Regulatory Impact Analysis for the Proposed 

Carbon Pollution Guidelines for Existing 

Power Plants and Emission Standards for 

Modified and Reconstructed Power Plants

June 2014
ii

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 

Linda M. Chappell, U.S. Environmental Protection Agency, Office of Air Quality Planning and 

Standards, Research Triangle Park, North Carolina 27711 (email: chappell.linda@epa.gov). 

ACKNOWLEDGEMENTS

Thank you to the many staff who worked on this document from EPA Offices including 

the Office of Air Quality Planning and Standards, the Office of Atmospheric Programs, and the 

Office of Policy. Contributions to this report were also made by RTI International and ICF 

International.

DEDICATION

This Regulatory Impact Analysis is dedicated to the memory of Lillian Grace Bradley 

who made substantial contributions to this analysis and report. Lillian freely expressed her 

fundamental belief that everyone has an inherent right to be treated with dignity and respect, 

regardless of their position or background, and she was a recognized leader in Environmental 

Justice and Tribal issues at EPA. She expressed her deep regard for others through individual 

acts of kindness as well as her dedication to increasing social awareness and emotional 

intelligence in the workplace. Her caring, integrity, and outspokenness were a gift to us 

all. Lillian challenged us to lead our very best life. She will be greatly missed.
iii

EPA -542/R-14-002

June 2014

Regulatory Impact Analysis for the Proposed Carbon Pollution 

Guidelines for Existing Power Plants and Emission Standards for 

Modified and Reconstructed Power Plants

U.S. Environmental Protection Agency

Office of Air Quality Planning and Standards

Health & Environmental Impacts Division

Air Economics Group

Research Triangle Park, North Carolina 27711
iv

TABLE OF CONTENTS

Table of Contents.........................................................................................................................................iv

List of Tables................................................................................................................................................x

List of Figures..........................................................................................................................................xviii

Acronyms...................................................................................................................................................xxi

Executive Summary................................................................................................................................ES-1

ES.1 Background and Context of Proposed EGU GHG Existing Source Guidelines ............................ES-1

ES.2 Summary of Proposed EGU GHG Existing Source Guidelines.....................................................ES-1

ES.3 Control Strategies for Existing EGUs............................................................................................ES-3

ES.4 Costs of Existing EGU Guidelines.................................................................................................ES-7

ES.5 Monetized Climate Benefits and Health Co-benefits for Existing EGUs......................................ES-9

ES.5.1 Estimating Global Climate Benefits .................................................................................ES-13

ES 5.2 Estimating Air Pollution Health Co-Benefits ...................................................................ES-15

ES 5.3 Combined Benefits Estimates...........................................................................................ES-17

ES.6 Monetized Benefits, Compliance Costs and Net Benefits of the Proposed Guidelines for Existing 

Sources..................................................................................................................................................ES-19

ES.7 Economic Impacts of the Proposed Emission Guidelines for Existing EGUs.............................ES-24

ES.8 Economic Impacts of the Proposed Guidelines for Existing EGUs for Sectors Other Than the EGU 

Sector and for Employment ..................................................................................................................ES-24

ES.9 Modified and Reconstructed Sources...........................................................................................ES-26

ES.10 References..................................................................................................................................ES-27

Chapter 1: Introduction and Background..................................................................................................1-1

1.1 Introduction..........................................................................................................................................1-1

1.2 Legal, Scientific and Economic Basis for this Rulemaking.................................................................1-1

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

1.2.2 Health and Welfare Impacts from Climate Change ......................................................................1-2

1.2.3 Market Failure...............................................................................................................................1-3

1.3 Summary of Regulatory Analysis........................................................................................................1-3

1.4 Background for the Proposed EGU Existing Source GHG Guidelines...............................................1-4

1.4.1 Base Case and Years of Analysis..................................................................................................1-4

1.4.2 Definition of Affected Sources .....................................................................................................1-5

1.4.3 Regulated Pollutant.......................................................................................................................1-5
v

1.4.4 Emission Guidelines .....................................................................................................................1-6

1.5 Organization of the Regulatory Impact Analysis.................................................................................1-7

1.6 References............................................................................................................................................1-8

Chapter 2: Electric Power Sector 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 .................................................................................................................................2-5

2.2.3 Distribution ...................................................................................................................................2-6

2.3 Deregulation and Restructuring ...........................................................................................................2-6

2.4 Emissions of Greenhouse Gases from Electric Utilities......................................................................2-8

2.5 Improving GHG Performance at Existing EGUs...............................................................................2-12

2.6 GHG and Clean Energy Regulation in the Power Sector ..................................................................2-14

2.6.1 State Policies...............................................................................................................................2-14

2.6.2 Federal Policies...........................................................................................................................2-16

2.7 Revenues, Expenses, and Prices ........................................................................................................2-19

2.8 Natural Gas Market............................................................................................................................2-22

2.9 Electricity Demand and Demand Response.......................................................................................2-25

2.10 References........................................................................................................................................2-28

Chapter 3: Cost, Economic, and Energy Impacts ......................................................................................3-1

3.1 Introduction...................................................................................................................................3-1

3.2 Overview.......................................................................................................................................3-1

3.3 Power Sector Modelling Framework ............................................................................................3-2

3.3.1 Recent Updates to EPA’s Base Case using IPM (v.5.13)..........................................................3-4

3.4 State Goals in this Proposal .........................................................................................................3-6

3.5 Compliance Scenarios Analyzed ..................................................................................................3-9

3.6 Demand Side Energy Efficiency.................................................................................................3-14

3.6.1 Projected Demand-Side Energy Savings.................................................................................3-14

3.6.2 Demand-Side Energy Efficiency Total Costs..........................................................................3-17

3.7 Projected Power Sector Impacts .................................................................................................3-19

3.7.1 Projected Emissions.................................................................................................................3-19

3.7.2 Projected Compliance Costs....................................................................................................3-22

3.7.3 Projected Compliance Actions for Emissions Reductions ......................................................3-24

3.7.4 Projected Generation Mix........................................................................................................3-25
vi

3.7.5 Projected Incremental Retirements..........................................................................................3-32

3.7.6 Projected Capacity Additions..................................................................................................3-34

3.7.7 Projected Coal Productions for the Electric Power Sector ......................................................3-36

3.7.8 Projected Fuel Price Impacts...................................................................................................3-37

3.7.9 Projected Retail Electricity Prices...........................................................................................3-38

3.7.10 Projected Electricity Bill Impacts ........................................................................................3-43

3.8 Projected Primary PM Emissions from Power Plants.................................................................3-44

3.9 Limitations of Analysis...............................................................................................................3-45

3.10 Significant Energy Impacts.........................................................................................................3-46

3.11 Monitoring, Reporting, and Recordkeeping Costs......................................................................3-47

3.12 Social Costs.................................................................................................................................3-48

3.13 References...................................................................................................................................3-53

Appendix 3A: Analysis of Potential Upstream Methane Emissions Changes in Natural Gas Systems and 

Coal Mining ............................................................................................................................................3A-1

3A.1 General Approach ......................................................................................................................3A-1

3A.1.1 Analytical Scope .....................................................................................................................3A-1

3A.1.2 Coal Mining Source Description.............................................................................................3A-3

3A.1.3 Natural Gas Systems Source Description................................................................................3A-4

3A.1.4 Compliance Approaches Examined ........................................................................................3A-6

3A.1.5 Activity Drivers.......................................................................................................................3A-6

3A.2 Results............................................................................................................................................3A-7

3A.2.1 Primary Results.......................................................................................................................3A-7

3A.2.2 Sensitivity Analysis: Side Case Incorporating More Coal Production Geographic Detail .....3A-9

3A.2.3 Uncertainties and Limitations ...............................................................................................3A-10

3A.3 Methodologies..............................................................................................................................3A-11

3A.3.1 Coal Mining ..........................................................................................................................3A-11

3A.3.2 Natural Gas Systems.............................................................................................................3A-14

3A.4 References....................................................................................................................................3A-31

Chapter 4: Estimated Climate Benefits and Human Health Co-benefits...................................................4-1

4.1 Introduction..........................................................................................................................................4-1

4.2 Estimated Climate Benefits from CO2 .................................................................................................4-1

4.2.1 Climate Change Impacts...............................................................................................................4-2

4.2.2 Social Cost of Carbon ...................................................................................................................4-7

4.3 Estimated Human Health Co-Benefits...............................................................................................4-14
vii

4.3.1 Health Impact Assessment for PM2.5 and Ozone ........................................................................4-16

4.3.2 Economic Valuation for Health Co-benefits...............................................................................4-21

4.3.3 Benefit-per-ton Estimates for PM2.5 ............................................................................................4-23

4.3.4 Benefit-per-ton Estimates for Ozone ..........................................................................................4-24

4.3.5 Estimated Health Co-Benefits Results........................................................................................4-25

4.3.6 Characterization of Uncertainty in the Estimated Health Co-benefits........................................4-39

4.4 Combined Climate Benefits and Health Co-benefits Estimates.........................................................4-46

4.5 Unquantified Co-benefits...................................................................................................................4-51

4.5.1 HAP Co-benefits.........................................................................................................................4-53

4.5.2 Additional NO2 Health Co-Benefits............................................................................................4-57

4.5.3 Additional SO2 Health Co-Benefits............................................................................................4-58

4.5.4 Additional NO2 and SO2 Welfare Co-Benefits...........................................................................4-59

4.5.5 Ozone Welfare Co-Benefits........................................................................................................4-60

4.5.6 Carbon Monoxide Co-Benefits...................................................................................................4-60

4.5.7 Visibility Impairment Co-Benefits..............................................................................................4-61

4.6 References..........................................................................................................................................4-62

Appendix 4A: Generating Regional Benefit-per-Ton Estimates for Electric Generating Units............4A-1

4A.1 Overview of Benefit-per-Ton Estimates........................................................................................4A-1

4A.2 Underlying Source Apportionment Air Quality Modeling ............................................................4A-2

4A.3 Regional PM2.5 Benefit-per-Ton Estimates for EGUs....................................................................4A-3

4A.4 Regional Ozone Benefit-per-Ton Estimates ................................................................................4A-16

4A.5 Evaluating the Benefit-per-Ton Estimates..................................................................................4A-20

4A.6 References....................................................................................................................................4A-26

Chapter 5: Economic Impacts – Markets Outside the Electricity Sector..................................................5-1

5.1 Introduction..........................................................................................................................................5-1

5.2 Summary of Secondary Market Impacts of Energy Price Changes.....................................................5-3

5.2.1 Share of Total Production Costs ...................................................................................................5-4

5.2.2 Ability to Substitute between Inputs to the Production Process ...................................................5-5

5.2.3 Availability of Substitute Goods and Services..............................................................................5-5

5.3 Conclusions..........................................................................................................................................5-6

Chapter 6: Employment Impact Analysis..................................................................................................6-1

6.1 Introduction..........................................................................................................................................6-1

6.2 Overview of the Proposed EGU GHG Existing Source Guidelines ....................................................6-2

6.2.1 Determining State Goals Utilizing the Four Building Blocks.......................................................6-2
viii

6.3 Economic Theory and Employment ....................................................................................................6-3

6.4 Current State of Knowledge Based on the Peer-Reviewed Literature.................................................6-7

6.4.1 Regulated Sector ...........................................................................................................................6-8

6.4.2 Labor Supply Impacts...................................................................................................................6-9

6.5 Recent Employment Trends...............................................................................................................6-10

6.5.1 Electric Power Generation ..........................................................................................................6-11

6.5.2 Fossil Fuel Extraction .................................................................................................................6-12

6.5.3 Clean Energy Employment Trends.............................................................................................6-15

6.6 Projected Sectoral Employment Changes due to Proposed Guidelines.............................................6-19

6.6.1 Projected Changes in Employment in Electricity Generation and Fossil Fuel Extraction..........6-21

6.6.2 Projected Changes in Employment in Demand-Side Energy Efficiency Activities....................6-27

6.7 Conclusion .........................................................................................................................................6-32

6.8 References..........................................................................................................................................6-35

Appendix 6A: Estimating Supply-Side Employment Impacts of the Proposed EGU GHG Existing Source 

Guidelines...............................................................................................................................................6A-1

6A.1 General Approach ..........................................................................................................................6A-1

6A.1.1 Employment Effects Included In the Analysis........................................................................6A-2

6A.2 Employment Changes due to Heat Rate Improvements.................................................................6A-3

6A.2.1 Employment Changes Due to Building (or Avoiding) New Generation Capacity .................6A-5

6A.2.2 Employment Changes due to Coal and Oil/Gas Retirements..................................................6A-8

6A.2.3 Employment Changes due to Coal and Oil/Gas Retirements..................................................6A-8

Chapter 7: Statutory and executive Order Analysis..................................................................................7-1

7.1 Executive Order 12866, Regulatory Planning and Review, and Executive Order 13563, Improving 

Regulation and Regulatory Review ...........................................................................................................7-1

7.2 Paperwork Reduction Act ....................................................................................................................7-3

7.3 Regulatory Flexibility Act ...................................................................................................................7-4

7.4 Unfunded Mandates Reform Act (UMRA) .........................................................................................7-5

7.5 Executive Order 13132, Federalism.....................................................................................................7-7

7.6 Executive Order 13045, Protection of Children from Environmental Health Risks and Safety..........7-8

7.7 Executive Order 13211, Actions Concerning Regulations that Significantly Affect Energy Supply, 

Distribution, or Use.................................................................................. .......................7-8

7.8 National Technology Transfer and Advancement Act (NTTAA) .......................................................7-9

7.9 Executive Order 12898: Federal Actions to Address Environmental Justice in Minority Populations 

and Low-Income Populations...............................................................................................7-9

7.10 Executive Order 13175 - Consultation and Coordination with Indian Tribal Governments ...........7-13
ix

Chapter 8: Comparison of Benefits and Costs...........................................................................................8-1

8.1 Comparison of Benefits and Costs.......................................................................................................8-1

8.2 Uncertainty Analysis............................................................................................................................8-7

8.2.1 Uncertainty in Abatement Costs and Compliance Approaches....................................................8-7

8.2.2 Uncertainty Associated with PM2.5 and Ozone Health Co-Benefits Assessment..........................8-8

8.2.3 Uncertainty Associated with Estimating the Social Cost of Carbon...........................................8-10

Chapter 9: Benefits, Costs, and Economic Impacts of Standards of Performance for Reconstructed and 

Modified Electric Utility Generating Units ...............................................................................................9-1

9.1 Introduction..........................................................................................................................................9-1

9.2 Legal Basis for this Rulemaking..........................................................................................................9-1

9.3 Background for the Proposed EGU Reconstructed and Modified Source GHG Standards.................9-2

9.3.1 Definition of Affected Sources .....................................................................................................9-2

9.3.2 Emission Limits............................................................................................................................9-3

9.4 Impacts of the Proposed Reconstructed and Modified Source Standards............................................9-5

9.4.1 Reconstructed Sources..................................................................................................................9-5

9.4.2 Modified Sources..........................................................................................................................9-6

9.5 Statutory and Executive Order Requirements....................................................................................9-14

9.5.1 Executive Order 12866, Regulatory Planning and Review, and Executive Order 13563, 

Improving Regulation and Regulatory Review....................................................................................9-14

9.5.2 Paperwork Reduction Act ...........................................................................................................9-14

9.5.3 Regulatory Flexibility Act ..........................................................................................................9-16

9.5.4 Unfunded Mandates Reform Act (UMRA).................................................................................9-18

9.5.5 Executive Order 13132, Federalism............................................................................................9-20

9.5.6 Executive Order 13175, Consultation and Coordination with Indian Tribal Governments........9-21

9.5.7 Executive Order 13045, Protection of Children from Environmental Health Risks and Safety 

Risks.....................................................................................................................................................9-22

9.5.8 Executive Order 13211, Actions Concerning Regulations that Significantly Affect Energy 

Supply, Distribution, or Use ................................................................................................................9-22

9.5.9 National Technology Transfer and Advancement Act................................................................9-22

9.5.10 Executive Order 12898: Federal Actions to Address Environmental Justice in Minority 

Populations and Low-Income Populations...........................................................................................9-23

9.6 References..........................................................................................................................................9-25
x

LIST OF TABLES

Table ES-1. Proposed State Goals (Adjusted MWh-Weighted-Average Pounds of CO2 per Net MWh 

from all Affected Fossil Fuel-Fired EGUs) for Options 1 and 2.........................................ES-4

Table ES-2. Summary of Climate and Air Pollutant Emission Reductions Option 11..........................ES-6

Table ES-3. Summary of Climate and Air Pollutant Emission Reductions Option 21..........................ES-7

Table ES-4. Summary of Illustrative Compliance Costs.......................................................................ES-8

Table ES-5. Quantified and Unquantified Benefits .............................................................................ES-11

Table ES-6. Combined Estimates of Climate Benefits and Health Co-Benefits for Proposed Existing 

EGU GHG Rule – Regional Compliance Approach (billions of 2011$)* ........................ES-18

Table ES-7. Combined Estimates of Climate Benefits and Health Co-Benefits for Proposed Existing 

EGU GHG Rule – State Compliance Approach (billions of 2011$)*...............................ES-19

Table ES-8. Summary of Estimated Monetized Benefits, Compliance Costs, and Net Benefits for the 

Proposed Guidelines – 2020 (billions of 2011$) a.............................................................ES-21

Table ES-9. Summary of Estimated Monetized Benefits, Compliance Costs, and Net Benefits for the 

Proposed Guidelines – 2025 (billions of 2011$) a.............................................................ES-22

Table ES-10. Summary of Estimated Monetized Benefits, Compliance Costs, and Net Benefits for the 

Proposed Guidelines –2030 (billions of 2011$) a..............................................................ES-23

Table 2-1. Existing Electricity Generating Capacity by Energy Source, 2012.........................................2-2

Table 2-2. Electricity Net Generation in 2012 (billion kWh) ...................................................................2-2

Table 2-3. Total U.S. Electric Power Industry Retail Sales in 2012 (billion kWh)..................................2-3

Table 2-4. Coal Steam Electricity Generating Units in 2015, by Size, Age, Capacity, and Thermal 

Efficiency (Heat Rate)............................................................................................................2-4
xi

Table 2-5. Domestic Emissions of Greenhouse Gases, by Economic Sector (million metric tons of CO2

equivalent)..............................................................................................................................2-9

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

Distribution), 2012 (million metric tons of CO2 equivalent)................................................2-10

Table 2-7. Fossil Fuel Emission Factors in EPA Modeling Applications ..............................................2-11

Table 2-8. Revenue and Expense Statistics for Major U.S. Investor-Owned Electric Utilities..............2-21

Table 2-9. Projected Revenues by Service Category in 2015 for Public Power and Investor-Owned 

Utilities (billions) .................................................................................................................2-21

Table 3-1. Proposed State Goals (Adjusted MWh-Weighted-Average Pounds of CO2 Per Net MWh from 

all Affected Fossil Fuel-Fired EGUs) for Options 1 and 2.....................................................3-6

Table 3-2. Projected Base Case CO2 Emissions Rate (Adjusted MWh-Weighted-Average Pounds of CO2

Per Net MWh from all Affected Fossil Fuel-Fired EGUs) ....................................................3-7

Table 3-3. Net Cumulative Savings as a Percent of Projected BAU Sales.............................................3-16

Table 3-4. Annualized Cost of Demand-Side Energy Efficiency (at discount rates of 3% and 7%, billions 

2011$)...................................................................................................................................3-19

Table 3-5. Projected CO2 Emission Impacts, Relative to Base Case......................................................3-20

Table 3-6. Projected CO2 Emission Impacts, Relative to 2005 ..............................................................3-20

Table 3-7. Projected Non-CO2 Emission Impacts, 2020-2030 ................................................................3-21

Table 3-8. Annualized Compliance Costs (billions of 2011$)................................................................3-22

Table 3-9. Total Power Sector Generating Costs (IPM) (billions 2011$) ..............................................3-23

Table 3-10. Projected Capacity Factor of Existing Coal Steam and Natural Gas Combined Cycle 

Capacity................................................................................................................................3-25

Table 3-11. Generation Mix (thousand GWh)........................................................................................3-27
xii

Table 3-12. Total Generation Capacity by 2020-2030 (GW) .................................................................3-34

Table 3-13. Projected Capacity Additions, Gas (GW).............................................................................3-35

Table 3-14. Projected Capacity Additions, Non-hydro Renewable (GW)...............................................3-35

Table 3-15. Coal Production for the Electric Power Sector, 2020..........................................................3-36

Table 3-16. Power Sector Gas Use .........................................................................................................3-36

Table 3-17. Projected Average Minemouth and Delivered Coal Prices (2011$/MMBtu)......................3-38

Table 3-18. Projected Average Minemouth and Delivered Coal Prices: Percent Change from Base Case

Projections............................................................................................................................3-38

Table 3-19. Projected Average Henry Hub (spot) and Delivered Natural Gas Prices (2011$/MMBtu).3-38

Table 3-20. Average Henry Hub (spot) and Delivered Natural Gas Prices: Percent Change from Base 

Case Projections...................................................................................................................3-38

Table 3-21. 2020 Projected Contiguous U.S. and Regional Retail Electricity Prices (cents/kWh)........3-40

Table 3-22. 2025 Projected Contiguous U.S. and Regional Retail Electricity Prices (cents/kWh)........3-41

Table 3-23. 2030 Projected Contiguous U.S. and Regional Retail Electricity Prices (cents/kWh)........3-42

Table 3-24. Projected Changes in Average Electricity Bills...................................................................3-43

Table 3-25. Years 2020, 2025 and 2030: Summary of Annual Respondent Burden and Cost of Reporting 

and Recordkeeping Requirements (2011$) ..........................................................................3-48

Table 3-26. Annualized Compliance Costs Including Monitoring, Reporting and Recordkeeping Costs 

Requirements (billions of 2011$).........................................................................................3-48

Table 3A-1. Base Year Upstream Methane-Related Emissions in the U.S. GHG Inventory ................3A-5

Table 3A-2. Projected Coal Production Impacts, Option 1 ...................................................................3A-7

Table 3A-3. Projected Coal Production Impacts, Option 2 ...................................................................3A-7
xiii

Table 3A-4. Projected Natural Gas Production Impacts, Option 1........................................................3A-7

Table 3A-5. Projected Natural Gas Production Impacts, Option 2........................................................3A-7

Table 3A-6. Upstream Emissions Changes, Baseline to Compliance Scenarios...................................3A-9

Table 3A-7. Upstream Emissions Changes, Baseline to Compliance Scenarios, Side Case Incorporating 

More Coal Production Geographic Detail.........................................................................3A-10

Table 4-1. Climate Effects.........................................................................................................................4-2

Table 4-2. Global Social Cost of CO2, 2015-2050 (in 2011$)*...............................................................4-12

Table 4-3. Estimated Global Climate Benefits of CO2 Reductions for Proposed EGU GHG Existing 

Source Guidelines in 2020 (billions of 2011$)*...................................................................4-12

Table 4-4. Estimated Global Climate Benefits of CO2 Reductions for Proposed EGU GHG Existing 

Source Guidelines in 2025 (billions of 2011$)*...................................................................4-12

Table 4-5. Estimated Global Climate Benefits of CO2 Reductions for Proposed EGU GHG Existing 

Source Guidelines in 2030 (billions of 2011$)*...................................................................4-12

Table 4-6. Human Health Effects of Ambient PM2.5 and Ozone .............................................................4-17

Table 4-7. Summary of National and Regional Benefit-per-ton Estimates for EGUs in 2020................4-26

Table 4-8. Summary of National and Regional Benefit-per-Ton Estimates for EGUs in 2025...............4-26

Table 4-9. Summary of National and Regional Benefit-per-Ton Estimates for EGUs in 2030...............4-27

Table 4-10. Emission Reductions of Criteria Pollutants for the Proposed EGU GHG Existing Source 

Guidelines in 2020 (thousands of short tons)* .....................................................................4-28

Table 4-11. Emission Reductions of Criteria Pollutants for the Proposed EGU GHG Existing Source 

Guidelines in 2025 (thousands of short tons)* .....................................................................4-29

Table 4-12. Emission Reductions of Criteria Pollutants for the Proposed EGU GHG Existing Source 

Guidelines in 2030 (thousands of short tons)* .....................................................................4-30
xiv

Table 4-13. Summary of Estimated Monetized Health Co-Benefits for the Proposed EGU GHG Existing 

Source Guidelines in 2020 (millions of 2011$) * ................................................................4-31

Table 4-14. Summary of Estimated Monetized Health Co-Benefits for the Proposed EGU GHG Existing 

Source Guidelines in 2025 (millions of 2011$) * ................................................................4-32

Table 4-15. Summary of Estimated Monetized Health Co-Benefits for the Proposed EGU GHG Existing 

Source Guidelines in 2030 (millions of 2011$) * ................................................................4-33

Table 4-16. Summary of Avoided Health Incidences from PM2.5-Related and Ozone-Related Co-benefits 

for Proposed EGU GHG Existing Source Guidelines in 2020*...........................................4-34

Table 4-17. Summary of Avoided Health Incidences from PM2.5-Related and Ozone-Related Co-benefits 

for Proposed EGU GHG Existing Source Guidelines in 2025*...........................................4-35

Table 4-18. Summary of Avoided Health Incidences from PM2.5-Related and Ozone-Related Co-Benefits 

for Proposed EGU GHG Existing Source Guidelines in 2030*...........................................4-36

Table 4-19. Population Exposure in the Baseline Sector Modeling (used to generate the benefit-per-ton 

estimates) Above and Below Various Concentrations Benchmarks in the Underlying 

Epidemiology Studies *........................................................................................................4-44

Table 4-20. Combined Climate Benefits and Health Co-Benefits for Proposed EGU GHG Existing Source 

Guidelines in 2020 (billions of 2011$)*...............................................................................4-48

Table 4-21. Combined Climate Benefits and Health Co-Benefits for Proposed EGU GHG Existing Source 

Guidelines in 2025 (billions of 2011$)*...............................................................................4-49

Table 4-22. Combined Climate Benefits and Health Co-Benefits for Proposed EGU GHG Existing Source 

Guidelines in 2030 (billions of 2011$)*...............................................................................4-50

Table 4-23. Unquantified Health and Welfare Co-benefits Categories...................................................4-52

Table 4A-1. Summary of National and Regional Emissions in Sector Modeling for EGUs in 2016 (in 

short tons)............................................................................................................................4A-7
xv

Table 4A-2. Summary of National and Regional PM2.5 Benefit-per-Ton Estimates for EGUs in 2020 

(2011$)* ............................................................................................................................4A-11

Table 4A-3. Summary of National and Regional PM2.5 Benefit-per-Ton Estimates for EGUs in 2025 

(2011$)* ............................................................................................................................4A-11

Table 4A-4. Summary of National and Regional PM2.5 Benefit-per-Ton Estimates for EGUs in 2030 

(2011$)* ............................................................................................................................4A-12

Table 4A-5. Summary of Regional PM2.5 Incidence-per-Ton Estimates for EGUs in 2020*..............4A-13

Table 4A-6. Summary of Regional PM2.5 Incidence-per-Ton Estimates for EGUs in 2025*..............4A-14

Table 4A-7. Summary of Regional PM2.5 Incidence-per-Ton Estimates for EGUs in 2030*..............4A-15

Table 4A-8. Summary of National and Regional Ozone Benefit-per-Ton Estimates for EGUs in 2020 

(2011$)* ............................................................................................................................4A-18

Table 4A-9. Summary of National and Regional Ozone Benefit-per-Ton Estimates for EGUs in 2025 

(2011$)* ............................................................................................................................4A-18

Table 4A-10. Summary of National and Regional Ozone Benefit-per-Ton Estimates for EGUs in 2030 

(2011$)* ............................................................................................................................4A-18

Table 4A-11. Summary of Regional Ozone Incidence-per-Ton Estimates for EGUs in 2020* ..........4A-19

Table 4A-12. Summary of Regional Ozone Incidence-per-Ton Estimates for EGUs in 2025* ..........4A-19

Table 4A-13. Summary of Regional Ozone Incidence-per-Ton Estimates for EGUs in 2030* ..........4A-19

Table 5-1. Estimated Percentage Changes in Average Energy Prices, by Energy Type and Regulatory 

Option.....................................................................................................................................5-4

Table 6-1. U. S. Green Goods and Services (GGS) Employment (annual average)................................6-17

Table 6-2. Renewable Electricity Generation-Related Employment.......................................................6-18

Table 6-3. Energy and Resources Efficiency-Related Employment........................................................6-19
xvi

Table 6-4. Engineering-Baseda Changes in Labor Utilization, Regional Compliance Approach - (Number 

of Job-Yearsb of Employment in a Single Year)..................................................................6-26

Table 6-5. Engineering-Baseda Changes in Labor Utilization, State Compliance Approach (Number of 

Job-Years of Employment in Year)......................................................................................6-27

Table 6-6. Estimated Demand-Side Energy Efficiency Employment Impacts For Option 1 and Option 2 

for Both Regional and State Compliance Approaches.........................................................6-31

Table 6A-1. Labor Productivity Growth Rate due to Heat Rate Improvement ......................................6A-4

Table 6A-2. Capital Charge Rate and Duration Assumptions................................................................6A-6

Table 6A-3. Expenditure Breakdown due to New Generating Capacity ................................................6A-6

Table 6A-4. Labor Productivity due to New Generating Capacity.........................................................6A-7

Table 6A-5. Average FOM Cost Assumptions.......................................................................................6A-8

Table 6A-6. Labor Productivity due to New Generating Capacity.........................................................6A-9

Table 7-1. Potentially Regulated Categories and Entitiesa........................................................................7-4

Table 8-1. Summary of Estimated Monetized Benefits, Compliance Costs, and Net Benefits

for the Proposed Guidelines – 2020 (billions of 2011$) a..........................................................................8-4

Table 8-2. Summary of Estimated Monetized Benefits, Compliance Costs, and Net Benefits for the 

Proposed Guidelines – 2025 (billions of 2011$) a..................................................................8-5

Table 8-3. Summary of Estimated Monetized Benefits, Compliance Costs, and Net Benefits for the 

Proposed Guidelines –2030 (billions of 2011$) a...................................................................8-6

Table 9-1. Proposed Emission Limits - Reconstructions..........................................................................9-3

Table 9-2. Proposed Emission Limits - Modifications.............................................................................9-5

Table 9-3. Annualized Costs and Fuel Savings of Efficiency Improvements (millions of 2011$) ..........9-9
xvii

Table 9-4. Emission Reductions from Efficiency Improvements (tons/year).........................................9-10

Table 9-5. Summary of National Benefit-per-ton (BPT) Estimates for EGUs in 2025 ..........................9-10

Table 9-6. Estimated Global Climate Benefits of Illustrative CO2 Reductions in 2025 (millions of 

2011$)*.................................................................................................................................9-11

Table 9-7. Summary of Estimated Monetized Health Co-benefits of Illustrative Efficiency Improvements 

in 2025 (thousands of 2011$)*.............................................................................................9-12

Table 9-8. Combined Estimated Climate Benefits and Health Co-Benefits of Illustrative Efficiency 

Improvements in 2025 (millions of 2011$)*........................................................................9-13
xviii

LIST OF FIGURES

Figure 2-1. Fossil Fuel-Fired Electricity Generating Facilities, by Size....................................................2-5

Figure 2-2. Status of State Electricity Industry Restructuring Activities..................................................2-8

Figure 2-3. Domestic Emissions of Greenhouse Gases, 2012 (million metric tons of CO2 equivalent)...2-9

Figure 2-4. Direct GHG Emissions from the Power Sector Relative to Total Domestic GHG Emissions 

(2012) ...................................................................................................................................2-11

Figure 2-5. National Average Retail Electricity Price (1960 – 2013, 2011$).........................................2-20

Figure 2-6. Average Retail Electricity Price by State (cents/kWh), 2011 ..............................................2-22

Figure 2-7. Natural Gas Spot Price, Annual Average (Henry Hub) .......................................................2-25

Figure 2-8. Electricity Growth Rate (3-Year Rolling Average) and Projections from the Annual Energy 

Outlook 2014........................................................................................................................2-26

Figure 3-1. NERC Regions.....................................................................................................................3-12

Figure 3-2. NERC Assessment Areas.....................................................................................................3-12

Figure 3-3. Regional Compliance Zones Used in this Analysis .............................................................3-13

Figure 3-4. Generation Mix with the Base Case and 111(d) Options, 2020-2030 (million GWh).........3-28

Figure 3-5. Electricity Market Module Regions.....................................................................................3-43

Figure 4-1. Monetized Health Co-benefits for each Option (Regional Compliance) of the Proposed EGU 

GHG Existing Source Guidelines in 2020 *.........................................................................4-37

Figure 4-2. Breakdown of Monetized Health Co-benefits by Precursor Pollutant at a 3% Discount Rate for 

each Option (Regional Compliance) for Proposed EGU GHG Existing Source Guidelines in 

2020*....................................................................................................................................4-38
xix

Figure 4-3. Breakdown of Monetized Health Co-Benefits by Region at a 3% Discount Rate for each 

Option (Regional compliance) for Proposed EGU GHG Existing Source Guidelines in 2020*

..............................................................................................................................................4-39

Figure 4-4. Percentage of Adult Population (age 30+) by Annual Mean PM2.5 Exposure in the Baseline 

Sector Modeling (used to generate the benefit-per-ton estimates)*.....................................4-45

Figure 4-5. Cumulative Distribution of Adult Population (age 30+) by Annual Mean PM2.5 Exposure in 

the Baseline Sector Modeling (used to generate the benefit-per-ton estimates)* ................4-46

Figure 4-6. Breakdown of Combined Monetized Climate and Health Co-benefits of Proposed EGU GHG 

Existing Source Guidelines in 2020 by Option (Regional Compliance) and Pollutant (3% 

discount rate)* ......................................................................................................................4-51

Figure 4A-1. Conceptual Overview of Benefit-per-Ton Calculation ....................................................4A-2

Figure 4A-2. Modeled PM2.5 Concentrations Attributable to EGUs in 2016 (Annual Mean, in μg/m3) 

(Source: BPT TSD) .............................................................................................................4A-4

Figure 4A-3. Modeled PM2.5 Concentrations Attributable to EGUs by Precursor in 2016 (Annual Mean, 

in μg/m3)..............................................................................................................................4A-6

Figure 4A-4. Regional Breakdown ........................................................................................................4A-7

Figure 4A-5. Breakdown of Directly Emitted PM2.5 Emissions from EGU in Sector Modeling...........4A-8

Figure 4A-6. Modeled Average Ozone Concentrations Attributable to EGUs in 2016 (Daily 8-hour 

maximum, May-September, in ppb)..................................................................................4A-17

Figure 4A-7. Comparison of Base Case SO2 Emissions in 2020.........................................................4A-21

Figure 4A-8. Comparison of Base Case Annual NOx Emissions in 2020...........................................4A-22

Figure 4A-9. Comparison of Base Case Ozone-season NOx Emissions in 2020................................4A-23

Figure 6.1. Electric Power Industry Employment....................................................................................6-12

Figure 6.2. Coal Production Employment ...............................................................................................6-14
xx

Figure 6.3. Oil and Gas Production Employment....................................................................................6-15
xxi

ACRONYMS

ACS American Cancer Society

AEO Annual Energy Outlook 

AQ Air quality

ASM Annual Survey of Manufactures

ATSDR Agency for Toxic Substances and Disease Registry

BACT Best Available Control Technology 

BenMAP Benefits Mapping and Analysis Program

BPT Benefit-per-Ton 

BSER Best System of Emissions Reduction 

Btu British Thermal Units 

C Celsius 

CAA Clean Air Act 

CAIR Clean Air Interstate Rule 

CCR Coal Combustion Residuals 

CCS Carbon Capture and Sequestration or Carbon Capture and Storage 

CCSP Climate Change Science Program

CFR Code of Federal Regulations 

CH

4 

Methane 

CO Carbon Monoxide

CO

2 

Carbon Dioxide 

CRF Capital Recovery Factor 

CSAPR Cross State Air Pollution Rule 

CT Combustion Turbines 

CUA Climate Uncertainty Adder 

DICE Dynamic Integrated Climate and Economy Model 

DOE U.S. Department of Energy 

EAB Environmental Appeals Board 

EC Elemental carbon

ECS Energy Cost Share

EG Emissions guidelines

EGR Enhanced Gas Recovery 
xxii

EGU Electric Generating Unit 

EIA U.S. Energy Information Administration 

EMM Electricity Market Module 

EO Executive Order 

EOR Enhanced Oil Recovery 

EPA U.S. Environmental Protection Agency 

ER Enhanced Recovery 

FERC Federal Energy Regulatory Commission 

FGD Flue Gas Desulfurization 

FOAK First of a Kind 

FOM Fixed Operating and Maintenance 

FR Federal Register 

FRCC Florida Reliability Coordinating Council 

FUND Framework for Uncertainty, Negotiation, and Distribution Model v

GDP Gross Domestic Product 

GHG Greenhouse Gas 

GS Geologic Sequestration 

Gt Gigaton 

H

2

S Hydrogen Sulfide 

HAP Hazardous air pollutant

HCl Hydrogen chloride

HFC Hydrofluorocarbons 

HIA Health impact assessment

IARC International Agency for Research on Cancer

IAM Integrated Assessment Model 

ICR Information Collection Request 

IGCC Integrated Gasification Combined Cycle 

IOU Investor Owned Utility 

IPCC Intergovernmental Panel on Climate Change 

IPM Integrated Planning Model 

IRIS Integrated Risk Information System

IRP Integrated Resource Plan 

ISA Integrated Science Assessment
xxiii

kWh Kilowatt-hour 

lbs Pounds 

LCOE Levelized Cost of Electricity 

LML Lowest measured level

LNB Low NO

X 

Burners 

MATS Mercury and Air Toxics Standards 

MEA Monoethanolamine

MECSA Manufacturing Energy Consumption Survey 

MeHg Methylmercury

MGD Millions of Gallons per Day 

mg/L Milligrams per Liter 

mmBtu Million British Thermal Units 

MW Megawatt 

MWh Megawatt-hour 

N

2

O Nitrous Oxide 

NAAQS National Ambient Air Quality Standards

NAICS North American Industry Classification System

NaOH Sodium Hydroxide 

NATCARB National Carbon Sequestration Database and Geographic Information System 

NEEDS National Electric Energy Data System 

NEMS National Energy Modeling System 

NERC North American Electric Reliability Corporation 

NETL National Energy Technology Laboratory 

NGCC Natural Gas Combined Cycle 

NMMAPS National Morbidity, Mortality Air Pollution Study

NOAK Next of a Kind or Nth of a Kind 

NO

X 

Nitrogen Oxide 

NRC National Research Council 

NSPS New Source Performance Standard 

NSR New Source Review 

NTTAA National Technology Transfer and Advancement Act 

OC Organic carbon

OFA Overfire Air 
xxiv

OMB Office of Management and Budget 

PAGE Policy Analysis of the Greenhouse Gas Effect Model 

PFC Perfluorocarbons 

PM2.5 Fine Particulate Matter 

ppm Parts per Million 

PRA Paperwork Reduction Act 

PSD Prevention of Significant Deterioration 

RCSP Regional Carbon Sequestration Partnerships 

RADS Relative Airways Dysfunction Syndrome

RES Renewable Electricity Standards 

RFA Regulatory Flexibility Act 

RGGI Regional Greenhouse Gas Initiative 

RIA Regulatory Impact Analysis 

RPS Renewable Portfolio Standards 

SAB-CASAC Science Advisory Board Clean Air Scientific Advisory Committee

SAB-HES Science Advisory Board Health Effects Subcommittee of the Advisory Council on 

Clean Air Compliance

SAB-EEAC Science Advisory Board Environmental Economics Advisory Committee

SBA Small Business Administration

SBREFA Small Business Regulatory Enforcement Fairness Act 

SCC Social Cost of Carbon 

SCPC Super Critical Pulverized Coal 

SCR Selective Catalytic Reduction 

SF

6 

Sulfur Hexafluoride 

SIP State Implementation Plan 

SO2 Sulfur Dioxide 

Tcf Trillion Cubic Feet 

TDS Total Dissolved Solids 

TSD Technical Support Document 

TSM Transportation Storage and Monitoring 

UMRA Unfunded Mandates Reform Act 

U.S.C. U.S. Code 
xxv

USGCRP U.S. Global Change Research Program 

USGS U.S. Geological Survey 

USG SCC U.S. Government’s Social Cost of Carbon

U.S. NRC U.S. Nuclear Regulatory Commission 

VCS Voluntary Consensus Standards 

VOC Volatile Organic Compounds

VOM Variable Operating and Maintenance 

VSL Value of a statistical life

WTP Willingness to pay
ES-1

EXECUTIVE SUMMARY

This Regulatory Impact Analysis (RIA) discusses potential benefits, costs, and economic 

impacts of the proposed Emission Guidelines for Greenhouse Gas Emissions from Existing 

Stationary Sources: Electric Utility Generating Units (herein referred to EGU GHG Existing 

Source Guidelines). This RIA also discusses the potential benefits, costs and economic impacts 

of the proposed Standards of Performance for Greenhouse Gas Emissions from Reconstructed 

and Modified Stationary Sources (EGU GHG Reconstructed and Modified Source Standards).

ES.1 Background and Context of Proposed EGU GHG Existing Source Guidelines

Greenhouse gas pollution threatens Americans' health and welfare by leading to longlasting changes in our climate that can have a range of severely negative effects on human health 

and the environment. Carbon Dioxide (CO2) is the primary greenhouse gas pollutant, accounting 

for nearly three-quarters of global greenhouse gas emissions and 84 percent of U.S. greenhouse 

gas emissions. Fossil fuel-fired electric generating units (EGUs) are, by far, the largest emitters 

of GHGs, primarily in the form of CO2, among stationary sources in the U.S.

In this action, the EPA is proposing emission guidelines for states to use in developing 

plans to address greenhouse gas emissions from existing fossil fuel-fired EGUs. Specifically, the 

EPA is proposing state-specific rate-based goals for carbon dioxide emissions from the power 

sector, as well as emission guidelines for states to use in developing plans to attain the statespecific goals. This rule, as proposed, would set in motion actions to lower the carbon dioxide 

emissions associated with existing power generation sources in the United States.

ES.2 Summary of Proposed EGU GHG Existing Source Guidelines

Under Clean Air Act (CAA) section 111(d), state plans must establish standards of 

performance that reflect the degree of emission limitation achievable through the application of 

the “best system of emission reduction” (BSER) that, taking into account the cost of achieving 

such reduction and any non-air quality health and environmental impact and energy 
ES-2

requirements, the Administrator determines has been adequately demonstrated.1 Consistent with 

CAA section 111(d), this proposed rule contains state-specific goals that reflect the EPA’s 

calculation of the emission reductions that a state can achieve through the application of BSER. 

The EPA is using the following four building blocks to determine state-specific goals:

1. Reducing the carbon intensity of generation at individual affected EGUs through heatrate improvements. 

2. Reducing emissions from the most carbon-intensive affected EGUs in the amount that 

results from substituting generation at those EGUs with generation from less carbonintensive affected EGUs (including natural gas combined cycle [NGCC] units that are 

under construction).

3. Reducing emissions from affected EGUs in the amount that results from substituting 

generation at those EGUs with expanded low- or zero-carbon generation.

4. Reducing emissions from affected EGUs in the amount that results from the use of

demand-side energy efficiency that reduces the amount of generation required.

The proposed rule also contains emission guidelines for states to use in developing plans 

that set their standards of performance. The EPA recognizes that each state has different policy 

considerations, including varying emission reduction opportunities and existing state programs 

and measures, and characteristics of the electricity system (e.g., utility regulatory structure, 

generation mix, electricity demand). The proposed emission guidelines provide states with 

options for establishing standards of performance in a manner that accommodates a diverse 

range of state approaches. The proposed guidelines would also allow states to collaborate and to 

demonstrate emission performance on a multi-state basis, in recognition of the fact that 

electricity is transmitted across state lines, and local measures often impact regional EGU CO2

 

1

 Under CAA sections 111(a)(1) and (d), the EPA is authorized to determine the BSER and to calculate the amount 

of emission reduction achievable through applying the BSER; and the state is authorized to identify the 

standard(s) of performance that reflects that amount of emission reduction. In addition, the state is required to 

include in its state plan the standards of performance and measures to implement and enforce those standards. The 

state must submit the plan to the EPA, and the EPA must approve the plan if the standards of performance and 

implementing and enforcing measures are satisfactory. 
ES-3

emissions. 

While the EPA must establish BSER and is proposing goals and guidelines that reflect 

BSER, CAA section 111(d) also provides the EPA with the flexibility to design goals and 

guidelines that recognize, and are tailored to, the uniqueness and complexity of the power 

generation sector and CO2 emissions. And, importantly, CAA section 111(d) allows the states 

flexibility in designing the measures for their state plans in response to the EPA’s guidelines. 

States are not required to use each of the measures that the EPA determines constitute BSER, or 

use those measures to the same degree of stringency that the EPA determines is achievable at a 

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, by way of meeting 

its state-specific goal. Given the flexibilities afforded states in complying with the emission 

guidelines, the benefits, cost and economic impacts reported in this RIA are not definitive 

estimates, but are instead illustrative of compliance actions states may take.

ES.3 Control Strategies for Existing EGUs 

States will ultimately determine approaches to comply with the goals established in this 

regulatory action. The EPA is proposing a BSER goal approach referred to as Option 1 and 

taking comment on a second approach referred to as Option 2. Each of these goal approaches use 

the four building blocks described above at different levels of stringency. Option 1 involves 

higher deployment of the four building blocks but allows a longer timeframe to comply (2030) 

whereas Option 2 has a lower deployment over a shorter timeframe (2025). 

Table ES-1 shows the proposed state goals for Options 1 and 2. This RIA depicts 

illustrative rate-based compliance scenarios for the goals set for Options 1 and 2, as well as 

regional and state compliance approaches for each option. With the state compliance approach, 

states are assumed to comply with the guidelines by implementing measures solely within the 

state and emissions rate averaging occurs between affected sources on an intrastate basis 

only. In contrast under the regional approach, groups of states are assumed to collaboratively 

comply with the guidelines. States have the discretion of choosing between a regional or state 

compliance approach, and this RIA reports the economic consequences of compliance under two 
ES-4

sets of assumptions: one that assumes all states individually take a rate-based compliance 

approach and the other that assumes certain groups of states take regional rate-based 

approaches. The analysis in the illustrative scenarios does not assume that states use any specific 

policy mechanism to achieve the state goals. The distributions of emissions and electricity 

generation reflected in the Integrated Planning Model (IPM) analysis of the illustrative scenarios

could be achieved by various policy mechanisms. Alternative compliance approaches are also 

possible. For example, the guidance allows flexibility of compliance, including the possibility of 

using a mass-based approach. While IPM finds a least cost way to achieve the state goals

implemented through the rate-based constraints imposed in the illustrative scenarios, individual 

states or multi-state regional groups may develop more cost effective approaches to achieve their 

state goals.

Table ES-1. Proposed State Goals (Adjusted MWh-Weighted-Average Pounds of CO2 per 

Net MWh from all Affected Fossil Fuel-Fired EGUs) for Options 1 and 2

State2

Option 1 Option 2

Interim Goal

(2020-2029)

Final Goal

(2030 Forward)

Interim Goal

(2020-2024)

Final Goal

(2025 Forward)

Alabama 1,147 1,059 1,270 1,237

Alaska 1,105 1,017 1,175 1,138

Arizona * 735 702 779 763

Arkansas 968 910 1,083 1,058

California 556 537 582 571

Colorado 1,159 1,108 1,265 1,227

Connecticut 597 540 651 627

Delaware 913 841 1,007 983

Florida 794 740 907 884

Georgia 891 834 997 964

Hawaii 1,378 1,306 1,446 1,417

Idaho 244 228 261 254

Illinois 1,366 1,271 1,501 1,457

Indiana 1,607 1,531 1,715 1,683

Iowa 1,341 1,301 1,436 1,417

Kansas 1,578 1,499 1,678 1,625

Kentucky 1,844 1,763 1,951 1,918

Louisiana 948 883 1,052 1,025

 

2 The EPA has not developed goals for Vermont and the District of Columbia because current information indicates 

those jurisdictions have no affected EGUs. Also, as noted in Chapter 3, EPA is not proposing goals for tribes or 

U.S. territories at this time.
ES-5

Table ES-1. Continued

Maine 393 378 418 410

Maryland 1,347 1,187 1,518 1,440

Massachusetts 655 576 715 683

Michigan 1,227 1,161 1,349 1,319

Minnesota 911 873 1,018 999

Mississippi 732 692 765 743

Missouri 1,621 1,544 1,726 1,694

Montana 1,882 1,771 2,007 1,960

Nebraska 1,596 1,479 1,721 1,671

Nevada 697 647 734 713

New Hampshire 546 486 598 557

New Jersey 647 531 722 676

New Mexico * 1,107 1,048 1,214 1,176

New York 635 549 736 697

North Carolina 1,077 992 1,199 1,156

North Dakota 1,817 1,783 1,882 1,870

Ohio 1,452 1,338 1,588 1,545

Oklahoma 931 895 1,019 986

Oregon 407 372 450 420

Pennsylvania 1,179 1,052 1,316 1,270

Rhode Island 822 782 855 840

South Carolina 840 772 930 897

South Dakota 800 741 888 861

Tennessee 1,254 1,163 1,363 1,326

Texas 853 791 957 924

Utah * 1,378 1,322 1,478 1,453

Virginia 884 810 1,016 962

Washington 264 215 312 284

West Virginia 1,748 1,620 1,858 1,817

Wisconsin 1,281 1,203 1,417 1,380

Wyoming 1,808 1,714 1,907 1,869

* Excludes EGUs located in Indian country. 

Table ES-2 shows the emission reductions associated with the compliance scenarios for 

the proposed Option 1 regional and state compliance approaches and Table ES-3 reports 

emission reductions associated with Option 2. In 2020, the EPA estimates that CO2 emissions 

will be reduced by 371 million metric tons under the regional compliance approach and by 383 

million metric tons assuming a state specific compliance approach compared to base case levels. 

CO2 emission reductions for Option 1 increase to 545 and 555 million metric tons annually in 

2030 when compared to the base case emissions for Option 1 regional and state compliance 

approaches, respectively. Tables ES-2 and ES-3 also show emission reductions for criteria air 
ES-6

pollutants. 

Table ES-2. Summary of Climate and Air Pollutant Emission Reductions Option 11

CO2 (million 

metric tons)

SO2 (thousands of 

tons)

NOX (thousands of 

tons)

PM2.5 (thousands 

of tons)

2020 Regional Compliance Approach

Base Case 2,161 1,476 1,559 212

Proposed Guidelines 1,790 1,184 1,213 156

Emissions Change -371 -292 -345 -56

2025 Regional Compliance Approach

Base Case 2,231 1,515 1,587 209

Proposed Guidelines 1,730 1,120 1,166 150

Emissions Change -501 -395 -421 -59

2030 Regional Compliance Approach

Base Case 2,256 1,530 1,537 198

Proposed Guidelines 1,711 1,106 1,131 144

Emission Change -545 -424 -407 -54

2020 State Compliance Approach

Base Case 2,161 1,476 1,559 212

Proposed Guidelines 1,777 1,140 1,191 154

Emissions Change -383 -335 -367 -58

2025 State Compliance Approach

Base Case 2,231 1,515 1,587 209

Proposed Guidelines 1,724 1,090 1,151 145

Emission Change -506 -425 -436 -63

2030 State Compliance Approach

Base Case 2,256 1,530 1,537 198

Proposed Guidelines 1,701 1,059 1,109 142

Emissions Change -555 -471 -428 -56

Source: Integrated Planning Model, 2014.

1 CO2 emission reductions are used to estimate the climate benefits of the guidelines. SO2, NOx, and directly emitted 

PM2.5 emission reductions are relevant for estimating air pollution health co-benefits of the proposed guidelines.
ES-7

Table ES-3. Summary of Climate and Air Pollutant Emission Reductions Option 21

CO2 (million 

metric tons)

SO2 (thousands 

of tons)

NOX (thousands 

of tons)

PM2.5 (thousands 

of tons) 

2020 Regional Compliance Approach

Base Case 2,161 1,476 1,559 212

Option 2 1,878 1,231 1,290 166

Emissions Change -283 -244 -268 -46

2025 Regional Compliance Approach

Base Case 2,231 1,515 1,587 209

Option 2 1,862 1,218 1,279 165

Emissions Change -368 -297 -309 -44

2020 State Compliance Approach

Base Case 2,161 1,476 1,559 212

Option 2 1,866 1,208 1,277 163

Emissions Change -295 -267 -281 -49

2025 State Compliance Approach

Base Case 2,231 1,515 1,587 209

Option 2 1,855 1,188 1,271 161

Emissions Change -376 -327 -317 -48

Source: Integrated Planning Model, 2014.

1 CO2 emission reductions are used to estimate the climate benefits of the guidelines. SO2, NOx, and directly emitted 

PM2.5 emission reductions are relevant for estimating air pollution health co-benefits of the guidelines.

ES.4 Costs of Existing EGU Guidelines

The ‘‘compliance costs’’ of this proposed action are represented in this analysis as the 

change in electric power generation costs between the base case and illustrative compliance 

scenario policy cases. The compliance scenario policy cases reflect the pursuit by states of a 

distinct set of strategies, which are not limited to the technologies and measures included in the 

BSER to meet the EGU GHG emission guidelines, and include cost estimates for demand side 

energy efficiency. The compliance assumptions, and therefore the projected “compliance costs”

set forth in this analysis, are illustrative in nature and do not represent the full suite of 

compliance flexibilities states may ultimately pursue.

The EPA projects that the annual incremental compliance cost of the proposed Option 1 

ranges from $5.4 to $7.4 billion in 2020 and from $7.3 to $8.8 billion in 2030 ($2011), excluding

the costs associated with monitoring, reporting, and recordkeeping. The estimated cost of Option 

2 is between $4.2 and $5.4 billion in 2020 and between $4.5 and $5.5 billion in 2025 (2011$). 

The estimated monitoring, reporting and recordkeeping costs for both options are $68.3 million 
ES-8

in 2020, $8.9 million in 2025, and $8.9 million in 2030 (2011$). The annual incremental cost is 

the projected additional cost of complying with the proposed rule in the year analyzed and 

includes the net change in the annualized cost of capital investment in new generating sources 

and heat rate improvements at coal steam facilities,3the change in the ongoing costs of operating 

pollution controls, shifts between or amongst various fuels, demand-side energy efficiency 

measures, and other actions associated with compliance. Costs for both options are reflected in 

Table ES-4 below and discussed more extensively in Chapter 3 of this RIA.

Table ES-4. Summary of Illustrative Compliance Costs

Incremental Cost from Base Case (billions of 2011$)

2020 2025 2030

Option 1 

State Compliance $7.4 $5.5 $8.8

Regional Compliance $5.4 $4.6 $7.3

Option 2 

State Compliance $5.4 $5.5 n/a 

Regional Compliance $4.2 $4.5 n/a 

Source: Integrated Planning Model, 2014, with post-processing to account for exogenous demand-side management 

energy efficiency costs. See Chapter 5 of the GHG Abatement Measures TSD for a full explanation. Compliance 

costs shown here do not include monitoring, reporting, and recordkeeping costs.

The costs reported in Table ES-4 represent the estimated incremental electric utility 

generating costs changes from the base case, plus end-use energy efficiency program costs (paid 

by electric utilities) and end-use energy efficiency participant costs (paid by electric utility 

consumers). For example in 2020 for the proposed Option 1 regional compliance approach, enduse energy efficiency program costs are estimated to be $5.1 billion and end-use efficiency 

participant costs are $5.1 billion using a 3% discount rate (see Table 3-4). This estimate for enduse energy efficiency costs of $10.2 billion is combined with the costs generated by the IPM that 

include the costs of states’ compliance with state goals associated with changes to reduce the 

carbon-intensity of electricity production and the energy demand decreases expected from enduse energy efficiency assumed in the illustrative scenarios. In order to reflect the full cost 

 

3 See Chapter 8 of EPA’s Base Case using IPM (v5.13) documentation, available at: 

http://www.epa.gov/powersectormodeling/BaseCasev513.html
ES-9

attributable to the policy, it is necessary to include this incremental -$4.8 billion (see Table 3-9) 

in electricity supply expenditure with the annualized expenditure needed to secure the end-use 

energy efficiency improvements. As a result, this analysis finds the cost of the Option 1 regional 

scenario in 2020 to be $5.4 billion (the sum of incremental supply-related and demand-related 

expenditures). Note that when monitoring, reporting and recordkeeping costs of $68.3 million 

are added to this estimate, compliance costs become $5.5 billion in 2020. 

The compliance costs reported in Table ES-4 are not social costs. These costs represent 

the illustrative real resources costs for states to comply with the BSER goals for Options 1 and 2. 

Electric sector compliance costs and monitoring, recordkeeping and reporting costs are compared 

to social benefits in Tables ES-8, ES-9 and ES-10 to derive illustrative net benefits of the 

guidelines. For a more extensive discussion of social costs, see Chapter 3 of this RIA. 

ES.5 Monetized Climate Benefits and Health Co-benefits for Existing EGUs

Implementing the proposed guidelines is expected to reduce emissions of CO2 and have 

ancillary emission reductions (i.e., co-benefits) of SO2, NO2, and directly emitted PM2.5, which 

would lead to lower ambient concentrations of PM2.5 and ozone. The climate benefits estimates

have been calculated using the estimated values of marginal climate impacts presented in the 

Technical Support Document: Technical Update of the Social Cost of Carbon for Regulatory 

Impact Analysis under Executive Order 12866, henceforth denoted as the 2013 SCC TSD.

4

 

Also, the range of combined benefits reflects different concentration-response functions for the 

air pollution health co-benefits, but it does not capture the full range of uncertainty inherent in 

the health co-benefits estimates. Furthermore, we were unable to quantify or monetize all of the 

climate benefits and health and environmental co-benefits associated with the proposed emission 

 

4 Technical Support Document: Technical Update of the Social Cost of Carbon for Regulatory Impact Analysis 

Under Executive Order 12866, Interagency Working Group on Social Cost of Carbon, with participation by 

Council of Economic Advisers, Council on Environmental Quality, Department of Agriculture, Department of 

Commerce, Department of Energy, Department of Transportation, Environmental Protection Agency, National 

Economic Council, Office of Energy and Climate Change, Office of Management and Budget, Office of Science 

and Technology Policy, and Department of Treasury (May 2013, Revised November 2013). Available at: 

http://www.whitehouse.gov/sites/default/files/omb/assets/inforeg/technical-update-social-cost-of-carbon-forregulator-impact-analysis.pdf
ES-10

guidelines, including reducing exposure to SO2, NOx, and hazardous air pollutants (e.g., mercury 

and hydrogen chloride), as well as ecosystem effects and visibility impairment. These 

unquantified benefits could be substantial, but it is difficult to approximate the potential 

magnitude of these unquantified benefits and previous quantification attempts have been 

incomplete. The omission of these endpoints from the monetized results should not imply that 

the impacts are small or unimportant. Table ES-5 provides the list of the quantified and 

unquantified environmental and health benefits in this analysis. 
ES-11

Table ES-5. Quantified and Unquantified Benefits

Benefits Category Specific Effect

Effect Has 

Been 

Quantified

Effect Has 

Been 

Monetized

More Information

Improved 

Environment

Reduced climate 

effects

Global climate impacts from CO2 —  SCC TSD

Climate impacts from ozone and black carbon (directly 

emitted PM) — —

Ozone ISA, PM 

ISA1

Other climate impacts (e.g., other GHGs such as methane, 

aerosols, other impacts) — — IPCC1

Improved Human Health (co-benefits)

Reduced incidence of 

premature mortality 

from exposure to 

PM2.5

Adult premature mortality based on cohort study estimates

and expert elicitation estimates (age >25 or age >30)   PM ISA

Infant mortality (age <1)   PM ISA

Reduced incidence of 

morbidity from 

exposure to PM2.5

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

Hospital admissions—respiratory (all ages)   PM ISA

Hospital admissions—cardiovascular (age >20)   PM ISA

Emergency room visits for asthma (all ages)   PM ISA

Acute bronchitis (age 8-12)   PM ISA

Lower respiratory symptoms (age 7-14)   PM ISA

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

Asthma exacerbation (asthmatics age 6-18)   PM ISA

Lost work days (age 18-65)   PM ISA

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

Chronic Bronchitis (age >26) — — PM ISA1

Emergency room visits for cardiovascular effects (all ages) — — PM ISA1

Strokes and cerebrovascular disease (age 50-79) — — PM ISA1

Other cardiovascular effects (e.g., other ages) — — PM ISA2

Other respiratory effects (e.g., pulmonary function, nonasthma ER visits, non-bronchitis chronic diseases, other 

ages and populations)

— — PM ISA2

Reproductive and developmental effects (e.g., low birth 

weight, pre-term births, etc) — — PM ISA2,3

Cancer, mutagenicity, and genotoxicity effects — — PM ISA2,3

Reduced incidence of 

mortality from 

exposure to ozone

Premature mortality based on short-term study estimates (all 

ages)   Ozone ISA

Premature mortality based on long-term study estimates 

(age 30–99) — — Ozone ISA1

Reduced incidence of 

morbidity from 

exposure to ozone

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

Hospital admissions—respiratory causes (age <2)   Ozone ISA

Emergency department visits for asthma (all ages)   Ozone ISA

Minor restricted-activity days (age 18–65)   Ozone ISA

School absence days (age 5–17)   Ozone ISA

Decreased outdoor worker productivity (age 18–65) — — Ozone ISA1

Other respiratory effects (e.g., premature aging of lungs) — — Ozone ISA2

Cardiovascular and nervous system effects — — Ozone ISA2

Reproductive and developmental effects — — Ozone ISA2,3
ES-12

Table ES-5. Continued

Reduced incidence of 

morbidity from 

exposure to NO2

Asthma hospital admissions (all ages) — — NO2 ISA1

Chronic lung disease hospital admissions (age > 65) — — NO2 ISA1

Respiratory emergency department visits (all ages) — — NO2 ISA1

Asthma exacerbation (asthmatics age 4–18) — — NO2 ISA1

Acute respiratory symptoms (age 7–14) — — NO2 ISA1

Premature mortality — — NO2 ISA1,2,3

Other respiratory effects (e.g., airway hyperresponsiveness 

and inflammation, lung function, other ages and 

populations)

— — NO2 ISA2,3

Reduced incidence of 

morbidity from 

exposure to SO2

Respiratory hospital admissions (age > 65) — — SO2 ISA1

Asthma emergency department visits (all ages) — — SO2 ISA1

Asthma exacerbation (asthmatics age 4–12) — — SO2 ISA1

Acute respiratory symptoms (age 7–14) — — SO2 ISA1

Premature mortality — — SO2 ISA1,2,3

Other respiratory effects (e.g., airway hyperresponsiveness 

and inflammation, lung function, other ages and 

populations)

— — SO2 ISA1,2

Reduced incidence of 

morbidity from 

exposure to 

methylmercury

Neurologic effects—IQ loss — — IRIS; NRC, 20001

Other neurologic effects (e.g., developmental delays, 

memory, behavior) — — IRIS; NRC, 20002

Cardiovascular effects — — IRIS; NRC, 20002,3

Genotoxic, immunologic, and other toxic effects — — IRIS; NRC, 20002,3

Reduced incidence of 

morbidity from 

exposure to HAP

Effects associated with exposure to hydrogen chloride — — ATSDR, IRIS1,2

Improved Environment (co-benefits)

Reduced visibility 

impairment

Visibility in Class 1 areas — — PM ISA1

Visibility in residential areas — — PM ISA1

Reduced effects on 

materials

Household soiling — — PM ISA1,2

Materials damage (e.g., corrosion, increased wear) — — PM ISA2

Reduced PM 

deposition (metals and 

organics)

Effects on Individual organisms and ecosystems — — PM ISA2

Reduced vegetation 

and ecosystem effects 

from exposure to 

ozone

Visible foliar injury on vegetation — — Ozone ISA1

Reduced vegetation growth and reproduction — — Ozone ISA1

Yield and quality of commercial forest products and crops — — Ozone ISA1

Damage to urban ornamental plants — — Ozone ISA2

Carbon sequestration in terrestrial ecosystems — — Ozone ISA1

Recreational demand associated with forest aesthetics — — Ozone ISA2

Other non-use effects Ozone ISA2

Ecosystem functions (e.g., water cycling, biogeochemical 

cycles, net primary productivity, leaf-gas exchange, 

community composition)

— — Ozone ISA2

Reduced effects from 

acid deposition

Recreational fishing — — NOx SOx ISA1

Tree mortality and decline — — NOx SOx ISA2

Commercial fishing and forestry effects — — NOx SOx ISA2

Recreational demand in terrestrial and aquatic ecosystems — — NOx SOx ISA2

Other non-use effects NOx SOx ISA2

Ecosystem functions (e.g., biogeochemical cycles) — — NOx SOx ISA2
ES-13

Table ES-5. Continued

Reduced effects from 

nutrient enrichment

Species composition and biodiversity in terrestrial and 

estuarine ecosystems — — NOx SOx ISA2

Coastal eutrophication — — NOx SOx ISA2

Recreational demand in terrestrial and estuarine ecosystems — — NOx SOx ISA2

Other non-use effects NOx SOx ISA2

Ecosystem functions (e.g., biogeochemical cycles, fire 

regulation) — — NOx SOx ISA2

Reduced vegetation 

effects from exposure 

to SO2 and NOx

Injury to vegetation from SO2 exposure — — NOx SOx ISA2

Injury to vegetation from NOx exposure — — NOx SOx ISA2

Reduced ecosystem 

effects from exposure 

to methylmercury

Effects on fish, birds, and mammals (e.g., reproductive 

effects) — —

Mercury Study 

RTC2

Commercial, subsistence and recreational fishing — —

Mercury Study 

RTC1

1 We assess these co-benefits qualitatively due to data and resource limitations for this 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.

ES.5.1 Estimating Global Climate Benefits

We estimate the global social benefits of carbon dioxide (CO2) emission reductions 

expected from this rulemaking using the SCC estimates presented in the 2013 SCC TSD. We 

refer to these estimates, which were developed by the U.S. government, as “SCC estimates” for 

the remainder of this document. The SCC is a metric that estimates the monetary value of 

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 have an 

incremental impact on cumulative global CO2 emissions). 

The SCC estimates used in this analysis have been developed over many years, using the 

best science available, and with input from the public. The EPA and other federal agencies have 

considered the extensive public comments on ways to improve SCC estimation received via the 

notice and comment period that was part of numerous rulemakings since 2006. In addition, 

OMB’s Office of Information and Regulatory Affairs recently sought public comment on the 
ES-14

approach used to develop the SCC estimates. The comment period ended on February 26, 2014, 

and OMB is reviewing the comments received.

An interagency process that included the EPA and other executive branch entities used 

three integrated assessment models (IAMs) to develop SCC estimates and selected four global 

values for use in regulatory analyses. The SCC estimates represent global measures because of 

the distinctive nature of the climate change problem. Emissions of greenhouse gases contribute 

to damages around the world, even when they are released in the United States, and the world’s 

economies are now highly interconnected. Therefore, the SCC estimates incorporate the 

worldwide damages caused by carbon dioxide emissions in order to reflect the global nature of 

the problem, and we expect other governments to consider the global consequences of their 

greenhouse gas emissions when setting their own domestic policies. See RIA Chapter 4 for more 

discussion.

The federal government first released the estimates in February 2010 and updated them in 

2013 using new versions of each IAM. The general approach to estimating the SCC values in 

2010 and 2013 was to run the three integrated assessment models (DICE, FUND, and PAGE)5

using the following three inputs in each model: a probabilistic distribution for climate sensitivity; 

five scenarios capturing economic, population, and emission trajectories; and constant annual 

discount rates. The 2010 SCC Technical Support Document (SCC TSD) provides a complete 

discussion of the methodology and the 2013 SCC TSD presents and discusses the updated 

estimates. The four SCC estimates, updated in 2013, are as follows: $13, $46, $68, and $137 per 

metric ton of CO2 emissions in the year 2020 (2011$), and each estimate increases over time.

These SCC estimates are associated with different discount rates. The first three estimates are the 

model average at 5 percent discount rate, 3 percent, and 2.5 percent, respectively, and the fourth 

estimate is the 95th percentile at 3 percent. 

The 2010 SCC TSD noted a number of limitations to the SCC analysis, including the 

incomplete way in which the IAMs capture catastrophic and non-catastrophic impacts, their 

 

5 The full models names are as follows: Dynamic Integrated Climate and Economy (DICE); Climate Framework for 

Uncertainty, Negotiation, and Distribution (FUND); and Policy Analysis of the Greenhouse Gas Effect (PAGE).
ES-15

incomplete treatment of adaptation and technological change, uncertainty in the extrapolation of 

damages to high temperatures, and assumptions regarding risk aversion. Current integrated 

assessment models do not assign value to all of the important physical, ecological, and economic 

impacts of climate change recognized in the climate change literature because of a lack of 

precise information on the nature of damages and because the science incorporated into these 

models understandably lags behind the most recent research. In particular, the IPCC Fourth 

Assessment Report concluded that “It is very likely that [SCC estimates] underestimate the 

damage costs because they cannot include many non-quantifiable impacts.” Nonetheless, these 

estimates and the discussion of their limitations represent the best available information about 

the social benefits of CO2 emission reductions to inform the benefit-cost analysis.

ES 5.2 Estimating Air Pollution Health Co-Benefits

The proposed guidelines would reduce emissions of precursor pollutants (e.g., SO2, NOx, 

and directly emitted particles), which in turn would lower ambient concentrations of PM2.5 and 

ozone. This co-benefits analysis quantifies the monetized benefits associated with the reduced 

exposure to these two pollutants.

6 Unlike the global SCC estimates, the air pollution health cobenefits are only estimated for the contiguous U.S.7 The estimates of monetized PM2.5 cobenefits include avoided premature deaths (derived from effect coefficients in two cohort studies 

[Krewski et al. 2009 and Lepeule et al. 2012] for adults and one for infants [Woodruff et al. 

1997]), as well as avoided morbidity effects for ten non-fatal endpoints ranging in severity from 

lower respiratory symptoms to heart attacks (U.S. EPA, 2012). The estimates of monetized 

ozone co-benefits include avoided premature deaths (derived from the range of effect 

coefficients represented by two short-term epidemiology studies [Bell et al. (2004) and Levy et 

al. (2005)]), as well as avoided morbidity effects for five non-fatal endpoints ranging in severity 

from school absence days to hospital admissions (U.S. EPA, 2008, 2011).

 

6 We did not estimate the co-benefits associated with reducing direct exposure to SO2 and NOx.

7 We do not have emission reduction information or air quality modeling available to estimate the air pollution 

health co-benefits in Alaska and Hawaii anticipated from implementation of the proposed guidelines.
ES-16

We used a “benefit-per-ton” approach to estimate the health co-benefits. To create the 

benefit-per-ton estimates for PM2.5, this approach uses an air quality model to convert emissions 

of PM2.5 precursors (e.g., SO2, NOx) and directly emitted particles into changes in ambient PM2.5 

concentrations and BenMAP to estimate the changes in human health associated with that 

change in air quality. We then divide these health impacts by the emissions in specific sectors at 

the regional level (i.e., East, West, and California). We followed a similar process to estimate 

benefit-per-ton estimates for the ozone precursor NOx. To calculate the co-benefits for the 

proposed guidelines, we then multiplied the regional benefit-per-ton estimates for the EGU 

sector by the corresponding emission reductions. All benefit-per-ton estimates reflect the 

geographic distribution of the modeled emissions, which may not exactly match the emission 

reductions in this rulemaking, and thus they may not reflect the local variability in population 

density, meteorology, exposure, baseline health incidence rates, or other local factors for any 

specific location. 

Our estimate of the monetized co-benefits is based on the EPA’s interpretation of the best 

available scientific literature (U.S. EPA, 2009) and methods and supported by the EPA’s Science 

Advisory Board and the NAS (NRC, 2002). Below are key assumptions underlying the estimates 

for PM2.5-related premature mortality, which accounts for 98 percent of the monetized PM2.5

health co-benefits: 

1. 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, 2009b).

2. We assume that the health impact function for fine particles is log-linear without a 

threshold in this analysis. Thus, the estimates include health co-benefits from 

reducing fine particles in areas with varied concentrations of PM2.5, including both 
ES-17

areas that do not meet the fine particle standard and those areas that are in attainment, 

down to the lowest modeled concentrations. 

3. 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, 2004c), which affects the valuation of mortality cobenefits at different discount rates.

Every benefits analysis examining the potential effects of a change in environmental 

protection requirements is limited, to some extent, by data gaps, model capabilities (such as 

geographic coverage) and uncertainties in the underlying scientific and economic studies used to 

configure the benefit and cost models. Despite these uncertainties, we believe this analysis 

provides a reasonable indication of the expected health co-benefits of the air pollution emission 

reductions for the illustrative compliance options for the proposed standards under a set of 

reasonable assumptions. This analysis does not include the type of detailed uncertainty 

assessment found in the 2012 PM2.5 National Ambient Air Quality Standard (NAAQS) RIA (U.S. 

EPA, 2012) because we lack the necessary air quality input and monitoring data to conduct a 

complete benefits assessment. In addition, using a benefit-per-ton approach adds another 

important source of uncertainty to the benefits estimates. 

ES 5.3 Combined Benefits Estimates

The EPA has evaluated the range of potential impacts by combining all four SCC values 

with health co-benefits values at the 3 percent and 7 percent discount rates. Different discount 

rates are applied to SCC than to the health co-benefit estimates; because CO2 emissions are longlived and subsequent damages occur over many years. Moreover, several discount rates are 

applied to SCC because the literature shows that the estimate of SCC is sensitive to assumptions 

about discount rate and because no consensus exists on the appropriate rate to use in an 

intergenerational context. The U.S. government centered its attention on the average SCC at a 3 

percent discount rate but emphasized the importance of considering all four SCC estimates. 
ES-18

Tables ES-6 and ES-7 provide the combined climate benefits and health co-benefits for each 

option evaluated for 2020, 2025 and 2030 for Options 1 and 2, respectively for each discount rate 

combination.

Table ES-6. Combined Estimates of Climate Benefits and Health Co-Benefits for 

Proposed Existing EGU GHG Rule – Regional Compliance Approach (billions of 2011$)*

Option SCC Discount Rate and 

Statistic**

Climate 

Benefits 

Only

Climate Benefits plus Health Co-Benefits 

(Discount Rate Applied to Health Co-Benefits)

3% 7%

Option 1 In 2020 371 million metric tonnes CO2

5% $4.7 $21 to $42 $19 to $39

3% $17 $33 to $54 $32 to $51

2.5% $25 $41 to $63 $40 to $59

3% (95th percentile) $51 $67 to $88 $65 to $85

In 2025 501 million metric tonnes CO2

5% $7.5 $30 to $61 $28 to $56

3% $25 $48 to $78 $46 to $74

2.5% $37 $60 to $90 $57 to $85

3% (95th percentile) $76 $99 to $130 $97 to $120

In 2030 545 million metric tonnes CO2

5% $9.3 $35 to $68 $32 to $63

3% $30 $55 to $89 $53 to $84

2.5% $44 $69 to $100 $66 to $97

3% (95th percentile) $92 $120 to $150 $120 to $150

Option 2 In 2020 283 million metric tonnes CO2

5% $3.6 $17 to $34 $16 to $32

3% $13 $26 to $44 $25 to $41

2.5% $19 $33 to $50 $31 to $47

3% (95th percentile) $39 $52 to $70 $51 to $67

In 2025 368 million metric tonnes CO2

5% $5.5 $23 to $46 $21 to $42

3% $18 $36 to $59 $34 to $55

2.5% $27 $44 to $67 $43 to $64

3% (95th percentile) $56 $73 to $96 $72 to $93

*All benefit estimates are rounded to two significant figures. Climate benefits are based on reductions in CO2

emissions. Co-benefits are based on regional benefit-per-ton estimates. Ozone co-benefits occur in analysis year, so 

they are the same for all discount rates. The health co-benefits reflect the sum of the PM2.5 and ozone co-benefits 

and reflect the range based on adult mortality functions (e.g., from Krewski et al. (2009) with Bell et al. (2004) to 

Lepeule et al. (2012) with Levy et al. (2005)). The monetized health co-benefits do not include reduced health 

effects from direct exposure to NO2, SO2, and HAP; ecosystem effects; or visibility impairment. See Chapter 4 for 

more information about these estimates and regarding the uncertainty in these estimates.

**Unless otherwise specified, it is the model average.
ES-19

Table ES-7. Combined Estimates of Climate Benefits and Health Co-Benefits for 

Proposed Existing EGU GHG Rule – State Compliance Approach (billions of 2011$)*

Option SCC Discount Rate and 

Statistic**

Climate 

Benefits 

Only

Climate Benefits plus Health Co-Benefits 

(Discount Rate Applied to Health Co-Benefits)

3% 7%

Option 

1

In 2020 383 million metric tonnes CO2

5% $4.9 $22 to $45 $20 to $41

3% $18 $35 to $57 $33 to $54

2.5% $26 $43 to $66 $42 to $62

3% (95th percentile) $52 $69 to $92 $68 to $88

In 2025 506 million metric tonnes CO2

5% $7.6 $31 to $62 $29 to $57

3% $25 $49 to $80 $46 to $75

2.5% $37 $61 to $92 $58 to $87

3% (95th percentile) $77 $100 to $130 $98 to $130

In 2030 n/a million metric tonnes CO2

5% $9.5 $36 to $72 $34 to $66

3% $31 $57 to $93 $55 to $87

2.5% $44 $71 to $110 $69 to $100

3% (95th percentile) $94 $120 to $160 $120 to $150

Option 

2

In 2020 295 million metric tonnes CO2

5% $3.8 $17 to $35 $16 to $32

3% $14 $27 to $45 $26 to $42

2.5% $20 $34 to $52 $32 to $49

3% (95th percentile) $40 $54 to $72 $53 to $69

In 2025 376 million metric tonnes CO2

5% $5.6 $23 to $47 $22 to $43

3% $19 $36 to $60 $35 to $56

2.5% $28 $45 to $69 $44 to $65

3% (95th percentile) $57 $75 to $98 $73 to $95

*All benefit estimates are rounded to two significant figures. Climate benefits are based on reductions in CO2

emissions. Co-benefits are based on regional benefit-per-ton estimates. Ozone co-benefits occur in analysis year, so 

they are the same for all discount rates. The health co-benefits reflect the sum of the PM2.5 and ozone co-benefits 

and reflect the range based on adult mortality functions (e.g., from Krewski et al. (2009) with Bell et al. (2004) to 

Lepeule et al. (2012) with Levy et al. (2005)). The monetized health co-benefits do not include reduced health 

effects from direct exposure to NO2, SO2, and HAP; ecosystem effects; or visibility impairment. See Chapter 4 for 

more information about these estimates and regarding the uncertainty in these estimates.

**Unless otherwise specified, it is the model average.

ES.6 Monetized Benefits, Compliance Costs and Net Benefits of the Proposed Guidelines 

for Existing Sources 

In this summary, the EPA provides the estimates of the climate benefits, health cobenefits, compliance costs and net benefits of the proposed Option 1 and alternative Option 2 

assuming a regional compliance approach and an alternative state compliance approach. In Table 
ES-20

ES-8, the EPA estimates that in 2020 the proposed Option 1 regional compliance approach will 

yield monetized climate benefits of $17 billion using a 3 percent discount rate (model average, 

2011$). The air pollution health co-benefits in 2020 are estimated to be $15 billion to $34 billion 

(2011$) for a 3 percent discount rate and $13 billion to $31 billion (2011$) for a 7 percent 

discount rate. The annual compliance costs, including monitoring and reporting costs, are 

approximately $5.5 billion (2011$) in 2020. The quantified net benefits (the difference between 

monetized benefits and costs) are $26 billion to $45 billion (2011$) for 2020 (Table ES-8 below) 

and $47 billion to $78 billion (2011$) for 2030 (Table ES-10 below), using a 3 percent discount 

rate (model average). For the Option 1 state compliance approach in 2020, the EPA estimates 

monetized climate benefits of approximately $18 billion using a 3 percent discount rate (model 

average). The air pollution health co-benefits in 2020 are estimated to be $15 billion to $36

billion for a 3 percent discount rate and $14 billion to $33 billion (2011$) for a 7 percent 

discount rate. The annual compliance costs including monitoring and reporting costs, are 

approximately $7.5 billion (2011$) in 2020. The quantified net benefits (the difference between 

monetized benefits and costs) are $26 billion to $46 billion for 2020 (Table ES-8 below) and $47

billion to $80 billion (2011$) for 2030 (Table ES-10 below). Benefit and cost estimates for 

Option 1 regional and state compliance approaches for 2020, 2025, and 2030 and are presented 

in Tables ES-8, ES-9, and ES-10, and similar estimates for Option 2 regional and state 

compliance approaches are presented in Tables ES-8 and ES-9 for 2020 and 2025. 

The EPA could not monetize some important benefits of the guidelines. Unquantified 

benefits include climate benefits from reducing emissions of non-CO2 greenhouse gases and cobenefits from reducing exposure to SO2, NOx, and hazardous air pollutants (e.g., mercury and 

hydrogen chloride), as well as ecosystem effects and visibility impairment. Upon considering 

these limitations and uncertainties, it remains clear that the benefits of this proposal are 

substantial and far outweigh the costs.
ES-21

Table ES-8. Summary of Estimated Monetized Benefits, Compliance Costs, and Net 

Benefits for the Proposed Guidelines – 2020 (billions of 2011$) a

Option 1 - state Option 2 – state

3% Discount 

Rate

7% Discount 

Rate 3% Discount Rate 7% Discount Rate

Climate Benefits b

5% discount rate

3% discount rate

2.5% discount rate

95th percentile at 3% discount rate

$4.9

$18

$26

$52

$3.8

$14

$20

$40

Air pollution health co-benefits c $17 to $40 $15 to $36 $14 to $32 $12 to $29

Total Compliance Costs d $7.5 $5.5

Net Benefits e $27 to $50 $26 to $46 $22 to $40 $20 to $37

Non-Monetized Benefits

Direct exposure to SO2 and NO2

1.5 tons of Hg 

Ecosystem Effects

Visibility impairment

Direct exposure to SO2 and NO2

1.2 tons of Hg 

Ecosystem Effects

Visibility impairment

Option 1 - regional Option 2 – regional

3% Discount 

Rate

7% Discount 

Rate 3% Discount Rate 7% Discount Rate

Climate Benefits b

5% discount rate

3% discount rate

2.5% discount rate

95th percentile at 3% discount rate

$4.7

$17

$25

$51

$3.6

$13

$19

$39

Air pollution health co-benefits c $16 to $37 $15 to $34 $13 to $31 $12 to $28

Total Compliance Costs d $5.5 $4.3

Net Benefits e $28 to $49 $26 to $45 $22 to $40 $21 to $37

Non-Monetized Benefits

Direct exposure to SO2 and NO2

1.3 tons of Hg 

Ecosystem effects

Visibility impairment

Direct exposure to SO2 and NO2

0.9 tons of Hg 

Ecosystem effects

Visibility impairment

a All estimates are for 2020 and are rounded to two significant figures, so figures may not sum.

b The climate benefit estimates in this summary table reflect global impacts from CO2 emission changes and do not 

account for changes in non-CO2 GHG emissions. Also, different discount rates are applied to SCC than to the 

other estimates because CO2 emissions are long-lived and subsequent damages occur over many years. The SCC 

estimates are year-specific and increase over time. 

c The air pollution health co-benefits reflect reduced exposure to PM2.5 and ozone associated with emission 

reductions of directly emitted PM2.5, SO2 and NOX. The range reflects the use of concentration-response functions 

from different epidemiology studies. The reduction in premature fatalities each year accounts for over 90 percent 

of total monetized co-benefits from PM2.5 and ozone. These models assume that all fine particles, regardless of 

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.

d Total social costs are approximated by the illustrative compliance costs which, in part, are estimated using the 

Integrated Planning Model for the proposed option and a discount rate of approximately 5%. This estimate also 

includes monitoring, recordkeeping, and reporting costs and demand side energy efficiency program and 

participant costs.

e The estimates of net benefits in this summary table are calculated using the global SCC at a 3 percent discount rate 

(model average). The RIA includes combined climate and health estimates based on these additional discount 

rates.
ES-22

Table ES-9. Summary of Estimated Monetized Benefits, Compliance Costs, and Net 

Benefits for the Proposed Guidelines – 2025 (billions of 2011$) a

Option 1 – state Option 2 – state

3% Discount 

Rate

7% Discount 

Rate

3% Discount 

Rate

7% Discount 

Rate

Climate Benefits b 

5% discount rate

3% discount rate

2.5% discount rate

95th percentile at 3% discount rate

$7.6

$25

$37

$77

$5.6

$19

$28

$57

Air pollution health co-benefits c $23 to $54 $21 to $49 $18 to $41 $16 to $37

Total Compliance Costs d $5.5 $5.5

Net Benefits e $43to $74 $41 to $69 $31 to $55 $29 to $51

Non-Monetized Benefits

Direct exposure to SO2 and NO2

2.0 tons of Hg 

Ecosystem Effects

Visibility impairment

Direct exposure to SO2 and NO2

1.7 tons of Hg 

Ecosystem Effects

Visibility impairment

Option 1 – regional Option 2 – regional

3% Discount 

Rate

7% Discount 

Rate

3% Discount 

Rate

7% Discount 

Rate

Climate Benefits b

5% discount rate

3% discount rate

2.5% discount rate

95th percentile at 3% discount rate

$7.5

$25

$37

$76

$5.5

$18

$27

$56

Air pollution health co-benefits c $23 to $53 $21 to $48 $17 to $40 $16 to $36

Total Compliance Costs d $4.6 $4.5

Net Benefits e $43 to $74 $41 to $69 $31 to $54 $29 to $50

Non-Monetized Benefits

Direct exposure to SO2 and NO2

1.7 tons of Hg 

Ecosystem effects

Visibility impairment

Direct exposure to SO2 and NO2

1.3 tons of Hg 

Ecosystem effects

Visibility impairment

a All estimates are for 2025 and are rounded to two significant figures, so figures may not sum.

b The climate benefit estimates in this summary table reflect global impacts from CO2 emission changes and do not 

account for changes in non-CO2 GHG emissions. Also, different discount rates are applied to SCC than to the 

other estimates because CO2 emissions are long-lived and subsequent damages occur over many years. The SCC 

estimates are year-specific and increase over time. 

c The air pollution health co-benefits reflect reduced exposure to PM2.5 and ozone associated with emission 

reductions of directly emitted PM2.5, SO2 and NOX. The range reflects the use of concentration-response functions 

from different epidemiology studies. The reduction in premature fatalities each year accounts for over 90 percent 

of total monetized co-benefits from PM2.5 and ozone. These models assume that all fine particles, regardless of 

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. 

d Total social costs are approximated by the illustrative compliance costs which, in part, are estimated using the 

Integrated Planning Model for the proposed option and a discount rate of approximately 5%. This estimate also 

includes monitoring, recordkeeping, and reporting costs and demand side energy efficiency program and 

participant costs.

e The estimates of net benefits in this summary table are calculated using the global SCC at a 3 percent discount rate 

(model average). The RIA includes combined climate and health estimates based on these additional discount 

rates.
ES-23

Table ES-10. Summary of Estimated Monetized Benefits, Compliance Costs, and Net 

Benefits for the Proposed Guidelines –2030 (billions of 2011$) a

Option 1– state

3% Discount Rate 7% Discount Rate

Climate Benefits b 

5% discount rate

3% discount rate

2.5% discount rate

95th percentile at 3% discount rate

$9.5

$31

$44

$94

Air pollution health co-benefits c $27 to $62 $24 to $56

Total Compliance Costs d $8.8

Net Benefits e $49 to $84 $46 to $79

Non-Monetized Benefits

Direct exposure to SO2 and NO2

2.1 tons of Hg and 590 tons of HCl

Ecosystem effects

Visibility impairment

Option 1– regional

3% Discount Rate 7% Discount Rate

Climate Benefits b

5% discount rate

3% discount rate

2.5% discount rate

95th percentile at 3% discount rate

$9.3

$30

$44

$92

Air pollution health co-benefits c $25 to $59 $23 to $54

Total Compliance Costs d $7.3

Net Benefits e $48 to $82 $46 to $77

Non-Monetized Benefits

Direct exposure to SO2 and NO2

1.7 tons of Hg and 580 tons of HCl

Ecosystem effects

Visibility impairment

a All estimates are for 2030, and are rounded to two significant figures, so figures may not sum.

b The climate benefit estimates in this summary table reflect global impacts from CO2 emission changes and do not 

account for changes in non-CO2 GHG emissions. Also, different discount rates are applied to SCC than to the 

other estimates because CO2 emissions are long-lived and subsequent damages occur over many years. The SCC 

estimates are year-specific and increase over time. 

c The air pollution health co-benefits reflect reduced exposure to PM2.5 and ozone associated with emission 

reductions of directly emitted PM2.5, SO2 and NOX. The range reflects the use of concentration-response functions 

from different epidemiology studies. The reduction in premature fatalities each year accounts for over 90 percent 

of total monetized co-benefits from PM2.5 and ozone. These models assume that all fine particles, regardless of 

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.

d Total social costs are approximated by the illustrative compliance costs which, in part, are estimated using the 

Integrated Planning Model for the proposed option and a discount rate of approximately 5%. This estimate also

includes monitoring, recordkeeping, and reporting costs and demand side energy efficiency program and 

participant costs.

e The estimates of net benefits in this summary table are calculated using the global SCC at a 3 percent discount rate 

(model average). The RIA includes combined climate and health estimates based on these additional discount 

rates.
ES-24

ES.7 Economic Impacts of the Proposed Emission Guidelines for Existing EGUs

The proposed guidelines have important energy market implications. Under Option 1, 

average nationwide retail electricity prices are projected to increase roughly 6 to 7 percent in 

2020, and roughly 3 percent in 2030 (contiguous U.S.), compared to base case price estimates 

modeled for these same years. Average monthly electricity bills are anticipated to increase by 

roughly 3 percent in 2020, but decline by roughly 9 percent by 2030 because increased energy 

efficiency will lead to reduced usage. 

The average delivered coal price to the power sector is projected to decrease by 16 to 17 

percent in 2020 and roughly 18 percent in 2030, relative to the base case (Option 1). The EPA 

projects coal production for use by the power sector, a large component of total coal production, 

will decline by roughly 25 to 27 percent in 2020 from base case levels. The use of coal by the 

power sector will decrease by roughly 30 to 32 percent in 2030.

The EPA also projects that the electric power sector-delivered natural gas prices will 

increase by 9 to 12 percent in 2020, with negligible changes by 2030 relative to the base case. 

Natural gas use for electricity generation will increase by as much as 1.2 trillion cubic feet (TCF) 

in 2020 relative to the base case, declining over time. 

Renewable energy capacity is anticipated to increase by roughly 12 GW in 2020 and by 9 

GW in 2030 under Option 1. Energy market impacts from the guidelines are discussed more 

extensively in Chapter 3 of this RIA. 

ES.8 Economic Impacts of the Proposed Guidelines for Existing EGUs for Sectors Other 

Than the EGU Sector and for Employment 

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 and these market changes may affect the profitability of firms and the 

economic welfare of their consumers. The EPA recognizes that these guidelines provide 

significant flexibilities and states implementing the guidelines may choose to mitigate impacts to 

some markets outside the EGU sector. Similarly, demand for new generation or energy 
ES-25

efficiency can result in changes in production and profitability for firms that supply those goods 

and services. The guidelines provide flexibility for states that may want to enhance demand for 

goods and services from those sectors.

Executive Order 13563 directs federal agencies to consider the effect of regulations on 

job creation and employment. According to the Executive Order, “our regulatory system must 

protect public health, welfare, safety, and our environment while promoting economic growth, 

innovation, competitiveness, and job creation. It must be based on the best available science” 

(Executive Order 13563, 2011). Although standard benefit-cost analyses have not typically 

included a separate analysis of regulation-induced employment impacts, during periods of 

sustained high unemployment, employment impacts are of particular concern and questions may 

arise about their existence and magnitude. 

States have the responsibility and flexibility to implement policies and practices for 

compliance with Proposed Electric Generating Unit Greenhouse Gas Existing Source Guidelines. 

Given the wide range of approaches that may be used, quantifying the associated employment 

impacts is difficult. The EPA’s illustrative employment analysis includes an estimate of 

projected employment impacts associated with these guidelines for the electric power industry, 

coal and natural gas production, and demand side energy efficiency activities. These projections 

are derived, in part, from a detailed model of the electricity production sector used for this 

regulatory analysis, and U.S government data on employment and labor productivity. In the 

electricity, coal, and natural gas sectors, the EPA estimates that these guidelines could result in 

an increase of approximately 28,000 to 25,900 job-years in 2020 for Option 1, state and regional 

compliance approaches, respectively. For Option 2, the state and regional compliance approach 

estimates reflect an increase of approximately 29,800 to 26,700 job-years in 2020. The Agency is 

also offering an illustrative calculation of potential employment effects due to demand-side 

energy efficiency programs. Employment impacts in 2020 could be an increase of approximately 

78,800 jobs for Option 1 (for both the state and regional compliance approaches). For Option 2

demand-side energy efficiency employment impacts in 2020 could be an increase of 

approximately 57,000 jobs (for both the state and regional compliance approaches). More detail 

about these analyses can be found in Chapter 6 of this RIA.
ES-26

ES.9 Modified and Reconstructed Sources 

The EPA is proposing emission limits for CO2 emitted from reconstructed and modified 

EGUs under section 111(b) of the CAA. Based on historical information that has been reported 

to the EPA, the EPA anticipates few, if any, covered units will trigger the reconstruction or 

modification provisions in the period of analysis (through 2025). As a result, we do not 

anticipate any significant costs or benefits associated with this proposal. However, because there 

have been a few units that have notified the EPA of modifications in the past, in Chapter 9 of this 

RIA we present an illustrative analysis of the costs and benefits for a hypothetical unit if it were 

to trigger the modification provision. 
ES-27

ES.10 References

Bell, M.L., et al. 2004. “Ozone and Short-Term Mortality in 95 U.S. Urban Communities, 1987-

2000.” Journal of the American Medical Association, 292(19): pp. 2372-8.

Docket ID EPA-HQ-OAR-2009-0472-114577, Technical Support Document: Social Cost of 

Carbon for Regulatory Impact Analysis Under Executive Order 12866, Interagency Working 

Group on Social Cost of Carbon, with participation by the Council of Economic Advisers, 

Council on Environmental Quality, Department of Agriculture, Department of Commerce, 

Department of Energy, Department of Transportation, Environmental Protection Agency, 

National Economic Council, Office of Energy and Climate Change, Office of Management 

and Budget, Office of Science and Technology Policy, and Department of Treasury (February 

2010). Available at: <http://www.whitehouse.gov/sites/default/files/omb/inforeg/foragencies/Social-Cost-of-Carbon-for-RIA.pdf>.

Docket ID EPA-HQ-OAR-2013-0495, Technical Support Document: Technical Update of the 

Social Cost of Carbon for Regulatory Impact Analysis Under Executive Order 12866, 

Interagency Working Group on Social Cost of Carbon, with participation by the Council of 

Economic Advisers, Council on Environmental Quality, Department of Agriculture, 

Department of Commerce, Department of Energy, Department of Transportation, 

Environmental Protection Agency, National Economic Council, Office of Energy and Climate 

Change, Office of Management and Budget, Office of Science and Technology Policy, and 

Department of Treasury (May 2013, Revised November 2013). Also available at: 

<http://www.whitehouse.gov/sites/default/files/omb/assets/inforeg/technical-update-socialcost-of-carbon-for-regulator-impact-analysis.pdf>.

Fann, N.; K.R. Baker and C.M. Fulcher. 2012. “Characterizing the PM2.5-Related Health 

Benefits of Emission Reductions for 17 Industrial, Area and Mobile Emission Sectors Across 

the U.S.” Environment International, 49 41–151. 

Krewski D; M. Jerrett; R.T. Burnett; R. Ma; E. Hughes and Y. Shi, et al. 2009. Extended FollowUp and Spatial Analysis of the American Cancer Society Study Linking Particulate Air 

Pollution and Mortality. HEI Research Report, 140, Health Effects Institute, Boston, MA.
ES-28

Intergovernmental Panel on Climate Change. 2007. Climate Change 2007: Synthesis Report 

Contribution of Working Groups I, II and III to the Fourth Assessment Report of the IPCC. 

Available at: 

<http://www.ipcc.ch/publications_and_data/publications_ipcc_fourth_assessment_report_synt

hesis_report.htm>.

Lepeule, J.; F. Laden; D. Dockery and J. Schwartz. 2012. “Chronic Exposure to Fine Particles 

and Mortality: An Extended Follow-Up of the Harvard Six Cities Study from 1974 to 2009.” 

Environmental Health Perspectives, July, 120(7): pp. 965-70. 

Levy, J.I.; S.M. Chemerynski and J.A. Sarnat. 2005. “Ozone Exposure and Mortality: An 

Empiric Bayes Meta-regression Analysis.” Epidemiology, 16(4): pp. 458-68.

National Research Council (NRC). 2000. Toxicological Effects of Methylmercury: Committee 

on the Toxicological Effects of Methylmercury. Board on Environmental Studies and 

Toxicology. Washington, DC: National Academies Press. 

National Research Council (NRC). 2002. Estimating the Public Health Benefits of Proposed Air 

Pollution Regulations. Washington, DC: National Academies Press. 

U.S. Environmental Protection Agency (U.S. EPA). 2008a. Integrated Science Assessment for 

Sulfur Oxides: Health Criteria (Final Report). Research Triangle Park, NC: National Center 

for Environmental Assessment, September. Available at 

<http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=198843>.

U.S. EPA. 2008b. Final Ozone NAAQS Regulatory Impact Analysis. Research Triangle Park, 

NC: Office of Air Quality Planning and Standards, Health and Environmental Impacts 

Division, Air Benefit and Cost Group. (EPA document number EPA-452/R-08-003, March). 

Available at <http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=194645>. 

U.S. EPA. 2008c. Integrated Science Assessment for Oxides of Nitrogen: Health Criteria (Final 

Report). Research Triangle Park, NC: National Center for Environmental Assessment. July. 

Available at <http://www.epa.gov/ttnecas1/regdata/RIAs/6-ozoneriachapter6.pdf>. 

U.S. EPA. 2008d. Integrated Science Assessment for Oxides of Nitrogen and Sulfur: Ecological 

Criteria National (Final Report). Research Triangle Park, NC: National Center for 

Environmental Assessment. (EPA document number EPA/600/R-08/139, December). 

Available at <http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=201485>.
ES-29

U.S. EPA. 2009. Integrated Science Assessment for Particulate Matter (Final Report). Research 

Triangle Park, NC: National Center for Environmental Assessment, RTP Division. (EPA 

document number EPA-600-R-08-139F, December). Available at 

<http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=216546>.

U.S. EPA. 2010d. Section 3: Re‐analysis of the Benefits of Attaining Alternative Ozone 

Standards to Incorporate Current Methods. Available at 

<http://www.epa.gov/ttnecas1/regdata/RIAs/s3-supplemental_analysis-updated_benefits11-

5.09.pdf >. 

U.S. EPA. 2012a. Regulatory Impact Analysis for the Final Revisions to the National Ambient 

Air Quality Standards for Particulate Matter. Research Triangle Park, NC: Office of Air 

Quality Planning and Standards, Health and Environmental Impacts Division. (EPA document 

number EPA-452/R-12-003, December). Available at 

<http://www.epa.gov/pm/2012/finalria.pdf>. 

U.S. EPA. 2013b. Integrated Science Assessment of Ozone and Related Photochemical Oxidants 

(Final Report). Washington, DC: U.S. Environmental Protection Agency. (EPA document 

number EPA/600/R-10/076F). Available at 

<http://cfpub.epa.gov/ncea/isa/recordisplay.cfm?deid=247492#Download>. 

Woodruff, T.J.; J. Grillo and K.C. Schoendorf. 1997. “The Relationship between Selected

Causes of Postneonatal Infant Mortality and Particulate Air Pollution in the United States,” 

Environmental Health Perspectives, 105 (6): 608-612.
1-1

CHAPTER 1: INTRODUCTION AND BACKGROUND

1.1 Introduction

This document presents the expected benefits, costs, and economic impacts of illustrative 

strategies states may implement to comply with the proposed Electric Utility Generating Unit 

(EGU) Existing Source GHG Guidelines in 2020, 2025, and 2030. Furthermore, this document 

provides a separate description of the expected benefits, costs, and economic impacts of 

proposed emission limits for reconstructed and modified EGU sources. This chapter contains 

background information on these rules and an outline of the chapters in the report.

1.2 Legal, Scientific and Economic Basis for this Rulemaking

1.2.1 Statutory Requirement

Section 111 of the Clean Air Act (CAA) requires performance standards for air pollutant 

emissions from categories of stationary sources that may reasonably contribute to endangerment 

of public health or welfare. In April 2007, the Supreme Court ruled in Massachusetts v. the 

Environmental Protection Agency (EPA) that greenhouse gases (GHGs) meet the definition of an 

“air pollutant” under the CAA. This ruling clarified that the authorities and requirements of the 

CAA apply to GHGs. As a result, the EPA must make decisions about whether to regulate GHGs 

under certain provisions of the CAA, based on relevant statutory criteria. The EPA issued a final 

determination that GHG emissions endanger both the public health and the public welfare of 

current and future generations in the Endangerment and Cause or Contribute Findings for 

Greenhouse Gases Under Section 202(a) of the CAA (74 FR 66,496; Dec. 15, 2009). 

Section 111(b) authorizes the EPA to issue new source performance standards (NSPS) for

carbon dioxide (CO2) from newly constructed, reconstructed, and modified sources. In January 

2014, under the authority of CAA section 111(b), the EPA proposed standards for emissions of 

CO2 from newly constructed fossil fuel-fired electric steam generating units (utility boilers and 

integrated gasification combined cycle [IGCC] units) and for natural gas-fired stationary 

combustion turbines. The EPA is currently proposing standards to address CO2 emissions from 

reconstructed and modified power plants under the authority of CAA section 111(b).
1-2

Furthermore, when the EPA establishes section 111(b) standards of performance for newly

constructed sources in a particular source category for a pollutant that is not regulated as a 

criteria pollutant or hazardous air pollutant, the EPA must establish requirements for existing 

sources in that source category for that pollutant under section 111(d). Under 111(d), the EPA 

develops “emission guidelines” that the states must develop plans to meet. The EPA is proposing 

state goals for GHG emissions from existing sources under section 111(d) of the CAA.

1.2.2 Health and Welfare Impacts from Climate Change

In 2009, the EPA Administrator found “six greenhouse gases taken in combination

endanger both the public health and the public welfare.”

8 These adverse impacts make it 

necessary for the EPA to regulate GHGs from EGU sources. This proposed rule is designed to 

reduce the rate at which atmospheric concentrations of these gases are increasing, and therefore 

reduce the risk of adverse effects.

A number of major peer-reviewed scientific assessments have been released since the 

administrative record concerning the Endangerment Finding closed following the EPA’s 2010 

Reconsideration Denial. The EPA has reviewed these assessments and finds that in general, the 

improved understanding of the climate system they present are consistent with the assessments 

underlying the 2009 Endangerment Finding. For example, the recently released National Climate 

Assessment stated, “Climate change is already affecting the American people in far reaching 

ways. Certain types of extreme weather events with links to climate change have become more 

frequent and/or intense, including prolonged periods of heat, heavy downpours, and, in some 

regions, floods and droughts. In addition, warming is causing sea level to rise and glaciers and 

Arctic sea ice to melt, and oceans are becoming more acidic as they absorb carbon dioxide. 

These and other aspects of climate change are disrupting people’s lives and damaging some 

sectors of our economy.” This and other assessments are outlined in Chapter 4 of this Regulatory 

Impact Assessment (RIA).

 

8 Endangerment and Cause or Contribute Findings for Greenhouse Gases under Section 202(a) of the Clean Air Act.
1-3

1.2.3 Market Failure

Many regulations are promulgated to correct market failures, which 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. 

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. These social costs associated with the health 

and welfare impacts are referred to as negative externalities. If an 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. The market price of electricity will fail to incorporate the full opportunity 

cost to society of generating electricity. All else equal, given this externality, the quantity of 

electricity generated in a free market will not be at the socially optimal level. More electricity 

will be produced than would occur if the power producers had to account for the full opportunity 

cost of production including the negative externality. Consequently, absent a regulation on 

emissions, the marginal social cost of the last unit of electricity produced will exceed its 

marginal social benefit. 

1.3 Summary of Regulatory Analysis

In accordance with Executive Order 12866, Executive Order 13563, and the EPA’s 

“Guidelines for Preparing Economic Analyses,” the EPA prepared this RIA for this “significant 

regulatory action.” This action is an economically significant regulatory action because it is 

expected to 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. 

This RIA addresses the potential costs, emission reductions, and benefits of the existing 

source emission guidelines that are the focus of this action. Additionally, this RIA includes 

information on potential impacts on electricity markets, employment, and markets outside the 

electricity sector. Chapter 9 of this document provides a separate description of the expected 
1-4

benefits, costs and economic impacts of proposed emission limits for reconstructed and modified 

EGU sources.

In evaluating the impacts of the proposed guidelines, we analyzed a number of 

uncertainties. For example, the analysis includes an evaluation of different potential spatial 

approaches to state compliance (i.e., state and regional) and in the estimated benefits of reducing

carbon dioxide and other air pollutants. For a further discussion of key evaluations of uncertainty 

in the regulatory analyses for this proposed rulemaking, see Chapter 8 of this RIA.

1.4 Background for the Proposed EGU Existing Source GHG Guidelines

1.4.1 Base Case and Years of Analysis

The rule on which the majority of the analysis in this RIA is based proposes GHG 

emission guidelines for states to limit CO2 emissions from certain existing EGUs. The base case 

for this analysis, which uses the Integrated Planning Model (IPM), includes state rules that have 

been finalized and/or approved by a state’s legislature or environmental agencies, as well as final 

federal rules. The IPM Base Case v.5.13 includes the Clean Air Interstate Rule (CAIR),9the 

Mercury and Air Toxics Rule (MATS), and other state and Federal regulations to the extent that 

they contain measures, permits, or other air-related limitations or requirements. Additional 

legally binding and enforceable commitments for GHG reductions considered in the base case 

are discussed in the documentation for IPM.10

Costs and benefits are presented for compliance in 2020, 2025, and 2030 for Option 1 

and 2020 and 2025 for Option 2. These years were selected because they represent partial and 

 

9 EPA Base Case v.5.13 includes the Clean Air Interstate Rule (CAIR), a Federal regulatory measure for achieving 

the 1997 National Ambient Air Quality Standards (NAAQS) for ozone (8-hour average of 0.08 ppm) and fine 

particles (24-hour average of 65 μg/m3 or less and annual average of 15 μg/m3for particles of diameter 2.5 

micrometers or less, i.e., PM2.5). Originally issued on March 10, 2005, CAIR was remanded back to EPA by the 

U.S. Court of Appeals for the District of Columbia Circuit in December 2008 and EPA was required to correct 

legal flaws in the regulations that had been cited in a ruling by the Court in July 2008. CAIR remains in effect 

until replaced by EPA pursuant to the Court’s ruling. CAIR’s provisions were still in effect when EPA Base 

Case v.5.13 was released. 

10 See Chapter 3 of EPA’s Base Case using IPM (v5.13) documentation, available 

at: http://www.epa.gov/powersectormodeling/BaseCasev513.html
1-5

full implementation dates for the two policy options analyzed. Analysis of employment impacts 

is presented for compliance in 2020, 2025, and 2030 for Option 1 and 2020 and 2025 for Option 

2. All estimates are presented in 2011 dollars. 

1.4.2 Definition of Affected Sources

This proposed rule under CAA section 111(d) will set emission guidelines for states to 

limit CO2 emissions from certain existing EGUs. This rulemaking does not address GHG 

emissions from newly constructed sources or sources modifying or reconstructing. Section 

111(b) emission limits for reconstructed and modified sources are being proposed in a separate 

action and are discussed in Chapter 9 of this RIA.

For the purposes of this proposed rule, an affected EGU is any fossil fuel-fired EGU that 

was in operation or had commenced construction as of January 8, 2014, and is therefore an 

“existing source” for purposes of CAA section 111, but in all other respects would meet the 

applicability criteria for coverage under the proposed GHG standards for newly constructed

fossil fuel-fired EGUs published in the Federal Register on that date. The proposed GHG 

standards for newly constructed EGUs generally define an affected EGU as any boiler, IGCC, or 

combustion turbine (in either simple cycle or combined cycle configuration) that (1) is capable of 

combusting at least 250 million British thermal units (Btu) per hour; (2) combusts fossil fuel for 

more than 10 percent of its total annual heat input; (3) sells the greater of 219,000 MWh per year 

or one-third of its potential electrical output to a utility distribution system; and (4) was not in 

operation or under construction as of January 8, 2014 (the date the proposed GHG standards of 

performance for newly constructed EGUs were published in the Federal Register). The minimum 

fossil fuel consumption condition applies over any consecutive three-year period (or as long as 

the unit has been in operation, if less). The minimum electricity sales condition applies over 

rolling three-year periods (or as long as the unit has been in operation, if less).

1.4.3 Regulated Pollutant

This rule sets emission guidelines for CO2 emissions from affected sources. The EPA is 

proposing these guidelines because CO2 is a GHG and fossil fuel-fired power plants are the 
1-6

country’s largest stationary source emitters of GHGs. In 2009, the EPA found that by causing or 

contributing to climate change, GHGs endanger both the public health and the public welfare of 

current and future generations.11

The EPA is aware that other GHGs such as nitrous oxide (N2O) (and to a lesser extent, 

methane [CH4]) may be emitted from fossil-fuel-fired EGUs, especially from coal-fired 

circulating fluidized bed combustors and from units with selective catalytic reduction and 

selective non-catalytic reduction systems installed for nitrogen oxide (NOX) control. The EPA is

not proposing separate N2O or CH4 guidelines or an equivalent CO2 emission limit because of a 

lack of available data for these affected sources. Additional information on the quantity and 

significance of emissions and on the availability of controls of reasonable cost would be needed 

before proposing standards for these pollutants.

1.4.4 Emission Guidelines

The EPA is proposing emission guidelines for states to use in developing plans to address 

greenhouse gas emissions from existing fossil fuel-fired electric generating units. Specifically, 

the EPA is proposing state-specific rate-based goals for CO2 emissions from the power sector, as 

well as guidelines for states to follow in developing plans to attain the state-specific goals.

(These state-based goals can be found in the preamble and Chapter 3 of this RIA.) The proposed 

emission guidelines provide states with options for establishing standards of performance in a 

manner that accommodates a diverse range of state approaches. The proposed guidelines would 

also allow states to collaborate and to demonstrate compliance on a multi-state basis, in 

recognition of the fact that electricity is transmitted across state lines, and measures often impact 

regional EGU CO2 emissions. The illustrative compliance strategies presented in this RIA 

include both regional and state-level compliance scenarios for each of the regulatory options 

presented. 

 

11 Endangerment and Cause or Contribute Findings for Greenhouse Gases under Section 202(a) of the Clean Air 

Act.
1-7

1.5 Organization of the Regulatory Impact Analysis

This report presents the EPA’s analysis of the potential benefits, costs, and other 

economic effects of the proposed EGU Existing Source GHG Guidelines to fulfill the 

requirements of an RIA. This RIA includes the following chapters:

 Chapter 2, Electric Power Sector Profile

 Chapter 3, Control Strategies, Cost, Economic, and Energy Impacts

 Chapter 4, Estimated Climate Benefits and Health Co-benefits

 Chapter 5, Economic Impacts – Markets Outside the Electricity Sector

 Chapter 6, Employment

 Chapter 7, Statutory and Executive Order Analyses

 Chapter 8, Summary of Benefits and Cost of the Proposed Regulation

 Chapter 9, Benefits, Costs, and Economic Impacts of Standards of Performance for 

Modified and Reconstructed Electric Generating Units
1-8

1.6 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.

75 FR 49556. August 13, 2010. “EPA’s Denial of the Petitions to Reconsider the Endangerment 

and Cause or Contribute Findings for Greenhouse Gases Under Section 202(a) of the Clean 

Air Act.”

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. Available at <http://nca2014.globalchange.gov>.

U.S. Environmental Protection Agency. Power Sector Modeling Platform v.5.13. November 27, 

2013. Available online at: http://www.epa.gov/powersectormodeling/BaseCasev513.html.
2-1

CHAPTER 2: ELECTRIC POWER SECTOR PROFILE

2.1 Introduction

This chapter discusses important aspects of the power sector that relate to the proposed 

EGU Existing Source GHG Standards, including the types of power-sector sources affected by 

the proposal, and provides background on the power sector and EGUs. In addition, this chapter 

provides some historical background on the EPA regulation of, and future projections for, the 

power sector.

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. Most 

of the existing capacity for generating electricity does so by creating heat to create high pressure 

steam that is released to rotate turbines which, in turn, create electricity. The existing power 

sector consists of over 18,000 generating units, comprising fossil-fuel-fired units, nuclear units, 

and hydroelectric and other renewable sources dispersed throughout the country (see Table 2-1).
2-2

Table 2-1. Existing Electricity Generating Capacity by Energy Source, 2012

Energy Source Number of 

Generators

Generator 

Nameplate 

Capacity (MW)

Net Summer 

Capacity 

(MW)

Net Winter 

Capacity 

(MW)

Coal 1,309 336,341 309,680 312,293

Petroleum 3,702 53,789 47,167 51,239

Natural Gas 5,726 485,957 422,364 455,214

Other Gases 94 2,253 1,946 1,933

Nuclear 104 107,938 101,885 104,182

Hydroelectric Conventional 4,023 78,241 78,738 78,215

Wind 947 59,629 59,075 59,082

Solar Thermal and Photovoltaic 553 3,215 3,170 3,053

Wood and Wood-Derived Fuels 351 8,520 7,508 7,570

Geothermal 197 3,724 2,592 2,782

Other Biomass 1,766 5,527 4,811 4,885

Hydroelectric Pumped Storage 156 20,858 22,368 22,271

Other Energy Sources 95 2,005 1,729 1,739

Total 19,023 1,167,995 1,063,033 1,104,459

Source: Table 4.3, EIA Electric Power Annual, 2013a

Note: This table presents generation capacity. Actual net generation is presented in Table 2-3.

In 2012, electric generating sources produced net 3,890 billion kWh to meet electricity 

demand. Roughly 70 percent of this electricity was produced through the combustion of fossil 

fuels, primarily coal and natural gas, with coal accounting for the largest single share.

Table 2-2. Electricity Net Generation in 2012 (billion kWh)

Net Generation (Billion kWh) Fuel Source Share

Coal 1,500.6 38.57%

Petroleum 20.1 0.52%

Natural Gas 1,132.8 29.12%

Other Gases 3.0 0.08%

Nuclear 769.3 19.78%

Hydroelectric 268.9 6.91%

Other 195.7 5.03%

Total 3,890 100%

Source: Tables 3.2.A and 3.3.A, EIA Electric Power Annual, 2013a

Note: Excludes generation from commercial and industrial sectors. Retail sales are not equal to net generation 

because net generation includes net exported electricity and loss of electricity that occurs through transmission and 

distribution.
2-3

These electric generating sources provide electricity for commercial, industrial, and 

residential uses, each of which consumes roughly a quarter to a third of the total electricity 

produced (see Table 2-3). 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.

Table 2-3. Total U.S. Electric Power Industry Retail Sales in 2012 (billion kWh)

Sales/Direct Use (Billion kWh) Share of Total End Use

Retail Sales

Residential 1,375 35.87%

Commercial 1,327 34.63%

Industrial 986 25.72%

Transportation 7.3 0.19%

Direct Use 138 3.59%

Total End Use 3,832 100%

Source: Table 2.2, EIA Electric Power Annual, 2013a

Coal-fired generating units have historically supplied “base-load” electricity, the portion 

of electricity loads which are continually present, and typically operate throughout the day. 

Along with nuclear generation, these coal units meet the part of demand that is relatively 

constant. Although much of the coal fleet operates as base load, there can be notable differences 

across various facilities (see Table 2-4). For example, coal-fired units less than 100 megawatts 

(MW) in size compose 32 percent of the total number of coal-fired units, but only 4 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. 

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 base load energy in addition to supplying electricity during peak load. Projections of changes 

in capacity and the impact of this rule on the future need for construction of new generation 
2-4

capacity sources are discussed in more detail in Chapter 3 of this RIA.

Table 2-4. Coal Steam Electricity Generating Units in 2015, by Size, Age, Capacity, and 

Thermal Efficiency (Heat Rate)

Unit Size Grouping 

(MW) No. Units % of All Units Avg. Age

Avg. Net 

Summer 

Capacity 

(MW)

Total Net 

Summer 

Capacity 

(MW)

% Total 

Capacity

Avg. Heat 

Rate 

(Btu/kWh)

0 To 25 133 14% 46 14 1,837 1% 11,860

>25 To 49 74 8% 38 37.5 2,775 1% 12,113

50 To 99 94 10% 44 72.0 6,765 2% 11,910

100 To 149 75 8% 49 124.4 9,329 3% 10,977

150 To 249 128 14% 47 91.3 24,492 9% 10,646

250 and up 432 46% 36 536.7 231,874 84% 10,336

Totals 1,266 316,480

Source: National Electric Energy Data System (NEEDS) v.5.13

Note: The average heat rate reported is the mean of the heat rate of the units in each size category (as opposed to a 

generation-weighted or capacity-weighted average heat rate.) A lower heat rate indicates a higher level of fuel 

efficiency. Table is limited to coal-steam units online in 2013 or earlier, and excludes those units with planned 

retirements.

The locations of existing fossil units in NEEDS v.5.13 are shown in Figure 2-1.
2-5

Figure 2-1. Fossil Fuel-Fired Electricity Generating Facilities, by Size

Source: National Electric Energy Data System (NEEDS) v.5.13

Note: This map displays fossil capacity at facilities in the NEEDS v.5.13 IPM frame. NEEDS reflects available 

fossil capacity on-line by the end of 2015. This includes planned new builds 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 movement 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,

12 each operating synchronously. Within each of these transmission 

 

12 These three network interconnections are the western US and Canada, corresponding approximately to the area 

west of the Rocky Mountains; eastern US and Canada, not including most of Texas; and a third network operating 

in most of Texas. These are commonly referred to as the Western Interconnect Region, Eastern Interconnect 

Region, and ERCOT, respectively.
2-6

networks, there are multiple areas where the operation of power plants is monitored and 

controlled 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 operator; in others, 

individual utilities coordinate the operations of their generation, transmission, and distribution 

systems to balance their common generation and load needs.

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.

Transmission has generally been developed by the larger vertically integrated utilities 

that typically operate generation and distribution networks. Often distribution is handled by a 

large number of utilities that purchase and sell electricity, but do not generate it. Over the last 

couple of decades, several jurisdictions in the United States began restructuring the power 

industry to separate transmission and distribution from generation, ownership, and operation. 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 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 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 
2-7

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 

establishing cost-based rates for various customer classes.

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. By the end of 2001, restructuring had either been delayed or suspended in 

eight states that previously enacted legislation or issued regulatory orders for its implementation 

(shown as “Suspended” in Figure 2-2 below). Eighteen other states that had seriously explored 

the possibility of deregulation in 2000 reported no legislative or regulatory activity in 2001 (EIA,

2003) (“Not Active” in Figure 2-2 below). Currently, there are 15 states where price deregulation 

of generation (restructuring) has occurred (“Active” in Figure 2-2 below). Power sector 

restructuring is more or less at a standstill; there have been no recent proposals to the Federal 

Energy Regulatory Commission (FERC) for actions aimed at wider restructuring, and no 

additional states have recently begun retail deregulation activity.
2-8

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

Source: EIA “Status of Electricity Restructuring by State” 2010a.

2.4 Emissions of Greenhouse Gases from Electric Utilities

The burning of fossil fuels, which generates about 70 percent of our electricity 

nationwide, results in emissions of greenhouse gases. The power sector is a major contributor of 

CO2 in particular, but also contributes to emissions of sulfur hexafluoride (SF6), CH4, and N2O.

In 2012, the power sector accounted for 32 percent of total nationwide greenhouse gas emissions, 

measured in CO2 equivalent,

13 a slight increase from its 30 percent share in 1990. Table 2-5 and 

Figure 2-3 show the contributions of the power sector relative to other major economic sectors. 

Table 2-6 and Figure 2-4 show the contributions of CO2 and other GHGs from the power sector.

 

13 All CO2 equivalent tons in this report are based on the 100-year time horizon Global Warming Potential.
2-9

Table 2-5. Domestic Emissions of Greenhouse Gases, by Economic Sector (million metric 

tons of CO2 equivalent)

Sector/Source 1990 2005 2008 2009 2010 2011 2012

Electricity Generation 1,866 2,446 2,402 2,187 2,303 2,201 2,064

Transportation 1,553 2,017 1,935 1,862 1,876 1,852 1,837

Industry 1,531 1,408 1,372 1,221 1,301 1,298 1,278

Agriculture 518 584 615 605 601 613 614

Commercial 385 370 379 382 377 378 353

Residential 345 371 365 358 3607 354 321

U.S. Territories 34 58 50 48 58 58 58

Total Emissions 6,233 7,254 7,118 6,663 6,875 6,753 6,523

Note that 2005 CO2 emissions from the electricity generation sector differ slightly from the 2005 CO2 emissions 

presented in Chapter 3 due to differences in methodology (e.g., distribution of cogeneration emissions in the 

commercial and industrial sectors). We believe that the methodology used in Chapter 3 better corresponds to the 

units covered by the proposal.

Source: EPA Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2012. April 2014.

Figure 2-3. Domestic Emissions of Greenhouse Gases, 2012 (million metric tons of CO2 

equivalent)

Source: EPA Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2012. April 2014.

Electricity 

Generation

2,064

Other 32%

4,459

68%
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Table 2-6. Greenhouse Gas Emissions from the Electricity Sector (Generation, 

Transmission and Distribution), 2012 (million metric tons of CO2 equivalent)

Source Total Emissions

CO2 from Electricity Generation 2,023

Coal 1,511

Natural Gas 492

Fuel Oil 19

Geothermal 0.4

CH4 from Electricity Generation 0.5

Coal 0.1

Fuel Oil 0.1

Natural Gas 0.4

Wood +

N2O from Electricity Generation 18.3

Coal 9.1

Fuel Oil 0.3

Natural Gas 8.7

Wood 0.1

SF6 from Electricity Transmission and Distribution 6.0

Total 2,048

Source: EPA Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2012. April 2014.

+ Does not exceed 0.05 Tg CO2 Eq. or 0.05 percent.

The amount of CO2 emitted during the combustion of fossil fuels varies according to the 

carbon content and heating value of the fuel used (EIA, 2000) (see Table 2-7). 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 does natural gas (EPA 

2013).
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Figure 2-4. Direct GHG Emissions from the Power Sector Relative to Total Domestic GHG 

Emissions (2012)

Source: EPA Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2012. April 2014.

Table 2-7. Fossil Fuel Emission Factors in EPA Modeling Applications

Fuel Type Carbon Dioxide (lbs/MMBtu)

Coal 

Bituminous 205.2 – 206.6

Subbituminous 212.7 – 213.1

Lignite 213.5 – 217.0

Natural Gas 117.1

Fuel Oil 

Distillate 161.4

Residual 161.4 – 173.9

Biomass* 195

Waste Fuels 

Waste Coal 205.7

Petroleum Coke 225.1

Fossil Waste 321.1

Non-Fossil Waste 0

Tires 189.5

Municipal Solid Waste 91.9

Source: Documentation for IPM Base Case v.513. See also Table 9.9 of IPM Documentation.

Note: CO2 emissions presented here for biomass reflect combustion only. They do not include any other biogenic or 

fossil emissions/sequestration related to biomass growth, harvest, transportation or any other biomass or processing 

emissions as part of the carbon cycle.

38%

72%

0% 4% 0.1%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

CO2 SF6 N2O CH4

Other

Power Sector
2-12

2.5 Improving GHG Performance at Existing EGUs

In proposing state goals, the analysis anticipates that states will pursue a mix of carbonreducing strategies appropriate to each state’s unique situation, developing an effective state plan 

that reflects the composition of the state’s economy, existing state programs and measures, and 

characteristics of the state’s existing electricity power system (e.g., utility regulatory structure, 

generation mix, transmission system and electricity demand). The analysis assumes states will 

develop plans involving four categories (“building blocks”) of demonstrated approaches to 

improve the GHG performance of existing EGUs in the power sector:

1. Reducing the carbon intensity of generation at individual affected EGUs through heatrate improvements. 

2. Reducing emissions from the most carbon-intensive affected EGUs in the amount that 

results from substituting generation at those EGUs with generation from less carbonintensive affected EGUs (including natural gas combined cycle [NGCC] units that are 

under construction).

3. Reducing emissions from affected EGUs in the amount that results from substituting 

generation at those EGUs with expanded low- or zero-carbon generation.

4. Reducing emissions from affected EGUs in the amount that results from the use of

demand-side energy efficiency that reduces the amount of generation required.

This section briefly describes each of these four building block categories. Our analyses 

of the magnitude of the opportunity for improvements from each category, and more detail about 

how we used each state’s unique situation and availability of demonstrated approaches to 

improve GHG performance to determine state-specific emission goals, can be found in Chapter 5 

of the Greenhouse Gas Abatement Measures TSD.

The first building block encompasses various improvements at existing EGUs that can 

lower the amount of CO2 they emit when generating power. Heat rate improvements result in any 

changes in equipment, operating procedures or maintenance practices that increase the efficiency 

of converting fuel energy into electricity by an EGU. Such efficiency changes result in more 
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electricity being generated by each unit of fuel (e.g., ton of coal or cubic foot of gas), thereby 

lowering the amount of CO2 per kWh of electricity produced as a byproduct of fuel combustion. 

The second building block consists of improvements to lower the electric system’s 

overall carbon intensity by shifting generation among existing EGUs. The nation’s EGUs are 

connected by transmission grids extending over large regions. Through these interconnections, 

EGU owners and/or grid operators prioritize among EGUs when deciding which ones to operate 

(i.e., “dispatch”) to meet electricity demand at any time, subject to various constraints. 

Opportunities exist to lower significantly the electric system’s carbon intensity through 

redispatch among existing EGUs, particularly by shifting generation from coal units to natural 

gas combined cycle (NGCC) units.14 Over the last several years, advances in the production of 

natural gas have helped reduce natural gas prices and improved the competitive position of gasfired units relative to coal-fired units. Operators have already shifted significant quantities of 

generation from coal units to NGCCs, absent any federal CO2 requirements. Additional 

redispatch opportunities exist to further reduce carbon intensity of the power system, with extent 

of the additional opportunities varying region, based on factors such as the mix of EGU types, 

the relative prices of coal and natural gas and the amount of available NGCC capacity.

The third building block consists of the potential to increase the amount of lower carbon 

intensity generation by expanding low-carbon and renewable generating capacity. Adding new

nuclear or renewable generating capacity to the electric system would tend to shift generation to 

the new units from existing EGUs with higher carbon intensity. Such expansion is consistent 

with current trends. While not included in the goal setting for building block 3, the addition of 

new NGCC capacity would have a similar impact and is one option states may choose to achieve 

the goal.

The fourth building block consists of improving the GHG performance of the power 

sector by reducing the total amount of generation required – in other words, improving demand-

 

14We view opportunities to shift generation to existing renewable EGUs such as wind or solar units as limited 

because such units already tend to run when capable of doing so due to their low variable operating costs.
2-14

side efficiency. Many studies have found that significant improvements in demand-side 

efficiency can be realized at less cost than the savings from avoided power generation.15 These 

electricity demand reductions can be achieved through policies or programs, such as subsidies 

for the purchase of energy-efficient appliances, which incentivize investment in cost-effective 

efficiency improvements by overcoming market imperfections that otherwise thwart these 

investments. States already employ a variety of mechanisms for this purpose. These include 

energy efficiency resource standards, building energy codes and appliance and equipment energy 

standards. Reducing electricity demand also enhances efficiency by reducing the absolute 

amount of transmission and distribution losses that occur across the grid between the electricity 

generation sites and the demand sites. Particularly when integrated into a comprehensive 

approach for addressing GHG emissions, demand-side efficiency improvements can improve the 

carbon profile of the electricity supply system. 

2.6 GHG and Clean Energy Regulation in the Power Sector

2.6.1 State Policies

Several states recently established emission performance standards or other measures to 

limit emissions of GHGs from existing EGUs that are comparable to this proposal for existing 

source guidance. 

In 2003, then-Governor George Pataki of New York sent a letter to his counterparts in the 

Northeast and Mid-Atlantic inviting them to participate in the development of a regional capand-trade program addressing power plant CO2 emissions. This program, known as the Regional 

Greenhouse Gas Initiative (RGGI), began in 2009 and sets a regional CO2 cap for participating 

states. The currently participating states include: Connecticut, Delaware, Maine, Maryland, 

Massachusetts, New Hampshire, New York, Rhode Island, and Vermont. The cap covers CO2

emissions from all fossil-fired EGUs greater than 25 MW in participating states, and limits total 

emissions to 91 million short tons in 2014. This emissions budget is reduced 2.5% annually 

 

15 See, e.g., Granade, et al., July 2009 and EPRI, 2009.
2-15

from 2015 to 2020.

In September 2006, then-Governor of California Arnold Schwarzenegger signed into law 

Senate Bill 1368. The law limits long-term investments in baseload generation by the state's 

utilities to power plants that meet an emissions performance standard jointly established by the 

California Energy Commission and the California Public Utilities Commission. The Energy 

Commission has designed regulations that establish a standard for new and existing baseload 

generation owned by, or under long-term contract to publicly owned utilities, of 1,100 lb 

CO2/MWh.

In 2006 Governor Schwarzenegger also signed into law Assembly Bill 32, the Global 

Warming Solutions Act of 2006. This act includes a multi-sector GHG cap-and-trade program 

which covers approximately 85% of the state GHG emissions. EGUs are included in phase I of 

the program, which began in 2013. Phase II begins in 2015 and includes upstream sources. The 

cap is based on a 2 percent reduction from total 2012 expected emissions, and declines 2 percent 

annually through 2014, then 3 percent each year until 2020. 

In May 2007, then-Governor Christine Gregoire of Washington signed Substitute Senate 

Bill 6001, which established statewide GHG emissions reduction goals, and imposed an emission 

standard that applies to any baseload electric generation that commenced operation after June 1, 

2008 and is located in Washington, whether or not that generation serves load located within the 

state. Baseload generation facilities must initially comply with an emission limit of 1,100 lb 

CO2/MWh. Bill 6001 also prohibited Washington electric utilities from entering new long-term 

power contracts after June 30, 2008 with any power plant exceeding the 1,100 lb CO2/MWh 

limit.

In July 2009, then-Governor Theodore Kulongoski of Oregon signed Senate Bill 101, 

which mandated that facilities generating baseload electricity, whether gas- or coal-fired, must 

have emissions equal to or less than 1,100 lb CO2/MWh, and prohibited utilities from entering 

into long-term purchase agreements for baseload electricity with out-of-state facilities that do not 

meet that standard. Natural gas- and petroleum distillate-fired facilities that are primarily used to 

serve peak demand or to integrate energy from renewable resources are specifically exempted 
2-16

from the performance standard.

In August 2011, New York Governor Andrew Cuomo signed the Power NY Act of 2011. 

This regulation establishes CO2 emission standards for new and modified electric generators 

greater than 25 MW. The standards vary based on the type of facility: baseload facilities must 

meet a CO2 standard of 925 lb/MWh or 120 lb/MMBtu, and peaking facilities must meet a CO2

standard of 1,450 lbs/MWh or 160 lbs/MMBtu.

Additionally, the majority of states have implemented Renewable Portfolio Standards 

(RPS), or Renewable Electricity Standards (RES). These programs are designed to increase the 

renewable share of a state’s total electricity generation. Currently 29 states and the District of 

Columbia have enforceable RPS or other mandatory renewable capacity policies, and 9 states 

have voluntary goals.16 These programs vary widely in structure, enforcement, and scope. For 

more information about existing state policies and programs that reduce power sector CO2

emissions, see the State Plan Considerations Technical Support Document Appendix. 

2.6.2 Federal Policies

In April 2007, the Supreme Court concluded that GHGs met the CAA definition of an air 

pollutant, giving the EPA the authority to regulate GHGs under the CAA contingent upon an 

agency determination that GHG emissions from new motor vehicles cause or contribute to air 

pollution that may reasonably be anticipated to endanger public health or welfare. This decision 

set in motion EPA’s finding that GHG endangered public health, welfare and its regulation of 

GHG emissions for motor vehicles and set the stage for the determination of whether other 

sources of GHG emissions, including stationary sources, would need to be regulated as well.

In response to the FY2008 Consolidated Appropriations Act (H.R. 2764; Public Law 

110–161), the EPA issued the Mandatory Reporting of Greenhouse Gases Rule (74 FR 5620) 

which required reporting of GHG data and other relevant information from fossil fuel suppliers 

and industrial gas suppliers, direct greenhouse gas emitters, and manufacturers of heavy-duty and 

 

16 Database of State Incentives for Renewables and Efficiency, March 2013 and Alaska House Bill 306, 2010. 
2-17

off-road vehicles and engines. The purpose of the rule was to collect accurate and timely GHG 

data to inform future policy decisions. As such, it did not require that sources control greenhouse 

gases, but sources above certain threshold levels must monitor and report emissions.

In August 2007, the EPA issued a prevention of significant deterioration (PSD) permit to 

Deseret Power Electric Cooperative, authorizing it to construct a new waste-coal-fired EGU near 

its existing Bonanza Power Plant, in Bonanza, Utah. The permit did not include emissions 

control requirements for CO2. The EPA acknowledged the Supreme Court decision, but found 

that decision alone did not require PSD permits to include limits on CO2 emissions. Sierra Club 

challenged the Deseret permit. In November 2008, the Environmental Appeals Board (EAB) 

remanded the permit to the EPA to reconsider “whether or not to impose a CO2 BACT (best 

available control technology) limit in light of the ‘subject to regulation’ definition under the 

CAA.” The remand was based in part on EAB’s finding that there was not an established EPA 

interpretation of the regulatory phrase “subject to regulation.” 

In December 2008, the Administrator issued a memo indicating that the PSD Permitting 

Program would apply to pollutants that are subject to either a provision in the CAA or a 

regulation adopted by the EPA under the CAA that requires actual control of emissions of that 

pollutant. The memo further explained that pollutants for which the EPA regulations only require 

monitoring or reporting, such as the provisions for CO2 in the Acid Rain Program, are not subject 

to PSD permitting. Fifteen organizations petitioned the EPA for reconsideration, prompting the 

agency to issue a revised finding in March 2009. After reviewing comments, the EPA affirmed 

the position that PSD permitting is not triggered for a pollutant such as GHGs until a final 

nationwide rule requires actual control of emissions of the pollutant. For GHGs, this meant 

January 2011 when the first national rule limiting GHG emissions for cars and light trucks was 

scheduled to take effect. Therefore, a permit issued after January 2, 2011, would have to address 

GHG emissions.

The Administrator signed two distinct findings in December 2009 regarding greenhouse 

gases under section 202(a) of the Clean Air Act. The endangerment finding indicated that current 

and projected concentrations of the six key well-mixed greenhouse gases — CO2, CH4, N2O, 

hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), and SF6 — in the atmosphere threaten the 
2-18

public health and welfare of current and future generations. These greenhouse gases have long 

lifetimes and, as a result, become homogeneously distributed through the lower level of the 

Earth’s atmosphere (IPCC, 2001). This differentiates them from other greenhouse gases that are 

not homogeneously distributed in the atmosphere. The cause and contribute finding indicated 

that the combined emissions of these well-mixed greenhouse gases from new motor vehicles and 

new motor vehicle engines contribute to the greenhouse gas pollution which threatens public 

health and welfare. Both findings were published in the Federal Register on December 15, 2009 

(Docket ID EPA-HQ-OAR-2009-0171). These findings did not themselves impose any 

requirements on any industry or other entities, but allowed the EPA to regulate greenhouse gases 

under the CAA (see preamble section II.D for regulatory background). This action was a 

prerequisite to implementing the EPA's proposed greenhouse gas emission standards for lightduty vehicles, which was finalized in January 2010. Once a pollutant is regulated under the 

CAA, it is subject to permitting requirements under the PSD and Title V programs. The 2009 

Endangerment Finding and a denial of reconsideration were challenged in a lawsuit; on June 26, 

2012, the DC Circuit Court upheld the Endangerment Finding and the Reconsideration Denial, 

ruling that the Finding was neither arbitrary nor capricious, was consistent with Massachusetts v. 

EPA, and was adequately supported by the administrative record. The Court found that the EPA 

had based its decision on “substantial scientific evidence,” noted that the EPA’s reliance on 

assessments was consistent with the methods decision-makers often use to make a science-based 

judgment, and stated that “EPA’s interpretation of the governing CAA provisions is 

unambiguously correct.”

In May 2010, the EPA issued the final Tailoring Rule which set thresholds for GHG 

emissions that define when permits under the New Source Review and Title V Operating Permit 

programs are required for new and existing industrial facilities. Facilities responsible for nearly 

70 percent of the national GHG emissions from stationary sources, including EGUs, were subject 

to permitting requirements under the rule. This rule was upheld by the D.C. Circuit in 2012.

On January 8, 2014 EPA proposed a new source performance standard (NSPS) for 

emissions of carbon dioxide for new fossil fuel-fired electric utility generating units. This action 

proposes to establish separate standards for fossil fuel-fired electric steam generating units 
2-19

(utility boilers and Integrated Gasification Combined Cycle (IGCC) units) and for natural gasfired stationary combustion turbines. These proposed standards reflect separate determinations of 

the best system of emission reduction (BSER) adequately demonstrated for utility boilers and 

IGCC units and for natural gas-fired stationary combustion turbines. This action proposes a 

standard of performance for utility boilers and IGCC units based on partial implementation of 

carbon capture and storage (CCS) as the BSER. The proposed emission limit for those sources is 

1,100 lb CO2/MWh. This action also proposes standards of performance for natural gas-fired 

stationary combustion turbines based on modern, efficient natural gas combined cycle (NGCC) 

technology as the BSER. The proposed emission limits for those sources are 1,000 lb CO2/MWh 

for larger units and 1,100 lb CO2/MWh for smaller units. 

2.7 Revenues, Expenses, and Prices

Due to lower retail electricity sales, total utility operating revenues declined in 2012 to 

$271 billion from a peak of almost $300 billion in 2008. Despite revenues not returning to 2008, 

operating expenses were appreciably lower and as a result, net income in 2012 rose in 

comparison previous years (see Table 2-8). Recent economic events and continued energy 

efficiency improvements have put downward pressure on electricity demand, thus dampening 

electricity prices and consumption (utility revenues), but have also reduced the price and cost of 

fossil fuels and other expenses. Electricity sales and revenues associated with the generation, 

transmission, and distribution of electricity are expected to rebound and increase modestly by 

2015, when revenues are projected to be roughly $359 billion (see Table 2-9).

Table 2-8 shows that investor-owned utilities (IOUs) earned income of about 13 percent

compared to total revenues in 2012. Based on EIA's Annual Energy Outlook 2013, Table 2-9

shows that the power sector is projected to derive revenues of $359 billion in 2015. Assuming 

the same income ratio from IOUs (with no income kept by public power), and using the same 

proportion of power sales from public power as observed in 2012, the EPA projects that the 

power sector will expend over $372 billion in 2015 to generate, transmit, and distribute 

electricity to end-use consumers.

Over the past 50 years, real national average retail electricity prices have ranged from 
2-20

around 8 cents per kWh in the early 1970s, to around 12 cents, reached in the early 1980s.

Generally, retail electricity prices do not change rapidly and do not display the variability of 

other energy or commodity prices, although the frequency at which these prices change varies 

across different types of customers. Retail rate regulation has largely insulated consumers from 

the rising and falling wholesale electricity price signals whose variation in the marketplace on an 

hourly, daily, and seasonal basis is critical for driving lowest-cost matching of supply and 

demand. In fact, the real price of electricity today is lower than it was in the early 1960s and 

1980s (see Figure 2-5).

Figure 2-5. National Average Retail Electricity Price (1960 – 2013, 2011$)

Source: EIA Monthly Energy Review 2013, BEA National Income Product Accounts 2014

0

2

4

6

8

10

12

14

1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010

cents/KWh (2011$)
2-21

Table 2-8. Revenue and Expense Statistics for Major U.S. Investor-Owned Electric 

Utilities ($millions)

2010 2011 2012

Utility Operating Revenues 285,512 280,520 270,912

Electric Utility 260,119 255,573 249,166

Other Utility 25,393 24,946 21,745

Utility Operating Expenses 253,022 247,118 235,694

Electric Utility 234,173 228,873 220,722

Operation 166,922 161,460 152,379

Production 128,831 122,520 111,714

Cost of Fuel 44,138 42,779 38,998

Purchased Power 67,284 61,447 54,570

Other 17,409 18,294 18,146

Transmission 6,948 6,876 7,183

Distribution 4,007 4,044 4,181

Customer Accounts 5,091 5,180 5,086

Customer Service 4,741 5,311 5,640

Sales 185 185 221

Admin. and General 17,120 17,343 18,353

Maintenance 14,957 15,772 15,489

Depreciation 20,951 22,555 23,677

Taxes and Other 31,343 29,086 29,177

Other Utility 18,849 18,245 14,972

Net Utility Operating Income 32,490 33,402 35,218

Source: Table 8.3, EIA Electric Power Annual, 2013a. Values are in millions of current year (i.e., nominal) terms.

Note: This data does not include information for public utilities.

Table 2-9. Projected Revenues by Service Category in 2015 for Public Power and InvestorOwned Utilities (billions)

Generation $207

Transmission $40

Distribution $111

Total $359

Source: EIA 2013b

Note: Data are derived by taking either total electricity use (for generation) or sales (transmission and distribution) 

and multiplying by forecasted prices by service category from Table 8 of EIA AEO 2013 (Electricity Supply, 

Disposition, Prices, and Emissions).

On a state-by-state basis, retail electricity prices vary considerably. The Northeast and 

California have average retail prices that can be as much as double those of other states (see 

Figure 2-6).
2-22

Figure 2-6. Average Retail Electricity Price by State (cents/kWh), 2011

2.8 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, and can undergo major price swings 

during short-lived weather events (such as cold snaps leading to short-run spikes in heating 

demand). Over the last decade, gas prices (both Henry Hub prices and delivered prices to the 

power sector) have ranged from below $3 to nearly $10/mmBtu on an annual average basis (see 

Figure 2-7). During that time, the daily price of natural gas reached as high as $15/mmBtu.

Note: Data are displayed as 5 groups of 10 States and the District of Columbia. U.S. total average price per kilowatthour is 9.90 cents.

Source: U.S. Energy Information Administration. 2011b.
2-23

Recent forecasts of natural gas availability have also experienced considerable revision as new 

sources of gas have been discovered and have come to market, although there continues to be 

some uncertainty surrounding the precise quantity of the resource base.

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. According to an EIA “Energy in Brief” (EIA 2012b):

“Shale gas refers to natural gas that is trapped within shale formations. Shales are finegrained sedimentary rocks that can be rich sources of petroleum and natural gas. Over the past 

decade, the combination of horizontal drilling and hydraulic fracturing has allowed access to 

large volumes of shale gas that were previously uneconomical to produce. The production of 

natural gas from shale formations has rejuvenated the natural gas industry in the United States.”

The U.S. Energy Information Administration's Annual Energy Outlook 2012 (Early 

Release) estimates that the United States possessed 2,214 trillion cubic feet (Tcf) of technically 

recoverable natural gas resources as of January 1, 2010. Natural gas from proven and unproven 

shale resources accounts for 542 Tcf of this resource estimate. The AEO 2012 (Early Release) 

notes that many shale formations, especially the Marcellus, are so large that only small portions 

of the entire formations have been intensively production-tested. Consequently, the estimate of 

technically recoverable resources is highly uncertain, and is regularly updated as more 

information is gained through drilling and production. At the 2010 rate of U.S. consumption 

(about 24.1 Tcf per year), 2,214 Tcf of natural gas is enough to supply over 90 years of use. 

Although the estimate of the shale gas resource base is lower than in the prior edition of the 

Outlook, shale gas production estimates increased between the 2011 and 2012 Outlooks, driven 

by lower drilling costs and continued drilling in shale plays with high concentrations of natural 

gas liquids and crude oil, which have a higher value in energy equivalent terms than dry natural 
2-24

gas.17

EIA’s projections of natural gas conditions did not change substantially in AEO 2014 

from the AEO 2013, and EIA is still forecasting abundant reserves consistent with the above 

findings. Recent historical data reported to EIA is also consistent with these trends, with 2013 

being the highest year on record for domestic natural gas production.18 The average delivered 

natural gas price to the power sector was $4.49 per MMBtu in 2013, higher than in 2012 

($3.54/MMBtu), but still down from $4.89/MMBtu in 2011.19

EIA projections of future natural gas prices assume trends that are consistent with 

historical and current market behavior, technological and demographic changes, and current laws 

and regulations.

20 Depending on actual conditions, there may be significant variation from the 

price projected in the reference case and the price observed. To address this uncertainty, EIA 

issues a range of alternative cases, including cases with higher and lower economic growth, 

which address many of the uncertainties inherent in the long-term projections. 

 

17 For more information, see: http://www.eia.gov/forecasts/archive/aeo11/IF_all.cfm#prospectshale; 

http://www.eia.gov/energy_in_brief/about_shale_gas.cfm

18 http://www.eia.gov/dnav/ng/hist/n9010us2a.htm

19 http://www.eia.gov/dnav/ng/hist/n3045us3A.htm; Assumes that 1 TCF = 1.023 MMBtu natural gas 

(http://www.eia.gov/tools/faqs/faq.cfm?id=45&t=8)

20 EIA 2010b.
2-25

Figure 2-7. Natural Gas Spot Price, Annual Average (Henry Hub)

Source: EIA 2010c, EIA 2012a, EIA 2013b, EIA 2014

2.9 Electricity Demand and Demand Response

Electricity performs a vital and high-value function in the economy. Historically, growth 

in electricity consumption has been closely aligned with economic growth. Overall, the U.S. 

economy has become more efficient over time, producing more output (gross domestic product –

GDP) per unit of energy input, with per capita energy use fairly constant over the past 30 years

(EIA, 2010d). The growth rate of electricity demanded has also been in overall decline for the 

past sixty years (see Figure 2-8), with several key drivers that are worth noting. First, there has 

been a significant structural shift in the U.S. economy towards less energy-intensive sectors, like 

0

2

4

6

8

10

12

1990 1995 2000 2005 2010 2015 2020 2025 2030 2035 2040

2011 dollars per million Btu

Natural Gas Spot Price, Annual Average (Henry 

Hub)

EIA Historical

EIA Projected (AEO 2012)

EIA Projected (AEO 2013)

EIA Projected (AEO 2014)
2-26

services.21 Second, companies face increasing financial incentives to reduce expenditures, 

including those for energy. Third, companies are responding to the marketplace and continually 

develop and bring to market new technologies that reduce energy consumption. Fourth, energy 

efficiency policies at the state and Federal level have reduced demand. These broader changes 

have altered the outlook for future electricity growth.

Figure 2-8. Electricity Growth Rate (3-Year Rolling Average) and Projections from the 

Annual Energy Outlook 2014

Source: EIA 2009, EIA 2014

State policies have driven a rapid increase in investment in utility energy efficiency 

programs (increasing from $1.6 billion in 2006 to $5.9 billion in 2011)22 and investments in 

energy efficiency are projected to continue to increase significantly (to $8 billion or more) for at 

least the next decade23, driven largely by the growing number of states that have adopted energy 

 

21 EIA 2013b

22 American Council for an Energy-Efficient Economy (ACEEE). November 2013. The 2013 State Energy 

Efficiency Scorecard. Available at http://www.aceee.org/state-policy/scorecard.

23 Barbose, G.L., C.A. Goldman, I. Ml. Hoffman, M. A. Billingsley. January 2013. The Future of Utility CustomerFunded Energy Efficiency Programs in the United States: Projected Spending and Savings to 2025. LBNL-5803E. 

-2%

0%

2%

4%

6%

8%

10%

12%

14%

1950 1960 1970 1980 1990 2000 2010 2020 2030 2040
2-27

efficiency resource standards. These investments, and other energy efficiency policies at both the 

state and federal level, create incentives to reduce electricity consumption and peak load. 

According to data reported to EIA, energy efficiency programs reduced annual electricity 

demand by 3.74% in 2012.24

Demand for electricity, especially in the short run, is not very sensitive to changes in 

prices and is considered relatively price inelastic, although some demand reduction does occur in 

response to price. With that in mind, the EPA modeling does not typically incorporate a “demand 

response” in its electric generation modeling (see the discussion in Chapter 3) to the increases in 

electricity prices typically projected for EPA rulemakings.

 

Available at http://emp.lbl.gov/publications/future-utility-customer-funded energy-efficiency-programs-unitedstates-projected-spend.

24 U.S. Energy Information Administration Form EIA-861 data files. 2012. Available at 

http://www.eia.gov/electricity/data/eia861/.
2-28

2.10 References

Alaska House Bill 306. Signed by Governor Sean Parnell June 16, 2010. Available online at: 

http://www.legis.state.ak.us/basis/get_bill_text.asp?hsid=HBO306Z&session=26

American Council for an Energy-Efficient Economy (ACEEE). November 2013. “The 2013 

State Energy Efficiency Scorecard”. Available at http://www.aceee.org/state-policy/scorecard

Barbose, G.L., C.A. Goldman, I. Ml. Hoffman, M. A. Billingsley. January 2013. The Future of 

Utility Customer-Funded Energy Efficiency Programs in the United States: Projected 

Spending and Savings to 2025. LBNL-5803E. Available at 

http://emp.lbl.gov/publications/future-utility-customer-funded energy-efficiency-programsunited-states-projected-spend

Database of State Incentives for Renewables and Efficiency. March 2013. Accessed May 23, 

2014. Available online at: http://www.dsire.org

Granade, Hannah Choi; Creyts, Jon; Derkach, Anton; Farese, Philip; Nyquist, Scott; and 

Ostrowski, Ken. 2009 “Unlocking Energy Efficiency in the U.S. Economy”. Available online 

at 

http://www.mckinsey.com/client_service/electric_power_and_natural_gas/latest_thinking/unl

ocking_energy_efficiency_in_the_us_economy

EPRI. 2009. “Assessment of Achievable Potential from Energy Efficiency and Demand 

Response Programs in the U.S. (2010 – 2030): Executive Summary”. Palo Alto, CA. EPRI 

Product ID. Available online at: 

http://www.epri.com/abstracts/pages/productabstract.aspx?ProductID=000000000001016987

Intergovernmental Panel on Climate Change. 2001. “Climate Change 2001: The Scientific 

Basis. 2001.” Available online at: 

http://www.grida.no/publications/other/ipcc_tar/?src=/climate/ipcc_tar/wg1/218.htm

International Energy Agency (IEA).2013. “Tracking Clean Energy Progress 2013.” Input to the 

Clean Energy Ministerial. 2013. Available online at: http://www.iea.org/etp/tracking/

Oil and Gas Journal. “Enhanced Oil Recovery Survey,” 2010. Volume 108, Issue 14, April 19, 

p.41. 
2-29

U.S. Bureau of Economic Analyis (BEA) “National Income Product Accounts Tables, Table 

1.1.9. Implicit Price Deflators for Gross Domestic Product, May 20, 2014.” Available online 

at: 

http://www.bea.gov/iTable/iTable.cfm?reqid=9&step=3&isuri=1&903=13#reqid=9&step=3&

isuri=1&904=2012&903=13&906=a&905=2014&910=x&911=1

U.S. Energy Information Administration (U.S. EIA). 2000. “Carbon Dioxide Emissions from the 

Generation of Electric Power in the United States,” July. Available online at: 

ftp://ftp.eia.doe.gov/environment/co2emiss00.pdf

U.S. U.S. EIA. 2003. “Electric Power Annual 2003. 2003.” Available online at: 

http://www.eia.gov/electricity/annual/archive/03482003.pdf

U.S. EIA. 2009. “Electric Power Annual 2009.” Available online at: 

http://www.eia.gov/electricity/annual/archive/03482009.pdf

U.S. EIA. 2010a. “Status of Electricity Restructuring by State.” Available online at: 

http://www.eia.gov/cneaf/electricity/page/restructuring/restructure_elect.html

U.S. EIA. 2010b. “AEO 2010 Retrospective Review.” Available online at: 

http://www.eia.gov/forecasts/aeo/retrospective/

U.S. EIA. 2010c. “Annual Energy Outlook 2010.”Available online at: 

http://www.eia.gov/oiaf/archive/aeo10/index.html

U.S. EIA. 2010d. “Annual Energy Review 2010.” Available online at: 

http://www.eia.gov/totalenergy/data/annual/pdf/aer.pdf

U.S. EIA. 2011. “Annual Energy Outlook 2011.” Available online at: 

http://www.eia.gov/forecasts/archive/aeo11/

U.S. EIA. 2011b. “Average Price by State by Provider, 1990-2011 (EIA-861).” Available online 

at: http://www.eia.gov/electricity/data/state/

U.S. EIA. 2012a. “Annual Energy Outlook 2012 (Early Release).” Available online at: 

http://www.eia.gov/forecasts/aeo/

U.S. EIA. 2012b. “Energy in Brief: What is Shale Gas and Why is it Important?” Available 

online at: http://www.eia.gov/energy_in_brief/about_shale_gas.cfm

U.S. EIA. 2013a. “Electric Power Annual 2012.”Available online at: 

http://www.eia.gov/electricity/annual/
2-30

U.S. EIA.2013b. “Annual Energy Outlook 2013.” Available online at: 

http://www.eia.gov/forecasts/aeo/

U.S. EIA.2014. “Annual Energy Outlook 2014.” Available online at: 

http://www.eia.gov/forecasts/aeo/

U.S. EIA. 2013. “Monthly Energy Review May 2014.” Available online at: 

http://www.eia.gov/totalenergy/data/annual/#electricity

U.S. Environmental Protection Agency (EPA). 2014. “Inventory of U.S. Greenhouse Gas 

Emissions and Sinks: 1990–2012,” April. Available online at: 

http://www.epa.gov/climatechange/ghgemissions/usinventoryreport.html
3-1

CHAPTER 3: COST, ECONOMIC, AND ENERGY IMPACTS

3.1 Introduction

This chapter reports the compliance cost, economic, and energy impact analysis 

performed for the proposed rule. EPA used the Integrated Planning Model (IPM), developed by 

ICF International, to conduct its analysis. IPM is a dynamic linear programming model that can 

be used to examine air pollution control policies for CO2, SO2, NOX, Hg, HCl, and other air 

pollutants throughout the United States for the entire power system. The IPM analysis is 

complemented by an analysis of the cost and scope of reductions in electricity demand that can 

be achieved through energy efficiency programs.

3.2 Overview

EPA is proposing emission guidelines for states to follow in developing plans to address 

greenhouse gas emissions from existing fossil fuel-fired electric generating units. Specifically, 

the EPA is proposing state-specific rate-based goals for CO2 emissions from the power sector, as 

well as guidelines for states to use in developing plans to attain the state-specific goals. This rule, 

as proposed, would set in motion actions to lower the carbon intensity of power generation in the 

United States.

Over the last decade, EPA has conducted extensive analyses of regulatory actions 

affecting the power sector. These efforts support the Agency’s understanding of key variables 

that influence the effects of a policy and provide the framework for how the Agency estimates 

the costs and benefits associated with its actions.

The estimated annual costs of the proposed action are between $5.4 and $7.4 billion in 

2020 and between $7.3 and $8.8 billion in 2030 for the primary Option (Option 1). The 

alternative option (Option 2) has annual estimated costs of between $4.2 and $5.4 billion in 2020 

and between $4.5 and $5.5 billion in 202525, 26

 

25 These costs do not include monitoring, reporting, and recordkeeping costs. For more information, see section 

3.11. 

26 All costs represent real dollars ($2011).
3-2

3.3 Power Sector Modelling Framework

The Integrated Planning Model (IPM), developed by ICF Consulting, is a state-of-the-art, 

peer-reviewed, dynamic linear programming model that can be used to project power sector 

behavior under future business-as-usual conditions and examine prospective air pollution control 

policies throughout the contiguous United States for the entire electric power system. EPA used 

IPM, inclusive of the electricity demand reductions achieved through the energy efficiency 

scenario included in the proposed rule, to project likely future electricity market conditions with 

and without the proposed rule. The level of energy efficiency-driven reductions in electricity 

demand and their associated costs are reported in section 3.6.

IPM is a multi-regional, dynamic, deterministic linear programming model of the U.S. 

electric power sector. 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 under future business-as-usual conditions and evaluate the economic and 

emission impacts of prospective environmental policies. The model is designed to reflect 

electricity markets as accurately as possible. 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 discussed here as well as all other model 

assumptions and inputs.27

Although the Agency typically focuses on broad system effects when assessing the 

economic impacts of a particular policy, EPA’s application of IPM includes a detailed and 

sophisticated regional representation of key variables affecting power sector behavior.

The model incorporates a detailed representation of the fossil-fuel supply system that is 

used to forecast equilibrium fuel prices. The model includes an endogenous representation of the 

 

27 Detailed information and documentation of EPA’s Base Case using IPM (v5.13), including all the underlying 

assumptions, data sources, and architecture parameters can be found on EPA’s website at: 

http://www.epa.gov/powersectormodeling
3-3

North American natural gas supply system through a natural gas module that reflects a partial 

supply/demand equilibrium of the North American gas market accounting for varying levels of 

potential power sector gas demand and corresponding gas production and price levels.28 This 

module consists of 118 supply, demand, and storage nodes and 15 liquefied natural gas regasification facility locations that are tied together by a series of linkages (i.e., pipelines) that 

represent the North American natural gas transmission and distribution network.

IPM also endogenously models the partial equilibrium of coal supply and EGU coal 

demand levels throughout the continental U.S., taking into account assumed non-power sector 

demand and imports/exports. IPM reflects 36 coal supply regions, 14 coal grades, and the coal 

transport network, which consists of over four thousand linkages representing rail, barge, and 

truck and conveyer linkages. The coal supply curves in IPM, which are publicly available, were 

developed during a thorough bottom-up, mine-by-mine approach that depicts the coal choices 

and associated supply costs that power plants would face if selecting that coal over the modeling 

time horizon. The IPM documentation outlines the methods and data used to quantify the 

economically recoverable coal reserves, characterize their cost, and build the 36 coal regions 

curves. The coal supply curves were developed in consultation with Wood Mackenzie, one of the 

leading energy consulting firms and specialists in coal supply. These curves have been 

independently reviewed by industry experts and have been made available for public review 

prior to this rulemaking process.29 

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, Cross-State Air Pollution 

Rule (CSAPR), the Mercury and Air Toxics Standards (MATS), and the proposed Carbon 

Pollution Standards for New Power Plants.

 

28 See Chapter 10 of EPA’s Base Case using IPM (v5.13) documentation, available at: 

http://www.epa.gov/powersectormodeling/BaseCasev513.html

29 See Chapter 9 of EPA’s Base Case using IPM (v5.13) documentation, available at: 

http://www.epa.gov/powersectormodeling/BaseCasev513.html
3-4

The model and EPA's assumptions input into the model 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. IPM has received extensive review by 

energy and environmental modeling experts in a variety of contexts. For example, in the late 

1990’s, the Science Advisory Board reviewed IPM as part of the CAA Amendments Section 812 

prospective studies that are periodically conducted. The model has also undergone considerable 

interagency scrutiny when it has been used to conduct over a dozen legislative analyses 

(performed at Congressional request) over the past decade. The Agency has also used the model 

in a number of comparative modeling exercises sponsored by Stanford University’s Energy 

Modeling Forum over the past 15 years.

IPM has also been employed by states (e.g., for RGGI, the Western Regional Air 

Partnership, Ozone Transport Assessment Group), other Federal and state agencies, 

environmental groups, and industry, all of whom subject the model to their own review 

procedures.

3.3.1 Recent Updates to EPA’s Base Case using IPM (v.5.13)

This new IPM modeling platform (v.5.13) incorporates important structural 

improvements and data updates with respect to the previous version (v.4.10_MATS), and 

includes notable changes to the modeling architecture. This is the fifth major iteration of EPA’s 

base case using IPM, and calibrates certain information and data from the Energy Information 

Agency's (EIA) Annual Energy Outlook (AEO), in this case AEO 2013 (hence the platform 

name of v5.13).

The current base case represents a projection of electricity sector activity that takes into 

account only those Federal and state laws and regulations whose provisions were either in effect 

or enacted and clearly delineated at the time the base case was finalized in August 2013. The 
3-5

EPA Base Case v.5.13 includes the Clean Air Interstate Rule (CAIR),30 the Mercury and Air 

Toxics Rule (MATS), and other state and Federal regulations to the extent that they contain 

measures, permits, or other air-related limitations or requirements.31

EPA has also updated the National Electric Energy Data System (NEEDS). This database 

contains the unit-level data that is used to construct the "model" plants that represent existing and 

committed32 units in EPA modeling applications of IPM. NEEDS includes basic geographic, 

operating, air emissions, and other data on these generating units.33

Other routine updates were also adopted in v5.13. These include changes based on public 

comments that have been received over the last few years, updates reflecting planned new power 

plant construction, retirements, new power plant cost and performance, pollution control costs 

and performance, emission rate assignments, and state rules and enforcement actions. The update 

also included further refinement to the modeled regions to reflect more recent power market 

 

30 EPA Base Case v.5.13 includes the Clean Air Interstate Rule (CAIR), a Federal regulatory measure for achieving 

the 1997 National Ambient Air Quality Standards (NAAQS) for ozone (8-hour average of 0.08 ppm) and fine 

particles (24-hour average of 65 μg/m3 or less and annual average of 15 μg/m3for particles of diameter 2.5 

micrometers or less, i.e., PM2.5). Originally issued on March 10, 2005, CAIR was remanded back to EPA by the 

U.S. Court of Appeals for the District of Columbia Circuit in December 2008 and EPA was required to correct 

legal flaws in the regulations that had been cited in a ruling by the Court in July 2008. CAIR remains in effect 

until replaced by EPA pursuant to the Court’s ruling. CAIR’s provisions were still in effect when EPA Base Case 

v.5.13 was released.

31 On May 19, 2014, EPA finalized the "National Pollutant Discharge Elimination System—Final Regulations to 

Establish Requirements for Cooling Water Intake Structures at Existing Facilities and Amend Requirements at 

Phase I Facilities". This finalized rule, which implements section 316(b) of the Clean Water Act, affects sources 

with a cooling water design intake flow greater than 2 million gallons a day. Many of the sources affected by the 

316(b) rule are electricity generating units, but not all of these generating units are subject to the proposed 111(d) 

rule. The 316(b) rule is not reflected in the base case of the 111(d) proposed rule analysis. However, the EPA 

estimated that the 316(b) rule will have relatively minor impacts on facilities affected by that regulation, with a 

net decrease in electricity generating capacity of 1 GW in 2030 (less than 0.1% of total 2030 base case generating 

capacity), generally reflecting the retirement of older, less efficient generating units with very low capacity 

utilization rates. It is not expected that the analysis described in this RIA would be meaningfully affected if the 

expected effects of the 316(b) rule were included in the base case. 

32 v.5.13 includes planned units that had broken ground or secured financing and were expected to be online by the 

end of 2015; one geothermal unit and four nuclear units that are scheduled to come online after 2015 were also 

included. For more information, see Chapter 4 of EPA’s Base Case using IPM (v5.13) documentation, available 

at: http://www.epa.gov/powersectormodeling/BaseCasev513.html

33 The NEEDS database can be found on the EPA’s website for the Base Case using IPM (v5.13), 

<http://www.epa.gov/powersectormodeling/BaseCasev513.html>.
3-6

structure based on NERC, FERC, EIA, and other data and planning sources, and now includes 

more regions that better reflect limitations on current power system dispatch and transmission 

behavior. 

3.4 State Goals in this Proposal

In this action, the EPA is proposing state-specific rate-based goals to guide states in the 

development of their plans. The agency is proposing one option (Option 1) for state-specific 

goals and requesting comment on a second set of state-specific goals and compliance period 

(Option 2).

Table 3-1. Proposed State Goals (Adjusted MWh-Weighted-Average Pounds of CO2 Per 

Net MWh from all Affected Fossil Fuel-Fired EGUs) for Options 1 and 2

State34

Option 1 Option 2

Interim Goal

(2020-2029)

Final Goal

(2030 Forward)

Interim Goal

(2020-2024)

Final Goal

(2025 Forward)

Alabama 1,147 1,059 1,270 1,237

Alaska 1,097 1,003 1,170 1,131

Arizona * 735 702 779 763

Arkansas 968 910 1,083 1,058

California 556 537 582 571

Colorado 1,159 1,108 1,265 1,227

Connecticut 597 540 651 627

Delaware 913 841 1,007 983

Florida 794 740 907 884

Georgia 891 834 997 964

Hawaii 1,378 1,306 1,446 1,417

Idaho 244 228 261 254

Illinois 1,366 1,271 1,501 1,457

Indiana 1,607 1,531 1,715 1,683

Iowa 1,341 1,301 1,436 1,417

Kansas 1,578 1,499 1,678 1,625

Kentucky 1,844 1,763 1,951 1,918

Louisiana 948 883 1,052 1,025

Maine 393 378 418 410

Maryland 1,347 1,187 1,518 1,440

Massachusetts 655 576 715 683

Michigan 1,227 1,161 1,349 1,319

Minnesota 911 873 1,018 999

Mississippi 732 692 765 743

 

34 The EPA has not developed goals for Vermont and the District of Columbia because current information indicates 

those jurisdictions have no affected EGUs. Also, as noted above, EPA is not proposing goals for tribes or U.S. 

territories at this time.
3-7

Table 3-1. Continued

Missouri 1,621 1,544 1,726 1,694

Montana 1,882 1,771 2,007 1,960

Nebraska 1,596 1,479 1,721 1,671

Nevada 697 647 734 713

New Hampshire 546 486 598 557

New Jersey 647 531 722 676

New Mexico * 1,107 1,048 1,214 1,176

New York 635 549 736 697

North Carolina 1,077 992 1,199 1,156

North Dakota 1,817 1,783 1,882 1,870

Ohio 1,452 1,338 1,588 1,545

Oklahoma 931 895 1,019 986

Oregon 407 372 450 420

Pennsylvania 1,179 1,052 1,316 1,270

Rhode Island 822 782 855 840

South Carolina 840 772 930 897

South Dakota 800 741 888 861

Tennessee 1,254 1,163 1,363 1,326

Texas 853 791 957 924

Utah * 1,378 1,322 1,478 1,453

Virginia 884 810 1,016 962

Washington 264 215 312 284

West Virginia 1,748 1,620 1,858 1,817

Wisconsin 1,281 1,203 1,417 1,380

Wyoming 1,808 1,714 1,907 1,869

* Excludes EGUs located in Indian country. 

Table 3-2. Projected Base Case CO2 Emissions Rate (Adjusted MWh-Weighted-Average 

Pounds of CO2 Per Net MWh from all Affected Fossil Fuel-Fired EGUs) 

State 2020 2025 2030

Alabama 1,491 1,511 1,557

Arizona* 1,458 1,439 1,523

Arkansas 1,563 1,576 1,577

California 691 692 633

Colorado 1,647 1,595 1,599

Connecticut 869 869 868

Delaware 1,076 1,104 937

Florida 1,211 1,285 1,345

Georgia 1,304 1,346 1,368

Idaho 544 596 592

Illinois 1,731 1,666 1,672

Indiana 1,938 1,791 1,753

Iowa 1,525 1,533 1,529

Kansas 1,833 1,795 1,790

Kentucky 2,163 2,165 2,168
3-8

Table 3-2. Continued

Louisiana 1,301 1,294 1,316

Maine 1,003 1,003 1,004

Maryland 1,746 1,747 1,721

Massachusetts 923 929 929

Michigan 1,794 1,829 1,826

Minnesota 1,697 1,687 1,695

Mississippi 1,053 1,078 1,144

Missouri 1,986 1,971 1,970

Montana 2,134 2,134 2,135

Nebraska 2,133 2,122 2,122

Nevada 989 876 879

New Hampshire 874 877 879

New Jersey 1,324 1,406 1,399

New Mexico* 1,396 1,246 1,330

New York 967 959 960

North Carolina 1,512 1,597 1,608

North Dakota 1,982 1,984 1,984

Ohio 1,704 1,777 1,794

Oklahoma 1,382 1,361 1,339

Oregon 531 503 536

Pennsylvania 1,566 1,639 1,684

Rhode Island 890 888 887

South Carolina 1,039 1,101 1,060

South Dakota 1,127 1,124 1,126

Tennessee 1,522 1,519 1,539

Texas 1,473 1,514 1,529

Utah* 1,829 1,749 1,800

Virginia 1,352 1,457 1,517

Washington 659 711 724

West Virginia 2,025 2,025 2,025

Wisconsin 1,935 1,944 1,938

Wyoming 2,053 2,054 2,055

 * Excludes EGUs located in Indian country. 

Note that the proposed state goals in Table 3-1 differ slightly from the state goals 

modelled in the illustrative compliance scenarios analyzed in this RIA. Any differences in the 

goals are minor and reflect small adjustments to the goal setting methodology due to minor 

adjustments in the assumed at-risk nuclear capacity, fossil units included in Indian Country, the 

treatment of cogeneration in historical data, and the assumed applicability for some units. The 

resulting changes to the state goals are minor, and any differences in results obtained by 

analyzing these new goals would be negligible. Furthermore, these small changes in the goals 
3-9

would be offset by comparable changes in the illustrative compliance scenarios (in which 

compliance is consistent with the assumptions used in setting state goals).

3.5 Compliance Scenarios Analyzed

In order to estimate the costs, benefits, and impacts of implementing the proposed 

guidelines, the EPA modeled two illustrative compliance scenarios. One of these scenarios 

allows averaging of emission rates within each individual state (these scenarios are referred to as 

“state”), and another set of scenarios where groups of states are assumed to collaborate and 

achieve compliance across larger regions (referred to as “regional”). Estimates of the benefits, 

costs, and economic impacts of this proposed action are presented for both state and regional 

compliance.35 These illustrative compliance scenarios are designed to reflect, to the extent 

possible, the scope and nature of the proposed guidelines. However, there is considerable 

uncertainty with regard to the precise measures that states will adopt to meet the proposed 

requirements, since there are considerable flexibilities afforded to the states in developing their 

state plans. Nonetheless, the analysis of the benefits, costs, and relevant impacts of the proposed 

rule attempts to encapsulate some of those flexibilities in order to inform states and stakeholders 

of the potential overall impacts of the proposal. The relevant impacts, costs, and benefits are 

provided for 2020, 2025, and 2030 for Option 1 and 2020 and 2025 for Option 2 (with both state 

and regional compliance). 

It is also important to note that the analysis does not specify any particular CO2 reduction 

measure to occur, with the exception of the level of demand-side energy efficiency (EE), which 

the model is not currently configured to include as an endogenous compliance option. In other 

words, aside from EE, the analysis allows the power system the flexibility to respond to average 

emissions rate constraints on affected sources in the illustrative scenarios that achieve the state 

rate-based goals in the most cost-effective manner determined by IPM, as specified below. 

In the illustrative compliance scenarios analyzed, the average emissions rate of the source 

types included in the calculation of the state goals must be, on average, less than or equal to the 

proposed goals over the entire compliance period. That is, the sources assumed to be directly 

 

35 For more details on the nature of the regulatory options, see the preamble.
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affected by the illustrative state compliance scenarios are only those included in the calculation 

of the state goals, and include affected sources as well as savings from demand-side energy 

efficiency.

36 These affected sources are:37

 Existing fossil steam boilers with nameplate capacity greater than 25 MW

 Existing NGCC units with nameplate capacity greater than 25 MW 

 Simple cycle combustion turbines with nameplate capacity greater than 25 MW, 

and 33% capacity factor and 219,000 MWh of generation in 2012

 New and existing non-hydro renewable capacity

 At risk and under construction nuclear.38

All compliance scenarios modeled include an assumption that affected sources within 

states are able to meet state goals collectively, by averaging all of their emissions relative to all 

of their generation. This approach enables some sources to emit at rates higher than the relevant 

goal, as long as there is corresponding generation coming from sources that emit at a lower rate 

such that the goal (in lbs/MWh) is met across all affected sources collectively. The average 

emissions rate at covered sources must be less than or equal to the applicable state goal, on 

average, over the entire compliance period, but not in any particular year. 

The illustrative compliance scenarios further assume that the states adopt intertemporal 

averaging in the initial compliance period for both Option 1 and Option 2. That is, the average 

emissions rate at covered sources in each state must be less than or equal to the applicable state 

goal over the compliance period. The initial compliance period for Option 1 is 2020 to 2029 and 

for Option 2 it is 2020 to 2024. After the initial compliance period the average emission rate of 

 

36 As discussed in the preamble, for compliance purposes states may be able to include sources of generation in the 

calculation of the state’s emission rate other than those sources of generation considered in constructing the 

building blocks for setting the state goals. However, this illustrative analysis does not include those options. 

37 For the illustrative scenarios renewable generation includes generation from wind, solar, geothermal, and biomass 

co-fired with coal. Dedicated biomass is not an affected source in the illustrative scenarios. 

38 See Greenhouse Gas Abatement Measures TSD
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the affected sources in each year must be less than or equal to the state goal in the illustrative 

compliance scenarios. 

For EE, the megawatt-hour (MWh) savings and associated costs are specified 

exogenously for Option 1 and Option 2 compliance scenarios consistent with the EE "best 

practice" performance levels informing calculation of state goals under each option. EPA has 

determined that these performance levels are achievable at costs that are generally less than 

avoided power system costs. EPA has specified and imposed EE-related costs and changes in 

future electricity demand exogenously when modeling the compliance scenarios presented for 

this rule. Details of the implementation of the demand reduction are reported in the following 

section.

EPA also analyzed a set of compliance scenarios that assume greater geographic 

flexibility for compliance where states may choose to cooperate in order to achieve more costeffective outcomes, since some states can reduce their emissions more easily relative to others. 

This scenario is modeled for both Option 1 and Option 2 and is referred to as the “Regional” 

scenario. The regional scenarios allow emission rate averaging across affected sources within six 

multi-state regions, informed by North American Electric Reliability Corporation (NERC) 

regions and Regional Transmission Organizations (RTOs). These regions do not always follow 

state borders, however, so certain states that fall into more than one region were grouped in 

regions where there was a majority of geographic territory (area) or generation. While Florida 

and Texas each have unique NERC regions unto themselves, for purposes of this compliance 

analysis, those states were each grouped with other neighboring states:

 West (WECC) (CA, WA, OR, ID, MT, UT, NV, CO, WY, NM, AZ)

 North Central (MISO)39– ND, SD, IA, MN, WI, MO, IL, IN, MI

 South Central (SPP + ERCOT) – NE, KS, OK, AR, TX, LA

 Southeast (SERC + FL) – KY, NC, SC, TN, MS, AL, GA, FL

 East Central (PJM) – OH, PA, WV, MD, DE, NJ, VA

 

39 Note that the MISO region expanded to integrate Entergy territory at the end of 2013.
3-12

 Northeast (NPCC) – NY, RI, MA, CT, NH, VT, ME

Figure 3-1. NERC Regions

Figure 3-2. NERC Assessment Areas
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Figure 3-3. Regional Compliance Zones Used in this Analysis

These regional groupings of states allow the state goals in each option analyzed to be met 

collectively by averaging emission rate performance across all affected units located in that 

region. Results are also presented for “State” scenarios (for both options), to illustrate potential 

impacts and benefits should states choose not to cooperate in a “Regional” manner. In the 

regional scenarios, as in the state scenarios, affected sources in each state must respond to their 

respective state goal. However, the ability to average is extended to all affected sources in each 

compliance region. In these scenarios, the average emissions rate from affected sources in each 

region must be less than or equal to the weighted average40 of the state goals in that region, over 

the compliance period. Furthermore, as in the “State” scenarios, the regions are assumed to 

adopt intertemporal averaging in the initial compliance period for each Option.

The analysis in the illustrative scenarios does not assume that states use any specific 

policy mechanism to achieve the state goals. While IPM produces a least cost solution to 

 

40 The weights are the generation of the covered sources plus the demand-side energy savings, in the respective 

state.
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achieve the state goals imposed in the illustrative scenarios, there may be less costly approaches 

that the states may adopt to achieve their state goals.

In addition to these four illustrative compliance scenarios which estimate the costs and 

impacts of the proposed state goals, EPA also analyzed the impacts of the individual building 

blocks used to construct the proposed goals. For each of these additional scenarios, EPA 

imposed CO2 emission rate constraints for each state that reflect the particular combination of 

building blocks analyzed in that scenario. The various building block combinations analyzed in 

these scenarios will be available in the docket for this rulemaking.

3.6 Demand Side Energy Efficiency

3.6.1 Projected Demand-Side Energy Savings41

To estimate the potential electricity demand reduction that could be achieved, and 

associated costs incurred, through implementation of demand-side energy efficiency policies, 

EPA developed scenarios that reflect increased levels of demand-side energy efficiency, rooted 

in what leading states have already accomplished or have requirements in place to accomplish.

42

For Option 1, adjustments were made to each state’s annual incremental reduction in electricity 

consumption by ramping up from an historical basis43 to a target rate of 1.5% of electricity 

demand annually over a period of years starting in 2017, and maintain that rate throughout the 

modeling horizon. Twelve leading states have either achieved, or have established requirements 

that will lead them to achieve, this rate of incremental electricity demand reduction, which we 

refer to as the “savings rate.” The pace of improvement from the state’s historical value is 

assumed to be 0.2% per year, beginning in 2017 until the target rate of reduction from baseline 

 

41 For a more detailed discussion of the demand-side energy efficiency savings projections, refer to the Greenhouse 

Gas Abatement Measures TSD.

42 This scenario is intended to represent a feasible pathway for additional EE resulting from accelerated use of 

energy efficiency policies, in all states, consistent with a level of performance that has already been demonstrated 

or required by policies (e.g., energy efficiency resource standards) of leading energy efficiency implementing 

states, and consistent with a demonstrated annual pace of performance improvement from current levels. It does 

not represent an estimate of the full potential for end-use EE.

43 The historical basis of the percentage of reduced electricity consumption differs for each state and is drawn from 

the data reported in Energy Information Administration (EIA) Form 861, 2012, available at 

http://www.eia.gov/electricity/data/eia861/.
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electricity demand is achieved. States already at or above the 1.5% annual incremental savings 

rate are assumed to have a 1.5% rate beginning in 2017 and sustain that rate. For Option 2, the 

annual incremental savings rate is ramped up from an historical basis to a target rate of 1.0% and 

at a pace of improvement of 0.15% per year, beginning in 2017, until the target rate is achieved. 

States already at or above the 1.0% annual incremental savings rate are assumed to sustain a 

1.0% rate beginning in 2017. Twenty leading states have either achieved, or have established 

requirements that will lead them to achieve, this rate of savings. The incremental savings rate for 

each state, for each year, is then used to derive cumulative annual energy savings based upon 

information/assumptions about the average life of EE measures and the distribution of measure 

lives within a state’s full portfolio of EE programs. The cumulative annual energy savings 

derived for Option 1 and Option 2 using this methodology are used consistently to set goals and 

to conduct power sector compliance modeling for each option.

44

To reflect the implementation of demand-side energy efficiency in modeling, the fixed 

total electricity demand in IPM was adjusted exogenously to reflect the estimated future-year 

energy savings calculated from the approach described above. State energy savings in sales were 

scaled up to account for transmission losses and applied to base case generation demand in each 

model year to derive adjusted demand for each state, reflecting the energy efficiency scenario

energy savings.45 The demand adjustments were applied proportionally across all segments (peak 

and non-peak) of the load duration curve.46 In order to reflect the adjusted state-level demand 

within IPM model regions that cross state borders, energy savings from a bisected state were 

distributed between the applicable IPM model regions using a distribution approach based on 

 

44 See Greenhouse Gas Abatement Measures TSD and the State Goal Setting TSD

45 That is, while the methodology for applying the energy efficiency savings rate was used consistently to set state 

goals and to conduct power sector compliance modeling for each option, the cumulative savings rate is applied to 

2012 electricity consumption in the calculation of state goals whereas compliance modeling applies that rate to 

forecasted base case demand. Thus, for a given rate of demand reduction, the quantity of electricity demand 

reduced is greater in the compliance modeling than in setting the state goals, consistent with forecasted base case 

demand growth from 2012 to 2020 and beyond.

46 For more information on load duration curves, see Chapter 2 of EPA’s Base Case using IPM (v5.13) 

documentation, available at: http://www.epa.gov/powersectormodeling/BaseCasev513.html
3-16

reported sales in 2013 as a proxy for the distribution of energy efficiency investment

opportunities.

Table 3-3. Net Cumulative Savings as a Percent of Projected BAU Sales

Option 1 Option 2

2020 2025 2030 2020 2025 2030

Alabama 1.36% 6.19% 10.09% 1.07% 4.54% n/a

Arizona 5.24% 9.50% 11.72% 3.52% 6.45% n/a

Arkansas 1.52% 6.46% 10.31% 1.24% 4.77% n/a

California 4.95% 9.46% 11.90% 3.55% 6.57% n/a

Colorado 3.92% 8.73% 11.39% 3.32% 6.35% n/a

Connecticut 4.71% 9.55% 12.27% 3.61% 6.77% n/a

Delaware 1.14% 5.94% 10.14% 0.86% 4.32% n/a

District of Columbia 1.14% 5.94% 10.14% 0.86% 4.32% n/a

Florida 2.03% 7.04% 10.50% 1.75% 5.26% n/a

Georgia 1.76% 6.74% 10.40% 1.48% 5.01% n/a

Idaho 3.80% 8.73% 11.50% 3.28% 6.38% n/a

Illinois 4.36% 9.26% 12.03% 3.52% 6.66% n/a

Indiana 3.20% 8.42% 11.59% 2.89% 6.26% n/a

Iowa 4.65% 9.39% 12.04% 3.58% 6.67% n/a

Kansas 1.22% 6.05% 10.17% 0.94% 4.42% n/a

Kentucky 1.91% 6.95% 10.57% 1.63% 5.18% n/a

Louisiana 1.14% 5.88% 9.97% 0.85% 4.28% n/a

Maine 5.37% 9.96% 12.48% 3.61% 6.77% n/a

Maryland 4.21% 9.13% 11.92% 3.47% 6.61% n/a

Massachusetts 4.43% 9.37% 12.18% 3.55% 6.73% n/a

Michigan 4.59% 9.43% 12.16% 3.59% 6.73% n/a

Minnesota 4.80% 9.49% 12.09% 3.58% 6.67% n/a

Mississippi 1.40% 6.28% 10.20% 1.12% 4.62% n/a

Missouri 1.58% 6.60% 10.53% 1.29% 4.88% n/a

Montana 3.36% 8.41% 11.33% 3.01% 6.21% n/a

Nebraska 2.20% 7.38% 10.95% 1.91% 5.51% n/a

Nevada 2.95% 8.07% 11.15% 2.67% 6.00% n/a

New Hampshire 2.84% 8.14% 11.52% 2.56% 6.08% n/a

New Jersey 1.25% 6.10% 10.23% 0.96% 4.46% n/a

New Mexico 3.10% 8.11% 11.03% 2.81% 6.02% n/a

New York 4.42% 9.35% 12.17% 3.54% 6.73% n/a

North Carolina 2.37% 7.45% 10.76% 2.09% 5.56% n/a

North Dakota 1.39% 6.32% 10.35% 1.11% 4.65% n/a

Ohio 4.17% 9.13% 11.97% 3.47% 6.63% n/a

Oklahoma 1.86% 6.88% 10.54% 1.57% 5.13% n/a

Oregon 4.66% 9.26% 11.76% 3.55% 6.55% n/a

Pennsylvania 4.67% 9.42% 12.07% 3.58% 6.68% n/a

Rhode Island 3.90% 9.02% 12.00% 3.35% 6.60% n/a

South Carolina 2.32% 7.40% 10.73% 2.04% 5.52% n/a

South Dakota 1.60% 6.62% 10.52% 1.32% 4.90% n/a

Tennessee 2.21% 7.33% 10.79% 1.93% 5.47% n/a
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Table 3-3. Continued

Texas 1.78% 6.79% 10.48% 1.50% 5.05% n/a

Utah 3.62% 8.62% 11.44% 3.19% 6.33% n/a

Vermont 5.37% 9.96% 12.48% 3.61% 6.77% n/a

Virginia 1.23% 5.98% 9.95% 0.95% 4.37% n/a

Washington 4.24% 9.01% 11.64% 3.45% 6.49% n/a

West Virginia 1.77% 6.86% 10.71% 1.49% 5.11% n/a

Wisconsin 4.68% 9.48% 12.17% 3.60% 6.73% n/a

Wyoming 1.61% 6.55% 10.32% 1.33% 4.85% n/a

Contiguous U.S. Total 3.05% 7.93% 11.14% 2.44% 5.76% n/a

Alaska 1.22% 6.02% 10.09% 0.94% 4.40% n/a

Hawaii 1.29% 6.13% 10.15% 1.01% 4.49% n/a

U.S. Total 3.04% 7.92% 11.13% 2.43% 5.75% n/a

Source: See Greenhouse Gas Abatement Measures TSD

3.6.2 Demand-Side Energy Efficiency Total Costs47

Total costs of achieving the demand-side energy efficiency scenarios for each year were 

determined at the state level, exogenous to power sector modeling. In addition to the energy 

savings data, the total cost was based upon first-year cost of saved energy, average measure life,

distribution of measure lives, and cost escalation factors.

The first year cost of saved energy used in the cost calculation accounts for both the costs 

to the utilities that are funding the demand-side energy efficiency programs (known as the 

program costs), and the additional cost to the end-user purchasing a more energy efficient 

technology (known as the participant costs). Total costs were found to be divided evenly, 50% 

each, between program costs and participant costs.48 To account for the potential for increasing 

costs as states realize greater levels of energy savings, the first-year costs were escalated when 

the annual incremental savings in each state reached 0.5% (a 20% additional cost escalation is 

applied to subsequent investments) and a 1.0% (a 40% additional cost escalation is applied to 

subsequent investments). 

 

47 For a more detailed discussion of the demand-side energy efficiency savings cost estimates, refer to the 

Greenhouse Gas Abatement Measures TSD.

48 For a more detailed discussion of the analysis of program versus participant costs, refer to the Greenhouse Gas 

Abatement Measures TSD.
3-18

To calculate total annualized energy efficiency costs, first-year costs for each year for 

each state were levelized (at 3% and 7% discount rates) over the estimated distribution of 

measure lives and the results summed for each year for each state. For example, the 2025 

estimate of annualized EE cost includes levelized value of first-year costs for 2017 through 2025. 

The annualized cost is rising in each analysis year as additional first-year costs are incurred. 

These annualized costs were determined for Options 1 and 2 and are summarized below in Table 

3-4. The total levelized cost of saved energy was calculated based upon the same inputs and 

using a 3% discount rate results in average values of 8.5 cents per kWh in 2020, 8.9 cents per 

kWh in 2025, and 9.0 cents per kWh in 2030.49

The utility funding for demand-side energy efficiency programs (to cover program costs)

is typically collected through a standard per kWh surcharge to the rate-payer; the regional retail 

price impacts analyzed from this RIA’s compliance scenarios assumes the recovery of these 

program costs through the following procedure.

50 For each state, the first-year EE program costs 

are calculated for each year (which are equal to 50% of the total first-year EE costs for that state 

as noted above). These EE program costs were distributed between the applicable IPM model

regions using a distribution approach based on reported sales in 2013 as a proxy for the 

distribution of energy efficiency investment opportunities. These regionalized EE program costs 

were then incorporated into the regional retail price calculation as discussed in section 3.7.9.

51

 

49 This analysis does not capture the potential effect on the quantity of electricity demand of lower electricity prices 

induced by energy efficiency policies. That is, the modeling does not capture a “system wide” rebound effect of 

energy efficiency policies. This is due to IPM’s assumption of fixed demand. However, the modeling also does 

not capture the effect of higher costs of producing electricity, which is attributable to other methods of complying 

with the state goals, on electricity demand for the same reason (i.e., fixed demand in IPM). For further discussion 

of these issues see the Limitations section at the end of this chapter.

50 The full retail price analysis method is discussed in section 3.7.9 of this chapter.

51 The effect on equilibrium supply and demand of electricity due to changing retail rates to fund energy efficiency 

programs is not captured in the IPM modeling.
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Table 3-4. Annualized Cost of Demand-Side Energy Efficiency (at discount rates of 3% 

and 7%, billions 2011$)

2018 2020 2025 2030

Option 1 at 3% 4.1 10.2 28.9 42.7

Option 1 at 7% 4.9 12.3 35.0 51.8

Option 2 at 3% 3.6 8.0 20.6 n/a

Option 2 at 7% 4.3 9.7 24.9 n/a

Source: Greenhouse Gas Abatement Measures TSD

Annualized demand-side energy efficiency (EE) costs are derived by using two key 

variables: the first-year (or “up front”) EE costs and EE investment lives (which vary by type of 

program). Chapter 5 of the GHG Abatement Measures TSD presents the calculations of 

annualized costs as well as a comprehensive set (by state, by year) of first-year EE costs.

3.7 Projected Power Sector Impacts

3.7.1 Projected Emissions

Under the proposed rule, EPA projects annual CO2 reductions between 17% and 18% 

below base case projections for Option 1 in 2020 (reaching 26% to 27% below 2005 

emissions52), and between 24% and 25% below the base case in 2030 (reaching 30% below 2005 

emissions). For Option 2, EPA projects annual CO2 reductions between 13% and 14% in 2020 

(reaching 23% below 2005 emissions) and 17% in 2025 (reaching 23% to 24% below 2005 

emissions). For each Option, the regional scenario achieves fewer emissions reductions largely 

because the ability to average emissions regionally allows those states that were projected to emit 

below their state goal in the base case to offset reductions that other states would otherwise have 

made. 

 

52 For purposes of these calculations, EPA has used historical CO2 emissions from eGRID for 2005, which reports 

EGU emissions as 2,434 million metric tonnes in the contiguous US.
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Table 3-5. Projected CO2 Emission Impacts, Relative to Base Case

CO2 Emissions

(MM Tonnes)

CO2 Emissions Change

from Base Case

(MM Tonnes)

CO2 Emissions: Percent 

Change from Base Case

2020 2025 2030 2020 2025 2030 2020 2025 2030

Base Case 2,161 2,231 2,256

Option 1 Regional 1,790 1,730 1,711 -371 -501 -545 -17% -22% -24%

Option 1 State 1,777 1,724 1,701 -383 -506 -555 -18% -23% -25%

Option 2 Regional 1,878 1,862 n/a -283 -368 n/a -13% -17% n/a

Option 2 State 1,866 1,855 n/a -295 -376 n/a -14% -17% n/a

Source: Integrated Planning Model run by EPA, 2014

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

CO2 Emissions

(MM Tonnes)

CO2 Emissions Change

from Base Case

(MM Tonnes)

CO2 Emissions: Percent 

Change from Base Case

2020 2025 2030 2020 2025 2030 2020 2025 2030

Base Case 2,161 2,231 2,256 -273 -203 -178 -11% -8% -7%

Option 1 Regional 1,790 1,730 1,711 -644 -704 -723 -26% -29% -30%

Option 1 State 1,777 1,724 1,701 -657 -710 -733 -27% -29% -30%

Option 2 Regional 1,878 1,862 n/a -556 -572 n/a -23% -23% n/a

Option 2 State 1,866 1,855 n/a -568 -579 n/a -23% -24% n/a

Source: Integrated Planning Model run by EPA, 2014

In 2020, EPA projects a 20% to 23% reduction of SO2, 22% to 24% reduction of NOX, 

and a 15% to 18% reduction of mercury, under the proposed Option 1 illustrative scenarios. 

EPA projects fewer emission reductions overall as a result of the proposed Option 2 in 2020: a 

17% to 18% reduction in SO2, 17% to 18% reduction in NOX, and an 11% to 14% reduction in 

mercury. The projected non-CO2 reductions presented in Table 37 below demonstrate similar 

trends in later years.
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Table 3-7. Projected Non-CO2 Emission Impacts, 2020-2030

Base 

Case

Option 1 Option 2 Option 1 Option 2

Reg. State Reg. State Reg. State Reg. State

2020

SO2

(thousand tons) 1,476 1,184 1,140 1,231 1,208 -19.8% -22.7% -16.6% -18.1%

NOX

(thousand tons) 1,559 1,213 1,191 1,290 1,277 -22.2% -23.6% -17.2% -18.0%

Hg

(tons) 8.3 7.0 6.8 7.3 7.1 -15.3% -18.1% -11.3% -14.0%

PM2.5

(thousand tons) 212 156 154 166 163 -26.4% -27.2% -21.5% -22.9%

2025

SO2

(thousand tons) 1,515 1,120 1,090 1,218 1,188 -26.1% -28.0% -19.6% -21.6%

NOX

(thousand tons) 1,587 1,166 1,151 1,279 1,271 -26.5% -27.5% -19.4% -19.9%

Hg

(tons) 8.7 7.0 6.7 7.4 7.1 -19.5% -23.2% -14.9% -19.3%

PM2.5

(thousand tons) 209 150 146 165 161 -28.1% -30.1% -21.0% -23.2%

2030

SO2

(thousand tons) 1,530 1,106 1,059 n/a n/a -27.7% -30.8% n/a n/a

NOX

(thousand tons) 1,537 1,131 1,109 n/a n/a -26.4% -27.9% n/a n/a

Hg

(tons) 8.8 7.0 6.6 n/a n/a -19.7% -24.1% n/a n/a

PM2.5

(thousand tons) 198 144 142 n/a n/a -27.2% -28.5% n/a n/a

Source: Integrated Planning Model run by EPA, 2014

While the EPA has not quantified the climate impacts of these other pollutants for the 

proposed guidelines, the Agency has analyzed the potential changes in upstream methane 

emissions from the natural gas and coal production sectors that may result from the compliance 

approaches examined in this RIA. The EPA assessed whether the net change in upstream 

methane emissions from natural gas and coal production is likely to be positive or negative and 

also assessed the potential magnitude of changes relative to CO2 emissions reductions anticipated 

at power plants. This assessment included CO2 emissions from the flaring of methane, but did 

not evaluate potential changes in other combustion-related CO2 emissions, such as emissions 

associated with drilling, mining, processing, and transportation in the natural gas and coal 

production sectors. This analysis found that the net upstream methane emissions from natural gas 

systems and coal mines and CO2 emissions from flaring of methane will likely decrease under 

the proposed guidelines. Furthermore, the changes in upstream methane emissions are small 
3-22

relative to the changes in direct emissions from power plants. The technical details supporting 

this analysis can be found in the Appendix to this chapter.

3.7.2 Projected 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 policy case in which the sector 

pursues flexible compliance approaches to meet the proposed rule as represented in the 

illustrative compliance scenarios. In simple terms, these costs are the resource costs of what the 

power industry will expend to comply with EPA’s requirements. 53 Program and participant enduse EE costs are also included in the compliance cost estimates.

EPA projects that the annual compliance cost of the proposed rule’s Option 1 ranges from 

$5.4 to $7.4 billion in 2020 and from $7.3 to $8.8 billion in 2030. The projected annual 

incremental compliance cost of the proposed rule’s Option 2 ranges from $4.2 to $5.4 billion in 

2020 and from $4.5 to $5.5 billion in 2025. The annual compliance cost is the projected 

additional cost of complying with the proposed rule in the year analyzed and includes the net 

change in the annualized cost of capital investment in new generating sources and heat rate 

improvements at coal steam facilities,54 the change in the ongoing costs of operating pollution 

controls, shifts between or amongst various fuels, demand-side energy efficiency measures, and 

other actions associated with compliance. 

Table 3-8. Annualized Compliance Costs (billions of 2011$)

2020 2025 2030

Option 1 Regional 5.4 4.6 7.3

Option 1 State 7.4 5.5 8.8

Option 2 Regional 4.2 4.5 n/a

Option 2 State 5.4 5.5 n/a

These costs do not include monitoring, reporting, and recordkeeping costs. For more information, see section 3.11.

Source: Integrated Planning Model run by EPA, 2014 and with post-processing to account for exogenous demand 

side management energy efficiency costs. See Chapter 5 of GHG Abatement Measures TSD for a full explanation.

 

53 The compliance costs also capture the effect of changes in equilibrium fuel prices on the expenditures of the 

electricity sector to serve demand. 

54 See Chapter 2 of the Greenhouse Gas Abatement Measures TSD and Chapter 8 of EPA’s Base Case using IPM 

(v5.13) documentation, available at: http://www.epa.gov/powersectormodeling/BaseCasev513.html
3-23

EPA’s projection of $4.2 to $7.4 billion in additional costs in 2020 across the illustrative 

compliance scenarios evaluated for both options should be put into context for power sector 

operations. As shown in section 2.7, the power sector is expected in the base case to expend over 

$359 billion in 2020 to generate, transmit, and distribute electricity to end-use consumers. 

Therefore, the projected costs of compliance with the proposed rule amount to a one to two 

percent increase in the cost to meet electricity demand, while securing public health and welfare 

benefits that are several times more valuable (as described in Chapters 4 and 8).

The annual compliance costs presented in Table 3 reflects the cost savings due to reduced 

electricity demand from energy efficiency measures presented earlier. 

The following example uses results from Option 1 Regional scenario in the year 2020 to 

illustrate how different components of estimated expenditures are combined to form the full 

compliance costs presented in Table 3. In Table 3-8 we present the IPM modeling results for the 

2020 Option 1 Regional scenario, which includes CO2 emission reductions from the four 

building blocks and the exogenous reductions in electricity demand due to end-use energy 

efficiency (EE) improvements, and the base case. The results show that annualized expenditures 

required to supply enough electricity to meet demand decline by $4.8 billion from the base 

case. This incremental decline is a net outcome of two simultaneous effects which move in 

opposite directions. First, imposing the CO2 constraints represented in the Option 1 Regional 

scenario on electric generators would, other things equal, result in an incremental increase in 

expenditures to supply any given level of electricity. However, once electricity demand is 

exogenously reduced in IPM to reflect the substantial reduction in electricity demand (induced 

by EE improvements), there is a substantial reduction in the expenditures needed to supply a 

correspondingly lower amount of electricity demand.

Table 3-9. Total Power Sector Generating Costs (IPM) (billions 2011$)

2020 2025 2030

Base Case $177.8 $202.9 $224.7

Option 1 Regional $173.0 $178.6 $189.2

Option 1 State $175.0 $179.5 $190.7

Option 2 Regional $174.0 $186.9 n/a

Option 2 State $175.2 $187.8 n/a

Source: Integrated Planning Model run by EPA, 2014
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In order to reflect the full compliance cost attributable to the policy, it is necessary to 

include this incremental -$4.8 billion in electricity supply expenditures with the annualized 

expenditures needed to secure the end-use energy efficiency improvements. EPA has estimated 

these EE-related expenditures to be $10.2 billion in 2020 (using a 3% discount rate). As a result, 

this analysis finds the cost of the Option 1 Regional scenario in 2020 to be $5.4 billion (the sum 

of incremental supply-related and demand-related expenditures).55

3.7.3 Projected Compliance Actions for Emissions Reductions

Heat rate improvements (HRI). EPA analysis assumes that the existing coal steam electric 

generating fleet has, on average, the ability to improve operating efficiency (i.e., reduce the 

average net heat rate, or the Btu of fuel energy needed to produce one kWh of net electricity 

output). All else held constant, HRI allow the EGU to generate the same amount of electricity 

using less fuel. The decrease in required fossil fuel results in a lower output-based CO2 emissions 

rate (lbs/MWh), as well as a lower variable cost of electricity generation. In the modeling 

conducted for these compliance scenarios, coal boilers have the choice to improve heat rates by 

6% under Option 1 and 4% under Option 2, at capital cost of $100 per kW in both options.56

The vast majority of existing coal boilers are projected to adopt the aforementioned heat 

rate improvements. EPA projects that 176 to 179 GW of existing coal steam capacity (greater 

than 25 MW) will improve operating efficiency (i.e., reduce the average net heat rate) under 

Option 1 in 2020. Under Option 2, EPA projects that 168 to 185 GW of existing coal steam 

capacity with improve operating efficiency in 2020.

Re-dispatch. Another approach for reducing the average emission rate from existing units is to 

shift some generation from more CO2-intensive generation to less CO2-intensive generation. 

Compared to the Base Case, existing coal steam capacity is projected to operate at a lower 

capacity factor for both Option 1 and Option 2, on average, although projected average capacity 

factors for existing coal steam boilers remains around 75% across scenarios. Existing natural gas 

 

55 For this analysis, we quantified and imposed end-use EE costs and impacts on electricity demand exogenously. 

56 The option for heat rate improvement is only made available in the illustrative scenarios for Options 1 and 2, and 

is not available in the base case. See GHG Abatement Measures TSD.
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combined cycle units, which are less carbon-intensive than coal steam capacity on an output 

basis, operate at noticeably higher capacity factor in Options 1 and 2, on average. See Table 3-2. 

The utilization of existing natural gas combined cycle capacity is lower than 70% on an annual 

average basis in these illustrative compliance scenarios, reflecting the fact that states have the 

flexibility to choose among alternative CO2 reduction strategies that were part of BSER, instead 

of employing re-dispatch to the maximum extent. In addition, future electric demand is 

considerably lower than the Base Case.

Table 3-10. Projected Capacity Factor of Existing Coal Steam and Natural Gas Combined 

Cycle Capacity

Existing Coal Steam Existing Natural Gas Combined Cycle

2020 2025 2030 2020 2025 2030

Base Case 78% 80% 79% 52% 48% 42%

Option 1 Regional 77% 74% 73% 55% 54% 50%

Option 1 State 76% 74% 73% 56% 55% 51%

Option 2 Regional 75% 74% n/a 57% 55% n/a

Option 2 State 75% 73% n/a 57% 56% n/a

Source: Integrated Planning Model run by EPA, 2014

Demand-Side Energy Efficiency. Another approach for reducing the average emission rate is to 

consider reductions in load attributable to demand-side energy efficiency savings, which will 

reduce the need for higher emitting generation.57 In the compliance scenario analyses presented 

in this RIA, each state is credited for total demand-side energy efficiency savings consistent with 

the savings that are used to construct the state goals. See section 3.6.1 for a description of the 

levels assumed for Options 1 and 2. Again, these reductions in demand as a result of demandside energy efficiency are made exogenously in both of the Options for the illustrative scenarios 

analyzed.

3.7.4 Projected Generation Mix

Table 3-3 and Figure 3-4 show the generation mix in the base case and under the 

proposed rule. The ability to average the emissions rate at covered sources (which exclude new 

 

57 Because fossil fuel-fired EGUs typically have higher variable costs than other EGUs (such as nuclear and 

renewable EGUs), they are typically the first to be displaced when demand is reduced. The influence of EE on 

new sources of generation depends on both their relative variable and capital costs. 
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NGCC) over the 2020-2030 compliance period provides an incentive to shift generation within 

the covered sources to less carbon-intensive generation. In 2020, for Option 1, total generation 

declines approximately three percent under Option 1 as a result of the reduction in total demand 

attributable to the demand-side energy savings applied in the illustrative scenarios. Coal-fired 

generation is projected to decline 20% to 22% in 2020, and natural-gas-fired generation from 

existing combined cycle capacity is projected to increase four to six percent relative to the base 

case under Option 1. In 2030, the cumulative demand-side energy savings under the Option 1 

result in an 11% reduction in total generation relative to the base case. The coal-fired fleet in 

2030 generates between 25% and 27% less than in the base case, while natural-gas-fired 

generation from existing combined cycles increases 18% to 19% relative to the base case. Gasfired generation from new combined cycle capacity increases in 2020, as projected new natural 

gas combined cycle capacity replaces retired coal capacity. By 2030, generation from newly 

built natural gas combined cycle decreases between 36% and 40% relative to the base case, 

consistent with the decrease in new capacity (see section 3.7.6). Generation from non-hydro 

renewables increases two percent relative to the base case in 2030.

Similar trends are projected for Option 2. In 2020, total generation declines two percent 

under Option 2 as a result of the reduction in total demand attributable to the demand-side energy 

savings applied in the illustrative scenarios. Coal generation is projected to decrease between 

16% and 17% in 2020, and natural gas-fired generation from existing combined cycle units is 

projected to increase nine percent, relative to the base case. 

Under both options, additional natural gas pipeline capacity is projected to be built 

through 2020 relative to the base case. In all of the illustrative compliance scenarios, pipeline 

capacity is projected to increase four to eight percent beyond base case projections by 2020. In 

2030, however, the total cumulative pipeline capacity built is projected to decrease, consistent 

with the projected decrease in total natural gas use. The projected increase in pipeline capacity 

in the near term is largely the result of building pipeline capacity a few years earlier than in is 

projected in the base case. Given the relatively small amount of additional infrastructure 

development that is projected to shift to earlier years, construction of this additional capacity 

should be easily manageable and not raise any reliability or cost concerns.
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Table 3-11. Generation Mix (thousand GWh) 

Base

Case

Option 1 Option 2 Option 1 Option 2

Reg. State Reg. State Reg. State Reg. State

2020

Pulverized Coal 1,665 1,337 1,302 1,406 1,375 -20% -22% -16% -17%

NG Combined Cycle 

(existing) 1,003 1,043 1,065 1,093 1,091 4% 6% 9% 9%

NG Combined Cycle 

(new) 85 238 248 155 185 181% 192% 83% 119%

Combustion Turbine 19 33 33 33 32 74% 76% 75% 67%

Oil/Gas Steam 52 15 14 18 16 -70% -73% -65% -69%

Non-Hydro Renewables 299 321 323 313 316 7% 8% 5% 5%

Hydro 280 282 281 282 281 1% 1% 1% 1%

Nuclear 817 817 819 814 819 0% 0% 0% 0%

Other 8 15 14 13 12 92% 88% 66% 61%

Total 4,227 4,102 4,100 4,128 4,128 -3% -3% -2% -2%

2025

Pulverized Coal 1,702 1,275 1,250 1,383 1,353 -25% -27% -19% -20%

NG Combined Cycle 

(existing) 919 1,022 1,035 1,055 1,068 11% 13% 15% 16%

NG Combined Cycle 

(new) 280 257 266 218 231 -8% -5% -22% -17%

Combustion Turbine 27 37 37 37 37 36% 39% 36% 36%

Oil/Gas Steam 37 13 15 16 16 -64% -59% -56% -58%

Non-Hydro Renewables 335 347 346 342 341 4% 3% 2% 2%

Hydro 280 282 281 282 282 1% 0% 0% 0%

Nuclear 817 817 819 814 819 0% 0% 0% 0%

Other 6 13 11 11 10 115% 86% 71% 64%

Total 4,404 4,063 4,062 4,158 4,156 -8% -8% -6% -6%

2030

Pulverized Coal 1,668 1,249 1,216 n/a n/a -25% -27% n/a n/a

NG Combined Cycle 

(existing) 810 955 961 n/a n/a 18% 19% n/a n/a

NG Combined Cycle 

(new) 599 359 384 n/a n/a -40% -36% n/a n/a

Combustion Turbine 23 32 31 n/a n/a 43% 35% n/a n/a

Oil/Gas Steam 23 10 12 n/a n/a -57% -46% n/a n/a

Non-Hydro Renewables 350 356 356 n/a n/a 2% 2% n/a n/a

Hydro 280 281 280 n/a n/a 0% 0% n/a n/a

Nuclear 797 796 797 n/a n/a 0% 0% n/a n/a

Other 6 16 14 n/a n/a 163% 132% n/a n/a

Total 4,557 4,054 4,051 n/a n/a -11% -11% n/a n/a

Note: “Other” mostly includes MSW and fuel cells. Source: Integrated Planning Model run by EPA, 2014
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Figure 3-4. Generation Mix with the Base Case and 111(d) Options, 2020-2030 (million GWh)

Source: Integrated Planning Model run by EPA, 2014

0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

4.00

4.50

5.00

Base

Case

Reg. State Reg. State Base

Case

Reg. State Reg. State Base

Case

Reg. State

Other

Oil

Non-hydro

Renewables

Hydroelectric

Natural Gas

Nuclear

2020 2025 2030

Option 1 Option 2 Option 1 Option 2 Option 1
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3.7.5 Projected Incremental Retirements

Relative to the base case, about 30 to 49 GW of coal-fired capacity is projected to be 

uneconomic to maintain (about 12% to 19% of all coal-fired capacity projected to be in service 

in the base case) by 2020 under the range of scenarios analyzed.

For the proposed rule, EPA examined whether these projected incremental retirements 

may adversely impact reserve margins and reliability planning. The IPM model is designed to 

ensure that generation resource adequacy is maintained in the projected results, and the model is 

required to meet reserve margin requirements in the 64 modeling regions in the contiguous US 

by retaining enough existing capacity and/or building enough new capacity. IPM also addresses 

reliable delivery of generation resources at a regional level by limiting the ability to transfer 

power between regions using the bulk power transmission system. Within each model region, 

IPM assumes that adequate transmission capacity is available to deliver any resources located in, 

or transferred to, the region.58

EPA examined the implications of each of the scenarios for regional resource adequacy 

and for potential concerns over on grid reliability. To conduct this analysis, EPA examined key 

parameters from the IPM projections to assess whether concerns over regional resource adequacy 

would be likely to arise or whether changes in generation and flow pattern impacts would raise 

issues for reliability management. The key parameters analyzed were:

Operating Reserve Margins. The IPM model ensures that target reserve margins from NERC 

will be met, by maintaining existing capacity or by building additional new capacity if needed. 

Capacity that is otherwise uneconomic to operate will be retired only when planned reserve 

margins can be met from within the region or by transfers from other regions. 

Operational Capacity. Since IPM ensures that NERC target margins will be met, EPA analyzed 

the remaining operating capacity for 2020 through 2030 to determine what types of changes in 

the generation fleet were projected to occur through retirements, additional generation and 

energy efficiency. Although there were changes from the base case to the policy cases, none of 

 

58 For more detail on IPM’s electric load modeling and power system operation, please see IPM documentation 

(http://www.epa.gov/powersector modeling).
3-33

the policy cases were found to raise concerns over regional resource adequacy. Moreover, the 

time horizon for compliance with this rule will permit environmental and reliability planners to 

coordinate these changes and address potential concerns before they arise.

Interregional Power Flows and Capacity Transfers. IPM constrains both interregional energy 

flows and interregional transfers of capacity to meet reserve requirements. These limits are based 

on grid operator data from ISOs, RTOs and other planning entities. Although these limits keep 

projected flows within expected bounds, EPA further examined how the policy options impacted 

these flows and transfers to identify any large shifts from the base case. None of the interregional 

changes in the policy cases suggested that there would be increases in flows that would raise 

significant concerns about grid congestion or grid management.

The increased energy efficiency anticipated with the proposed rule also contributes to 

meeting resource adequacy, by lowering the regional load and thereby lowering operating 

capacity needed to meet planning reserve margins. Demand-side energy efficiency would also 

reduce the overall load on the grid and thus generally reduces the burden on the transmission 

infrastructure needed to maintain reliability. EPA concludes that the proposed rule will not raise 

significant concerns over regional resource adequacy or raise the potential for interregional grid 

problems. EPA believes any remaining local issues can be managed through standard reliability 

planning processes.59

Capacity changes from the base case in 2020 are shown in Table 3-12.

 

59 For further discussion of EPA’s examination of these projected incremental retirements on reserve margins and 

reliability planning, see the Resource Adequacy and Reliability Analysis TSD. 
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Table 3-12. Total Generation Capacity by 2020-2030 (GW)

Base 

Case

Option 1 Option 2 Option 1 Option 2

Reg. State Reg. State Reg. State Reg. State

2020

Pulverized Coal 244 198 195 214 211 -19% -20% -12% -14%

NG Combined Cycle 

(existing) 219 216 217 218 218 -1% -1% -1% 0%

NG Combined Cycle (new) 12 33 35 21 26 174% 190% 79% 117%

Combustion Turbine 146 144 143 146 145 -2% -2% -1% -1%

Oil/Gas Steam 83 66 66 72 71 -20% -20% -13% -14%

Non-Hydro Renewables 93 105 105 101 102 13% 13% 9% 10%

Hydro 101 101 101 101 101 0% 0% 0% 0%

Nuclear 103 103 103 103 103 0% 0% 0% 0%

Other 5 5 5 5 5 2% 2% 2% 2%

Total 1,005 971 970 981 981 -3% -3% -2% -2%

2025

Pulverized Coal 243 197 193 214 211 -19% -21% -12% -13%

NG Combined Cycle 

(existing) 219 216 217 218 218 -1% -1% -1% 0%

NG Combined Cycle (new) 39 34 36 30 32 -11% -7% -23% -18%

Combustion Turbine 149 145 144 148 147 -3% -3% -1% -2%

Oil/Gas Steam 82 65 66 72 71 -20% -19% -12% -13%

Non-Hydro Renewables 103 113 112 110 110 10% 8% 7% 7%

Hydro 101 101 101 101 101 0% 0% 0% 0%

Nuclear 103 103 103 103 103 0% 0% 0% 0%

Other 5 5 5 5 5 2% 2% 2% 2%

Total 1,044 980 977 1,000 997 -6% -6% -4% -4%

2030

Pulverized Coal 240 195 191 n/a n/a -19% -21% n/a n/a

NG Combined Cycle 

(existing) 219 216 217 n/a n/a -1% -1% n/a n/a

NG Combined Cycle (new) 84 49 52 n/a n/a -42% -38% n/a n/a

Combustion Turbine 156 146 145 n/a n/a -7% -7% n/a n/a

Oil/Gas Steam 82 65 66 n/a n/a -20% -19% n/a n/a

Non-Hydro Renewables 107 117 115 n/a n/a 9% 7% n/a n/a

Hydro 101 101 101 n/a n/a 0% 0% n/a n/a

Nuclear 101 100 101 n/a n/a 0% 0% n/a n/a

Other 5 5 5 n/a n/a 2% 2% n/a n/a

Total 1,095 994 992 n/a n/a -9% -9% n/a n/a

Source: Integrated Planning Model run by EPA, 2014

3.7.6 Projected Capacity Additions

Due largely to the demand reduction attributable to the demand-side energy savings 

applied in the illustrative scenarios, EPA projects less new natural gas combined cycle capacity 

built under the proposed rule than is built in the base case over the time horizon presented in this 
3-35

RIA. While this new NGCC capacity cannot be directly counted towards the average emissions 

rate used for compliance, it can displace some generation from covered sources and thus 

indirectly lower the average emissions rate from covered sources. Conversely, EPA projects an 

overall increase in new non-hydro renewable capacity. As affected sources in the illustrative 

scenarios, the generation from new non-hydro renewables are able to contribute to the average 

emissions rate in each state or region.

Under the Option 1 illustrative scenarios, new natural gas combined cycle capacity is 

projected to increase by 21 to 23 GW in 2020 (174% to 190% increase relative to the base case), 

and decrease by 32 to 35 GW (38% to 42% reduction relative to base case) by 2030. New nonhydro renewable capacity is projected to increase by about 12 GW (67% increase) above the 

base case in 2020, and 8 to 9 GW (24% to 28% increase) by 2030.

Under the Option 2 illustrative scenarios, new natural gas combined cycle capacity is 

projected to increase by about 9 to 14 GW (79% to 117% increase relative to base case) in 2020, 

and decrease between 7 and 9 GW (17% to 23% reduction) in 2025. New non-hydro renewable 

capacity is projected to increase by about 9 GW (50% increase) above the base case in 2020, and 

about 7 GW (25% increase) by 2025.

Table 3-13. Projected Capacity Additions, Gas (GW)

Cumulative Capacity Additions: Gas 

Combined Cycle

Incremental Cumulative Capacity 

Additions: Gas Combined Cycle

2020 2025 2030 2020 2025 2030

Base Case 11.9 38.9 83.8

Option 1 Regional 32.7 34.4 49.0 20.8 -4.4 -34.8

Option 1 State 34.7 36.1 51.6 22.7 -2.7 -32.1

Option 2 Regional 21.4 30.0 n/a 9.4 -8.9 n/a

Option 2 State 25.9 32.0 n/a 14.0 -6.8 n/a

Source: Integrated Planning Model run by EPA, 2014

Table 3-14. Projected Capacity Additions, Non-hydro Renewable (GW)

Cumulative Capacity Additions: 

Renewables

Incremental Cumulative Capacity 

Additions: Renewables

2020 2025 2030 2020 2025 2030

Base Case 17.8 28.4 32.7

Option 1 Regional 29.9 38.4 41.9 12.1 9.9 9.2

Option 1 State 29.6 37.0 40.6 11.8 8.5 7.9

Option 2 Regional 26.4 35.5 n/a 8.6 7.1 n/a

Option 2 State 26.7 35.0 n/a 8.8 6.6 n/a

Source: Integrated Planning Model run by EPA, 2014
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3.7.7 Projected Coal Productions for the Electric Power Sector

Coal production is projected to decrease in 2020 and beyond in the illustrative scenarios 

due to (1) improved heat rates (generating efficiency) at existing coal units, (2) demand 

reduction attributable to the demand-side energy savings, and (3) a shift in generation from coal 

to less-carbon intensive generation. As shown in Table 15, the largest decrease in coal 

production is projected to occur in the western region. Waste coal production is projected to 

increase slightly under the proposed rule due to the operation under both options of less than 1 

GW of coal steam capacity that is projected to retire under the base case.

Table 3-15. Coal Production for the Electric Power Sector, 2020

Coal Production (MM Tons) Percent Change from Base Case

Base 

Case

Option 1 Option 2 Option 1 Option 2

Reg. State Reg. State Reg. State Reg. State

Appalachia 140 87 91 n/a n/a -37% -35% n/a n/a

Interior 249 231 222 n/a n/a -7% -11% n/a n/a

West 446 308 292 n/a n/a -31% -34% n/a n/a

Waste Coal 9 10 10 n/a n/a 8% 5% n/a n/a

Imports 0 0 0 n/a n/a n/a n/a

Total 844 636 616 n/a n/a -25% -27% n/a n/a

Source: Integrated Planning Model run by EPA, 2014

Power sector natural gas use is projected to increase between 12% and 14% in 2020 under 

Option 1 and between 10% and 12% under Option 2. In later years, gas use declines under both 

options. These trends are consistent with the change in generation mix described above in 

Section 3.7.4.

Table 3-16. Power Sector Gas Use

Power Sector Gas Use (TCF) Percent Change in Power Sector Gas 

Use

2020 2025 2030 2020 2025 2030

Base Case 8.35 8.88 9.89

Option 1 Regional 9.32 9.31 9.37 11.7% 4.8% -5.3%

Option 1 State 9.54 9.52 9.61 14.3% 7.2% -2.9%

Option 2 Regional 9.20 9.34 n/a 10.2% 5.1% n/a

Option 2 State 9.35 9.52 n/a 12.0% 7.2% n/a

Source: Integrated Planning Model run by EPA, 2014
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3.7.8 Projected Fuel Price Impacts

The impacts of the proposed rule on coal and natural gas prices before shipment are 

shown below in Table 3-17, Table 3-18, Table 3-19, and Table 3-20, and are attributable to the 

policy-induced changes in overall power sector demand for each fuel. 

Coal demand decreases over the 2020-2030 time horizon, resulting in a decrease in the 

price of coal delivered to the electric power sector. In 2020, the increase in natural gas demand 

results in an increase in the price of gas delivered to the electric power sector. In 2030, gas 

demand and price decrease below the base case projections, due to the cumulative impact of 

national demand-side energy efficiency savings and the consequent reduced overall electricity 

demand.

IPM modeling of natural gas prices uses both short- and long-term price signals to 

balance supply and demand for the fuel across the modeled time horizon. As such, it should be 

understood that the pattern of IPM natural gas price projections over time is not a forecast of 

natural gas prices incurred by end-use consumers at any particular point in time. The natural gas 

market in the United States has historically experienced some degree of price volatility from year 

to year, between seasons within a year, and during short-lived weather events (such as cold snaps 

leading to short-run spikes in heating demand). These short-term price signals are fundamental 

for allowing the market to successfully align immediate supply and demand needs. However, 

end-use consumers are typically shielded from experiencing these rapid fluctuations in natural 

gas prices by retail rate regulation and by hedging through longer-term fuel supply contracts by 

the power sector. IPM assumes these longer-term price arrangements take place “outside of the 

model” and on top of the “real-time” shorter-term price variation necessary to align supply and 

demand. Therefore, the model’s natural gas price projections should not be mistaken for 

traditionally experienced consumer price impacts related to natural gas, but a reflection of 

expected average price changes over the period of time represented by the modeling horizon.
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Table 3-17. Projected Average Minemouth and Delivered Coal Prices (2011$/MMBtu)

Minemouth Delivered - Electric Power Sector

2020 2025 2030 2020 2025 2030

Base Case 1.73 1.88 2.06 2.62 2.80 2.98

Option 1 Regional 1.46 1.57 1.72 2.19 2.29 2.44

Option 1 State 1.45 1.56 1.70 2.18 2.30 2.44

Option 2 Regional 1.49 1.62 n/a 2.25 2.40 n/a

Option 2 State 1.49 1.62 n/a 2.26 2.41 n/a

Source: Integrated Planning Model run by EPA, 2014

Table 3-18. Projected Average Minemouth and Delivered Coal Prices: Percent Change 

from Base Case Projections

Minemouth Delivered - Electric Power Sector

2020 2025 2030 2020 2025 2030

Option 1 Regional -15.5% -16.6% -16.4% -16.3% -18.3% -18.1%

Option 1 State -16.1% -17.0% -17.6% -16.5% -17.9% -18.2%

Option 2 Regional -14.1% -14.1% n/a -13.8% -14.4% n/a

Option 2 State -14.0% -14.1% n/a -13.6% -14.1% n/a

 Source: Integrated Planning Model run by EPA, 2014

Table 3-19. Projected Average Henry Hub (spot) and Delivered Natural Gas Prices 

(2011$/MMBtu)

Henry Hub Delivered - Electric Power Sector

2020 2025 2030 2020 2025 2030

Base Case 4.98 5.68 6.00 5.36 6.11 6.39

Option 1 Regional 5.50 5.60 6.02 5.86 5.91 6.33

Option 1 State 5.61 5.57 6.07 5.98 5.90 6.39

Option 2 Regional 5.40 5.79 n/a 5.76 6.13 n/a

Option 2 State 5.43 5.80 n/a 5.80 6.16 n/a

Source: Integrated Planning Model run by EPA, 2014

Table 3-20. Average Henry Hub (spot) and Delivered Natural Gas Prices: Percent Change 

from Base Case Projections

Henry Hub Delivered - Electric Power Sector

2020 2025 2030 2020 2025 2030

Option 1 Regional 10.4% -1.5% 0.4% 9.3% -3.3% -0.9%

Option 1 State 12.5% -2.0% 1.2% 11.5% -3.5% 0.0%

Option 2 Regional 8.5% 2.0% n/a 7.5% 0.2% n/a

Option 2 State 9.0% 2.1% n/a 8.1% 0.8% n/a

Source: Integrated Planning Model run by EPA, 2014

3.7.9 Projected Retail Electricity Prices

EPA’s analysis projects an increase in the national average (contiguous U.S.) retail 

electricity price between 5.9% and 6.5% in 2020 and between 2.7% and 3.1% by 2030 under the 
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proposed Option 1, compared to the modeled base case price estimate in those years. Under 

Option 2, on average, EPA projects an average retail price increase ranging from 23.6% to 4.0% 

in 2020, and from 2.4% to 2.7% in 2025. 

Retail electricity prices embody generation, transmission, distribution, taxes, and utility 

demand-side EE program costs. IPM modeling projects changes in regional wholesale power 

prices and capacity payments related to imposition of the represented policy that are combined 

with EIA regional transmission and distribution costs to calculate changes to regional retail 

prices. As described in Section 3.6.2, the utility funding for demand-side energy efficiency 

programs (to cover program costs) is typically collected through a standard per kWh surcharge to 

the ratepayer and the regional retail price impacts assume that first-year costs of these policies 

are recovered by utilities in retail rates. There are many factors influencing the estimated retail 

electricity price impacts, namely projected changes in generation mix, fuel prices, and 

development of new generating capacity. These projected changes vary regionally under each 

compliance scenario in response to the goals under the two options, and they also vary depending 

upon retail electricity market structure (e.g., cost-of-service vs. competitive). 
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Table 3-21. 2020 Projected Contiguous U.S. and Regional Retail Electricity Prices

(cents/kWh)

2020 Projected Retail Price (cents/kWh) Percent Change from Base Case

Base 

Case

Option 1 

Regional

Option 1 

State

Option 2 

Regional

Option 2 

State

Option 1 

Regional

Option 1 

State

Option 2 

Regional

Option 2 

State

ERCT 9.9 10.6 10.8 10.4 10.5 7.4% 9.9% 5.3% 6.0%

FRCC 10.6 11.3 11.6 11.2 11.4 6.5% 8.7% 5.2% 7.1%

MROE 10.4 10.9 11.0 10.8 10.9 4.7% 5.8% 3.4% 4.2%

MROW 9.2 9.8 9.8 9.6 9.6 6.4% 7.0% 4.6% 4.9%

NEWE 13.8 15.2 15.4 14.5 14.8 10.1% 11.4% 5.1% 6.9%

NYCW 18.0 19.7 19.8 18.8 18.9 9.5% 10.0% 4.6% 5.0%

NYLI 14.7 16.1 16.1 15.3 15.3 9.3% 9.5% 3.7% 4.2%

NYUP 12.7 14.0 14.0 13.4 13.3 9.9% 9.8% 4.8% 4.5%

RFCE 12.2 13.3 13.2 12.6 12.8 8.6% 7.7% 3.2% 4.6%

RFCM 10.7 11.2 11.3 11.1 11.1 5.1% 6.0% 3.6% 3.9%

RFCW 10.1 10.7 10.8 10.4 10.3 6.1% 6.3% 2.5% 2.2%

SRDA 9.0 9.4 9.4 9.3 9.2 4.1% 4.5% 3.0% 2.8%

SRGW 9.3 9.7 9.7 9.5 9.5 4.9% 4.7% 2.1% 1.7%

SRSE 10.4 10.7 10.7 10.6 10.6 2.8% 3.4% 2.2% 2.2%

SRCE 8.2 8.4 8.4 8.3 8.4 1.5% 1.7% 1.3% 1.6%

SRVC 10.7 10.9 10.9 10.8 10.8 2.4% 2.2% 1.6% 1.5%

SPNO 10.6 11.7 10.7 11.4 10.7 9.9% 0.6% 7.5% 1.1%

SPSO 8.3 9.0 9.3 8.8 9.0 8.3% 11.7% 5.3% 7.7%

AZNM 10.5 11.1 11.3 10.9 11.1 5.3% 7.4% 3.9% 5.6%

CAMX 14.3 15.2 15.2 14.9 14.9 6.3% 6.4% 4.7% 4.5%

NWPP 7.3 7.7 7.8 7.6 7.6 6.1% 6.7% 4.5% 4.5%

RMPA 8.9 9.5 9.9 9.4 9.6 6.3% 10.4% 5.0% 6.9%

Contiguous 

U.S. 10.4 11.1 11.1 10.8 10.9 5.9% 6.5% 3.6% 4.0%
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Table 3-22. 2025 Projected Contiguous U.S. and Regional Retail Electricity Prices

(cents/kWh)

2025 Projected Retail Price (cents/kWh) Percent Change from Base Case

Base 

Case

Option 1 

Regional

Option 1 

State

Option 2 

Regional

Option 2 

State

Option 1 

Regional

Option 1 

State

Option 2 

Regional

Option 2 

State

ERCT 11.2 11.5 11.4 11.6 11.6 2.9% 1.8% 3.3% 3.7%

FRCC 10.9 11.4 11.5 11.3 11.5 4.5% 5.4% 4.1% 5.4%

MROE 10.5 10.9 11.0 10.8 10.9 4.0% 4.2% 2.5% 4.0%

MROW 9.2 9.8 9.8 9.6 9.6 5.8% 5.7% 4.3% 4.4%

NEWE 14.2 14.4 14.5 14.3 14.5 1.4% 1.9% 0.2% 1.6%

NYCW 18.8 19.1 19.1 18.9 18.9 1.5% 1.3% 0.5% 0.4%

NYLI 15.6 15.5 15.4 15.4 15.3 -0.4% -1.4% -1.4% -1.5%

NYUP 13.2 13.3 13.2 13.2 13.1 0.2% -0.4% -0.3% -0.8%

RFCE 12.6 12.7 12.9 13.0 13.0 0.4% 2.2% 3.0% 3.1%

RFCM 10.7 11.2 11.2 11.1 11.1 4.0% 4.5% 3.1% 3.6%

RFCW 10.9 10.9 11.0 10.9 11.0 -0.4% 0.0% -0.1% 0.3%

SRDA 9.3 9.7 9.7 9.7 9.7 4.1% 3.7% 3.9% 3.6%

SRGW 10.1 10.1 10.1 10.1 10.1 -0.1% -0.1% -0.1% -0.1%

SRSE 10.3 10.7 10.7 10.6 10.7 3.5% 3.8% 2.8% 2.9%

SRCE 8.2 8.5 8.5 8.4 8.4 3.1% 3.2% 2.2% 2.2%

SRVC 10.6 10.9 10.8 10.8 10.8 3.0% 2.5% 2.4% 2.0%

SPNO 10.3 11.8 10.8 11.3 10.7 14.1% 4.5% 8.7% 3.0%

SPSO 8.8 9.5 9.7 9.4 9.5 7.4% 9.4% 6.0% 7.7%

AZNM 10.8 11.2 11.3 11.0 11.2 3.9% 5.0% 2.4% 4.0%

CAMX 13.9 14.4 14.3 14.3 14.3 3.2% 2.9% 2.9% 2.9%

NWPP 7.4 7.8 7.7 7.6 7.6 5.0% 4.6% 3.4% 3.3%

RMPA 9.4 9.6 10.0 9.6 9.8 2.3% 6.4% 2.7% 4.8%

Contiguous 

U.S. 10.8 11.1 11.1 11.0 11.1 2.7% 2.9% 2.4% 2.7%
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Table 3-23. 2030 Projected Contiguous U.S. and Regional Retail Electricity Prices

(cents/kWh)

2030 Projected Retail Price (cents/kWh) Percent Change from Base Case

Base 

Case

Option 1 

Regional

Option 1 

State

Option 2 

Regional

Option 2 

State

Option 1 

Regional

Option 1 

State

Option 2 

Regional

Option 2 

State

ERCT 11.6 12.0 12.0 n/a n/a 3.6% 3.9% n/a n/a

FRCC 10.9 11.5 11.5 n/a n/a 4.7% 5.6% n/a n/a

MROE 10.5 10.9 11.1 n/a n/a 4.0% 5.9% n/a n/a

MROW 9.4 9.8 9.8 n/a n/a 4.3% 4.3% n/a n/a

NEWE 15.1 15.1 15.3 n/a n/a -0.1% 1.0% n/a n/a

NYCW 19.9 20.1 20.1 n/a n/a 1.0% 0.8% n/a n/a

NYLI 16.9 16.5 16.3 n/a n/a -2.3% -3.3% n/a n/a

NYUP 14.2 14.2 14.2 n/a n/a 0.0% -0.4% n/a n/a

RFCE 12.4 12.8 12.9 n/a n/a 3.4% 4.2% n/a n/a

RFCM 10.8 11.2 11.2 n/a n/a 3.8% 4.5% n/a n/a

RFCW 11.2 11.3 11.3 n/a n/a 0.4% 0.6% n/a n/a

SRDA 9.5 9.9 9.9 n/a n/a 4.6% 4.5% n/a n/a

SRGW 10.4 10.3 10.2 n/a n/a -1.2% -1.6% n/a n/a

SRSE 10.4 10.7 10.8 n/a n/a 3.2% 4.0% n/a n/a

SRCE 8.1 8.4 8.4 n/a n/a 2.6% 2.7% n/a n/a

SRVC 10.4 10.7 10.6 n/a n/a 3.0% 2.5% n/a n/a

SPNO 10.2 11.5 10.5 n/a n/a 12.7% 3.1% n/a n/a

SPSO 9.1 9.7 9.9 n/a n/a 7.2% 9.2% n/a n/a

AZNM 11.5 11.4 11.7 n/a n/a -0.2% 2.1% n/a n/a

CAMX 14.1 14.7 14.7 n/a n/a 4.2% 3.9% n/a n/a

NWPP 7.4 7.7 7.7 n/a n/a 2.8% 2.8% n/a n/a

RMPA 9.9 9.9 10.3 n/a n/a 0.7% 4.7% n/a n/a

Contiguous 

U.S. 10.9 11.2 11.3 n/a n/a 2.7% 3.1% n/a n/a
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Figure 3-5. Electricity Market Module Regions

Source: EIA <http://www.eia.gov/forecasts/aeo/pdf/nerc_map.pdf)>.

3.7.10 Projected Electricity Bill Impacts

The electricity price changes addressed in section 3.7.9 combine with the significant 

reductions in electricity demand applied in the illustrative scenarios to affect average electricity 

bills. The estimated changes to average bills are summarized in Table 3-24. Under Option 1, 

EPA estimates an average bill increase of 2.7% to 3.2% in 2020 and an average bill decrease of 

8.4% to 8.7% in 2030. Under Option 2, EPA estimates an average bill increase of 1.1% to 1.4% 

in 2020 and an average bill decrease of 3.2% to 3.5% in 2025. These reduced electricity bills 

reflect the combined effects of changes in both average retail rates (driven by the effects of all 

four building blocks) and lower electricity demand (driven by the fourth building block, demandside energy efficiency). 

Table 3-24. Projected Changes in Average Electricity Bills

2020 2025 2030

Option 1 Regional 2.7% -5.4% -8.7%

Option 1 State 3.2% -5.3% -8.4%

Option 2 Regional 1.1% -3.5% n/a

Option 2 State 1.4% -3.2% n/a
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3.8 Projected Primary PM Emissions from Power Plants

IPM is not configured to endogenously model primary PM emissions from power plants. 

These emissions are calculated as a function of IPM outputs, emission factors and control 

configuration. IPM-projected fuel use (heat input) is multiplied by PM emission factors (based in 

part on the presence of PM-relevant pollution control devices) to determine PM emissions. 

Primary PM emissions are calculated by adding the filterable PM and condensable PM 

emissions.

Filterable PM emissions for each unit are based on historical information regarding 

existing emissions controls and types of fuel burned and ash content of the fuel burned, as well 

as the projected emission controls (e.g., scrubbers and fabric filters).

Condensable PM emissions are based on plant type, sulfur content of the fuel, and 

SO2/HCl and PM control configurations. Although EPA’s analysis is based on the best available 

emission factors, these emission factors do not account for the potential changes in condensable 

PM emissions due to the installation and operation of SCRs. The formation of additional 

condensable PM (in the form of SO3 and H2SO4) in units with SCRs depends on a number of 

factors, including coal sulfur content, combustion conditions and characteristics of the catalyst 

used in the SCR, and is likely to vary widely from unit to unit. SCRs are generally designed and 

operated to minimize increases in condensable PM. This limitation means that IPM postprocessing is potentially underestimating condensable PM emissions for units with SCRs. In 

contrast, it is possible that IPM post-processing overestimates condensable PM emissions in a 

case where the unit is combusting a low-sulfur coal in the presence of a scrubber.

EPA applied this methodology to develop primary PM emission projections for 2025 in 

the base case, and for Option 1 State and Option 2 Regional. Using these results, EPA then 

estimated primary PM emissions for the remainder of the base case and policy scenarios over the 

2020-2030 time horizon using simplified emissions factors. These factors were developed for 

the eastern and western regions (excluding California) using EPA’s emissions and fuel use 

projections for fossil plus biomass. Separate factors were developed for base case and policy 

case scenarios. These factors were applied to the projected fuel use from fossil and biomass 

plant types. While this methodology provides a reasonable estimate, EPA notes that applying the 
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methodology discussed above would likely yield different results. Nevertheless, EPA has 

determined that this estimation is sufficient for the purpose of estimating benefits on a large 

regional level.

For a more complete description of the methodologies used to post-process PM emissions 

from IPM, see “IPM ORL File Generation Methodology” (March, 2011), available in the docket.

3.9 Limitations of Analysis

EPA’s modeling is based on expert judgment of various input assumptions for variables 

whose outcomes are in fact uncertain. As a general matter, the Agency reviews the best available 

information from engineering studies of air pollution controls, the ability to improve operating 

efficiency, and new capacity construction costs to support a reasonable modeling framework for 

analyzing the cost, emission changes, and other impacts of regulatory actions.

The costs presented in this RIA include both the IPM-projected annualized estimates of 

private compliance costs as well as the estimated costs incurred by utilities and ratepayers to 

achieve demand-side energy efficiency improvements. The IPM-projected annualized estimates 

of private compliance costs provided in this analysis are meant to show the increase in 

production (generating) costs to the power sector in response to the final rule. To estimate these 

annualized 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.60

The demand-side energy efficiency costs are developed based on a review of energy 

efficiency data and studies, and expert judgment. EPA recognizes that significant variation exists 

in these analyses reflecting data and methodological limitations. The method used for estimating 

the demand-side energy efficiency costs is discussed in more detail in the Greenhouse Gas 

Abatement Measures TSD. The TSD also discusses the economic literature of energy efficiency 

costs and related considerations, energy savings potential studies, and discusses the associated 

 

60 See Chapter 8 of EPA’s Base Case using IPM (v5.13) documentation, available at: 

http://www.epa.gov/powersectormodeling/BaseCasev513.html.
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uncertainties. The evaluation, measurement and verification (EM&V) of demand-side energy 

efficiency is addressed in the State Plan Considerations TSD.

The base case electricity demand in IPM v.5.13 is calibrated to reference case demand in 

AEO 2013. AEO 2013 demand reflects, to some degree, a continuation of the impacts of state 

demand-side energy efficiency policies but does not explicitly represent many of the existing 

state policies in this area (e.g., energy efficiency resource standards). To some degree the 

implicit representation of state policies in the EPA’s base case alters the impacts assessment, but 

the direction of change is not known with certainty. This issue is discussed in more detail in the 

Greenhouse Gas Abatement Measures TSD.

Cost estimates for the proposed rule are based on rigorous power sector modeling using

ICF’s Integrated Planning Model.61 IPM assumes “perfect foresight” of market conditions over 

the time horizon modeled; to the extent that utilities and/or energy regulators misjudge future 

conditions affecting the economics of pollution control, costs may be understated as well.

Furthermore, IPM does not represent electricity markets in Alaska, Hawaii, and U.S. territories 

outside the contiguous U.S. and therefore the costs (and benefits) that may be expected from the 

proposed rule in this states and territories are not accounted for in the compliance cost modeling. 

3.10 Significant Energy Impacts

The proposed rule (Option 1) would have a significant impact according to E.O. 13211: 

Actions that Significantly Affect Energy Supply, Distribution, or Use. Under the provisions of 

this rule, EPA projects that approximately 46 to 49 GW of additional coal-fired generation 

(about 19% of all coal-fired capacity and 4.6% of total generation capacity in 2020) may be 

removed from operation by 2020. 

EPA also projects the average delivered coal price decreases by 16.3% to 16.5% with 

decreased production of 208 to 228 million tons (24.6% to 27.7% of US production) in 2020 and 

that electric power sector delivered natural gas prices will increase by about 9.3% to 11.5% with 

increased power sector consumption of between 979 to 1,194 billion cubic feet (BCF) in 2020. 

 

61 Full documentation for IPM can be found at <http:// http://www.epa.gov/powersectormodeling>.
3-47

Average retail electricity prices are projected to increase in the contiguous U.S. by 5.9% to 6.5% 

in 2020.

3.11 Monitoring, Reporting, and Recordkeeping Costs

EPA projected monitoring, reporting and recordkeeping costs for both state entities and 

affected EGUs for the compliance years 2020, 2025 and 2030. 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 1,228 facilities would be affected.

In calculating the cost for affected EGUs to comply, EPA assumed that the state plan 

would utilize a rate-based emission limit. The 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. However, an affected EGU will still need to be 

modify their equipment to comply with a rate-based limit as described in the emission 

guidelines. The EPA estimates that it would take 3 working months for a technician to retrofit 

any existing energy meters to meet the requirements set in the state plan. Additionally EPA 

believes that 1 hour will be needed for each EGU operator to read the rule and understand how 

the facility will comply with the rule. Also, after all modifications are made at a facility to 

measure net energy output, each EGUs 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 2020. 

Recordkeeping and reporting costs substantially decrease for the period 2021-2030. The 

projected costs for 2020, 2025, and 2030 are summarized below.

In calculating the cost for states to comply, EPA estimates that each state will rely on the 

equivalent of 2 full time staff to oversee program implementation, assess progress, develop 

possible contingency measures, state plan revisions and the subsequent public meetings if 

revisions are indeed needed, download data from the ECMPS for their annual reporting and 
3-48

develop their annual EPA report. Table 3-25 shows the annual respondent burden costs and costs 

of reporting and recordkeeping for 2020, 2025 and 2030.

Table 3-25. Years 2020, 2025 and 2030: Summary of Annual Respondent Burden and Cost 

of Reporting and Recordkeeping Requirements (2011$)

Nationwide Totals Total Annual 

Labor Burden 

(Hours)

Total

Annual

Labor Costs

Total 

Annualized

Capital Costs

Total

Annual

O&M Costs

Total 

Annualized 

Costs

Year 2020 900,048 $65,573,900 $0 $2,701,100 $68,275,000

Year 2025 217,280 $8,215,240 $0 $638,500 $8,853,740

Year 2030 217,280 $8,215,240 $0 $638,500 $8,853,740

The annual costs of this proposal Option1 and regulatory alternative Option 2 including 

monitoring reporting and recordkeeping costs are shown in Table 3-26 below.

Table 3-26. Annualized Compliance Costs Including Monitoring, Reporting and 

Recordkeeping Costs Requirements (billions of 2011$)

2020 2025 2030

Option 1 Regional 5.5 4.6 7.3

Option 1 State 7.5 5.5 8.8

Option 2 Regional 4.3 4.5 n/a

Option 2 State 5.5 5.5 n/a

Source: Integrated Planning Model run by EPA, 2014 and GHG Abatement Measures TSD. Monitoring, reporting 

and recordkeeping costs calculated outside IPM.

3.12 Social Costs

As discussed in the EPA 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 towards pollution mitigation. 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. This section 

provides a qualitative discussion of the relationship between social costs and compliance cost 

estimates presented in this chapter. 
3-49

For the illustrative compliance scenarios cost estimates presented in this chapter are the 

sum of expenditures on end-use energy efficiency programs and the change in expenditures 

required by the electricity sector to comply with the proposed guidelines. These two components 

are estimated separately. The expenditures required to achieve the assumed demand reductions 

through end-use energy efficiency programs are estimated using historical data and expert 

judgment. The change in the expenditures required by the electricity sector to meet demand and 

maintain compliance are estimated by IPM and reflect both the reduction in electricity 

production costs due to the reduction in demand caused by the end-use energy efficiency 

programs and the increase in electricity production costs required to achieve the additional 

emission reductions necessary to comply with the state goals.62 

As described in section 3.6.1, the illustrative scenarios generally assume that, in 

achieving their goals, states adopt energy efficiency programs which lead to net demand 

reductions in each year equivalent to those applied in the calculation of their respective goals. 

The estimated expenditures required to achieve those net demand reductions through end-use 

energy efficiency programs are presented in this chapter and detailed in the GHG Abatement 

Measures TSD chapter on end-use energy efficiency. The social cost of achieving these energy 

savings comes in the form of increased expenditures on technologies and/or services that are 

required to lower end-user’s electricity consumption beyond the business as usual. Under the 

assumption of complete and well-functioning markets the expenditures required to reduce 

electricity consumption on the margin will represent society’s opportunity cost of the resources 

required to produce the energy savings. 

The social cost of achieving these net electricity demand reductions may differ from the 

expenditures associated with the end-use energy efficiency programs. For example, some 

participants in end-use energy efficiency programs might have chosen to adopt the energy 

 

62 As described in section 3.5, IPM provides the least-cost solution to attaining the constraints representing the 

regulation required used to achieve the state goals but for the component of the state goals that is achieved by end 

use energy efficiency programs. 
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efficiency improvements even in the absence of the program.63 Therefore the expenditures, 

except for any program administration expenditures, associated with those demand reductions 

are not part of the program’s social costs, as they would have accrued even in its absence.64 The 

compliance cost estimates for the illustrative scenarios includes this limited share of the program 

costs that is expended on end-users who would have reduced their demand in the absence of the 

program. Thus, with respect to this particular issue, the social cost of the illustrative scenarios 

would be less than the estimated compliance cost reported in Table 3-8 to a limited degree. If the 

program expenditures that do not lead to additional demand reductions are paid for by electricity 

rate or tax payer funded programs then that portion of program expenditures represents a transfer 

among electricity consumers and has no net welfare gain or loss to society as a whole. 

Due to the flexibility held by states in implementing their compliance with the proposed 

standards these energy efficiency expenditures may be borne by end-users through direct 

participant expenditures or electricity rate increases or by producers through reductions in their 

profits. While the allocation of these expenditures between consumers and producers is 

important for understanding the distributional impact of potential compliance strategies, it does 

not necessarily affect the opportunity cost required for the production of the energy savings from 

a social perspective. However, specific design elements of demand-side or end-use energy 

efficiency programs included to address distributional outcomes may have an effect on the 

economic efficiency of the programs and therefore the social cost.

Another reason the expenditures associated with end-use energy efficiency programs may 

differ from social costs is due to differences in the services provided by more energy efficient 

technologies and services adopted under the program relative to the baseline. For example, if 

 

63 For example if a rebate program is offered for energy efficient appliances, some participants that claim the rebate 

may have purchased the more energy efficient appliances even without the rebate and so the rebate and 

investment made by these end-users is not a social cost of the program. However, the administrative cost of the 

rebate program is a social cost as it requires the reallocation of societal resources. 

64 The demand reductions assumed in the illustrative compliance scenarios only include those additional demand 

reductions motivated by the program costs. That is, the demand reductions assumed for the illustrative compliance 

scenarios already account for the potential that some program costs do not lead to additional energy efficiency 

investments by end-users. For more information see the GHG Abatement Measures TSD chapter on end-use 

energy efficiency.
3-51

under the program end-users adopted more energy efficient products which were associated with 

quality or service attributes deemed less desirable, then there would be an additional welfare loss 

that should be accounted for in social costs but is not necessarily captured in the measure of 

expenditures. However, there is an analogous (and more common) possibility that in some cases 

the quality of services, outside of the energy savings, provided by the more energy efficient 

products and practices are deemed more desirable by some end-users. For example, 

weatherization of buildings to reduced electricity demand associated with cooling will likely 

have a significant impact on natural gas use associated with heating. In either case these real 

welfare impacts are not fully captured by end-use energy efficiency expenditure estimates.

The fact that such quality and service differences may exist in reality but may not be 

reflected in the price difference between more and less energy efficient products is one potential 

hypothesis for the energy paradox. The energy paradox is the observation that end-users do not 

always purchase products that are more energy efficient when the additional cost is less than the 

reduction in the net present value of expected electricity expenditures achieved by those 

products.65 Such circumstances are present in the analysis presented in this chapter, whereby in 

some regions the base case and illustrative scenarios suggest that cost of reducing demand 

through energy efficiency programs is less than the retail electricity price. In addition to 

heterogeneity in product services and consumer preferences, there are other explanations for the 

energy paradox, falling both within and outside the neoclassical rational expectations paradigm 

that is used in benefit/cost analysis. The end-use energy efficiency chapter of the GHG 

Abatement Measures TSD discusses the energy paradox in detail and provides additional 

hypothesis for why consumers may not make energy efficiency investments that ostensibly seem 

to be in their own interest. The TSD discussion also provides details on how the presence of 

additional market failures can lead to levels of energy efficiency investment that may be too low 

from society’s perspective even if that is not the case for the end-user. In such cases there is the 

potential for properly designed energy efficiency programs to address the source of underinvestment, such as principal-agent problems where there is a disconnect between those making 

 

65 An analogous situation is present when some EGUs have assumed to have the ability to make heat rate 

improvements at a capital cost that is less than the anticipated fuel expenditure savings. 
3-52

the purchase decision regarding energy efficient investments and energy use and those that 

would receive the benefits associated with reduced energy use through lower electricity bills.

The other component of compliance cost reported in this chapter is the change in resource 

cost (i.e., expenditures) required by the electricity sector to fulfill the remaining demand while 

making additional CO2 emissions intensity reductions necessary to comply with the state goals. 

Included in the estimate of these compliance costs, developed using IPM, are the cost savings 

associated with the reduction in required electricity generation due to the demand reductions 

from end-use energy efficiency programs and improvements in heat rate. By shifting the demand 

curve for electricity, energy efficiency programs reduce the production cost in the sector. The 

resource cost estimates from IPM therefore account for the increased cost of providing electricity 

at a lower average emissions rate net of the reduction in production costs due to lower demand 

from end-use energy efficiency programs. 

Under the assumption that impacts outside the electricity market and markets providing 

inputs to the electricity sector do not meaningfully affect the prices in those markets, and the 

assumption of fixed electricity demand, then the social costs associated with the regulatory 

action would be equal to the resource costs measured by IPM (net of tax and subsidy payments 

and recovery of sunk costs). Under these assumptions the change in compliance cost will equal 

the reduction in producer66 and consumer surplus67 in the electricity sector from the preregulatory action market equilibrium. However, IPM forecasts production and price changes in 

fuel markets, which implies that there are changes in producer and consumer surplus in those 

markets, and therefore the resource cost estimate by IPM may differ from the social cost for this 

reason. For a theoretically consistent method for estimating partial-equilibrium changes in 

welfare where prices and outputs change in multiple markets see Chapter 9 of Just et al. (2004).

 

66 Producer surplus is a welfare measure representing the amount gained by producers from selling output at a 

market price higher than the price they are willing to accept.

67 Consumer surplus is a welfare measure representing the monetized value of the benefit consumers receive from 

consumption of purchased goods or services beyond their opportunity cost as defined by the market price.
3-53

3.13 References

Just, R.J., D.L. Hueth and A. Schmitz, (2004) The Welfare Economics of Public Policy: A 

Practical Guide to Policy and Project Evaluation, Edwin Elgar Press, Cheltenham UK.

US EPA (2010) EPA Guidelines for Preparing Economic Analyses, Chapters 8 and Appendix A. 

http://yosemite.epa.gov/ee/epa/eed.nsf/webpages/guidelines.html
3A-1

APPENDIX 3A: ANALYSIS OF POTENTIAL UPSTREAM METHANE EMISSIONS

CHANGES IN NATURAL GAS SYSTEMS AND COAL MINING

The purpose of this appendix is to describe the methodology for estimating upstream 

methane (CH4) emissions related to natural gas systems and coal mining sectors that may result 

from the compliance approaches examined in the Regulatory Impact Analysis (RIA). The US 

Environmental Protection Agency (EPA) assessed whether the net change in upstream methane 

emissions from natural gas and coal production is likely to be positive or negative and also 

assessed the potential magnitude of these upstream changes relative to CO2 emissions reductions 

anticipated at power plants from the compliance strategies examined in the RIA. In addition to 

estimating changes in upstream methane emissions, this assessment included estimating CO2

from the flaring of methane, but did not examine other potential changes in other upstream 

greenhouse gas emissions changes from natural gas systems and coal mining sectors.

The methodologies used to project upstream emissions were previously developed for the 

purpose of the Sixth U.S. Climate Action Report, and were subject to peer review and public 

review as part of the publication of that report. In section 3A.1, the overall approach is described 

in brief. In section 3A.2, results are presented. In section 3A.3, detailed methodologies are 

presented for how CH4 and flaring-related CO2 projections were calculated for coal mining and 

natural gas systems. Finally, section 3A.4 contains a bibliography of cited resources.

3A.1 General Approach

3A.1.1 Analytical Scope

Upstream CH4 and flaring-related CO2 emissions associated with coal mining and natural 

gas systems were estimated for 2020 through 2030 using methodologies developed for the 2014 

U.S. Climate Action Report (U.S. Department of State 2014). The base year for the projections is 

2011, as reported in the 2013 U.S. GHG Inventory (EPA 2013a). The projection methodologies 

use activity driver data outputs such as coal and natural gas production from the base case and 

policy scenarios generated by the Integrated Planning Model (IPM), which was used in the RIA 

to model illustrative compliance strategies. The projection methodologies use similar activity 
3A-2

data and emissions factors as are used in the U.S. Greenhouse Gas (GHG) Inventory. The 

projection methodologies estimate reductions associated with both voluntary and regulatory 

programs affecting upstream CH4-related emissions. In the case of the voluntary programs, the 

rate of reductions is based on the historical average decrease from these programs over recent 

years. In the case of regulatory reductions, the reductions are based on the reduction rates 

estimated in the RIAs of relevant regulations. The methodologies to estimate upstream emissions 

were subject to expert peer review and public review in the context of the Sixth U.S. Climate 

Action Report. For more information on the review, or for the detailed methodologies used for 

non-CO2 source projections in that report, including CH4-related emissions from coal production 

and natural gas systems, see “Methodologies for U.S. Greenhouse Gas Emissions Projections: 

Non-CO2 and Non-Energy CO2 Sources” (EPA, 2013b). Uncertainties and limitations are 

discussed, including a side case which incorporates additional geographic information for 

estimating CH4 from coal mining.

The term “upstream emissions” in this memo refers to vented, fugitive and flared 

emissions associated with fuel production, processing, transmission, storage, and distribution of 

fuels prior to fuel combustion in electricity plants. For this analysis, the EPA focused on 

upstream CH4 from the natural gas systems and coal mining sectors. In addition, the analysis 

included CO2 resulting from flaring in natural gas production. This analysis does not assess other 

upstream GHG emissions changes, such as CO2 emissions from the combustion of fuel used in 

natural gas and coal production activities or other non-combustion CO2 emissions from natural 

gas systems, such as vented CO2 and CO2 emitted from acid-gas removal processes.

Also, the EPA assessed potential upstream methane emissions from natural gas systems 

and coal mining sectors within the domestic U.S., but did not examine emissions from potential 

changes in upstream emissions generated by changes in natural gas and coal production, 

processing, and transportation activities outside of the US.68 Last, the EPA did not assess 

potential changes in other upstream non-GHG emissions, such as nitrogen oxides, volatile 

 

68 While the analysis does not estimate methane emissions changes outside of the United States, activity factors 

include imports and exports of natural gas to help estimate domestic methane emissions related to trade of natural 

gas, such as emissions from LNG terminals in the US or from pipelines transporting imported natural gas within 

the US (or transporting natural gas within the US while en route for export).
3A-3

organic compounds, and particulate matter. Table 3A-1 presents estimates of the upstream 

emissions discussed in this analyses for 2011, based on the 2013 U.S. GHG Inventory. 

EPA defined the boundaries of this assessment in order to provide targeted insights into 

the potential net change in CH4 emissions from natural gas systems and coal production activities 

specifically. CO2 emissions from flared methane are included because regulatory and voluntary 

programs influence the rate of CH4 flaring over time and the CO2 remaining after flaring is a 

CH4 -related GHG. Because of the multiple compliance strategies adopted in the illustrative 

compliance scenarios, a more comprehensive assessment of upstream GHG emissions would 

require examination of the broader power sector and related input markets and their potential 

changes in response to the rule. This analysis would be complex and likely subject to data 

limitations and substantial uncertainties. Rather, EPA chose to limit the scope of this upstream 

analysis to evaluate the potential for changes in GHG emissions that may be of significant scale 

relative to the impacts of the rule and for which EPA had previously-reviewed projection 

techniques, which are presented in detail below.

3A.1.2 Coal Mining Source Description

Within coal mining, this analysis covers fugitive CH4 emissions from coal mining 

(including pre-mining drainage) and post-mining activities (i.e., coal handling), including both 

underground and surface mining. Emissions from abandoned mines are not included. Energyrelated CO2 emissions, such as emissions from mining equipment and vehicles transporting coal 

are not included. CH4, which is contained within coal seams and the surrounding rock strata, is 

released into the atmosphere when mining operations reduce the pressure above and/or 

surrounding the coal bed. The quantity of CH4 emitted from these operations is a function of two 

primary factors: coal rank and coal depth. Coal rank is a measure of the carbon content of the 

coal, with higher coal ranks corresponding to higher carbon content and generally higher CH4

content. Pressure increases with depth and prevents CH4 from migrating to the surface; as a 

result, underground mining operations typically emit more CH4 than surface mining. In addition 

to emissions from underground and surface mines, post-mining processing of coal and 

abandoned mines also release CH4. Post-mining emissions refer to CH4 retained in the coal that 

is released during processing, storage, and transport of the coal.
3A-4

3A.1.3 Natural Gas Systems Source Description

Within natural gas systems, this analysis covers vented and fugitive CH4 emissions from 

the production, processing, transmission and storage, and distribution segments of the natural gas 

system. It also includes CO2 from flaring of natural gas. Not included are vented and fugitive 

CO2 emissions from natural gas systems, such as vented CO2 emissions removed during natural 

gas processing, or energy-related CO2 such as emissions from stationary or mobile combustion. 

The U.S. natural gas system encompasses hundreds of thousands of wells, hundreds of 

processing facilities, and over a million miles of transmission and distribution pipelines. CH4 and 

non-combustion69 CO2 emissions from natural gas systems are generally process-related, with 

normal operations, routine maintenance, and system upsets being the primary contributors. There 

are four primary stages of the natural gas system which are briefly described below.

Production: In this initial stage, wells are used to withdraw raw gas from underground 

formations. Emissions arise from the wells themselves, gathering pipelines, and well-site gas

treatment facilities (e.g., dehydrators, separators). Major emissions source categories within the 

production stage include pneumatic devices, gas wells with liquids unloading, and gas well 

completions and re-completions (i.e., workovers) with hydraulic fracturing (EPA 2013). Flaring 

emissions account for the majority of the non-combustion CO2 emissions within the production 

stage. 

Processing: In this stage, natural gas liquids and various other constituents from the raw gas are 

removed, resulting in “pipeline-quality” gas, which is then injected into the transmission system. 

Fugitive CH4 emissions from compressors, including compressor seals, are the primary 

emissions source from this stage. In the U.S. GHG Inventory, the majority of non-combustion 

CO2 emissions in the processing stage come from acid gas removal units, which are designed to 

remove CO2 from natural gas. 

 

69 In this document, consistent with IPCC accounting terminology, the term “combustion emissions” refers to the 

emissions associated with the combustion of fuel for useful heat and work, while “non-combustion emissions” 

refers to emissions resulting from other activities, including flaring and CO2 removed from raw natural gas. 
3A-5

Transmission and Storage: Natural gas transmission involves high-pressure, large-diameter 

pipelines that transport gas long distances from field production and processing areas to 

distribution systems or large-volume customers such as power plants or chemical plants. 

Compressor station facilities, which contain large reciprocating and turbine compressors, are 

used to move the gas throughout the U.S. transmission system. Fugitive CH4 emissions from 

these compressor stations and from metering and regulating stations account for the majority of 

the emissions from this stage. Pneumatic devices and non-combusted engine exhaust are also 

sources of CH4 emissions from transmission facilities. Natural gas is also injected and stored in 

underground formations, or liquefied and stored in above-ground tanks, during periods of lower 

demand (e.g., summer), and withdrawn, processed, and distributed during periods of higher 

demand (e.g., winter). Compressors and dehydrators are the primary contributors to emissions 

from these storage facilities. Emissions from LNG import terminals are included within the 

transportation and storage stage.

Distribution: Distribution pipelines take the high-pressure gas from the transmission system at 

“city gate” stations, reduce the pressure, and then distribute the gas through primarily 

underground mains and service lines to individual end users.

Table 3A-1. Base Year Upstream Methane-Related Emissions in the U.S. GHG Inventory

Emissions Source 2011 Emissions (TgCO2e)

CH4 from Coal Mining 75.2

Underground Mining and Post-Mining 57.4

Surface Mining and Post-Mining 18.0

CH4 from Natural Gas Systems 172.3

Production 63.6

Processing 23.3

Transmission and Storage 52.1

Distribution 33.2

CO2 from flaring of natural gas 10.3

Source: 2013 U.S. GHG Inventory (EPA, 2013). A Global Warming Potential of 25 was used to convert methane 

emissions to CO2e.

It is important to note that in Table 3A-1, CO2-equivalent methane emissions are 

presented using the Fourth Assessment Report Global Warming Potential (GWP) of 25, whereas 

CO2-equivalent methane emissions in the 1990-2011 U.S. GHG Inventory (EPA 2013) are 

presented using the Second Assessment Report GWP of 21. EPA plans to use 25 as the methane 

GWP starting with the 1990-2013 U.S. GHG Inventory, to be published in April 2015.
3A-6

3A.1.4 Compliance Approaches Examined

States will ultimately determine optimal approaches to comply with the goals established 

in this regulatory action. Each of these goal approaches use the four building blocks described in 

the Executive Summary of the RIA at different levels of stringency. Option 1 involves higher 

deployment of the four building blocks but allows a longer timeframe to comply (2030) whereas 

Option 2 has a lower deployment over a shorter timeframe (2025). The RIA depicts illustrative 

state compliance scenarios for the goals set for Options 1 and 2, inclusive of regional and state 

compliance approaches for each option.

3A.1.5 Activity Drivers

IPM-based activity driver projections from base case and illustrative compliance

scenarios underlie the estimates of upstream CH4 emissions. These activity drivers include 

domestic coal and natural gas production, imports and exports, and natural gas consumption. For 

a sensitivity analysis described later, regional coal production is used for the Appalachian, 

Interior, and Western regions. The following Tables 3A-2 to 3A-5 summarize the IPM-based 

activity driver results from the baseline, Option 1 Regional and State scenarios, and Option 2 

Regional and State scenarios.70

Under Option 1, the state and regional compliance scenarios result in 24 percent and 22 

percent reductions in coal production in 2020, respectively, relative to base case coal production. 

Natural gas production in 2020 increases by 4 percent as a result of the Option 1 State

compliance scenario, and increases by 3 percent in the Option 1 Regional compliance scenario 

relative to the base case. 

Under Option 2, the state and regional compliance scenarios result in 19 percent and 17 

percent reductions in coal production in 2020, respectively, relative to base case coal production. 

Natural gas production in 2020 increases by 3 percent as a result of either the Option 2 state 

compliance scenario or the Option 2 Regional compliance scenario, relative to the base case. 

 

70 Uncertainties related to activity drivers are discussed in the uncertainties and limitations section.
3A-7

Table 3A-2. Projected Coal Production Impacts, Option 1

Coal Production

(million short tons)

Coal Production Change 

from Base Case 

(million short tons)

Coal Production Percent 

Change from Base Case 

2020 2025 2030 2020 2025 2030 2020 2025 2030

Base Case 921 952 951

Option 1 State 695 682 682 -225 -270 -269 -24% -28% -28%

Option 1 Regional 716 698 702 -205 -254 -249 -22% -27% -26%

Table 3A-3. Projected Coal Production Impacts, Option 2

Coal Production

(million short tons)

Coal Production Change 

from Base Case 

(million short tons)

Coal Production Percent 

Change from Base Case 

2020 2025 2030 2020 2025 2030 2020 2025 2030

Base Case 921 952 n/a

Option 2 State 747 749 n/a -174 -203 n/a -19% -21% n/a

Option 2 Regional 763 764 n/a -157 -187 n/a -17% -20% n/a

Table 3A-4. Projected Natural Gas Production Impacts, Option 1

Dry Gas Production

(trillion cubic feet)

Dry Gas Production 

Change from Base Case 

(trillion cubic feet)

Dry Gas Production Percent 

Change from Base Case 

2020 2025 2030 2020 2025 2030 2020 2025 2030

Base Case 26.6 29.3 31.9

Option 1 State 27.7 29.8 31.7 +1.1 +0.6 -0.2 +4% +2% -1%

Option 1 Regional 27.5 29.7 31.5 +0.9 +0.4 -0.4 +3% +1% -1%

Table 3A-5. Projected Natural Gas Production Impacts, Option 2

Dry Gas Production

(trillion cubic feet)

Dry Gas Production 

Change from Base Case 

(trillion cubic feet)

Dry Gas Production Percent 

Change from Base Case 

2020 2025 2030 2020 2025 2030 2020 2025 2030

Base Case 26.6 29.3 n/a

Option 2 State 27.5 29.8 n/a +0.9 +0.5 n/a +3% +2% n/a

Option 2 Regional 27.4 29.7 n/a +0.8 +0.4 n/a +3% +1% n/a

3A.2 Results

This section presents results for the main analysis and sensitivity analysis. The detailed 

methods used to perform the analysis are presented in section 3A.3.

3A.2.1 Primary Results

Both Options 1 and 2, in either the state or regional compliance scenarios, result 

in total net reductions in upstream CH4 emissions and CO2 emissions (see Table 3A-6). Under 
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Option 1, the state compliance scenario results in net emissions reduction of 10.6 TgCO2e in 

2020, while the regional compliance scenario results in 9.9 TgCO2e reduction. Under Option 2, 

the net emissions reduction is smaller: 7.7 and 7.4 TgCO2e in the state and regional compliance 

scenarios in 2020, respectively. These net emissions changes represent the sum of changes in 

CH4 from coal mining, CH4 from natural gas systems, and CO2 from flaring in natural gas 

production. 

Under the regional compliance scenario, Option 1 would result in decreases in CH4

emissions from coal mining of 14.7 TgCO2e in 2020, 18.2 TgCO2e in 2025, and 17.7 TgCO2e in 

2030. Under the same scenario, CH4 from natural gas systems increases relative to the base case 

in 2020 (4.4 TgCO2e) and 2025 (2.1 TgCO2e) but decreases relative to the base case in 2030 (by 

1.7 TgCO2e). Following a similar pattern, CO2 from flaring in natural gas production increases 

by 0.4 TgCO2e in 2020 and 0.3 TgCO2e in 2025 while decreasing by 0.3 TgCO2e in 2030. The 

total net change under the Option 1 regional compliance scenario is a reduction in emissions.

Under the regional compliance scenario, Option 2 would result in decreases in CH4

emissions from coal mining of 11.3 TgCO2e in 2020 and 13.4 TgCO2e in 2025. CH4 from natural 

gas systems increases relative to the base case in 2020 (3.6 TgCO2e) and 2025 (1.8 TgCO2e). 

CO2 from flaring in natural gas production increases in 2020 and 2025. Like Option 1, the total 

net change under the Option 2 Regional compliance scenario is a reduction in emissions. The full 

results are listed in Table 3A-6.
3A-9

Table 3A-6. Upstream Emissions Changes, Baseline to Compliance Scenarios

Emissions (TgCO2e)

2020 2025 2030

Option 1 State

CH4 from Coal Mining -16.1 -19.3 -19.1

CH4 from Natural Gas Systems +5.1 +2.6 -1.0

CO2 from NG flaring +0.4 +0.2 -0.3

Total CH4 + CO2 -10.6 -16.4 -20.5

Option 1 Regional

CH4 from Coal Mining -14.7 -18.2 -17.7

CH4 from Natural Gas Systems +4.4 +2.1 -1.7

CO2 from NG flaring +0.4 +0.1 -0.5

Total CH4 + CO2 -9.9 -16.0 -19.8

Option 2 State

CH4 from Coal Mining -12.4 -14.5 n/a

CH4 from Natural Gas Systems +4.4 +2.4 n/a

CO2 from NG flaring +0.4 +0.3 n/a

Total CH4 + CO2 -7.7 -11.8 n/a

Option 2 Regional

CH4 from Coal Mining -11.3 -13.4 n/a

CH4 from Natural Gas Systems +3.6 +1.8 n/a

CO2 from NG flaring +0.2 +0.1 n/a

Total CH4 + CO2 -7.4 -11.4 n/a

Note: A Global Warming Potential of 25 was used to convert methane emissions to CO2e.

3A.2.2 Sensitivity Analysis: Side Case Incorporating More Coal Production Geographic Detail

A number of factors could potentially affect the changes in upstream emission as a result 

of the policy. Here, we present sensitivity analysis examining the possible effect of shifts in 

regional coal production that might result in a changing the relative share of production 

originating in underground mines and surface mines. This sensitivity analysis is presented 

because underground mining generally emits more CH4 emissions per ton of coal production 

than surface mining. The compliance scenarios show generally larger reductions in coal 

production in the western region where more of the generally lower-emitting surface mining 

takes place. This analysis is characterized as a “sensitivity analysis” as the methods that were 

peer-reviewed as part of the EPA’s contribution to the Climate Action Report, which did not 

examine alternative coal production scenarios, did not incorporate this geographic component. 

After incorporating this effect, the net upstream CH4 emissions reductions that may result from 

the compliance scenarios are approximately 1 to 2 TgCO2e lower than the results found in the 

main analysis (see Table 3A-7).
3A-10

Table 3A-7. Upstream Emissions Changes, Baseline to Compliance Scenarios, Side Case 

Incorporating More Coal Production Geographic Detail

Emissions (TgCO2e)

2020 2025 2030

Option 1 State

CH4 from Coal Mining -14.1 -17.4 -17.8

CH4 from Natural Gas Systems +5.1 +2.6 -1.0

CO2 from NG flaring +0.4 +0.2 -0.3

Total CH4 + CO2 -8.6 -14.6 -19.2

Option 1 Regional

CH4 from Coal Mining -13.1 -16.5 -16.3

CH4 from Natural Gas Systems +4.4 +2.1 -1.7

CO2 from NG flaring +0.4 +0.1 -0.5

Total CH4 + CO2 -8.3 -14.3 -18.5

Option 2 State

CH4 from Coal Mining -11.2 -13.2 n/a

CH4 from Natural Gas Systems +4.4 +2.4 n/a

CO2 from NG flaring +0.4 +0.3 n/a

Total CH4 + CO2 -6.4 -10.4 n/a

Option 2 Regional

CH4 from Coal Mining -10.3 -12.3 n/a

CH4 from Natural Gas Systems +3.6 +1.8 n/a

CO2 from NG flaring +0.2 +0.1 n/a

Total CH4 + CO2 -6.5 -10.3 n/a

Note: A Global Warming Potential of 25 was used to convert methane emissions to CO2e.

3A.2.3 Uncertainties and Limitations

Projections of upstream CH4 emissions and CO2 emitted from flaring of CH4 are subject 

to a range of uncertainties and limitations. These uncertainties and limitations include estimating 

the effect of the compliance approach on activity drivers, uncertainty in base year emissions, and 

uncertainties in changes in emissions factors over relatively long periods of time. For example, 

EPA’s application of IPM relies on EIA projections for coal imports and exports. Consequently, 

coal imports and exports are not able to fully respond within the IPM framework to significant 

fluctuations in power sector coal demand. To the extent international markets may be expected 

to offset reduced domestic coal demand, changes in U.S. upstream emissions as a result of the 

policy scenarios would be smaller than what is presented here.

Discussion of uncertainty in historical estimates of emissions from coal mining and 

natural gas systems can be found in the 2013 U.S. GHG Inventory. Projected changes in activity 

drivers and emissions factors are based on a combination of policy, macroeconomic, energy 
3A-11

market, and technology factors which are uncertain in both baseline and compliance scenarios. 

Relatively higher or lower economic growth, or changes in the relative prices or availability of 

various technologies could result in alternative estimates in the net change in upstream CH4

emissions and related CO2 emissions.

3A.3 Methodologies

3A.3.1 Coal Mining

The scope of CH4 from coal mining emissions covered in this analysis is discussed above 

in the “General Approach” section.

Methodology

EPA calculated emissions projections for this source by summing emissions associated 

with underground mining, post-underground mining, surface mining, and post-surface mining.

Emissionsy = ∑Emissionsy,s

s

Equation 3

Where:

s = Sources (underground, post-underground, surface, and post-surface 

mining)

Emissionsy,s = Emissions in year y from source s

EPA projected emissions from each source by multiplying an aggregate emissions factor 

by projected coal production (for underground or surface mining as appropriate). Projected 

reductions due to recovery and use are then subtracted from potential emissions.71

 

71 Current CH4 recovery and use projects apply to underground mining, but projects related to surface mining could 

be implemented in the future.
3A-12

Emissionsy,s = EFagg,s × InventoryProductionb,s × (

ProjectedProductiony,s

ProjectedProductionb,s

)

× (1 − CH4RecoveryUseFracs

)

Equation 4

Where:

EFagg,s = Aggregate emissions factor associated with source s

InventoryProductionb,s = Coal production associated with source s in the base 

year from the U.S. GHG Inventory72

ProjectedProductiony,s = Projected coal production associated with the 

emissions source (e.g., either underground or surface 

mining) in year y

CH4RecoveryUseFracs = Fraction of CH4 recovered from source s

Emissions Factors

To calculate potential emissions from each category, EPA calculated an aggregate CH4

emissions factor using historical CH4 emissions and coal production data contained in the most 

recent U.S. GHG Inventory (EPA 2013a). For example, historical CH4 liberated by underground 

mining was divided by the total underground coal production for the corresponding year. The 

aggregate emissions factor is the average of this ratio over the most recent five years. Similar 

calculations were performed for post-underground mining emissions, surface mining emissions, 

and post-surface mining emissions, using either historical underground or surface mining 

production data as appropriate. 

The projection methodology differs from the estimation methodology used in the U.S. 

GHG Inventory. The inventory does not use emissions factors to calculate CH4 emissions from 

underground mines. The U.S. GHG Inventory estimates total CH4 emitted from underground 

mines as the sum of CH4 liberated from ventilation systems (mine-by-mine measurements) and 

 

72 Because of slight differences between historical and projection datasets, values for production in the base year 

from each dataset do not cancel
3A-13

CH4 liberated by means of degasification systems, minus CH4 recovered and used. EPA 

estimated surface mining and post-mining CH4 emissions by multiplying basin-specific coal 

production, obtained from EIA’s Annual Coal Report (EIA 2012), by basin-specific emissions 

factors.

Coal Production Projections

For the Sixth U.S. Climate Action Report, EPA projected emissions using projections of 

underground, surface, and total coal production from the EIA Annual Energy Outlook (AEO) 

(EIA 2013). For this analysis, projections of total coal production and production by region were 

drawn from IPM runs performed for the proposal RIA. Projections of underground and surface 

mining were not available outputs for the various scenarios, so two different approaches were 

used to estimate underground and surface mining in the baseline and compliance scenarios.

The 2013 AEO projects the total coal production for the United States, as well as the coal 

production by region, and by various characteristics including underground and surface mining 

(see AEO table “Coal Production by Region and Type”). In the primary results presented above, 

the breakout between underground and surface mining for projection years in both baseline and 

compliance scenarios is based on the 2013 AEO proportions. This means that the compliance 

approach is assumed not to affect the relative proportion of underground and surface mining in 

the main results. Also, note there has been a general trend toward increasing surface mining 

relative to underground mining (EPA 2013).

In the sensitivity analysis that incorporated more geographical details into coal 

production, EPA used IPM outputs for coal production from the Appalachian, Interior, and 

Western regions to estimate the proportion of underground versus surface mining in each 

scenario. This was done by assuming that the relative proportion of underground versus surface 

mining in each coal production region would remain constant through the projection period, but 

that the national proportion of underground and surface production would depend on regional 

production changes. In general, a larger proportion of coal production in the Appalachian and 

Interior regions is from underground mining, while a large majority of coal production in 

western regions is from surface mining. The proportion of underground and surface mining in 

each region was based on averaging the years 2011 and 2012 historical percentages. EIA 
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provides historical regional coal production data broken out between underground and surface 

mining in its Annual Coal Report (EIA 2012). EPA collated and calculated the proportion of 

production, underground versus surface, for each year. 

CH4 Mitigation (Recovery and Use)

EPA projected coal mine CH4 mitigation by calculating the historical fraction of methane 

recovered in relation to generation from underground mines, and applying that fraction to future 

generation. The historical fraction was averaged over the most recent five years. Future 

mitigation was estimated by applying the historical rate of recovery and use to projected 

potential emissions generated.

The U.S. GHG Inventory uses quantitative estimates of CH4 recovery and use from 

several sources. Several gassy underground coal mines in the United States employ ventilation 

systems to ensure that CH4 levels remain within safe concentrations. Additionally, some U.S. 

coal mines supplement ventilation systems with degasification systems, which remove CH4 from 

the mine and allow the captured CH4 to be used as an energy source. 

CH4RecoveryUseFracs = ∑

CH4RecoveryUses,y

PotentialEmissionss,y

⁄5

b−4

y=b

Equation 5

Where:

CH4RecoveryUses,y = Recovered emissions from source s in year y

Potential Emissionss, y = Potential emissions from source s in year y

b = base year

3A.3.2 Natural Gas Systems

The scope of CH4 and CO2 from natural gas systems emissions covered in this analysis 

are discussed above in the “General Approach” section.

Methodology

The methodology for natural gas emissions projections involves the calculation of CH4
3A-15

and CO2 emissions for over 100 emissions source categories across the four natural gas sector 

stages, and then the summation of emissions for each sector stage. The calculation of emissions 

for each source of emissions in natural gas systems generally occurs in three steps:

1. Calculate potential CH4 (CH4 that would be released in the absence of controls)

2. Estimate reductions data associated with voluntary action and regulations 

3. Calculate net emissions

EPA calculated projections of potential CH4 emissions from natural gas systems by 

summing the projections associated with (1) production, (2) processing, (3) transmission and 

storage, and (4) distribution. For the 2014 U.S. Climate Action Report in general, activity data 

were projections of natural gas production and consumption from the Department of Energy’s 

Energy Information Administration, or EIA (EIA 2013). Additional activity data for projections 

included liquefied natural gas (LNG) imports, pipeline length, and number of service lines. For 

this report, activity driver data were taken from IPM outputs for baseline and compliance 

scenarios consistent with the analysis presented in the RIA. Because the base year inventory 

emissions explicitly include reductions due to voluntary and regulatory requirements, the 

projections also include appropriate explicit mitigation projections as well. Emissions for each 

source were estimated using the following equation:

NEs,y = PEs,y − VRs,y − RRs,y

Equation 6

Where:

NEs,y = Projected net emissions for source s in year y

PEs,y = Projected potential emissions for source s in year y

VRs,y, y = Projected voluntary reductions for source s in year y

RRs,y, y = Projected regulatory reductions for source s in year y

The sections below describe detailed calculations for projections of CH4 from natural gas 

systems. This analysis also includes CO2 emissions that result from flaring in the production 
3A-16

sector.73 The U.S. Greenhouse Gas Inventory also includes other sources of venting, fugitive, and 

flaring CO2 such as CO2 processing emissions from acid gas removal units, which are designed 

to remove CO2 from natural gas. This analysis does not include these other non-combustion CO2

sources. EPA calculated projected flaring CO2 emissions by scaling emissions in the base year 

by the increase in projected natural gas production in the IPM scenario outputs consistent with 

the analysis presented in the RIA.

Production Stage

The production stage includes a total of 35 emissions source categories. Regional 

emissions were estimated in the base year inventory for the six supply regions (i.e., Northeast, 

Gulf Coast, Midcontinent, Southwest, Rocky Mountain, and West Coast) for 33 of these sources.

Potential Emissions

EPA estimated future year potential emissions for the production stage using the 

following equation.

 

73 The GHG Inventory estimate for CO2 from natural gas flaring includes some CO2 from flaring of associated gas.
3A-17

PEs,y = PEs,b × (

Gas Productiony

Gas Productionb

)

Equation 7

Where:

PEs,y, y = Projected future potential emissions for source s in year y

PEs,b, b = Estimated potential emissions for source s in base year b

Gas Productiony = Projected natural gas dry production year y

Gas Productionb = Estimated natural gas dry production for base year b

The natural gas dry production estimates were obtained from the IPM scenario outputs.

Voluntary Reductions

Projections of voluntary reductions for the production stage were based on historical data 

reported by industry to the Natural Gas STAR program for projects implemented to reduce 

emissions. Natural Gas STAR tracks projects on an annual basis and assigns a lifetime of limited 

duration to each reduction project; for purposes of the base year emissions inventory and the 

future year projections, the reductions associated with each project were either considered to be a 

“one-year” project or a “permanent” project based on sunset dates provided by the Natural Gas 

STAR program. Reductions from “one-year” projects were typically from the implementation of 

new or modified practices, while reductions from “permanent” projects tended to be from 

equipment installation, replacement, or modification. In the base year emissions inventory and 

the future year projections, reductions for a “one-year” project were limited to the project’s 

reported start year, while reductions for a “permanent” project were assigned to the project’s 

reported start year and every subsequent year thereafter. Thus, the reductions due to “permanent” 

projects gradually accumulated throughout the inventory time series, while the reductions due to 

“one-year” projects were replaced every year.

The following production stage voluntary reductions were reported to Natural Gas STAR 

and applied to individual sources in the emissions inventory:
3A-18

 Completions for gas wells with hydraulic fracturing (one year)—perform reduced 

emissions completions (RECs).74

 Pneumatic device vents (one year)—reduce gas pressure on pneumatic devices; 

capture/use gas released from gas-operated pneumatic pumps.

 Pneumatic device vents (permanent)—identify and replace high-bleed pneumatic 

devices; convert pneumatic devices to mechanical controls; convert to instrument air 

systems; install no-bleed controllers.

 Kimray pumps (permanent)—install/convert gas-driven pumps to electric, 

mechanical, or solar pumps.

 Gas engines compressor exhaust (one year)—replace ignition/reduce false starts; 

turbine fuel use optimization.

 Gas engines compressor exhaust (permanent)—convert engine starting to Nitrogenand/or CO2-rich gas; install automated air/fuel ratio controls; install lean burn 

compressors; replace gas starters with air or N.

In addition to these reductions that were applied to specific individual sources in the 

emissions inventory, there were reductions classified as “Other Production” that were applied to 

the overall production stage emissions. 

It was assumed that the percentage of voluntary reductions relative to potential CH4 in the 

most recent base year inventory for the production stage would remain constant in each 

subsequent future year.75 In addition, implementation of the Oil and Natural Gas Sector New 

 

74 The 2014 GHG Inventory includes an update to the methodology for calculating emissions from hydraulically 

fractured gas well completions and workovers. The update uses control/practice-specific emission factors and no 

longer uses potential methane, or reductions data from Gas STAR or from regulations to calculate emissions, as 

emissions are directly calculated using the emission factors.

75 The assumption of a constant rate of voluntary reductions relative to the base year inventory for sources 

unaffected by regulatory changes is meant to simulate a constant level of effort toward voluntary reductions into 

the future. No enhancements to the voluntary program are assumed. This assumption is a source of uncertainty; 

due to the voluntary nature of the program, reduction levels can fluctuate based on participation and investment. 

Where new regulatory requirements apply to new and modified equipment, voluntary reductions are assumed to 

continue to apply to existing equipment, but no voluntary reductions are applied to new equipment. As a potential 
3A-19

Source Performance Standards (NSPS)—discussed further below—necessitates the 

reclassification of certain production reductions from voluntary to regulatory. 

Regulatory Reductions

As part of the regulatory reductions for the production stage, reductions due to existing 

Oil and Natural Gas Sector National Standards for Hazardous Air Pollutants (NESHAP) 

requirements (EPA. 2012a) for dehydrator vents and condensate tanks without control devices 

were included in the base year inventory. These reductions were carried forward in the future 

year projections. 

In addition, the base year inventory accounted for state-level requirements in Wyoming 

and Colorado for RECs.76 In the base year inventory, a national-level reduction was estimated by 

applying a 95 percent REC reduction to the fraction of national emissions occurring in Wyoming 

and Colorado (i.e., 15.1 percent); this resulted in a national-level reduction of 14.35 percent for 

gas well completions and workovers with hydraulic fracturing. These reductions were modified 

as described below.

The Oil and Natural Gas Sector NSPS for VOCs (EPA 2012a, finalized in 2012) 

significantly increased the amount of regulatory reductions applicable to the production stage, 

resulting in substantial CH4 emissions reductions co-benefits. These reductions are not currently 

reflected in the 2013 U.S. GHG Inventory for the base year 2011, but are projected for future 

years as discussed in detail below. The specific Oil and Natural Gas Sector NSPS requirements 

impact the following production stage sources with regard to VOC (and the associated CH4) 

emissions:

 Hydraulically fractured natural gas well completions

 

future improvement to these projections, EPA may develop an alternate methodology to model equipment 

turnover. However, a revised and reviewed methodology that incorporates equipment turnover is would likely be 

developed within the context of preparing the Agency’s submission to the next Climate Action Report, which will 

probably be in the 2017 to 2018 timeframe.

76 The 2014 GHG Inventory includes an update to the methodology for calculating emissions from hydraulically 

fractured gas well completions and workovers. The update uses control/practice-specific emission factors and no 

longer uses potential methane, or reductions data from Gas STAR or from regulations to calculate emissions, as 

emissions are directly calculated using the emission factors. 
3A-20

 Hydraulically refractured natural gas well recompletions

 New and modified high-bleed, gas-driven pneumatic controllers

 New storage tanks (with VOC emissions of 6 tons per year or more)

 New and modified reciprocating and centrifugal compressors at gathering and 

boosting stations

The impact of these requirements on the future year projections is discussed below. The 

specific quantitative reductions calculated for these projections are based on information from 

the Oil and Natural Gas Sector NSPS Background Technical Support Document for the 

Proposed Standards (EPA 2011) and the Background Supplemental Technical Support 

Document for the Final New Source Performance Standards (EPA 2012b), referred to 

collectively in this document as the Oil and Natural Gas Sector NSPS TSD.

Hydraulically Fractured Well Completions

The Oil and Natural Gas Sector NSPS requires the use of RECs (or “green completions”) 

for all new hydraulically-fractured natural gas well completions. A phase-in period prior to 

January 1, 2015, also allows for the alternate use of a completion combustion device (i.e., flare), 

instead of RECs. In addition, RECs are not required for exploratory “wildcat” wells, delineation 

wells (i.e., used to define the borders of a natural gas reservoir), and low-pressure wells (i.e., 

completions where well pressure is too low to perform RECs); in these instances, emissions must 

be reduced using combustion. Based on the Oil and Natural Gas Sector NSPS TSD (EPA 

2012b), EPA assumed for the purpose of these projections a 95 percent reduction for both RECs 

and completion combustion.77

Although the base year inventory included a national-level reduction of 14.35 percent to 

account for the required use of RECs in Wyoming and Colorado, there does not appear to be an 

 

77 The Oil and Natural Gas Sector NSPS TSD indicates that 90 percent of flowback gas can be recovered during an 

REC (based on Natural Gas STAR data) and that any amount of gas that cannot be recovered can be directed to a 

completion combustion device in order to achieve a minimum 95 percent reduction in emissions. The Oil and 

Natural Gas Sector NSPS TSD indicates that although industrial flares are required to meet a combustion 

efficiency of 98 percent, this is not required for completion combustion devices. Completion combustion devices 

(i.e., exploration and production flares) can be expected to achieve 95 percent combustion efficiency.
3A-21

appreciable difference in emissions reductions resulting from the Oil and Natural Gas Sector 

NSPS requirements and the state requirements in Wyoming and Colorado. Therefore, for future 

year projections, the national-level reduction of 14.35 percent was replaced with a 95 percent 

reduction for new hydraulically fractured well completions.

Hydraulically Refractured Well Workovers

The Oil and Natural Gas Sector NSPS also requires the use of RECs for gas wells that are 

refractured and recompleted. The phase-in period before January 1, 2015, is also applicable.78 As 

with completions, a 95 percent reduction was assumed for both RECs and completion 

combustion. This replaced the national-level reduction of 14.35 percent that was used in the base 

year inventory.

For both well completions and workovers (or refractured well completions) with 

hydraulic fracturing, in conjunction with the Oil and Natural Gas Sector NSPS, EPA removed 

REC-related reductions from the projected voluntary reductions from the production stage to 

avoid double-counting. This removal was very straightforward, since these REC-related 

reductions were calculated separately and then applied to the well completions and workovers 

source in the base year inventory.

New and Modified High-Bleed, Gas-Driven Pneumatic Controllers

The Oil and Natural Gas Sector NSPS also requires the installation of new low-bleed 

pneumatic devices (i.e., bleed rates less than or equal to 6 standard cubic feet per hour) instead of 

high-bleed pneumatic devices (i.e., bleed rates greater than 6 standard cubic feet per hour) with 

exceptions where high bleed devices are required for safety reasons. The TSD indicates that a 

typical production stage high-bleed pneumatic device emits 6.91 tons of CH4 per year and that 

replacing the high-bleed device with a typical low-bleed pneumatic device would result in a 

reduction of 6.65 tons CH4 per year; this is a reduction of 96.2 percent. The TSD also indicates 

that only 51 percent of all pneumatic devices installed are continuous bleed natural gas driven 

controllers. In addition, it is assumed that 20 percent of the situations where bleed pneumatic 

 

78 Use of RECs is not considered to be “modified” and would not trigger state permitting requirements, while use of 

flaring or completion combustion would be considered to be “modified.”
3A-22

devices are installed require a high-bleed device (i.e., instances where a minimal response time is 

needed, large valves require a high bleed rate to actuate, or a safety isolation valve is involved) 

(EPA 2011). Based on this information, for the purpose of these projections EPA applied a 

national-level reduction of 77 percent (i.e., 0.962 × 0.8) to each future year’s annual increase in 

emissions from pneumatic device vents in the production stage.

In conjunction with the Oil and Natural Gas Sector NSPS, no removal of production 

stage voluntary reductions was required. The reductions included in the base year inventory 

already occurred in the past and the associated effects carry forward into the future or were 

unrelated to the requirements of the Oil and Natural Gas Sector NSPS.

New Storage Tanks

The Oil and Natural Gas Sector NSPS also requires that new storage tanks with VOC 

emissions of 6 tons per year or greater must reduce VOC emissions by at least 95 percent, likely 

to be accomplished by routing emissions to a combustion device or rerouting emissions into 

process streams. The Oil and Natural Gas Sector NSPS TSD indicates that approximately 74 

percent of the total condensate produced in the United States passes through storage tanks with 

VOC emissions of 6 tons per year or greater (EPA 2011). Based on this information, for the 

purpose of these projections EPA applied a national-level reduction of 70.3 percent (i.e., 0.95 × 

0.74) to each future year’s annual increase in emissions from condensate storage tanks in the 

production stage.

In conjunction with the Oil and Natural Gas Sector NSPS, no removal of production 

stage voluntary reductions associated with storage tanks was required. The reductions included 

in the base year inventory already occurred in the past and the associated effects carry forward 

into the future.

New and Modified Reciprocating Compressors

The Oil and Natural Gas Sector NSPS requires the replacement of rod packing systems in

new and modified reciprocating compressors at gathering and boosting stations. There are two 

options for this replacement: every 26,000 hours of operation if operating hours are monitored 

and documented, or every 36 months if operating hours are not monitored or documented. The 

Oil and Natural Gas Sector NSPS TSD estimated baseline emissions of 3,773 tons per year of 
3A-23

CH4 for new reciprocating compressors used in the production stage; the TSD also estimated 

total reductions from replacing the rod packing for these compressors as 2,384 tons per year of 

CH4 (EPA 2011). Based on this information, for the purpose of these projections EPA applied a 

national-level reduction of 63.2 percent to each future year’s annual increase in emissions from 

gathering reciprocating compressors in the production stage.

Processing Stage

The processing stage includes a total of 11 emissions source categories. EPA estimated 

the base year inventory emissions for the processing stage at the national level, instead of at the 

region level, like the base year inventory emissions for the production stage.

Potential Emissions

Because projections of future year processing activity were not available, EPA also used 

Equation 7 to estimate future year potential emissions for the processing stage by assuming that 

the quantity of processed natural gas would track closely with the quantity of produced natural 

gas.

As with the production stage, EPA used the natural gas dry production estimates from the 

IPM scenario outputs.

Voluntary Reductions

Projections of voluntary reductions for the processing stage were based on historical data 

reported by industry to the Natural Gas STAR program for projects implemented to reduce 

emissions (EPA 2012c). The following processing stage voluntary reductions were reported to 

Natural Gas STAR and applied to individual sources in the emissions inventory:

 Blowdowns/venting (one year)—recover gas from pipeline pigging operations; 

redesign blowdown/alter ESD practices; reduce emissions when taking compressors 

offline; use composite wrap repair; use hot taps for in-service pipeline connections; 

use inert gas and pigs to perform pipeline purges.

 Blowdowns/venting (permanent)—rupture pin shutoff device to reduce venting.

In addition to these reductions that were applied to specific individual sources in the 
3A-24

emissions inventory, there were reductions classified as “Other Processing” that were applied to 

the overall processing stage emissions. 

It was assumed that the percentage of voluntary reductions relative to potential CH4 in the 

most recent base year inventory for the processing stage would remain constant in each 

subsequent future year.79 In addition, implementation of the Oil and Natural Gas Sector NSPS 

(discussed further below) necessitates the reclassification of certain processing reductions from 

voluntary to regulatory. 

Regulatory Reductions

The only regulatory reductions included in the base year inventory for the processing 

stage were existing Oil and Natural Gas Sector NESHAP requirements for dehydrator vents 

(EPA 2013). These reductions were carried forward in the future year projections.

The Oil and Natural Gas Sector NSPS significantly increased the amount of regulatory 

reductions applicable to the processing stage relative to the base year 2011 inventory estimates. 

The specific Oil and Natural Gas Sector NSPS requirements affect the following processing 

stage sources with regard to VOC (and the associated CH4) emissions:

 New and modified reciprocating compressors

 New and modified centrifugal compressors

 New and modified high-bleed, gas-driven pneumatic controllers

 New storage tanks (with VOC emissions of at least 6 tons per year)

The impact of these requirements on the future year projections is discussed below.

New and Modified Reciprocating Compressors

The Oil and Natural Gas Sector NSPS requires the replacement of rod packing systems in 

new and modified reciprocating compressors. There are two options for this replacement: every 

26,000 hours of operation if operating hours are monitored and documented, or every 36 months 

 

79 This assumption is discussed in Footnote 7. 
3A-25

if operating hours are not monitored or documented. The Oil and Natural Gas Sector NSPS TSD 

estimated baseline emissions of 4,870 tons per year of CH4 for new reciprocating compressors 

used in the processing stage; the Oil and Natural Gas Sector NSPS TSD also estimated total 

reductions from replacing the rod packing for these compressors as 3,892 tons per year of CH4

(EPA 2011). Based on this information, for the purpose of these projections EPA applied a 

national-level reduction of 79.9 percent to each future year’s annual increase in emissions from 

reciprocating compressors in the processing stage.

New and Modified Centrifugal Compressors

The Oil and Natural Gas Sector NSPS requires a 95 percent reduction in VOC emissions 

from new and modified centrifugal compressors with wet seal systems, which can be 

accomplished through flaring or by routing captured gas back to a compressor suction or fuel 

system, or switching to dry seal systems. The Oil and Natural Gas Sector NSPS does not apply to 

centrifugal compressors with dry seal systems, because they have low VOC emissions. A 

national-level reduction of 95 percent was applied to each future year’s annual increase in 

emissions from centrifugal compressors with wet seals in the processing stage.

In conjunction with the Oil and Natural Gas Sector NSPS, no removal of processing stage 

voluntary reductions was required. The reductions included in the base year inventory already 

occurred in the past and the associated effects carry forward into the future or were unrelated to 

the requirements of the Oil and Natural Gas Sector NSPS.

New and Modified High-Bleed, Gas-Driven Pneumatic Controllers

The Oil and Natural Gas Sector NSPS also requires that the VOC emissions limit for 

continuous-bleed, gas-driven pneumatic controls at gas processing plants be zero. Accordingly, 

emissions from new pneumatic device vents in the processing stage were set to zero.

New Storage Tanks

As described above in the production sector.

Transmission and Storage Stage

The transmission and storage stage includes a total of 37 emissions source categories: 25 
3A-26

associated with natural gas transmission and storage and 12 associated with liquefied natural gas 

(LNG) transmission and storage. The natural gas and LNG emissions were estimated at the 

national level.

Potential Emissions

Future year potential emissions for the natural gas sources and the six LNG storage 

sources within the transmission and storage stage were estimated using the following equation:

PEs,y = PEs,b × (

Gas Consumptiony

Gas Consumptionb

)

Equation 8

Where:

PEs,y = Projected future potential emissions for source s in year y

PEs,b = Estimated potential emissions for source s in base year b

Gas Consumptiony = Projected national natural gas consumption in year y

Gas Consumptionb = Estimated national natural gas consumption in base year b

The national natural gas consumption estimates were obtained from the IPM scenario 

outputs. 

Future year potential emissions for the six LNG import terminal sources within the 

transmission and storage stage were estimated using the following equation:

PEs,y = PEs,b × (

LNG Importsy

LNG Importsb

)

Equation 9

Where:

PEs,y = Projected future potential emissions for source s in year y

PEs,b = Estimated potential emissions for source s in base year b

LNG Importsy = Projected LNG imports in year y

LNG Importsb = Estimated LNG imports in base year b
3A-27

The LNG import estimates were obtained from the table titled “Natural Gas Imports and 

Exports” of the IPM scenario outputs. The specific estimates used were for the “Liquefied 

Natural Gas Imports” line item.

Voluntary Reductions

Projections of voluntary reductions for the transmission and storage stage were also based 

on historical data reported by industry to the Natural Gas STAR program for projects 

implemented to reduce emissions. The following transmission and storage stage voluntary 

reductions were reported to Natural Gas STAR and applied to individual sources in the base year 

emissions inventory:

 Reciprocating compressors (one year)—replace compressor rod packing systems.

 Reciprocating compressors (permanent)—replace wet seals with dry seals.

 Pipeline venting (one year)—recover gas from pipeline pigging operations; use 

composite wrap repair; use hot taps for in-service pipeline connections; use inert gas 

and pigs to perform pipeline purges; use pipeline pump-down techniques to lower 

gas line pressure.

 Pneumatic devices (permanent)—identify and replace high-bleed pneumatic devices; 

convert pneumatic devices to mechanical controls; convert to instrument air systems.

In addition to these reductions that were applied to specific individual sources in the 

emissions inventory, there were reductions classified as “Other Transmission and Storage” that 

were applied to the overall transmission and storage stage emissions. 

It was assumed that the percentage of voluntary reductions relative to potential CH4 in the 

most recent base year inventory for the transmission and storage stage would remain constant in 

each subsequent future year.80 In addition, implementation of the Oil and Natural Gas Sector 

NSPS (discussed further below) necessitates the reclassification of certain reductions from 

voluntary to regulatory. 

 

80 This assumption is discussed in Footnote 7. 
3A-28

Regulatory Reductions

No regulatory reductions were previously included in the inventory for the transmission 

and storage stage.

The Oil and Natural Gas Sector NSPS includes requirements applicable to the natural gas 

transmission and storage stage for VOC reductions of at least 95 percent for new storage tanks 

with VOC emissions of 6 tons per year or more.

The impact of these requirements on the future year projections is discussed below.

New Storage Tanks

As described above in the production sector.

Distribution Stage

The distribution stage includes a total of 23 emissions source categories consisting of 10 

city gate sources, two customer meter sources, three vented sources, and eight pipeline leak 

sources. For all sources, emissions were estimated at the national level.

Potential Emissions

Because future year distribution projections were not available, EPA estimated future 

year potential emissions for the distribution stage (except for the pipeline leak sources) using 

Equation 3, assuming that the quantity of distributed natural gas tracks closely with the quantity 

of consumed natural gas.81 The natural gas consumption estimates were obtained from the IPM 

scenario outputs. Sector-specific consumption estimates were used. For most sources (i.e., all 

city gate, all vented, and the residential customer meter sources), the “Natural Gas” line item 

 

81 Many natural gas power plants are connected directly to transmission and distribution pipelines, and thus an 

increase in consumption of natural gas by power plants might not lead to a proportional change in CH4 emissions 

from the distribution segment of the natural gas system. The current projection methodology uses total natural gas 

consumption as an activity driver to project emissions from non-pipeline leak sources in the distribution segment. 

EPA examined how the results would differ if the distribution segment were excluded. Depending on the scenario 

and year, the net emissions change between the base case and policy cases was reduced by up to 0.9 TgCO2e or 

increased by up to 0.4 TgCO2e, assuming the change in power plant natural gas consumption had no effect on 

distribution-segment methane emissions.
3A-29

under “Residential Consumption” was used. For the commercial/industry sources, EPA used the 

summation of the “Natural Gas” line item under “Commercial Consumption” and the “Natural 

Gas Subtotal” line item (including natural gas, natural gas-to-liquids heat and power, and lease 

and plant fuel) under “Industrial Consumption.”

Unlike most other sources in the natural gas systems emissions inventory, projected 

pipeline leak emissions in the distribution stage were not estimated using natural gas production 

or consumption estimates. Instead, linear extrapolation of historical pipeline miles was used to 

project leak emissions from distribution mains, while linear extrapolation of the historical 

number of service lines was used to project leak emissions from services. Linear extrapolation 

was used because the historical statistics for pipeline miles and number of services show fairly 

consistent behavioral trends over the entire time series from 1990 to 2011. In particular, the 

historical statistics show a distinct trend toward the use of plastic and away from other materials 

(i.e., cast iron, copper, unprotected steel, and protected steel). Historical pipeline length data was 

drawn from the US GHG Inventory (EPA 2013), which draws pipeline data from a variety of 

sources.

Voluntary Reductions

Projections of voluntary reductions for the distribution stage were based on historical data 

reported by industry to the Natural Gas STAR program for projects implemented to reduce 

emissions. Unlike the production, processing, and transmission and storage stages, no 

distribution stage voluntary reductions reported to Natural Gas STAR were applied to individual 

sources in the emissions inventory. However, there were reductions classified as “Other 

Distribution” that were applied to the overall distribution stage emissions.

It was assumed that the percentage of voluntary reductions relative to potential CH4 in the 

most recent base year inventory for the transmission stage would remain constant in each 

subsequent future year.82

 

82 This assumption is discussed in Footnote 7. 
3A-30

Regulatory Reductions

There were no requirements in the Oil and Natural Gas Sector NSPS that impact 

emissions from the distribution stage.
3A-31

3A.4 References

IPCC. 2006. “2006 IPCC Guidelines for National Greenhouse Gas Inventories.” Available at: 

<http://www.ipcc-nggip.iges.or.jp/public/2006gl/>.

U. S. Department of State. 2014. “2014 U.S. Climate Action Report to the UNFCCC.” Available 

at: < http://www.state.gov/e/oes/rls/rpts/car6/index.htm>.

U. S. Energy Information Administration (EIA). 2012. “Annual Coal Report 2011.” Available at:

<http://www.eia.gov/coal/annual/>.

U. S. EIA. 2013. “Annual Energy Outlook 2013.” Available at: 

<http://www.eia.gov/forecasts/aeo/>.

U. S. Environmental Protection Agency. 1993. Anthropogenic Methane Emissions in the United 

States, Estimates for 1990: Report to Congress. (EPA document number EPA-430-R-93-003).

U. S. EPA. 2011. “Oil and Natural Gas Sector: Standards of Performance for Crude Oil and 

Natural Gas Production, Transmission, and Distribution: Background Technical Support 

Document for the Proposed Standards.” Available at: 

<http://epa.gov/airquality/oilandgas/pdfs/20110728tsd.pdf>.

U. S. EPA. 2012a. “Oil and Natural Gas Sector: New Source Performance Standards and 

National Emission Standards for Hazardous Air Pollutants Reviews.” 40 CFR Parts 60 and 63 

[EPA-HQ-OAR-2010-0505; FRL-9665-1], RIN 2060-AP76. Available at:

<http://www.gpo.gov/fdsys/pkg/FR-2012-08-16/pdf/2012-16806.pdf>.

U. S. EPA. 2012b. “Oil and Natural Gas Sector: Standards of Performance for Crude Oil and 

Natural Gas Production, Transmission, and Distribution: Background Supplemental Technical 

Support Document for the Final New Source Performance Standards.” Available at:

<http://epa.gov/airquality/oilandgas/pdfs/20120418tsd.pdf>.

U.S. EPA. 2012c. “Natural Gas STAR Reductions 1990-2011.” Natural Gas STAR Program.

U.S. EPA. 2013a. Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2011. (EPA 

document number EPA-430-R-13-001). Available at:

<http://www.epa.gov/climatechange/ghgemissions/usinventoryreport.html>.

U.S. EPA. 2013b. “Methodologies for U.S. Greenhouse Gas Emissions Projections: Non-CO2

and Non-Energy CO2 Sources.” Available at: 

<http://www.state.gov/documents/organization/219472.pdf>
4-1

CHAPTER 4: ESTIMATED CLIMATE BENEFITS AND HUMAN HEALTH CO-BENEFITS

4.1 Introduction

Implementing the proposed Emission Guidelines for Greenhouse Gas (GHG) Emissions 

from Existing Stationary Sources: Electric Utility Generating Units (EGUs) (hereafter, EGU 

GHG Existing Source Guidelines) is expected to reduce emissions of carbon dioxide (CO2) and 

have ancillary human health benefits (i.e., co-benefits) associated with lower ambient 

concentrations of criteria air pollutants. This chapter describes the methods used to estimate the 

monetized climate benefits and the monetized air pollution health co-benefits associated with 

reducing exposure to ambient fine particulate matter (PM2.5) and ozone by reducing emissions of 

precursor pollutants (i.e., sulfur dioxide (SO2), nitrogen dioxide (NO2), and directly emitted 

PM2.5). Data, resource, and methodological limitations prevent EPA from monetizing the 

benefits from several important co-benefit categories, including reducing direct exposure to SO2, 

NO2, and hazardous air pollutants (HAP), as well as ecosystem effects and visibility impairment. 

We qualitatively discuss these unquantified benefits in this chapter.

This chapter provides estimates of the monetized climate benefits and air pollution health 

co-benefits associated with emission reductions for two options with two illustrative compliance 

scenarios across several analysis years and discount rates. The estimated benefits associated with 

these emission reductions are beyond those achieved by previous EPA rulemakings, including 

the Mercury and Air Toxics Standards (MATS).

4.2 Estimated Climate Benefits from CO2

The primary goal of the proposed guidelines is to reduce emissions of CO2. In this 

section, we provide an overview of the climate science assessments released since the 2009 

Endangerment Finding. We also provide information regarding the economic valuation of CO2

using the Social Cost of Carbon (SCC), a metric that estimates the monetary value of impacts 

associated with marginal changes in CO2 emissions in a given year. Table 4-1 summarizes the 

quantified and unquantified climate benefits in this analysis. 
4-2

Table 4-1. Climate Effects

Benefits 

Category

Specific Effect Effect Has Been 

Quantified

Effect Has Been 

Monetized

More 

Information

Improved Environment

Reduced 

climate effects

Global climate impacts from CO2 —  SCC TSD

Climate impacts from ozone and black

carbon (directly emitted PM)

— — Ozone ISA, PM 

ISA*

Other climate impacts (e.g., other GHGs 

such as methane, aerosols, other impacts)

— — IPCC*

* We assess these co-benefits qualitatively because we do not have sufficient confidence in available data or 

methods.

4.2.1 Climate Change Impacts 

In 2009, the EPA Administrator found that “six greenhouse gases taken in combination 

endanger both the public health and the public welfare of current and future generations.”83 The 

specific public health and public welfare impacts are detailed in the 2009 Endangerment Finding 

and its record.

A number of major peer-reviewed scientific assessments have been released since the 

administrative record concerning the Endangerment Finding closed following the EPA’s 2010 

Reconsideration Denial84. These assessments include the “Special Report on Managing the Risks 

of Extreme Events and Disasters to Advance Climate Change Adaptation” (SREX) (IPCC, 

2012), the 2013-14 Fifth Assessment Report (AR5) (IPCC, 2013, 2014a, 2014b), the 2014 

National Climate Assessment report (Melillo et al., 2014), the “Ocean Acidification: A National 

Strategy to Meet the Challenges of a Changing Ocean” (Ocean Acidification) (NRC, 2010), 

“Report on Climate Stabilization Targets: Emissions, Concentrations, and Impacts over Decades 

to Millennia” (Climate Stabilization Targets) (NRC, 2011a), “National Security Implications for 

U.S. Naval Forces” (National Security Implications) (NRC, 2011b), “Understanding Earth’s 

Deep Past: Lessons for Our Climate Future” (Understanding Earth’s Deep Past) (NRC, 2012a), 

“Sea Level Rise for the Coasts of California, Oregon, and Washington: Past, Present, and 

 

83 “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”).

84 “EPA’s Denial of the Petitions to Reconsider the Endangerment and Cause or Contribute Findings for Greenhouse 

Gases Under Section 202(a) of the Clean Air Act”, 75 Fed. Reg. 49,556 (Aug. 13, 2010) (“Reconsideration 

Denial”).
4-3

Future” (NRC, 2012b), “Climate and Social Stress: Implications for Security Analysis” (Climate 

and Social Stress) (NRC, 2013a), and “Abrupt Impacts of Climate Change” (Abrupt Impacts) 

assessments (NRC, 2013b). 

The EPA has reviewed these assessments and finds that in general, the improved 

understanding of the climate system they present are consistent with the assessments underlying 

the 2009 Endangerment Finding. 

The IPCC report (IPCC, 2013), the National Climate Assessment (Melillo et al., 2014), 

and three of the new NRC assessments (NRC, 2011a, 2011b, 2012b) provide estimates of 

projected global sea level rise. These estimates, while not always directly comparable as they 

assume different emissions scenarios and baselines, are at least 40 percent larger than, and in 

some cases more than twice as large as, the rise estimated in a 2007 IPCC assessment (IPCC, 

2007) of between 0.18 and 0.59 meters by the end of the century, relative to 1990. (It should be 

noted that in 2007, the IPCC stated that including poorly understood ice sheet processes could 

lead to an increase in the projections.) While these NRC and IPCC assessments continue to 

recognize and characterize the uncertainty inherent in accounting for ice sheet processes, these 

revised estimates are consistent with the assessments underlying the existing finding that GHGs 

are reasonably anticipated to endanger public health and welfare. Other key findings of the 

recent assessments are described briefly below.

According to the IPCC in the SREX (IPCC, 2012), “A changing climate leads to changes 

in the frequency, intensity, spatial extent, duration, and timing of extreme weather and climate 

events, and can result in unprecedented extreme weather and climate events.” The SREX 

documents observational evidence of changes in some weather and climate extremes that have 

occurred globally since 1950. The assessment also provides evidence of anthropogenic influence 

(e.g., elevated concentrations of GHGs) regarding the cause of some of these changes, including 

warming of extreme daily temperatures, intensified extreme precipitation events, and increases in 

extreme coastal high water levels due to rising sea level. The SREX projects further increases in 

some extreme weather and climate events during the 21st century. Combined with increasing 

vulnerability and exposure of populations and assets, increases in extreme weather and climate 

events have consequences for disaster risk, with particular impacts on the water, agriculture and 

food security and health sectors. 
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In its Climate Stabilization Targets assessment (NRC, 2011a), the NRC states, 

“Emissions of carbon dioxide from the burning of fossil fuels have ushered in a new epoch 

where human activities will largely determine the evolution of Earth’s climate. Because carbon 

dioxide in the atmosphere is long lived, it can effectively lock Earth and future generations into a 

range of impacts, some of which could become very severe.” 

The assessment concludes that carbon dioxide emissions will alter the atmosphere’s 

composition and therefore the climate for thousands of years; and attempts to quantify the results 

of stabilizing GHG concentrations at different levels. The report also projects the occurrence of 

several specific climate change impacts, finding warming could lead to increases in heavy 

rainfall and decreases in crop yields and Arctic sea ice extent, along with other significant 

changes in precipitation and stream flow. For an increase in global average temperature of 1 to 2 

oC above pre-industrial levels, the assessment projects that the area burnt by wildfires in western 

North America will likely more than double and that coral bleaching events and coastal erosion 

are projected to increase due both to warming and ocean acidification. An increase of 3 oC is 

projected to lead to a sea level rise of 0.5 to 1 meter by 2100. With an increase of 4 oC, the 

average summer in the United States is projected to be as warm as the warmest summers of the 

past century. The assessment notes that although many important aspects of climate change are 

difficult to quantify, the risk of adverse impacts is likely to increase with increasing temperature, 

and the risk of surprises can be expected to increase with the duration and magnitude of the 

warming.

The NRC assessment on Sea Level Rise (NRC, 2012b) projects a global sea level rise of 

0.5 to 1.4 meters by 2100, which is sufficient to lead to rising relative sea level even in the 

northern states. The NRC National Security Implications assessment (NRC, 2011b) considers 

potential impacts of sea level rise and suggests that “the Department of the Navy should expect 

roughly 0.4 to 2 meters global average sea-level rise by 2100." This assessment also 

recommends preparing for increased needs for humanitarian aid; responding to the effects of 

climate change in geopolitical hotspots, including possible mass migrations; and addressing 

changing security needs in the Arctic as sea ice retreats. The NRC Climate and Social Stress 

assessment (NRC, 2013a) found that it would be “prudent for security analysts to expect climate 

surprises in the coming decade . . . and for them to become progressively more serious and more 

frequent thereafter[.]” 
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The NRC Understanding Earth’s Deep Past assessment (NRC, 2012a) finds that “the 

magnitude and rate of the present greenhouse gas increase place the climate system in what 

could be one of the most severe increases in radiative forcing of the global climate system in 

Earth history.” This assessment finds that CO2 concentrations by the end of the century, without 

a reduction in emissions, are projected to increase to levels that Earth has not experienced for 

more than 30 million years. 

Similarly, the NRC Ocean Acidification assessment (NRC, 2010) finds that “[t]he 

chemistry of the ocean is changing at an unprecedented rate and magnitude due to anthropogenic 

carbon dioxide emissions; the rate of change exceeds any known to have occurred for at least the 

past hundreds of thousands of years.” The assessment notes that the full range of consequences is 

still unknown, but the risks “threaten coral reefs, fisheries, protected species, and other natural 

resources of value to society.” 

The most recent assessments to be released were the IPCC AR5 assessments (IPCC, 

2013, 2014a, 2014b) between September 2013 and April 2014, the NRC Abrupt Impacts 

assessment (NRC, 2013b) in December of 2013, and the U.S. National Climate Assessment 

(Melillo et al., 2014) in May of 2014. The NRC Abrupt Impacts report examines the potential for 

tipping points, thresholds beyond which major and rapid changes occur in the Earth’s climate 

system or other systems impacted by the climate. The Abrupt Impacts report did find less cause 

for concern than some previous assessments regarding some abrupt events within the next 

century such as disruption of the Atlantic Meridional Overturning Circulation (AMOC) and 

sudden releases of high-latitude methane from hydrates and permafrost, but found that the 

potential for abrupt changes in ecosystems, weather and climate extremes, and groundwater 

supplies critical for agriculture now seem more likely, severe, and imminent. The assessment 

found that some abrupt changes were already underway (Arctic sea ice retreat and increases in 

extinction risk due to the speed of climate change), but cautioned that even abrupt changes such 

as the AMOC disruption that are not expected in this century can have severe impacts when they 

happen. 

The IPCC AR5 assessments (IPCC, 2013, 2014a, 2014b) are also generally consistent 

with the underlying science supporting the 2009 Endangerment Finding. For example, 

confidence in attributing recent warming to human causes has increased: the IPCC stated that it 
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is extremely likely (>95 percent confidence) that human influences have been the dominant 

cause of recent warming. Moreover, the IPCC found that the last 30 years were likely (>66 

percent confidence) the warmest 30 year period in the Northern Hemisphere of the past 1400 

years, that the rate of ice loss of worldwide glaciers and the Greenland and Antarctic ice sheets 

has likely increased, that there is medium confidence that the recent summer sea ice retreat in the 

Arctic is larger than has been in 1450 years, and that concentrations of carbon dioxide and 

several other of the major greenhouse gases are higher than they have been in at least 800,000 

years. Climate-change induced impacts have been observed in changing precipitation patterns, 

melting snow and ice, species migration, negative impacts on crops, increased heat and 

decreased cold mortality, and altered ranges for water-borne illnesses and disease vectors. 

Additional risks from future changes include death, injury, and disrupted livelihoods in coastal 

zones and regions vulnerable to inland flooding, food insecurity linked to warming, drought, and 

flooding, especially for poor populations, reduced access to drinking and irrigation water for 

those with minimal capital in semi-arid regions, and decreased biodiversity in marine 

ecosystems, especially in the Arctic and tropics, with implications for coastal livelihoods. The 

IPCC determined that “[c]ontinued emissions of greenhouse gases will cause further warming 

and changes in all components of the climate system. Limiting climate change will require 

substantial and sustained reductions of greenhouse gases emissions.” 

Finally, the recently released National Climate Assessment (Melillo et al., 2014) stated, 

“Climate change is already affecting the American people in far reaching ways. Certain types of 

extreme weather events with links to climate change have become more frequent and/or intense, 

including prolonged periods of heat, heavy downpours, and, in some regions, floods and 

droughts. In addition, warming is causing sea level to rise and glaciers and Arctic sea ice to melt, 

and oceans are becoming more acidic as they absorb carbon dioxide. These and other aspects of 

climate change are disrupting people’s lives and damaging some sectors of our economy.”

Assessments from these bodies represent the current state of knowledge, 

comprehensively cover and synthesize thousands of individual studies to obtain the majority 

conclusions from the body of scientific literature and undergo a rigorous and exacting standard 

of review by the peer expert community and U.S. government.
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4.2.2 Social Cost of Carbon

We estimate the global social benefits of CO2 emission reductions expected from the 

proposed guidelines using the SCC estimates presented in the 2013 Technical Support 

Document: Technical Update of the Social Cost of Carbon for Regulatory Impact Analysis 

Under Executive Order 12866 (2013 SCC TSD).

85 We refer to these estimates, which were 

developed by the U.S. government, as “SCC estimates.” The SCC is a metric that estimates the 

monetary value of 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 have an incremental impact on cumulative global CO2 emissions). 

The SCC estimates used in this analysis were developed over many years, using the best 

science available, and with input from the public. The EPA and other federal agencies have 

considered the extensive public comments on ways to improve SCC estimation received via the 

notice and comment period that was part of numerous rulemakings since 2006. In addition, 

OMB’s Office of Information and Regulatory Affairs recently sought public comment on the 

approach used to develop the SCC estimates. The comment period ended on February 26, 2014, 

and OMB is reviewing the comments received.

An interagency process that included the EPA and other executive branch entities used 

three integrated assessment models (IAMs) to develop SCC estimates and selected four global 

values for use in regulatory analyses. The SCC estimates were first released in February 2010

 

85 Docket ID EPA-HQ-OAR-2013-0495, Technical Support Document: Technical Update of the Social Cost of 

Carbon for Regulatory Impact Analysis Under Executive Order 12866, Interagency Working Group on Social 

Cost of Carbon, with participation by Council of Economic Advisers, Council on Environmental Quality, 

Department of Agriculture, Department of Commerce, Department of Energy, Department of Transportation, 

Environmental Protection Agency, National Economic Council, Office of Energy and Climate Change, Office of 

Management and Budget, Office of Science and Technology Policy, and Department of Treasury (May 2013, 

Revised November 2013). Available at:

http://www.whitehouse.gov/sites/default/files/omb/assets/inforeg/technical-update-social-cost-of-carbon-forregulator-impact-analysis.pdf. 
4-8

and updated in 2013 using new versions of each IAM. 86

The SCC estimates represent global measures because of the distinctive nature of the 

climate change problem. The climate change problem is highly unusual in at least two respects.

First, emissions of most GHGs contribute to damages around the world even when they are 

emitted in the United States. The SCC must therefore incorporate the full (global) damages 

caused by GHG emissions in order to address the global nature of the problem. Second, climate 

change presents a problem that the United States alone cannot solve. The US now operates in a 

global, highly interconnected economy such that impacts on the other side of the world now 

affect our economy. Climate damages in other countries can affect U.S. companies. Climateexacerbated conflict can require military expenditures by the U.S. All of this means that the true 

cost of climate change to U.S. is much larger than impacts that simply occur in the U.S. Climate 

change presents a problem that the United States alone cannot solve. A global number is the 

economically appropriate reference point for collective actions to reduce climate change.

A key objective in the development of the SCC estimates was to enable a consistent 

exploration of three integrated assessment models (DICE, FUND, and PAGE)87 while respecting 

the different approaches to quantifying damages taken by the key modelers in the field. The 

selection of the three input parameters (equilibrium climate sensitivity, reference socioeconomic 

scenarios, discount rate) was based on an extensive review of the literature. Specifically, a 

probability distribution for climate sensitivity was specified as an input into all three models. In 

addition, the interagency group used a range of scenarios for the socio-economic parameters and 

a range of values for the discount rate. All other model features were left unchanged, relying on 

the model developers’ best estimates and judgments. In DICE, these parameters are handled 

deterministically and represented by fixed constants; in PAGE, most parameters are represented 

 

86 Docket ID EPA-HQ-OAR-2009-0472-114577, Technical Support Document: Social Cost of Carbon for 

Regulatory Impact Analysis Under Executive Order 12866, Interagency Working Group on Social Cost of 

Carbon, with participation by Council of Economic Advisers, Council on Environmental Quality, Department of 

Agriculture, Department of Commerce, Department of Energy, Department of Transportation, Environmental 

Protection Agency, National Economic Council, Office of Energy and Climate Change, Office of Management 

and Budget, Office of Science and Technology Policy, and Department of Treasury (February 2010). Available at: 

http://www.whitehouse.gov/sites/default/files/omb/inforeg/for-agencies/Social-Cost-of-Carbon-for-RIA.pdf. See 

previous citation for 2013 SCC TSD.

87 The full models names are as follows: Dynamic Integrated Climate and Economy (DICE); Climate Framework for 

Uncertainty, Negotiation, and Distribution (FUND); and Policy Analysis of the Greenhouse Gas Effect (PAGE).
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by probability distributions. FUND was also run in a mode in which parameters were treated 

probabilistically. The use of three models and these input parameters allowed for exploration of 

important uncertainties in the way climate damages are estimated, equilibrium climate 

sensitivity, reference socioeconomic and emission trajectories, and discount rate. As stated in the 

2010 SCC TSD, however, key uncertainties remain as the existing models are imperfect and 

incomplete. See the 2010 SCC TSD for a complete discussion of the methods used to develop 

the estimates and the key uncertainties, and the 2013 SCC TSD for the updated estimates. 

Notably, the 2013 process did not revisit the 2010 interagency modeling decisions (e.g., 

with regard to the discount rate, reference case socioeconomic and emission scenarios or 

equilibrium climate sensitivity). Rather, improvements in the way damages are modeled are 

confined to those that have been incorporated into the latest versions of the models by the 

developers themselves and used in peer-reviewed publications. The model updates that are

relevant to the SCC estimates include: an explicit representation of sea level rise damages in the

DICE and PAGE models; updated adaptation assumptions, revisions to ensure damages are 

constrained by GDP, updated regional scaling of damages, and a revised treatment of potentially 

abrupt shifts in climate damages in the PAGE model; an updated carbon cycle in the DICE 

model; and updated damage functions for sea level rise impacts, the agricultural sector, and 

reduced space heating requirements, as well as changes to the transient response of temperature 

to the buildup of GHG concentrations and the inclusion of indirect effects of methane emissions 

in the FUND model. The 2013 SCC TSD provides complete details.

When attempting to assess the incremental economic impacts of carbon dioxide 

emissions, the analyst faces a number of serious challenges. A report from the National 

Academies of Science (NRC, 2009) points out that any assessment will suffer from uncertainty, 

speculation, and lack of information about (1) future emissions of greenhouse gases, (2) the 

effects of past and future emissions on the climate system, (3) the impact of changes in climate 

on the physical and biological environment, and (4) the translation of these environmental 

impacts into economic damages.88 As a result, any effort to quantify and monetize the harms 

associated with climate change will raise serious questions of science, economics, and ethics and 

 

88 National Research Council (2009). Hidden Costs of Energy: Unpriced Consequences of Energy Production and 

Use. National Academies Press. See docket ID EPA-HQ-OAR-2009-0472-11486.
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should be viewed as provisional. 

The 2010 SCC TSD noted a number of limitations to the SCC analysis, including the 

incomplete way in which the integrated assessment models capture catastrophic and noncatastrophic impacts, their incomplete treatment of adaptation and technological change, 

uncertainty in the extrapolation of damages to high temperatures, and assumptions regarding risk 

aversion. Current integrated assessment models do not assign value to all of the important 

physical, ecological, and economic impacts of climate change recognized in the climate change 

literature due to a lack of precise information on the nature of damages and because the science 

incorporated into these models understandably lags behind the most recent research. The limited 

amount of research linking climate impacts to economic damages makes the modeling exercise 

even more difficult. These individual limitations do not all work in the same direction in terms of 

their influence on the SCC estimates, though taken together they suggest that the SCC estimates 

are likely conservative. In particular, the IPCC Fourth Assessment Report (2007) concluded that 

“It is very likely that [SCC estimates] underestimate the damage costs because they cannot 

include many non-quantifiable impacts.”

Nonetheless, these estimates and the discussion of their limitations represent the best 

available information about the social benefits of CO2 reductions to inform benefit-cost analysis. 

The new versions of the models used to estimate the values presented below offer some 

improvements in these areas, although further work remains warranted. Accordingly, the EPA 

and other agencies continue to engage in research on modeling and valuation of climate impacts 

with the goal to improve these estimates. Additional details are provided in the SCC TSDs.

The four SCC estimates, updated in 2013, are as follows: $13, $46, $68, and $137 per 

metric ton of CO2 emissions in the year 2020 (2011$).89 The first three values are based on the 

average SCC from the three IAMs, at discount rates of 5, 3, and 2.5 percent, respectively. SCC 

estimates for several discount rates are included because the literature shows that the SCC is 

quite sensitive to assumptions about the discount rate, and because no consensus exists on the 

appropriate rate to use in an intergenerational context (where costs and benefits are incurred by 

 

89 The 2010 and 2013 TSDs present SCC in 2007$. The estimates were adjusted to 2011$ using GDP Implicit Price 

Deflator, http://www.gpo.gov/fdsys/pkg/ECONI-2013-02/pdf/ECONI-2013-02-Pg3.pdf.
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different generations). The fourth value is the 95th percentile of the SCC from all three models at 

a 3 percent discount rate. It is included to represent higher-than-expected impacts from 

temperature change further out in the tails of the SCC distribution (representing less likely, but 

potentially catastrophic, outcomes).

Table 4-2 presents the updated global SCC estimates for the years 2015 to 2050. In order 

to calculate the dollar value for emission reductions, the SCC estimate for each emissions year 

would be applied to changes in CO2 emissions for that year, and then discounted back to the 

analysis year using the same discount rate used to estimate the SCC.90 The SCC 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 climate change. Note that the 

interagency group estimated the growth rate of the SCC directly using the three integrated 

assessment models rather than assuming a constant annual growth rate. This helps to ensure that 

the estimates are internally consistent with other modeling assumptions. Tables 4-3 through 4-5 

report the incremental climate benefits estimated in three analysis years (2020, 2025, and 2030) 

for the two illustrative compliance scenarios (i.e., state and regional) for two options evaluated.

 

90 This analysis considered the climate impacts of only CO2 emission change. As discussed below, the climate 

impacts of other pollutants were not calculated for the proposed guidelines. Furthermore, the U.S. Interagency 

Working Group on the Social Cost of Carbon has so far only considered estimates for the social cost of CO2. 

While CO2 is the dominant GHG emitted by the sector, we recognize the representative facilities within these 

comparisons may also have different emission rates for other climate forcers that will serve a minor role in 

determining the overall social cost of generation.
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Table 4-2. Global Social Cost of CO2, 2015-2050 (in 2011$)*

Year

Discount Rate and Statistic

5% Average 3% Average 2.5% Average 3% (95th percentile)

2015 $12 $39 $61 $116

2020 $13 $46 $68 $137

2025 $15 $50 $74 $153

2030 $17 $55 $80 $170

2035 $20 $60 $85 $187

2040 $22 $65 $92 $204

2045 $26 $70 $98 $220

2050 $28 $76 $104 $235

* The SCC values 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. These SCC values are stated in $/metric ton.

Table 4-3. Estimated Global Climate Benefits of CO2 Reductions for Proposed EGU GHG 

Existing Source Guidelines in 2020 (billions of 2011$)*

Discount Rate and Statistic Option 1 –

state

Option 1 –

regional

Option 2 –

state

Option 2 -

regional

Million metric tonnes of CO2 reduced 383 371 295 283

5% (average) $4.9 $4.7 $3.8 $3.6

3% (average) $18 $17 $14 $13

2.5% (average) $26 $25 $20 $19

3% (95th percentile) $52 $51 $40 $39

* The SCC values are dollar-year and emissions-year specific. SCC values represent only a partial accounting of 

climate impacts.

Table 4-4. Estimated Global Climate Benefits of CO2 Reductions for Proposed EGU GHG 

Existing Source Guidelines in 2025 (billions of 2011$)*

Discount Rate and Statistic Option 1 –

state

Option 1 -

regional

Option 2 –

state

Option 2 -

regional

Million metric tonnes of CO2 reduced 506 501 376 368

5% (average) $7.6 $7.5 $5.6 $5.5

3% (average) $25 $25 $19 $18

2.5% (average) $37 $37 $28 $27

3% (95th percentile) $77 $76 $57 $56

* The SCC values are dollar-year and emissions-year specific. SCC values represent only a partial accounting of 

climate impacts.

Table 4-5. Estimated Global Climate Benefits of CO2 Reductions for Proposed EGU GHG 

Existing Source Guidelines in 2030 (billions of 2011$)*

Discount Rate and Statistic Option 1 –

state

Option 1 -

regional

Option 2 –

state

Option 2 -

regional

Million metric tonnes of CO2 reduced 555 545 n/a n/a

5% (average) $9.5 $9.3 n/a n/a 

3% (average) $31 $30 n/a n/a

2.5% (average) $44 $44 n/a n/a

3% (95th percentile) $94 $92 n/a n/a

* The SCC values are dollar-year and emissions-year specific. SCC values represent only a partial accounting of 

climate impacts.
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It is important to note that the climate benefits presented above are associated with 

changes in CO2 emissions only. Implementing these guidelines, however, will have an impact on 

the emissions of other pollutants that would affect the climate. Both predicting reductions in 

emissions and estimating the climate impacts of these other pollutants, however, is complex. The 

climate impacts of these other pollutants have not been calculated for the proposed guidelines.91

The other emissions potentially reduced as a result of these guidelines include other 

greenhouse gases (such as methane), aerosols and aerosol precursors such as black carbon, 

organic carbon, sulfur dioxide and nitrogen oxides, and ozone precursors such as nitrogen oxides 

and volatile organic carbon compounds. Changes in emissions of these pollutants (both increases 

and decreases) could directly result from changes in electricity generation, upstream fossil fuel 

extraction and transport, and/or downstream secondary market impacts. Reductions in black 

carbon or ozone precursors are projected to lead to further cooling, but reductions in the other 

aerosol species and precursors are projected to lead to warming. Therefore, changes in non-CO2

pollutants could potentially augment or offset the climate benefits calculated here. These 

pollutants can act in different ways and on different timescales than carbon dioxide. For 

example, aerosols reflect (and in the case of black carbon, absorb) incoming radiation, whereas 

greenhouse gases absorb outgoing infrared radiation. In addition, these aerosols are thought to 

affect climate indirectly by altering properties of clouds. Black carbon can also deposit on snow 

and ice, darkening these surfaces and accelerating melting. In terms of lifetime, while carbon 

dioxide emissions can increase concentrations in the atmosphere for hundreds or thousands of 

years, many of these other pollutants are short lived and remain in the atmosphere for short 

periods of time ranging from days to weeks and can therefore exhibit large spatial and temporal 

variability. 

While the EPA has not quantified the climate impacts of these other pollutants for the 

proposed guidelines, the Agency has analyzed the potential changes in upstream methane 

emissions from the natural gas and coal production sectors that may result from the compliance 

scenarios examined in this RIA in the appendix to Chapter 3. The EPA assessed whether the net 

change in upstream methane emissions from natural gas and coal production is likely to be 

 

91 The federal government’s SCC estimates used in this analysis are designed to assess the climate benefits 

associated with changes in CO2 emissions only.
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positive or negative and also assessed the potential magnitude of changes relative to CO2

emissions reductions anticipated at power plants. This assessment included CO2 emissions from 

the flaring of methane, but did not evaluate potential changes in other combustion-related CO2

emissions, such as emissions associated with drilling, mining, processing, and transportation in 

the natural gas and coal production sectors. This analysis found that the net upstream CH4

emissions from natural gas systems and coal mines and CO2 emissions from flaring of methane 

will likely decrease under the proposed guidelines. Furthermore, the analysis suggests that the 

changes in upstream methane emissions are small relative to the changes in direct emissions 

from power plants. 

4.3 Estimated Human Health Co-Benefits

In addition to CO2, implementing these proposed guidelines is expected to reduce 

emissions of SO2 and NOX, which are precursors to formation of ambient PM2.5, as well as 

directly emitted fine particles.92 Therefore, reducing these emissions would also reduce human 

exposure to ambient PM2.5 and the incidence of PM2.5-related health effects. In addition, in the 

presence of sunlight, NOX and VOCs can undergo a chemical reaction in the atmosphere to form 

ozone. Depending on localized concentrations of volatile organic compounds (VOCs), reducing 

NOX emissions would also reduce human exposure to ozone and the incidence of ozone-related 

health effects. Although we do not have sufficient data to quantify these impacts in this analysis, 

reducing emissions of SO2 and NOx would also reduce ambient exposure to SO2 and NO2, 

respectively. In this section, we provide an overview of the monetized PM2.5 and ozone-related 

co-benefits estimated for the proposed guidelines. The estimated co-benefits associated with 

these emission reductions are beyond those achieved by previous EPA rulemakings, including 

MATS. A full description of the underlying data, studies, and assumptions is provided in the PM 

NAAQS RIA (U.S. EPA, 2012a) and Ozone NAAQS RIA (U.S. EPA, 2008b, 2010d).

There are several important considerations in assessing the air pollution-related health cobenefits for a climate-focused rulemaking. First, these estimated health co-benefits do not 

 

92 We estimate the health co-benefits associated with emission reductions of two categories of directly emitted 

particles: elemental carbon plus organic carbon (EC+OC) and crustal. Crustal emissions are composed of

compounds associated with minerals and metals from the earth’s surface, including carbonates, silicates, iron, 

phosphates, copper, and zinc. Often, crustal material represents particles not classified as one of the other species 

(e.g., organic carbon, elemental carbon, nitrate, sulfate, chloride, etc.).
4-15

account for any climate-related air quality changes (e.g., increased ambient ozone associated 

with higher temperatures) but rather changes in precursor emissions affected by this rulemaking. 

Excluding climate-related air quality changes may underestimate ozone-related health cobenefits. It is unclear how PM2.5-related health co-benefits would be impacted by excluding 

climate-related air quality changes since the science is unclear as to how climate change may 

affect PM2.5 exposure. Second, the estimated health co-benefits also do not consider temperature 

modification of PM2.5 and ozone risks (Roberts 2004; Ren 2006a, 2006b, 2008a, 2008b). Third, 

the estimated climate benefits reported in this RIA reflect global benefits, while the estimated 

health co-benefits are calculated for the contiguous U.S. only. Excluding temperature 

modification of air pollution risks and international air pollution-related health benefits implies 

that the quantified health co-benefits likely lead to underestimation.

Implementing these guidelines may lead to reductions in ambient PM2.5 concentrations 

below the National Ambient Air Quality Standards (NAAQS) for PM and ozone in some areas 

and assist other areas with attaining these NAAQS. Because the NAAQS RIAs (U.S. EPA, 

2012a, 2008b, 2010d) also calculated PM and ozone benefits, there are important differences 

worth noting in the design and analytical objectives of each RIA. The NAAQS RIAs illustrate 

the potential costs and benefits of attaining a revised air quality standard nationwide based on an 

array of emission reduction strategies for different sources including known and unknown 

controls, incremental to implementation of existing regulations and controls needed to attain the 

current standards. In short, NAAQS RIAs hypothesize, but do not predict, the reduction 

strategies that States may choose to enact when implementing a revised NAAQS. The setting of 

a NAAQS does not directly result in costs or benefits, and as such, EPA’s NAAQS RIAs are 

merely illustrative and the estimated costs and benefits are not intended to be added to the costs 

and benefits of other regulations that result in specific costs of control and emission reductions. 

However, it is possible that some costs and benefits estimated in this RIA may account for the 

same air quality improvements as estimated in the illustrative NAAQS RIAs. 

Similar to NAAQS RIAs, the emission reduction scenarios estimated for the proposed 

guidelines are also illustrative. In contrast to NAAQS RIAs, all of the emission reductions for the 

illustrative compliance scenarios would occur in one well-characterized sector (i.e., the EGU

sector). In general, EPA is more confident in the magnitude and location of the emission 

reductions for implementation rules, which typically require specific emission reductions in a 
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specific sector. As such, emission reductions achieved under promulgated implementation rules 

will ultimately be reflected in the baseline of future NAAQS analyses, which would reduce the 

incremental costs and benefits associated with attaining revised future NAAQS. EPA does not 

re-issue illustrative RIAs outside of the rulemaking process that retroactively update the baseline 

to account for implementation rules promulgated after an RIA was completed. For more 

information on the relationship between illustrative analyses, such as for the NAAQS and this 

proposal, and implementation rules, please see section 1.3 of the PM NAAQS RIA (U.S. EPA, 

2012a).

4.3.1 Health Impact Assessment for PM2.5 and Ozone

The Integrated Science Assessment for Particulate Matter (PM ISA) (U.S. EPA, 2009b) 

identified the human health effects associated with ambient PM2.5 exposure, which include 

premature morality and a variety of morbidity effects associated with acute and chronic 

exposures. Similarly, the Integrated Science Assessment for Ozone and Related Photochemical 

Oxidants (Ozone ISA) (U.S. EPA, 2013b) identified the human health effects associated with 

ambient ozone exposure, which include premature morality and a variety of morbidity effects 

associated with acute and chronic exposures. Table 4-6 identifies the quantified and unquantified 

co-benefit categories captured in EPA’s health co-benefits estimates for reduced exposure to 

ambient PM2.5 and ozone. Although the table below does not list unquantified health effects such 

as those associated with exposure to SO2, NO2, and mercury nor welfare effects such as 

acidification and nutrient enrichment, these effects are described in detail in Chapters 5 and 6 of 

the PM NAAQS RIA (U.S. EPA, 2012a) and summarized later in this chapter. It is important to 

emphasize that the list of unquantified benefit categories is not exhaustive, nor is quantification 

of each effect complete.
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Table 4-6. Human Health Effects of Ambient PM2.5 and Ozone

Category Specific Effect

Effect Has 

Been 

Quantified

Effect Has

Been 

Monetized

More 

Information

Improved Human Health

Reduced incidence of 

premature mortality 

from exposure to 

PM2.5

Adult premature mortality based on cohort study 

estimates and expert elicitation estimates (age >25 

or age >30)

  PM ISA

Infant mortality (age <1)   PM ISA

Reduced incidence of 

morbidity from 

exposure to PM2.5

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

Hospital admissions—respiratory (all ages)   PM ISA

Hospital admissions—cardiovascular (age >20)   PM ISA

Emergency room visits for asthma (all ages)   PM ISA

Acute bronchitis (age 8-12)   PM ISA

Lower respiratory symptoms (age 7-14)   PM ISA

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

Asthma exacerbation (asthmatics age 6-18)   PM ISA

Lost work days (age 18-65)   PM ISA

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

Chronic Bronchitis (age >26) — — PM ISA1

Emergency room visits for cardiovascular effects 

(all ages) — — PM ISA1

Strokes and cerebrovascular disease (age 50-79) — — PM ISA1

Other cardiovascular effects (e.g., other ages) — — PM ISA2

Other respiratory effects (e.g., pulmonary function, 

non-asthma ER visits, non-bronchitis chronic 

diseases, other ages and populations)

— — PM ISA2

Reproductive and developmental effects (e.g., low 

birth weight, pre-term births, etc) — — PM ISA2,3

Cancer, mutagenicity, and genotoxicity effects — — PM ISA2,3

Reduced incidence of 

mortality from 

exposure to ozone

Premature mortality based on short-term study 

estimates (all ages)   Ozone ISA

Premature mortality based on long-term study 

estimates (age 30–99) — — Ozone ISA1

Reduced incidence of 

morbidity from 

exposure to ozone

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

Hospital admissions—respiratory causes (age <2)   Ozone ISA

Emergency department visits for asthma (all ages)   Ozone ISA

Minor restricted-activity days (age 18–65)   Ozone ISA

School absence days (age 5–17)   Ozone ISA

Decreased outdoor worker productivity (age 18–65) — — Ozone ISA1

Other respiratory effects (e.g., premature aging of 

lungs) — — Ozone ISA2

Cardiovascular and nervous system effects — — Ozone ISA2

Reproductive and developmental effects — — Ozone ISA2,3

1 We assess these co-benefits qualitatively due to data and resource limitations for this analysis, but we have quantified them in 

sensitivity analyses for other analyses.

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.
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We follow a “damage-function” approach in calculating benefits, which estimates 

changes in individual health endpoints (specific effects that can be associated with changes in air 

quality) and assigns values to those changes assuming independence of the values for those 

individual endpoints. Because EPA rarely has the time or resources to perform new research to 

measure directly, either health outcomes or their values for regulatory analyses, our estimates are 

based on the best available methods of benefits transfer, which is the science and art of adapting 

primary research from similar contexts to estimate benefits for the environmental quality change 

under analysis. We use a “benefit-per-ton” approach to estimate the PM2.5 and ozone co-benefits

in this RIA. This section describes the underlying basis for the health and economic valuation 

estimates, and the subsequent section provides an overview of the benefit-per-ton estimates, 

which are described in detail in the appendix to this chapter.

The health impact assessment (HIA) quantifies the changes in the incidence of adverse 

health impacts resulting from changes in human exposure to PM2.5 and ozone. We use the 

environmental Benefits Mapping and Analysis Program (BenMAP) (version 4.0.66) to

systematize health impact analyses by applying a database of key input parameters, including 

population projections, health impact functions, and valuation functions (Abt Associates, 2012). 

For this assessment, the HIA is limited to those health effects that are directly linked to ambient 

PM2.5 and ozone concentrations. There may be other indirect health impacts associated with 

reducing emissions, such as occupational health exposures. Epidemiological studies generally 

provide estimates of the relative risks of a particular health effect for a given increment of air 

pollution (often per 10 μg/m3for PM2.5 or ppb for ozone). These relative risks can be used to 

develop risk coefficients that relate a unit reduction in PM2.5 to changes in the incidence of a 

health effect. We refer the reader to the PM NAAQS RIA (U.S. EPA, 2012a) and Ozone 

NAAQS RIA (U.S. EPA, 2008b, 2010d) for more information regarding the epidemiology 

studies and risk coefficients applied in this analysis, and we briefly elaborate on adult premature 

mortality below. The size of the mortality effect estimates from epidemiological studies, the 

serious nature of the effect itself, and the high monetary value ascribed to prolonging life make 

mortality risk reduction the most significant health endpoint quantified in this analysis.
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4.3.1.1 Mortality Concentration-Response Functions for PM2.5

Considering a substantial body of published scientific literature and reflecting thousands 

of epidemiology, toxicology, and clinical studies, the PM ISA documents the association 

between elevated PM2.5 concentrations and adverse health effects, including increased premature 

mortality (U.S. EPA, 2009b). The PM ISA, which was twice reviewed by the Clean Air 

Scientific Advisory Committee of EPA’s Science Advisory Board (SAB-CASAC) (U.S. EPASAB, 2009b, 2009c), 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 consistently finds that a no-threshold loglinear model most adequately portrays the PM-mortality concentration-response relationship 

while recognizing potential uncertainty about the exact shape of the concentration-response 

function. In addition to adult mortality discussed in more detail below, we use effect coefficients 

from Woodruff et al. (1997) to estimate PM-related infant mortality.

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 PM 

ISA (U.S. EPA, 2009b) concluded that the ACS and Six Cities cohorts provide the strongest 

evidence of the association between long-term PM2.5 exposure and premature mortality with 

support from a number of 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, 2010a). As both the ACS and Six Cities cohort studies have inherent strengths 

and weaknesses, we present PM2.5 co-benefits estimates using relative risk estimates from both 

these cohorts (Krewski et al., 2009; Lepeule et al., 2012).

As a characterization of uncertainty regarding the adult PM2.5-mortality relationship, EPA 

graphically presents the PM2.5 co-benefits derived from EPA’s expert elicitation study (Roman et 

al., 2008; IEc, 2006). The primary goal of the 2006 study was to elicit from a sample of health 

experts probabilistic distributions describing uncertainty in estimates of the reduction in 

mortality among the adult U.S. population resulting from reductions in ambient annual average 

PM2.5 concentrations. In that study, twelve experts provided independent opinions regarding the 

PM2.5-mortality concentration-response function. Because the experts relied upon the ACS and 
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Six Cities cohort studies to inform their concentration-response functions, the benefits estimates 

derived from the expert responses generally fall between results derived from these studies (see 

Figure 4-1). We do not combine the expert results in order to preserve the breadth and diversity 

of opinion on the expert panel. This presentation of the expert-derived results is generally 

consistent with SAB advice (U.S. EPA-SAB, 2008), which recommended that the EPA 

emphasize that “scientific differences existed only with respect to the magnitude of the effect of 

PM2.5 on mortality, not whether such an effect existed” and that the expert elicitation “supports 

the conclusion that the benefits of PM2.5 control are very likely to be substantial”. Although it is 

possible that newer scientific literature could revise the experts’ quantitative responses if elicited 

again, we believe that these general conclusions are unlikely to change.

4.3.1.2 Mortality Concentration-Response Functions for Ozone

In 2008, the National Academies of Science (NRC, 2008) issued a series of 

recommendations to the EPA regarding the quantification and valuation of ozone-related shortterm 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 NMMAPS 

[National Morbidity, Mortality, and Air Pollution Study] studies without exclusion of the metaanalyses” (NRC, 2008). In view of the findings of the National Academies panel, we estimate the 

co-benefits of avoiding short-term ozone mortality using the Bell et al. (2004) NMMAPS 

analysis, the Schwartz (2005) multi-city study, the Huang et al. (2005) multi-city study as well as 

effect estimates from the three meta-analyses (Bell et al. (2005), Levy et al. (2005), and Ito et al. 

(2005)). These studies are consistent with the studies used in the Ozone NAAQS RIA (U.S. 

EPA, 2008b, 2010d).93 For simplicity, we report the ozone mortality estimates in this RIA as a 

range from Bell et al. (2004) to Levy et al. (2005) to represent the lowest and the highest cobenefits estimates based on these six ozone mortality studies. In addition, we graphically present 

 

93 Since EPA received NAS advice, EPA published the Ozone ISA (U.S. EPA, 2013b) and the second draft Ozone 

Health Risk and Exposure Assessment (U.S. EPA, 2014a). Therefore, the ozone mortality studies applied in this 

analysis, while current at the time of the previous Ozone NAAQS RIAs, do not reflect the most updated literature 

available. The selection of ozone mortality studies used to estimate benefits in RIAs will be revisited in the 

forthcoming RIA accompanying the on-going review of the Ozone NAAQS.
4-21

estimated co-benefits derived from all six studies mentioned above as a characterization of 

uncertainty regarding the ozone -mortality relationship in Figure 4-1.

4.3.2 Economic Valuation for Health Co-benefits

After quantifying the change in adverse health impacts, we estimate the economic value 

of these avoided impacts. Reductions in ambient concentrations of air pollution generally lower 

the risk of future adverse health effects by a small amount for a large population. 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 generally not available, 

so we use the cost of treating or mitigating the effect. These cost-of-illness (COI) estimates 

generally (although not necessarily in every case) understate the true value of reductions in risk 

of a health effect. They tend to reflect the direct expenditures related to treatment but not the 

value of avoided pain and suffering from the health effect. The unit values applied in this 

analysis are provided in Table 5-9 of the PM NAAQS RIA for each health endpoint (U.S. EPA, 

2012a).

Avoided premature deaths account for 98 percent of 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 

adoption of a 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), the 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 reductions in mortality risk (U.S. EPA-SAB, 

2000). The VSL approach is a summary measure for the value of small changes in mortality risk 

experienced by a large number of people.

EPA 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 has decided to apply the VSL 

that was vetted and endorsed by the SAB in the Guidelines for Preparing Economic Analyses 
4-22

(U.S. EPA, 2000)94 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 $6.3 million (2000$).95 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 2011$ after adjusting for income growth are $9.9 million for 2020 and $10.1 million 

for 2025 and 2030.

96

The Agency is committed to using scientifically sound, appropriately reviewed evidence 

in valuing mortality risk reductions and has made significant progress in responding to the SABEEAC’s specific recommendations. In the process, the Agency has identified a number of 

important issues to be considered in updating its mortality risk valuation estimates. These are 

detailed in a white paper, “Valuing Mortality Risk Reductions in Environmental Policy” (U.S. 

EPA, 2010c), which recently underwent review by the SAB-EEAC. A meeting with the SAB on 

this paper was held on March 14, 2011 and formal recommendations were transmitted on 

July 29, 2011 (U.S. EPA-SAB, 2011). EPA is taking SAB’s recommendations under advisement.

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 (OMB, 2003). We 

assume that there is a “cessation” lag between changes in PM exposures and the total realization 

of changes in health effects. Although the structure of the lag is uncertain, the EPA follows the 

advice of the SAB-HES to assume a segmented lag structure characterized by 30 percent of 

mortality reductions 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, 2004c). 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, 

 

94 In the updated Guidelines for Preparing Economic Analyses (U.S. EPA, 2010e), EPA retained the VSL endorsed 

by the SAB with the understanding that further updates to the mortality risk valuation guidance would be 

forthcoming in the near future.

95 In 1990$, this base VSL is $4.8 million.

96 Income growth projections are only currently available in BenMAP through 2024, so both the 2025 and 2030 

estimates use income growth only through 2024 and are therefore likely underestimates. 
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the estimated ozone-related co-benefits are identical for all discount rates.

4.3.3 Benefit-per-ton Estimates for PM2.5

We used a “benefit-per-ton” approach to estimate the PM2.5 co-benefits in this RIA. EPA 

has applied this approach in several previous RIAs (e.g., U.S. EPA, 2011b, 2011c, 2012b). These 

benefit-per-ton estimates provide the total monetized human health co-benefits (the sum of 

premature mortality and premature morbidity), of reducing one ton of PM2.5 (or PM2.5 precursor 

such as NOX or SO2) from a specified source. Specifically, in this analysis, we multiplied the 

estimates for the EGU sector by the corresponding emission reductions based on regional (i.e., 

East, West, and California) benefit-per-ton estimates. 

The method used to calculate the regional benefit-per-ton estimates is a slight 

modification of the national benefit-per-ton estimates described in the TSD: Estimating the 

Benefit per Ton of Reducing PM2.5 Precursors from 17 Sectors (U.S. EPA, 2013a). The national 

estimates were derived using the approach published in Fann et al. (2012), but they have since 

been updated to reflect the epidemiology studies and Census population data first applied in the 

final PM NAAQS RIA (U.S. EPA, 2012a). The approach in Fann et al. (2012) is similar to the 

work previously published by Fann et al. (2009), but the newer study includes improvements that 

provide more refined estimates of PM2.5-related health benefits for emissions reductions in the 

various sectors. Specifically, the air quality modeling data reflect industrial sectors that are more 

narrowly defined. In addition, the updated air quality modeling data reflects more recent 

emissions data (2005 rather than 2001) and has higher spatial resolution (12km rather than 36 km 

grid cells).97 For this rulemaking, to generate the regional benefit-per-ton estimates we simply 

aggregated the EGU impacts in BenMAP to the region (i.e., East, West, and California) rather 

than aggregating to the nation as was done in Fann et al. (2012). We then divided the regional 

benefits by the regional emissions rather than the national emissions. The appendix to this 

chapter provides additional detail regarding these calculations.

As noted below in the characterization of uncertainty, all benefit-per-ton estimates have 

 

97 Although the modeling underlying the benefit-per-ton estimates does not reflect emission reductions anticipated 

from MATS, the EGU contribution to ambient PM2.5 and ozone on a per-ton basis would be similar. (Fann, 

Fulcher, and Baker, 2013) Because the emission reductions in this RIA are calculated from an IPM base case that 

includes MATS (see Chapter 3), there is no double-counting concern with the resulting co-benefits estimates.
4-24

inherent limitations. Specifically, all benefit-per-ton estimates reflect the geographic distribution 

of the modeled sector emissions, which may not match the emission reductions anticipated by 

the proposed guidelines, and they may not reflect local variability in population density, 

meteorology, exposure, baseline health incidence rates, or other local factors for any specific 

location. In addition, these estimates reflect the regional average benefit-per-ton for each ambient 

PM2.5 precursor emitted from EGUs, which assumes a linear atmospheric response to emission 

reductions. The regional benefit-per-ton estimates, although less subject to these types of 

uncertainties than national estimates, still should be interpreted with caution. Even though we 

assume that all fine particles have equivalent health effects, the benefit-per-ton estimates vary 

between precursors depending on the location and magnitude of their impact on PM2.5 levels, 

which drive population exposure. 

4.3.4 Benefit-per-ton Estimates for Ozone

Similar to PM2.5, we used a “benefit-per-ton” approach in this RIA to estimate the ozone

co-benefits, which represent the total monetized human health co-benefits (the sum of premature 

mortality and premature morbidity) of reducing one ton of NOx (an ozone precursor). Also 

consistent with the PM2.5 estimates, we generated regional benefit-per-ton estimates for ozone for 

the EGU sector using the air quality modeling data described in Fann et al. (2012) and using the 

updated Census population data first applied in the final PM NAAQS RIA (U.S. EPA, 2012a). In 

contrast to the PM2.5 estimates, the ozone estimates are not based on changes to annual 

emissions. Instead, the regional estimates (i.e., East, West, and California) correspond to NOX

emissions from U.S. EGUs during the ozone-season (May to September). Because we estimate 

ozone health impacts from May to September only, this approach underestimates ozone cobenefits in areas with a longer ozone season such as southern California and Texas. These 

estimates assume that EGU-attributable ozone formation at the regional-level is due to NOx 

alone. Because EGUs emit little VOC relative to NOX emissions, it is unlikely that VOCs 

emitted by EGUs would contribute substantially to regional ozone formation. All benefit-per-ton 

estimates have inherent limitations and should be interpreted with caution. We provide more 

detailed information regarding the generation of these estimates in the appendix to this chapter.
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4.3.5 Estimated Health Co-Benefits Results

Tables 4-7 through 4-9 provide the national and regional benefit-per-ton estimates for 

2020, 2025, and 2030. Tables 4-10 through 4-12 provide the emission reductions estimated to 

occur in three analysis years (2020, 2025, and 2030) for two illustrative compliance scenarios

(i.e., state and regional) for two options by region (i.e., East, West, and California).

98 Tables 4-13

through 4-15 summarize the national monetized PM and ozone-related health co-benefits 

estimated to occur in three analysis years (2020, 2025, and 2030) for the options by precursor 

pollutant using discount rates of 3 percent and 7 percent. Tables 4-16 through 4-18 provide 

national summaries of the reductions in health incidences estimated for the options associated 

with these pollution reductions in 2020, 2025, and 2030. Figure 4-1 provides a visual 

representation of the range of estimated PM2.5 and ozone-related co-benefits using concentrationresponse functions from different studies and expert opinion for the options evaluated in 2020 as 

an illustrative analysis year. Figures 4-2 and 4-3 provide a breakdown of the monetized health 

co-benefits for each of the options evaluated in 2020 as an illustrative analysis year by precursor 

pollutant and region, respectively. 

 

98 See Chapter 3 of this RIA for more information regarding the expected emission reductions used to calculate the 

health co-benefits in this chapter. Chapter 3 also provides more information regarding the illustrative compliance 

scenarios.
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Table 4-7. Summary of National and Regional Benefit-per-ton Estimates for EGUs in 2020 

(2011$)*

Pollutant Discount 

Rate National Regional

East West California

SO2

3% $38,000 to $86,000 $40,000 to $90,000 $7,800 to $18,000 $160,000 to $320,000

7% $34,000 to $77,000 $36,000 to $82,000 $7,100 to $16,000 $140,000 to $320,000

Directly emitted 

PM2.5 (EC+OC)

3% $140,000 to $320,000 $140,000 to $320,000 $56,000 to $130,000 $280,000 to $570,000

7% $130,000 to $290,000 $130,000 to $280,000 $50,000 to $110,000 $250,000 to $570,000

Directly emitted 

PM2.5 (crustal)

3% $18,000 to $40,000 $18,000 to $41,000 $11,000 to $25,000 $110,000 to $220,000

7% $16,000 to $36,000 $16,000 to $37,000 $10,000 to $23,000 $95,000 to $220,000

NOX (as PM2.5)

3% $5,600 to $13,000 $6,700 to $15,000 $1,200 to $2,600 $17,000 to $34,000

7% $5,000 to $11,000 $6,000 to $14,000 $1,000 to $2,400 $15,000 to $34,000

NOX (as Ozone) N/A $3,800 to $16,000 $4,600 to $19,000 $930 to $4,000 $7,400 to $31,000

* The range of estimates reflects the range of epidemiology studies for avoided premature mortality for PM2.5 and 

ozone. All estimates are rounded to two significant figures. The monetized co-benefits do not include reduced 

health effects from direct exposure to NO2, SO2, ecosystem effects, or visibility impairment. All fine particles are 

assumed to have equivalent health effects, but the benefit per ton estimates vary depending on the location and 

magnitude of their impact on PM2.5 concentrations, which drive population exposure. The monetized co-benefits 

incorporate the conversion from precursor emissions to ambient fine particles and ozone. Benefit-per-ton 

estimates for ozone are based on ozone season NOX emissions. Ozone co-benefits occur in analysis year, so they 

are the same for all discount rates. Confidence intervals are unavailable for this analysis because of the benefitper-ton methodology. In general, the 95th percentile confidence interval for monetized PM2.5 benefits ranges from 

approximately -90 percent to +180 percent of the central estimates based on Krewski et al. (2009) and Lepeule et 

al. (2012).

Table 4-8. Summary of National and Regional Benefit-per-Ton Estimates for EGUs in 2025 

(2011$)*

Pollutant Discount 

Rate National Regional

East West California

SO2

3% $41,000 to $93,000 $44,000 to $98,000 $8,800 to $20,000 $180,000 to $410,000

7% $37,000 to $84,000 $39,000 to $89,000 $8,000 to $18,000 $160,000 to $370,000

Directly emitted 

PM2.5 (EC+OC)

3% $150,000 to $350,000 $150,000 to $340,000 $64,000 to $140,000 $320,000 to $720,000

7% $140,000 to $310,000 $140,000 to $310,000 $58,000 to $130,000 $290,000 to $650,000

Directly emitted 

PM2.5 (crustal)

3% $17,000 to $39,000 $18,000 to $40,000 $12,000 to $27,000 $43,000 to $96,000

7% $15,000 to $35,000 $16,000 to $36,000 $11,000 to $24,000 $38,000 to $87,000

NOX (as PM2.5)

3% $6,000 to $14,000 $7,200 to $16,000 $1,300 to $2,900 $19,000 to $42,000

7% $5,400 to $12,000 $6,500 to $15,000 $1,200 to $2,600 $17,000 to $38,000

NOX (as Ozone) N/A $4,900 to $21,000 $5,900 to $25,000 $1,200 to $5,400 $9,900 to $42,000

* The range of estimates reflects the range of epidemiology studies for avoided premature mortality for PM2.5 and 

ozone. All estimates are rounded to two significant figures. The monetized co-benefits do not include reduced 

health effects from direct exposure to NO2, SO2, ecosystem effects, or visibility impairment. All fine particles are 

assumed to have equivalent health effects, but the benefit per ton estimates vary depending on the location and 

magnitude of their impact on PM2.5 concentrations, which drive population exposure. The monetized co-benefits 

incorporate the conversion from precursor emissions to ambient fine particles and ozone. Benefit-per-ton 

estimates for ozone are based on ozone season NOX emissions. Ozone co-benefits occur in analysis year, so they 

are the same for all discount rates. Confidence intervals are unavailable for this analysis because of the benefitper-ton methodology. In general, the 95th percentile confidence interval for monetized PM2.5 benefits ranges from 

approximately -90 percent to +180 percent of the central estimates based on Krewski et al. (2009) and Lepeule et 

al. (2012).
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Table 4-9. Summary of National and Regional Benefit-per-Ton Estimates for EGUs in 2030 

(2011$)*

Pollutant Discount 

Rate National Regional

East West California

SO2

3% $44,000 to $100,000 $47,000 to $110,000 $9,800 to $22,000 $200,000 to $450,000

7% $40,000 to $90,000 $42,000 to $95,000 $8,800 to $20,000 $180,000 to $410,000

Directly emitted 

PM2.5 (EC+OC)

3% $170,000 to $370,000 $160,000 to $370,000 $71,000 to $160,000 $360,000 to $800,000

7% $150,000 to $340,000 $150,000 to $330,000 $64,000 to $150,000 $320,000 to $730,000

Directly emitted 

PM2.5 (crustal)

3% $18,000 to $42,000 $19,000 to $43,000 $13,000 to $30,000 $47,000 to $110,000

7% $17,000 to $38,000 $17,000 to $38,000 $12,000 to $27,000 $43,000 to $96,000

NOX (as PM2.5)

3% $6,400 to $14,000 $7,600 to $17,000 $1,400 to $3,200 $21,000 to $42,000

7% $5,800 to $13,000 $6,900 to $16,000 $1,300 to $2,900 $19,000 to $47,000

NOX (as Ozone) N/A $5,300 to $23,000 $6,300 to $27,000 $1,400 to $6,000 $11,000 to $47,000

* The range of estimates reflects the range of epidemiology studies for avoided premature mortality for PM2.5 and 

ozone. All estimates are rounded to two significant figures. The monetized co-benefits do not include reduced 

health effects from direct exposure to NO2, SO2, ecosystem effects, or visibility impairment. All fine particles are 

assumed to have equivalent health effects, but the benefit per ton estimates vary depending on the location and 

magnitude of their impact on PM2.5 concentrations, which drive population exposure. The monetized co-benefits 

incorporate the conversion from precursor emissions to ambient fine particles and ozone. Benefit-per-ton 

estimates for ozone are based on ozone season NOX emissions. Ozone co-benefits occur in analysis year, so they 

are the same for all discount rates. Confidence intervals are unavailable for this analysis because of the benefitper-ton methodology. In general, the 95th percentile confidence interval for monetized PM2.5 benefits ranges from 

approximately -90 percent to +180 percent of the central estimates based on Krewski et al. (2009) and Lepeule et 

al. (2012).
4-28

Table 4-10. Emission Reductions of Criteria Pollutants for the Proposed EGU GHG 

Existing Source Guidelines in 2020 (thousands of short tons)*

Region SO2

All-year 

NOx

Ozone-Season 

NOx

Directly emitted 

PM2.5 (EC+OC)

Directly emitted 

PM2.5 (crustal)

Option 1 - State

East 311 315 135 5 41

West 25 51 22 <1 4

California <1 1 1 N/A N/A

National Total 335 367 157 6 45

Option 1 - Regional

East 279 305 130 5 41

West 10 32 13 0 3

California 2 8 3 N/A N/A

National Total 292 345 146 6 44

Option 2 - State

East 247 240 101 4 35

West 20 40 18 <1 3

California <1 1 1 N/A N/A

National Total 267 281 119 5 38

Option 2 - Regional

East 234 235 97 4 33

West 8 25 11 <1 2

California 2 8 3 N/A N/A

National Total 244 268 111 5 36

*All emissions shown in the table are rounded, so regional emission reductions may appear to not sum to national 

total.
4-29

Table 4-11. Emission Reductions of Criteria Pollutants for the Proposed EGU GHG 

Existing Source Guidelines in 2025 (thousands of short tons)*

Region SO2

All-year 

NOx

Ozone-Season 

NOx

Directly emitted 

PM2.5 (EC+OC)

Directly emitted 

PM2.5 (crustal)

Option 1 - State

East 395 378 164 6 44

West 30 53 23 1 6

California 1 5 2 N/A N/A

National Total 425 436 190 6 49

Option 1 - Regional

East 376 372 160 5 42

West 16 34 15 1 4

California 3 16 5 N/A N/A

National Total 395 421 180 6 46

Option 2 - State

East 301 271 114 4 34

West 25 42 20 <1 4

California 1 4 2 N/A N/A

National Total 327 317 136 5 38

Option 2 - Regional

East 281 270 113 4 32

West 13 24 11 <1 3

California 3 14 5 N/A N/A

National Total 297 309 129 4 34

*All emissions shown in the table are rounded, so regional emission reductions may appear to not sum to national 

total.
4-30

Table 4-12. Emission Reductions of Criteria Pollutants for the Proposed EGU GHG 

Existing Source Guidelines in 2030 (thousands of short tons)*

Region SO2

All-year 

NOx

Ozone-Season 

NOx

Directly emitted 

PM2.5 (EC+OC)*

Directly emitted 

PM2.5 (crustal)*

Option 1 - State

East 441 376 163 5 39

West 30 52 24 1 5

California <1 <1 <1 N/A N/A

National Total 471 428 187 6 44

Option 1 - Regional

East 406 366 158 5 39

West 16 33 15 <1 4

California 2 7 3 N/A N/A

National Total 424 407 176 5 42

*All emissions shown in the table are rounded, so regional emission reductions may appear to not sum to national 

total.
4-31

Table 4-13. Summary of Estimated Monetized Health Co-Benefits for the Proposed EGU 

GHG Existing Source Guidelines in 2020 (millions of 2011$) *

Pollutant 3% Discount Rate 7% Discount Rate

Option 1 - State

SO2 $13,000 to $29,000 $11,000 to $26,000

Directly emitted PM2.5 (EC+OC) $760 to $1,700 $690 to $1,600

Directly emitted PM2.5 (crustal) $790 to $1,800 $710 to $1,600

NOx (as PM2.5) $2,200 to $4,900 $2,000 to $4,400

NOx (as Ozone) $640 to $2,700 $640 to $2,700

Total $17,000 to $40,000 $15,000 to $36,000

Option 1 - Regional

SO2 $12,000 to $26,000 $11,000 to $24,000

Directly emitted PM2.5 (EC+OC) $750 to $1,700 $670 to $1,500

Directly emitted PM2.5 (crustal) $770 to $1,700 $690 to $1,600

NOx (as PM2.5) $2,200 to $5,000 $2,000 to $4,500

NOx (as Ozone) $630 to $2,700 $630 to $2,700

Total $16,000 to $37,000 $15,000 to $34,000

Option 2 - State

SO2 $10,000 to $23,000 $9,100 to $21,000

Directly emitted PM2.5 (EC+OC) $640 to $1,500 $580 to $1,300

Directly emitted PM2.5 (crustal) $660 to $1,500 $600 to $1,400

NOx (as PM2.5) $1,700 to $3,800 $1,500 to $3,400

NOx (as Ozone) $480 to $2,100 $480 to $2,100

Total $14,000 to $32,000 $12,000 to $29,000

Option 2 - Regional

SO2 $9,800 to $22,000 $8,900 to $20,000

Directly emitted PM2.5 (EC+OC) $610 to $1,400 $550 to $1,200

Directly emitted PM2.5 (crustal) $630 to $1,400 $570 to $1,300

NOx (as PM2.5) $1,700 to $3,900 $1,600 to $3,500

NOx (as Ozone) $470 to $2,000 $470 to $2,000

Total $13,000 to $31,000 $12,000 to $28,000

* All estimates are rounded to two significant figures so numbers may not sum down columns. The estimated 

monetized co-benefits do not include climate benefits or reduced health effects from direct exposure to NO2, SO2, 

ecosystem effects, or visibility impairment. All fine particles are assumed to have equivalent health effects, but the 

benefit-per-ton estimates vary depending on the location and magnitude of their impact on PM2.5 levels, which drive 

population exposure. The monetized co-benefits incorporate the conversion from precursor emissions to ambient 

fine particles and ozone. Co-benefits for PM2.5 precursors are based on regional benefit-per-ton estimates. Cobenefits for ozone are based on ozone season NOx emissions. Ozone co-benefits occur in analysis year, so they are 

the same for all discount rates. Confidence intervals are unavailable for this analysis because of the benefit-per-ton 

methodology. In general, the 95th percentile confidence interval for monetized PM2.5 benefits ranges from 

approximately -90 percent to +180 percent of the central estimates based on Krewski et al. (2009) and Lepeule et al. 

(2012).
4-32

Table 4-14. Summary of Estimated Monetized Health Co-Benefits for the Proposed EGU 

GHG Existing Source Guidelines in 2025 (millions of 2011$) *

Pollutant 3% Discount Rate 7% Discount Rate

Option 1 - State

SO2 $18,000 to $40,000 $16,000 to $36,000

Directly emitted PM2.5 (EC+OC) $900 to $2,000 $810 to $1,800

Directly emitted PM2.5 (crustal) $830 to $1,900 $750 to $1,700

NOx (as PM2.5) $2,900 to $6,500 $2,600 to $5,800

NOx (as Ozone) $1,000 to $4,400 $1,000 to $4,400

Total $23,000 to $54,000 $21,000 to $49,000

Option 1 - Regional

SO2 $17,000 to $38,000 $15,000 to $35,000

Directly emitted PM2.5 (EC+OC) $850 to $1,900 $760 to $1,700

Directly emitted PM2.5 (crustal) $780 to $1,800 $700 to $1,600

NOx (as PM2.5) $3,000 to $6,800 $2,700 to $6,100

NOx (as Ozone) $1,000 to $4,300 $1,000 to $4,300

Total $23,000 to $53,000 $21,000 to $48,000

Option 2 - State

SO2 $14,000 to $30,000 $12,000 to $27,000

Directly emitted PM2.5 (EC+OC) $690 to $1,600 $630 to $1,400

Directly emitted PM2.5 (crustal) $640 to $1,400 $580 to $1,300

NOx (as PM2.5) $2,100 to $4,700 $1,900 to $4,200

NOx (as Ozone) $720 to $3,100 $720 to $3,100

Total $18,000 to $41,000 $16,000 to $37,000

Option 2 - Regional

SO2 $13,000 to $29,000 $12,000 to $26,000

Directly emitted PM2.5 (EC+OC) $640 to $1,400 $580 to $1,300

Directly emitted PM2.5 (crustal) $590 to $1,300 $530 to $1,200

NOx (as PM2.5) $2,200 to $5,000 $2,000 to $4,500

NOx (as Ozone) $730 to $3,100 $730 to $3,100

Total $17,000 to $40,000 $16,000 to $36,000

* All estimates are rounded to two significant figures so numbers may not sum down columns. The estimated 

monetized co-benefits do not include climate benefits or reduced health effects from direct exposure to NO2, SO2, 

ecosystem effects, or visibility impairment. All fine particles are assumed to have equivalent health effects, but the 

benefit-per-ton estimates vary depending on the location and magnitude of their impact on PM2.5 levels, which drive 

population exposure. The monetized co-benefits incorporate the conversion from precursor emissions to ambient 

fine particles and ozone. Co-benefits for PM2.5 precursors are based on regional benefit-per-ton estimates. Cobenefits for ozone are based on ozone season NOx emissions. Ozone co-benefits occur in analysis year, so they are 

the same for all discount rates. Confidence intervals are unavailable for this analysis because of the benefit-per-ton 

methodology. In general, the 95th percentile confidence interval for monetized PM2.5 benefits ranges from 

approximately -90 percent to +180 percent of the central estimates based on Krewski et al. (2009) and Lepeule et al. 

(2012).
4-33

Table 4-15. Summary of Estimated Monetized Health Co-Benefits for the Proposed EGU 

GHG Existing Source Guidelines in 2030 (millions of 2011$) *

Pollutant 3% Discount Rate 7% Discount Rate

Option 1 - State

SO2 $21,000 to $47,000 $19,000 to $43,000

Directly emitted PM2.5 (EC+OC) $870 to $2,000 $780 to $1,800

Directly emitted PM2.5 (crustal) $800 to $1,800 $720 to $1,600

NOx (as PM2.5) $2,900 to $6,600 $2,600 to $6,000

NOx (as Ozone) $1,100 to $4,600 $1,100 to $4,600

Total $27,000 to $62,000 $24,000 to $57,000

Option 1 - Regional

SO2 $20,000 to $44,000 $18,000 to $40,000

Directly emitted PM2.5 (EC+OC) $840 to $1,900 $760 to $1,700

Directly emitted PM2.5 (crustal) $770 to $1,700 $700 to $1,600

NOx (as PM2.5) $3,000 to $6,700 $2,700 to $6,100

NOx (as Ozone) $1,100 to $4,500 $1,100 to $4,500

Total $25,000 to $59,000 $23,000 to $54,000

* All estimates are rounded to two significant figures so numbers may not sum down columns. The estimated 

monetized co-benefits do not include climate benefits or reduced health effects from direct exposure to NO2, SO2, 

ecosystem effects, or visibility impairment. All fine particles are assumed to have equivalent health effects, but the 

benefit-per-ton estimates vary depending on the location and magnitude of their impact on PM2.5 levels, which drive 

population exposure. The monetized co-benefits incorporate the conversion from precursor emissions to ambient 

fine particles and ozone. Co-benefits for PM2.5 precursors are based on regional benefit-per-ton estimates. Cobenefits for ozone are based on ozone season NOx emissions. Ozone co-benefits occur in analysis year, so they are 

the same for all discount rates. Confidence intervals are unavailable for this analysis because of the benefit-per-ton 

methodology. In general, the 95th percentile confidence interval for monetized PM2.5 benefits ranges from 

approximately -90 percent to +180 percent of the central estimates based on Krewski et al. (2009) and Lepeule et al. 

(2012).
4-34

Table 4-16. Summary of Avoided Health Incidences from PM2.5-Related and OzoneRelated Co-benefits for Proposed EGU GHG Existing Source Guidelines in 2020*

Option 1 -

state

Option 1 -

regional

Option 2 -

state

Option 2 -

regional

PM2.5-related Health Incidences

Avoided Premature Mortality

Krewski et al. (2009) (adult) 1,800 1,700 1,400 1,400

Lepeule et al. (2012) (adult) 4,100 3,800 3,200 3,200

Woodruff et al. (1997) (infant) 4 4 3 3

Avoided Morbidity

Emergency department visits for asthma (all ages) 950 890 760 740

Acute bronchitis (age 8–12) 2,600 2,500 2,100 2,000

Lower respiratory symptoms (age 7–14) 33,000 31,000 27,000 26,000

Upper respiratory symptoms (asthmatics age 9–11) 48,000 45,000 38,000 38,000

Minor restricted-activity days (age 18–65) 1,300,000 1,200,000 1,000,000 1,000,000

Lost work days (age 18–65) 220,000 210,000 180,000 170,000

Asthma exacerbation (age 6–18) 100,000 95,000 82,000 80,000

Hospital admissions—respiratory (all ages) 530 490 420 410

Hospital admissions—cardiovascular (age > 18) 650 610 520 500

Non-Fatal Heart Attacks (age >18)

Peters et al. (2001) 2,100 1,900 1,600 1,600

Pooled estimate of 4 studies 220 210 180 170

Ozone-related Health Incidences

Avoided Premature Mortality

Bell et al. (2004) (all ages) 36 35 27 27

Levy et al. (2005) (all ages) 170 160 120 120

Avoided Morbidity

Hospital admissions—respiratory causes (ages > 65) 220 210 160 160

Hospital admissions—respiratory causes (ages < 2) 100 98 76 74

Emergency room visits for asthma (all ages) 120 110 89 87

Minor restricted-activity days (ages 18-65) 210,000 210,000 160,000 160,000

School absence days 72,000 71,000 55,000 54,000

* All estimates are rounded to whole numbers with two significant figures. Co-benefits for PM2.5 precursors are 

based on regional benefit-per-ton estimates for all precursors. Co-benefits for ozone are based on ozone season NOx 

emissions. Confidence intervals are unavailable for this analysis because of the benefit-per-ton methodology. In 

general, the 95th percentile confidence interval for the health impact function alone ranges from approximately ±30 

percent for mortality incidence based on Krewski et al. (2009) and ±46 percent based on Lepeule et al. (2012).
4-35

Table 4-17. Summary of Avoided Health Incidences from PM2.5-Related and OzoneRelated Co-benefits for Proposed EGU GHG Existing Source Guidelines in 2025*

Option 1 -

state

Option 1 -

regional

Option 2 -

state

Option 2 -

regional

PM2.5-related Health Incidences

Avoided Premature Mortality

Krewski et al. (2009) (adult) 2,400 2,300 1,800 1,800

Lepeule et al. (2012) (adult) 5,400 5,300 4,100 4,000

Woodruff et al. (1997) (infant) 5 5 4 4

Avoided Morbidity

Emergency department visits for asthma (all ages) 1,200 1,200 930 900

Acute bronchitis (age 8–12) 3,400 3,300 2,600 2,500

Lower respiratory symptoms (age 7–14) 43,000 43,000 33,000 32,000

Upper respiratory symptoms (asthmatics age 9–11) 63,000 62,000 48,000 46,000

Minor restricted-activity days (age 18–65) 1,700,000 1,600,000 1,300,000 1,200,000

Lost work days (age 18–65) 280,000 280,000 210,000 210,000

Asthma exacerbation (age 6–18) 130,000 130,000 100,000 100,000

Hospital admissions—respiratory (all ages) 730 710 560 540

Hospital admissions—cardiovascular (age > 18) 890 870 680 650

Non-Fatal Heart Attacks (age >18)

Peters et al. (2001) 2,800 2,700 2,100 2,100

Pooled estimate of 4 studies 310 300 230 220

Ozone-related Health Incidences

Avoided Premature Mortality

Bell et al. (2004) (all ages) 93 92 65 66

Levy et al. (2005) (all ages) 420 420 300 300

Avoided Morbidity

Hospital admissions—respiratory causes (ages > 65) 600 600 430 430

Hospital admissions—respiratory causes (ages < 2) 250 240 180 180

Emergency room visits for asthma (all ages) 290 290 210 210

Minor restricted-activity days (ages 18-65) 520,000 520,000 370,000 370,000

School absence days 180,000 180,000 130,000 130,000

* All estimates are rounded to whole numbers with two significant figures. Co-benefits for PM2.5 precursors are 

based on regional benefit-per-ton estimates for all precursors. Co-benefits for ozone are based on ozone season NOx 

emissions. In general, the 95th percentile confidence interval for the health impact function alone ranges from 

approximately ±30 percent for mortality incidence based on Krewski et al. (2009) and ±46 percent based on Lepeule 

et al. (2012).
4-36

Table 4-18. Summary of Avoided Health Incidences from PM2.5-Related and OzoneRelated Co-Benefits for Proposed EGU GHG Existing Source Guidelines in 2030*

Option 1 -

state

Option 1 -

regional

PM2.5-related Health Incidences

Avoided Premature Mortality

Krewski et al. (2009) (adult) 2,700 2,600

Lepeule et al. (2012) (adult) 6,200 5,900

Woodruff et al. (1997) (infant) 5 5

Avoided Morbidity

Emergency department visits for asthma (all ages) 1,400 1,300

Acute bronchitis (age 8–12) 3,700 3,500

Lower respiratory symptoms (age 7–14) 48,000 45,000

Upper respiratory symptoms (asthmatics age 9–11) 69,000 66,000

Minor restricted-activity days (age 18–65) 1,800,000 1,700,000

Lost work days (age 18–65) 310,000 290,000

Asthma exacerbation (age 6–18) 150,000 140,000

Hospital admissions—respiratory (all ages) 870 820

Hospital admissions—cardiovascular (age > 18) 1,000 980

Non-Fatal Heart Attacks (age >18)

Peters et al. (2001) 3,300 3,100

Pooled estimate of 4 studies 360 340

Ozone-related Health Incidences

Avoided Premature Mortality

Bell et al. (2004) (all ages) 97 96

Levy et al. (2005) (all ages) 440 430

Avoided Morbidity

Hospital admissions—respiratory causes (ages > 65) 670 660

Hospital admissions—respiratory causes (ages < 2) 250 240

Emergency room visits for asthma (all ages) 290 290

Minor restricted-activity days (ages 18-65) 510,000 510,000

School absence days 180,000 180,000

* All estimates are rounded to whole numbers with two significant figures. Co-benefits for PM2.5 precursors are 

based on regional benefit-per-ton estimates for all precursors. Co-benefits for ozone are based on ozone season NOx

emissions. In general, the 95th percentile confidence interval for the health impact function alone ranges from 

approximately ±30 percent for mortality incidence based on Krewski et al. (2009) and ±46 percent based on Lepeule 

et al. (2012).
4-37

Figure 4-1. Monetized Health Co-benefits for each Option (Regional Compliance) of the 

Proposed EGU GHG Existing Source Guidelines in 2020 *

*The PM2.5 graphs show the estimated PM2.5 co-benefits at discount rates of 3% and 7% using effect coefficients 

derived from the Krewski et al. (2009) study and the Lepeule et al. (2012) study, as well as 12 effect coefficients 

derived from EPA’s expert elicitation on PM mortality (Roman et al., 2008). The results shown are not the direct 

results from the studies or expert elicitation; rather, the estimates are based in part on the concentration-response 

functions provided in those studies. The ozone graphs show the estimated ozone co-benefits derived from six ozone 

mortality studies (i.e., Bell et al. (2004), Schwartz (2005), Huang et al. (2005), Bell et al. (2005), Levy et al. (2005),

and Ito et al. (2005). Ozone co-benefits occur in the analysis year, so they are the same for all discount rates. These 

estimates do not include climate benefits. The monetized co-benefits do not include climate benefits or reduced 

health effects from direct exposure to NO2, SO2, ecosystem effects, or visibility impairment. Results would be 

similar if the state compliance scenario was shown.

$0

$10,000

$20,000

$30,000

$40,000

$50,000

$60,000

Krewski et al.

Lepeule et al. Millions (2011$)

Benefits estimates derived from 2 epidemiology and 12 expert functions

Option 1 - Regional

3%

7%

$0

$500

$1,000

$1,500

$2,000

$2,500

$3,000

Bell et al. (2004)

Levy et al. (2005) Millions (2011$)

Benefits estimates derived from 6 epidemiology functions 

Option 1 - Regional

$0

$10,000

$20,000

$30,000

$40,000

$50,000

Krewski et al.

Lepeule et al. Millions (2011$)

Benefits estimates derived from 2 epidemiology and 12 expert functions

Option 2 - Regional

3%

7%

$0

$500

$1,000

$1,500

$2,000

$2,500

Bell et al. (2004)

Levy et al. (2005) Millions (2011$)

Benefits estimates derived from 6 epidemiology functions 

Option 2 - Regional

PM2.5 Ozone 
4-38

Figure 4-2. Breakdown of Monetized Health Co-benefits by Precursor Pollutant at a 3% 

Discount Rate for each Option (Regional Compliance) for Proposed EGU GHG 

Existing Source Guidelines in 2020*

* “Low Health Co-benefits” refers to the combined health co-benefits estimated using the Bell et al. (2004) 

mortality study for ozone with the Krewski et al. (2009) mortality study for PM2.5. “High Health Co-benefits” refers 

to the combined health co-benefits estimated using the Levy et al. (2005) mortality study for ozone with the Lepeule 

et al. (2012) mortality study for PM2.5. Results would be similar if the state compliance scenario was shown.

SO2 73%

Directly 

emitted 

PM2.5 

(EC+OC) 4%

Directly 

emitted 

PM2.5 

(Crustal)

5%

NOx (as 

PM2.5)

14% NOx (as 

Ozone)

4%

Option 1 - Regional

SO2 70%

Directly 

emitted 

PM2.5 

(EC+OC) 5%

Directly 

emitted 

PM2.5 

(Crustal)

5%

NOx (as 

PM2.5)

13%

NOx (as 

Ozone)

7%

Option 1 - Regional

SO2 74%

Directly 

emitted 

PM2.5 

(EC+OC)

5%

Directly 

emitted 

PM2.5 

(Crustal)

5%

NOx (as 

PM2.5)

13%

NOx (as 

Ozone)

3%

Option 2 - Regional

SO2 72%

Directly 

emitted 

PM2.5 

(EC+OC) 4%

Directly 

emitted 

PM2.5 

(Crustal)

5%

NOx (as 

PM2.5)

13%

NOx (as 

Ozone)

6%

Option 2 - Regional

Low Health Co-benefits High Health Co-benefits 
4-39

Figure 4-3. Breakdown of Monetized Health Co-Benefits by Region at a 3% Discount Rate 

for each Option (Regional compliance) for Proposed EGU GHG Existing Source 

Guidelines in 2020*

* “Low Health Co-benefits” refers to the combined health co-benefits estimated using the Bell et al. (2004) 

mortality study for ozone with the Krewski et al. (2009) mortality study for PM2.5. “High Health Co-benefits” 

refers to the combined health co-benefits estimated using the Levy et al. (2005) mortality study for ozone with 

the Lepeule et al. (2012) mortality study for PM2.5. Results would be similar if the state compliance scenario was 

shown.

4.3.6 Characterization of Uncertainty in the Estimated Health Co-benefits

In any complex analysis using estimated parameters and inputs from numerous models, 

there are likely to be many sources of uncertainty. This analysis is no exception. This analysis 

includes many data sources as inputs, including emission inventories, air quality data from 

East 95.6%

West

1.1%

California

3.3%

Option 1 - Regional

East 95.5%

West 1.2%

California

3.3%

Option 1 - Regional

East 95.1%

West

1.1%

California

3.8%

Option 2 - Regional

East 95.0%

West 1.1%

California

3.9%

Option 2 - Regional

Low Health Co-benefits High Health Co-benefits 
4-40

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). Each of these inputs may be uncertain and would affect the estimate of co-benefits. 

When the uncertainties from each stage of the analysis are compounded, even small uncertainties 

can have large effects on the total quantified benefits. Therefore, the estimates of co-benefits in 

each analysis year should be viewed as representative of the general magnitude of co-benefits of 

the illustrative compliance scenarios, rather than the actual co-benefits anticipated from 

implementing the proposed guidelines.

This RIA does not include the type of detailed uncertainty assessment found in the PM 

NAAQS RIA (U.S. EPA, 2012a) or the Ozone NAAQS RIA (U.S. EPA, 2008b) because we lack 

the necessary air quality modeling input and/or monitoring data to run the benefits model. 

However, the results of the quantitative and qualitative uncertainty analyses presented in the PM 

NAAQS RIA and Ozone NAAQS RIAs can provide some information regarding the uncertainty 

inherent in the estimated co-benefits results presented in this analysis. For example, sensitivity 

analyses conducted for the PM NAAQS RIA indicate that alternate cessation lag assumptions 

could change the estimated PM2.5-related mortality co-benefits discounted at 3 percent by 

between 10 percent and –27 percent and that alternate income growth adjustments could change 

the PM2.5-related mortality co-benefits by between 33 percent and −14 percent. Although we 

generally do not calculate confidence intervals for benefit-per-ton estimates and they can provide 

an incomplete picture about the overall uncertainty in the benefits estimates, the PM NAAQS RIA 

can provide an indication of the random sampling error in the health impact and economic 

valuation functions using Monte Carlo methods. In general, the 95th percentile confidence interval 

for monetized PM2.5 benefits ranges from approximately -90 percent to +180 percent of the 

central estimates based on Krewski et al. (2009) and Lepeule et al. (2012). The 95th percentile 

confidence interval for the health impact function alone ranges from approximately ±30 percent for 

mortality incidence based on Krewski et al. (2009) and ±46 percent based on Lepeule et al. (2012). 

Unlike RIAs for which EPA conducts air quality modeling, we do not have information

on the specific location of the air quality changes associated with the proposed guidelines. As 

such, it is not feasible to estimate the proportion of co-benefits occurring in different locations, 

such as designated nonattainment areas. Instead, we applied benefit-per-ton estimates, which 
4-41

reflect specific geographic patterns of emissions reductions and specific air quality and benefits 

modeling assumptions. For example, these estimates may not reflect local variability in 

population density, meteorology, exposure, baseline health incidence rates, or other local factors 

that might lead to an over-estimate or under-estimate of the actual co-benefits of controlling PM 

and ozone precursors. Use of these benefit-per-ton values to estimate co-benefits may lead to 

higher or lower benefit estimates than if co-benefits were calculated based on direct air quality 

modeling. Great care should be taken in applying these estimates to emission reductions 

occurring in any specific location, as these are all based on broad emission reduction scenarios 

and therefore represent average benefits-per-ton over the entire region. The benefit-per-ton for 

emission reductions in specific locations may be very different than the estimates presented here. 

To the extent that the geographic distribution of the emissions reductions achieved by 

implementing the proposed guidelines is different than the emissions in the sector modeling, the 

co-benefits may be underestimated or overestimated. For more information regarding the 

limitations of benefit-per-ton estimates derived from the sector modeling, see the TSD describing 

the calculation of the national benefit-per-ton estimates (U.S. EPA, 2013a) and Fann et al. 

(2012). In addition, the appendix to this chapter provides additional uncertainty information 

regarding the benefit-per-ton estimates applied in this RIA, including an evaluation of the 

similarities and differences in the spatial distribution of EGU emissions in the sector modeling 

and the IPM base case discussed in Chapter 3 of this RIA.

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, which accounts for 98 percent of the monetized PM2.5 health 

co-benefits.

1. 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, 2009b).
4-42

2. We assume that the health impact function for fine particles is log-linear without a 

threshold in this analysis. 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. 

3. 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. EPASAB, 2004c), which affects the valuation of mortality co-benefits at different discount 

rates.

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. Concentration benchmark analyses (e.g., lowest measured level 

[LML], one standard deviation below the mean of the air quality data in the study, etc.) 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. In this analysis, we apply two concentration benchmark 

approaches (LML and one standard deviation below the mean) that have been incorporated into 

recent RIAs and EPA’s Policy Assessment for Particulate Matter (U.S. EPA, 2011d). There are 

uncertainties inherent in identifying any particular point at which our confidence in reported 

associations becomes appreciably less, and the scientific evidence provides no clear dividing 

line. However, the EPA does not view these concentration benchmarks as a concentration 

threshold below which we would not quantify health co-benefits of air quality improvements.99

Rather, the co-benefits estimates reported in this RIA are the best estimates because they reflect 

the full range of air quality concentrations associated with the emission reduction strategies. The 

PM ISA concluded that the scientific evidence collectively is sufficient to conclude that the 

 

99 For a summary of the scientific review statements regarding the lack of a threshold in the PM2.5-mortality 

relationship, see the TSD entitled Summary of Expert Opinions on the Existence of a Threshold in the 

Concentration-Response Function for PM2.5-related Mortality (U.S. EPA, 2010b).
4-43

relationship between long-term PM2.5 exposures and mortality is causal and that overall the 

studies support the use of a no-threshold log-linear model to estimate PM-related long-term 

mortality (U.S. EPA, 2009b). 

For this analysis, policy-specific air quality data is not available, and the compliance 

strategies are illustrative of what states may choose to do. For this RIA, we are unable to 

estimate the percentage of premature mortality associated with the emission reductions at each 

PM2.5 concentration, as we have done for previous rules with air quality modeling (e.g., U.S. 

EPA, 2011b, 2012a). However, we believe that it is still important to characterize the distribution 

of exposure to baseline concentrations. As a surrogate measure of mortality impacts, we provide 

the percentage of the population exposed at each PM2.5 concentration in the baseline of the 

source apportionment modeling used to calculate the benefit-per-ton estimates for this sector

using 12 km grid cells across the contiguous U.S.

100 It is important to note that baseline exposure 

is only one parameter in the health impact function, along with baseline incidence rates 

population and change in air quality. In other words, the percentage of the population exposed to 

air pollution below the LML is not the same as the percentage of the population experiencing 

health impacts as a result of a specific emission reduction policy. The most important aspect, 

which we are unable to quantify without rule-specific air quality modeling, is the shift in 

exposure anticipated by implementing the proposed guidelines. Therefore, caution is warranted 

when interpreting the LML assessment in this RIA because these results are not consistent with 

results from RIAs that had air quality modeling. 

Table 4-19 provides the percentage of the population exposed above and below two 

concentration benchmarks (i.e., LML and one standard deviation below the mean) in the 

modeled baseline for the sector modeling. Figure 4-4 shows a bar chart of the percentage of the 

population exposed to various air quality levels in the baseline, and Figure 4-5 shows a 

cumulative distribution function of the same data. Both figures identify the LML for each of the 

major cohort studies.

 

100 As noted above, the modeling used to generate the benefit-per-ton estimates does not reflect emission reductions 

anticipated from MATS rule. Therefore, the baseline PM2.5 concentrations in the LML assessment are higher 

than would be expected if MATS was reflected.
4-44

Table 4-19. Population Exposure in the Baseline Sector Modeling (used to generate the 

benefit-per-ton estimates) Above and Below Various Concentrations Benchmarks in the 

Underlying Epidemiology Studies *

Epidemiology Study

Below 1 Standard

Deviation.

Below AQ Mean

At or Above 1 

Standard Deviation

Below AQ Mean

Below LML At or Above LML

Krewski et al. (2009) 89% 11% 7% 93%

Lepeule et al. (2012) N/A N/A 23% 67%

*One standard deviation below the mean is equivalent to the middle of the range between the 10th and 25th

percentile. For Krewski, the LML is 5.8 μg/m3and one standard deviation below the mean is 11.0 μg/m3. For 

Lepeule et al., the LML is 8 μg/m3and we do not have the data for one standard deviation below the mean. It is 

important to emphasize that although we have lower levels of confidence in levels below the LML for each study, 

the scientific evidence does not support the existence of a level below which health effects from exposure to PM2.5

do not occur.
4-45

Figure 4-4. Percentage of Adult Population (age 30+) by Annual Mean PM2.5 Exposure in 

the Baseline Sector Modeling (used to generate the benefit-per-ton estimates)*

* This graph shows the population exposure in the modeling baseline used to generate the benefit-per-ton estimates. 

Similar graphs for analyses with air quality modeling show premature mortality impacts at each PM2.5 concentration. 

Therefore, caution is warranted when interpreting this graph because it is not derived in a manner consistent with 

similar graphs from RIAs that had been based on air quality modeling (e.g., MATS).

Among the populations exposed to PM2.5 in the baseline:

93% are exposed to PM2.5 levels at or above the LML of the Krewski et al. (2009) study

67% are exposed to PM2.5 levels at or above the LML of the Lepeule et al. (2012) study

0%

5%

10%

15%

20%

25%

<1 1-2 2-3 3-4 4-5 5-6 6-7 7-8 8-9 9-10 10-11 11-12 12-13 13-14 14-15 15-16 16-17 17-18 18-19 19-20 Percentage of Population Exposed

Baseline Annual Mean PM2.5 Level (μg/m3

)

Percentage of Population Exposed by Baseline Air Quality Level

LML of Lepeule et al. 

(2012) study

LML of Krewski et 

al. (2009) study
4-46

Figure 4-5. Cumulative Distribution of Adult Population (age 30+) by Annual Mean PM2.5 

Exposure in the Baseline Sector Modeling (used to generate the benefit-per-ton 

estimates)*

* This graph shows the population exposure in the modeling baseline used to generate the benefit-per-ton estimates. 

Similar graphs for analyses with air quality modeling show premature mortality impacts at each PM2.5 concentration. 

Therefore, caution is warranted when interpreting this graph because it is not derived in a manner consistent with 

similar graphs from RIAs that had based on air quality modeling (e.g., MATS).

4.4 Combined Climate Benefits and Health Co-benefits Estimates

In this analysis, we were able to monetize the estimated co-benefits associated with the 

decreased emissions of CO2 and reduced exposure to PM2.5 and ozone, but we were unable to 

monetize the co-benefits associated with reducing exposure to mercury, hydrogen chloride, 

carbon monoxide, SO2, and NO2, as well as ecosystem effects and visibility impairment. 

Specifically, we estimated the combined climate benefits at discount rates of 5 percent, 3 

percent, 2.5 percent, and 3 percent (95th percentile) (as estimated by the interagency working 

group), and health co-benefits at discount rates of 3 percent and 7 percent (as recommended by 

Among the populations exposed to PM2.5 in the baseline:

93% are exposed to PM2.5 levels at or above the LML of the Krewski et al. (2009) study

67% are exposed to PM2.5 levels at or above the LML of the Lepeule et al. (2012) study

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Cumulative percentage of population exposed

Baseline Annual Mean PM2.5 Level (μg/m3

)

Cumulative Percentage of Population Exposed by Baseline Air Quality 

Level

LML of Krewski et 

al. (2009) study

LML of Lepeule et 

al. (2012) study
4-47

EPA’s Guidelines for Preparing Economic Analyses (U.S. EPA, 2010e) and OMB’s Circular A4 [OMB, 2003]). 

Different discount rates are applied to SCC than to the health co-benefit estimates 

because CO2 emissions are long-lived and subsequent damages occur over many years.

Moreover, several rates are applied to SCC because the literature shows that it is sensitive to 

assumptions about discount rate and because no consensus exists on the appropriate rate to use in 

an intergenerational context. The SCC interagency group centered its attention on the 3 percent 

discount rate but emphasized the importance of considering all four SCC estimates.101 The EPA 

has evaluated the range of potential impacts by combining all SCC values with health co-benefits 

values at the 3 percent and 7 percent discount rates. To be consistent with concepts of 

intergenerational discounting, values for health benefits, which occur within a generation, would 

only be combined with SCC values using a lower discount rate (e.g., the 7 percent health benefit 

estimates would be combined with 5 percent or lower SCC values, but the 3 percent health 

benefit would not be combined with the 5 percent SCC value). While the 5 percent SCC and 3 

percent health benefit estimate falls within the range of values we analyze, this individual 

estimate should not be used independently in an analysis, as it represents a combination of 

discount rates that is unlikely to occur. Combining the 3 percent SCC values with the 3 percent 

health benefit values assumes that there is no difference in discount rates between

intragenerational and intergenerational impacts.

Tables 4-20 through 4-22 provide the combined climate and health benefits for each 

option evaluated for 2020, 2025, and 2030. Figure 4-6 shows the breakdown of the monetized 

benefits by pollutant for each option evaluated in 2020 as an illustrative analysis year using a 3 

percent discount rate.

 

101 See the 2010 SCC TSD. Docket ID EPA-HQ-OAR-2009-0472-114577 or 

http://www.whitehouse.gov/sites/default/files/omb/inforeg/for-agencies/Social-Cost-of-Carbon-for-RIA.pdf for 

details. 
4-48

Table 4-20. Combined Climate Benefits and Health Co-Benefits for Proposed EGU GHG 

Existing Source Guidelines in 2020 (billions of 2011$)*

SCC Discount Rate

Climate 

Benefits 

Only

Climate Benefits plus Health Co-Benefits 

(Discount Rate Applied to Health Co-Benefits)

3% 7%

Option 1 - state 383 million metric tonnes CO2

5% $4.9 $22 to $45 $20 to $41

3% $18 $35 to $57 $33 to $54

2.5% $26 $43 to $66 $42 to $62

3% (95th percentile) $52 $69 to $92 $68 to $88

Option 1 - regional 371 million metric tonnes CO2

5% $4.7 $21 to $42 $19 to $39

3% $17 $33 to $54 $32 to $51

2.5% $25 $41 to $63 $40 to $59

3% (95th percentile) $51 $67 to $88 $65 to $85

Option 2 - state 295 million metric tonnes CO2

5% $3.8 $17 to $35 $16 to $32

3% $14 $27 to $45 $26 to $42

2.5% $20 $34 to $52 $32 to $49

3% (95th percentile) $40 $54 to $72 $53 to $69

Option 2 - regional 283 million metric tonnes CO2

5% $3.6 $17 to $34 $16 to $32

3% $13 $26 to $44 $25 to $41

2.5% $19 $33 to $50 $31 to $47

3% (95th percentile) $39 $52 to $70 $51 to $67

*All estimates are rounded to two significant figures. Climate benefits are based on reductions in CO2 emissions. 

Co-benefits are based on regional benefit-per-ton estimates. Co-benefits for ozone are based on ozone season NOx 

emissions. Ozone co-benefits occur in analysis year, so they are the same for all discount rates. The health cobenefits reflect the sum of the PM2.5 and ozone co-benefits and reflect the range based on adult mortality functions 

(e.g., from Krewski et al. (2009) with Bell et al. (2004) to Lepeule et al. (2012) with Levy et al. (2005)). The 

monetized health co-benefits do not include reduced health effects from direct exposure to NO2, SO2, and HAP;

ecosystem effects; or visibility impairment. 
4-49

Table 4-21. Combined Climate Benefits and Health Co-Benefits for Proposed EGU GHG 

Existing Source Guidelines in 2025 (billions of 2011$)*

SCC Discount Rate

Climate

Benefits 

Only

Climate Benefits plus Health Co-Benefits 

(Discount Rate Applied to Health Co-Benefits)

3% 7%

Option 1 - state 506 million metric tonnes CO2

5% $7.6 $31 to $62 $29 to $57

3% $25 $49 to $80 $46 to $75

2.5% $37 $61 to $92 $58 to $87

3% (95th percentile) $77 $100 to $130 $98 to $130

Option 1 - regional 501 million metric tonnes CO2

5% $7.5 $30 to $61 $28 to $56

3% $25 $48 to $78 $46 to $74

2.5% $37 $60 to $90 $57 to $85

3% (95th percentile) $76 $99 to $130 $97 to $120

Option 2 - state 376 million metric tonnes CO2

5% $5.6 $23 to $47 $22 to $43

3% $19 $36 to $60 $35 to $56

2.5% $28 $45 to $69 $44 to $65

3% (95th percentile) $57 $75 to $98 $73 to $95

Option 2 - regional 368 million metric tonnes CO2

5% $5.5 $23 to $46 $21 to $42

3% $18 $36 to $59 $34 to $55

2.5% $27 $44 to $67 $43 to $64

3% (95th percentile) $56 $73 to $96 $72 to $93

*All estimates are rounded to two significant figures. Climate benefits are based on reductions in CO2 emissions. 

Co-benefits are based on regional benefit-per-ton estimates. Co-benefits for ozone are based on ozone season NOx 

emissions. Ozone co-benefits occur in analysis year, so they are the same for all discount rates. The health cobenefits reflect the sum of the PM2.5 and ozone co-benefits and reflect the range based on adult mortality functions 

(e.g., from Krewski et al. (2009) with Bell et al. (2004) to Lepeule et al. (2012) with Levy et al. (2005)). It is 

important to note that the monetized health co-benefits do not include reduced health effects from direct exposure to 

NO2, SO2, and HAP; ecosystem effects; or visibility impairment. 
4-50

Table 4-22. Combined Climate Benefits and Health Co-Benefits for Proposed EGU GHG 

Existing Source Guidelines in 2030 (billions of 2011$)*

SCC Discount Rate

Climate 

Benefits 

Only

Climate Benefits plus Health Co-Benefits 

(Discount Rate Applied to Health Co-Benefits)

3% 7%

Option 1 - state 555 million metric tonnes CO2

5% $9.5 $36 to $72 $34 to $66

3% $31 $57 to $93 $55 to $87

2.5% $44 $71 to $110 $69 to $100

3% (95th percentile) $94 $120 to $160 $120 to $150

Option 1 - regional 545 million metric tonnes CO2

5% $9.3 $35 to $68 $32 to $63

3% $30 $55 to $89 $53 to $84

2.5% $44 $69 to $100 $66 to $97

3% (95th percentile) $92 $120 to $150 $120 to $150

*All estimates are rounded to two significant figures. Climate benefits are based on reductions in CO2 emissions. 

Co-benefits are based on regional benefit-per-ton estimates. Co-benefits for ozone are based on ozone season NOx 

emissions. Ozone co-benefits occur in analysis year, so they are the same for all discount rates. The health cobenefits reflect the sum of the PM2.5 and ozone co-benefits and reflect the range based on adult mortality functions 

(e.g., from Krewski et al. (2009) with Bell et al. (2004) to Lepeule et al. (2012) with Levy et al. (2005)). It is 

important to note that the monetized health co-benefits do not include reduced health effects from direct exposure to 

NO2, SO2, and HAP; ecosystem effects; or visibility impairment. 
4-51

Figure 4-6. Breakdown of Combined Monetized Climate and Health Co-benefits of 

Proposed EGU GHG Existing Source Guidelines in 2020 by Option (Regional 

Compliance) and Pollutant (3% discount rate)*

* “Low Health Co-benefits” refers to the combined health co-benefits estimated using the Bell et al. (2004) 

mortality study for ozone with the Krewski et al. (2009) mortality study for PM2.5. “High Health Co-benefits” refers 

to the combined health co-benefits estimated using the Levy et al. (2005) mortality study for ozone with the Lepeule 

et al. (2012) mortality study for PM2.5. Results would be similar if the state compliance scenario was shown.

4.5 Unquantified Co-benefits

The monetized co-benefits estimated in this RIA only reflect a subset of co-benefits 

attributable to the health effect reductions associated with ambient fine particles and ozone. Data, 

time, and resource limitations prevented EPA from quantifying the impacts to, or monetizing the 

CO2 52%

SO2 35%

Directly 

emitted 

PM2.5 

(EC+OC) 2%

Directly 

emitted 

PM2.5 

(Crustal) 2%

NOx (as 

PM2.5)

7%

NOx (as 

Ozone) 2%

Option 1 - Regional

CO2 31%

SO2 49%

Directly 

emitted 

PM2.5 

(EC+OC) 3%

Directly 

emitted 

PM2.5 

(Crustal) 3%

NOx (as 

PM2.5)

9%

NOx (as 

Ozone) 5%

Option 1 - Regional

CO2 50%

SO2 37%

Directly 

emitted 

PM2.5 

(EC+OC) 2%

Directly 

emitted 

PM2.5 

(Crustal) 2%

NOx (as 

PM2.5)

7%

NOx (as 

Ozone) 2%

Option 2 - Regional

CO2 30%

SO2 50%

Directly 

emitted 

PM2.5 

(EC+OC) 3%

Directly 

emitted 

PM2.5 

(Crustal) 3%

NOx (as 

PM2.5)

NOx (as 9%

Ozone) 5%

Option 2 - Regional

Low Health Co-benefits High Health Co-benefits 
4-52

co-benefits from several important benefit categories, including co-benefits associated with 

exposure to several HAP (including mercury and hydrogen chloride) SO2 and NO2, as well as 

ecosystem effects, and visibility impairment due to the absence of air quality modeling data for 

these pollutants in this analysis. This does not imply that there are no co-benefits associated with 

these emission reductions. In this section, we provide a qualitative description of these benefits, 

which are listed in Table 4-23. 

Table 4-23. Unquantified Health and Welfare Co-benefits Categories

Category Specific Effect

Effect Has 

Been 

Quantified

Effect Has 

Been 

Monetized

More Information

Improved Human Health

Reduced incidence of 

morbidity from exposure 

to NO2

Asthma hospital admissions (all ages) — — NO2 ISA1

Chronic lung disease hospital admissions (age > 

65) — — NO2 ISA1

Respiratory emergency department visits (all 

ages) — — NO2 ISA1

Asthma exacerbation (asthmatics age 4–18) — — NO2 ISA1

Acute respiratory symptoms (age 7–14) — — NO2 ISA1

Premature mortality — — NO2 ISA1,2,3

Other respiratory effects (e.g., airway 

hyperresponsiveness and inflammation, lung 

function, other ages and populations)

— — NO2 ISA2,3

Reduced incidence of 

morbidity from exposure 

to SO2

Respiratory hospital admissions (age > 65) — — SO2 ISA1

Asthma emergency department visits (all ages) — — SO2 ISA1

Asthma exacerbation (asthmatics age 4–12) — — SO2 ISA1

Acute respiratory symptoms (age 7–14) — — SO2 ISA1

Premature mortality — — SO2 ISA1,2,3

Other respiratory effects (e.g., airway 

hyperresponsiveness and inflammation, lung 

function, other ages and populations)

— — SO2 ISA1,2

Reduced incidence of 

morbidity from exposure 

to CO

Cardiovascular effects — — CO ISA 1,2

Respiratory effects — — CO ISA 1,2,3

Central nervous system effects — — CO ISA 1,2,3

Premature mortality — — CO ISA 1,2,3

Reduced incidence of 

morbidity from exposure 

to methylmercury

Neurologic effects—IQ loss — —

IRIS; NRC, 

20001

Other neurologic effects (e.g., developmental 

delays, memory, behavior) — —

IRIS; NRC, 

20002

Cardiovascular effects — —

IRIS; NRC, 

20002,3

Genotoxic, immunologic, and other toxic effects — —

IRIS; NRC, 

20002,3

Reduced incidence of 

morbidity from exposure 

to HAP

Effects associated with exposure to hydrogen 

chloride — — ATSDR, IRIS1,2

Improved Environment

Reduced visibility 

impairment

Visibility in Class 1 areas — — PM ISA1

Visibility in residential areas — — PM ISA1
4-53

Category Specific Effect

Effect Has 

Been 

Quantified

Effect Has 

Been 

Monetized

More Information

Reduced effects on 

materials

Household soiling — — PM ISA1,2

Materials damage (e.g., corrosion, increased 

wear) — — PM ISA2

Reduced effects from PM 

deposition (metals and 

organics)

Effects on Individual organisms and ecosystems — — PM ISA2

Reduced vegetation and 

ecosystem effects from 

exposure to ozone

Visible foliar injury on vegetation — — Ozone ISA1

Reduced vegetation growth and reproduction — — Ozone ISA1

Yield and quality of commercial forest products 

and crops — — Ozone ISA1

Damage to urban ornamental plants — — Ozone ISA2

Carbon sequestration in terrestrial ecosystems — — Ozone ISA1

Recreational demand associated with forest 

aesthetics — — Ozone ISA2

Other non-use effects Ozone ISA2

Ecosystem functions (e.g., water cycling, 

biogeochemical cycles, net primary productivity, 

leaf-gas exchange, community composition)

— — Ozone ISA2

Reduced effects from 

acid deposition

Recreational fishing — — NOx SOx ISA1

Tree mortality and decline — — NOx SOx ISA2

Commercial fishing and forestry effects — — NOx SOx ISA2

Recreational demand in terrestrial and aquatic 

ecosystems — — NOx SOx ISA2

Other non-use effects NOx SOx ISA2

Ecosystem functions (e.g., biogeochemical 

cycles) — — NOx SOx ISA2

Reduced effects from 

nutrient enrichment

Species composition and biodiversity in terrestrial 

and estuarine ecosystems — — NOx SOx ISA2

Coastal eutrophication — — NOx SOx ISA2

Recreational demand in terrestrial and estuarine 

ecosystems

— — NOx SOx ISA2

Other non-use effects NOx SOx ISA2

Ecosystem functions (e.g., biogeochemical 

cycles, fire regulation) — — NOx SOx ISA2

Reduced vegetation 

effects from ambient 

exposure to SO2 and NOx

Injury to vegetation from SO2 exposure — — NOx SOx ISA2

Injury to vegetation from NOx exposure — — NOx SOx ISA2

Reduced ecosystem 

effects from exposure to 

methylmercury

Effects on fish, birds, and mammals (e.g., 

reproductive effects) — —

Mercury Study 

RTC2

Commercial, subsistence and recreational fishing — —

Mercury Study 

RTC1

1 We assess these co-benefits qualitatively due to data and resource limitations for this RIA.

2We 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.

4.5.1 HAP Co-benefits

Due to methodology and resource limitations, we were unable to estimate the co-benefits 

associated with reducing emissions of the hazardous air pollutants in this analysis. EPA’s SABHES concluded that “the challenges for assessing progress in health improvement as a result of 
4-54

reductions in emissions of hazardous air pollutants (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” (U.S. EPA-SAB, 2008). 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). 

Chapter 4 of the MATS RIA (U.S. EPA, 2011b) describes the health effects associated 

with HAP emitted by EGUs. Below we describe the health effects associated with the two HAP 

for which we were able to quantify emission reductions for the proposed guidelines: mercury and 

hydrogen chloride. Using the IPM modeling described in Chapter 3 of this RIA, we estimate that 

the illustrative compliance scenarios for the proposed guidelines would reduce mercury 

emissions by up to 2.1 tons and hydrogen chloride by up to 590 tons by 2030. These HAP 

emission reductions are beyond those achieved by MATS.

4.5.1.1 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).

102 Many of the peer-reviewed articles cited in this section are publications 

originally cited in the Mercury Study.

103 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 

 

102 National Research Council (NRC). 2000. Toxicological Effects of Methylmercury. Washington, DC: National 

Academies Press.

103 U.S. Environmental Protection Agency (U.S. EPA). 1997. Mercury Study Report to Congress, EPA–HQ–OAR–

2009–0234–3054. December. Available on the Internet at <http://www.epa.gov/hg/report.htm>.
4-55

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 in animals reported sensory effects 

as well as effects on brain development and memory functions and support the conclusions based 

on epidemiology studies. The NAS noted that their recommended endpoints for an 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.”

Additional cardiovascular studies have been published since 2000. EPA did not develop a 

quantitative dose-response assessment for cardiovascular effects associated with MeHg 

exposures, as there is no consensus among scientists on the dose-response functions for these 

effects. In addition, there is inconsistency among available studies as to the association between 

MeHg exposure and various cardiovascular system effects. The pharmacokinetics of some of the 

exposure measures (such as toenail Hg levels) are not well understood. The studies have not yet 

received the review and scrutiny of the more well-established neurotoxicity data base. 

The Mercury Study noted that MeHg is not a potent mutagen but is capable of causing 

chromosomal damage in a number of experimental systems. The NAS concluded that evidence 

that human exposure to MeHg caused genetic damage is inconclusive; they note that some earlier 

studies showing chromosomal damage in lymphocytes may not have controlled sufficiently for 

potential confounders. One study of adults living in the Tapajós River region in Brazil (Amorim 

et al., 2000) reported a direct relationship between MeHg concentration in hair and DNA damage 
4-56

in lymphocytes, as well as effects on chromosomes.

104 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).

105

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)106 and in IRIS 

(U.S. EPA, 2002).107 The existing evidence supporting the possibility of carcinogenic effects 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.

 

104 Amorim, M.I.M., D. Mergler, M.O. Bahia, H. Dubeau, D. Miranda, J. Lebel, R.R. Burbano, and M. Lucotte. 

2000. Cytogenetic damage related to low levels of methyl mercury contamination in the Brazilian Amazon. An. 

Acad. Bras. Ciênc. 72(4): 497-507.

105 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.

106 International Agency for Research on Cancer (IARC). 1994. IARC Monographs on the Evaluation of 

Carcinogenic Risks to Humans and their Supplements: Beryllium, Cadmium, Mercury, and Exposures in the 

Glass Manufacturing Industry. Vol. 58. Jalili, H.A., and A.H. Abbasi. 1961. Poisoning by ethyl mercury toluene 

sulphonanilide. Br. J. Indust. Med. 18(Oct.):303-308 (as cited in NRC, 2000).

107 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.
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4.5.1.2 Hydrogen Chloride

Hydrogen chloride (HCl) is a corrosive gas that can cause irritation of the mucous 

membranes of the nose, throat, and respiratory tract. Brief exposure to 35 ppm causes throat 

irritation, and levels of 50 to 100 ppm are barely tolerable for 1 hour.108 The greatest impact is on 

the upper respiratory tract; exposure to high concentrations can rapidly lead to swelling and 

spasm of the throat and suffocation. Most seriously exposed persons have immediate onset of 

rapid breathing, blue coloring of the skin, and narrowing of the bronchioles. Exposure to HCl can 

lead to Reactive Airways Dysfunction Syndrome (RADS), a chemically, or irritant-induced type 

of asthma. Children may be more vulnerable to corrosive agents than adults because of the 

relatively smaller diameter of their airways. Children may also be more vulnerable to gas 

exposure because of increased minute ventilation per kg and failure to evacuate an area promptly 

when exposed. Hydrogen chloride has not been classified for carcinogenic effects.109

4.5.2 Additional NO2 Health Co-Benefits

In addition to being a precursor to PM2.5 and ozone, NOx emissions are also associated 

with a variety of adverse health effects associated with direct exposure. Unfortunately, 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, 2008c) concluded that there is a likely causal relationship between respiratory health 

effects and short-term exposure to NO2. The NOx ISA concluded that the evidence “is sufficient 

to infer a likely causal relationship between short-term NO2 exposure and adverse effects on the 

respiratory system.” These epidemiologic and experimental studies encompass a number of 

 

108 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.

109 U.S. Environmental Protection Agency (U.S. EPA). 1995. “Integrated Risk Information System File of Hydrogen 

Chloride.” Washington, DC: Research and Development, National Center for Environmental Assessment. This 

material is available at http://www.epa.gov/iris/subst/0396.htm.
4-58

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. 

4.5.3 Additional SO2 Health Co-Benefits

In addition to being a precursor to PM2.5, SO2 emissions are also associated with a variety 

of adverse health effects associated with direct exposure. Unfortunately, we were unable to 

estimate the health co-benefits associated with reduced SO2 in this analysis because we do not 

have air quality modeling data available. 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, 2008a). The immediate effect of SO2 on the respiratory system in 

humans is bronchoconstriction. Asthmatics are more sensitive to the effects of SO2 likely 

resulting from preexisting inflammation associated with this disease. A clear concentrationresponse relationship has been demonstrated in laboratory studies following exposures to SO2 at 

concentrations between 20 and 100 ppb, both in terms of increasing severity of effect and 

percentage of asthmatics adversely affected. Based on our review of this information, we 

identified four 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 
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associations to adjustment for pollutants. We did not quantify these co-benefits due to data 

constraints.

4.5.4 Additional NO2 and SO2 Welfare Co-Benefits

As described in the Integrated Science Assessment for Oxides of Nitrogen and Sulfur —

Ecological Criteria (NOx ISA) (U.S. EPA, 2008d), SO2 and NOx emissions also contribute to a 

variety of adverse welfare effects, including 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, which restricts 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 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, 2008d)

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, 

2008d)
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4.5.5 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, 2013b). Sensitivity to ozone is highly variable 

across species, with over 65 plan 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 are considered adverse to the public welfare 

and can include reduced growth and/or biomass production in sensitive plant species, including 

forest trees, reduced crop yields, visible foliar injury, reduced plant vigor (e.g., increased 

susceptibility to harsh weather, disease, insect pest infestation, and competition), species 

composition shift, and changes in ecosystems and associated ecosystem services.

4.5.6 Carbon Monoxide Co-Benefits

CO in ambient air is formed primarily by the incomplete combustion of carboncontaining fuels and photochemical reactions in the atmosphere. The amount of CO emitted from 

these reactions, relative to carbon dioxide (CO2), is sensitive to conditions in the combustion 

zone, such as fuel oxygen content, burn temperature, or mixing time. Upon inhalation, CO 

diffuses through the respiratory system to the blood, which can cause hypoxia (reduced oxygen 

availability). Carbon monoxide can elicit a broad range of effects in multiple tissues and organ 

systems that are dependent upon 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.
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4.5.7 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, 2009b). 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 

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, 2009b). Previous analyses (U.S. 

EPA, 2011a) show that visibility co-benefits can be a significant welfare benefit category. 

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 proposed 

guidelines would be likely to have a significant impact on visibility in urban areas or Class I 

areas.
4-62

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Ozone and Related Photochemical Oxidants (Final Report). U.S. Environmental Protection 

Agency, Washington, DC, EPA/600/R-10/076F. Available at:

<http://cfpub.epa.gov/ncea/isa/recordisplay.cfm?deid=247492#Download>. 

U.S. Environmental Protection Agency (U.S. EPA). 2014a. Welfare Risk and Exposure 

Assessment for Ozone Second External Review Draft. EPA-452/P-14-003a. Office of Air 

Quality Planning and Standards, Research Triangle Park, NC. February. Available at:

<http://www.epa.gov/ttn/naaqs/standards/ozone/data/20140131welfarerea.pdf>.

U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 2002. 

Workshop on the Benefits of Reductions in Exposure to Hazardous Air Pollutants: 

Developing Best Estimates of Dose-Response Functions An SAB Workshop Report of an 

EPA/SAB Workshop (Final Report). EPA-SAB-EC-WKSHP-02-001. January. Available at:

<http://yosemite.epa.gov/sab%5CSABPRODUCT.NSF/34355712EC011A358525719A005B

F6F6/$File/ecwkshp02001%2Bappa-g.pdf>.

U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 2008. 

Characterizing Uncertainty in Particulate Matter Benefits Using Expert Elicitation. EPACOUNCIL-08-002. July. Available at:

<http://yosemite.epa.gov/sab/sabproduct.nsf/0/43B91173651AED9E85257487004EA6CB/$F

ile/EPA-COUNCIL-08-002-unsigned.pdf>.

U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 2000. An 

SAB Report on EPA’s White Paper Valuing the Benefits of Fatal Cancer Risk Reduction.

EPA-SAB-EEAC-00-013. July. Available at:

<http://yosemite.epa.gov/sab%5CSABPRODUCT.NSF/41334524148BCCD6852571A70051

6498/$File/eeacf013.pdf>.
4-69

U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 2004c.

Advisory Council on Clean Air Compliance Analysis Response to Agency Request on 

Cessation Lag. EPA-COUNCIL-LTR-05-001. December. Available at:

<http://yosemite.epa.gov/sab/sabproduct.nsf/0/39F44B098DB49F3C85257170005293E0/$Fil

e/council_ltr_05_001.pdf>.

U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 2008. 

Benefits of Reducing Benzene Emissions in Houston, 1990–2020. EPA-COUNCIL-08-001. 

July. Available at:

<http://yosemite.epa.gov/sab/sabproduct.nsf/D4D7EC9DAEDA8A548525748600728A83/$Fi

le/EPA-COUNCIL-08-001-unsigned.pdf>.

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Review of EPA’s Integrated Science Assessment for Particulate Matter (First External Review 

Draft, December 2008). EPA-COUNCIL-09-008. May. Available at:

<http://yosemite.epa.gov/sab/SABPRODUCT.NSF/81e39f4c09954fcb85256ead006be86e/73

ACCA834AB44A10852575BD0064346B/$File/EPA-CASAC-09-008-unsigned.pdf>.

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Review of Integrated Science Assessment for Particulate Matter (Second External Review 

Draft, July 2009). EPA-CASAC-10-001. November. Available at:

<http://yosemite.epa.gov/sab/SABPRODUCT.NSF/81e39f4c09954fcb85256ead006be86e/15

1B1F83B023145585257678006836B9/$File/EPA-CASAC-10-001-unsigned.pdf>.

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Review of EPA’s DRAFT Health Benefits of the Second Section 812 Prospective Study of the 

Clean Air Act. EPA-COUNCIL-10-001. June. Available at: <

http://yosemite.epa.gov/sab/sabproduct.nsf/9288428b8eeea4c885257242006935a3/72D4EFA

39E48CDB28525774500738776/$File/EPA-COUNCIL-10-001-unsigned.pdf>.

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(December 10, 2010). EPA-SAB-11-011 July. Available at:

<http://yosemite.epa.gov/sab/sabproduct.nsf/298E1F50F844BC23852578DC0059A616/$File/

EPA-SAB-11-011-unsigned.pdf>.

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postneonatal infant mortality and particulate air pollution in the United States.” 

Environmental Health Perspectives 105 (6): 608-612.
4A-1

APPENDIX 4A: GENERATING REGIONAL BENEFIT-PER-TON ESTIMATES FOR 

ELECTRIC GENERATING UNITS

The purpose of this appendix is to provide additional detail regarding the generation of 

the benefit-per-ton estimates applied in Chapter 4 of this Regulatory Impact Analysis (RIA). 

Specifically, this appendix describes the methods for generating benefit-per-ton estimates by 

region for PM2.5 and ozone precursors emitted by the electrical generating unit (EGU) sector.

4A.1 Overview of Benefit-per-Ton Estimates

As described in the Technical Support Document: Estimating the Benefit per Ton of 

Reducing PM2.5 Precursors from 17 Sectors (U.S. EPA, 2013a) (hereafter “BPT TSD”), the

procedure for calculating average benefit-per-ton coefficients generally follows three steps, 

shown graphically in Figure 4A-1. As an example, in order to calculate national average benefitper-ton estimates for each ambient PM2.5 precursor emitted from EGU sources, we:

1. Use air quality modeling to predict ambient concentrations of primary PM2.5, nitrate and 

sulfate across the contiguous U.S. that are attributable to the EGU sector.

2. Estimate the health impacts, and the economic value of these impacts, associated with the 

attributable ambient concentrations of primary PM2.5, sulfate and nitrate PM2.5 using the 

environmental Benefits Mapping and Analysis Program (BenMAP v4.0.66)110 (Abt 

Associates, Inc, 2012).

3. Divide the PM2.5-related health impacts attributable to each type of PM2.5, and the 

monetary value of these impacts, by the level of associated precursor emissions. That is, 

primary PM2.5 benefits are divided by direct PM2.5 emissions, sulfate benefits are divided 

by SO2 emissions, and nitrate benefits are divided by NOx emissions.

 

110 In this stage we estimate the PM2.5-related impacts associated with changes in directly emitted PM2.5, nitrate and 

sulfate separately, so that we may ultimately calculate the benefit-per-ton reduced of the corresponding PM2.5

precursor, or directly emitted PM2.5, in step 3. When estimating these impacts we apply effect coefficients that relate 

changes in total PM2.5 mass to the risk of adverse health outcomes; we do not apply effect coefficients that are 

differentiated by PM2.5 specie. 
4A-2

Figure 4A-1. Conceptual Overview of Benefit-per-Ton Calculation

4A.2 Underlying Source Apportionment Air Quality Modeling

EPA performed a national-scale air quality modeling analysis using the Comprehensive 

Air Quality Model with Extensions (CAMx) model111 to estimate PM2.5 and ozone 

concentrations attributable to 17 industrial sectors, including EGUs. In this section, we provide a 

short summary of the air quality modeling used to generate these benefit-per-ton estimates. 

Readers interested in a full discussion of the air quality modeling may consult the Air Quality 

Modeling Technical Support Document: Source Sector Assessments (U.S. EPA, 2011a). The 

source apportionment modeling is also discussed in Fann, Baker and Fulcher (2012).

CAMx simulates the numerous physical and chemical processes involved in the 

formation, transport, and destruction of ozone, particulate matter and air toxics. Emissions of 

precursor species are injected into the model where they react to form secondary species such as 

ozone and then transport around the modeling domain before ultimately being removed by 

 

111 Environ International Corporation. 2010. Comprehensive Air Quality Model with Extensions, Version 5.3. 

User’s Guide. Novato, CA. December. Available at <http://www.camx.com>.

PM2.5 air quality 

change for a 

given sector

Human health 

benefits

Benefit-per-ton 

calculation
4A-3

deposition or chemical reaction. Source apportionment techniques track the formation and 

transport of ozone and PM from specific emissions sources and calculate the contribution of 

sources and precursors to ozone and PM2.5 at individual receptor locations. In contrast to “zeroout” modeling techniques, the tracking of emissions and resulting pollution in source 

apportionment modeling does not affect the transport, chemical transformation, or atmospheric 

chemical relationships within the modeling. More details on the implementation of source 

apportionment in CAMx can be found in the CAMx user’s guide.

The modeling analyses were performed for a domain covering the continental U.S. This 

domain has a parent horizontal grid of 36 km with two finer-scale 12 km grids over the eastern 

and western U.S. The base year of data used to construct the modeling platform includes 

emissions and meteorology for 2005. Specifically, the starting point for the emission projections 

in this modeling was the 2005 v4.3 emissions platform (U.S. EPA, 2005). EGU emission 

estimates for 2016 are from the Integrated Planning Model (IPM), and the projections include

emission reductions related to the NOx State Implementation Plan Call (U.S. EPA, 1998) and the 

proposed Transport Rule (U.S. EPA, 2010c).112

4A.3 Regional PM2.5 Benefit-per-Ton Estimates for EGUs

The approach for generating regional benefit-per-ton estimates for PM2.5 from EGU 

emissions is a small modification of the approach described above from the BPT TSD. The 

regional benefit-per-ton estimates reflect the ambient PM2.5 attributable to the EGU sector in the 

source apportionment modeling (“sector modeling”) from Fann, Baker, and Fulcher (2012), as 

illustrated in Figures 4A-2 and 4A-3 for 2016. After estimating the PM2.5 benefits for each of the 

analysis years applied in this RIA (i.e., 2020, 2025, and 2030), we aggregated the benefits results 

regionally (i.e., East, West, and California) rather than nationally, as shown in Figure 4A-4.113

Due to the low emissions of SO2, NOX, and directly emitted particles from EGUs in California 

 

112 The 2016 air quality modeling simulations underlying the benefit-per-ton estimates do not reflect emission 

reductions anticipated from the EGU sector as a result of the recently promulgated MATS rule, and so are likely to 

overstate the total PM2.5 from this sector. (Fann, Fulcher, and Baker [2013]). The EGU contribution on a per-ton 

basis would be similar. Because the emission reductions in this RIA are calculated from an IPM base case that 

includes MATS (see Chapter 3), there is no double-counting concern with the resulting co-benefits estimates.

113 This aggregation is identified as the shapefile “Report Regions” in BenMAP’s grid definitions.
4A-4

and the high population density, we separated out California in order not to bias the benefit-perton estimates for the rest of the Western U.S. In order to calculate the benefit-per-ton estimates, 

we divided the regional benefits estimates by the corresponding emissions, as shown in Table 

4A-1. Lastly, we adjusted the benefit-per-ton estimates for a currency year of 2011$.114

Figure 4A-2. Modeled PM2.5 Concentrations Attributable to EGUs in 2016 (Annual Mean, 

in μg/m3) (Source: BPT TSD)

This method provides estimates of the regional average benefit-per-ton for each ambient 

PM2.5 precursor emitted from EGU sources. For some precursor emissions, such as NOx, there is 

generally a non-linear relationship between emissions and formation of PM2.5. This means that 

each ton of NOx reduced would have a different impact on ambient PM2.5 depending on the 

initial level of emissions and potentially on the levels of emissions of other pollutants. In 

contrast, SO2 is generally linear in forming PM2.5. For non-linear pollutants like NOx, a marginal 

 

114 Currently, BenMAP does not have an inflation adjustment to 2011$. We ran BenMAP for a currency year of 

2010$ and calculated the benefit-per-ton estimates in 2010$. We then adjusted the resulting benefit-per-ton 

estimates to 2011$ using the Consumer Price Index.
4A-5

benefit-per-ton approach would better approximate the specific benefits associated with an 

emissions reduction scenario for a given set of base case emissions, but we do not have sufficient 

air quality modeling data to calculate a marginal benefit-per-ton estimates for the EGU sector. 

Therefore, using an average benefit-per-ton estimate for NOx adds uncertainty to the co-benefits 

estimated in this RIA. Because most of the estimated co-benefits for the proposed guidelines are 

attributable to reductions in SO2 emissions, the added uncertainty is likely to be small.
4A-6

Figure 4A-3. Modeled PM2.5 Concentrations Attributable to EGUs by Precursor in 2016 (Annual Mean, in μg/m3)

a) Sulfate b) Nitrate

c) Elemental Carbon and Organic Carbon d) Crustal PM2.5
4A-7

Figure 4A-4. Regional Breakdown

Table 4A-1. Summary of National and Regional Emissions in Sector Modeling for EGUs in 

2016 (in short tons)

Pollutant National EGU 

emissions

Regional EGU Emissions

East West California

SO2 3,793,362 3,520,296 273,070 4,886

Directly emitted PM2.5 (EC+OC) 30,078 26,172 3,892 1,523

Directly emitted PM2.5 (crustal) 243,497 214,219 28,438 840

NOx (all year) 1,826,582 1,425,148 401,584 13,485

NOx (ozone season) 931,189 728,402 195,748 7,039

In this RIA, we estimate emission reductions from EGUs using IPM.115 IPM outputs 

provide endogenously projected unit level emissions of SO2, NOx, CO2, Hg, HCl from EGUs, 

but CO, VOC, NH3 and total directly emitted PM2.5 and PM10 emissions are post-calculated.116 In 

 

115 See Chapter 3 of this RIA for additional information regarding IPM.

116 Detailed documentation of this post-processing is available at 

http://www.epa.gov/powersectormodeling/docs/v513/FlatFile_Methodology.pdf 
4A-8

addition, directly emitted particle emissions calculated from IPM outputs do not include 

speciation, i.e. they are only the total emissions. In order to estimate the benefits associated with 

reduced emissions of directly emitted particles, we must determine the fraction of total PM2.5 

emissions comprised of elemental carbon and organic carbon (EC+OC) and crustal emissions.117

Figure 4A-5 illustrates the relative breakdown of directly emitted PM2.5 components from EGUs 

in the modeling by Fann, Baker, and Fulcher (2012). In this modeling, the national average

EC+OC fraction of emitted PM2.5 is 10% with a range of 5% to 63% in different states due to the 

different proportion of fuels. The national average is similar to the averages for the east and west 

regions at 10% and 7%, respectively. Only five states had EC+OC fractions greater than 30%. 

For crustal emissions, the national average fraction of emitted PM2.5 from EGUs is 78% with a 

range of 26% to 83%. The national average is similar to the averages for the east and west 

regions at 78% and 81%, respectively. Only four states had crustal fractions less than 50%. 

Figure 4A-5. Breakdown of Directly Emitted PM2.5 Emissions from EGU in Sector 

Modeling

 

117 Crustal emissions are composed of compounds associated with minerals and metals from the earth’s surface, 

including carbonates, silicates, iron, phosphates, copper, and zinc. Often, crustal material represents particles not 

classified as one of the other species (e.g., organic carbon, elemental carbon, nitrate, sulfate, chloride, etc.).

-

5,000 

10,000 

15,000 

20,000 

25,000 Texas Pennsylvania Ohio Indiana Florida West Virginia Georgia Kentucky Alabama North Carolina South Carolina Illinois Minnesota Missouri Wyoming Arizona Tennessee Virginia Michigan Wisconsin North Dakota Utah Iowa New Mexico Kansas Oklahoma Colorado Maryland New York Arkansas Louisiana Nevada California Nebraska Montana New Jersey Mississippi Massachusetts Connecticut Washington South Dakota New Hampshire Oregon Delaware Rhode Island Maine Idaho Emissions in Sector Modeling (Tons)

Breakdown of Directly Emitted PM2.5 Emissions from EGUs in Sector 

Modeling

PNO3 PSO4

POC PEC

PMFINE (crustal)
4A-9

There are several uncertainties associated with estimating the benefits associated with 

reducing emissions of EC+OC and crustal PM2.5 from EGUs. As previously mentioned, IPM 

does not estimate PM2.5 emissions by component, and total PM2.5 is estimated as an IPM postprocessing step using emission factors. In order to conduct air quality modeling, PM2.5 from 

EGUs is speciated into components during the emissions modeling process based on emission 

profiles for EGUs by source classification code. Even though these speciation profiles are not 

unit-specific, an emission profile based on the source classification code is highly sophisticated 

and reflects the fuel and the unit configuration. In addition, the air quality model output has been 

adjusted to match an interpolated surface of speciated PM2.5 measurements, using the Model 

Attainment Test Software (Abt Associates, Inc, 2010). Uncertainties in some emissions sectors 

can sometimes be large enough that air quality estimates from well-characterized sectors such as 

EGUs may be changed to match observed monitor data. While these changes are usually 

minimal, in this instance, uncertainties in crustal emissions from other sectors impact the 

relationship between EGU crustal emissions and resulting air quality impacts. Because the cobenefits associated with directly emitted particles for the proposed guidelines are small relative 

to the co-benefits of reducing SO2 emissions, these uncertainties would not substantially affect 

the estimate of total monetized health co-benefits.118 In calculating the PM2.5 co-benefits in this 

RIA, we estimate the emission reductions of EC+OC and crustal emissions by applying the 

national average fractions (i.e., 78% crustal and 10% EC+OC) to the emission reductions of all 

directly emitted particles from EGUs. Because the benefit-per-ton estimates for reducing 

emissions of EC+OC are larger than the benefit-per-ton estimate for crustal emissions, this 

assumption underestimates the monetized PM2.5 co-benefits in certain states with higher EC+OC 

fractions, such as California and North Dakota. We further underestimate the co-benefits by not 

quantifying or monetizing the co-benefits associated with reducing directly emitted particulate 

nitrate and sulfate.

Although it is possible to calculate 95th percentile confidence intervals using the approach 

described in this appendix (e.g., U.S. EPA, 2011b), we generally do not calculate confidence 

intervals for benefit-per-ton estimates. Instead, we refer the reader to Chapter 5 of PM NAAQS 

 

118 See Figure 5-2 of this RIA for the relative proportion of health co-benefits from directly emitted particles relative 

to co-benefits from SO2 emissions.
4A-10

RIA (U.S. EPA, 2012a) for an indication of the combined random sampling error in the health 

impact and economic valuation functions using Monte Carlo methods. In general, the 95th

percentile confidence interval for the total monetized PM2.5 benefits ranges from approximately90% to +180% of the central estimates based on concentration-response functions from Krewski 

et al. (2009) and Lepeule et al. (2012). The 95th percentile confidence interval for the health 

impact function alone ranges from approximately ±30% for mortality incidence based on 

Krewski et al. (2009) and ±46% based on Lepeule et al. (2012). These confidence intervals do 

not reflect other sources of uncertainty inherent within the estimates, such as baseline incidence 

rates, populations exposed, and transferability of the effect estimate to diverse locations. As a 

result, the reported confidence intervals and range of estimates give an incomplete picture about 

the overall uncertainty in the benefits estimates.

Tables 4A-2 through 4A-4 provide the national and regional benefit-per-ton estimates for 

the EGU sector at discount rates of 3% and 7% in 2020, 2025, and 2030 respectively. Tables 4A5 through 4A-7 provide the incidence per ton estimates for the EGU sector in 2020, 2025, and 

2030 respectively.
4A-11

Table 4A-2. Summary of National and Regional PM2.5 Benefit-per-Ton Estimates for EGUs in 2020 (2011$)*

Pollutant Discount 

Rate National Region

East West California

SO2

3% $38,000 to $86,000 $40,000 to $90,000 $7,800 to $18,000 $160,000 to $360,000

7% $34,000 to $77,000 $36,000 to $82,000 $7,100 to $16,000 $140,000 to $320,000

Directly emitted 

PM2.5 (EC+OC)

3% $140,000 to $320,000 $140,000 to $320,000 $56,000 to $130,000 $280,000 to $640,000

7% $130,000 to $290,000 $130,000 to $280,000 $50,000 to $110,000 $250,000 to $570,000

Directly emitted 

PM2.5 (Crustal)

3% $18,000 to $40,000 $18,000 to $41,000 $11,000 to $25,000 $110,000 to $240,000

7% $16,000 to $36,000 $16,000 to $37,000 $10,000 to $23,000 $95,000 to $220,000

NOx (as PM2.5)

3% $5,600 to $13,000 $6,700 to $15,000 $1,200 to $2,600 $17,000 to $38,000

7% $5,000 to $11,000 $6,000 to $14,000 $1,000 to $2,400 $15,000 to $34,000

* The range of estimates reflects the range of epidemiology studies for avoided premature mortality for PM2.5. All estimates are rounded to two significant figures. All fine 

particles are assumed to have equivalent health effects, but the benefit per ton estimates vary depending on the location and magnitude of their impact on PM2.5 levels, 

which drive population exposure. The monetized benefits incorporate the conversion from precursor emissions to ambient fine particles. The estimates do not include 

reduced health effects from direct exposure to ozone, NO2, SO2, ecosystem effects, or visibility impairment. 

Table 4A-3. Summary of National and Regional PM2.5 Benefit-per-Ton Estimates for EGUs in 2025 (2011$)*

Pollutant Discount 

Rate National Region

East West California

SO2

3% $41,000 to $93,000 $44,000 to $98,000 $8,800 to $20,000 $180,000 to $410,000

7% $37,000 to $84,000 $39,000 to $89,000 $8,000 to $18,000 $160,000 to $370,000

Directly emitted 

PM2.5 (EC+OC)

3% $150,000 to $350,000 $150,000 to $340,000 $64,000 to $140,000 $320,000 to $720,000

7% $140,000 to $310,000 $140,000 to $310,000 $58,000 to $130,000 $290,000 to $650,000

Directly emitted 

PM2.5 (Crustal)

3% $17,000 to $39,000 $18,000 to $40,000 $12,000 to $27,000 $43,000 to $96,000

7% $15,000 to $35,000 $16,000 to $36,000 $11,000 to $24,000 $38,000 to $87,000

NOx (as PM2.5)

3% $6,000 to $14,000 $7,200 to $16,000 $1,300 to $2,900 $19,000 to $42,000

7% $5,400 to $12,000 $6,500 to $15,000 $1,200 to $2,600 $17,000 to $38,000

* The range of estimates reflects the range of epidemiology studies for avoided premature mortality for PM2.5. All estimates are rounded to two significant figures. All fine 

particles are assumed to have equivalent health effects, but the benefit per ton estimates vary depending on the location and magnitude of their impact on PM2.5 levels, 

which drive population exposure. The monetized benefits incorporate the conversion from precursor emissions to ambient fine particles. The estimates do not include 

reduced health effects from direct exposure to ozone, NO2, SO2, ecosystem effects, or visibility impairment.
4A-12

Table 4A-4. Summary of National and Regional PM2.5 Benefit-per-Ton Estimates for EGUs in 2030 (2011$)*

Pollutant Discount 

Rate National Region

East West California

SO2

3% $44,000 to $100,000 $47,000 to $110,000 $9,800 to $22,000 $200,000 to $450,000

7% $40,000 to $90,000 $42,000 to $95,000 $8,800 to $20,000 $180,000 to $410,000

Directly emitted 

PM2.5 (EC+OC)

3% $170,000 to $370,000 $160,000 to $370,000 $71,000 to $160,000 $360,000 to $800,000

7% $150,000 to $340,000 $150,000 to $330,000 $64,000 to $150,000 $320,000 to $730,000

Directly emitted 

PM2.5 (Crustal)

3% $18,000 to $42,000 $19,000 to $43,000 $13,000 to $30,000 $47,000 to $110,000

7% $17,000 to $38,000 $17,000 to $38,000 $12,000 to $27,000 $43,000 to $96,000

NOx (as PM2.5)

3% $6,400 to $14,000 $7,600 to $17,000 $1,400 to $3,200 $21,000 to $42,000

7% $5,800 to $13,000 $6,900 to $16,000 $1,300 to $2,900 $19,000 to $47,000

* The range of estimates reflects the range of epidemiology studies for avoided premature mortality for PM2.5. All estimates are rounded to two significant figures. All fine 

particles are assumed to have equivalent health effects, but the benefit per ton estimates vary depending on the location and magnitude of their impact on PM2.5 levels, 

which drive population exposure. The monetized benefits incorporate the conversion from precursor emissions to ambient fine particles. The estimates do not include 

reduced health effects from direct exposure to ozone, NO2, SO2, ecosystem effects, or visibility impairment.
4A-13

Table 4A-5. Summary of Regional PM2.5 Incidence-per-Ton Estimates for EGUs in 2020*

Health Endpoint

East West California

SO2 NOx EC+OC Crustal SO2 NOx EC+OC Crustal SO2 NOx EC+OC Crustal

Premature Mortality

Krewski et al. (2009) – adult 0.004400 0.000730 0.015000 0.002000 0.000860 0.000130 0.006100 0.001200 0.017000 0.001800 0.031000 0.012000

Lepeule et al. (2012) – adult 0.009900 0.001700 0.035000 0.004500 0.001900 0.000290 0.014000 0.002800 0.039000 0.004100 0.070000 0.026000

Woodruff et al. (1997) – infants 0.000010 0.000002 0.000033 0.000004 0.000002 0.000000 0.000016 0.000003 0.000036 0.000004 0.000067 0.000025

Morbidity

Emergency department visits for asthma 0.002300 0.000410 0.008700 0.001000 0.000380 0.000055 0.002800 0.000520 0.008600 0.001000 0.016000 0.005900

Acute bronchitis 0.006300 0.001100 0.022000 0.002800 0.001600 0.000330 0.011000 0.002300 0.031000 0.003400 0.057000 0.021000

Lower respiratory symptoms 0.081000 0.014000 0.290000 0.036000 0.021000 0.004200 0.150000 0.029000 0.390000 0.044000 0.720000 0.270000

Upper respiratory symptoms 0.120000 0.020000 0.410000 0.051000 0.030000 0.006000 0.210000 0.042000 0.560000 0.062000 1.000000 0.390000

Minor restricted-activity days 3.200000 0.540000 11.000000 1.400000 0.780000 0.130000 5.400000 1.000000 16.00000 1.700000 29.00000 11.00000

Lost work days 0.540000 0.090000 1.900000 0.240000 0.130000 0.023000 0.910000 0.180000 2.700000 0.290000 4.900000 1.800000

Asthma exacerbation 0.290000 0.020000 0.420000 0.053000 0.074000 0.006200 0.220000 0.043000 1.400000 0.064000 1.100000 0.400000

Hospital Admissions, Respiratory 0.001300 0.000220 0.004500 0.000580 0.000190 0.000026 0.001400 0.000270 0.004100 0.000440 0.007400 0.002800

Hospital Admissions, Cardiovascular 0.001600 0.000270 0.005600 0.000720 0.000220 0.000032 0.001800 0.000340 0.005000 0.000520 0.009000 0.003400

Non-fatal Heart Attacks (Peters) 0.005000 0.000860 0.018000 0.002300 0.000810 0.000110 0.006000 0.001200 0.018000 0.001900 0.032000 0.012000

Non-fatal Heart Attacks (All others) 0.000550 0.000093 0.001900 0.000250 0.000087 0.000012 0.000640 0.000130 0.001900 0.000210 0.003400 0.001300

* All estimates are rounded to two significant figures. All fine particles are assumed to have equivalent health effects, but the incidence-per-ton estimates vary depending on 

the location and magnitude of their impact on PM2.5 levels, which drive population exposure. The incidence benefit-per-ton estimates incorporate the conversion from 

precursor emissions to ambient fine particles. 
4A-14

Table 4A-6. Summary of Regional PM2.5 Incidence-per-Ton Estimates for EGUs in 2025*

Health Endpoint

East West California

SO2 NOx EC+OC Crustal SO2 NOx EC+OC Crustal SO2 NOx EC+OC Crustal

Premature Mortality

Krewski et al. (2009) – adult 0.004700 0.000770 0.016000 0.002100 0.000940 0.000140 0.006800 0.001400 0.019000 0.002000 0.034000 0.013000

Lepeule et al. (2012) – adult 0.011000 0.001700 0.037000 0.004800 0.002100 0.000310 0.015000 0.003100 0.043000 0.004500 0.077000 0.029000

Woodruff et al. (1997) – infants 0.000009 0.000001 0.000031 0.000004 0.000002 0.000000 0.000016 0.000003 0.000034 0.000004 0.000063 0.000024

Morbidity

Emergency department visits for asthma 0.002400 0.000420 0.009000 0.001100 0.000410 0.000059 0.003000 0.000560 0.009000 0.001000 0.016000 0.006100

Acute bronchitis 0.006600 0.001100 0.023000 0.002900 0.001700 0.000350 0.012000 0.002400 0.032000 0.003600 0.059000 0.022000

Lower respiratory symptoms 0.084000 0.014000 0.300000 0.037000 0.022000 0.004400 0.150000 0.031000 0.410000 0.046000 0.760000 0.290000

Upper respiratory symptoms 0.120000 0.020000 0.430000 0.053000 0.032000 0.006300 0.220000 0.044000 0.590000 0.065000 1.100000 0.410000

Minor restricted-activity days 3.200000 0.540000 12.000000 1.400000 0.820000 0.140000 5.700000 1.100000 16.00000 1.70000 30.00000 11.00000

Lost work days 0.550000 0.091000 2.000000 0.240000 0.140000 0.024000 0.970000 0.190000 2.700000 0.300000 5.000000 1.800000

Asthma exacerbation 0.300000 0.021000 0.440000 0.055000 0.078000 0.006500 0.230000 0.046000 1.400000 0.067000 1.100000 0.420000

Hospital Admissions, Respiratory 0.001400 0.000240 0.005000 0.000640 0.000220 0.000030 0.001600 0.000320 0.004800 0.000500 0.008500 0.003200

Hospital Admissions, Cardiovascular 0.001800 0.000290 0.006100 0.000780 0.000250 0.000036 0.002000 0.000390 0.005600 0.000580 0.010000 0.003800

Non-fatal Heart Attacks (Peters) 0.005500 0.000930 0.019000 0.002500 0.000910 0.000120 0.006800 0.001300 0.020000 0.002100 0.036000 0.013000

Non-fatal Heart Attacks (All others) 0.000600 0.000100 0.002100 0.000270 0.000098 0.000013 0.000730 0.000140 0.002200 0.000230 0.003900 0.001500

* All estimates are rounded to two significant figures. All fine particles are assumed to have equivalent health effects, but the incidence-per-ton estimates vary depending on 

the location and magnitude of their impact on PM2.5 levels, which drive population exposure. The incidence benefit-per-ton estimates incorporate the conversion from 

precursor emissions to ambient fine particles. 
4A-15

Table 4A-7. Summary of Regional PM2.5 Incidence-per-Ton Estimates for EGUs in 2030*

Health Endpoint

East West California

SO2 NOx EC+OC Crustal SO2 NOx EC+OC Crustal SO2 NOx EC+OC Crustal

Premature Mortality 0.005000 0.000810 0.017000 0.002300 0.001000 0.000150 0.007600 0.001500 0.021000 0.002200 0.038000 0.014000

Krewski et al. (2009) – adult 0.011000 0.001800 0.040000 0.005100 0.002400 0.000340 0.017000 0.003400 0.048000 0.005000 0.086000 0.032000

Lepeule et al. (2012) – adult 0.000009 0.000001 0.000030 0.000004 0.000002 0.000000 0.000015 0.000003 0.000032 0.000004 0.000060 0.000023

Woodruff et al. (1997) – infants

Morbidity 0.002500 0.000430 0.009300 0.001100 0.000430 0.000062 0.003200 0.000600 0.009400 0.001100 0.017000 0.006400

Emergency department visits for asthma 0.006800 0.001100 0.024000 0.003000 0.001800 0.000370 0.013000 0.002600 0.033000 0.003700 0.061000 0.023000

Acute bronchitis 0.087000 0.014000 0.310000 0.038000 0.023000 0.004700 0.170000 0.033000 0.420000 0.047000 0.780000 0.300000

Lower respiratory symptoms 0.130000 0.021000 0.440000 0.055000 0.034000 0.006700 0.240000 0.047000 0.610000 0.067000 1.100000 0.420000

Upper respiratory symptoms 3.300000 0.540000 12.000000 1.500000 0.860000 0.150000 6.000000 1.200000 17.00000 1.800000 30.00000 11.00000

Minor restricted-activity days 0.560000 0.092000 2.000000 0.250000 0.150000 0.025000 1.000000 0.200000 2.800000 0.300000 5.100000 1.900000

Lost work days 0.310000 0.021000 0.460000 0.057000 0.082000 0.006900 0.240000 0.048000 1.500000 0.069000 1.200000 0.430000

Asthma exacerbation 0.001600 0.000260 0.005500 0.000710 0.000250 0.000034 0.001800 0.000360 0.005500 0.000580 0.009900 0.003700

Hospital Admissions, Respiratory 0.001900 0.000320 0.006700 0.000850 0.000280 0.000041 0.002300 0.000440 0.006400 0.000660 0.012000 0.004300

Hospital Admissions, Cardiovascular 0.006000 0.001000 0.021000 0.002700 0.001000 0.000140 0.007700 0.001500 0.023000 0.002400 0.041000 0.015000

Non-fatal Heart Attacks (Peters) 0.000660 0.000110 0.002300 0.000290 0.000110 0.000015 0.000830 0.000160 0.002500 0.000260 0.004400 0.001700

Non-fatal Heart Attacks (All others) 0.005000 0.000810 0.017000 0.002300 0.001000 0.000150 0.007600 0.001500 0.021000 0.002200 0.038000 0.014000

* All estimates are rounded to two significant figures. All fine particles are assumed to have equivalent health effects, but the incidence-per-ton estimates vary depending on 

the location and magnitude of their impact on PM2.5 levels, which drive population exposure. The incidence benefit-per-ton estimates incorporate the conversion from 

precursor emissions to ambient fine particles. 
4A-16

4A.4 Regional Ozone Benefit-per-Ton Estimates

The process for generating the regional ozone benefit-per-ton estimates is consistent with 

the process for PM2.5. The key difference is substituting the ozone impacts from EGUs in the 

sector modeling for the PM2.5 impacts. We have not historically generated ozone benefit-per-ton 

estimates due to the complex non-linearity in the atmospheric response to changes in ozone 

precursor pollutants from different sectors in different geographic areas. However, for EGUs, we 

have increased confidence in the magnitude and location of ozone-season NOx emissions and 

ambient ozone concentrations attributable to these emissions. In addition, the sector modeling 

provides information regarding the total EGU contribution to ozone formation, which provides 

greater transferability than an air quality modeling scenario for a regulation that might include 

both increases and decreases in emissions in different locations. Figure 4A-6 provides the 

average ambient ozone concentrations from the EGU sector in the sector modeling for 2016.119

 

119 The 2016 air quality modeling simulations do not reflect emission reductions anticipated from the EGU sector as 

a result of the recently promulgated MATS rule, and so may overstate the total ozone contribution from this sector. 

(Fann, Fulcher, and Baker (2013). However, the EGU contribution on a per-ton basis would likely be similar. 

Because the emission reductions in this RIA are calculated from an IPM base case that includes MATS (see Chapter 

3), there is no double-counting concern with the resulting co-benefits estimates.
4A-17

Figure 4A-6. Modeled Average Ozone Concentrations Attributable to EGUs in 2016 (Daily 

8-hour maximum, May-September, in ppb)

We assume that all of the ozone impacts from EGUs are attributable to NOx emissions. 

VOC emissions, which are also a precursor to ambient ozone formation, are insignificant from 

the EGU sector relative to NOx emissions from EGUs and the total VOC emissions inventory. 

Therefore, we believe that our assumption that EGU-attributable ozone formation at the regionallevel is due to NOx alone is reasonable.

Similar to PM2.5, this method provides estimates of the regional average benefit-per-ton. 

Due to the non-linear chemistry between NOx emissions and ambient ozone, using an average 

benefit-per-ton estimate for NOx adds uncertainty to the ozone co-benefits estimated for the 

proposed guidelines. Because most of the estimated co-benefits for the proposed guidelines are 

attributable to changes in ambient PM2.5, the added uncertainty is likely to be small.

In the ozone co-benefits estimated in this RIA, we apply the benefit-per-ton estimates 

calculated using NOX emissions from U.S. EGUs during the ozone-season only (May to 

September). As shown in Table 4A-1, ozone-season NOx emissions from EGUs are 

approximately half of all-year NOX emissions. Because we estimate ozone health impacts from 

May to September only, this approach underestimates ozone co-benefits in areas with a longer 
4A-18

ozone seasons such as southern California and Texas. When the underestimated benefit-per-ton 

estimate is multiplied by ozone-season only NOx emission reductions, this results in an 

underestimate of the monetized ozone co-benefits. For illustrative purposes, Tables 4A-8 through 

4A-10 provide the ozone benefit-per-ton estimates using both all-year NOx emissions and ozoneseason only NOx for 2020, 2025, and 2030, respectively. Tables 4A-11 through 4A-13 provide 

the ozone season incidence-per-ton estimates for 2020, 2025, and 2030, respectively.

Table 4A-8. Summary of National and Regional Ozone Benefit-per-Ton Estimates for 

EGUs in 2020 (2011$)*

Ozone precursor 

Pollutant National Regional

East West California

Ozone season NOx $3,800 to $16,000 $4,600 to $19,000 $930 to $4,000 $7,400 to $31,000

All-year NOx $1,900 to $8,000 $2,300 to $9,600 $450 to $2,000 $3,800 to $16,000

* The range of estimates reflects the range of epidemiology studies for avoided premature mortality for ozone. All 

estimates are rounded to two significant figures. The monetized benefits incorporate the conversion from NOx

precursor emissions to ambient ozone. 

Table 4A-9. Summary of National and Regional Ozone Benefit-per-Ton Estimates for 

EGUs in 2025 (2011$)*

Ozone precursor 

Pollutant National Regional

East West California

Ozone season NOx $4,900 to $21,000 $5,900 to $25,000 $1,200 to $5,400 $9,900 to $42,000

All-year NOx $2,500 to $11,000 $3,000 to $13,000 $650 to $2,800 $5,200 to $22,000

* The range of estimates reflects the range of epidemiology studies for avoided premature mortality for ozone. All 

estimates are rounded to two significant figures. The monetized benefits incorporate the conversion from NOx 

precursor emissions to ambient ozone. 

Table 4A-10. Summary of National and Regional Ozone Benefit-per-Ton Estimates for 

EGUs in 2030 (2011$)*

Ozone precursor 

Pollutant National Regional

East West California

Ozone season NOx $5,300 to $23,000 $6,300 to $27,000 $1,400 to $6,000 $11,000 to $47,000

All-year NOx $2,700 to $12,000 $3,200 to $14,000 $730 to $3,200 $5,700 to $25,000

* The range of estimates reflects the range of epidemiology studies for avoided premature mortality for ozone. All 

estimates are rounded to two significant figures. The monetized benefits incorporate the conversion from NOx 

precursor emissions to ambient ozone. 
4A-19

Table 4A-11. Summary of Regional Ozone Incidence-per-Ton Estimates for EGUs in 2020*

Health Endpoint East West California

Premature Mortality – adult 

Bell et al. (2004) 0.000260 0.000053 0.000420

Levy et al. (2005) 0.001200 0.000250 0.001900

Morbidity

Hospital Admissions, Respiratory (ages > 65) 0.001600 0.000250 0.002200

Hospital Admissions, Respiratory (ages < 2) 0.000710 0.000220 0.001200

Emergency Room Visits, Respiratory 0.000840 0.000150 0.001300

Acute Respiratory Symptoms 1.500000 0.380000 3.000000

School Loss Days 0.510000 0.140000 1.000000

* All estimates are rounded to two significant figures. The incidence benefit-per-ton estimates incorporate the 

conversion from NOx precursor emissions to ambient ozone. These estimates reflect ozone-season NOx 

emissions. 

Table 4A-12. Summary of Regional Ozone Incidence-per-Ton Estimates for EGUs in 2025*

Health Endpoint East West California

Premature Mortality – adult 

Bell et al. (2004) 0.000530 0.000110 0.000890

Levy et al. (2005) 0.002400 0.000520 0.004100

Morbidity

Hospital Admissions, Respiratory (ages > 65) 0.003500 0.000590 0.004900

Hospital Admissions, Respiratory (ages < 2) 0.001400 0.000440 0.002300

Emergency Room Visits, Respiratory 0.001700 0.000300 0.002700

Acute Respiratory Symptoms 3.000000 0.770000 5.900000

School Loss Days 1.000000 0.290000 2.100000

* All estimates are rounded to two significant figures. The incidence benefit-per-ton estimates incorporate the 

conversion from NOx precursor emissions to ambient ozone. These estimates reflect ozone-season NOx 

emissions. 

Table 4A-13. Summary of Regional Ozone Incidence-per-Ton Estimates for EGUs in 2030*

Health Endpoint East West California

Premature Mortality – adult 

Bell et al. (2004) 0.000570 0.000130 0.001000

Levy et al. (2005) 0.002600 0.000580 0.004500

Morbidity

Hospital Admissions, Respiratory (ages > 65) 0.004000 0.000690 0.005900

Hospital Admissions, Respiratory (ages < 2) 0.001400 0.000460 0.002400

Emergency Room Visits, Respiratory 0.001800 0.000320 0.002800

Acute Respiratory Symptoms 3.000000 0.810000 6.000000

School Loss Days 1.100000 0.310000 2.200000

* All estimates are rounded to two significant figures. The incidence benefit-per-ton estimates incorporate the 

conversion from NOx precursor emissions to ambient ozone. These estimates reflect ozone-season NOx 

emissions.
4A-20

4A.5 Evaluating the Benefit-per-Ton Estimates

In this section, we provide information that can be used to evaluate the regional benefitper-ton estimates and provide some characterization of the uncertainty inherent in these estimates 

relative to benefits estimated using scenario-specific air quality modeling. First, we evaluated 

how well the spatial distribution of emissions underlying the regional benefit-per-ton estimates 

match the spatial distribution of the emissions for the illustrative compliance scenarios for the 

proposed guidelines. Second, we evaluated how well the national and regional benefit-per-ton 

estimates predicted modeled benefits estimated for MATS (U.S. EPA, 2011b) and CSAPR (U.S. 

EPA, 2011c).

For the first evaluation, we provide graphs of the EGU emissions in each state in the 

sector modeling base case used to generate the regional benefit-per-ton estimates and the 

emissions in the IPM base case for this RIA, as shown in Figures 4A-7 through 4A-9. It is 

important to note that these graphs do not show the emission reductions anticipated from 

implementation of the proposed guidelines. Rather, these graphs illustrate the similarities and 

differences in the spatial distribution of the emissions from the EGU sector from the different 

base cases in order to evaluate the potential uncertainty associated with using the benefit-per-ton 

estimates to estimate the health co-benefits of the proposed guidelines. As noted in section 4A-2 

above, the sector modeling base case does not reflect emission reductions anticipated from the 

EGU sector as a result of the recently promulgated MATS rule,120 but the IPM base case does 

reflect those emission reductions. Even though the sector modeling has substantially more SO2

emissions from EGUs (3.8 million tons in 2016 vs. 1.5 million tons in 2020), the spatial 

distribution is relatively similar across states and thus the resulting benefit-per-ton would likely 

be similar. Therefore, we would not expect this difference to have a substantial effect on the 

resulting co-benefits estimated for the proposed guidelines. Base case emissions for annual NOx 

and ozone-season NOx are also reasonably similar.

 

120 See Figure 5C-1 from the MATS RIA (U.S. EPA, 2011b) for more information regarding the state-level SO2

emission reductions anticipated from MATS.
4A-21

Figure 4A-7. Comparison of Base Case SO2 Emissions in 2020

 -

 50,000

 100,000

 150,000

 200,000

 250,000

 300,000

 350,000

 400,000

 450,000 Alabama Arizona Arkansas California Colorado Connecticut Delaware District of Columbia Florida Georgia Idaho Illinois Indiana Iowa Kansas Kentucky Louisiana Maine Maryland Massachusetts Michigan Minnesota Mississippi Missouri Montana Nebraska Nevada New Hampshire New Jersey New Mexico New York North Carolina North Dakota Ohio Oklahoma Oregon Pennsylvania Rhode Island South Carolina South Dakota Tennessee Texas Utah Vermont Virginia Washington West Virginia Wisconsin Wyoming Tons

SO2 Emissions in 2020

EGU sector air quality modeling base case

IPM base case for this RIA
4A-22

Figure 4A-8. Comparison of Base Case Annual NOx Emissions in 2020

 -

 20,000

 40,000

 60,000

 80,000

 100,000

 120,000

 140,000

 160,000 Alabama Arizona Arkansas California Colorado Connecticut Delaware District of Columbia Florida Georgia Idaho Illinois Indiana Iowa Kansas Kentucky Louisiana Maine Maryland Massachusetts Michigan Minnesota Mississippi Missouri Montana Nebraska Nevada New Hampshire New Jersey New Mexico New York North Carolina North Dakota Ohio Oklahoma Oregon Pennsylvania Rhode Island South Carolina South Dakota Tennessee Texas Utah Vermont Virginia Washington West Virginia Wisconsin Wyoming Tons

Annual NOx Emissions in 2020

EGU sector air quality modeling base case

IPM base case for this RIA
4A-23

Figure 4A-9. Comparison of Base Case Ozone-season NOx Emissions in 2020

 -

 10,000

 20,000

 30,000

 40,000

 50,000

 60,000

 70,000

 80,000 Alabama Arizona Arkansas California Colorado Connecticut Delaware District of Columbia Florida Georgia Idaho Illinois Indiana Iowa Kansas Kentucky Louisiana Maine Maryland Massachusetts Michigan Minnesota Mississippi Missouri Montana Nebraska Nevada New Hampshire New Jersey New Mexico New York North Carolina North Dakota Ohio Oklahoma Oregon Pennsylvania Rhode Island South Carolina South Dakota Tennessee Texas Utah Vermont Virginia Washington West Virginia Wisconsin Wyoming Tons

Ozone Season NOx Emissions in 2020

EGU sector air quality modeling base case

IPM base case for this RIA
4A-24

For the second evaluation, we used the national and regional benefit-per-ton estimates to 

estimate the modeled sulfate benefits estimated for MATS and CSAPR.121 We focused on sulfate 

because the vast majority of PM2.5 benefits for both rules were from reductions in ambient 

sulfate. We found that the national benefit-per-ton estimates overestimated the modeled CSAPR 

benefits by 7 percent and the MATS benefits by 33 percent. The regional benefit-per-ton 

estimates overestimated the CSAPR benefits by 14 percent and MATS benefits by 29 percent. 

This evaluation shows that the national and regional benefit-per-ton estimates perform 

reasonably well, with a slight overestimate, but this does not mean that the co-benefits estimated 

for the proposed guidelines would necessarily be within a similar range. 

We also evaluated whether available modeling information could be used to generate 

reliable state-level benefit-per-ton estimates for PM2.5.

122 In general, we would expect state-level 

estimates to better reflect the spatial differences in emission reductions and associated health 

impacts across scenarios than regional estimates, which implicitly assume a constant percentage 

of emission reductions across the region. Varying percentage reductions across states are further 

magnified by spatial differences in population density, base case health incidence rates, air 

quality response, and interstate pollution transport. 

We tested several methods for generating state-level benefit-per-ton estimates. The 

method that performed best used state-by-state contribution modeling in the Eastern U.S. 

conducted for CSAPR (U.S. EPA, 2011a) to adjust the regional benefit-per-ton estimates for the 

EGU sector. Although the CSAPR modeling is the best available for estimating state-level 

benefit-per-ton estimates, it zeroed-out all anthropogenic emission sources within a state rather 

than just zeroing-out the emissions from the EGU sector alone. Currently, we do not have air 

quality modeling that provides information regarding the PM2.5 response to EGU only emissions 

for each state separately. We evaluated the performance of the state-level estimates by 

comparing the modeled benefits for two previous rulemakings (i.e., CSAPR and MATS) to the 

 

121 The benefits analyses for both the MATS and CSAPR RIAs relied on benefit-per-ton estimates generated from 

similar policy-specific air quality modeling conducted specifically for those rulemakings. For this evaluation, we 

compared the benefits estimated from the air quality modeling, not the benefits estimated for the final rules in the 

RIA generated using the benefit-per-ton estimates for those rulemakings.

122 We did not attempt to generate state-level ozone benefit-per-ton estimates due to inter-state transport, complex 

atmospheric chemistry, and localized impacts from VOC emissions.
4A-25

benefits estimated using state-level benefit-per-ton. When we evaluated the state-level estimates 

in the same manner as the national and regional estimates, we found that the state-level estimates 

performed similarly, in general, to the regional estimates for estimating total national benefits but 

were unreliable in estimating the benefits that would accrue to each state. In addition, because 

the spatial differences in emission reductions between the options evaluated in this RIA are 

small, using the state-level estimates did not make a difference in the relative co-benefits across 

options. 
4A-26

4A.6 References

Abt Associates, Inc. 2010. “User’s Guide: Modeled Attainment Test Software.” Available at: 

<http://www.epa.gov/scram001/modelingapps_mats.htm>. 

Abt Associates, Inc. 2012. “BenMAP User’s Manual Appendices,” prepared for U.S. Research 

Triangle Park, NC: U. S. Environmental Protection Agency, Office of Air Quality Planning 

and Standards. Available at: 

<http://www.epa.gov/air/benmap/models/BenMAPAppendicesOct2012.pdf>. 

Bell, M.L., et al. 2004. “Ozone and Short-Term Mortality in 95 U.S. Urban Communities, 1987-

2000.” Journal of the American Medical Association, 292(19): pp. 2372-8.

Bell, M.L.; F. Dominici and J.M. Samet. 2005. “A Meta-Analysis of Time-Series Studies of 

Ozone and Mortality with Comparison to the National Morbidity, Mortality, and Air Pollution 

Study.” Epidemiology. 16(4): pp. 436-45.

Fann, N.; C.M. Fulcher and B.J. Hubbell. 2009. “The Influence of Location, Source, and 

Emission Type in Estimates of the Human Health Benefits of Reducing a Ton of Air 

Pollution.” Air Quality and Atmospheric Health, 2:169–176.

Fann, N.; K.R. Baker and C.M. Fulcher. 2012. “Characterizing the PM2.5-Related Health 

Benefits of Emission Reductions for 17 Industrial, Area and Mobile Emission Sectors Across 

the U.S.” Environment International, 49 41–151. 

Fann, N.; K.R. Baker and C.M. Fulcher. 2013. “The Recent and Future Health Burden of Air 

Pollution Apportioned Across 23 U.S. Sectors.” Environmental Scientific Technology, 47 (8) 

(2013), pp. 3580–3589. 

Huang Y.; F. Dominici and M. Bell. 2005. “Bayesian Hierarchical Distributed Lag Models for 

Summer Ozone Exposure and Cardio-Respiratory Mortality.” Environmetrics, 16, 547-562.

Ito, K.; S.F. De Leon and M. Lippmann. 2005. “Associations Between Ozone and Daily 

Mortality: Analysis and Meta-Analysis.” Epidemiology, 16(4): p. 446-57.

Krewski D.; M. Jerrett; R.T. Burnett; R. Ma; E. Hughes; Y. Shi, et al. 2009. Extended Follow-Up 

and Spatial Analysis of the American Cancer Society Study Linking Particulate Air Pollution 

and Mortality. HEI Research Report, 140, Health Effects Institute, Boston, MA.

Lepeule, J.; F. Laden; D. Dockery and J. Schwartz. 2012. “Chronic Exposure to Fine Particles 

and Mortality: An Extended Follow-Up of the Harvard Six Cities Study from 1974 to 2009.” 

Environmental Health Perspective, Jul; 120(7):965-70. 

Levy, J.I.; S.M. Chemerynski and J.A. Sarnat. 2005. “Ozone Exposure and Mortality: An 

Empiric Bayes Metaregression Analysis.” Epidemiology, 16(4): pp. 458-68.

Roman, H.A.; K. D. Walker; T. L. Walsh; L. Conner; H. M. Richmond; B. J. Hubbell and P. L. 

Kinney. 2008. “Expert Judgment Assessment of the Mortality Impact of Changes in Ambient 

Fine Particulate Matter in the U.S.” Environmental Scientific Technology, 42(7):2268-2274.

Schwartz, J. 2005. “How Sensitive is the Association Between Ozone and Daily Deaths to 

Control for Temperature?” American Journal of Respiratory and Critical Care Medicine, Vol. 

171 (6): 627-31.
4A-27

U.S. Environmental Protection Agency. 1998. Regulatory Impact Analysis for the NOx SIP Call, 

FIP, and Section 126 Petitions. (EPA document number EPA-452/R-98-003). Available at: 

<http://http://www.epa.gov/ttn/oarpg/otag/sipriav2.zip>.

U.S. EPA. 2005. “2005 National Emissions Inventory Data & Documentation.” Available at: 

<http://www.epa.gov/ttnchie1/net/2005inventory.html>.

U.S. EPA. 2010. Regulatory Impact Analysis for the Proposed Federal Transport Rule. (EPA 

document number EPA-HQ-OAR-2009-0491). Research Triangle Park, NC. Available at: 

<http://www.epa.gov/ttn/ecas/regdata/RIAs/proposaltrria_final.pdf>.

U.S. EPA. 2011a. “Air Quality Modeling Final Rule Technical Support Document [Cross-State 

Rule].” Research Triangle Park, NC: Office of Air Quality Planning and Standards, Air 

Quality Assessment Division, June. Available on the Internet at:

<http://www.epa.gov/crossstaterule/pdfs/AQModeling.pdf >.

U.S. EPA. 2011b. Regulatory Impact Analysis for the Final Mercury and Air Toxics Standards. 

Research Triangle Park, NC: Office of Air Quality Planning and Standards, Health and 

Environmental Impacts Division. (EPA document number EPA-452/R-11-011, December).

Available at <http://www.epa.gov/ttn/ecas/regdata/RIAs/matsriafinal.pdf>.

U.S. EPA. 2011c. “Regulatory Impact Analysis (RIA) for the Final Transport Rule.” Research 

Triangle Park: Office of Air and Radiation, July. Available at:

<http://www.epa.gov/airtransport/pdfs/FinalRIA.pdf>.

U.S. EPA. 2011d. “Air Quality Modeling Technical Support Document: Source Sector 

Assessments.” Research Triangle Park, NC. Available at: 

<http://www.epa.gov/scram001/reports/EPA454_R11_006.pdf>.

U.S. EPA. 2012a. “Regulatory Impact Analysis for the Final Revisions to the National Ambient 

Air Quality Standards for Particulate Matter.” Research Triangle Park, NC: Office of Air 

Quality Planning and Standards, Health and Environmental Impacts Division. (EPA document 

number EPA-452/R-12-003, December). Available at:

<http://www.epa.gov/pm/2012/finalria.pdf>. 

U.S. EPA. 2013a. “Technical Support Document: Estimating the Benefit per Ton of Reducing 

PM2.5 Precursors from 17 Sectors.” Research Triangle Park, NC: Office of Air Quality 

Planning and Standards, February. Available at:

<http://www.epa.gov/airquality/benmap/models/Source_Apportionment_BPT_TSD_1_31_13

.pdf>.

U.S. EPA. 2013b. Integrated Science Assessment of Ozone and Related Photochemical Oxidants 

(Final Report). Washington, DC: U.S. Environmental Protection Agency. (EPA document 

number EPA/600/R-10/076F). Available at:

<http://cfpub.epa.gov/ncea/isa/recordisplay.cfm?deid=247492#Download>. 
5-1

CHAPTER 5: ECONOMIC IMPACTS – MARKETS OUTSIDE THE 

ELECTRICITY SECTOR

5.1 Introduction

The energy sector impacts presented in Chapter 3 of this regulatory impact analysis 

(RIA) include projected changes in the prices for electricity, natural gas, and coal resulting from 

the proposed Electric Utility Generating Unit (EGU) Existing Source Greenhouse Gas (GHG) 

Guidelines. This chapter addresses the impact of these changes on other markets and discusses 

some of the determinants of the magnitude of these impacts. 

Under the proposed EGU Existing Source GHG Guidelines states are not required to use 

any of the measures that the EPA determines constitute BSER, or use those measures to the same 

degree of stringency that the 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, by way of meeting its state-specific goal. Given the 

flexibilities afforded states in complying with the emission guidelines, the benefits, cost and 

economic impacts reported in this RIA are illustrative of compliance actions states may take. The 

compliance approaches adopted by the states will ultimately drive the magnitude and timing of 

impacts on the prices of electricity, natural gas, and coal and in turn other markets that use these 

commodities in the production process. The flexibility afforded to states by the Clean Air Act 

also allows them to adopt programs which include design elements that may mitigate or promote 

particular impacts based on their priorities. For example, states in the Regional Greenhouse Gas 

Initiative use the revenues from allowance auctions to support direct bill assistance for retail 

consumers, fund investments in clean energy and electricity demand reduction for business 

consumers, and support employment in the development of clean and renewable energy

technologies. In its recent regulations to limit GHG emissions, California’s Air Resources Board 

designated a portion of allowances to be allocated to electric distribution companies for the 

purpose of mitigating electricity rate increases and their associated impacts. Other states may 

choose to comply with particularly robust deployment of renewables, energy efficiency, or 

natural gas in order to promote manufacturing demand or employment in those sectors. For 

example, energy efficiency investments may be targeted towards reducing both electricity 

consumption and natural gas or heating oil consumption, such as weatherization projects. The 
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composition of these programs will influence the effects of the state’s compliance with the 

proposed rulemaking.

To support the Transport Rule proposed in the summer of 2010, among other 

rulemakings, the EPA used the Economic Model for Policy Analysis (EMPAX) to estimate the 

effect of impacts projected by the Integrated Planning Model (IPM) on markets outside the 

electricity sector. EMPAX is a dynamic computable general equilibrium (CGE) model that 

forecasts a new equilibrium for the entire economy after a policy intervention. While the external 

Council on Clean Air Compliance Analysis (Council) peer review of this study stated that 

inclusion of benefits in an economy-wide model, specifically adapted for use in that study, 

“represent[ed] a significant step forward in benefit-cost analysis,” EPA recognizes that serious 

technical challenges remain when attempting to evaluate the benefits and costs of potential 

regulatory actions using economy-wide models. Consistent with the Council’s advice regarding 

the importance of including benefit-side effects demonstrated by the 1990 to 2020 Clean Air Act 

study, and the lack of available multi-year air quality projections needed to include these benefitside effects, EPA has not conducted CGE modeling for this proposal.

However, the EPA recognizes that serious technical challenges remain when attempting 

to evaluate the impacts of potential regulatory actions using economy-wide models. The EPA is 

therefore establishing a new Science Advisory Board (SAB) panel on economy-wide modeling 

to consider the technical merits and challenges of using this analytical tool to evaluate costs, 

benefits, and economic impacts in regulatory development. The EPA will use the 

recommendations and advice of this panel as an input into its process for improving benefit-cost 

and economic impact analyses that are used to inform decision-making at the agency. The panel 

will also be asked to identify potential paths forward for improvements that could address the 

challenges posed when economy-wide models are used to evaluate the effects of regulations.

The advice from the Science Advisory Board (SAB) panel formed specifically to address 

the subject of economy-wide modeling will not be available in time for this proposal. Given the 

ongoing SAB panel on economy-wide modeling, the uncertain nature of the ultimate energy 

price impacts due to the compliance flexibility for states, and the ongoing challenges of 

accurately representing costs, energy efficiency savings and economic benefits in economy-wide 

modeling, this chapter proceeds by presenting the energy impacts associated with the illustrative 
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scenarios analyzed in Chapter 3. This chapter then presents a qualitative discussion of the factors 

that will, in part, determine the timing and magnitude of effects in other markets.

5.2 Summary of Secondary Market Impacts of Energy Price Changes

Electricity, natural gas, and coal are important inputs to the production of other goods and 

services. Therefore, changes in the price of these commodities will shift the production costs for 

sectors that use electricity, natural gas, and coal in the production of other goods and services. 

Such changes in production costs may lead to changes in the quantities and/or prices of the goods 

or services produced and changes in imports and exports. We refer to these changes as secondary 

market impacts. 

The EPA used IPM to estimate electricity, natural gas, and coal price changes based on 

the illustrative compliance approaches modeled for this proposal. IPM is a multi-regional, 

dynamic, deterministic linear programming model of the U.S. electric power sector that is 

described in more detail in Chapter 3. The prices are average prices weighted by the amount 

used. Table 5-1 shows these estimated price changes. For other results generated by IPM, please 

refer to Chapter 3.

There are many factors influencing the projected natural gas prices. Firstly, as discussed 

in Chapter 3, there are key differences between the two regulatory options (both stringency and 

over time). Second, IPM (and its integrated gas resource and supply module) will develop 

natural gas reserves and bring to market appropriate natural gas supplies based on a multitude of 

factors. Since the model simulates perfect foresight, it anticipates future demand for natural gas 

and respond accordingly. In addition, IPM (and the natural gas module) are viewing a very long 

time horizon (through 2050, longer than the compliance timeframe), such that the impacts in 

certain years may be responsive to other modeling assumptions or drivers. The modeling 

framework is simultaneously solving for all of these key market and policy parameters (both 

electric and natural gas), resulting in the impacts shown. For more information on the modeling 

framework see http://www.epa.gov/airmarkt/progsregs/epa-ipm/BaseCasev513.html.
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Table 5-1. Estimated Percentage Changes in Average Energy Prices, by Energy Type and 

Regulatory Option

Option 1 Regional Compliance 2020 2025 2030

Electricity Price Change 5.9% 2.7% 2.7%

Delivered Natural Gas Price Change 9.3% -3.3% -0.9%

Delivered Coal Price Change -16.3% -18.3% -18.1%

Option 2 Regional Compliance 2020 2025 2030

Electricity Price Change 3.6% 2.4% n/a

Delivered Natural Gas Price Change 7.5% 0.2% n/a

Delivered Coal Price Change -13.8% -14.4% n/a

Option 1 State Compliance: 2020 2025 2030

Electricity Price Change 6.5% 2.9% 3.1%

Delivered Natural Gas Price Change 11.5% -3.5% 0.0%

Delivered Coal Price Change -16.5% -17.9% -18.2%

Option 2 State Compliance 2020 2025 2030

Electricity Price Change 4.0% 2.7% n/a

Delivered Natural Gas Price Change 8.1% 0.8% n/a

Delivered Coal Price Change -13.6% -14.1% n/a

For options and years when the price of electricity, natural gas, or coal increased, one 

would expect decreases in production and increases in market prices in sectors for which these 

commodities are inputs, ceteris paribus. Conversely, in options and years when prices of these 

inputs decreased, one would expect increases in production and decreases in market prices within 

these sectors. Smaller changes in input price changes are assumed to lead to smaller impacts 

within secondary markets. However, a number of factors in addition to the magnitude and sign of 

the energy price changes, influence the magnitude of the impact on production and market prices 

for sectors using electricity, natural gas, or coal as inputs to production. These factors are 

discussed below.

5.2.1 Share of Total Production Costs

The impact of energy price changes in a particular sector depends, in part, on the share of 

total production costs attributable to those commodities. For sectors in which the directly 

affected inputs are only a small portion of production costs, the impact will be smaller than for 

sectors in which these inputs make up a greater portion of total production costs. Therefore more 

energy-intensive sectors would potentially experience greater cost increases when electricity, 

natural gas, or coal prices increase, but would also experience greater savings when these input 
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prices decrease.123

5.2.2 Ability to Substitute between Inputs to the Production Process

The ease with which producers are able to substitute other inputs for electricity, natural 

gas, or coal, or even amongst those commodities, influences the impact of price changes for 

these inputs. Those sectors with a greater ability to substitute across energy inputs or to other 

inputs will be able to, at least partially, offset the increased cost of these inputs resulting in 

smaller market impacts. Similarly, when prices for electricity, natural gas, or coal decrease, some 

sectors may choose to use more of these inputs in place of other more costly substitutes.

5.2.3 Availability of Substitute Goods and Services

The ability of producers in sectors experiencing changes in their input prices to pass 

along the increased costs to their customers in the form of higher prices for their products 

depends, in part, on the availability of substitutes for the sectors’ products. Substitutes may either 

be other domestic products or foreign imports. If close substitutes exist, the demand for the 

product will in general be more elastic and the producers will be less able to pass on the added 

cost through a price increase.

Such substitution can also take place between foreign and domestic goods within the 

same sector. Changes in the price of electricity, natural gas, and coal can influence the quantities 

of goods imported or exported from sectors using these inputs. When the cost of domestic 

production increases due to more expensive inputs, imports may increase as consumers substitute 

towards relatively less costly foreign-produced goods. If imports increase as a result of a 

regulation and those imports come from countries with fewer emission controls, this can result in 

foreign emission increases that offset some portion of domestic decreases, an effect commonly 

referred to as “leakage.” The potential for leakage is noteworthy for global pollutants such as 

carbon dioxide (CO2) and other GHG emissions. Unlike most criteria pollutants and hazardous 

air pollutants, the impacts of CO2 emissions are not affected by the location from which those 

 

123 The net direct effect of this rulemaking on the production costs of a sector that is attributable to a change in the 

electricity price also depends on the expenditures the sector makes to reduce its demand for electricity under any 

energy efficiency program that was adopted to achieve a state goal. That said, those expenditures may lead to 

other reduced expenditures for the sector, such as reduced natural gas use from weatherization projects.
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emissions originate. EPA does not anticipate significant leakage in the EGU sector to occur from 

this regulation because the nature of electricity transmission does not lend itself to significant 

imports or exports of electricity.

5.3 Conclusions

Changes in the price of electricity, natural gas, and coal can impact markets for goods 

and services produced by sectors that use these energy inputs in the production process. The 

direction and magnitude of these impacts is influenced by a number of factors. Changes in cost 

of production may lead to changes in price, quantity produced, and profitability of firms within 

secondary markets. If regulation results in changes in domestic markets that lead to an increase 

in imports, increases in production in countries with more energy-intensive production may lead 

to emissions leakage. 
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CHAPTER 6: EMPLOYMENT IMPACT ANALYSIS 

6.1 Introduction 

Executive Order 13563 directs federal agencies to consider regulatory impacts on job 

creation and employment. According to the Executive Order, “our regulatory system must 

protect public health, welfare, safety, and our environment while promoting economic growth, 

innovation, competitiveness, and job creation. It must be based on the best available science” 

(Executive Order 13563, 2011). Although standard benefit-cost analyses have not typically 

included a separate analysis of regulation-induced employment impacts,124 during periods of 

sustained high unemployment, employment impacts are of particular concern and questions may 

arise about their existence and magnitude. The chapter discusses and projects potential 

employment impacts of the Proposed Electric Generating Unit Greenhouse Gas (EGU GHG) 

Existing Source Guidelines for the electric power industry, coal and natural gas production, and 

demand-side energy efficiency.

Section 6.2 presents the overview of the guidelines and a general description of the 

associated building block framework. Section 6.3 describes the theoretical framework used to 

analyze regulation-induced employment impacts, discussing how economic theory alone cannot 

predict whether such impacts are positive or negative. Section 6.4 presents an overview of the 

peer-reviewed literature relevant to evaluating the effect of environmental regulation on 

employment. Section 6.5 provides background regarding recent employment trends in the 

electricity generation, coal and natural gas extraction, renewable energy, and demand-side 

energy efficiency-related sectors. Section 6.6 presents the EPA’s quantitative projections of 

potential employment impacts in these sectors. These projections are based in part on a detailed 

model of the electricity production sectors used for this regulatory analysis. Additionally, this 

section discusses projected employment impacts due to demand-side energy efficiency activities. 

Section 6.7 offers several conclusions.

 

124 Labor expenses do, however, contribute toward total costs in the EPA’s standard benefit-cost analyses.
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6.2 Overview of the Proposed EGU GHG Existing Source Guidelines

The EPA is proposing emission guidelines for states to use in developing plans to address 

greenhouse gas emissions from existing fossil fuel-fired EGUs. Specifically, EPA is proposing 

state-specific rate-based goals for carbon dioxide (CO2) emissions from the power sector, as well 

as emission guidelines for states to use in developing plans to attain the state-specific goals. The 

guidelines, as proposed, will lower carbon intensity of power generation in the United States. 

Under the Clean Air Act (CAA) section 111(d), state plans must establish standards of 

performance that reflect the degree of emission limitation achievable through the application of 

the “best system of emission reduction” (BSER) that, taking into account the cost of achieving 

such reductions and non-air quality health and environmental impact and energy requirements, 

the Administrator determines has been adequately demonstrated. Consistent with CAA section 

111(d), this proposed rule contains state-specific goals that reflect the EPA’s calculation of 

emission reductions that a state can achieve through the cost-effective application of BSER. The 

EPA is using four building blocks as a basis to determine state-specific goals.

6.2.1 Determining State Goals Utilizing the Four Building Blocks

In proposing state goals, EPA is applying the following four building blocks. Each 

represents a demonstrated approach to improving the GHG performance of existing EGUs in the 

power sector:125

1. Reducing the carbon intensity of generation at individual affected EGUs through heat rate 

improvements.

2. Reducing emissions from the most carbon-intensive affected EGUs in the amount that 

results from substituting generation at those EGUs with generation from less carbonintensive affected EGUs (including natural gas combined cycle [NGCC] units under 

construction).

 

125 Refer to Chapter 3 for information regarding estimates of emissions reductions achievable by each building block 

and the determination of state-specific goals.
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3. Reducing emissions from affected EGUs in the amount that results from substituting 

generation at those EGUs with expanded low- or zero-carbon generation.

4. Reducing emissions from affected EGUs in the amount that results from the use of 

demand-side energy efficiency that reduces the amount of generation required.

6.3 Economic Theory and Employment

Regulatory employment impacts are difficult to disentangle from other economic changes 

affecting employment decisions over time and across regions and industries. Labor market 

responses to regulation are complex. They depend on labor demand and supply elasticities and 

possible labor market imperfections (e.g., wage stickiness, long-term unemployment, etc). The 

unit of measurement (e.g., number of jobs, types of job hours worked, and earnings) may affect 

observability of that response. Net employment impacts are composed of a mix of potential 

declines and gains in different areas of the economy (e.g., the directly regulated sector, upstream 

and downstream sectors, etc.) over time. In light of these difficulties, economic theory provides a 

constructive framework for analysis.

Microeconomic theory describes how firms adjust their use of inputs in response to 

changes in economic conditions.126 Labor is one of many inputs to production, along with 

capital, energy, and materials. In competitive markets, firms choose inputs and outputs to 

maximize profit as a function of market prices and technological constraints.127,128

Berman and Bui (2001) and Morgenstern, Pizer, and Shih (2002) adapt this model to 

analyze how environmental regulations affect labor demand.129 They model environmental 

regulation as effectively requiring certain factors of production, such as pollution abatement 

capital, at levels that firms would not otherwise choose.

 

126 See Layard and Walters (1978), a standard microeconomic theory textbook, for a discussion, in Chapter 9. 

127 See Hamermesh (1993), Ch. 2, for a derivation of the firm’s labor demand function from cost-minimization. 

128 In this framework, labor demand is a function of quantity of output and prices (of both outputs and inputs). 

129 Berman and Bui (2001) and Morgenstern, Pizer, and Shih (2002) use a cost-minimization framework, which is a 

special case of profit-maximization with fixed output quantities.
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Berman and Bui (2001, pp. 274-75) model two components that drive changes in firmlevel labor demand: output effects and substitution effects.130 Regulation affects the profitmaximizing quantity of output by changing the marginal cost of production. If regulation causes 

marginal cost to increase, it will place upward pressure on output prices, leading to a decrease in 

demand, and resulting in a decrease in production. The output effect describes how, holding 

labor intensity constant, a decrease in production causes a decrease in labor demand. As noted by 

Berman and Bui, although many assume that regulation increases marginal cost, it need not be 

the case. A regulation could induce a firm to upgrade to less polluting and more efficient 

equipment that lowers marginal production costs. In such a case, output could increase. For 

example, in the context of the current rule, improving the heat rate of utility boiler increases fuel 

efficiency, lowering marginal production costs, and thereby potentially increasing the boiler’s 

generation. An unregulated profit-maximizing firm may not have chosen to install such an 

efficiency-improving technology if the investment cost were too high.

The substitution effect describes how, holding output constant, regulation affects laborintensity of production. Although increased environmental regulation may increase use of 

pollution control equipment and energy to operate that equipment, the impact on labor demand is 

ambiguous. For example, equipment inspection requirements, specialized waste handling, or 

pollution technologies that alter the production process may affect the number of workers 

necessary to produce a unit of output. Berman and Bui (2001) model the substitution effect as the 

effect of regulation on pollution control equipment and expenditures required by the regulation 

and the corresponding change in labor-intensity of production. 

In summary, as output and substitution effects may be positive or negative, theory alone 

cannot predict the direction of the net effect of regulation on labor demand at the level of the 

regulated firm. Operating within the bounds of standard economic theory, however, empirical 

estimation of net employment effects on regulated firms is possible when data and methods of 

sufficient detail and quality are available. The literature, however, illustrates difficulties with 

 

130 The authors also discuss a third component, the impact of regulation on factor prices, but conclude that this effect 

is unlikely to be important for large competitive factor markets, such as labor and capital. Morgenstern, Pizer and 

Shih (2002) use a very similar model, but they break the employment effect into three parts: 1) a demand effect; 

2) a cost effect; and 3) a factor-shift effect.
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empirical estimation. For example, studies sometimes rely on confidential plant-level 

employment data from the U.S. Census Bureau, possibly combined with pollution abatement 

expenditure data that are too dated to be reliably informative. In addition, the most commonly 

used empirical methods do not permit estimation of net effects.

The conceptual framework described thus far focused on regulatory effects on plant-level 

decisions within a regulated industry. Employment impacts at an individual plant do not 

necessarily represent impacts for the sector as a whole. The approach must be modified when 

applied at the industry level.

At the industry-level, labor demand is more responsive if: (1) the price elasticity of 

demand for the product is high, (2) other factors of production can be easily substituted for labor, 

(3) the supply of other factors is highly elastic, or (4) labor costs are a large share of total 

production costs.131 For example, if all firms in an industry are faced with the same regulatory 

compliance costs and product demand is inelastic, then industry output may not change much, 

and output of individual firms may change slightly.132 In this case, the output effect may be 

small, while the substitution effect depends on input substitutability. Suppose, for example, that 

new equipment for heat rate improvements requires labor to install and operate. In this case, the 

substitution effect may be positive, and with a small output effect, the total effect may be 

positive. As with potential effects for an individual firm, theory cannot determine the sign or 

magnitude of industry-level regulatory effects on labor demand. Determining these signs and 

magnitudes requires additional sector-specific empirical study. For environmental rules, much of 

the data needed for these empirical studies is not publicly available, would require significant 

time and resources in order to access confidential U.S. Census data for research, and also would 

not be necessary for other components of a typical Regulatory Impact Analysis (RIA). 

In addition to changes to labor demand in the regulated industry, net employment impacts 

encompass changes in other related sectors. For example, the proposed guidelines may increase 

demand for heat rate improving equipment and services. This increased demand may increase 

 

131 See Ehrenberg & Smith, p. 108. 

132 This discussion draws from Berman and Bui (2001), pp. 293. 
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revenue and employment in the firms supporting this technology. At the same time, the regulated 

industry is purchasing the equipment, and these costs may impact labor demand at regulated 

firms. Therefore, it is important to consider the net effect of compliance actions on employment 

across multiple sectors or industries.

If the U.S. economy is at full employment, even a large-scale environmental regulation is 

unlikely to have a noticeable impact on aggregate net employment.133 Instead, labor 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).134

Affected sectors may experience transitory effects as workers change jobs. Some workers 

may retrain or relocate in anticipation of new requirements or require time to search for new 

jobs, while shortages in some sectors or regions could bid up wages to attract workers. These 

adjustment costs can lead to local labor disruptions. Although the net change in the national 

workforce is expected to be small, localized reductions in employment may adversely impact 

individuals and communities just as localized increases may have positive impacts.

If the economy is operating at less than full employment, economic theory does not 

clearly indicate the direction or magnitude of the net impact of environmental regulation on 

employment; it could cause either a short-run net increase or short-run net decrease 

(Schmalansee and Stavins, 2011). An important research question is how to accommodate 

unemployment as a structural feature in economic models. This feature may be important in 

assessing large-scale regulatory impacts on employment (Smith, 2012).

Environmental regulation may also affect labor supply. In particular, pollution and other 

environmental risks may impact labor productivity or employees’ ability to work.135 While the 

 

133 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. 

134 Arrow et al. 1996; see discussion on bottom of p. 8. In practice, distributional impacts on individual workers can 

be important, as discussed in later paragraphs of this section.

135 E.g. Graff Zivin and Neidell (2012).
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theoretical framework for analyzing labor supply effects is analogous to that for labor demand, it 

is more difficult to study empirically. There is a small emerging literature described in the next 

section that uses detailed labor and environmental data to assess these impacts.

To summarize, economic theory provides a framework for analyzing the impacts of 

environmental regulation on employment. The net employment effect incorporates expected 

employment changes (both positive and negative) in the regulated sector and elsewhere. Labor 

demand impacts for regulated firms, and also for the regulated industry, can be decomposed into 

output and substitution effects which may be either negative or positive. Estimation of net 

employment effects for regulated sectors is possible when data of sufficient detail and quality are 

available. Finally, economic theory suggests that labor supply effects are also possible. In the 

next section, we discuss the empirical literature.

6.4 Current State of Knowledge Based on the Peer-Reviewed Literature

The labor economics literature contains an extensive body of peer-reviewed empirical 

work analyzing various aspects of labor demand, relying on the theoretical framework discussed 

in the preceding section.136 This work focuses primarily on effects of employment policies such 

as labor taxes and minimum wages.137 In contrast, the peer-reviewed empirical literature 

specifically estimating employment effects of environmental regulations is more limited. 

Empirical studies, such as Berman and Bui (2001), suggest that net employment impacts 

were not statistically different from zero in the regulated sector. Other research suggests that 

more highly regulated counties may generate fewer jobs than less regulated ones (Greenstone,

2002). Environmental regulations may affect sectors that support pollution reduction earlier than 

the regulated industry. Rules are usually announced well in advance of their effective dates and 

then typically provide a period of time for firms to invest in technologies and process changes to 

meet the new requirements. When a regulation is promulgated, the initial response of firms is 

often to order pollution control equipment and services to enable compliance when the regulation 

 

136 Again, see Hamermesh (1993) for a detailed treatment. 

137 See Ehrenberg & Smith (2000), Chapter 4: “Employment Effects: Empirical Estimates” for a concise overview. 
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becomes effective. Estimates of short-term increases in demand for specialized labor within the 

environmental protection sector have been prepared for several EPA regulations in the past, 

including the Mercury and Air Toxics Standards (MATS).138 Overall, the peer-reviewed 

literature does not contain evidence that environmental regulation has a large impact on net 

employment (either negative or positive) in the long run across the whole economy. 

6.4.1 Regulated Sector 

Berman and Bui (2001) examine how an increase in local air quality regulation affects 

manufacturing employment in the South Coast Air Quality Management District (SCAQMD), 

which includes Los Angeles and its suburbs. From 1979 to 1992, the SCAQMD enacted some of 

the country’s most stringent air quality regulations. Using SCAQMD’s local air quality 

regulations, Berman and Bui identify the effect of environmental regulations on net employment 

in regulated manufacturing industries relative to other plants in the same 4-digit standard 

industrial classification (SIC) industries but in regions not subject to local regulations.139 The 

authors find that “while regulations do impose large costs, they have a limited effect on 

employment” (Berman and Bui, 2001, p. 269). Their conclusion is that local air quality 

regulation “probably increased labor demand slightly” but that “the employment effects of both 

compliance and increased stringency are fairly precisely estimated zeros, even when exit and 

dissuaded entry effects are included” (Berman and Bui, 2001, p. 269).140

The few studies in peer-reviewed journals evaluating employment impacts of policies 

that reduce CO2 emissions in the electric power generation sector are in the European context. In 

a sample of 419 German firms, 13 percent of which were in the electricity sector, Anger and 

Oberndorfer (2008) find that the initial allocation of emission permits did not significantly affect 

employment growth in the first year of the Eurpoean Union (EU) Emissions Trading Scheme 

(ETS). Examining European firms from 1996-2007, Commins et al. (2011) find that a 1 percent 

 

138 U.S. EPA (2011b).

139 Berman and Bui include over 40 4-digit SIC industries in their sample. They do not estimate the number of jobs 

created in the environmental protection sector.

140 Including the employment effect of existing plants and plants dissuaded from opening will increase the estimated 

impact of regulation on employment.
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increase in energy taxes is associated with a 0.01 percent decrease in employees in the electricity 

and gas sector. Chan et al. (2013) estimate the impact of the EU ETS on a panel of almost 6,000 

firms in 10 European countries from 2005-2009. They find that firms in the power sector that 

participated in the ETS had 2-3 percent fewer employees relative to those that did not participate, 

but this effect is not statistically significant.

This literature suggests that the employment impacts of controlling CO2 emissions in the 

European power sector were small. The degree to which these studies’ results apply to the U.S. 

context is unclear. European policies analyzed in these studies effectively put a price on 

emissions either through taxes or tradable permits. A performance standard may not generate 

similar employment effects. Moreover, European firms face relative fuel prices and market 

regulatory structures different from their U.S. counterparts, further complicating attempts to 

transfer quantitative results from the EU experience to evaluate this rule.

A small literature examines impacts of environmental regulations on manufacturing 

employment. Kahn and Mansur (2013) study environmental regulatory impacts on geographic 

distribution of manufacturing employment, controlling for electricity prices and labor regulation 

(right to work laws). Their methodology identifies employment impacts by focusing on 

neighboring counties with different air quality regulations. They find limited evidence that 

environmental regulations may cause employment to be lower within “county-border-pairs.” 

This result suggests that regulation may cause an effective relocation of labor across a county

border, but since one county’s loss is another’s gain, such shifts cannot be transformed into an 

estimate of a national net effect on employment. Moreover this result is sensitive to model 

specification choices.

6.4.2 Labor Supply Impacts

The empirical literature on environmental regulatory employment impacts focuses 

primarily on labor demand. However, there is a nascent literature focusing on regulation-induced 

effects on labor supply.141 Although this literature is limited by empirical challenges, researchers 

 

141 For a recent review see Graff-Zivin and Neidell (2013).
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have found that air quality improvements lead to reductions in lost work days (e.g., Ostro, 1987). 

Limited evidence suggests worker productivity may also improve when pollution is reduced. 

Graff Zivin and Neidell (2012) used detailed worker-level productivity data from 2009 and 2010, 

paired with local ozone air quality monitoring data for one large California farm growing 

multiple crops, with a piece-rate payment structure. Their quasi-experimental structure identifies 

an effect of daily variation in monitored ozone levels on productivity. They find “ozone levels 

well below federal air quality standards have a significant impact on productivity: a 10 parts per 

billion (ppb) decreases in ozone concentrations increases worker productivity by 5.5 percent.” 

(Graff Zivin and Neidell, 2012, p. 3654).142

This section has outlined the challenges associated with estimating regulatory effects on 

both labor demand and supply for specific sectors. These challenges make it difficult to estimate 

net national employment estimates that would appropriately capture the way in which costs, 

compliance spending, and environmental benefits propagate through the macro-economy. 

Quantitative estimates are further complicated by the fact that macroeconomic models often have 

little sectoral detail and usually assume that the economy is at full employment. The EPA is 

currently seeking input from an independent expert panel on modeling economy-wide regulatory 

impacts, including employment effects.143

6.5 Recent Employment Trends 

The U.S. electricity system includes employees that support electric power generation, 

transmission and distribution; the extraction of fossil fuels; renewable energy generation; and 

supply-side and demand-side energy efficiency. This section describes recent employment trends 

in the electricity system. 

 

142 The EPA is not quantifying productivity impacts of reduced pollution in this rulemaking using this study. In light 

of this recent research, however, the EPA is considering how best to incorporate possible productivity effects in 

the future.

143 For further information see: <https://www.federalregister.gov/articles/2014/02/05/2014-02471/draft-supportingmaterials-for-the-science-advisory-board-panel-on-the-role-of-economy-wide-modeling>.
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6.5.1 Electric Power Generation

In 2013, the electric power generation, transmission and distribution sector (NAICS 

2211) employed 394,000 workers in the U.S.144 Installation, maintenance, and repair occupations 

accounted for the largest share of workers (30 percent).145 These categories include inspection, 

testing, repairing and maintaining of electrical equipment and/or installation and repair of cables 

used in electrical power and distribution systems. Other major occupation categories include 

office and administrative support (17 percent), production occupations (15 percent), architecture 

and engineering (11 percent), business and financial operations (7 percent) and management (6 

percent). 

As shown in Figure 6.1, employment in the Electric Power Industry averaged 435,000 

workers in the early 2000s, declining to an average of 400,000 workers later in the decade, and to 

394,000 workers in 2013.

 

144 U.S. Bureau of Labor Statistics. “Current Employment Survey Seasonally Adjusted Employment for Electric 

Power Generation, Transmission, and Distribution (national employment).” Series ID: CES4422110001. Data 

extracted on: February 19, 2014. Available at: <http://www.bls.gov/ces/data.htm>.

145 U.S. Bureau of Labor Statistics, Occupational Employment Statistics, May 2012 National Industry-Specific 

Occupational Employment and Wage Estimates, Electric Power Generation, Transmission, and Distribution 

(NAICS 2211). Available at: <http://www.bls.gov/oes/current/naics4_221100.htm>. 
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Figure 6.1. Electric Power Industry Employment

6.5.2 Fossil Fuel Extraction

6.5.2.1 Coal Extraction

The coal extraction sector is primarily engaged in coal mining and coal mine site 

development, excluding metal ore mining and nonmetallic mineral mining and quarrying. There 

are two sources of U.S. government data on coal mining employment, the Bureau of Labor 

Statistics (BLS) Current Employment Statistics (NAICS 2121), and the Department of Labor’s 

Mine Safety and Health Administration (MSHA).146 Both sources show similar national levels 

and trends, though one is survey-based (BLS) and the other is census-based (MSHA). MSHA 

tracks direct coal mine employment and independent contractor employment, whereas BLS does 

 

146 U.S. Bureau of Labor Statistics. “Current Employment Statistics Seasonally Adjusted Employment for Coal 

Mining (national employment),” NAICS 2121, Series ID: CES1021210001. Data extracted on: February 19, 2014. 

Available at: < http://www.bls.gov/ces/data.htm>.

350 400 450 500

2000 2005 2010 2015

Year

Electric Power Industry national employment (in thousands)
6-13

not track contractors. Contractor employment reported by MSHA focuses primarily on mine 

development, construction, reconstruction or demolition of mine facilities, construction of dams, 

excavation or earth moving, equipment installation, service or repair, and material handling, 

drilling, or blasting.147 In 2013, BLS reported 79,000 coal mining employees, and MSHA 

reported 80,000 coal mining employees and 32,000 contractors.148 Both sets of data reveal a 

stable trend in employment over the past 10 years, with the exception of a small temporary 

increase in 2011. See Figure 6.2 below.

 

147 Mine Safety and Health Administration, CFR Part 50 Title 30 Employment Data – selected contract employment 

is included, p. 2. Available at: 

<http://www.msha.gov/Stats/Part50/WQ/MasterFiles/MIWQ%20Master_20133.pdf>. U.S. Bureau of Labor 

Statistics, Current Employment Statistics – contract employment not included. Available at: 

<http://www.bls.gov/ces/idcf/forme_sp.pdf>.

148 Annual averages calculated for: (i) BLS monthly coal mining employment data for 2013, and (ii) MSHA Part 50 

quarterly data for 2013. Available at: <http://www.msha.gov/STATS/PART50/P50Y2K/AETABLE.HTM>.
6-14

Figure 6.2. Coal Production Employment

6.5.2.2 Oil and Gas Extraction

In 2013, there were 198,000 employees in the oil and gas extraction sector (NAICS 

211).149 This sector includes production of crude petroleum, oil from oil shale and oil sands, 

production of natural gas, sulfur recovery from natural gas, and recovery of hydrocarbon liquids. 

Activities include the development of gas and oil fields, exploration activities for crude 

petroleum and natural gas, drilling, completing, and equipping wells, and other production 

activities.150 In contrast with coal, and looking at Figure 6.3, there has been a sharp increase in 

employment in this sector over the past decade.

 

149 BLS, Current Employment Statistics. Seasonally adjusted employment for oil and gas extraction (national 

employment), NAICS 211. Series ID: CES1021100001. Data extracted on: February 19, 2014. Available at: 

<http://www.bls.gov/ces/data.htm

150 U.S. Bureau of Labor Statistics. 20014. Available at: <http://www.bls.gov/iag/tgs/iag211.htm Accessed Feb. 19>.

20 40 60 80

100

2000 2005 2010 2015

Year

BLS - employees MSHA - employees

MSHA - contractors

Coal mining employment - national (in thousands)
6-15

Figure 6.3. Oil and Gas Production Employment

6.5.3 Clean Energy Employment Trends

Clean energy resources, such as energy efficiency and renewable energy, are used to 

meet energy demand, reduce peak electricity system loads, and reduce reliance on the most 

carbon-intensive sources of electricity. However, there is not a single clean energy sector in 

standard national accounts classifications. Renewable generation is not reported to the BLS

separately from other electric power generation. Similarly, manufacturers of energy efficient 

appliances are not reported separately from conventional appliance manufacturers and green 

building design is not separate from the construction sector. Instead, clean energy technology 

and services are supported by industries throughout the economy. 

Without a specific industrial classification, it is difficult to quantify the exact number of 

clean energy-related jobs or document the trends. Employees engaged in clean energy can span 

many job classifications, such as experts required to design and produce a renewable or energyefficient technology, workers that supply inputs and technicians who install service or operate 

100 150 200 250

2000 2005 2010 2015

Year

Oil and Gas Extraction national employment (in thousands)
6-16

equipment. As such, there are a variety of definitions of clean or green jobs used, some more 

expansive than others. 

6.5.3.1 Defining Clean Energy Jobs

Two U.S. Government sources, the 2010 Department of Commerce (DOC) report, 

Measuring the Green Economy and the 2010 and 2011 BLS Green Goods and Services surveys 

have subdivided industrial classifications into “green” categories. In both cases the approach was 

to determine which product classifications, rather than industries, were green. They multiplied 

green production by product revenue and defined an industrial sector as green if it met a 

threshold of green revenue as a proportion of total revenue.

DOC broadly defined green jobs in 2010 as those “created and supported in businesses 

that produce green products and services.”151 They further classified green jobs into a broad and 

a narrow category. The narrow category includes only products deemed to be green without 

disagreement, while the broad category is more inclusive definition of green goods and services 

to over 22,000 product codes in the 2007 Economic Census to estimate their contribution to the 

U.S. economy. The report found that the number of green jobs in 2007 ranged from 1.8 million 

to 2.4 million jobs, accounting for between 1.5 and 2 percent of total private sector 

employment.152

BLS used an expansive definition of clean or green jobs in 2010 and 2011. It goes 

beyond direct clean energy-related investments and includes “those in businesses that produce 

goods and provide services that benefit the environment or conserve natural resources. These 

goods and services, which are sold to customers, include research and development, installation, 

and maintenance services for renewable energy and energy efficiency and education and training 

related to green technologies and practices” but also include recycling and natural resource 

 

151 U.S. Department of Commerce Economics and Statistics Administration. 2010. “Measuring the Green 

Economy,” April. Available at: 

<http://www.esa.doc.gov/sites/default/files/reports/documents/greeneconomyreport_0.pdf>. 

152 U.S. Department of Commerce Economics and Statistics Administration. 2010. “Measuring the Green Economy, 

April. Available at: <http://www.esa.doc.gov/sites/default/files/reports/documents/greeneconomyreport_0.pdf>.
6-17

conservation, such as forestry management.153 Based on surveys across the 325 industries it 

identified as potential producers of green goods and services, BLS counts approximately 2.3 

million jobs in the green economy in 2010, rising 7.4 percent to 2.5 million in 2011,154 compared 

to increases of about one percent across all occupations in the entire economy over the same 

period.155 The table below, Table 6-1, presents BLS green job estimates nationally and for the 

utility sector. 

Table 6-1. U. S. Green Goods and Services (GGS) Employment (annual average)

Total GGS 

Employment

Utility GGS 

Employment

Total GGS Growth 

2010-11

Utility GGS Growth 

2010-11

2010 2,342,562 69,031 NA NA

2011 2,515,200 71,129 7.4% 3.0%

Source: Bureau of Labor Statistics

6.5.3.2 Renewable Electricity Generation Employment Trends

The DOC report does not separate renewable energy data and the BLS data include only 

privately owned electricity generating facilities. As such, neither source isolates renewable 

electricity generation employment. For historical trends in this sector, we therefore, rely on a 

Brookings Institution study, Muro et al. (2011). This study built a national database of “clean 

economy” jobs from the bottom up, verifying each company individually.156 They include a list 

of categories similar but not identical to that of BLS, including agricultural and natural resources 

conservation, education and compliance, energy and resource efficiency, greenhouse gas 

reduction, environmental management and recycling, and renewable energy. This study found 

about 138,000 jobs in the renewable energy sector in 2010, with an overall average annual 

growth rate of 3.1 percent from 2003-2010. Table 6-2 details the national results by energy 

 

153 BLS has identified 325 detailed industries (6-digit NAICS) as potential producers of green goods and services. 

Available at: <http://www.bls.gov/ggs/ggsoverview.htm>. (Accessed on 1-14-14, last modified date: March 19, 

2013.

154 U.S. Department of Labor, U.S. Bureau of Labor Statistics. (n.d.). 2011. “Green Goods and Services 2010-2011.” 

(Retrieved on January 14, 2014). Available at :< http://www.bls.gov/ggs/ggsoverview.htm>.

155U.S. Department of Labor, U.S. Bureau of Labor Statistics. 2010. National Occupational Employment and Wage 

Estimates, United States. Available at: <http://www.bls.gov/oes/2010/may/oes_nat.htm>, May. National 

Occupational Employment and Wage Estimates, United States http://www.bls.gov/oes/2010/may/oes_nat.htm. 

156 <http://www.brookings.edu/~/media/Series/resources/0713_clean_economy.pdf> p. 15.
6-18

source.

Table 6-2. Renewable Electricity Generation-Related Employment

Sector Jobs, 2010 2003-2010 Average Annual Growth Rate (%)

Biofuels/Biomass 20,680 8.9

Geothermal 2,720 6.7

Hydropower 55,467 -3.6

Renewable Energy Services 1,981 6.3

Solar Photovoltaic 24,152 10.7

Solar Thermal 5,379 18.4

Waste-to-Energy 3,320 3.7

Wave/Ocean Power 371 20.9

Wind 24,294 14.9

Total 138,364 3.1

Source: http://www.brookings.edu/~/media/Series/resources/0713_clean_economy.pdf, Appendix A.

6.5.3.3 Employment Trends in Demand-Side Energy Efficiency Activities

U.S. government data used for calculating the historical trends in the demand-side energy 

efficiency sector come from the BLS green goods and services surveys. BLS reports an energy 

efficiency category, finding 1.49 million private sector energy efficiency jobs in 2010 and 1.64 

million in 2011.

For a longer term trend the Brookings Institution study (Muro et al., 2011) built a 

national database of “clean economy” jobs from the bottom up, verifying each company 

individually.157 This study found about 428,000 jobs in the Energy and Resource Efficiency 

sector in 2010, with an overall average annual growth rate of 2.6 percent from 2003-2010. Table 

6-3 details the results by energy sector. 

 

157 <http://www.brookings.edu/~/media/Series/resources/0713_clean_economy.pdf> p. 15
6-19

Table 6-3. Energy and Resources Efficiency-Related Employment

Sector Jobs, 2010 2003-2010 Average Annual 

Growth Rate (%)

Appliances 36,608 -3.1

Energy-saving Building Materials 161,896 2.5

Energy-saving Consumer Products 19,210 -2.9

Green Architecture and Construction Services 56,190 6.4

HVAC and Building Control Systems 73,600 3.3

Lighting 14,298 -1.8

Professional Energy Services 49,863 6.9

Smart Grid 15,987 8.6

Total 427,652 2.6

 Source: http://www.brookings.edu/~/media/Series/resources/0713_clean_economy.pdf, Appendix A

In addition, other research institutes and industry groups have clean economy or clean 

energy employment databases. While definitions and timeframes vary, all show positive 

employment trends of 1.9 percent or more growth in clean energy-related jobs annually. 

6.6 Projected Sectoral Employment Changes due to Proposed Guidelines

EGUs may respond to these proposed guidelines by placing new orders for efficiencyrelated or renewable energy equipment and services to reduce GHG emissions. Installing and 

operating new equipment or improving heat rate efficiency could increase labor demand in the 

electricity generating sector itself, as well as associated equipment and services sectors. 

Specifically, the direct employment effects of supply-side initiatives include changes in labor 

demand for manufacturing, installing, and operating higher efficiency or renewable energy 

electricity generating assets supported by the initiative while reducing the demand for labor that 

would have been used by less efficient or higher emitting generating assets. Once implemented, 

increases in operating efficiency would impact the power sector’s demand for fuel and plans for 

EGU retirement and new construction.

In addition, EPA expects state compliance plans to also include demand-side energy 

efficiency policies and programs that typically change energy consumption patterns of business 

and residential consumers by reducing the quantity of energy required for a given level of 

production or service. Demand-side initiatives generally aim to increase the use of cost-effective 

energy efficiency technologies (e.g., including more efficient appliances and air conditioning 

systems, more efficient lighting devices, more efficient design and construction of new homes 

and businesses), and advance efficiency improvements in motor systems and other industrial 
6-20

processes. Demand-side initiatives can also directly reduce energy consumption, such as through 

programs encouraging changing the thermostat during the hours a building is unoccupied or 

motion-detecting room light switches. Such demand-side energy efficiency initiatives directly 

affect employment by encouraging firms and consumers to shift to more efficient products and 

processes than would otherwise be the case. Employment in the sectors that provide these more 

efficient devices and services would be expected to increase, while employment in the sectors 

that produce less efficient devices would be expected to contract.

This analysis uses the cost projections from the engineering-based Integrated Planning 

Model (IPM) to project labor demand impacts of the proposed guidelines for the electricity 

generation sector (fossil, renewable, and nuclear), and the fuel production sector (coal and 

natural gas). These projections include effects attributable to heat rate improvements, 

construction of new EGUs, changes in fuel use, and reductions in electricity generation due to 

demand-side energy efficiency activities. To project labor requirements for demand-side energy 

efficiency activities, the analysis uses a different approach that combines data on historic 

changes in employment and expenditures in the energy efficiency sector with projected changes 

in expenditures in the sector arising from state implementation of the proposed guidelines.

We project labor impacts for two options for establishing the “best system of emission 

reduction” (BSER) for GHG emissions from existing EGUs. The EPA is proposing a BSER goal 

approach referred to as Option 1 and taking comment on a second approach referred to as Option 

2. Each of these goal approaches use the four building blocks described above at different levels 

of stringency. Option 1 involves higher deployment of the four building blocks but allows a 

longer timeframe to comply (2030) whereas Option 2 has a lower deployment over a shorter 

timeframe (2025). This analysis estimates labor impacts of illustrative state and regional 

compliance approaches for the goals set for Options 1 and 2. With the state compliance 

approach, states are assumed to comply with the guidelines by implementing measures solely 

within the state and emissions rate averaging occurs between affected sources on an intrastate 

basis only. In contrast under the regional approach, groups of states are assumed to 

collaboratively comply with the guidelines.
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6.6.1 Projected Changes in Employment in Electricity Generation and Fossil Fuel Extraction 

The analytical approach used in this analysis is a bottom-up engineering method 

combining EPA’s cost analysis of the proposed guidelines with data on labor productivity, 

engineering estimates of the amount and types of labor needed to manufacture, construct, and 

operate different types of generating units, and prevailing wage rates for skilled and general 

labor categories. This approach is different from the types of economic analyses discussed in 

section 6.3. Rather than projecting employment impacts throughout the U.S. economy, the 

engineering-based analysis focuses on the direct impact on labor demand in industries closely 

involved with electricity generation. The engineering approach projects labor changes measured 

as the change in each analysis year in job-years158 employed in the power generation and directly 

related sectors (e.g., equipment manufacturing, fuel supply and generating efficiency services). 

For example, this approach projects the amounts and types of labor required to implement 

improvements in generating efficiency. It then uses the EPA’s estimated effect of efficiency 

improvements on fuel demand to project reductions in the amount of labor required to produce 

coal and gas.

This analysis relies on projections and costing analysis from the IPM, which uses 

industry-specific data and assumptions to estimate costs and energy impacts of the proposed 

guidelines (see Chapter 3). The EPA uses the IPM to predict coal generating capacity that is 

likely to undertake improvements in heat rate efficiency (HRI).159 IPM also predicts the 

guidelines’ impacts on fuel use, retirement of existing units, and construction of new ones.

The methods we use to estimate the labor impacts are based on the analytical methods 

used in the Regulatory Impact Analysis for the Mercury and Air Toxics Standards (MATS). 

While the methods used in this analysis to estimate the recurring labor impacts (e.g., labor 

associated with operating and maintaining generating units, as well as labor needed to mine coal 

 

158 Job-years are not individual jobs, but rather the amount of work performed by the equivalent of one full-time 

individual for one year. For example, 20 job-years in 2020 may represent 20 full-time jobs or 40 half-time jobs in 

that year.

159 Heat rate improvements (HRI) could include a range of activities in the power plant to lower the heat rate 

required to generate a net electrical output. Assuming all other things being equal, a lower heat rate is more 

efficient because more electricity is generated from each ton of coal.
6-22

and natural gas) are the same as we used in MATS (with updated data where available), the 

methods used to estimate the labor associated with installing new capacity and implementing 

heat rate improvements were developed for the GHG guidelines analysis.

The bottom-up engineering-based labor analysis in the MATS RIA primarily was 

concerned with the labor needs of retrofitting pollution control equipment. A central feature of 

the GHG guidelines labor analysis, however, involves the quantity and timing of the labor needs 

of building new renewable (primarily wind) and NGCC generating capacity. The EPA IPM 

analysis finds that by 2020 a significantly larger amount of renewable and NGCC capacity will 

be built to implement the GHG guidelines under all of the option scenarios. For example, in the 

base case IPM estimates that 11 GW of non-hydro renewable capacity will be built between 

2016 and 2020, while under Option 1 with regional compliance almost twice as much (21.3 GW) 

renewable capacity will be built. Similarly, in the base case 7.9 GW of NGCC capacity is built 

between 2016 and 2020, while almost 3 times as much (24.43 GW) is built under Option 1 with 

regional compliance.

An important aspect of building new units is that all of the construction-related labor 

occurs before the new units become operational. While the financial costs of building the new 

units are amortized and recouped over the book life of the new equipment, the labor involved 

with manufacturing equipment and constructing the new units occurs, and is actually paid for, in 

a concentrated amount of time before the new capacity begins to generate electricity. IPM 

assumes160 that new NGCC units take 3 years to build, and both natural gas combustion turbines 

and wind-powered renewables take 2 years.

In addition to the amount of labor needed to build new generating capacity, IPM also 

estimates that there will be significant labor impacts in later years from avoiding having to build 

additional new capacity. Because of the demand-side energy efficiency programs, the total 

amount of electricity needed by 2030 is substantially lower with all of the Options than in the 

base case. For example, in Option 1 with regional compliance only half the total new NGCC 

capacity is built between 2016 and 2030 (40.6 GW), compared with 79.7 GW in the base case. 

 

160 Table 4.7, IPM 5.13 Documentation.
6-23

The avoided new capacity results in both a significant net cost savings to consumers and the 

power sector, as well reduced emissions of both CO2 and precursor pollutants from fossil fuel 

generation. The avoided new capacity, however, also has significant labor impacts. A portion of 

the employment that would have been used to build the new capacity in the base case will not 

occur with the implementation of the GHG guidelines. Similarly, less labor involved with 

operating and providing fuel for new units will be needed with the GHG guidelines than in the 

base case.

Overall, the impact of much more rapid construction of new renewable and fossil 

generation capacity in the early years of implementing the GHG guidelines, followed by the need 

to build substantially less capacity in later years as the demand-side energy efficiency programs 

reduce the overall electricity demand relative to the base case, creates an important but complex 

temporal dynamic to the supply-side labor analysis. In the early years of implementation there 

will be a sizable net increase in the amount of labor needed to construct and operate the new 

generating capacity. However, in later years, the reduced need for additional newly built capacity 

results in a sizable net decrease in the amount of labor needed relative to the base case.

The changes in the timing and overall need for new capacity have direct labor impacts 

not only on the construction-related one-time labor, but also on the subsequent needs for 

operating and fuel supply labor to operate the plants. In the case of avoided new capacity, the 

labor impacts include the loss of those ongoing labor needs. In addition, there are similar labor 

impacts from the loss of operating and fuel-related jobs arising from the retirement of existing 

coal generating capacity.

A critical component of the overall labor impacts of implementing the GHG guidelines is 

the impact of the labor associated with the demand-side energy efficiency activities. The 

demand-side labor impacts are presented in section 6.6.2. All of the labor impacts of the demandside energy efficiency activities are increases in labor needs, which more than offset the loss of 

supply-side jobs associated with the decreasing demand for electricity arising from the demandside programs. The IPM labor expenditure projections are distributed across different labor 

categories (e.g., general construction labor, boilermakers and engineering) using data from 

engineering analyses of labor’s overall share of total expenditures, and apportionment of total 
6-24

labor cost to various labor categories. Hourly labor expenditures (including wages, fringe 

benefits, and employer-paid costs including taxes, insurance and administrative costs) for each 

category are used to estimate the labor quantity (measured in full-time job-years) consistent with 

the compliance scenario projections. Projected labor impacts arising from changes in fuel 

demand are primarily derived from labor productivity data for coal mining (tons mined per 

employee hour) and natural gas extraction (MMBtu produced/job-year). Tables 6.4 and 6.5 

present projected changes relative to the baseline of four labor categories: 

1. manufacturing, engineering and construction for building, designing and implementing 

heat rate improvements;

2. manufacturing and construction for new generating capacity; 

3. operating and maintenance for existing generating capacity; and

4. extraction of coal and natural gas fuel.

All of the employment estimates presented in Tables 6-4 and 6-5 are estimates occurring 

in a single year. For the construction-related (one-time) labor impacts, including the installation 

of HRI, Tables 6-4 and 6-5 present the average annual impact occurring in each year of three 

different intervals of years. The multi-year intervals correspond with the analytical years 

reported by IPM. The three intervals are from 2017 through 2020 (a four year interval), from 

2021 through 2025 (five years), and 2026 through 2030 (5 years). The construction-related labor 

analysis are based on the IPM estimates of the net change in capital investment that occurs 

during each multi-year interval to fund building new units completed during that interval. The 

new build labor analysis uses the net change in capital investment to estimate the amount and 

type of labor needed during the interval to build the new capacity. The analysis assumes that the 

new built labor within each interval is evenly distributed throughout the interval. Tables 6-4 and 

6-5 reflect this assumption by presenting the average labor utilization per year during each of the 

three intervals.

The HRI-related labor impacts are estimated based on the assumed capital cost of 

$100/kw (see section 3.7.3). Note that all of the HRI-related labor impacts occur in the first 

interval (2017 to 2020), and are assumed to be occur evenly throughout that four year interval.

Therefore, the HRI-related labor estimates in Tables 6-4 and 6-5 are the annual average labor 
6-25

impacts for of the four years. There are no HRI improvements made after 2020.

The labor estimates for operating and maintaining generating units annually are based on 

IPMs estimates of Fixed Operating and Maintenance (FOM) Costs. IPM estimates FOM for each 

year individually, so the net changes in O&M-related labor estimates in Tables 6-4 and 6-5 are 

single year estimates for 2020, 2025 and 2030. These O&M labor estimates are not the average 

annual averages labor needs throughout each multi-year interval. There are O&M labor changes 

occurring in the all years throughout the entire period 2017-2030, but labor impact changes each 

year. The fuel-related labor estimates are also single-year estimates, and not multi-year averages. 

The labor analysis uses IPM’s estimates of the net changes in the amount of coal and natural gas 

in 2020, 2025 and 2030, which are inherently estimates of the fuel usage in a single year. As 

with the O&M labor impacts, the fuels-related labor impacts occur in every year throughout 

2017-2030, and the labor impact changes every year.

It should be noted that the supply-side labor impact estimates in Tables 6-4 and 6-5 

reflect all the supply-side changes that will occur with each alternative option and compliance 

alternative. These labor impacts include not only the impacts of Building Blocks 1 through 3, but 

also the changes in total generation needed that result from the demand-side energy efficiency 

activities in Building Block 4. The additional upstream labor impacts from the demand-side 

activities are presented below in section 6.2.2.

More details on methodology, assumptions, and data sources used to estimate the supplyside labor impacts discussed in this section can be found in Appendix 6A.
6-26

Table 6-4. Engineering-Baseda Changes in Labor Utilization, Regional Compliance 

Approach - (Number of Job-Yearsb of Employment in a Single Year)

Category Option 1 Option 2

Construction-related (One-time) Changes*

2017-

2020

2021-

2025

2026-

2030

2017-

2020

2021-

2025

2026-

2030

Heat Rate Improvement: Total 32,900 0 0 33,900 0 n/a

Boilermakers and General 

Construction 22,800 0 0 23,600 0 n/a

Engineering and Management 6,000 0 0 6,200 0 n/a

Equipment-related 2,900 0 0 3,000 0 n/a

Material-related 1,100 0 0 1,100 0 n/a

New Capacity Construction: Total 24,700 -33,300 -37,000 14,700 -23,100 n/a

Renewables 17,000 -4,700 -2,100 11,600 -3,100 n/a

Natural Gas 7,700 -28,600 -34,900 3,100 -20,000 n/a

Recurring Changes**

2020 2025 2030 2020 2025 2030

Operation and Maintenance: 

Total -22,900 -23,800 -23,700 -15,300 -15,500 n/a

Changes in Gas 2,300 -600 -3,400 1,000 -1,000 n/a

Retired Coal -22,600 -20,800 -18,200 -14,600 -13,100 n/a

Retired Oil and Gas -2,600 -2,400 -2,100 -1,700 -1,400 n/a

Fuel Extraction: Total -8,800 -14,900 -19,200 -6,600 -10,600 n/a

Coal -13,700 -17,000 -16,600 -10,900 -12,900 n/a

Natural Gas 4,900 2,100 -2,600 4,300 2,300 n/a

Supply-Side Employment Impacts 

- Quantified 25,900 -72,000 -79,900 26,700 -49,200 n/a

a

Job-year estimates are derived from IPM investment and O&M cost estimates, as well as IPM fuel use estimates 

(tons coals or MMBtu gas).

b All job-year estimates on this are full-time equivalent (FTE) jobs. Job estimates in the Demand-Side energy 

efficiency section (below) include both full-time and part-time jobs.

*Construction-related job-year changes are one-time impacts, occurring during each year of the 2 to 4 year period 

during which construction and HRI installation activities occur. Figures in table are average job-years during each of 

the years in each range. Negative job-year estimates when additional generating capacity must be built in the base 

case, but is avoided in the Guideline implementation scenarios due to HRI or Demand-side energy efficiency 

programs.

**Recurring Changes are job-years associated with annual recurring jobs including operating and maintenance 

activities and fuel extraction jobs. Newly built generating capacity creates a recurring stream of positive job-years, 

while retiring generating capacity, as well as avoided new built capacity, create a stream of negative job-years. In 

addition, there are recurring jobs prior to 2020 to fuel and operate new generating capacity brought online before 

2020; the recurring jobs prior to 2020 are not estimated.
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Table 6-5. Engineering-Baseda Changes in Labor Utilization, State Compliance Approach 

(Number of Job-Years of Employment in Year)

Category Option 1 Option 2

Construction-related (One-time) Changes*

2017-

2020

2021-

2025

2026-

2030

2017-

2020

2021-

2025

2026-

2030

Heat Rate Improvement: Total 32,200 0 0 30,800 0 n/a

Boilermakers and General 

Construction 22,400 0 0 21,400 0 n/a

Engineering and Management 5,900 0 0 5,700 0 n/a

Equipment-related 2,900 0 0 2,800 0 n/a

Material-related 1,000 0 0 1,000 0 n/a

New Capacity Construction: Total 28,200 -38,000 -36,100 23,000 -29,500 n/a

Renewables 19,100 -8,900 -2,200 15,800 -6,300 n/a

Natural Gas 9,100 -29,100 -33,900 7,200 -23,200 n/a

Recurring Changes**

2020 2025 2030 2020 2025 2030

Operation and Maintenance: Total -24,100 -25,300 -24,900 -16,800 -17,100 n/a

Changes in Gas 2,500 -500 -3,200 1,500 -800 n/a

Retired Coal -24,000 -22,500 -19,700 -16,400 -14,800 n/a

Retired Oil and Gas -2,600 -2,300 -2,000 -1,900 -1,500 n/a

Fuel Extraction: Total -8,300 -14,600 -19,400 -6,500 -10,300 n/a

Coal -14,300 -17,800 -18,000 -11,500 -13,500 n/a

Natural Gas 6,000 3,200 -1,400 5,000 3,200 n/a

Supply-Side Employment Impacts –

Quantified 28,000 -77,900 -80,400 29,800 -56,900 n/a

a

Job-year estimates are derived from IPM investment and O&M cost estimates, as well as IPM fuel use estimates 

(tons coals or MMBtu gas)

b All job-year estimates on this are Full-Time Equivalent (FTE) jobs. Job estimates in the Demand-Side energy 

efficiency section (below) include both full-time and part-time jobs

*Construction-related job-year changes are one-time impacts, occurring during each year of the 2 to 4 year period 

during which construction and HRI installation activities occur. Figures in table are average job-years during each of 

the years in each range. Negative job-year estimates when additional generating capacity must be built in the base 

case, but is avoided in the Guideline implementation scenarios due to HRI or Demand-side energy efficiency 

programs.

**Recurring Changes are job-years associated with annual recurring jobs including operating and maintenance 

activities and fuel extraction jobs. Newly built generating capacity creates a recurring stream of positive job-years, 

while retiring generating capacity, as well as avoided new built capacity, create a stream of negative job-years. In 

addition, there are recurring jobs prior to 2020 to fuel and operate new generating capacity brought online before 

2020; the recurring jobs prior to 2020 are not estimated.

6.6.2 Projected Changes in Employment in Demand-Side Energy Efficiency Activities

EPA anticipates that this rule may stimulate investment in clean energy technologies and 

services, resulting in considerable increases in energy efficiency in particular. We expect these 

increases in energy efficiency, specifically, to support a significant number of jobs existing in 
6-28

related industries.

In this section, we project employment impacts in demand-side energy efficiency 

activities arising from these guidelines using illustrative calculations. The approach uses 

information from power sector modeling and projected impacts on energy efficiency investments 

analyzed as part of Building Block 4 (see Chapter 3), and U.S. government data on employment 

and expenditures in energy efficiency. This approach is limited by the fact that we do not know 

which options states will choose for demand-side energy efficiency activities and by 

uncertainties associated with methods. These illustrative employment projections are gross; thus

they do not include impacts of any shift in resources from other sectors. Nor does this analysis 

attempt to quantify employment impacts arising from changes in consumer expenditures away 

from energy towards other sectors. In other words, these projections are not attempts at 

estimating net national job creation. Also, this approach attempts to calculate the number of 

employees (full-time and part-time) rather than job-years as discussed in section 6.6.1. EPA 

requests public comment on all aspects of this proposed approach to partially quantifying 

demand-side management and energy efficiency employment impacts. 

Investments in demand-side energy efficiency reduce energy required for a given activity 

by encouraging more efficient technologies (e.g., ENERGY STAR appliances), implementing 

energy improvements for existing systems (e.g., weatherization of older homes), or encouraging 

changes in behavior (e.g., reducing air conditioning during periods of high electricity demand).

Employment impacts of demand-side energy efficiency programs have not been 

extensively studied in the peer-reviewed, published economics literature. Instead, most research 

has focused on consumer response to and amount of energy savings achieved by these programs 

(e.g., Allcott (2011a, 2011b), Arimura et al. (2012)). Results suggest that demand-side energy 

efficiency programs reduce energy use and generate small increases in consumer welfare. These 

policy impacts are due to low investment in energy efficiency as described in “energy paradox” 

literature (Gillingham, Newell, and Palmer (2009), Gillingham and Palmer (2014)).161

Two recent articles discuss employment effects of demand-side energy efficiency 

 

161 For more information on this efficiency paradox see Chapter 3.
6-29

programs. Aldy (2013) describes clean energy investments funded by the American Recovery 

and Reinvestment Act of 2009, which “included more than $90 billion for strategic clean energy 

investments intended to promote job creation and the deployment of low-carbon technologies” 

(p. 137), with nearly $20 billion for energy efficiency investments. The Council of Economic 

Advisors (CEA) (2011) estimated higher economic activity and employment than would have 

otherwise occurred without the American Recovery and Reinvestment Act. Using CEA’s 

methods to quantify job creation for the Recovery Act, Aldy uses the share of stimulus funds for 

clean energy investments to estimate job-years supported by the Recovery Act. The largest 

sources of job creation in clean energy are those that received the largest shares of stimulus 

funds: renewable energy, energy efficiency, and transit. Aldy’s estimates, while informative, are 

not directly applicable for employment analysis in this rulemaking as there are important 

differences in expected employment impacts from a historically large fiscal stimulus specifically 

targeting job creation during a period of exceptionally high unemployment versus environmental 

regulations taking effect several years from now. 

Yi (2013) analyzes clean energy policies and employment for U.S. metropolitan areas in 

2006, prior to the Recovery Act, to evaluate impacts on clean energy job growth. Implementing

an additional state clean energy policy tool (renewable energy policies, GHG emissions policies, 

and energy efficiency polices such as energy efficiency resource standards, appliance or 

equipment energy efficiency standards, tax incentives, and public building energy efficiency 

standards) is associated with 1% more clean energy employment within that MSA. These 

estimates are not transferable to this rulemaking since states are likely to change intensity as well 

as number of clean energy programs.

Lacking a peer-reviewed methodology, we propose the following approach to illustrate 

possible effects on labor demand in the energy efficiency sector due to demand-side management 

strategies. We use U.S. government data to divide the historical change in employment in the 

energy efficiency sector by the historical change in expenditure in the sector and multiply this 

fraction by projected expenditure in the sector undertaken in response to these proposed 

guidelines.

Data used for calculating the numerator of the fraction comes from the “energy 
6-30

efficiency” category of the 2010 and 2011 BLS Green Goods and Services surveys.162 BLS does 

not report the denominator of the job per additional dollar fraction, however. Instead, the data 

include the fraction of green revenues received relative to total revenues in each North American 

Industrial Classification System (NAICS) code.163 We multiply data on total revenues by NAICS 

by the fraction of green revenues reported by BLS to obtain green revenues. The only U.S. 

Government data source containing this revenue information for all NAICS sectors is the U.S. 

Economic Census. This Census is conducted at 5-year intervals (the latest available year is 

2007), however, making it unsuitable for identifying the change in revenues from 2010 to 2011. 

Instead, we use data from the Annual Survey of Manufacturers. The disadvantage of this data 

source is that the manufacturing sector makes up only 50 percent of the 132 NAICS codes 

belonging to the energy efficiency sector as defined by the BLS Green Goods and Services

surveys, with the remainder in the construction or service sectors. Thus, this analysis implicitly 

assumes that the same number of jobs per dollar are supported in construction and service sectors 

as in manufacturing. Using this approach we obtain a factor of 2.56 additional demand-side 

energy efficiency jobs per additional million 2011 dollars of expenditure. 

Having calculated the fraction of additional jobs per additional dollar of energy efficiency 

expenditure, we use energy sector model projections of the first-year costs required for states to 

attain the goal of demand-side efficiency improvements set by building block four.164

Multiplying this dollar expenditure by the jobs per additional dollar figure results in projected 

employment impacts for demand-side energy efficiency activities of 78,800 in 2020, 112,000 in 

2025, and 111,800 in 2030 for both the proposed Option 1 regional and state compliance 

approaches. The estimates for Option 2 are shown on Table 6-6 below. 

 

162For more details on these surveys, see section 7.5.3. 

163 See detailed listing available here: http://www.bls.gov/ggs/naics_2012.xlsx.

164 See Greenhouse Gas Abatement Measures TSD, Appendix 5-4.
6-31

Table 6-6. Estimated Demand-Side Energy Efficiency Employment Impacts For Option 1 

and Option 2 for Both Regional and State Compliance Approaches

Source Factor

Employment impact (jobs)*

Option 1 Option 2

2020 2025 2030 2020 2025 2030

BLS GGS additional jobs per 

additional million dollars 2.56 78,800 112,000 111,800 57,000 76,200 n/a

*Since these figures represent number of employees (full- or part-time) they should not be added to the full-time 

equivalent job-years reported in Table 6-5.

Although this approach has the advantage of illuminating the change in jobs for an 

incremental change in expenditures, this approach is limited by its focus on manufacturing 

sectors and direction of bias (overestimation or underestimation) cannot be determined at this 

time. The EPA is requesting comment on this method, data, identification of related studies and 

peer reviewed articles and other methods. 

There is more uncertainty involved in this approach than the standard bottom-up 

engineering analysis used to estimate electricity generation and fuel production employment 

impacts of this rulemaking. For those, the EPA was able to identify a limited set of activities 

(e.g., constructing a new NGCC power plant), and study associated labor requirements. Demandside energy efficiency improvements, in contrast, encompass a wide array of activities (subsidies 

for efficient appliances, “smart meters,” etc.). In addition, there is considerable uncertainty 

regarding which activities a state will choose. Thus, the validity of the jobs per dollar approach 

used here relies on the assumption that states will use a mix of activities similar to the 2011 

composition of energy efficiency sectors identified by BLS. 

In addition, the EPA does not have access to bottom-up information regarding labor

requirements for these activities. Use of a constant job per dollar fraction is at best a crude 

approximation of these labor requirements. The EPA has identified several other limitations of 

this approach, outlined below.

Job Reclassification. Job numbers in this chapter represent gross changes in the affected 

sector. As such they may over-estimate impacts to the extent that jobs created displace 

workers employed elsewhere in the economy. For demand-side efficiency activities this 

potential over-statement is may be higher than in other sectors. If states encourage 

consumers to purchase ENERGY STAR appliances, for example, currently employed 
6-32

workers in factories and retail outlets may simply be given a different task. This 

approach, however, would count these workers as jobs created

Imports. The job per additional dollar fraction used in the employment projection is 

calculated based on jobs per dollar of revenue for domestic firms only. To the extent that 

spending on demand-side energy efficiency activities goes toward the purchase of 

imported goods this projection will overstate the U.S. employment impact of those 

expenditures.

Fixed Coefficient. Implicit in this approach is the assumption that employment impacts 

can be projected decades into the future on the basis of a single calculation from 2010-

2011 data. The labor intensity of demand-side energy efficiency will likely change with 

technological innovation in the sector. In addition, even absent technological change, 

labor intensity of expenditures will likely change over time as states alter their portfolio 

of efficiency activities (e.g., by moving to higher cost activities after exhausting 

opportunities for low cost efficiency gains).

Non-additional Activities. Here we assume that all activities financed by demand-side 

energy efficiency expenditures are additional to what would have been undertaken in the 

absence of these programs. If utilities finance some actions customers would have 

undertaken in the absence of these programs (e.g., if a customer receives a rebate for an 

energy efficient appliance that would have been purchased without the rebate), these 

numbers would overestimate employment impacts of the proposed guidelines.

6.7 Conclusion

This chapter presents qualitative and quantitative discussions of potential employment 

impacts of the proposed guidelines for electricity generation, fuel production, and demand-side 

energy efficiency sectors. The qualitative discussion identifies challenges associated with 

estimating net employment effects and discusses anticipated impacts related to the rule. It 

includes an in-depth discussion of economic theory underlying analysis of employment impacts. 

Labor demand impacts for regulated firms can be decomposed into output and substitution 

effects, both of which may be positive or negative. Consequently, theory alone cannot predict the 

direction or magnitude of a regulation’s employment impact. It is possible to combine theory 
6-33

with empirical study specific to the regulated firms and other relevant sectors if data and 

methods of sufficient detail and quality are available. Finally, economic theory suggests that 

labor supply effects are also possible.

We examine the peer-reviewed economics literature analyzing various aspects of labor 

demand, relying on the above theoretical framework. Determining the direction of employment 

effects in regulated industries is challenging because of the complexity of the output and 

substitution effects. Complying with a new or more stringent regulation may require additional 

inputs, including labor, and may alter the relative proportions of labor and capital used by 

regulated firms (and firms in other relevant industries) in their production processes. The 

available literature illustrates some of the difficulties for empirical estimation: studies sometimes 

rely on confidential plant-level employment data from the U.S. Census Bureau, possibly 

combined with pollution abatement expenditure data that are too dated to be reliably informative. 

In addition, the methods do not permit estimation of net economy-wide effects. Empirical 

analysis at the industry level requires estimates of product demand elasticity; production factor 

substitutability; supply elasticity of production factors; and the share of total costs contributed by 

wages, by industry, and perhaps even by facility. For environmental rules, many of these data 

items are not publicly available, would require significant time and resources in order to access 

confidential U.S. Census data for research, and also would not be necessary for other 

components of a typical RIA. Econometric studies of environmental rules converge on the 

finding that employment effects, whether positive or negative, have been small in regulated 

sectors.

The illustrative quantitative analysis in this chapter projects a subset of potential 

employment impacts in the electricity generation, fuel production, and demand-side energy 

efficiency sectors. States have the responsibility and flexibility to implement policies and 

practices for compliance with Proposed EGU GHG Existing Source Guidelines. As such, given 

the wide range of approaches that may be used, quantifying the associated employment impacts 

is difficult. EPA’s employment analysis includes projected employment impacts associated with 

these guidelines for the electric power industry, coal and natural gas production, and demandside energy efficiency activities. These projections are derived, in part, from a detailed model of 

the electricity production sector used for this regulatory analysis, and U.S. government data on 
6-34

employment and labor productivity. In the electricity, coal, and natural gas sectors, the EPA 

estimates that these guidelines could have an employment impact of roughly 25,900 job-years in 

2020 for Option 1 and 26,700 for Option 2 of that same year (see Tables 6-4 and 6-5). 

Employment impacts from demand-side energy efficiency activities are based on historic data on 

jobs supported per dollar of expenditure. Demand-side energy efficiency employment impacts 

would approximately be 78,800 jobs in 2020 for both Option 1 regional and state compliance 

approaches (see Table 6-6). The IPM-generated job-year numbers for the electricity, coal and 

natural gas sectors should not be added to the demand-side efficiency job impacts since the 

former are reported in full-time equivalent jobs, whereas the latter do not distinguish between 

full- and part-time employment. Finally, note again that this is an illustrative analysis, and 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, by way of meeting its state-specific goal. Given the 

flexibilities afforded states in complying with the emission guidelines, the impacts reported in 

this chapter are illustrative of compliance actions states may take.
6-35

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Graff Zivin, J. and M. Neidell. 2013. “Environment, Health, and Human Capital.” Journal of 

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6A-1

APPENDIX 6A: ESTIMATING SUPPLY-SIDE EMPLOYMENT IMPACTS OF THE 

PROPOSED EGU GHG EXISTING SOURCE GUIDELINES

This appendix presents the methods used to estimate the supply-side employment impacts 

of the Proposed Electric Generating Unit Greenhouse Gas (EGU GHG) Existing Source 

Guidelines. The focus of the employment analysis is limited to the direct changes in the amount 

of labor needed in the power, fuels and generating equipment sectors directly influenced by 

compliance with the Guidelines. It does not include the ripple effects of these impacts on the 

broader economy (i.e., the “multiplier” effect), nor does it include the wider economy-wide 

effects of the changes to the energy markets, such as changes in electricity prices. 

The methods used to estimate the supply-side employments are based on methods 

previously developed for the Mercury and Air Toxics Standards (MATS) Regulatory Impact 

Analysis (RIA). The methods used in this analysis to estimate the recurring labor impacts (e.g., 

labor associated with operating and maintaining generating units, as well as labor needed to mine 

coal and natural gas) are the same as was used in MATS (with updated data where available). 

The labor analysis in the MATS RIA was primarily concerned with the labor needs of 

retrofitting pollution control equipment. The EGU GHG Existing Source Guidelines labor 

analysis, however, involves the quantity and timing of the labor needs of building new renewable 

and natural gas, as well as making heat rate improvements (HRI) at existing coal fired EGUs. 

These construction-related compliance activities in the EGU GHG Existing Source Guidelines 

required developing additional appropriate analytical methods that were not needed for the 

MATS analysis. The newly developed analytical methods for the construction-related activities 

are similar in structure and overall approach to the methods used in MATS, but required 

additional data and engineering information not needed in the MATS RIA.

6A.1 General Approach

The analytical approach used in this analysis is a bottom-up engineering method 

combining the EPA’s cost analysis of the proposed guidelines with data on labor productivity, 

engineering estimates of the amount and types of labor needed to manufacture, construct, and 

operate different types of generating units, and prevailing wage rates for skilled and general 
6A-2

labor categories. The approach involved using power sector projections and various energy 

market implications under the proposed EGU GHG Existing Source Guidelines from modeling 

conducted with the EPA Base Case version 5.13, using the Integrated Planning Model 

(IPM®)165, along with data from secondary sources, to estimate the first order employment 

impacts for 2020, 2025, and 2030. 

Throughout the supply-side labor analysis the engineering approach projects labor

changes measured as the change in each analysis year in job-years166 employed in the power 

generation and directly related sectors (e.g., equipment manufacturing, fuel supply and 

generating efficiency services). Job-years are not individual jobs, nor are they necessarily 

permanent nor full time jobs. Job-years the amount of work performed by one full time 

equivalent (FTE) employee in one year. For example, 20 job-years in 2020 may represent 20 

full-time jobs or 40 half-time jobs in that year, or any combination of full- and part-time workers 

such that total 20 FTEs.

6A.1.1 Employment Effects Included In the Analysis

The estimates of the employment impacts (both positive and negative) are divided into five 

categories: 

 additional employment to make HRI167 at existing coal fired EGUs; 

 additional construction-related employment to manufacture and install additional new 

generating capacity (renewables, and natural gas combined cycle or combustion turbine 

units) when needed as part of early compliance actions;

 

165 Results for this analysis were developed using various outputs from EPA’s Base Case v.5.13 using ICF’s 

Integrated Planning Model (IPM®). This case includes all of the underlying modeling that was developed by EPA 

with technical support from ICF International, Inc. See 

http://www.epa.gov/powersectormodeling/BaseCasev513.html for more information.

166 Job-years are not individual jobs, but rather the amount of work performed by the equivalent of one full-time 

individual for one year. For example, 20 job-years in 2020 may represent 20 full-time jobs or 40 half-time jobs in 

that year.

167 Heat rate improvements could include a range of activities in the power plant to lower the heat rate required to 

generate a net electrical output. Assuming all other things being equal, a lower heat rate is more efficient because 

less fuel is needed per unit of electric output.
6A-3

 lost construction-related employment opportunities due to reductions in the total amount 

of new generating capacity needed to be built in the later years because of reduced 

overall demand for electricity because of demand-side energy efficiency activities;

 lost operating and maintenance employment opportunities due to increased retirements of 

coal and small oil/gas units; 

 changes (both positive and negative) in coal mining and natural gas extraction 

employment due to the aggregate net changes in fuel demands arising from all the 

activities occurring due to compliance with the proposed guidelines.

Some of the changes are one-time labor effects which are associated with the building (or 

avoiding building) new generating capacity and installing HRI. This type of employment effects 

involves project-specific labor that is used for 2 to 4 years to complete a specific construction 

and installation type of project. There are other labor effects, however, which continue year after 

year. For example, bringing new generating capacity online creates an ongoing need for labor to 

operate and maintain the new generating capacity throughout the expected service life of the 

unit. New generating capacity also creates a need for additional employment to provide the fuel 

annually to run the new capacity. There are also continuing effects from the lost operations and 

maintenance (O&M) and fuel sector labor opportunities from decisions to retire existing 

capacity, as well as similar lost labor opportunities from decisions to reduce a portion of the 

amount of additional capacity needed in the base case.

6A.2 Employment Changes due to Heat Rate Improvements

The employment changes due to HRI were estimated based on the incremental MW 

capacity estimated to implement such improvements by 2020 as indicated by the analysis 

conducted by EPA. The heat rate improvement job impacts were assumed to have all occurred by 

2020 and thus this study assumes there will be no HRI related jobs after 2020 (i.e., no permanent 

O&M related jobs due to HRI for 2025 or 2030). EPA modeled the heat rate improvements 

exogenously in IPM using the assumption that all “relevant” units can improve their heat rate by 

6 percent at a capital cost of $100/kW. This study assumes that these investments will occur over 

a four-year period culminating in 2020. Hence, the per-year cost of heat rate was calculated to be 

$25/kW, and this cost was used in the next step. 
6A-4

This cost was then allocated to four categories based on the estimates provided by 

Andover Technology Partners (ATP), which were adapted from proxy projects involving 

installation of combustion control retrofits, such as those installed under the Best Available 

Retrofit Technology (BART) submissions from coal-fired power plants located in Wyoming and 

Arizona. For more details, refer to the Staudt (2014) report.168 These proxies were chosen to 

ensure that the types of activities involved and their associated costs would be representative of 

those investments EPA expects power plants to undertake for efficiency upgrades.

Information on cost for these proxies were then extrapolated to approximate the labor 

requirements for four broad categories of labor – boilermakers and general construction, 

engineering and management support labor, labor required to produce the equipment in upstream 

sectors, and labor required to supply the materials (assumed to be primarily steel) in upstream 

sectors. More details about these estimates are provided in the Staudt (2014) report.

Based on the cost allocated in each categories and output per worker figures for 

respective industries in 2020, the employment gains for heat rate improvement were estimated169

for 2020 using the assumptions summarized in Table 6A-1 below. Output per workers in future 

years were adjusted to account for growth in labor productivity, based on historical evidence of 

productivity growth rates for the relevant sectors.

Table 6A-1. Labor Productivity Growth Rate due to Heat Rate Improvement

Share of the Total Capital 

Cost

Output/Worker 

(2020)

Labor Productivity Growth 

Rate

Boilermaker and Gen. Const. 40% $78,500 0%

Management/Engineering 20% $141,000 1.3%

Equipment 30% $458,000 3.2%

Materials 10% $424,000 -1.2%

 

168 Staudt, James, Andover Technology Partners, Inc. Estimating Labor Effects of Heat Rate Improvements. Report 

prepared for the proposed EGU GHG Existing Source Guidelines, March 6, 2014. 

169 Total value of shipments or receipts in 2007 and total employees were taken from 2007 Economic Census, 

Statistics by Industry for Mining and Manufacturing sectors. The average annual growth rate of labor productivity 

was taken from the Bureau of Labor Statistics. Average growth rate calculated for years 1992-2007, applied to 

2007 productivity to determine 2020 estimates of productivity. For the construction sector, BLS productivity 

growth rate data was unavailable. Because of this, and lack of reliable data on construction sector productivity 

growth, the output per worker for the construction sector was not forecasted to 2020, and the most recent available 

value from 2007 was used. 
6A-5

For these output per worker figures, a power sector construction industry (NAICS 

237130) was used for general construction and boilermakers, Engineering Services (NAICS 

54133) was used for the engineering and management component, Machinery Manufacturing 

(NAICS 333) was used for the equipment sector, and steel manufacturing (NAICS 33121) was 

used for materials. Use of machinery manufacturing for equipment and steel for materials was 

based on an analysis of the types of materials and equipment needed for these projects, and what 

EPA determined to be the most appropriate industry sectors for those. For more details, refer to 

the Staudt (2014) report. 

6A.2.1 Employment Changes Due to Building (or Avoiding) New Generation Capacity

Employment changes due to new generation units were based on the incremental changes 

in capacity (MW), capital costs ($MM), and fixed operations and maintenance (FOM) costs 

($MM) between the policy scenario and the base case in a given year. 

New capacities were aggregated by generation type into the following categories:

• Combined Cycle, 

• Combustion Turbine, and 

• Renewables (which includes biomass, geothermal, landfill gas, onshore wind, and 

solar).

For each category, the analysis estimated the impacts due to both the construction and 

operating labor requirements for corresponding capacity changes. The construction labor was 

estimated using information on the capital costs, while the operating labor was estimated using 

the FOM costs. 

Because IPM outputs provide annualized capital costs ($MM), EPA first converted the 

annualized capital costs to changes in the total capital investment using the corresponding capital 

charge rates.170 These total capital investments were then converted to annual capital investments 

using assumptions about the estimated duration of the construction phase, in order to estimate the 

 

170 Capital charge rates obtained from EPA’s resource, EPA #450R13002: Documentation for EPA Base Case v.5.13 

using the Integrated Programming Model (IPM).
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annual impacts on construction phase labor. Duration estimates were based on assumptions for 

construction lengths used in EPA’s IPM modeling.171 Specific assumptions used for different 

generating technologies are shown in Table 6A-2 below.

Table 6A-2. Capital Charge Rate and Duration Assumptions

New Investment Technology Capital Charge Rate Duration (Years)

Advanced Combined Cycle 10.3% 3

Advanced Combustion Turbine 10.6% 2

Renewables

Biomass 9.5% 3

Wind (Onshore) 10.9% 3

Landfill Gas 10.9% 3

Solar 10.9% 3

Geothermal 10.9% 3

Annual capital costs for each generation type were then broken down into four 

categories: equipment, material (which is assumed to be primarily steel), installation labor, and 

support labor in engineering and management. The percentage breakdowns shown in Table 6A-3 

were estimated using information provided by Staudt (2014), based primarily on published 

budgets for new unit assembled in a study for the National Energy Technology Laboratory 

(NETL). For more details, refer to the Staudt (2014) report. Annual capital costs for each 

generation type provided by the IPM output were allocated according to this breakdown.

Table 6A-3. Expenditure Breakdown due to New Generating Capacity

Equipment Material Labor Eng. and Const. Mgt

Renewables 54% 6% 31% 9%

Combined Cycle 65% 10% 18% 7%

Combustion Turbine 65% 10% 18% 7%

The short-term construction labor of the new generation units were based on output ($ per 

worker) figures for the respective sectors. The total direct workers per $1 million of output for 

the baseline year 2007 were forecasted to the years under analysis using the relevant labor 

productivity growth rate. Table 6A-4 shows the figures for each of the five productivities: 

general power plant construction; engineering and management; material use; equipment use; 

 

171 Ibid.
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and plant operators. The resulting values were multiplied by the capital costs to get the job 

impact.

Table 6A-4. Labor Productivity due to New Generating Capacity

Labor Productivity Growth Rate Workers per Million $ (2007)

General Power Plant Construction 0.0% 5.7

Engineering and Management 1.3% 5.2

Material Use (Steel) -1.2% 2.0

Equipment Use (Machinery) 3.2% 3.3

Plant Operators 2.8% 10.8

General installation labor, assumed to be mostly related to the general power plant 

construction phase, was matched with the power industry specific construction sector. 

Engineering/management was matched to the engineering services sector to determine their 

respective output per worker. For materials, EPA assumed steel to be the proxy and used the 

steel manufacturing sector for this productivity. Equipment was assumed to primarily come from 

machinery manufacturing sector (such as turbines, engines and fans).

The net labor impact for construction labor for a given year was adjusted to account for 

changes in capacity that has already taken place in the prior IPM run year. Because IPM reports 

cumulative changes for new generating capacity for any given run year, this adjustment ensured 

that the short-term construction phase job impacts in any given run year does not reflect the 

cumulative effects of prior construction changes for the given policy scenario. The estimated 

amount of the change in construction-related labor in a single IPM run year (e.g., 2025) 

represents the average labor impact that occurs in all years between that IPM run year and the 

previous run year (i.e., the labor estimates derived from the 2025 IPM run year are the average 

annual labor impacts in 2021 through 2025). The construction labor results for 2020 represent 

the average labor impacts in 2017 through 2020.

The plant operating employment estimates used a simpler methodology as the one 

described above. The operating employment estimates use the IPM estimated change in FOM 

costs for the IPM run year. Because the FOM costs are inherently estimates for a single year, the 

operating employment estimates are for a single year only. While there are obviously operating 

employment effects occurring in every year throughout the entire IPM estimation period (2017-

2030), the labor analysis only estimates the single year labor impacts in the IPM run years: 2020, 
6A-8

2025 and 2030. The total direct workers for $1 million and labor productivity growth rate 

provided for plant operators in Table 6A-4 were used to estimate the employment impact.

6A.2.2 Employment Changes due to Coal and Oil/Gas Retirements

Employment changes due to plant retirements were calculated using the IPM projected 

changes in retirement capacities for coal and oil/gas units for the relevant year and the estimated 

changes in total FOM costs due to those retiring units. Thus, the basic assumption in this analysis 

was that increased retirements (over the base case) will lead to reduced FOM expenditures at 

those plants which were assumed to lead to direct job losses for plant workers.

In order to estimate the total FOM changes due to retirements, EPA first estimated the 

average FOM costs ($/kW) for existing coal-fired and oil/gas-fired units in the base case, as 

shown in Table 6A-5 below. It was assumed that the average FOM cost of existing units in the 

base case can be used as a proxy for the lost economic output due to fossil retirements. Thus, 

changes in the FOM costs for these retiring units were derived by taking the product of the 

incremental change in capacity and the average FOM costs. These values were converted to lost 

employment using data from the Economic Census and BLS on the output/worker estimates for 

the utility sector.172

Table 6A-5. Average FOM Cost Assumptions

2020 2025 2030

Coal 65 68 69

Oil and Gas 21 22 22

Note that the retirement related employment losses are assumed to include losses directly 

affecting the utility sector, and do not include losses in upstream sectors that supply other inputs 

to the EGU sector (except fuel related job losses, which are estimated separately and discussed in 

the next section).

6A.2.3 Employment Changes due to Coal and Oil/Gas Retirements

Two types of employment impacts due to projected fuel use changes were estimated in 

 

172 The same specific sources as cited before, however, used workers and total payroll.
6A-9

this section. First, employment losses due to either reductions or shifts in coal demand were 

estimated using an approach similar to EPA’s coal employment analyses under Title IV of the 

Clean Air Act Amendments. Using this approach, changes in coal demand (in short tons) for 

various coal supplying regions were taken from EPA’s base and policy case runs for the 

proposed EGU GHG NSPS. These changes were converted to job-years using U.S. Energy 

information Administration (EIA) data on regional coal mining productivity (in short tons per 

employee hour), using 2008 labor productivity estimates.173,174

Specifically, the incremental changes to coal demand were calculated based on the coal 

supply regions in IPM -- Appalachia, Interior, and West and Waste Coal (which was estimated 

using U.S. total productivity). Worker productivity values used for estimating coal related job 

impacts are shown in Table 6A-6 below.

Table 6A-6. Labor Productivity due to New Generating Capacity

Labor Productivity

Coal (Short tons/ employee hour)

Appalachia 2.91

Interior 4.81

West 19.91

Waste 5.96

Natural Gas (MMBtu/ employee hour) 126

Pipeline Construction (Workers per $Million) 5.1

For natural gas demand, labor productivity per unit of natural gas was unavailable, unlike 

coal labor productivities used above. Most secondary data sources (such as Census and EIA) 

provide estimates for the combined oil and gas extraction sector. This section thus used an 

adjusted labor productivity estimate for the combined oil and gas sector that accounts for the 

relative contributions of oil and natural gas in the total sector output (in terms of the value of 

energy output in MMBtu). This estimate of labor productivity was then used with the 

 

173 From EIA Annual Energy Review, Coal Mining Productivity Data. Used 2008. 

174 Unlike the labor productivity estimates for various equipment resources which were forecasted to 2020 using 

BLS average growth rates, this study uses the most recent historical productivity estimates for fuel sectors. In 

general, labor productivity for the fuel sectors (both coal and natural gas) showed a significantly higher degree of 

variability in recent years than the manufacturing sectors, which would have introduced a high degree of 

uncertainty in forecasting productivity growth rates for future years.
6A-10

incremental natural gas demand for the respective IPM runs to estimate the job-years for the 

specific year (converting the TCF of gas used projected by IPM into MMBtu using the 

appropriate conversion factors). In addition, the pipeline construction costs were estimated using 

endogenously determined gas market model parameters in IPM used by EPA for the MATS rule 

(using assumptions for EPA’s Base Case v4.10). This analysis assumed that the need for 

additional pipeline would be proportionate to those projected for the MATS rule and were hence 

extrapolated from those estimates.175 The job-years associated with the pipeline construction 

were included in the natural gas employment estimates.

 

175 See “Employment Estimates of Direct Labor in Response to the Proposed Toxics Rule in 2015”. Technical 

Support Document, March 2011.
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CHAPTER 7: STATUTORY AND EXECUTIVE ORDER ANALYSIS 

7.1 Executive Order 12866, Regulatory Planning and Review, and Executive Order 13563, 

Improving Regulation and Regulatory Review

Under section 3(f) (1) of Executive Order 12866 (58 FR 51735, October 4, 1993), this 

action is an "economically significant regulatory action” because it is likely to have an annual 

effect on the economy of $100 million or more or to 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. The $100 million 

threshold can be triggered by either costs or benefits, or a combination of them. Accordingly, the 

EPA submitted this action to OMB for review under Executive Orders 12866 and 13563 (76 FR 

3821, January 21, 2011), and any changes made in response to OMB recommendations have 

been documented in the docket for this action. The EPA also prepared an analysis of the 

potential costs and benefits associated with this action in this Regulatory Impact Analysis (RIA). 

Consistent with EO 12866 and EO 13563, the EPA estimated the costs and benefits for 

illustrative compliance approaches of implementing the proposed guidelines. This proposal sets 

goals to reduce CO2 emissions from the electric power industry. Actions taken to comply with 

the proposed guidelines will also reduce the emissions of directly emitted PM2.5, sulfur dioxide 

(SO2) and nitrogen oxides (NOX). The benefits associated with these PM, SO2 and NOX

reductions are referred to as co-benefits, as these reductions are not the primary objective of this 

rule.

The EPA has used the U.S. government’s social cost of carbon (USG SCC) estimates 

(i.e., the monetary value of impacts associated with a marginal change in CO2 emissions in a 

given year), to analyze CO2 climate impacts of this rulemaking. The four USG SCC estimates 

are associated with different discount rates (model average at 2.5 percent discount rate, 3

percent, and 5 percent; 95th percentile at 3 percent), and each increases over time. In this 

summary, EPA provides the estimate of climate benefits associated with the SCC value deemed 

to be central by the USG (the model average at 3% discount rate). The EPA estimates that in 

2020, the Option 1 regional compliance approach will yield monetized climate benefits of 

approximately $17 billion with a 3 percent model average (2011$). The air pollution health co-
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benefits in 2020 are estimated to be $16 billion to $37 billion for a 3 percent discount rate, and 

$15 billion to $34 billion (2011$) for a 7 percent discount rate. The annual illustrative 

compliance costs estimated by IPM, inclusive of demand side energy efficiency program and 

participant costs and MRR costs, are approximately $5.5 billion (2011$) in 2020. The quantified 

net benefits (the difference between monetized benefits and costs) in 2020, are estimated to be 

$28 billion to $49 billion using a 3 percent discount rate (model average) and $26 to $45 billion 

using a 7 percent discount rate (model average, 2011$). For Option 1 state compliance approach, 

the climate benefits are estimated to be $18 billion (2011$). The air pollution health co-benefits 

in 2020 for the Option 1 state compliance approach are estimated to be $17 billion to $40 billion 

for a 3 percent discount rate, and $15 billion to $36 billion (2011$) for a 7 percent discount rate. 

The annual illustrative compliance costs estimated by IPM, inclusive of demand side energy 

efficiency program and participant costs and MRR costs, are approximately $7.5 billion (2011$) 

in 2020. The quantified net benefits in 2020, are estimated to be $27 billion to $50 billion using 

a 3 percent discount rate (model average) and $26 to $46 billion using a 7 percent discount rate 

(model average, 2011$). 

For Option 1 regional compliance approach in 2030, the EPA estimates this proposal will 

yield monetized climate benefits of approximately $30 billion (3 percent, model average, 2011$). 

The air pollution health co-benefits in 2030 are estimated to be $25 billion to $59 billion for a 3 

percent discount rate, and $23 billion to $54 billion (2011$) for a 7 percent discount rate. The 

annual illustrative compliance costs estimated using IPM, inclusive of demand side energy 

efficiency program and participant costs and MRR costs, are approximately $7.3 billion (2011$) 

in 2030. The quantified net benefits (the difference between monetized benefits and costs) in 

2030, are estimated to be $48 billion to $82 billion using a 3 percent discount rate and $46 to $77 

billion assuming a 7% discount rate (model average, 2011$). For the Option 1 state compliance 

approach, net benefits in 2030 are estimated to be $49 to $84 billion (3 percent discount rate) and 

$46 to 79 billion (7 percent discount rate (2011$). Compliance costs are estimated to be $8.8 

billion (2011$). Based upon the foregoing discussion, it remains clear that the benefits of this 

proposal are substantial, and far exceed the costs. Additional benefit, cost and net benefit 

estimates for Option 1 and Option 2 are provided in Chapter 8 of this report.
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7.2 Paperwork Reduction Act

The information collection requirements in this proposed rule have been submitted for 

approval to the Office of Management and Budget (OMB) under the Paperwork Reduction Act, 

44 U.S.C. 3501 et seq. The Information Collection Request (ICR) document prepared by the 

EPA has been assigned the EPA ICR number 2503.01. 

The information collection requirements are based on the recordkeeping and reporting 

burden associated with developing, implementing, and enforcing a state plan to limit CO2

emissions from existing sources in the power sector. These recordkeeping and reporting 

requirements are specifically authorized by CAA section 114 (42 U.S.C. 7414). All information 

submitted to the EPA pursuant to the recordkeeping and reporting requirements for which a 

claim of confidentiality is made is safeguarded according to agency policies set forth in 40 CFR 

part 2, subpart B.

The annual burden for this collection of information for the states (averaged over the first 

3 years following promulgation of this proposed action) is estimated to be a range of 316,217

hours at a total annual labor cost of $22,381,044, to 633,001 hours at a total annual labor cost of 

$44,802,243. The lower bound estimate reflects the assumption that some states already have 

energy efficiency and renewable energy programs in place. The higher bound estimate reflects 

the assumption that no states have energy efficiency and renewable energy programs in place. 

The total annual burden for the federal government (averaged over the first 3 years following 

promulgation of this proposed action) is estimated to be 53,300 hours at a total annual labor cost 

of $2,958,005. Burden means the total time, effort, or financial resources expended by persons to 

generate, maintain, retain, or disclose or provide information to or for a federal agency. This 

includes the time needed to review instructions; develop, acquire, install, and utilize technology 

and systems for the purposes of collecting, validating, and verifying information, processing and 

maintaining information, and disclosing and providing information; adjust the existing ways to 

comply with any previously applicable instructions and requirements; train personnel to be able 

to respond to a collection of information; search data sources; complete and review the collection 

of information; and transmit or otherwise disclose the information.

An agency may not conduct or sponsor, and a person is not required to respond to a 
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collection of information unless it displays a currently valid OMB control number. The OMB 

control numbers for the EPA’s regulations are listed in 40 CFR part 9 and 48 CFR Chapter 15.

7.3 Regulatory Flexibility Act

The Regulatory Flexibility Act (RFA) generally requires an agency to prepare a 

regulatory flexibility analysis of any rule, subject to notice and comment rulemaking 

requirements, under the Administrative Procedure Act or any other statute unless the agency 

certifies that the rule will not have a significant economic impact on a substantial number of 

small entities. Small entities include small businesses, small organizations, and small 

governmental jurisdictions.

For purposes of assessing the impacts of this rule on small entities, small entity is defined 

as: 

1. A small business that is defined by the SBA’s regulations at 13 CFR 121.201 (for 

the electric power generation industry, the small business size standard is an 

ultimate parent entity with less than 750 employees). The NAICS codes for the 

affected industry are in Table 7-1 below.

2. A small governmental jurisdiction that is a government of a city, county, town, 

school district or special district with a population of less than 50,000.

A small organization that is any not-for-profit enterprise which is independently 

owned and operated and is not dominant in its field.

Table 7-1. Potentially Regulated Categories and Entitiesa

Category NAICS Code Examples of Potentially Regulated Entities

Industry 221112 Fossil fuel electric power generating units.

State/Local Government 221112b Fossil fuel electric power generating units 

owned by municipalities.

a

Include NAICS categories for source categories that own and operate electric power generating units (includes 

boilers and stationary combined cycle combustion turbines).

b State or local government-owned and operated establishments are classified according to the activity in which they 

are engaged.

After considering the economic impacts of this proposed rule on small entities, EPA 

certifies that this action will not have a significant economic impact on a substantial number of 

small entities. 
7-5

The proposed rule will not impose any requirements on small entities. Specifically, 

emission guidelines established under CAA section 111(d) do not impose any requirements on 

regulated entities and, thus, will not have a significant economic impact upon a substantial 

number of small entities. After emission guidelines are promulgated, states establish standards on 

existing sources and it is those state requirements that could potentially impact small 

entities. Our analysis here is consistent with the analysis of the analogous situation arising when 

the 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) national standards 

for allowable concentrations of particulate matter in ambient air as required by section 109 of the 

CAA. See also American Trucking Associations v. EPA. 175 F.3d at 1044–45 (NAAQS do not 

have significant impacts upon small entities because NAAQS themselves impose no regulations 

upon small entities). 

Nevertheless, the EPA is aware that there is substantial interest in the proposed rule 

among small entities (municipal and rural electric cooperatives). As detailed in section III.A.of 

the preamble, the EPA has conducted an unprecedented amount of stakeholder outreach on 

setting emission guidelines for existing EGUs. While formulating the provisions of the proposed 

rule, the EPA considered the input provided over the course of the stakeholder outreach. Section 

III.B. of the preamble describes the key messages from stakeholders. In addition, as described in 

the RFA section of the preamble to the proposed standards of performance for GHG emissions 

from new EGUs (79 FR 1499-1500, January 8, 2014), the EPA conducted outreach to 

representatives of small entities while formulating the provisions of the proposed standards. 

Although only new EGUs would be affected by those proposed standards, the outreach regarded 

planned actions for new and existing sources. We invite comments on all aspects of the proposal 

and its impacts, including potential impacts on small entities.

7.4 Unfunded Mandates Reform Act (UMRA)

This proposed action does not contain a federal mandate that may result in expenditures 

of $100 million or more for state, local, and tribal governments, in the aggregate, or the private 
7-6

sector in any one year. Specifically, the emission guidelines proposed under CAA section 111(d) 

do not impose any direct compliance requirements on regulated entities, apart from the 

requirement for states to develop state plans. The burden for states to develop state plans in the 3 

year period following promulgation of the rule was estimated and is listed in section IX B. of the 

preamble for the rulemaking, but this burden is estimated to be below $100 million in any one 

year. Thus, this proposed rule is not subject to the requirements of section 202 or section 205 of 

UMRA.

This proposed rule is also not subject to the requirements of section 203 of UMRA

because it contains no regulatory requirements that might significantly or uniquely affect small 

governments.

In light of the interest among governmental entities, the EPA initiated consultations with 

governmental entities while formulating the provisions of the proposed standards for new EGUs. 

Although only new EGUs would be affected by those proposed standards, the outreach regarded 

planned actions for new and existing sources. As described in the UMRA discussion in the 

preamble to the proposed standards of performance for GHG emissions from new EGUs (79 FR 

1500-1501, January 8, 2014), the EPA consulted with the following 10 national organizations 

representing state and local elected officials: 

 National Governors Association

 National Conference of State Legislatures

 Council of State Governments

 National League of Cities

 U.S. Conference of Mayors

 National Association of Counties

 International City/County Management Association

 National Association of Towns and Townships

 County Executives of America
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 Environmental Council of States

On February 26, 2014, the EPA re-engaged with those governmental entities to provide a 

pre-proposal update on the emission guidelines for existing EGUs and emission standards for 

modified and reconstructed EGUs.

While formulating the provisions of these proposed emission guidelines, the EPA also 

considered the input provided over the course of the extensive stakeholder outreach conducted 

by the EPA.

7.5 Executive Order 13132, Federalism

Under Executive Order 13132, EPA may not issue an action that has federalism 

implications, that imposes substantial direct compliance costs, and that is not required by statute, 

unless the Federal government provides the funds necessary to pay the direct compliance costs 

incurred by State and local governments, or the EPA consults with state and local officials early 

in the process of developing the proposed action.

The EPA has concluded that this action may have federalism implications, because it may 

impose substantial direct compliance costs on state or local governments, and the federal 

government will not provide the funds necessary to pay those costs. As discussed in the 

Supporting Statement found in the docket for this rulemaking, the development of state plans 

will entail many hours of staff time to develop and coordinate programs for compliance with the 

proposed rule, as well as time to work with state legislatures as appropriate, and develop a plan 

submittal.

The EPA consulted with state and local officials early in the process of developing the 

proposed action to permit them to have meaningful and timely input into its development. As 

described in the Federalism discussion in the preamble to the proposed standards of performance 

for GHG emissions from new EGUs (79 FR 1501, January 8, 2014), the EPA consulted with 

state and local officials in the process of developing the proposed standards for newly 

constructed EGUs. This outreach regarded planned actions for new, reconstructed, modified and 

existing sources. The EPA invited the following 10 national organizations representing state and 

local elected officials to a meeting on April 12, 2011, in Washington DC: (1) National Governors 

Association; (2) National Conference of State Legislatures, (3) Council of State Governments, 
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(4) National League of Cities, (5) U.S. Conference of Mayors, (6) National Association of 

Counties, (7) International City/County Management Association, (8) National Association of 

Towns and Townships, (9) County Executives of America, and (10) Environmental Council of 

States. These 10 organizations representing elected state and local officials have been identified 

by the EPA as the “Big 10” organizations appropriate to contact for purpose of consultation with 

elected officials. On February 26, 2014, the EPA re-engaged with those governmental entities to 

provide a pre-proposal update on the emission guidelines for existing EGUs and emission 

standards for modified and reconstructed EGUs. In addition, extensive stakeholder outreach 

conducted by the EPA allowed state leaders, including governors, environmental commissioners, 

energy officers, public utility commissioners, and air directors, opportunities to engage with EPA 

officials and provide input regarding reducing carbon pollution from power plants.

A detailed Federalism Summary Impact Statement (FSIS) describing the most pressing 

issues raised in pre-proposal and post-proposal comments will be forthcoming with the final rule, 

as required by section 6(b) of Executive Order 13132. In the spirit of Executive Order 13132, 

and consistent with EPA policy to promote communications between EPA and State and local 

governments, EPA specifically solicits comment on this proposed action from State and local 

officials.

7.6 Executive Order 13045, Protection of Children from Environmental Health Risks and 

Safety

The EPA interprets EO 13045 (62 FR 19885, April 23, 1997) as applying to those 

regulatory actions that concern health or safety risks, such that the analysis required under 

section 5-501 of the Order has the potential to influence the regulation. This action is not subject 

to EO 13045 because it does not involve decisions on environmental health or safety risks that 

may disproportionately affect children. The EPA believes that the CO2 emission reductions 

resulting from implementation of the proposed guidelines, as well as substantial ozone and PM2.5 

emission reductions as a co-benefit, would further improve children’s health.

7.7 Executive Order 13211, Actions Concerning Regulations that Significantly Affect 

Energy Supply, Distribution, or Use

Executive Order 13211 (66 FR 28355; May 22, 2001) requires the EPA to prepare and 

submit a Statement of Energy Effects to the Administrator of the Office of Information and 
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Regulatory Affairs, OMB, for actions identified as ‘‘significant energy actions.’’ This action, 

which is a significant regulatory action under EO 12866, is likely to have a significant effect on 

the supply, distribution, or use of energy. We have prepared a Statement of Energy Effects for 

this action as follows. We estimate a 4 to 7 percent increase in retail electricity prices, on 

average, across the contiguous U.S. in 2020, and a 16 to 22 percent reduction in coal-fired 

electricity generation as a result of this rule. The EPA projects that electric power sector 

delivered natural gas prices will increase by about 8 to 12 percent in 2020 and reflect no change 

to a decrease of approximately 3 percent in 2030. Additional information is available in Chapter 

3 of this RIA.

7.8 National Technology Transfer and Advancement Act (NTTAA)

Section 12(d) of the NTTAA of 1995 (Public Law No. 104-113; 15 U.S.C. 272 note) 

directs the EPA to use Voluntary Census Standards (VCS) in its regulatory and procurement 

activities unless to do so would be inconsistent with applicable law or otherwise impractical. 

Voluntary consensus standards are technical standards (e.g., materials specifications, test 

methods, sampling procedures, business practices) developed or adopted by one or more 

voluntary consensus bodies. The NTTAA directs the EPA to provide Congress, through annual 

reports to the OMB, with explanations when an agency does not use available and applicable 

VCS. This proposed rulemaking does not involve technical standards. 

The EPA welcomes comments on this aspect of the proposed rulemaking and specifically 

invites the public to identify potentially-applicable VCS and to explain why such standards 

should be used in this action. 

7.9 Executive Order 12898: Federal Actions to Address Environmental Justice in 

Minority Populations and Low-Income Populations

Executive Order 12898 (59 FR 7629, February 16, 1994) establishes federal executive 

policy on environmental justice. Its main provision directs federal agencies, to the greatest extent 

practicable and permitted by law, to make environmental justice part of their mission by 

identifying and addressing, as appropriate, disproportionately high and adverse human health or 

environmental effects of their programs, policies and activities on minority populations and lowincome populations in the U.S.
7-10

Section II.A of the preamble summarizes the public health and welfare impacts from 

GHG emissions that were detailed in the 2009 Endangerment Finding under CAA section 

202(a)(1).176 As part of the Endangerment Finding, the Administrator considered climate change 

risks to minority or low-income populations, finding that certain parts of the population may be 

especially vulnerable based on their circumstances. These include the poor, the elderly, the very 

young, those already in poor health, the disabled, those living alone, and/or indigenous 

populations dependent on one or a few resources. The Administrator placed weight on the fact 

that certain groups, including children, the elderly, and the poor, are most vulnerable to climaterelated health effects.

Strong scientific evidence that the potential impacts of climate change raise 

environmental justice issues is found in the major assessment reports by the U.S. Global Change 

Research Program (USGCRP), the Intergovernmental Panel on Climate Change (IPCC), and the 

National Research Council (NRC) of the National Academies, summarized in the record for the 

Endangerment Finding. Their conclusions include that poor communities can be especially 

vulnerable to climate change impacts because they tend to have more limited adaptive capacities 

and are more dependent on climate-sensitive resources such as local water and food supplies. In 

addition, Native American tribal communities possess unique vulnerabilities to climate change, 

particularly those on established reservations that are restricted to reservation boundaries and 

therefore have limited relocation options. Tribal communities whose health, economic wellbeing, and cultural traditions depend upon the natural environment will likely be affected by the 

degradation of ecosystem goods and services associated with climate change. Southwest native 

cultures are especially vulnerable to water quality and availability impacts. Native Alaskan 

communities are likely to experience disruptive impacts, including shifts in the range or 

abundance of wild species crucial to their livelihoods and well-being. The most recent 

assessments continue to strengthen scientific understanding of climate change risks to minority 

and low-income populations.

This proposed rule would limit GHG emissions by establishing CO2 emission guidelines 

 

176 “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”).
7-11

for existing fossil fuel-fired EGUs. In addition to reducing CO2 emissions, implementing the 

proposed rule would reduce other emissions from EGUs that become dispatched less frequently 

due to their relatively low energy efficiency. These emission reductions will include SO2 and 

NOx, which form ambient PM2.5 and ozone in the atmosphere, and hazardous air pollutants 

(HAP), such as mercury and hydrochloric acid. In the final rule revising the annual PM2.5

NAAQS177, the EPA identified persons with lower socioeconomic status as an at-risk population 

for experiencing adverse health effects related to PM exposures. Persons with lower 

socioeconomic status have been generally found to have a higher prevalence of pre-existing 

diseases, limited access to medical treatment, and increased nutritional deficiencies, which can 

increase this population’s risk to PM-related and ozone-related effects.178 Therefore, in areas 

where this rulemaking reduces exposure to PM2.5, ozone, and methylmercury persons with low 

socioeconomic status would also benefit. The regulatory impact analysis (RIA) for this 

rulemaking, included in the docket for this rulemaking, provides additional information 

regarding the health and ecosystem effects associated with these emission reductions. 

While there will be many locations with improved air quality for PM2.5, ozone, and HAP, 

there may also be EGUs whose emissions of one or more of these pollutants or their precursors 

increase as a result of the proposed emission guidelines for existing fossil fuel-fired EGUs. This 

may occur at EGUs that become dispatched more intensively than in the past because they 

become more energy efficient. The EPA has considered the potential for such increases and the 

environmental justice implications of such increases. 

As we noted in the NSR discussion in this preamble, as part of a state’s CAA section 

111(d) plan, the state may require an affected EGU to undertake a physical or operational 

changes to improve the unit’s efficiency that result in an increase in the unit’s dispatch and an 

increase in the unit’s annual emissions of GHGs and/or other regulated pollutants. A state can 

take steps to avoid increased utilization of particular EGUs and thus to avoid any significant 

 

177 U.S. EPA (2013). National Ambient Air Quality Standards for Particulate Matter, Final Rule. Federal Register

78 (15 January 2013): 3086-3287.

178 U.S. Environmental Protection Agency (U.S. EPA). 2009. Integrated Science Assessment for Particulate Matter 

(Final Report). EPA-600-R-08-139F. National Center for Environmental Assessment – RTP Division. December. 

Available on the Internet at <http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=216546>.
7-12

increases in emissions including emissions of other regulated pollutants whose environmental 

effects would be more localized around the affected EGU. To the extent that states take this path, 

there would be no new environmental justice concerns in the areas near such EGUs. For any 

EGUs that make modifications that do trigger NSR permitting, the applicable local, state, or 

federal permitting program will ensure that there are no new NAAQS violations and that no 

existing NAAQS violations are made worse. For those EGUs in a permitting situation for which 

the EPA is the permit reviewing authority, the EPA will consider environmental justice issues as 

required by Executive Order 12898.

In addition to some EGUs possibly being required by a state to make modifications for 

increased energy efficiency, another effect of the proposed CO2 emission guidelines for existing 

fossil fuel-fired EGUs would be increased utilization of other, unmodified EGUs with relatively 

low GHG emissions per unit of electrical output, in particular high efficiency gas-fired EGUs. 

Because such EGUs would not have been modified physically nor changed their method of 

operation, they would not be subject to review in the NSR permitting program. Such plants 

would have more hours in the year in which they operate and emit pollutants, including 

pollutants whose environmental effects if any would be localized rather than global as is the case 

with GHG emissions. Changes in utilization already occur now as demands for and sources of 

electrical energy evolve, but the proposed CO2 emission guidelines for existing fossil fuel-fired 

EGUs can be expected to cause more such changes. Because gas-fired EGUs emit essentially no 

mercury, increased utilization would not increase methylmercury concentrations in their 

vicinities. Increased utilization generally would not cause higher peak concentrations of PM2.5, 

NOx, or ozone around such EGUs than is already occurring because peak hourly or daily 

emissions generally would not change, but increased utilization may make periods of relatively 

high concentrations more frequent. It should be noted that the gas-fired sources that are likely to 

become dispatched more frequently than at present have very low emissions of primary 

particulate matter, SO2 and HAP per unit of electrical output, such that local (or regional) air 

quality for these pollutants is likely to be affected very little. For natural gas-fired EGUS, the 

EPA found that regulation of HAP emissions “is not appropriate or necessary because the 

impacts due to HAP emissions from such units are negligible based on the results of the study 

documented in the utility RTC” 65 FR 79831. In studies done by DOE/NETL comparing cost 
7-13

and performance of coal- and NG-fired generation, they assumed SO2, PM (and Hg) emissions to 

be “negligible.” Their studies predict NOx emissions from a NGCC unit to be approximately 10 

times lower than a subcritical or supercritical coal-fired boiler. Many are also very well 

controlled for emission of NOx through the application of after combustion controls such as 

selective catalytic reduction, although not all gas-fired sources are so equipped. Depending on 

the specificity of the state CAA section 111(d) plan, the state may be able to predict which EGUs 

and communities may be in this type of situation and to address any concerns about localized 

NO2 concentrations in the design of the CAA section 111(d) program, or separately from the 

CAA section 111(d) program but before its implementation. In any case, existing tracking 

systems will allow states and the EPA to be aware of the EGUs whose utilization has increased 

most significantly, and thus to be able to prioritize our efforts to assess whether air quality has 

changed in the communities in the vicinity of such EGUs. There are multiple mechanisms in the 

CAA to address situations in which air quality has degraded significantly. In conclusion, this 

proposed rule would result in regional and national pollutant reductions; however, there likely 

would also be some locations with more times during the year of relatively higher concentrations 

of pollutants with potential for effects on localized communities than would be experienced in 

the absence of the proposed rule. The EPA cannot exactly predict how emissions from specific 

EGUs would change as an outcome of the proposed rule due to the state-led implementation. 

Therefore, the EPA has concluded that it is not practicable to determine whether there would be 

disproportionately high and adverse human health or environmental effects on minority, low 

income, or indigenous populations from this proposed rule. 

In order to provide opportunities for meaningful involvement early on in the rule making 

process, the EPA has hosted webinars and conference calls on August 27, 2013 and September 9, 

2013 on the proposed rule specifically for environmental justice communities and has taken all 

comments and suggestions into consideration in the design of the emission guidelines. 

7.10 Executive Order 13175 - Consultation and Coordination with Indian Tribal 

Governments

This action does not have tribal implications, as specified in Executive Order 13175 (65 

FR 67249, November 9, 2000). It would not impose substantial direct compliance costs on tribal 

governments that have affected EGUs located in their area of Indian country. Tribes are not 
7-14

required to, but may, develop or adopt CAA programs. Tribes are not required to develop plans 

to implement the guidelines under CAA section 111(d) for affected EGUs. To the extent that a 

tribal government seeks and attains treatment in a manner similar to a state (TAS) status for that 

purpose and is delegated authority for air quality planning purposes, these proposed emission 

guidelines would require that planning requirements be met and emission management 

implementation plans be executed by the tribes. The EPA is aware of three coal-fired EGUs and 

one natural gas-fired EGU located in Indian country but is not aware of any affected EGUs that 

are owned or operated by tribal entities. The EPA notes that this proposal does not directly 

impose specific requirements on EGU sources, including those located in Indian country, such as 

the three coal-fired EGUs and one natural gas-fired EGU, but provides guidance to any tribe with 

delegated authority to address CO2 emissions from EGU sources found subject to section 111(d) 

of the CAA. Thus, Executive Order 13175 does not apply to this action.

The EPA conducted outreach to tribal environmental staff and offered consultation with 

tribal officials in developing this action. Because the EPA is aware of tribal interest in this 

proposed rule, prior to the April 13, 2012 proposal (77 FR 22392-22441), the EPA offered 

consultation with tribal officials early in the process of developing the proposed regulation to 

permit them to have meaningful and timely input into its development. The EPA's consultation 

regarded planned actions for new and existing sources. In addition, on April 15, 2014, prior to 

proposal, the EPA met with Navajo Energy Development Group officials. For this proposed 

action for existing EGUs, a tribe that has one or more affected EGUs located in its area of Indian 

Country179 would have the opportunity, but not the obligation, to establish a CO2 performance 

standard and a CAA section 111(d) plan for its area of Indian country. 

Consultation letters were sent to 584 tribal leaders. The letters provided information 

regarding the EPA's development of both the NSPS and emission guidelines for fossil fuel-fired 

EGUs and offered consultation. No tribes have requested consultation. Tribes were invited to 

 

179 The EPA is aware of at least four affected EGUs located in Indian country: two on Navajo lands, the Navajo 

Generating Station and the Four Corners Generating Station; one on Ute lands, the Bonanza Generating Station; 

and one on Fort Mojave lands, the South Point Energy Center. The affected EGUs at the first three plants are coalfired EGUs. The fourth affected EGU is an NGCC facility.
7-15

participate in the national informational webinar held August 27, 2013. In addition, 

consultation/outreach meeting was held on September 9, 2013, with tribal representatives from 

some of the 584 tribes. The EPA also met with tribal environmental staff via National Tribal Air

Association teleconferences on July 25, 2013, and December 19, 2013. In those teleconferences, 

the EPA provided background information on the GHG emission guidelines to be developed and 

a summary of issues being explored by the agency. Tribes have expressed varied points of view. 

Some tribes raised concerns about the impacts of the regulations on EGUs and the subsequent 

impact on jobs and revenue for their tribes. Other tribes expressed concern about the impact the 

regulations would have on the cost of water to their communities as a result of increased costs to 

the EGU that provide energy to transport the water to the tribes. Other tribes raised concerns 

about the impacts of climate change on their communities, resources, life ways and hunting and 

treaty rights. The tribes were also interested in the scope of the guidelines being considered by 

the agency (e.g., over what time period, relationship to state and multi-state plans) and how 

tribes will participate in these planning activities. In addition, the EPA held a series of listening 

sessions prior to development of this proposed action. In 2013, tribes participated in a session 

with the state agencies, as well as a separate session with tribes.

During the public comment period for this proposal, the EPA will hold meetings with 

tribal environmental staff to inform them of the content of this proposal, as well as offer further 

consultation with tribal elected officials where it is appropriate. We specifically solicit comment 

from tribal officials on this proposed rule. 
8-1

CHAPTER 8: COMPARISON OF BENEFITS AND COSTS

8.1 Comparison of Benefits and Costs

The benefits, costs, and net benefits of the Option 1 and Option 2 illustrative compliance 

scenarios are presented in this chapter of the Regulatory Impact Analysis (RIA). As discussed in 

Chapter 1, the EPA is proposing two options for state-specific rate-based CO2 goals that reflect 

application of measures from four building blocks. For both Options 1 and 2, two illustrative 

compliance scenarios, reflecting possible compliance approaches with state-specific CO2 goals, 

are analyzed in this RIA. These scenarios are a reflection of what compliance could look like for 

Options 1 and 2, assuming states comply with the CO2 goals on an individual state basis 

(referred to as state approach) or a number of states comply collectively on a regional basis 

(referred to as regional approach). EPA is proposing Option 1 and taking comment on Option 2. 

The guidelines allow flexibility of compliance, and EPA recognizes that actual compliance may 

differ from the illustrative approaches analyzed in this RIA.

The EPA has used the social cost of carbon estimates presented in the 2013 Technical 

Support Document: Technical Update of the Social Cost of Carbon for Regulatory Impact 

Analysis Under Executive Order 12866 (2013 SCC TSD) to analyze CO2 climate impacts of this 

rulemaking.

180

 We refer to these estimates, which were developed by the U.S. government, as 

“SCC estimates.” The SCC is an estimate of the monetary value of impacts associated with a 

marginal change in CO2 emissions in a given year). The four SCC estimates are associated with 

different discount rates (model average at 2.5 percent discount rate, 3 percent, and 5 percent; 95th

percentile at 3 percent), and each increases over time. In this comparison of benefits and costs, 

the EPA provides the estimate of climate benefits associated with the SCC value deemed to be 

 

180 Docket ID EPA-HQ-OAR-2013-0495, Technical Support Document: Technical Update of the Social Cost of 

Carbon for Regulatory Impact Analysis Under Executive Order 12866, Interagency Working Group on Social 

Cost of Carbon, with participation by Council of Economic Advisers, Council on Environmental Quality, 

Department of Agriculture, Department of Commerce, Department of Energy, Department of Transportation, 

Environmental Protection Agency, National Economic Council, Office of Energy and Climate Change, Office of 

Management and Budget, Office of Science and Technology Policy, and Department of Treasury (May 2013, 

Revised November 2013). Available at: 

http://www.whitehouse.gov/sites/default/files/omb/assets/inforeg/technical-update-social-cost-of-carbon-forregulator-impact-analysis.pdf.
8-2

central in the SCC TSD (the model average at 3% discount rate). As Table 8-1 shows, the EPA 

estimates that under the proposed Option 1, regional approach monetized climate benefits (in 

2011$) are $17 billion in 2020, assuming a 3 percent discount rate (model average). The air 

pollution health co-benefits in 2020 are estimated to be $16 billion to $37 billion for a 3 percent 

discount rate and $15 billion to $34 billion (2011$) for a 7 percent discount rate. The annual, 

illustrative compliance costs estimated by IPM and inclusive of demand side energy efficiency 

program and participant costs and monitoring, reporting, and recordkeeping (MRR) costs, are 

approximately $5.5 billion (2011$) in 2020. The quantified net benefits (the difference between 

monetized benefits and costs) in 2020, are $28 billion to $49 billion using a 3 percent discount 

rate and $26 billion to $45 billion assuming a 7 percent discount rate (2011$). In comparison, net 

benefits for the Option 1 state compliance approach in 2020 are estimated to be $27 billion to 

$50 billion, using a 3 percent discount rate and $26 billion to $46 billion assuming a 7 percent 

discount rate. Option 2 regional and state compliance approach benefits, costs and net benefits 

for 2020 are also shown on Table 8-1. Table 8-2 reflects estimates for the Options 1 and 2 

regional and state compliance approaches for 2025. For the regional compliance approach to the 

proposal Option 1 in 2030, the EPA estimates this approach will yield monetized climate 

benefits of $30 billion (2011$) (using 3 percent discount rate (model average) as shown on Table 

8-3. 

In addition to CO2, implementing these proposed guidelines is expected to reduce 

emissions of SO2 and NOX, which are precursors to formation of ambient PM2.5, as well as 

directly emitted fine particles. Therefore, reducing these emissions would also reduce human 

exposure to ambient PM2.5 and ozone precursors, thus the incidence of PM2.5- and ozone related 

health effects. These air pollution health co-benefits in 2030 are estimated to be $254 billion to 

$59 billion for a 3 percent discount rate and $23 billion to $54 billion (2011$) for a 7 percent 

discount rate. The annual illustrative compliance costs estimated using IPM, inclusive of demand 

side energy efficiency program and participant costs and MRR costs, are approximately $7.3

billion (2011$) in 2030. The quantified net benefits for the Option 1 regional in 2030 are $48

billion to $82 billion, assuming a 3 percent discount rate, and $46 to $77 billion assuming a 7 

percent discount rate (2011$). In 2030, quantified net benefits are $49 billion to $84 billion, 

assuming a 3 percent discount rate, and $46 to $79 billion assuming a 7 percent discount rate 
8-3

(2011$) for the Option 1 state compliance approach..

Based upon the foregoing discussion, it remains clear that this proposal’s combined 

climate benefits and human health co-benefits associated with the reduction in other air 

pollutants substantial and far outweigh the compliance costs for all of the regulatory options and 

compliance approaches. The EPA could not monetize important categories of impacts. 

Unquantified impacts include those associated with changes in emissions of other pollutants that 

affect the climate, such as methane. In addition, the analysis does not quantify co-benefits from 

reducing exposure to SO2, NOx, and hazardous air pollutants (e.g., mercury and hydrogen 

chloride), as well as ecosystem effects and visibility impairment.
8-4

Table 8-1. Summary of Estimated Monetized Benefits, Compliance Costs, and Net Benefits 

for the Proposed Guidelines – 2020 (billions of 2011$) a

Option 1 - state Option 2 – state

3% Discount 

Rate

7% Discount 

Rate 3% Discount Rate 7% Discount Rate

Climate Benefits b 

5% discount rate

3% discount rate

2.5% discount rate

95th percentile at 3% discount rate

$4.9

$18

$26

$52

$3.8

$14

$20

$40

Air pollution health co-benefits c $17 to $40 $15 to $36 $14 to $32 $12 to $29

Total Compliance Costs d $7.5 $5.5

Net Benefits e $27 to $50 $26 to $46 $22 to $40 $20 to $37

Non-Monetized Benefits

Direct exposure to SO2 and NO2

1.5 tons of Hg

Ecosystem effects

Visibility impairment

Direct exposure to SO2 and NO2

1.2 tons of Hg 

Ecosystem effects

Visibility impairment

Option 1 – regional Option 2 – regional

3% Discount 

Rate

7% Discount 

Rate 3% Discount Rate 7% Discount Rate

Climate Benefits b

5% discount rate

3% discount rate

2.5% discount rate

95th percentile at 3% discount rate

$4.7

$17

$25

$51

$3.6

$13

$19

$39

Air pollution health co-benefits c $16 to $37 $15 to $34 $13 to $31 $12 to $28

Total Compliance Costs d $5.5 $4.3

Net Benefits e $28 to $49 $26 to $45 $22 to $40 $21 to $37

Non-Monetized Benefits

Direct exposure to SO2 and NO2

1.3 tons of Hg 

Ecosystem effects

Visibility impairment

Direct exposure to SO2 and NO2

0.9 tons of Hg 

Ecosystem effects

Visibility impairment

a All estimates are for 2020, and are rounded to two significant figures, so figures may not sum.

b The climate benefit estimates in this summary table reflect global impacts from CO2 emission changes and do not 

account for changes in non-CO2 GHG emissions. Different discount rates are applied to SCC than to the other 

estimates because CO2 emissions are long-lived and subsequent damages occur over many years. The SCC 

estimates are year-specific and increase over time. 

c The air pollution health co-benefits reflect reduced exposure to PM2.5 and ozone associated with emission 

reductions of directly emitted PM2.5, SO2 and NOX. The range reflects the use of concentration-response 

functions from different epidemiology studies. The reduction in premature fatalities each year accounts for over 

90 percent of total monetized co-benefits from PM2.5 and ozone. These models assume that all fine particles, 

regardless of 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. 

d Total social costs are approximated by the illustrative compliance costs estimated, in part, using the Integrated 

Planning Model for the proposed option and a discount rate of approximately 5%. This estimate includes 

monitoring, recordkeeping, and reporting costs and demand side energy efficiency program and participant costs.

e The estimates of net benefits in this summary table are calculated using the global social cost of carbon at a 3 

percent discount rate (model average). The RIA includes combined climate and health estimates based on these 

additional discount rates.
8-5

Table 8-2. Summary of Estimated Monetized Benefits, Compliance Costs, and Net Benefits 

for the Proposed Guidelines – 2025 (billions of 2011$) a

Option 1 – state Option 2 – state

3% Discount 

Rate

7% Discount 

Rate

3% Discount 

Rate

7% Discount 

Rate

Climate Benefits b

5% discount rate

3% discount rate

2.5% discount rate

95th percentile at 3% discount rate

$7.6

$25

$37

$77

$5.6

$19

$28

$27

Air pollution health co-benefits c $23 to $54 $21 to $49 $18 to $41 $16 to $37

Total Compliance Costs d $5.5 $5.5

Net Benefits e $43 to $74 $41 to $69 $31 to $55 $29 to $51

Non-Monetized Benefits

Direct exposure to SO2 and NO2

2.0 tons of Hg 

Ecosystem effects

Visibility impairment

Direct exposure to SO2 and NO2

1.7 tons of Hg 

Ecosystem effects

Visibility impairment

Option 1 – regional Option 2 – regional

3% Discount 

Rate

7% Discount 

Rate

3% Discount 

Rate

7% Discount 

Rate

Climate Benefits b

5% discount rate

3% discount rate

2.5% discount rate

95th percentile at 3% discount rate

$7.5

$25

$37

$76

$5.5

$18

$27

$56

Air pollution health co-benefits c $23 to $53 $21 to $48 $17 to $40 $16 to $36

Total Compliance Costs d $4.6 $4.5

Net Benefits e $43 to $74 $41 to $69 $31 to $54 $29 to $50

Non-Monetized Benefits

Direct exposure to SO2 and NO2

1.7 tons of Hg 

Ecosystem effects

Visibility impairment

Direct exposure to SO2 and NO2

1.3 tons of Hg 

Ecosystem effects

Visibility impairment

a All estimates are 2025, and are rounded to two significant figures, so figures may not sum.

b The climate benefit estimates in this summary table reflect global impacts from CO2 emission changes and do not 

account for changes in non-CO2 GHG emissions. Different discount rates are applied to SCC than to the other 

estimates because CO2 emissions are long-lived and subsequent damages occur over many years. The SCC 

estimates are year-specific and increase over time. 

c The air pollution health co-benefits reflect reduced exposure to PM2.5 and ozone associated with emission 

reductions of directly emitted PM2.5, SO2 and NOX. The range reflects the use of concentration-response 

functions from different epidemiology studies. The reduction in premature fatalities each year accounts for over 

90 percent of total monetized co-benefits from PM2.5 and ozone. These models assume that all fine particles, 

regardless of 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. 

d Total social costs are approximated by the illustrative compliance costs estimated, in part, using the Integrated 

Planning Model for the proposed option and a discount rate of approximately 5%. This estimate includes 

monitoring, recordkeeping, and reporting costs and demand side energy efficiency program and participant costs.

e The estimates of net benefits in this summary table are calculated using the global social cost of carbon at a 3 

percent discount rate (model average). The RIA includes combined climate and health estimates based on these 

additional discount rates.
8-6

Table 8-3. Summary of Estimated Monetized Benefits, Compliance Costs, and Net Benefits 

for the Proposed Guidelines –2030 (billions of 2011$)

a

Option 1– state

3% Discount Rate 7% Discount Rate

Climate Benefits b 

5% discount rate

3% discount rate

2.5% discount rate

95th percentile at 3% discount rate

$9.5

$31

$44

$94

Air pollution health co-benefits c $27 to $62 $24 to $56

Total Compliance Costs d $8.8

Net Benefits e $49 to $84 $46 to $79

Non-Monetized Benefits

Direct exposure to SO2 and NO2

2.1 tons of Hg and 590 tons of HCl

Ecosystem effects

Visibility impairment

Option 1– regional

3% Discount Rate 7% Discount Rate

Climate Benefits b

5% discount rate

3% discount rate

2.5% discount rate

95th percentile at 3% discount rate

$9.3

$30

$44

$92

Air pollution health co-benefits c $25 to $59 $23 to $54

Total Compliance Costs d $7.3

Net Benefits e $48 to $82 $46 to $77

Non-Monetized Benefits

Direct exposure to SO2 and NO2

1.7 tons of Hg and 580 tons of HCl

Ecosystem effects

Visibility impairment

a All estimates are 2030, and are rounded to two significant figures, so figures may not sum.

b The climate benefit estimates in this summary table reflect global impacts from CO2 emission changes and do not 

account for changes in non-CO2 GHG emissions. Also, different discount rates are applied to SCC than to the 

other estimates because CO2 emissions are long-lived and subsequent damages occur over many years. The SCC 

estimates are year-specific and increase over time. 

c The air pollution health co-benefits reflect reduced exposure to PM2.5 and ozone associated with emission 

reductions of directly emitted PM2.5, SO2 and NOX. The range reflects the use of concentration-response 

functions from different epidemiology studies. The reduction in premature fatalities each year accounts for over 

90 percent of total monetized co-benefits from PM2.5 and ozone. These models assume that all fine particles,

regardless of 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. 

d Total social costs are approximated by the illustrative compliance costs estimated, in part, using the Integrated 

Planning Model for the proposed option and a discount rate of approximately 5%. This estimate includes 

monitoring, recordkeeping, and reporting costs and demand side energy efficiency program and participant costs.

e The estimates of net benefits in this summary table are calculated using the global social cost of carbon at a 3 

percent discount rate (model average). The RIA includes combined climate and health estimates based on these 

additional discount rates.
8-7

8.2 Uncertainty Analysis

The Office of Management and Budget’s circular Regulatory Analysis (Circular A-4) 

provides guidance on the preparation of regulatory analyses required under E.O. 12866, and 

requires an uncertainty analysis for rules with annual benefits or costs of $1 billion or more.181

This proposed rulemaking surpasses that threshold for both benefits and costs. Throughout the 

RIA, we considered a number of sources of uncertainty, both quantitatively and qualitatively, on

benefits and costs. We summarize three key elements of our analysis of uncertainty here:

 Evaluating uncertainty in the compliance approaches that states will implement under 

Option 1 or Option 2, which influences both costs and benefits. 

 Assess uncertainty in the methods used to calculate the health co-benefits associated with 

the reduction in PM2.5 and ozone and the use of a benefits-per-ton approach in estimating 

these co-benefits. 

 Characterizing uncertainty in the monetization of climate related benefits. 

Some of these elements are evaluated using probabilistic techniques, whereas for others 

the underlying likelihoods of certain outcomes are unknown and so we use scenario analysis to 

evaluate their potential effect on the benefits and costs of this rulemaking.

8.2.1 Uncertainty in Abatement Costs and Compliance Approaches

The calculation of the state goals is based on an evaluation of methods for reducing the 

carbon emissions intensity of electricity generation that may be achieved at reasonable cost. Our 

best estimates of the costs of these methods of intensity reduction are reported within the cost 

analysis of this rule and are included in the cost modeling in the RIA (e.g., the cost of demandside energy efficiency programs and non-emitting generation). We have also conducted cost 

analyses for alternative quantifications of the degree to which the building blocks of the BSER 

determination can reduce the carbon emissions intensity of electricity generation, and scenarios 

 

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

http://www.whitehouse.gov/omb/circulars_a004_a-4 and OMB, 2011. Regulatory Impact Analysis: A Primer.

http://www.whitehouse.gov/sites/default/files/omb/inforeg/regpol/circular-a-4_regulatory-impact-analysis-aprimer.pdf
8-8

modeled and presented in this RIA illustrate the potential range of impacts of the varying 

specifications of building block potential between Option 1 and Option 2 scenarios. Comments 

received by EPA on this proposal will have the potential to shed light on some of the uncertain 

components associated with the cost of technologies or other methods of reducing emissions 

intensity and will inform the basis of analyses in the final rule. However, we recognize that 

systematic uncertainty will persist in the analysis even with this additional information and 

analysis, especially given the state-level flexibility in meeting the guidelines. 

A significant source of uncertainty under this regulation is the ultimate approach states 

will take to comply with the guidelines, which will affect both the costs and benefits of this rule. 

For this reason we modeled two potential compliance scenarios for each regulatory option: the 

state scenario and the regional scenario. In general, for both Option 1 and Option 2, the 

compliance cost of the regulation fell under the regional scenario as compared to the state 

scenario, while the reductions of CO2 and other pollutants from fossil fuel combustion also fell 

(but by lesser proportions). However, from this analysis we see that the net benefits of the two 

scenarios, given consistent assumptions, do not differ notably. 

8.2.2 Uncertainty Associated with PM2.5 and Ozone Health Co-Benefits Assessment 

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, which accounts for 98 percent of the monetized PM2.5 health 

co-benefits. 

 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, 2009b).
8-9

 We assume that the health impact function for fine particles is log-linear without a 

threshold in this analysis. 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, 2004c), which affects the valuation of mortality cobenefits at different discount rates.EPA quantitatively assessed uncertainty in the air 

pollution health co-benefits, including probabilistic approaches. 

As a further example, EPA provides the 95th percentile confidence interval for avoided 

PM-related premature deaths and the associated economic valuation using two key epidemiology 

studies. Further, EPA provides the PM-related results using alternate concentration-response 

relationship provided by an expert elicitation and alternate ozone-related results using 

concentration-response relationships provided by alternate epidemiology studies. Additional 

quantitative analyses include sensitivity analyses for alternate income growth adjustments and 

cessation lags, assessments of the distribution of population exposure in the modeling underlying 

the benefit-per-on estimates, and a quantitative evaluation of the benefit-per-ton estimates 

relative to recent analyses. A qualitative description of uncertainties associated with certain 

assumptions for PM, such as the linear model and equal potency across constituents, is also 

provided. For further discussion and characterization of those uncertainties influencing the 

benefit assessment, see Chapter 4. 

As noted and described in Chapter 4, we use a benefit-per-ton approach to quantify health 

co-benefits. All benefit-per-ton have inherent limitations, including that the estimates reflect the 

geographic distribution of the modeled sector emissions, which may not match the emission 

reductions anticipated by the proposed guidelines, and they may not reflect local variability in 

population density, meteorology, exposure, baseline health incidence rates, or other local factors 
8-10

for any specific location. In addition, these estimates reflect the regional average benefit-per-ton 

for each ambient PM2.5 precursor emitted from EGUs, which assumes a linear atmospheric 

response to emission reductions. The regional benefit-per-ton estimates, although less subject to 

these types of uncertainties than national estimates, still should be interpreted with caution. Even 

though we assume that all fine particles have equivalent health effects, the benefit-per-ton 

estimates vary between precursors depending on the location and magnitude of their impact on 

PM2.5 levels, which drive population exposure. 

8.2.3 Uncertainty Associated with Estimating the Social Cost of Carbon

The 2010 SCC TSD noted a number of limitations to the SCC analysis, including the 

incomplete way in which the integrated assessment models capture catastrophic and noncatastrophic impacts, their incomplete treatment of adaptation and technological change, 

uncertainty in the extrapolation of damages to high temperatures, and assumptions regarding risk 

aversion. Current integrated assessment models do not assign value to all of the important 

physical, ecological, and economic impacts of climate change recognized in the climate change 

literature due to a lack of precise information on the nature of damages and because the science 

incorporated into these models understandably lags behind the most recent research. The limited 

amount of research linking climate impacts to economic damages makes the modeling exercise 

even more difficult. These individual limitations do not all work in the same direction in terms of 

their influence on the SCC estimates, though taken together they suggest that the SCC estimates 

are likely conservative. In particular, the IPCC Fourth Assessment Report (2007) concluded that 

“It is very likely that [SCC estimates] underestimate the damage costs because they cannot 

include many non-quantifiable impacts.”

EPA characterized significant sources of uncertainty in the estimate of climate benefits of 

the emission reductions forecast by the compliance cost modeling.182 The modeling underlying 

 

182 As discussed in Chapter 4, EPA estimated the global social benefits of CO2 emission reductions expected from 

the proposed guidelines using the SCC estimates presented in the 2013 Technical Support Document: Technical 

Update of the Social Cost of Carbon for Regulatory Impact Analysis Under Executive Order 12866 (2013 SCC 

TSD). The estimates were first developed by the U.S. government and published in the 2010 Technical Support 

Document: Social Cost of Carbon for Regulatory Impact Analysis Under Executive Order 12866, Interagency 
8-11

the development of the SCC estimates addressed uncertainty in several ways. Three integrated 

assessment models (IAMs) were used to generate the SCC estimates. Each IAM relied on Monte 

Carlo simulations specifying different possible outcomes for climate sensitivity (represented by a 

Roe and Baker Distribution), used five different emissions growth scenarios and three discount 

rates. The distribution of results were depicted by four point estimates. The use of this range of 

point estimates in this rulemaking helps to reflect the uncertainty in the SCC estimation exercise. 

See both the 2010 SCC TSD and 2013 SCC TSD for a full description. 

 

Working Group on Social Cost of Carbon (2010 SCC TSD) and updated in the 2013 SCC TSD. We refer to these 

estimates as “SCC estimates.”
9-1

CHAPTER 9: BENEFITS, COSTS, AND ECONOMIC IMPACTS OF STANDARDS OF 

PERFORMANCE FOR RECONSTRUCTED AND MODIFIED ELECTRIC UTILITY 

GENERATING UNITS

9.1 Introduction

The U.S. Environmental Protection Agency (EPA) is proposing emission limits for 

carbon dioxide (CO2) emitted from reconstructed and modified electric utility generating units 

(EGUs) under section 111(b) of the Clean Air Act (CAA). This chapter presents the proposed 

standards of performance for reconstructed and modified EGUs, as well as the expected 

economic impacts of the proposed standards. Based on historical information that has been 

reported to the EPA, the EPA anticipates few covered units will trigger the reconstruction or 

modification provisions in the period of analysis (through 2025). As a result, we do not 

anticipate any significant costs or benefits associated with this proposal. However, because there 

have been a few units that have notified EPA of modifications in the past, in this chapter we 

present an illustrative analysis of the costs and benefits for a hypothetical unit if it were to trigger 

the modification provision. We also discuss how the costs and benefits for compliance with the 

proposed standards may vary as a result of interactions with the CAA 111(d) emission guidelines 

for existing sources. This chapter also presents the relevant executive order and statutory 

requirements related to this proposal.

9.2 Legal Basis for this Rulemaking

Section 111 of the CAA requires performance standards for air pollutant emissions from 

categories of stationary sources that may reasonably contribute to the endangerment of public 

health or welfare. In April 2007, the Supreme Court ruled in Massachusetts v. EPA that 

greenhouse gases (GHGs) meet the definition of an “air pollutant” under the CAA. This ruling 

clarified that the authorities and requirements of the CAA apply to GHGs. The EPA issued a 

final determination that GHG emissions endanger both the public health and the public welfare 

of current and future generations in the Endangerment and Cause or Contribute Findings for 

Greenhouse Gases Under Section 202(a) of the CAA (74 FR 66,496; Dec. 15, 2009).
9-2

On September 20, 2013, the EPA announced its first steps under President Obama’s 

Climate Action Plan to reduce CO2 emissions from power plants by proposing standards for 

newly constructed power plants built in the future (79 FR 1430, January 8, 2014). Specifically, 

under the authority of CAA section 111(b), the EPA proposed standards for emissions of CO2

from newly constructed utility boilers and integrated gasification combined cycle (IGCC) units,

and for natural gas-fired stationary combustion turbines. This action proposes standards to 

address CO2 emissions from reconstructed and modified power plants under the authority of 

CAA section 111(b).

9.3 Background for the Proposed EGU Reconstructed and Modified Source GHG 

Standards

9.3.1 Definition of Affected Sources

This action proposes emission limits for EGUs that undergo reconstruction or 

modification. This rulemaking does not address GHG emissions from newly constructed sources 

or existing sources that do not undertake reconstruction or modification. (Analysis of the impact 

of emission guidelines for existing sources is presented in the preceding chapters.) The EPA is 

proposing that an existing unit that becomes subject to requirements under section 111(d) will 

continue to be subject to those requirements even after it becomes a modified or reconstructed 

source. Under this interpretation, the modified or reconstructed source would be subject to both 

the section 111(d) requirements that it had previously been subject to and the modified or 

reconstructed source standard under 111(b) that it became subject to as a modified or 

reconstructed source.

9.3.1.1 Reconstructed Sources

Under the EPA’s CAA section 111 standards of performance for new stationary sources, 

reconstructed sources are defined, in general, as existing sources that replace components to such 

an extent that the capital costs of the new components exceed 50 percent of the capital costs of 

an entirely new facility, and for which compliance with standards of performance for newly 

constructed sources is technologically and economically feasible. As described in the preamble, 

determinations regarding reconstructed status are made on a case-by-case basis, based on 

information submitted regarding the planned project.
9-3

9.3.1.2 Modified Sources

A modification is any physical or operational change to a source that increases the 

source’s maximum achievable hourly rate of emissions (i.e., lbs/MWh). The EPA, through 

regulations, has determined that certain types of changes (such as pollution control projects) are 

exempt from consideration as a modification (40 CFR 60.2, 60.14(e)).

9.3.2 Emission Limits

This action proposes standards of performance for reconstructed utility boilers and IGCC 

units based on current best-performing generating technology (e.g., use of the highest 

demonstrated steam temperatures and pressures) as the best system of emission reductions 

(BSER). This action also proposes standards of performance for reconstructed natural gas-fired 

stationary combustion turbines based on modern, efficient natural gas combined cycle (NGCC) 

technology as the BSER. The proposed emission limits are calculated on a 12-month rolling 

average basis. The proposed emission limits for these units are shown in table 9-1. All existing 

sources that reconstruct after becoming subject to an approved CAA 111(d) state plan will 

remain in the plan and remain subject to regulatory requirements in the plan in addition to its 

regulatory requirements as a reconstructed unit.

Table 9-1. Proposed Emission Limits - Reconstructions

Subcategory Emission Limit

Reconstructed Utility Boilers and IGCC Units 

(heat input rating > 2,000 MMBtu/h)

1,900 lb CO2/MWh-net

Reconstructed Utility Boilers and IGCC Units 

(heat input rating <= 2,000 MMBtu/h) 2,100 lb CO2/MWh-net

Reconstructed Natural Gas-Fired Stationary Combustion Turbines

(heat input rating > 850 MMBtu/h) 1,000 lb CO2/MWh-gross

Reconstructed Natural Gas-Fired Stationary Combustion Turbines 

(heat input rating <= 850 MMBtu/h) 1,100 lb CO2/MWh-gross

This action proposes that modified utility boilers and IGCC units must meet one of two 

alternative requirements, depending on the timing of the modification.183 Sources that modify 

 

183 Units triggering both the modification and reconstruction provisions would be subject to the reconstructed source 

standards.
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prior to becoming subject to an approved section 111(d) state plan would be required to meet a 

unit-specific numerical emission standard that is based on the source’s best demonstrated 

historical performance. Specifically, a modified utility boiler or IGCC unit would be required to 

maintain an emission rate that is two percent less than the unit’s best demonstrated annual 

performance during the years from 2002 to the year the modification occurs (or the emission 

limitation applicable to a corresponding reconstructed source, whichever is less stringent). The 

EPA has determined that this standard can be met through a combination of best operating 

practices and equipment upgrades. In co-proposed alternative #2, existing utility boilers and 

IGCC units that modify after becoming subject to requirements under an approved 111(d) plan 

would be required to meet a unit-specific emission limitation that is determined from a third 

party assessment to identify energy efficiency improvement opportunities for the affected 

source.184 All existing sources that are subject to requirements under an approved section 111(d) 

plan would remain subject to those requirements after modifying.

For affected modified natural gas-fired stationary combustion turbines not subject to a 

111(d) plan, this action proposes standards of performance based on efficient NGCC technology 

as the BSER. The EPA is also proposing that affected stationary combustion turbines that are 

subject to an approved section 111(d) plan must remain in the plan. The proposed emission limits 

for modified sources (for co-proposed alternative #2) are shown in Table 9-2.

 

184 In co-proposed alternative #1, units would be required to meet the same 111(b) standard both before and after 

participation in a state 111(d) program.
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Table 9-2. Proposed Emission Limits - Modifications

Subcategory Emission Limit

Not Subject to State 

CAA 111(d) Plan

Modified Utility Boilers and IGCC Units 

(heat input rating > 2,000 MMBtu/h)

2% less than unit’s best demonstrated 

annual performance

NO LOWER THAN

1,900 lb CO2/MWh-net

Modified Utility Boilers and IGCC Units 

(heat input rating <= 2,000 MMBtu/h)

2% less than unit’s best demonstrated 

annual performance

NO LOWER THAN

2,100 lb CO2/MWh-net

Modified Natural Gas-Fired Stationary 

Combustion Turbines (heat input rating > 

850 MMBtu/h)

1,000 lb CO2/MWh-gross

Modified Natural Gas-Fired Stationary 

Combustion Turbines (heat input rating <= 

850 MMBtu/h )

1,100 lb CO2/MWh-gross

After Becoming 

Subject to State CAA 

111(d) Plan

Modified Utility Boilers and IGCC Units

Unit-specific emission limit determined 

by the expected performance after 

implementation of identified energy 

efficiency improvement opportunities

AND

111(d) requirements

Modified Natural Gas-Fired Stationary 

Combustion Turbines (heat input rating > 

850 MMBtu/h)

1,000 lb CO2/MWh-gross

AND

111(d) requirements

Modified Natural Gas-Fired Stationary 

Combustion Turbines (heat input rating <= 

850 MMBtu/h )

1,100 lb CO2/MWh-gross

AND

111(d) requirements

Note: The proposed emission limits are calculated on a 12-month rolling average basis. 

9.4 Impacts of the Proposed Reconstructed and Modified Source Standards

9.4.1 Reconstructed Sources

Historically, we are only aware of one EGU that has notified the EPA that it has 

reconstructed under the reconstruction provision of section 111(b). As a result, we anticipate that 

few EGUs will undertake reconstruction through 2025. For this reason, the proposed standards 

will not result in any significant emission reductions, costs, or quantified benefits in the period of 

analysis. Likewise, the Agency does not anticipate any impacts on the price of electricity or 

energy supply. The proposed rule is not expected to raise any resource adequacy concerns, since 

reserve margins will not be impacted and the rule does not impose any additional requirements 

on existing facilities not triggering the reconstruction provision. There are no macroeconomic or

employment impacts expected as a result of these proposed standards.
9-6

Due to the extremely limited data available on reconstructions, it is not possible to 

conduct a representative illustrative analysis of what costs and benefits might result from this 

proposal in the unlikely case that a unit were to reconstruct.

9.4.2 Modified Sources

Historically, few EGUs have notified the EPA that they have modified under the 

modification provision of section 111(b). The EPA’s current regulations define an NSPS

“modification” as a physical or operational change that increases the source’s maximum 

achievable hourly rate of emissions, but specifically exempt from that definition projects that 

entail the installation of pollution control equipment or systems. The EPA expects that most of 

the actions EGUs are likely to take in the foreseeable future that would be classified as 

“modifications” would qualify as pollution control projects. In many cases, those projects would 

involve the installation of add-on control equipment needed to meet CAA requirements for 

criteria and air toxics air pollutants. Any associated CO2 emissions increases would generally be 

small and would occur as a chemical byproduct of the operation of the control equipment. In 

other cases, those projects would involve equipment changes to improve fuel efficiency to meet 

state requirements for implementation of the future CAA section 111(d) rulemaking for existing 

sources and would have the effect of increasing a source’s maximum achievable hourly emission 

rate (lb CO2/hr), even while decreasing its actual output based emission rate (lb CO2/MWh). 

Because all of these actions would be treated as pollution control projects under the EPA’s 

current NSPS regulations, they would be specifically exempted from the definition of 

modification.

Based on this information, we anticipate that few EGUs will take actions that would be 

considered modifications during the period of analysis. For this reason, the proposed standards 

will result in minimal emission reductions, costs, or quantified benefits by 2025. Likewise, the 

Agency does not anticipate any impacts on the price of electricity or energy supplies. This

proposed rule is not expected to raise any resource adequacy concerns, since reserve margins 

will not be impacted and the rule does not impose any additional requirements on existing 

facilities not triggering the modification provision. There are no macroeconomic or employment 

impacts expected as a result of these proposed standards.
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9.4.2.1 Illustrative Analysis – Emission Reductions

In the unlikely event that a unit were to trigger the modification provision, the proposed 

standards would result in some costs and benefits. Based on available data, we conducted an 

analysis of a hypothetical unit to illustrate the potential costs and benefits of the proposed 

standards. The hypothetical plant presented in this section is a coal-fired boiler185 and is assumed 

to be in compliance with all on-the-books federal regulations. In the illustrative analysis, costs 

and benefits are presented for compliance in 2025. Section 111(b) standards are subject to review 

every eight years. While we do not know when the hypothetical plant would choose to undertake 

a modification, it is necessary to select a year of analysis in order to value the climate benefits 

and health co-benefits resulting from these proposed standards. We have selected 2025 to 

approximate the end of the eight year period. All estimates are presented in 2011 dollars.

The hypothetical unit used in this analysis is a 33 percent efficient 500 MW bituminous 

coal-fired unit operating at 78 percent capacity. The assumptions for capacity and efficiency 

were based on the average projected coal fleet in IPM in 2020. The emission rate specifications 

are (1) SO2 = 0.30 lb/MMBtu (3.3% weight Illinois bituminous coal with 95% efficient 

scrubber), (2) NOX = 0.30 lb/MMBtu (low NOX burners plus overfire air and SNCR), and (3) 

PM2.5 = 0.024 lb/MMBtu (8 lb/MMBtu ash with 99.7% control). In this analysis, we examine the 

costs and benefits that would occur if this unit were able to make a 4 percent, 6 percent, or 8 

percent heat rate improvement as a result of measures implemented in order to comply with the

proposed standards for modified sources. 

 

185 IGCC and NGCC units are significantly less likely to trigger the modification provision due to the way in which 

these units are constructed. Simple-cycle combustion turbines (CT) have lower fuel efficiencies and produce a 

significantly higher cost of electricity (cost per kWh) at higher capacity factors and consequently are typically 

utilized at levels well below the proposed threshold for covered sources (1/3 of potential electric output). 

Historically, these units have most often been built to ensure reserve margins are met during peak periods 

(typically in the summer), and in some instances are able to generate additional revenues by selling capacity into 

power markets. Thus, in practice, EPA expects that potential CT units would not meet the applicability threshold 

in this proposed action and would not be subject to any standard. During the public comment period for the 

proposed 111(b) standards for newly constructed sources, the EPA has received comments regarding more recent 

integration of CT units and renewables and is taking comment on the treatment of these units.
9-8

Costs. We assume the hypothetical unit will implement a range of energy efficiency 

improvements at an annual cost of $8.3 million.186 At this cost, we estimate the typical unit 

would be able to implement a set of efficiency improvement measures that would achieve a heat 

rate improvement of approximately 6 percent. A heat rate improvement of 6 percent is 

approximately equivalent to an absolute efficiency improvement of 2 percent. The actual 

efficiency improvement a unit can achieve for this cost will depend on its starting efficiency and 

operating capacity. Less efficient units may have more efficiency improvement options available 

and be able to achieve greater efficiency for the same cost. Relatively more efficient units may 

have fewer remaining cost-effective options to improve efficiency. Improvements in efficiency 

will result in fuel savings, which can offset the cost of implementing those improvements. Cost 

and fuel savings information is shown in Table 9-3. 

For units that modify prior to being part of a section 111(d) existing source program that 

choose to comply with a source-specific standard that is a 2 percent improvement from the 

previous best performance, costs will vary depending on the current and prior performance of 

that unit. For a unit that modifies after being covered by a section 111(d) program, it is possible 

that the energy assessment may not identify any further efficiency improvements that unit can 

economically undertake considering the overall 111(d) program requirements. In those cases, 

there would be no additional control costs to the unit as a result of this proposal.

 

186 Calculated based on a total capital cost of $50 million ($100/kilowatt), financed over 15 years at a capital charge 

rate of 14.29%. The capital charge rate value and useful life are based on assumptions used when modeling the 

electricity sector in the Integrated Planning Model (IPM). Please see Chapter 8 of EPA’s documentation for the 

IPM for more information on the assumed capital charge rate: http://www.epa.gov/airmarkets/progsregs/epaipm/docs/v513/Chapter_8.pdf. See the See Technical Support Document – GHG Abatement Measures” for 

information about the cost of efficiency improvements.
9-9

Table 9-3. Annualized Costs and Fuel Savings of Efficiency Improvements (millions of 

2011$)

4% Efficiency

Improvement

6% Efficiency

Improvement

8% Efficiency

Improvement

Annual Cost $8.3 $8.3 $8.3

Fuel Savings $3.7 $5.6 $7.5

Net Cost $4.5 $2.6 $0.78

Emission Reductions. By improving the efficiency of its operations, the hypothetical unit 

would reduce emissions of CO2 and may reduce emissions of (SO2), nitrogen dioxide (NOx), and 

fine particulate matter (PM2.5), which would lead to lower ambient concentrations of PM2.5 and 

ozone. Table 9-4 shows the expected emission reductions from each level of efficiency 

improvement for the hypothetical unit. For a unit participating in a 111(d) plan, it is possible that 

the CO2 emission reductions could be offset by increases elsewhere in the system depending on 

the structure of that plan. Ancillary emission reductions may be offset if, as a result of these 

reductions, a unit is able to reduce operation of pollution control equipment and still meet outputbased standards for these pollutants or pollutants related to their control (such as the Mercury 

and Air Toxics Standards limit for SO2 to control hydrogen chloride). Additionally, if efficiency 

improvements lead to decreased operating costs, it may result in changes to the frequency of 

dispatch for the unit. In that case, the unit may run more often, offsetting some of the emission 

decreases. Furthermore, for units that choose to comply with a source-specific standard that is a 

2 percent improvement from the previous best performance, emission reductions will vary 

depending on the current and prior performance of that unit. Additionally, it is possible that the 

energy assessment for a unit participating in a 111(d) program may not identify any further fuel 

efficiency improvements that unit can economically undertake after implementing the overall 

111(d) program requirements. In those cases, there would be no additional emission reductions 

or control costs as a result of this proposal.
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Table 9-4. Emission Reductions from Efficiency Improvements (tons/year)

4% Efficiency

Improvement

6% Efficiency

Improvement

8% Efficiency

Improvement

CO2 132,790 199,184 265,579

SO2 214 321 428

NOx (Total) 214 321 428

NOx (Ozone Season) 89 134 178

PM2.5 17 26 34

Notes: SO2, NOX, and PM2.5 in short tons, CO2 in metric tonnes.

Benefits. Reducing CO2 and criteria pollutant emissions will result in both climate and human 

health benefits. The impacts of these pollutants on the environment and health are discussed in 

detail in Chapter 4 of this RIA. In sum, to estimate climate benefits the EPA uses the U.S. 

government’s global social cost of carbon (USG SCC) estimates—i.e, the monetary value of 

impacts associated with a marginal change in CO2 emissions in a given year. The EPA has 

applied the USG SCC estimates to the CO2 reductions described above for the hypothetical unit. 

The four USG SCC estimates are as follows: $15, $50, $74, and $153 per metric ton of CO2

emissions in the year 2025 (2011$). To estimate human health co-benefits for this hypothetical 

scenario, the EPA used benefit-per-ton estimates for PM2.5 and ozone precursors described in 

detail in Chapter 4 of this RIA. Table 9-5 shows the quantified per ton co-benefits for reductions

in SO2, directly emitted PM2.5, and NOX emissions in 2025.

Table 9-5. Summary of National Benefit-per-ton (BPT) Estimates for EGUs in 2025

Discount Rate

Pollutant 3% 7%

SO2 $41,000 to $93,000 $37,000 to $84,000

Directly emitted PM2.5 (EC+OC) $150,000 to $350,000 $140,000 to $310,000

Directly emitted PM2.5 (crustal) $17,000 to $39,000 $15,000 to $35,000

NOX (as PM2.5) $6,000 to $14,000 $5,400 to $12,000

NOX (as Ozone)* $4,900 to $21,000

* Ozone co-benefits occur in analysis year, so they are the same for all discount rates.

Note: See section 4.3.6 for discussion of the assumptions, and uncertainty for these estimates.

To estimate the benefits associated with improving efficiency of operations, we 

determine the emission reductions for efficiency improvements in Table 9-4 and apply the 2025 

social benefit values discussed in Chapter 4. Specifically, we multiply the reduction in CO2

emissions by the estimates of the SCC, multiply the reduction in SO2, NOX, and PM2.5 emissions 

by the PM2.5-related BPT estimates, multiply the reductions in ozone season NOX emissions by 
9-11

the ozone-related BPT estimate, and add those values to get a measure of 2025 benefits. Tables 

9-6, 9-7, and 9-8 show the climate, human health, and combined benefits expected based on the 

estimated emission reductions that would occur. These benefits are subject to the same 

uncertainties discussed for emission reductions in the previous section.

Table 9-6. Estimated Global Climate Benefits of Illustrative CO2 Reductions in 2025 

(millions of 2011$)*

Discount Rate and Statistic 4% Efficiency 

Improvement

6% Efficiency 

Improvement

8% Efficiency 

Improvement

Metric tonnes of CO2 reduced 132,790 199,184 265,579 

5% (average) $2.0 $3.0 $4.0

3% (average) $6.7 $10 $13

2.5% (average) $9.8 $15 $20

3% (95th percentile) $20 $30 $41

* The SCC values are dollar-year and emissions-year specific. SCC values represent only a partial accounting of 

climate impacts.
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Table 9-7. Summary of Estimated Monetized Health Co-benefits of Illustrative Efficiency 

Improvements in 2025 (thousands of 2011$)*

Pollutant 3% Discount Rate 7% Discount Rate

4% Efficiency Improvement

SO2 $8,900 to $20,000 $8,000 to $18,000

Directly emitted PM2.5 (EC+OC) $260 to $590 $240 to $530

Directly emitted PM2.5 (crustal) $230 to $510 $200 to $460

NOx (as PM2.5) $1,300 to $2,900 $1,200 to $2,600

NOx (as Ozone) $440 to $1,900 $440 to $1,900

Total $11,000 to $26,000 $10,000 to $23,000

6% Efficiency Improvement

SO2 $13,000 to $30,000 $12,000 to $27,000

Directly emitted PM2.5 (EC+OC) $400 to $900 $360 to $810

Directly emitted PM2.5 (crustal) $350 to $780 $310 to $710

NOx (as PM2.5) $1,900 to $4,300 $1,700 to $3,900

NOx (as Ozone) $660 to $2,800 $660 to $2,800

Total $17,000 to $39,000 $15,000 to $35,000

8% Efficiency Improvement

SO2 $18,000 to $40,000 $16,000 to $36,000

Directly emitted PM2.5 (EC+OC) $520 to $1,200 $470 to $1,100

Directly emitted PM2.5 (crustal) $450 to $1,000 $410 to $930

NOx (as PM2.5) $2,600 to $5,800 $2,300 to $5,200

NOx (as Ozone) $880 to $3,800 $880 to $3,800

Total $22,000 to $52,000 $20,000 to $47,000

* All estimates are rounded to two significant figures so numbers may not sum down columns. The estimated 

monetized co-benefits do not include climate benefits or health effects from direct exposure to NO2, SO2, and 

hazardous air pollutants (HAP); ecosystem effects; or visibility impairment. All fine particles are assumed to have 

equivalent health effects. The monetized co-benefits incorporate the conversion from precursor emissions to 

ambient fine particles and ozone. Co-benefits for PM2.5 and ozone are based on national benefit-per-ton estimates. 

Co-benefits for ozone are based on ozone season NOx emissions. Ozone co-benefits occur in analysis year, so 

they are the same for all discount rates. The health co-benefits reflect the sum of the PM2.5 and ozone co-benefits 

and reflect the range based on adult mortality functions (e.g., from Krewski et al. (2009) with Bell et al. (2004) to 

Lepeule et al. (2012) with Levy et al. (2005)). Confidence intervals are unavailable for this analysis because of the 

benefit-per-ton methodology. In general, the 95th percentile confidence interval for monetized PM2.5 benefits 

ranges from approximately -90 percent to +180 percent of the central estimates based on Krewski et al. (2009) and 

Lepeule et al. (2012). Emissions of directly emitted particles are disaggregated into EC+OC or crustal components 

using the method discussed in Appendix 4A of this RIA.
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Table 9-8. Combined Estimated Climate Benefits and Health Co-Benefits of Illustrative 

Efficiency Improvements in 2025 (millions of 2011$)*

SCC Discount Rate Climate 

Benefits Only

Climate Benefits and Health Co-Benefits 

(Discount Rate Applied to Health Co-Benefits)

3% 7%

4% Efficiency Improvement 132,790 metric tonnes CO2

5% $2.0 $13 to $28 $12 to $25

3% $6.7 $18 to $33 $17 to $30

2.5% $9.8 $21 to $36 $20 to $33

3% (95th percentile) $20 $31 to $46 $30 to $44

6% Efficiency Improvement 199,184 metric tonnes CO2

5% $3.0 $20 to $42 $18 to $38

3% $10 $27 to $49 $25 to $45

2.5% $15 $31 to $53 $30 to $50

3% (95th percentile) $30 $47 to $69 $45 to $66

8% Efficiency Improvement 265,579 metric tonnes CO2

5% $4.0 $26 to $56 $24 to $51

3% $13 $35 to $65 $33 to $60

2.5% $20 $42 to $71 $40 to $67

3% (95th percentile) $41 $63 to $92 $61 to $88

*All estimates are rounded to two significant figures. Climate benefits are based on reductions in CO2 emissions. 

The estimated monetized co-benefits do not include climate benefits or health effects from direct exposure to 

NO2, SO2, and HAP; ecosystem effects; or visibility impairment. All fine particles are assumed to have equivalent 

health effects. The monetized co-benefits incorporate the conversion from precursor emissions to ambient fine 

particles and ozone. Co-benefits for PM2.5 and ozone are based on national benefit-per-ton estimates. Co-benefits 

for ozone are based on ozone season NOx emissions. Ozone co-benefits occur in analysis year, so they are the 

same for all discount rates. The health co-benefits reflect the sum of the PM2.5 and ozone co-benefits and reflect 

the range based on adult mortality functions (e.g., from Krewski et al. (2009) with Bell et al. (2004) to Lepeule et 

al. (2012) with Levy et al. (2005)). Confidence intervals are unavailable for this analysis because of the benefitper-ton methodology. In general, the 95th percentile confidence interval for monetized PM2.5 benefits ranges from 

approximately -90 percent to +180 percent of the central estimates based on Krewski et al. (2009) and Lepeule et 

al. (2012). Emissions of directly emitted particles are disaggregated into EC+OC or crustal components using the 

method discussed in Appendix 4A of this RIA. 

Summary. In the unlikely event that a unit were to trigger the modification provision, the 

proposed standards would result in some costs and benefits. Based on the illustrative analysis of 

a hypothetical unit implementing efficiency measures in 2025 to comply with the proposed 

requirements, this proposal would result in a net benefit (benefits outweigh costs) for all levels of 

efficiency improvement and combinations of benefits. The net benefit would be even greater 

when potential fuel savings are taken into account. The actual costs, emission reductions, and 

benefits resulting from this proposal will vary based on the specifications of the modified unit, 

the efficiency improvements undertaken, the unit’s participation in a section 111(d) plan, and the 

influence of the control requirements it faces for non-CO2 pollutants.
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9.5 Statutory and Executive Order Requirements

9.5.1 Executive Order 12866, Regulatory Planning and Review, and Executive Order 13563, 

Improving Regulation and Regulatory Review

Under Executive Order (EO) 12866 (58 FR 51,735, October 4, 1993), this action is a 

“significant regulatory action” because it “raises novel legal or policy issues arising out of legal 

mandates.” Accordingly, the EPA submitted this action to the Office of Management and Budget 

(OMB) for review under Executive Orders 12866 and 13563 (76 FR 3821, January 21, 2011) and 

any changes made in response to OMB recommendations have been documented in the docket 

for this action. In addition, the EPA prepared an analysis of the potential costs and benefits 

associated with this action. This analysis is presented in the preceding sections of this chapter. 

In the period of analysis (through 2025) the EPA anticipates few sources will trigger 

either the modification or the reconstruction provisions proposed. Because there have been a few 

units that have notified the EPA of NSPS modifications in the past, we have conducted an 

illustrative analysis of the costs and benefits for a representative unit that is presented above.

9.5.2 Paperwork Reduction Act

This proposed action is not expected to impose an information collection burden under 

the provisions of the Paperwork Reduction Act, 44 U.S.C. 3501 et seq. Burden is defined at 5 

CFR 1320.3(b). As previously stated, the EPA expects few modified or reconstructed EGUs in 

the period of analysis. Specifically, the EPA believes it unlikely that fossil fuel-fired electric 

utility steam generating units (utility boilers and IGCC units) or stationary combustion turbines 

will take actions that would constitute modifications or reconstructions as defined under the 

EPA’s NSPS regulations. Accordingly, this proposed action is not anticipated to impose any 

information collection burden over the 3-year period covered by the Information Collection 

Request (ICR) for this proposed rule. We have estimated, however, the information collection 

burden that would be imposed on an affected EGU if it was modified or reconstructed. The 

information collection requirements in this proposed rule have been submitted for approval to 

OMB under the Paperwork Reduction Act, 44 U.S.C. 3501 et seq. The information collection 

request prepared by the EPA has been assigned the EPA ICR number 2465.03.
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The EPA intends to codify the standards of performance in the same way for both this 

proposed action and the January 2014 proposal for newly constructed sources and is proposing

the same recordkeeping and reporting requirements that were included in the January 2014 

proposal.187 (See 79 FR 1,498 and 1,499.) Although not anticipated, if an EGU were to modify or 

reconstruct, this proposed action would impose minimal information collection burden on 

affected sources, beyond what those sources would already be subject to under the authorities of 

CAA parts 75 and 98. The OMB has previously approved the information collection 

requirements contained in the existing part 75 and 98 regulations (40 CFR part 75 and 40 CFR 

part 98) under the provisions of the Paperwork Reduction Act, 44 U.S.C. 3501 et seq. and has 

assigned OMB control numbers 2060-0626 and 2060-0629, respectively. Apart from potential 

energy metering modifications to comply with net energy output based emission limits proposed 

in this action and certain reporting costs, which are mandatory for all owners/operators subject to 

CAA section 111 national emission standards, there would be no new information collection 

costs, as the information required by this proposed rule is already collected and reported by other 

regulatory programs. The recordkeeping and reporting requirements are specifically authorized 

by CAA section 114 (42 U.S.C. 7414). All information submitted to the EPA pursuant to the 

recordkeeping and reporting requirements for which a claim of confidentiality is made is 

safeguarded according to agency policies set forth in 40 CFR part 2, subpart B.

Although, as stated above, the EPA expects few sources will trigger either the NSPS 

modification or reconstruction provisions that we are proposing, if an EGU were to modify or 

reconstruct during the three-year period covered by the ICR, it is likely that the EGU’s energy 

metering equipment would need to be modified to comply with proposed net energy output based 

CO2 emission limits. Specifically, the EPA estimates that it would take approximately three 

working months for a technician to retrofit existing energy metering equipment to meet the 

proposed net energy output requirements. In addition, after modifications are made that enable a 

facility to measure net energy output, each EGU’s Data Acquisition System (DAS) would need 

 

187 The information collection requirements in the January 2014 proposal have been submitted for approval to the 

OMB under the Paperwork Reduction Act, 44 U.S.C. 3501 et seq. The ICR document prepared by the EPA for the 

January 2014 proposal has been assigned the EPA ICR number 2465.02.
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to be upgraded to accommodate reporting of net energy output rate based emissions. A modified 

or reconstructed EGU would be required to prepare a quarterly summary report, which includes 

reporting of emissions and downtime, every three months. The reporting burden for such a unit 

(averaged over the first three years after the effective date of the standards) is estimate to be 

$17,217 and 205 labor hours. Estimated cost burden is based on 2013 Bureau of Labor Statistics 

(BLS) labor cost data. Average burden hours per response are estimated to be 47.3 hours and the 

average number of annual responses over the three-year ICR period is 4.33 per year. Burden is 

defined at 5 CFR 1320.3(b).

An agency may not conduct or sponsor, and a person is not required to respond to, a 

collection of information unless it displays a currently valid OMB control number. The OMB 

control numbers for EPA’s regulations in 40 CFR are listed in 40 CFR part 9.

To comment on the Agency’s need for this information, the accuracy of the provided 

burden estimates, and any suggested methods for minimizing respondent burden, the EPA has 

established a public docket for this rule, which includes the ICR, under Docket ID number EPAHQ-OAR-2013-0603. Submit any comments related to the ICR to the EPA and OMB. See the 

addresses section in the preamble for where to submit comments to the EPA. Send comments to 

OMB at the Office of Information and Regulatory Affairs, Office of Management and Budget, 

725 17th Street, NW, Washington, DC 20503, Attention: Desk Officer for the EPA. Since OMB 

is required to make a decision concerning the ICR between 30 and 60 days after publication in 

the Federal Register, a comment to OMB is best assured of having its full effect if OMB receives 

it by 30 days after publication in the Federal Register. The final rule will respond to any OMB or 

public comments on the information collection requirements contained in this proposal. 

9.5.3 Regulatory Flexibility Act

The Regulatory Flexibility Act (RFA) generally requires an agency to prepare a 

regulatory flexibility analysis of any rule subject to notice and comment rulemaking 

requirements under the Administrative Procedure Act or any other statute, unless the agency 

certifies that the rule will not have a significant economic impact on a substantial number of 

small entities. Small entities include small businesses, small organizations, and small 

governmental jurisdictions.
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For purposes of assessing the impacts of this rule on small entities, small entity is defined 

as: 

1. A small business that is defined by the SBA’s regulations at 13 CFR 121.201 (for the 

electric power generation industry, the small business size standard is an ultimate 

parent entity with less than 750 employees). 

2. A small governmental jurisdiction that is a government of a city, county, town, school 

district or special district with a population of less than 50,000; and

3. A small organization that is any not-for-profit enterprise which is independently 

owned and operated and is not dominant in its field.

After considering the economic impacts of this proposed rule on small entities, we certify 

that this action will not have a significant economic impact on a substantial number of small 

entities.

The EPA expects few modified utility boilers, IGCC units, or stationary combustion 

turbines in the period of analysis. An NSPS modification is defined as a physical or operational 

change that increases the source’s maximum achievable hourly rate of emissions. The EPA does 

not believe that there are likely to be EGUs that will take actions that would constitute 

modifications as defined under the EPA’s NSPS regulations. 

Because there have been a limited number of units that have notified the EPA of NSPS 

modifications in the past, the RIA for this proposed rule includes an illustrative analysis of the 

costs and benefits for a representative unit. 

Based on the analysis, the EPA estimates that this proposed rule could result in CO2

emission changes, quantified benefits, or costs for a hypothetical unit that triggered the 

modification provision. However, we do not anticipate this proposed rule would impose 

significant costs on those sources, including any that are owned by small entities.

In addition, the EPA expects few reconstructed fossil fuel-fired electric utility steam 

generating units (utility boilers and IGCC units) or stationary combustion turbines in the period 

of analysis. Reconstruction occurs when a single project replaces components or equipment in an 

existing facility and exceeds 50 percent of the fixed capital cost that would be required to 

construct a comparable entirely new facility. Due to the limited data available on reconstructions, 
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it is not possible to conduct a representative illustrative analysis of what costs and benefits might 

result from this proposal in the unlikely case that a unit were to reconstruct. However, based on 

the low number of previous reconstructions and the BSER determination based on the most 

efficient available generating technology, we would expect this proposal to result in no 

significant CO2 emission changes, quantified benefits, or costs for NSPS reconstructions.

Nevertheless, the EPA is aware that there is substantial interest in the proposed rule 

among small entities (municipal and rural electric cooperatives). As summarized in section II.G. 

of the preamble, the EPA has conducted an unprecedented amount of stakeholder outreach. As 

part of that outreach, agency officials participated in many meetings with individual utilities as 

well as meetings with electric utility associations. Specifically, the EPA Administrator, Gina 

McCarthy, participated in separate meetings with both the National Rural Electric Cooperative 

Association (NRECA) and the American Public Power Association (APPA). The meetings 

brought together leaders of the rural cooperatives and public power utilities from across the 

country. The Administrator discussed and exchanged information on the unique challenges, in 

particular the financial structure of NRECA and APPA member utilities. A detailed discussion of 

the stakeholder outreach is included in the preamble to the emission guidelines for existing 

affected electric utility generating units being proposed in a separate action.

In addition, as described in the RFA section of the preamble to the proposed standards of 

performance for GHG emissions from newly constructed EGUs (79 FR 1499 and 1500, January 

8, 2014), the EPA conducted outreach to representatives of small entities while formulating the 

provisions of the proposed standards. Although only newly constructed EGUs would be affected 

by those proposed standards, the outreach regarded planned actions for newly constructed, 

reconstructed, modified and existing sources.

While formulating the provisions of the proposed rule, the EPA considered the input 

provided over the course of the stakeholder outreach. We invite comments on all aspects of this 

proposal and its impacts, including potential impacts on small entities.

9.5.4 Unfunded Mandates Reform Act (UMRA)

This proposed rule does not contain a federal mandate that may result in expenditures of 

$100 million or more for state, local, and tribal governments, in the aggregate, or the private 
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sector in any one year. As previously stated, the EPA expects few modified or reconstructed

fossil fuel-fired electric utility steam generating units (utility boilers and IGCC units) or 

stationary combustion turbines in the period of analysis. Accordingly, this proposed rule is not 

subject to the requirements of sections 202 or 205 of UMRA.

This proposed rule is also not subject to the requirements of section 203 of UMRA 

because it contains no regulatory requirements that might significantly or uniquely affect small 

governments.

In light of the interest among governmental entities, the EPA initiated consultations with 

governmental entities while formulating the provisions of the proposed standards for newly 

constructed EGUs. This outreach regarded planned actions for newly constructed, reconstructed, 

modified and existing sources. As described in the UMRA discussion in the preamble to the 

proposed standards of performance for GHG emissions from new EGUs (79 FR 1500 and 1501, 

January 8, 2014), the EPA consulted with the following 10 national organizations representing 

state and local elected officials: 

 National Governors Association

 National Conference of State Legislatures

 Council of State Governments

 National League of Cities

 U.S. Conference of Mayors

 National Association of Counties

 International City/County Management Association

 National Association of Towns and Townships

 County Executives of America

 Environmental Council of State

On February 26, 2014, the EPA re-engaged with those governmental entities to provide a 

pre-proposal update on the emission guidelines for existing EGUs and emission standards for 
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modified and reconstructed EGUs.

While formulating the provisions of these proposed standards, the EPA also considered 

the input provided over the course of extensive stakeholder outreach conducted by the EPA (see 

section II.G. of the preamble for more information).

9.5.5 Executive Order 13132, Federalism

This proposed action does not have federalism implications. It would not have substantial 

direct effects on the States, on the relationship between the national government and the States, 

or on the distribution of power and responsibilities among the various levels of government, as 

specified in EO 13132. This proposed action would not impose substantial direct compliance 

costs on state or local governments, nor would it preempt state law. Thus, EO 13132 does not 

apply to this action.

However, as described in the Federalism discussion in the preamble to the proposed 

standards of performance for GHG emissions from newly constructed EGUs (79 FR 1501, 

January 8, 2014), the EPA consulted with state and local officials in the process of developing 

the proposed standards for newly constructed EGUs. This outreach regarded planned actions for 

newly constructed, reconstructed, modified and existing sources. The EPA engaged 10 national 

organizations representing state and local elected officials. The UMRA discussion in the 

preamble to the proposed standards of performance for GHG emissions from newly constructed

EGUs (79 FR 1500 and 1501, January 8, 2014) includes a description of the consultation. In 

addition, on February 26, 2014, the EPA re-engaged with those governmental entities to provide 

a pre-proposal update on the emission guidelines for existing EGUs and emission standards for 

modified and reconstructed EGUs. While formulating the provisions of these proposed 

standards, the EPA also considered the input provided over the course of the extensive 

stakeholder outreach conducted by the EPA (see section II.G. of the preamble). In the spirit of 

EO 13132, and consistent with the EPA policy to promote communications between the EPA 

and state and local governments, the EPA specifically solicits comment on this proposed action 

from state and local officials.
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9.5.6 Executive Order 13175, Consultation and Coordination with Indian Tribal Governments

This action does not have tribal implications, as specified in Executive Order 13175 (65 

FR 67249, November 9, 2000). It would neither impose substantial direct compliance costs on 

tribal governments, nor preempt Tribal law. This proposed rule would impose requirements on 

owners and operators of reconstructed and modified EGUs. The EPA is aware of three coal-fired 

EGUs located in Indian Country but is not aware of any EGUs owned or operated by tribal 

entities. The EPA notes that this proposal would only affect existing sources such as the three 

coal-fired EGUs located in Indian Country, if those EGUs were to take actions constituting 

modifications or reconstructions as defined under the EPA’s NSPS regulations. However, as 

previously stated, the EPA expects few modified or reconstructed EGUs in the period of 

analysis. Thus, Executive Order 13175 does not apply to this action.

Although Executive Order 13175 does not apply to this action, the EPA conducted 

outreach to tribal environmental staff and offered consultation with tribal officials in developing 

this action. Because the EPA is aware of tribal interest in carbon pollution standards for the 

power sector, prior to proposal of GHG standards for newly constructed power plants, the EPA 

offered consultation with tribal officials early in the process of developing the proposed 

regulation to permit them to have meaningful and timely input into its development. The EPA’s 

consultation regarded planned actions for newly constructed, reconstructed, modified, and 

existing sources. The Consultation and Coordination with Indian Tribal Governments discussion 

in the preamble to the proposed standards of performance for GHG emissions from newly

constructed EGUs (79 FR 1501, January 8, 2014) includes a description of that consultation.

During development of this proposed regulation, consultation letters were sent to 584 

tribal leaders. The letters provided information regarding the EPA’s development of both the 

NSPS for modified and reconstructed EGUs and emission guidelines for existing EGUs and 

offered consultation. None have requested consultation. Tribes were invited to participate in the 

national informational webinar held August 27, 2013, and to which tribes were invited. In 

addition, a consultation/outreach meeting was held on September 9, 2013, with tribal 

representatives from some of the 584 tribes. The EPA also met with tribal environmental staff 

with the National Tribal Air Association, by teleconference, on July 25, 2013, and December 19, 
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2013. In those teleconferences, the EPA provided background information on the GHG emission 

guidelines to be developed and a summary of issues being explored by the Agency. Additional 

detail regarding this stakeholder outreach is included in the executive order discussion for the 

emission guidelines for existing affected electric utility generating units in Chapter 7 of this RIA.

The EPA also held a series of listening sessions prior to proposal of GHG standards for newly 

constructed power plants. Tribes participated in a session on February 17, 2011, with state 

agencies, as well as in a separate session with tribes on April 20, 2011.

The EPA will also hold additional meetings with tribal environmental staff during the 

public comment period, to inform them of the content of this proposal, and offer further 

consultation with tribal elected officials where it is appropriate. We specifically solicit additional 

comment from tribal officials on this proposed rule.

9.5.7 Executive Order 13045, Protection of Children from Environmental Health Risks and 

Safety Risks

The EPA interprets EO 13045 (62 FR 19885, April 23, 1997) as applying to those 

regulatory actions that concern health or safety risks, such that the analysis required under 

section 5-501 of the Order has the potential to influence the regulation. This action is not subject 

to EO 13045 because it is based solely on technology performance.

9.5.8 Executive Order 13211, Actions Concerning Regulations that Significantly Affect 

Energy Supply, Distribution, or Use

This proposed action is not a “significant energy action” as defined in EO 13211 (66 FR 

28355, May 22, 2001), because it is not likely to have a significant adverse effect on the supply, 

distribution, or use of energy. As previously stated, the EPA expects few reconstructed or 

modified EGUs in the period of analysis and impacts on emissions, costs or energy supply 

decisions for the affected electric utility industry to be minimal as a result.

9.5.9 National Technology Transfer and Advancement Act

Section 12(d) of the NTTAA of 1995 (Public Law No. 104-113; 15 U.S.C. 272 note) 

directs the EPA to use voluntary consensus standards (VCS) in their regulatory and procurement 
9-23

activities unless to do so would be inconsistent with applicable law or otherwise impractical. 

VCS are technical standards (e.g., materials specifications, test methods, sampling procedures, 

business practices) developed or adopted by one or more voluntary consensus bodies. The 

NTTAA directs the EPA to provide Congress, through annual reports to the OMB, with 

explanations when an agency does not use available and applicable VCS.

This proposed rulemaking involves technical standards. The EPA proposes to use the 

following standards in this proposed rule: 

 ASTM D388-12 (Standard Classification of Coals by Rank)

 ASTM D396-13c (Standard Specification for Fuel Oils)

 ASTM D975-14 (Standard Specification for Diesel Fuel Oils)

 D3699-13b (Standard Specification for Kerosene)

 D6751-12 (Standard Specification for Biodiesel Fuel Blend Stock [B100] 

for Middle Distillate Fuels)

 ASTM D7467-13 (Standard Specification for Diesel Fuel Oil, Biodiesel 

Blend [B6 to B20])

 ANSI C12.20 (American National Standards for Electricity Meters – 0.2 

and 0.5 Accuracy Classes).

The EPA is proposing use of Appendices A, B, D, F and G to 40 CFR part 75. These 

Appendices contain standards that have already been reviewed under the NTTAA. The EPA 

welcomes comments on this aspect of the proposed rulemaking and, specifically, invites the 

public to identify potentially-applicable VCS and to explain why such standards should be used 

in this action.

9.5.10 Executive Order 12898: Federal Actions to Address Environmental Justice in Minority 

Populations and Low-Income Populations

Executive Order 12898 (59 FR 7629, February 16, 1994) establishes federal executive 

policy on environmental justice. Its main provision directs federal agencies, to the greatest extent 

practicable and permitted by law, to make environmental justice part of their mission by 
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identifying and addressing, as appropriate, disproportionately high and adverse human health or 

environmental effects of their programs, policies, and activities on minority populations and lowincome populations in the U.S.

This proposed rule limits GHG emissions from modified and reconstructed fossil fuelfired electric utility steam generating units (utility boilers and IGCC units) and stationary 

combustion turbines by establishing national emission standards for CO2. The EPA has 

determined that this proposed rule would not result in disproportionately high and adverse 

human health or environmental effects on minority, low-income, and indigenous populations 

because it does not affect the level of protection provided to human health or the 

environment. As previously stated, the EPA expects few modified or reconstructed fossil fuelfired electric utility steam generating units (utility boilers and IGCC units) or stationary 

combustion turbines in the period of analysis.
9-25

9.6 References

Bell, M.L., et al. 2004. “Ozone and Short-Term Mortality in 95 U.S. Urban Communities, 1987-

2000.” Journal of the American Medical Association, 292(19): p. 2372-8.

Krewski D.; M. Jerrett; R.T. Burnett; R. Ma; E. Hughes; Y. Shi, et al. 2009. Extended Follow-Up 

and Spatial Analysis of the American Cancer Society Study Linking Particulate Air Pollution 

and Mortality. HEI Research Report, 140, Health Effects Institute, Boston, MA.

Lepeule, J.; F. Laden; D. Dockery; J. Schwartz. 2012. “Chronic Exposure to Fine Particles and 

Mortality: An Extended Follow-Up of the Harvard Six Cities Study from 1974 to 2009.” 

Environmental Health Perspectives, July, 120(7):965-70. 

Levy, J.I.; S.M. Chemerynski and J.A. Sarnat. 2005. “Ozone Exposure and Mortality: An 

Empiric Bayes Metaregression Analysis.” Epidemiology, 16(4): pp. 458-68.
United States Environmental 

Protection Agency

Office of Air Quality Planning and Standards

Health and Environmental Impacts Division

Research Triangle Park, NC

Publication No. EPA-542/R-14-002

June 2014