0.24 0.21 0.23 0.24 0.23 0.24 0.25

VA 0.97 1.06 0.98 0.92 0.99 0.97 0.91 0.99 0.97 0.91 0.99 0.96 0.91 0.96 0.96

WV 0.18 0.45 1.02 0.28 0.38 0.89 0.18 0.35 0.93 0.18 0.22 0.94 0.18 0.25 0.94

WI 0.90 0.90 0.95 0.83 0.84 0.92 0.82 0.84 0.93 0.81 0.84 0.91 0.83 0.86 0.93

WY 0.04 0.08 0.08 0.02 0.08 0.08 0.06 0.08 0.08 0.05 0.08 0.08 0.08 0.08 0.08

Tribal 11.16 12.28 12.10 10.83 12.31 12.03 10.80 12.44 12.07 10.73 12.36 12.17 10.82 12.42 11.95
8-42

8.4 Creating Fused Fields Based on Observations and Model Surfaces

In Chapter 4 we describe steps taken to estimate PM2.5 and ozone gridded surfaces 

associated with the base case and each of the four illustrative scenarios for every year. For PM2.5, 

steps (4) - (8) (Chapter 4) describe how daily gridded PM2.5 species were processed into annual 

average surfaces which combine observed values with model predictions using the enhanced 

Veronoi Neighbor Average (eVNA) method (Gold et al., 1997; US EPA, 2007; Ding et al., 

2015). These steps were performed using EPA’s software package, Software for the Modeled 

Attainment Test – Community Edition (SMAT-CE)1 and have been previously documented both 

in the user’s guide for the predecessor software (Abt, 2014) and in EPA’s modeling guidance 

document (U.S. EPA, 2014b). As explained in Chapter 4, we first create a 2011 eVNA surface 

for each PM component species. To create the 2011 eVNA surface, SMAT-CE first calculates 

quarterly average values (January-March; April-June; July-September; October-December) for 

each PM2.5 component species at each monitoring site with available measured data. For this 

calculation we used 3 years of monitoring data (2010-2012)2. SMAT-CE then creates an 

interpolated field of the quarterly-average observed data for each PM2.5 component species using 

inverse distance squared weighting resulting in a separate 3-year average interpolated observed 

field for each PM2.5 species and each quarter. The interpolated observed fields are then adjusted 

to match the spatial gradients from the modeled data. These two steps can be calculated using 

Equation (12):

eVNAg,s,q,2011 = ∑ WeightxMonitorx,s,q,2010−2012

Modelg,s,q,2011

Modelx,s,q,2011

(Eq-12)

Where:

• eVNAg,s,q,current is the gradient adjusted quarterly-average eVNA value at gridcell, g, for PM component species, s, during quarter, q for the year 2011;

• Weightx is the inverse distance weight for monitor x at the location of grid-cell, 

g;

 

1 Software download and documentation available at https://www.epa.gov/scram/photochemical-modeling-tools

2 Three years of ambient data is used to provide a more representative picture of air pollution concentrations.
8-43

• Monitorx,s,q,2010−2012 is the 3-year (2010-2012) average of the quarterly 

monitored concentration for species, s, at monitor, x, during quarter, q;

• Modelg,s,q,2011 is the 2011 modeled quarterly-average concentrations of species, 

s, at grid cell, g, during quarter, q; and

• Modelx,s,q,2011 is the 2011 modeled quarterly-average concentration of species, s, 

at the location of monitor, x, during quarter q.

The 2011 eVNA field serves as the starting point for future-year projections. As 

described in Chapter 4, to create a gridded future-year eVNA surfaces for the base case and 

illustrative scenarios for 2025/2030/2035, we take the ratio of the modeled future year3 quarterly 

average concentration to the modeled 2011 concentration in each grid cell and multiply that by 

the corresponding 2011 eVNA quarterly PM2.5 component species value in that grid cell 

(Equation 13). 

eVNAg,s,q,future = (eVNAg,s,q,2011) ×

Modelg,s,q,future

Modelg,s,q,2011

(Eq-13)

This results in a gridded future-year projection which accounts for adjustments to match 

observations in the 2011 modeled data. 

Finally, particulate ammonium concentrations are impacted both by emissions of 

precursor ammonia gas as well as ambient concentrations of particulate sulfate and nitrate. 

Because of uncertainties in ammonium speciation measurements combined with sparse 

ammonium measurements in rural areas, the SMAT-CE default is to calculate ammonium values 

using the degree of sulfate neutralization (i.e., the relative molar mass of ammonium to sulfate 

with the assumption that all nitrate is fully neutralized). Degree of neutralization values are 

mainly available in urban areas while sulfate measurements are available in both urban and rural 

areas. Ammonium is thus calculated by multiplying the interpolated degree of neutralization 

value by the interpolated sulfate value at each grid-cell location which allows the ammonium 

fields to be informed by rural sulfate measurements in locations where no rural ammonium 

 

3

In this analysis the “future year” modeled concentration is the result of Equations 9, 10 or 11 that represents either 

the base case or one of the illustrative scenarios for 2025, 2030, or 2035.
8-44

measurements are available. The degree of neutralization is not permitted to exceed the 

maximum theoretical molar ratio of 2:1 for ammonium:sulfate. When creating the future year 

surface for particulate ammonium, we use the default SMAT-CE assumption that the degree of 

neutralization for the aerosol remains at 2011 levels.

A similar method for creating future-year eVNA surfaces is followed for the two ozone 

metrics with a few key differences. First, while PM2.5 is split into quarterly averages and then 

averaged up to an annual value, we look at ozone as a summer-season average using definitions 

that match metrics from epidemiology studies (May-Sep for MDA8 and Apr-Oct for MDA1). 

The other main difference in the SMAT-CE calculation for ozone is that the spatial interpolation 

of observations uses an inverse distance weighting rather than an inverse distance squared 

weighting. This results in interpolated observational fields that better replicate the more gradual 

spatial gradients observed in ozone compared to PM2.5.
8-45

8.5 References

82 FR 1733. Notice of Availability of the Environmental Protection Agency’s Preliminary 

Interstate Ozone Transport Modeling Data for the 2015 Ozone National Ambient Air 

Quality Standard (NAAQS), (January 6, 2017).

Abt Associates, 2014. User’s Guide: Modeled Attainment Test Software. 

http://www.epa.gov/scram001/modelingapps_mats.htm

Cohan, D.S., Hakami, A., Hu, Y.T., Russell, A.G., 2005. Nonlinear response of ozone to 

emissions: Source apportionment and sensitivity analysis. Environ. Sci. Technol. 39, 6739–

6748.

Cohan, D.S., Napelenok, S.L., 2011. Air Quality Response Modeling for Decision Support. 

Atmosphere. 2, 407-425.

Ding, D., Zhu, Y., Jang, C., Lin, C., Wang, S., Fu, J., Gao, J., Deng, S., Xie, J., Qui, X., 2015. 

Evaluation of heath benefit using BenMAP-CE with an integrated scheme of model and 

monitor data during Guangzhou Asian Games. Journal of Environmental Science. 29, 178-

188.

Dunker, A.M., Yarwood, G., Ortmann, J.P., Wilson, G.M., 2002. The decoupled direct method 

for sensitivity analysis in a three-dimensional air quality model—Implementation, 

accuracy, and efficiency. Environ. Sci. Technol. 36, 2965–2976.

Gold C, Remmele P.R., Roos T., 1997. In: Algorithmic Foundation of Geographic Information 

Systems. In: Lecture Notes in Computer Science, Vol. 1340 (van Kereveld M, Nievergelt J, 

Roos T, Widmayer P, eds) Berlin, Germany: Springer-Verlag. Voronoi methods in GIS. pp. 

21–35.

Henderson, B.H., Akhtar, F., Pye, H.O.T., Napelenok, S.T., Hutzell, W.T., 2014. A Database and 

Tool for Boundary Conditions for Regional Air Quality Modeling: Description and 

Evaluations, Geoscientific Model Development. 7, 339-360.

Koo, B., Dunker, A.M., Yarwood, G., 2007. Implementing the decoupled direct method for 

sensitivity analysis in a particulate matter air quality model. Environ. Sci. Technol. 41, 

2847–2854.

Napelenok, S.L, Cohan, D.S., Hu, Y., Russell, A.G., 2006. Decoupled direct 3D sensitivity 

analysis for particulate matter (DDM-3D/PM). Atmospheric Environment. 40, 6112-6121.

Ramboll Environ, 2016. User's Guide Comprehensive Air Quality Model with Extensions 

version 6.40. Ramboll Environ International Corporation, Novato, CA.

Simon, H., Baker, K. R., Phillips, S., 2012. Compilation and interpretation of photochemical 

model performance statistics published between 2006 and 2012, Atmos. Environ. 61, 124-

139.
8-46

Skamarock, W.C., Klemp, J.B., Dudhia,J., Gill, D.O., Barker, D.M., Duda, M.G., Huang, X.-Y., 

Wang, W., Powers, J.G., 2008. A Description of the Advanced Research WRF Version 3. 

NCAR Tech. Note NCAR/TN-475+STR. 

(http://wwww.mmm.ucar.edu/wrf/users/docs/arw_v3.pdf).

US EPA, 2007, Technical Report on Ozone Exposure, Risk, and Impact Assessments for 

Vegetation. EPA 452/R-07-002. Prepared by Abt Associates Inc. for U.S. Environmental 

Protection Agency, Office of Air Quality Planning and Standards, Health and 

Environmental Impacts Division. Research Triangle Park, NC. 

(https://www3.epa.gov/ttn/naaqs/standards/ozone/data/2007_01_environmental_tsd.pdf).

US EPA, 2014a. Meteorological Model Performance for Annual 2011 Simulation WRF v3.4, 

Research Triangle Park, NC. (http://www.epa.gov/scram001/).

US EPA, 2014b, Modeling Guidance for Demonstrating Attainment of Air Quality Goals for 

Ozone, PM2.5, and Regional Haze- December 2014 DRAFT, Research Triangle Park, NC. 

(https://www3.epa.gov/ttn/scram/guidance/guide/Draft_O3-PM-RH_Modeling_Guidance2014.pdf).

US EPA, 2015, Regulatory Impact Analysis of the Final Revisions to the National Ambient Air 

Quality Standards for Ground-Level Ozone, EPA-452/R-15-07, Research Triangle Park, 

NC. (https://www.epa.gov/sites/production/files/2016-02/documents/20151001ria.pdf).

US EPA, 2017a, Technical Support Document (TSD) Additional Updates to Emissions 

Inventories for the Version 6.3, 2011 Emissions Modeling Platform for the Year 2023, 

Research Triangle Park, NC. (https://www.epa.gov/sites/production/files/2017-

11/documents/ 2011v6.3_2023en_update_emismod_tsd_oct2017.pdf).

US EPA, 2017b. Documentation for the EPA’s Preliminary 2028 Regional Haze Modeling. 

Research Triangle Park, NC 

(https://www3.epa.gov/ttn/scram/reports/2028_Regional_Haze_Modeling-TSD.pdf).

US EPA, 2017c. Air Quality Modeling Technical Support Document for the 2015 Ozone 

NAAQS Preliminary Interstate Transport Assessment. Research Triangle Park, NC 

(https://www.epa.gov/airmarkets/notice-data-availability-preliminary-interstate-ozonetransport-modeling-data-2015-ozone).

Yantosca, B. 2004. GEOS-CHEMv7-01-02 User’s Guide, Atmospheric Chemistry Modeling 

Group, Harvard University, Cambridge, MA. 

Zavala, M., Lei, W., Molina, M.J., Molina, L.T., 2009. Modeled and observed ozone sensitivity 

to mobile-source emissions in Mexico City. Atmos. Chem. Phys. 9, 39–55.
United States

Environmental Protection 

Agency

Office of Air Quality Planning and Standards

Health and Environmental Impacts Division

Research Triangle Park, NC

Publication No. EPA-452/R-18-006

August 2018