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


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

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ii

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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
111

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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).
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TABLE OF CONTENTS
LIST OF TABLES	VIII
LIST OF FIGURES	XII
EXECUTIVE SUMMARY	ES-1
ES.l 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	B SER 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
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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 HI AI TH 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 C02	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
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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-C02 Estimates	7-1
7.2	Treatment of Uncertainty in Interim Domestic SC-C02 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
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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

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Table
3-3
Table
3-4
Table
3-5
Table
3-6
Table
3-7
Table
3-8
Table
3-9
Table
3-10
Table
3-11
Table
3-12
Table
3-13
Table
3-14
Table
3-15
Table
3-16
Table
3-17
Table
3-18
Table
3-19
Table
3-20
Table
3-21
Table
3-22
Table
3-23
Table
3-24
Table
3-25
Table
3-26
Table
3-27
Table
3-28
Table
3-29
Table
3-30
Table
3-31
Table
3-32
Table
3-33
Table
3-34
Table
3-35
Table
3-36
Table
3-37
Table
3-38
Table
3-39
Table
3-40
Table
3-41
Table
3-42
Table
3-43
Table
4-1
Table
4-2
Table
4-3
Table
4-4
Table
4-5
Table
4-6
Years 2025, 2030, and 2035: Total State and Industry Annual Cost of Reporting and Recordkeeping
Requirements, Relative to the Base Case (Million 2016$)	3-13
Projected CO2 Emission Impacts, Relative to Base Case (CPP) Scenario	3-14
Projected CO2 Emission Impacts, Relative to No CPP Scenario	3-15
Projected CO2 Emission Impacts, Relative to 2005 	 3-15
Projected Emissions of SO2, NOx, and Hg	3-16
Percent Change in Projected SO2, NOx, and Hg Emissions, Relative to Base Case (CPP) Scenario3-16
Percent Difference in Projected SO2, NOx, and Hg Emissions, Relative to No CPP Scenario	3-17
Total Projected Power Sector System Costs (billions of 2016$)	3-18
Annualized Compliance Costs, Relative to Base Case (CPP) Scenario (billions of 2016$)	3-18
Annualized Compliance Costs, Relative to No CPP Scenario (billions of 2016$)	3-18
Projected CO2 Emissions by Generation Source (MM short tons)	3-19
Projected SO2 Emissions by Generation Source (thousand short tons)	3-20
Projected NOx Emissions by Generation Source (thousand short tons)	3-20
Projected Mercury Emissions by Generation Source (short tons)	3-21
Projected Generation Mix (thousand GWh)	3-23
Percent Change in Projected Generation Mix, Relative to Base Case (CPP) Scenario	3-24
Percent Change in Projected Generation Mix, Relative to No CPP Scenario	3-25
Total Generation Capacity by 2025-2035 (GW)	3-27
Percent Change in Total Generation Capacity by 2025-2035, Relative to Base Case Scenario (CPP) 3-
28
Percent Change in Total Generation Capacity by 2025-2035, Relative to No CPP Scenario	3-29
Projected Natural Gas Combined Cycle Capacity Additions and Changes Relative to Base Case (CPP)
3-30
Projected Renewable Capacity Additions and Changes Relative to Base Case (CPP)	3-30
Projected Natural Gas Combined Cycle Capacity Additions and Changes Relative to No CPP
Scenario	3-30
Projected Renewable Capacity Additions and Changes Relative to No CPP Scenario	3-31
2025 Projected Coal Production for the Electric Power Sector (million short tons)	3-31
2030 Projected Coal Production for the Electric Power Sector (million short tons)	3-32
2035 Projected Coal Production for the Electric Power Sector (million short tons)	3-32
Projected Power Sector Gas Use	3-32
Projected Average Minemouth and Delivered Coal Prices (2016$/MMBtu)	3-33
Projected Average Henry Hub (spot) and Delivered Natural Gas Prices (2016$/MMBtu)	3-33
Percent Change in Projected Average Henry Hub (spot) and Delivered Natural Gas Prices, Relative to
Base Case (CPP)	3-33
Percent Change in Projected Average Henry Hub (spot) and Delivered Natural Gas Prices, Relative to
No CPP Scenario	3-34
Projected Contiguous U.S. Retail Electricity Prices (cents/kWh), 2025-2035	 3-34
Percent Change in Projected Contiguous U.S. Retail Electricity Prices, Relative to Base Case (CPP),
2025-2035	 3-35
Percent Change in Projected Contiguous U.S. Retail Electricity Prices, Relative to No CPP Scenario,
2025-2035	 3-35
Change in Electricity Demand Due to Demand-side Energy Efficiency, CPP Scenario vs. No CPP
Scenario in AEO2017	3-36
Costs of Demand-side Energy Efficiency (billions of 2016$)	3-37
Annualized Compliance Costs of the No CPP Scenario (billions of 2016$)	3-39
Projected CO2 Emission Impacts, Relative to Illustrative No CPP Scenario	3-40
Projected SO2, NOx, and Mercury Emissions	3-41
Projected SO2, NOx, and Mercury Emission Impacts, Relative to Illustrative No CPP Scenario.... 3-41
Interim Domestic Social Cost of C02, 2015-2050 (in 2016$ per metric ton)*	4-4
Estimated Forgone Domestic Climate Benefits, Relative to Base Case (CPP) (billions 2016$)*	4-5
Projected EGU Emissions of SO2, NOx, and PM2.5*	4-8
Human Health Effects of Ambient PM2.5 and Ozone	4-18
Estimated Incremental PM25 and Ozone-Related Premature Deaths and Illnesses in 2025*	4-32
Estimated Incremental PM25 and Ozone-Related Premature Deaths and Illnesses in 2030*	4-33
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Table 4-7 Estimated Incremental PM25 and Ozone-Related Premature Deaths and Illnesses in 2035*	4-34
Table 4-8 PM-Related Premature Deaths Estimated Using Alternative Approaches to Evaluate Uncertainty at
Low-Concentations (95% Confidence Interval), Relative to Base Case (CPP)*	4-35
Table 4-9 Estimated Economic Value of Incremental PM2 5 and Ozone-Attributable Deaths and Illnesses for
Illustrative Scenarios & Three Alternative Approaches to Representing PM Effects in 2025, Relative
to Base Case (CPP) (95% Confidence Interval; Billions of 2016$)A	4-36
Table 4-10 Estimated Economic Value of Forgone PM25 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 PM25 Concentration Cut
Points	4-40
Table 4-13 Forgone Climate Benefits and Ancillary Health Co-Benefits, Relative to Base Case (CPP) (billion
2016$)	4-42
Table 4-14 Forgone Climate Benefits and Ancillary Health Co-Benefits, showing only PM25 Related Benefits
above the Lowest Measured Level of Each Long-Term PM2 5 Mortality Study, Relative to Base Case
(CPP) (billion 2016$)	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 Unqualified 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
(C02), 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
(C02), 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
(C02), 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
(C02), 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
(C02), Relative to Base Case (CPP), 3 and 7 Percent Discount Rates, 2023 -2037 (billion 2016$) .6-11
Table 6-8 Illustrative No CPP Scenario: Present Value of Compliance Costs, Benefits (Inclusive of Health Co-
Benefits), and Net Benefits, Relative to Base Case (CPP), 20230-2037 (billion 2016$)	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$)	6-14
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Table 6-13 Present Value of Compliance Costs, Benefits, and Net Benefits assuming that PM2.5 Related Benefits
Fall to Zero Below the Lowest Measured Level of Each Long-Term PM2 5 Mortality Study, Relative
to Base Case (CPP), 3 and 7 Percent Discount Rates, 2023-2037 (billion 2016$)	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
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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 ($/MMBtu)	
	2-32
Figure 3-1 Generation Mix (thousand GWh)	3-26
Figure 4-1 Relationship between the PM2 5 Concentrations Considered in Epidemiology Studies and our
Confidence in the Estimated PM-related Premature Deaths	4-26
Figure 4-2 Number of Individuals Exposed According to Annual Mean PM2 5 Concentration in 2030	4-28
Figure 4-3 Number of PM2 5-Related Premature Deaths According to PM2 5 Concentration in 2030 	4-29
Figure 4-4 Change in Annual Mean PM2 5 (|ig/m3) and Summer Season Average Daily 8hr Maximum Ozone
(ppb) in 2025 (Difference Calculated as Illustrative Scenario - Base Case)	4-30
Figure 4-5 Estimated Forgone Avoided PM2 5 and Ozone Deaths for Each Illustrative Scenario in 2025, Relative
to Base Case (CPP) (Deaths per 100k People)	4-39
Figure 7-1 Frequency Distribution of Interim Domestic SC-C02 Estimates for 2030 (in 2016$ per metric ton
C02)	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 MD A8 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

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EXECUTIVE SUMMARY
ES.l 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-1

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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-2

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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 coal-
fired 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-3

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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-4

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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$)	



Domestic
Climate Benefits
Net Benefits

Costs
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-5

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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$)		
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.2 In 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
„	Domestic
0S S	Climate Benefits
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-6

-------
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-7

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

CO2 Emissions
(MM Short Tons)
2025 2030 2035
CO2 Emissions Change
(MM Short Tons)
2025 2030 2035
CO2 Emissions Change
Percent Change
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
CO2 Emissions Change
CO2 Emissions Change

(MM Short Tons)
(MM Short Tons)
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-8

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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-9

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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-10

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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 |ig/m3. We
estimate that only about 1 percent of the PIVh.s-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-11

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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 PIVh.s-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 |ig/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-12

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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 Ancillary
Climate Health 0
Benefits Co-Benefits ene 1 S
Domestic Ancillary
Climate Health 0
Benefits Co-Benefits ene 1 S
No CPP
2025 (0.3) (2.8) to (6.6) (3.2) to (7.0)
2030 (0.5) (4.9) to (11.4) (5.4) to (11.9)
2035 (0.5) (3.8) to (8.8) (4.3) to (9.3)
(0.1) (2.6) to (6.1) (2.7) to (6.1)
(0.1) (4.5) to (10.5) (4.6) to (10.6)
(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)
2030 (0.4) (4.5) to (10.6) (4.9) to (11.0)
2035 (0.4) (3.0) to (7.0) (3.4) to (7.4)
(0.0) (2.4) to (5.4) (2.4) to (5.5)
(0.1) (4.1) to (9.8) (4.2) to (9.9)
(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)
2030 (0.4) (4.2) to (9.8) (4.6) to (10.2)
2035 (0.5) (4.0) to (9.3) (4.4) to (9.8)
(0.0) (2.5) to (5.7) (2.5) to (5.7)
(0.1) (3.9) to (9.0) (3.9) to (9.1)
(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)
2030 (0.3) (3.6) to (8.2) (3.9) to (8.6)
2035 (0.3) (2.6) to (6.0) (2.9) to (6.3)
(0.0) (2.0) to (4.4) (2.0) to (4.4)
(0.1) (3.3) to (7.6) (3.3) to (7.6)
(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 el al. (2009) with Smith el al. (2009) to Lepeule el 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-13

-------
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$)	



Domestic
Climate Benefits
Net Benefits

Costs
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-14

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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
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-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$)	
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 el al. (2009) with Smith el al. (2009) to Lepeule el al. (2012)
with Jerrett et al. (2009)).
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$)	

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 el al. (2009) with Smith el al. (2009) to Lepeule el al. (2012)
with Zanobetti & Schwartz. (2008)).
ES-17

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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$)	
Net Benefits
„ Domestic associated with
0S S Climate Benefits 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-18

-------
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 el al. (2009) with Smith el al. (2009) to Lepeule el 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-19

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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-20

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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-21

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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-22

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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 co-
benefits 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 PM25 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-amendment-
2012
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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/NEJMoa0803 894
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 short-
term 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/R-
15-003. 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.
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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
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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 11 l(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."4 If 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).
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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.
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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
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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.
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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.
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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.
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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 coal-
fired 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).
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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
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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
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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.
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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.
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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
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Within each capacity bin, the units were divided into heat rate quartiles based on reported
heat rates used in NEEDS, with the first quartile (Ql) 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 Ql 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 on-
line 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.
1-14

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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.
1-15

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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.
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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
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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 fossil-
steam 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
1-18

-------
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.
1-19

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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
1-20

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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
2-1

-------
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.
2-2

-------
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.
2-3

-------
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.
New Build
Retirement
250,000
200,000
150,000
100,000
50,000
(50,000)
(100,000)
(150,000)
I Coal ¦ Natural Gas ¦ Nuclear ¦ Hydro ¦ Petroleum ¦ Wind ¦ Other RE ¦ Misc.
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
2-4

-------
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. l.A, 3. l.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.
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-------
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 i
Heat Rat
e)
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.
2-6

-------
100%
>~
-M
ro 80%
Q_
TO
u
° 60%
CD
i_
TO
_C
a 40%
>
_rc
1 20%
u
0%
•	Coal
•	Gas
»
•
•
•
%
•
•
•
•
\
*
•
•
*••••


•
•
•
•
•




•
•
•
•
•




•
•
•
•


aN<




20	40	60
Age in 2025 (years)
80
100
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.
2-7

-------
2016 Annual Average Capacity Factor, Coal Steam by Capacity
m.


I^J v • *P
••It •» * t 0
& • •
. I
200 400 600 800 1000
Capacity (MW)
1200
1400
1600
Figure 2-3 2016 Annual Average Capacity Factor for Coal Steam Generators, by
Capacity
Source: EIA Forms 860 and 923
2-8

-------
ID
*—I
O
(N
C
i_
o
4-»
u
ro
O
o
ro
Q.
ro
U
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
2016 Annual Average Capacity Factor, Coal Steam by Age
•v . .; ^ ¦' - i j* •
v;. ¦¦¦>¦ • iHSfo .
. V'.-. i'iSwi..
10 20 30 40 50 60 70 80
Age in 2016 (years)
90
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.
2-9

-------
•••* . » J W. • . •.
. •• .*?. . . •
• 9	•6	6,	•
:J ,®fc -< .
V ¦«&!#*» -J? f-'-'JB



•a
Coal Steam Combined Cycle Combustion Turbine Renewable	Nuclear 0/G Steam Other
•	Under 25 MW • Under 25 MW Under 25 MW	• Under 25 MW • Under 25 MW • Under 25 MW • Under 25 MW
•	25 to 100 MW • 25 to 100 MW 25 to 100 MW	• 25 to 100 MW • 25 to 100 MW • 25 to 100 MW • 25 to 100 MW
•	100 to 250 MW • 100 to 250 MW O 100 to 250 MW	• 100 to 250 MW • 100 to 250 MW • 100 to 250 MW • 100 to 250 MW
•	250 to 500 MW • 250 to 500 MW O 250 to 500 MW	• 250 to 500 MW • 250 to 500 MW • 250 to 500 MW • 250 to 500 MW
•	Over 500 MW • Over 500 MW 0 Over 500 MW	# Over 500 MW $ Over 500 MW # Over 500 MW • Over 500 MW
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,2 each 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
2-10

-------
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.eom/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.
2-11

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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

Residential
1,352
35%
1,411
36.2%

Commercial
1,300
34%
1,367
35.0%
Sales
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.
2-12

-------
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.
2-13

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Creeled with mapcharl.net ®
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.
2-14

-------
14
12
w
"S
¦| 10
o
c 8



























—








-C
5
< 6
tn
+-»
c
CJ 4












tj 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
_oj
LU
Residential Commercial Industrial — — Total
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/.
2-15

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0%
2006 2008 2010 2012 2014	2016
— Residential	Commercial	Industrial	CPI-U — — — GDP Price
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.
2-16

-------
18
16
14
Real Electricity Price, 1960-2016 (including taxes)

















12
10
o

~





^ —

O
£.




D
A





H-
9






Z
0
19






60 1970 1980 1990 2000 2010
Residential Commercial Industrial — — Total
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.
Relative Change in Electricity Prices, 1960 -
2016 (including taxes)
50%
-2 1960	1970	1980	1990	2000	2010
Residential	Commercial	Industrial — — Total
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.
2-17

-------
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.
Average
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.
2-18

-------
20%
15%
10%
5%
0%
-5%
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
	Real GDP 	Population 	Generation
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 $1 million of GDP to 244 MWh/$l 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.
2-19

-------
10%
5%
0%
-5%
-10%
-15%
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
Real GDP/Capita	Generation/Capita	Generation/Real GDP
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
2-20

-------
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.
2-21

-------
i Not considering restructuring at this time (24 + AK & HI)
| Allow retail access (13 + DC)
| Limited access (5)
Restructuring law repealed or delayed (4)
Retail access with generation price control (AZ)
j Retail access suspended (CA)
Figure 2-14 Status of State Electricity Industry Restructuring Activities
Source: Map adapted from Rose, Ken. 2017. Retail Electricity Markets. 59th Animal 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.
2-22

-------
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.
8IPP data presented in this section include both combined and non-combined heat and power plants.
2-23

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700,000
cnn nnn
















Capacity (MW)
l—4 NJ UJ -P^ LT1 C
o o o o o
o o o o o
o o o o o
o o o o o
D O O O O O




¦



m



_

¦



1












1

1

2006
- 2016
2006
- 2016
_
Utility Utility
IPP

IPP

¦ Nuclear BCoal Gas ¦ Hydro IWind IAN Other
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/)
3,000,000
2,500,000
¦ ¦
>2,000,000
I
o 1,500,000
I	¦
£ 1,000,000
II	11
Utilities Utilities	IPP 2006 IPP 2016
2006 2016
¦ Nuclear BCoal Gas ¦ Hydro aWind All Other
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,
2-24

-------
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.
140,000
120,000
^ 100,000
£¦ 80,000
'u
(O
-------
80,000
U
Utility IPP
¦ Coal ¦ Natural Gas ¦ Wind & Solar ¦ Other
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 (SFe), CH4, and N2O.
Including both generation and transmission (a source of SFe), 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 emissions9 from 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.
70,000
60,000
~ 50,000
g. 40,000
03
u
-a 30,000
L_
H 20,000
10,000
0
9 CO2 equivalent data in this section are calculated with the IPCC SAR (Second Assessment Report) GWP potential
factors.
2-26
-------
Table 2-5 Domestic Emissions of Greenhouse Gases, by Economic Sector (million tons
of CO2 equivalent)

2006
2015
Change Between '06 and '15

%of
Sector/Source
GHG
Emissions
% Total
GHG
Emissions
GHG
Emissions
% Total
GHG
Emissions
Change
in
Emissions
%
Change in
Emissions
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.

9,000
-------
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

%of

Total

% of Total

Gas/Fuel Type or Source
GHG
Emissions
GHG
Emissions
from
GHG
Emissions
GHG
Emissions
from Power
Change in
GHG
Emissions
% Change
in
Emissions

Power

Sector

C02

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

N20

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
Electrical
7.6
0.29%
4.6
0.22%
-3.0
-39%

Transmission and
7.6
0.29%
4.6
0.22%
-3.0
-39%

Distribution

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.
2-28
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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).
2-29
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Table 2-7 Revenue and Expense Statistics for Major U.S. Investor-Owned Electric
Utilities for 2006, 2011 and 2016 (nominal Smillions)

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
14,580
17,343
17,575
General

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
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$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.
vv
LO
CD
U
L-
Q.
LO
(O
15
11.00
10.00
9.00
8.00
7.00
6.00
5.00
4.00
3.00
(O
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1.00
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11
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
111 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.
2-31
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100%
80%
60%
40%
20%
0%
-20%
-40%
-60%
-80%
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
Coal
Gas
Average
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
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technology. Proven reserves make up 12 percent of the technically recoverable total estimate,
with the remaining 88 percent from unproven reserves.
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2.8 References
Federal Reserve Bank of St. Louis. Available online at:
https://fred.stlouisfed.0rg/series/GDPDEF#O. 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/b kmk/table/1.0/en/PEP/2017/PEPANNRES/O100000US |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/aeol7/. 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-sinks-
1990-2015-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.
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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_l_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.
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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.
3-1
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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.5 It 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-markets-
power-sector-modeling
6 For documentation, see https://www.epa.gov/airmarkets/clean-air-markets-power-sector-modeling
3-2
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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.
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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
3-4
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• 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, HC1 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
3-5
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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 i
Heat Rat
e)
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,
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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.
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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
3-8
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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
BSERto 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.
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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
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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.
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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$)

Total
Annual
Total
Total
Annualized
Capital
Costs
Total
Total
Total
Annual
Respondent
Costs
Totals
Labor
Burden
(Hours)
Annual
Labor Costs
Annual
O&M Costs
Annualized
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. EPA-
HQ-OAR-2003 -0162-0001.
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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,
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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)
2025 2030 2035
CO2 Emissions Change
(MM Short Tons)
2025 2030 2035
CO2 Emissions Change
Percent Change
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.
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Table 3-5 Projected CO2 Emission Impacts, Relative to No CPP Scenario

CO2 Emissions
(MM Short Tons)
2025 2030 2035
CO2 Emissions Change
(MM Short Tons)
2025 2030 2035
CO2 Emissions Change
Percent Change
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)
2020 2025 2030
CO2 Emissions:
Change from 2005
(MM Short Tons)
2020 2025 2030
CO2 Emissions:
Percent Change from
2005
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.
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Table 3-7 Projected Emissions of SO2, NOx, and Hg

SO2
NOx
Hg

(thousand tons)
(thousand tons)
(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
NOx
Hg

(thousand tons)
(thousand tons)
(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%
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Table 3-9 Percent Difference in Projected SO2, NOx, and Hg Emissions, Relative to No
CPP Scenario

SO2
NOx
Hg

(thousand tons)
(thousand tons)
(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).
3-17
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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
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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.
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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
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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
3-21
-------
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.
3-22
-------
Table 3-17 Projected Generation Mix (thousand GWh)

No
Base Case
2% HRI at
4.5% HRI at
4.5% HRI at

CPP
(CPP)
$50/kW
$50/kW
$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
3-23
-------
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%
3-24
-------
Table 3-19 Percent Change in Projected Generation Mix, Relative to No CPP Scenario

No
Base Case
2% HRI at
4.5% HRI at
4.5% HRI at

CPP
(CPP)
$50/kW
$50/kW
$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%
3-25
-------
¦ Coal ¦ Natural Gas ¦ Nuclear ¦ Non-hydro Renewables ¦ Hydroelectric BOther
5,000
4,500
4,000
3,500
3,000
2,500
2,000
1,500
1,000
500
0
2025
704
586
586
670
588
1,677 ! 700 1,662 1,647 1,649
980 ^¦1,008
713
660
2030
683
711
708
658 646 654
708
1,718 1745 1,715 1,701 1,706
2035
674
716
658
713
646
713
1,946 1 ggg 1,943 1,927 1,936

5 3
so '
ON LT)
lo -uy
cd js*
JZ o"
S? o
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
3-26
-------
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
Base Case
2% HRI at
4.5% HRI at
4.5% HRI at

CPP
(CPP)
$50/kW
$50/kW
$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
3-27
-------
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%
3-28
-------
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%
3-29
-------
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
Incremental Cumulative
Capacity Additions,
Relative to the Base Case
(CPP)
Percent Change in
Incremental Cumulative

Additions: Renewables
(GW)
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%
3-30
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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
3-31
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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;
$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;
$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 Pro
ected Power Sector Gas Use
Power Sector Gas Use (TCF)
2025 2030 2035
Percent Change in Power
Sector Gas Use, Relative to
Base Case (CPP)
2025 2030 2035
Percent Change in Power
Sector Gas Use, Relative to
No CPP
2025 2030 2035
No CPP
Base Case (CPP)
2% HRI at $50/kW
4.5% HRI at $50/kW
4.5% HRI at $100/kW
11.96
12.14
11.84
11.72
11.73
12.18
12.36
12.15
12.05
12.08
13.65
13.78
13.62
13.52
13.56
-1.5%
-1.5% -0.9%
-2.5% -1.7% -1.1%
-3.4% -2.5% -1.9%
-3.4% -2.3% -1.6%
1.5%
-1.0%
-1.9%
-1.9%
1.5%
-0.3%
-1.0%
-0.8%
0.9%
-0.2%
-1.0%
-0.7%
3-32
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3.7.7 Projected Fuel Price, Market, and Infrastructure Impacts
Relative to the base case (CPP), the illustrative policy scenarios result in small changes in
electric power sector delivered coal and natural gas prices, on a Btu-weighted average basis.
Depending on the illustrative policy scenario and year, the model projects changes to delivered
coal prices that range from a small increase to a small decrease, less than one percent.
Additionally, depending on the illustrative policy scenario and year, EPA projects a reduction in
delivered natural gas prices on the order of about 1 percent.
Table 3-31 Projected Average Minemouth and Delivered Coal Prices (2016$/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%
3-33
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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-modeling-
platform-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.
3-34
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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
3-35
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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 program-
years 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.
3-36
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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$/2012$) = $57.3
2016$/netMWh.
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.
3-37
-------
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
$10.5 billion in 2035 (Table 3-40). These excluded energy cost savings reflect both forgone
variable cost savings (e.g., fuel and variable O&M) as well as forgone fixed cost savings (e.g.,
new power plants, and fixed O&M).
24 For the purposes of this document, "energy cost savings" is the value of the reduced costs of producing electricity
that is attributable to the demand-side energy efficiency programs. We estimate this value by calculating the
difference in projected system costs between the two CPP modeling scenarios, with and without EE. The term
"energy savings" is also commonly used to describe the amount of energy saved as a result of demand-side energy
efficiency measures, usually expressed in terms of megawatt-hours, but in this document, it will refer to the financial
value of those savings unless otherwise noted.
3-38
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Table 3-40 Annualized Compliance Costs of the No CPP Scenario (billions of 2016$)

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.
3-39
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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%
3-40
-------
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%
3-41
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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
3-42
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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 rate-
based 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 demand-
side 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.
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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.
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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.
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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-of-
saved-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-Research-
14-053.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/guidelines-
preparing-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-ll/documents/tsd-cpp-demand-
side-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.
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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 co-
benefits, 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
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welfare.1 It 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; andUSGCRP, 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.
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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/valuing-
climate-changes-updating-estimation-of-the-social-cost-of
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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 CQ2, 2015-2050 (in 2016$ per metric ton)*
Discount Rate and Statistic
Year
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 $/short ton using the conversion factor 0.90718474 metric tons in a short ton for application to the short
ton CO2 emission impacts provided in this rulemaking. Such a conversion does not change the underlying
methodology nor does it change the meaning of the SC-CO2 estimates. For both metric and short tons denominated
SC-CO2 estimates, the estimates vary depending on the year of CO2 emissions and are defined in real terms, i.e.,
adjusted for inflation using the GDP implicit price deflator.
Table 4-2 reports the forgone domestic climate benefits in the three analysis years (2025,
2030, 2035) for the four illustrative scenarios, compared to the base case.
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Table 4-2 Estimated Forgone Domestic Climate Benefits, Relative to Base Case (CPP)
(billions 2016$)*

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 inter-
sectoral 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
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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
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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).
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Table 4-3 Projected EGU Emissions of SO2, NOx, and PJVh.s*

SO2
NOx
PM25

(thousand tons)
(thousand tons)
(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.
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2015). The estimated ancillary health co-benefits also do not consider the potential for climate-
induced 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 co-
benefits 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.
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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.
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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 CAMx7 to 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 EGUNOx 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.
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fired units or non-coal units.9 In 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.
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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.
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(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.
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(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.
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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. sand 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-to-
be-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
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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.
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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
Adult premature mortality based on cohort study
estimates and expert elicitation estimates (age >25
or age >30)
•/
~
PM ISA
PM2.5
Infant mortality (age <1)
•/
~
PM ISA

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)
V
~
PM ISA

Lost work days (age 18-65)
S
~
PM ISA
Morbidity from
Minor restricted-activity days (age 18-65)
S
~
PM ISA
exposure to PM2 5
Chronic Bronchitis (age >26)
—
—
PM ISA1

Emergency room visits for cardiovascular effects

PM ISA1

(all ages)

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

PM ISA2

diseases, other ages and populations)

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
Premature mortality based on short-term study
estimates (all ages)
~
~
Ozone ISA
exposure to ozone
Premature mortality based on long-term study
estimates (age 30-99)
~
~
Ozone ISA1

Hospital admissions—respiratory causes (age > 65)
V
~
Ozone ISA

Emergency department visits for asthma (all ages)
V
~
Ozone ISA

Exacerbated asthma (asthmatics age 6-18)
s
~
O/.onc ISA

Minor restricted-activity days (age 18-65)
V
~
Ozone ISA
Morbidity from
School absence days (age 5-17)
V
~
Ozone ISA
exposure to ozone
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
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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 PIVh.s-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=l,.. ,,J where J is the total number of counties) as
yij= Ea yija
yija = moija x(ep cij-l) x Pija, Eq[l]
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, P 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.
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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 concentration-
response 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-A ttributable 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 long-
term 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 PM-
mortality 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-
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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 PIVh.s-related forgone benefits are calculated using a log-
linear 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
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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-A ttributable 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 long-
term 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"
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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.
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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. EPA-
SAB 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.
4-24
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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).
4-25
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More confident

Less confident
Mean of PM2.5 data in epidemiology study
I standard deviation below the mean PM2.5
data in epidemiology study
Below LML of PM2.5 data in epidemiology
study (extrapolation)
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 201 la) 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 PAF.s-related Mortality (U.S. EPA, 2010b).
4-26
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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 PM-
related 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 |ig/m3 in red. For Krewski, the LML is 5.8 |ig/m3 and
for Lepeule et al., the LML is 8 |ig/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 |ig/m3 in 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.
4-27
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45p100d
45p50d
o.
002
00
0 25 0 5
pm25 (ng/m3)
Figure 4-2 Number of Individuals Exposed According to Annual Mean PM2.5
Concentration in 2030
4-28
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Annual Mean PM2.5
Summer Season Average Ozone
Figure 4-4 Change in Annual Mean PM2.5 (jig/ni3) and Summer Season Average Daily
8hr Maximum Ozone (ppb) in 2025 (Difference Calculated as Illustrative
Scenario - Base Case)
4-30
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4.5.2 Estimated Number and Economic Value of Forgone Ancillary Health Co-
Benefits
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.
4-31
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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

h Krewski et al. (2009)
280
(190 to 370)
260
(170 to 340)
280
(19 to 370)
220
(150 to 300)
Ph
Lcpculc et al. (2012)
640
(320 to 960)
590
(290 to 890)
630
(310 to 950)
510
(250 to 760)
S Smith el 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
29
31
25
(12 to 85)
(11 to 78)
(12 to 84)
(9 to 68)
All other morbidity effects

Hospital admissions—
72
67
72
58
cardiovascular (PM2 5)
(32 to 130)
(29 to 120)
(31 to 130)
(25 to 110)
Hospital admissions—
90
75
77
55
respiratory (PM2 5 & O3)
(27 to 170)
(27 to 140)
(30 to 140)
(26 to 100)
ED visits for asthma
190
150
140
84
(PM2 5 & 03)
(-44 to 440)
(-44 to 330)
(-48 to 310)
(-97 to 220)
Exacerbated asthma
23,000
12,000
8,600
-2,200
(PM2 5 & 03)
(-15,000 to 56,000)
(-5,600 to 29,000)
(-2,200 to 20,000)
(-18,000 to 18,000)
Minor restricted-activity
210,000
170,000
170,000
120,000
days (PM25 & O3)
(150,000 to 260,000)
(130,000 to 210,000)
(140,000 to 210,000)
(100,000 to 130,000)
Acute bronchitis
310
290
320
250
(PM25)
(-73 to 700)
(-68 to 650)
(-75 to 710)
(-59 to 560)
Upper resp. symptoms
5,600
5,300
5,700
4,600
(PM25)
(1,000 to 10,000)
(960 to 9,600)
(1,000 to 10,000)
(830 to 8,300)
Lower resp. symptoms
4,000
3,700
4,000
3,200
(PM25)
(1,500 to 6,400)
(1,400 to 6,000)
(1,500 to 6,500)
(1,200 to 5,200)
Lost work days
28,000
26,000
29,000
23,000
(PM25)
(24,000 to 33,000)
(22,000 to 30,000)
(24,000 to 33,000)
(19,000 to 26,000)
School absence days
12,000
4,500
1,500
-5,400
(03)
(4,400 to 28,000)
(1,600 to 10,000)
(540 to 3,400)
(-12,000 to -1,900)
* Values rounded to two significant figures
4-32
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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

3 Krewski et al. (2009)
470
(320 to 630)
410
(280 to 550)
410
(280 to 550)
350
(240 to 470)
Ph
Lcpculc et al. (2012)
1,100
(540 to 1,600)
940
(470 to 1,400)
940
(470 to 1,400)
800
(400 to 1,200)
S Smith el 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
46
47
39
(20 to 140)
(17 to 120)
(17 to 120)
(15 to 110)
All other morbidity effects

Hospital admissions—
120
110
110
91
cardiovascular (PM2 5)
(53 to 230)
(46 to 200)
(47 to 200)
(40 to 170)
Hospital admissions—
130
110
140
87
respiratory (PM2 5 & O3)
(210 to 250)
(26 to 210)
(35 to 280)
(24 to 170)
ED visits for asthma
250
210
280
170
(PM2 5 & 03)
(-50 to 620)
(-37 to 530)
(-51 to 690)
(-34 to 410)
Exacerbated asthma
44,000
40,000
48,000
29,000
(PM2 5 & 03)
(-31,000 to 110,000)
(-29,000 to 96,000)
(-34,000 to 120,000)
(-20,000 to 69,000)
Minor restricted-activity
290,000
230,000
300,000
190,000
days (PM25 & O3)
(200,000 to 370,000)
(160,000 to 310,000)
(210,000 to 390,000)
(140,000 to 250,000)
Acute bronchitis
570
500
500
420
(PM25)
(-130 to 1,300)
(-120 to 1,100)
(-120 to 1,100)
(-99 to 940)
Upper resp. symptoms
10,000
9,000
9,000
7,700
(PM25)
(1,900 to 19,000)
(1,600 to 16,000)
(1,600 to 16,000)
(1,400 to 14,000)
Lower resp. symptoms
7,200
6,300
6,300
5,400
(PM25)
(2,800 to 12,000)
(2,400 to 10,000)
(2,400 to 10,000)
(2,000 to 8,700)
Lost work days
48,000
42,000
42,000
35,000
(PM25)
(40,000 to 55,000)
(35,000 to 48,000)
(35,000 to 48,000)
(30,000 to 41,000)
School absence days
31,000
60,000
21,000
16,000
(03)
(11,000 to 71,000)
(22,000 to 140,000)
(7,700 to 48,000)
(5,600 to 35,000)
* Values rounded to two significant figures
4-33
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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
^ Krewskietal. (2009) ^Oto^O)
290
(190 to 380)
380
(260 to 510)
250
(170 to 330)
Ph
Lcpculc et al. (2012)
830
(420 to 1,200)
650
(330 to 980)
870
(440 to 1,300)
570
(280 to 850)
S Smith el 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
33
44
29
(15 to 110)
(12 to 89)
(16 to 120)
(11 to 77)
All other morbidity effects

Hospital admissions—
97
77
100
67
cardiovascular (PM2 5)
(42 to 180)
(33 to 140)
(44 to 190)
(29 to 120)
Hospital admissions—
130
110
140
87
respiratory (PM2 5 & O3)
(-8 to 250)
(26 to 210)
(35 to 280)
(24 to 170)
ED visits for asthma
250
210
280
170
(PM2 5 & 03)
(-50 to 620)
(-37 to 530)
(-51 to 690)
(-34 to 410)
Exacerbated asthma
44,000
40,000
48,000
29,000
(PM2 5 & 03)
(-31,000 to 110,000)
(-29,000 to 96,000)
(-34,000 to 120,000)
(-20,000 to 69,000)
Minor restricted-activity
290,000
230,000
300,000
190,000
days (PM25 & O3)
(200,000 to 370,000)
(160,000 to 310,000)
(210,000 to 390,000)
(140,000 to 250,000)
Acute bronchitis
430
330
440
290
(PM25)
(-100 to 960)
(-78 to 740)
(-100 to 980)
(-69 to 650)
Upper resp. symptoms
(PM25)
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
(PM25)
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
36,000
28,000
37,000
24,000
(PM25)
(31,000 to 42,000)
(24,000 to 32,000)
(31,000 to 43,000)
(21,000 to 28,000)
School absence days
16,000
24,000
29,000
16,000
(03)
(5,900 to 37,000)
(8,600 to 54,000)
(10,000 to 64,000)
(5,900 to 37,000)
* Values rounded to two significant figures
4-34
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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)*

No CPP
2% HRI
4.5% HRI
4.5% HRI

at $50/kW
at $50/kW
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)
240
220
230
190
(LML= 5.8 ng/m3)
(160 to 310)
(150 to 290)
(160 to 310)
(130 to 250)
Lepeule et al. (2012)
140
130
150
110
(LML=8ug/m3)
(67 to 200)
(64 to 190)
(74 to 220)
(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)
400
350
350
300
(LML= 5.8 ng/m3)
(270 to 530)
(240 to 460)
(240 to 460)
(200 to 390)
Lepeule et al. (2012)
260
220
220
180
(LML=8ug/m3)
(130 to 380)
(110 to 330)
(110 to 330)
(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)
320
250
330
220
(LML= 5.8 ng/m3)
(220 to 420)
(170 to 330)
(220 to 440)
(150 to 290)
Lepeule et al. (2012)
220
170
210
150
(LML=8ug/m3)
(110 to 330)
(83 to 250)
(110 to 320)
(73 to 220)
* Values rounded to two significant figures
4-35
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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:
No-threshold t
B ($0.3 to to ($0.6 to
ta $7.7) $19)
Oh
$2.6 $5.9
($0.3 to to ($0.5 to
$7) $17)
$2.7 $6.2
($0.3 to to ($0.6 to
$7.4) $18)
$2.1 $4.9
($0.2 to to ($0.2 to
$5.9) $14)
I T ¦* A* U $L8 $2'4
g Limited to above , .
8 tivttc ($0.1 to to ($0.1 to
•a $5.2) $7)
$1.5 $2.2
($0.1 to to ($0.2 to
$4) $6)
$1.6 $2.3
($0.2 to to ($0.2 to
$4.6) $6)
$1.1 $1.8
(-$.1 to to ($0.1 to
$3.3) $5)
•? , . $0.12 $0.4
£ Effects above ^
naao<;d ($0to t0 ($0to
$0.4) $1.3)
$0.06 $0.21
($0 to to ($0 to
$0.2) $0.6)
$0.04 $0.12
($0 to to ($0 to
$0.1) $0.4)
-$0.07 -$0.02
(-$0.2 to- to (-$0.1 to
$0) $0)

Ozone benefits summed with PM benefits:
-------
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:
No-threshold J)!
« modelB ($0.47 to to ($1 to
3 oae $13) $33)
$4.5 $11
($0.4 to to ($lto
$12) $30)
$4.2 $9.8
($0.4 to to ($0.9 to
$11) $28)
$3.6 $8.2
($0.34 to to ($0.8 to
$9.7) $24)
^ $3.5 $4.2
g LML model0 ($0.33 to to ($0.4 to
y $10) $11)
$3.7 $3.8
($0.3 to to ($0.4 to
$11) $10)
$2.9 $3.6
($0.3 to to ($0.4 to
$8.3) $10)
$2.3 $3
($0.22 to to ($0.3 to
$6.7) $8)
Q ™ + u $0.26 $0.92
Effects above ,CA. , ,
m WAAnS" ($0 tO to ($0 tO
JNAA^S, $0?5) $2?)
$0.43 $1.5
($0 to to ($0.1 to
$1.2) $4)
-------
Table 4-11 Estimated Economic Value of Forgone PM2.5 and Ozone-Attributable Deaths and Illnesses for Illustrative
Scenarios & Three Alternative Approaches to Representing PM Effects in 2035, Relative to Base Case (CPP)
(95% Confidence Interval; Billions of 2016$)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:
No-threshold $3.8 $8.8
B model0 ($0.4 to $10) ($1 to $25)
$3 $7
($0.29 to to ($0.6 to
$8.1) $20)
^ $9.3
($$°il)t0 t0 ($110 $2?)
$2.6
($0^25 to to ($lt0$17)
Zj $2.9 $3.3
g LML model0 ($0.3 to to ($0.3 to
y $8.4) $9)
$2.4 $2.6
($0.2 to to ($0.3 to
$7) $7)
$3 $3.5
($0.3 to to ($0.3 to
$8.6) $9)
$2.3
($0.2 to $6) t0
Q en 73
Effects above $0.21 '
m NAAQSD ($0 to $0.6) c '
U>2)
$0.2 $0.69
($0 to to ($0.1 to
$0.6) $2)
$0.24
($0 to $0.7) $3)
$0.14 $0.48
($0 to $0.4) ($0 to $1)

Ozone benefits summed with PM benefits:
No-threshold $3.5 $8.1
B model0 ($0.3 to $9) ($1 to $23)
$2.7
to ($lto$19)
$3.7 .
($$°io)t0 t0 ($110 $25)
$2.4 $5.5
($0.2 to $6) ($1 to $16)
S
-------
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
-------
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

Epidemiological
Total
Above
Below NAAQS

Year
Policy option
study
mortality
NAAQS
and Above LML A
Below LML A

Krewski
280
<1
240
44

No CPP
(< 1%)
<1
(<1%)
(84%)
140
(21%)
(16%)
510
(79%)

Lepeule
640

Krewski
260
<1
220
41

2% HRI @
(< 1%)
(84%)
(16%)

$50/kW
Lepeule
590
<1
(< 1%)
130
(22%)
460
(78%)

Krewski
280
<1
230
42

4.5% HRI @
(< 1%)
(85%)
(15%)

$50/kW
Lepeule
630
<1
(< 1%)
150
(23%)
480
(77%)

Krewski
220
<1
190
34

4.5% HRI @
(< 1%)
(85%)
(15%)

$100/kW
Lepeule
510
<1
(< 1%)
110
(23%)
390
(77%)

Krewski
470
<1
400
71

No CPP
(<1%)
(85%)
240
(22%)
(15%)
840
(78%)

Lepeule
1,100
L
(
-------
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.
4-41
-------
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 Forgone Total
Domestic ,, " . _
Health Forgone
Climate Co-Benefits Benefits
Benefits
Forgone Forgone Total
Domestic ,, " . _
Health Forgone
Climate Co-Benefits Benefits
Benefits
No CPP
2025 0.3 2.8 to 6.6 3.2 to 7.0
2030 0.5 4.9 to 11.4 5.4 to 11.9
2035 0.5 3.8 to 8.8 4.3 to 9.3
0.1 2.6 to 6.1 2.7 to 6.1
0.1 4.5 to 10.5 4.6 to 10.6
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
2030 0.4 4.5 to 10.6 4.9 to 11.0
2035 0.4 3.0 to 7.0 3.4 to 7.4
0.0 2.4 to 5.4 2.4 to 5.5
0.1 4.1 to 9.8 4.2 to 9.9
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
2030 0.4 4.2 to 9.8 4.6 to 10.2
2035 0.5 4.0 to 9.3 4.4 to 9.8
0.0 2.5 to 5.7 2.5 to 5.7
0.1 3.9 to 9.0 3.9 to 9.1
0.1 3.7 to 8.6 3.7 to 8.7
4.5% HRI at $100/kW
2025 0.1 2.1 to 4.9 2.3 to 5.0
2030 0.3 3.6 to 8.2 3.9 to 8.6
2035 0.3 2.6 to 6.0 2.9 to 6.3
0.0 2.0 to 4.4 2.0 to 4.4
0.1 3.3 to 7.6 3.3 to 7.6
0.1 2.4 to 5.5 2.4 to 5.6
Notes: Estimates rounded to one decimal point, so figures may not sum due to independent rounding. The forgone
climate benefit estimates in this table reflect the value of domestic impacts from CO2 emission changes and do not
account for changes in non-CCh GHG emissions. Forgone ozone co-benefits occur in analysis year, so they are the
same for all discount rates. The health co-benefits reflect the sum of the PM2 5 and ozone co-benefits and reflect the
range based on adult mortality functions (e.g., from Krewski el al. (2009) with Smith el al. (2009) to Lepeule el al.
(2012) with Zanobetti & Schwartz. (2008)). The forgone health co-benefits do not account for direct exposure to
NO2, SO2, and HAP; ecosystem effects; or, visibility impairment.
4-42
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Table 4-14 Forgone Climate Benefits and Ancillary Health Co-Benefits, showing only
PM2.5 Related Benefits above the Lowest Measured Level of Each Long-
Term PM2.5 Mortality Study, Relative to Base Case (CPP) (billion 2016$)
Values Calculated using 3% Discount Rate
Values Calculated using 7% Discount Rate
Forgone Forgone Total
Domestic ,, " . _
Health Forgone
Climate Co-Benefits Benefits
Benefits
Forgone Forgone Total
Domestic ,, " . _
Health Forgone
Climate Co-Benefits Benefits
Benefits
No CPP
2025 0.3 2.4 to 1.8 2.8 to 2.1
2030 0.5 4.2 to 3.5 4.7 to 4.0
2035 0.5 3.3 to 2.9 3.8 to 3.4
0.1 2.2 to 1.7 2.3 to 1.7
0.1 3.8 to 3.3 3.9 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
2030 0.4 3.8 to 3.7 4.3 to 4.1
2035 0.4 2.6 to 2.4 3.1 to 2.8
0.0 2.0 to 1.4 2.0 to 1.4
0.1 3.5 to 3.5 3.6 to 3.6
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
2030 0.4 3.6 to 2.9 4.0 to 3.3
2035 0.5 3.5 to 3.0 3.9 to 3.5
0.0 2.1 to 1.5 2.1 to 1.5
0.1 3.3 to 2.7 3.4 to 2.8
0.1 3.2 to 2.8 3.3 to 2.9
4.5% HRI at $100/kW
2025 0.1 1.8 to 1.1 1.9 to 1.2
2030 0.3 3.0 to 2.3 3.4 to 2.7
2035 0.3 2.3 to 2.0 2.6 to 2.3
0.0 1.7 to 1.0 1.7 to 1.0
0.1 2.8 to 2.2 2.8 to 2.2
0.1 2.1 to 1.8 2.1 to 1.9
Notes: Estimates rounded to one decimal point, so figures may not sum due to independent rounding. The forgone
climate benefit estimates in this table reflect the value of domestic impacts from CO2 emission changes and do not
account for changes in non-CCh GHG emissions. Forgone ozone co-benefits occur in analysis year, so they are the
same for all discount rates. The health co-benefits reflect the sum of the PM2 5 and ozone co-benefits and reflect the
range based on adult mortality functions (e.g., from Krewski el al. (2009) with Smith el al. (2009) to Lepeule el al.
(2012) with Zanobetti & Schwartz. (2008)). The forgone health co-benefits do not account for direct exposure to
NO2, SO2, and HAP; ecosystem effects; or, visibility impairment.
4-43
-------
Table 4-15 Forgone Climate Benefits and Ancillary Health Co-Benefits, showing only
PM2.5 Related Benefits above PM2.5 National Ambient Air Quality Standard
(billion 2016$)
Values Calculated using 3% Discount Rate
Values Calculated using 7% Discount Rate
Forgone Total
Domestic Forgone Health _
Climate Co-Benefits I0**0™
„ Benefits
Benefits
Forgone
Domestic Forgone Health _
Climate Co-Benefits I0**0™
„ Benefits
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 $I00/kW
2025
0.1
(0.0)
to
(0.1)
0.1
to
0.1
0.0
(0.0)
to
(0.1)
(0.0)
to
(0.0)
2030
0.3
0.1
to
0.5
0.5
to
0.8
0.1
0.1
to
0.5
0.2
to
0.5
2035
0.3
0.1
to
0.5
0.5
to
0.8
0.1
0.1
to
0.5
0.2
to
0.5
Notes: Estimates rounded to one decimal point, so figures may not sum due to independent rounding. The forgone
climate benefit estimates in this table reflect the value of domestic impacts from CO2 emission changes and do not
account for changes in non-CCh GHG emissions. Forgone ozone co-benefits occur in analysis year, so they are the
same for all discount rates. The health co-benefits reflect the sum of the PM2 5 and ozone co-benefits and reflect the
range based on adult mortality functions (e.g., from Krewski el al. (2009) with Smith el al. (2009) to Lepeule el al.
(2012) with Zanobetti & Schwartz. (2008)). The forgone health co-benefits do not account for direct exposure to
NO2, SO2, and HAP; ecosystem effects; or, visibility impairment.
4-44
-------
Table 4-16 Forgone Climate Benefits and Ancillary Health Co-Benefits using Alternate
Method for Representing PM2.5 Benefits at Low Levels, Relative to Base Case
(CPP) (billion 2016$)
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-CCh 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 el al. (2009) with Smith el 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 co-
benefits 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).
4-45
-------
Table 4-17 Unquantified Forgone Ancillary Health and Welfare Co-Benefits Categories
Category
Effect
Effect
Quantified
Effect
Monetized
More
Information
Improved Human Health

Asthma hospital admissions (all ages)
—
—
NO2 ISA1

Chronic lung disease hospital admissions (age >
65)
—
—
NO2 ISA1
Reduced incidence of
morbidity from
exposure to NO2
Respiratory emergency department visits (all
ages)
Asthma exacerbation (asthmatics age 4-18)
Acute respiratory symptoms (age 7-14)
=
=
NO2 ISA1
NO2 ISA1
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
Respiratory hospital admissions (age > 65)
Asthma emergency department visits (all ages)
Asthma exacerbation (asthmatics age 4-12)
—
—
SO2 ISA1
SO2 ISA1
SO2 ISA1
Acute respiratory symptoms (age 7-14)
—
—
SO2 ISA1
exposure to SO2
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

Neurologic effects—IQ loss
—
—
IRIS; NRC,
20001
Reduced incidence of
morbidity from
Other neurologic effects (e.g., developmental
delays, memory, behavior)
—
—
IRIS; NRC,
20002
exposure to
methylmercury
Cardiovascular effects
—
—
IRIS; NRC,
20002'3

Genotoxic, immunologic, and other toxic effects
—
—
IRIS; NRC,
20002'3
Improved Environment
Reduced visibility
Visibility in Class 1 areas
—
—
PM ISA1
impairment
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 organic s)
Effects on Individual organisms and ecosystems
—
—
PM ISA2

Visible foliar injury on vegetation
—
—
Ozone ISA1

Reduced vegetation growth and reproduction
—
—
Ozone ISA1
Reduced vegetation
Yield and quality of commercial forest products
and crops
—
—
Ozone ISA1
and ecosystem effects
from exposure to
ozone
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
4-46
-------
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

Recreational fishing
—
—
NOxSOxISA1

Tree mortality and decline
—
—
NOxSOxISA2
Reduced effects from
acid deposition
Commercial fishing and forestry effects
—
—
NOxSOxISA2
Recreational demand in terrestrial and aquatic
ecosystems
—
—
NOxSOxISA2

Other non-use effects

NOxSOxISA2

Ecosystem functions (e.g., biogeochemical
cycles)
—
—
NOxSOxISA2

Species composition and biodiversity in terrestrial
and estuarine ecosystems
—
—
NOxSOxISA2

Coastal eutrophication
—
—
NOxSOxISA2
Reduced effects from
nutrient enrichment
Recreational demand m terrestrial and estuarine
ecosystems
Other non-use effects
—
—
NOxSOxISA2
NOxSOxISA2

Ecosystem functions (e.g., biogeochemical
cycles, fire regulation)
—
—
NOxSOxISA2
Reduced vegetation
Injury to vegetation from SO2 exposure
—
—
NOxSOxISA2
effects from ambient

exposure to SO2 and
NOx
Injury to vegetation from NOx exposure
—
—
NOxSOxISA2
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).
4-47
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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.2 In 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 .
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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 Tapajos 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. Cienc. 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(0ct.):303-308 (as cited in NRC, 2000).
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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 (HC1) 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 HC1 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/table 1 .pdf
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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 co-
benefits 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.
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humans is bronchoconstriction. Asthmatics are more sensitive to the effects of SO2 likely
resulting from preexisting inflammation associated with this disease. A clear concentration-
response 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
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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
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ecosystem services.
4.7.6 Forgone Carbon Monoxide Co-Benefits
CO in ambient air is formed primarily by the incomplete combustion of carbon-
containing 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
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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 201 Id). 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.
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Sisler JF. 1996. Spatial and Seasonal P atterns and Long - Term Variability of the Composition
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Cancer Risk Reduction.
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U.S. EPA. 2008a. Integrated Science Assessment (ISA) for Nitrogen Dioxide (Health Criteria).
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Ecological Criteria (Final Report, Dec 2008).
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(CSAPR) Update for the 2008 Ozone National Ambient Air Quality Standards (NAAQS).
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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.
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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.
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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.1 In September 2017, the SAB issued its final
1 Science Advisory Board, USEPA. Economy-wide Modeling of the Benefits and Costs of Environmental
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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-SAB-
17-012.pdf
3 Executive Order 12898, Federal Actions to Address Environmental Justice in Minority Populations and Low-
Income 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.
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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 sub-
section 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.
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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).
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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.
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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.7 In 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.
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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.8 Instead, 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.
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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).
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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/.
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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).
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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. Community-
wide 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
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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.
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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 andR. 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
. 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:
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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
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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 All-
Cause 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:
.
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/2017/0 l/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
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https://www.energy.gov/sites/prod/files/2017/01/04/2017%20US%20Energy%20and%2
0Jobs%20Report%20State%20Charts%202_0.pdf >. Accessed 5/14/2018.
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Productivity." Journal of Environmental Economics and Management, 44(2): 261-270.
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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-CCh 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
6-1
-------
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.
6-2
-------
negative benefit values indicate forgone benefits, and negative net benefits indicate forgone net
benefits.
6.3 Results
6.3.1 Analysis of2023-2037for 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.
6-3
-------
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.
0 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 $1.6 billion when discounted at
7 percent. These avoided compliance cost estimates represent the regulatory cost savings related
to the regulatory allowance under E.O. 13771. Table 6-2 also presents the equivalent annualized
6-4
-------
value, which is a calculation that yields and even-flow of figures that would yield an equivalent
present value.
Table 6-2 Present Value of Compliance Costs for the Illustrative Scenario, Relative to
Base Case (CPP), 3 and 7 Percent Discount Rates, 2023-2037 (billion 2016$)
2% HRI at 4.5% HRI at 4.5% HRI at
$50/kW $50/kW $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 ( )
(0.3)
(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 Targe t 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
6-5
-------
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
$100/kW scenario. All values in Table 6-3 through Table 6-7 are present value estimates.
5 When considering whether a regulatory action is a potential welfare improvement (i.e., potential Pareto
improvement) it is necessary to consider all impacts of the action.
6-6
-------
Table 6-3 Present Value of Compliance Costs, Benefits, and Net Benefits Associated
with Targeted Pollutant (CO2), Illustrative No CPP Scenario, Relative to
Base Case (CPP), 3 and 7 Percent Discount Rates, 2023-2037 (billion 2016$)
Costs
Domestic
Climate Benefits
Net Benefits
associated with the
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.
6-7
-------
Table 6-4 Present Value of Compliance Costs, Benefits, and Net Benefits Associated
with Targeted Pollutant (CO2), Illustrative 2 Percent HRI at $50/kW
Scenario, Relative to Base Case (CPP), 3 and 7 Percent Discount Rates, 2023-
2037 (billion 2016$)
Costs
Domestic
Climate Benefits
Net Benefits
associated with the
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.
6-8
-------
Table 6-5 Present Value of Compliance Costs, Benefits, and Net Benefits Associated
with Targeted Pollutant (CO2), Illustrative 4.5 Percent HRI at $50/kW
Scenario, Relative to Base Case (CPP), 3 and 7 Percent Discount Rates, 2023-
2037 (billion 2016$)
Costs
Domestic
Climate Benefits
Net Benefits
associated with the
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.
6-9
-------
Table 6-6 Present Value of Compliance Costs, Benefits, and Net Benefits Associated
with Targeted Pollutant (CO2), Illustrative 4.5 Percent HRI at $100/kW
Scenario, Relative to Base Case (CPP), 3 and 7 Percent Discount Rates, 2023-
2037 (billion 2016$)
Costs
Domestic
Climate Benefits
Net Benefits
associated with the
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.
6-10
-------
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$)

Net Benefits

Domestic
associated with the

vOSlS
Climate Benefits
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 $100/kW
0.3
0.2
(0.2)
(0.0)
(0.5)
(0.2)
Note: Negative costs indicate avoided costs, negative benefits indicate forgone benefits, and negative net benefits
indicate forgone net benefits.
6.3.2.2 Net Benefits Including Forgone Air Pollutant Co-Benefits
When considering whether a regulatory action is a potential welfare improvement (i.e.,
potential Pareto improvement) it is necessary to consider all impacts of the action. Therefore,
tables in this section provide the estimates of the benefits, costs, and net benefits of the
illustrative scenarios, inclusive of the benefit impacts from the SO2 and NOx emission changes
that are projected to accompany the CO2 changes. In these tables, the estimates for the ancillary
health co-benefits are derived using PM2.5 log-linear concentration-response functions that
quantify risk associated with the full range of PM2.5 exposures experienced by the population.
There are additional important benefit impacts that EPA could not monetize. Due to
current data and modeling limitations, our estimates of the benefit impacts from changing CO2
emissions do not include important impacts like ocean acidification or potential tipping points in
natural or managed ecosystems. Unquantified benefits also include climate benefits from
changing emissions of non-CC>2 greenhouse gases and co-benefits from changes in exposure to
SO2, NOx, and hazardous air pollutants (e.g., mercury), as well as ecosystem effects and
visibility impairment.
6-11
-------
Table 6-8 through Table 6-12 contain present value estimates of compliance costs,
benefits, and net benefits inclusive of ancillary health co-benefits for the four illustrative
scenarios.
Table 6-8 Illustrative No CPP Scenario: Present Value of Compliance Costs, Benefits
(Inclusive of Health Co-Benefits), and Net Benefits, Relative to Base Case
(CPP), 20230-2037 (billion 2016$)
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.
6-12
-------
Table 6-9 Illustrative 2 Percent HRI at $50/kW Scenario: Present Value of Compliance
Costs, Benefits (Inclusive of Health Co-Benefits), and Net Benefits, Relative
to Base Case (CPP), 2023-2037 (billion 2016$)
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.
6-13
-------
Table 6-11 Illustrative 4.5 Percent HRI at $100/kW Scenario: Present Value of
Compliance Costs, Benefits (Inclusive of Health Co-Benefits), and Net
Benefits, Relative to Base Case (CPP), 2023-2037 (billion 2016$)
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 Co-
Benefits), 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.
6-14
-------
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 PIVh.s-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 PIVh.s-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 PIVh.s-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 PIVh.s-related forgone
benefits using an alternative concentration-response parameter to quantify PIVh.s-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.
6-15
-------
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$)
„ Benefits Excluding Benefits Net Benefits Excluding Benefits
below LML 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 $100/kW
0.3
0.2
(2.0) to (1.6)
(1.3) to (1.0)
(2.2) to (1.9)
(1.5) to (1.2)
Note: Negative costs indicate avoided costs, negative benefits indicate forgone benefits, and negative net benefits
indicate forgone net benefits.
Table 6-14 Present Value of Compliance Costs, Benefits, and Net Benefits assuming that
PM2.5 Related Benefits Fall to Zero Below the PM2.5 National Ambient Air
Quality Standard, Relative to Base Case (CPP), 3 and 7 Percent Discount
Rates, 2023-2037 (billion 2016$)
Benefits Excluding Benefits Net Benefits Excluding Benefits
below NAAQS 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 $100/kW
0.3
0.2
(0.3) to (0.5)
(0.1) to (0.3)
(0.5) to (0.8)
(0.3) to (0.5)
Note: Negative costs indicate avoided costs, negative benefits indicate forgone benefits, and negative net benefits
indicate forgone net benefits.
6-16
-------
Table 6-15 Present Value of Compliance Costs, Benefits, and Net Benefits assuming
Alternate Method for Calculating PM2.5 Benefits at Low Levels, Relative to
Base Case (CPP), 3 and 7 Percent Discount Rates, 2023-2037 (billion 2016$)
Benefits Net Benefits
Costs Assuming Alternate Assuming Alternate
Concentration-Response 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.
6-17
-------
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.
6-18
-------
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 Ml 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-HQ-
OAR-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.
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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
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(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.
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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 multi-
model 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
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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.
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Interim U.S. Domestic Social Cost of Carbon in 2030 [2016$ / metric ton C02]
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Figure 7-1 Frequency Distribution of Interim Domestic SC-C02 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).
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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
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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.
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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.
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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 C02: 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 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:
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.
Whittington, D., & MacRae, D. (1986). The Issue of Standing in Cost-Benefit Analysis. Journal
of Policy Analysis and Management, 5(4): 665-682.
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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.
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a'srjsrr^ ' " V'
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_201 l_fmal_l l-26-14.pdf
The lateral boundary and initial species condition concentrations are provided by a three-
dimensional 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).
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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 201 1NEIv2 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 201 1NEIv2, 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 (NOD A)
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 201 len 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
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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.
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by EPA in response to the January 2017 NOD A, 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 co-
benefits 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.
8-5
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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. Non-
U.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 = (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:
8-6
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ME = i2;|P-0|
(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:
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:
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/ttnamtil/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.
nmb = £;(np 0) * 100
Zi(0)
(Eq-3)
NME = * 100
Zi(o)
(Eq-4)
8-7
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BasmsttBBipfe
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
U.S. Climate Regions
Figure 8-2 NO A A 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.g0v/monitoring-references/maps/us-cli1nate-regions.php#references.
8-8
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Table 8-1
Model Performance Statistics by Region for PM2.5
Region
Network
No. of
Obs
MB
(jig/m3)
ME
(jig/m3)
NMB
(%)
NME
(%)
Northeast
IMPROVE
CSN
1577
2788
0.87
0.97
2.21
4.04
17.70
9.70
44.90
40.40
Ohio Valley
IMPROVE
CSN
680
2475
0.10
0.13
2.96
3.85
1.20
1.10
35.50
32.80
Upper Midwest
IMPROVE
CSN
700
1343
0.83
1.37
2.37
3.66
14.20
13.60
40.40
36.30
Southeast
IMPROVE
CSN
1172
1813
0.52
0.19
3.54
3.92
6.30
1.70
43.20
34.20
South
IMPROVE
CSN
933
962
-0.47
-0.08
2.69
4.48
-6.50
-0.75
37.40
39.50
Southwest
IMPROVE
CSN
3695
746
-1.12
-0.08
1.86
3.93
-28.00
-1.00
46.30
47.10
N. Rockies/
IMPROVE
1952
0.07
1.39
2.40
44.90
Plains
CSN
275
-2.07
4.18
-21.80
43.90
Northwest
IMPROVE
CSN
1901
668
1.19
5.77
2.28
7.25
43.20
69.90
82.90
87.90
West
IMPROVE
CSN
1782
936
-1.08
-2.92
2.08
5.08
-25.30
-23.10
48.50
40.30
Model performance statistics for May through September modeled and MDA8 ozone
concentrations for 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).
8-9
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Table 8-2 Model Performance Statistics by Region for Ozone on Days Above 60 ppb
„ . Y1B ME NMB NME
ReS,on No. of Obs (ppb) (ppb) (%) (%)
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 coal-
fired EGUs.
8-10
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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
•
EGU retirements
Arizona

through 2025
Arkansas
•
EGU retirements
California

2026-2030
Colorado
•
All U.S.
Connecticut + Rhode Island

anthropogenic
Delaware + New Jersey

emissions from
Florida

source sectors
Georgia

other than EGUs
Idaho + Oregon + Washington
Illinois
•
International
within-domain
Indiana
Iowa

emissions

(sources
Kansas

occurring in
Canada, Mexico,
Kentucky

and from
Louisiana

offshore marine
Maine + Mass. + New Hamp. +

vessels and
Vermont

drilling
Maryland

platforms)
Michigan
•
Fires (wildfires
Minnesota

and prescribed
Mississippi

fires)
Missouri
•
Biogenic sources
Montana
•
Boundary
Nebraska

conditions
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
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.
8-11
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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
PA Coal EGU Ozone Contributions
May-Sep Mean of MDA8
Min = O.OOE+O at (1,1), Max = 2.039 at (327,151)
8-12
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PA Non-coal EGU Ozone Contributions
May-Sep Mean of MDA8
1 k
1 396 PPD
Min = 0.00E+0 at (1,1), Max = 1.808 at (344,152)
Figure 8-4 Map of Pennsylvania Non-Coal EGU Tag Contribution to Seasonal Average
MDA8 Ozone
TX Coal EGU Ozone Contributions
May-Sep Mean of MDA8
Min =0.00E-H] at (1,1), Max = 4.818 at (221,66)
396ppb
Figure 8-5 Map of Texas Coal EGU Tag Contribution to Seasonal Average MDA8
Ozone
8-13
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TX Non-coal EGU Ozone Contributions
May-Sep Mean of MDA8
il ,
1 396ppb
Min = 0.00E+0 at (1,1), Max = 2.310 at (227,44)
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.
8-14
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IN Coal EGU Nitrate Contributions
Quarterly Avg ¦ Winter
Min = 0.00E+Q at (1,1), Max = 0.151 at (274,124)
Figure 8-7 Map of Indiana Coal EGU Tag Contributions to Wintertime Average
(January-March) Nitrate
IN Coal EGU Nitrate Contributions
Quarterly Avg - Summer
1 396 ug/m3
Min = 0.00E+0 at (1,1), Max = 0.022 at (274,125)
Figure 8-8 Map of Indiana Coal EGU Tag Contributions to Summertime Average (July-
September) Nitrate
8-15
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396 ug/m3
Min = O.OOE+O at (1,1), Max = 0.129 at (269,120)
IN Coal EGU Sulfate Contributions
Quarterly Avg ¦ Winter
Figure 8-9 Map of Indiana Coal EGU Tag Contributions to Wintertime Average
(January-March) Sulfate
396 ug/m3
IN Coal EGU Sulfate Contributions
Quarterly Avg - Summer
Min =0.00E+€ at (1,1), Max = 0.229 at (272,126)
Figure 8-10 Map of Indiana Coal EGU Tag Contributions to Summertime Average (July-
September) Sulfate
8-16
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IN Coal EGU Primary PM Contributions
Quarterly Avg ¦ Winter
Min = 0.00E+0 at (1,1), Max = 0.108 at (272,120)
396 ug/m'3
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 EG1J Tag Contributions to Summertime Average (July-
September) Primary PM2.5
IN Coal EGU Primary PM Contributions
Quarterly Avg - Summer
Min = O.OOE+O at (1,1), Max = 0.099 at (272,120)
396 ug/m3
8-17
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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.
8-18
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• 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).
Total 2023 EGU COstate fuei
2023 Heat Scalarstate fuel = ,J ^ ^
state,fuel 2023 CEMS COstateJuel (Eq-5)
Total 2023 Hcatg^^g fuel 2023 Heat Scalavs^a^efuel ^ 2023 CEMS Hcatg^^gfuel (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).
P^2.S ScalaVScenario,state,fuel
IPM Heatscenari0 Sfafejuei
Total 2023 HeatSfafejuel (Eq-7)
P^2.S scenario,state,fuel ~ P^2.S ScalaTSCenario,state,fuel * 2023 EGU PM2 5 state,fuel
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/ch02s09sl9.html
8-19
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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 non-
coal 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 non-
coal 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 state-
fractions 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.
8-20
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Table 8-4 Tribal Fractions by State in the 2023 Emissions
State
NOx
SO2
PM25
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 ("03N") 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.
8-21
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0 ZOnem g (i i y Cm,g,d,BC Cm,g,d,int Cm,g,d,bio ^m,g,d,fires
T
Cm,g,d,USanthro Cm,g,d,y,EGUret / CVOC,m,g,d,t
I
^ ' CNOx,m,g,d,t^t,i,y
t=1
T
(Eq-9)
t=1
where:
• Ozonemg d i y is the estimated ozone for metric, "m" (MDA8 or MDA1), grid-
cell, "g", day, "d", scenario, "i", and year, "y";
• Cm,g,d,Bc's 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;
• Cmg d bio is the total ozone contribution from biogenic emissions;
• Cm,g,d,fireS's the total ozone contribution from fires;
• Cmg 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,t's the ozone contribution from EGU emissions of VOCs from tag, "t";
• CNOx,m,g,d,t is the ozone contribution from EGU emissions of NOx from tag, "t";
and
• St iy 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):
8-22
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s,g,d,USanthro
's,g,d,y,EGUret
(Eq-10)
OAgdiy — CpQA g age + CpoA,g,d,int CpOA,g,d,bio CpOA,g,d,fires
CpOA,g,d,USanthro CpOA,g,d,y,EGUret SOAg Ci
T
(Eq-11)
^ ' CpOA,g,d,tSpri,t,i,y
t=1
PMSig:d,i,y 1S 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,t is 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".
8-23
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Similarly, for Equation (11):
• 0Ag d iy 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
• SOAgd represents the modeled secondary organic aerosol concentration for gird-
cell, "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
8-24
-------
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.
8-25
-------
Table 8-5 Scaling Ratios for Primary PM2.5 for Coal EGUs
Base Case (CPP) No CPP
State
2025
2030
2035
2025
2030
2035
AL
0.58
0.64
0.58
0.47
0.61
0.54
AZ
0.48
0.46
0.40
0.48
0.46
0.40
AR
0.38
0.45
0.38
0.45
0.52
0.5 1
CA
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
CT+RI
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
FL
0.44
0.42
0.51
0.39
0.40
0.50
GA
0.42
0.49
0.43
0.50
0.5 1
0.45
IL
0.85
0.82
0.75
0.82
0.81
0.74
IN
0.67
0.68
0.58
0.66
0.67
0.57
IA
0.74
0.70
0.64
0.93
0.89
0.88
KS
0.68
0.59
0.59
0.91
0.86
0.79
KY
0.36
0.34
0.25
0.35
0.31
0.24
LA
0.16
0.19
0.22
0.15
0.19
0.23
ME+MA+NH+

VT
0.22
0.22
0.22
0.17
0.17
0.17
MD
0.10
0.00
0.00
0.07
0.00
0.00
MI
0.79
0.73
0.67
0.95
0.96
0.87
MN
1.11
0.91
0.87
1.27
0.96
0.95
MS
0.28
0.30
0.30
0.23
0.27
0.28
MO
0.81
0.77
0.72
0.95
0.93
0.90
MT
0.94
0.94
0.88
1.04
1.04
1.04
NE
0.75
0.67
0.67
1.01
1.00
0.99
NV
0.59
0.50
0.54
0.45
0.45
0.44
NM
0.51
0.48
0.46
0.50
0.47
0.46
NY
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
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
0.47
0.59
0.53
0.53
0.63
0.55
0.50
0.59
0.53
0.48
0.45
0.39
0.47
0.44
0.38
0.47
0.44
0.38
0.48
0.53
0.51
0.5 1
0.54
0.53
0.51
0.53
0.52
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.08
1.08
0.99
1.06
1.05
0.98
1.06
1.05
0.98
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
0.00
0.00
0.00
0.41
0.41
0.50
0.42
0.43
0.50
0.41
0.43
0.50
0.50
0.51
0.48
0.50
0.5 1
0.48
0.49
0.51
0.48
0.82
0.80
0.74
0.81
0.79
0.74
0.79
0.76
0.71
0.65
0.66
0.56
0.64
0.65
0.56
0.64
0.65
0.56
0.92
0.88
0.87
0.91
0.88
0.86
0.90
0.87
0.86
0.90
0.87
0.80
0.90
0.86
0.80
0.90
0.86
0.80
0.35
0.31
0.24
0.34
0.32
0.25
0.34
0.31
0.24
0.15
0.16
0.20
0.15
0.21
0.21
0.15
0.19
0.19
0.06
0.06
0.06
0.13
0.13
0.13
0.00
0.00
0.00
0.10
0.01
0.00
0.10
0.05
0.00
0.10
0.05
0.00
0.94
0.95
0.87
0.92
0.93
0.89
0.92
0.92
0.87
1.23
0.95
0.93
1.16
0.97
0.91
1.16
0.97
0.91
0.22
0.27
0.29
0.23
0.30
0.30
0.27
0.30
0.30
0.93
0.90
0.88
0.94
0.91
0.89
0.92
0.89
0.86
1.02
1.02
1.02
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.98
0.97
0.96
0.96
0.95
0.96
0.96
0.95
0.55
0.47
0.43
0.54
0.46
0.42
0.54
0.46
0.42
0.50
0.47
0.46
0.50
0.47
0.46
0.49
0.46
0.46
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.48
0.39
0.29
0.49
0.40
0.3 1
0.49
0.40
0.31
-------
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
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
0.1 1
0.13
0.10
0.08
0.18
0.16
0.1 1
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
0.3 1
0.28
0.32
0.31
0.28
8-27
-------
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 $5()/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
0.3 1
0.33
0.31
0.32
0.33
0.30
0.3 1
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
8-28
-------
Base Case (CPP) No CPP 2% HRI at $50/kW 4.5% HRI at $5()/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
1.1 1
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
8-29
-------
Table 8-7 Scaling Ratios for Sulfate for Coal EGUs
Base Case (CPP) No CPP
State
2025
2030
2035
2025
2030
2035
AL
0.91
1.02
0.85
0.71
0.91
0.79
AZ
1.02
0.99
0.81
1.02
0.99
0.81
AR
1.47
1.97
1.69
1.92
2.45
2.35
CA
0.98
0.00
0.00
0.98
0.00
0.00
CO
0.78
0.71
0.66
0.98
1.00
0.90
CT+RI
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
FL
0.77
0.74
0.88
0.67
0.69
0.86
GA
1.72
1.65
1.45
2.1 1
1.74
1.53
IL
0.86
0.83
0.77
0.85
0.83
0.75
IN
0.96
0.91
0.81
0.95
0.90
0.80
IA
0.40
0.37
0.35
0.50
0.47
0.46
KS
1.96
1.70
1.69
2.53
2.45
2.26
KY
0.34
0.34
0.24
0.33
0.30
0.23
LA
0.23
0.26
0.30
0.22
0.24
0.29
ME+MA+NH+VT
0.67
0.67
0.67
0.52
0.52
0.52
MD
0.05
0.00
0.00
0.04
0.00
0.00
MI
0.77
0.64
0.61
0.96
0.98
0.74
MN
1.33
1.31
1.29
1.38
1.31
1.30
MS
0.86
0.91
0.91
0.70
0.83
0.86
MO
1.10
1.12
1.09
1.20
1.27
1.24
MT
0.46
0.55
0.51
0.52
0.61
0.61
NE
0.72
0.69
0.87
0.78
0.78
0.96
NV
5.04
4.47
4.67
2.89
2.93
2.85
NM
1.40
1.30
1.26
1.39
1.30
1.27
NY
0.00
0.00
0.00
0.00
0.00
0.00
NC
0.57
0.46
0.37
0.53
0.44
0.35
ND+SD
0.56
0.55
0.49
0.61
0.64
0.63
8-30
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
0.71
0.89
0.77
0.76
0.94
0.79
0.74
0.89
0.77
1.00
0.97
0.80
0.98
0.94
0.78
0.98
0.94
0.78
2.10
2.50
2.35
2.28
2.57
2.40
2.25
2.55
2.37
0.96
0.00
0.00
0.93
0.00
0.00
0.93
0.00
0.00
0.96
0.98
0.88
0.95
0.95
0.86
0.95
0.95
0.86
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
0.00
0.00
0.00
0.72
0.72
0.86
0.74
0.76
0.87
0.73
0.76
0.86
2.04
1.72
1.63
2.06
1.71
1.64
2.01
1.71
1.62
0.84
0.83
0.74
0.82
0.81
0.75
0.81
0.79
0.73
0.94
0.88
0.80
0.93
0.87
0.79
0.93
0.87
0.79
0.49
0.47
0.46
0.48
0.47
0.46
0.48
0.46
0.45
2.50
2.44
2.26
2.50
2.41
2.27
2.51
2.40
2.26
0.32
0.30
0.23
0.32
0.30
0.24
0.31
0.30
0.23
0.22
0.23
0.28
0.21
0.28
0.28
0.21
0.28
0.27
0.17
0.17
0.17
0.41
0.41
0.41
0.01
0.01
0.01
0.05
0.01
0.00
0.05
0.03
0.00
0.06
0.03
0.00
0.95
0.97
0.77
0.93
0.95
0.87
0.94
0.94
0.80
1.36
1.30
1.29
1.31
1.30
1.26
1.32
1.29
1.26
0.68
0.82
0.89
0.71
0.91
0.91
0.84
0.90
0.91
1.18
1.24
1.22
1.20
1.22
1.20
1.20
1.21
1.18
0.51
0.60
0.60
0.50
0.58
0.58
0.50
0.58
0.58
0.76
0.76
0.94
0.74
0.74
0.92
0.74
0.74
0.92
3.54
3.03
2.79
3.45
2.96
2.72
3.49
2.99
2.76
1.38
1.29
1.28
1.37
1.28
1.27
1.36
1.28
1.27
0.01
0.00
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.54
0.45
0.36
0.54
0.46
0.39
0.54
0.46
0.39
0.60
0.62
0.62
0.59
0.61
0.61
0.59
0.61
0.61
-------
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
0.3 1
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
0.5 1
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
8-31
-------
Table 8-8 Scaling Ratios for Sulfate for Non-Coal EGUs
Base Case (CPP) No CPP

2025
2030
2035
2025
2030
2035
AL
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
AR
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
CO
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
DE+NJ
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
GA
0.05
0.06
0.09
0.05
0.05
0.09
ID+OR+WA
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
IN
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
KS
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
LA
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
MD
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
MN
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
MO
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
NE
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
NM
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
NC
0.01
0.01
0.01
0.01
0.01
0.01
8-32
2% HRI at $50/kW 4.5% HRI at $5()/kW 4.5% HRI at $100/kW
2025
2030
2035
2025
2030
2035
2025
2030
2035
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
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.19
0.01
0.02
0.19
0.01
0.02
0.19
0.01
0.02
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
1.96
1.96
1.96
1.96
1.96
1.96
1.96
1.96
1.96
2.67
2.67
2.67
2.67
2.67
2.67
2.67
2.67
2.67
0.68
0.68
0.67
0.68
0.68
0.67
0.68
0.68
0.67
0.04
0.05
0.09
0.04
0.05
0.05
0.04
0.05
0.05
0.07
0.07
0.07
0.07
0.07
0.07
0.07
0.07
0.07
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.20
0.20
0.20
0.20
0.20
0.20
0.20
0.20
0.20
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
0.00
0.00
0.00
0.03
0.03
0.02
0.03
0.03
0.02
0.03
0.03
0.02
0.06
0.06
0.06
0.06
0.06
0.06
0.06
0.06
0.06
0.59
0.54
0.59
0.59
0.54
0.59
0.58
0.54
0.59
0.45
0.45
0.45
0.45
0.45
0.45
0.45
0.45
0.45
0.06
0.06
0.06
0.06
0.06
0.06
0.06
0.06
0.06
0.36
0.36
0.36
0.36
0.36
0.36
0.36
0.36
0.36
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
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
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
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.69
0.64
0.64
0.69
0.64
0.64
0.69
0.64
0.64
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
-------
Base Case (CPP) No CPP 2% HRI at $50/kW 4.5% HRI at $5()/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
8-33
-------
Table 8-9 Scaling Ratios for Nitrate for Coal EGUs
Base Case (CPP) No CPP
State
2025
2030
2035
2025
2030
2035
AL
0.43
0.48
0.44
0.34
0.45
0.40
AZ
0.77
0.77
0.68
0.77
0.77
0.68
AR
0.87
0.98
0.82
1.02
1.14
1.10
CA
0.18
0.00
0.00
0.18
0.00
0.00
CO
0.92
0.81
0.77
0.89
0.92
0.83
CT+RI
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
FL
0.52
0.54
0.62
0.47
0.50
0.60
GA
0.37
0.43
0.39
0.44
0.45
0.41
IL
1.10
1.05
0.97
1.06
1.04
0.95
IN
0.79
0.80
0.69
0.78
0.78
0.68
IA
0.98
0.91
0.84
1.25
1.19
1.17
KS
0.85
0.71
0.73
1.08
1.03
0.93
KY
0.53
0.49
0.36
0.52
0.45
0.35
LA
0.22
0.25
0.29
0.21
0.23
0.28
ME+MA+NH+VT
0.23
0.23
0.23
0.18
0.18
0.18
MD
0.07
0.00
0.00
0.05
0.00
0.00
MI
0.91
0.81
0.75
1.12
1.14
0.95
MN
1.17
0.94
0.92
1.30
0.98
0.97
MS
0.29
0.50
0.50
0.23
0.41
0.49
MO
0.84
0.76
0.71
1.07
1.03
0.97
MT
0.91
0.91
0.85
0.97
0.97
0.97
NE
0.82
0.75
0.76
1.14
1.14
1.13
NV
1.75
1.55
1.62
1.01
1.02
0.99
NM
0.75
0.67
0.63
0.74
0.66
0.62
NY
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
ND+SD
0.58
0.56
0.53
0.81
0.84
0.83
8-34
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
0.35
0.44
0.39
0.39
0.48
0.42
0.37
0.44
0.39
0.76
0.75
0.66
0.74
0.73
0.65
0.74
0.73
0.65
1.08
1.15
1.11
1.13
1.17
1.13
1.13
1.16
1.12
0.17
0.00
0.00
0.17
0.00
0.00
0.17
0.00
0.00
0.88
0.90
0.82
0.86
0.88
0.80
0.86
0.88
0.80
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
0.00
0.00
0.00
0.49
0.53
0.60
0.51
0.55
0.60
0.49
0.54
0.60
0.43
0.46
0.44
0.44
0.46
0.44
0.43
0.46
0.44
1.05
1.03
0.94
1.05
1.02
0.95
1.02
0.98
0.91
0.77
0.77
0.67
0.76
0.76
0.67
0.76
0.76
0.66
1.23
1.18
1.17
1.22
1.18
1.15
1.22
1.16
1.15
1.08
1.03
0.94
1.07
1.02
0.94
1.07
1.02
0.94
0.51
0.44
0.35
0.50
0.45
0.36
0.50
0.45
0.35
0.21
0.22
0.27
0.21
0.27
0.27
0.20
0.26
0.26
0.06
0.06
0.06
0.14
0.14
0.14
0.00
0.00
0.00
0.07
0.01
0.00
0.07
0.03
0.00
0.07
0.04
0.00
1.10
1.13
0.97
1.08
1.10
1.04
1.08
1.10
0.99
1.25
0.96
0.96
1.18
0.98
0.93
1.18
0.98
0.93
0.23
0.41
0.49
0.24
0.53
0.53
0.36
0.52
0.52
1.04
0.99
0.94
1.07
1.01
0.96
1.03
0.98
0.92
0.95
0.95
0.95
0.92
0.92
0.92
0.92
0.92
0.92
1.12
1.12
1.11
1.09
1.09
1.08
1.09
1.09
1.08
1.24
1.06
0.97
1.20
1.03
0.95
1.22
1.04
0.96
0.73
0.65
0.62
0.71
0.64
0.61
0.71
0.63
0.61
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.71
0.58
0.39
0.71
0.59
0.44
0.71
0.59
0.44
0.80
0.83
0.82
0.79
0.80
0.80
0.79
0.80
0.80
-------
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
0.5 1
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
0.5 1
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
0.1 1
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
8-35
-------
Table 8-10 Scaling Ratios for Nitrate for Non-Coal EGUs
Base Case (CPP) No CPP
State
2025
2030
2035
2025
2030
2035
AL
0.79
0.90
1.12
0.80
0.89
1.12
AZ
0.23
0.30
0.34
0.22
0.30
0.33
AR
0.79
0.79
0.82
0.79
0.79
0.81
CA
3.42
0.57
0.66
2.99
0.57
0.66
CO
0.39
0.56
0.62
0.23
0.35
0.45
CT+R.I
1.19
1.18
1.19
1.20
1.18
1.19
DE+NJ
1.25
1.30
1.29
1.33
1.42
1.45
FL
1.00
1.02
1.01
1.01
1.03
1.01
GA
1.19
1.32
1.33
1.18
1.33
1.31
ID+OR+WA
0.58
0.66
0.69
0.57
0.65
0.67
IL
0.87
0.94
1.00
0.80
0.93
0.98
IN
1.00
1.03
1.24
1.01
1.05
1.23
IA
1.01
1.01
1.17
0.91
0.99
1.24
KS
1.14
0.86
1.04
1.14
1.08
1.18
KY
1.23
1.47
1.54
1.26
1.41
1.54
LA
0.48
0.41
0.43
0.48
0.41
0.42
ME+MA+NH+VT
1.07
0.64
0.71
0.73
0.65
0.71
MD
1.26
1.25
1.22
1.26
1.28
1.21
MI
1.15
1.17
1.23
1.17
1.20
1.23
MN
0.71
0.72
0.79
0.68
0.73
0.80
MS
0.44
0.51
0.51
0.42
0.48
0.49
MO
0.48
0.49
0.59
0.48
0.52
0.62
MT
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
NV
0.84
1.10
1.32
0.90
1.02
1.16
NM
0.41
0.23
0.20
0.41
0.22
0.19
NY
1.00
0.98
0.98
1.01
0.99
0.99
NC
0.88
0.86
0.90
0.89
0.86
0.89
8-36
2% HRI at $50/kW 4.5% HRI at $5()/kW 4.5% HRI at $100/kW
2025
2030
2035
2025
2030
2035
2025
2030
2035
0.77
0.88
1.09
0.78
0.90
1.08
0.77
0.87
1.07
0.22
0.30
0.33
0.21
0.30
0.33
0.21
0.30
0.33
0.79
0.78
0.80
0.78
0.78
0.80
0.78
0.79
0.79
2.98
0.57
0.66
2.98
0.58
0.67
2.97
0.58
0.67
0.23
0.35
0.45
0.23
0.35
0.45
0.24
0.36
0.44
1.20
1.19
1.20
1.20
1.19
1.19
1.21
1.19
1.20
1.32
1.40
1.45
1.32
1.39
1.44
1.31
1.38
1.42
1.00
1.02
1.02
0.99
1.01
1.02
0.98
1.01
1.02
1.14
1.32
1.28
1.21
1.36
1.25
1.10
1.35
1.24
0.57
0.65
0.67
0.58
0.65
0.67
0.58
0.65
0.68
0.80
0.93
0.98
0.79
0.92
0.98
0.80
0.91
0.99
0.97
1.03
1.24
0.97
1.03
1.24
0.96
1.03
1.24
0.90
1.03
1.25
0.90
1.01
1.22
0.91
1.04
1.26
1.14
1.07
1.23
1.12
1.11
1.21
1.14
1.11
1.21
1.23
1.37
1.55
1.14
1.36
1.51
1.12
1.31
1.54
0.48
0.41
0.42
0.48
0.40
0.42
0.48
0.41
0.41
0.73
0.64
0.71
0.73
0.64
0.71
0.72
0.64
0.71
1.25
1.25
1.21
1.24
1.24
1.19
1.24
1.23
1.19
1.16
1.20
1.23
1.16
1.21
1.23
1.16
1.20
1.21
0.67
0.73
0.80
0.66
0.73
0.80
0.66
0.74
0.81
0.42
0.48
0.50
0.43
0.48
0.49
0.41
0.49
0.50
0.47
0.51
0.63
0.45
0.5 1
0.62
0.46
0.51
0.62
0.01
0.01
0.02
0.01
0.01
0.02
0.01
0.01
0.02
0.87
0.95
0.97
0.93
0.94
0.97
0.90
0.94
0.97
0.87
1.03
1.16
0.88
1.03
1.17
0.87
1.03
1.16
0.41
0.22
0.19
0.41
0.22
0.18
0.41
0.22
0.18
1.02
0.99
0.99
1.01
0.99
1.00
1.02
0.99
0.99
0.88
0.87
0.91
0.90
0.89
0.97
0.89
0.89
0.95
-------
Base Case (CPP) No CPP 2% HRI at $50/kW 4.5% HRI at $5()/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
8-37
-------
Table 8-11 Scaling Ratios for Ozone for Coal EGUs
Base Case (CPP) No CPP
State
2025
2030
2035
2025
2030
2035
AL
0.53
0.61
0.65
0.43
0.57
0.61
AZ
0.75
0.78
0.80
0.75
0.78
0.80
AR
0.94
1.18
1.20
1.04
1.42
1.46
CA
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
CT+RI
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
FL
0.66
0.79
1.05
0.65
0.73
1.03
GA
0.61
0.79
0.89
0.72
0.81
0.93
IL
1.00
1.02
0.96
0.99
1.01
0.94
IN
0.80
0.82
0.68
0.79
0.79
0.68
IA
0.85
0.84
0.82
1.10
1.11
1.09
KS
0.86
0.90
0.90
1.07
1.12
1.11
KY
0.60
0.56
0.52
0.60
0.54
0.49
LA
0.32
0.33
0.44
0.31
0.34
0.44
ME+MA+NH+VT
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
MI
0.81
0.82
0.77
1.02
1.05
0.94
MN
1.20
0.96
0.94
1.28
0.95
0.95
MS
0.31
0.66
0.66
0.23
0.66
0.66
MO
0.93
0.94
0.93
1.08
1.10
1.10
MT
1.02
1.02
0.95
1.08
1.08
1.08
NE
0.92
0.89
0.91
1.15
1.15
1.15
NV
0.71
0.88
1.05
0.71
0.83
0.78
NM
0.84
0.84
0.84
0.82
0.82
0.82
NY
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
ND+SD
0.66
0.67
0.66
0.84
0.91
0.89
8-38
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
0.44
0.55
0.59
0.50
0.60
0.64
0.47
0.53
0.57
0.73
0.76
0.78
0.71
0.74
0.76
0.71
0.74
0.76
1.14
1.45
1.47
1.17
1.42
1.45
1.17
1.42
1.45
0.17
0.00
0.00
0.17
0.00
0.00
0.17
0.00
0.00
0.90
0.93
0.90
0.88
0.89
0.88
0.88
0.91
0.88
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
0.00
0.00
0.00
0.65
0.77
1.03
0.64
0.78
1.04
0.63
0.77
1.03
0.71
0.80
1.02
0.70
0.80
0.99
0.69
0.80
0.99
0.98
1.00
0.95
0.95
0.98
0.95
0.92
0.93
0.92
0.77
0.78
0.67
0.76
0.77
0.65
0.76
0.77
0.65
1.07
1.09
1.09
1.07
1.08
1.08
1.07
1.08
1.08
1.04
1.09
1.09
1.02
1.06
1.08
1.02
1.06
1.09
0.59
0.54
0.50
0.58
0.53
0.53
0.57
0.55
0.50
0.30
0.32
0.42
0.30
0.38
0.42
0.29
0.36
0.41
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.08
0.00
0.00
0.09
0.03
0.00
0.09
0.03
0.00
1.00
1.02
0.95
0.99
1.00
0.98
0.99
1.00
0.93
1.23
0.95
0.94
1.16
1.03
0.92
1.16
1.01
0.92
0.23
0.65
0.65
0.26
0.63
0.63
0.50
0.63
0.63
1.04
1.06
1.06
1.05
1.06
1.08
1.02
1.03
1.04
1.06
1.06
1.06
1.03
1.03
1.03
1.03
1.03
1.03
1.13
1.13
1.13
1.10
1.10
1.10
1.10
1.10
1.10
0.69
0.83
0.76
0.68
0.81
0.74
0.68
0.81
0.74
0.81
0.81
0.81
0.79
0.79
0.79
0.78
0.78
0.78
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.79
0.65
0.55
0.77
0.67
0.63
0.77
0.67
0.63
0.84
0.89
0.88
0.81
0.87
0.86
0.81
0.87
0.86
-------
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
8-39
-------
Table 8-12 Scaling Ratios for Ozone for Non-Coal EGUs
Base Case (CPP) No CPP

2025
2030
2035
2025
2030
2035
AL
1.19
1.22
1.16
1.20
1.21
1.15
AZ
0.25
0.31
0.32
0.24
0.32
0.32
AR
0.66
0.63
0.58
0.67
0.59
0.53
CA
2.82
0.55
0.73
2.39
0.55
0.73
CO
0.46
0.57
0.49
0.31
0.37
0.42
CT+RI
1.15
1.16
1.19
1.16
1.16
1.19
DE+NJ
1.15
1.21
1.12
1.19
1.26
1.25
FL
0.97
0.97
0.90
0.97
0.97
0.90
GA
1.22
1.35
1.15
1.20
1.41
1.14
ID+OR+WA
0.46
0.52
0.57
0.45
0.52
0.55
IL
1.01
1.00
1.09
0.95
0.99
1.06
IN
1.19
1.14
1.36
1.20
1.17
1.33
IA
1.15
1.10
1.27
1.03
1.03
1.40
KS
1.58
1.10
1.41
1.57
1.41
1.61
KY
1.28
1.17
1.14
1.37
1.28
1.15
LA
0.57
0.50
0.39
0.57
0.49
0.39
ME+MA+NH+VT
1.10
0.66
0.79
0.75
0.66
0.79
MD
1.13
1.08
1.01
1.12
1.12
1.02
MI
1.15
1.14
1.18
1.19
1.20
1.18
MN
0.75
0.73
0.87
0.72
0.75
0.89
MS
0.41
0.47
0.38
0.39
0.46
0.36
MO
0.59
0.50
0.57
0.54
0.54
0.61
MT
0.03
0.03
0.04
0.02
0.02
0.05
NE
0.71
0.88
0.88
0.83
0.89
0.93
NV
0.67
0.78
1.05
0.68
0.77
0.98
NM
0.50
0.38
0.26
0.52
0.38
0.28
NY
0.92
0.90
0.88
0.93
0.91
0.90
NC
1.10
0.95
0.89
1.1 1
0.98
0.88
8-40
2% HRI at $50/kW
2025 2030 2035
4.5% HRI at $50/kW 4.5% HRI at $100/kW
2025 2030 2035 2025 2030 2035
1.18
1.21
1.14
1.17
1.21
1.14
1.17
1.21
1.13
0.24
0.32
0.32
0.24
0.32
0.33
0.24
0.32
0.32
0.67
0.58
0.52
0.65
0.59
0.54
0.66
0.60
0.53
2.39
0.55
0.72
2.39
0.56
0.74
2.39
0.56
0.74
0.31
0.37
0.42
0.31
0.37
0.42
0.31
0.37
0.42
1.16
1.17
1.20
1.16
1.17
1.20
1.17
1.17
1.21
1.19
1.25
1.24
1.19
1.25
1.24
1.19
1.24
1.22
0.96
0.96
0.93
0.96
0.96
0.93
0.95
0.96
0.93
1.14
1.39
1.10
1.29
1.47
1.03
1.10
1.46
1.02
0.45
0.52
0.55
0.45
0.52
0.55
0.46
0.52
0.56
0.95
1.00
1.08
0.95
1.01
1.08
0.96
1.02
1.09
1.16
1.18
1.34
1.17
1.18
1.36
1.15
1.18
1.36
1.02
1.10
1.43
1.02
1.10
1.39
1.04
1.14
1.45
1.57
1.42
1.69
1.55
1.47
1.65
1.57
1.48
1.66
1.29
1.21
1.16
1.13
1.20
1.12
1.09
1.10
1.13
0.57
0.50
0.39
0.57
0.49
0.38
0.56
0.50
0.38
0.75
0.66
0.79
0.75
0.66
0.79
0.73
0.66
0.79
1.12
1.11
1.02
1.11
1.11
1.00
1.11
1.08
1.00
1.17
1.20
1.17
1.17
1.21
1.20
1.18
1.20
1.16
0.71
0.75
0.88
0.70
0.75
0.87
0.71
0.76
0.87
0.39
0.47
0.37
0.39
0.46
0.34
0.37
0.47
0.36
0.54
0.54
0.65
0.54
0.55
0.63
0.56
0.55
0.64
0.02
0.02
0.05
0.02
0.02
0.05
0.02
0.03
0.05
0.82
0.90
0.92
0.88
0.89
0.92
0.84
0.89
0.92
0.68
0.77
0.98
0.68
0.76
0.98
0.68
0.77
0.98
0.51
0.38
0.28
0.5 1
0.38
0.27
0.51
0.38
0.27
0.94
0.91
0.90
0.94
0.91
0.90
0.94
0.91
0.89
1.11
1.02
0.88
1.10
1.05
0.95
1.09
1.05
0.94
-------
Base Case (CPP) No CPP 2% HRI at $50/kW 4.5% HRI at $5()/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
0.5 1
0.60
0.48
0.51
0.60
0.48
0.5 1
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
8-41
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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 = Z VKeightxMonitorx s q 2010_2012 ^^fl,s,g,2°11 (Eq-12)
Moaeix,s,q, 2011
Where:
• eVNAg s q current is the gradient adjusted quarterly-average eVNA value at grid-
cell, 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.
8-42
-------
• Monitorxsq,2010-2012 1S 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
• Modelxsq 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 = (eVNAgsq2011) x Mod°lgqs'fq20ii (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.
8-43
-------
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.
8-44
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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
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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.
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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_03-PM-RH_Modeling_Guidance-
2014.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/ 201 Iv6.3_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-ozone-
transport-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.
8-46
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United States Office of Air Quality Planning and Standards Publication No. EPA-452/R-18-006
Environmental Protection Health and Environmental Impacts Division August 2018
Agency Research Triangle Park, NC
-------