### From: Culligan, Kevin [mailto:Culligan.Kevin@epa.gov] Sent: Friday, May 30, 2014 8:14 PM To: Higgins, Cortney Cc: Chappell, Linda; Wayland, Robertj Subject: FW: clean RIA Importance: High

<< EO 12866 RIA OMB Final Version 53014 master 053014.docx >> << EO 12866 RIA OMB Final Version 53014 master_clean.docx >>***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

i 

# Carbon Pollution Guidelines for Existing
Power Plants:
Emission Guidelines for Greenhouse Gas Emissions from Existing Stationary Sources: Electric Utility Generating Units

June 2014 Style Definition: Style 10 Caption Figure + After: 6 pt: Font: Bold, Line spacing: single Style Definition: 10 Caption Figure: Line spacing: single***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

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 anal ysis 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 expresse d 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.***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

iii EPA -542/R-14-002

June 2014



## Carbon Pollution Guidelines for Existing Power Plants:

Emission Guidelines for Greenhou se Gas Emissions from Existing Stationary Sources: Electric Utility Generating Units

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***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

iv 

### T ABLE OF 

### C ONTENTS

Table of Contents ............................................................................................................. ................................................ iv
List of Tables ................................................................................................................ .................................................... xi List of Figures ............................................................................................................... ................................................. xvi
ACRONYMS

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

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

............................................................... 1
ES.3 Control Strategies for Existing EGUs

..................................................................................................... 3
ES.4 Costs of Existing EGU Guidelines

........................................................................................................... 7
ES.5 Monetized Climate Benefits and Health Co

‐ benefits for Existing EGUs

............................................... 9

ES.5.1 Estimating Global Climate Benefits

......................................................................................... 13
ES 5.2 Estimating Air Pollution Health Co

‐ Benefits

........................................................................... 15
ES 5.3 Combined Benefits Estimates

................................................................................................. 17
ES.6 Monetized Benefits, Compliance Costs and Net Benefits of the Proposed Guidelines for Existing Sources

............................................................................................................................... ......................... 20
ES.7 Economic Impacts of the Proposed Emission Guidelines for Existing EGUs

....................................... 24
ES.8 Economic Impacts of the Proposed Guidelines for Existing EGUs for Sectors Other Than the EGU Sector and for Employment

....................................................................................................................... 24
ES.9 Modified and Reconstructed Sources

................................................................................................. 26
ES.10 References

............................................................................................................................... .......... 26
CHAPTER 1: INTRODUCTION AND BACKGROUND ....................................................................................... ..... 1
1.1 Introduction

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

....................................................................... 1

1.2.1 Statutory Requirement

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

......................................................................... 2
1.2.3 Market Failure

............................................................................................................................... ... 3
1.3 Summary of Regulatory Analysis

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

..................................................... 5

1.4.1 Base Case and Years of Analysis

....................................................................................................... 5***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

v 1.4.2 Definition of Affected Sources

......................................................................................................... 5
1.4.3 Regulated Pollutant

.......................................................................................................................... 6
1.4.4 Emission Guidelines

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

..................................................................................... 7
1.6 References

............................................................................................................................... ............... 9
CHAPTER 2: ELECTRIC POWER SECTOR PROFILE ..................................................................................... ......... 1
2.1 Introduction

............................................................................................................................... ............. 1
2.2 Power Sector Overview

........................................................................................................................... 1

2.2.1 Generation

............................................................................................................................... ........ 1
2.2.2 Transmission

............................................................................................................................... ..... 5
2.2.3 Distribution

............................................................................................................................... ....... 6
2.3 Deregulation and Restructuring

.............................................................................................................. 6
2.4 Emissions of Greenhouse Gases from Electric Utilities

.......................................................................... 8
2.5 Improving GHG Performance at Existing EGUs

..................................................................................... 12
2.6 GHG and Clean Energy Regulation in the Power Sector

....................................................................... 14

2.6.1 State Policies

............................................................................................................................... ... 14
2.6.2 Federal Policies

.............................................................................................................................. 16
2.7 Revenues, Expenses, and Prices

........................................................................................................ 19
2.8 Natural Gas Market

............................................................................................................................... 24
2.9 Electricity Demand and Demand Response

.......................................................................................... 27
2.10 References

............................................................................................................................... ........... 30 CHAPTER 3: COST, ECONOMIC, AND ENERGY IMPACTS ................................................................................ . 1
3.1 Introduction

............................................................................................................................... ...... 1
3.2 Overview

............................................................................................................................... ........... 1
3.3 Power Sector Modelling Framework

............................................................................................... 2

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

............................................................... 4
3.4 State Goals in this Proposal

............................................................................................................ 6
3.5 Compliance Scenarios Analyzed

.................................................................................................... 10
3.6 Demand Side Energy Efficiency

...................................................................................................... 15

3.6.1 Projected Demand

‐ Side Energy Savings

..................................................................................... 15
3.6.2 Demand

‐ Side Energy Efficiency Total Costs

................................................................................... 18
3.7 Projected Power Sector Impacts

.................................................................................................... 20***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

vi 3.7.1 Projected Emissions

.................................................................................................................... 20
3.7.2 Projected Compliance Costs

....................................................................................................... 23
3.7.3 Projected Compliance Actions for Emissions Reductions

.......................................................... 26
3.7.4 Projected Generation Mix

.......................................................................................................... 27
3.7.5 Projected Incremental Retirements

............................................................................................. 1
3.7.6 Projected Capacity Additions

........................................................................................................ 4
3.7.7 Projected Coal Productions for the Electric Power Sector

........................................................... 5
3.7.8 Projected Fuel Price Impacts

........................................................................................................ 6
3.7.9 Projected Retail Electricity Prices

................................................................................................. 8
3.7.10 Projected Electricity Bill Impacts

............................................................................................. 13
3.8 Projected Primary PM Emissions from Power Plants

.................................................................... 14
3.9 Limitations of Analysis

................................................................................................................... 15
3.10 Significant Energy Impacts

............................................................................................................. 16
3.11 Monitoring, Reporting, and Recordkeeping Costs

......................................................................... 17
3.12 Social Costs

............................................................................................................................... ..... 18
3.13 References

............................................................................................................................... ...... 23

............................................................................................................................. ............................................................. 1 Appendix 3A: Analysis of Potential Upstream Meth ane Emissions Changes in Natural Gas Systems and Coal Mining ........................................................................................................................ ....................................................... 1
3A.1 General Approach

............................................................................................................................ 1

3A.1.1 Analytical Scope

............................................................................................................................. 1
3A.1.2 Coal Mining Source Description

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

....................................................................................... 4
3A.1.4 Compliance Approaches Examined

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

............................................................................................................................... 6
3A.2 Results

............................................................................................................................... .................... 7

3A.2.1 Primary Results

.............................................................................................................................. 8
3A.2.2 Sensitivity Analysis: Side Case Incorporating More Coal Production Geographic Detail

.............. 9
3A.2.3 Uncertainties and Limitations

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

............................................................................................................................... ..... 11

3A.3.1 Coal Mining

............................................................................................................................... ... 11
3A.3.2 Natural Gas Systems

.................................................................................................................... 15
3A.4 References

............................................................................................................................... ........... 30***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

vii Chapter 4: Estimated Climate Benefits and Human Health Co-benefits ............................................................ ............. 2
4.1 Introduction

............................................................................................................................... ............. 2
4.2 Estimated Climate Benefits from CO 2 ..................................................................................................... 2

4.2.1 Climate Change Impacts................................................................................................................... 3
4.2.2 Social Cost of Carbon

....................................................................................................................... 7
4.3 Estimated Human Health Co

‐ Benefits

.................................................................................................. 15

4.3.1 Health Impact Assessment for PM 2.5 and Ozone

........................................................................... 17
4.3.2 Economic Valuation for Health Co

‐ benefits

................................................................................... 22
4.3.3 Benefit

‐ per
‐ ton Estimates for PM 2.5 ............................................................................................... 24
4.3.4 Benefit

‐ per
‐ ton Estimates for Ozone

............................................................................................. 25
4.3.5 Estimated Health Co

‐ Benefits Results

............................................................................................ 25
4.3.6 Characterization of Uncertainty in the Estimated Health Co

‐ benefits

.......................................... 39
4.4 Combined Climate Benefits and Health Co

‐ benefits Estimates

............................................................ 46
4.5 Unquantified Co

‐ benefits

...................................................................................................................... 51

4.5.1 HAP Co

‐ benefits

............................................................................................................................. 53
4.5.2 Additional NO 2 Health Co

‐ Benefits

................................................................................................ 57
4.5.3 Additional SO 2 Health Co

‐ Benefits

................................................................................................. 58
4.5.4 Additional NO 2 and SO 2 Welfare Co

‐ Benefits

................................................................................ 58
4.5.5 Ozone Welfare Co

‐ Benefits

............................................................................................................ 59
4.5.6 Carbon Monoxide Co

‐ Benefits

....................................................................................................... 60
4.5.7 Visibility Impairment Co

‐ Benefits

.................................................................................................. 60
4.6 References

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

‐ per
‐ Ton Estimates

............................................................................................... 1
4A.2 Underlying Source Apportionment Air Quality Modeling

.................................................................... 2
4A.3 Regional PM 2.5 Benefit

‐ per
‐ Ton Estimates for EGUs

............................................................................. 3
4A.4 Regional Ozone Benefit

‐ per
‐ Ton Estimates

........................................................................................ 18
4A.5 Evaluating the Benefit

‐ per
‐ Ton Estimates

......................................................................................... 22
4A.6 References

............................................................................................................................... ............. 3 CHAPTER 5: ECONOMIC IMPACTS – MARKETS Outside the Electricity Sector .................................................. 1
5.1 Introduction

............................................................................................................................... ...... 1
5.2 Summary of Secondary Market Impacts of Energy Price Changes

......................................................... 3***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

viii 5.3.1 Share of Total Production Costs

....................................................................................................... 4
5.3.2 Ability to Substitute between Inputs to the Production Process

.................................................... 5
5.3.3 Availability of Substitute Goods and Services

.................................................................................. 5
5.4 Conclusions

............................................................................................................................... .............. 6 CHAPTER 6: Employment Impact Analysis ......................................................................................... .......................... 1
6.1 Introduction

............................................................................................................................... ............. 1
6.2 Overview of the Proposed EGU GHG Existing Source Guidelines

........................................................... 2

6.2.1 Determining State Goals Utilizing the Four Building Blocks

............................................................ 2
6.3 Economic Theory and Employment

........................................................................................................ 3
6.4 Current State of Knowledge Based on the Peer

‐ Reviewed Literature

................................................... 7

6.4.1 Regulated Sector

.............................................................................................................................. 8
6.4.2 Labor Supply Impacts

....................................................................................................................... 9
6.5 Recent Employment Trends

.................................................................................................................. 10

6.5.1 Electric Power Generation

............................................................................................................. 10
6.5.2 Fossil Fuel Extraction

...................................................................................................................... 11
6.5.3 Clean Energy Employment Trends

................................................................................................. 14
6.6 Projected Sectoral Employment Changes due to Proposed Guidelines

............................................... 18

6.6.1 Projected Changes in Employment in Electricity Generation and Fossil Fuel Extraction

.............. 20
6.6.2 Projected Changes in Employment in Demand

‐ Side Energy Efficiency Activities

............................. 26
6.7 Conclusion

............................................................................................................................... .............. 32
6.8 References

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

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

............................................................................................................................... .. 1

6A.1.1 Employment Effects Included In the Analysis

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

...................................................................... 3

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

.......................... 5
6A.2.2 Employment Changes due to Coal and Oil/Gas Retirements

........................................................ 9
6A.2.3 Employment Changes due to Coal and Oil/Gas Retirements

........................................................ 9 CHAPTER 7: Statutory and executive Order Analysis ............................................................................ ...................... 1
7.1 Executive Order 12866, Regulatory Planning and Review, and Executive Order 13563, Improving Regulation and Regulatory Review

............................................................................................................... 1
7.2 Paperwork Reduction Act

....................................................................................................................... 3***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

ix 7.3 Regulatory Flexibility Act

........................................................................................................................ 4
7.4 Unfunded Mandates Reform Act (UMRA)

.............................................................................................. 6
7.5 Executive Order 13132, Federalism

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

.............. 8

7.7 Executive Order 13211, Actions Concerning Regulations that Significantly Affect Energy Supply, Distribution, or Use 9
7.8 National Technology Transfer and Advancement Act (NTTAA)

.............................................................. 9
7.8 Executive Order 12898: Federal Actions to Address Environmental Justice in Minority Populations and Low

‐ Income Populations 10
7.10 Executive Order 13175 ‐ Consultation and Coordination with Indian Tribal Governments

........... 14
CHAPTER 8: COMPARISON OF BENEFITS AND COSTS .................................................................................. .... 1
8.1 Comparison of Benefits and Costs

.......................................................................................................... 1
8.2 Uncertainty Analysis

............................................................................................................................... 7 CHAPTER 9: Benefits, Costs, and Economic Impacts of Standards of Performance for Reconstructed and Modified Electric Utility Generating Units ............................................................................................. ......................................... 1
9.1 Introduction

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

............................................................................................................... 1
9.3 Background for the Proposed EGU Reconstructed and Modified Source GHG Standards

..................... 2

9.3.1 Definition of Affected Sources

......................................................................................................... 2
9.3.2 Emission Limits

............................................................................................................................... .. 3
9.4 Impacts of the Proposed Reconstructed and Modified Source Standards

............................................. 5

9.4.1 Reconstructed Sources

.................................................................................................................... 5
9.4.2 Modified Sources

............................................................................................................................. 6
9.5 Statutory and Executive Order Requirements

...................................................................................... 14

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

......................................................................................................... 14
9.5.2 Paperwork Reduction Act

.............................................................................................................. 15
9.5.3 Regulatory Flexibility Act................................................................................................................ 17
9.5.4 Unfunded Mandates Reform Act (UMRA)

..................................................................................... 19
9.5.5 Executive Order 13132, Federalism

............................................................................................... 20
9.5.6 Executive Order 13175, Consultation and Coordination with Indian Tribal Governments

........... 21
9.5.7 Executive Order 13045, Protection of Children from Environmental Health Risks and Safety Risks

............................................................................................................................... .................................. 22***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

x 9.5.8 Executive Order 13211, Actions Concerning Regulations that Significantly Affect Energy Supply, Distribution, or Use

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

................................................................... 23
9.5.10 Executive Order 12898: Federal Actions to Address Environmental Justice in Minority Populations and Low

‐ Income Populations

............................................................................................. 23
9.6 References

............................................................................................................................... ............. 24***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

xi 

### L IST OF 

### T ABLES

Table ES-1. Proposed State Goals (Adj usted MWh-Weighted-Average Pounds of CO 2 per Net MWh from all Affected Fossil Fuel-Fired EGUs) for Options 1 and 2 ............................................... 4

Table 3-2. Projected Capacity Factor of Existing Coal Steam and Natural Gas Combined Cycle Capacity

.............................................................................................................................. ................... 27

Table 3-3. Generation Mix (thousand GWh) ..................................................................................... ........ 29

Table 3-4. Total Generation Capacity by 2020-2030 (GW) ....................................................................... . 3

Table 3-5. Projected Capacity Additions, Gas (GW) ............................................................................. ....... 4

Table 3-64. Projected Capacity Additions, Non-hydro Renewable (GW) .................................................... 5

Table 3-75. Coal Production for the Electric Power Sector, 2020 .............................................................. . 5

Table 3-8. Power Sector Gas Use .............................................................................................. .................. 6

Table 3-9. Projected Average Mine mouth and Delivered Coal Prices (2011$/MMBtu) ............................. 7

Table 3-10. Projected Average Minemouth and Delive red Coal Prices: Percent Change from Base Case Projections ................................................................................................................... .............. 7

Table 3-11. Average Henry Hub (spot) and Delivere d Natural Gas Prices: Percent Change from Base Case Projections .............................................................................................................. .......... 8

Table 3-12. 2020 Projected Contiguous U.S. and Regional Retail Electricity Prices (cents/kWh) ........... 10

Table 3-13. 2025 Projected Contiguous U.S. and Regional Retail Electricity Prices (cents/kWh) ........... 11

Table 3-14. 2030 Projected Contiguous U.S. and Regional Retail Electricity Prices (cents/kWh) ........... 12

Table 3-15. Projected Changes in Average Electricity Bills ................................................................... ... 13

Table 3-26. Annualized Compliance Costs Including Monitoring, Reporting and Recordkeeping Costs Requirements (billions of 2011$) ............................................................................................ 1 8 Formatted: 01 ChapterTitle1, Tab stops: Not at 6.49"***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

xii Table 3-19. 2025 Projected Contiguous U.S. and Regi onal Retail Electricity Prices (cents/kWh) ....... 3-31
Table 3-20. 2030 Projected Contiguous U.S. and Region al Retail Electricity Pric es (cents/kWh) ........ 3-32
Table 3-21. Projected Changes in Average Electricity Bills ................................................................... 3-33
Table 3A-1. Base Year Upstream Methane-Related Em issions 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 -8
Table 3A-4. Projected Natural Gas Production Impacts, Option 1 ........................................................ 3A-8
Table 3A-5. Projected Natural Gas Production Impacts, Option 2 ........................................................ 3A-9
Table 3A-6. Upstream Emissions Changes, Baseline to Compliance Scenarios ................................. 3A-11
Table 3A-7. Upstream Emissions Changes, Baseline to Compliance Scenarios, Side Case Incorporating More Coal Production Geographic Detail ......................................................................... 3A-12

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

Table 4-2. Global Social Cost of CO 2 , 2015-2050 (in 2011$) ................................................................ 4-11

Table 4-3. Estimated Global Climate Benefits of CO 2 Reductions for the Proposed EGU GHG Existing Source Guidelines in 2020 (billions of 2011$) ..................................................................... 4-12

Table 4-4. Estimated Global Climate Benefits of CO 2 Reductions Proposed EGU GHG Exisitng Source Guidelines in 2025 (billions of 2011$) ................................................................................. 4-13

Table 4-5. Estimated Global Climate Benefits of CO 2 Reductions for the Proposed EGU GHG Existing Source Guidelines in 2030 (billions of 2011$) ..................................................................... 4-13

Table 4-6. Human Health Effects of Ambient PM 2.5 and Ozone ............................................................ 4-19
Table 4-7. Summary of National and Regional Benefi t-per-Ton Estimates for EGUs in 2020 (2011$) 4-29
Table 4-8. Summary of National and Regional Benefi t-per-Ton Estimates for EGUs in 2025 (2011$) 4-30
Table 4-9. Summary of National and Regional Benefi t-per-Ton Estimates for EGUs in 2020 (2011$) 4-29
Table 4-10. Emission Reductions of Criteria Pollutants for the Proposed EGU GHG Existing Source Guidelines in 2020 (thousands of short tons) ....................................................................... 4-32
Table 4-11. Emission Reductions of Criteria Pollutants for the Proposed EGU GHG Existing Source Guidelines in 2025 (thousands of short tons) ....................................................................... 4-33
Table 4-12. Emission Reductions of Criteria Pollutants for the Proposed EGU GHG Existing Source Guidelines in 2030 (thousands of short tons) ....................................................................... 4-35***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

xiii Table 4-13. Summary of Estimated Monetized Health Co-Benefits for the Proposed EGU GHG Existing Source Guidelines in 2020 (millions of 2011$) ................................................................... 4-36
Table 4-14. Summary of Estimated Monetized Health Co-Benefits for the Proposed EGU GHG Existing Source Guidelines in 2025 (millions of 2011$) ................................................................... 4-39
Table 4-15. Summary of Estimated Monetized Health Co-Benefits for the Proposed EGU GHG Existing Source Guidelines in 2030 (millions of 2011$) ................................................................... 4-42
Table 4-16. Summary of Avoided Health Incidences from PM 2.5 -Related and Ozone-Related Co-Benefits for Proposed EGU GHG Existing Source Guidelines in 2020 ............................................. 4-45
Table 4-17. Summary of Avoided Health Incidences from PM 2.5 -Related and Ozone-Related Co-Benefits for Proposed EGU GHG Existing Source Guidelines in 2025 ............................................. 4-48
Table 4-18. Summary of Avoided Health Incidences from PM 2.5 -Related and Ozone-Related Co-Benefits for Proposed EGU GHG Existing Source Guidelines in 2030 ............................................. 4-51
Table 4-19. Population Exposure in the Baseline Modeling Above and Below Various Concentrations Benchmarks in the Underlying Epidemiology Studies......................................................... 4-61

Table 4-20. Combined Climate Benefits and He alth Co-Benefits for Proposed EGU GHG Existing Source Guidelines in 2020 (billions of 2011$) ..................................................................... 4-65

Table 4-21. Combined Climate Benefits and He alth Co-Benefits for Proposed EGU GHG Existing Source Guidelines in 2025 (billions of 2011$) ..................................................................... 4-67

Table 4-22. Combined Climate Benefits and He alth Co-Benefits for Proposed EGU GHG Existing Source Guidelines in 2030 (billions of 2011$) ..................................................................... 4-69
Table 4-23. Unquantified Health and Welfare Co-Benefits Categories .................................................. 4-72

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

EGUs in 2016

.................................................................................................................. 4A-100

Table 4A-2

. Summary of National and Regional PM 2.5 Benefit-per-Ton

Estimates for EGUs in 2020

............................................................................................ 4A

- 104

Table 4A-3

. Summary of National and Regional PM 2.5 Benefit-per-Ton

Estimates for EGUs in 2025

............................................................................................ 4A

- 105

Table 4A-4

. Summary of National and Regional PM2.5 Benefit-per-Ton

Estimates for EGUs in 2030

............................................................................................ 4A

- 105

Table 4A-5

. Summary of Regional PM 2.5 Incidence-per-Ton Estimates for EGUs in 2020

............. 4A

- 107***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

xiv Table 4A-6

. Summary of Regional PM 2.5 Incidencet-per-Ton Estimates for EGUs in 2025

............ 4A

- 109

Table 4A-7

. Summary of Regional PM 2.5 Incidencet-per-Ton Estimates for EGUs in 2030

............ 4A

- 111

Table 4A-8

. Summary of National and Regional Ozone Benefit-per-Ton Estimates

for EGUs in 2020

............................................................................................................ 4A

- 114

Table 4A-9

. Summary of National and Regional Ozone Benefit-per-Ton Estimates

for EGUs in 2025

........................................................................................................... 4A

- 114

Table 4A-10

. Summary of National and Regional Ozone Benefit-per-Ton Estimates

for EGUs in 2030

........................................................................................................... 4A

- 115

Table 4A-11

. Summary of Regional Ozone Incidence-per-Ton Estimates for EGUs in 2020

.......... 4A

- 115

Table 4A-12

. Summary of Regional Ozone Incidence-per-Ton Estimates for EGUs in 2025

.......... 4A

- 115

Table 4A-13

. Summary of Regional Ozone Incidene -per-Ton Estimates for EGUs in 2030

............ 4A

- 116
Table 5-1. Estimated Percentage Changes in Energy Prices, by Energy Type and Regulatory Option ... 5-4
Table 6-1. U. S. Green Goods and Services (GGS) Employment (annual average) ............................... 6-16
Table 6-2. Renewable Electricity Generation-Related Employment ...................................................... 6-17
Table 6-3. Energy and Resources Efficiency-Related Employment ....................................................... 6-18
Table 6-4. Engineering-Based Changes in Labor Demand in 2020, 2025 and 2030, Regional Compliance Approach (number of years of employment in year) ........................................................... 6-26
Table 6-5. Engineering-Based Changes in Labor Demand in 2020, 2025 and 2030, State Compliance Approach (number of job-years of employment in year) ..................................................... 6-28
Table 6-6. Estimated Demand-Side Energy Efficien cy Employment Impacts for Option 1 and Option 2 Both Regional and State Compliance Approaches ............................................................... 6-33

Table 6A-1

. Labor Productivity Growth Rate Due to Heat Rate Improvement

.................................... 6A-5

Table 6A-2

. Captial Charge Rate and Duration Assumptions

............................................................... 6A-6

Table 6A-3

. Expenditure Breakdown due to New Generating Capacity

............................................... 6A-7

Table 6A-4

. Labor Productivity due to New Generating Capacity

........................................................ 6A-7

Table 6A-5

. Average FOM Cost Assumptions

...................................................................................... 6A-9

Table 6A-6

. Labor Productivity due to New Generating Capacity

...................................................... 6A-10***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

xv Table 7-1. Potentially Regulated Categories and Entities. .................................................................... .... 7-5
Table 8-1. Summary of Monetized Benefits, Compliance Costs, and Net Benefits for the Proposed Guidelines – 2020 (billions of 2011$) .................................................................................... 8-4
Table 8-2. Summary of Monetized Benefits, Compliance Costs, and Net Benefits for the Proposed Guidelines – 2025 (billions of 2011$) .................................................................................... 8-5
Table 8-3. Summary of Monetized Benefits, Compliance Costs, and Net Benefits for the Proposed Guidelines – 2030 (billions of 2011$) .................................................................................... 8-6
Table 9-1. Proposed Emission Limits - Reconstructions ........................ 9- Error! Bookmark not defined.
Table 9-2. Proposed Emission Limits - Modifications ........................... 9- Error! Bookmark not defined.
Table 9-3. Annualized Costs and Fuel Savings of Efficiency Improvements (millions of 2011$) .... Error! Bookmark not defined. -9
Table 9-4. Emission Reductions from Efficiency Improvements (tons/year) ........ 9- Error! Bookmark not defined.
Table 9-5. Summary of National Benefit-per-ton Estimates for EGUs in 2025 .... 9- Error! Bookmark not defined.

Table 9-6. Estimated Global Climate Benefits of Illustrative CO 2 Reductions in 2025 (millions of 2011$)* ....................................................................................................................... ............ 9-2
Table 9-7. Summary of Estimated Monetized Health Co-benefits of Illustrative Efficiency Improvements in 2025 (thousands of 2011$)* ............................................. 9- Error! Bookmark not defined.
Table 9-8. Combined Estimated Climate Benefits and Health Co-Benefits of Illustrative Efficiency Improvements in 2025 (millions of 2011$)* ........................ 9- Error! Bookmark not defined.

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xvi 

### L IST OF 

### F IGURES

Figure 2-1. Fossil Fuel-Fired Electricity Generating Facilities, by Size ...................................................... . 5
Figure 2-2. Status of State Electricity Industry Restructuring Activities .................................................... . 8
Figure 2-6. Domestic Emissions of Greenhouse Gases, 2012 (million metric tons of CO 2 equivalent) .... 10
Figure 2-3. Direct GHG Emission s from the Power Sector Relative to Total Domestic GHG Emissions (2012) ........................................................................................................................ .............. 12
Figure 2-5. National Average Retail Electricity Price (1960 – 20113, 2011$) .......................................... 22
Figure 2-6. Average Retail Electricity Price by State (cents/kWh), 2011 ................................................. 27
Figure 2-7. Natural Gas Spot Price, Annual Average (Henry Hub) .......................................................... 30
Figure 2-8 . Electricity Growth Rate (3-Year Rolling Average) and Projections from the Annual Energy Outlook 2014 .................................................................................................................. ......... 31
Figure 3-11111. NERC Regions ................................................................................................. ............... 13
Figure 3-4. Generation Mix with the Base Case and 111(d) Options, 2020-2030 (million GWh) ............ 34
Figure 3-5. Electricity Market Module Regions ................................................................................ ........ 13
Figure 4-1. Monetized Health Co-benefits for each Option (Regional Compliance) of the Proposed EGU GHG Existing Source Guidelines in 2020 * ............................................................................ 54
Figure 4-3. Breakdown of Monetized Health Co-Ben efits by Region at a 3% Discount Rate for each Option (Regional compliance) for Proposed EGU GHG Existing Source Guidelines in 2020*

.............................................................................................................................. ................... 56
Figure 4-4. Percentage of Adult Popul ation (age 30+) by Annual Mean PM 2.5 Exposure in the Baseline Sector Modeling (used to generate the benefit-per-ton estimates)* ........................................ 62
Figure 4-5. Cumulative Distribution of Adu lt Population (age 30+) by Annual Mean PM 2.5 Exposure in the Baseline Sector Modeling (used to generate the benefit-per-ton estimates)* ................... 63
Figure 4-6. Breakdown of Combined Monetized Cl imate and Health Co-benefits of Proposed EGU GHG Existing Source Guidelines in 2020 by Option (Regional Compliance) and Pollutant (3% discount rate)* ............................................................................................................... .......... 71
Figure 4A-1

. Conceptual Overview of Benefit-per-Ton Calculation .......................................................... 3
Figure 4A-2. Modeled PM 2.5 Concentrations Attributable to EGUs in 2016 (Annual Mean, in μg/m 3 ) (Source: BPT TSD) ............................................................................................................. ...... 6***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

xvii Figure 4A-3. Modeled PM 2.5 Concentrations Attributable to EGUs by Precursor in 2016 (Annual Mean, in μg/m 3 ) ............................................................................................................................. ....... 8
Figure 4A-4. Regional Breakdown .............................................................................................. ................ 9
Figure 4A-5. Breakdown of Directly Emitted PM 2.5 Emissions from EGU in Sector Modeling ............... 11
Figure 4A-6. Modeled Average Ozone Concentrations Attributable to EGUs in 2016 (Daily 8-hour maximum, May-September, in ppb) ........................................................................................ 23
Figure 4A-7. Comparison of Base Case SO 2 Emissions in 2020 ............................................................... 28
Figure 4A-8. Comparison of Base Case Annual NOx Emissions in 2020 ................................................. 29
Figure 4A-9. Comparison of Base Case Ozone-season NOx Emissions in 2020 ...................................... 30
Figure 6.1. Electric Power Industry Employment ................................................................................ ....... 11
Figure 6.2. Coal Production Employment ........................................................................................ .......... 13
Figure 6.3. Oil and Gas Production Employment ................................................................................. ...... 14
Figure 4A-1

. Conceptual Overview of Benefit-per-Ton Calculation .......................................................... 2
Figure 4A-2. Modeled PM 2.5 Concentrations Attributable to EGUs in 2016 (Annual Mean, in μg/m 3 ) (Source: BPT TSD) ............................................................................................................. ...... 4
Figure 4A-3. Modeled PM 2.5 Concentrations Attributable to EGUs by Precursor in 2016 (Annual Mean, in μg/m 3 ) ............................................................................................................................. ....... 6
Figure 4A-4. Regional Breakdown .............................................................................................. ................ 7
Figure 4A-5. Breakdown of Directly Emitted PM 2.5 Emissions from EGU in Sector Modeling ................. 8
Figure 4A-6. Modeled Average Ozone Concentrations Attributable to EGUs in 2016 (Daily 8-hour maximum, May-September, in ppb) ........................................................................................ 17
Figure 4A-7. Comparison of Base Case SO 2 Emissions in 2020 ............................................................... 21
Figure 4A-8. Comparison of Base Case Annual NOx Emissions in 2020 ................................................. 22
Figure 4A-9. Comparison of Base Case Ozone-season NOx Emissions in 2020 ...................................... 23
Figure 6.1. Electric Power Industry Employment ............................................................................... ....... 11
Figure 6.2. Coal Production Employment ....................................................................................... .......... 12
Figure 6.3. Oil and Gas Production Employment ................................................................................ ...... 13
Figure 2-1. Fossil Fuel-Fired Electricity Generating Facilities, by Size .................................................... 2

- 5***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

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

- 8
Figure 2-6. Domestic Emissions of Greenhouse Gases, 2012 (million metric tons of CO 2 equivalent) ... 2- 9
Figure 2-3. Direct GHG Emission s from the Power Sector Relative to Total Domestic GHG Emissions (2012) ........................................................................................................................ ........... 2- 11
Figure 2-5. National Average Retail Electricity Price (1960 – 20113, 2011$) ....................................... 2- 20
Figure 2-6. Average Retail Electricity Price by State (cents/kWh), 2011 .............................................. 2- 24
Figure 2-7. Natural Gas Spot Price, Annual Average (Henry Hub) ....................................................... 2- 27
Figure 2-8 . Electricity Growth Rate (3-Year Rolling Average) and Projections from the Annual Energy Outlook 2014 .................................................................................................................. ...... 2- 28
Figure 3-11111. NERC Regions ................................................................................................. ........ 3- 13 12
Figure 3-2. NERC Assessment Areas ............................................................................................ ......... 3-13
Figure 3-3. Regional Compliance Zones Used in the Analysis .............................................................. 3-13

Figure 3-4. Generation Mix with the Base Case and 111(d) Options, 2020-2030 (million GWh) ..... 3- 31 33
Figure 3-5. Electricity Market Module Regions ................................................................................ ..... 3- 13
Figure 4-1. Monetized Health Co-benefits for each Option (Regional Compliance) of the Proposed EGU GHG Existing Source Guidelines in 2020 * ..................................................................... 4-54 37
Figure 4-23. Breakdown of Monetized Health Co-Benef its by RegionPrecursor Pollutant at a 3 at a 3% Discount Rate for each Option (Regional compliance) for Proposed EGU GHG Existing Source Guidelines in 2020* ................................................................................................. 4- 55
Figure 4-3. Breakdown of Monetized Health Co-Ben efits by Region at a 3% Discount Rate for each Option (Regional compliance) for Proposed EGU GHG Existing Source Guidelines in 2020*

.............................................................................................................................. ................ 4-56

39
Figure 4-44. Percentage of Adult Population (age 30+) by Annual Mean PM 2.5 Exposure in the Baseline Sector Modeling (used to generate the benefit-per-ton estimates)* ................................. 4-62 45
Figure 4-5. Cumulative Distribution of Adu lt Population (age 30+) by Annual Mean PM 2.5 Exposure in the Baseline Sector Modeling (used to generate the benefit-per-ton estimates)* ............ 4-63 46
Figure 4-6. Breakdown of Combined Monetized Cl imate and Health Co-benefits of Proposed EGU GHG Existing Source Guidelines in 2020 by Option (Regional Compliance) and Pollutant (3% discount rate)* ............................................................................................................... ... 4-71 51 Formatted: Font: (Default) Times New Roman, Complex Script Font: Times New Roman, Check spelling and grammar
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xix Figure 4A-1

. Conceptual Overview of Benefit-per-Ton Calculation ................................................ 4A-94 2
Figure 4A-2. Modeled PM 2.5 Concentrations Attributable to EGUs in 2016 (Annual Mean, in μg/m 3 ) (Source: BPT TSD) ............................................................................................................ 4-96 4
Figure 4A-3. Modeled PM 2.5 Concentrations Attributable to EGUs by Precursor in 2016 (Annual Mean, in μg/m 3 ) .......................................................................................................................... 4A -98 7
Figure 4A-4. Regional Breakdown .............................................................................................. .... 4A- 8 100
Figure 4A-5. Breakdown of Directly Emitted PM 2.5 Emissions from EGU in Sector Modeling ... 4A- 10 102
Figure 4A-6. Modeled Average Ozone Concentrations Attributable to EGUs in 2016 (Daily 8-hour maximum, May-September, in ppb) ............................................................................ 4A-113 19
Figure 4A-7. Comparison of Base Case SO 2 Emissions in 2020 ................................................... 4A-118 23
Figure 4A-8. Comparison of Base Case Annual NOx Emissions in 2020 ..................................... 4A-119 24
Figure 4A-9. Comparison of Base Case Ozone-season NOx Emissions in 2020 .......................... 4A- 25 120
Figure 6.1. Electric Power Industry Employment ................................................................................ .... 6- 11
Figure 6.2. Coal Production Employment ........................................................................................ ....... 6- 13
Figure 6.3. Oil and Gas Production Employment ................................................................................. ... 6- 14***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

xx 

### 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 Sequestrati on 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***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

xxi 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, Ne gotiation, 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***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

xxii 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 Indus try Classification System NaOH Sodium Hydroxide NATCARB National Carbon Seque stration 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***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

xxiii OMB Office of Management and Budget PAGE Policy Analysis of the Greenhouse Gas Effect Model
PFC Perfluorocarbons
PM
2.5 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 Cl ean 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
SO

2 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***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

xxiv 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***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review*** Draft Deliberative

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 Re constructed 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 long lasting changes in our climate that can have a range of severely negative effects on human health and the environment. Carbon Dioxide (CO 2 ) 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 CO 2 , 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 goal s 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***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review*** Draft Deliberative

ES-2 such reduction and any non-air quality health and environmental impact and energy 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 buildi ng 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-i ntensive affected EGUs in the amount thatresults 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 gui delines 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 reduc tion 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-sta te basis, in recognition of the fact that

1 Under CAA sections 111(a)(1) and (d), the EPA is aut horized 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 em ission reduction. In addition, the state is required to include in its state plan the standards of performance a nd measures to implement and enforce those standards. The state must submit the plan to the EPA, and the EPA mu st approve the plan if the standards of performance and implementing and enforcing measures are satisfactory.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review*** Draft Deliberative

ES-3 electricity is transmitted across state lines, a nd local measures often impact regional EGU CO 2 emissions. While the EPA must establish BSER and is pr oposing goals and guidelines that reflect BSER, CAA section 111(d) also provides the EP A with the flexibility to design goals and guidelines that recognize, and are tailored to, the uniqueness and complexity of the power generation sector and CO 2 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 measur es that the EPA determines constitute BSER, or use those measures to the same degree of stringe ncy 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 a fforded 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 a regional and state compliance approaches for each option. With the state compliance approach, states are assumed to comply with the guidelin es by implementing measures solely within the state and emissions rate averaging occurs betw een 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 di scretion of choosing between a regional or state compliance approach, and this RIA reports the economic consequences of compliance under two***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review*** Draft Deliberative

ES-4 sets of assumptions: one that assumes all stat es 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 scenar ios does not assume that states use any specific policy mechanism to achieve the state goals. There may be various policy mechanisms that result
in tT he same distribution s of emissions and electricity generation asreflected 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 to achieve the rate-based constraints imposed in the illustrative scenarios, individual states or multi-state regional groupsthere may develop be more cost effective approaches that the states may adopt to achieve their state goals.
Table ES-1. Proposed State Goals (Adjusted MWh-Weighted-Average Pounds of CO 2 per Net MWh from all Affected Fossil Fuel-Fired EGUs) for Options 1 and 2

State 2 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

2 The EPA has not developed goals for Vermont and the Dist rict of Columbia because current information indicates those jurisdictions have no affected EGUs. Also, as noted in Chapter 3above , EPA is not proposing goals for tribes or U.S. territories at this time.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review*** Draft Deliberative

ES-5 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 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 s 1 regional and state compliance approaches and Table ES-3 reports***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review*** Draft Deliberative

ES-6 emission reductions associated with Option 2. In 2020, the EPA estimates that CO 2 emissions will be reduced by 371 million metric tons under the regional compliance approach and by 383 million metric tons assuming a state specific co mpliance approach compared to base case levels.
CO 2 emission reductions for Option 1 increase to 545 and 555 million metric tons annually in 2030 when compared to the base case emissi ons for Option 1 regional and state compliance approaches, respectively. Tables ES-2 and ES-3 also show emission reductions for criteria air pollutants.

Table ES-2. Summary of Climate and Air Pollutant Emission Reductions Option s 1 1
CO 2 (million metric tons) SO 2 (thousands of tons) NO X (thousands of tons) PM 2.5 (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 Complia nce 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. N/A – not yet available 1 CO 2 emission reductions are used to estimate the climate benefits of the guidelines. SO 2 , NOx, and directly emitted PM 2.5 emission reductions are relevant for estimating air pollution health co-benefits of the proposed guidelines.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review*** Draft Deliberative

ES-7 Table ES-3. Summary of Climate and Air Pollutant Emission Reductions Option 2 1
CO 2 (million metric tons) SO 2 (thousands of tons) NO X (thousands of tons) PM 2.5 (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 2030 Regional Compliance Approach Base Case n/a n/a n/a n/a Option 2 n/a n/a n/a n/a Emissions Change n/a n/a n/a n/a 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 Complia nce 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 2030 State Complian ce Approach Base Case n/a n/a n/a n/a Option 2 n/a n/a n/a n/a Emissions Change n/a n/a n/a n/a Source: Integrated Planning Model, 2014. N/A– not yet available.

1 CO 2 emission reductions are used to estimate the climate benefits of the guidelines. SO 2 , NOx, and directly emitted PM 2.5 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 in which states ofpursue 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.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review*** Draft Deliberative

ES-8 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 monitori ng, 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 record keeping costs for both options are $68.3 million 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, 3 the change in the ongoing costs of operating pollution controls, shifts between or amongst va rious fuels, demand-side energy efficiency measures, and other actions associated with comp liance. 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 GH G 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 estimate d 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 efficien cy participant costs (p aid by electric utilityconsumers). For example in 2020 for the proposed Option 1 regional compliance approach, enduse energy efficiency program costs are estimat ed to be $5.1 billion and end-use efficiency

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

http://www.epa.gov/powersectormodeling/BaseCasev513.html***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review*** Draft Deliberative

ES-9 participant costs are $5.1 billi on using a 3% discount rate (see Table 3-43) . This estimate for end-use energy efficiency costs of $10.2 billion is combined with the costs generated by the Integrated Planning Model ( IPM ) that include the costs of stat es’ compliance with state goals associated with changes to reduce the carbon-intensity of electricity production and the energy demand decreases expected from end-use energy efficiency assumed in the illustrative scenarios.
In order to reflect the full cost 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). Thus the end-use energy efficiency costs are combined with the costs of reducing carbon-intensity of production less the energy demand reductions due to end-use energy efficiency or by a net value derived by IPM of - $4.8 billion. The resulting compliance cost estimate of the rule considering all components is $5.4 billion ($10.2 billion - $4.8 billion) in 202 0 for the Option 1 regional compliance approach. Note that when monitoring, reporting and record keeping 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 He alth Co-benefits for Existing EGUs

Implementing the proposed guidelines is expected to reduce emissions of CO 2 and have ancillary emission reductions (i.e., co-benefits) of SO 2 , NO 2 , and directly emitted PM 2.5 , which
would lead to lower ambient concentrations of PM 2.5 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***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review*** Draft Deliberative

ES-10 Impact Analysis under Executive Order 12866 , henceforth denoted as the 2013 SCC TSD. 4 Also, the range of combined benefits reflects di fferent concentration-response functions for the air pollution health co-benefits, but it does not cap ture the full range of un certainty 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 guidelines, including reducing exposure to SO 2 , NO x , and hazardous air pollutants (e.g., mercury and hydrogen chloride), as well as ecosyste m effects and visibility impairment. These unquantified benefits could be substantial, but it is difficult to approximate the potential magnitude of these unquantified benefits because and previous quantification attempts have been incomplete. The omission of these endpoints fr om 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.

4 Technical Support Document: Technical Update of the So cial 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 Ag riculture, Department of Commerce, Department of Energy, Department of Tran sportation, 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***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review*** Draft Deliberative

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 CO 2 —  SCC TSD Climate impacts from ozone and black carbon (directly emitted PM) — — Ozone ISA, PM ISA 1 Other climate impacts (e.g., other GHGs such as methane, aerosols, other impacts) — — IPCC 1 Improved Human Health (co-benefits) Reduced incidence of premature mortality from exposure to PM 2.5 Adult premature mortality ba sed 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 PM 2.5 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 ISA 1 Emergency room visits for cardiovascular effects (all ages) — — PM ISA 1 Strokes and cerebrovascular disease (age 50-79) — — PM ISA 1 Other cardiovascular effects (e.g., other ages) — — PM ISA 2Other respiratory effects (e.g., pulmonary function, nonasthma ER visits, non-bronchitis chronic diseases, other ages and populations) — — PM ISA 2

Reproductive and developmental effects (e.g., low birth weight, pre-term births, etc) — — PM ISA 2,3 Cancer, mutagenicity, a nd genotoxicity effects — — PM ISA 2,3

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 ISA 1

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 ISA 1 Other respiratory effects (e.g., premature aging of lungs) — — Ozone ISA 2 Cardiovascular and ne rvous system effects — — Ozone ISA 2 Reproductive and developmental effects — — Ozone ISA 2,3

Reduced incidence of morbidity from exposure to NO 2 Asthma hospital admissions (all ages) — — NO 2 ISA 1 Chronic lung disease hospital admissions (age > 65) — — NO 2 ISA 1 Respiratory emergency department visits (all ages) — — NO 2 ISA 1 Asthma exacerbation (asthmatics age 4–18) — — NO 2 ISA 1 Acute respiratory symptoms (age 7–14) — — NO 2 ISA 1 Premature mortality — — NO 2 ISA 1,2,3 Other respiratory effects (e.g., airway hyperresponsiveness and inflammation, lung function, other ages and populations) — — NO 2 ISA 2,3

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ES-12 morbidity from exposure to SO 2 Asthma emergency department visits (all ages) — — SO 2 ISA 1 Asthma exacerbation (asthmatics age 4–12) — — SO 2 ISA 1 Acute respiratory symptoms (age 7–14) — — SO 2 ISA 1 Premature mortality — — SO 2 ISA 1,2,3 Other respiratory effects (e.g., airway hyperresponsiveness and inflammation, lung function, other ages and populations) — — SO 2 ISA 1,2

Reduced incidence of morbidity from exposure to methylmercury Neurologic effects—IQ loss — — IRIS; NRC, 2000 1 Other neurologic effects (e.g., developmental delays, memory, behavior) — — IRIS; NRC, 2000 2

Cardiovascular effects — — IRIS; NRC, 2000 2,3 Genotoxic, immunologic, and other toxic effects — — IRIS; NRC, 2000 2,3 Reduced incidence of morbidity from exposure to HAP Effects associated with exposure to hydrogen chloride — — ATSDR, IRIS 1,2

Improved Environment (co-benefits) Reduced visibility impairment Visibility in Class 1 areas — — PM ISA 1 Visibility in residential areas — — PM ISA 1 Reduced effects on materials Household soiling — — PM ISA 1,2 Materials damage (e.g., corrosion, increased wear) — — PM ISA 2 Reduced PM deposition (metals and organics) Effects on Individual organisms and ecosystems — — PM ISA 2

Reduced vegetation and ecosystem effects from exposure to

ozone Visible foliar injury on vegetation — — Ozone ISA 1 Reduced vegetation growth and reproduction — — Ozone ISA 1 Yield and quality of commercial forest products and crops — — Ozone ISA 1 Damage to urban ornamental plants — — Ozone ISA 2 Carbon sequestration in terrestrial ecosystems — — Ozone ISA 1 Recreational demand associated with forest aesthetics — — Ozone ISA 2 Other non-use effects Ozone ISA 2 Ecosystem functions (e.g., water cycling, biogeochemical cycles, net primary productivity, leaf-gas exchange, community composition) — — Ozone ISA 2

Reduced effects from acid deposition Recreational fishing — — NO x SO x ISA 1 Tree mortality and decline — — NO x SO x ISA 2 Commercial fishing and forestry effects — — NO x SO x ISA 2 Recreational demand in terrestrial and aquatic ecosystems — — NO x SO x ISA 2 Other non-use effects NO x SO x ISA 2 Ecosystem functions (e.g., biogeochemical cycles) — — NO x SO x ISA 2

Reduced effects from nutrient enrichment Species composition and biodiversity in terrestrial and estuarine ecosystems — — NO x SO x ISA 2 Coastal eutrophication — — NO x SO x ISA 2 Recreational demand in terrestrial and estuarine ecosystems — — NO x SO x ISA 2 Other non-use effects NO x SO x ISA 2 Ecosystem functions (e.g., biogeochemical cycles, fire regulation) — — NO x SO x ISA 2 Reduced vegetation effects from exposure to SO 2 and NO x Injury to vegetation from SO 2 exposure — — NO x SO x ISA 2 Injury to vegetation from NO x exposure — — NO x SO x ISA 2 Reduced ecosystem effects from exposure to methylmercury Effects on fish, birds, and mammals (e.g., reproductive effects) — — Mercury Study RTC 2
Commercial, subsistence and recreational fishing — — Mercury Study RTC 1

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

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ES-13 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 suggest ive 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 (CO 2 ) 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 CO 2 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 CO 2 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 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 es timates represent global measures because of the distinctive nature of the climate change pr oblem. 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. Th erefore, the SCC estimates incorporate the worldwide damages caused by carbon dioxide emission s in order to reflect the global nature of the problem, and we expect other governments to consider the global consequences of their***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review*** Draft Deliberative

ES-14 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, a nd 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 S CC TSD presents and discusses the updated estimates. The four SCC estimates, updated in 2013, are as follows: $13, $46, $68, and $137 per
metric ton of CO 2 emissions in the year 2020 (2011$), 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 95 th 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 catas trophic and non-catastrophic impacts, their incomplete treatment of adaptation and technological change, uncertainty in the extrapolation of damages to high temperatures, and assumptions regarding risk aversion. Current integrated assessment models do not assign value to all of the important physical, ecological, and economic impacts of climate chan ge recognized in the c limate 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 CO 2 emission reductions to inform the benefit-cost analysis.

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

Uncertainty, Negotiation, and Distribution (FUND); and Po licy Analysis of the Greenhouse Gas Effect (PAGE).***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review*** Draft Deliberative

ES-15 ES 5.2 Estimating Air Po llution Health Co-Benefits

The proposed guidelines would reduce emissions of precursor pollutants (e.g., SO 2 , NOx, and directly emitted particles), which in turn would lower ambient concentrations of PM 2.5 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 PM 2.5 cobenefits include avoided premature deaths (derived from effect coefficients in two cohort studies [Krewski et al. 2009 and Lepeule et al. 2012] fo r 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 epidem iology 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). We used a “benefit-per-ton” approach to estim ate the health co-benefits. To create the benefit-per-ton estimates for PM 2.5 , this approach uses an air quality model to convert emissions
of PM 2.5 precursors (e.g., SO 2 , NO x ) and directly emitted particle s into changes in ambient PM 2.5 concentrations and BenMAP to estimate the ch anges 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 Californi a). We followed a similar process to estimate benefit-per-ton estimates for the ozone precursor NO x . 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 reduc tions. 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

6 We did not estimate the co-benefits associ ated with reducing direct exposure to SO 2 and NOx.

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

health co-benefits in Alaska and Hawaii anticipated from implementation of the proposed guidelines.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review*** Draft Deliberative

ES-16 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 PM 2.5 -related premature mortality, which accounts for 98 percent of the monetized PM 2.5 health co-benefits:
1. We assume that all fine particles, rega rdless of their chemical composition, are equally potent in causing premature mortality. This is an important assumption, because PM 2.5 varies considerably in composition across sources, but the scientific evidence is not yet sufficient to allow differ entiation of effect es timates by particle type. The PM ISA concluded that “many constituents of PM 2.5 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 PM 2.5 , including both areas that do not meet the fine particle stan dard 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 change s in mortality effects. Specifically, we assume that some of the incidences of premature mortality related to PM 2.5 exposures occur in a distributed fashion over the 20 years following exposure based on the advice of theSAB-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 ex tent, by data gaps, model capabilities (such as geographic coverage) and uncertainties in the unde rlying 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***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review*** Draft Deliberative

ES-17 reasonable assumptions. This analysis does not include the type of detailed uncertainty
assessment found in the 2012 PM 2.5 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 CO 2 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 ce ntered its attention on the average SCC at a 3 percent discount rate but emphasized the importance of considering all four SCC estimates. 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.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review*** Draft Deliberative

ES-18 Table ES-6. Combined Estimates of Clim ate 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 CO 2 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% (95 th percentile) $51 $67 to $88 $65 to $85 In 2025 501 million metric tonnes CO 2

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% (95 th percentile) $76 $99 to $130 $97 to $120 In 2030 545 million metric tonnes CO 2 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% (95 th percentile) $92 $120 to $150 $120 to $150 Option 2 In 2020 283 million metric tonnes CO 2 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% (95 th percentile) $39 $52 to $70 $51 to $67 In 2025 368 million metric tonnes CO 2 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% (95 th percentile) $56 $73 to $96 $72 to $93 In 2030 n/a million metric tonnes CO 2

5% n/a n/a n/a 3% n/a n/a n/a 2.5% n/a n/a n/a 3% (95 th percentile) n/a n/a n/a *All benefit estimates are rounded to two significant figures. Climate benefits are based on reductions in CO 2 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. Th e health co-benefits reflect the sum of the PM 2.5 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 mone tized health co-benefits do not include reduced health effects from direct exposure to NO 2 , SO 2 , and HAP; ecosystem effects; or visi bility impairment. See chapter 4 for more information about these estimates and re garding the uncertainty in these estimates. **Unless otherwise specified, it is the model average.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review*** Draft Deliberative

ES-19 Table ES-7. Combined Estimates of Clim ate 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 CO 2 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% (95 th percentile) $52 $69 to $92 $68 to $88 In 2025 506 million metric tonnes CO 2

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% (95 th percentile) $77 $100 to $130 $98 to $130 In 2030 n/a million metric tonnes CO 2 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% (95 th percentile) $94 $120 to $160 $120 to $150 Option

2 In 2020 295 million metric tonnes CO 2 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% (95 th percentile) $40 $54 to $72 $53 to $69 In 2025 376 million metric tonnes CO 2 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% (95 th percentile) $57 $75 to $98 $73 to $95 In 2030 n/a million metric tonnes CO 2

5% n/a n/a n/a 3% n/a n/a n/a 2.5% n/a n/a n/a 3% (95 th percentile) n/a n/a n/a *All benefit estimates are rounded to two significant figures. Climate benefits are based on reductions in CO 2 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. Th e health co-benefits reflect the sum of the PM 2.5 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 mone tized health co-benefits do not include reduced health effects from direct exposure to NO 2 , SO 2 , and HAP; ecosystem effects; or visi bility impairment. See chapter 4 for more information about these estimates and re garding the uncertainty in these estimates. **Unless otherwise specified, it is the model average.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review*** Draft Deliberative

ES-20 ES.6 Monetized Benefits, Compliance Costs and Net Benefits of the Proposed Guidelines for Existing Sources
In this summary, the EPA provides the esti mates 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-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 $78 billion (20 11$) 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 bi llion 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 Options 1 and 2 regional and state compliance approaches for 2020, 2025, and 2030 are presented in Tables ES-8, ES-9, and ES-10. The EPA could not monetize some important benefits of the guidelines. Unquantified
benefits include climate benefits from reducing emissions of non-CO 2 greenhouse gases and cobenefits from reducing exposure to SO 2 , NO x , and hazardous air pollutants (e.g., mercury and hydrogen chloride), as well as ecosystem effect s and visibility impairment. Upon considering these limitations and uncertainties, it remains cl ear that the benefits of this proposal are substantial and far outweigh the costs.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review*** Draft Deliberative

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 su mmary table reflect global impacts from CO 2 emission changes and do not

account for changes in non-CO 2 GHG emissions. Also, different discount rates are applied to SCC than to the other estimates because CO 2 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 PM 2.5 and ozone associated with emission

reductions of directly emitted PM 2.5 , SO 2 and NO X . The range reflects the use of concentration-response functions from different epidemiology studies. The reduction in pr emature fatalities each year accounts for over 90 percent of total monetized co-benefits from PM 2.5 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 di scount 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 cal culated 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.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review*** Draft Deliberative

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 su mmary table reflect global impacts from CO 2 emission changes and do not

account for changes in non-CO 2 GHG emissions. Also, different discount rates are applied to SCC than to the other estimates because CO 2 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 PM 2.5 and ozone associated with emission

reductions of directly emitted PM 2.5 , SO 2 and NO X . The range reflects the use of concentration-response functions from different epidemiology studies. The reduction in pr emature fatalities each year accounts for over 90 percent of total monetized co-benefits from PM 2.5 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 di scount 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 cal culated 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.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review*** Draft Deliberative

ES-23 Table ES-10. Summary of Estimated Monetized Benefits, Compliance Costs, and Net Benefits for the Proposed Guid elines –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 su mmary table reflect global impacts from CO 2 emission changes and do not

account for changes in non-CO 2 GHG emissions. Also, different discount rates are applied to SCC than to the other estimates because CO 2 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 PM 2.5 and ozone associated with emission

reductions of directly emitted PM 2.5 , SO 2 and NO X . The range reflects the use of concentration-response functions from different epidemiology studies. The reduction in pr emature fatalities each year accounts for over 90 percent of total monetized co-benefits from PM 2.5 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 di scount 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 cal culated 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.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review*** Draft Deliberative

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 s ector 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 increas e 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 forin Sectors Other Than the EGU Sector and for Employment Impacts
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, changes in quantity produced , and these market changes may affectconsequently changes in the profitability of firms affected and the economic welfare of effect on their consumers. The EPA recognizes that these guidelines provide signifi cant 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 efficiency can result in changes in production and***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review*** Draft Deliberative

ES-25 profitability for firms that su pply those goods and services . and tT he 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 jo b 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 flexibil ity to implement polic ies 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 EP A estimates that these guidelines could result in an increase of approximately 28,000 to 25,900 j ob-years in 2020 for Option 1, state and regional compliance approaches, respectively. For Option 2, the state and regional compliance approach
estimates reflect both an increase of approximately 29,800 to 26,700 job-years in 2020. The Agency is also offering an illustrative calcu lation of potential employment effects due to
dD emand-side energy efficiency programs. Ee mployment impacts in 2020 could bemay range from an increase of approximately 78,800 jobs in 2020 for Option 1 ( for both the state and regional compliance approaches ) . For Option 2 demand-side energy efficiency employment impacts in 2020 could beare an increase of approximately 57,000 jobs in 2020 ( for both the state and regional compliance approaches ) . More detail about these analyses can be found in Chapter
67 of this RIA.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review*** Draft Deliberative

ES-26 ES.9 Modified and Reconstructed Sources

The EPA is proposing emission limits for CO 2 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 associ ated 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.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, Tec hnical Support Document: Social Cost of Carbon for Regulatory Impact Analysis Under Executive Order 12866, Interagency Working Group on Social Cost of Carbon, with partic ipation 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:***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review*** Draft Deliberative

ES-27 <http://www.whitehouse.gov/sites/default/files/ omb/assets/inforeg/tec hnical-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 Indust rial, 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. 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/pub lications_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 Methylmerc ury. 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>.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review*** Draft Deliberative

ES-28 U.S. EPA. 2008c. Integrated Science Assessment fo r Oxides of Nitrogen: Health Criteria (Final Report). Research Triangle Park, NC: National Center for Environmental Assessment. July. Available at <http://www.epa.gov/ttnecas1/re gdata/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/n cea/cfm/recordisplay.cfm?deid=201485>. U.S. EPA. 2009. Integrated Science Assessment for Particulate Matter (Final Report). Research Triangle Park, NC: National Center for Envi ronmental 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 En vironmental 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.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review*** Draft Deliberative

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 w ith 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 rele vant 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 (CO 2 ) 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
CO 2 from newly constructed fossil fuel-fired electr ic steam generating units (utility boilers and integrated gasification combined cycle [IGCC] units) and for natural gas-fired stationary
combustion turbines. The EPA is curren tly proposing standards to address CO 2 emissions from reconstructed and modified power plants u nder the authority of CAA section 111(b). Furthermore, when the EPA establishes section 111(b) standards of performance for newly***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review*** Draft Deliberative

1-2 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 mu st 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 combinationthat elevated concentrations of greenhouse gases in the atmosphere may reasonably be anticipated
both to endanger both the public health and to endangerthe public welfare. ” 8 These adverse impacts make it necessary for the EPA to regulat e 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 Endang erment Finding closed following the EPA’s 2010
Reconsideration Denial. The EPA has reviewed thes e 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 a ffecting the American people in far reaching ways. Certain types of extreme weather events w ith 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, warmi ng 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 ar e disrupting people’s lives and damaging some sectors of our economy.” This an d other assessments are outlined in Chapter 4 of this Regulatory Impact Assessment (RIA). Climate change threatens public health in a number of ways. Heat is already the leading

8 Endangerment and Cause or Contribute Findings for Greenh ouse Gases under Section 202(a) of the Clean Air Act.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review*** Draft Deliberative

1-3 cause of weather-related deaths in the United States. In the future, severe heat waves are projected to intensify in magnitude and duration over the portions of the U.S. where these events have already been observed. If observed warming continues as projected, it will increase heatrelated mortality and illness, especially among the elderly, young, and frail. There is also a relationship between climate change and other aspects of air quality. Climate change is expected to increase regional ozone pollution compared to the expected ozone levels in the absence of climate change. These incr eases are expected to in crease associated risks for respiratory illnesses and premature death. In addition to impacts on heat-related morta lity and air quality, there is also the potential for increased deaths, injuries, infectious diseas es, stress-related disorders, and other adverse
effects associated with social disruption and migration from more frequent extreme weather events. Climate change can also impact the spread of vector-borne diseases. There are multiple pathways by which greenhouse gas emissions and the resultant climate change affect climate-sensitive economic sector s and the environment. These sectors include agriculture, forestry, water resources, sea level rise and coastal areas, energy, infrastructure, settlements, and ecosystems and wildlife. For exampl e, changes in the intensity and frequency of extreme weather events, such as droughts and heavy storms, have the potential to cause serious negative impacts on U.S. agriculture. Additionally , climate change will likely further constrain already over-allocated water resources in some regions of the United States, increasing competition among agricultural, municipal, indust rial, and ecological uses. Furthermore, climate change impacts in other regions of the world could raise national security concerns for the
United States. The specific health and welfare impacts of climate change are addressed in more detail in Chapter 4 of this Regulatory Impact Assessment (RIA).
1.2.3 Market Failure
Many regulations are promulgated to correct ma rket failures, which lead to a suboptimal
allocation of resources within the free market. Air quality and pollution control regulations address “negative externa lities” whereby the market does not in ternalize the full opportunity cost of production borne by society as public goods such as air quality are unpriced.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review*** Draft Deliberative

1-4 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 el ectricity, this cost will be borne not by the polluting firm but by society as a whole. The mark et price of electricity will fail to incorporate the full opportunity cost to society of generating el ectricity. 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 o ccur 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,” th e EPA prepared this RIA for this “significant regulatory action.” This action is an economical ly 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, emi ssion 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 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 include s 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 furt her discussion of key evaluations of uncertainty***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review*** Draft Deliberative

1-5 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 CO 2 emissions from certain existing EGUs. The base case for this analysis, which uses the Integrated Plan ning Model (IPM), includes state rules that have been finalized and/or approved by a state’s legisl ature or environmental agencies, as well as final
federal rules. The IPM Base Case v.5.13 incl udes the Clean Air Interstate Rule (CAIR), 9 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. 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 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 C AA section 111(d) will set emission guidelines for states to
limit CO 2 emissions from certain existing EGUs. This rulemaking does not address GHG emissions from newly constructed sources or s ources modifying or reconstructing. Section 111(b) emission limits for reconstructed and modifi ed sources are being proposed in a separate action and are discussed in Chapter 9 of this RIA.

9 EPA Base Case v.5.13 includes the Clean Air Interstate Rule (CAIR), a Federal regulatory measure for achieving the 1997 Natio nal Ambient Air Quality Standards (NAAQS)

for ozone (8-hour average of 0.08 ppm) and fine particles (24-hour average of 65 μ g/m3 or less and annual average of 15 μ g/m3 for 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 Dis trict 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/BaseCase v513.html***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review*** Draft Deliberative

1-6 For the purposes of this proposed rule, an af fected 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 unit s (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 elect rical 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 mi nimum 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 CO 2 emissions from affected sources. The EPA is proposing these guidelines because CO 2 is a GHG and fossil fuel-fired power plants are the 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 GH Gs such as nitrous oxide (N 2 O) (and to a lesser extent, methane [CH 4 ]) may be emitted from fossil-fuel-fire d 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 (NO X ) control. The EPA is not proposing separate N 2 O or CH 4 guidelines or an equivalent CO 2 emission limit because of a

11 Endangerment and Cause or Contribute Findings for Gr eenhouse Gases under Section 202(a) of the Clean Air

Act.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review*** Draft Deliberative

1-7 lack of available data for these affected so urces. 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 st ates 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 CO 2 emissions from the power sector, as well as guidelines for states to follow in deve loping 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 transm itted across state lines, and measures often impact regional EGU CO 2 emissions. The illustrative compliance strategies presented in this RIA include both regional and state-level compliance scenarios for each of the regulatory options presented. 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***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review*** Draft Deliberative

1-8  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***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review*** Draft Deliberative

1-9 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 Nationa l 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.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review*** Draft Deliberative

1-10***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review*** Draft Deliberative

32 -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 th e delivery of electricity to consumers. Most of the existing capacity for generating electricity do es so by creating heat to create high pressure
steam that is released to rotate turbines whic h, 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).***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review*** Draft Deliberative

32 -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. Formatted Table
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32 -3 These electric generating sources provide el ectricity 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 Po wer Industry Retail Sale s 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 exam ple, 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 be tter able to vary output and is the primary option used to meet the variable portion of th e 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 elect ricity during peak load. Projections of changes Formatted: Font: Times New Roman, Font color: Auto
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32 -4 in capacity and the impact of this rule on the future need for construction of new generation 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. Formatted: Font: Not Bold, Complex Script Font: 10 pt
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32 -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 pla nned 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.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review*** Draft Deliberative

32 -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 tr ansmission 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 elect ricity, 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 generati on 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 the aim of enhancing competition in the generation segment of the industry.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review*** Draft Deliberative

32 -7 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, an d energy, which were all thought to be natural monopolies (prior to 1970) that warranted g overnmental 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 includ ed the desire for more efficient investment choices, the economic incentive to provide least-co st 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 powe r industry slowed significantly in response to market volatility in California and financial tu rmoil 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). Eight een 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). Currentl y, 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 actio ns aimed at wider restructuring, and no additional states have recently begun retail deregulation activity.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review*** Draft Deliberative

32 -8 Figure 2- 2 . Status of State Electricity In dustry 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 gase s. The power sector is a major contributor of
CO 2 in particular, but also contributes to emissions of sulfur hexafluoride (SF 6 ), CH 4 , and N 2 O. In 2012, the power sector accounted for 32 percent of total nationwide greenhouse gas emissions,
measured in CO 2 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 s ector relative to other major economic sectors. Table 2-6 and Figure 2-4 show the contributions of CO 2 and other GHGs from the power sector.

13 All CO 2 equivalent tons in this report are based on th e 100-year time horizon Global Warming Potential. Field Code Changed***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review*** Draft Deliberative

32 -9 Table 2-5. Domestic Emissions of Greenhouse Gases, by Economic Sector (million metric tons of CO 2 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 CO 2 emissions from the electricity generation sector differ slightly from the 2005 CO 2 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 Sink s: 1990–2012 . April 2014.

Figure 2-6. Domestic Emissions of Gree nhouse Gases, 2012 (million metric tons of CO 2 equivalent)
Source: EPA Inventory of U.S. Greenhouse Gas Emissions and Sink s: 1990–2012 . April 2014. 

### Other
4,459
68% Electricity
Generation
2,064
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32 -10 Table 2-6. Greenhouse Gas Emissions fr om the Electricity Sector (Generation, Transmission and Distribution), 2012 (million metric tons of CO 2 equivalent) Source Total Emissions CO 2 from Electricity Generation 2,023 Coal 1,511 Natural Gas 492 Fuel Oil 19 Geothermal 0.4 CH 4 from Electricity Generation 0.5 Coal 0.1 Fuel Oil 0.1 Natural Gas 0.4 Wood + N 2 O from Electricity Generation 18.3 Coal 9.1 Fuel Oil 0.3 Natural Gas 8.7 Wood 0.1 SF 6 from Electricity Transmission and Distribution 6.0 Total 2,048

Source: EPA Inventory of U.S. Greenhouse Gas Emissions and Sink s: 1990–2012 . April 2014. + Does not exceed 0.05 Tg CO 2 Eq. or 0.05 percent.

The amount of CO 2 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 CO 2 during combustion. Coal emits around 1.7 times as much carbon per unit of energy when burned as does natural gas (EPA 2013). Formatted Table ... [26] Formatted ... [27] Formatted ... [28] Formatted ... [29] Formatted ... [30] Formatted ... [31] Formatted ... [32] Formatted ... [33] Formatted ... [34] Formatted ... [35] Formatted ... [36] Formatted ... [37] Formatted ... [38] Formatted ... [39] Formatted ... [40] Formatted ... [41] Formatted ... [42] Formatted ... [43] Formatted ... [44] Formatted ... [45] Formatted ... [46] Formatted ... [47] Formatted ... [48] Formatted ... [49] Formatted ... [50] Formatted ... [51] Formatted ... [52] Formatted ... [53] Formatted ... [54] Formatted ... [55] Formatted ... [56] Formatted ... [57] Formatted ... [58] Formatted ... [59] Formatted ... [60] Formatted ... [61] Formatted ... [62] Formatted ... [63] Formatted ... [64] Formatted ... [65] Formatted ... [66] Formatted ... [67] Formatted ... [68] Formatted ... [69] Formatted ... [70]***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review*** Draft Deliberative

32 -11 Figure 2- 3 . Direct GHG Emissions from the Power Sector Relative to Total Domestic GHG Emissions (2012)
Source: EPA Inventory of U.S. Greenhouse Gas Emissions and Sink s: 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: CO 2 emissions presented here for biomass reflect combus tion only. They do not include any other biogenic or fossil emissions/sequestration related to biomass growth, ha rvest, transportation or any other biomass or processing emissions as part of the carbon cycle. 38% 72%

4% 0.1% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

CO2 SF6 N2O CH4 Other

Power Sector***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review*** Draft Deliberative

32 -12 2.5 Improving GHG Performance at Existing EGUsIn proposing state goals, the analysis anticipates that states will pursue a mix of carbonreducing strategies appropriate to each state’s uni que 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 po wer system (e.g., utility regulatory structure, generation mix, transmission syst em and electricity demand). Th e analysis assumes states will develop plans involving four categories (“buildi ng 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-i ntensive affected EGUs in the amount thatresults 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 CO 2 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***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review*** Draft Deliberative

32 -13 electricity being generated by each unit of fuel (e .g., ton of coal or cubic foot of gas), thereby lowering the amount of CO 2 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 shif ting 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 th e 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 CO 2 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 ca rbon intensity. Such expansion is consistent with current trends. While not included in the go al 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 powersector by reducing the total amount of generation required – in other words, improving demandside efficiency. Many studies have found th at significant improvements in demand-side

14 We view opportunities to shift generation to existing re newable EGUs such as wind or solar units as limited

because such units already tend to r un when capable of doing so due to their low variable operating costs.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review*** Draft Deliberative

32 -14 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 th at occur across the grid between the electricity generation sites and the demand s ites. 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 par ticipate in the development of a regional capand-trade program addressing power plant CO 2 emissions. This program, known as the Regional Greenhouse Gas Initiative (RGGI), began in 2009 and sets a regional CO 2 cap for participating states. The currently participating states in clude: Connecticut, Delaware, Maine, Maryland, Massachusetts, New Hampshire, New York, Rhode Island, and Vermont. The cap covers CO 2 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
from 2015 to 2020. In September 2006, then-Governor of California Arnold Schwarzenegger signed into law

15 See, e.g., Granade, et al., July 2009 and EPRI, 2009.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review*** Draft Deliberative

32 -15 Senate Bill 1368. The law limits long-term inve stments 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 Californ ia Public Utilities Commission. The Energy Commission has designed regulations that establ ish a standard for new and existing baseload generation owned by, or under long-term contract to publicly owned utilities, of 1,100 lb
CO 2 /MWh. In 2006 Governor Schwarzenegger also signe d into law Assembly Bill 32, the Global Warming Solutions Act of 2006. This act incl udes a multi-sector GHG cap-and-trade program which covers approximately 85% of the state GHG emissions. EGUs are includes 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 Gregoi re of Washington signed Substitute Senate Bill 6001, which established statewide GHG emissi ons 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 initia lly comply with an emission limit of 1,100 lb
CO 2 /MWh. Bill 6001also prohibited Washington electr ic utilities from entering new long-term power contracts after June 30, 2008 with any power plant exceeding the 1,100 lb CO 2 /MWh
limit. In July 2009, then-Governor Theodore Kulongoski of Or egon signed Senate Bill 101, which mandated that facilities ge nerating baseload electricity, whet her gas- or coal-fired, must have emissions equal to or less than 1,100 lb CO 2 /MWh, and prohibited utilities from entering into long-term purchase ag reements for baseload electricity w ith out-of-state facilities that do not meet that standard. Natural gas- and petroleum dist illate-fired facilities that are primarily used to serve peak demand or to integrate energy from renewable resources are specifically exempted from the performance standard. In August 2011, New York Governor Andrew Cuomo signed the Power NY Act of 2011. This regulation establishes CO 2 emission standards for new a nd modified electric generators***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review*** Draft Deliberative

32 -16 greater than 25 MW. The standards vary based on the type of facility: baseload facilities must
meet a CO 2 standard of 925 lb/MWh or 120 lb/MMBtu, and peaking facilities must meet a CO 2 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 genera tion. Currently 29 states and the District of
Columbia have enforceable RPS or other mandat ory 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 CO 2 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 re levant information from fossil fuel suppliers and industrial gas suppliers, direct greenhouse gas emitters, and manufacturers of heavy-duty and 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

16 Database of State Incentives for Renewables and Effi ciency, March 2013 and Alaska House Bill 306, 2010.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review*** Draft Deliberative

32 -17 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 CO 2 . The EPA acknowledged the Supreme Court decision, but found that decision alone did not require PSD permits to include limits on CO 2 emissions. Sierra Club challenged the Deseret permit. In November 2008, the Environmental Appeals Board (EAB) remanded the permit to the EPA to recons ider “whether or not to impose a CO 2 BACT (best available control technology) limit in light of th e ‘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 subj ect 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 pollut ants for which the EPA regulations only require monitoring or reporting, such as the provisions for CO 2 in the Acid Rain Program, are not subject to PSD permitting. Fifteen organizations petiti oned 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 emis sions of the pollutant. For GHGs, this meant January 2011 when the first na tional 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 find ings 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 — CO 2 , CH 4 , N 2 O, hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), and SF 6 — in the atmosphere threaten the public health and welfare of current and future generations. These greenhouse gases have long lifetimes and, as a result, become homogeneous ly 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***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review*** Draft Deliberative

32 -18 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 aprerequisite 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 challe nged 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 Tail oring Rule which set thresholds for GHG emissions that define when permits under the Ne w Source Review and Title V Operating Permit programs are required for new and existing industr ial facilities. Facilities responsible for nearly 70 percent of the national GHG emissions from st ationary 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-fir ed electric utility generating units. This action proposes to establish separate standards for fo ssil fuel-fired electric steam generating units (utility boilers and Integrated Gasification Co mbined 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***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review*** Draft Deliberative

32 -19 standard of performance for utili ty boilers and IGCC units base d on partial implementation of carbon capture and storage (CCS) as the BSER. The proposed emission limit for those sources is 1,100 lb CO 2 /MWh. This action also proposes standard s 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 CO 2 /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 pr essure 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 de rive 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 around 8 cents per kWh in the early 1970s, to around 12 cents, reached in the early 1980s. Generally, retail electricity prices do not cha nge 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***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review*** Draft Deliberative

32 -20 hourly, daily, and seasonal basis is critical for driving lowest-cost matching of supply and demand. In fact, the real price of electricity t oday 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 Reviewok 2013, BEA National Income Product Accoun ts 2014

0 2 4 6 8 10 12 14

1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010cents/KWh(2011$)

Formatted: Font: Bold***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review*** Draft Deliberative

32 -21***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review*** Draft Deliberative

32 -22 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 Utilit y O p eratin g Ex p enses 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 Othe r 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.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review*** Draft Deliberative

32 -23Table 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 us e (for generation) or sales (transmission and distribution) and multiplying by forecasted prices by service categor y 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).***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review*** Draft Deliberative

32 -24 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 with in 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 Note: Data are displayed as 5 groups of 10 States and the District of U.S. total average price per kilowatt-hour is 9.90 cents. Source: U.S. Energy Information Administration, Annual Energy Review – Electricity Section, Table 4, September 27, 2012 2011b..***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review*** Draft Deliberative

32 -25 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. Recent forecasts of natural gas availability have also experienced consider able revision as new sources of gas have been discovered and have come to market, although there continues to be some uncertainty surrounding the pr ecise quantity of the resource base. Current and projected natural gas prices are c onsiderably 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. Accordin g 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 th e 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 posse ssed 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 re source 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 ba se 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***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review*** Draft Deliberative

32 -26 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, technolo gical 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 in herent 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.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review*** Draft Deliberative

32 -27 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 al igned 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. ec onomy towards less energy-intensive sectors, like 0 2 4 6 8 10 12

1990 1995 2000 2005 2010 2015 2020 2025 2030 2035 20402011dollarspermillionBtu 

## Natural Gas Spot Price, Annual Average (Henry Hub)

EIA Historical

EIA Projected (AEO 2012)

EIA Projected (AEO 2013)

EIA Projected (AEO 2014)***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review*** Draft Deliberative

32 -28 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-Y ear 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 in crease significantly (to $8 billion or more) for at

21 EIA 2013b

22 American Council for an Energy-Efficient Economy (ACEEE). November 2013. The 2013 State Energy

Efficiency Scorecard. Available at h ttp://www.aceee.org/state-policy/scorecard. ‐ 2% 0% 2% 4% 6% 8% 10% 12% 14%

1950 1960 1970 1980 1990 2000 2010 2020 2030 2040***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review*** Draft Deliberative

32 -29 least the next decade 23 , driven largely by the growing number of states that have adopted energy 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 ef ficiency programs reduced annual electricity demand by 3.74% in 2012. 24 Demand for electricity, especially in the shor t run, is not very sensitive to changes in prices and is considered relatively price inelas tic, 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 pr ojected for EPA rulemakings.

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. Available at http://emp.lbl.gov/publications/future-ut ility-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/.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review*** Draft Deliberative

32 -30 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/electri c_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/pro ductabstract.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. U.S. Bureau of Economic Analyis (BEA) “ National Income Product Accoun ts Tables, Table 1.1.9. Implicit Price Deflators for Gross Dome stic Product, May 20, 2014.” Available online Formatted: Bibliography style

Formatted: Bibliography style, Indent: Before: 0", First line: 0"

Formatted: Bibliography style

Formatted: Bibliography style, Indent: Before: 0", First line: 0"

Formatted: Bibliography style***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review*** Draft Deliberative

32 -31 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/envi ronment/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 Re structuring by State.” Available online at: http://www.eia.gov/cneaf/elect ricity/page/restructurin g/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/ U.S. EIA.2013b. “Annual Energy Outlook 2013.” Available online at: http://www.eia.gov/forecasts/aeo/***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review*** Draft Deliberative

32 -32 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/ghge missions/usinventoryreport.html chapter 2 Formatted: Font: Not Bold

Formatted: Default Paragraph Font***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

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 CO 2 , SO 2 , NO X , 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 CO 2 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***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

3-2 alternative option (Option 2) has annual estimated costs of between $4.2 and $5.4 billion in 2020 and between $4.51.9 and $5.53.1 billion in 202530. 2526

3.3 Power Sector Modelling Framework
The Integrated Planning Model (IPM), develope d 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, determin istic linear programming model of the U.S. electric power sector. It provides forecasts of leas t cost capacity expansion, electricity dispatch, and emission control strategies while meeting en ergy 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 environmenta l 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

25 These costs do not include monitoring, reporting, and recordkeeping costs. For more information, sSee section

3.11.

26 All costs represent real dollars ($2011).

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***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

3-3 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 mo del includes an endogenous representation of the North American natural gas supply system thro ugh 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 Thismodule consists of 118 supply, demand, and storage nodes and 15 liquefied natural gas regasification facility locations that are tied togeth er 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 wo uld 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, character ize 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

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 usi ng IPM (v5.13) documentation, available at:

http://www.epa.gov/powersectormodeling/BaseCasev513.html***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

3-4 EPA has used IPM extensively over the past two decades to analyze options for reducing power sector emissions. Previously, the model ha s 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.
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 operato rs 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 condu cted. 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 w hom 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 architectur e. 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).***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

3-5 The current base case represents a projection of electricity sector activity that takes into account only those Federal and state laws and regul ations whose provisions were either in effect or enacted and clearly delineated at the time the base case was finalized in August 2013. The
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 constr uct the "model" plants that represent existing and
committed 32 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 plan t cost and performance, pollution control costs

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 μ g/m3 or less and annual average of 15 μ g/m 3 for particles of diameter 2.5 micrometers or less, i.e., PM 2.5 ). Originally issued on March 10, 2005, CAIR was remanded back to EPA by the U.S. Court of Appeals for the District of Columbia Ci rcuit in December 2008 and EPA was required to correct legal flaws in the regulations that had been cited in a ru ling 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 Struct ures 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 mill ion 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 tota l 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 desc ribed in this RIA would be m eaningfully 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/powersector modeling/BaseCasev513.html>.***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

3-6 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 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-speci fic 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 CO 2 Per Net MWh from all Affected Fossil Fuel-Fired EGUs) for Options 1 and 2

State 34 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

34 The EPA has not developed goals for Vermont and the Dist rict 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. Formatted Table***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

3-7 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
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***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

3-8 Wyoming 1,808 1,714 1,907 1,869
* Excludes EGUs located in Indian country.

Table 3- 2 . Projected Base Case CO 2 Emissions Rate (Adjusted MWh-Weighted-Average Pounds of CO 2 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
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 Formatted: 11 Caption Table
Formatted: Subscript

Formatted Table***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

3-9 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 Tabl e 3-51 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 capacit y, 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 would be offset by comparable changes in the illustrative compliance scenarios (in which compliance is consistent with the assumptions used in setting state goals). Formatted: Indent: First line: 0"***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

3-10 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 indivi dual state (these scenarios are referred to as “state”), and another set of scenarios where gr oups 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 ar e 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 flexibil ities afforded to the states in developing their state plans. Nonetheless, the analysis of the bene fits, costs, and relevant impacts of the proposed rule attempts to encapsulate some of those flexib ilities 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 Options 1 and 2020 and 2025 for Option 2 (with both state and regional compliance).
It is also important to note that the an alysis does not specify any particular CO 2 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 affected by the illustrative state compliance scen arios are only those included in the calculation

35 For more details on the nature of the regulatory options, see the preamble.***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

3-11 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 so urces 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 as sume 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 the affected sources in each year must be less th an or equal to the state goal in the illustrative compliance scenarios.

36 As discussed in the preamble, for compliance purposes stat es may be able to include sources of generation in the

calculation of the state’s emission rate other than thos e 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 includ es generation from wind, solar, geothermal, and biomass

co-fired with coal. Dedicated biomass is not an af fected source in the illustrative scenarios.

38 See Greenhouse Gas Abatement Measures TSD Tec hnical Support Document – GHG Abatement Measures***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

3-12 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 th e 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 choos e 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 ra te averaging across affected sources within six multi-state regions, informed by North Amer ican 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

 Northeast (NPCC) – NY, RI, MA, CT, NH, VT, ME

39 Note that the MISO region expanded to integrate Entergy territory at the end of 2013. Formatted: Bulleted + Level: 1 + Aligned at: 1.25" + Indent at: 1.5"***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

3-13 Figure 3- 1 . NERC Regions

Figure 3- 2 . NERC Assessment Areas***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

3-14 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” sc enarios (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, aff ected sources in each state must respond to their respective state goal. However, the ability to aver age 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 average 40 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.***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

3-15 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 CO 2 emission rate constraints for each state th at 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 Savings 41
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 stat e’s annual incremental reduction in electricity consumption by ramping up from an historical basis 43 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 e ither 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 ener gy efficiency savings projections, refer to the Greenhouse

Gas Abatement Measures TSD

. 42 This scenario is intended to represent a feasible pa thway 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 electri city consumption differs for each state and is drawn from

the data reported in Energy Information Admi nistration (EIA) Form 861, 2012, available at http://www.eia.gov/electricity/data/eia861/.***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

3-16 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 in cremental savings rate are assumed to sustain a 1.0% rate beginning in 2017. Twenty leading stat es 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 border s, 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 co mpliance 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 m odeling 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***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

3-17 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 Formatted Table***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

3-18 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
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 Costs 47
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

47 For a more detailed discussion of the demand-side en ergy efficiency savings cost estimates, refer to the

Greenhouse Gas Abatement Measures TSD.***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

3-19 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 th e 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 reach ed 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).
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-s ide energy efficiency progra ms (to cover program costs) is typically collected through a standard per kWh surcharge to the rate-payer; the regional retail

48 For a more detailed discussion of the analysis of program versus participant costs, refer to the Greenhouse Gas

Abatement Measures TSD.

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 assu mption of fixed demand. However, the modeling also does not capture the effect of higher costs of producing electric ity, which is attributable to other methods of complying with the state goals, on electricity demand for the same r eason (i.e., fixed demand in IPM). For further discussion of these issues see the Limitations section at the end of this chapter.***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

3-20 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 oppor tunities. These regionalized EE program costs were then incorporated into the regional retail price calculation as discussed in Section 3.7.9. 51

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 CO 2 reductions between 17% and 18% below base case projections for Option 1 in 2020 (reaching 26% to 27% below 2005
emissions 52 ), and between 24% and 25% below the base case in 2030 (reaching 30% below 2005

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.

52 For purposes of these calculations, EPA has used historical CO 2 emissions from eGRID for 2005, which reports

EGU emissions as 2,434 million metric tonnes in the contiguous US. Formatted Table

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Formatted: 05 Body text

Formatted: 05 Body Text***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

3-21 emissions). For Option 2, EPA projects annual CO 2 reductions between 13% and 14% in 2020 (reaching 23% below 2005 emissions) and 17% in 20 25 (reaching 2 3 % to 2 4 % below 2005 emissions). For each Option, the regional scenario achieves fewer emissions reductions largely because the ability to average emissions regionally al lows 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.

Table 3- 5 . Projected CO 2 Emission Impacts, Relative to Base Case

CO 2 Emissions (MM Tonnes) CO 2 Emissions Change from Base Case (MM Tonnes) CO 2 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 CO 2 Emission Impacts, Relative to 2005

CO 2 Emissions (MM Tonnes) CO 2 Emissions Change from Base Case (MM Tonnes) CO 2 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 SO 2 , 22% to 24% reduction of NO X , 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 Formatted Table

Formatted Table***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

3-22 17% to 18% reduction in SO 2 , 17% to 18% reduction in NO X , and an 11% to 14% reduction in mercury. The projected non-CO 2 reductions presented in Table 3 7 below demonstrate similar
trends in later years

.

Table 3- 7 . Projected Non-CO 2 Emission Impacts, 2020-2030

Base Case Option 1 Option 2 Option 1 Option 2
Reg. State Reg. State Reg. State Reg. State

2020
SO 2 (thousand tons) 1,476 1,184 1,140 1,231 1,208 -19.8% -22.7% -16.6% -18.1%

NO X (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%

PM 2.5 (thousand tons) 212 156 154 166 163 -26.4% -27.2% -21.5% -22.9%

2025
SO 2 (thousand tons) 1,515 1,120 1,090 1,218 1,188 -26.1% -28.0% -19.6% -21.6%

NO X (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%

PM 2.5 (thousand tons) 209 150 146 165 161 -28.1% -30.1% -21.0% -23.2%

2030
SO 2 (thousand tons) 1,530 1,106 1,059 n/a n/a -27.7% -30.8% n/a n/a

NO X (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

PM 2.5 (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 Formatted Table***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

3-23 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 produc tion is likely to be positive or negative and also assessed the potential magni tude of changes relative to CO 2 emissions reductions anticipated at power plants. This assessment included CO 2 emissions from the flaring of methane, but did not evaluate potential changes in other combustion-related CO 2 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 CO 2 emissions from flaring of methane will likely decrease under the proposed guidelines. Furthermore, the changes in upstream methane emissions are small relative to the changes in direct emissions fro m 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 20 25 . 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 inve stment in new generating sources and heat rate

53 The compliance costs also capture the effect of change s in equilibrium fuel prices on the expenditures of the

electricity sector to serve demand.***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

3-24 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 mana gement energy efficiency costs. See Chapter 5 of GHG Abatement Measures TSD for a full explanation

.

EPA’s projection of $4.2 to $7.4 billion in ad ditional 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 di stribute 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 valu able (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.

Table 3-7. Annualized Cost of Demand-Side Energy Efficiency (at discount rates of 3% and 7%, billions 2011$Source: Greenhouse Gas Abatement Measures TSD The following example uses results from Option 1 Regional scenario in the year 2020 to illustrate how different components of estima ted expenditures are comb ined to form the full

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://ww w.epa.gov/powersectormodeling/BaseCasev513.html Formatted Table

Formatted: Line spacing: single***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

3-25 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 CO 2 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 CO 2 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 elect ricity. 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

In order to reflect the full compliance cost attributable to the policy, it is necessary to include this incremental -$4.8 billion in electri city 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, Formatted: Font: Not Italic

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3-26 this analysis finds the cost of the Option 1 Regi onal 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 EG U to generate the same amount of electricity using less fuel. The decrease in required foss il fuel results in a lower output-based CO 2 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 CO 2 -intensive generation to less CO 2 -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 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 scenar ios, reflecting the fact that states have the

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 availa ble in the illustrative scenarios for Options 1 and 2, and is not available in the base case. See GHG Abatement Measures TSD

.***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

3-27 flexibility to choose among alternative CO 2 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- 2 . 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 de mand-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 Section 1 for a description ofthe 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 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 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

57 Because fossil fuel-fired EGUs typically have higher va riable costs than other EGUs (such as nuclear and

renewable EGUs), they are typically the first to be di splaced when demand is redu ced. The influence of EE on new sources of generation depends on both their relative variable and capital costs. Formatted Table***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

3-28 declines approximately three percent under Option 1 as a result of the reduction in total demand attributable to the demand-side energy savings ap plied 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-firedgeneration 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 0 ). 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 tota l 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 pe rcent beyond base case projections by 2020. In
20 30 , however, the total cumulative pipeline capacity built is projected to decrease, consistent with the projected decrease in total natural gas us e. 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 relativ ely 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.***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

3-29 Table 3- 3 . 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***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

3-30 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***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***3-31Figure 3-4. Generation Mix with the Base Case and 111(d) Options, 2020-2030 (million GWh)Source: Integrated Planning Model run by EPA, 20140.000.501.001.502.002.503.003.504.004.505.00BaseCaseReg. State Reg. State BaseCaseReg. State Reg. State BaseCaseReg. StateOtherOilNon‐hydroRenewablesHydroelectricNaturalGasNuclear2020 2025 2030Option1Option2Option1Option2Option1***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

3-1 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 tr ansmission 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 reliabilit y. 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 oper ate 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).***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

3-2 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 othe r 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 Adeq uacy and Reliability Analysis TSD.***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

3-3 Table 3- 4 . 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***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

3-4 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 re duction attributable to the demand-side energy savings applied in the illustrative scenarios, EPA projec ts less new natural gas combined cycle capacity built under the proposed rule than is built in th e base case over the time horizon presented in this 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 20 25 . 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 20 25

.

Table 3- 5 . 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 Formatted Table***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

3-5 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- 64 . 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

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 3- 75 , the largest decrease in coal production is projected to occur in the we stern 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- 75 . 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 Formatted Table

Formatted: No bullets or numbering

Formatted Table***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

3-6 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- 8 . 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

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- 97 , Table 3- 108 , Table 3 19 , and

Table 3- 11 , 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 Formatted Table***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

3-7 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 pr ice 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 expe rienced 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.

Table 3- 9 . 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- 10 . 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% Formatted Table

Formatted Table***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

3-8 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- 11 . 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 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 2 3.6% to 4.0% in 2020, and from 2.4 % to 2.7 % in 20 25

. Formatted Table

Formatted Table

Formatted: Indent: Before: 0", Hanging: 0.5", No bullets or numbering***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

3-9 Retail electricity prices embody generation, tr ansmission, 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 co sts 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).***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

3-10 Table 3- 12 . 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%***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

3-11 Table 3- 13 . 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%***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

3-12 Table 3- 14 . 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***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

3-13 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 il lustrative scenarios to affect average electricity bills. The estimated changes to average bills are summarized in Table 3- 154 . 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 20 25 . 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, demand-side energy efficiency).

Table 3- 15 . 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 Formatted: Indent: Before: 0"
Formatted: Default Paragraph Font

Formatted Table***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

3-14 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
SO 2 /HCl and PM control configurations. Although EP A’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 SO 3 and H 2 SO 4 ) 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 Ca lifornia) 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***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

3-15 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 de scription 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 improv ements. 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 usi ng IPM (v5.13) documentation, available at:

http://www.epa.gov/powersectormodeling/BaseCasev513.html.***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

3-16 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 degr ee, 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 effi ciency 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 ba sed on rigorous power sector modeling using ICF’s Integrated Planning Model. 61 IPM assumes “perfect foresigh t” of market conditions over the time horizon modeled; to the extent that ut ilities 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 ma rkets 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

61 Full documentation for IPM can be found at <http:// http://www.epa.gov/powersectormodeling>.***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

3-17 increased power sector consumption of between 979 to 1,194 billion cubic feet (BCF) in 2020. 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 reco rdkeeping 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 st ate’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. Howe ver, an affected EGU will still need to be modify their equipment to comply with a ra te-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 require ments 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, af ter 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 revi sions and the subsequent public meetings if revisions are indeed needed, download data from the ECMPS for their annual reporting and***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

3-18 develop their annual EPA report. Table 3-2 5 shows the annual respondent burden costs and costs of reporting and recordkeeping for 2020, 2025 and 2030.

Table 3-2 5 . 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 3-2 6 below.

Table 3- 16 . Annualized Compliance Costs Including Monitoring, Reporting and Recordkeeping Costs Requirem ents (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 M easures 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 co mpared to the social Formatted Table

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3-19 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 relation ship between social costs and compliance cost estimates presented in this chapter.
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 produc tion costs required to achieve the additional emission reductions necessary to comply with the state goals. 62

As described in section 3.6.1, the illustr ative scenarios generally assume that, in achieving their goals, states adopt energy ef ficiency 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 expenditu res 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 repres ent society’s opportunity cost of the resources required to produce the energy savings.

62 As described in section 3.5, IPM provides the least-co st 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.***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

3-20 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 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 scenar ios 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 issu e, 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 increas es 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.

63 For example if a rebate program is offered for energy effi cient appliances, some participants that claim the rebate

may have purchased the more energy efficient applian ces 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 co mpliance 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 th e potential that some program costs do not lead to additional energy efficiency investments by end-users. For more information s ee the GHG Abatement Measures TSD chapter on end-use energy efficiency.***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

3-21 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 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 sa vings, provided by the more energy efficient products and practices are deemed more desirable by some end-users. For example, weatherization of buildings to reduced electric ity demand associated with cooling will likely have a significant impact on natural gas use associ ated 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 e fficient 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

65 An analogous situation is present when some EGUs ha ve assumed to have the ability to make heat rate

improvements at a capital cost that is less th an the anticipated fuel expenditure savings.***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

3-22 from society’s perspective even if that is not the case for the end-user. In such cases there is thepotential 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 the purchase decision regarding energy efficien t investments and energy use and those that
would receive the benefits associated with re duced 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 electric ity sector to fulfill the remaining demand while making additional CO 2 emissions intensity reductions necessa ry 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 elect ricity 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 fo r 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 th e electricity market and markets providing inputs to the electricity sector do not meaningful ly affect the prices in those markets, and the assumption of fixed electricity demand, then th e 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 producer 66 and consumer surplus 67 in the electricity sector from the preregulatory action market equilib rium. However, IPM forecasts pr oduction 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

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 meas ure representing the monetized value of the benefit consumers receive from consumption of purchased goods or services beyond thei r opportunity cost as defined by the market price.***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

3-23 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).

3.13 References 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***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

3-1 CHAPTER 3

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3-1 Formatted: Normal, Tab stops: 3.25", Centered***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

33AA -1 

### A PPENDIX 

### 3A: A NALYSIS OF 

### P OTENTIAL 

### U PSTREAM 

### M ETHANE 

### E MISSIONS 

### C HANGES IN 

### N ATURAL 

### G AS 

### S YSTEMS AND 

### C OAL 

### M INING

The purpose of this appendix is to describe the methodology for estimating upstream methane (CH 4 ) 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 CO 2 emissions reductions anticipated at power plants from the compliance stra tegies examined in the RIA. In addition to estimating changes in upstream methane emis sions, this assessment included estimating CO 2 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 CH 4 and flaring-related CO 2 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 CH 4 and flaring-related CO 2 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. Th e projection methodologies use similar activity***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

33AA -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 CH 4 -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 me thodologies used for
non-CO 2 source projections in that report, including CH 4 -related emissions from coal production and natural gas systems, see “Methodologies for U.S. Greenhouse Gas Emissions Projections:
Non-CO 2 and Non-Energy CO 2 Sources” (EPA, 2013b). Uncertainties and limitations are discussed, including a side case which incor porates additional geogr aphic information for estimating CH 4 from coal mining. The term “upstream emissions” in this memo refers to vented, fugitive and flared emissions associated with fuel production, proces sing, transmission, storag e, and distribution of fuels prior to fuel combustion in electricity plants. For this analysis, the EPA focused on upstream CH 4 from the natural gas systems and coal mining sectors. In addition, the analysis
included CO 2 resulting from flaring in natural gas production. This analysis does not assess other upstream GHG emissions changes, such as CO 2 emissions from the combustion of fuel used in natural gas and coal production activities or other non-combustion CO 2 emissions from natural gas systems, such as vented CO 2 and CO 2 emitted from acid-gas removal processes. Also, the EPA assessed potential upstream me thane 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 emi ssions, 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 estima te 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).***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

33AA -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 CH 4 emissions from natural gas syst ems and coal production activities specifically. CO 2 emissions from flared methane are included because regulatory and voluntary programs influence the rate of CH 4 flaring over time and the CO 2 remaining after flaring is a
CH 4 -related GHG. Because of the multiple comp liance 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 pr esented in detail below.

3A.1.2 Coal Mining Source Description Within coal mining, this analysis covers fugitive CH 4 emissions from coal mining (including pre-mining drainage) a nd post-mining activities (i.e., coal handling), including both underground and surface mining. Emissions from abandoned mines are not included. Energyrelated CO 2 emissions, such as emissions from mining equipment and vehicles transporting coal
are not included. CH 4 , 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 CH 4 emitted from these opera tions 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 CH 4 content. Pressure increases with depth and prevents CH 4 from migrating to the surface; as a result, underground mining operations typically emit more CH 4 than surface mining. In addition to emissions from underground and surface mi nes, post-mining processing of coal and
abandoned mines also release CH 4 . Post-mining emissions refer to CH 4 retained in the coal that is released during processing, storage, and transport of the coal.***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

33AA -4 3A.1.3 Natural Gas Systems Source Description Within natural gas systems, this anal ysis covers vented and fugitive CH 4 emissions from the production, processing, transmission and storag e, and distribution segments of the natural gas system. It also includes CO 2 from flaring of natural gas. Not included are vented and fugitive
CO 2 emissions from natural gas systems, such as vented CO 2 emissions removed during natural gas processing, or energy-related CO 2 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 distri bution pipelines. CH 4 and
non-combustion 69 CO 2 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 them selves, gathering pipelines, and well-site gas treatment facilities (e.g., dehydr ators, separators). Major emissi ons 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 CO 2 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-qu ality” gas, which is then inject ed into the transmission system. Fugitive CH 4 emissions from compressors, includi ng compressor seals, are the primary emissions source from this stage. In the U.S. GHG Inventory, the majority of non-combustion
CO 2 emissions in the processing stage come from acid gas removal units, which are designed to
remove CO 2 from natural gas.

69 In this document, consistent with IPCC accounting term inology, 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 ot her activities, including flaring and CO 2 removed from raw natural gas.***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

33AA -5 Transmission and Storage: Natural gas transmission involv es 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 CH 4 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 CH 4 emissions from transmission facilities. Natu ral 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-press ure 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-Re lated Emissions in the U.S. GHG Inventory Emissions Source 2011 Emissions (TgCO 2 e) CH 4 from Coal Mining 75.2

Underground Mining and Post-Mining 57.4 Surface Mining and Post-Mining 18.0 CH 4 from Natural Gas S y stems 172.3

Production 63.6 Processing 23.3 Transmission and Storage 52.1 Distribution 33.2 CO 2 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 CO 2 e.

It is important to note that in Table 3A-1, CO 2 -equivalent methane emissions are presented using the Fourth Assessment Report Global Warming Potential (GWP) of 25, whereas***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

33AA -6 CO 2 -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.1.4 Compliance Approaches Examined States will ultimately determine optimal approa ches to comply with the goals established in this regulatory action. Each of these goal appr oaches 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 CH 4 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 pe rcent as a result of either the Option 2 state

70 Uncertainties related to activity drivers are disc ussed in the uncertainties and limitations section.***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

33AA -7 compliance scenario or the Option 2 Regional comp liance scenario, relative to the base case. 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.***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

33AA -8 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 CH 4 emissions and CO 2 emissions (see Table 3A-6). Under Option 1, the state compliance scenario results in net emissions reduction of 10.6 TgCO 2 e in 2020, while the regional compliance scenario results in 9.9 TgCO 2 e reduction. Under Option 2, the net emissions reduction is smaller: 7.7 and 7.4 TgCO 2 e in the state and regional compliance scenarios in 2020, respectively. These net emissions changes represent the sum of changes in
CH 4 from coal mining, CH 4 from natural gas systems, and CO 2 from flaring in natural gas production. Under the regional compliance scenario, Option 1 would result in decreases in
CH 4 emissions from coal mining of 14.7 TgCO 2 e in 2020, 18.2 TgCO 2 e in 2025, and 17.7 TgCO 2 e in 2030. Under the same scenario, CH 4 from natural gas systems increases relative to the base case in 2020 (4.4 TgCO 2 e) and 2025 (2.1 TgCO2e) but decreases relative to the base case in 2030 (by 1.7 TgCO 2 e). Following a similar pattern, CO 2 from flaring in natural gas production increases by 0.4 TgCO 2 e in 2020 and 0.3 TgCO 2 e in 2025 while decreasing by 0.3 TgCO 2 e 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
CH 4 emissions from coal mining of 11.3 TgCO 2 e in 2020 and 13.4 TgCO 2 e in 2025. CH 4 from natural gas systems increases relative to the base case in 2020 (3.6 TgCO 2 e) and 2025 (1.8 TgCO 2 e). CO 2 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.***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

33AA -9 Table 3A-6. Upstream Emissions Changes, Baseline to Compliance Scenarios Emissions (TgCO 2 e) 2020 2025 2030 Option 1 State

CH 4 from Coal Mining -16.1 -19.3 -19.1
CH 4 from Natural Gas Systems +5.1 +2.6 -1.0
CO 2 from NG flaring +0.4 +0.2 -0.3
Total CH 4 + CO 2 -10.6 -16.4 -20.5 Option 1 Regional

CH 4 from Coal Mining -14.7 -18.2 -17.7
CH 4 from Natural Gas Systems +4.4 +2.1 -1.7
CO 2 from NG flaring +0.4 +0.1 -0.5
Total CH 4 + CO 2 -9.9 -16.0 -19.8 Option 2 State

CH 4 from Coal Mining -12.4 -14.5 n/a
CH 4 from Natural Gas Systems +4.4 +2.4 n/a
CO 2 from NG flaring +0.4 +0.3 n/a
Total CH 4 + CO 2 -7.7 -11.8 n/a Option 2 Regional

CH 4 from Coal Mining -11.3 -13.4 n/a
CH 4 from Natural Gas Systems +3.6 +1.8 n/a
CO 2 from NG flaring +0.2 +0.1 n/a
Total CH 4 + CO 2 -7.4 -11.4 n/a
Note: A Global Warming Potential of 25 was used to convert methane emissions to CO 2 e.

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***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

33AA -10 originating in underground mines and surface mines. This sensitivity analysis is presented because underground mining generally emits more CH 4 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 CH 4 emissions reductions that may result from the compliance scenarios are approximately 1 to 2 TgCO 2 e lower than the results found in the main analysis (see Table 3A-7). Table 3A-7. Upstream Emissions Changes, Baseline to Compliance Scenarios, Side Case Incorporating More Coal Production Geographic Detail Emissions (TgCO 2 e) 2020 2025 2030 Option 1 State

CH 4 from Coal Mining -14.1 -17.4 -17.8 CH 4 from Natural Gas Systems +5.1 +2.6 -1.0 CO 2 from NG flaring +0.4 +0.2 -0.3 Total CH 4 + CO 2 -8.6 -14.6 -19.2 Option 1 Regional

CH 4 from Coal Mining -13.1 -16.5 -16.3 CH 4 from Natural Gas Systems +4.4 +2.1 -1.7 CO 2 from NG flaring +0.4 +0.1 -0.5 Total CH 4 + CO 2 -8.3 -14.3 -18.5 Option 2 State

CH 4 from Coal Mining -11.2 -13.2 n/a CH 4 from Natural Gas Systems +4.4 +2.4 n/a CO 2 from NG flaring +0.4 +0.3 n/a Total CH 4 + CO 2 -6.4 -10.4 n/a Option 2 Regional

CH 4 from Coal Mining -10.3 -12.3 n/a CH 4 from Natural Gas Systems +3.6 +1.8 n/a CO 2 from NG flaring +0.2 +0.1 n/a Total CH 4 + CO 2 -6.5 -10.3 n/a Note: A Global Warming Potential of 25 was used to convert methane emissions to CO 2 e.***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

33AA -11 3A.2.3 Uncertainties and Limitations Projections of upstream CH 4 emissions and CO 2 emitted from flaring of CH 4 are subject to a range of uncertainties and limitations. Thes e 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 es timates 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 market, and technology factors which are uncertain in both baseline and compliance scenarios. Relatively higher or lower economic growth, or chan ges in the relative prices or availability of various technologies could result in alternative estimates in the net change in upstream CH 4 emissions and related CO 2 emissions. 3A.3 Methodologies

3A.3.1 Coal Mining
The scope of CH 4 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 mi ning, surface mining, and post-surface mining.

ݏ݊݋݅ݏݏ݉݅ܧ ௬ ݏ݊݋݅ݏݏ݉݅ܧ ෍	 ൌ ௦,௬

௦ Equation 3***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

33AA -12 Where:

s = Sources (underground, post-underground, surface, and post-surface mining)

Emissions y,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

ݏ݊݋݅ݏݏ݉݅ܧ ௦,௬ ܨܧ ൌ ௦,௔௚௚ ݊݋݅ݐܿݑ݀݋ݎܲݕݎ݋ݐ݁݊ݒ݊ܫ ൈ ௦,௕ ൈ ቆ ݊݋݅ݐܿݑ݀݋ݎ݁݀ܲݐ݆݁ܿ݋ݎܲ ௦,௬ ݊݋݅ݐܿݑ݀݋ݎ݁݀ܲݐ݆݁ܿ݋ݎܲ ௦,௕ ቇ

ൈ ሺ ܪܥ	 െ 1 4 ܽܿݎܨ݁ݏܷݕݎ݁ݒ݋ܴ݁ܿ ௦ ሻ
Equation 4

Where:

EF agg,s = Aggregate emissions factor associated with source s InventoryProduction b,s = Coal production associated with source s in the base year from the U.S. GHG Inventory 72
ProjectedProduction y,s = Projected coal production associated with the emissions source (e.g., either underground or surface mining) in year y

CH 4 RecoveryUseFrac s = Fraction of CH 4 recovered from source s

Emissions Factors

71 Current CH 4 recovery and use projects apply to underground mining, but projects related to surface mining could

be implemented in the future.

72 Because of slight differences between historical and pr ojection datasets, values for production in the base year

from each dataset do not cancel***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

33AA -13 To calculate potential emissions from each category, EPA calculated an aggregate CH 4 emissions factor using historical CH 4 emissions and coal production data contained in the most recent U.S. GHG Inventory (EPA 20 13a). For example, historical CH 4 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-undergro und 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 CH 4 emissions from underground mines. The U.S. GHG Inventory estimates total CH 4 emitted from underground
mines as the sum of CH 4 liberated from ventilation systems (mine-by-mine measurements) and
CH 4 liberated by means of degasification systems, minus CH 4 recovered and used. EPA estimated surface mining and post-mining CH 4 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, EP A 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 characteris tics 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***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

33AA -14 relative to underground mining (EPA 2013). In the sensitivity analysis that incorporat ed 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 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.
CH 4 Mitigation (Recovery and Use)
EPA projected coal mine CH 4 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 histor ical rate of recovery and use to projected potential emissions generated. The U.S. GHG Inventory uses quantitative estimates of CH 4 recovery and use from several sources. Several gassy underground coal mines in the United States employ ventilation systems to ensure that CH 4 levels remain within safe concen trations. Additionally, some U.S. coal mines supplement ventilation systems with degasification systems, which remove CH 4 from the mine and allow the captured CH 4 to be used as an energy source.

ܪܥ 4 ܽܿݎܨ݁ݏܷݕݎ݁ݒ݋ܴ݁ܿ ௦ ෍	ൌ ݁ݏܷݕݎ݁ݒ݋4ܴ݁ܿܪܥ ௬,௦ ݏ݊݋݅ݏݏ݉݅ܧ݈݅ܽݐ݁݊ݐ݋ܲ ௬,௦ 5 ൗ ௕ିସ

௬ୀ௕ Equation 5

Where:***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

33AA -15 CH4RecoveryUse s,y = Recovered emissions from source s in year y

Potential Emissions s, y = Potential emissions from source s in year y

b = base year

3A.3.2 Natural Gas Systems
The scope of CH 4 and CO 2 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 CH 4 and CO 2 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 CH 4 (CH 4 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 CH 4 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 mi tigation projections as well. Emissions for each source were estimated using the following equation:
ܰܧ ௦,௬ ൌ ܲܧ ௦,௬ െ ܸܴ ௦,௬ െ ܴܴ ௦,௬***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

33AA -16 Equation 6

Where:

NE s,y = Projected net emissions for source s in year y

PE s,y = Projected potential emissions for source s in year y

VR s,y, y = Projected voluntary reductions for source s in year y

RR s,y, y = Projected regulatory reductions for source s in year y
The sections below describe detailed calculations for projections of CH 4 from natural gas systems. This analysis also includes CO 2 emissions that result from flaring in the production

sector. 73 The U.S. Greenhouse Gas Inventory also incl udes other sources of venting, fugitive, and flaring CO 2 such as CO 2 processing emissions from acid gas removal units, which are designed
to remove CO 2 from natural gas. This analysis does not include these other non-combustion CO 2 sources. EPA calculated projected flaring CO 2 emissions by scaling emis sions in the base year by the increase in projected natural gas productio n 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 emi ssions for the production stage using the following equation.

ܲܧ ௦,௬ ൌ ܲܧ ௦,௕ ൈ ቀ ீ௔௦	௉௥௢ௗ௨௖௧௜௢௡ ೤ ீ௔௦	௉௥௢ௗ௨௖௧௜௢௡ ್ ቁ

Equation 7

73 The GHG Inventory estimate for CO2 from natural gas flari ng includes some CO2 from fl aring of associated gas.***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

33AA -17 Where:

PE s,y, y = Projected future potential emissions for source s in year y

PE s,b, b = Estimated potential emissions for source s in base year b

Gas Production y = Projected natural gas dry production year y

Gas Production b = 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 ST AR 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:***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

33AA -18  Completions for gas wells with hydraulic fract uring (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 Nitrogen- and/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 vol untary reductions relative to potential CH 4 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 Source Performance Standards (NSPS)—discussed further below—necessitates the reclassification of certain production reductions from voluntary to regulatory.

74 The 2014 GHG Inventory includes an update to the methodology for calculating emissions from hydraulically

fractured gas well completions and workovers. The upda te uses control/practice-specific emission factors and no longer uses potential methane, or reductions data from Ga s STAR or from regulations to calculate emissions, as emissions are directly calculate d using the emission factors.

75 The assumption of a constant rate of voluntary reducti ons relative to the base year inventory for sources

unaffected by regulatory changes is meant to simulate a c onstant 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 leve ls can fluctuate based on participation and investment. Where new regulatory requirements apply to new and modifi ed equipment, voluntary reductions are assumed to continue to apply to existing equipment, but no voluntary reductions are applied to new equipment. As a potential future improvement to these projections, EPA may de velop an alternate methodology to model equipment turnover. However, a revised and reviewed methodology th at incorporates equipment turnover is would likely be developed within the context of prep aring the Agency’s submission to the next Climate Action Report, which will probably be in the 2017 to 2018 timeframe.***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

33AA -19 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 natio nal-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 CH 4 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 CH 4 )
emissions:

 Hydraulically fractured natural gas well completions

 Hydraulically refractured natural gas well recompletions

 New and modified high-bleed, gas-driven pneumatic controllers

 New storage tanks (with VOC emission s of 6 tons per year or more)

 New and modified reciprocating and centrifu gal compressors at gathering and boosting stations The impact of these requirements on the future year projections is discussed below. The

76 The 2014 GHG Inventory includes an update to the methodology for calculating emissions from hydraulically

fractured gas well completions and workovers. The upda te uses control/practice-specific emission factors and no longer uses potential methane, or reductions data from Ga s STAR or from regulations to calculate emissions, as emissions are directly calculated using the emission factors.***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

33AA -20 specific quantitative reductions calculated for th ese 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 re quired 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 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

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 comp letion combustion devices. Completion combustion devices (i.e., exploration and production flares) can be exp ected to achieve 95 percent combustion efficiency.***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

33AA -21 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 reductio n 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 gr eater 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 CH 4 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 CH 4 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 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

78 Use of RECs is not considered to be “modified” and woul d not trigger state permitting requirements, while use of

flaring or completion combustion would be considered to be “modified.”***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

33AA -22 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
CH 4 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
CH 4 (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***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

33AA -23 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 ST AR program for projects implemented to reduce emissions (EPA 2012c). The following processing stage voluntary reductions were reported to Natural Gas STAR and applied to individu al 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 emissions inventory, there were reductions classifi ed as “Other Processing” that were applied to the overall processing stage emissions. It was assumed that the percentage of vol untary reductions relative to potential CH 4 in the most recent base year inventory for the processing stage would remain constant in each subsequent future year. 79 In addition, implementation of th e Oil and Natural Gas Sector NSPS (discussed further below) necessitates the reclassification of certain processing reductions from

79 This assumption is discussed in Footnote 7.***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

33AA -24 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 re lative 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 CH 4 ) 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 fu ture 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. Th ere 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 4,870 tons per year of CH 4 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 CH 4 (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***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

33AA -25 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 associated with natural gas transm ission and storage and 12 associated with liquefied natural gas (LNG) transmission and storage. The natural ga s 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 stag e were estimated using the following equation:

ܲܧ ௦,௬ ൌ ܲܧ ௦,௕ ൈ ቀ ீ௔௦	஼௢௡௦௨௠௣௧௜௢௡ ೤ ீ௔௦	஼௢௡௦௨௠௣௧௜௢௡ ್ ቁ

Equation 8

Where:***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

33AA -26 PE s,y = Projected future potential emissions for source s in year y

PE s,b = Estimated potential emissions for source s in base year b
Gas Consumption y = Projected national natural gas consumption in year y Gas Consumption b = 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 es timated using the following equation:

ܲܧ ௦,௬ ൌ ܲܧ ௦,௕ ൈ ቀ ௅ேீ	ூ௠௣௢௥௧௦ ೤ ௅ேீ	ூ௠௣௢௥௧௦ ್ ቁ

Equation 9

Where:

PE s,y = Projected future potential emissions for source s in year y

PE s,b = Estimated potential emissions for source s in base year b
LNG Imports y = Projected LNG imports in year y LNG Imports b = Estimated LNG imports in base year b

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 tr ansmission 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 followi ng transmission and storage stage voluntary reductions were reported to Natu ral Gas STAR and applied to individual sources in the base year emissions inventory:

 Reciprocating compressors (one year)—replace compressor rod packing systems.***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

33AA -27  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 classi fied as “Other Transmission and Storage” that were applied to the overall transm ission and storage stage emissions. It was assumed that the percentage of vol untary reductions relative to potential CH 4 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.

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.

80 This assumption is discussed in Footnote 7.***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

33AA -28 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 st age (except for the pipeline leak sources) using Equation 3, assuming that the quantity of distri buted 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 es timates were used. For most sources (i.e., all city gate, all vented, and the residential customer meter sources), the “Natural Gas” line item 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 pi peline miles and number of services show fairly consistent behavioral trends over the entire time series from 1990 to 2011. In particular, the

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 CH 4 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 fro m non-pipeline leak sources in the distribution segment. EPA examined how the results would differ if the distribut ion 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 TgCO 2 e or increased by up to 0.4 TgCO 2 e, assuming the change in power plant natural gas consumption had no effect on distribution-segment methane emissions.***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

33AA -29 historical statistics show a distinct trend toward the use of plastic and away from other materials (i.e., cast iron, copper, unprotected steel, and prot ected 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 dist ribution stage were based on historical data reported by industry to the Natural Gas ST AR 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 vol untary reductions relative to potential CH 4 in the most recent base year inventory for the transmission stage would remain constant in each subsequent future year. 82 Regulatory Reductions
There were no requirements in the Oil and Natural Gas Sector NSPS that impact emissions from the distribution stage.

82 This assumption is discussed in Footnote 7.***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

33AA -30 3A.4 References

IPCC. 2006. “2006 IPCC Guidelines for Nationa l 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 Di stribution: 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-CO 2 and Non-Energy CO 2 Sources.” Available at:

< http://www.state.gov/documents/organization/219472.pdf >***E.O. 12866 Review – Draft – Do Not Ci te, Quote or Release During Review***

4-1***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-2 

### C HAPTER 

### 4: E STIMATED 

### C LIMATE 

### B ENEFITS AND 

### H UMAN 

### H EALTH 

### C O 

### - 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 (CO 2 ) 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 ai r pollution health co-benefits associated with reducing exposure to ambient fine particulate matter (PM 2.5 ) and ozone by reducing emissions of precursor pollutants (i.e., sulfur dioxide (SO 2 ), nitrogen dioxide (NO 2 ), and directly emitted
PM 2.5 ). Data, resource, and methodological limitations prevent EPA from monetizing the benefits from several important co-benefit cate gories, including reducing direct exposure to SO 2 , NO 2 , and hazardous air pollutants (HAP), as well as ecosystem effects and visibility impairment. We qualitatively discuss these unquant ified 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 CO 2 The primary goal of the proposed guidelines is to reduce emissions of CO 2 . 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 CO 2 using the Social Cost of Carbon (SCC), a metric that estimates the monetary value of impacts associated with marginal changes in CO 2 emissions in a given year. Table 4-1 summarizes the quantified and unquantified climate benefits in this analysis. Formatted: 01 ChapterTitle1***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-3 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 CO 2 —  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 Endang erment Finding closed following the EPA’s 2010
Reconsideration Denial 84 . These assessments include the IPCC’s 2012 “Special Report on Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation” (SREX) (IPCC, 2012), the IPCC and 2013-14 Fifth Assessment Report (AR5) (IPCC, 2013, 2014a, 2014b), the 2014 National Climate Assessme nt report (Melillo et al., 2014), the NRC 2010 “Ocean Acidification: A National Strategy to Meet the Challenges of a Changing Ocean” (Ocean Acidification) (NRC, 2010), NRC 2011 “Report on Climate Stabilization Targets: Emissions, Concentrations, and Impacts over Decades to Millennia” (Climate Stabilization Targets) (NRC, 2011a), NRC 2011 “National Secu rity Implications for U.S. Naval Forces” (National Security Implications) (NRC, 2011b), NRC 2011 “Understanding Earth’s Deep Past: Lessons for Our Climate Future” (Understanding Earth’s Deep Past) (NRC, 2012a), NRC 2012

83 “Endangerment and Cause or Contribute Findings for Gree nhouse 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 E ndangerment 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”).***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-4 “Sea Level Rise for the Coasts of California, Oregon, and Washington: Past, Present, and Future” (NRC, 2012b), NRC 2012 “Climate and So cial Stress: Implications for Security Analysis” (Climate and Social Stress) (NRC, 2 013a), and NRC 2013 “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 includ ing 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 asse ssments underlying the existing finding that GHGs are reasonably anticipated to endanger public h ealth 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 extrem e 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 to elevated concentrations of GHGs, including warming of extreme daily temperatures, intensified extreme precipitation events, and increases in extreme coas tal high water levels due to rising sea level. The SREX projects further increases in some ex treme weather and climate events during the 21 st 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. Formatted: Superscript***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-5 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 dioxid e 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 le vels. 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 pr ojects that the area burnt by wildfires in western North America will likely more than double and that coral bleaching events and coastal erosionprojects that erosion will likely are proj ected to increase due both to warming and ocean acidification. An increase of 3 oC is projected to l ead to a sea level rise of 0.5 to 1 meter by 2100. With an increase of 4 oC, the average summer in th e 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 quantif y, 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, 2 012b) 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, includ ing 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 “pru dent for security analysts to expect climate surprises in the coming decade . . . and for them to become progressively more serious and more frequent thereafter[.]”***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-6 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 CO 2 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 as sessment (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 assessme nt 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 an d 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 permafro st, 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 is extremely likely (>95% percent confidence) that human influences have been the dominant Formatted: Subscript***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-7 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 be en observed in changing precipitation patterns, melting snow and ice, species migration, ne gative 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 deat h, 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 regi ons, 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.
4.2.2 Social Cost of Carbon We estimate the global social benefits of CO 2 emission reductions expected from the proposed guidelines using the SCC estimates presented in the 2013 Technical Support***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-8 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 es timates, 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 CO 2 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 incremen tal impact on cumulative global CO 2 emissions). The SCC estimates used in this analysis were developed over many years, using the best science available, and with input from the pu blic. 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 es timates were first released in February 2010 and updated in 2013 using new versions of each IAM. 86

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, De partment of Energy, Department of Transportation, Environmental Protection Agency, National Economic Counc il, Office of Energy and Climate Change, Office of Management and Budget, Office of Science and Technol ogy 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.

86 Docket ID EPA-HQ-OAR-2009-0472- 114577, Technical Support Documen t: 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 Advi sers, 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:***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-9 The SCC estimates represent global measures because of the distinctive nature of the climate change problem. The climate change proble m 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 th erefore 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 nowaffect 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 (equilibri um 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 consta nts; in PAGE, most parameters are represented by probability distributions. FUND was also run in a mode in which parameters were treated probabilistically. The use of three models and thes e input parameters allowed for exploration of important uncertainties in the way climate damages are estimated, equilibrium climate sensitivity, reference socioeconomic and emission traj ectories, and discount rate. As stated in the

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 Po licy Analysis of the Greenhouse Gas Effect (PAGE).***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-10 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, improv ements 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 incl usion of indirect effect s 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 Scien ce (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 climat e system, (3) the impact of changes in climate on the physical and biological environment, a nd (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, eco nomics, and ethics and should be viewed as provisional. The 2010 SCC TSD noted a number of limitations to the SCC analysis, including theincomplete way in which the integrated assessment models capture catastrophic and noncatastrophic impacts, their incomplete treatme nt of adaptation and technological change,

88 National Research Council (2009). Hidden Costs of Ener gy: Unpriced Consequences of Energy Production and

Use. National Academies Press. See docket ID EPA-HQ-OAR -2009-0472-11486.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-11 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 clim ate 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 individua l 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 discussi on of their limitations represent the best
available information about the social benefits of CO 2 reductions to inform benefit-cost analysis.
The new versions of the models used to esti mate the values presen ted 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 $137 per
metric ton of CO 2 emissions in the year 2020 (2011$). 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 different generations). The fourth value is the 95 th 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

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.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-12 to calculate the dollar value for emission reducti ons, the SCC estimate for each emissions year would be applied to changes in CO 2 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 co nsidered the climate impacts of only CO 2 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 CO 2 . While CO 2 is the dominant GHG emitted by the sector, we reco gnize the representative facilities within these comparisons may also have different emission rates for ot her climate forcers that will serve a minor role in determining the overall social cost of generation.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-13 Table 4- 2 . Global Social Cost of CO 2 , 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 CO 2 emissions and are defined in real terms, i.e., adjusted for

inflation using the GDP implicit price deflator. Th ese SCC values are stated in $/metric ton.

Table 4- 3 . Estimated Global Climate Benefits of CO 2 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 CO 2 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% (95 th 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 CO 2 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 CO 2 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% (95 th 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 CO 2 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 CO 2 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% (95 th 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.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-14 It is important to note that the climate be nefits presented above are associated with changes in CO 2 emissions only. Implementi ng these guidelines, however, will have an impact on the emissions of other pollutants that would aff ect 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 em issions 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-CO 2 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

91 The federal government’s SCC estimates used in this analysis are designed to assess the climate benefits

associated with changes in CO 2 emissions only.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-15 change in upstream methane emissions from natural gas and coal production is likely to be positive or negative and also assessed the pote ntial magnitude of changes relative to CO 2 emissions reductions anticipated at pow er plants. This assessment included CO 2 emissions from the flaring of methane, but did not evaluate potential changes in other combustion-related CO 2 emissions, such as emissions associated with dr illing, mining, processing, and transportation in the natural gas and coal production sectors. This analysis found that the net upstream CH 4 emissions from natural gas systems and coal mines and CO 2 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 CO 2 , implementing these proposed guidelines is expected to reduce
emissions of SO 2 and NO X , which are precursors to formation of ambient PM 2.5 , as well as directly emitted fine particles. 92 Therefore, reducing these emissions would also reduce human exposure to ambient PM 2.5 and the incidence of PM 2.5 -related health effects. In addition, in the presence of sunlight, NO X and VOCs can undergo a chemical reaction in the atmosphere to form ozone. Depending on localized c oncentrations of vol atile organic compounds (VOCs), reducing
NO X 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 SO 2 and NOx would also reduce ambient exposure to SO 2 and NO 2 , respectively. In this section, we provide an overview of the monetized PM 2.5 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 co-

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 th e earth’s surface, including carbonates, silicates, iron, phosphates, copper, and zinc. Often, crustal material represen ts particles not classified as one of the other species (e.g., organic carbon, elemental carbon, nitrate, sulfate, chloride, etc.).***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-16 benefits for a climate-focused rulemaking. Firs t, these estimated health co-benefits do not account for any climate-related air quality chan ges (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 PM 2.5 -related health co-benefits would be impacted by excluding climate-related air quality changes since the scie nce is unclear as to how climate change may
affect PM 2.5 exposure. Second, the estimated health co-benefits also do not consider temperature
modification of PM 2.5 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 internati onal air pollution-related h ealth benefits implies that the quantified health co-benefit s likely lead to underestimation. Implementing these guidelines may lead to reductions in ambient PM 2.5 concentrations below the National Ambient Air Quality Standard s (NAAQS) for PM and ozone in some areas and assist other areas with attaining thes e 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 obj ectives of each RIA. The NAAQS RIAs illustrate the potential costs and benefits of attaining a re vised air quality standard nationwide based on an array of emission reduction strategies for di fferent 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 c hoose to enact when implementi ng 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 benef its 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 bene fits estimated in this RIA may account for the same air quality improvements as estim ated 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***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-17 reductions for implementation rules, which typically require specific emission reductions in a 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 illustrativ e 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 PM 2.5 and Ozone

The Integrated Science Assessment for Particulate Matter (PM ISA) (U.S. EPA, 2009b)
identified the human health effects associated with ambient PM 2.5 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. Tabl e 4-6 identifies the quantified and unquantified co-benefit categories captured in EPA’s health co-benefits estimates for reduced exposure to
ambient PM 2.5 and ozone. Although the table below does not list unquantified health effects such as those associated with exposure to SO 2 , NO 2 , 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 unquan tified benefit categories is not e xhaustive, nor is quantification of each effect complete.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-18 Table 4- 6 . Human Health Effects of Ambient PM 2.5 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 PM 2.5 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 PM 2.5 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 ISA 1 Emergency room visits for cardiovascular effects (all ages) — — PM ISA 1
Strokes and cerebrovascular disease (age 50-79) — — PM ISA 1 Other cardiovascular effects (e.g., other ages) — — PM ISA 2 Other respiratory effects (e.g., pulmonary function, non-asthma ER visits, non-bronchitis chronic diseases, other ages and populations) — — PM ISA 2

Reproductive and developmental effects (e.g., low birth weight, pre-term births, etc) — — PM ISA 2,3
Cancer, mutagenicity, a nd genotoxicity effects — — PM ISA 2,3
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 ISA 1

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 ISA 1 Other respiratory effects (e.g., premature aging of lungs) — — Ozone ISA 2

Cardiovascular and ne rvous system effects — — Ozone ISA 2 Reproductive and developmental effects — — Ozone ISA 2,3

1 We assess these co-benefits qualitatively due to data and res ource 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 significa nt concerns over the strength of the association.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-19 We follow a “damage-function” approach in calculating benefits, which estimates changes in individual health endpoints (specific eff ects 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 estima te benefits for the environmental quality change under analysis. We use a “benefit-per-ton” approach to estimate the PM 2.5 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 PM 2.5 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
PM 2.5 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 partic ular health effect for a given increment of air pollution (often per 10 μg/m 3 for PM 2.5 or ppb for ozone). These relative risks can be used to develop risk coefficients that relate a unit reduction in PM 2.5 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 eff ect 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 h ealth endpoint quantified in this analysis.

4.3.1.1 Mortality Concentration-Response Functions for PM 2.5***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-20 Considering a substantial body of published sc ientific literature and reflecting thousands of epidemiology, toxicology, and clinical studies, the PM ISA documents the association
between elevated PM 2.5 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 Sc ience 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 PM 2.5 based on the entire body of scientific evidence. The
PM ISA also concluded that the scientific litera ture 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 mort ality discussed in more detail below, we use effect coefficients from Woodruff et al. (1997) to es timate 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 PM 2.5 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 PM 2.5 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 PM 2.5 -mortality relationship, EPA graphically presents the PM 2.5 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
PM 2.5 concentrations. In that study, twelve experts provided independent opinions regarding the
PM 2.5 -mortality concentration-response function. Because the experts relied upon the ACS and 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***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-21 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
PM 2.5 on mortality, not whether such an effect existed” and that the expert elicitation “supports
the conclusion that the benefits of PM 2.5 control are very likely to be substantial”. Although it is possible that newer scientific literature could revi se 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 ofrecommendations 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 commit tee 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 mort ality 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 arange 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
estimated co-benefits derived from all six studies mentioned above as a characterization of uncertainty regarding the ozone -mortality relationship in Figure 4-1.

93 Since EPA received NAS advice, EPA published the Ozon e ISA (U.S. EPA, 2013b) and the second draft Ozone

Health Risk and Exposure Assessment (U.S. EPA, 2014a). Th erefore, 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.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-22 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 th e 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 di rect 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 per cent 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 pr emature mortality risk is still developing. The adoption of a value for the projected reduction in th e 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 Econom ics 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 provi des 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-EE AC 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***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-23 (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 analys is year. Specifically, the VSLs applied in this analysis in 2011$ after adjusting for income gr owth 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 evidencein 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 PM 2.5 -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 ch anges in PM exposures and the total realization of changes in health effects. Although the struct ure 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 PM 2.5 (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 prematur e mortality occurs within the analysis year,
the estimated ozone-related co-benefits are identical for all discount rates.

94 In the updated Guidelines for Preparing Economic Analyses (U.S. EPA, 2010e), EPA re tained 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.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-24 4.3.3 Benefit-per-ton Estimates for PM 2.5 We used a “benefit-per-ton” approach to estimate the PM 2.5 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 PM 2.5 (or PM 2.5 precursor such as NO X or SO 2 ) 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 regiona l 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 PM 2.5 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 appro ach 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 PM 2.5 -related health benefits for emissions reductions in the various sectors. Specifically, the air quality modelin g 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 regi onal 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 inherent limitations. Specifically, all benefit-pe r-ton estimates reflect the geographic distribution

97 Although the modeling underlying the benefit-per-ton es timates does not reflect emission reductions anticipated

from MATS, the EGU contribution to ambient PM 2.5 and ozone on a per-ton basis would be similar. (Fann, Fulcher, and Baker, 2013) Because th e emission reductions in this RIA are calculated from an IPM base case that includes MATS (see Chapter 3), there is no double-coun ting concern with the resulting co-benefits estimates.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-25 of the modeled sector emissions, which may not match the emission reductions anticipated by the proposed guidelines, and they may not re flect 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
PM 2.5 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 PM 2.5 levels, which drive population exposure. 4.3.4 Benefit-per-ton Estimates for Ozone

Similar to PM 2.5 , 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 PM 2.5 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 PM 2.5 estimates, the ozone estimates are not based on changes to annual emissions. Instead, the regional estimates (i.e., East, West, and California) correspond to NO X emissions from U.S. EGUs during the ozone-season (May to September). Because we estimateozone 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 NO X 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 cau tion. We provide more detailed information regarding the generation of th ese estimates in the appendix to this chapter.
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***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-26 (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 PM 2.5 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. 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

SO 2 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 PM 2.5 (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 PM 2.5 (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

NO X (as PM 2.5 ) 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 NO X (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 epidem iology studies for avoided premature mortality for PM 2.5 and

ozone. All estimates are rounded to two significant figur es. The monetized co-benefits do not include reduced health effects from direct exposure to NO 2 , SO 2 , ecosystem effects, or visibility impairment. All fine particles are assumed to have equivalent health effects, but the be nefit per ton estimates vary depending on the location and magnitude of their impact on PM 2.5 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 NO X emissions. Ozone co-benefits occu r in analysis year, so they are the same for all discount rates. Confidence intervals ar e unavailable for this analysis because of the benefitper-ton methodology. In general, the 95 th percentile confidence interval for monetized PM 2.5 benefits ranges from approximately -90 percent to +180 per cent of the central estimates based on Krewski et al. (2009) and Lepeule et al. (2012).

98 See Chapter 3 of this RIA for more information regardin g the expected emission reductions used to calculate the

health co-benefits in this chapter. Chapter 3 also provides more inform ation regarding the illustrative compliance scenarios.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-27 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

SO 2 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 PM 2.5 (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 PM 2.5 (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

NO X (as PM 2.5 ) 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 NO X (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 epidem iology studies for avoided premature mortality for PM 2.5 and

ozone. All estimates are rounded to two significant figur es. The monetized co-benefits do not include reduced health effects from direct exposure to NO 2 , SO 2 , ecosystem effects, or visibility impairment. All fine particles are assumed to have equivalent health effects, but the be nefit per ton estimates vary depending on the location and magnitude of their impact on PM 2.5 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 NO X emissions. Ozone co-benefits occu r in analysis year, so they are the same for all discount rates. Confidence intervals ar e unavailable for this analysis because of the benefitper-ton methodology. In general, the 95 th percentile confidence interval for monetized PM 2.5 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 4-9. Summary of National and Regional Benefit-per-Ton Estimates for EGUs in 2030 (2011$)*

Pollutant Discount Rate National Regional East West California

SO 2 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 PM 2.5 (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 PM 2.5 (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

NO X (as PM 2.5 ) 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 NO X (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 epidem iology studies for avoided premature mortality for PM 2.5 and

ozone. All estimates are rounded to two significant figur es. The monetized co-benefits do not include reduced health effects from direct exposure to NO 2 , SO 2 , ecosystem effects, or visibility impairment. All fine particles are assumed to have equivalent health effects, but the be nefit per ton estimates vary depending on the location and magnitude of their impact on PM 2.5 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 NO X emissions. Ozone co-benefits occu r in analysis year, so they are the same for all discount rates. Confidence intervals ar e unavailable for this analysis because of the benefitper-ton methodology. In general, the 95 th percentile confidence interval for monetized PM 2.5 benefits ranges from approximately -90 percent to +180 percent of the central estimates based on Krewski et al. (2009) and Lepeule et al. (2012).***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

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 SO 2 All-year NOx Ozone-Season NOx Directly emitted PM 2.5 (EC+OC) Directly emitted PM 2.5 (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 regi onal emission reductions may appear to not sum to national total.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

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 SO 2 All-year NOx Ozone-Season NOx Directly emitted PM 2.5 (EC+OC) Directly emitted PM 2.5 (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 regi onal emission reductions may appear to not sum to national total.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

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 SO 2 All-year NOx Ozone-Season NOx Directly emitted PM 2.5 (EC+OC)* Directly emitted PM 2.5 (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 regi onal emission reductions may appear to not sum to national total.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

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

SO 2 $13,000 to $29,000 $11,000 to $26,000 Directly emitted PM 2.5 (EC+OC) $760 to $1,700 $690 to $1,600 Directly emitted PM 2.5 (crustal) $790 to $1,800 $710 to $1,600 NOx (as PM 2.5 ) $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

SO 2 $12,000 to $26,000 $11,000 to $24,000 Directly emitted PM 2.5 (EC+OC) $750 to $1,700 $670 to $1,500 Directly emitted PM 2.5 (crustal) $770 to $1,700 $690 to $1,600 NOx (as PM 2.5 ) $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

SO 2 $10,000 to $23,000 $9,100 to $21,000 Directly emitted PM 2.5 (EC+OC) $640 to $1,500 $580 to $1,300 Directly emitted PM 2.5 (crustal) $660 to $1,500 $600 to $1,400 NOx (as PM 2.5 ) $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

SO 2 $9,800 to $22,000 $8,900 to $20,000 Directly emitted PM 2.5 (EC+OC) $610 to $1,400 $550 to $1,200 Directly emitted PM 2.5 (crustal) $630 to $1,400 $570 to $1,300 NOx (as PM 2.5 ) $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 NO 2 , SO 2 , ecosystem effects, or visibility impairment. All fine partic les are assumed to have equivalent health effects, but the benefit-per-ton estimates vary depending on th e location and magnitude of their impact on PM 2.5 levels, which drive population exposure. The monetized co-benefits incorporate the conversion from precursor emissions to ambient fine particles and ozone. Co-benefits for PM 2.5 precursors are based on regional benefit-per-ton estimates. Cobenefits for ozone are based on ozone season NOx emissions. Ozone co-benefits occur in an alysis year, so they are the same for all discount rates. Confidence intervals are una vailable for this analysis because of the benefit-per-ton methodology. In general, the 95 th percentile confidence interval for monetized PM 2.5 benefits ranges from approximately -90 percent to +180 percent of the central es timates based on Krewski et al. (2009) and Lepeule et al. (2012). Formatted: Style2, Indent: Before: 0", First line: 0", Space After: 0 pt, Don't keep lines together***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

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

SO 2 $18,000 to $40,000 $16,000 to $36,000 Directly emitted PM 2.5 (EC+OC) $900 to $2,000 $810 to $1,800 Directly emitted PM 2.5 (crustal) $830 to $1,900 $750 to $1,700 NOx (as PM 2.5 ) $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

SO 2 $17,000 to $38,000 $15,000 to $35,000 Directly emitted PM 2.5 (EC+OC) $850 to $1,900 $760 to $1,700 Directly emitted PM 2.5 (crustal) $780 to $1,800 $700 to $1,600 NOx (as PM 2.5 ) $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

SO 2 $14,000 to $30,000 $12,000 to $27,000 Directly emitted PM 2.5 (EC+OC) $690 to $1,600 $630 to $1,400 Directly emitted PM 2.5 (crustal) $640 to $1,400 $580 to $1,300 NOx (as PM 2.5 ) $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

SO 2 $13,000 to $29,000 $12,000 to $26,000 Directly emitted PM 2.5 (EC+OC) $640 to $1,400 $580 to $1,300 Directly emitted PM 2.5 (crustal) $590 to $1,300 $530 to $1,200 NOx (as PM 2.5 ) $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 NO 2 , SO 2 , ecosystem effects, or visibility impairment. All fine partic les are assumed to have equiva lent health effects, but the benefit-per-ton estimates vary depending on th e location and magnitude of their impact on PM 2.5 levels, which drive population exposure. The monetized co-benefits incorporate the conversion from precursor emissions to ambient fine particles and ozone. Co-benefits for PM 2.5 precursors are based on regional benefit-per-ton estimates. Cobenefits for ozone are based on ozone season NOx emissions. Ozone co-benefits occur in an alysis year, so they are the same for all discount rates. Confidence intervals are una vailable for this analysis because of the benefit-per-ton methodology. In general, the 95 th percentile confidence interval for monetized PM 2.5 benefits ranges from approximately -90 percent to +180 percent of the central es timates based on Krewski et al. (2009) and Lepeule et al. (2012). Formatted: Style2, Indent: Before: 0", First line: 0", Space After: 0 pt, Don't keep lines together***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

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

SO 2 $21,000 to $47,000 $19,000 to $43,000 Directly emitted PM 2.5 (EC+OC) $870 to $2,000 $780 to $1,800 Directly emitted PM 2.5 (crustal) $800 to $1,800 $720 to $1,600 NOx (as PM 2.5 ) $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

SO 2 $20,000 to $44,000 $18,000 to $40,000 Directly emitted PM 2.5 (EC+OC) $840 to $1,900 $760 to $1,700 Directly emitted PM 2.5 (crustal) $770 to $1,700 $700 to $1,600 NOx (as PM 2.5 ) $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 NO 2 , SO 2 , ecosystem effects, or visibility impairment. All fine partic les are assumed to have equiva lent health effects, but the benefit-per-ton estimates vary depending on th e location and magnitude of their impact on PM 2.5 levels, which drive population exposure. The monetized co-benefits incorporate the conversion from precursor emissions to ambient fine particles and ozone. Co-benefits for PM 2.5 precursors are based on regional benefit-per-ton estimates. Cobenefits for ozone are based on ozone season NOx emissions. Ozone co-benefits occur in an alysis year, so they are the same for all discount rates. Confidence intervals are una vailable for this analysis because of the benefit-per-ton methodology. In general, the 95 th percentile confidence interval for monetized PM 2.5 benefits ranges from approximately -90 percent to +180 percent of the central es timates based on Krewski et al. (2009) and Lepeule et al. (2012). Formatted: Style2, Indent: Before: 0", First line: 0", Space After: 0 pt, Don't keep lines together***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-34 Table 4-16. Summary of Avoided Health Incidences from PM 2.5 -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 PM 2.5 -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 PM 2.5 precursors are based on regional benefit-per-ton estimates for all precursor s. 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 95 th percentile confidence interval for the health im pact function alone ranges from approximately ±30 percent for mortality incidence based on Krewski et al. (2009) and ±46 per cent based on Lepeule et al. (2012). Formatted: Style2, Space After: 0 pt, Don't keep lines together***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-35 Table 4-17. Summary of Avoided Health Incidences from PM 2.5 -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 PM 2.5 -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 PM 2.5 precursors are based on regional benefit-per-ton estimates for all precursor s. Co-benefits for ozone are based on ozone season NOx emissions. In general, the 95 th percentile confidence interval for the he alth impact function alone ranges from approximately ±30 percen t for mortality incidence based on Krewski et al. (2009) and ±46 pe rcent based on Lepeule et al. (2012). Formatted: Style2, Space After: 0 pt, Don't keep lines together***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-36 Table 4-18. Summary of Avoided Health Incidences from PM 2.5 -Related and OzoneRelated Co-Benefits for Proposed EGU GHG Existing Source Guidelines in 2030* Option 1 - state Option 1 - regional PM 2.5 -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 PM 2.5 precursors are based on regional benefit-per-ton estimates for all precursor s. Co-benefits for ozone are based on ozone season NOx emissions. In general, the 95 th percentile confidence interval for the he alth impact function alone ranges from approximately ±30 percen t for mortality incidence based on Krewski et al. (2009) and ±46 pe rcent based on Lepeule et al. (2012). Formatted: Style2, Space After: 0 pt, Don't keep lines together***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

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 PM 2.5 graphs show the estimated PM 2.5 co-benefits at discount rates of 3% and 7% using effect coefficients derived from the Krewski et al. (2009) study and the Lepe ule 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 o ccur 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 NO 2 , SO 2 , ecosystem effects, or visibility impairment. Results would be similar if the state compliance scenario was shown. PM 2.5 Ozone

Formatted: Style2, Space After: 0 pt, Don't keep lines together***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

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-b enefits estimated using the Bell et al. (2004) mortality study for ozone with the Krew ski et al. (2009) mortality study for PM 2.5 . “High Health Co-benefits” refers to the combined health co-b enefits estimated using the Levy et al. (2005) mortality study for ozone with the Lepeule et al. (2012) mortality study for PM 2.5 . 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

Formatted: Font: Not Bold

Formatted: Style 10 Caption Figure + After: 6 pt, Space After: 0 pt

Formatted: Style2, Space After: 0 pt, Don't keep lines together***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

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 Krewsk i et al. (2009) mortality study for PM 2.5 . “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 PM 2.5 . 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 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

Formatted: Space After: 0 pt

Formatted: paragraph under tables, Space After: 0 pt, Don't keep lines together***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-40 includes many data sources as inputs, includi ng emission inventories, air quality data from models (with their associated parameters an d inputs), population data, population estimates, health effect estimates from epidemiology studies, economic data for monetizing co-benefits, and assumptions regarding the future state of th e 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 benef its. 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 deta iled uncertainty assessment found in the PM NAAQS RIA (U.S. EPA, 2012a) or the Ozone NAAQS RIA (U.S. EP A, 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 qualita tive 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 pres ented in this analysis. For example, sensitivity analyses conducted for the PM NAAQS RIA indicat e that alternate cessation lag assumptions could change the estimated PM 2.5 -related mortality co-benefits discounted at 3 percent by between 10 percent and –27 percent and that alternate income growth adjustments could change
the PM 2.5 -related mortality co-benefit s by between 33 percent and

− 14 percent. Although we generally do not calculate confidence in tervals 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 95 th percentile confidence interval
for monetized PM 2.5 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 95 th 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 qual ity 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 proporti on of co-benefits occurring in different locations,***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-41 such as designated nonattainment areas. Instead, we applied benefit-per-ton estimates, which reflect specific geographic patterns of emissions reductions and specific air quality and benefits modeling assumptions. For example, these esti mates 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 distributio n 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 th e 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-benef its 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
PM 2.5 -related premature mortality, which accounts for 98 percent of the monetized PM 2.5 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 PM 2.5 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 PM 2.5 can be linked with multiple health effects, and the evidence is not yet sufficient to allow di fferentiation of those constituents or sources that are more closely related to specific outcomes” (U.S. EPA, 2009b).***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

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 va ried concentrations of PM 2.5 , 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 PM 2.5 exposures occur in a distributed fashionover 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 PM 2.5 concentrations that coincide with the bul k of the observed PM concentrations in the epidemiological studies that are used to estima te the benefits. Likewise, we are less confident
in the risk we estimate from simulated PM 2.5 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 PM 2.5 levels at or above different concentrations, which provides some in sight into the level of uncertainty in the
estimated PM 2.5 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 identif ying 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 asso ciated with the emission reduction strategies. The
PM ISA concluded that the scientific evidence co llectively is sufficient to conclude that the

99 For a summary of the scientific review statements regarding the lack of a threshold in the PM 2.5 -mortality

relationship, see the TSD entitled Summary of Expert Opinions on the Existence of a Threshold in the Concentration-Response Function for PM 2.5 -related Mortality (U.S. EPA, 2010b).***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-43 relationship between long-term PM 2.5 exposures and mortality is causal and that overall the studies support the use of a no-threshold log-lin ear 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 c hoose to do. For this RIA, we are unable to estimate the percentage of premature mortality associated with the emission reductions at each
PM 2.5 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 surro gate measure of mortality impacts, we provide the percentage of the population exposed at each PM 2.5 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-sp ecific 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 be nefit-per-ton estimates does not reflect emission reductions anticipated from MATS rule. Therefore, the baseline PM 2.5 concentrations in the LML assessment are higher than would be expected if MATS was reflected.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

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 equi valent to the middle of the range between the 10 th and 25 th percentile. For Krewski, the LML is 5.8 μg/m 3 and one standard deviation below the mean is 11.0 μg/m 3 . For Lepeule et al., the LML is 8 μg/m 3 and we do not have the data for one st andard 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 PM 2.5 do not occur. Formatted: Style2***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-45 Figure 4-4 . Percentage of Adult Population (age 30+) by Annual Mean PM 2.5 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 th e benefit-per-ton estimates. Similar graphs for analyses with air quality modeling show premature mortality impacts at each PM 2.5 concentration. Therefore, caution is warranted when interpreting this gra ph 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 PM 2.5 in the baseline:
93% are exposed to PM 2.5 levels at or above the LML of the Krewski et al. (2009) study
67% are exposed to PM 2.5 levels at or above the LML of the Lepeule et al. (2012) study 0% 5% 10% 15% 20% 25%

<1 1

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Baseline Annual Mean PM 2.5 Level (μg/m 3 ) LML of Lepeule et al. (2012) study LML of Krewski et al. (2009) study Formatted: Style2***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-46 Figure 4-5 . Cumulative Distribution of Adult Population (age 30+) by Annual Mean PM 2.5 Exposure in the Baseline Sector Modeling (used to generate the benefit-perton 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 PM 2.5 concentration. Therefore, caution is warranted when interpreting this gra ph 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 CO 2 and reduced exposure to PM 2.5 and ozone, but we were unable to monetize the co-benefits associated with reducing exposure to mercury, hydrogen chloride, carbon monoxide, SO 2 , and NO 2 , 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 (95 th 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 PM 2.5 in the baseline:
93% are exposed to PM 2.5 levels at or above the LML of the Krewski et al. (2009) study
67% are exposed to PM 2.5 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 20Cumulativepercentageofpopulationexposed

Baseline Annual Mean PM 2.5 Level (μg/ m 3 ) LML of Krewski et al. (2009) study LML of Lepeule et al. (2012) study***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

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 CO 2 emissions are long-lived and subsequent damages occur over many years. Moreover, several rates are applied to SCC beca use 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 co mbining all SCC values w ith 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. Combin ing the 3 percent SCC values with the 3 percent
health benefit values assumes that there is no difference in discount rates between intragenerational and inte rgenerational 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/infore g/for-agencies/Social-Cost-of-Carbon-for-RIA.pdf for details.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

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 CO 2 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% (95 th percentile) $52 $69 to $92 $68 to $88 Option 1 - regional 371 million metric tonnes CO 2 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% (95 th percentile) $51 $67 to $88 $65 to $85 Option 2 - state 295 million metric tonnes CO 2 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% (95 th percentile) $40 $54 to $72 $53 to $69 Option 2 - regional 283 million metric tonnes CO 2 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% (95 th percentile) $39 $52 to $70 $51 to $67 *All estimates are rounded to two significant figures. Climate benefits are based on reductions in CO 2 emissions. Co-benefits are based on regional benefit-per-ton estimat es. Co-benefits for ozone are based on ozone season NOx emissions. Ozone co-benefits occur in an alysis year, so they are the same for all discount rates. The health cobenefits reflect the sum of the PM 2.5 and ozone co-benefits and reflect the ra nge 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 redu ced health effects from direct exposure to NO 2 , SO 2 , and HAP; ecosystem effects; or visibility impairment.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

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 CO 2 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% (95 th percentile) $77 $100 to $130 $98 to $130 Option 1 - regional 501 million metric tonnes CO 2 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% (95 th percentile) $76 $99 to $130 $97 to $120 Option 2 - state 376 million metric tonnes CO 2 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% (95 th percentile) $57 $75 to $98 $73 to $95 Option 2 - regional 368 million metric tonnes CO 2 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% (95 th percentile) $56 $73 to $96 $72 to $93 *All estimates are rounded to two significant figures. Climate benefits are based on reductions in CO 2 emissions. Co-benefits are based on regional benefit-per-ton estimat es. Co-benefits for ozone are based on ozone season NOx emissions. Ozone co-benefits occur in an alysis year, so they are the same for all discount rates. The health cobenefits reflect the sum of the PM 2.5 and ozone co-benefits and reflect the ra nge 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 NO 2 , SO 2 , and HAP; ecosystem effects; or visibility impairment.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

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 CO 2 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% (95 th percentile) $94 $120 to $160 $120 to $150 Option 1 - regional 545 million metric tonnes CO 2 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% (95 th percentile) $92 $120 to $150 $120 to $150 *All estimates are rounded to two significant figures. Climate benefits are based on reductions in CO 2 emissions. Co-benefits are based on regional benefit-per-ton estimat es. Co-benefits for ozone are based on ozone season NOx emissions. Ozone co-benefits occur in an alysis year, so they are the same for all discount rates. The health cobenefits reflect the sum of the PM 2.5 and ozone co-benefits and reflect the ra nge 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 NO 2 , SO 2 , and HAP; ecosystem effects; or visibility impairment.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

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 Krew ski et al. (2009) mortality study for PM 2.5 . “High Health Co-benefits” refers
to the combined health co-b enefits estimated using the Levy et al. (2005) mortality study for ozone with the Lepeule et al. (2012) mortality study for PM 2.5 . 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 associ ated with ambient fine particles and ozone. Data, 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)

9% NOx (as Ozone) 5% 

### Option 2 ‐ Regional Low Health Co

‐ benefits High Health Co

‐ benefits

Formatted: Space After: 0 pt***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-52 time, and resource limitations prevented EPA from quantifying the impacts to, or monetizing the co-benefits from several important benefit cate gories, including co-benefits associated with exposure to several HAP (including mercury and hydrogen chloride) SO 2 and NO 2 , 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 NO 2 Asthma hospital admissions (all ages) — — NO 2 ISA 1 Chronic lung disease hospital admissions (age > 65) — — NO 2 ISA 1 Respiratory emergency department visits (all ages) — — NO 2 ISA 1 Asthma exacerbation (asthmatics age 4–18) — — NO 2 ISA 1 Acute respiratory symptoms (age 7–14) — — NO 2 ISA 1 Premature mortality — — NO 2 ISA 1,2,3 Other respiratory effects (e.g., airway hyperresponsiveness and inflammation, lung function, other ages and populations) — — NO 2 ISA 2,3

Reduced incidence of morbidity from exposure to SO 2 Respiratory hospital admissions (age > 65) — — SO 2 ISA 1 Asthma emergency department visits (all ages) — — SO 2 ISA 1 Asthma exacerbation (asthmatics age 4–12) — — SO 2 ISA 1 Acute respiratory symptoms (age 7–14) — — SO 2 ISA 1 Premature mortality — — SO 2 ISA 1,2,3 Other respiratory effects (e.g., airway hyperresponsiveness and inflammation, lung function, other ages and populations) — — SO 2 ISA 1,2

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, 2000 1

Other neurologic effects (e.g., developmental delays, memory, behavior) — — IRIS; NRC, 2000 2

Cardiovascular effects — — IRIS; NRC, 2000 2,3 Genotoxic, immunologic, a nd other toxic effects — — IRIS; NRC, 2000 2,3 Reduced incidence of morbidity from exposure to HAP Effects associated with exposure to hydrogen chloride — — ATSDR, IRIS 1,2

Improved Environment Table 4-12. Continued Reduced visibility Visibility in Class 1 areas — — PM ISA 1***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-53 Category Specific Effect Effect Has Been Quantified Effect Has Been Monetized More Information
impairment Visibility in residential areas — — PM ISA 1
Reduced effects on materials Household soiling — — PM ISA 1,2 Materials damage (e.g., corrosion, increased wear) — — PM ISA 2 Reduced effects from PM deposition (metals and organics) Effects on Individual organisms and ecosystems — — PM ISA 2

Reduced vegetation and ecosystem effects from exposure to ozone Visible foliar injury on vegetation — — Ozone ISA 1 Reduced vegetation growth and reproduction — — Ozone ISA 1 Yield and quality of commercial forest products and crops — — Ozone ISA 1 Damage to urban ornamental plants — — Ozone ISA 2 Carbon sequestration in terrestrial ecosystems — — Ozone ISA 1 Recreational demand associated with forest aesthetics — — Ozone ISA 2

Other non-use effects Ozone ISA 2 Ecosystem functions (e.g., water cycling, biogeochemical cycles, net primary productivity, leaf-gas exchange, community composition) — — Ozone ISA 2

Reduced effects from acid deposition Recreational fishing — — NO x SO x ISA 1 Tree mortality and decline — — NO x SO x ISA 2 Commercial fishing and forestry effects — — NO x SO x ISA 2 Recreational demand in terrestrial and aquatic ecosystems — — NO x SO x ISA 2

Other non-use effects NO x SO x ISA 2 Ecosystem functions (e.g., biogeochemical cycles) — — NO x SO x ISA 2

Reduced effects from nutrient enrichment Species composition and biodiversity in terrestrial and estuarine ecosystems — — NO x SO x ISA 2 Coastal eutrophication — — NO x SO x ISA 2 Recreational demand in te rrestrial and estuarine ecosystems — — NO x SO x ISA 2

Other non-use effects NO x SO x ISA 2 Ecosystem functions (e.g., biogeochemical cycles, fire regulation) — — NO x SO x ISA 2 Reduced vegetation effects from ambient exposure to SO 2 and NO x Injury to vegetation from SO 2 exposure — — NO x SO x ISA 2 Injury to vegetation from NO x exposure — — NO x SO x ISA 2
Reduced ecosystem effects from exposure to methylmercury Effects on fish, birds, and mammals (e.g., reproductive effects) — — Mercury Study RTC 2 Commercial, subsistence and recreational fishing — — Mercury Study RTC 1

1 We assess these co-benefits qualitatively due to data and resource limitations for this RIA.
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 significa nt 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 hazard ous air pollutants in this analysis. EPA’s SAB-***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-54 HES concluded that “the challenges for assessing progress in health improvement as a result of reductions in emissions of hazardous air pollut ants (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 co mplexities, 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-res ponse 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 th e 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 describe d 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 transforme d into a more toxic form, methylmercury (MeHg). Because Hg is a persistent pollutant, Me Hg 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 c ited 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 literatu re, 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 In ternet at <http://www.epa.gov/hg/report.htm>.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-55 sensitive and best documented endp oints 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 th at 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 poin ts (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 charac terize the effect of meth ylmercury 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 car diovascular 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 betw een MeHg concentration in hair and DNA damage***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

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 fr om 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 te nuous. Multiple human epidemiological studies have found no significant association between Hg exposure and overall cancer incidence, although a few studies have shown an association between Hg exposure and specific types of cancer incidence (e.g., acute leukemia and liver cancer) (NRC, 2000). There is also some evidence of reproductive and renal toxicity in humans from MeHg exposure. However, overall, human data regarding reproductive, renal, and hematological toxicity from MeHg are very limited and are based on either studies of the two high-dose poisoning episodes in Iraq and Japan or animal data, rather than epidemiological studies of chronic exposures at the levels of interest in this analysis. 4.5.1.2 Hydrogen Chloride

Hydrogen chloride (HCl) is a corrosive gas that can cause irritation of the mucous

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 Assess ment. Office of Research and Development. Available at http://www.epa.gov/iris/subst/0073.htm.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-57 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 conc entrations can rapidly lead to swelling and spasm of the throat and suffocation. Most seri ously 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 (R ADS), 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 NO 2 Health Co-Benefits In addition to being a precursor to PM 2.5 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 NO 2 exposure in this analysis. Therefore, this analysis only quantified and monetized the PM 2.5 and ozone co-benefits
associated with the reductions in NO 2 emissions. Following a comprehensive review of health evidence from epidemiologic and laboratory studies, the Integrated Science Assessment for Ox ides 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 NO 2 . The NOx ISA concluded that the evidence “is sufficient to infer a likely causal relationship between short-term NO 2 exposure and adverse effects on the respiratory system.” These epidemiologic and experimental studies encompass a number of endpoints including emergency department visi ts and hospitalizations, respiratory symptoms, airway hyperresponsiveness, airway inflammation, and lung function. The NOx ISA also concluded that the relationship between short-term NO 2 exposure and premature mortality was

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.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-58 “suggestive but not sufficient to infer a causal rela tionship,” because it is difficult to attribute the mortality risk effects to NO 2 alone. Although the NOx ISA stat ed that studies consistently reported a relationship between NO 2 exposure and mortality, the effect was generally smaller than that for other pollutants such as PM.
4.5.3 Additional SO 2 Health Co-Benefits In addition to being a precursor to PM 2.5 , SO 2 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 SO 2 in this analysis because we do not have air quality modeling data available. Therefore, this analysis only quantifies and monetizes
the PM 2.5 co-benefits associated with the reductions in SO 2 emissions. Following an extensive evaluation of health evidence from epidemiologic and laboratory studies, the Integrated Science Assessment for Oxides of Sulfur —Health Criteria (SO 2 ISA) concluded that there is a causal relationship between respiratory health effects and short-term exposure to SO 2 (U.S. EPA, 2008a). The immediate effect of SO 2 on the respiratory system in
humans is bronchoconstriction. Asthmatics are more sensitive to the effects of SO 2 likely resulting from preexisting inflammation associat ed with this disease. A clear concentrationresponse relationship has been demonstrated in laboratory studies following exposures to SO 2 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 SO 2 ISA identified as a “causal relationship”: asthma exacerbation, respiratory-related emergency department visits, and respiratory-related hospitalizations. The diffe ring evidence and associated strength of the
evidence for these different effects is described in detail in the SO 2 ISA. The SO 2 ISA also concluded that the relationship between short-term SO 2 exposure and premature mortality was “suggestive of a causal relationship” because it is di fficult to attribute the mortality risk effects to
SO 2 alone. Although the SO 2 ISA stated that studies are generally consistent in reporting a relationship between SO 2 exposure and mortality, there was a lack of robustness of the observed associations to adjustment for pollutants. We did not quantify these co-benefits due to data
constraints.
4.5.4 Additional NO 2 and SO 2 Welfare Co-Benefits

As described in the Integrated Science Assessment for Oxides of Nitrogen and Sulfur —***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-59 Ecological Criteria (NOx ISA) (U.S. EPA, 2008d), SO 2 and NOx emissions also contribute to a variety of adverse welfare effects, including acidi c 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 aesth etic 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 temp eratures, insect pests, and disease leading to increased mortality of canopy trees. Terrestrial ac idification affects several important ecological services, including declines in habitat for threat ened and endangered species (cultural), declines in forest aesthetics (cultural), declines in forest productivity (provisioning), and increases in
forest soil erosion and reductions in water rete ntion (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, particular ly 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 fre quent and more intense wildfires. (U.S. EPA, 2008d)
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 sp ecies identified as “ozone-sensitive”, many of which occur in state and national parks and forests. These effects include those that damage or impair the***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-60 intended use of the plant or ecosystem. Such effects are considered adverse to the public welfare and can include reduced growth and/or biomass pro duction in sensitive plant species, including forest trees, reduced crop yields, visible foliar injury, reduced plant vigor (e.g., increased susceptibility to harsh weather, disease, ins ect 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 th e atmosphere. The amount of CO emitted from these reactions, relative to carbon dioxide (CO 2 ), 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 cardi ovascular morbidity, particularly in individuals with coronary heart disease. Epidemiologic st udies 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 pulmona ry 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 follow ing long-term exposure to CO, and for central nervous system effects linked to short- and long-term exposure to CO. 4.5.7 Visibility Impairment Co-Benefits Reducing secondary formation of PM 2.5 would improve levels visibility in the U.S. because suspended particles and gases degrade visi bility by scattering and absorbing light (U.S. EPA, 2009b). Fine particles with significant li ght-extinction efficiencies include sulfates, nitrates, organic carbon, elemental carbon, and soil (Sisler, 1996). Visibility has direct significance to people’s enjoyment of daily activ ities and their overall sense of wellbeing. Good***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-61 visibility increases the quality of life where indi viduals 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 im pact on visibility in urban areas or Class I

areas.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-62 4.6 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. Abt Associates, Inc. 2012. BenMAP User’s Manual Appendices . Prepared for U.S. Environmental Protection Agency Office of Air Quality Planning and Standards. Research Triangle Park, NC. September. 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): p. 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 Na tional 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, Atmosphere, & Health 2:169–176. Fann, N.; K.R. Baker and C.M. Fulcher. 2012. “Characterizing the PM 2.5 -related health benefits of emission reductions for 17 industrial, area and mobile emission sectors across the U.S.” Environment International 49 41–151. Gwinn, M.R.; J. Craig; D.A. Axelrad; R. Cook; C. Dockins; N. Fann; R. Fegley; D.E. Guinnup; G. Helfand; B. Hubbell; S.L. Mazur; T. Palma; R.L. Smith; J. Vandenberg and B. Sonawane. 2011. “Meeting report: Estimating the benefits of reducing hazardous air pollutants— summary of 2009 workshop and future considerations.” Environmental Health Perspectives Jan; 119(1): p. 125-30. Huang, Y.; F. Dominici; M. Bell. 2005. “Bayes ian Hierarchical Distributed Lag Models for Summer Ozone Exposure and Cardio-Respiratory Mortality.” Environmetrics , 16, 547-562. Industrial Economics, Incorporated (IEc). 2006. Expanded Expert Judgment Assessment of the Concentration-Response Relationship Between PM 2.5 Exposure and Mortality . Prepared for: Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC. September. Available at: <http://www.epa.gov/ttn/ecas/regdata/Uncertain ty/pm_ee_tsd_expert_interview_summaries.p df>. Formatted: Bibliography style***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-63 Industrial Economics, Incorporated (IEc). 2009. Section 812 Prospective Study of the Benefits and Costs of the Clean Air Act: Air Toxics Case Study: Health Benefits of Benzene Reductions in Houston, 1990–2020. Final Report, July 14, 2009. Available at: <http://www.epa.gov/air/sect812/dec09/812C AAA_Benzene_Houston_Final_Report_July_20 09.pdf>. Intergovernmental Panel on Climate Change (IPCC). 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/pub lications_ipcc_fourth_assessment_report_synt hesis_report.htm>. Intergovernmental Panel on Climate Change (IPCC). 2012: Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation . A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, UK, and New York, NY, USA. Intergovernmental Panel on Climate Change (IPCC). 2013. Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. Intergovernmental Panel on Climate Change (IPCC). 2014a. Climate Change 2014: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. Intergovernmental Panel on Climate Change (IPCC). 2014b. Climate Change 2014: Mitigation of Climate Change. Cont ribution of Working Group III to th e Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. Ito, K.; S.F. De Leon and M. Lippmann. 2005. “Associations between Ozone and Daily Mortality: Analysis and Meta-Analysis.” Epidemiology, 16(4): pp. 446-57. Karl, T.; J. Melillo and T. Peterson, Eds. 2009. Global Climate Change Impacts in the United States . Cambridge, United Kingdom: Cambridge University Press. 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 So ciety Study Linking Particulate Air Pollution and Mortality. HEI Research Report, 140, Health Effects Institute, Boston, MA. Formatted: Bibliography style, Indent: Before: 0", First line: 0", Space After: 0 pt, Line spacing: single, Adjust space between Latin and Asian text, Adjust space between Asian text and numbers

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4-64 Lepeule, J.; F. Laden; D. Dockery; J. Schwartz. 2012. “Chronic Exposure to Fine Particles and Mortality: An Extended Follow-Up of the Ha rvard 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. 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> National Research Council (NRC). 2000. Toxicological Effects of Methylmercury: Committee on the Toxicological Effects of Methylmercury .” Board on Environmental Studies and Toxicology. National Academies Press. Washington, DC. National Research Council (NRC). 2002. Estimating the Public Health Benefits of Proposed Air Pollution Regulations . National Academies Press. Washington, DC. National Research Council (NRC). 2008. Estimating Mortality Risk Reduction and Economic Benefits from Controlling Ozone Air Pollution . National Academies Press. Washington, DC. National Research Council (NRC). 2010. Ocean Acidification: A National Strategy to Meet the Challenges of a Changing Ocean . National Academies Press. Washington, DC. National Research Council (NRC). 2011a. Climate Stabilization Ta rgets: Emissions, Concentrations, and Impacts over Decades to Millennia . National Academies Press, Washington, DC. National Research Council (NRC) 2011b. National Security Implications of Climate Change for U.S. Naval Forces . National Academies Press. Washington, DC. National Research Council (NRC, 2011c). Understanding Earth's Deep Past: Lessons for Our Climate Future . National Academies Press. Washington, DC National Research Council (NRC). 2012a. Sea-Level Rise for the Coas ts of California, Oregon, and Washington: Past, Present, and Future . National Academies Press. Washington, DC. National Research Council (NRC). 2013a. Climate and Social Stress: Implications for Security Analysis . National Academies Press. Washington, DC. National Research Council (NRC). 2013b. Abrupt Impacts of Climate Change: Anticipating Surprises . National Academies Press. Washington, DC. Office of Management and Budget (OMB). 2003. Circular A-4: Regulatory Analysis. Washington, DC. Available at: < http://www.w hitehouse.gov/omb/circulars/a004/a-4.html>. Formatted: Font: Italic

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4-65 Ren, C.; G.M. William; L. Morawska; K. Mengensen and S. Tong. 2008a. “Ozone Modifies Associations between Temperature and Cardiovascular Mortality: Analysis of the NMMAPS Data.” Occupational and Environmental Medicine, 65:255-260. Ren, C.; G.M. Williams; K. Mengersen; L. Morawska; and S. Tong. 2008b. “Does Temperature Modify Short-Term Effects of Ozone on Total Mortality in 60 Large Eastern U.S. Communities? An Assessment Using the NMMAPS Data.” Environment International , 34 (2008), pp. 451–458. Ren, C.; S. Tong. 2006b. “Temperature Modifies the Health Effects of Particulate Matter in Brisbane, Australia.” International Journal of Biometeorology, 51:87–96. Ren. C.; G.M. Williams; S. Tong. 2006a. “Does Particulate Matter Modify the Association between Temperature and Cardiorespiratory Diseases? Environmental Health Persp ectives, 114:1690–1696. Roberts, S. 2004. “Interactions between Particulate Air Pollution and Temperature in Air Pollution Mortality Time Series Studies.” Environmental Research, 96:328–337. Roman, H.; 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 Science and Technol ogy, 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. Sisler, J.F. 1996. Spatial and Seasonal Patterns and Long-Term Variability of the Composition of the Haze in the United States: An analysis of data from the IMPROVE network. CIRA Report, ISSN 0737-5352-32, Colorado State University. U.S. Environmental Protection Agency (U.S. EPA). 1995. Regulatory Impact Analysis for the Petroleum Refinery NESHAP. Revised Draft for Promulgation . Office of Air Quality Planning and Standards, Research Triangle Park, N.C. Available on the Internet at <http://www.regmarkets.org/admin/authorpdfs/page.php?id=705>. U.S. Environmental Protection Agency (U.S. EPA). 2000. Guidelines for Preparing Economic Analyses. EPA 240-R-00-003. National Center for Environmental Economics, Office of Policy Economics and Innovation. Washington, DC. September. Available at: <http://yosemite.epa.gov/ee/epa/eed.nsf/webpages/Guidelines.html/$file/cover.pdf>.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-66 U.S. Environmental Protection Agency (U.S. EPA). 2006. Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final) . EPA/600/R-05/004aF-cF. Washington, DC: U.S. EPA. February. Available at: <http://cfpub.epa.gov/ncea/CFM/recordisplay.cfm?deid=149923>. U.S. Environmental Protection Agency (U.S. EPA). 2008a. Integrated Science Assessment for Sulfur Oxides—Health Criteria (Final Report) . National Center for Environmental Assessment, Research Triangle Park, NC. September. Available at: <http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=198843>. U.S. Environmental Protection Agency (U.S. EPA). 2008b. Final Ozone NAAQS Regulatory Impact Analysis . EPA-452/R-08-003. Office of Air Qual ity Planning and Standards Health and Environmental Impacts Division, Air Benefit and Cost Group Research Triangle Park, NC. March. Available at: <http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=194645>. U.S. Environmental Protection Agency (U.S. EPA). 2008c. Integrated Science Assessment for Oxides of Nitrogen - Health Criteria (Final Report). National Center for Environmental Assessment, Research Triangle Park, NC. July. Available at: <http://www.epa.gov/ttnecas1/regdata/RIAs/6-ozoneriachapter6.pdf>. U.S. Environmental Protection Agency (U.S. EPA). 2008d. Integrated Science Assessment for Oxides of Nitrogen and Sulfur–Ecological Criteria National (Final Report ). National Center for Environmental Assessment, Research Tria ngle Park, NC. EPA/600/R-08/139. December. Available at: <http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=201485>. U.S. Environmental Protection Agency (U.S. EPA). 2009b. Integrated Science Assessment for Particulate Matter (Final Report) . EPA-600-R-08-139F. National Center for Environmental Assessment – RTP Division. December. Available at: <http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=216546>. U.S. Environmental Protection Agency (U.S. EPA). 2010a. Integrated Science Assessment for Carbon Monoxide. National Center for Environmental Assessment, Research Triangle Park, NC. EPA/600/R-09/019F. January. Available at: <http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=218686>. U.S. Environmental Protection Agency (U.S. EPA). 2010b. Technical Support Document: Summary of Expert Opinions on the Existence of a Threshold in the Concentration-Response Function for PM 2.5 -related Mortality. Research Triangle Park, NC. June. Available at: <http://www.epa.gov/ttn/ecas/regda ta/Benefits/thresholdstsd.pdf>.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-67 U.S. Environmental Protection Agency (U.S. EPA). 2010c. Valuing Mortality Risk Reductions for Environmental Policy: A Wh ite Paper: SAB Review Draft . National Center for Environmental Economics December. Available at: <http://yosemite.epa.gov/ee/epa/eerm.nsf/ vwAN/EE-0563-1.pdf/$file/EE-0563-1.pdf>. U.S. Environmental Protection Agency (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. Environmental Protection Agency (U.S. EPA). 2010e. Guidelines for Preparing Economic Analyses . EPA 240-R-10-001. National Center for Environmental Economics, Office of Policy Economics and Innovation. Washington, DC. December. Available at: <http://yosemite.epa.gov/ee/epa/eerm.nsf/v wAN/EE-0568-50.pdf/$file/EE-0568- 50.pdf>. U.S. Environmental Protection Agency (U.S. EPA). 2011a. The Benefits and Costs of the Clean Air Act from 1990 to 2020 . Office of Air and Radiation, Washington, DC. March. Available at: <http://www.epa.gov/cleanairactbe nefits/feb11/fullreport_rev_a.pdf>. U.S. Environmental Protection Agency (U.S. EPA). 2011b. Regulatory Impact Analysis for the Final Mercury and Air Toxics Standards. EPA-452/R-11-011. December. Available at: <http://www.epa.gov/ttn/ecas/regda ta/RIAs/matsriafinal.pdf>. U.S. Environmental Protection Agency (U.S. EPA). 2011c. Regulatory Impact Analysis: National Emission Standards for Hazardous Air Pollutants for Industrial, Commercial, and Institutional Boilers and Process Heaters. February. Available at: <http://www.epa.gov/ttnecas1/regdata/RIA s/boilersriafinal110221_psg.pdf>. U.S. Environmental Protection Agency (U.S. EPA). 2011d. Policy Assessment for the Review of the Particulate Matter National Ambient Air Quality Standards . EPA-452/D-11-003. Office of Air Quality Planning and Standards, Health and Environmental Impacts Division. April. Available at: <http://www.epa .gov/ttn/naaqs/standards/pm/data/20110419pmpafinal.pdf>. U.S. Environmental Protection Agency (U.S. EPA). 2012a. Regulatory Impact Analysis for the Final Revisions to the National Ambient Ai r Quality Standards for Particulate Matter . EPA452/R-12-003. Office of Air Quality Planning and Standards, Health and Environmental Impacts Division. December. Available at: <http://www.epa.gov/pm/2012/finalria.pdf>. U.S. Environmental Protection Agency (U.S. EPA). 2012b. Regulatory Impact Analysis: Petroleum Refineries New Source Performance Standards Ja. Office of Air Quality Planning***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-68 and Standards, Health and Environmental Impacts Division. June. Available at: <http://www.epa.gov/ttnecas1/regdata/RIAs/re fineries_nsps_ja_final_ria.pdf>. U.S. Environmental Protection Agency (U.S. EPA). 2013a. Technical Support Document: Estimating the Benefit per ton of Reducing PM 2.5 Precursors from 17 sectors . Office of Air Quality Planning and Standards, Research Triangle Park, NC. February. Available at: <http://www.epa.gov/airquality/benmap/models/Source_Apportionment_BPT_TSD_1_31_13 .pdf>. U.S. Environmental Protection Agency (U.S. EPA). 2013b. Integrated Science Assessment of 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%5CSAB PRODUCT.NSF/34355712E C011A358525719A005B 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%5CSAB PRODUCT.NSF/413345241 48BCCD6852571A70051 6498/$File/eeacf013.pdf>. 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:***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-69 <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>. U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 2009b. 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/$File/EPA-CASAC-09-008-unsigned.pdf>. U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 2009c. Review of Integrated Science Assessment for Particulate Matter (Second External Review Draft, July 2009) . EPA-CASAC-10-001. November. Available at: <http://yosemite.epa.gov/sab/SABPRODUCT.NSF/81e39f4c09954fcb85256ead006be86e/15 1B1F83B023145585257678006836B9/$File/EPA-CASAC-10-001-unsigned.pdf>. U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 2010a. 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>. U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 2011. Review of Valuing Mortality Risk Reductio ns for Environmental Policy: A White Paper (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>. Woodruff, T.J., J. Grillo, and K.C. Schoendorf. 1997. “The Relationship between Selected of postneonatal infant mortalit y and particulate air pollution in the United States.” Environmental Health Perspectiv es 105 (6): 608-612.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-1***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

5-1***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4A-1 

### A PPENDIX 

### 4A: G ENERATING 

### R EGIONAL 

### B ENEFIT 

### - PER 

### -T ON 

### E STIMATES FOR


### E LECTRIC 

### G ENERATING 

### U NITS

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 PM 2.5 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: Es timating the Benefit per Ton of Reducing PM 2.5 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 PM 2.5 precursor emitted from EGU sources, we:

1. Use air quality modeling to predict ambient concentrations of primary PM 2.5 , 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 PM 2.5 , sulfate and nitrate PM 2.5 using the environmental Benefits Mapping and Analysis Program (BenMAP v4.0.66) 1 (Abt Associates, Inc, 2012).

3. Divide the PM 2.5 -related health impacts attributable to each type of PM 2.5 , and the monetary value of these impacts, by the level of associated precursor emissions. That is, primary PM 2.5 benefits are divided by direct PM 2.5 emissions, sulfate benefits are divided by SO 2 emissions, and nitrate benefits are divided by NOx emissions.

1 In this stage we estimate the PM 2.5 -related impacts associated with changes in directly emitted PM 2.5 , nitrate and sulfate separately, so that we may ultimately calcula te the benefit-per-ton reduced of the corresponding PM 2.5 precursor, or directly emitted PM 2.5 , in step 3. When estimating these impacts we apply effect coefficients that relate changes in total PM 2.5 mass to the risk of adverse health outcomes; we do not apply effect coefficients that are differentiated by PM 2.5 specie. Formatted: 01 ChapterTitle1, Indent: Before: 0", First line: 0", Border: Bottom: (No border)

Formatted: Numbered + Level: 1 + Numbering Style: 1, 2, 3, ... + Start at: 1 + Alignment: Left + Aligned at: 0.25" + Indent at: 0.5"***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

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 Ex tensions (CAMx) model 2 to estimate PM 2.5 and ozone concentrations attributable to 17 industrial sectors, including E GUs. 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 a nd 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

2 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>. 

## PM 2.5 

## air quality

change for a given sector Human health benefits Benefit-per- t on calculation Formatted: Font: 11.5 pt, Bold, Complex Script Font: 11.5 pt***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4A-3 deposition or chemical reaction. Source apportionment techniques track the formation and transport of ozone and PM from specific emissi ons sources and calculate the contribution of sources and precursors to ozone and PM 2.5 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 transp ort, 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 Im plementation Plan Call (U.S. EPA, 1998) and the proposed Transport Rule (U.S. EPA, 2010c). 3
4A.3 Regional PM 2.5 Benefit-per-Ton Estimates for EGUs The approach for generating regional benefit-per-ton estimates for PM 2.5 from EGU emissions is a small modification of the approach described above from the BPT TSD. The regional benefit-per-ton esti mates reflect the ambient PM 2.5 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 PM 2.5 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. 4
Due to the low emissions of SO 2 , NO X , and directly emitted particles from EGUs in California

3 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 PM 2.5 from this sector. (Fann, Fulcher, and Ba ker [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-count ing concern with the resulting co-benefits estimates.

4 This aggregation is identified as the shapefile “Report Regions” in BenMAP’s grid definitions.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4A-4and 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$. 5

Figure 4A-2. Modeled PM 2.5 Concentrations Attributable to EGUs in 2016 (Annual Mean, in μg/m 3 ) (Source: BPT TSD)
This method provides estimates of the regional average benefit-per-ton for each ambient
PM 2.5 precursor emitted from EGU sources. For some pr ecursor emissions, such as NOx, there is generally a non-linear relationship between emissions and formation of PM 2.5 . This means that each ton of NOx reduced would have a different impact on ambient PM 2.5 depending on the initial level of emissions and potentially on the levels of emissions of other pollutants. In contrast, SO 2 is generally linear in forming PM 2.5 . For non-linear pollutants like NOx, a marginal

5 Currently, BenMAP does not have an inflation adjustment to 2011$ . We ran BenMAP for a currency year of 2010$ and calculated t he benefit-per-ton estimates in 2010$. We then adjusted the resulting benefit-per-ton estimates to 2011$ using the Consumer Price Index.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

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 es timated co-benefits for the proposed guidelines are
attributable to reductions in SO 2 emissions, the added uncertainty is likely to be small.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***4A-6a)Sulfateb)Nitratec)ElementalCarbonandOrganicCarbond)CrustalPM2.5***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***4A-7Figure 4A-3. Modeled PM2.5Concentrations Attributable to EGUs byPrecursor in 2016 (Annual Mean, in μg/m3)***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4A-8 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 SO 2 3,793,362 3,520,296 273,070 4,886 Directly emitted PM 2.5 (EC+OC) 30,078 26,172 3,892 1,523 Directly emitted PM 2.5 (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 SO 2 , NOx, CO 2 , Hg, HCl from EGUs,

115 See Chapter 3 of this RIA for additional information regarding IPM.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4A-9 but CO, VOC, NH 3 and total directly emitted PM 2.5 and PM 10 emissions are post-calculated. 116 In 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 particle s, we must determine the fraction of total PM 2.5 emissions comprised of elemental carbon and organic carbon (EC+OC) and crustal emissions. 117 Figure 4A-5 illustrates the relative breakdown of dir ectly emitted PM 2.5 components from EGUs in the modeling by Fann, Baker, and Fulcher (2012). In this modeling, the national average
EC+OC fraction of emitted PM 2.5 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 PM 2.5 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%.

116 Detailed documentation of this post-processing is available at http://www.epa.gov/powersectormodeling/do cs/v513/FlatFile_Methodology.pdf

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 carbo n, elemental carbon, nitrate, sulfate, chloride, etc.).***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4A-10 Figure 4A-5. Breakdown of Directly Emitted PM 2.5 Emissions from EGU in Sector Modeling

There are several uncertainties associated with estimating the benefits associated with reducing emissions of EC+OC and crustal PM 2.5 from EGUs. As previously mentioned, IPM
does not estimate PM 2.5 emissions by component, and total PM 2.5 is estimated as an IPM postprocessing step using emission factors. In order to conduct air quality modeling, PM 2.5 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 s ource 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 PM 2.5 measurements, using the Model Attainment Test Software (Abt Associates, In c, 2010). Uncertainties in some emissions sectors can sometimes be large enough that air quality es timates from well-character ized 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 particle s for the proposed guidelines are small relative ‐ 5,000 10,000 15,000 20,000 25,000TexasPennsylvaniaOhioIndianaFloridaWestVirginiaGeorgiaKentuckyAlabamaNorthCarolinaSouthCarolinaIllinoisMinnesotaMissouriWyomingArizonaTennesseeVirginiaMichiganWisconsinNorthDakotaUtahIowaNewMexicoKansasOklahomaColoradoMarylandNewYorkArkansasLouisianaNevadaCaliforniaNebraskaMontanaNewJerseyMississippiMassachusettsConnecticutWashingtonSouthDakotaNewHampshireOregonDelawareRhodeIslandMaineIdahoEmissionsinSectorModeling(Tons) PNO3 PSO4 POC PEC PMFINE (crustal)***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4A-11 to the co-benefits of reducing SO 2 emissions, these uncertainties would not substantially affect
the estimate of total monetized health co-benefits. 118 In calculating the PM 2.5 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 PM 2.5 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 monetizi ng the co-benefits associated with reducing directly emitted particulate
nitrate and sulfate.
Although it is possible to calculate 95 th 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 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 95 th percentile confidence interval for the total monetized PM 2.5 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 95 th 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 4A-

118 See Figure 5-2 of this RIA for the rela tive proportion of health co-benefits from directly emitted particles relative to co-benefits from SO 2 emissions.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4A-12 5 through 4A-7 provide the incidence per ton estimates for the EGU sector in 2020, 2025, and 2030 respectively.***E.O. 12866 Review – Draft – Do NotCite, Quote or Release During Review***4A-13Table 4A-2. Summary of National and Regional PM2.5Benefit-per-Ton Estimates for EGUs in 2020 (2011$)*PollutantDiscountRateNationalRegionEastWestCaliforniaSO23%$38,000 to $86,000$40,000 to $90,000$7,800 to $18,000$160,000 to $360,0007%$34,000 to $77,000$36,000 to $82,000$7,100 to $16,000$140,000 to $320,000Directly emittedPM2.5(EC+OC)3%$140,000 to $320,000$140,000 to $320,000$56,000 to $130,000$280,000 to $640,0007%$130,000 to $290,000$130,000 to $280,000$50,000 to $110,000$250,000 to $570,000Directly emittedPM2.5(Crustal)3%$18,000 to $40,000$18,000 to $41,000$11,000 to $25,000$110,000 to $240,0007%$16,000 to $36,000$16,000 to $37,000$10,000 to $23,000$95,000 to $220,000NOx (as PM2.5)3%$5,600 to $13,000$6,700 to $15,000$1,200 to $2,600$17,000 to $38,0007%$5,000 to $11,000$6,000 to $14,000$1,000 to $2,400$15,000 to $34,000* The range of estimates reflects the range of epidemiology studies for avoidedpremature mortality for PM2.5. All estimates are rounded to twosignificant figures. All fineparticles are assumed to have equivalent health effects, butthe benefit per ton estimates vary depending on the location and magnitude of their impact on PM2.5levels,which drive population exposure. The monetized benefits incorporate the conversion from precursor emissions to ambient fine particles. The estimates do not includereduced health effects from direct exposure to ozone, NO2, SO2, ecosystem effects, or visibility impairment.Table 4A-3. Summary of National and Regional PM2.5Benefit-per-Ton Estimates for EGUs in 2025 (2011$)*PollutantDiscountRateNationalRegionEastWestCaliforniaSO23%$41,000 to $93,000$44,000 to $98,000$8,800 to $20,000$180,000 to $410,0007%$37,000 to $84,000$39,000 to $89,000$8,000 to $18,000$160,000 to $370,000Directly emittedPM2.5(EC+OC)3%$150,000 to $350,000$150,000 to $340,000$64,000 to $140,000$320,000 to $720,0007%$140,000 to $310,000$140,000 to $310,000$58,000 to $130,000$290,000 to $650,000Directly emittedPM2.5(Crustal)3%$17,000 to $39,000$18,000 to $40,000$12,000 to $27,000$43,000 to $96,0007%$15,000 to $35,000$16,000 to $36,000$11,000 to $24,000$38,000 to $87,000NOx (as PM2.5)3%$6,000 to $14,000$7,200 to $16,000$1,300 to $2,900$19,000 to $42,0007%$5,400 to $12,000$6,500 to $15,000$1,200 to $2,600$17,000 to $38,000* The range of estimates reflects the range of epidemiology studies for avoidedpremature mortality for PM2.5. All estimates are rounded to twosignificant figures. All fineparticles are assumed to have equivalent health effects, butthe benefit per ton estimates vary depending on the location and magnitude of their impact on PM2.5levels,which drive population exposure. The monetized benefits incorporate the conversion from precursor emissions to ambient fine particles. The estimates do not includereduced health effects from direct exposure to ozone, NO2, SO2, ecosystem effects, or visibility impairment.***E.O. 12866 Review – Draft – Do NotCite, Quote or Release During Review***4A-14Table 4A-4. Summary of National and Regional PM2.5Benefit-per-Ton Estimates for EGUs in 2030 (2011$)*PollutantDiscountRateNationalRegionEastWestCaliforniaSO23%$44,000 to $100,000$47,000 to $110,000$9,800 to $22,000$200,000 to $450,0007%$40,000 to $90,000$42,000 to $95,000$8,800 to $20,000$180,000 to $410,000Directly emittedPM2.5(EC+OC)3%$170,000 to $370,000$160,000 to $370,000$71,000 to $160,000$360,000 to $800,0007%$150,000 to $340,000$150,000 to $330,000$64,000 to $150,000$320,000 to $730,000Directly emittedPM2.5(Crustal)3%$18,000 to $42,000$19,000 to $43,000$13,000 to $30,000$47,000 to $110,0007%$17,000 to $38,000$17,000 to $38,000$12,000 to $27,000$43,000 to $96,000NOx (as PM2.5)3%$6,400 to $14,000$7,600 to $17,000$1,400 to $3,200$21,000 to $42,0007%$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 avoidedpremature mortality for PM2.5. All estimates are rounded to twosignificant figures. All fineparticles are assumed to have equivalent health effects, butthe benefit per ton estimates vary depending on the location and magnitude of their impact on PM2.5levels,which drive population exposure. The monetized benefits incorporate the conversion from precursor emissions to ambient fine particles. The estimates do not includereduced health effects from direct exposure to ozone, NO2, SO2, ecosystem effects, or visibility impairment.***E.O. 12866 Review – Draft – Do NotCite, Quote or Release During Review***4A-15Table 4A-5. Summary of Regional PM2.5Incidence-per-Ton Estimates for EGUs in 2020*Health EndpointEastWestCaliforniaSO2NOxEC+OCCrustalSO2NOxEC+OCCrustalSO2NOxEC+OCCrustalPremature MortalityKrewski et al. (2009) – adult0.0044000.0007300.0150000.0020000.0008600.0001300.0061000.0012000.0170000.0018000.0310000.012000Lepeule et al. (2012) – adult0.0099000.0017000.0350000.0045000.0019000.0002900.0140000.0028000.0390000.0041000.0700000.026000Woodruff et al. (1997) – infants0.0000100.0000020.0000330.0000040.0000020.0000000.0000160.0000030.0000360.0000040.0000670.000025MorbidityEmergency department visits for asthma0.0023000.0004100.0087000.0010000.0003800.0000550.0028000.0005200.0086000.0010000.0160000.005900Acute bronchitis0.0063000.0011000.0220000.0028000.0016000.0003300.0110000.0023000.0310000.0034000.0570000.021000Lower respiratory symptoms0.0810000.0140000.2900000.0360000.0210000.0042000.1500000.0290000.3900000.0440000.7200000.270000Upper respiratory symptoms0.1200000.0200000.4100000.0510000.0300000.0060000.2100000.0420000.5600000.0620001.0000000.390000Minor restricted-activity days3.2000000.54000011.0000001.4000000.7800000.1300005.4000001.00000016.000001.70000029.0000011.00000Lost work days0.5400000.0900001.9000000.2400000.1300000.0230000.9100000.1800002.7000000.2900004.9000001.800000Asthma exacerbation0.2900000.0200000.4200000.0530000.0740000.0062000.2200000.0430001.4000000.0640001.1000000.400000Hospital Admissions, Respiratory0.0013000.0002200.0045000.0005800.0001900.0000260.0014000.0002700.0041000.0004400.0074000.002800Hospital Admissions, Cardiovascular0.0016000.0002700.0056000.0007200.0002200.0000320.0018000.0003400.0050000.0005200.0090000.003400Non-fatal Heart Attacks (Peters)0.0050000.0008600.0180000.0023000.0008100.0001100.0060000.0012000.0180000.0019000.0320000.012000Non-fatal Heart Attacks (All others)0.0005500.0000930.0019000.0002500.0000870.0000120.0006400.0001300.0019000.0002100.0034000.001300* All estimates are rounded to two significantfigures. All fine particles are assumed tohave equivalent health effects, but the incidence-per-ton estimates vary depending onthe location and magnitudeof their impact on PM2.5levels, which drive population exposure. The incidencebenefit-per-ton estimates incorporate the conversion fromprecursor emissions to ambient fine particles.***E.O. 12866 Review – Draft – Do NotCite, Quote or Release During Review***4A-16Table 4A-6. Summary of Regional PM2.5Incidence-per-Ton Estimates for EGUs in 2025*Health EndpointEastWestCaliforniaSO2NOxEC+OCCrustalSO2NOxEC+OCCrustalSO2NOxEC+OCCrustalPremature MortalityKrewski et al. (2009) – adult0.0047000.0007700.0160000.0021000.0009400.0001400.0068000.0014000.0190000.0020000.0340000.013000Lepeule et al. (2012) – adult0.0110000.0017000.0370000.0048000.0021000.0003100.0150000.0031000.0430000.0045000.0770000.029000Woodruff et al. (1997) – infants0.0000090.0000010.0000310.0000040.0000020.0000000.0000160.0000030.0000340.0000040.0000630.000024MorbidityEmergency department visits for asthma0.0024000.0004200.0090000.0011000.0004100.0000590.0030000.0005600.0090000.0010000.0160000.006100Acute bronchitis0.0066000.0011000.0230000.0029000.0017000.0003500.0120000.0024000.0320000.0036000.0590000.022000Lower respiratory symptoms0.0840000.0140000.3000000.0370000.0220000.0044000.1500000.0310000.4100000.0460000.7600000.290000Upper respiratory symptoms0.1200000.0200000.4300000.0530000.0320000.0063000.2200000.0440000.5900000.0650001.1000000.410000Minor restricted-activity days3.2000000.54000012.0000001.4000000.8200000.1400005.7000001.10000016.000001.7000030.0000011.00000Lost work days0.5500000.0910002.0000000.2400000.1400000.0240000.9700000.1900002.7000000.3000005.0000001.800000Asthma exacerbation0.3000000.0210000.4400000.0550000.0780000.0065000.2300000.0460001.4000000.0670001.1000000.420000Hospital Admissions, Respiratory0.0014000.0002400.0050000.0006400.0002200.0000300.0016000.0003200.0048000.0005000.0085000.003200Hospital Admissions, Cardiovascular0.0018000.0002900.0061000.0007800.0002500.0000360.0020000.0003900.0056000.0005800.0100000.003800Non-fatal Heart Attacks (Peters)0.0055000.0009300.0190000.0025000.0009100.0001200.0068000.0013000.0200000.0021000.0360000.013000Non-fatal Heart Attacks (All others)0.0006000.0001000.0021000.0002700.0000980.0000130.0007300.0001400.0022000.0002300.0039000.001500* All estimates are rounded to two significantfigures. All fine particles are assumed tohave equivalent health effects, but the incidence-per-ton estimates vary depending onthe location and magnitudeof their impact on PM2.5levels, which drive population exposure. The incidencebenefit-per-ton estimates incorporate the conversion fromprecursor emissions to ambient fine particles.***E.O. 12866 Review – Draft – Do NotCite, Quote or Release During Review***4A-17Table 4A-7. Summary of Regional PM2.5Incidence-per-Ton Estimates for EGUs in 2030*Health EndpointEastWestCaliforniaSO2NOxEC+OCCrustalSO2NOxEC+OCCrustalSO2NOxEC+OCCrustalPremature Mortality0.0050000.0008100.0170000.0023000.0010000.0001500.0076000.0015000.0210000.0022000.0380000.014000Krewski et al. (2009) – adult0.0110000.0018000.0400000.0051000.0024000.0003400.0170000.0034000.0480000.0050000.0860000.032000Lepeule et al. (2012) – adult0.0000090.0000010.0000300.0000040.0000020.0000000.0000150.0000030.0000320.0000040.0000600.000023Woodruff et al. (1997) – infantsMorbidity0.0025000.0004300.0093000.0011000.0004300.0000620.0032000.0006000.0094000.0011000.0170000.006400Emergency department visits for asthma0.0068000.0011000.0240000.0030000.0018000.0003700.0130000.0026000.0330000.0037000.0610000.023000Acute bronchitis0.0870000.0140000.3100000.0380000.0230000.0047000.1700000.0330000.4200000.0470000.7800000.300000Lower respiratory symptoms0.1300000.0210000.4400000.0550000.0340000.0067000.2400000.0470000.6100000.0670001.1000000.420000Upper respiratory symptoms3.3000000.54000012.0000001.5000000.8600000.1500006.0000001.20000017.000001.80000030.0000011.00000Minor restricted-activity days0.5600000.0920002.0000000.2500000.1500000.0250001.0000000.2000002.8000000.3000005.1000001.900000Lost work days0.3100000.0210000.4600000.0570000.0820000.0069000.2400000.0480001.5000000.0690001.2000000.430000Asthma exacerbation0.0016000.0002600.0055000.0007100.0002500.0000340.0018000.0003600.0055000.0005800.0099000.003700Hospital Admissions, Respiratory0.0019000.0003200.0067000.0008500.0002800.0000410.0023000.0004400.0064000.0006600.0120000.004300Hospital Admissions, Cardiovascular0.0060000.0010000.0210000.0027000.0010000.0001400.0077000.0015000.0230000.0024000.0410000.015000Non-fatal Heart Attacks (Peters)0.0006600.0001100.0023000.0002900.0001100.0000150.0008300.0001600.0025000.0002600.0044000.001700Non-fatal Heart Attacks (All others)0.0050000.0008100.0170000.0023000.0010000.0001500.0076000.0015000.0210000.0022000.0380000.014000* All estimates are rounded to two significantfigures. All fine particles are assumed tohave equivalent health effects, but the incidence-per-ton estimates vary depending onthe location and magnitudeof their impact on PM2.5levels, which drive population exposure. The incidence benefit-per-ton estimates incorporate the conversion fromprecursor emissions to ambient fine particles.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4A-18 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 PM 2.5 . The key difference is substituting the ozone impacts from EGUs in the sector modeling for the PM 2.5 impacts. We have not historically generated ozone benefit-per-ton estimates due to the complex non-linearity in th e 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 attr ibutable 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 ma y overstate the total ozone contribution from this sector. (Fann, Fulcher, and Baker (2013). However, the EGU cont ribution on a per-ton basis would likely be similar. Because the emission reductions in this RIA are calculate d from an IPM base case that includes MATS (see Chapter 3), there is no double-counting concern wi th the resulting co-benefits estimates.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4A-19 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 fr om 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 PM 2.5 , 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 uncertain ty 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 PM 2.5 , 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 NO X 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 NO X emissions. Because we estimate ozone health impacts from***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4A-20 May to September only, this approach underestimat es ozone co-benefits in areas with a longer 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-benefit s. For illustrative purposes, Tables 4A-8 through 4A-10 provide the ozone benefit-per-ton estimate s 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 Regi onal Ozone Benefit-p er-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 epidemiol ogy studies for avoided premature mortality for ozone. All estimates are rounded to two significant figures. The m onetized benefits incorporat e the conversion from NOx precursor emissions to ambient ozone.

Table 4A-9. Summary of National and Regi onal Ozone Benefit-p er-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 epidemiol ogy studies for avoided premature mortality for ozone. All estimates are rounded to two significant figures. The m onetized benefits incorporat e 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 epidemiol ogy studies for avoided premature mortality for ozone. All estimates are rounded to two significant figures. The m onetized benefits incorporat e the conversion from NOx precursor emissions to ambient ozone.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4A-21 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 ambien t 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 ambien t 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 ambien t ozone. These estimates reflect ozone-season NOx emissions.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4A-22 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-sp ecific 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 fo r 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 em issions from the EGU sector from the different base cases in order to evaluate the potential uncer tainty associated with using the benefit-per-ton
estimates to estimate the health co-benefits of th e 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 SO 2 emissions from EGUs (3.8 million tons in 2 016 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 SO 2

emission reductions anticipated from MATS.***E.O. 12866 Review – Draft – Do NotCite, Quote or Release During Review***4A-23Figure 4A-7. Comparison of Base Case SO2Emissions in 2020‐50,000100,000150,000200,000250,000300,000350,000400,000450,000 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

TonsEGUsectorairqualitymodelingbasecaseIPMbasecaseforthisRIA***E.O. 12866 Review – Draft – Do NotCite, Quote or Release During Review***4A-24Figure 4A-8. Comparison of BaseCase Annual NOx Emissions in 2020‐20,00040,00060,00080,000100,000120,000140,000160,000 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

TonsEGUsectorairqualitymodelingbasecaseIPMbasecaseforthisRIA***E.O. 12866 Review – Draft – Do NotCite, Quote or Release During Review***4A-25Figure 4A-9. Comparison of Base CaseOzone-season NOx Emissions in 2020‐10,00020,00030,00040,00050,00060,00070,00080,000 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

TonsEGUsectorairqualitymodelingbasecaseIPMbasecaseforthisRIA***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4A-1 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 PM 2.5 benefits for both rules were from reductions in ambient sulfate. We found that the national benefit-pe r-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 th is 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 PM 2.5 . 122 In general, we would expect state-level estimates to better reflect the sp atial 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 intersta te 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 th e best available for estimating state-level benefit-per-ton estimates, it zeroed-out all anthr opogenic 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 PM 2.5 response to EGU only emissions for each state separately. We evaluated the performance of the state-level estimates by

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 speci fically for those rulemakings. For this evaluation, we compared the benefits estimated from the air quality modeli ng, 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 stat e-level ozone benefit-per-ton estimates due to inter-state transport, complex

atmospheric chemistry, and localized impacts from VOC emissions.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4A-2 comparing the modeled benefits for two previous rulemakings (i.e., CSAPR and MATS) to the benefits estimated using state-level benefit-per-t on. 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.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4A-3 4A.6 References
Abt Associates, Inc. 2010. “User’s Guide: Mode led Attainment Test So ftware.” 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 Prot ection Agency, Office of Air Quality Planning and Standards. Available at: <http://www.epa.gov/air/benmap/model s/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 Na tional 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 PM 2.5 -Related Health Benefits of Emission Reductions for 17 Indust rial, 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 Technol ogy, 47 (8) (2013), pp. 3580–3589. Huang Y.; F. Dominici and M. Bell. 2005. “Bay esian 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 So ciety 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. Co nner; 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 Asso ciation Between Ozone and Daily Deaths to Control for Temperature?” American Journal of Respiratory and Critical Care Medicine, Vol. 171 (6): 627-31.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4A-4 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/crossstate rule/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 Qu ality Planning and Standards, Health and Environmental Impacts Division. (EPA docu ment 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/airtrans port/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 En vironmental 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 PM 2.5 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>.

4A***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

45A -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 Gu idelines states are not required to use any of the measures that the EPA determines cons titute 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 illustra tive of compliance actions states may take. The compliance approaches adopted by the states w ill ultimately drive the magnitude and timing of impacts on the prices of electricity, natural gas, a nd coal and in turn other markets that use these commodities in the production process. The flexib ility afforded to states by the Clean Air Act also allows them to adopt programs which includ e 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 auct ions 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 allo cated to electric distribution companies for the purpose of mitigating electricity rate increases an d 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***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

45A -2 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 fo r 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 comput able 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 bene fit-cost analysis,” EPA recognizes that serious technical challenges remain when attempting to evaluate the be nefits 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 Actstudy, 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 pote ntial 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 regulat ory 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 us ed 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 scenarios analyzed in Chapter 3. This Chapte r then presents a qualitative discussion of the***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

45A -3 factors that will, in part, determine the timi ng 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 co al 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 foresi ght, 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/progs regs/epa-ipm/BaseCasev513.html

.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

45A -4 Table 5-1. Estimated Percentage Changes in Average Energy Prices, by Energy Type and Regulatory Option 2020 2025 2030 Option 1 Regional Compliance 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. Conversel y, 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 co al as inputs to production. These factors are
discussed below.
5.3.1 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 attr ibutable to those commodities. For sectors in which the directly affected inputs are only a small portion of producti on costs, the impact will be smaller than for sectors in which these inputs make up a greater po rtion of total production costs. Therefore more energy-intensive sectors would potentially experi ence greater cost increases when electricity, natural gas, or coal prices increase, but would also experience greater savings when these input***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

45A -5 prices decrease. 123 5.3.2 Ability to Substitute between Inputs to the Production Process
The ease with which producers are able to s ubstitute 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 fo r electricity, natural gas, or coal decrease, some sectors may choose to use more of these inputs in place of other more costly substitutes. 5.3.3 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 pr oducers will be less able to pass on the added cost through a price increase.
Such substitution can also ta ke 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 usi ng 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 wi th fewer emission controls, this can result in foreign emission increases that offset some po rtion of domestic decreases, an effect commonly referred to as “leakage.” The potential for leakage is noteworthy for global pollutants such as carbon dioxide (CO 2 ) and other GHG emissions. Unlike most criteria pollutants and hazardous air pollutants, the impacts of CO 2 emissions are not affected by the location from which those emissions originate. EPA does not anticipate significant leakage in the EGU sector to occur from

123 The net direct effect of this rulemaking on the production co sts of a sector that is attributable to a change in the

electricity price also depends on the expenditures the sect or 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.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

45A -6 this regulation because the nature of electricity transmission does not lend itself to significant imports or exports of electricity.
5.4 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.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

6A-1 

### CHAPTER 6: E MPLOYMENT 

### I MPACT 

### A NALYSIS

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 jo b 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 Elect ric 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 evaluatin g 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 sect ors. Section 6.6 presents the EPA’s quantitative projections of potential employment impacts in these sectors. Th ese 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.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

6A-2 6.2 Overview of the Proposed EGU GHG Existing Sour ce Guidelines The EPA is proposing emission guidelines for st ates 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 (CO 2 ) 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 intens ity 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-speci fic 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 thos e EGUs with generation from less carbonintensive affected EGUs (including natural gas combined cycle [ NGCC ] units 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.

125 Refer to Chapter 3 for information regarding estimates of emissions reductions achievable by each building block

and the determination of state-specific goals.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

6A-3 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 an d 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, et c.) over time. In light of these diff iculties, 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.Berman and Bui (2001, pp. 274-75) model two components that drive changes in firmlevel labor demand: output e ffects and substitution effects. 130 Regulation affects the profit-

126 See Layard and Walters (1978), a standard microeconom ic 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.

130 The authors also discuss a third component, the impact of re gulation on factor prices, but conclude that this effect

is unlikely to be important for large competitive factor ma rkets, such as labor and cap ital. 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.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

6A-4 maximizing quantity of output by changing the marginal cost of production. If regulation causes marginal cost to increase, it will place upward pr essure 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, improv ing 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 th e investment cost were too high. The substitution effect describes how, holding output constant, regul ation 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 re quirements, 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 an d 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 eff ects may be positive or ne gative, 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 lit erature, however, illustrates difficulties with empirical estimation , . For example, studies sometimes re ly on confidential plant-level employment data from the U.S. Census Bureau, possibly combined with pollution abatement expenditure data that are too da ted 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. Employ ment impacts at an individual plant do not***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

6A-5 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 i nput 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 indivi dual firm, theory cannot determine the sign or magnitude of industry-level regulatory effect s on labor demand. Determining these signs and magnitudes requires additional s ector-specific empirical study. For environmental rules, much of the data needed for these empirical studies are 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 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

131 See Ehrenberg & Smith, p. 108.

132 This discussion draws from Berman and Bui (2001), pp. 293.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

6A-6 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 disr uptions. 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 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 enviro nmental 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

133 Full employment is a conceptual targ et 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 pr actice, distributional impacts on individual workers can

be important, as discussed in late r paragraphs of this section.

135 E.g. Graff Zivin and Neidell (2012).***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

6A-7 demand impacts for regulated firms, and also for the regulated industry, can be decomposed into output and substitution effects which may be eith er negative or positive. Estimation of net employment effects for regulated sectors is possi ble 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-rev iewed empirical literature specifically estimating employment effects of en vironmental regulations is more limited. Empirical studies, such as Berman and Bui (2001) , suggest that net employment impacts were not statistically different from zer oo small 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 regu lated industry. Rules are usually announced well in advance of their effective dates and then typically provide a pe riod 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 c ontrol equipment and services to enable compliance when the regulation 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 enviro nmental regulation has a large impact on net employment (eith er negative or positive) in the long run across the whole

economy.

136 Again, see Hamermesh (1993) for a detailed treatment.

137 See Ehrenberg & Smith (2000), Chapter 4: “Employment Ef fects: Empirical Estimates” for a concise overview.

138 U.S. EPA (2011b).***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

6A-8 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 re gulations. 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 sli ghtly” 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 CO 2 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 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. Th ey find that firms in the power sector that participated in the ETS had 2-3 percent fewer empl oyees relative to those that did not participate, but this effect is not statistically significant. This literature suggests that the em ployment impacts of controlling CO 2 emissions in the

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 exi s ting plants and plants dissuaded from opening will increase the estimated

impact of regulation on employment.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

6A-9 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, controlli ng 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 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

141 For a recent review see Graff-Zivin and Neidell (2013).***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

6A-10 billion (ppb) decreases in ozone concentrations in creases 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 a ppropriately capture the way in which costs, compliance spending, and environmental benefits propagate through the macro-economy. Quantitative estimates are further complicated by th e 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 employ ees that support electric power generation,
transmission and distribution , ; the extraction of fossil fuels , ; renewable energy generation ; and supply-side and demand-side energy efficiency. Th is section describes recent employment trends in the electricity system.
6.5.1 Electric Power Generation

In 2013 , the electric power generation, transmi ssion 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 e quipment and/or installation and repair of cables used in electrical power and distribution syst ems. Other major occupation categories include

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 ho w 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-p anel-on-the-role-of-economy-wide-modeling>.

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, Electri c Power Generation, Transmission, and Distribution (NAICS 2211). Available at: < http://www.bls.gov/oes/current/naics4_221100.htm >.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

6A-11 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.

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’s350400450500

2000 2005 2010 2015 Year 

### Electric Power Industry national employment (in thousands)***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

6A-12 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 not track contractors. Contractor employment reported by MSHA focuses primarily on mine development, construction, reconstruction or demoli tion 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.

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>.

147 Mine Safety and Health Administration, CFR Part 50 Ti tle 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 – cont ract 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 >.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

6A-13 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>.20406080100

2000 2005 2010 2015 Year BLS - employees MSHA - employees MSHA - contractors 

### Coal mining employment - national (in thousands)***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

6A-14 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. Re newable generation is not reported to the Bureau of
Labor and StatisticsBLS 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 requi red to design and produce a renewable or energyefficient technology, workers that supply inputs and technicians who install service or operate100150200250

2000 2005 2010 2015 Year 

### Oil and Gas Extraction national employment (in thousands)***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

6A-15 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 Bureau of Labor Statistics ( 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 inclusiveconservative 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>

. Formatted: Font: Italic
Formatted: Font: Italic***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

6A-16 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 eco nomy in 2010, rising 7.4 pe rcent 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 tr ends 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 identi cal 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 indust ries (6-digit NAICS) as potential pr oducers of green goods and services. Available at: < http://www.bls.gov/ggs/ggsoverview.htm>. (Accessed on 1-14-14, l ast 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>.

155 U.S. Department of Labor, U.S. Bureau of Labor St atistics. 2010. National Occ upational 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.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

6A-17 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/Seri es/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 Brooki ngs 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. 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

157 < http://www.brookings.edu/~/media/Series/resources/0713_clean_economy.pdf> p. 15***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

6A-18 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 ope rating 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 mo re 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 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 dema nd-side energy efficiency initiatives directly affect employment by encouraging firms and consumers to shift to more efficient products and***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

6A-19 processes than would otherwise be the case. Empl oyment 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 st ate implementation of the proposed guidelines. We project labor impacts for two options for establishing the “best system of emission reduction” (BSER) for GHG em issions 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).Option 1 has a relatively stringe nt target, but allows a longer timeframe to comply (2030). Option 2 has a less stringen t target with 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. Under the regional approach, states may form regional groups to comply with the guidelines. With the state compliance approach, states are assumed to comply with the guidelines by implementing measures solely within the state and emissions ra te 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.W ith the state compliance approach, states are assumed to comply with the guidelines by impl ementing measures solely within the state and emissions rate averaging occurs between affected s ources on an intrastate basis only. In contrast***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

6A-20 under the regional approach, groups of states are assumed to collaboratively comply with the guidelines. 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 employme nt 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 engine ering approach projects labor changes measured as the change in each analysis year in job-years 158 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).

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.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

6A-21 While the methods used in this analysis to estimate the recurring labor impacts (e.g., labor associated with operating and main taining generating units, as well as labor needed to mine coal 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, involv es the quantity and timing of the labor needs of building new renewable (primarily wind) and natural gas combined cycle (NG CC ) generating capacity. The EPA IPM analysis finds that by 20 20 a significantly larger amount of renewable
and NG CC 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, wh ile 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 NG CC 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

assumes 160 that new NG CC 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 imp acts 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 substant ially lower with all of the Options than in the

160 Table 4.7, IPM 5.13 Documentation.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

6A-22 base case. For example, in Option 1 with re gional compliance only half the total new NG CC capacity is built between 2016 and 2030 (40.6 GW), compared with 79.7 GW in the base case. The avoided new capacity results in both a significant net cost savings to consumers and the power sector, as well reduced emissions of both CO 2 and precursor pollutants from fossil fuel generation. The avoided needed to build new capacity, however, also has significant labor impacts. A portion of the employme nt that would have been used to build the new capacity in the base case will not occur with the implementatio n 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 la bor 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 imp acts 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 demand-side energy efficiency ac tivities are increases in labor needs, which more than offset the loss of supply-side jobs associated with the d ecreasing demand for electricity arising from the demand-side programs. The IPM labor expenditure projections are distributed across different labor categories (e.g., general construction labor, boilermakers and engineering) using data from***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

6A-23 engineering analyses of labor’s overall share of total expenditures, and apportionment of total labor cost to various labor categories. Hourly labor expenditures (including wages, fringe benefits, and employer-paid costs including taxe s, 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 ( 5 five years), and 2026 through 2030 (5 y ears). 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 buil dt labor analysis uses the net change in cap ital 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 di stributed throughout the interval. Tables 6-4 and 6-5 reflect this assumption by presenting the aver age labor utilization per year during each of the
3 three intervals. The HRI-related labor impact s are estimated based on the assumed capital cost of $100/kw (see Section 3.7.3). Note that all of th e HRI-related labor imp acts occur in the first interval (2017 to 2020), and are assumed to be occur evenly throughout that four year interval.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

6A-24 Therefore, the HRI-related labor estimates in Tables 6-4 and 6-5 are the annual average labor impacts for of the 4 four years. There are no HRI improvements made after 2020. The labor estimates for operating and mainta ining 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-re lated 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 th roughout 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 s -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 additi onal 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.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

6A-25 Table 6-4. Engineering-Based a Changes in Labor Utilizat ion, Regional Compliance Approach - (Number of Job-Years b 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 impact s, occurring during each year of the 2 to 4 year period during which construction and HRI instal lation activities occur. Figures in tabl e 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 s cenarios due to HRI or Demand-side energy efficiency programs. **Recurring Changes are job-years associated with a nnual recurring jobs including operating and maintenance activities and fuel extraction jobs. Newly built generating capacity creates a recurring stre am 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.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

6A-26 Table 6-5. Engineering-Based a Changes in Labor Utilizatio n, 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 impact s, occurring during each year of the 2 to 4 year period during which construction and HRI instal lation activities occur. Figures in tabl e 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 s cenarios due to HRI or Demand-side energy efficiency programs. **Recurring Changes are job-years associated with a nnual recurring jobs including operating and maintenance activities and fuel extraction jobs. Newly built generating capacity creates a recurring stre am 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 mayll 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***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

6A-27 in 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 proj ected impacts on energy efficiency investments analyzed as part of bB uilding bB lock four4 (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,
in the sense that they do not include impacts of any shift in resources from other sectors. Nor does this analysis attempt to quantify employme nt 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 conditi oning 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 Palm er (2009), Gillingham and Palmer (2014)). 161 Two recent articles discuss employment ef fects of demand-side energy efficiency

161 For more information on this efficiency paradox see Chapter 3.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

6A-28 programs. Aldy (2013) describes clean energy investments funded by the American Recovery and Reinvestment Act of 2009, which “included mo re 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 e fficiency 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 thos e 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 polic y tool (renewable energy policies, greenhouse gasGHG 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% mo re clean energy employment within that MSA.
These estimates are not transferable to this rulema king since states are likely to change intensity as well as number of clean energy programs. Outside of the peer-reviewed, published economics literature, analysts have estimated employment effects of existing and proposed stat e and regional demand-side energy efficiency policies, programs and investments. While these studies may use modeling approaches that would not be appropriate for national-level anal yses or may include job impacts not considered in this chapter, they are often used by state government officials in their decision-making process. As such, they can provide useful insight s as to how states analyze demand-side energy efficiency activities. Lacking a peer-reviewed methodology, we pr opose the following approach to illustrate***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

6A-29 po ssibletential 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 th e 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 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)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 re venue 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 e xpenditure by the jobs per additional dollar figure results in projected employment impacts for demand-side energy effi ciency activities of 78,800 in 2020, 112,000 in

162 For 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. Formatted: Font: Italic, Complex Script Font: Italic

Formatted: Font: Italic, Complex Script Font: Italic***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

6A-30 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.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

6A-31 Table 6-6. Estimated Demand-Side Energy Effi ciency 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 expenditu res, 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 met hod, 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 improveme nts, in contrast, encompass a wi de 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 impact s 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 Formatted: Indent: Before: 0.5"***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

6A-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 fractio n 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 overstat e 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 ad dition, even absent technological change, labor intensity of expenditures will likely chan ge 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 assu me 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 over

- estimate employment impacts of the proposed guidelines.

6.7 Conclusion
This chapter presents qualitative and quantita tive discussions of potential employment impacts of the proposed guidelines for electrici ty generation, fuel production, and demand-side energy efficiency sectors. The qualitative disc ussion 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 regulat ed firms can be decomposed into output and substitution effects, both of which ma y be positive or negative. Consequent ly, theory alone cannot predict the direction or magnitude of a regulation’s employment impact. It is possible to combine theory***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

6A-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 litera ture 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 mo re 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 i ndustries) in their production processes. The
available literature illustrates some of the diffic ulties 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 date d to be reliably informative. In addition, the methods do not permit estimati on of net economy-wide effects. Empirical analysis at the industry level requires estimates of product demand elasticity; production factor substitutability; supply elasticity of production fact ors; 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 positiv e or negative, have been small in regulated

sectors. QThe illustrative q uantitative 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 responsi bility and flexibility to implement policies and practices for compliance with Proposed Electric Generating Unit Greenhouse GasEGU 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 demand-side energy efficiency activities. These projections are derived, in part, from a detailed model of the electricity production sector used for this***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

6A-34 regulatory analysis, and U.S

. government data on employment and labor productivity. In the electricity, coal, and natural gas sectors, the EP A 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 im pacts would approximately be 78,800 jobs in 2020 for both Option 1 regional and state comp liance approaches (see Table 6-6). The IPMgenerated 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 no t distinguish between full

- and part - time employment. Finally, note again that this is an illustrative analysis, a nd 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 fl exibilities afforded stat es in complying with the emission guidelines, the impacts reported in this chapter are illustrative of compliance actions states may take.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

6A-35 6.8 References Aldy, J.E. 2013. “A Preliminary Assessment of the American Recovery and Reinvestment Act’s Clean Energy Package.” Review of Environmental Economics and Policy , vol. 7, issue 1, Winter, pp. 136-155. Allcott, H. 2011a. “Social Norms and Energy Conservation.” Journal of Public Economics , Volume 95, Issues 9–10 , October, pgs. 1082–1095.
Allcott, H. 2011b. “ Rethinking Real-Time Electricity Pricing,” Resource and Energy Economics , 33 (2011) 820–842. Anger, N.; and U. Oberndorfer. 2008. “Firm Performance and Employment in the EU Emissions Trading Scheme: An Empirical Assessment for Germany.” Energy Policy , (36):12–22.

Arimura , T. H.; S. Li; R. G. Newell and K. Palmer. 2012 “Cost-Effectiveness of Electricity Energy Efficiency Programs.” Energy Journal , Volume 33, Number 2, pp. 63-99. Arrow, K. J.; M. L. Cropper; G. C. Eads; R. W. Hahn; L. B. Lave; R. G. Noll; Paul R. Portney; M. Russell; R. Schmalensee; V. K. Smith and R. N. Stavins. 1996. “Benefit-Cost Analysis in Environmental, Health, and Safety Regulati on: A Statement of Principles.” American Enterprise Institute, the Annapolis Center, and Resources for the Future; AEI Press. Available
at: < http://www.hks.harvard.edu/fs/rstavins/ Papers/Benefit Cost Analysis in Environmental.AEI.1996.pdf >, accessed Aug. 5, 2013. Barbose, G. L.; I. M. Hoffman and M. Billingsley. 2013. The Future of Utility Customer-Funded Energy Efficiency Programs in the United Stat es: Projected Spending and Savings to 2025, http://emp.lbl.gov/sites/ all/files/lbnl-5803e.pdf . Lawrence Berkeley National Laboratory. Berman, E. and L. T. M. Bui. 2001. “Environmental Regulation and Labor Demand: Evidence from the South Coast Air Basin.” Journal of Public Economics 79 (2): 265-295. Bezdek, R. H.; R. M. Wendling and P. Diperna. 2008. “Environmental Protection, the Economy, and Jobs: National and Regional Analyses.” Journal of Environmental Management 86 (1): 63-79. Brown, R.S. and L. R. Christensen. 1981. “Estim ating the Elasticities of Substitution in a Model of Partial Static Equilibrium: An Application to U.S. Agriculture 1947 to 1974,” in: Berndt, E.R.; B. C. Field. (Eds.), Modeling and Measuring Natural Resource Substitution. Chan, H. S.; S. Li; F. Zhang. (2013) “Firm Competitiveness and the European Union Emissions Trading Scheme.” Energy Policy , (63):1056-1064.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

6A-36 Commins, N.; S. Lyons; M. Schiffbauer; R. S.J. Tol. 2011. “Climate Policy and Corporate Behaviour.” The Energy Journal , (32):51-68. Council of Economic Advisers. 2011. “The Economic Impact of the American Recovery and Reinvestment Act of 2009.” Sixth quarterly report. Downs, A.; A. Chittum; S. Hayes; M. Neubau er; S. Nowak; S. Vaidyanathan, et al. 2013. The 2013 State Energy Efficiency Scorecard. American Council for an Energy Efficient Economy. www.aceee.org/sites/default/files/publ ications/researchreports/e13k.pdf

. Ehrenberg, R. G. and R. S. Smith. 2000. Modern Labor Economics: Theory and Public Policy. Addison Wesley Longman, Inc., Chapter 4. Environmental Entrepreneurs. 2013. Clean Energy Works For US:2013 Third Quarter Clean Energy/Clean Transportation Jobs Report.

< http://www.e2.org/jsp/controller;jsessi onid=8287977D43FA05E237DD5FEF58B00B3A?do cName=cleanjobsarchives >. Environmental Entrepreneurs. 2012. “What Clean Energy Jobs? These Clean Energy Jobs: Third Quarter 2012.” Clean Energy Jobs Roundup

. Executive Order 13563 (January 21, 2011). “Improving Regulation and Regulatory Review. Section 1. General Principles of Regulation.” Federal Register 76 (14): 3821-3823. Gillingham, K.; R. Newell and Karen Palmer. 20 09. “Energy Efficiency Economics and Policy.” Annual Review of Resource Economics, 1 (1): 597-619. Gillingham, K. and K. Palmer. 2014. “Bridging the Energy Efficiency Gap: Policy Insights from Economic Theory and Empirical Evidence.” Review of Environmental Economics and Policy, vol. 8, issue 1, pp. 18-38. Global Insight. 2008. U.S. Metro Economies: Current and Potential Green Jobs in the U.S. Economy. U.S. Conference of Mayors. Available at:

< http://www.usmayors.org/pressreleases/uploads/greenjobsreport.pdf >. Goldman, C.; M. C. Fuller; E. Stuart; J. S. Peters; M. McRae and N. Albers, et al. 2010. Energy Efficiency Services Sector: Workforce Size and Expectat ions for Growth. Lawrence Berkeley National Laboratory. Graff Zivin J. and M. Neidell. 2012. “The Impact of Pollution on Worker Productivity.” American Economic Review , 102(7), pp. 3652-73.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

6A-37 Graff Zivin, J. and M. Neidell. 2013. “Environment, Health, and Human Capital.” Journal of Economic Literature , September 2013, v. 51, iss. 3, pp. 689-730. Greenstone, M. 2002. “The Impacts of Environmental Regulations on Industrial Activity: Evidence from the 1970 and 1977 Clean Air Act Amendments and the Census of Manufactures.” Journal of Political Economy 110(6): 1175-1219. Hamermesh, D. S. 1993. Labor Demand. Princeton, NJ: Princeton University Press. Chapter 2. Kahn, M.E. and E. T. Mansur. 2013.“Do Local Energy Prices and Regulation Affect the Geographic Concentration of Employment?” Journal of Public Economics , 101 (2013) 105– 114. Layard, P.R.G. and A. A. Walters. 1978. Microeconomic Theory . McGraw-Hill, Inc. Chapter 9. List, J. A.; D. L. Millimet; P. G. Fredriksson and W. W. McHone. 2003. “Effects of Environmental Regulations on Manufacturing Pl ant Births: Evidence from a Propensity Score Matching Estimator.” The Review of Economics and Statistics, 85 (4): 944-952. Marshall, A. 1920. Principles of Economics . Library of Economics and Liberty. Available at:

< http://www.econlib.org/lib rary/Marsha ll/marP.html>

. Morgenstern, R. D.; W. A. Pizer and J.S. Sh ih. 2002. “Jobs Versus the Environment: An Industry-Level Perspective.” Journal of Environmental Economics and Management, 43(3): 412-436. Muro, M.; J. Rothwell; D. Saha & Partnership, w. B. 2011. Sizing the Clean Economy: A National and Regional Green Jobs Assessment. Brookings Institution, Metropolitan Policy Program. Available at: http://www.brookings.edu/~/media/series/resources/0713_clean_economy.pdf

. Ostro, B.D. 1987. “Air Pollution and Morbidity Revisited: A Specification Test.” Journal of Environmental Economics Management 14:87-98. Schmalansee, R. and R. Stavins (2011). “A Guide to Economic and Policy Analysis for the Transport Rule.” White Paper. Boston, MA. Exelon Corp. Smith, K. 2012. “Reflections: In Search of Crosswalks between Macroeconomics and Environmental Economics.” Review of Environmental Economics and Policy , 6(2), pp 298- 317. Southwest Energy Efficiency Project. 2002. “The New Mother Lode: The Potential for More Efficient Electricity Use in the Southwest.” Available at: < http://www.swenergy.org/nml >.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

6A-38 The Pew Charitable Trust. 2009. 2009. “The Clean Energy Economy: Repowering Jobs, Businesses and Investments Across America

. ” Available at:

< http://www.pewenvironment.org/uploadedFile s/PEG/Publications/Report/Clean%20Energy %20Economy.pdf >. The Solar Foundation in partnership with BW Research and Cornell University. 2012. “The National Solar Jobs Census.” Available at: < http://thesolarfoundation.org/research/nationalsolar-jobs-census-2012 >. Sommers, D. 2013. “BLS Geen Jobs Overview,” Monthly Labor Review. U.S. Bureau of Labor Statistics, January, p. 3-16. Available at:

< http://www.bls.gov/opub/mlr/2013/01/art1full.pdf>. U.S. Department of Commerce, Economics and Statistics Administration. 2010a. “Measuring the Green Economy.” Available at: < http://www.esa.doc.gov/Reports/measuring-greeneconomy>

. U.S. Department of Commerce, International Trade Administration. 2010b. Environmental Industries Fact Sheet, using 2008 data from Envi ronmental Business Intern ational, Inc., April. Available at:

< http://web.ita.doc.gov/ete/eteinfo.nsf/068f3801d047f26e85256883006ffa54/4878b7e2fc08ac 6d85256883006c452c?OpenDocument>. U.S. Department of Labor, Bureau of Labor Statistics. 2012a. Databases, Tables & Calculators by Subject. Acccessed on April 16, 2012 and available at:

< http://data.bls.gov/timeseries/LNS14000000>. U.S. Department of Labor, Bureau of Labor Statistics (2012b, April 6). “The Employment Situation—March 2012.” News Release. Accessed April 16, 2012 and available at:

< http://www.bls.gov/news.release/pdf/empsit.pdf>

. U.S. Department of Labor, U.S. Bureau of La bor Statistics, (n.d.). “Employment Projections Program.” Retrieved January 14, 2014, and available at:

< http://www.bls.gov/emp/ep_table_102.htm >. U.S. Department of Labor, U.S. Bureau of Labor Statistics. (n.d.). “Green Goods and Services, 2010-2011.” Retrieved January 14, 2014, and available at:

< http://www.bls.gov/ggs/ggsoverview.htm>. U.S. Energy Information Agency. 2013. “Annual Energy Outlook 2014: Early Release.” Available at: http://www.eia.gov/forecast/aeo/er/early_elecgen.cfm

.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

6A-39 U.S. Environmental Protection Agency (EPA). 2 011a. “The Benefits and Costs of the Clean Air Act from 1990 to 2020 Final Report – Rev. A.” Office of Air and Radiation. April.Available
at: < http://www.epa.gov/air/sect81 2/feb11/fullreport_rev_a.pdf>. U.S. EPA. 2011b. “Regulatory Impact Analysis for the Mercury and Air Toxics Standards (MATS),” December. Available at:

< http://www.epa.gov/ttn/ecas/re gdata/RIAs/matsriafinal.pdf>

. U.S. International Trade Commi ssion. “Environmental and Related Services.” Publication No. 4389, Investigation No. 332-533, March, 2013. Available at:

< http://www.usitc.gov/publications/332/pub4389.pdf> Walker, R. 2011.“Environmental Regulation and Labor Reallocation." American Economic Review, Papers and Proceedings, 101(2). Wei, M.; S. Patadia & D. M. Kammen. 2010. “ 2010 Putting Renewables And Energy Efficiency To Work: How Many Jobs Can The Clean Energy Industry Generate In The US?” Energy Policy, 38, 919-931. Available at: https://rael.berkeley.edu/sites/default/file s/WeiPatadiaKammen_CleanEnergyJobs_EPolicy20 10.pdf

. Yi, H. 2013. “Clean Energy Policies and Green Jobs: An Evaluation of Green Jobs in U.S. Metropolitan Areas.” Energy Policy , 56: 644-65.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

6A-1 

### A PPENDIX 

### 6A: E STIMATING 

### S UPPLY 

### -S IDE 

### E MPLOYMENT 

### I MPACTS OF THE


### P ROPOSED 

### EGU GHG E XISTING 

### S OURCE 

### G UIDELINES

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 gene rating 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 gG uidelines 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 ac tivities in the EGU GHG Existing Source gG uidelines required developing additional appropriate anal ytical methods that were not needed for the MATS analysis. The newly developed analytical methods for the construc tion-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.
A 6 A .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 labor categories. The approach involved using power sector projections and various energy***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

6A-2 market implications under the proposed EGU GHG Existing Source gG uidelines 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-years 166 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 (bot h positive and negative) are divided into five categories:

 additional employment to make heat rate improvementsHRI 167 (HTI) 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 vari ous 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.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

6A-3  lost construction-related employment opportuniti es 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 op portunities 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 in stalling 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.
A 6 A .2 Employment Changes due to Heat Rate Improvements The employment changes due to heat rate improvement ( 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 culm inating 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. This cost was then allocated to four categories based on the estimates provided by***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

6A-4 Andover Technology Partners (ATP), which we re adapted from proxy projects involving installation of combustion control retrofits, su ch as those installed under the Best Available Retrofit Technology (BART) submissions from co al-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 th eir 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 la bor – 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 estimated 169 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 Pa rtners, 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 to tal 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 gr owth 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 l ack of reliable data on cons truction sector productivity growth, the output per worker for the construction sector wa s not forecasted to 2020, and the most recent available value from 2007 was used.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

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 sect or, 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.
A 6 A .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 genera tion type into the following categories:

• Combined Cycle,

• Combustion Turbine, and

• Renewables (which includes biomass, geot hermal, 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 annual impacts on construction phase labor. Duration estimates were based on assumptions for

170 Capital charge rates obtained from EPA’s resource, EP A #450R13002: Documentation for EPA Base Case v.5.13

using the Integrated Programming Model (IPM)

.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

6A-6 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 assume d 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 fo r 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:

171 Ibid.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

6A-7 general power plant construction; engineering an d management; material use; equipment use; and plant operators. The resulting values were multiplied by the capital costs to get the job impact.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

6A-8 Table 6A-4. Labor Productivity du e 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 th e general power plant construction phase, was matched with the power industry specific construction sector. Engineering/management was matched to the engi neering 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, 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.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

6A-9 A 6 A .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/ga s 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 unit s 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).
A 6 A .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 this section. First, employment losses due to e ither reductions or shifts in coal demand were estimated using an approach similar to EPA’s co al employment analyses under Title IV of the Clean Air Act Amendments. Using this approach, changes in coal demand (in short tons) for

172 The same specific sources as cited before, however, used workers and total payroll.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

6A-10 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
i nformation 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- 46 . 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 combin ed 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 US Energy Information Administration ( EIA ) Annual Energy Review, Coal Mining Productivity Data.

Used 2008.

174 Unlike the labor productivity estimates for various e quipment resources which were forecasted to 2020 using

BLS average growth rates, this study uses the most recen t 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 s ectors, which would have introduced a high degree of uncertainty in forecasting productivit y growth rates for future years.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

6A-11 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 thos e 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 Respon se to the Proposed Toxics Rule in 2015”. Technical

Support Document, March 2011.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

7 7-1***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

7 7-1 

### CHAPTER 7: S TATUTORY AND EXECUTIVE 

### O RDER 

### A NALYSIS

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 ac tion” 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 ac tion 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 CO 2 emissions from the electric power industry. Actions taken to comply with the proposed guidelines will also re duce the emissions of directly emitted PM 2.5 , sulfur dioxide (SO 2 ) and nitrogen oxides (NO X ). The benefits associated with these PM, SO 2 and NO X 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 CO 2 emissions in a given year), to analyze CO 2 climate impacts of this rulemaking. The four USG SCC estimates are associated with different discount rates (m odel average at 2.5 percent discount rate, 3 percent, and 5 percent; 95 th 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***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

7 7-2 approximately $17 billion with a 3 percent mode l average (2011$). The air pollution health cobenefits in 2020 are estimated to be $16 billion to $37 billion fo r a 3 percent discount rate, and $15 billion to $34 bil lion (2011$) 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 bene fits and costs) in 2020, are estimated to be $28 billion to $49 billio n 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 $18 b illion (2011$). 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, an d $15 billion to $36 billion (2011$ ) for a 7 percent discount rate. The annual illustrative compliance costs estima ted by IPM, inclusive of demand side energy efficiency program and participant costs and MR R 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 approximatel y $30 billion (3 percent, model average, 2011$). The air pollution health co-benefit s in 2030 are estimated to be $2 5 billion to $59 billion for a 3 percent discount rate, and $23 billion to $54 bill ion (2011$) 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 MR R 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 billi on 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 fore going 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.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

7 7-3 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 CO 2 emissions from existing sources in the power sector. These recordkeeping and reporting requirements are specifically authorized by C AA 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 govern ment (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 prov iding information; adjust the existing ways to comply with any previously appli cable 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 collection of information unless it displays a currently valid OMB control number. The OMB***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

7 7-4 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) genera lly 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 signifi cant economic impact on a substantial number of
small entities. Small entities include small businesses, small orga nizations, and small governmental jurisdictions. For purposes of assessing the impacts of this ru le 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 w ith a population of less than 50,000. A small organization that is any not-for-pr ofit enterprise which is independently owned and operated and is not dominant in its field.

Table 7-1. Potentially Regulated Categories and Entities a

Category NAICS Code Examples of Potentially Regulated
Entities***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

7 7-5 Industry 221112 Fossil fuel electric power generating
units.

State/Local Government 221112 b 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 establishment s 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 signifi cant economic impact on a substantial number of
small entities.
The proposed rule will not impose any requi rements 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 imp act upon a substantial number of small entities. After emission guidelines are promulgated, states establish standards on existing sources and it is those state require ments 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 woul d 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 becau se 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***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

7 7-6 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 formulati ng 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 sector in any one year. Specifically, the emissi on 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 EP A 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 performa nce 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***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

7 7-7  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 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 pr oposed 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 complia nce 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 offici als early in the process of developing the***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

7 7-8 proposed action to permit them to have meaningf ul 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 Le gislatures, (3) Council of State Governments, (4) National League of Cities, (5) U.S. Conference of Mayors, (6) National Association of Counties, (7) International City/County Manageme nt 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 redu cing carbon pollution from power plants. A detailed Federalism Summary Impact Statement (FSIS) describing the most pressing issues raised in pre-proposal and post-proposal co mments 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 co mmunications 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 potentia l to influence the regulation. This action is not subject***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

7 7-9 to EO 13045 because it does not involve decisions on environmental health or safety risks that may disproportionately affect chil dren. The EPA believes that the CO 2 emission reductions resulting from implementation of the proposed guidelines, as well as substantial ozone and PM 2.5 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 Ad ministrator of the Office of Information and Regulatory Affairs, OMB, for ac tions 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 ha ve 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 ru le. The EPA projects that electric power sector delivered natural gas prices will increase by abou t 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.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

7 7-10 7.8 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 en vironmental justice part of their mission by identifying and addressing, as appropriate, disproportionately high and adverse human health orenvironmental effects of their programs, policies and activities on minority populations and lowincome populations in the U.S. 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, th e Administrator considered climate change risks to minority or low-income populations, find ing 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 disa bled, 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 elderl y, 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 incl ude that poor communities can be especially vulnerable to climate change imp acts 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 trib al communities possess unique vulnerabilities to climate change, particularly those on established reservations that are restricted to reservation boundaries and therefore have limited relocation options. Tr ibal communities whose health, economic well-

176 “Endangerment and Cause or Contribute Findings for Gr eenhouse Gases Under Section 202(a) of the Clean Air

Act,” 74 Fed. Reg. 66,496 (Dec. 15, 2009) (“Endangerment Finding”).***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

7 7-11 being, and cultural traditions depend upon the na tural environment will likely be affected by the degradation of ecosystem goods and services a ssociated 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 CO 2 emission guidelines for existing fossil fuel-fired EGUs . In addition to reducing CO 2 emissions, implementing the proposed rule would reduce other emissions from EG Us that become dispatched less frequently due to their relatively low energy efficiency. These emission reductions will include SO 2 and NOx, which form ambient PM 2.5 and ozone in the atmosphere, and hazardous air pollutants (HAP), such as mercury and hydrochloric acid. In the final rule revising the annual PM 2.5 NAAQS 177 , 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 PM 2.5 , 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 PM 2.5 , 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 guide lines for existing fossil fuel-fired EGUs. This

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 Enviro nmental Assessment – RTP Division. December. Available on the Internet at <http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=216546>.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

7 7-12 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 pa rticular EGUs and thus to avoid any significant increases in emissions including emissions of other regulated pollutant s 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 th ere 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 EP A will consider environmental justice issues as required by Executive Order 12898. In addition to some EGUs possibly being requ ired by a state to make modifications for increased energy efficiency, another effect of the proposed CO 2 emission guidelines for existing
fossil fuel-fired EGUs would be increased utiliza tion 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 al ready occur now as demands for and sources of electrical energy evolve, but the proposed CO 2 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 woul d not cause higher peak concentrations of PM 2.5, NOx, or ozone around such EGUs than is already occurring because peak hourly or daily***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

7 7-13 emissions generally would not change, but increas ed utilization may make periods of relatively high concentrations more frequent. It should be no ted that the gas-fired sources that are likely to become dispatched more frequently than at present have very low emissions of primary particulate matter, SO 2 and HAP per unit of electrical output, such that local (or regional) air quality for these pollutants is likely to be af fected 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 and performance of coal- and NG-fired generation, they assumed SO 2 , PM (and Hg) emissions to be “negligible.” Their studies predict NOx emis sions 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 appli cation 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
NO 2 concentrations in the design of the CAA section 111(d) program, or separately from the CAA section 111(d) program but before its im plementation. 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 priori tize 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 dur ing the year of relatively higher concentrations of pollutants with potential for effects on locali zed 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 propos ed 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***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

7 7-14 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 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-fire d EGU, but provides guidance to any tribe with delegated authority to address CO 2 emissions from EGU sources foun d 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 inpu t 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***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

7 7-15 Country 179 would have the opportunity, but not the obligation, to establish a CO 2 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 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 tr ibal 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 wate r to the tribes. Other tribes raised concerns about the impacts of climate change on their co mmunities, resources, life ways and hunting and treaty rights. The tribes were al so 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 pr oposal, the EPA will hold meetings with
tribal environmental staff to info rm 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.

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.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

7 7-16***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

778 -1 

### CHAPTER 8: COMPARISON OF BENEFITS AND COSTS

8.1 Comparison of Benefits and Costs

The illustrative benefits, costs, and net benefits of the compliance with Option s 1 and Option 2 illustrative compliance scenarios are presented in this chapter of the Regulatory Impact Analysis (RIA). As discussed in Chapter 3 1, the EPA is proposing two options for state-specific
rate-based CO 2 goals that reflect application of cost-effective measures from all four building
blocks discussed . For both Options 1 and 2, presented in this RIA, two illustrative compliance
scenarios , reflecting possible compliance approaches with state-specific CO 2 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 CO 2 goals on an individual state basis (referred to as state approach) or a number of states comply co llectively 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 CO 2 climate impacts of this rulemaking. 180 We refer to these estimates, which we re 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 CO 2 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; 95 th percentile at 3 percent), and each increases over time. In this comparison of benefits and costs,

180 Docket ID EPA-HQ-OAR-2013-0495, Technical Support Do cument: 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, De partment of Energy, Department of Transportation, Environmental Protection Agency, National Economic Counc il, Office of Energy and Climate Change, Office of Management and Budget, Office of Science and Technol ogy 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.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

778 -2 the EPA provides the estimate of climate benefits associated with the SCC value deemed to be 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 $34 billion (201 1$) 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 M monitoring, R reporting, and R 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 a nd $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 di scount rate (model average) as shown on Table
8-3.
In addition to CO 2 , implementing these proposed guidelines is expected to reduce
emissions of SO 2 and NO X , which are precursors to formation of ambient PM 2.5 , as well as directly emitted fine particles. Therefore, reducing these em issions would also reduce human exposure to ambient PM 2.5 and ozone precursors, thus the incidence of PM 2.5 - and ozone related
health effects. The se 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 participan t costs and MRR costs, are approximately $7.3 billion (2011$) in 2030. The quantified net benef its for the Option 1 regional in 2030 are $48 billion to $82 billion, assuming a 3 percent di scount rate, and $46 to $77 billion assuming a 7 percent discount rate (2011$). In 2030, quantifie d net benefits are $49 billion to $84 billion, Formatted: Not Highlight***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

778 -3 assuming a 3 percent discount rate, and $46 to $79 billion assuming a 7 percent discount rate (2011$) for the Option 1 state compliance approach. This table also reflects benefit, cost, and net benefit estimates in 2030 for Option 2 regional and state compliance approaches

. Based upon the foregoing discussion, it remains clear that the this proposal’s combined
climate benefits and human health co-benefits associated with the reduction in other air pollutants of this proposal are substantial and far outweigh the compliance costs for the all of the regulatory options and compliance approaches. The EPA could not monetize important categories of impacts. Unquantified impacts includ e 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 SO 2 , NOx, and hazardous air pollutants (e.g., mercury and hydrogen chloride), as well as ecosystem effects and visibility impairment.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

778 -4 Table 8-1. Summary of Estimated Monetized Benefits, Complian ce 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 Eeffects Visibility impairment Direct exposure to SO2 and NO2 1.2 tons of Hg Ecosystem Eeffects 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 CO 2 emission changes and do not account for changes in non-CO 2 GHG emissions. Different discount rates are applied to SCC than to the other estimates because CO 2 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 PM 2.5 and ozone associated with emission reductions of directly emitted PM 2.5 , SO 2 and NO X . The range reflects the use of concentration-response functions from different epidemiology studies. The re duction in premature fatalities each year accounts for over 90 percent of total monetized co-benefits from PM 2.5 and ozone. These models assume that all fine particles, regardless of their chemical composition, are equally pot ent 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 illu strative 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.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

778 -5 Table 8-2. Summary of Estimated Monetized Benefits, Complian ce 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 CO 2 emission changes and do not account for changes in non-CO 2 GHG emissions. Different discount rates are applied to SCC than to the other estimates because CO 2 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 PM 2.5 and ozone associated with emission reductions of directly emitted PM 2.5 , SO 2 and NO X . The range reflects the use of concentration-response functions from different epidemiology studies. The reduc tion in premature fatalities each year accounts for over 90 percent of total monetized co-benefits from PM 2.5 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 complian ce 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 si de 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.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

778 -6 Table 8-3. Summary of Estimated Monetized Benefits, Complian ce Costs, and Net Benefits for the Proposed Guidelin es –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 CO 2 emission changes and do not account for changes in non-CO 2 GHG emissions. Also, different discount rates are applied to SCC than to the other estimates because CO 2 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 PM 2.5 and ozone associated with emission reductions of directly emitted PM 2.5 , SO 2 and NO X . The range reflects the use of concentration-response functions from different epidemiology studies. The reduc tion in premature fatalities each year accounts for over 90 percent of total monetized co-benefits from PM 2.5 and ozone. These models assume that all fine particles, regardless of their chemical composition, are equally pot ent 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 ar e 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.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

778 -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 so urces of uncertainty, both qu antitatively and qualitatively, on
benefits and costs . TheWe summarize three key elements of our notable source of analysis of uncertainty are called out here:

 Evaluating uncertainty in the compliance approaches There is a significant amount of uncertainty associated with the simulation of the choices that states will implement make regarding how they will reduce CO2 (i.e., as illu strated under Options 1 and or Option 2, below). These simulations , which influences both the costs and the benefits (see below).

 The uncertainty associated with these illust rative scenarios is further compounded by Assess uncertainty int the methods approach used to calculate the health co-benefits
associated with the reduction in PM 2.5 and ozone and the use of a benefits-per-ton approach in estimating these co-benefits. that would happen when CO2-reduction technologies are implemented.
The usual uncertainties associated with the PM2.5 and ozone related health benefits
estimates hold here as well

 The Characterizing uncertainty in the mo netization of climate related benefits, particularly those associated with the so cial cost of carbon introduce separate
uncertainties.

. We summarize key elements of our uncertainty analysis here. Some of these elements are evaluated using probabilistic techniques, whereas for others the underlying likelihoods of certain

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 Formatted: Indent: First line: 0", Space Before: 0 pt, After: 0 pt

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Comment [A1]: We appreciate that commenters see the value in this section, but have revised the summary text for this section to make it more parsimonious.
Furthermore, we were concerned that the tone of the proposed edits suggested that we did a more formal joint uncertainty analysis across components of uncertainty than we intended to convey. Stated conclusions to that effect may be unwarranted at this time as they would require further evaluation and explanation that unfortunately time does not allow. That said, the uncertainty analyses are sophisticated and well documented, but generally stand alone, although the benefits are clearly modeled based on the emission forecasts from the different illustrative scenarios, which should be clear from the body of the RIA and the previous section.
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Formatted: Indent: First line: 0"***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

778 -8 outcomes are unknown and so we use scenario analysis to evaluate their potential effect on the benefits and costs of this rulemaking.
Uncertainty in Abatement Co sts 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 costanalysis of this rule and are included in the cost modeling in the RIA (e.g., the cost of demandside energy efficiency programs and non-emi tting 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 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 technol ogies 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 anal ysis 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 compli ance 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 CO 2 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.
Uncertainty Associated with PM2.5 an d Ozone Health Co-Benefits Assessment
Uncertainty associated with Benefits-Ton Approach
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 Formatted: Font: Italic
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Formatted: Font: Bold***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

778 -9 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 esti mates reflect the regional average benefit-per-ton
for each ambient PM 2.5 precursor emitted from EGUs, which assumes a linear atmospheric response to emission reductions. The regional be nefit-per-ton estimates, although less subject to these types of uncertainties than national estimate s, 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
PM 2.5 levels, which drive population exposure.

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
PM 2.5 -related premature mortality, which accounts for 98 percent of the monetized PM 2.5 health
co-benefits.

4. We assume that all fine particles, rega rdless of their chemical composition, are equally potent in causing premature mortality. This is an important assumption, because PM 2.5 varies considerably in composition across sources, but the scientific evidence is not yet sufficient to allow differ entiation of effect es timates by particle type. The PM ISA concluded that “many constituents of PM 2.5 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).

5. 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 PM 2.5 , including both areas that do not meet the fine particle stan dard and those areas that are in attainment, down to the lowest modeled concentrations.

4.6. 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 PM 2.5 exposures occur in a distributed fashion over the 20 years following exposure based on the advice of theSAB-HES (U.S. EPA-SAB, 2004c), which affects the valuation of mortality cobenefits at different discount rates. Comment [A2]: Moved down.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

778 -10 EPA also evaluated significant sources of uncertainty in the estimate of climate benefits of the emission reductions forecast by the compliance cost modeling. 182 The modeling underlying 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 differen t 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 resu lts were depicted by four point estimates. The use of this range of point estimates in this rule making helps to reflect the uncertainty in the SCC estimation exercise. See both the 2010 SCC TSD and 2013 SCC TSD for a full description.
In addition, EPA quantitatively assessed uncertain ty in the air pollution health cobenefits, including probabilistic approaches. For example, EPA provides the 95 th percentile confidence interval for avoided PM-related pr emature 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 analys es include sensitivity analyses for alternate income growth adjustments and cessation lags, assessments of the distribution of population exposure in the modeling underly ing 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 bene fit 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

182 As discussed in Chapter 4, EPA estimated the global social benefits of CO 2 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 Regul atory 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 Im pact Analysis Under Executive Order 12866, Interagency 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.”***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

778 -11 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 esti mates reflect the regional average benefit-per-ton
for each ambient PM 2.5 precursor emitted from EGUs, which assumes a linear atmospheric response to emission reductions. The regional be nefit-per-ton estimates, although less subject to these types of uncertainties than national estimate s, 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
PM 2.5 levels, which drive population exposure.
Uncertainty Associated with Estimat ing the Social Cost of Carbon
The 2010 SCC TSD noted a number of limitations to the SCC analysis, including theincomplete way in which the integrated assessment models capture catastrophic and noncatastrophic impacts, their incomplete treatme nt 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 clim ate 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.”***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

778 -12 EPA characterized also evaluated significant sources of uncertainty in the estimate of climate benefits of the emission reductions forecast by the compliance cost modeling. 183 The modeling underlying the development of the S CC 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 sp ecifying different possible outcomes for climate sensitivity (represented by a Roe and Baker Distri bution), used five different emissions growth
scenarios and three discount rates. The distri bution of results were depicted by four point estimates. The use of this range of point esti mates 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.

183 As discussed in Chapter 4, EPA estimated the global social benefits of CO 2 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 Im pact Analysis Under Executive Order 12866, Interagency 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.”***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

7 9-1 

### CHAPTER 9: B ENEFITS 

### , C OSTS 

### , AND 

### E CONOMIC 

### I MPACTS OF 

### S TANDARDS OF


### P ERFORMANCE FOR 

### R ECONSTRUCTED AND 

### M ODIFIED 

### E LECTRIC 

### U TILITY 

### G ENERATING 

### U NITS

9.1 Introduction The U.S. Environmental Protection Agency (EPA) is proposing emission limits for carbon dioxide (CO 2 ) 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. Base d on historical information that has been reported to the EPA, the EPA anticipates few c overed 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 associ ated 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 benefi ts 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).***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

7 9-2 On September 20, 2013, the EPA announced its first steps under President Obama’s
Climate Action Plan to reduce CO 2 emissions from power plants by proposing standards for newly constructed power plants built in the futu re (79 FR 1430, January 8, 2014). Specifically, under the authority of CAA section 111(b), the EPA proposed standards for emissions of CO 2 from newly constructed utility boi lers and integrated gasification combined cycle (IGCC) units, and for natural gas-fired stationary combustion turbines. This action proposes standards to
address CO 2 emissions from reconstructed and modifi ed 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 fo r EGUs that undergo reconstruction or modification. This rulemaking does not addres s GHG emissions from newly constructed sources or existing sources that do not undertake recons truction 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 subj ect 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 prev iously 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 co mponents 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 econom ically 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.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

7 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 performan ce 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 emissi on limits are calculated on a 12-month rolling average basis. The proposed emission limits for th ese 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 su bject 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 CO 2 /MWhnet

Reconstructed Utility Boilers and IGCC Units (heat input rating <= 2,000 MMBtu/h) 2,100 lb CO 2 /MWhnet
Reconstructed Natural Gas-Fired Stationary Combustion Turbines (heat input rating > 850 MMBtu/h) 1,000 lb CO 2 /MWhgross Reconstructed Natural Gas-Fired Stationary Combustion Turbines (heat input rating <= 850 MMBtu/h) 1,100 lb CO 2 /MWhgross

This action proposes that modified utility boilers and IGCC units must meet one of two***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

7 9-4 alternative requirements, depending on the timing of the modification. 184 Sources that modify prior to becoming subject to an approved sectio n 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 u tility 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 emissi on limitation that is determined from a third party assessment to identify energy efficiency improvement opportunities for the affected

source. 185 All existing sources that are subject to requirements under an approved section 111(d) plan would remain subject to thos e requirements after modifying. For affected modified natural gas-fired stat ionary 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 re main in the plan. The proposed emission limits for modified sources (for co-proposed alternative #2) are shown in Table 9-2.

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

184 Units triggering both the modification and reconstruction provisions would be subject to the reconstructed source

standards.

185 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.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

7 9-5 1,900 lb CO 2 /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 CO 2 /MWh-net
Modified Natural Gas-Fired Stationary Combustion Turbines (heat input rating > 850 MMBtu/h) 1,000 lb CO 2 /MWh-gross

Modified Natural Gas-Fired Stationary Combustion Turbines (heat input rating <= 850 MMBtu/h ) 1,100 lb CO 2 /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 CO 2 /MWh-gross

AND
111(d) requirements

Modified Natural Gas-Fired Stationary Combustion Turbines (heat input rating <= 850 MMBtu/h ) 1,100 lb CO 2 /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***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

7 9-6 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 anticipa te 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 reconstruc tion provision. There are no macroeconomic or employment impacts expected as a result of these proposed standards. 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 specifi cally exempt from that definition projects that entail the installation of pollution control equipm ent 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 CO 2 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 futu re CAA section 111(d) rulemaking for existing
sources and would have the effect of increas ing a source’s maximum achievable hourly emission rate (lb CO 2 /hr), even while decreasing its actual output based emission rate (lb CO 2 /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***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

7 9-7 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 reconstructi on provision. There are no macroeconomic or employment impacts expected as a result of these proposed standards. 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 th e potential costs and benefits of the proposed standards. The hypothetical plant presented in this section is a coal-fired boiler 186 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) SO 2 = 0.30 lb/MMBtu (3.3% weight Illinois bituminous coal with 95% efficient scrubber), (2) NO X = 0.30 lb/MMBtu (low NO X burners plus overfire air and SNCR), and (3)

186 IGCC and NGCC units are significantly less likely to tr igger the modification provision due to the way in which

these units are constructed. Simple-cycle combustion tu rbines (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 fo r 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 so me instances are able to generate additional revenues by selling capacity into power markets. Thus, in practice, EPA expects that poten tial 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) st andards 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.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

7 9-8 PM = 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.
Costs. We assume the hypothetical unit will implement a range of energy efficiency improvements at an annual cost of $8.3 million. 187 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 cove red 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 costs to the unit as a result of this proposal.

187 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 Docu ment – GHG Abatement Measures” for information about the cost of efficiency improvements.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

7 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 it s operations, the hypothetical unit
would reduce emissions of CO 2 and may reduce emissions of (SO 2 ), nitrogen dioxide (NO x ), and fine particulate matter (PM 2.5 ), which would lead to lower ambient concentrations of PM 2.5 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 CO 2 emission reductions could be offset by increases elsewhere in the system depending on the structure of that plan. Ancilla ry 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 SO 2 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, offsettin g 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.

Table 9-4. Emission Reductions from Efficiency Improvements (tons/year) 4% Efficiency Improvement 6% Efficiency Improvement 8% Efficiency Improvement
CO 2 132,790 199,184 265,579
SO 2 214 321 428***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

7 9-10 NO x (Total) 214 321 428
NO x (Ozone Season) 89 134 178
PM 2.5 17 26 34
Notes: SO 2 , NO X , and PM 2.5 in short tons, CO 2 in metric tonnes.

Benefits. Reducing CO 2 and criteria pollutant emissions w ill 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 CO 2 emissions in a given year. The EPA has applied the USG SCC estimates to the CO 2 reductions described above for the hypothetical unit. The four USG SCC estimates are as follows: $15, $50, $74, and $153 per metric ton of CO 2 emissions in the year 2025 (2011$). To estimate human health co-benefits for this hypothetical scenario, the EPA used benefit-per-ton estimates for PM 2.5 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 SO 2 , directly emitted PM 2.5 , and NO X emissions in 2025.

Table 9-5. Summary of National Bene fit-per-ton Estimates for EGUs in 2025
Discount Rate
Pollutant 3% 7%
SO 2 $41,000 to $93,000 $37,000 to $84,000 Directly emitted PM 2.5 (EC+OC) $150,000 to $350,000 $140,000 to $310,000 Directly emitted PM 2.5 (crustal) $17,000 to $39,000 $15,000 to $35,000
NO X (as PM 2.5 ) $6,000 to $14,000 $5,400 to $12,000
NO X (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 CO 2 emissions by the estimates of the SCC, multiply the reduction in SO 2 , NO X , and PM 2.5 emissions by the PM 2.5 -related BPT estimates, multiply the reductions in ozone season NO X emissions by***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

7 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 occu r. These benefits are subject to the same
uncertainties discussed for emission re ductions in the previous section.

Table 9-6. Estimated Global Climate Benefits of Illustrative CO 2 Reductions in 2025 (millions of 2011$)*

Discount Rate and Statistic 4% Efficiency Improvement 6% Efficiency Improvement 8% Efficiency Improvement

Metric tonnes of CO 2 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% (95 th percentile) $20 $30 $41
* The SCC values are dollar-year and emissions-year specific. SCC values represent only a partial accounting of

climate impacts.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

7 9-12 Table 9-7. Summary of Estimated Monetized He alth Co-benefits of Illustrative Efficiency Improvements in 2025 (thousands of 2011$)* Pollutant 3% Discount Rate 7% Discount Rate
4% Efficiency Improvement

SO 2 $8,900 to $20,000 $8,000 to $18,000

Directly emitted PM 2.5 (EC+OC) $260 to $590 $240 to $530

Directly emitted PM 2.5 (crustal) $230 to $510 $200 to $460

NOx (as PM 2.5 ) $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

SO 2 $13,000 to $30,000 $12,000 to $27,000

Directly emitted PM 2.5 (EC+OC) $400 to $900 $360 to $810

Directly emitted PM 2.5 (crustal) $350 to $780 $310 to $710

NOx (as PM 2.5 ) $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

SO 2 $18,000 to $40,000 $16,000 to $36,000

Directly emitted PM 2.5 (EC+OC) $520 to $1,200 $470 to $1,100

Directly emitted PM 2.5 (crustal) $450 to $1,000 $410 to $930

NOx (as PM 2.5 ) $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 visi bility impairment. All fine particles are assumed to have equivalent health effects. The monetized co-benefits incorporate the conversion from precursor emissions to***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

7 9-13 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 heal th co-benefits reflect the sum of the PM2.5 and ozone cobenefits and reflect the range based on ad ult mortality functions (e.g., from Krew ski 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 dir ectly emitted particles are disaggregated into EC+OC or crustal components using the method discussed in Appendix 4A of this RIA. Table 9-8. Combined Estimated Climate Benefi ts 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 CO 2

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% (95 th percentile) $20 $31 to $46 $30 to $44
6% Efficiency Improvement 199,184 metric tonnes CO 2

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% (95 th percentile) $30 $47 to $69 $45 to $66
8% Efficiency Improvement 265,579 metric tonnes CO 2

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% (95 th percentile) $41 $63 to $92 $61 to $88

*All estimates are rounded to two significant figures. C limate 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***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

7 9-14 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 heal th co-benefits reflect the sum of the PM2.5 and ozone cobenefits and reflect the range based on ad ult mortality functions (e.g., from Krew ski 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 dir ectly 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 an d 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-CO 2 pollutants.
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 “rai ses novel legal or policy issues arising out of legal mandates.” Accordingly, th e 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 a ddition, 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***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

7 9-15 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 ex pects 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 modifi cations or reconstructions as defined under the EPA’s NSPS regulations. Accordingly, this prop osed 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 EG U if it was modified or reconstructed. The information collection requirements in this propos ed 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. 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. 188 (See 79 FR 1,498 and 1,499.) Although not anticipated, if an EGU were to modify or reconstruct, this proposed action w ould impose minimal information collection burden on affected sources, beyond what those sources woul d 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

188 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.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

7 9-16 in this action and certain reporting costs, which ar e 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 expect s 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
CO 2 emission limits. Specifically, the EPA estimat es 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 additi on, after modifications ar e made that enable a facility to measure net energy output, each E GU’s Data Acquisition System (DAS) would need 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 effec tive 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 hasestablished 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***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

7 9-17 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 17 th Street, NW, Washington, DC 20503, Attenti on: 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 Re gister. 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) genera lly 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 signifi cant economic impact on a substantial number of
small entities. Small entities include small businesses, small orga nizations, and small governmental jurisdictions. For purposes of assessing the impacts of this ru le 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 dist rict with a population of less than 50,000; and 3. A small organization that is any not-for-p rofit 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 eco nomic impact on a substa ntial number of small
entities. The EPA expects few modified utility boilers, IGCC units, or stationary combustion turbines in the period of analysis. An NSPS modi fication 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.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

7 9-18 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 CO 2 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 so urces, including any that are owned by small entities.

In addition, the EPA expects few The EPA expects few reconstructed fossil fuel-fired electric utility steam generating units (utility boi lers 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, 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 availabl e generating technology, we would expect this proposal to result in no significant CO 2 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 participat ed in many meetings with individual utilities as well as meetings with electric utility associ ations. Specifically, the EPA Administrator, Gina McCarthy, participated in sepa rate meetings with both the Na tional Rural Electric Cooperative Association (NRECA) and the American Public Power Association (APPA). The meetings brought together leaders of the rural cooperativ es 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 AP PA member utilities. A detailed discussion of the stakeholder outreach is included in the preamble to the emission guidelines for existing***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

7 9-19 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 and1500, January 8, 2014), the EPA conducted outreach to representatives of small entities while formulating the provisions of the proposed standards. Although on ly 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 sector in any one year. As previously stated, the EPA expects few modified or reconstructed
fossil fuel-fired electric utilit y steam generating units (utilit y 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 EP A 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***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

7 9-20  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 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 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 relationshi p 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 woul d 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 el ected 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***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

7 9-21 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 form ulating 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. 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 awar e 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 modifi ed 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 c onsultation 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 standard s 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 ha ve 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 Coordinati on 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.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

7 9-22 During development of this proposed regulation, consultation letters were sent to 584 tribal leaders. The letters provided informatio n 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. Th e EPA also met with tribal environmental staff with the National Tribal Air Association, by teleconference, on July 25, 2013, and December 19, 2013. In those teleconferences, the EPA provide d 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 ut ility generating units in Chapter 7 of this RIA. The EPA also held a series of listening sessio ns 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 potentia l 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***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

7 9-23 modified EGUs in the period of analysis and impacts on emissions, costs or energy supply
decisions for the affected electric utilit y 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
activities unless to do so would be inconsistent with applicable law or otherwise impractical. VCS are technical standards (e.g., materials spec ifications, 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 Specificati on 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 prop osed 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***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

7 9-24 policy on environmental justice. Its main provision directs federal agencies, to the greatest extent practicable and permitted by law, to make en vironmental justice part of their mission by identifying and addressing, as appropriate, disproportionately high and adverse human health orenvironmental 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 fuel-fired electric utility steam generating units (utility boilers and IGCC units) and stationary combustion turbines by establishi ng national emission standards for CO 2 . 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 theenvironment. As previously stated, the EPA expects few modified or reconstructed fossil fuelfired electric utility steam generating units (u tility boilers and IGCC units) or stationary combustion turbines in the period of analysis.

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 Ha rvard 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. Formatted: Font: Not Italic

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i 

# Carbon Pollution Guidelines for Existing
Power Plants:
Emission Guidelines for Greenhouse Gas Emissions from Existing Stationary Sources: Electric Utility Generating Units

June 2014

## ***E.O. 12866 Review – Draft – Do Not C ite, Quote or Release During Review***

ii CONTACT INFORMATION

This document has been prepared by staff from the Office of Air Quality Planning and Standards, the Office of Atmospheric Program s, and the Office of Policy of the U.S. Environmental Protection Agency. Questions related to this docu ment should be addressed to Linda M. Chappell, U.S. Environmental Protec tion Agency, Office of Air Quality Planning and Standards, Research Triangle Park, North Ca rolina 27711 (email: chappe ll.linda@epa.gov).

ACKNOWLEDGEMENTS
Thank you to the many staff who worked on this docum ent from EPA Offices including the Office of Air Quality Planni ng and Standards, the Office of Atmospheric Programs, and the Office of Policy. Contributions to this report were also ma de by RTI International and ICF
International.

DEDICATION
This Regulatory Impact Analysis is dedica ted to the memory of Lillian Grace Bradley
who made substantial contributions to this analysis and report . Lillian freely expressed her fundamental belief that everyone ha s an inherent right to be tr eated with dignity and respect, regardless of their position or background, and sh e was a recognized leader in Environmental
Justice and Tribal issues at EP A. She expressed her deep regard for others through individual
acts of kindness as well as her dedication to increasing so cial awareness and emotional intelligence in the workplace. Her caring, integrity, and outsp okenness were a gift to us all. Lillian challenged us to lead our ve ry best life. She will be greatly missed.

## ***E.O. 12866 Review – Draft – Do Not C ite, Quote or Release During Review***

iii EPA -542/R-14-002

June 2014



# Carbon Pollution Guidelines for Existing Power Plants:

Emission Guidelines for Greenhous e Gas Emissions from Existing Stationary Sources: Electr ic Utility Generating Units

U.S. Environmental Protection Agency Office of Air Quality Planning and Standards

Health & Environmenta l Impacts Division Air Economics Group Research Triangle Park, North Carolina 27711

## ***E.O. 12866 Review – Draft – Do Not C ite, Quote or Release During Review***

iv 

## T ABLE OF 

## C ONTENTS

Table of Contents.............................................................................................................. ............................................iv
List of Tables ................................................................................................................ ................................................xi List of Figures ............................................................................................................... .............................................. xvi
ACRONYMS

.............................................................................................................................. ............................ xviii
EXECUTIVE SUMMARY ............................................................................................................. .............................. 1


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

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

............................................................... 1
ES.3 Control Strategies for Existing EGUs

..................................................................................................... 3
ES.4 Costs of Existing EGU Guidelines

........................................................................................................... 7
ES.5 Monetized Climate Benefits and Health Co

‐ benefits for Existing EGUs

............................................... 9
ES.5.1 Estimating Global Climate Benefits

......................................................................................... 13
ES 5.2 Estimating Air Pollution Health Co

‐ Benefits

........................................................................... 15
ES 5.3 Combined Benefits Estimates

................................................................................................. 17
ES.6 Monetized Benefits, Compliance Costs and Net Benefits of the Proposed Guidelines for Existing Sources

............................................................................................................................... ......................... 20
ES.7 Economic Impacts of the Proposed Emission Guidelines for Existing EGUs

....................................... 24
ES.8 Economic Impacts of the Proposed Guidelines for Existing EGUs for Sectors Other Than the EGU Sector and for Employment

....................................................................................................................... 24
ES.9 Modified and Reconstructed Sources

................................................................................................. 26
ES.10 References

............................................................................................................................... .......... 26
CHAPTER 1: INTRODUC TION AND BA CKGROUND ....................................................................................... ... 1


### 1.1 Introduction

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

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

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

......................................................................... 2
1.2.3 Market Failure

............................................................................................................................... ... 3
1.3 Summary of Regulatory Analysis

............................................................................................................ 3
1.4 Background for the Proposed EGU Existing Source GHG Guidelines

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

....................................................................................................... 4

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v 1.4.2 Definition of Affected Sources

......................................................................................................... 5
1.4.3 Regulated Pollutant

.......................................................................................................................... 5
1.4.4 Emission Guidelines

......................................................................................................................... 6
1.5 Organization of the Regulatory Impact Analysis

..................................................................................... 6
1.6 References

............................................................................................................................... ............... 8
CHAPTER 2: ELECTRIC PO WER SECTOR PROFILE ..................................................................................... ....... 1


### 2.1 Introduction

............................................................................................................................... ............. 1
2.2 Power Sector Overview

........................................................................................................................... 1
2.2.1 Generation

............................................................................................................................... ........ 1
2.2.2 Transmission

............................................................................................................................... ..... 5
2.2.3 Distribution

............................................................................................................................... ....... 6
2.3 Deregulation and Restructuring

.............................................................................................................. 6
2.4 Emissions of Greenhouse Gases from Electric Utilities

.......................................................................... 8
2.5 Improving GHG Performance at Existing EGUs

..................................................................................... 12
2.6 GHG and Clean Energy Regulation in the Power Sector

....................................................................... 14
2.6.1 State Policies

............................................................................................................................... ... 14
2.6.2 Federal Policies

.............................................................................................................................. 16
2.7 Revenues, Expenses, and Prices

........................................................................................................ 19
2.8 Natural Gas Market

............................................................................................................................... 22
2.9 Electricity Demand and Demand Response

.......................................................................................... 25
2.10 References

............................................................................................................................... ........... 28 CHAPTER 3: COST, ECONOMI C, AND ENERGY IMPACTS ................................................................................ 1


### 3.1 Introduction

............................................................................................................................... ...... 1
3.2 Overview

............................................................................................................................... ........... 1
3.3 Power Sector Modelling Framework

............................................................................................... 2
3.3.1 Recent Updates to EPA’s Base Case using IPM (v.5.13)

............................................................... 4
3.4 State Goals in this Proposal

............................................................................................................ 6
3.5 Compliance Scenarios Analyzed

.................................................................................................... 10
3.6 Demand Side Energy Efficiency

...................................................................................................... 15
3.6.1 Projected Demand

‐ Side Energy Savings

..................................................................................... 15
3.6.2 Demand

‐ Side Energy Efficiency Total Costs

................................................................................... 18
3.7 Projected Power Sector Impacts

.................................................................................................... 20

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vi 3.7.1 Projected Emissions

.................................................................................................................... 20
3.7.2 Projected Compliance Costs

....................................................................................................... 23
3.7.3 Projected Compliance Actions for Emissions Reductions

.......................................................... 26
3.7.4 Projected Generation Mix

.......................................................................................................... 27
3.7.5 Projected Incremental Retirements

........................................................................................... 32
3.7.6 Projected Capacity Additions

...................................................................................................... 35
3.7.7 Projected Coal Productions for the Electric Power Sector

......................................................... 36
3.7.8 Projected Fuel Price Impacts

...................................................................................................... 37
3.7.9 Projected Retail Electricity Prices

............................................................................................... 39
3.7.10 Projected Electricity Bill Impacts

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

.................................................................... 45
3.9 Limitations of Analysis

................................................................................................................... 46
3.10 Significant Energy Impacts

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

......................................................................... 48
3.12 Social Costs

............................................................................................................................... ..... 49
3.13 References

............................................................................................................................... ...... 54

............................................................................................................................. ......................................................... 1 Appendix 3A: Analysis of Potential Upstream Meth ane Emissions Changes in Natural Gas Systems and Coal Mining ........................................................................................................................ ................................................... 1


### 3A.1 General Approach

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

............................................................................................................................. 1
3A.1.2 Coal Mining Source Description

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

....................................................................................... 4
3A.1.4 Compliance Approaches Examined

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

............................................................................................................................... 6
3A.2 Results

............................................................................................................................... .................... 8
3A.2.1 Primary Results

.............................................................................................................................. 8
3A.2.2 Sensitivity Analysis: Side Case Incorporating More Coal Production Geographic Detail

.............. 9
3A.2.3 Uncertainties and Limitations

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

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

............................................................................................................................... ... 11
3A.3.2 Natural Gas Systems

.................................................................................................................... 15
3A.4 References

............................................................................................................................... ........... 30

## ***E.O. 12866 Review – Draft – Do Not C ite, Quote or Release During Review***

vii Chapter 4: Estimated Climate Benef its and Human Hea lth Co-benefits ............................................................ ......... 31


### 4.1 Introduction

............................................................................................................................... ........... 31
4.2 Estimated Climate Benefits from CO 2 

### ................................................................................................... 31
4.2.1 Climate Change Impacts................................................................................................................. 32
4.2.2 Social Cost of Carbon

..................................................................................................................... 36
4.3 Estimated Human Health Co

‐ Benefits

.................................................................................................. 44
4.3.1 Health Impact Assessment for PM 2.5 

### and Ozone

........................................................................... 46
4.3.2 Economic Valuation for Health Co

‐ benefits

................................................................................... 51
4.3.3 Benefit

‐ per
‐ ton Estimates for PM 2.5 

### ............................................................................................... 53
4.3.4 Benefit

‐ per
‐ ton Estimates for Ozone

............................................................................................. 54
4.3.5 Estimated Health Co

‐ Benefits Results

............................................................................................ 54
4.3.6 Characterization of Uncertainty in the Estimated Health Co

‐ benefits

.......................................... 68
4.4 Combined Climate Benefits and Health Co

‐ benefits Estimates

............................................................ 75
4.5 Unquantified Co

‐ benefits

...................................................................................................................... 80
4.5.1 HAP Co

‐ benefits

............................................................................................................................. 82
4.5.2 Additional NO 2 

### Health Co

‐ Benefits

................................................................................................ 86
4.5.3 Additional SO 2 

### Health Co

‐ Benefits

................................................................................................. 87
4.5.4 Additional NO 2 

### and SO 2 

### Welfare Co

‐ Benefits

................................................................................ 87
4.5.5 Ozone Welfare Co

‐ Benefits

............................................................................................................ 88
4.5.6 Carbon Monoxide Co

‐ Benefits

....................................................................................................... 89
4.5.7 Visibility Impairment Co

‐ Benefits

.................................................................................................. 89
4.6 References

............................................................................................................................... ............. 91 Appendix 4A: Generating Regional Benefit-per- Ton Estimates for Electri c Generating Units ................................... 1


### 4A.1 Overview of Benefit

‐ per
‐ Ton Estimates

............................................................................................... 1
4A.2 Underlying Source Apportionment Air Quality Modeling

.................................................................... 2
4A.3 Regional PM 2.5 

### Benefit

‐ per
‐ Ton Estimates for EGUs

............................................................................. 3
4A.4 Regional Ozone Benefit

‐ per
‐ Ton Estimates

........................................................................................ 17
4A.5 Evaluating the Benefit

‐ per
‐ Ton Estimates

......................................................................................... 21
4A.6 References

............................................................................................................................... ........... 27
CHAPTER 5: ECONOMIC IMPACTS – MARKET S Outside the Electri city Sector ................................................. 1


### 5.1 Introduction

............................................................................................................................... ...... 1
5.2 Summary of Secondary Market Impacts of Energy Price Changes

......................................................... 3

## ***E.O. 12866 Review – Draft – Do Not C ite, Quote or Release During Review***

viii 5.3.1 Share of Total Production Costs

....................................................................................................... 4
5.3.2 Ability to Substitute between Inputs to the Production Process

.................................................... 5
5.3.3 Availability of Substitute Goods and Services

.................................................................................. 5
5.4 Conclusions

............................................................................................................................... .............. 6 CHAPTER 6: Employment Impact Analysis ......................................................................................... ....................... 1


### 6.1 Introduction

............................................................................................................................... ............. 1
6.2 Overview of the Proposed EGU GHG Existing Source Guidelines

........................................................... 2
6.2.1 Determining State Goals Utilizing the Four Building Blocks

............................................................ 2
6.3 Economic Theory and Employment

........................................................................................................ 3
6.4 Current State of Knowledge Based on the Peer

‐ Reviewed Literature

................................................... 7
6.4.1 Regulated Sector

.............................................................................................................................. 8
6.4.2 Labor Supply Impacts

....................................................................................................................... 9
6.5 Recent Employment Trends

.................................................................................................................. 10
6.5.1 Electric Power Generation

............................................................................................................. 10
6.5.2 Fossil Fuel Extraction

...................................................................................................................... 11
6.5.3 Clean Energy Employment Trends

................................................................................................. 14
6.6 Projected Sectoral Employment Changes due to Proposed Guidelines

............................................... 18
6.6.1 Projected Changes in Employment in Electricity Generation and Fossil Fuel Extraction

.............. 19
6.6.2 Projected Changes in Employment in Demand

‐ Side Energy Efficiency Activities

............................. 26
6.7 Conclusion

............................................................................................................................... .............. 31
6.8 References

............................................................................................................................... ............. 34 Appendix 6A: Estimating Supply-Side Employment Impact s of the Proposed EGU GHG Existing Source Guidelines

.............................................................................................................................. ......................................................... 1


### 6A.1 General Approach

............................................................................................................................... .. 1
6A.1.1 Employment Effects Included In the Analysis

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

...................................................................... 3
6A.2.1 Employment Changes Due to Building (or Avoiding) New Generation Capacity

.......................... 5
6A.2.2 Employment Changes due to Coal and Oil/Gas Retirements

........................................................ 9
6A.2.3 Employment Changes due to Coal and Oil/Gas Retirements

........................................................ 9 CHAPTER 7: Statutory and executive Order Analysis ............................................................................ .................... 1


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

............................................................................................................... 1
7.2 Paperwork Reduction Act

....................................................................................................................... 3

## ***E.O. 12866 Review – Draft – Do Not C ite, Quote or Release During Review***

ix 7.3 Regulatory Flexibility Act

........................................................................................................................ 4
7.4 Unfunded Mandates Reform Act (UMRA)

.............................................................................................. 6
7.5 Executive Order 13132, Federalism

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

.............. 8

7.7 Executive Order 13211, Actions Concerning Regulations that Significantly Affect Energy Supply, Distribution, or Use 9
7.8 National Technology Transfer and Advancement Act (NTTAA)

.............................................................. 9
7.8 Executive Order 12898: Federal Actions to Address Environmental Justice in Minority Populations and Low

‐ Income Populations 10
7.10 Executive Order 13175 ‐ Consultation and Coordination with Indian Tribal Governments

........... 14
CHAPTER 8: COMPARISON OF BENEFITS AND COSTS .................................................................................. .. 1


### 8.1 Comparison of Benefits and Costs

.......................................................................................................... 1
8.2 Uncertainty Analysis

............................................................................................................................... 7 CHAPTER 9: Benefits, Costs, and Economic Impacts of Standards of Performance for Reconstructed and Modified Electric Utility Ge nerating Units ............................................................................................. ...................................... 1


### 9.1 Introduction

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

............................................................................................................... 1
9.3 Background for the Proposed EGU Reconstructed and Modified Source GHG Standards

..................... 2
9.3.1 Definition of Affected Sources

......................................................................................................... 2
9.3.2 Emission Limits

............................................................................................................................... .. 3
9.4 Impacts of the Proposed Reconstructed and Modified Source Standards

............................................. 5
9.4.1 Reconstructed Sources

.................................................................................................................... 5
9.4.2 Modified Sources

............................................................................................................................. 6
9.5 Statutory and Executive Order Requirements

...................................................................................... 14
9.5.1 Executive Order 12866, Regulatory Planning and Review, and Executive Order 13563, Improving Regulation and Regulatory Review

......................................................................................................... 14
9.5.2 Paperwork Reduction Act

.............................................................................................................. 15
9.5.3 Regulatory Flexibility Act................................................................................................................ 17
9.5.4 Unfunded Mandates Reform Act (UMRA)

..................................................................................... 19
9.5.5 Executive Order 13132, Federalism

............................................................................................... 20
9.5.6 Executive Order 13175, Consultation and Coordination with Indian Tribal Governments

........... 21
9.5.7 Executive Order 13045, Protection of Children from Environmental Health Risks and Safety Risks

............................................................................................................................... .................................. 22

## ***E.O. 12866 Review – Draft – Do Not C ite, Quote or Release During Review***

x 9.5.8 Executive Order 13211, Actions Concerning Regulations that Significantly Affect Energy Supply, Distribution, or Use

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

................................................................... 23
9.5.10 Executive Order 12898: Federal Actions to Address Environmental Justice in Minority Populations and Low

‐ Income Populations

............................................................................................. 23
9.6 References

............................................................................................................................... ............. 24

## ***E.O. 12866 Review – Draft – Do Not C ite, Quote or Release During Review***

xi 

## L IST OF 

## T ABLES

Table ES-1. Proposed State Goals (Adj usted MWh-Weighted-Average Pounds of CO 2 

### per Net MWh from all Affected Fossil Fuel-Fired EGUs) for Options 1 and 2 ............................................... 4

Table 3-2. Projected Capacity Factor of Existing Coal Steam and Natural Gas Combined Cycle Capacity

.............................................................................................................................. ................... 27

Table 3-3. Generation Mix (thousand GWh) ..................................................................................... ........ 29

Table 3-4. Total Generation Capacity by 2020-2030 (GW) ...................................................................... 34

Table 3-5. Projected Capac ity Additions, Gas (GW) ............................................................................. ..... 35

Table 3-64. Projected Capacity Add itions, Non-hydro Renewable (GW) .................................................. 36

Table 3-75. Coal Production for the Electric Power Sector, 2020 ............................................................. 36

Table 3-8. Power Sector Gas Use .............................................................................................. ................ 37

Table 3-9. Projected Average Minemouth a nd Delivered Coal Prices (2011$/MMBtu) ........................... 38

Table 3-10. Projected Average Minemouth and Deliv ered Coal Prices: Percent Change from Base Case Projections ................................................................................................................... ............ 38

Table 3-11. Average Henry Hub (spot) and Delivered Natural Gas Prices: Percent Change from Base Case Projections .............................................................................................................. ........ 39

Table 3-12. 2020 Projected Contiguous U.S. and Regional Retail Electricity Prices (cents/kWh) ........... 41

Table 3-13. 2025 Projected Contiguous U.S. and Regional Retail Electricity Prices (cents/kWh) ........... 42

Table 3-14. 2030 Projected Contiguous U.S. and Regional Retail Electricity Prices (cents/kWh) ........... 43

Table 3-15. Projected Changes in Average Elect ricity Bills ................................................................... ... 44

Table 3-26. Annualized Compliance Costs Includi ng Monitoring, Reporting and Recordkeeping Costs Requirements (billions of 2011$) ............................................................................................ 4 9

## ***E.O. 12866 Review – Draft – Do Not C ite, Quote or Release During Review***

xii Table 3-19. 2025 Projected Contiguous U.S. and Re gional Retail Electricity Prices (cents/kWh) ....... 3-31
Table 3-20. 2030 Projected Contiguous U.S. and Re gional Retail Electricity Prices (cents/kWh) ........ 3-32
Table 3-21. Projected Changes in Average Elect ricity Bills ................................................................... 3-33
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 -8
Table 3A-4. Projected Natural Gas Production Impacts, Option 1 ........................................................ 3A-8
Table 3A-5. Projected Natural Gas Production Impacts, Option 2 ........................................................ 3A-9
Table 3A-6. Upstream Emissions Changes, Baseline to Compliance Scenarios ................................. 3A-11
Table 3A-7. Upstream Emissions Changes, Baseline to Compliance Scenarios, Side Case Incorporating More Coal Production Geogr aphic Detail ......................................................................... 3A-12

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

Table 4-2. Global Social Cost of CO 2 

### , 2015-2050 (in 2011$) ................................................................ 4-11

Table 4-3. Estimated Global Climate Benefits of CO 2 

### Reductions for the Proposed EGU GHG Existing Source Guidelines in 2020 (billions of 2011$) ..................................................................... 4-12

Table 4-4. Estimated Global Climate Benefits of CO 2 

### Reductions Proposed EGU GHG Exisitng Source Guidelines in 2025 (billi ons of 2011$) ................................................................................. 4-13

Table 4-5. Estimated Global Climate Benefits of CO 2 

### Reductions for the Proposed EGU GHG Existing Source Guidelines in 2030 (billions of 2011$) ..................................................................... 4-13

Table 4-6. Human Health Effects of Ambient PM 2.5 

### and Ozone ............................................................ 4-19
Table 4-7. Summary of National and Regional Bene fit-per-Ton Estimates for EGUs in 2020 (2011$) 4-29
Table 4-8. Summary of National and Regional Bene fit-per-Ton Estimates for EGUs in 2025 (2011$) 4-30
Table 4-9. Summary of National and Regional Bene fit-per-Ton Estimates for EGUs in 2020 (2011$) 4-29
Table 4-10. Emission Reductions of Criteria Pollutants for the Proposed EGU GHG Existing Source Guidelines in 2020 (thousands of short tons) ....................................................................... 4-32
Table 4-11. Emission Reductions of Criteria Pollutants for the Proposed EGU GHG Existing Source Guidelines in 2025 (thousands of short tons) ....................................................................... 4-33
Table 4-12. Emission Reductions of Criteria Pollutants for the Proposed EGU GHG Existing Source Guidelines in 2030 (thousands of short tons) ....................................................................... 4-35

## ***E.O. 12866 Review – Draft – Do Not C ite, Quote or Release During Review***

xiii Table 4-13. Summary of Estimated Monetized Heal th Co-Benefits for the Pr oposed EGU GHG Existing Source Guidelines in 2020 (m illions of 2011$) ................................................................... 4-36
Table 4-14. Summary of Estimated Monetized Heal th Co-Benefits for the Pr oposed EGU GHG Existing Source Guidelines in 2025 (m illions of 2011$) ................................................................... 4-39
Table 4-15. Summary of Estimated Monetized Heal th Co-Benefits for the Pr oposed EGU GHG Existing Source Guidelines in 2030 (m illions of 2011$) ................................................................... 4-42
Table 4-16. Summary of Avoided Health Incidences from PM 2.5 

### -Related and Ozone-Related Co-Benefits for Proposed EGU GHG Existing Sour ce Guidelines in 2020 ............................................. 4-45
Table 4-17. Summary of Avoided Health Incidences from PM 2.5 

### -Related and Ozone-Related Co-Benefits for Proposed EGU GHG Existing Sour ce Guidelines in 2025 ............................................. 4-48
Table 4-18. Summary of Avoided Health Incidences from PM 2.5 

### -Related and Ozone-Related Co-Benefits for Proposed EGU GHG Existing Sour ce Guidelines in 2030 ............................................. 4-51
Table 4-19. Population Exposure in the Baseline Modeling Above and Below Various Concentrations Benchmarks in the Underlying Epidemiology Studies......................................................... 4-61
Table 4-20. Combined Climate Benefits and Health Co-Benefits for Proposed EGU GHG Existing Source Guidelines in 2020 (billions of 2011$) ..................................................................... 4-65
Table 4-21. Combined Climate Benefits and Health Co-Benefits for Proposed EGU GHG Existing Source Guidelines in 2025 (billions of 2011$) ..................................................................... 4-67
Table 4-22. Combined Climate Benefits and Health Co-Benefits for Proposed EGU GHG Existing Source Guidelines in 2030 (billions of 2011$) ..................................................................... 4-69
Table 4-23. Unquantified Health and Welfare Co-Benefits Categories .................................................. 4-72
Table 4A-1. Summary of National and Regi onal Emissions in Sector Modeling for

EGUs in 2016 ................................................................................................................. . 4A-100
Table 4A-2. Summary of National and Regional PM 2.5 

### Benefit-per-Ton

Estimates for EGUs in 2020 ............................................................................................ 4A-104
Table 4A-3. Summary of National and Regional PM 2.5 

### Benefit-per-Ton

Estimates for EGUs in 2025 ............................................................................................ 4A-105
Table 4A-4. Summary of National and Regional PM2.5 Benefit-per-Ton

Estimates for EGUs in 2030 ............................................................................................ 4A-105
Table 4A-5. Summary of Regional PM 2.5 

### Incidence-per-Ton Estimates for EGUs in 2020 ............. 4A-107

## ***E.O. 12866 Review – Draft – Do Not C ite, Quote or Release During Review***

xiv Table 4A-6. Summary of Regional PM 2.5 

### Incidencet-per-Ton Estimates for EGUs in 2025 ............ 4A-109
Table 4A-7. Summary of Regional PM 2.5 

### Incidencet-per-Ton Estimates for EGUs in 2030 ............ 4A-111
Table 4A-8. Summary of National and Regional Ozone Benefit-per-Ton Estimates

for EGUs in 2020 ............................................................................................................ 4A-114
Table 4A-9. Summary of National and Regional Ozone Benefit-per-Ton Estimates

for EGUs in 2025 ........................................................................................................... 4A-114
Table 4A-10. Summary of National and Regional Ozone Benefit-per-Ton Estimates

for EGUs in 2030 ........................................................................................................... 4A-115
Table 4A-11. Summary of Regional Ozone Incide nce-per-Ton Estimates for EGUs in 2020 .......... 4A-115
Table 4A-12. Summary of Regional Ozone Incide nce-per-Ton Estimates for EGUs in 2025 .......... 4A-115
Table 4A-13. Summary of Regional Ozone Incide ne-per-Ton Estimates for EGUs in 2030 ............ 4A-116
Table 5-1. Estimated Percentage Changes in Energy Prices, by Energy Type and Regulatory Option ... 5-4

Table 6-1. U. S. Green Goods and Servi ces (GGS) Employment (annual average) ............................... 6-16
Table 6-2. Renewable Electricity Ge neration-Related Employment ...................................................... 6-17
Table 6-3. Energy and Resources Effi ciency-Related Employment ....................................................... 6-18
Table 6-4. Engineering-Based Ch anges in Labor Demand in 2020, 2025 and 2030, Regional Compliance Approach (number of years of employment in year) ........................................................... 6-26
Table 6-5. Engineering-Based Changes in Labor Demand in 2020, 2025 and 2030, State Compliance Approach (number of job-years of employment in year) ..................................................... 6-28
Table 6-6. Estimated Demand-Side Energy Effici ency Employment Impacts for Option 1 and Option 2 Both Regional and State Compliance Approaches ............................................................... 6-33
Table 6A-1. Labor Productivity Growth Rate Due to Heat Rate Improvement .................................... 6A-5
Table 6A-2. Captial Charge Rate and Duration Assumptions ............................................................... 6A-6
Table 6A-3. Expenditure Breakdown due to New Generating Capacity ............................................... 6A-7
Table 6A-4. Labor Productivity due to New Generating Capacity ........................................................ 6A-7
Table 6A-5. Average FOM Cost Assumptions ..................................................................................... . 6A-9
Table 6A-6. Labor Productivity due to New Generating Capacity ...................................................... 6A-10

## ***E.O. 12866 Review – Draft – Do Not C ite, Quote or Release During Review***

xv Table 7-1. Potentially Regulat ed Categories and Entities. .................................................................... .... 7-5
Table 8-1. Summary of Monetized Benefits, Comp liance Costs, and Net Benefits for the Proposed Guidelines – 2020 (billions of 2011$) .................................................................................... 8-4
Table 8-2. Summary of Monetized Benefits, Comp liance Costs, and Net Benefits for the Proposed Guidelines – 2025 (billions of 2011$) .................................................................................... 8-5
Table 8-3. Summary of Monetized Benefits, Comp liance Costs, and Net Benefits for the Proposed Guidelines – 2030 (billions of 2011$) .................................................................................... 8-6
Table 9-1. Proposed Emission Limits - Reconstructions ........................ 9- Error! Bookmark not defined.
Table 9-2. Proposed Emission Limits - Modifications ........................... 9- Error! Bookmark not defined.
Table 9-3. Annualized Costs and Fuel Savings of Efficiency Improvements (millions of 2011$) .... Error! Bookmark not defined. -9
Table 9-4. Emission Reductions from Efficiency Improvements (tons/year) ........ 9- Error! Bookmark not defined.
Table 9-5. Summary of National Benef it-per-ton Estimates for EGUs in 2025 .... 9- Error! Bookmark not defined.

Table 9-6. Estimated Global Climate Benefits of Illustrative CO 2 

### Reductions in 2025 (millions of 2011$)* ....................................................................................................................... ............ 9-2
Table 9-7. Summary of Estimated Monetized Health Co-benefits of Illustrative Efficiency Improvements in 2025 (thousands of 2011$)* ............................................. 9- Error! Bookmark not defined.

Table 9-8. Combined Estimated Climate Benefits and Health Co-Benefits of Illustrative Efficiency Improvements in 2025 (millions of 2011$)* ........................ 9- Error! Bookmark not defined.

## ***E.O. 12866 Review – Draft – Do Not C ite, Quote or Release During Review***

xvi 

## L IST OF 

## F IGURES

Figure 2-1. Fossil Fuel-Fired Electric ity Generating Facilities, by Size .................................................... 2 -5
Figure 2-2. Status of State Electric ity Industry Restructuring Activities .................................................. 2 -8
Figure 2-6. Domestic Emissions of Gree nhouse Gases, 2012 (million metric tons of CO 2 

### equivalent) ... 2-9
Figure 2-3. Direct GHG Emissions from the Power Sector Relative to Total Domestic GHG Emissions (2012) ........................................................................................................................ ........... 2-11
Figure 2-5. National Average Retail Electri city Price (1960 – 20 113, 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 Ro lling Average) and Projections from the Annual Energy Outlook 2014 .................................................................................................................. ...... 2-26
Figure 3-1. NERC Regions ..................................................................................................... ................ 3-12
Figure 3-2. NERC Assessment Areas ............................................................................................ ......... 3-13
Figure 3-3. Regional Compliance Zones Used in the Analysis .............................................................. 3-13
Figure 3-4. Generation Mix with the Base Case and 111(d) Options, 2020-2030 (million GWh) ......... 3-33
Figure 3-5. Electricity Market Module Regions ................................................................................ ..... 3-44
Figure 4-1. Monetized Health Co-benefits for each Option (Regional Compliance) of the Proposed EGU GHG Existing Source Guide lines in 2020 * ..................................................................... 4-5466
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* ...................................................................................................................... .......... 4-55
Figure 4-3. Breakdown of Monetized Health Co-Ben efits by Region at a 3% Discount Rate for each Option (Regional compliance) for Proposed EGU GHG Existing Source Guidelines in 2020*

.............................................................................................................................. ................ 4-56
Figure 4-4. Percentage of Adult Population (age 30+) by Annual Mean PM 2.5 

### Exposure in the Baseline Sector Modeling (used to generate the benefit-per-ton estimates)* ..................................... 4-62
Figure 4-5. Cumulative Distribution of Adult Population (age 30+) by Annual Mean PM 2.5 

### Exposure in the Baseline Sector Modeling (used to gene rate the benefit-per-ton estimates)* ................ 4-63

## ***E.O. 12866 Review – Draft – Do Not C ite, Quote or Release During Review***

xvii Figure 4-6. Breakdown of Combined Monetized Clim ate and Health Co-benefits of Proposed EGU GHG Existing Source Guidelines in 2020 by Opti on (Regional Compliance) and Pollutant (3% discount rate)* ............................................................................................................... ....... 4-71
Figure 4A-1

. Conceptual Overview of Bene fit-per-Ton Calculation .................................................. 4A-94
Figure 4A-2. Modeled PM 2.5 

### Concentrations Attributable to EGUs in 2016 (Annual Mean, in μg/m 3 

### ) (Source: BPT TSD) ............................................................................................................. . 4-96
Figure 4A-3. Modeled PM 2.5 

### Concentrations Attributable to EGUs by Precursor in 2016 (Annual Mean, in μg/m 3 

### ) ............................................................................................................................ 4A-98
Figure 4A-4. Regional Breakdown .............................................................................................. ...... 4A-100
Figure 4A-5. Breakdown of Directly Emitted PM 2.5 

### Emissions from EGU in Sector Modeling ....... 4A-102
Figure 4A-6. Modeled Average Ozone Concentrations Attributable to EGUs in 2016 (Daily 8-hour maximum, May-September, in ppb) ................................................................................ 4A-113
Figure 4A-7. Comparison of Base Case SO 2 

### Emissions in 2020 ....................................................... 4A-118
Figure 4A-8. Comparison of Base Case Annual NOx Emissions in 2020 ......................................... 4A-119
Figure 4A-9. Comparison of Base Case Ozone -season NOx Emissions in 2020 .............................. 4A-120
Figure 6.1. Electric Power Industry Employment ................................................................................ .... 6-11
Figure 6.2. Coal Production Employment ........................................................................................ ....... 6-13
Figure 6.3. Oil and Gas Production Employment ................................................................................. ... 6-14

## ***E.O. 12866 Review – Draft – Do Not C ite, Quote or Release During Review***

xviii 

## 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

## ***E.O. 12866 Review – Draft – Do Not C ite, Quote or Release During Review***

xix 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

## ***E.O. 12866 Review – Draft – Do Not C ite, Quote or Release During Review***

xx 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 Databa se 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

## ***E.O. 12866 Review – Draft – Do Not C ite, Quote or Release During Review***

xxi OMB Office of Management and Budget PAGE Policy Analysis of the Greenhouse Gas Effect Model
PFC Perfluorocarbons
PM
2.5 

### 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
SO
2 

### 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

## ***E.O. 12866 Review – Draft – Do Not C ite, Quote or Release During Review***

xxii 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

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ES-1 

## EXECUTIVE SUMMARY

This Regulatory Impact Analysis (RIA) discu sses potential benefits, costs, and economic impacts of the proposed Emission Guidelines for Greenhouse Gas Emissions from Existing Stationary Sources: Electric Utility Generati ng Units (herein referre d to EGU GHG Existing Source Guidelines). This RIA also discusses th e potential benefits, costs and economic impacts of the proposed Standards of Performance fo r Greenhouse Gas Emissions from Reconstructed and Modified Stationary Sour ces (EGU GHG Reconstructed a nd 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 ra nge of severely negative effects on human health and the environment. Carbon Dioxide (CO 2 

## ) 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 CO 2 

## , among stationary sources in the U.S. In this action, the EPA is proposing emission gui delines for states to use in developing plans to address greenhouse gas emissions from ex isting fossil fuel-fired EGUs. Specifically, the EPA is proposing state-specific rate-based goals for carbon dioxide emis sions from the power sector, as well as emission guideli nes for states to use in devel oping 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), st ate plans must establish standards of performance that reflect the degree of emission limitation achievable thro ugh the application of the “best system of emission reduction” (BSER) that, taking into account the cost of achieving

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ES-2 such reduction and any non-air quality hea lth and environmental impact and energy 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 reducti ons that a state can achieve th rough the applica tion 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 affect ed EGUs through heatrate improvements.
2. Reducing emissions from the most carbon-intensive affected EGUs in the amount that results from substituting generation at t hose 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 expa nded 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 redu ces the amount of generation required.
The proposed rule also contains emission guide lines for states to use in developing plans that set their standards of performance. The EP A recognizes that each st ate has different policy considerations, includ ing varying emission reduction opportuni ties and existing state programs and measures, and characteristics of the electri city system (e.g., utility regulatory structure, generation mix, electricity dema nd). The proposed emission guide lines provide states with options for establishing standards of performa nce in a manner that accommodates a diverse range of state approaches. The pr oposed guidelines would also allow states to collaborate and to demonstrate emission performance on a multi-stat e basis, in recognition of the fact that

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 reflect s that amount of emission reduction. In addition, the state is required to include in its state plan the standard s of performance and measures to impl ement 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.

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ES-3 electricity is transmitted across state lines, a nd local measures often impact regional EGU CO 2 

## emissions.
While the EPA must establish BSER and is proposing goals and guidelines that reflect BSER, CAA section 111(d) also provides the EP A with the flexibility to design goals and guidelines that recognize, and are tailored to, the uniqueness and complexity of the power generation sector and CO 2 

## emissions. And, importantly, CAA section 111(d) allows the states flexibility in designing the measures for their st ate plans in response to the EPA’s guidelines. States are not required to use each of the measur es that the EPA determines constitute BSER, or use those measures to the same degree of stringen cy that the EPA determin es is achievable at a reasonable cost; rather, CAA s ection 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 im pacts 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 describe d 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 deployme nt 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 eac h option. With the state compliance approach, states are assumed to comply with the guideli nes by implementing measures solely within the state and emissions rate averaging occurs betw een affected sources on an intrastate basis only. In contrast under the region al approach, groups of states are assumed to collaboratively comply with the guidelines. States have the discretion of choosi ng between a regional or state compliance approach, and this RIA reports the economic consequences of compliance under two

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ES-4 sets of assumptions: one that assumes all stat es 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 scen arios does not assume that states use any specific policy mechanism to achieve the state goals. Th e distributions of emi ssions and electricity generation reflected in the Integr ated 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 flexib ility of compliance, incl uding 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 illust rative scenarios, individual states or multi-state regional groups may develop mo re cost effective approaches to achieve their state goals.
Table ES-1. Proposed State Goals (Adj usted MWh-Weighted-Average Pounds of CO 2 

## per Net MWh from all Affected Fossil Fu el-Fired EGUs) for Options 1 and 2

State 2 

### 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

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 note d in Chapter 3, EPA is not proposing goals for tribes or U.S. territories at this time.

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ES-5 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 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 asso ciated with the compliance scenarios for the proposed Option 1 regional and state comp liance approaches and Table ES-3 reports emission reductions associated with Op tion 2. In 2020, the EPA estimates that CO 2 

## emissions

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ES-6 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.
CO 2 

## emission reductions for Option 1 increase to 545 and 555 million metric tons annually in 2030 when compared to the base case emissi ons for Option 1 regional and state compliance approaches, respectively. Tables ES-2 and ES-3 also show emission reductions for criteria air pollutants.

Table ES-2. Summary of Climate and Ai r Pollutant Emission Reductions Option 1 1


### CO 2 

### (million metric tons) SO 2 

### (thousands of tons) NO X 

### (thousands of tons) PM 2.5 

### (thousands of tons) 2020 Regional Co mpliance 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 Co mpliance 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 Co mpliance 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 

### CO 2 

### emission reductions are used to estimate the climate benefits of the guidelines. SO 2 

### , NOx, and directly emitted PM 2.5 

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

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ES-7 Table ES-3. Summary of Climate and Air Pollutant Emission Reductions Option 2 1


### CO 2 

### (million metric tons) SO 2 

### (thousands of tons) NO X 

### (thousands of tons) PM 2.5 

### (thousands of tons) 2020 Regional Co mpliance 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 Co mpliance 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 2030 Regional Complia nce Approach Base Case n/a n/a n/a n/a Option 2 n/a n/a n/a n/a Emissions Change n/a n/a n/a n/a 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 2030 State Compliance Approach Base Case n/a n/a n/a n/a Option 2 n/a n/a n/a n/a Emissions Change n/a n/a n/a n/a Source: Integrated Planning Model, 2014.

1 

### CO 2 

### emission reductions are used to estimate the climate benefits of the guidelines. SO 2 

### , NOx, and directly emitted PM 2.5 

### 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 th is proposed action are represented in this analysis as the change in electric power genera tion costs between the base case and illustrative compliance scenario policy cases. The compliance scenario po licy cases reflect the pu rsuit by states of a distinct set of strategies, which are not limited to the technologies and m easures included in the BSER to meet the EGU GHG emission guidelines, and include cost estimates for demand side energy efficiency. The compliance assumptions, a nd therefore the projected “compliance costs” set forth in this analysis, are illustrative in nature and do not repr esent the full suite of compliance flexibilities states may ultimately pursue.

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ES-8 The EPA projects that the annual incremental compliance cost of th e proposed Option 1 ranges from $5.4 to $7.4 billion in 2020 and fr om $7.3 to $8.8 billion in 2030 ($2011), excluding
the costs associated with mon itoring, reporting, and recordkeeping. The estimated cost of Option 2 is between $4.2 and $5.4 billion in 2020 a nd between $4.5 and $5.5 billion in 2025 (2011$). The estimated monitoring, reporting and record keeping costs for both options are $68.3 million 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, 3 

## the change in the ongoing costs of operating pollution controls, shifts betw een or amongst various fuels, demand-side energy efficiency measures, and other actions associated with comp liance. Costs for both options are reflected in
Table ES-4 below and discussed more ex tensively 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 Chapte r 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) a nd end-use energy efficiency particip ant costs (paid by electric utility consumers). For example in 2020 for the proposedOption 1 regional compliance approach, enduse energy efficiency program costs are estimat ed to be $5.1 billion and end-use efficiency

3 

### 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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ES-9 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’ comp liance with state goals associat ed with changes to reduce the carbon-intensity of electricity production and th e energy demand decreases expected from enduse energy efficiency assumed in the illustrative scenarios. In order to reflect the full cost attributable to the policy, it is necessary to include this increm ental -$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-re lated and demand-related expenditures). Note that when monitoring, re porting 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 a nd 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 so cial costs, see Chapter 3 of this RIA.
ES.5 Monetized Climate Benefits and He alth Co-benefits for Existing EGUs

Implementing the proposed guidelines is expected to reduce emissions of CO 2 

## and have ancillary emission reductions (i.e., co-benefits) of SO 2 

## , NO 2 

## , and directly emitted PM 2.5 

## , which
would lead to lower ambient concentrations of PM 2.5 

## and ozone. The climate benefits estimates have been calculated using the estimated values of marginal c limate 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

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 Environmenta l 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 Manage ment and Budget, Office of Science and Technology Policy, and Department of Treasury (May 2013, Revised November 2013). Available at:

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ES-10 Also, the range of combined benefits reflects different concentration-re sponse functions for the air pollution health co-b enefits, but it does not ca pture 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 guidelines, including reducing exposure to SO 2 

## , NO x 

## , 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 pr evious 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.

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

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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 CO 2 —  SCC TSD Climate impacts from ozone and black carbon (directly emitted PM) — — Ozone ISA, PM ISA 1 Other climate impacts (e.g., other GHGs such as methane, aerosols, other impacts) — — IPCC 1 Improved Human Health (co-benefits) Reduced incidence of premature mortality from exposure to PM 2.5 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 PM 2.5 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-activ ity days (age 18-65)   PM ISA Chronic Bronchitis (age >26) — — PM ISA 1 Emergency room visits fo r cardiovascular effects (all ages) — — PM ISA 1 Strokes and cerebrovascular disease (age 50-79) — — PM ISA 1 Other cardiovascular effects (e .g., other ages) — — PM ISA 2Other respiratory effects (e.g., pulmonary function, nonasthma ER visits, non-bronchitis chronic diseases, other ages and populations) — — PM ISA 2

Reproductive and developmenta l effects (e.g., low birth weight, pre-term births, etc) — — PM ISA 2,3 Cancer, mutagenicity, and genotoxicity effects — — PM ISA 2,3

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 ISA 1

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-activ ity days (age 18–65)   Ozone ISA School absence days (age 5–17)   Ozone ISA Decreased outdoor worker productivity (age 18–65) — — Ozone ISA 1 Other respiratory effects (e.g., prem ature aging of lungs) — — Ozone ISA 2 Cardiovascular and nervous sy stem effects — — Ozone ISA 2 Reproductive and developmen tal effects — — Ozone ISA 2,3

Reduced incidence of morbidity from exposure to NO 2 Asthma hospital admissions (all ages) — — NO 2 ISA 1 Chronic lung disease hospital admissions (age > 65) — — NO 2 ISA 1 Respiratory emergency department visits (all ages) — — NO 2 ISA 1 Asthma exacerbation (asthmatics age 4–18) — — NO 2 ISA 1 Acute respiratory symptoms (age 7–14) — — NO 2 ISA 1 Premature mortality — — NO 2 ISA 1,2,3 Other respiratory effects (e.g ., airway hyperresponsiveness and inflammation, lung function, other ages and populations) — — NO 2 ISA 2,3 Reduced incidence of Respiratory hospital admissions (age > 65) — — SO 2 ISA 1

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ES-12 morbidity from exposure to SO 2 Asthma emergency department visits (all ages) — — SO 2 ISA 1 Asthma exacerbation (asthmatics age 4–12) — — SO 2 ISA 1 Acute respiratory symptoms (age 7–14) — — SO 2 ISA 1 Premature mortality — — SO 2 ISA 1,2,3 Other respiratory effects (e.g ., airway hyperresponsiveness and inflammation, lung function, other ages and populations) — — SO 2 ISA 1,2

Reduced incidence of morbidity from exposure to methylmercury Neurologic effects—IQ loss — — IRIS; NRC, 2000 1 Other neurologic effects (e.g ., developmen tal delays, memory, behavior) — — IRIS; NRC, 2000 2
Cardiovascular effects — — IRIS; NRC, 2000 2,3 Genotoxic, immunologic, and other toxic effects — — IRIS; NRC, 2000 2,3 Reduced incidence of morbidity from exposure to HAP Effects associated with exposure to hydrogen chloride — — ATSDR, IRIS 1,2

Improved Environment (co-benefits) Reduced visibility impairment Visibility in Class 1 areas — — PM ISA 1 Visibility in residential areas — — PM ISA 1 Reduced effects on materials Household soiling — — PM ISA 1,2 Materials damage (e.g., corrosi on, increased wear) — — PM ISA 2 Reduced PM deposition (metals and organics) Effects on Individual organism s and ecosystems — — PM ISA 2

Reduced vegetation and ecosystem effects from exposure to

ozone Visible foliar injury on vegetation — — Ozone ISA 1 Reduced vegetation growth and reproduction — — Ozone ISA 1 Yield and quality of co mmercial forest products and crops — — Ozone ISA 1 Damage to urban ornament al plants — — Ozone ISA 2 Carbon sequestration in terrestrial ecosystems — — Ozone ISA 1 Recreational demand associated with forest aesthetics — — Ozone ISA 2 Other non-use effects Ozone ISA 2 Ecosystem functions (e.g., wa ter cycling, biogeochemical cycles, net primary productivity, leaf-gas exchange, community composition) — — Ozone ISA 2

Reduced effects from acid deposition Recreational fishing — — NO x SO x ISA 1 Tree mortality and decline — — NO x SO x ISA 2 Commercial fishing and fo restry effects — — NO x SO x ISA 2 Recreational demand in terrestrial and aquatic ecosystems — — NO x SO x ISA 2 Other non-use effects NO x SO x ISA 2 Ecosystem functions (e.g., bi ogeochemical cycles) — — NO x SO x ISA 2

Reduced effects from nutrient enrichment Species composition and biodiversity in terrestrial and estuarine ecosystems — — NO x SO x ISA 2 Coastal eutrophication — — NO x SO x ISA 2 Recreational demand in terrestrial and estuarine ecosystems — — NO x SO x ISA 2 Other non-use effects NO x SO x ISA 2 Ecosystem functions (e.g., bi ogeochemical cycles, fire regulation) — — NO x SO x ISA 2 Reduced vegetation effects from exposure to SO 2 and NO x Injury to vegetation from SO 2 exposure — — NO x SO x ISA 2 Injury to vegetation from NO x exposure — — NO x SO x ISA 2 Reduced ecosystem effects from exposure to methylmercury Effects on fish, birds, and mammals (e.g., reproductive effects) — — Mercury Study RTC 2
Commercial, subsistence and recreati onal fishing — — Mercury Study RTC 1

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 confiden ce in available data or

methods.

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ES-13 3 

### We assess these co-benefits qualitatively because current ev idence is only sugg estive 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 (CO 2 

## ) emission reductions expected from this rulemaking using the SCC es timates 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 CO 2 

## emissions in a given year. It includes a wide range of anticipated climate impacts, such as net changes in agricultu ral productivity and human health, property damage from increased flood ris k, and changes in energy system costs, such as reduced costs for heating and increased costs for air conditioning. It is ty pically 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 CO 2 

## emissions). The SCC estimates used in this analysis ha ve been developed over many years, using the best science available, and with input from the public. The EPA a nd other federal agencies have considered the extensive public comments on ways to improve SCC estima tion 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 EP A and other executive branch entities used three integrated assessment models (IAMs) to de velop 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. Ther efore, 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 do mestic policies. See RIA Chapter 4 for more

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ES-14 discussion. The federal government first released the es timates in February 2010 and updated them in 2013 using new versions of each IAM. The genera l approach to estimating the SCC values in 2010 and 2013 was to run the three integrated assessment models (DIC E, FUND, and PAGE) 5 

## using the following three inputs in each model: a probabilistic distribution fo r climate sensitivity; five scenarios capturing economic, population, a nd emission trajectories; and constant annual
discount rates. The 2010 SCC T echnical Support Document (S CC TSD) provides a complete discussion of the methodology and the 2013 S CC 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 CO 2 

## 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 percen t, and 2.5 percent, respectively, and the fourth
estimate is the 95 th 

## percentile at 3 percent.
The 2010 SCC TSD noted a number of limitatio ns to the SCC analysis, including the incomplete way in which the IAMs capture catastrophic and non-catastrophic impacts, their incomplete treatment of adaptation and technologi cal 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 c limate change literature because of a lack of precise information on the nature of damages a nd because the science incorporated into these models understandably lags behind the most recen t 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 CO 2 

## emission reductions to inform the benefit-cost analysis.

5 

### The full models names are as follows: Dynamic Integr ated Climate and Economy (D ICE); Climate Framework for

Uncertainty, Negotiation, and Distribution (FUND); and Po licy Analysis of the Greenh ouse Gas Effect (PAGE).

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

The proposed guidelines would reduce emi ssions of precursor pollutants (e.g., SO 2 

## , NOx, and directly emitted particles), which in turn would lower ambient concentrations of PM 2.5 

## 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 PM 2.5 

## 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 on e 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 es timates of monetized ozone co-benefits include avoided premature d eaths (derived from the range of effect coefficients represented by two short-term epid emiology studies [Bell et al. (2004) and Levy et al. (2005)]), as well as avoided mo rbidity effects for five non-fata l endpoints ranging in severity from school absence days to hospital admissions (U.S. EPA, 2008, 2011). We used a “benefit-per-ton” approach to esti mate the health co-benefits. To create the benefit-per-ton estimates for PM 2.5 

## , this approach uses an air qua lity model to convert emissions
of PM 2.5 

## precursors (e.g., SO 2 

## , NO x 

## ) and directly emitted particles into changes in ambient PM 2.5 

## concentrations and BenMAP to estimate the ch anges 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 Californi a). We followed a similar process to estimate benefit-per-ton estimates for the ozone precursor NO x 

## . To calculate the co-benefits for the proposed guidelines, we then multiplied the re gional 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 emissi ons, which may not exactly match the emission reductions in this rulemaking, and thus they may not reflect the local va riability in population

6 

### We did not estimate the co-b enefits associated with redu cing direct exposure to SO 2 

### 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 anticipa ted from implementation of the proposed guidelines.

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ES-16 density, meteorology, exposure, baseline health inci dence 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 PM 2.5 

## -related premature mortality, which account s for 98 percent of the monetized PM 2.5 

## health co-benefits:
1. We assume that all fine particles, re gardless of their chemical composition, are equally potent in causing premature mortality. This is an important assumption, because PM 2.5 

## varies considerably in composition across sources, but the scientific evidence is not yet sufficient to allow diffe rentiation of effect estimates by particle type. The PM ISA concluded that “many constituents of PM 2.5 

## 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 wi th varied concentrations of PM 2.5 

## , including both areas that do not meet the fine particle sta ndard and those areas th at are in attainment, down to the lowest modeled concentrations.

3. We assume that there is a “cessation” lag between the ch ange in PM exposures and the total realization of changes in mortality effects. Specifically, we assume that some of the incidences of premat ure mortality related to PM 2.5 

## exposures occur in a distributed fashion over the 20 years follo wing exposure based on the advice of the SAB-HES (U.S. EPA-SAB, 2004c), which a ffects the valuation of mortality cobenefits at different discount rates.
Every benefits analysis examining the potenti al effects of a change in environmental protection requirements is limited, to some exte nt, by data gaps, model capabilities (such as geographic coverage) and uncertain ties 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 indicat ion of the expected health co-ben efits of the air pollution emission reductions for the illustrative compliance options for the proposed standards under a set of

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ES-17 reasonable assumptions. This analysis does not include the type of detailed uncertainty
assessment found in the 2012 PM 2.5 

## 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, us ing a benefit-per-ton a pproach 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 di scount rates. Different discount rates are applied to SCC than to the health co-benefit estimates; because CO 2 

## 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 ex ists 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 importa nce of considering all four SCC estimates. 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.

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ES-18 Table ES-6. Combined Estimates of Clim ate 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 CO 2 

### 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% (95 th 

### percentile) $51 $67 to $88 $65 to $85 In 2025 501 million metric tonnes CO 2



### 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% (95 th 

### percentile) $76 $99 to $130 $97 to $120 In 2030 545 million metric tonnes CO 2 

### 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% (95 th 

### percentile) $92 $120 to $150 $120 to $150 Option 2 In 2020 283 million metric tonnes CO 2 

### 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% (95 th 

### percentile) $39 $52 to $70 $51 to $67 In 2025 368 million metric tonnes CO 2 

### 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% (95 th 

### percentile) $56 $73 to $96 $72 to $93 In 2030 n/a million metric tonnes CO 2



### 5% n/a n/a n/a 3% n/a n/a n/a 2.5% n/a n/a n/a 3% (95 th 

### percentile) n/a n/a n/a *All benefit estimates are rounded to two significant figu res. Climate benefits are based on reductions in CO 2 

### 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. Th e health co-benefits reflect the sum of the PM 2.5 

### 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 NO 2 

### , SO 2 

### , and HAP; ecosystem effects; or vi sibility 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.

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ES-19 Table ES-7. Combined Estimates of Clim ate 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 CO 2 

### 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% (95 th 

### percentile) $52 $69 to $92 $68 to $88 In 2025 506 million metric tonnes CO 2



### 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% (95 th 

### percentile) $77 $100 to $130 $98 to $130 In 2030 n/a million metric tonnes CO 2 

### 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% (95 th 

### percentile) $94 $120 to $160 $120 to $150 Option 2 In 2020 295 million metric tonnes CO 2 

### 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% (95 th 

### percentile) $40 $54 to $72 $53 to $69 In 2025 376 million metric tonnes CO 2 

### 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% (95 th 

### percentile) $57 $75 to $98 $73 to $95 In 2030 n/a million metric tonnes CO 2



### 5% n/a n/a n/a 3% n/a n/a n/a 2.5% n/a n/a n/a 3% (95 th 

### percentile) n/a n/a n/a *All benefit estimates are rounded to two significant figu res. Climate benefits are based on reductions in CO 2 

### 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. Th e health co-benefits reflect the sum of the PM 2.5 

### 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 NO 2 

### , SO 2 

### , and HAP; ecosystem effects; or vi sibility 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.

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ES-20 ES.6 Monetized Benefits, Compliance Costs and Net Benefits of the Proposed Guidelines for Existing Sources
In this summary, the EPA provides the estimat es of the climate benefits, health cobenefits, compliance costs and ne t benefits of the proposed Op tion 1 and alternative Option 2 assuming a regional compliance approach and an a lternative state compliance approach. In Table ES-8, the EPA estimates that in 2020 the propos ed Option 1 regional comp liance 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 2 020 are estimated to be $15 billion to $34 billion (2011$) for a 3 percent discount rate and $13 bi llion 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 quan tified 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 (Tab le ES-10 below), usin g a 3 percent discount rate (model average). For the Option 1 state co mpliance approach in 2020, the EPA estimates
monetized climate benefits of approximately $18 billion using a 3 percent discount rate (model average). The air pollutio n 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 quan tified 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 a nd cost estimates for Options 1 and 2 regional and state comp liance approaches for 2020, 2025, and 2030 are presented in Tables ES-8, ES-9, and ES-10. The EPA could not monetize some important benefits of the guide lines. Unquantified
benefits include climate benefits from reducing emissions of non-CO 2 

## greenhouse gases and cobenefits from reducing exposure to SO 2 

## , NO x 

## , and hazardous air pollutants (e.g., mercury and hydrogen chloride), as well as ecosystem effect s and visibility impairment. Upon considering these limitations and uncertainties, it remains cl ear that the benefits of this proposal are substantial and far outweigh the costs.

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ES-21 Table ES-8. Summary of Estimated Moneti zed Benefits, Compliance Costs, and Net Benefits for the Proposed Guid elines – 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 su mmary table reflect global impacts from CO 2 

### emission changes and do not

account for changes in non-CO 2 

### GHG emissions. Also, different discount rates are applied to SCC than to the other estimates because CO 2 

### 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 PM 2.5 

### and ozone associated with emission

reductions of directly emitted PM 2.5 

### , SO 2 

### and NO X 

### . The range reflects the use of concentration-response functions from different epidemiology studies. The reduction in prematur e fatalities each year accounts for over 90 percent of total monetized co-benefits from PM 2.5 

### and ozone. These models assume that all fine particles, regardless of their chemical composition, are equally potent in causing premature mortality because th e 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.

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ES-22 Table ES-9. Summary of Estimated Moneti zed Benefits, Compliance Costs, and Net Benefits for the Proposed Guid elines – 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 su mmary table reflect global impacts from CO 2 

### emission changes and do not

account for changes in non-CO 2 

### GHG emissions. Also, different discount rates are applied to SCC than to the other estimates because CO 2 

### 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 PM 2.5 

### and ozone associated with emission

reductions of directly emitted PM 2.5 

### , SO 2 

### and NO X 

### . The range reflects the use of concentration-response functions from different epidemiology studies. The reduction in prematur e fatalities each year accounts for over 90 percent of total monetized co-benefits from PM 2.5 

### and ozone. These models assume that all fine particles, regardless of their chemical composition, are equally potent in causing premature mortality because th e 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.

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ES-23 Table ES-10. Summary of Estimated Moneti zed Benefits, Compliance Costs, and Net Benefits for the Proposed Guid elines –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 su mmary table reflect global impacts from CO 2 

### emission changes and do not

account for changes in non-CO 2 

### GHG emissions. Also, different discount rates are applied to SCC than to the other estimates because CO 2 

### 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 PM 2.5 

### and ozone associated with emission

reductions of directly emitted PM 2.5 

### , SO 2 

### and NO X 

### . The range reflects the use of concentration-response functions from different epidemiology studies. The reduction in prematur e fatalities each year accounts for over 90 percent of total monetized co-benefits from PM 2.5 

### and ozone. These models assume that all fine particles, regardless of their chemical composition, are equally potent in causing premature mortality because th e 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.

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ES-24 ES.7 Economic Impacts of the Proposed Em ission Guidelines for Existing EGUs
The proposed guidelines have important ener gy market implications. Under Option 1, average nationwide retail electric ity prices are projected to incr ease 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 el ectricity bills are antic ipated 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 pe rcent in 2030, relative to the base case (Option 1). The EPA projects coal production for use by the power sect or, a large component of total coal production, will decline by roughly 25 to 27 percent in 2020 fr om 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 pow er sector-delivered na tural gas prices will increase by 9 to 12 percent in 2020, with neglig ible changes by 2030 relativ e to the base case. Natural gas use for electricity ge neration 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 impact s from the guidelines are discussed more extensively in Chapter 3 of this RIA. ES.8 Economic Impacts of the Proposed Gu idelines for Existing EGUs for Sectors Other Than the EGU Sector and for Employment
Changes in supply or demand for electricity, na tural gas, and coal can impact markets for goods and services produced by sectors that use these energy inputs in th e production process or that supply those sectors. Cha nges in cost of production may re sult in changes in price and/or quantity produced and these market changes ma y affect the profitability of firms and the
economic welfare of their consumers. The EP A recognizes that these guidelines provide significant flexibilities and states implementing the guidelines may choose to mitigate impacts to
some markets outside the EGU sector. Sim ilarly, demand for new generation or energy efficiency can result in changes in production an d profitability for firms that supply those goods

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ES-25 and services. The guidelines provide flexibility fo r 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 mu st be based on the best available science” (Executive Order 13563, 2011). Alth ough standard benefit-cost an alyses have not typically included a separate analysis of regulation-i nduced 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 flexibil ity to implement policies and practices for compliance with Proposed Electric Generating Un it 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 a ssociated 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 de tailed 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 fo r Option 1, state and regional compliance approaches, respectively. For Option 2, the state and regional compliance approach estimates reflect an increase of approximat ely 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 re gional 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 appro aches). More detail about these analyses can be f ound in Chapter 6 of this RIA.

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ES-26 ES.9 Modified and Reconstructed Sources

The EPA is proposing emission limits for CO 2 

## emitted from reconstructed and modified EGUs under section 111(b) of the CAA. Based on hi storical information that has been reported to the EPA, the EPA anticipates few, if any, c overed units will trigger the reconstruction or modification provisions in the period of anal ysis (through 2025). As a result, we do not anticipate any significant costs or benefits associ ated 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 co sts and benefits for a hypothetical unit if it were to trigger the modification provision.

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 Medi cal Association, 292(19): pp. 2372-8. Docket ID EPA-HQ-OAR-2009-0472-114577, Technica l Support Document: Social Cost of Carbon for Regulatory Impact Analysis U nder Executive Order 12866, Interagency Working Group on Social Cost of Carbon, with partic ipation by the Council of Economic Advisers, Council on Environmental Quality, Department of Agriculture, Department of Commerce, Department of Energy, Department of Tran sportation, Environmental Protection Agency, National Economic Council, Office of Energy a nd Climate Change, Office of Management and Budget, Office of Science and Technology Po licy, and Department of Treasury (February 2010). Available at: <http://www.whitehouse.gov/sites/default/files/omb/inforeg/foragencies/Social-Cost-o f-Carbon-for-RIA.pdf>. Docket ID EPA-HQ-OAR-2013-0495, Technical Suppor t Document: Technical Update of the Social Cost of Carbon fo r 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 Environmenta l Quality, Department of Agriculture, Department of Commerce, Department of Energy, Department of Transportation, Environmental Protection Agency, National Econ omic Council, Office of Energy and Climate Change, Office of Management and Budget, O ffice of Science and Technology Policy, and Department of Treasury (May 2013, Revise d November 2013). Also available at:

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ES-27 <http://www.whitehouse.gov/sites/default/files/o mb/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 Industr ial, 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. 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/public ations_ipcc_fourth_assessment_report_synt hesis_report.htm>. Lepeule, J.; F. Laden; D. Dockery and J. Sc hwartz. 2012. “Chronic Exposur e to Fine Particles and Mortality: An Extended Follow-Up of th e Harvard Six Cities Study from 1974 to 2009.” Environmental Health Perspe ctives, 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 Analys is.” Epidemiology, 16(4): pp. 458-68. National Research Council (NRC). 2000. Toxicol ogical Effects of Methylmercury: Committee on the Toxicological Effects of Methylme rcury. Board on Environmental Studies and Toxicology. Washington, DC: Na tional Academies Press. National Research Council (NRC). 2002. Estimating th e 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/reco rdisplay.cfm?deid=198843>.

U.S. EPA. 2008b. Final Ozone NAA QS Regulatory Impact Analysis. Research Triangle Park, NC: Office of Air Quality Planning and Sta ndards, Health and Environmental Impacts Division, Air Benefit and Cost Group. (EPA document number EPA-452/R-08-003, March). Available at <http://cf pub.epa.gov/ncea/cfm/recordisplay.cfm?deid=194645>.

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ES-28 U.S. EPA. 2008c. Integrated Science Assessment fo r Oxides of Nitrogen: Health Criteria (Final Report). Research Triangle Park, NC: National Center for Environmental Assessment. July. Available at <http://www.epa.gov/ttnecas1/re gdata/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 docum ent number EPA/600/R-08/139, December). Available at <http://cf pub.epa.gov/ncea/cfm/recordisplay.cfm?deid=201485>. U.S. EPA. 2009. Integrated Science Assessment fo r Particulate Matter (Final Report). Research Triangle Park, NC: National Center for Envi ronmental Assessment, RTP Division. (EPA document number EPA-600-R-08-139F , December). Available at

< http://cfpub.epa.gov/ncea/cfm/r ecordisplay.cfm?deid=216546 >.
U.S. EPA. 2010d. Section 3: Re

‐ analysis of the Benefits of Attaining Alternative Ozone Standards to Incorporate Cu rrent 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 fo r 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, Dece mber). 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. Envi ronmental Protection Agency. (EPA document number EPA/600/R-10/076F). Available at <http://cfpub.epa.gov/ncea/is a/recordisplay.cfm?deid=247492#Download>. Woodruff, T.J.; J. Grillo and K.C. Schoendor f. 1997. “The Relationship Between Selected Causes of Postneonatal Infant Mortality and Pa rticulate Air Pollution in the United States,” Environmental Health Perspectives , 105 (6): 608-612.

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1-1 

## CHAPTER 1: INTRODUCTION AND BACKGROUND

1.1 Introduction
This document presents the expe cted benefits, costs, and eco nomic impacts of illustrative strategies states may implement to comply w ith the proposed Electric Utility Generating Unit (EGU) Existing Source GHG Guidelines in 202 0, 2025, and 2030. Furthermore, this document provides a separate description of the expected benefits, co sts, and economic impacts of proposed emission limits for reconstructed and m odified EGU sources. This chapter contains background information on these rules and an outline of the chapte rs 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) require s performance standards for air pollutant emissions from categories of stationary source s 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 C AA. This ruling clarified that the authorities and requirements of the CAA apply to GHGs. As a result, the EPA must make decisions a bout whether to regulate GHGs under certain provisions of the CAA, based on relevant statutory cr iteria. 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 Endange rment 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 (CO 2 

## ) from newly constructed, reconstructe d, and modified sources. In January 2014, under the authority of CAA s ection 111(b), the EPA proposed standards for emissions of
CO 2 

## from newly constructed fossil fuel-fired electr ic steam generating units (utility boilers and integrated gasification combined cycle [IGCC] units) and for natural gas-fired stationary
combustion turbines. The EPA is curren tly proposing standards to address CO 2 

## emissions from reconstructed and modified power plants under the authority of CAA section 111(b). Furthermore, when the EPA establishes secti on 111(b) standards of performance for newly

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1-2 constructed sources in a particul ar source category for a pollutant that is not regulated as a criteria pollutant or hazardous air pollutant, th e EPA must establish requirements for existing sources in that source category for that pol lutant under section 111(d). Under 111(d), the EPA develops “emission guidelines” that the states mu st 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 hea lth and the public welfare.” 8 

## These adverse impacts make it necessary for the EPA to regulate GHGs from E GU sources. This proposed rule is designed to reduce the rate at which atmosphe ric concentrations of these ga ses 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 Endange rment Finding closed following the EPA’s 2010
Reconsideration Denial. The EPA has reviewed thes e 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 w ith links to climate change have become more frequent and/or intense, in cluding 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 becomi ng 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.

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1-3 1.2.3 Market Failure Many regulations are promulgated to correct ma rket failures, which lead to a suboptimal
allocation of resources within the free market . Air quality and polluti on 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, su ch 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 so cial costs associated with the health and welfare impacts are referred to as negative externalities. If an electricity producer pollutes the atmosphere wh en it generates electricity, this cost will be borne not by the polluting firm but by society as a whole. The mark et price of electricity will fail to incorporate the full opportunity cost to society of generating el ectricity. 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. Co nsequently, 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 econom y of $100 million or more or adversely affect in a material way the economy, a sector of th e economy, productivity, competition, jobs, the environment, public health or safety, or state, local, or tribal governments or communities. This RIA addresses the potential costs, emi ssion 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 benefits, costs and economic impacts of proposed emission limits for reconstructed and modified

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1-4 EGU sources. In evaluating the impacts of the proposed guidelines, we analyzed a number of uncertainties. For example, the analysis include s an evaluation of different potential spatial approaches to state compliance (i.e., state and regi onal) and in the estimated benefits of reducing carbon dioxide and other air pollutants. For a furthe r discussion of key evaluations of uncertainty in the regulatory analyses for this propos ed 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 an alysis in this RIA is based proposes GHG emission guidelines for states to limit CO 2 

## emissions from certain existing EGUs. The base case for this analysis, which uses the Integrated Plan ning Model (IPM), includes state rules that have been finalized and/or approved by a state’s legislature or environm ental agencies, as well as final
federal rules. The IPM Base Case v.5.13 incl udes the Clean Air Interstate Rule (CAIR), 9 

## the Mercury and Air Toxics Rule (MAT S), and other state and Federal re gulations to the extent that they contain measures, permits, or other air- related limitations or re quirements. Additional legally binding and enforceable commitments fo r 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 se lected because they represent partial and full implementation dates for the two policy options analyzed. Analysis of employment impacts is presented for complian ce in 2020, 2025, and 2030 for Option 1 and 2020 and 2025 for Option 2. All estimates are pres ented in 2011 dollars.

9 EPA Base Case v.5.13 includes the Clean Air Interstate Rule (CAIR), a Federal regulatory measure for achieving the 1997 Natio nal Ambient Air Quality Standards (NAAQS)

for ozone (8-hour average of 0.08 ppm) and fine particles (24-hour average of 65 μ g/m3 or less and annual average of 15 μ g/m3 for 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 Dis trict 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/BaseCase v513.html

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1-5 1.4.2 Definition of Affected Sources
This proposed rule under CAA section 111(d) will set emission guidelines for states to
limit CO 2 

## 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 modifi ed 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 a ffected EGU is any fossil fuel-fired EGU that was in operation or had commenced constructi on 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 Fede ral Register on that date. The proposed GHG standards for newly constructed EGUs generally de fine an affected EGU as any boiler, IGCC, or combustion turbine (in either simple cycle or combin ed 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 grea ter of 219,000 MWh per year or one-third of its potential el ectrical output to a utility distri bution 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 pub lished in the Federal Register). The minimum fossil fuel consumption condition applies over an y 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 (o r as long as the unit has b een in operation, if less). 1.4.3 Regulated Pollutant This rule sets emission guidelines for CO 2 

## emissions from affected sources. The EPA is proposing these guidelines because CO 2 

## is a GHG and fossil fuel-fired power plants are the 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

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1-6 current and future generations. 11 

## The EPA is aware that other GHGs such as nitrous oxide (N 2 

## O) (and to a lesser extent, methane [CH 4 

## ]) may be emitted from fossil-fuel-fired EGUs, especially from coal-fired circulating fluidized bed combustors and from units with selective cat alytic reduction and selective non-catalytic reduction syst ems installed for nitrogen oxide (NO X 

## ) control. The EPA is not proposing separate N 2 

## O or CH 4 

## guidelines or an equivalent CO 2 

## emission limit because of a
lack of available data for these affected s ources. Additional information on the quantity and significance of emissions and on the availability of controls of r easonable cost would be needed before proposing standards for these pollutants.
1.4.4 Emission Guidelines The EPA is proposing emission guidelines for stat es to use in developing plans to address greenhouse gas emissions from existing fossil fuel-f ired electric generating units. Specifically, the EPA is proposing state-spec ific rate-based goals for CO 2 

## emissions from the power sector, as well as guidelines for states to follow in deve loping plans to attain the state-specific goals. (These state-based goals can be found in the prea mble and Chapter 3 of this RIA.) The proposed emission guidelines provide states with options fo r establishing standards of performance in a manner that accommodates a diverse range of st ate approaches. The proposed guidelines would
also allow states to collaborate and to de monstrate compliance on a multi-state basis, in recognition of the fact that electricity is transmitt ed across state lines, and measures often impact regional EGU CO 2 

## emissions. The illustrative compliance strategies presented in this RIA include both regional and state-level complian ce scenarios for each of the regulatory options presented. 1.5 Organization of the Regu latory Impact Analysis This report presents the EPA’s analysis of the potential benefits, costs, and other economic effects of the proposed EGU Exis ting Source GHG Guidelines to fulfill the requirements of an RIA. This RI A includes the following chapters:

11 

### Endangerment and Cause or Contribute Findings for Greenhouse Gases under Section 202(a) of the Clean Air

Act.

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1-7  Chapter 2, Electric Power Sector Profile

 Chapter 3, Control Strategies, Cost, Economic, and Energy Impacts

 Chapter 4, Estimated Climate Be nefits and Health Co-benefits

 Chapter 5, Economic Impacts – Market s 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

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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 Findi ngs 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 Findi ngs 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 Nati onal 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

.

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2-1 

## CHAPTER 2: ELECTRIC POWER SECTOR PROFILE

2.1 Introduction
This chapter discusses important aspects of th e power sector that relate to the proposed EGU Existing Source GHG Standards, including th e 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 re gulation 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 th e delivery of electricity to consumers. Most of the existing capacity for genera ting electricity does so by creati ng heat to create high pressure
steam that is released to rotate turbines wh ich, in turn, create electr icity. The existing power sector consists of over 18,000 ge nerating units, comprising fossil-fu el-fired units, nuclear units, and hydroelectric and other renewable sources di spersed throughout the c ountry (see Table 2-1).

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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. Actu al 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 Gen eration 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 export ed electricity and loss of electricity that occurs through transmission and distribution.

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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 rela tively constant, such as industrial processes that operate 24 hours a day.
Table 2-3. Total U.S. Electric Pow er 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 El ectric Power Annual, 2013a



## Coal-fired generating units have historically supplied “base-load” electricity, the portion of electricity loads which are continually pr esent, and typically ope rate 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 exam ple, coal-fired units less than 100 megawatts (MW) in size compose 32 percent of the total nu mber of coal-fired units , but only 4 percent of total coal-fired capacity. Gas-fired generation is better able to vary output a nd is the primary option used to meet the variable portion of the electricity load and ha s historically supplied “peak” and “intermediate” power, when there is in creased demand for electricity (for example, when businesses operate throughout the day or when people return home from work and run appliances and heating/air-conditi oning), 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 lo w natural gas prices, have resulted in more gas being utilized as base load energy in addition to supplying electricity during p eak load. Projections of changes in capacity and the impact of this rule on the future need for construction of new generation

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2-4 capacity sources are discussed in more detail in Chapter 3 of this RIA.
Table 2-4. Coal Steam Electrici ty 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 h eat 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-s team 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.

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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, so me facilities may be obscured.



## 2.2.2 Transmission

Transmission is the term used to describe th e movement of electrici ty 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 interconn ected 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 Re gion, Eastern Interconnect Region, and ERCOT, respectively.

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2-6 networks, there are multiple areas where the operation of power plants is monitored and
controlled to ensure that electric ity generation and load are kept in balance. In some areas, the operation of the transmission system is under the c ontrol of a single regional operator; in others,
individual utilities coordinate the operations of thei r generation, transmission, and distribution systems to balance their comm on generation and load needs.
2.2.3 Distribution

Distribution of electrici ty involves networks of lower vo ltage lines and substations that take the higher voltage power from the transmi ssion system and step it down to lower voltage
levels to match the needs of customers. The tr ansmission 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 so urces to substations or from substations to
residences and businesses. Transmission has generally been developed by th e larger vertically integrated utilities that typically operate generati on and distribution networks. Ofte n distribution is handled by a large number of utilities that purchase and sell electricity, but do not gene rate it. Over the last couple of decades, several jurisdictions in th e United States began restructuring the power industry to separate transmissi on and distribution from genera tion, ownership, and operation. As discussed below, electricity restructuring has focused primarily on efforts to reorganize the industry to encourage competition in the genera tion segment of the industry, including ensuring open access of generation to the transmission and di stribution 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-regul ated throughout the country base d 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 powe r industry. In addition to reorganizing asset management between companies, restructuri ng sought a functional unbund ling of the generation, transmission, distribution, and anci llary services the power sector has historically provided, with the aim of enhancing competition in th e generation segment of the industry.

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2-7 Beginning in the 1970s, government policy shifted against traditional regulatory approaches and in favor of de regulation for many important i ndustries, including transportation (notably commercial airlines), communications, and energy, which were all thought to be natural monopolies (prior to 1970) that warranted gove rnmental 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 includ ed the desire for more efficient investment choices, the economic incentive to provide least-cost electric rate s through market competition,
reduced costs of combustion turb ine 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 pow er 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, restructuri ng had either been dela yed or suspended in eight states that previously enac ted legislation or issu ed regulatory orders for its implementation (shown as “Suspended” in Figure 2-2 below). Eight een 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). Currentl y, there are 15 states wh ere price deregulation of generation (restructuring) has occurred (“Ac tive” in Figure 2-2 below). Power sector restructuring is more or less at a standstill; ther e have been no recent proposals to the Federal Energy Regulatory Commission (FERC) for acti ons aimed at wider restructuring, and no additional states have recently begun retail deregulation activity.

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2-8 Figure 2-2. Status of St ate Electricity Industry Re structuring Activities
Source: EIA “Status of Electricity Restructuring by State” 2010a.



## 2.4 Emissions of Greenhouse Ga ses from Electric Utilities The burning of fossil fuels, which genera tes about 70 percent of our electricity nationwide, results in emissions of greenhouse gases. The power sect or is a major contributor of
CO 2 

## in particular, but also contributes to emissions of sulfur hexafluoride (SF 6 

## ), CH 4 

## , and N 2 

## O. In 2012, the power sector accounted for 32 percen t of total nationwide greenhouse gas emissions,
measured in CO 2 

## 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 s ector relative to other major economic sectors. Table 2-6 and Figure 2-4 show the contributions of CO 2 

## and other GHGs from the power sector.

13 

### All CO 2 

### equivalent tons in this report are based on th e 100-year time horizon Global Warming Potential.

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2-9 Table 2-5. Domestic Emissions of Greenhouse Gases, by Econ omic Sector (million metric tons of CO 2 

## 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 CO 2 

### emissions from the electricity generation sector differ slightly from the 2005 CO 2 

### 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 Sink s: 1990–2012 . April 2014.



## Figure 2-6. Domestic Emission s of Greenhouse Gases, 2012 (million metric tons of CO 2 

## equivalent)
Source: EPA Inventory of U.S. Greenhouse Gas Emissions and Sink s: 1990–2012 . April 2014.

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2-10 Table 2-6. Greenhouse Gas Emissions fr om the Electricity Sector (Generation, Transmission and Distribution), 2012 (million metric tons of CO 2 

## equivalent) Source Total Emissions CO 2 

### from Electricity Generation 2,023 Coal 1,511 Natural Gas 492 Fuel Oil 19 Geothermal 0.4 CH 4 

### from Electricity Generation 0.5 Coal 0.1 Fuel Oil 0.1 Natural Gas 0.4 Wood + N 2 

### O from Electricity Generation 18.3 Coal 9.1 Fuel Oil 0.3 Natural Gas 8.7 Wood 0.1 SF 6 

### from Electricity Transmission and Distribution 6.0 Total 2,048
Source: EPA Inventory of U.S. Greenhouse Gas Emissions and Sink s: 1990–2012 . April 2014. + Does not exceed 0.05 Tg CO 2 

### Eq. or 0.05 percent.



## The amount of CO 2 

## 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 CO 2 

## 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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2-11 Figure 2-3. Direct GHG Emission s from the Power Sector Rela tive to Total Domestic GHG Emissions (2012)
Source: EPA Inventory of U.S. Greenhouse Gas Emissions and Sink s: 1990–2012 . April 2014.



## Table 2-7. Fossil Fuel Emission Fa ctors 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: CO 2 

### 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%

4% 0.1% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

CO2 SF6 N2O CH4 Other

Power Sector

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2-12 2.5 Improving GHG Performance at Existing EGUs In proposing state goals, the analysis anticip ates that states wi ll pursue a mix of carbonreducing strategies appropriate to each state’s unique situation, deve loping an effective state plan that reflects the composition of the state’s ec onomy, existing state programs and measures, and
characteristics of the state’s ex isting electricity power system (e .g., utility regulatory structure, generation mix, transmission system and electric ity demand). The analysis assumes states will develop plans involving four cat egories (“building bloc ks”) of demonstrated approaches to improve the GHG performance of existing EGUs in the power sector: 1. Reducing the carbon intensity of generation at individual affect ed EGUs through heatrate improvements.
2. Reducing emissions from the most carbon-intensive affected EGUs in the amount that results from substituting generation at t hose 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 expa nded 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 redu ces 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 improve ments from each category, and more detail about how we used each state’s unique situation and av ailability of demonstrated approaches to improve GHG performance to determine state-sp ecific 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 CO 2 

## they emit when generating power. Heat rate improvements result in any changes in equipment, operating pr ocedures 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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2-13 electricity being generated by each unit of fuel (e .g., ton of coal or cubic foot of gas), thereby lowering the amount of CO 2 

## per kWh of electricity produced as a byproduct of fuel combustion. The second building block consists of impr ovements to lower the electric system’s overall carbon intensity by shif ting generation among existing EGUs. The nation’s EGUs are connected by transmission grids extending over la rge regions. Through th ese interconnections, EGU owners and/or grid operato rs 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 sy stem’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 ofnatural gas have helped reduce natural gas prices and improved the competitive position of gasfired units relative to coal-fir ed units. Operators have already shifted significant quantities of generation from coal units to NGCCs, absent any federal CO 2 

## 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 su ch as the mix of EGU types, the relative prices of coal and natural ga s and the amount of available NGCC capacity. The third building block consists of the poten tial to increase the amount of lower carbon intensity generation by expanding low-carbon a nd renewable generating capacity. Adding new nuclear or renewable generating cap acity to the electric system w ould 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 a nd 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 generatio n required – in other words, improving demandside efficiency. Many studies have found th at significant improvements in demand-side

14 

### We view opportunities to shift generation to existing re newable EGUs such as wind or solar units as limited

because such units already tend to ru n when capable of doing so due to their low variable operating costs.

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2-14 efficiency can be realized at less cost th an the savings from av oided 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 em ploy 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 e nhances efficiency by reducing the absolute
amount of transmission and distri bution losses that occu r across the grid betw een the electricity generation sites and the demand sites. Particul arly when integrated into a comprehensive approach for addressing GHG emissions, demand-s ide 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 pe rformance 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 Yo rk sent a letter to hi s counterparts in the
Northeast and Mid-Atlantic inviti ng them to participate in the development of a regional capand-trade program addressing power plant CO 2 

## emissions. This program, known as the Regional Greenhouse Gas Initiative (RGGI), be gan in 2009 and sets a regional CO 2 

## cap for participating states. The currently particip ating states include: Connectic ut, Delaware, Maine, Maryland, Massachusetts, New Hampshire, New York, Rh ode Island, and Vermont. The cap covers CO 2 

## emissions from all fossil-fired EGUs greater than 25 MW in participating states, and limits total
emissions to 91 million short tons in 2014. Th is emissions budget is reduced 2.5% annually
from 2015 to 2020. In September 2006, then-Governor of Califor nia Arnold Schwarzene gger signed into law

15 

### See, e.g., Granade, et al., July 2009 and EPRI, 2009.

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2-15 Senate Bill 1368. The law limits long-term invest ments in baseload generation by the state's utilities to power plants that meet an emissions performance standard jo intly established by the California Energy Commission and the Californi a 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 contr act to publicly owned utilities, of 1,100 lb
CO 2 

## /MWh. In 2006 Governor Schwarzenegger also signed into law Assembly Bill 32, the Global Warming Solutions Act of 2006. This act incl udes a multi-sector GHG cap-and-trade program which covers approximately 85% of the state GHG emissions. EGUs are includes 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 Gregoi re of Washington signed Substitute Senate Bill 6001, which established statew ide GHG emissions reduction goals, and imposed an emission standard that applies to any ba seload electric generation that commenced operation after June 1, 2008 and is located in Washington, whether or not th at generation serves load located within the state. Baseload genera tion facilities must init ially comply with an emission limit of 1,100 lb
CO 2 

## /MWh. Bill 6001also prohibited Wa shington electric utilities from entering new long-term power contracts after June 30, 2008 with any power plant exceeding the 1,100 lb CO 2 

## /MWh
limit. In July 2009, then-Governor Theodore Kulongoski of Oregon signed Senate Bill 101, which mandated that facilities ge nerating baseload electricity, whet her gas- or coal-fired, must have emissions equal to or less than 1,100 lb CO 2 

## /MWh, and prohibited u tilities from entering into long-term purchase agreements for baseload el ectricity with out-of-state facilities that do not meet that standard. Natural gas- a nd petroleum distillate-fired facilities that are primarily used to serve peak demand or to integrate energy from renewable resources are specifically exempted from the performance standard. In August 2011, New York Governor Andrew Cuomo signed the Power NY Act of 2011. This regulation establishes CO 2 

## emission standards for new and modified electric generators

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2-16 greater than 25 MW. The standards vary based on the type of fac ility: baseload facilities must
meet a CO 2 

## standard of 925 lb/MWh or 120 lb/MMBtu, and peaking facilities must meet a CO 2 

## 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 tota l electricity generation. Currently 29 states and the District of
Columbia have enforceable RPS or other mandato ry 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 policie s and programs that reduce power sector CO 2 

## emissions, see the State Plan Considerations Technical Support Document Appendix.
2.6.2 Federal Policies
In April 2007, the Supreme Court concluded th at GHGs met the CAA definition of an air pollutant, giving the EPA the authority to re gulate GHGs under the CAA contingent upon an agency determination that GHG emissions from ne w motor vehicles cause or contribute to air pollution that may reasonably be anticipated to en danger 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 st age for the determination of whether other sources of GHG emissions, includin g stationary sources, would need to be regulated as well. In response to the FY2008 Consolidated A ppropriations Act (H.R. 2764; Public Law 110–161), the EPA issued the Mandatory Reporti ng of Greenhouse Gases Rule (74 FR 5620) which required reporting of GHG data and other re levant information from fossil fuel suppliers and industrial gas suppliers, direct greenhouse gas emitters, and manufactur ers of heavy-duty and off-road vehicles and engines. The purpose of the rule was to collect accurate and timely GHG data to inform future policy decisions. As suc h, it did not require that sources control greenhouse gases, but sources above certa in threshold levels must m onitor and report emissions. In August 2007, the EPA issued a prevention of significant deterioration (PSD) permit to

16 

### Database of State Incentives for Renewables and Efficiency, March 2013 and Alaska House Bill 306, 2010.

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2-17 Deseret Power Electric Cooperative , authorizing it to construct a new waste-coal-fired EGU near its existing Bonanza Power Plan t, in Bonanza, Utah. The permit did not include emissions control requirements for CO 2 

## . The EPA acknowledged the Supreme Court decision, but found that decision alone did not require PSD permits to include limits on CO 2 

## 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 CO 2 

## 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 fi nding that there was not an established EPA interpretation of the regulatory phrase “subj ect to regulation.” In December 2008, the Administrator issued a memo indicating that the PSD Permitting Program would apply to pollutant s that are subject to either a provision in the CAA or a regulation adopted by the EPA under the CAA that re quires actual control of emissions of that pollutant. The memo further explained that pollu tants for which the EPA regulations only require monitoring or reporting, such as the provisions for CO 2 

## in the Acid Rain Program, are not subject to PSD permitting. Fifteen organizations petitione d 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 triggere d 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 limitin g GHG emissions for cars and light trucks was scheduled to take effect. Therefore, a permit i ssued after January 2, 2011, would have to address
GHG emissions. The Administrator signed two distinct fi ndings 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 — CO 2 

## , CH 4 

## , N 2 

## O, hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), and SF 6 

## — in the atmosphere threaten the public health and welfare of current and futu re generations. These greenhouse gases have long lifetimes and, as a result, become homogeneous ly distributed through th e lower level of the Earth’s atmosphere (IPCC, 2001). This differentia tes them from other greenhouse gases that are not homogeneously distributed in the atmosphere . The cause and contribute finding indicated

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2-18 that the combined emissions of these well-mixe d 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 publishe d in the Federal Register on December 15, 2009 (Docket ID EPA-HQ-OAR-2009-0171). These fi ndings 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 propos ed 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 reconsider ation were challenged in a lawsuit; on June 26, 2012, the DC Circuit Court upheld the Endangerm ent Finding and the Reconsideration Denial, ruling that the Finding was neither arbitrary nor capricious, was cons istent with Massachusetts v. EPA, and was adequately supported by the admini strative record. The Court found that the EPA
had based its decision on “substantial scientif ic evidence,” noted that the EPA’s reliance on
assessments was consistent with the methods deci sion-makers often use to make a science-based judgment, and stated that “EPA’s interpre tation of the governing CAA provisions is unambiguously correct.” In May 2010, the EPA issued the final Tail oring 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 stat ionary 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 sour ce performance standard (NSPS) for
emissions of carbon dioxide for new fossil fuel-fir ed electric utility gene rating units. This action proposes to establish separate standards for fossil fuel-fired electric steam generating units (utility boilers and Integrated Gasification Co mbined Cycle (IGCC) units) and for natural gasfired stationary combustion turbines. These proposed standards reflect separate determinations of the best system of emission reduction (BSER) ad equately demonstrated for utility boilers and IGCC units and for natural gas-fired stationary combustion turbines. This action proposes a

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2-19 standard of performance for utility boilers a nd IGCC units based on partial implementation of carbon capture and storage (CCS) as the BSER. Th e proposed emission limit for those sources is 1,100 lb CO 2 

## /MWh. This action also proposes standards of performance for natural gas-fired stationary combustion turbines based on modern, efficient natura l gas combined cycle (NGCC) technology as the BSER. The proposed emission limits for those source s are 1,000 lb CO2/MWh for larger units and 1,100 lb CO 2 

## /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 billi on in 2008. Despite revenues not returning to 2008, operating expenses were appreciably lower a nd 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 pr essure 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 sale s and revenues associated with the generation, transmission, and distribution of electricity are expected to rebound a nd increase modestly by 2015, when revenues are projected to be roughly $359 billion (see Table 2-9).
Table 2-8 shows that investor-owned utilit ies (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 reta il electricity prices have ranged from around 8 cents per kWh in the early 1970s, to around 12 cents, reached in the early 1980s. Generally, retail electricity prices do not cha nge rapidly and do not disp lay the variability of other energy or commodity prices, although the fr equency at which these prices change varies across different types of customers. Retail rate regulation has largely in sulated consumers from the rising and falling wholesale electricity price si gnals whose variation in the marketplace on an

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2-20 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 Reta il Electricity Price (1960 – 2013, 2011$)

Source: EIA Monthly Energy Reviewok 2013, BEA National Income Product Accoun ts 2014



## 0 2 4 6 8 10 12 14

1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010cents/KWh(2011$)

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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 Cate gory 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 categor y 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 pric es that can be as much as d ouble those of other states (see Figure 2-6).

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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 experi enced significant price volatility from year to year, between seasons w ithin a year, and can unde rgo 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 n early $10/mmBtu on an annual average basis (see Note: Data are displayed as 5 groups of 10 States and the District of Columbia. U.S. total average price per kilowatt-hour is 9.90 cents. Source: U.S. Energy Information Administration, 2011b.

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2-23 Figure 2-7). During that time, th e daily price of natural gas reached as high as $15/mmBtu. 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 preci se quantity of the resource base. Current and projected natural ga s prices are considerably lowe r 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 res ources and substantially increased the supply of economically recoverable natural gas. Accordi ng 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 source s 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 previ ously 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 Administra tion's Annual Energy Outlook 2012 (Early Release) estimates that the United States posse ssed 2,214 trillion cubic feet (Tcf) of technically recoverable natural gas resources as of Ja nuary 1, 2010. Natural gas from proven and unproven
shale resources accounts for 542 Tc f 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. Conseque ntly, the estimate of technically recoverable resour ces is highly uncertain, and is regularly updated as more information is gained through drilling and produ ction. At the 2010 rate of U.S. consumption (about 24.1 Tcf per year), 2,214 Tcf of natural ga s is enough to supply over 90 years of use. Although the estimate of the shale gas resource base is lower than in th e prior edition of the Outlook, shale gas production estimates increa sed between the 2011 and 2012 Outlooks, driven by lower drilling costs and continue d drilling in shale plays with high concentrations of natural gas liquids and crude oil, which have a higher valu e in energy equivalent terms than dry natural

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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 st ill 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 wa s $4.49 per MMBtu in 2013, higher than in 2012 ($3.54/MMBtu), but still dow n from $4.89/MMBtu in 2011. 19 

## EIA projections of future natural gas prices assume trends that are consistent with
historical and current market be havior, technological and demogra phic 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 pr ice 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 un certainties inherent in th e 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/fa qs/faq.cfm?id=45&t=8)

20 

### EIA 2010b.

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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 a ligned 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 c onstant over the past 30 years (EIA, 2010d). The growth rate of electricity dema nded has also been in overall decline for the past sixty years (see Figure 2-8), with several key driver s 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 20 15 2020 2025 2030 2035 20402011dollarspermillionBtu 

# Natural Gas Spot Price, Annual Average (Henry Hub)

EIA Historical

EIA Projected (AEO 2012)

EIA Projected (AEO 2013)

EIA Projected (AEO 2014)

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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 le vel have reduced demand. These broader changes
have altered the outlook for future electricity growth.

Figure 2-8 . Electricity Growth Rate (3-Y ear 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 in crease significantly (to $8 billion or more) for at

21 

### EIA 2013b

22 

### American Council for an Energy-Efficient Econ omy (ACEEE). November 2013. The 2013 State Energy

Efficiency Scorecard. Available at ht tp://www.aceee.org/state-policy/scorecard. 

## ‐ 2% 0% 2% 4% 6% 8% 10% 12% 14%

1950 1960 1970 1980 1990 2000 2010 2020 2030 2040

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2-27 least the next decade 23 

## , driven largely by the growing number of states that have adopted energy efficiency resource standards. These investments, and other energy efficien cy policies at both the state and federal level, create incentives to reduce electricit y consumption and peak load. According to data reported to EIA, energy e fficiency 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 relativ ely 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 generati on modeling (see the discussion in Ch apter 3) to the increases in electricity prices typically pr ojected for EPA rulemakings.

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.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/.

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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 Pr ograms in the United States: Projected Spending and Savings to 2025. LBNL-5803E. Available at http://emp.lbl.gov/publications/f uture-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; Derkac h, 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/elec tric_power_and_natural_ga s/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/publica tions/other/ipcc_tar/?src=/ climate/ipcc_tar/wg1/218.htm International Energy Agency (IEA).2 013. “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. U.S. Bureau of Economic Analyis (BEA) “ National Income Product Accoun ts Tables, Table 1.1.9. Implicit Price Deflators for Gross Dome stic Product, May 20, 2014.” Available online

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2-29 at: http://www.bea.gov/iTable/iTable.cfm?reqi d=9&step=3&isuri=1&9 03=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 Unite d 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/electr icity/annual/archive/03482003.pdf

U.S. EIA. 2009. “Electric Power Annual 2009.” Available online at: http://www.eia.gov/electr icity/annual/archive/03482009.pdf U.S. EIA. 2010a. “Status of Electricity Re structuring by State.” Available online at: http://www.eia.gov/cneaf/elect ricity/page/restructurin g/restructur e_elect.html U.S. EIA. 2010b. “AEO 2010 Retrospec tive Review.” Ava ilable online at: http://www.eia.gov/forecas ts/aeo/retrospective/ U.S. EIA. 2010c. “Annual Energy Outlook 2010.”Available online at: http://www.eia.gov/oiaf/a rchive/aeo10/index.html U.S. EIA. 2010d. “Annual Energy Re view 2010.” Available online at: http://www.eia.gov/totalene rgy/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/elect ricity/data/state/ U.S. EIA. 2012a. “Annual Energy Outlook 2012 (E arly Release).” Available online at: http://www.eia.gov/forecasts/aeo/ U.S. EIA. 2012b. “Energy in Brie f: What is Shale Gas and Why is it Important?” Available online at: http://www.eia.gov/energy_in_br ief/about_shale_gas.cfm

U.S. EIA. 2013a. “Electric Power Annual 2012.”Available online at: http://www.eia.gov/electricity/annual/ U.S. EIA.2013b. “Annual Energy Ou tlook 2013.” Available online at: http://www.eia.gov/forecasts/aeo/

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2-30 U.S. EIA.2014. “Annual Energy Outlook 2014.” Available online at: http://www.eia.gov/forecasts/aeo/ U.S. EIA. 2013. “Monthly Energy Revi ew May 2014.” Available online at: http://www.eia.gov/totalene rgy/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

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3-1 

## CHAPTER 3: COST, ECONOM IC, 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. IP M is a dynamic linear prog ramming model that can be used to examine air pollution control policies for CO 2 

## , SO 2 

## , NO X 

## , 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 sc ope of reductions in elect ricity 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-f ired electric generating units. Specifically, the EPA is proposing state-spec ific rate-based goals for CO 2 

## emissions from the power sector, as well as guidelines for states to us e in developing plans to attain th e 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 framewor k 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

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3-2 alternative option (Option 2) ha s annual estimated costs of between $4.2 and $5.4 billion in 2020 and between $4.5 and $5.5 billion in 2025. 2526



## 3.3 Power Sector Modelling Framework
The Integrated Planning Model (IPM), develo ped by ICF Consulting, is a state-of-the-art, peer-reviewed, dynamic linear prog ramming model that can be us ed to project power sector
behavior under future business-as-usual conditions and examine prospectiv e air pollution control policies throughout the contiguous Un ited States for the entire elec tric power system. EPA used IPM, inclusive of the electricity demand re ductions 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 ener gy efficiency-driven redu ctions in electricity
demand and their associated cost s are reported in Section 3.6.
IPM is a multi-regional, dynamic, determinis tic linear programming model of the U.S. electric power sector. It provide s 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 futu re business-as-usual conditions and evaluate the economic and emission impacts of prospectiv e environmental policies. The model is designed to reflect electricity markets as accurately as possible. EPA uses the best avai lable information from utilities, industry experts, ga s and coal market experts, fina ncial 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

25 

### These costs do not include monitoring, reporting, and re cordkeeping costs. For more information, See section

3.11.

26 

### All costs represent real dollars ($2011).

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

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3-3 Although the Agency typically focuses on br oad 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 mo del includes an endogenous representation of the 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 co rresponding gas producti on and price levels. 28 

## This module consists of 118 supply, demand, and stor age nodes and 15 liquefied natural gas regasification facility locations that are tied togeth er by a series of linkage s (i.e., pipelines) that represent the North American natural ga s transmission and di stribution network.
IPM also endogenously models the partial eq uilibrium 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 w ould 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 re serves, characterize th eir 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 industr y experts and have been made available for public review prior to this rulemaking process. 29

28 

### See Chapter 10 of EPA’s Base Case usin g 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

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3-4 EPA has used IPM extensively over the past two decades to analyze options for reducing power sector emissions. Previously, the model ha s been used to forecast the costs, emission changes, and power sector impacts for the Clea n 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.
The model and EPA's assumptions input into the model undergo peri odic formal peer review. The rulemaking process also provides op portunity for expert re view and comment by a variety of stakeholders, including owners and operators of capacity in the elec tricity sector that is represented by the model, public interest groups, a nd other developers of U. S. electricity sector models. The feedback that the Agency receives provides a highly detaile d review of key input assumptions, model representation, and modeling re sults. IPM has received extensive review by energy and environmental modeling experts in a va riety of contexts. For example, in the late 1990’s, the Science Advisory Board reviewed IP M as part of the CAA Amendments Section 812 prospective studies that are pe riodically conducted. The model ha s 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 exercise s 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 Gr oup), 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 previ ous version (v.4.10_MATS), and includes notable changes to the modeling architectu re. This is the fifth major iteration of EPA’s base case using IPM, and calibrates certain info rmation 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).

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3-5 The current base case represents a projection of electricity sector activity that takes into account only those Federal and stat e laws and regulations whose provi sions were either in effect or enacted and clearly delineated at the time the base case was finalized in August 2013. The
EPA Base Case v.5.13 includes the Clean Air Interstate Rule (CAIR), 30 

## the Mercury and Air Toxics Rule (MATS), and other st ate and Federal regulations to the extent that they contain measures, permits, or other air-re lated limitations or requirements. 31


## EPA has also updated the National Electric En ergy Data System (NEEDS). This database
contains the unit-level data that is used to cons truct the "model" plants that represent existing and
committed 32 

## 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 refl ecting planned new power plant construction, retirements, new power plan t cost and performance, pollution control costs

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 μ g/m3 or less and annual average of 15 μ g/m 3 

### for particles of diameter 2.5 micrometers or less, i.e., PM 2.5 

### ). Originally issued on March 10, 2005, CAIR was remanded back to EPA by the U.S. Court of Appeals for the District of Columbia Ci rcuit 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 purs uant to the Court’s ruling. CAIR’s provision s 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 Stru ctures 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) propos ed 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 th at the analysis described in this RIA would be meaningfully affected if the expected effects of the 316(b) rule we re 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/powersect ormodeling/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/powersector modeling/BaseCasev513.html>.

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3-6 and performance, emission rate assignments, and state rules a nd enforcement actions. The update
also included further refinement to the modele d regions to reflect more recent power market structure based on NERC, FERC, EIA, and other data and planning sources, and now includes more regions that better reflect limitations on current power sy stem dispatch and transmission
behavior.
3.4 State Goals in this Proposal
In this action, the EPA is propos ing state-specific rate-based goals to guide states in the development of their plans. The agency is pr oposing 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 (Adjus ted MWh-Weighted-Average Pounds of CO 2 

## Per Net MWh from all Affected Fossil Fu el-Fired EGUs) for Options 1 and 2

State 34 

### 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

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 note d above, EPA is not proposing goals for tribes or U.S. territories at this time.

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3-7 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 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

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3-8 Wyoming 1,808 1,714 1,907 1,869 * Excludes EGUs located in Indian country.



## Table 3-2. Projected Base Case CO 2 

## Emissions Rate (Adjuste d MWh-Weighted-Average Pounds of CO 2 

## 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 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

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3-9 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 sli ghtly from the state goals modelled in the illustrative comp liance scenarios analyzed in this RIA. Any differences in the goals are minor and reflect small adjustments to the goal sett ing methodology due to minor adjustments in the assumed at-ris k nuclear capacity, fossil units in cluded 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 differe nces in results obtained by analyzing these new goals would be negligible. Furthermore, th ese small changes in the goals would be offset by comparable changes in th e illustrative complianc e scenarios (in which compliance is consistent with the assumptions used in setting state goals).

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3-10 3.5 Compliance Scenarios Analyzed
In order to estimate the costs, benefits , and impacts of implementing the proposed guidelines, the EPA modeled tw o illustrative compliance scenar ios. One of these scenarios allows averaging of emission rates within each in dividual state (these scen arios are referred to as “state”), and another set of scenarios where gr oups of states are assumed to collaborate and achieve compliance across larger re gions (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 ar e 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 flexibili ties afforded to the stat es in developing their state plans. Nonetheless, the analysis of the be nefits, costs, and relevant impacts of the proposed rule attempts to encapsulate some of those flexib ilities in order to inform states and stakeholders of the potential overall impacts of the proposal. Th e 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 an alysis does not spec ify any particular CO 2 

## reduction measure to occur, with the exception of the leve l of demand-side energy efficiency (EE), which the model is not currently configured to incl ude as an endogenous compliance option. In other words, aside from EE, the analysis allows the po wer 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 ma nner 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 aver age, less than or equal to the proposed goals over the entire co mpliance period. That is, the s ources assumed to be directly affected by the illustrative state compliance scen arios are only those included in the calculation

35 

### For more details on the nature of the regulatory options, see the preamble.

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3-11 of the state goals, and include affected sources as well as sa vings 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 s ources to emit at rates hi gher than the relevant goal, as long as there is corre sponding generation coming from sour ces 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 a pplicable state goal, on average, over the entire compliance pe riod, 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. Af ter the initial compliance period the average emission rate of the affected sources in each year must be less th an or equal to the state goal in the illustrative compliance scenarios.

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 ra te 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 Abat ement Measures TSD

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3-12 For EE, the megawatt-hour (MWh) savings and associated costs are specified exogenously for Option 1 and Option 2 complian ce scenarios consistent with the EE "best practice" performance levels informing calcula tion 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 mo deling 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 s cenarios 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 ra te 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 fa ll into more than one region were grouped in regions where there was a majority of geographi c 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

 Northeast (NPCC) – NY, RI, MA, CT, NH, VT, ME

39 

### Note that the MISO region expanded to in tegrate Entergy territory at the end of 2013.

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3-13 Figure 3-1. NERC Regions



## Figure 3-2. NERC Assessment Areas

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3-14 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 aff ected units located in that region. Results are also presented for “State” scen arios (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, affe cted sources in each state must respond to their respective state goal. However, the ability to aver age 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 e qual to the weighted average 40 

## 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 init ial compliance period for each Option.
The analysis in the illustrative scenarios doe s 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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3-15 achieve the state goals imposed in the illustrative scenarios, ther e 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 CO 2 

## emission rate constraints for each state that reflect the particular combination of building blocks analyzed in that scenario. The various building block comb inations 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 Savings 41


## To estimate the potential electricity dema nd reduction that could be achieved, and associated costs incurred, through implementation of demand-side energy efficiency policies, EPA developed scenarios that refl ect increased levels of demand- side energy efficiency, rooted in what leading states have al ready accomplished or have requir ements in place to accomplish. 42 

## For Option 1, adjustments were made to each stat e’s annual incremental reduction in electricity consumption by ramping up from an historical basis 43 

## to a target rate of 1.5% of electricity demand annually over a period of years starting in 2017, and mainta in that rate throughout the modeling horizon. Twelve leading st ates 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 im provement 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 resu lting from accelerated use of

energy efficiency policies, in all stat es, consistent with a level of performance that has already been demonstrated or required by policies (e.g., energy efficiency resour ce 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 di ffers 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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3-16 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 sustai n 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, begi nning in 2017, until the target rate is achieved. States already at or above th e 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 ra te of savings. The incremental savings rate for each state, for each year, is then used to de rive 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 pr ograms. The cumulative annual energy savings derived for Option 1 and Option 2 using this met hodology 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 e xogenously to reflect the estimated future-year energy savings calculated from the approach descri bed above. State energy savings in sales were scaled up to account for transmission losses and a pplied to base case generation demand in each model year to derive adjusted demand for each st ate, reflecting the energy efficiency scenario energy savings. 45 

## The demand adjustments were applied proportionally across a ll 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 borde rs, energy savings from a bisected state were distributed between the applicab le 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 co mpliance modeling for each option, the cu mulative 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 settin g 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

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3-17 reported sales in 2013 as a proxy for the distri bution of energy effi ciency 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

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3-18 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
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 Costs 47


## Total costs of achieving the demand-side ener gy efficiency scenarios for each year were determined at the state level, exogenous to power sector modeling. In addition to the energy

47 

### For a more detailed discussion of the demand-side energy efficiency savings cost estimates, refer to the

Greenhouse Gas Abatem ent Measures TSD.

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3-19 savings data, the total cost was based upon first-y ear 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 acc ounts for both the costs
to the utilities that are fu nding the demand-side energy effi ciency programs (known as the program costs), and the additional cost to the end-user purchasing a more energy efficient technology (known as the participan t costs). Total costs were f ound 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 le vels of energy savings, the first- year costs were escalated when the annual incremental savings in each state re ached 0.5% (a 20% additio nal cost escalation is applied to subsequent investments) and a 1.0% (a 40% additional cost es calation is applied to subsequent investments).
To calculate total annualized energy efficiency costs, first-year costs for each year for each state were levelized (at 3% and 7% disc ount 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 leveli zed value of first-year costs for 2017 through 2025. The annualized cost is rising in each analysis year as additiona l first-year costs are incurred.
These annualized costs were dete rmined for Options 1 and 2 and are summarized below in Table
3-4. The total levelized cost of saved ener gy was calculated based upon the same inputs and using a 3% discount rate result s 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 effi ciency programs (to cover program costs) is typically collected through a st andard per kWh surcharge to the rate-payer; the re gional retail

48 

### For a more detailed discussion of the analysis of program versus participant costs, refer to the Greenhouse Gas

Abatement Measures TSD.

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 s ection at the end of this chapter.

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3-20 price impacts analyzed from this RIA’s complia nce 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 sa les in 2013 as a proxy for the distribution of energy efficiency investment opportunities. Thes e regionalized EE program costs were then incorporated into the regional reta il price calculation as discussed in Section 3.7.9. 51



## Table 3-4. Annualized Cost of Demand-Side En ergy 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 (w hich vary by type of program). Chapter 5 of the GHG Abatement Measures TSD presents the calculations of annualized costs as well as a comprehensive se t (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 CO 2 

## reductions between 17% and 18% below base case projections for Option 1 in 2020 (reaching 26% to 27% below 2005
emissions 52 

## ), and between 24% and 25% below the base case in 2030 (reaching 30% below 2005

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.

52 

### For purposes of these calculations, EPA has used historical CO 2 

### emissions from eGRID for 2005, which reports

EGU emissions as 2,434 million metric tonnes in the contiguous US.

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3-21 emissions). For Option 2, EPA projects annual CO 2 

## 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 emi ssions regionally allows those stat es that were projected to emit below their state goal in the base case to offset reductions that ot her states would otherwise have
made.
Table 3-5. Projected CO 2 

## Emission Impacts, Relative to Base Case

CO 2 

### Emissions (MM Tonnes) CO 2 

### Emissions Change from Base Case

(MM Tonnes) CO 2 

### 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 CO 2 

## Emission Impacts, Relative to 2005

CO 2 

### Emissions (MM Tonnes) CO 2 

### Emissions Change from Base Case

(MM Tonnes) CO 2 

### 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 SO 2 

## , 22% to 24% reduction of NO X 

## , and a 15% to 18% reduction of mercury, under th e proposed Option 1 illu strative scenarios. EPA projects fewer emission reductions overall as a result of the proposed Option 2 in 2020: a

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3-22 17% to 18% reduction in SO 2 

## , 17% to 18% reduction in NO X 

## , and an 11% to 14% reduction in mercury. The projected non-CO 2 

## reductions presented in Tabl e 37 below demonstrate similar trends in later years.

Table 3-7. Projected Non-CO 2 

## Emission Impacts, 2020-2030

Base Case Option 1 Option 2 Option 1 Option 2
Reg. State Reg. State Reg. State Reg. State

2020
SO 2 

### (thousand tons) 1,476 1,184 1,140 1,231 1,208 -19.8% -22.7% -16.6% -18.1%

NO X 

### (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%

PM 2.5 

### (thousand tons) 212 156 154 166 163 -26.4% -27.2% -21.5% -22.9%

2025
SO 2 

### (thousand tons) 1,515 1,120 1,090 1,218 1,188 -26.1% -28.0% -19.6% -21.6%

NO X 

### (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%

PM 2.5 

### (thousand tons) 209 150 146 165 161 -28.1% -30.1% -21.0% -23.2%

2030
SO 2 

### (thousand tons) 1,530 1,106 1,059 n/a n/a -27.7% -30.8% n/a n/a

NO X 

### (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

PM 2.5 

### (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

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3-23 emissions from the natural gas and coal producti on 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 pro duction is likely to be positive or negative and also assessed the potential magn itude of changes relative to CO 2 

## emissions reductions anticipated at power plants. This assessment included CO 2 

## emissions from the flaring of methane, but did not evaluate potential changes in other combustion-related CO 2 

## 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 CO 2 

## emissions from flaring of methane will likely decrease under the proposed guidelines. Furthermore, the chan ges in upstream methane emissions are small relative to the changes in direct emissions from power pl ants. 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 repr esented in this analysis as the change in electric power generation costs between the ba se case and policy case in which the sector pursues flexible compliance approaches to m eet 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 ru le’s Option 2 ranges from $4.2 to $5.4 billion in 2020 and from $4.5 to $5.5 billion in 2025. The a nnual compliance cost is the projected additional cost of complying with the proposed ru le in the year analyzed and includes the net change in the annualized cost of capital investment in new ge nerating sources and heat rate

53 

### The compliance costs also capture the effect of changes in equilibrium fuel prices on the expenditures of the

electricity sector to serve demand.

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3-24 improvements at coal steam facilities, 54 

## the change in the ongoing costs of operating pollution controls, shifts between or amongst various fuel s, 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 ma nagement energy effici ency costs. See Chapter 5 of GHG Abatement Measures TSD for a full explanation.



## EPA’s projection of $4.2 to $7.4 billion in a dditional costs in 2020 ac ross 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 di stribute electricity to end-use consumers. Therefore, the projected costs of compliance wi th the proposed rule amount to a one to two percent increase in the cost to meet electricity demand, while s ecuring public hea lth and welfare
benefits that are several times more valu able (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 efficien cy measures presented earlier.
The following example uses results from Opti on 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 Ta ble 3-8 we present the IPM modeling results for

54 

### See Chapter 2 of the Greenhouse Ga s Abatement Measures TSD and Chapte r 8 of EPA’s Base Case using IPM

(v5.13) documentation, available at: http://www .epa.gov/powersectormodeling/BaseCasev513.html

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3-25 the 2020 Option 1 Regional scenario, which includes CO 2 

## 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 outco me of two simultaneous effects which move in opposite directions. First, imposing the CO 2 

## constraints represented in the Option 1 Regional scenario on electric generators w ould, other things equal, result in an incremental increase in expenditures to supply any given level of elec tricity. However, once electricity demand is exogenously reduced in IPM to reflect the subs tantial reduction in elec tricity demand (induced by EE improvements), there is a substantial re duction in the expenditure s needed to supply a correspondingly lower amount of electricity demand.

Table 3-9. Total Power Sector Gen erating 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



## In order to reflect the full compliance cost at tributable to the policy, it is necessary to include this incremental -$4.8 billion in electr icity 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,

## ***E.O. 12866 Review – Draft – Do Not C ite, Quote or Release During Review***

3-26 this analysis finds the cost of the Option 1 Regional scenario in 2020 to be $5.4 billion (the sum of incremental supply-related a nd demand-related expenditures). 55


## 3.7.3 Projected Compliance Action s 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 ener gy 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 fo ssil fuel results in a lower output-based CO 2 

## emissions rate (lbs/MWh), as well as a lower variable cost of electri city generation. In the modeling conducted for these compliance scen arios, coal boilers have the choice to improve heat rates by 6% under Option 1 and 4% under Option 2, at ca pital cost of $100 per kW in both options. 56


## The vast majority of existing coal boilers ar e 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 th at 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 CO 2 

## -intensive generation to less CO 2 

## -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 combined cycle units, which are less carbon-inte nsive 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 illustrativ e compliance scenarios, reflecting the fact that states have the

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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3-27 flexibility to choose among alternative CO 2 

## reduction strategies that we re 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 3-2. Projected Capacity Factor of Ex isting 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 credit ed for total demand-side energy e fficiency savings consistent with the savings that are used to construct the state goals. See section Section 1 for a description of the levels assumed for Options 1 and 2. Again, th ese reductions in dema nd as a result of demandside energy efficiency are made e xogenously in both of the Options for the illustrative scenarios analyzed.
3.7.4 Projected Generation Mix Table 3-3 and Figure 3 show the generation mi x in the base case and under the proposed rule. The ability to average the emissions rate at covered sources (w hich exclude new NGCC) over the 2020-2030 compliance period provides an incentive to shift ge neration within the covered sources to less carbon-intensive gene ration. In 2020, for Option 1, total generation

57 

### Because fossil fuel-fired EGUs typica lly 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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3-28 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 i llustrative scenarios. Coal-fired generation is projected to dec line 20% to 22% in 2020, and natu ral-gas-fired generation from existing combined cycle capacity is projected to incr ease 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 rela tive to the base case. Th e coal-fired fleet in 2030 generates between 25% and 27 % less than in the base ca se, while natural-gas-fired generation from existing combined cycles increases 18% to 19% relative to the base case. Gasfired generation from new combined cycle capac ity increases in 2020, as projected new natural gas combined cycle capacity replaces retire d coal capacity. By 2030, generation from newly built natural gas combined cycle decreases betw een 36% and 40% relative to the base case, consistent with the decrease in new capacity (see section 0). 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 tota l demand attributable to the demand-side energy savings applied in the illustrative scenarios. Co al generation is projecte d to decrease between 16% and 17% in 2020, and natural gas-fired genera tion from existing combined cycle units is projected to increase nine percen t, relative to the base case.
Under both options, additional natural gas pi peline capacity is projected to be built through 2020 relative to the base cas e. In all of the illustrative compliance scenarios, pipeline capacity is projected to increase four to eigh t percent beyond base case projections by 2020. In 2030, however, the total cumulative pipeline capacity built is projec ted to decrease, consistent with the projected decrease in total natural gas use. The projected incr ease 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 relativ ely small amount of additional infrastructure development that is projected to shift to earlie r years, construction of this additional capacity should be easily manageable and not ra ise any reliability or cost concerns.

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3-29 Table 3-3. Generation Mix (thousand GWh)
Base Case Option 1 Option 2 Option 1 Option 2 Reg. State Reg. Stat e 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

## ***E.O. 12866 Review – Draft – Do Not C ite, Quote or Release During Review***

3-30 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***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***3-31Figure 3-4. Generation Mix with the BaseCase and 111(d) Options, 2020-2030 (million GWh)Source: Integrated Planning Model run by EPA, 20140.000.501.001.502.002.503.003.504.004.505.00BaseCaseReg. State Reg. State BaseCaseReg. State Reg. State BaseCaseReg. StateOtherOilNon‐hydroRenewablesHydroelectricNaturalGasNuclear2020 2025 2030Option1Option2Option1Option2Option1

## ***E.O. 12866 Review – Draft – Do Not C ite, Quote or Release During Review***

3-32 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 co al-fired capacity projec ted to be in service in the base case) by 2020 under th e range of scenarios analyzed.
For the proposed rule, EPA examined whethe r these projected incremental retirements may adversely impact reserve margins and reliab ility planning. The IPM model is designed to ensure that generation resource adequacy is mainta ined 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 bu ilding enough new capacity. IPM also addresses reliable delivery of generation re sources 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 a ny resources located in, or transferred to, the region. 58


## EPA examined the implications of each of th e scenarios for regional resource adequacy and for potential concerns over on grid reliabilit y. 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 whet her changes in generation and fl ow pattern impacts would raise issues for reliability management. The key parameters analyzed were:
Operating Reserve Margins. The IPM model en sures 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 opera te 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 NE RC 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 fr om 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).

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3-33 the policy cases were found to raise concerns over regional resource ad equacy. Moreover, the time horizon for compliance with this rule will permit environmental and reliability planners to coordinate these changes and address pot ential concerns before they arise.
Interregional Power Flows and Capacity Transf ers. 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 othe r planning entities. Alth ough these limits keep projected flows within expected bounds, EPA furt her examined how the policy options impacted these flows and transfers to identify any large shifts from the base case. N one 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 w ith the proposed rule also contributes to meeting resource adequacy, by lowering the regional load an d thereby lowering operating capacity needed to meet planning reserve margin s. 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. EP A concludes that the prop osed 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 ca se in 2020 are shown in Table 3-12.

59 

### For further discussion of EPA’s examination of these projected incremental retiremen ts on reserve margins and

reliability planning, see the Resource Adequacy and Reliability Analysis TSD.

## ***E.O. 12866 Review – Draft – Do Not C ite, Quote or Release During Review***

3-34 Table 3-4. 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) 2 19 216 217 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

## ***E.O. 12866 Review – Draft – Do Not C ite, Quote or Release During Review***

3-35 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 attri butable to the demand-side energy savings applied in the illustrative scenarios, EPA proj ects 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 RIA. While this new NGCC capacity cannot be di rectly 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 capac ity. As affected source s in the illustrative scenarios, the generation from new non-hydro renewa bles are able to contribute to the average
emissions rate in e ach state or region.
Under the Option 1 illustrative scenarios, ne w 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 increas e 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, ne w natural gas combined cycle capacity is projected to increase by about 9 to 14 GW (79% to 117% increas e 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 increas e by about 9 GW (50% increase) above the base case in 2020, and about 7 GW (25% increase) by 2025.

Table 3-5. 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

## ***E.O. 12866 Review – Draft – Do Not C ite, Quote or Release During Review***

3-36 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 3-64. 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 

## 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 rate s (generating efficiency) at existing coal units, (2) demand
reduction attributable to the dema nd-side energy savings, and (3) a shift in generation from coal to less-carbon intensive gene ration. As shown in Table 3-75, the largest decrease in coal production is projected to occur in the western region. Waste coal production is projected to increase slightly under the propos ed rule due to the operation unde r both options of less than 1 GW of coal steam capacity that is pr ojected to retire unde r the base case.

Table 3-75. 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. Stat e 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

## ***E.O. 12866 Review – Draft – Do Not C ite, Quote or Release During Review***

3-37 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 us e declines under both options. These trends are consistent with the cha nge in generation mix described above in section
3.7.4.

Table 3-8. 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



## 3.7.8 Projected Fuel Price Impacts
The impacts of the proposed rule on coal a nd natural gas prices before shipment are shown below in Table 3-97, Table 3-108, Table 319, and
Table 3-11, and are attributable to the polic y-induced changes in overall power sector
demand for each fuel.

Coal demand decreases over the 2020-2030 tim e horizon, resulting in a decrease in the price of coal delivered to th e electric power sector. In 2020, th e increase in natural gas demand results in an increase in the price of gas de livered to the electric power sector. In 2030, gas demand and price decrease below the base case pr ojections, due to the cumulative impact of national demand-side energy efficiency savings and the consequent reduced overall electricity
demand.

## ***E.O. 12866 Review – Draft – Do Not C ite, Quote or Release During Review***

3-38 IPM modeling of natural gas prices uses bot h 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 pr ice 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 expe rienced 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 ex periencing these rapid fluctuations in natural gas prices by retail rate re gulation and by hedging through longe r-term fuel supply contracts by the power sector. IPM assumes thes e longer-term price arrangements take place “outside of the model” and on top of the “real- time” shorter-term price variati on necessary to align supply and demand. Therefore, the model’s natural gas pr ice 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.
Table 3-9. 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 3-10. 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

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3-39 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 3-11. 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 th e 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 proposed Option 1, compared to the modeled base case price estimate in those years. Under Option 2, on average, EPA projects an average re tail 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, tr ansmission, distribution, taxes, and utility demand-side EE program costs. IPM modeling pr ojects changes in regional wholesale power prices and capacity payments related to impositi on of the represented policy that are combined with EIA regional transmission a nd distribution costs to calcula te changes to regional retail

## ***E.O. 12866 Review – Draft – Do Not C ite, Quote or Release During Review***

3-40 prices. As described in Section 3.6.2, the ut ility funding for demand-side energy efficiency programs (to cover program costs) is typically co llected 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).

## ***E.O. 12866 Review – Draft – Do Not C ite, Quote or Release During Review***

3-41 Table 3-12. 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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3-42 Table 3-13. 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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3-43 Table 3-14. 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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3-44 Figure 3-5. Electricity Market Module Regions Source: EIA <http://www.e ia.gov/forecasts/aeo/p df/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 ap plied in the illustrative scenario s to affect average electricity bills. The estimated changes to average bills are summarized in Table 3-154. 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 Optio n 2, EPA estimates an average bill increase of 1.1% to 1.4% in 2020 and an average bill decrea se of 3.2% to 3.5% in 2025. These reduced electricity bills reflect the combined effects of ch anges in both average re tail rates (driven by the effects of all four building blocks) and lower el ectricity demand (driven by the fourth building block, demand-side energy efficiency).
Table 3-15. 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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3-45 3.8 Projected Primary PM Emi ssions from Power Plants IPM is not configured to e ndogenously model primary PM emissions from power plants.
These emissions are calculated as a function of IPM outputs, emissi on factors and control configuration. IPM-projected fuel use (heat input) is multiplied by PM emission factors (based in part on the presence of PM-rele vant pollution control devices) to determine PM emissions. Primary PM emissions are calculated by a dding 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 bur ned 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 plan t type, sulfur conten t of the fuel, and
SO 2 

## /HCl and PM control configurat ions. Although EPA’s analysis is based on the best available emission factors, these emission factors do not a ccount 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 SO 3 

## and H 2 

## SO 4 

## ) in units with SCRs depends on a number of factors, including coal sulfur c ontent, 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 condensab le PM. This limitation means that IPM postprocessing is potentially underestimating conden sable PM emissions for units with SCRs. In contrast, it is possible that IPM post-processi ng overestimates condensable PM emissions in a case where the unit is combusting a low-sulf ur coal in the pres ence of a scrubber.
EPA applied this methodology to develop pr imary 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 remainde r of the base case and policy scenarios over the 2020-2030 time horizon using simplified emissions f actors. These factors were developed for the eastern and western regions (excluding Calif ornia) using EPA’s em issions 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 met hodology provides a reasonable estimate, EPA notes that applying the

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3-46 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 met hodologies used to post-process PM emissions from IPM, see “IPM ORL File Generation Meth odology” (March, 2011), avai lable in the docket.
3.9 Limitations of Analysis EPA’s modeling is based on expe rt judgment of various input assumptions for variables whose outcomes are in fact uncertain. As a genera l matter, the Agency reviews the best available information from engineering studies of air po llution 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 bot h the IPM-projected a nnualized estimates of private compliance costs as well as the estimated costs incurred by utilit ies and ratepayers to achieve demand-side energy effi ciency 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 th e power sector in response to th e final rule. To estimate these annualized costs, EPA uses a conventional and wi dely-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 estima tes 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 signif icant variation exists in these analyses reflecting data and methodologi cal limitations. The method used for estimating the demand-side energy efficiency costs is di scussed in more detail in the Greenhouse Gas
Abatement Measures TSD. The TSD also discusse s 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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3-47 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 th e 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 effici ency resource standards). To some degree the implicit representation of state policies in the EP A’s base case alters the impacts assessment, but the direction of change is not know n with certainty. This issue is discussed in more detail in the
Greenhouse Gas Abatement Measures TSD.
Cost estimates for the proposed rule are ba sed 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 ut ilities 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 ma rkets in Alaska, Hawaii, and U.S. territories outside the contiguous U.S. and therefore the cost s (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 co al 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 deli vered natural gas prices will incr ease by about 9.3% to 11.5% with

61 

### Full documentation for IPM can be found at <http:// http://www.epa.gov/powersectormodeling >.

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3-48 increased power sector consumption of betw een 979 to 1,194 billion cubic feet (BCF) in 2020. Average retail electricity prices are projected to increase in th e contiguous U.S. by 5.9% to 6.5%
in 2020.
3.11 Monitoring, Reporting, and Recordkeeping Costs EPA projected monitoring, reporting and reco rdkeeping costs for bot h state entities and affected EGUs for the compliance years 2020, 2 025 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 st ate’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 EP A 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 availa ble to them, EPA believes a new system for calculating net energy output is not needed. Howe ver, 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 requireme nts set in the state pl an. Additionally EPA
believes that 1 hour will be needed for each E GU 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 Ac quisition System (DAS) would need to be upgraded to supply the rate-based emissions va lue 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 substantia lly 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 pr ogram implementation, assess progress, develop possible contingency measures, st ate plan revisions and the subs equent public meetings if revisions are indeed needed, download data fr om the ECMPS for their annual reporting and

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3-49 develop their annual EPA report. Table 3-25 show s the annual respondent burden costs and costs of reporting and recordk eeping 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 a nd regulatory alternativ e Option 2 including monitoring reporting and recordkeeping co sts are shown in Table 3-26 below.

Table 3-16. 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 Pr eparing 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 acti on, where an opportunity cost is the value lost to society of any goods and services that will not be produced an d consumed as a result of reallocating some resources towards pollution mitigation. Estimates of social costs may be compared to the social

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3-50 benefits expected as a result of a regulation to assess its net impact on soci ety. The social costs of a regulatory action will not necessarily be equi valent 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 relations hip between social costs and compliance cost estimates presented in this chapter.
For the illustrative compliance scenarios cost estimates presented in this Chapter are the sum of expenditures on end-use energy efficien cy 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 requ ired to achieve the assumed demand reductions through end-use energy efficiency programs are es timated using historical data and expert judgment. The change in the expe nditures required by th e 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 dema nd caused by the end-us e energy efficiency programs and the increase in electricity produc tion costs required to achieve the additional emission reductions necessary to comply with the state goals. 62



## As described in section 3.6.1, the illustrativ e scenarios generally assume that, in achieving their goals, states adopt energy effi ciency programs which lead to net demand reductions in each year equivalent to those appl ied 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 th is chapter and detailed in the GHG Abatement Measures TSD chapter on end-use energy efficienc y. The social cost of achieving these energy savings comes in the form of increased expend itures on technologies and/ or services that are required to lower end-user’s el ectricity 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 repres ent society’s opportunity cost of the resources required to produce the energy savings.

62 

### As described in section 3.5, IPM provides the least-co st 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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3-51 The social cost of achieving these net elec tricity demand reductions may differ from the expenditures associated with the end-use ener gy efficiency programs. For example, some participants in end-use energy efficiency pr ograms might have chosen to adopt the energy efficiency improvements even in the absence of the program. 63 

## Therefore the expenditures, except for any program administration expenditure s, 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 scen arios includes this limited share of the program costs that is expended on end-us ers who would have reduced thei r demand in the absence of the program. Thus, with respect to this particular issue, the social co st 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 additiona l demand reductions are paid for by electricity rate or tax payer funded programs then that porti on of program expenditures represents a transfer among electricity consumers and has no net welfar e gain or loss to society as a whole.
Due to the flexibility held by states in im plementing their compliance with the proposed standards these energy efficien cy expenditures may be borne by end-users through direct participant expenditures or electricity rate incr eases or by producers thro ugh reductions in their profits. While the allocation of these expenditures between consumers and producers is important for understanding the di stributional impact of potential compliance strategies, it does not necessarily affect the opportunity cost requir ed for the production of the energy savings from a social perspective. However, specific desi gn elements of demand-side or end-use energy efficiency programs included to address distri butional outcomes may have an effect on the economic efficiency of the program s and therefore the social cost.

63 

### For example if a rebate program is offered for energy effi cient 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 requi res 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.

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3-52 Another reason the expenditures associated w ith 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 prog ram relative to the baseline. For example, if under the program end-users adopted more energy efficient products which were associated with quality or service attributes deemed less desirabl e, 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 ener gy savings, provided by the more energy efficient products and practices are deemed more de sirable by some end-users. For example, weatherization of buildings to reduced electricity demand associated with cooling will likely have a significant impact on natu ral gas use associated with hea ting. In either case these real welfare impacts are not fully captured by end-us e energy efficiency expenditure estimates.
The fact that such quality and service diffe rences may exist in reality but may not be reflected in the price difference between more a nd less energy efficient pr oducts is one potential hypothesis for the energy paradox. The energy para dox is the observation th at end-users do not always purchase products that are more energy efficient when the a dditional cost is less than the reduction in the net present value of expect ed electricity expenditures achieved by those products. 65 

## Such circumstances are present in the analys is presented in this chapter, whereby in some regions the base case and illustrative s cenarios suggest that cost of reducing demand through energy efficiency programs is less than the retail electricity pr ice. In addition to heterogeneity in product services and consumer pr eferences, 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 e fficiency investments that ostensibly seem
to be in their own interest. The TSD discussi on also provides details on how the presence of
additional market failures can lead to levels of energy efficiency investme nt that may be too low

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.

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3-53 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 th ere is a disconnect between those making the purchase decision regarding energy efficien t investments and energy use and those that
would receive the benefits associated with re duced energy use through lo wer electricity bills.
The other component of complian ce cost reported in this chap ter is the change in resource cost (i.e., expenditures) required by the electric ity sector to fulfill the remaining demand while making additional CO 2 

## emissions intensity reductions necessa ry to comply with the state goals. Included in the estimate of these compliance cost s, 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 impr ovements in heat rate. By shifting the demand curve for electricity, energy efficiency program s 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 re duction in production co sts due to lower demand from end-use energy efficiency programs.
Under the assumption that impacts outside th e electricity market and markets providing inputs to the electricity sector do not meaningfully affect the pr ices 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 meas ured by IPM (net of tax and subsidy payments and recovery of sunk costs). Under these assump tions the change in co mpliance cost will equal the reduction in producer 66 

## and consumer surplus 67 

## 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 chan ges in producer and cons umer surplus in those markets, and therefore the resource cost estimate by IPM may differ from the social cost for this

66 

### Producer surplus is a welfare measure representing th e amount gained by producer s from selling output at a

market price higher than the pr ice they are willing to accept.

67 

### Consumer surplus is a welfare measure representing the monetized value of the bene fit consumers receive from

consumption of purchased goods or services beyond their opportunity cost as defined by the market price.

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3-54 reason. For a theoretically consistent method fo r estimating partial-equilibrium changes in welfare where prices and outputs change in multiple markets see Chapter 9 of Just et al. (2004).

3.13 References
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

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3A-1 

## A PPENDIX 

## 3A: A NALYSIS OF 

## P OTENTIAL 

## U PSTREAM 

## M ETHANE 

## E MISSIONS 

## C HANGES IN 

## N ATURAL 

## G AS 

## S YSTEMS AND 

## C OAL 

## M INING



## The purpose of this appendix is to desc ribe the methodology for estimating upstream methane (CH 4 

## ) emissions related to natural gas system s and coal mining sectors that may result from the compliance approaches examined in th e 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 th ese upstream changes relative to CO 2 

## emissions reductions anticipated at power plants from the compliance st rategies examined in the RIA. In addition to estimating changes in upstream methane emissions, this assessment included estimating CO 2 

## from the flaring of methane, but did not exam ine other potential changes in other upstream greenhouse gas emissions changes from natura l 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 S ection 3A.1, the overall approach is described in brief. In Section 3A.2, results are presen ted. In Section 3A.3, de tailed methodologies are presented for how CH 4 

## and flaring-related CO 2 

## projections were calculated for coal mining and natural gas systems. Finally, Section 3A.4 c ontains a bibliography of cited resources.

3A.1 General Approach

3A.1.1 Analytical Scope
Upstream CH 4 

## and flaring-related CO 2 

## emissions associated with coal mining and natural gas systems were estimated for 2020 through 203 0 using methodologies developed for the 2014 U.S. Climate Action Report (U.S. Department of St ate 2014). The base year for the projections is 2011, as reported in the 2013 U.S. GHG Invent ory (EPA 2013a). The projection methodologies use activity driver data outputs such as coal and natural gas production from the base case and

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3A-2 policy scenarios generated by the Integrated Pl anning Model (IPM), which was used in the RIA to model illustrative compliance strategies. Th e projection methodologies use similar activity
data and emissions factors as are used in the U.S. Greenhouse Gas (GHG) Inventory. The projection methodologies estimate reductions a ssociated with both vol untary and regulatory programs affecting upstream CH 4 

## -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 regulato ry 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 re view and public review in the co ntext of the Sixth U.S. Climate Action Report. For more information on the review , or for the detailed methodologies used for
non-CO 2 

## source projections in th at report, including CH 4 

## -related emissions from coal production and natural gas systems, see “Methodologies for U.S. Greenhouse Gas Emissions Projections:
Non-CO 2 

## and Non-Energy CO 2 

## Sources” (EPA, 2013b). Uncerta inties and limitations are discussed, including a side case which incorpor ates additional geographic information for estimating CH 4 

## from coal mining. The term “upstream emissions” in this memo refers to vented, fugitive and flared emissions associated with fuel production, proces sing, transmission, storage, and distribution of fuels prior to fuel combustion in electricity plants. For this analysis, the EPA focused on upstream CH 4 

## from the natural gas systems and coal mining sectors. In a ddition, the analysis
included CO 2 

## resulting from flaring in natural gas produc tion. This analysis does not assess other upstream GHG emissions changes, such as CO 2 

## emissions from the combustion of fuel used in natural gas and coal production ac tivities or other non-combustion CO 2 

## emissions from natural gas systems, such as vented CO 2 

## and CO 2 

## emitted from acid-gas removal processes. Also, the EPA assessed potential upstream me thane 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 ga s and coal production, processing, and transportation act ivities outside of the US. 68 

## . Last, the EPA did not assess

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 estimat e domestic methane emissions related to trade of natural

## ***E.O. 12866 Review – Draft – Do Not C ite, Quote or Release During Review***

3A-3 potential changes in other upstream non-GHG emi ssions, such as nitrogen oxides, volatile organic compounds, and particulate matter. Tabl e 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 CH 4 

## emissions from natural gas syst ems and coal production activities specifically. CO 2 

## emissions from flared methane are included because regulatory and voluntary programs influence the rate of CH 4 

## flaring over time and the CO 2 

## remaining after flaring is a
CH 4 

## -related GHG. Because of the multiple complia nce strategies adopte d in the illustrative compliance scenarios, a more comprehensive assessment of upstream GHG emissions would require examination of the broa der power sector and related i nput markets and their potential changes in response to the rule. This analysis would be comple x 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 pres ented in detail below.

3A.1.2 Coal Mining Source Description
Within coal mining, this analysis covers fugitive CH 4 

## emissions from coal mining (including pre-mining drainage) and post-mining activities (i.e., coal handling), including both underground and surface mining. Emissions fromabandoned mines are not included. Energyrelated CO 2 

## emissions, such as emissions from mining equipment and vehicles transporting coal
are not included. CH 4 

## , which is contained within coal seam s 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 CH 4 

## emitted from these operatio ns 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 CH 4 

## content. Pressure increases with depth and prevents CH 4 

## from migrating to the surface; as a

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).

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3A-4 result, underground mining operati ons typically emit more CH 4 

## than surface mining. In addition to emissions from underground and surface mi nes, post-mining processing of coal and
abandoned mines also release CH 4 

## . Post-mining emissions refer to CH 4 

## retained in the coal that is released during processing, stor age, and transport of the coal.

3A.1.3 Natural Gas Systems Source Description
Within natural gas systems, this analysis covers vented and fugitive CH 4 

## emissions from the production, processing, transmission and storage, and distribution segments of the natural gas system. It also includes CO 2 

## from flaring of natural gas. Not included are vented and fugitive
CO 2 

## emissions from natural gas systems, such as vented CO 2 

## emissions removed during natural gas processing, or energy-related CO 2 

## 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. CH 4 

## and
non-combustion 69 

## CO 2 

## 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 them selves, gathering pipelin es, and well-site gas treatment facilities (e.g., dehydrat ors, separators). Major emissions source categories within the production stage include pneuma tic 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 CO 2 

## emissions within the production stage.

Processing: In this stage, natural gas liquids and vari ous other constituents from the raw gas are removed, resulting in “pipeline-quali ty” gas, which is then injected into the transmission system.

69 

### In this document, consistent with IPCC accounting term inology, 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 CO 2 

### removed from raw natural gas.

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3A-5 Fugitive CH 4 

## emissions from compressors, includi ng compressor seals, are the primary emissions source from this stage. In the U.S. GHG Inventory, the majority of non-combustion
CO 2 

## emissions in the processing stage come from acid gas removal units, which are designed to
remove CO 2 

## from natural gas.

Transmission and Storage: Natural gas transmission involve s high-pressure, large-diameter pipelines that transport gas long distances from field production a nd processing areas to distribution systems or large-volume customers such as power plants or chemical plants. Compressor station facilities, which contain la rge reciprocating and turbine compressors, are used to move the gas throughout the U.S. transmission system. Fugitive CH 4 

## emissions from these compressor stations and from metering and re gulating stations accountfor the majority of the emissions from this stage. Pneumatic devi ces and non-combusted engi ne exhaust are also
sources of CH 4 

## emissions from transmission facilities. Natu ral gas is also injected and stored in underground formations, or liquefi ed 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 contri butors 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-pressu re gas from the transmission system at “city gate” stations, reduce the pressure, a nd then distribute the gas through primarily underground mains and service lines to individual end users.

Table 3A-1. Base Year Upstream Methane-Rel ated Emissions in the U.S. GHG Inventory Emissions Source 2011 Emissions (TgCO 2 

### e) CH 4 

### from Coal Mining 75.2

Underground Mining and Post-Mining 57.4 Surface Mining and Post-Mining 18.0 CH 4 

### from Natural Gas Systems 172.3

Production 63.6 Processing 23.3 Transmission and Storage 52.1 Distribution 33.2 CO 2 

### from flaring of natural gas 10.3

## ***E.O. 12866 Review – Draft – Do Not C ite, Quote or Release During Review***

3A-6 Source: 2013 U.S. GHG Inventory (EPA, 2013). A Global Warming Potential of 25 was used to convert methane
emissions to CO 2 

### e.



## It is important to note that in Table 3A-1, CO 2 

## -equivalent methane emissions are presented using the Fourth Assessment Report Gl obal Warming Potential (GWP) of 25, whereas
CO 2 

## -equivalent methane emissions in the 1990- 2011 U.S. GHG Inventory (EPA 2013) are presented using the Second Asse ssment Report GWP of 21. EPA plans to use 25 as the methane GWP starting with the 1990-2013 U.S. GHG I nventory, to be published in April 2015. 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 appr oaches use the four build ing blocks described in the Executive Summary of the RI A at different levels of stri ngency. Option 1 involves higher deployment of the four building blocks but allo ws a longer timeframe to comply (2030) whereas Option 2 has a lower deployment over a shorter ti meframe (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 fr om base case and illustrative compliance scenarios underlie the estimates of upstream CH 4 

## emissions. These activity drivers include domestic coal and natural gas production, imports and exports, a nd natural gas consumption. For a sensitivity analysis described later, regiona l 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, Opti on 1 Regional and State scenarios, and Option 2 Regional and State scenarios. 70 

## Under Option 1, the state and regional complia nce scenarios result in 24 percent and 22 percent reductions in coal pr oduction in 2020, respectively, relative to base case coal production. Natural gas production in 2020 increases by 4 pe rcent as a result of the Option 1 State

70 

### Uncertainties related to activity drivers are discus sed in the uncertainties and limitations section.

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3A-7 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 complia nce scenarios result in 19 percent and 17 percent reductions in coal pr oduction in 2020, respectively, relative to base case coal production. Natural gas production in 2020 increases by 3 percen t as a result of eith er the Option 2 state compliance scenario or the Option 2 Regional comp liance scenario, relative to the base case. 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 Ga s 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 Ga s 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

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3A-8 3A.2 Results

This section presents results for the main an alysis and sensitivity analysis. The detailed methods used to perform the analys is are presented in section 3A.3.

3A.2.1 Primary Results
Both Options 1 and 2, in either the stat e or regional compliance scenarios, result in total net reductions in upstream CH 4 

## emissions and CO 2 

## emissions (see Table 3A-6). Under Option 1, the state compliance scenario resu lts in net emissions reduction of 10.6 TgCO 2 

## e in 2020, while the regional compliance scenario results in 9.9 TgCO 2 

## e reduction. Under Option 2, the net emissions reduction is smaller: 7.7 and 7.4 TgCO 2 

## e in the state and regional compliance scenarios in 2020, respectively. Th ese net emissions changes repr esent the sum of changes in
CH 4 

## from coal mining, CH 4 

## from natural gas systems, and CO 2 

## from flaring in natural gas production. Under the regional compliance scenario, Option 1 would result in decreases in
CH 4 

## emissions from coal mining of 14.7 TgCO 2 

## e in 2020, 18.2 TgCO 2 

## e in 2025, and 17.7 TgCO 2 

## e in 2030. Under the same scenario, CH 4 

## from natural gas systems increases relative to the base case in 2020 (4.4 TgCO 2 

## e) and 2025 (2.1 TgCO2e) but decr eases relative to the base case in 2030 (by 1.7 TgCO 2 

## e). Following a similar pattern, CO 2 

## from flaring in natural gas production increases by 0.4 TgCO 2 

## e in 2020 and 0.3 TgCO 2 

## e in 2025 while decreasing by 0.3 TgCO 2 

## e in 2030. The total net change under the Op tion 1 regional compliance scenario is a
reduction in emissions. Under the regional compliance scenario, Option 2 would result in decreases in
CH 4 

## emissions from coal mining of 11.3 TgCO 2 

## e in 2020 and 13.4 TgCO 2 

## e in 2025. CH 4 

## from natural gas systems increases relativ e to the base case in 2020 (3.6 TgCO 2 

## e) and 2025 (1.8 TgCO 2 

## e). CO 2 

## from flaring in natural gas producti on increases in 2020 an d 2025. Like Option 1, the total net change under the Op tion 2 Regional compliance scenario is a reduction in emissions.
The full results are listed in Table 3A-6.

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3A-9 Table 3A-6. Upstream Emissions Change s, Baseline to Compliance Scenarios Emissions (TgCO 2 

### e) 2020 2025 2030 Option 1 State CH 4 

### from Coal Mining -16.1 -19.3 -19.1
CH 4 

### from Natural Gas Systems +5.1 +2.6 -1.0
CO 2 

### from NG flaring +0.4 +0.2 -0.3
Total CH 4 

### + CO 2 

### -10.6 -16.4 -20.5 Option 1 Regional CH 4 

### from Coal Mining -14.7 -18.2 -17.7
CH 4 

### from Natural Gas Systems +4.4 +2.1 -1.7
CO 2 

### from NG flaring +0.4 +0.1 -0.5
Total CH 4 

### + CO 2 

### -9.9 -16.0 -19.8 Option 2 State CH 4 

### from Coal Mining -12.4 -14.5 n/a
CH 4 

### from Natural Gas Systems +4.4 +2.4 n/a
CO 2 

### from NG flaring +0.4 +0.3 n/a
Total CH 4 

### + CO 2 

### -7.7 -11.8 n/a Option 2 Regional CH 4 

### from Coal Mining -11.3 -13.4 n/a
CH 4 

### from Natural Gas Systems +3.6 +1.8 n/a
CO 2 

### from NG flaring +0.2 +0.1 n/a
Total CH 4 

### + CO 2 

### -7.4 -11.4 n/a


## Note: A Global Warming Potential of 25 was used to convert methane emissions to CO 2 

### e.



## 3A.2.2 Sensitivity Analysis: Side Case Incorp orating 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 analys is examining the possible effect of shifts in regional coal production that might result in a changing the relative share of production

## ***E.O. 12866 Review – Draft – Do Not C ite, Quote or Release During Review***

3A-10 originating in underground mines and surface mine s. This sensitivity analysis is presented because underground mining generally emits more CH 4 

## emissions per ton of coal production than surface mining. The compliance scenarios sh ow generally larger reductions in coal production in the western region where more of the generally lower-e mitting 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 contributi on to the Climate Action Report, which did not examine alternative coal producti on scenarios, did not incorporat e this geographic component. After incorporating this effect, the net upstream CH 4 

## emissions reductions that may result from the compliance scenarios are approximately 1 to 2 TgCO 2 

## e lower than the results found in the main analysis (see Table 3A-7). Table 3A-7. Upstream Emissions Change s, Baseline to Compliance Scenarios, Side Case Incorporating More Coal Production Geographic Detail Emissions (TgCO 2 

### e) 2020 2025 2030 Option 1 State CH 4 

### from Coal Mining -14.1 -17.4 -17.8 CH 4 

### from Natural Gas Systems +5.1 +2.6 -1.0 CO 2 

### from NG flaring +0.4 +0.2 -0.3 Total CH 4 

### + CO 2 

### -8.6 -14.6 -19.2 Option 1 Regional CH 4 

### from Coal Mining -13.1 -16.5 -16.3 CH 4 

### from Natural Gas Systems +4.4 +2.1 -1.7 CO 2 

### from NG flaring +0.4 +0.1 -0.5 Total CH 4 

### + CO 2 

### -8.3 -14.3 -18.5 Option 2 State CH 4 

### from Coal Mining -11.2 -13.2 n/a CH 4 

### from Natural Gas Systems +4.4 +2.4 n/a CO 2 

### from NG flaring +0.4 +0.3 n/a Total CH 4 

### + CO 2 

### -6.4 -10.4 n/a Option 2 Regional CH 4 

### from Coal Mining -10.3 -12.3 n/a CH 4 

### from Natural Gas Systems +3.6 +1.8 n/a CO 2 

### from NG flaring +0.2 +0.1 n/a Total CH 4 

### + CO 2 

### -6.5 -10.3 n/a 

## Note: A Global Warming Potential of 25 was used to convert methane emissions to CO 2 

### e.

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3A-11 3A.2.3 Uncertainties and Limitations Projections of upstream CH 4 

## emissions and CO 2 

## emitted from flaring of CH 4 

## are subject to a range of uncertainties and limitations. Thes e uncertainties and limitations include estimating the effect of the compliance approach on activity dr ivers, uncertainty in base year emissions, and uncertainties in changes in emissions factors ove r relatively long periods of time. For example, EPA’s application of IPM relies on EIA projections for coal impor ts and exports. Consequently, coal imports and exports are not able to fully respond within the IPM fr amework to significant fluctuations in power sector co al demand. To the extent intern ational 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 es timates 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 market, and technology factors which are uncertain in both baseline and compliance scenarios. Relatively higher or lower economic growth, or changes in the relativ e prices or availability of various technologies could result in alternative estimates in the net change in upstream CH 4 

## emissions and related CO 2 

## emissions. 3A.3 Methodologies

3A.3.1 Coal Mining
The scope of CH 4 

## from coal mining emissions covered in this analysis is discussed above in the “General Approach” section.

Methodology
EPA calculated emissions projections for th is source by summing emissions associated with underground mining, post-underground mini ng, surface mining, and post-surface mining.

ݏ݊݋݅ݏݏ݉݅ܧ ௬ 

## ݏ݊݋݅ݏݏ݉݅ܧ ෍	 ൌ ௦,௬

௦ 

## Equation 3

## ***E.O. 12866 Review – Draft – Do Not C ite, Quote or Release During Review***

3A-12 Where:

s = Sources (underground, post-underground, surface, and post-surface mining)

Emissions y,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



## ݏ݊݋݅ݏݏ݉݅ܧ ௦,௬ 

## ܨܧ ൌ ௦,௔௚௚ 

## ݊݋݅ݐܿݑ݀݋ݎܲݕݎ݋ݐ݁݊ݒ݊ܫ ൈ ௦,௕ 

## ቆ ൈ ݊݋݅ݐܿݑ݀݋ݎ݁݀ܲݐ݆݁ܿ݋ݎܲ ௦,௬ 

## ݊݋݅ݐܿݑ݀݋ݎ݁݀ܲݐ݆݁ܿ݋ݎܲ ௦,௕ 

## ቇ

ൈ ሺ ܪܥ	 െ 1 4 ܽܿݎܨ݁ݏܷݕݎ݁ݒ݋ܴ݁ܿ ௦ 

## ሻ
Equation 4

Where:

EF agg,s 

## = Aggregate emissions factor associated with source s InventoryProduction b,s 

## = Coal production associated w ith source s in the base year from the U.S. GHG Inventory 72


## ProjectedProduction y,s 

## = Projected coal produc tion associated with the emissions source (e.g., either underground or surface mining) in year y

CH 4 

## RecoveryUseFrac s 

## = Fraction of CH 4 

## recovered from source s

Emissions Factors

71 

### Current CH 4 

### recovery and use projects apply to underground mi ning, but projects related to surface mining could

be implemented in the future.

72 

### Because of slight differences betwee n historical and projection datasets, va lues for production in the base year

from each dataset do not cancel

## ***E.O. 12866 Review – Draft – Do Not C ite, Quote or Release During Review***

3A-13 To calculate potential emissions from each category, EPA calculated an aggregate CH 4 

## emissions factor using historical CH 4 

## emissions and coal production data contained in the most recent U.S. GHG Inventory (EPA 2013a ). For example, historical CH 4 

## liberated by underground mining was divided by the total underground co al 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-undergroun d mining emissions, surface mining emissions, and post-surface mining emissions, using eith er 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 us e emissions factors to calculate CH 4 

## emissions from underground mines. The U.S. GHG Inventory estimates total CH 4 

## emitted from underground
mines as the sum of CH 4 

## liberated from ventilation systems (mine-by-mine measurements) and
CH 4 

## liberated by means of degasification systems, minus CH 4 

## recovered and used. EPA estimated surface mining and post-mining CH 4 

## 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, EP A projected emissions using projections of underground, surface, and total coal production from the EIA Annual Energy Outlook (AEO) (EIA 2013). For this analysis, pr ojections of total coal product ion 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 Stat es, 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 Typ e”). 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 relativ e proportion of underground and surface mining in the main results. Also, note there has been a general trend toward increasing surface mining

## ***E.O. 12866 Review – Draft – Do Not C ite, Quote or Release During Review***

3A-14 relative to undergr ound mining (EPA 2013). In the sensitivity analysis that incorpor ated more geographical details into coal production, EPA used IPM outputs for coal prod uction 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 re main constant through the projection period, but that the national proportion of underground a nd surface production would depend on regional production changes. In general, a larger propor tion of coal production in the Appalachian and Interior regions is from unde rground mining, while a large ma jority 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 year s 2011 and 2012 historical percentages. EIA provides historical regi onal 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.
CH 4 

## Mitigation (Recovery and Use)
EPA projected coal mine CH 4 

## mitigation by calculating the hi storical fraction of methane recovered in relation to genera tion from underground mines, and appl ying 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 CH 4 

## recovery and use from several sources. Several gassy underground coal mi nes in the United States employ ventilation systems to ensure that CH 4 

## levels remain within safe con centrations. Additionally, some U.S. coal mines supplement ventilation systems w ith degasification systems, which remove CH 4 

## from the mine and allow the captured CH 4 

## to be used as an energy source.

ܪܥ 4 ܽܿݎܨ݁ݏܷݕݎ݁ݒ݋ܴ݁ܿ ௦ 

## ෍	ൌ ݁ݏܷݕݎ݁ݒ݋4ܴ݁ܿܪܥ ௬,௦ 

## ݏ݊݋݅ݏݏ݉݅ܧ݈݅ܽݐ݁݊ݐ݋ܲ ௬,௦ 

## 5 ൗ ௕ିସ

௬ୀ௕ 

## Equation 5

Where:

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3A-15 CH4RecoveryUse s,y 

## = Recovered emissions from source s in year y

Potential Emissions s, y 

## = Potential emissions from source s in year y

b = base year

3A.3.2 Natural Gas Systems
The scope of CH 4 

## and CO 2 

## from natural gas systems emissi ons covered in this analysis
are discussed above in the “G eneral Approach” section. Methodology
The methodology for natural gas emissions pr ojections involves the calculation of CH 4 

## and CO 2 

## emissions for over 100 emissions source cat egories 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 CH 4 

## (CH 4 

## that would be released in the absence of controls) 2. Estimate reductions data associated w ith voluntary action and regulations 3. Calculate net emissions
EPA calculated projections of potential CH 4 

## emissions from natural gas systems by summing the projections associated with (1) production, (2) processi ng, (3) transmission and storage, and (4) distribution. For the 2014 U.S. C limate Action Report in general, activity data were projections of natural gas production a nd 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, pi peline length, and number of service lines. For this report, activity driver data were take n from IPM outputs for baseline and compliance scenarios consistent with the analysis presente d 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 projec tions as well. Emissions for each source were estimated using the following equation:
ܰܧ ௦,௬ 

## ൌ ܲܧ ௦,௬ 

## െ ܸܴ ௦,௬ 

## െ ܴܴ ௦,௬

## ***E.O. 12866 Review – Draft – Do Not C ite, Quote or Release During Review***

3A-16 Equation 6

Where:

NE s,y 

## = Projected net emissions for source s in year y

PE s,y 

## = Projected potential emissions for source s in year y

VR s,y, y 

## = Projected voluntary reductions for source s in year y

RR s,y, y 

## = Projected regulatory reductions for source s in year y

The sections below describe detailed calculations for projections of CH 4 

## from natural gas systems. This analysis also includes CO 2 

## emissions that result from flaring in the production

sector. 73 

## The U.S. Greenhouse Gas Inventory also incl udes other sources of venting, fugitive, and flaring CO 2 

## such as CO 2 

## processing emissions from acid ga s removal units, which are designed
to remove CO 2 

## from natural gas. This analysis does not include these other non-combustion CO 2 

## sources. EPA calculated projected flaring CO 2 

## emissions by scaling emi ssions 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 inve ntory for the six supply regions (i.e., Northeast, Gulf Coast, Midcontinent, Southwest, Rocky Mount ain, and West Coast) for 33 of these sources.
Potential Emissions
EPA estimated future year potential emi ssions for the production stage using the following equation.

ܲܧ ௦,௬ 

## ൌ ܲܧ ௦,௕ 

## ൈ ቀ ீ௔௦	௉௥௢ௗ௨௖௧௜௢௡ ೤ ீ௔௦	௉௥௢ௗ௨௖௧௜௢௡ ್ 

## ቁ

Equation 7

73 

### The GHG Inventory estimate for CO2 from natural gas flari ng includes some CO2 from flaring of associated gas.

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3A-17 Where:

PE s,y, y 

## = Projected future potential emissions for source s in year y

PE s,b, b 

## = Estimated potential emissions for source s in base year b

Gas Production y 

## = Projected natural gas dry production year y

Gas Production b 

## = 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 assi gns a lifetime of limited duration to each reduction projec t; for purposes of the base year emissions inventory and the future year projections, the reductio ns associated with each project we re either considered to be a “one-year” project or a “perma nent” project based on sunset da tes provided by the Natural Gas STAR program. Reductions from “one-year” project s were typically from the implementation of new or modified practices, while reductions from “permanent” projec ts tended to be from equipment installation, replacement, or modificati on. 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 “perma nent” project were assigned to the project’s reported start year and every subs equent year thereafter. Thus, th e reductions due to “permanent” projects gradually accumu lated throughout the invent ory time series, while the reductions due to “one-year” projects were replaced every year. The following production stage voluntary reducti ons were reported to Natural Gas STAR and applied to individual sources in the emissions inventory:

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3A-18  Completions for gas wells with hydraulic fract uring (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 a nd 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-driv en pumps to electric, mechanical, or solar
pumps.

 Gas engines compressor exhaust (one year)—repla ce ignition/reduce false starts; turbine fuel use optimization.

 Gas engines compressor exhaust (permanent)—conve rt engine starting to Nitrogen- and/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 indi vidual sources in the emissions inventory, there were re ductions classified as “Other Production” that were applied to the overall production stage emissions. It was assumed that the percentage of vol untary reductions relative to potential CH 4 

## in the most recent base year inventory for the produc tion stage would remain constant in each subsequent future year. 75 

## In addition, implementation of th e Oil and Natural Gas Sector New Source Performance Standards (NSPS)—disc ussed further belo w—necessitates the reclassification of certain production re ductions from voluntary to regulatory.

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 vo luntary program are assumed. This assu mption 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 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.

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3A-19 Regulatory Reductions
As part of the regulatory reductions for the production stage, reductions due to existing
Oil and Natural Gas Sector National Standa rds for Hazardous Air Pollutants (NESHAP) requirements (EPA. 2012a) for dehydrator vents and condensate tanks without control devices were included in the base year inventory. Thes e reductions were carried forward in the future year projections. In addition, the base year inventory account ed for state-level requirements in Wyoming
and Colorado for RECs. 76 

## In the base year inventory, a natio nal-level reduction was estimated by applying a 95 percent REC reduction to the fracti on of national emissions occurring in Wyoming and Colorado (i.e., 15.1 percent); this resulted in a national-level redu ction 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 fo r VOCs (EPA 2012a, finalized in 2012) significantly increased the amount of regulatory reductions applicab le to the production stage, resulting in substantial CH 4 

## 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 specifi c Oil and Natural Gas Sector NSPS requirements impact the following production stage sources w ith regard to VOC (and the associated CH 4 

## )
emissions:

 Hydraulically fractured natural gas well completions

 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 centrif ugal compressors at gathering and boosting stations 

## The impact of these requirements on the future year projections is discussed below. The

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.

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3A-20 specific quantitative reductions calculated for th ese 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 docu ment 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 us e of a completion combus tion device (i.e., flare), instead of RECs. In addition, RECs are not requi red for exploratory “wild cat” wells, delineation wells (i.e., used to define the borders of a natu ral gas reservoir), and low-pressure wells (i.e., completions where well pressure is too low to perf orm RECs); in these instances, emissions must be reduced using combustion. Based on the O il and Natural Gas Sector NSPS TSD (EPA 2012b), EPA assumed for the purpose of these pr ojections 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 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-le vel reduction of 14.35 percent wa s 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

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 exp ected to achieve 95 percent combustion efficiency.

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3A-21 refractured and recompleted. The phase-in peri od before January 1, 2015, is also applicable. 78 

## As with completions, a 95 percent reduction wa s assumed for both RECs and completion combustion. This replaced the natio nal-level reduction of 14.35 percen t that was used in the base year inventory. For both well completions and workovers (or refractured well completions) with hydraulic fracturing, in conjuncti on with the Oil and Natural Ga s Sector NSPS, EPA removed REC-related reductions from the projected vol untary 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 re quires 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 great er than 6 standard cubi c feet per hour) with exceptions where high bleed devices are required for safety reasons. The TSD indicates that a typical production stage high-bleed pn eumatic device emits 6.91 tons of CH 4 

## per year and that replacing the high-bleed device with a typical low-bleed pneumatic de vice would result in a
reduction of 6.65 tons CH 4 

## 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 pe rcent of the situations where bleed pneumatic 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 safe ty isolation valve is involved) (EPA 2011). Based on this information, for the purpose of these project ions EPA applied a national-level reduction of 77 pe rcent (i.e., 0.962 × 0.8) to each fu ture 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

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.”

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3A-22 already occurred in the past and the associated effects carry forward into the future or were unrelated to the requirements of th e Oil and Natural Gas Sector NSPS. New Storage Tanks

The Oil and Natural Gas Sector NSPS also re quires that new stor age tanks with VOC emissions of 6 tons per year or greater must re duce VOC emissions by at least 95 percent, likely to be accomplished by routing emissions to a co mbustion device or rerouting emissions into process streams. The Oil and Natural Gas Sect or NSPS TSD indicates that approximately 74 percent of the total condensate produced in the United States pa sses 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 natio nal-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 asso ciated with storage tanks was required. The reductions included in the base year inventory alre ady 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 operati ng hours are not monitored or documented. The
Oil and Natural Gas Sector NSPS TSD estimated baseline emissions of 3,773 tons per year of
CH 4 

## for new reciprocating compressors used in the production stage; the TSD also estimated total reductions from replacing th e rod packing for these compressors as 2,384 tons per year of
CH 4 

## (EPA 2011). Based on this information, for th e purpose of these projections EPA applied a national-level reduction of 63.2 per cent to each future year’s annua l increase in emissions from gathering reciprocating compresso rs in the production stage. Processing Stage
The processing stage includes a total of 11 emissions source categories. EPA estimated

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3A-23 the base year inventory emissions for the processi ng stage at the national level, instead of at the region level, like the base year invent ory 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 emi ssions for the processing stage by assuming that the quantity of processed natura l 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 reducti ons were reported to Natural Gas STAR and applied to individua l 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 pi n shutoff device to reduce venting.



## In addition to these reductions that were applied to specific indi vidual sources in the emissions inventory, there were redu ctions classified as “Other Pr ocessing” that were applied to the overall processing stage emissions. It was assumed that the percentage of vol untary reductions relative to potential CH 4 

## in the most recent base year inventory for the proces sing stage would remain constant in each subsequent future year. 79 

## In addition, implementation of th e Oil and Natural Gas Sector NSPS (discussed further below) necessitates the recla ssification of certain processing reductions from

79 

### This assumption is discussed in Footnote 7.

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3A-24 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 Sect or NESHAP requirement s for dehydrator vents (EPA 2013). These reductions were carried fo rward in the future year projections. The Oil and Natural Gas Sector NSPS signifi cantly increased the amount of regulatory reductions applicable to the proc essing 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 CH 4 

## ) 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 futu re 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. Th ere are two options for this replacement: every 26,000 hours of operation if operating hours are m onitored and documented, or every 36 months if operating hours are not monitored or documen ted. The Oil and Natura l Gas Sector NSPS TSD
estimated baseline emissions of 4,870 tons per year of CH 4 

## 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 th ese compressors as 3,892 tons per year of CH 4 

## (EPA 2011). Based on this information, for the purpose of these project ions EPA applied a national-level reduction of 79.9 per cent to each future year’s annua l increase in emissions from reciprocating compressors in the processing stage. New and Modified Centrifugal Compressors
The Oil and Natural Gas Sector NSPS require s a 95 percent reduction in VOC emissions

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3A-25 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 Ga s Sector NSPS does not apply to centrifugal compressors with dry seal system s, because they have low VOC emissions. A national-level reduction of 95 percent was applie d to each future year’s annual increase in emissions from centrifugal compressors w ith 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 in cluded in the base ye ar inventory already occurred in the past and the associated effects car ry 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 pneum atic controls at gas processi ng 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 associated with natural gas transmission and stor age and 12 associated with liquefied natural gas (LNG) transmission and storage. The natural ga s and LNG emissions were estimated at the
national level.
Potential Emissions
Future year potential emissions for the na tural gas sources and the six LNG storage sources within the transmission and storage stag e were estimated using the following equation:

ܲܧ ௦,௬ 

## ൌ ܲܧ ௦,௕ 

## ൈ ቀ ீ௔௦	஼௢௡௦௨௠௣௧௜௢௡ ೤ ீ௔௦	஼௢௡௦௨௠௣௧௜௢௡ ್ 

## ቁ

Equation 8

Where:

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3A-26 PE s,y 

## = Projected future potential emissions for source s in year y

PE s,b 

## = Estimated potential emissions for source s in base year b
Gas Consumption y 

## = Projected national natura l gas consumption in year y Gas Consumption b 

## = Estimated national natural gas consumption in base year b

The national natural gas consumption estimat es were obtained from the IPM scenario outputs. Future year potential emissions for the si x LNG import terminal sources within the transmission and storage stage were estimated using the following equation:

ܲܧ ௦,௬ 

## ൌ ܲܧ ௦,௕ 

## ൈ ቀ ௅ேீ	ூ௠௣௢௥௧௦ ೤ ௅ேீ	ூ௠௣௢௥௧௦ ್ 

## ቁ

Equation 9

Where:

PE s,y 

## = Projected future potential emissions for source s in year y

PE s,b 

## = Estimated potential emissions for source s in base year b
LNG Imports y 

## = Projected LNG imports in year y LNG Imports b 

## = Estimated LNG imports in base year b

The LNG import estimates were obtained from the table titled “Natural Gas Imports and Exports” of the IPM scenario outputs. The speci fic 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 th e Natural Gas STAR program for projects implemented to reduce emissions. The following transmission and storage stage voluntary reductions were reported to Natura l Gas STAR and applied to indivi dual sources in the base year emissions inventory:

 Reciprocating compressors (one year)—replace compressor rod packing systems.

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3A-27  Reciprocating compressors (permanent)—replace wet seals with dry seals.

 Pipeline venting (one year)—recover gas from pi peline pigging operations; use composite wrap repair; use hot taps for in-service pipeline connec tions; use inert gas and pigs to perform pipeline purges; use pipeline pump-down techniques to lower gas line pressure.

 Pneumatic devices (permanent)—identify and re place 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 indi vidual sources in the emissions inventory, there were re ductions classified as “Other Transmission and Storage” that were applied to the overall transmis sion and storage stage emissions. It was assumed that the percentage of vol untary reductions relative to potential CH 4 

## in the most recent base year inventory for the transmis sion 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 reclassificati on of certain reductions from voluntary to regulatory.

Regulatory Reductions
No regulatory reductions were previously incl uded 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 futu re year projections is discussed below.

New Storage Tanks
As described above in the production sector.

80 

### This assumption is discussed in Footnote 7.

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3A-28 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 s ources, 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 stag e (except for the pipeline leak sources) using Equation 3, assuming that the quantity of distribu ted natural gas tracks cl osely with the quantity of consumed natural gas. 81 

## The natural gas consumption estimat es were obtained from the IPM scenario outputs. Sector-specific consumption es timates were used. For most sources (i.e., all city gate, all vented, and the re sidential customer meter sources), the “Natural Gas” line item 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 h eat and power, and lease and plant fuel) under “Industrial Consumption.” Unlike most other sources in the natural ga s systems emissions inventory, projected pipeline leak emissions in the distribution stag e were not estimated using natural gas production or consumption estimates. Instead, linear extrapolat ion of historical pipeline miles was used to project leak emissions from dist ribution mains, while linear ex trapolation of the historical
number of service lines was used to project leak emissions from services. Linear extrapolation was used because the historical statistics for pipe line miles and number of services show fairly
consistent behavioral trends over the entire time series from 1990 to 2011. In particular, the

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 plan ts might not lead to a proportional change in CH 4 

### 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 distri bution 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 TgCO 2 

### e or increased by up to 0.4 TgCO 2 

### e, assuming the change in power plant natural gas consumption had no effect on distribution-segment methane emissions.

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3A-29 historical statistics show a dist inct trend toward the use of plas tic and away from other materials (i.e., cast iron, copper, unprotecte d steel, and protected st eel). Historical pipeli ne length data was drawn from the US GHG Inventory (EPA 2013), wh ich draws pipeline data from a variety of

sources. Voluntary Reductions
Projections of voluntary reductions for the distribution stag e were based on historical data reported by industry to the Natural Gas STAR program for projects implemented to reduce emissions. Unlike the production, processing, a nd transmission and storage stages, no distribution stage voluntary reduc tions reported to Natural Gas STAR were applied to individual sources in the emissions inventory. However, th ere were reductions classified as “Other Distribution” that were applied to th e overall distribution stage emissions. It was assumed that the percentage of vol untary reductions relative to potential CH 4 

## in the most recent base year inventory for the transm ission stage would remain constant in each subsequent future year. 82 

## Regulatory Reductions
There were no requirements in the Oil a nd Natural Gas Sector NSPS that impact emissions from the distribution stage.

82 

### This assumption is discussed in Footnote 7.

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3A-30 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. C limate 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 Di stribution: 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 Sect or: New Source Performance Standards and National Emission Standards for Hazardous Ai r 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 Natura l Gas Sector: Standards of Performance for Crude Oil and Natural Gas Production, Transmission, and Dist ribution: 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 Reduc tions 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-CO 2 

## and Non-Energy CO 2 

## Sources.” Available at:

< http://www.state.gov/documents/organization/219472.pdf >

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4-31 

## C HAPTER 

## 4: E STIMATED 

## C LIMATE 

## B ENEFITS AND 

## H UMAN 

## H EALTH 

## C O 

## - BENEFITS



## 4.1 Introduction Implementing the proposed Emission Guide lines for Greenhouse Gas (GHG) Emissions from Existing Stationary Sources: Electric Ut ility Generating Units (EGUs) (hereafter, EGU GHG Existing Source Guidelines) is expected to reduce emissions of carbon dioxide (CO 2 

## ) and have ancillary human health benefits (i.e., co-benefits) associated with lower ambient
concentrations of criteri a air pollutants. This chapter describe s 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 (PM 2.5 

## ) and ozone by reducing emissions of precursor pollutants (i.e ., sulfur dioxide (SO 2 

## ), nitrogen dioxide (NO 2 

## ), and directly emitted
PM 2.5 

## ). Data, resource, and methodological limita tions prevent EPA from monetizing the benefits from several important co-benefit categories, including reducing direct exposure to SO 2 

## , NO 2 

## , and hazardous air pollutants (HAP), as well as ecosystem effects and visibility impairment. We qualitatively discuss these unquantif ied 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 ye ars and discount rates. The esti mated benefits associated with these emission reductions are beyond those ach ieved by previous EPA rulemakings, including the Mercury and Air Toxics Standards (MATS).
4.2 Estimated Climate Benefits from CO 2 

## The primary goal of the proposed guidel ines is to reduce emissions of CO 2 

## . In this section, we provide an overview of the clim ate science assessments released since the 2009 Endangerment Finding. We also provide informa tion regarding the econo mic valuation of CO 2 

## using the Social Cost of Carbon (SCC), a metric that estimates the monetary value of impacts associated with marginal changes in CO 2 

## emissions in a given year. Table 4-1 summarizes the quantified and unquantified climate benefits in this analysis.

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4-32 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 CO 2 

### —  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 confid ence in available data or
methods. 

## 4.2.1 Climate Change Impacts
In 2009, the EPA Administrator found that “s ix greenhouse gases taken in combination endanger both the public health and the public welfare of cu rrent 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 Endange rment Finding closed following the EPA’s 2010
Reconsideration Denial 84 

## . These assessments include the “Spe cial Report on Managing the Risks
of Extreme Events and Disasters to Advan ce 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 Cha nging Ocean” (Ocean Acidification) (NRC, 2010), “Report on Climate Stabilization Targets: Emissions, Concentrations, and Impacts over Decades to Millennia” (Climate Stabilization Targets) (N RC, 2011a), “National Security Implications for U.S. Naval Forces” (National Security Imp lications) (NRC, 2011b), “Understanding Earth’s Deep Past: Lessons for Our Climate Future” (U nderstanding 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 Endang erment 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”).

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4-33 Future” (NRC, 2012b), “Climate and Social Stress: Implications for Securi ty Analysis” (Climate and Social Stress) (NRC, 2013a), and “Abrupt Im pacts of Climate Chan ge” (Abrupt Impacts) assessments (NRC, 2013b).
The EPA has reviewed these assessments a nd finds that in general, the improved understanding of the climate syst em they present are consistent with the assessments underlying the 2009 Endangerment Finding. The IPCC report (IPCC, 2013), the National C limate Assessment (Mel illo 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 baseline s, are at least 40 percen t larger than, and in some cases more than twice as large as, th e 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 incl uding 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 uncer tainty inherent in accounting for ice sheet processes, these
revised estimates are consistent with the a ssessments underlying the existing finding that GHGs are reasonably anticipated to endanger public h ealth and welfare. Other key findings of the recent assessments are described briefly below. According to the IPCC in the SREX (IPCC, 2012), “A changing climat e leads to changes in the frequency, intensity, spatial extent, dura tion, and timing of extreme weather and climate events, and can result in unprecedented extrem e weather and climate events.” The SREX documents observational evidence of changes in so me weather and climate extremes that have occurred globally since 1950. The assessment al so provides evidence of anthropogenic influence (e.g., elevated concentrations of GHGs) regarding the cause of some of these changes, including warming of extreme daily temperat ures, intensified extreme precipi tation events, and increases in extreme coastal high water levels due to rising s ea level. The SREX projects further increases in
some extreme weather and c limate events during the 21 st 

## century. Combined with increasing vulnerability and exposure of populations and asse ts, 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. In its Climate Stabilization Targets assessment (NRC, 2011a), the NRC states,

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4-34 “Emissions of carbon dioxide from the burning of fossil fuels have ushered in a new epoch where human activities will largely determine th e evolution of Earth’s climate. Because carbon dioxide in the atmosphere is long lived, it can effectively lock Eart h 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, findi ng warming could lead to increases in heavy rainfall and decreases in crop yields and Arctic sea ice exte nt, 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, th e assessment projects that the ar ea burnt by wildfires in western North America will likely more than double and th at 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 le vel 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 l ead to rising relative sea level even in the northern states. The NRC National Security Im plications assessment (NRC, 2011b) considers potential impacts of sea level ri se and suggests that “the Depart ment of the Navy should expect roughly 0.4 to 2 meters global average sea-le vel 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 ma ss migrations; and addressing changing security needs in the Arctic as sea ice retreats. Th e NRC Climate and Social Stress assessment (NRC, 2013a) found that it would be “prude nt for security analysts to expect climate surprises in the coming decade . . . and for them to become progressively more serious and more frequent thereafter[.]” The NRC Understanding Earth’s Deep Past assessment (NRC, 2012a) finds that “the

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4-35 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 CO 2 

## concentrations by the e nd 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 a ssessment (NRC, 2010) finds that “[t]he chemistry of the ocean is changing at an unpreced ented rate and magnitude due to anthropogenic carbon dioxide emissions; the rate of change exceed s any known to have occu rred 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, fi sheries, 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 Abr upt 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 re garding some abrupt events within the next century such as disrup tion of the Atlantic Meridional Ov erturning 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 ex tremes, and groundwater supplies critical for agriculture now seem more likely, severe, and imminent. The assessment found that some abrupt changes we re already underway (Arctic s ea ice retreat a nd 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 th e 2009 Endangerment Finding. For example, confidence in attributing recent warming to human causes has increased: the IPCC stated that it is extremely likely (>95 percent confidence) th at human influences have been the dominant cause of recent warming. Moreover, the IPCC found that the last 30 years were likely (>66

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4-36 percent confidence) the warmest 30 year period in the Northern Hemisphere of the past 1400 years, that the rate of ice lo ss of worldwide glaciers and the Greenland and Antarctic ice sheets has likely increased, that there is medium confiden ce 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 be en observed in changing precipitation patterns, melting snow and ice, species migration, ne gative impacts on crops, increased heat and decreased cold mortality, and altered ranges fo r water-borne illnesses and disease vectors. Additional risks from future changes include d eath, injury, and disrupted livelihoods in coastal zones and regions vulnerable to inland flooding, food insecurity linked to warming, drought, and flooding, especially for poor popul ations, reduced access to dri nking and irrigation water for those with minimal capital in semi-arid regi ons, and decreased biodiversity in marine ecosystems, especially in the Ar ctic and tropics, with implicati ons 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 sy stem. Limiting climate change will require
substantial and sustained reductions of greenhouse gases emissions.” Finally, the recently released National Climat e 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 cha nge 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 cau sing sea level to rise and glacier s and Arctic sea ice to melt, and oceans are becoming more acidic as they ab sorb carbon dioxide. These and other aspects of climate change are disrupting people’s lives and damaging so me sectors of our economy.” Assessments from these bodies repres ent the current state of knowledge, comprehensively cover and synthesize thousands of individual studies to obtain the majority conclusions from the body of scientific literatur e and undergo a rigorous and exacting standard of review by the peer expert community and U.S. government.
4.2.2 Social Cost of Carbon
We estimate the global social benefits of CO 2 

## emission reductions expected from the proposed guidelines using the S CC estimates presented in the 2013 Technical Support Document: Technical Update of the Social Cost of Carbon for Regulatory Impact Analysis

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4-37 Under Executive Order 12866 (2013 SCC TSD). 85 

## We refer to these estimates, which were developed by the U.S. government, as “SCC estima tes.” The SCC is a metric that estimates the monetary value of impacts associat ed with marginal changes in CO 2 

## emissions in a given year. It includes a wide range of anticip ated 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 ac tions (i.e., benefits of rulemakings that have an incremental impact on cumulative global CO 2 

## emissions). The SCC estimates used in this analysis we re developed over many years, using the best science available, and with i nput from the public. The EPA and other federal agencies have considered the extensive public comments on ways to improve SCC estima tion 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 EP A and other executive branch entities used three integrated assessment models (IAMs) to de velop SCC estimates and selected four global values for use in regulatory an alyses. The SCC estimates were first released in February 2010 and updated in 2013 using new versions of each IAM. 86

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 Coun cil, 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/defau lt/files/omb/assets/inforeg/technical- update-social-cost-of-carbon-forregulator-impact-analysis.pdf

. 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 Advi sers, 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/defau lt/files/omb/inforeg/for-agencies/Soci al-Cost-of-Carbon-for-RIA.pdf. See previous citation for 2013 SCC TSD.

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4-38 The SCC estimates represent global measures because of the distinctive nature of the climate change problem. The climate change prob lem 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 c ountries 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 th an impacts that simply occur in the U.S. Climate change presents a problem that the United Stat es 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 S CC estimates was to enable a consistent exploration of three inte grated assessment models (DICE, FUND, and PAGE) 87 

## while respecting the different approaches to quantifying damage s taken by the key modelers in the field. The selection of the three input parameters (equilib rium climate sensitivity, reference socioeconomic scenarios, discount rate) was ba sed on an extensive review of the literature. Specifically, a probability distribution for climate sensitivity was sp ecified as an input into all three models. In addition, the interagency group used a range of sc enarios for the socio-economic parameters and a range of values for the discount rate. All othe r model features were left unchanged, relying on the model developers’ best estimates and judgm ents. In DICE, these parameters are handled deterministically and represented by fixed consta nts; in PAGE, most parameters are represented by probability distributions. FUND was also run in a mode in which parameters were treated probabilistically. The use of three models and these input para meters allowed for exploration of important uncertainties in the way climate damages are estimated, equilibrium climate sensitivity, reference socioeconomic and emission traj ectories, and discount ra te. 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

87 

### The full models names are as follows: Dynamic Integrat ed Climate and Economy (DIC E); Climate Framework for

Uncertainty, Negotiation, and Distribution (FUND); and Po licy Analysis of the Greenh ouse Gas Effect (PAGE).

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4-39 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 mode ling decisions (e.g., with regard to the discount rate, referen ce 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-rev iewed publications. The model updates that are relevant to the SCC estimates include: an explicit representation of sea le vel rise damages in the DICE and PAGE models; updated adaptation assu mptions, revisions to ensure damages are constrained by GDP, updated regional scaling of damages, and a re vised treatment of potentially abrupt shifts in climate damages in the PAGE model; an updated car bon cycle in the DICE model; and updated damage functions for sea leve l rise impacts, the ag ricultural sector, and reduced space heating requirements, as well as changes to the tr ansient response of temperature to the buildup of GHG concentrations and the incl usion of indirect effects of methane emissions
in the FUND model. The 2013 SCC TS D provides complete details. When attempting to assess the incremen tal economic impacts of carbon dioxide emissions, the analyst faces a number of seri ous 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 climat e 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 should be viewed as provisional.
The 2010 SCC TSD noted a number of limitatio ns to the SCC analysis, including the incomplete way in which the integrated a ssessment models capture catastrophic and noncatastrophic impacts, their in complete treatment of adapta tion 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

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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4-40 physical, ecological, and economic impacts of clim ate 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 la gs 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 t ogether they suggest that the SCC estimates are likely conservative. In part icular, the IPCC Fourth Assessme nt Report (2007) concluded that “It is very likely that [SCC estimates] undere stimate the damage costs because they cannot include many non-quantifiable impacts.” Nonetheless, these estimates and the discussi on of their limitations represent the best
available information about the social benefits of CO 2 

## reductions to inform benefit-cost analysis.
The new versions of the models used to esti mate the values presented below offer some improvements in these areas, although further wo rk 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. Addi tional 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 CO 2 

## 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 in tergenerational context (where co sts and benefits are incurred by different generations). The fourth value is the 95 th 

## 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 distribu tion (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 reduc tions, the SCC estimate for each emissions year would be applied to changes in CO 2 

## emissions for that year, and then discounted back to the

89 

### The 2010 and 2013 TSDs present SCC in 2007$. The estim ates were adjusted to 2011$ using GDP Implicit Price

Deflator, http://www.gpo.gov/fdsys/pkg/ECONI-2013-02/pdf/ECONI-2013-02-Pg3.pdf

.

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4-41 analysis year using the same disc ount 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 re sponse 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 intern ally consistent with other modeli ng 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 CO 2 

### 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 fa r only considered estimates for the social cost of CO 2 

### . While CO 2 

### 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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4-42 Table 4-2. Global Social Cost of CO 2 

## , 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 CO 2 

### emissions and are defined in r eal terms, i.e., adjusted for

inflation using the GDP implicit price deflator. Th ese SCC values are stated in $/metric ton.



## Table 4-3. Estimated Global Climate Benefits of CO 2 

## 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 CO 2 

### 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% (95 th 

### percentile) $52 $51 $40 $39 * The SCC values are dollar-year and emissions-year speci fic. SCC values represent only a partial accounting of climate impacts.



## Table 4-4. Estimated Global Climate Benefits of CO 2 

## 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 CO 2 

### 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% (95 th 

### percentile) $77 $76 $57 $56 * The SCC values are dollar-year and emissions-year speci fic. SCC values represent only a partial accounting of climate impacts.



## Table 4-5. Estimated Global Climate Benefits of CO 2 

## 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 CO 2 

### 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% (95 th 

### percentile) $94 $92 n/a n/a * The SCC values are dollar-year and emissions-year speci fic. SCC values represent only a partial accounting of climate impacts.

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4-43 It is important to note that the climate be nefits presented above are associated with changes in CO 2 

## 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 pollutant s have not been calculated for the proposed guidelines. 91 

## The other emissions potentially reduced as a result of these guidel ines include other greenhouse gases (such as methane), aerosols an d aerosol precursors such as black carbon, organic carbon, sulfur dioxide a nd nitrogen oxides, and ozone precursors such as nitrogen oxides and volatile organic carbon compoun ds. Changes in emissions of these pollutants (both increases and decreases) could directly re sult from changes in electricity generation, upstream fossil fuel extraction and transport, and/or downstream se condary market impacts. Reductions in black carbon or ozone precursors are proj ected to lead to further coo ling, but reductions in the other aerosol species and precursors are projected to lead to warm ing. Therefore, changes in non-CO 2 

## pollutants could potentially augment or offset the climate benefits calculated here. These pollutants can act in different ways and on di fferent timescales than carbon dioxide. For example, aerosols reflect (and in the case of black carbon, absorb) incoming radiation, whereas greenhouse gases absorb outgoing in frared radiation. In addition, these aerosols are thought to affect climate indirectly by a ltering properties of clouds. Black carbon can also deposit on snow and ice, darkening these surfaces and accelera ting melting. In terms of lifetime, while carbon
dioxide emissions can increase c oncentrations in the atmosphere for hundreds or thousands of years, many of these other pollu tants are short lived and remain in the atmosphere for short periods of time ranging from days to weeks and ca n therefore exhibit larg e 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 producti on sectors that may result from the compliance scenarios examined in this RIA in the appendix to Chapter 3. The EPA assessed whether the net

91 

### The federal government’s SCC estimates used in this an alysis are designed to assess the climate benefits

associated with changes in CO 2 

### emissions only.

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4-44 change in upstream methane emissions from na tural gas and coal produc tion is likely to be positive or negative and also assessed the poten tial magnitude of changes relative to CO 2 

## emissions reductions antici pated at power plants. This assessment included CO 2 

## emissions from the flaring of methane, but did not evaluate potential changes in othe r combustion-related CO 2 

## 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 CH 4 

## emissions from natural gas systems and coal mines and CO 2 

## emissions from flaring of methane will likely decrease under the proposed guidelines . Furthermore, the anal ysis suggests that the changes in upstream methane emissions are small relative to the change s in direct emissions from power plants.
4.3 Estimated Human Health Co-Benefits

In addition to CO 2 

## , implementing these proposed guidelines is expected to reduce
emissions of SO 2 

## and NO X 

## , which are precursors to formation of ambient PM 2.5 

## , as well as directly emitted fine particles. 92 

## Therefore, reducing these em issions would also reduce human exposure to ambient PM 2.5 

## and the incidence of PM 2.5 

## -related health effect s. In addition, in the presence of sunlight, NO X 

## and VOCs can undergo a chemical r eaction in the atmosphere to form ozone. Depending on localized concentrations of volatile organic com pounds (VOCs), reducing
NO X 

## emissions would also reduce human exposure to ozone and the inci dence of ozone-related health effects. Although we do not have sufficient data to quantify these impacts in this analysis, reducing emissions of SO 2 

## and NOx would also reduce ambient exposure to SO 2 

## and NO 2 

## , respectively. In this section, we pr ovide an overview of the monetized PM 2.5 

## and ozone-related co-benefits estimated for the pr oposed guidelines. The estimated co-benefits associated with these emission reductions are beyond those ach ieved by previous EPA rulemakings, including MATS. A full description of the underlying data, studies, and a ssumptions is provided in the PM NAAQS RIA (U.S. EPA, 2012a) and Oz one NAAQS RIA (U.S. EPA, 2008b, 2010d). There are several important cons iderations in assessing the air pollution-related health co-

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 th e earth’s surface, including carbonates, silicates, iron, phosphates, copper, and zinc. Often, crustal material repres ents particles not classified as one of the other species (e.g., organic carbon, elemental carbon, nitrate, sulfate, chloride, etc.).

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4-45 benefits for a climate-focused rulemaking. Firs t, these estimated hea lth co-benefits do not account for any climate-related air quality chan ges (e.g., increased ambient ozone associated with higher temperatures) but rath er changes in precursor emissi ons affected by this rulemaking. Excluding climate-related air quality changes may underestimate ozone-related health cobenefits. It is unclear how PM 2.5 

## -related health co-benefits wo uld be impacted by excluding climate-related air quality changes since the scie nce is unclear as to how climate change may
affect PM 2.5 

## exposure. Second, the estimated health co-b enefits also do not consider temperature
modification of PM 2.5 

## 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-benef its likely lead to underestimation. Implementing these guidelines may lead to reductions in ambient PM 2.5 

## concentrations below the National Ambient Air Quality Standa rds (NAAQS) for PM and ozone in some areas and assist other areas with attaining thes e NAAQS. Because the NAAQS RIAs (U.S. EPA, 2012a, 2008b, 2010d) also calculated PM and ozone be nefits, there are important differences worth noting in the design and analytical objec tives 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 s ources including known and unknown controls, incremental to implementation of existing regulations and controls needed to attain the current standards. In short, NAAQS RIAs hypot hesize, but do not predict, the reduction strategies that States may choose to enact wh en implementing a revise d 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 contro l and emission reductions. However, it is possible that some costs and bene fits estimated in this RIA may account for the same air quality improvements as estima ted in the illustrative NAAQS RIAs. Similar to NAAQS RIAs, the emission reduc tion 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

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4-46 reductions for implementation rules, which typica lly require specific emission reductions in a 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 revi sed future NAAQS. EPA does not
re-issue illustrative RIAs outside of the rulemaking process that retroactively update the baseline to account for implementation rules promulga ted after an RIA was completed. For more information on the relationship be tween illustrative an alyses, 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 PM 2.5 

## and Ozone

The Integrated Science Assessment for Particulate Matter (PM ISA) (U.S. EPA, 2009b)
identified the human health effects associated with ambient PM 2.5 

## 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 eff ects associated with ambient ozone exposure, which include prematur e morality and a variety of morbidity effects associated with acute and chronic exposures. Ta ble 4-6 identifies the qua ntified and unquantified co-benefit categories captured in EPA’s health co -benefits estimates for reduced exposure to
ambient PM 2.5 

## and ozone. Although the table below does not list unquantified health effects such as those associated with exposure to SO 2 

## , NO 2 

## , and mercury nor welf are 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 unquantif ied benefit categories is not e xhaustive, nor is quantification of each effect complete.

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4-47 Table 4-6. Human Health Effects of Ambient PM 2.5 

## 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 PM 2.5 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 PM 2.5 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-activ ity days (age 18-65) 

###   PM ISA Chronic Bronchitis (age >26) 

### — — PM ISA 1 Emergency room visits for cardiovascular effects (all ages) 

### — — PM ISA 1
Strokes and cerebrovascular disease (age 50-79) 

### — — PM ISA 1 Other cardiovascular effects (e.g., other ages) 

### — — PM ISA 2 Other respiratory effects (e.g., pulmonary function, non-asthma ER visits, non-bronchitis chronic diseases, other ages and populations) 

### — — PM ISA 2

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

### — — PM ISA 2,3
Cancer, mutagenicity, and genotoxicity effects 

### — — PM ISA 2,3


### 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 ISA 1



### 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-activ ity days (age 18–65) 

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

###   Ozone ISA Decreased outdoor worker productivity (age 18–65) 

### — — Ozone ISA 1 Other respiratory effects (e.g., premature aging of lungs) 

### — — Ozone ISA 2
Cardiovascular and nervous system effects 

### — — Ozone ISA 2 Reproductive and developmental effects 

### — — Ozone ISA 2,3

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 eviden ce is only suggestive of causality or there are other significa nt concerns over the strength of the association.

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4-48 We follow a “damage-function” approach in calculating benefits, which estimates changes in individual health endpoints (specific eff ects that can be associat ed with changes in air quality) and assigns values to those changes assuming independe nce 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 thei r values for regulatory analyses, our estimates are
based on the best available methods of benefits tr ansfer, which is the science and art of adapting primary research from similar contexts to estimat e benefits for the environmental quality change under analysis. We use a “benefit-per-ton” approach to estimate the PM 2.5 

## and ozone co-benefits
in this RIA. This section desc ribes the underlying basis for th e 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) quantifie s the changes in the incidence of adverse health impacts resulting from changes in human exposure to PM 2.5 

## and ozone. We use the environmental Benefits Mapping and Analys is Program (BenMAP) (version 4.0.66) to systematize health impact analyses by applyi ng a database of key input parameters, including population projections, health imp act functions, and valuation functions (Abt Associates, 2012). For this assessment, the HIA is limited to those h ealth effects that are di rectly linked to ambient
PM 2.5 

## and ozone concentrations. There may be other indirect health impacts associated with reducing emissions, such as occ upational health exposures. Epid emiological studies generally provide estimates of the relative risks of a partic ular health effect for a given increment of air pollution (often per 10 μg/m 3 

## for PM 2.5 

## or ppb for ozone). These relative risks can be used to develop risk coefficients that relate a unit reduction in PM 2.5 

## to changes in the incidence of a
health effect. We refer the reader to th e 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 an alysis, and we briefly elaborate on adult premature mortality below. The size of the mortality eff ect estimates from epidemiological studies, the serious nature of the effect itself, and the hi gh monetary value ascribed to prolonging life make mortality risk reduction the most significant he alth endpoint quantified in this analysis.

4.3.1.1 Mortality Concentration-Response Functions for PM 2.5

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4-49 Considering a substantial body of published scie ntific literature a nd reflecting thousands of epidemiology, toxicology, and clinical studi es, the PM ISA documents the association
between elevated PM 2.5 

## concentrations and adverse health e ffects, 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 Scie nce Advisory Board (SAB-CASAC) (U.S. EPASAB, 2009b, 2009c), concluded that there is a causal relationshi p between mortality and both long-term and short-term exposure to PM 2.5 

## based on the entire body of sc ientific evidence. The
PM ISA also concluded that the scientific litera ture consistently finds that a no-threshold loglinear model most adequately portrays the PM -mortality concentratio n-response relationship while recognizing potential uncer tainty about the exact shape of the concentr ation-response
function. In addition to adult mortal ity discussed in more detail belo w, we use effect coefficients from Woodruff et al. (1997) to es timate PM-related infant mortality. For adult PM-related mortality, we use the e ffect coefficients from the most recent epidemiology studies examining two large popula tion 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 a nd Six Cities cohorts provide the strongest evidence of the association between long-term PM 2.5 

## exposure and premature mortality with support from a number of additional cohort st udies. The SAB’s Health Effects Subcommittee (SAB-HES) also supported using thes e 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 PM 2.5 

## co-benefits estimates using relative risk estimates from both these cohorts (Krewski et al., 2009; Lepeule et al., 2012).
As a characterization of uncer tainty regarding the adult PM 2.5 

## -mortality relationship, EPA graphically presents the PM 2.5 

## co-benefits derived from EPA’s e xpert elicitation study (Roman et al., 2008; IEc, 2006). The primary goal of the 2006 st udy was to elicit from a sample of health experts probabilistic distributi ons describing uncertainty in estimates of the reduction in mortality among the adult U.S. population resulti ng from reductions in ambient annual average
PM 2.5 

## concentrations. In that study, twelve experts provided indepe ndent opinions regarding the
PM 2.5 

## -mortality concentration-response function. Because the experts relied upon the ACS and Six Cities cohort studies to inform their concentration-response functions, the benefits estimates derived from the expert responses generally fall between results derived fr om these studies (see

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4-50 Figure 4-1). We do not combine the expert results in order to pres erve the breadth and diversity of opinion on the expert panel. Th is presentation of the expert -derived results is generally consistent with SAB advice (U.S. EPA-SA B, 2008), which recommended that the EPA emphasize that “scientific differences existed only w ith respect to the magnitude of the effect of
PM 2.5 

## on mortality, not whether such an effect exis ted” and that the expe rt elicitation “supports
the conclusion that the benefits of PM 2.5 

## control are very likely to be substantial”. Although it is possible that newer scientific lite rature 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 Scie nce (NRC, 2008) issued a series of recommendations to the EPA regarding the quan tification 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 committ ee recommended that “o zone-related mortality
be included in future estimates of the health benefits of reducing oz one exposures...” The NAS also recommended that “...the greatest em phasis be placed on the multicity and NMMAPS [ National Morbidity, Mortal ity, 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 (Be ll et al. (2005), Levy et al. (2005), and Ito et al. (2005)). These studies are consistent with th e studies used in th e 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 represen t the lowest and the highest cobenefits estimates based on these six ozone morta lity studies. In addition, we graphically present
estimated co-benefits derived from all six studi es mentioned above as a characterization of uncertainty regarding the ozone -mortality relationsh ip in Figure 4-1.

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). Th erefore, the ozone mortality studies applied in this analysis, while current at the time of the previous Ozon e 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-g oing review of the Ozone NAAQS.

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4-51 4.3.2 Economic Valuation for Health Co-benefits After quantifying the change in adverse hea lth 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 admi ssions, 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 dire ct expenditures related to treatment but not the value of avoided pain and suffering from the hea lth 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 perc ent of monetized PM -related co-benefits and over 90 percent of m onetized ozone-related co-benefits. The economics literature concerning the appropriate method for valuing reductions in pr emature mortality risk is still developing. The adoption of a value for the projected reduction in th e risk of premature mortality is the subject of continuing discussion within the economics a nd public policy analysis community. Following
the advice of the SAB’s Environmental Econom ics 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 provid es 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 ha s received. Therefore, EPA has decided to apply the VSL
that was vetted and endorsed by the SAB in the Guidelines for Preparing Economic Analyses

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4-52 (U.S. EPA, 2000) 94 

## while the Agency continues its effort s to update its guidance on this issue. This approach calculates a mean value across VS L estimates derived from 26 labor market and contingent valuation studies published be tween 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 analys is 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 scientifi cally sound, appropriately reviewed evidence in valuing mortality risk reductions and has ma de significant progress in responding to the SABEEAC’s specific recommendations. In the proce ss, the Agency has identified a number of important issues to be consider ed in updating its mortality risk valuation estimates. These are detailed in a white paper, “V aluing 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 fo rmal recommendations were transmitted on July 29, 2011 (U.S. EPA-SAB, 2011). EPA is ta king SAB’s recommendations under advisement. In valuing PM 2.5 

## -related premature mortality, we discount the value of premature mortality occurring in future years using rates of 3 percen t and 7 percent (OMB, 2003). We assume that there is a “cessation” lag between ch anges in PM exposures a nd 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 characte rized 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 PM 2.5 

## (U.S. EPA-SAB, 2004c). Changes in the cessation lag assumptions do not change the tota l number of estimated deaths but rather the timing of those deaths. Because short-term ozone-related prematur e mortality occurs within the analysis year,
the estimated ozone-related co-benefits are identical for al l discount rates.

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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4-53 4.3.3 Benefit-per-ton Estimates for PM 2.5 

## We used a “benefit-per-ton” approach to estimate the PM 2.5 

## co-benefits in this RIA. EPA has applied this approach in several previ ous 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 mo rbidity), of reducing one ton of PM 2.5 

## (or PM 2.5 

## precursor such as NO X 

## or SO 2 

## ) from a specified source. Specifically, in this analysis, we multiplied the estimates for the EGU sector by the correspond ing 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-pe r-ton estimates described in the TSD: Estimating the Benefit per Ton of Reducing PM 2.5 

## Precursors from 17 Sectors (U.S. EPA, 2013a). The national estimates were derived using the approach publishe d 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 Fa nn et al. (2009), but the newer study includes improvements that provide more refined estimates of PM 2.5 

## -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, th e updated air quality modeling data reflects more recent emissions data (2005 rather than 2001) and has hi gher spatial resolution ( 12km rather than 36 km grid cells). 97 

## For this rulemaking, to generate the regi onal 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 Fa nn et al. (2012). We then divided the regional benefits by the regional emissi ons rather than the national em issions. The appendix to this chapter provides additional detail regarding these calculations.
As noted below in the characte rization of uncertainty, all be nefit-per-ton estimates have inherent limitations. Specificall y, all benefit-per-ton estimates reflect the geographic distribution

97 

### Although the modeling underlying the benefit-per-ton es timates does not reflect emission reductions anticipated

from MATS, the EGU contribution to ambient PM 2.5 

### 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-cou nting concern with the resu lting co-benefits estimates.

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4-54 of the modeled sector emissions, which may not match the emission reductions anticipated by the proposed guidelines, and they may not refl ect local variability in population density, meteorology, exposure, baseline health incidence rates, or other local factors for any specific location. In addition, these estima tes reflect the regional average benefit-per-ton for each ambient
PM 2.5 

## precursor emitted from EGUs, which assumes a linear atmospheric response to emission reductions. The regional benefit-per-ton estima tes, although less subject to these types of uncertainties than national estimates, still shou ld 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 locati on and magnitude of their impact on PM 2.5 

## levels, which drive population exposure. 4.3.4 Benefit-per-ton Estimates for Ozone

Similar to PM 2.5 

## , 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 PM 2.5 

## estimates, we generated regional bene fit-per-ton estimates for ozone for the EGU sector using the air quali ty 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 PM 2.5 

## estimates, the ozone estimates ar e not based on changes to annual emissions. Instead, the regional estimates (i.e., East, West, and California) correspond to NO X 

## emissions from U.S. EGUs during the ozone-seas on (May to September). Because we estimate ozone health impacts from May to September only, this approach underestimates ozone cobenefits in areas with a longe r ozone season such as southern California and Texas. These
estimates assume that EGU-attributable ozone fo rmation at the regional -level is due to NOx
alone. Because EGUs emit little VOC relative to NO X 

## 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 cau tion. We provide more detailed information regarding th e generation of these estimates in the appendix to this chapter.
4.3.5 Estimated Health Co-Benefits Results Tables 4-7 through 4-9 provide the national and regional be nefit-per-ton estimates for 2020, 2025, and 2030. Tables 4-10 through 4-12 provide the emission reductions estimated to occur in three analysis year s (2020, 2025, and 2030) for two illu strative compliance scenarios

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4-55 (i.e., state and regional) for two options by region (i.e., East, We st, 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 PM 2.5 

## and ozone-related co-benefits using concentrationresponse functions from different studies and expe rt 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 2 020 as an illustrative analysis year by precursor pollutant and region, respectively. 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

SO 2 

### 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 PM 2.5 

### (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 PM 2.5 

### (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

NO X 

### (as PM 2.5 

### ) 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 NO X 

### (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 th e range of epidemiology studies fo r avoided premature mortality for PM 2.5 

### and

ozone. All estimates are ro unded to two significant figures. The mone tized co-benefits do not include reduced health effects from direct exposure to NO 2 

### , SO 2 

### , 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 PM 2.5 

### 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 NO X 

### emissions. Ozone co-benefits occu r in analysis year, so they are the same for all discount rates. Confidence intervals are unavailable for this anal ysis because of the benefitper-ton methodology. In general, the 95 th 

### percentile confidence interval for monetized PM 2.5 

### benefits ranges from approximately -90 percent to +180 percent of the central estimates based on Krewski et al. (2009) and Lepeule et al. (2012).

98 

### See Chapter 3 of this RIA for more information regardin g 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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4-56 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

SO 2 

### 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 PM 2.5 

### (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 PM 2.5 

### (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

NO X 

### (as PM 2.5 

### ) 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 NO X 

### (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 th e range of epidemiology studies fo r avoided premature mortality for PM 2.5 

### and

ozone. All estimates are ro unded to two significant figures. The mone tized co-benefits do not include reduced health effects from direct exposure to NO 2 

### , SO 2 

### , 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 PM 2.5 

### 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 NO X 

### emissions. Ozone co-benefits occu r in analysis year, so they are the same for all discount rates. Confidence intervals are unavailable for this anal ysis because of the benefitper-ton methodology. In general, the 95 th 

### percentile confidence interval for monetized PM 2.5 

### 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 4-9. Summary of National and Regional Benefit-per-Ton Estimates for EGUs in 2030 (2011$)*

Pollutant Discount Rate National Regional East West California

SO 2 

### 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 PM 2.5 

### (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 PM 2.5 

### (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

NO X 

### (as PM 2.5 

### ) 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 NO X 

### (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 th e range of epidemiology studies fo r avoided premature mortality for PM 2.5 

### and

ozone. All estimates are ro unded to two significant figures. The mone tized co-benefits do not include reduced health effects from direct exposure to NO 2 

### , SO 2 

### , 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 PM 2.5 

### 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 NO X 

### emissions. Ozone co-benefits occu r in analysis year, so they are the same for all discount rates. Confidence intervals are unavailable for this anal ysis because of the benefitper-ton methodology. In general, the 95 th 

### percentile confidence interval for monetized PM 2.5 

### benefits ranges from approximately -90 percent to +180 percent of the central estimates based on Krewski et al. (2009) and Lepeule et al. (2012).

## ***E.O. 12866 Review – Draft – Do Not C ite, Quote or Release During Review***

4-57 Table 4-10. Emission Reductions of Criter ia Pollutants for the Proposed EGU GHG Existing Source Guidelines in 2020 (thousands of short tons)* Region SO 2 

### All-year NOx Ozone-Season NOx Directly emitted PM 2.5 

### (EC+OC) Directly emitted PM 2.5 

### (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.

## ***E.O. 12866 Review – Draft – Do Not C ite, Quote or Release During Review***

4-58 Table 4-11. Emission Reductions of Criter ia Pollutants for the Proposed EGU GHG Existing Source Guidelines in 2025 (thousands of short tons)* Region SO 2 

### All-year NOx Ozone-Season NOx Directly emitted PM 2.5 

### (EC+OC) Directly emitted PM 2.5 

### (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.

## ***E.O. 12866 Review – Draft – Do Not C ite, Quote or Release During Review***

4-59 Table 4-12. Emission Reductions of Criter ia Pollutants for the Proposed EGU GHG Existing Source Guidelines in 2030 (thousands of short tons)* Region SO 2 

### All-year NOx Ozone-Season NOx Directly emitted PM 2.5 

### (EC+OC)* Directly emitted PM 2.5 

### (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.

## ***E.O. 12866 Review – Draft – Do Not C ite, Quote or Release During Review***

4-60 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

SO 2 

### $13,000 to $29,000 $11,000 to $26,000 Directly emitted PM 2.5 

### (EC+OC) $760 to $1,700 $690 to $1,600 Directly emitted PM 2.5 

### (crustal) $790 to $1,800 $710 to $1,600 NOx (as PM 2.5 

### ) $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

SO 2 

### $12,000 to $26,000 $11,000 to $24,000 Directly emitted PM 2.5 

### (EC+OC) $750 to $1,700 $670 to $1,500 Directly emitted PM 2.5 

### (crustal) $770 to $1,700 $690 to $1,600 NOx (as PM 2.5 

### ) $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

SO 2 

### $10,000 to $23,000 $9,100 to $21,000 Directly emitted PM 2.5 

### (EC+OC) $640 to $1,500 $580 to $1,300 Directly emitted PM 2.5 

### (crustal) $660 to $1,500 $600 to $1,400 NOx (as PM 2.5 

### ) $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

SO 2 

### $9,800 to $22,000 $8,900 to $20,000 Directly emitted PM 2.5 

### (EC+OC) $610 to $1,400 $550 to $1,200 Directly emitted PM 2.5 

### (crustal) $630 to $1,400 $570 to $1,300 NOx (as PM 2.5 

### ) $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 si gnificant figures so numbers may not sum down columns. The estimated monetized co-benefits do not include climate benefits or reduced hea lth effects from direct exposure to NO 2 

### , SO 2 

### , ecosystem effects, or visibility impairment. All fine particle s are assumed to have equivalent health effects, but the benefit-per-ton estimates vary depending on the location and magnitude of their impact on PM 2.5 

### levels, which drive population exposure. The monetized co-b enefits incorporate the co nversion from precursor emissions to ambient fine particles and ozone. Co-benefits for PM 2.5 

### 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 95 th 

### percentile confidence interval for monetized PM 2.5 

### benefits ranges from approximately -90 percent to +180 percent of the central es timates based on Krewski et al. (2009) and Lepeule et al. (2012).

## ***E.O. 12866 Review – Draft – Do Not C ite, Quote or Release During Review***

4-61 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

SO 2 

### $18,000 to $40,000 $16,000 to $36,000 Directly emitted PM 2.5 

### (EC+OC) $900 to $2,000 $810 to $1,800 Directly emitted PM 2.5 

### (crustal) $830 to $1,900 $750 to $1,700 NOx (as PM 2.5 

### ) $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

SO 2 

### $17,000 to $38,000 $15,000 to $35,000 Directly emitted PM 2.5 

### (EC+OC) $850 to $1,900 $760 to $1,700 Directly emitted PM 2.5 

### (crustal) $780 to $1,800 $700 to $1,600 NOx (as PM 2.5 

### ) $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

SO 2 

### $14,000 to $30,000 $12,000 to $27,000 Directly emitted PM 2.5 

### (EC+OC) $690 to $1,600 $630 to $1,400 Directly emitted PM 2.5 

### (crustal) $640 to $1,400 $580 to $1,300 NOx (as PM 2.5 

### ) $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

SO 2 

### $13,000 to $29,000 $12,000 to $26,000 Directly emitted PM 2.5 

### (EC+OC) $640 to $1,400 $580 to $1,300 Directly emitted PM 2.5 

### (crustal) $590 to $1,300 $530 to $1,200 NOx (as PM 2.5 

### ) $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 si gnificant figures so numbers may not sum down columns. The estimated monetized co-benefits do not include climate benefits or reduced hea lth effects from direct exposure to NO 2 

### , SO 2 

### , ecosystem effects, or visibility impairment. All fine particle s are assumed to have equivalent health effects, but the benefit-per-ton estimates vary depending on the location and magnitude of their impact on PM 2.5 

### levels, which drive population exposure. The monetized co-b enefits incorporate the co nversion from precursor emissions to ambient fine particles and ozone. Co-benefits for PM 2.5 

### 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 95 th 

### percentile confidence interval for monetized PM 2.5 

### benefits ranges from approximately -90 percent to +180 percent of the central es timates based on Krewski et al. (2009) and Lepeule et al. (2012).

## ***E.O. 12866 Review – Draft – Do Not C ite, Quote or Release During Review***

4-62 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

SO 2 

### $21,000 to $47,000 $19,000 to $43,000 Directly emitted PM 2.5 

### (EC+OC) $870 to $2,000 $780 to $1,800 Directly emitted PM 2.5 

### (crustal) $800 to $1,800 $720 to $1,600 NOx (as PM 2.5 

### ) $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

SO 2 

### $20,000 to $44,000 $18,000 to $40,000 Directly emitted PM 2.5 

### (EC+OC) $840 to $1,900 $760 to $1,700 Directly emitted PM 2.5 

### (crustal) $770 to $1,700 $700 to $1,600 NOx (as PM 2.5 

### ) $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 si gnificant figures so numbers may not sum down columns. The estimated monetized co-benefits do not include climate benefits or reduced hea lth effects from direct exposure to NO 2 

### , SO 2 

### , ecosystem effects, or visibility impairment. All fine particle s are assumed to have equivalent health effects, but the benefit-per-ton estimates vary depending on the location and magnitude of their impact on PM 2.5 

### levels, which drive population exposure. The monetized co-b enefits incorporate the co nversion from precursor emissions to ambient fine particles and ozone. Co-benefits for PM 2.5 

### 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 95 th 

### percentile confidence interval for monetized PM 2.5 

### benefits ranges from approximately -90 percent to +180 percent of the central es timates based on Krewski et al. (2009) and Lepeule et al. (2012).

## ***E.O. 12866 Review – Draft – Do Not C ite, Quote or Release During Review***

4-63 Table 4-16. Summary of Avoided Health Incidences from PM 2.5 

## -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 PM 2.5 

### -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 w ith two significant figures. Co-benefits for PM 2.5 

### 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 an alysis because of the benefit-per-ton methodology. In general, the 95 th 

### 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).

## ***E.O. 12866 Review – Draft – Do Not C ite, Quote or Release During Review***

4-64 Table 4-17. Summary of Avoided Health Incidences from PM 2.5 

## -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 PM 2.5 

### -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) 13 0,000 130,000 100,000 100,000 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 w ith two significant figures. Co-benefits for PM 2.5 

### 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 95 th 

### 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).

## ***E.O. 12866 Review – Draft – Do Not C ite, Quote or Release During Review***

4-65 Table 4-18. Summary of Avoided Health Incidences from PM 2.5 

## -Related and OzoneRelated Co-Benefits for Proposed EGU GHG Existing Source Guidelines in 2030* Option 1 - state Option 1 - regional PM 2.5 

### -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 w ith two significant figures. Co-benefits for PM 2.5 

### 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 95 th 

### 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).

## ***E.O. 12866 Review – Draft – Do Not C ite, Quote or Release During Review***

4-66 Figure 4-1. Monetized Health Co-benefits for each Option (Regional Compliance) of the Proposed EGU GHG Existing Source Guidelines in 2020 *

*The PM 2.5 

### graphs show the estimated PM 2.5 

### 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 benef its. The monetized co-benefits do not include climate benefits or reduced health effects from direct exposure to NO 2 

### , SO 2 

### , ecosystem effects, or visibility impairment. Results would be similar if the state complia nce scenario was shown. 

## PM 2.5 

## Ozone

## ***E.O. 12866 Review – Draft – Do Not C ite, Quote or Release During Review***

4-67 Figure 4-2. Breakdown of Monetized Health Co -benefits by Precursor Pollutant at a 3% Discount Rate for each Option (Reg ional Compliance) for Proposed EGU GHG Existing Source Guidelines in 2020*
* “Low Health Co-benefits” refers to the combined health co-benefits es timated using the Bell et al. (2004) mortality study for ozone with the Krewski et al. (2009) mortality study for PM 2.5 

### . “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 PM 2.5 

### . Results would be similar if the st ate 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

## ***E.O. 12866 Review – Draft – Do Not C ite, Quote or Release During Review***

4-68 Figure 4-3. Breakdown of Monetized Health Co-B enefits 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 es timated using the Bell et al. (2004) mortality study for ozone with the Krewski et al. (2009) mortality study for PM 2.5 

### . “High Health Co-benefits” refers to the combined health co-benefits estimated usin g the Levy et al. (2005) mortality study for ozone with the Lepeule et al. (2012) mortality study for PM 2.5 

### . 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 para meters and inputs from numerous models, there are likely to be many sources of uncertainty. This analysis is no exception. This analysis 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

## ***E.O. 12866 Review – Draft – Do Not C ite, Quote or Release During Review***

4-69 includes many data sources as i nputs, including emission invent ories, air quality data from models (with their associated parameters a nd inputs), population data, population estimates, health effect estimates from epidemiology stud ies, economic data for monetizing co-benefits, and assumptions regarding the future state of the world (i.e., regulati ons, 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 an alysis are compounded, even small uncertainties can have large effects on the total quantified benef its. Therefore, the estimates of co-benefits in each analysis year should be viewed as represen tative of the general magn itude 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 detail ed uncertainty assessment found in the PM NAAQS RIA (U.S. EPA, 2012a) or the Ozone NAA QS RIA (U.S. EPA, 2008b) because we lack the necessary air quality modeli ng input and/or monitoring data to run the benefits model. However, the results of the quantitative and qualit ative uncertainty analyses presented in the PM NAAQS RIA and Ozone NAAQS RIAs can provide so me information regarding the uncertainty inherent in the estimated co-benefits results pres ented in this analysis. For example, sensitivity analyses conducted for the PM NAAQS RIA indi cate that alternate cessation lag assumptions could change the estimated PM 2.5 

## -related mortality co-benefit s discounted at 3 percent by between 10 percent and –27 percent and that alternate income grow th adjustments could change
the PM 2.5 

## -related mortality co-benef its 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 sampli ng error in the health impact and economic valuation functions using Monte Carlo methods. 

## In general, the 95 th 

## percentile confidence interval
for monetized PM 2.5 

## benefits ranges from approximately -90 percent to + 180 percent of the
central estimates based on Krewski et al. (2009) and Lepe ule et al. (2012). The 95 th 

### 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 quali ty modeling, we do not have information on the specific location of the air quality chan ges associated with th e proposed guidelines. As such, it is not feasible to estim ate the proportion of co-benefits oc curring in different locations,

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4-70 such as designated nonattainment areas. Instead, we applied benefit-pe r-ton estimates, which reflect specific geographic patterns of emissions reductions and specific air quality and benefits modeling assumptions. For example, these estim ates may not reflect local variability in population density, meteorology, exposure, baseline h ealth incidence rates, or other local factors that might lead to an over-estimate or under-estim ate of the actual co-benefits of controlling PM and ozone precursors. Use of these benefit-per-to n values to estimate co-benefits may lead to higher or lower benefit estimates than if co-benefits were calcula ted based on direct air quality modeling. Great care should be taken in applying these estim ates to emission reductions occurring in any specific location, as these ar e 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 ve ry different than the estimates presented here. To the extent that the geographic distribu tion 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 overes timated. 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-t on estimates (U.S. EPA, 2013a) and Fann et al. (2012). In addition, the appendix to this chapte r provides additional uncertainty information regarding the benefit-per-ton es timates applied in this RIA, including an evaluation of the similarities and differences in the spatial dist ribution 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 scientif ic literature and met hods and supported by the SAB-HES and the National Academies of Science (NRC, 2002). Below are key assumptions underlying the estimates for
PM 2.5 

## -related premature mortality, which accoun ts for 98 percent of the monetized PM 2.5 

## health
co-benefits.
1. We assume that all fine particles, regard less of their chemical composition, are equally potent in causing premature mortality. This is an important assumption, because PM 2.5 

## varies considerably in compos ition across sources, but the scie ntific evidence is not yet sufficient to allow differentiation of effect estimates by particle type. The PM ISA concluded that “many constituents of PM 2.5 

## can be linked with multiple health effects, and the evidence is not yet sufficient to allow di fferentiation of those constituents or sources that are more closely related to specific outcomes” (U.S. EPA, 2009b).

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4-71 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 va ried concentrations of PM 2.5 

## , including both areas that do not meet the fine particle standard a nd those areas that are in attainment, down to the lowest modeled concentrations.

3. We assume that there is a “cessation” lag between the cha nge in PM exposures and the total realization of changes in mortality effect s. Specifically, we assume that some of the incidences of premature mortality related to PM 2.5 

## exposures occur in a distributed fashionover 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 th e magnitude of the risks we estimate from
simulated PM 2.5 

## concentrations that coincide with the bul k of the observed PM concentrations in the epidemiological studies that are used to esti mate the benefits. Likewi se, we are less confident
in the risk we estimate from simulated PM 2.5 

## concentrations that fall below the bulk of the
observed data in these studies. Concentration be nchmark 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 p opulation exposed to annual mean PM 2.5 

## levels at or above different concentrations, which provides some insight into the level of uncertainty in the
estimated PM 2.5 

## mortality benefits. In this analysis, we apply two concentration benchmark approaches (LML and one standard deviation belo w the mean) that have been incorporated into recent RIAs and EPA’s Policy Assessment for Pa rticulate Matter (U.S. EPA, 2011d). There are uncertainties inherent in identifying any particul ar point at which our c onfidence in reported associations becomes appreciably less, and the scientific evidence prov ides no clear dividing line. However, the EPA does not view these co ncentration benchmarks as a concentration threshold below which we would not quantify h ealth 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 concen trations associated with the em ission reduction strategies. The
PM ISA concluded that the scientific evidence co llectively is sufficient to conclude that the

99 

### For a summary of the scientific review statemen ts regarding the lack of a threshold in the PM 2.5 

### -mortality

relationship, see the TSD entitled Summary of Expert Opinions on the Existence of a Threshold in the Concentration-Response Function for PM 2.5 

### -related Mortality (U.S. EPA, 2010b).

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4-72 relationship between long-term PM 2.5 

## 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-speci fic air quality data is not available, and the compliance strategies are illustrati ve of what states may choose to do. For this RIA, we are unable to estimate the percentage of premature mortality as sociated with the emission reductions at each
PM 2.5 

## 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 char acterize the distribution of exposure to baseline concentrations. As a surro gate measure of mortality impacts, we provide the percentage of the popul ation exposed at each PM 2.5 

## 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 not e that baseline exposure is only one parameter in the health impact function, along with base line incidence rates population and change in air qualit y. 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 emi ssion reduction policy. The most important aspect, which we are unable to quantify without rule-s pecific 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 th e 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 m odeling. Figure 4-4 shows a bar ch art of the percentage of the population exposed to various ai r quality levels in the base line, 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 PM 2.5 

### concentrations in the LML assessment are higher than would be expected if MATS was reflected.

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4-73 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 10 th 

### and 25 th 

### percentile. For Krewski, the LML is 5.8 μg/m 3 

### and one standard deviation below the mean is 11.0 μg/m 3 

### . For Lepeule et al., the LML is 8 μg/m 3 

### and 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 PM 2.5 

### do not occur.

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4-74 Figure 4-4. Percentage of Adult Popul ation (age 30+) by Annual Mean PM 2.5 

## Exposure in the Baseline Sector Modeling (used to ge nerate 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 m odeling show premature mortality impacts at each PM 2.5 

### concentration. Therefore, caution is warranted when interpreting this gr aph 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 PM 2.5 

### in the baseline:
93% are exposed to PM 2.5 

### levels at or above the LML of the Krewski et al. (2009) study
67% are exposed to PM 2.5 

### 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

‐ 20PercentageofPopulationExposed

Baseline Annual Mean PM 2.5 Level (μg/m 3 ) LML of Lepeule et al. (2012) study LML of Krewski et al. (2009) study

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4-75 Figure 4-5. Cumulative Distri bution of Adult Population (age 30+) by Annual Mean PM 2.5 

## 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 m odeling show premature mortality impacts at each PM 2.5 

### concentration. Therefore, caution is warranted when interpreting this gr aph because it is not derived in a manner consistent with similar graphs from RIAs that had base d on air quality modeling (e.g., MATS).



## 4.4 Combined Climate Benefits an d Health Co-benefits Estimates In this analysis, we were able to monetize the estimated co-benefits associated with the
decreased emissions of CO 2 

## and reduced exposure to PM 2.5 

## and ozone, but we were unable to
monetize the co-benefits associ ated with reducing exposure to mercury, hydrogen chloride, carbon monoxide, SO 2 

## , and NO 2 

## , as well as ecosystem effects and visibility impairment. Specifically, we estimated the combined climate benefits at discount ra tes of 5 percent, 3 percent, 2.5 percent, and 3 percent (95 th 

## percentile) (as estimated by the interagency working group), and health co-benefits at discount rate s of 3 percent and 7 pe rcent (as recommended by

Among the populations exposed to PM 2.5 

### in the baseline:
93% are exposed to PM 2.5 

### levels at or above the LML of the Krewski et al. (2009) study
67% are exposed to PM 2.5 

### 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 20Cumulativepercentageofpopulationexposed

Baseline Annual Mean PM 2 5 Level (μg/ m 3 ) LML of Krewski et al. (2009) study LML of Lepeule et al. (2012) study

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4-76 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 CO 2 

## emissions are long-lived and subse quent 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 cons ensus exists on the appropriate rate to use in an intergenerational context. The SCC interage ncy group centered its atte ntion on the 3 percent discount rate but emphasized the importance of considering all four SCC estimates. 101 

## The EPA has evaluated the range of poten tial 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, valu es 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/defa ult/files/omb/inforeg/for-agencies/ Social-Cost-of-Carbon-for-RIA.pdf for details.

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4-77 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 CO 2 

### 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% (95 th 

### percentile) $52 $69 to $92 $68 to $88 Option 1 - regional 371 million metric tonnes CO 2 

### 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% (95 th 

### percentile) $51 $67 to $88 $65 to $85 Option 2 - state 295 million metric tonnes CO 2 

### 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% (95 th 

### percentile) $40 $54 to $72 $53 to $69 Option 2 - regional 283 million metric tonnes CO 2 

### 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% (95 th 

### percentile) $39 $52 to $70 $51 to $67 *All estimates are rounded to two significant figures. Climate benefits are based on reductions in CO 2 

### 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 th ey are the same for all discount rates. The health cobenefits reflect the sum of the PM 2.5 

### and ozone co-benefits and reflect the ra nge 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 effect s from direct exposure to NO 2 

### , SO 2 

### , and HAP; ecosystem effects; or visibility impairment.

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4-78 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 CO 2 

### 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% (95 th 

### percentile) $77 $100 to $130 $98 to $130 Option 1 - regional 501 million metric tonnes CO 2 

### 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% (95 th 

### percentile) $76 $99 to $130 $97 to $120 Option 2 - state 376 million metric tonnes CO 2 

### 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% (95 th 

### percentile) $57 $75 to $98 $73 to $95 Option 2 - regional 368 million metric tonnes CO 2 

### 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% (95 th 

### percentile) $56 $73 to $96 $72 to $93 *All estimates are rounded to two significant figures. Climate benefits are based on reductions in CO 2 

### 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 th ey are the same for all discount rates. The health cobenefits reflect the sum of the PM 2.5 

### and ozone co-benefits and reflect the ra nge 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 eff ects from direct exposure to NO 2 

### , SO 2 

### , and HAP; ecosystem effects; or visibility impairment.

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4-79 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 CO 2 

### 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% (95 th 

### percentile) $94 $120 to $160 $120 to $150 Option 1 - regional 545 million metric tonnes CO 2 

### 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% (95 th 

### percentile) $92 $120 to $150 $120 to $150 *All estimates are rounded to two significant figures. Climate benefits are based on reductions in CO 2 

### 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 th ey are the same for all discount rates. The health cobenefits reflect the sum of the PM 2.5 

### and ozone co-benefits and reflect the ra nge 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 eff ects from direct exposure to NO 2 

### , SO 2 

### , and HAP; ecosystem effects; or visibility impairment.

## ***E.O. 12866 Review – Draft – Do Not C ite, Quote or Release During Review***

4-80 Figure 4-6. Breakdown of Combined Moneti zed Climate and Heal th 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 es timated using the Bell et al. (2004) mortality study for ozone with the Krewski et al. (2009) mortality study for PM 2.5 

### . “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 PM 2.5 

### . 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 ambien t fine particles and ozone. Data, 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)

9% NOx (as Ozone) 5% 

# Option 2 ‐ Regional Low Health Co

‐ benefits High Health Co

‐ benefits

## ***E.O. 12866 Review – Draft – Do Not C ite, Quote or Release During Review***

4-81 time, and resource limitations prevented EPA from quantifying the impacts to, or monetizing the co-benefits from several importa nt benefit categories, including co-benefits associated with exposure to several HAP (includi ng mercury and hydrogen chloride) SO 2 

## and NO 2 

## , 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 pr ovide a qualitative description of these benefits,
which are listed in Table 4-23. Table 4-23. Unquantified Health an d 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 NO 2 Asthma hospital admissions (all ages) — — NO 2 ISA 1 Chronic lung disease hospital admissions (age > 65) — — NO 2 ISA 1 Respiratory emergency department visits (all ages) — — NO 2 ISA 1 Asthma exacerbation (asthmatics age 4–18) — — NO 2 ISA 1 Acute respiratory symptoms (age 7–14) — — NO 2 ISA 1 Premature mortality — — NO 2 ISA 1,2,3 Other respiratory effects (e.g., airway hyperresponsiveness and inflammation, lung function, other ages and populations) — — NO 2 ISA 2,3

Reduced incidence of morbidity from exposure to SO 2 Respiratory hospital admissions (age > 65) — — SO 2 ISA 1 Asthma emergency department visits (all ages) — — SO 2 ISA 1 Asthma exacerbation (asthmatics age 4–12) — — SO 2 ISA 1 Acute respiratory symptoms (age 7–14) — — SO 2 ISA 1 Premature mortality — — SO 2 ISA 1,2,3 Other respiratory effects (e.g., airway hyperresponsiveness and inflammation, lung function, other ages and populations) — — SO 2 ISA 1,2

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, 2000 1

Other neurologic effects (e.g., developmental delays, memory, behavior) — — IRIS; NRC, 2000 2

Cardiovascular effects — — IRIS; NRC, 2000 2,3 Genotoxic, immunologi c, and other toxic effects — — IRIS; NRC, 2000 2,3 Reduced incidence of morbidity from exposure to HAP Effects associated with exposure to hydrogen chloride — — ATSDR, IRIS 1,2

Improved Environment Reduced visibility impairment Visibility in Class 1 areas — — PM ISA 1 Visibility in residential areas — — PM ISA 1

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4-82 Category Specific Effect Effect Has Been Quantified Effect Has Been Monetized More Information

Reduced effects on materials Household soiling — — PM ISA 1,2 Materials damage (e.g., corrosi on, increased wear) — — PM ISA 2 Reduced effects from PM deposition (metals and organics) Effects on Individual organism s and ecosystems — — PM ISA 2

Reduced vegetation and ecosystem effects from exposure to ozone Visible foliar injury on vegetation — — Ozone ISA 1 Reduced vegetation growth and reproduction — — Ozone ISA 1 Yield and quality of co mmercial forest products and crops — — Ozone ISA 1 Damage to urban ornament al plants — — Ozone ISA 2 Carbon sequestration in terrestrial ecosystems — — Ozone ISA 1 Recreational demand associated with forest aesthetics — — Ozone ISA 2

Other non-use effects Ozone ISA 2 Ecosystem functions (e .g., water cycling, biogeochemical cycles, ne t primary productivity, leaf-gas exchange, community composition) — — Ozone ISA 2

Reduced effects from acid deposition Recreational fishing — — NO x SO x ISA 1 Tree mortality and decline — — NO x SO x ISA 2 Commercial fishing and fo restry effects — — NO x SO x ISA 2 Recreational demand in te rrestrial and aquatic ecosystems — — NO x SO x ISA 2

Other non-use effects NO x SO x ISA 2 Ecosystem functions (e.g., bi ogeochemical cycles) — — NO x SO x ISA 2

Reduced effects from nutrient enrichment Species composition and biodiversity in terrestrial and estuarine ecosystems — — NO x SO x ISA 2 Coastal eutrophication — — NO x SO x ISA 2 Recreational demand in terrestrial and estuarine ecosystems — — NO x SO x ISA 2

Other non-use effects NO x SO x ISA 2 Ecosystem functions (e.g., biogeochemical cycles, fire regulation) — — NO x SO x ISA 2 Reduced vegetation effects from ambient exposure to SO 2 and NO x Injury to vegetation from SO 2 exposure — — NO x SO x ISA 2 Injury to vegetation from NO x exposure — — NO x SO x ISA 2
Reduced ecosystem effects from exposure to methylmercury Effects on fish, birds, and mammals (e.g., reproductive effects) — — Mercury Study RTC 2 Commercial, subsistence and recreational fishing — — Mercury Study RTC 1

1 We assess these co-benefits qualitatively due to data and resource limitations for this RIA.
2 We assess these co-benefits qual itatively because we do not have sufficient confidence in available data or methods.
3 We assess these co-benefits qualitatively because current eviden ce is only suggestive of causality or there are other significa nt 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 hazard ous air pollutants in this analysis. EPA’s SABHES concluded that “the challenge s for assessing progress in health improvement as a result of

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4-83 reductions in emissions of hazardous air polluta nts (HAPs) are daunting...due to a lack of exposure-response functions, uncertainties in em issions 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 ha ve 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 rese arch priorities, focu sing on susceptible and vulnerable populations, and improving dose-respon se 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 re ductions for the proposed guidelines: mercury and hydrogen chloride. Using the IPM m odeling described in Chapter 3 of this RIA, we estimate that the illustrative compliance scenarios for th e proposed guidelines would reduce mercury emissions by up to 2.1 tons and hydrogen ch loride by up to 590 tons by 2030. These HAP
emission reductions are be yond those achieved by MATS. 4.5.1.1 Mercury

Mercury in the environment is transforme d 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 thes e 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 ci ted in this section are publications originally cited in the Mercury Study. 103 

## In addition, EPA has conducte d literature searches to
obtain other related and more r ecent publications to complement the material summarized by the
NRC in 2000. In its review of the literature, the NAS f ound neurodevelopmental effects to be the most sensitive and best documented endpoints and appr opriate for establishing a reference dose (RfD)

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>.

## ***E.O. 12866 Review – Draft – Do Not C ite, Quote or Release During Review***

4-84 (NRC, 2000); in particular NAS supported the use of results from neurobehavioral or neuropsychological tests. The NAS report noted that stud ies in animals reported sensory effects as well as effects on brain development and memo ry 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 lear n 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 “Alt hough the data base is not as extensive for cardiovascular effects as it is for other end point s (i.e., neurologic effect s), 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 ch aracterize the effect of methylmercury exposure on blood pressure and cardiovascular f unction at various stages of life.”
Additional cardiovascular studies have been published since 2000. EPA did not develop a quantitative dose-response assessment for car diovascular effects associated with MeHg exposures, as there is no consensus among scient ists on the dose-response functions for these effects. In addition, there is in consistency 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 neurot oxicity data base. The Mercury Study noted that MeHg is not a potent mutagen but is capable of causing chromosomal damage in a number of experiment al 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 lymphocyt es 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 re lationship between MeHg concentration in hair and DNA damage

## ***E.O. 12866 Review – Draft – Do Not C ite, Quote or Release During Review***

4-85 in lymphocytes, as well as effects on chromosomes. 104 

## Long-term MeHg exposures in this population were believed to occur through consump tion of fish, suggesting that genotoxic effects (largely chromosomal aberrations ) may result from dietary, and ch ronic MeHg exposures similar
to and above those seen in the Faroes and Seychelles populations. Although exposure to some forms of Hg can resu lt 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 pos sibility of carcinogenic effects in humans from low-dose chronic exposures is te nuous. Multiple human ep idemiological studies have found no significant association between Hg exposure and overall cancer incidence, although a few studies have shown an associatio n between Hg exposure and specific types of cancer incidence (e.g., acute leuke mia and liver cancer) (NRC, 2000). There is also some evidence of reproductiv e and renal toxicity in humans from MeHg exposure. However, overall, human data rega rding reproductive, renal, and hematological toxicity from MeHg are very limited and are based on either studies of the two high-dose poisoning episodes in Iraq and Japan or animal data, rather than epidemiological studies of chronic exposures at the levels of interest in this analysis. 4.5.1.2 Hydrogen Chloride

Hydrogen chloride (HCl) is a corrosive gas that can cause irritation of the mucous

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 (A TSDR). 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 c ited 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

.

## ***E.O. 12866 Review – Draft – Do Not C ite, Quote or Release During Review***

4-86 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; exposu re to high concentrations can rapidly lead to swelling and spasm of the throat and suffocation. Most seriou sly exposed persons have immediate onset of rapid breathing, blue coloring of the skin, and na rrowing 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 al so be more vulnerable to gas exposure because of increased minute ventilation pe r kg and failure to evacuate an area promptly when exposed. Hydrogen chloride has not been classified for carcinogenic effects. 109


## 4.5.2 Additional NO 2 

## Health Co-Benefits In addition to being a precursor to PM 2.5 

## 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-b enefits associated with reduced NO 2 

## exposure in this analysis. Therefore, this analysis only quantified and monetized the PM 2.5 

## and ozone co-benefits
associated with the reductions in NO 2 

## 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 NO 2 

## . The NOx ISA concluded that the evidence “is sufficient to infer a likely causal relationship between short-term NO 2 

## exposure and adverse effects on the respiratory system.” These epidemiologic and experimental studies encompass a number of endpoints including emergency depa rtment visits and hospitalizat ions, respiratory symptoms, airway hyperresponsiveness, ai rway inflammation, and lung function. The NOx ISA also
concluded that the relations hip between short-term NO 2 

## exposure and premature mortality was

108 

### Agency for Toxic Substances and Disease Registry (A TSDR). 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

.

## ***E.O. 12866 Review – Draft – Do Not C ite, Quote or Release During Review***

4-87 “suggestive but not sufficient to infer a causal rela tionship,” because it is di fficult to attribute the mortality risk effects to NO 2 

## alone. Although the NOx ISA stated that studies consistently reported a relationship between NO 2 

## exposure and mortality, the e ffect was generally smaller than that for other pollutants such as PM.
4.5.3 Additional SO 2 

## Health Co-Benefits In addition to being a precursor to PM 2.5 

## , SO 2 

## emissions are also associated with a variety
of adverse health effects associated with dire ct exposure. Unfortunately, we were unable to
estimate the health co-benefits associated with reduced SO 2 

## in this analysis because we do not have air quality modeling data available. Therefor e, this analysis only quantifies and monetizes
the PM 2.5 

## co-benefits associated with the reductions in SO 2 

## emissions. Following an extensive evaluation of health evidence from epidemiologic and laboratory studies, the Integrated Science Assessment for Oxides of Sulfur —Health Criteria (SO 2 

## ISA)
concluded that there is a causal relationship between respiratory health effects and short-term exposure to SO 2 

## (U.S. EPA, 2008a). The immediate effect of SO 2 

## on the respiratory system in
humans is bronchoconstriction. Asthmatics ar e more sensitive to the effects of SO 2 

## likely resulting from preexisting inflammation associat ed with this disease. A clear concentrationresponse relationship has been demonstrated in laboratory studies following exposures to SO 2 

## at concentrations between 20 and 100 ppb, both in te rms 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 SO 2 

## ISA identified as a “causal relationship”: asthma exacerbation, respiratory -related emergency department visits, and respiratory-related hospitalizations. The differi ng evidence and associated strength of the
evidence for these different effects is described in detail in the SO 2 

## ISA. The SO 2 

## ISA also
concluded that the relations hip between short-term SO 2 

## exposure and prem ature mortality was “suggestive of a causal relationship” because it is difficult to attribut e the mortality risk effects to
SO 2 

## alone. Although the SO 2 

## ISA stated that studies are gene rally consistent in reporting a relationship between SO 2 

## exposure and mortality, there was a lack of robustness of the observed associations to adjustment for pollutants. We did not quantify these co -benefits due to data
constraints.
4.5.4 Additional NO 2 

## and SO 2 

## Welfare Co-Benefits

As described in the Integrated Science Assessment for Oxides of Nitrogen and Sulfur —

## ***E.O. 12866 Review – Draft – Do Not C ite, Quote or Release During Review***

4-88 Ecological Criteria (NOx ISA) (U.S. EPA, 2008d), SO 2 

## and NOx emissions also contribute to a variety of adverse welfare effects, including acidic deposition, visibility impairment, and nutrient enrichment. Deposition of nitrogen causes acidifica tion, 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 (Pic ea 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 subsis tence fishermen and for other consumers and support several cultural services, including aesthetic and educati onal services and recreational fishing. Biological effects of aci dification in terrestrial ecosys tems are generally linked to aluminum toxicity, which can cause reduced root growth, which rest ricts the ability of the plant to take up water and nutrients. These direct effect s can, in turn, increase the sensitivity of these plants to stresses, such as droughts, cold temp eratures, insect pests, and disease leading to increased mortality of canopy trees. Terrestrial ac idification affects seve ral important ecological services, including declines in ha bitat for threatened and endangered species (cultural), declines in forest aesthetics (cultural), declines in forest productivity (p rovisioning), 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 terrestria l nutrient enrichment. In estuarine waters, excess nutri ent enrichment can lead to eutrophication. Eutrophication of estuaries can disrupt an importa nt source of food production, part icularly fish and shellfish production, and a variety of cultural ecosystem se rvices, including water-based recreational and
aesthetic services. Terrestrial nut rient enrichment is associated with changes in the types and number of species and biodiversit y in terrestrial systems. Exce ssive nitrogen deposition upsets the balance between native and nonnative plants, ch anging the ability of an area to support biodiversity. When the composition of species ch anges, then fire frequency and intensity can also change, as nonnative grasses fuel more fre quent and more intense wildfires. (U.S. EPA, 2008d)
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 oz one 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 eff ects include those that damage or impair the

## ***E.O. 12866 Review – Draft – Do Not C ite, Quote or Release During Review***

4-89 intended use of the plant or ecosystem. Such eff ects 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 fo liar injury, reduced plant vigor (e.g., increased susceptibility to harsh weather, disease, inse ct pest infestation, and competition), species composition shift, and changes in ecosystem s 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, relativ e to carbon dioxide (CO 2 

## ), is sensitive to conditions in the combustion zone, such as fuel oxygen content, burn temp erature, or mixing time. Upon inhalation, CO diffuses through the respiratory system to th e 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 concentr ation and duration of exposure. The Integrated Science Assessment for Carbon Monoxide (U.S. EPA, 2010a) concluded th at short-term exposure to CO is “likely to have a causal relati onship” with cardiovascular morbid ity, particularly in individuals with coronary heart disease. Epidemiologic st udies associate short-te rm CO exposure with increased risk of emergency department visits and hospital admissions. Coronary heart disease includes those who have angina pectoris (cardi ac chest pain), as we ll as those who have experienced a heart attack. Othe r subpopulations potentially at risk include individuals with diseases such as chronic obstructive pulmonary disease (COPD), anemia, or diabetes, and individuals in very early or la te life stages, such as older adults or the developing young. The evidence is suggestive of a cau sal relationship between shor t-term exposure to CO and respiratory morbidity and mortality. The evidence is also suggestive of a causal relationship for birth outcomes and developmental effects follo wing long-term exposure to CO, and for central nervous system effects linked to s hort- and long-term exposure to CO. 4.5.7 Visibility Impairment Co-Benefits Reducing secondary formation of PM 2.5 

## would improve levels visibility in the U.S. because suspended particles and gases degrade vi sibility by scattering an d absorbing light (U.S. EPA, 2009b). Fine particles with significant lig ht-extinction efficiencies include sulfates, nitrates, organic carbon, elemental carbon, and soil (Sisler, 1996). Visibility has direct significance to people’s enjoyment of daily activ ities and their overall sense of wellbeing. Good

## ***E.O. 12866 Review – Draft – Do Not C ite, Quote or Release During Review***

4-90 visibility increases the quality of life where indi viduals live and work, and where they engage in
recreational activities. Particulat e sulfate is the dominant source of regional haze in the eastern U.S. and particulate nitrate is an important cont ributor to light extincti on in California and the upper Midwestern U.S., particularly during wint er (U.S. EPA, 2009b). Previous analyses (U.S. EPA, 2011a) show that visibility co-benefits can be a significant we lfare benefit category. Without air quality modeling, we are unable to es timate visibility relate d benefits, and we are
also unable to determine whether the emissi on reductions associated with the proposed guidelines would be likely to have a significant impact on visibility in urban areas or Class I

areas.

## ***E.O. 12866 Review – Draft – Do Not C ite, Quote or Release During Review***

4-91 4.6 References
40 CFR Chapter I [EPA–HQ–OAR–2009–0171; FRL–9091–8] RIN 2060–ZA14, “Endangerment and Cause or Contribute Findi ngs 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. Abt Associates, Inc. 2012. BenMAP User’s Manual Appendices . Prepared for U.S. Environmental Protection Agency Office of Air Quality Planning and Standards. Research Triangle Park, NC. September. Available at: <http://www.epa.gov/air/benmap/mode ls/BenMAPAppendicesOct2012.pdf>. Bell, M.L., et al. 2004. “Ozone and Short-Term Mortality in 95 U.S. Urban Communities, 1987- 2000.” Journal of the Americ an Medical Association , 292(19): p. 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 locatio n, source, and emission type in estimates of the human health be nefits of reducing a ton of air pollution.” Air Quality, Atmosphere, & Health 2:169–176. Fann, N.; K.R. Baker and C.M. Fulcher. 2012. “Characterizing the PM 2.5 

## -related health benefits of emission reductions for 17 industrial, area and mobile emission sectors across the U.S.” Environment International 49 41–151. Gwinn, M.R.; J. Craig; D.A. Axelra d; R. Cook; C. Dockins; N. Fann; R. Fegley; D.E. Guinnup; G. Helfand; B. Hubbell; S.L. Mazur; T. Palma; R.L. Smith; J. Vandenberg and B. Sonawane. 2011. “Meeting report: Estimating the benefits of reducing hazardous air pollutants— summary of 2009 workshop and future considerations.” Environmental Health Perspectives Jan; 119(1): p. 125-30. Huang, Y.; F. Dominici; M. Bell. 2005. “Bayesia n Hierarchical Distributed Lag Models for Summer Ozone Exposure and Card io-Respiratory Mortality.” Environmetrics , 16, 547-562. Industrial Economics, Incorporated (IEc). 2006. Expanded Expert Judgment Assessment of the Concentration-Response Relationship Between PM 2.5 

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## ***E.O. 12866 Review – Draft – Do Not C ite, Quote or Release During Review***

4-92 Industrial Economics, Incorporated (IEc). 2009. S ection 812 Prospective Study of the Benefits and Costs of the Clean Air Act: Air Toxics Case Study: Health Benefits of Benzene Reductions in Houston, 1990–2020. Final Report, July 14, 2009. Available at: <http://www.epa.gov/air/sect812/dec09/812CAAA_ Benzene_Houston_Final_Report_July_20 09.pdf>. Intergovernmental Panel on Climate Change (IPCC). 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/public ations_ipcc_fourth_assessment_report_synt hesis_report.htm>. Intergovernmental Panel on Climate Change (IPCC). 2012: Managing the Risks of Extreme Events and Disasters to Adv ance Climate Change Adaptation . A Special Report of Working Groups I and II of the Intergovernmental Pane l on Climate Change. Cambridge University Press, Cambridge, UK, and New York, NY, USA. Intergovernmental Panel on Climate Change (IPCC). 2013. Climate Change 2013: The Physical Science Basis. Contribution of Working Gr oup I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. Intergovernmental Panel on Climate Change (IPCC). 2014a. Climate Change 2014: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Fifth Assessment Report of the Inte rgovernmental Pane l on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. Intergovernmental Panel on Climate Change (IPCC). 2014b. Climate Change 2014: Mitigation of Climate Change. Contribution of Working Gr oup III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. Ito, K.; S.F. De Leon and M. Lippmann. 2005. “Associations between Ozone and Daily Mortality: Analysis and Meta-Analysis.” Epidemiology, 16(4): pp. 446-57. Karl, T.; J. Melillo and T. Peterson, Eds. 2009. Global Climate Change Impacts in the United States . Cambridge, United Kingdom: Camb ridge University Press. 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 Soc iety Study Linking Particulate Air Pollution and Mortality. HEI Research Report, 140, Health Effects Institute, Boston, MA.

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4-93 Lepeule, J.; F. Laden; D. Dock ery; 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. Melillo, Jerry M., Terese (T.C.) Ri chmond, 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> National Research Council (NRC). 2000. Toxicological Effects of Methylmercury: Committee on the Toxicological Effects of Methylmercury .” Board on Environmental Studies and Toxicology. National Academies Press. Washington, DC. National Research Council (NRC). 2002. Estimating the Public Health Benefits of Proposed Air Pollution Regulations . National Academies Pr ess. Washington, DC. National Research Council (NRC). 2008. Estimating Mortality Risk Reduction and Economic Benefits from Controlling Ozone Air Pollution . National Academies Press. Washington, DC. National Research Council (NRC). 2010. Ocean Acidification: A National Strategy to Meet the Challenges of a Changing Ocean . National Academies Pr ess. Washington, DC. National Research Council (NRC). 2011a. Climate Stabilization Targets: Emissions, Concentrations, and Impacts over Decades to Millennia . National Academies Press, Washington, DC. National Research Council (NRC) 2011b. National Security Implicati ons of Climate Change for U.S. Naval Forces . National Academies Press. Washington, DC. National Research Council (NRC, 2011c). Understanding Earth's Deep Past: Lessons for Our Climate Future . National Academies Press. Washington, DC National Research Council (NRC). 2012a. Sea-Level Rise for the Coasts of California, Oregon, and Washington: Past, Present, and Future . National Academies Press. Washington, DC. National Research Council (NRC). 2013a. Climate and Social Stress: Im plications for Security Analysis . National Academies Press. Washington, DC. National Research Council (NRC). 2013b. Abrupt Impacts of Climate Change: Anticipating Surprises . National Academies Pr ess. Washington, DC. Office of Management and Budget (OMB). 2003. Circular A-4: Regulatory Analysis. Washington, DC. Available at: < http://www. whitehouse.gov/omb/circulars/a004/a-4.html>.

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4-94 Ren, C.; G.M. William; L. Morawska; K. Me ngensen and S. Tong. 2008a. “Ozone Modifies Associations between Temperature and Cardi ovascular Mortality: Analysis of the NMMAPS Data.” Occupational and Environmental Medicine, 65:255-260. Ren, C.; G.M. Williams; K. Mengersen; L. Mo rawska; and S. Tong. 2008b. “Does Temperature Modify Short-Term Effects of Ozone on To tal Mortality in 60 Large Eastern U.S. Communities? An Assessment Using the NMMAPS Data.” Environment International , 34 (2008), pp. 451–458. Ren, C.; S. Tong. 2006b. “Temperature Modifies th e Health Effects of Particulate Matter in Brisbane, Australia.” International Journal of Biometeorology, 51:87–96. Ren. C.; G.M. Williams; S. Tong. 2006a. “Does Pa rticulate Matter Modify the Association between Temperature and Ca rdiorespiratory Diseases? Environmental Health Persp ectives, 114:1690–1696. Roberts, S. 2004. “Interactions between Partic ulate Air Pollution and Temperature in Air Pollution Mortality Time Series Studies.” Environmental Research, 96:328–337. Roman, H.; K. D. Walker; T.L. Walsh; L. C onner; 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 Science and Technol ogy, 42(7):2268- 2274. Schwartz, J. 2005. “How Sensitive is the Associ ation between Ozone and Daily Deaths to Control for Temperature?” American Journal of Respirat ory and Critical Care Medicine, Vol. 171 (6): 627-31. Sisler, J.F. 1996. Spatial and Seas onal Patterns and Long-Term Vari ability of the Composition of the Haze in the United States: An analysis of data from the IMPROVE network. CIRA Report, ISSN 0737-5352-32, Colorado State University. U.S. Environmental Protection Agency (U.S. EPA). 1995. Regulatory Impact Analysis for the Petroleum Refinery NESHAP. Revised Draft for Promulgation . Office of Air Quality Planning and Standards, Research Triangle Park, N.C. Available on the Internet at <http://www.regmarkets.org/admin/authorpdfs/page.php?id=705>. U.S. Environmental Protection Agency (U.S. EPA). 2000. Guidelines for Preparing Economic Analyses. EPA 240-R-00-003. National Center for E nvironmental Economics, Office of Policy Economics and Innovation. Washingt on, DC. September. Available at: <http://yosemite.epa.gov/ee/epa/eed.nsf/webpages/Guidelines.html/$file/cover.pdf>.

## ***E.O. 12866 Review – Draft – Do Not C ite, Quote or Release During Review***

4-95 U.S. Environmental Protection Agency (U.S. EPA). 2006. Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final) . EPA/600/R-05/004aF-cF. Washington, DC: U.S. EPA. February. Available at: <http://cfpub.epa.gov/ncea/CFM/reco rdisplay.cfm?deid=149923>. U.S. Environmental Protection Agency (U.S. EPA). 2008a. Integrated Science Assessment for Sulfur Oxides—Health Criteria (Final Report) . National Center for Environmental Assessment, Research Triangle Par k, NC. September. Available at: <http://cfpub.epa.gov/ncea/cfm/reco rdisplay.cfm?deid=198843>. U.S. Environmental Protection Agency (U.S. EPA). 2008b. Final Ozone NAAQS Regulatory Impact Analysis . EPA-452/R-08-003. Office of Air Qual ity Planning and Standards Health and Environmental Impacts Division, Air Bene fit and Cost Group Research Triangle Park, NC. March. Available at: <http://cfpub.epa.gov/ ncea/cfm/recordisplay.cfm?deid=194645>. U.S. Environmental Protection Agency (U.S. EPA). 2008c. Integrated Science Assessment for Oxides of Nitrogen - Health Criteria (Final Report). National Center for Environmental Assessment, Research Triangle Park, NC. July. Available at: <http://www.epa.gov/ttnecas1/regdata/RIAs/6-ozoneriachapter6.pdf>. U.S. Environmental Protection Agency (U.S. EPA). 2008d. Integrated Science Assessment for Oxides of Nitrogen and Sulfur–Ecologic al Criteria National (Final Report ). National Center for Environmental Assessment, Research Tr iangle Park, NC. EPA/600/R-08/139. December. Available at: <http://cfpub.epa.gov/ncea/ cfm/recordisplay.cfm?deid=201485>. U.S. Environmental Protection Agency (U.S. EPA). 2009b. Integrated Science Assessment for Particulate Matter (Final Report) . EPA-600-R-08-139F. National Center for Environmental Assessment – RTP Division. December. Available at: <http://cfpub.epa.gov/ncea/cfm/reco rdisplay.cfm?deid=216546>. U.S. Environmental Protection Agency (U.S. EPA). 2010a. Integrated Science Assessment for Carbon Monoxide. National Center for Environmental A ssessment, Research Triangle Park, NC. EPA/600/R-09/019F. January. Available at: <http://cfpub.epa.gov/ncea/cfm/reco rdisplay.cfm?deid=218686>. U.S. Environmental Protection Agency (U .S. EPA). 2010b. Technical Support Document: Summary of Expert Opinions on the Existence of a Threshold in the Concentration-Response Function for PM 2.5 

## -related Mortality. Research Triangl e Park, NC. June. Available at: <http://www.epa.gov/ttn/ecas/regdata/B enefits/thresholdstsd.pdf>.

## ***E.O. 12866 Review – Draft – Do Not C ite, Quote or Release During Review***

4-96 U.S. Environmental Protection Agency (U.S. EPA). 2010c. Valuing Mortality Risk Reductions for Environmental Policy: A White Paper: SAB Review Draft . National Center for Environmental Economics December. Available at: <http://yosemite.epa.gov/ee/epa/eerm.nsf/ vwAN/EE-0563-1.pdf/$file/EE-0563-1.pdf>. U.S. Environmental Protection Agency (U.S. EPA). 2010d. Section 3: Re

‐ analysis of the Benefits of Attaining Alternative Ozone Standar ds to Incorporate Current Methods . Available at: <http://www.epa.gov/ttnecas1/regdata/RIAs/s3- supplemental_analysis-updated_benefits11- 5.09.pdf >. U.S. Environmental Protection Agency (U.S. EPA). 2010e. Guidelines for Preparing Economic Analyses . EPA 240-R-10-001. National Center for E nvironmental Economics, Office of Policy Economics and Innovation. Washi ngton, DC. December. Available at: <http://yosemite.epa.gov/ee/epa/eerm.nsf/ vwAN/EE-0568-50.pdf/$file/EE-0568- 50.pdf>. U.S. Environmental Protection Agency (U.S. EPA). 2011a. The Benefits and Costs of the Clean Air Act from 1990 to 2020 . Office of Air and Radiation, Washington, DC. March. Available at: <http://www.epa.gov/cleanairactbene fits/feb11/fullreport_rev_a.pdf>. U.S. Environmental Protection Agency (U.S. EPA). 2011b. Regulatory Impact Analysis for the Final Mercury and Air Toxics Standards. EPA-452/R-11-011. December. Available at: <http://www.epa.gov/ttn/ecas/regda ta/RIAs/matsriafinal.pdf>. U.S. Environmental Protection Agency (U.S . EPA). 2011c. Regulatory Impact Analysis: National Emission Standards for Hazardous Air Pollutants for Industrial, Commercial, and Institutional Boilers and Process Heaters. February. Available at: <http://www.epa.gov/ttnecas1/regdata/ RIAs/boilersriafinal110221_psg.pdf>. U.S. Environmental Protection Agency (U.S. EPA). 2011d. Policy Assessment for the Review of the Particulate Matter National Ambient Air Quality Standards . EPA-452/D-11-003. Office of Air Quality Planning and Standards, Health and Environmental Impacts Division. April. Available at: <http://www.epa.gov/ttn/naaq s/standards/pm/data/20110419pmpafinal.pdf>. U.S. Environmental Protection Agency (U.S. EPA). 2012a. Regulatory Impact Analysis for the Final Revisions to the National Ambient Ai r Quality Standards fo r Particulate Matter . EPA452/R-12-003. Office of Air Qual ity Planning and Standards, Health and Environmental Impacts Division. December. Available at: <http://www.epa.gov/pm/2012/finalria.pdf>. U.S. Environmental Protection Agency (U.S. EPA). 2012b. Regulatory Impact Analysis: Petroleum Refineries New Source Performance Standards Ja. Office of Air Quality Planning

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4-97 and Standards, Health and Environmenta l Impacts Division. June. Available at: <http://www.epa.gov/ttnecas1/regdata/RIAs/r efineries_nsps_ja_final_ria.pdf>. U.S. Environmental Protection Agency (U.S. EPA). 2013a. Technical Support Document: Estimating the Benefit per ton of Reducing PM 2.5 

## Precursors from 17 sectors . Office of Air Quality Planning and Standards, Research Triangle Park, NC. February. Available at: <http://www.epa.gov/airquality/benmap/m odels/Source_Apportionment_BPT_TSD_1_31_13 .pdf>. U.S. Environmental Protection Agency (U.S. EPA). 2013b. Integrated Science Assessment of Ozone and Related Photochemica l Oxidants (Final Report). U.S. Environmental Protection Agency, Washington, DC, EPA/600/R-10/076F. Available at: <http://cfpub.epa.gov/ncea/is a/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—Sci ence 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-SA B-EC-WKSHP-02-001. January. Available at: <http://yosemite.epa.gov/sab%5CSABPRODUCT.NSF/34355712EC011A358525719A005B F6F6/$File/ecwkshp02001%2Bappa-g.pdf>. U.S. Environmental Protection Agency—Sci ence Advisory Board (U.S. EPA-SAB). 2008. Characterizing Uncertainty in Particulate Ma tter Benefits Using Expert Elicitation. EPACOUNCIL-08-002. July. Available at: <http://yosemite.epa.gov/sab/sabproduc t.nsf/0/43B91173651AED9E85257487004EA6CB/$F ile/EPA-COUNCIL-08-002-unsigned.pdf>. U.S. Environmental Protection Agency—Sci ence Advisory Board (U.S. EPA-SAB). 2000. An SAB Report on EPA’s White Paper Valuing the Be nefits of Fatal Cancer Risk Reduction. EPA-SAB-EEAC-00-013. July. Available at: <http://yosemite.epa.gov/sab%5CSA BPRODUCT.NSF/41334524148BCCD6852571A70051 6498/$File/eeacf013.pdf>. U.S. Environmental Protection Agency—Scien ce 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:

## ***E.O. 12866 Review – Draft – Do Not C ite, Quote or Release During Review***

4-98 <http://yosemite.epa.gov/sab/sabproduct .nsf/0/39F44B098DB49F3C85257170005293E0/$Fil e/council_ltr_05_001.pdf>. U.S. Environmental Protection Agency—Sci ence Advisory Board (U.S. EPA-SAB). 2008. Benefits of Reducing Benzene Emissions in Houston, 1990–2020 . EPA-COUNCIL-08-001. July. Available at: <http://yosemite.epa.gov/sab/sabproduc t.nsf/D4D7EC9DAE DA8A548525748600728A83/$Fi le/EPA-COUNCIL-08-001-unsigned.pdf>. U.S. Environmental Protection Agency—Sci ence Advisory Board (U.S. EPA-SAB). 2009b. 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/$File/E PA-CASAC-09-008-unsigned.pdf>. U.S. Environmental Protection Agency—Scien ce Advisory Board (U.S. EPA-SAB). 2009c. Review of Integrated Science Assessment fo r Particulate Matter (S econd External Review Draft, July 2009) . EPA-CASAC-10-001. November. Available at: <http://yosemite.epa.gov/sab/SABPRODUCT.NSF/81e39f4c09954fcb85256ead006be86e/15 1B1F83B023145585257678006836B9/$File/EPA-C ASAC-10-001-unsigned.pdf>. U.S. Environmental Protection Agency—Scien ce Advisory Board (U.S. EPA-SAB). 2010a. 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-COUNC IL-10-001-unsigned.pdf>. U.S. Environmental Protection Agency—Sci ence Advisory Board (U.S. EPA-SAB). 2011. Review of Valuing Mortality Risk Reductions for Environmental Po licy: A White Paper (December 10, 2010). EPA-SAB-11-011 July

. Available at: <http://yosemite.epa.gov/sab/sabproduct.ns f/298E1F50F844BC23852578DC0059A616/$File/ EPA-SAB-11-011-unsigned.pdf>. Woodruff, T.J., J. Grillo, and K.C. Schoendorf. 1997. “The Relationship between Selected of postneonatal infant mortality and particul ate air pollution in the United States.” Environmental Health Perspectiv es 105 (6): 608-612.

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4A-1 

## A PPENDIX 

## 4A: G ENERATING 

## R EGIONAL 

## B ENEFIT 

## - PER 

## -T ON 

## E STIMATES FOR


## E LECTRIC 

## G ENERATING 

## U NITS



## 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 met hods for generating benefit-per-ton estimates by region for PM 2.5 

## 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 PM 2.5 

## Precursors from 17 Sectors (U.S. EPA, 2013a) (hereafter “BPT TSD”), the procedure for calculating average benefit-per-ton coefficients ge nerally follows three steps, shown graphically in Figure 4A-1. As an example, in order to calculate national average benefitper-ton estimates for each ambient PM 2.5 

## precursor emitted from EGU sources, we:

1. Use air quality modeling to predict am bient concentrations of primary PM 2.5 

## , nitrate and sulfate across the contiguous U.S. that are attributable to the EGU sector.

2. Estimate the health impacts, and the economic va lue of these impacts, associated with the attributable ambient con centrations of primary PM 2.5 

## , sulfate and nitrate PM 2.5 

## using the environmental Benefits Mapping and Analysis Program (BenMAP v4.0.66) 1 

## (Abt Associates, Inc, 2012).

3. Divide the PM 2.5 

## -related health impacts attri butable to each type of PM 2.5 

## , and the monetary value of these impacts, by the level of associated precursor emissions. That is, primary PM 2.5 

## benefits are divided by direct PM 2.5 

## emissions, sulfate benefits are divided by SO 2 

## emissions, and nitrate benefits are divided by NOx emissions.

1 

### In this stage we estimate the PM 2.5 

### -related impacts associated with changes in directly emitted PM 2.5 

### , nitrate and sulfate separately, so that we may ultimately calcula te the benefit-per-ton reduc ed of the corresponding PM 2.5 

### precursor, or directly emitted PM 2.5 

### , in step 3. When estimating these impacts we apply effect coefficients that relate changes in total PM 2.5 

### mass to the risk of adverse health outcomes; we do not apply effect coefficients that are differentiated by PM 2.5 

### specie.

## ***E.O. 12866 Review – Draft – Do Not C ite, Quote or Release During Review***

4A-2 Figure 4A-1

. Conceptual Overview of Benefit-per-Ton Calculation

4A.2 Underlying Source Appo rtionment Air Quality Modeling EPA performed a national-scale air quality modeling analysis using the Comprehensive Air Quality Model with Extensions (CAMx) model 2 

## to estimate PM 2.5 

## and ozone concentrations
attributable to 17 industrial sect ors, including EGUs. In this sec tion, we provide a short summary of the air quality modeling used to generate thes e 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). Th e source apportionment modeling is also discussed in Fann, Baker and Fulcher (2012).
CAMx simulates the numerous physical a nd 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

2 

### Environ International Corporation. 2010. Comprehensive Air Quality Model with Extensions, Version 5.3. User’s Guide. Novato, CA. December. Av ailable at <http ://www.camx.com>. 

# PM 2.5 

# air quality change for a given sector Human health benefits Benefit-pe r -ton calculation

## ***E.O. 12866 Review – Draft – Do Not C ite, Quote or Release During Review***

4A-3 deposition or chemical reaction. Source appor tionment techniques track the formation and transport of ozone and PM from specific emissi ons sources and calculate the contribution of sources and precursors to ozone and PM 2.5 

## at individual receptor locations. In contrast to “zeroout” modeling techniques, the tracking of emissions and re sulting pollution in source apportionment modeling does not affect the trans port, 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 mode ling 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 Pla nning Model (IPM), and the projections include
emission reductions related to the NOx State Im plementation Plan Call (U.S. EPA, 1998) and the proposed Transport Rule (U.S. EPA, 2010c). 3


## 4A.3 Regional PM 2.5 

## Benefit-per-Ton Estimates for EGUs The approach for generating regiona l benefit-per-ton estimates for PM 2.5 

## from EGU emissions is a small modification of the appr oach described above from the BPT TSD. The regional benefit-per-ton estimat es reflect the ambient PM 2.5 

## attributable to the EGU sector in the source apportionment modeling (“sector modelin g”) from Fann, Baker, and Fulcher (2012), as illustrated in Figures 4A-2 and 4A-3 for 2016. After estimating the PM 2.5 

## 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) rath er than nationally, as shown in Figure 4A-4. 4


## Due to the low emissions of SO 2 

## , NO X 

## , and directly emitted particles from EGUs in California

3 

### 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 PM 2.5 

### from this sector. (Fann, Fulcher, and Baker [2013]). The EGU contribution on a per-ton basis would be similar. Because the em ission reductions in this RIA are cal culated from an IPM base case that includes MATS (see Chapter 3), there is no double-cou nting concern with the resu lting co-benefits estimates.

4 

### This aggregation is identified as the shapefile “Report Regions” in BenMAP’s grid definitions.

## ***E.O. 12866 Review – Draft – Do Not C ite, Quote or Release During Review***

4A-4 and the high population density, we separated out California in orde r 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 benef its estimates by the corresponding emissions, as shown in Table 4A-1. Lastly, we adjusted the benefit-pe r-ton estimates for a currency year of 2011$. 5



## Figure 4A-2. Modeled PM 2.5 

## Concentrations Attributable to EGUs in 2016 (Annual Mean, in μg/m 3 

## ) (Source: BPT TSD)
This method provides estimates of the region al average benefit-per-ton for each ambient
PM 2.5 

## precursor emitted from EGU sources. For some precursor emissions, such as NOx, there is generally a non-linear relationship betw een emissions and formation of PM 2.5 

## . This means that
each ton of NOx reduced would have a different impact on ambient PM 2.5 

## depending on the
initial level of emissions and potentially on the levels of emi ssions of other pollutants. In contrast, SO 2 

## is generally linear in forming PM 2.5 

## . For non-linear pollutants like NOx, a marginal

5 Currently, BenMAP does not have an inflation adjustment to 2011$. We ran BenMAP for a currency year of 2010$ and calculated t he benefit-per-ton estimates in 2010$. We then adjusted the resulting benefit-per-ton estimates to 2011$ using the Consumer Price Index.

## ***E.O. 12866 Review – Draft – Do Not C ite, Quote or Release During Review***

4A-5 benefit-per-ton approach would better approximate the specific be nefits 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 estimat e for NOx adds uncertain ty to the co-benefits
estimated in this RIA. Because most of the es timated co-benefits for the proposed guidelines are
attributable to reductions in SO 2 

## emissions, the added uncertainty is likely to be small.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***4A-6a)Sulfateb)Nitratec)ElementalCarbonandOrganicCarbond)CrustalPM2.5***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***4A-7Figure 4A-3. Modeled PM2.5Concentrations Attributable to EGUs byPrecursor in 2016 (Annual Mean, in μg/m3)

## ***E.O. 12866 Review – Draft – Do Not C ite, Quote or Release During Review***

4A-8 Figure 4A-4. Regional Breakdown

Table 4A-1. Summary of Nation al and Regional Emissions in Sector Modeling for EGUs in 2016 (in short tons)

Pollutant National EGU emissions Regional EGU Emissions East West California SO 2 

### 3,793,362 3,520,296 273,070 4,886 Directly emitted PM 2.5 

### (EC+OC) 30,078 26,172 3,892 1,523 Directly emitted PM 2.5 

### (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 projecte d unit level emissions of SO 2 

## , NOx, CO 2 

## , Hg, HCl from EGUs,

115 

### See Chapter 3 of this RIA for additional information regarding IPM.

## ***E.O. 12866 Review – Draft – Do Not C ite, Quote or Release During Review***

4A-9 but CO, VOC, NH 3 

## and total directly emitted PM 2.5 

## and PM 10 

## emissions are post-calculated. 116 

## In addition, directly emitted particle emissions calculated from IPM outputs do not include speciation, i.e. they are only the total emissions. In order to estimat e the benefits associated with reduced emissions of directly emitted particles, we must determine the fraction of total PM 2.5 

## emissions comprised of elemental carbon and organic carbon (EC+OC) and crustal emissions. 117 

## Figure 4A-5 illustrates the relative breakdown of directly emitted PM 2.5 

## components from EGUs in the modeling by Fann, Baker, and Fulcher (2 012). In this modeli ng, the national average
EC+OC fraction of emitted PM 2.5 

## 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 PM 2.5 

## from EGUs is 78% with a range of 26% to 83%. The national average is si milar to the averages for the east and west regions at 78% and 81%, respectively. Only four states had crustal frac tions less than 50%.

116 

### Detailed documentation of this post-processing is available at http://www.epa.gov/powersectormodeling/do cs/v513/FlatFile_Methodology.pdf

117 

### Crustal emissions are composed of compounds associat ed with minerals and meta ls from the earth’s surface, including carbonates, silicates, iron, phosphates, copper, an d zinc. Often, crustal material represents particles not classified as one of the other species (e.g., organic carbon, elemental carbon, nitrate, sulfate, chloride, etc.).

## ***E.O. 12866 Review – Draft – Do Not C ite, Quote or Release During Review***

4A-10 Figure 4A-5. Breakdown of Directly Emitted PM 2.5 

## Emissions from EGU in Sector Modeling

There are several uncertainties associated wi th estimating the benefits associated with reducing emissions of EC+OC and crustal PM 2.5 

## from EGUs. As previously mentioned, IPM
does not estimate PM 2.5 

## emissions by component, and total PM 2.5 

## is estimated as an IPM postprocessing step using emission factors. In order to conduct air quality modeling, PM 2.5 

## from EGUs is speciated into components during th e emissions modeling process based on emission profiles for EGUs by source classi fication code. Even though thes e speciation profiles are not unit-specific, an emission profile based on the so urce classification code is highly sophisticated
and reflects the fuel and the uni t configuration. In addition, the ai r quality model output has been adjusted to match an inter polated surface of speciated PM 2.5 

## 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 mon itor data. While these changes are usually minimal, in this instance, uncertainties in cr ustal emissions from other sectors impact the relationship between EGU crustal emissions and resulting air quali ty impacts. Because the cobenefits associated with directly emitted particles for the proposed guidelines are small relative to the co-benefits of reducing SO 2 

## emissions, these uncertainties w ould not substantially affect ‐ 5,000 10,000 15,000 20,000 25,000TexasPennsylvaniaOhioIndianaFloridaWestVirginiaGeorgiaKentuckyAlabamaNorthCarolinaSouthCarolinaIllinoisMinnesotaMissouriWyomingArizonaTennesseeVirginiaMichiganWisconsinNorthDakotaUtahIowaNewMexicoKansasOklahomaColoradoMarylandNewYorkArkansasLouisianaNevadaCaliforniaNebraskaMontanaNewJerseyMississippiMassachusettsConnecticutWashingtonSouthDakotaNewHampshireOregonDelawareRhodeIslandMaineIdahoEmissionsinSectorModeling(Tons) PNO3 PSO4 POC PEC PMFINE (crustal)

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4A-11 the estimate of total mone tized health co-benefits. 118 

## In calculating the PM 2.5 

## 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 fr om EGUs. Because the benefit-per-ton estimates for reducing emissions of EC+OC are larger than the benef it-per-ton estimate for crustal emissions, this assumption underestimates the monetized PM 2.5 

## 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 associat ed with reducing directly emitted particulate
nitrate and sulfate.
Although it is possibl e to calculate 95 th 

## percentile confidence inte rvals using the approach described in this appendix (e.g., U.S. EPA, 2011b ), we generally do not calculate confidence intervals for benefit-per-ton estim ates. Instead, we refer the reader to Chapter 5 of PM NAAQS RIA (U.S. EPA, 2012a) for an indication of the co mbined random sampling error in the health impact and economic valuation functions usi ng Monte Carlo methods. In general, the 95 th 

## percentile confidence interval for the total monetized PM 2.5 

## benefits ranges from approximately90% to +180% of the central estimates based on concentration-response functions from Krewski et al. (2009) and Lepeul e et al. (2012). The 95 th 

## percentile confidence in terval for the health impact function alone ranges from approximate ly ±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 uncerta inty inherent within the estimate s, such as baseline incidence rates, populations exposed, and tran sferability of the effect estimate to diverse locations. As a result, the reported confidence inte rvals and range of estimates give an incomplete picture about the overall uncertainty in the benefits estimates.
Tables 4A-2 through 4A-4 provide the nationa l and regional benefit- per-ton estimates for
the EGU sector at discount rates of 3% a nd 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.

118 

### See Figure 5-2 of this RIA for the relative proportion of he alth co-benefits from directly emitted particles relative

to co-benefits from SO 2 

### emissions.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***4A-12Table 4A-2. Summary ofNational and Regional PM2.5Benefit-per-Ton Estimates for EGUs in 2020 (2011$)*PollutantDiscountRateNationalRegionEast West CaliforniaSO23% $38,000 to $86,000 $40,000 to $90,000 $7,800 to $18,000 $160,000 to $360,0007% $34,000 to $77,000 $36,000 to $82,000 $7,100 to $16,000 $140,000 to $320,000Directly emittedPM2.5(EC+OC)3% $140,000 to $320,000 $140,000 to $320,000 $56,000 to $130,000 $280,000 to $640,0007% $130,000 to $290,000 $130,000 to $280,000 $50,000 to $110,000 $250,000 to $570,000Directly emittedPM2.5(Crustal)3% $18,000 to $40,000 $18,000 to $41,000 $11,000 to $25,000 $110,000 to $240,0007% $16,000 to $36,000 $16,000 to $37,000 $10,000 to $23,000 $95,000 to $220,000NOx (as PM2.5)3% $5,600 to $13,000 $6,700 to $15,000 $1,200 to $2,600 $17,000 to $38,0007% $5,000 to $11,000 $6,000 to $14,000 $1,000 to $2,400 $15,000 to $34,000* The range of estimates reflects the range of epidemiology studies for avoided premature mortality for PM2.5. All estimates are rounded to two significant figures. All fineparticles are assumed to have equivalent health effects, but the benefit per ton estimates vary depending on the location and magnitude of their impact on PM2.5levels,which drive population exposure. The monetized benefits incorporate the conversion from precursor emissions to ambient fine particles. The estimates do not includereduced health effects from direct exposure to ozone, NO2, SO2, ecosystem effects, or visibility impairment.Table 4A-3. Summary ofNational and Regional PM2.5Benefit-per-Ton Estimates for EGUs in 2025 (2011$)*PollutantDiscountRateNationalRegionEast West CaliforniaSO23% $41,000 to $93,000 $44,000 to $98,000 $8,800 to $20,000 $180,000 to $410,0007% $37,000 to $84,000 $39,000 to $89,000 $8,000 to $18,000 $160,000 to $370,000Directly emittedPM2.5(EC+OC)3% $150,000 to $350,000 $150,000 to $340,000 $64,000 to $140,000 $320,000 to $720,0007% $140,000 to $310,000 $140,000 to $310,000 $58,000 to $130,000 $290,000 to $650,000Directly emittedPM2.5(Crustal)3% $17,000 to $39,000 $18,000 to $40,000 $12,000 to $27,000 $43,000 to $96,0007% $15,000 to $35,000 $16,000 to $36,000 $11,000 to $24,000 $38,000 to $87,000NOx (as PM2.5)3% $6,000 to $14,000 $7,200 to $16,000 $1,300 to $2,900 $19,000 to $42,0007% $5,400 to $12,000 $6,500 to $15,000 $1,200 to $2,600 $17,000 to $38,000* The range of estimates reflects the range of epidemiology studies for avoided premature mortality for PM2.5. All estimates are rounded to two significant figures. All fineparticles are assumed to have equivalent health effects, but the benefit per ton estimates vary depending on the location and magnitude of their impact on PM2.5levels,which drive population exposure. The monetized benefits incorporate the conversion from precursor emissions to ambient fine particles. The estimates do not includereduced health effects from direct exposure to ozone, NO2, SO2, ecosystem effects, or visibility impairment.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***4A-13Table 4A-4. Summary ofNational and Regional PM2.5Benefit-per-Ton Estimates for EGUs in 2030 (2011$)*PollutantDiscountRateNationalRegionEast West CaliforniaSO23% $44,000 to $100,000 $47,000 to $110,000 $9,800 to $22,000 $200,000 to $450,0007% $40,000 to $90,000 $42,000 to $95,000 $8,800 to $20,000 $180,000 to $410,000Directly emittedPM2.5(EC+OC)3% $170,000 to $370,000 $160,000 to $370,000 $71,000 to $160,000 $360,000 to $800,0007% $150,000 to $340,000 $150,000 to $330,000 $64,000 to $150,000 $320,000 to $730,000Directly emittedPM2.5(Crustal)3% $18,000 to $42,000 $19,000 to $43,000 $13,000 to $30,000 $47,000 to $110,0007% $17,000 to $38,000 $17,000 to $38,000 $12,000 to $27,000 $43,000 to $96,000NOx (as PM2.5)3% $6,400 to $14,000 $7,600 to $17,000 $1,400 to $3,200 $21,000 to $42,0007% $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 fineparticles are assumed to have equivalent health effects, but the benefit per ton estimates vary depending on the location and magnitude of their impact on PM2.5levels,which drive population exposure. The monetized benefits incorporate the conversion from precursor emissions to ambient fine particles. The estimates do not includereduced health effects from direct exposure to ozone, NO2, SO2, ecosystem effects, or visibility impairment.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***4A-14Table 4A-5. Summary of Regional PM2.5Incidence-per-Ton Estimates for EGUs in 2020*Health EndpointEast West CaliforniaSO2NOx EC+OC Crustal SO2NOx EC+OC Crustal SO2NOx EC+OC CrustalPremature MortalityKrewski et al. (2009) – adult 0.004400 0.000730 0.015000 0.002000 0.000860 0.000130 0.0061000.001200 0.017000 0.001800 0.031000 0.012000Lepeule et al. (2012) – adult 0.009900 0.001700 0.035000 0.004500 0.001900 0.000290 0.0140000.002800 0.039000 0.004100 0.070000 0.026000Woodruff et al. (1997) – infants 0.000010 0.000002 0.000033 0.000004 0.000002 0.000000 0.0000160.000003 0.000036 0.000004 0.000067 0.000025MorbidityEmergency department visits for asthma 0.0023000.000410 0.008700 0.001000 0.000380 0.0000550.002800 0.000520 0.008600 0.001000 0.016000 0.005900Acute bronchitis 0.006300 0.001100 0.022000 0.002800 0.001600 0.000330 0.011000 0.002300 0.031000 0.003400 0.057000 0.021000Lower respiratory symptoms 0.081000 0.014000 0.2900000.036000 0.021000 0.004200 0.150000 0.029000 0.390000 0.044000 0.720000 0.270000Upper respiratory symptoms 0.120000 0.020000 0.4100000.051000 0.030000 0.006000 0.210000 0.042000 0.560000 0.062000 1.000000 0.390000Minor restricted-activity days 3.200000 0.540000 11.000000 1.400000 0.780000 0.130000 5.4000001.000000 16.00000 1.700000 29.00000 11.00000Lost work days 0.540000 0.090000 1.900000 0.2400000.130000 0.023000 0.910000 0.1800002.700000 0.290000 4.900000 1.800000Asthma exacerbation 0.290000 0.020000 0.420000 0.0530000.074000 0.006200 0.220000 0.0430001.400000 0.064000 1.100000 0.400000Hospital Admissions, Respiratory 0.001300 0.000220 0.004500 0.000580 0.000190 0.000026 0.0014000.000270 0.004100 0.000440 0.007400 0.002800Hospital Admissions, Cardiovascular 0.001600 0.000270 0.005600 0.000720 0.000220 0.0000320.001800 0.000340 0.005000 0.000520 0.009000 0.003400Non-fatal Heart Attacks (Peters) 0.005000 0.000860 0.018000 0.002300 0.000810 0.000110 0.0060000.001200 0.018000 0.001900 0.032000 0.012000Non-fatal Heart Attacks (All others) 0.000550 0.000093 0.001900 0.000250 0.000087 0.0000120.000640 0.000130 0.001900 0.000210 0.0034000.001300* All estimates are rounded to two significant figures. All fine particles are assumed tohave equivalent health effects, but the incidence-per-ton estimates vary depending onthe location and magnitude of their impact on PM2.5levels, which drive population exposure. The incidence benefit-per-ton estimates incorporate the conversion fromprecursor emissions to ambient fine particles.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***4A-15Table 4A-6. Summary of Regional PM2.5Incidence-per-Ton Estimates for EGUs in 2025*Health EndpointEast West CaliforniaSO2NOx EC+OC Crustal SO2NOx EC+OC Crustal SO2NOx EC+OC CrustalPremature MortalityKrewski et al. (2009) – adult 0.004700 0.000770 0.016000 0.002100 0.000940 0.000140 0.0068000.001400 0.019000 0.002000 0.034000 0.013000Lepeule et al. (2012) – adult 0.011000 0.001700 0.037000 0.004800 0.002100 0.000310 0.0150000.003100 0.043000 0.004500 0.077000 0.029000Woodruff et al. (1997) – infants 0.000009 0.000001 0.000031 0.000004 0.000002 0.000000 0.0000160.000003 0.000034 0.000004 0.000063 0.000024MorbidityEmergency department visits for asthma 0.0024000.000420 0.009000 0.001100 0.000410 0.0000590.003000 0.000560 0.009000 0.001000 0.016000 0.006100Acute bronchitis 0.006600 0.001100 0.023000 0.0029000.001700 0.000350 0.012000 0.0024000.032000 0.003600 0.059000 0.022000Lower respiratory symptoms 0.084000 0.014000 0.3000000.037000 0.022000 0.004400 0.1500000.031000 0.410000 0.046000 0.760000 0.290000Upper respiratory symptoms 0.120000 0.020000 0.4300000.053000 0.032000 0.006300 0.2200000.044000 0.590000 0.065000 1.100000 0.410000Minor 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 11.00000Lost work days 0.550000 0.091000 2.000000 0.2400000.140000 0.024000 0.970000 0.1900002.700000 0.300000 5.000000 1.800000Asthma exacerbation 0.300000 0.021000 0.440000 0.055000 0.078000 0.006500 0.230000 0.046000 1.400000 0.067000 1.100000 0.420000Hospital Admissions, Respiratory 0.001400 0.000240 0.005000 0.000640 0.000220 0.000030 0.0016000.000320 0.004800 0.000500 0.008500 0.003200Hospital Admissions, Cardiovascular 0.001800 0.000290 0.006100 0.000780 0.000250 0.0000360.002000 0.000390 0.005600 0.000580 0.010000 0.003800Non-fatal Heart Attacks (Peters) 0.005500 0.000930 0.019000 0.002500 0.000910 0.000120 0.0068000.001300 0.020000 0.002100 0.036000 0.013000Non-fatal Heart Attacks (All others) 0.000600 0.000100 0.002100 0.000270 0.000098 0.0000130.000730 0.000140 0.002200 0.000230 0.0039000.001500* All estimates are rounded to two significant figures. All fine particles are assumed tohave equivalent health effects, but the incidence-per-ton estimates vary depending onthe location and magnitude of their impact on PM2.5levels, which drive population exposure. The incidence benefit-per-ton estimates incorporate the conversion fromprecursor emissions to ambient fine particles.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***4A-16Table 4A-7. Summary of Regional PM2.5Incidence-per-Ton Estimates for EGUs in 2030*Health EndpointEast West CaliforniaSO2NOx EC+OC Crustal SO2NOx EC+OC Crustal SO2NOx EC+OC CrustalPremature Mortality 0.005000 0.000810 0.017000 0.002300 0.001000 0.000150 0.007600 0.001500 0.021000 0.002200 0.038000 0.014000Krewski et al. (2009) – adult 0.011000 0.001800 0.0400000.005100 0.002400 0.000340 0.0170000.003400 0.048000 0.005000 0.086000 0.032000Lepeule et al. (2012) – adult 0.000009 0.000001 0.000030 0.000004 0.000002 0.000000 0.0000150.000003 0.000032 0.000004 0.000060 0.000023Woodruff et al. (1997) – infantsMorbidity 0.002500 0.000430 0.009300 0.001100 0.0004300.000062 0.003200 0.000600 0.009400 0.001100 0.017000 0.006400Emergency department visits for asthma 0.0068000.001100 0.024000 0.003000 0.001800 0.0003700.013000 0.002600 0.033000 0.003700 0.061000 0.023000Acute bronchitis 0.087000 0.014000 0.310000 0.0380000.023000 0.004700 0.170000 0.0330000.420000 0.047000 0.780000 0.300000Lower respiratory symptoms 0.130000 0.021000 0.4400000.055000 0.034000 0.006700 0.2400000.047000 0.610000 0.067000 1.100000 0.420000Upper respiratory symptoms 3.300000 0.540000 12.0000001.500000 0.860000 0.150000 6.000000 1.200000 17.00000 1.800000 30.00000 11.00000Minor restricted-activity days 0.560000 0.092000 2.000000 0.250000 0.150000 0.025000 1.0000000.200000 2.800000 0.300000 5.100000 1.900000Lost work days 0.310000 0.021000 0.460000 0.0570000.082000 0.006900 0.240000 0.0480001.500000 0.069000 1.200000 0.430000Asthma exacerbation 0.001600 0.000260 0.005500 0.000710 0.000250 0.000034 0.001800 0.000360 0.005500 0.000580 0.009900 0.003700Hospital Admissions, Respiratory 0.001900 0.000320 0.006700 0.000850 0.000280 0.000041 0.0023000.000440 0.006400 0.000660 0.012000 0.004300Hospital Admissions, Cardiovascular 0.006000 0.001000 0.021000 0.002700 0.001000 0.0001400.007700 0.001500 0.023000 0.002400 0.041000 0.015000Non-fatal Heart Attacks (Peters) 0.000660 0.000110 0.002300 0.000290 0.000110 0.000015 0.0008300.000160 0.002500 0.000260 0.004400 0.001700Non-fatal Heart Attacks (All others) 0.005000 0.000810 0.017000 0.002300 0.001000 0.0001500.007600 0.001500 0.021000 0.002200 0.0380000.014000* All estimates are rounded to two significant figures. All fine particles are assumed tohave equivalent health effects, but the incidence-per-ton estimates vary depending onthe location and magnitude of their impact on PM2.5levels, which drive population exposure. The incidence benefit-per-ton estimates incorporate the conversion fromprecursor emissions to ambient fine particles.

## ***E.O. 12866 Review – Draft – Do Not C ite, Quote or Release During Review***

4A-17 4A.4 Regional Ozone Be nefit-per-Ton Estimates The process for generating the regional ozone be nefit-per-ton estimates is consistent with the process for PM 2.5 

## . The key difference is s ubstituting the ozone imp acts from EGUs in the sector modeling for the PM 2.5 

## impacts. We have not historica lly 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 geographi c areas. However, for EGUs, we have increased confidence in the magnitude a nd 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 c ontribution to ozone fo rmation, which provides greater transferability than an air quality mode ling scenario for a regulation that might include
both increases and decreases in emissions in different locations. Fi gure 4A-6 provides the average ambient ozone concentr ations 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 calculate d from an IPM base case that includes MATS (see Chapter 3), there is no double-counting concern with the resulting co-benefits estimates.

## ***E.O. 12866 Review – Draft – Do Not C ite, Quote or Release During Review***

4A-18 Figure 4A-6. Modeled Average Ozone Concentrat ions Attributable to EGUs in 2016 (Daily 8-hour maximum, May-September, in ppb)
We assume that all of the ozone impacts fr om EGUs are attributable to NOx emissions. VOC emissions, which are also a precursor to ambient ozone form ation, 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 PM 2.5 

## , this method provides estimates of the regional averag e benefit-per-ton. Due to the non-linear chemistry between NOx em issions 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 estimat ed co-benefits for the proposed guidelines are attributable to changes in ambient PM 2.5 

## , 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 NO X 

## emissions from U.S. EGUs duri ng the ozone-season only (May to September). As shown in Table 4A-1, oz one-season NOx emissions from EGUs are approximately half of all-year NO X 

## emissions. Because we estimate ozone health impacts from

## ***E.O. 12866 Review – Draft – Do Not C ite, Quote or Release During Review***

4A-19 May to September only, this approach underestimat es ozone co-benefits in areas with a longer
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 oz one co-benefits. For illustrative purposes, Tables 4A-8 through 4A-10 provide the ozone benefit-per-ton estimate s using both all-year NOx emissions and ozoneseason only NOx for 2020, 2025, and 2030, respectiv ely. Tables 4A-11 through 4A-13 provide the ozone season incidence-per-ton estim ates for 2020, 2025, and 2030, respectively.
Table 4A-8. Summary of National and Regi onal 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 ra nge of epidemiology studies for avoided premature mortality for ozone. All estimates are rounded to two significa nt figures. The monetized benefits incorporate the conversion from NOx precursor emissions to ambient ozone.



## Table 4A-9. Summary of National and Regi onal 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 ra nge of epidemiology studies for avoided premature mortality for ozone. All estimates are rounded to two significa nt figures. The monetized benefits incorporate the conversion from NOx precursor emissions to ambient ozone.



## Table 4A-10. Summary of National and Re gional 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 ra nge of epidemiology studies for avoided premature mortality for ozone. All estimates are rounded to two significa nt figures. The monetized benefits incorporate the conversion from NOx precursor emissions to ambient ozone.

## ***E.O. 12866 Review – Draft – Do Not C ite, Quote or Release During Review***

4A-20 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 si gnificant figures. The incidence bene fit-per-ton estimates incorporate the conversion from NOx precursor em issions 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 si gnificant figures. The incidence bene fit-per-ton estimates incorporate the conversion from NOx precursor em issions 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 si gnificant figures. The incidence bene fit-per-ton estimates incorporate the conversion from NOx precursor em issions to ambient ozone. These estimates reflect ozone-season NOx emissions.

## ***E.O. 12866 Review – Draft – Do Not C ite, Quote or Release During Review***

4A-21 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 qualit y modeling. First, we evaluated how well the spatial distributi on of emissions underlying the re gional benefit-per-ton estimates match the spatial distribution of the emissions fo r 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 pr ovide graphs of the EGU emis sions 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 distri bution 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 guidel ines. As noted in section 4A-2 above, the sector modeling base case does not re flect 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 modeli ng has substantially more SO 2 

## emissions from EGUs (3.8 million tons in 2016 vs. 1.5 million tons in 2020), the spatial distribution is relatively simila r across states and thus the resu lting benefit-per-ton would likely be similar. Therefore, we woul d 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 SO 2



### emission reductions anticipated from MATS.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***4A-22Figure 4A-7. Comparison of Base Case SO2Emissions in 2020‐50,000100,000150,000200,000250,000300,000350,000400,000450,000 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

TonsEGUsectorairqualitymodelingbasecaseIPMbasecaseforthisRIA***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***4A-23Figure 4A-8. Comparison of BaseCase Annual NOx Emissions in 2020‐20,00040,00060,00080,000100,000120,000140,000160,000 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

TonsEGUsectorairqualitymodelingbasecaseIPMbasecaseforthisRIA***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***4A-24Figure 4A-9. Comparison of Base Case Ozone-season NOx Emissions in 2020‐10,00020,00030,00040,00050,00060,00070,00080,000 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

TonsEGUsectorairqualitymodelingbasecaseIPMbasecaseforthisRIA

## ***E.O. 12866 Review – Draft – Do Not C ite, Quote or Release During Review***

4A-25 For the second evaluation, we used the nationa l and regional benefit- per-ton estimates to
estimate the modeled sulfate benef its estimated for MATS and CSAPR. 121 

## We focused on sulfate because the vast majority of PM 2.5 

## benefits for both rules were from reductions in ambient
sulfate. We found that the natio nal 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 bene fits by 29 percent.
This evaluation shows that the national and regional benefit-per-t on estimates perform reasonably well, with a slight ov erestimate, but this does not mean that the co-benefits estimated for the proposed guidelines would nece ssarily be within a similar range.

We also evaluated whether available modeli ng information could be used to generate
reliable state-level benefi t-per-ton estimates for PM 2.5 

## . 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 pe rcentage reductions acro ss states are further magnified by spatial differences in population de nsity, base case health incidence rates, air quality response, and inters tate pollution transport.
We tested several methods for generating state-level benefit-per-ton estimates. The method that performed best used state-by-stat e contribution modeling in the Eastern U.S. conducted for CSAPR (U.S. EPA, 2011a) to adjust the regional benefit-pe r-ton estimates for the EGU sector. Although the CSAPR m odeling is the best availabl e for estimating state-level benefit-per-ton estimates , it zeroed-out all anth ropogenic emission sources within a state rather than just zeroing-out the emissions from the EGU sector alone. Current ly, we do not have air quality modeling that provides information regarding the PM 2.5 

## response to EGU only emissions for each state separately. We evaluated the pe rformance of the state-level estimates by

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 modeli ng, 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 benef it-per-ton estimates due to in ter-state transport, complex atmospheric chemistry, and localized impacts from VOC emissions.

## ***E.O. 12866 Review – Draft – Do Not C ite, Quote or Release During Review***

4A-26 comparing the modeled benefits for two previous rulemakings (i.e., CSAPR and MATS) to the benefits estimated using state-le vel benefit-per-ton. When we evaluated the state-level estimates in the same manner as the national and regional es timates, 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.

## ***E.O. 12866 Review – Draft – Do Not C ite, Quote or Release During Review***

4A-27 4A.6 References
Abt Associates, Inc. 2010. “User’s Guide: Modele d 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 Prot ection 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 Americ an 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 H ealth 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 PM 2.5 

## -Related Health Benefits of Emission Reductions for 17 Industr ial, 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 Technol ogy, 47 (8) (2013), pp. 3580–3589. Huang Y.; F. Dominici and M. Bell. 2005. “Bayes ian Hierarchical Distributed Lag Models for Summer Ozone Exposure and Card io-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 Soc iety Study Linking Particulate Air Pollution and Mortality. HEI Research Report, 140, Health Effects Institute, Boston, MA. Lepeule, J.; F. Laden; D. Dockery and J. Sc hwartz. 2012. “Chronic Exposur e to Fine Particles and Mortality: An Extended Follow-Up of th e 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. Co nner; 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 Associ ation Between Ozone and Daily Deaths to Control for Temperature?” American Journal of Respirat ory and Critical Care Medicine, Vol. 171 (6): 627-31.

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4A-28 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 th e Proposed Federal Transport Rule. (EPA document number EPA-HQ-OAR-2009-0491). Resear ch Triangle Park, NC. Available at: <http://www.epa.gov/ttn/ecas/regdata/R IAs/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 Qu ality Planning and Standards, Health and Environmental Impacts Division. (EPA doc ument 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 Tech nical Support Document: Source Sector Assessments.” Research Tria ngle 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 Partic ulate Matter.” Research Tria ngle 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 Documen t: Estimating the Benefit per Ton of Reducing PM 2.5 

## Precursors from 17 Sectors.” Research Triangle Park, NC: Office of Air Quality Planning and Standards, February. Available at: <http://www.epa.gov/airquality/benmap/m odels/Source_Apportionment_BPT_TSD_1_31_13 .pdf>. U.S. EPA. 2013b. Integrated Science Assessment of Oz one and Related Photochemical Oxidants (Final Report). Washington, DC: U.S. Environmenta l Protection Agency. (EPA document number EPA/600/R-10/076F). Available at: <http://cfpub.epa.gov/ncea/is a/recordisplay.cfm?deid=247492#Download>.

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5-1 

## CHAPTER 5: ECONOMIC IMPACTS – MARKETS OUTSIDE THE
ELECTRICITY SECTOR

5.1 Introduction The energy sector impacts presented in Chapte r 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 Un it (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 Guid elines states are not required to use any of the measures that the EPA determines cons titute 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 th e appropriate combination of, and the extent of its reliance on, measures for its state plan, by wa y of meeting its state-specific goal. Given the
flexibilities afforded states in complying with the emission gui delines, the benefits, cost and economic impacts reported in this RIA are illustrative of complian ce 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, a nd coal and in turn other markets that use these commodities in the production process. The flexib ility afforded to states by the Clean Air Act also allows them to adopt programs which includ e 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 aucti ons to support direct bi ll assistance for retail consumers, fund investments in clean energy and electricity demand reduction for business consumers, and support employment in the de velopment of clean and renewable energy technologies. In its recent regulations to lim it 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 an d their associated impacts. Other states may choose to comply with particularly robust depl oyment of renewables, energy efficiency, or natural gas in order to promote manufacturi ng 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 cons umption, such as weathe rization projects. The

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5-2 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 comput able 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 mode l, specifically adapted for use in that study, “represent[ed] a significant step forward in benefit-cost analys is,” EPA recognizes that serious technical challenges remain when attempting to evaluate the benefits and costs of potential regulatory actions using economy-wi de models. Consistent with the Council’s advice regarding the importance of including bene fit-side effects demonstrated by the 1990 to 2020 Clean Air Act study, and the lack of available multi-year air qual ity projections needed to include these benefitside effects, EPA has not conducte d CGE modeling for this proposal. However, the EPA recognizes that serious t echnical challenges remain when attempting to evaluate the impacts of pot ential regulatory actions using ec onomy-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 t ool 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 fo r 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 fo rward for improvements that could address the challenges posed when economy-wide models are us ed to evaluate the e ffects 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 unc ertain nature of the ultimate energy price impacts due to the compliance flexibil ity 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 en ergy impacts associated with the illustrative scenarios analyzed in Chapter 3. This Chapter then presents a qualitative discussion of the

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5-3 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 th ese commodities will shift the production costs for sectors that use electricity, natu ral gas, and coal in the produc tion of other goods and services. Such changes in production costs may lead to cha nges 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, natu ral gas, and coal price changes based on the illustrative compliance approaches modele d 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 pr ices are average prices weighted by the amount used. Table 5-1 shows these estimated price chan ges. For other results generated by IPM, please refer to Chapter 3. There are many factors influenci ng the projected natural gas pr ices. 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 approp riate natural gas supplies based on a multitude of factors. Since the model simulates perfect fore sight, it anticipates future demand for natural gas and respond accordingly. In addition, IPM (and th e natural gas module) are viewing a very long time horizon (through 2050, longer th an the compliance timeframe), such that the impacts in certain years may be responsive to other mode ling 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 imp acts shown. For more in formation on the modeling
framework see http://www.epa.gov/airmarkt/progsregs/epa-ipm/BaseCasev513.html

.

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5-4 Table 5-1. Estimated Percentage Ch anges in Average Energy Prices, by Energy Type and Regulatory Option 2020 2025 2030 Option 1 Regional Compliance 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 elect ricity, natural gas, or coal increased, one would expect decreases in producti on and increases in market pri ces in sectors for which these commodities are inputs, ceteris paribus. Conversel y, in options and years when prices of these inputs decreased, one would expect increases in production and decrea ses in market prices within these sectors. Smaller changes in input price ch anges are assumed to lead to smaller impacts within secondary markets. However, a number of factors in addition to th e 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 produc tion. These factors are
discussed below.
5.3.1 Share of Total Production Costs
The impact of energy price changes in a partic ular sector depends, in part, on the share of total production costs attr ibutable to those commodities. Fo r sectors in which the directly affected inputs are only a small portion of produc tion costs, the impact will be smaller than for sectors in which these inputs make up a greater po rtion of total production costs. Therefore more energy-intensive sectors would pot entially experience gr eater cost increases when electricity, natural gas, or coal prices incr ease, but would also experience greater savings when these input

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5-5 prices decrease. 123 

## 5.3.2 Ability to Substitute between Inputs to the Production Process
The ease with which producers are able to subs titute 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 increase d cost of these in puts 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. 5.3.3 Availability of Substitute Goods and Services
The ability of producers in s ectors experiencing changes in their input prices to pass along the increased costs to thei r customers in the form of hi gher 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 betw een 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 usin g 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 increa se as a result of a regulation and those imports come from countries w ith fewer emission contro ls, this can result in foreign emission increases that offset some porti on of domestic decrease s, an effect commonly referred to as “leakage.” The potential for leak age is noteworthy for gl obal pollutants such as carbon dioxide (CO 2 

## ) and other GHG emissions. Unlike most criteria pollutants and hazardous air pollutants, the impacts of CO 2 

## emissions are not affected by the location from which those emissions originate. EPA does not anticipate signi ficant leakage in the EGU sector to occur from

123 

### The net direct effect of this rulemaking on the production co sts 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 th at was adopted to achieve a state goal. Th at said, those expenditures may lead to other reduced expenditures for the sector, such as re duced natural gas use from weatherization projects.

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5-6 this regulation because the nature of electricit y transmission does not lend itself to significant imports or exports of electricity.
5.4 Conclusions Changes in the price of electricity, natura l gas, and coal can impact markets for goods and services produced by sectors that use thes e energy inputs in the production process. The direction and magnitude of these impacts is influenced by a nu mber of factors. Changes in cost of production may lead to changes in price, quantity produced, and profitabili ty of firms within secondary markets. If regulation results in change s in domestic markets that lead to an increase in imports, increases in production in countries with more energy-intens ive production may lead to emissions leakage.

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6-1 

## CHAPTER 6: E MPLOYMENT 

## I MPACT 

## A NALYSIS



## 6.1 Introduction Executive Order 13563 directs federal agencies to consider regulatory impacts on job creation and employment. According to the Exec utive Order, “our regulatory system must protect public health, welfare, safety, and our environment while promoting economic growth, innovation, competitiveness, and job creation. It mu st be based on the best available science” (Executive Order 13563, 2011). Alth ough standard benefit-cost an alyses have not typically included a separate analysis of re gulation-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. Th e chapter discusses a nd projects potential employment impacts of the Proposed Elect ric 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 gui delines and a general description of the associated building block framework. Section 6.3 describes the theoretical framework used to analyze regulation-induced empl oyment impacts, discussing ho w economic theory alone cannot predict whether such impacts are positive or ne gative. Section 6.4 presents an overview of the peer-reviewed literature relevant to evalua ting the effect of envi ronmental regulation on employment. Section 6.5 provides background rega rding recent employment trends in the electricity generation, coal and natural gas extraction, renewable energy, and demand-side energy efficiency-related sectors. Section 6.6 pr esents the EPA’s quantit ative projections of potential employment impacts in these sectors. Th ese projections are based in part on a detailed model of the electricity production sectors used fo r 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 6.2 Overview of the Proposed EGU GHG Existing Source Guidelines
The EPA is proposing emission guidelines for stat es to use in developing plans to address greenhouse gas emissions from existing fossil fu el-fired EGUs. Specifically, EPA is proposing state-specific rate-based goals for carbon dioxide (CO 2 

## ) emissions from the power sector, as well as emission guidelines for states to use in develo ping plans to attain the state-specific goals. The guidelines, as proposed, will lower carbon intens ity 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 thro ugh 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 adequate ly demonstrated. Consistent with CAA section 111(d), this proposed rule contai ns state-specific goals that re flect the EPA’s calculation of
emission reductions that a state can achieve thro ugh the cost-effective a pplication of BSER. The EPA is using four building blocks as a ba sis to determine state-specific goals.
6.2.1 Determining State Goals Utiliz ing the Four Building Blocks In proposing state goals, EPA is applying th e following four building blocks. Each represents a demonstrated approach to improvi ng 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-inte nsive affected EGUs in the amount that results from substituting generation at t hose EGUs with generation from less carbonintensive affected EGUs (including natura l gas combined cycl e [NGCC] units under construction).

3. Reducing emissions from affected EGUs in the amount that results from substituting generation at those EGUs with expa nded low- or zero-carbon generation.

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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6-3 4. Reducing emissions from affected EGUs in the amount that results from the use of demand-side energy efficiency that redu ces 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 depe nd 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, type s 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 ec onomy (e.g., the directly re gulated 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 ad just their use of inputs in response to changes in economic conditions. 126 

## Labor is one of many i nputs 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 fact ors of production, such as pollution abatement capital, at levels that firm s would not otherwise choose. Berman and Bui (2001, pp. 274-75) model two co mponents that drive changes in firmlevel labor demand: output eff ects and substitution effects. 130 

## Regulation affects the profit-

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 Morgenst ern, Pizer, and Shih (2002) use a cost-minimization framework, which is a

special case of profit-maximization with fixed output quantities.

130 

### The authors also discuss a third componen t, the impact of regulation on factor prices, but conclude that this effect

is unlikely to be important for large competitive factor ma rkets, such as labor and cap ital. 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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6-4 maximizing quantity of output by changing the marg inal cost of production. If regulation causes marginal cost to increase, it will place upward pre ssure on output prices, l eading to a decrease in demand, and resulting in a decrease in produc tion. The output effect describes how, holding labor intensity constant, a decr ease in production causes a decr ease in labor demand. As noted by Berman and Bui, although many assume that regulat ion 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, outpu t could increase. For example, in the context of the current rule, improvi ng 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, holdi ng output constant, regulation affects laborintensity of production. Although increased environmental regul ation may increase use of pollution control equipment and energy to operate that equipment, the impact on labor demand is ambiguous. For example, equipment inspection re quirements, specialized waste handling, or pollution technologies that alte r 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 equipm ent and expenditures required by the regulation and the corresponding change in labor-intensity of production. In summary, as output and subs titution effects may be positiv e 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 with in the bounds of standard econo mic 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 empirical estimation. For example, studies sometimes rely on confidential plant-level employment data from the U.S. Census Bur eau, possibly combined w ith pollution abatement expenditure data that are too da ted 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. Employ ment impacts at an individual plant do not

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6-5 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) th e 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 indus try are faced with the same regulatory compliance costs and product demand is inelastic, then industry output may not change much, and output of individual fi rms may change slightly. 132 

## In this case, the output effect may be small, while the substitution effect depends on inpu t substitutability. Suppos e, for example, that new equipment for heat rate impr ovements requires labor to install and operate. In this case, the substitution effect may be posit ive, and with a small output effect, the total effect may be positive. As with potential effects for an indivi dual firm, theory cannot determine the sign or magnitude of industry-level re gulatory effects on labor demand. Determining these signs and magnitudes requires additional sector-specific empi rical study. For environmental rules, much of the data needed for these empirical studies is not publicly available, w ould 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 t ypical Regulatory Impact Analysis (RIA). In addition to changes to labor demand in th e 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 se rvices. This increased demand may increase revenue and employment in the firms supporting this technology. At the same time, the regulated industry is purchasing the equipment, and thes e 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

131 

### See Ehrenberg & Smith, p. 108.

132 

### This discussion draws from Berman and Bui (2001), pp. 293.

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6-6 unlikely to have a noticeable imp act 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 em ployment effects from environmental regulation would be small and tran sitory (e.g., as workers move from one job to another). 134 

## Affected sectors may experience transitory e ffects as workers change jobs. Some workers may retrain or relocate in anticip ation of new requirements or require time to search for new jobs, while shortages in some s ectors or regions could bid up wa ges to attract workers. These adjustment costs can lead to local labor disr uptions. Although the net change in the national workforce is expected to be small, localized reductions in employment may adversely impact individuals and communities ju st 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 al so affect labor supply. In pa rticular, pollution and other environmental risks may impact labor productivity or employees’ ability to work. 135 

## While the 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 e nvironmental data to assess these impacts. To summarize, economic theory provides a framework for analyzing the impacts of environmental regulation on employment. The ne t employment effect incorporates expected employment changes (both positive and negative) in the regulated sector and elsewhere. Labor

133 

### Full employment is a conceptual target for the econom y 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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6-7 demand impacts for regulated firms, and also for the regulated industry, can be decomposed into output and substitution effects which may be eith er negative or positive. Estimation of net employment effects for regulated sectors is possibl e 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 e ffects of employment policies such as labor taxes and minimum wages. 137 

## In contrast, the peer-revi ewed empirical literature specifically estimating employment effects of environmental re gulations is more limited. Empirical studies, such as Berman and Bui (2001), suggest that ne t employment impacts were not statistically different from zero in the regulated sector. Other re search suggests that more highly regulated counties may generate fe wer jobs than less regulated ones (Greenstone, 2002). Environmental regulations may affect sector s that support pollution reduction earlier than the regulated industry. Rules are usually announced well in advan ce of their effective dates and then typically provide a period of time for firms to invest in technologie s and process changes to meet the new requirements. When a regulation is promulgated, the initial response of firms is often to order pollution c ontrol equipment and services to enable compliance when the regulation
becomes effective. Estimates of short-term incr eases in demand for specialized labor within the environmental protection sector have been prepared for severa l EPA regulations in the past, including the Mercury and Ai r Toxics Standards (MATS). 138 

## Overall, the peer-reviewed
literature does not contain evidence that envi ronmental regulation has a large impact on net employment (either negative or positive) in the long run acros s the whole economy.

136 

### Again, see Hamermesh (1993) for a detailed treatment.

137 

### See Ehrenberg & Smith (2000), Chapter 4: “Employment Effects: Empirical Estimates” for a concise overview.

138 

### U.S. EPA (2011b).

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6-8 6.4.1 Regulated Sector
Berman and Bui (2001) examine how an increas e in local air qualit y regulation affects manufacturing employment in the South Coast Air Quality Ma nagement 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 re gulations. Using SCAQMD’s local air quality regulations, Berman and Bui identify the effect of environmental regulations on net employment in regulated manufacturing indus tries 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). Thei r conclusion is that local air quality regulation “probably increased la bor demand slightly” but that “t he employment effects of both compliance and increased stringency are fairly precisely estimated zeros, even when exit and dissuaded entry effects are incl uded” (Berman and Bui, 2001, p. 269). 140 

## The few studies in peer-reviewed journals evaluating employment impacts of policies
that reduce CO 2 

## emissions in the electric power generation sector are in the European context. In a sample of 419 German firms, 13 percent of wh ich were in the electricity sector, Anger and Oberndorfer (2008) find that the ini tial 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, Co mmins et al. (2011) find that a 1 percent increase in energy taxes is associated with a 0.01 percent decrease in employ ees 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 employ ees relative to those th at did not participate, but this effect is not statistically significant. This literature suggests that the employment impacts of controlling CO 2 

## emissions in the

139 

### Berman and Bui include over 40 4-dig it 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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6-9 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 pe rmits. A performance standard may not generate similar employment effects. Moreover, European firms face relative fu el prices and market regulatory structures different from their U.S. counterparts, further co mplicating 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 regulator y impacts on geographic distribution of manufacturing em ployment, controlling for electrici ty prices and labor regulation (right to work laws). Their methodology iden tifies 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 regula tion may cause an effective relo cation of labor across a county border, but since one county’s lo ss 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 environmenta l regulatory employment impacts focuses primarily on labor demand. However, there is a nascent literature focu sing on regulation-induced effects on labor supply. 141 

## Although this literature is limited by empirical challenges, researchers 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) us ed detailed worker-level pr oductivity 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 structur e. Their quasi-experimental structure identifies an effect of daily variation in monitored ozone levels on productivity. Th ey find “ozone levels well below federal air quality standards have a significant impact on prod uctivity: a 10 parts per

141 

### For a recent review see Graff-Zivin and Neidell (2013).

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6-10 billion (ppb) decreases in ozone concentrations increases work er productivity by 5.5 percent.” (Graff Zivin and Neidell, 2012, p. 3654). 142


## This section has outlined the ch allenges associated with es timating regulatory effects on both labor demand and supply for specific sectors. These challenges make it difficult to estimate net national employment estimates that would ap propriately capture the way in which costs, compliance spending, and environmental bene fits 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 empl oyees that support elect ric power generation, transmission and distribution; the extraction of fossil fuels; renewable energy generation; and supply-side and demand-side ener gy efficiency. This section de scribes recent employment trends in the electricity system.
6.5.1 Electric Power Generation In 2013, the electric power ge neration, transmission and di stribution sector (NAICS 2211) employed 394,000 workers in the U.S. 144 

## Installation, maintenance, and repair occupations accounted for the largest shar e 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 di stribution systems. Other majo r occupation categories include

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 incorp orate possible productivity effects in the future.

143 

### For further information see: < https://www.federalregister.gov/article s/2014/02/05/2014-02471/draft-supportingmaterials-for-the-science-advisory-board-panel-on-the-role-of-economy-wide-modeling>.

144 

### U.S. Bureau of Labor Statistics. “Current Employmen t Survey Seasonally Adjust ed 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 Empl oyment 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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6-11 office and administrative support (17 percent), production occupatio ns (15 percent), architecture and engineering (11 percent), business and financia l operations (7 percen t) 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 aver age of 400,000 workers later in the decade, and to 394,000 workers in 2013.

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 e ngaged in coal mining and coal mine site development, excluding metal ore mining and no nmetallic mineral mining and quarrying. There are two sources of U.S. government data on co al mining employment, the Bureau of Labor Statistics (BLS) Current Employment Statistics (NAICS 2121), and the Department of Labor’s350400450500



### 2000 2005 2010 2015 Year 

## Electric Power Industry national employment (in thousands)

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6-12 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 indepe ndent contractor employment, whereas BLS does not track contractors. Contractor employmen t reported by MSHA focuses primarily on mine development, construction, reconstruction or demo lition of mine facilities, construction of dams, excavation or earth moving, equipment installati on, 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 ove r the past 10 years, with the exception of a small temporary
increase in 2011. See Figure 6.2 below.

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>.

147 

### Mine Safety and Health Administration, CFR Part 50 T itle 30 Employment Data – selected contract employment

is included, p. 2. Available at:
< http://www.msha.gov/Stats/Part50/WQ/Ma sterFiles/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/P ART50/P50Y2K/AETABLE.HTM >.

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6-13 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 producti on of crude petroleum, oil from oil shale and oil sands, production of natural gas, sulfur recovery from natural gas, and recove ry of hydrocarbon liquids. Activities include the developm ent of gas and oil fields, e xploration activiti es for crude petroleum and natural gas, drilling, comple ting, and equipping wells, and other production
activities. 150 

## In contrast with coal, and looking at Fi gure 6.3, there has been a sharp increase in employment in this sector over the past decade.

149 

### BLS, Current Employment Statistics. Seasonally adju sted 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>.20406080100



### 2000 2005 2010 2015 Year BLS - employees MSHA - employees MSHA - contractors 

## Coal mining employment - national (in thousands)

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6-14 Figure 6.3. Oil and Gas Production Employment
6.5.3 Clean Energy Employment Trends Clean energy resources, such as energy effi ciency and renewable energy, are used to meet energy demand, reduce peak electricity sy stem loads, and reduce reliance on the most
carbon-intensive sources of electri city. However, there is not a single clean energy sector in
standard national accounts cla ssifications. Renewable generati on is not reported to the BLS separately from other electric power generation. Similarly, ma nufacturers of energy efficient appliances are not reported separately from conventional appliance manufacturers and green building design is not separate from the cons truction sector. Instea d, clean energy technology and services are supported by indus tries throughout the economy. Without a specific industrial classification, it is difficult to quantify the exact number of clean energy-related jobs or doc ument the trends. Employees enga ged in clean energy can span many job classifications, such as experts require d to design and produce a renewable or energyefficient technology, workers that supply inputs and technicians w ho install service or operate equipment. As such, there are a variety of defini tions of clean or green jobs used, some more100150200250



### 2000 2005 2010 2015 Year 

## Oil and Gas Extraction national employment (in thousands)

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6-15 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 classifi cations into “green” categories. In both cases the approach was to determine which product classifications, rath er 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 s upported 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 th e 2007 Economic Census to estim ate 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-relate d investments and includes “thos e in businesses that produce goods and provide services that benefit the envi ronment or conserve natural resources. These goods and services, which are sold to customers, include research and development, installation, and maintenance services for renewable energy a nd 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 Sta tistics Administration. 2010. “Measuring the Green

Economy ,” April. Available at:
< http://www.esa.doc.gov/sites/default/files/re ports/documents/greeneconomyreport_0.pdf >.

152 

### U.S. Department of Commerce Economics and Statistic s Administration. 2010. “Measuring the Green Economy,

April. Available at: < http://www.esa.doc.gov/sites/default/files/re ports/documents/greeneconomyreport_0.pdf>

.

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6-16 conservation, such as forestry management. 153 

## Based on surveys acro ss 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 o ccupations 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 Servi ces (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 Gene ration Employment Trends

The DOC report does not separate renewable en ergy data and the BLS data include only privately owned electricity generating facilities. As such, neither sour ce 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 na tional database of “clean economy” jobs from the bottom up, veri fying each company individually. 156 

## They include a list of categories similar but not identic al to that of BLS, including agricultural and natural resources conservation, education and compliance, en ergy and resource effi ciency, greenhouse gas reduction, environmental management and recy cling, and renewable energy. This study found about 138,000 jobs in the renewa ble energy sector in 2010, with an overall average annual growth rate of 3.1 percent fr om 2003-2010. Table 6-2 details the national results by energy

153 

### BLS has identified 325 detailed industries (6-digit NAIC S) as potential producers of green goods and services.

Available at: < http://www.bls.gov/ggs/ggsoverview.htm>. (Accessed on 1-14-14, l ast modified date: March 19, 2013.

154 

### U.S. Department of Labor, U.S. Bureau of Labor Sta tistics. (n.d.). 2011. “Green Goods and Services 2010-2011.”

(Retrieved on January 14, 2014). Available at :< http://www.bls.gov/ggs/ggsoverview.htm>.

155 

### U.S. Department of Labor, U.S. Bureau of Labor St atistics. 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/Seri es/resources/0713_clean_economy.pdf> p. 15.

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6-17 source. Table 6-2. Renewable Electricity Ge neration-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/Seri es/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 s ector energy efficiency jobs in 2010 and 1.64
million in 2011. For a longer term trend the Brookings In stitution 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 annua l growth rate of 2.6 percent from 2003-2010. Table
6-3 details the results by energy sector. 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

157 

### < http://www.brookings.edu/~/media/Seri es/resources/0713_clean_economy.pdf> p. 15

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6-18 Total 427,652 2.6 Source: http://www.brookings.edu/~/media/Serie s/resources/0713_clean_economy.pdf , Appendix A



## In addition, other research in stitutes and industry groups ha ve clean economy or clean energy employment databases. While definitions and timeframes vary, all show positive employment trends of 1.9 percent or more gr owth in clean energy-re lated jobs annually. 6.6 Projected Sectoral Employment Chan ges due to Proposed Guidelines EGUs may respond to these proposed guideli nes by placing new orders for efficiencyrelated or renewable energy equipment and se rvices to reduce GHG em issions. Installing and operating new equipment or improving heat rate efficiency could increase labor demand in the electricity generating sector its elf, as well as associated equipment and services sectors. Specifically, the direct employment effects of supply-side initia tives include changes in labor demand for manufacturing, instal ling, and operating higher effi ciency or renewable energy electricity generating assets s upported by the initiative while re ducing 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 pl ans 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 incr ease the use of cost-effective energy efficiency technologies (e.g., including mo re efficient appliances and air conditioning systems, more efficient lighting devices, more efficient design and construction of new homes and businesses), and advance efficiency improve ments in motor systems and other industrial processes. Demand-side initiatives can also dire ctly 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 dema nd-side energy efficiency initiatives directly affect employment by encouraging firms and consum ers 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 empl oyment in the sectors

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6-19 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 (foss il, renewable, and nuclear), and th e fuel production sector (coal and natural gas). These projections include effect s attributable to heat rate improvements, construction of new EGUs, changes in fuel use, and reductions in electri city generation due to demand-side energy efficiency activities. To pr oject labor requirements for demand-side energy efficiency activities, the analysis uses a differe nt approach that comb ines data on historic changes in employment and expendi tures in the energy efficiency sector with projected changes in expenditures in the sector arising from state implementatio n of the proposed guidelines. We project labor impacts for two options fo r establishing the “best system of emission reduction” (BSER) for GHG emissions from exis ting 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 desc ribed above at different levels of stringency. Option 1 involves higher deploymen t of the four building blocks but allows a longer timeframe to comply (2030) whereas Op tion 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 oc curs 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. 6.6.1 Projected Changes in Employment in Electricity Generation and Fossil Fuel Extraction The analytical approach us ed 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 gene rating units, and prevailing wage rates for skilled and general labor categories. This approach is different fr om the types of economic analyses discussed in Section 6.3. Rather than proj ecting employment impacts th roughout the U.S. economy, the

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6-20 engineering-based analysis focuses on the dir ect impact on labor demand in industries closely involved with electricity genera tion. The engineering approach pr ojects labor changes measured as the change in each analysis year in job-years 158 

## employed in the power generation and directly related sectors (e.g., equipment ma nufacturing, fuel supply and gene rating efficiency services). For example, this approach projects the am ounts and types of labor required to implement improvements in generating efficiency. It then us es the EPA’s estimated effect of efficiency improvements on fuel demand to project reduction s in the amount of la bor required to produce coal and gas. This analysis relies on projections and co sting analysis from the IPM, which uses industry-specific data and assumptions to esti mate 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 im pacts are based on the analytical methods used in the Regulatory Impact Analysis for the Mercury and Air Toxi cs Standards (MATS). While the methods used in this analysis to estimate the recurring la bor impacts (e.g., labor associated with operating and maintaining generati ng units, as well as labor needed to mine coal 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 develo ped for the GHG guidelines analysis. The bottom-up engineering-based labor anal ysis 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, involv es the quantity and timing of the labor needs of building new renewable (primarily wind) and NGCC generating capacity. The EPA IPM

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.

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6-21 analysis finds that by 2020 a significantly la rger 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-hydr o 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 c onstructing the new units occurs, a nd is actually paid for, in
a concentrated amount of time before the new ca pacity begins to generate electricity. IPM

assumes 160 

## that new NGCC units take 3 years to buil d, and both natural gas combustion turbines and wind-powered renewa bles 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 imp acts 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 th e Options than in the base case. For example, in Option 1 with re gional 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. The avoided new capacity results in both a signif icant net cost savings to consumers and the power sector, as well reduced emissions of both CO 2 

## 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 gui delines. Similarly, less labor involved with operating and providing fuel for ne w units will be needed with the GHG guidelines than in the
base case.

160 

### Table 4.7, IPM 5.13 Documentation.

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6-22 Overall, the impact of much more rapi d construction of new renewable and fossil generation capacity in the early years of im plementing the GHG guidelines, followed by the need to build substantially less capacity in later year s as the demand-side ener gy efficiency programs reduce the overall electricity dema nd relative to the base case, cr eates an important but complex temporal dynamic to the supply-side labor analys is. 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, th e reduced need for additional newly built capacity
results in a sizable net decrease in the amount of labor needed relativ e 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 la bor, 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 impl ementing 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 demand-side energy efficiency activities are increase s in labor needs, which more than offset the loss of supply-side jobs associated with the decreasing demand for electricity arising from the demand-side programs. The IPM labor expenditure projections are distri buted across different labor categories (e.g., general c onstruction labor, boilermakers and engineering) using data from engineering analyses of labor’s overall share of total expenditures, and apportionment of total 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 (m easured 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 producti vity data for coal mining (tons mined per employee hour) and natural gas extraction (M MBtu produced/job-year). Tables 6.4 and 6.5 present projected changes relative to th e baseline of four labor categories:

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6-23 1. manufacturing, engineering and constructi on for building, designing and implementing heat rate improvements;
2. manufacturing and construction for new generating capacity;
3. operating and maintenance for ex isting generatin g 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 interv als are from 2017 through 2020 (a four year interval), from 2021 through 2025 (five years), a nd 2026 through 2030 (5 years). Th e construction-related labor analysis are based on the IPM estimates of the ne t 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 th e net change in capital investme nt 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 interv al is evenly distributed throughout the interval. Tables 6-4 and 6-5 reflect this assumption by presenting the averag e 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 th e HRI-related labor impacts occur in the first interval (2017 to 2020), and are a ssumed to be occur evenly thr oughout that four year interval. Therefore, the HRI-related labor estimates in Ta bles 6-4 and 6-5 are the annual average labor impacts for of the four years. Ther e are no HRI improvements made after 2020. The labor estimates for operating and maintain ing generating units annually are based on IPMs estimates of Fixed Operating and Maintena nce (FOM) Costs. IPM estimates FOM for each year individually, so the net ch anges in O&M-related labor estimat es 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 th roughout the entire period 2017-2030, but labor impact changes each

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6-24 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 ne t changes in the amount of coal and natural gas in 2020, 2025 and 2030, which are inhere ntly estimates of the fuel usage in a single year. As with the O&M labor impacts, th e fuels-related labor impacts o ccur in every year throughout 2017-2030, and the labor impact changes every year.
It should be noted that the supply-side labor impact estim ates 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 impact s include not only the impacts of Building Blocks 1 through 3, but also the changes in total genera tion needed that result from th e demand-side energy efficiency activities in Building Block 4. The additional up stream labor impacts from the demand-side activities are presented below in Section 6.2.2. More details on methodology, assumptions, and da ta sources used to estimate the supplyside labor impacts discussed in this section can be found in Appendix 6A.

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6-25 Table 6-4. Engineering-Based a 

## Changes in Labor Utilization, Regional Compliance Approach - (Number of Job-Years b 

### 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: To tal 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 eac h year of the 2 to 4 year period during which construction and HRI insta llation 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 asso ciated with annual recurring jobs including operating and maintenance activities and fuel extraction jobs. Newly built generating cap acity creates a recurring stre am 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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6-26 Table 6-5. Engineering-Based a 

## 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: To tal 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 eac h year of the 2 to 4 year period during which construction and HRI insta llation 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 asso ciated with annual recurring jobs including operating and maintenance activities and fuel extraction jobs. Newly built generating cap acity creates a recurring stre am 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 De mand-Side Energy Efficiency Activities
EPA anticipates that this rule may stimulate investment in clean energy technologies and services, resulting in considerable increases in en ergy efficiency in particular. We expect these increases in energy efficiency, specifically, to support a significant number of jobs existing in

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6-27 related industries. In this section, we projec t employment impacts in demand-side energy efficiency activities arising from these guidelines using illustrative calculations. The approach uses information from power sector modeling and proj ected 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 appro ach is limited by the fact that we do not know which options states will choose for dema nd-side energy efficiency activities and by
uncertainties associated with methods. These illu strative employment projections are gross; thus they do not include impacts of a ny 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 wo rds, these projections are not attempts at estimating net national job creation. Also, this ap proach attempts to cal culate the number of employees (full-time and part-time) rather th an job-years as discu ssed 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., weat herization of older homes), or encouraging changes in behavior (e.g., reduc ing air conditioning during periods of high electricity demand). Employment impacts of demand-side ener gy efficiency programs have not been extensively studied in the peer -reviewed, published economics literat ure. 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 ener gy efficiency as described in “energy paradox” literature (Gillingham, Newell, and Palm er (2009), Gillingham and Palmer (2014)). 161 

## Two recent articles discuss employment ef fects of demand-side energy efficiency

161 

### For more information on this e fficiency paradox see Chapter 3.

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6-28 programs. Aldy (2013) describes clean energy in vestments 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 effi ciency investments. The Council of Economic Advisors (CEA) (2011) estimated higher economi c activity and employment than would have
otherwise occurred without the American Rec overy 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-ye ars supported by the Recovery Act. The largest sources of job creation in clean energy are thos e that received the largest shares of stimulus funds: renewable energy, energy effi ciency, and transit. Aldy’s esti mates, while informative, are not directly applicable for employment analys is in this rulemaking as there are important differences in expected employment impacts from a historically large fiscal stimulus specifically targeting job creation during a pe riod of exceptionally high unemployment versus environmental regulations taking effect several years from now. Yi (2013) analyzes clean ener gy 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 pol icy tool (renewable energy policies, GHG emissions policies, and energy efficiency polices such as energy e fficiency resource standards, appliance or equipment energy efficiency standards, tax in centives, 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 si nce states are likely to change intensity as well as number of clean energy programs. Lacking a peer-reviewed methodology, we pr opose the following approach to illustrate possible effects on labor demand in the energy effi ciency 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.

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6-29 Data used for calculating the numerator of the fraction comes from the “energy efficiency” category of the 2010 and 2011 BLS Green Goods and Services surveys. 162 

## BLS does not report the denominator of the job per addi tional dollar fraction, howev er. Instead, the data include the fraction of green revenues received re lative 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-y ear intervals (the late st available year is 2007), however, making it unsuitable for identifyi ng the change in revenues from 2010 to 2011. Instead, we use data from the Annual Survey of Manufacturers. The disa dvantage of this data source is that the manufacturing sector make s 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 additiona l 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 efficien cy 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 Op tion 1 regional and state compliance approaches. The estimates for Option 2 are shown on Table 6-6 below.

162 

### For more details on these su rveys, see section 7.5.3.

163 

### See detailed listing available here: h ttp://www.bls.gov/ggs/naics_2012.xlsx.

164 

### See Greenhouse Gas Abatement Measures TSD, Appendix 5-4.

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6-30 Table 6-6. Estimated Demand-Side Energy Effi ciency 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 appr oach 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 met hod, 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 electr icity generation and fuel production employment impacts of this rulemaking. For those, the EPA wa s able to identify a li mited set of activities (e.g., constructing a new NGCC power plant), and study associated labor requirements. Demandside energy efficiency improvement s, in contrast, encompass a wide array of activities (subsidies for efficient appliances, “smart meters,” etc.). In addition, there is co nsiderable 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 activ ities similar to the 2011 composition of energy efficiency sectors identif ied by BLS. In addition, the EPA does not have access to bottom-up information regarding labor requirements for these activities. Use of a consta nt job per dollar fracti on is at best a crude approximation of these labor requirements. The EP A has identified several other limitations of this approach, outlined below. Job Reclassification. Job numbers in this chap ter 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. Fo r demand-side efficiency activities this potential over-statement is may be higher th an in other sectors. If states encourage consumers to purchase ENERGY STAR applia nces, for example, currently employed

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6-31 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 fracti on 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 appro ach 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 ab sent technological change, labor intensity of expenditures will likely cha nge 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 fi nance 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 quant itative discussions of potential employment impacts of the proposed guidelines for electric ity generation, fuel pr oduction, and demand-side energy efficiency sectors. The qualitative disc ussion 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 anal ysis of employment impacts. Labor demand impacts for regulated firms can be decomposed into output and substitution effects, both of which may be pos itive or negative. Consequently, theory alone cannot predict the direction or magnitude of a re gulation’s employment impact. It is possible to combine theory

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6-32 with empirical study specific to the regulated firms and other relevant sectors if data and methods of sufficient detail and quality are avai lable. 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 framewor k. 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 ma y alter the relative proportions of labor and capital used by regulated firms (and firms in other relevant industries) in their pr oduction processes. The
available literature illustrates some of the diffi culties for empirical estimation: studies sometimes rely on confidential plant-level employment da ta 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 estimati on of net economy-wide effects. Empirical analysis at the industry level requires estimat es of product demand elasticity; production factor substitutability; supply elasticity of production f actors; 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 si gnificant time and resources in order to access
confidential U.S. Census data for research, and also woul d not be necessary for other components of a typical RIA. Econometric studi es 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 th is chapter projects a subset of potential employment impacts in the electricity gene ration, fuel production, and demand-side energy efficiency sectors. States ha ve the responsibility and flexib ility 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 use d, quantifying the associated employment impacts is difficult. EPA’s employment an alysis includes projected employ ment 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

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6-33 employment and labor productivity. In the electri city, 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 effi ciency 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 j ob-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 th e appropriate combination of, and the extent of its reliance on, measures for its state plan, by wa y of meeting its state-specific goal. Given the
flexibilities afforded states in complying with the emission gui delines, the impacts reported in this chapter are illustrative of compliance actions states may take.

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6-34 6.8 References Aldy, J.E. 2013. “A Preliminary Assessment of the American Recovery and Reinvestment Act’s Clean Energy Package.” Review of Environmental Economics and Policy , vol. 7, issue 1, Winter, pp. 136-155. Allcott, H. 2011a. “Social Norms and Energy Conservation.” Journal of Public Economics , Volume 95, Issues 9–10 , October, pgs. 1082–1095.
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6-37 The Pew Charitable Trust. 2009. 2009. “The Clean Energy Economy: Repowering Jobs, Businesses and Investments Across America

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< http://www.bls.gov/opub/mlr/2013/01/art1full.pdf>. U.S. Department of Commerce, Economics and Statistics Ad ministration. 2010a. “Measuring the Green Economy.” Available at: < http://www.esa.doc.gov/Repor ts/measuring-greeneconomy>

. U.S. Department of Commerce, Internati onal Trade Administration. 2010b. Environmental Industries Fact Sheet, using 2008 data from Envi ronmental Business Intern ational, Inc., April. Available at:

< http://web.ita.doc.gov/ete/eteinf o.nsf/068f3801d047f26e85256883006ffa54/4878b7e2fc08ac 6d85256883006c452c?OpenDocument>. U.S. Department of Labor, Bureau of Labor Stat istics. 2012a. Databases, Tables & Calculators by Subject. Acccessed on April 16, 2012 and available at:

< http://data.bls.gov/timeseries/LNS14000000>. U.S. Department of Labor, Bureau of Labor Statistics (2012b, April 6). “The Employment Situation—March 2012.” News Release. A ccessed April 16, 2012 and available at:

< http://www.bls.gov/news.release/pdf/empsit.pdf>

. U.S. Department of Labor, U.S. Bureau of La bor Statistics, (n.d.). “Employment Projections Program.” Retrieved January 14, 2014, and available at:

< http://www.bls.gov/emp/ep_table_102.htm >. U.S. Department of Labor, U.S. Bureau of Labor Statistics. (n.d.). “Green Goods and Services, 2010-2011.” Retrieved January 14, 2014, and available at:

< http://www.bls.gov/ggs/ggsoverview.htm>. U.S. Energy Information Agency. 2013. “A nnual Energy Outlook 2014: Early Release.” Available at: http://www.eia.gov/forecast/ aeo/er/early_elecgen.cfm

.

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6-38 U.S. Environmental Protection Agency (EPA). 2011a . “The Benefits and Costs of the Clean Air Act from 1990 to 2020 Final Report – Rev. A.” Of fice of Air and Radiation. April.Available
at: < http://www.epa.gov/air/sect81 2/feb11/fullreport_rev_a.pdf>. U.S. EPA. 2011b. “Regulatory Impact Analysis for the Mercury and Air Toxics Standards (MATS),” December. Available at:

< http://www.epa.gov/ttn/ecas/regda ta/RIAs/matsriafinal.pdf>

. U.S. International Trade Commission. “Environmen tal and Related Servic es.” Publication No. 4389, Investigation No. 332-533, March, 2013. Available at:

< http://www.usitc.gov/publications/332/pub4389.pdf> Walker, R. 2011.“Environmental Regu lation and Labor Reallocation." American Economic Review, Papers and Proceedings, 101(2). Wei, M.; S. Patadia & D. M. Kammen. 2010. “ 2010 Putting Renewables And Energy Efficiency To Work: How Many Jobs Can The Clean Energy Industry Generate In The US?” Energy Policy, 38, 919-931. Available at: https://rael.berkeley.edu/site s/default/files/WeiPatadiaKammen_CleanEnergyJobs_EPolicy20 10.pdf

. Yi, H. 2013. “Clean Energy Policies and Green J obs: An Evaluation of Green Jobs in U.S. Metropolitan Areas.” Energy Policy , 56: 644-65.

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6A-1 

## A PPENDIX 

## 6A: E STIMATING 

## S UPPLY 

## -S IDE 

## E MPLOYMENT 

## I MPACTS OF THE


## P ROPOSED 

## EGU GHG E XISTING 

## S OURCE 

## G UIDELINES



## 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 gene rating equipment sectors directly influenced by compliance with the Guidelines. It does not in clude 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 ar e 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 da ta where available). The labor analysis in the MA TS RIA was primarily concerne d with the labor needs of retrofitting pollution control equipment. Th e 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 comp liance activities in the EGU GHG Existing Source Guidelines required developing additional appr opriate analytical methods that were not needed for the MATS analysis. The newly developed analytical methods for the construc tion-related activities are similar in structure and overall approach to the methods used in MATS, but required additional data and engineering inform ation not needed in the MATS RIA. 6A.1 General Approach The analytical approach us ed in this analysis is a bottom-up engineering method combining the EPA’s cost analysis of the propos ed guidelines with data on labor productivity, engineering estimates of the amount and types of labor needed to manufacture, construct, and operate different types of gene rating units, and prevailing wage rates for skilled and general labor categories. The approach involved using power sector projections and various energy

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6A-2 market implications under the proposed EGU GHG Existing Source Guidelines from modeling
conducted with the EPA Base Case versi on 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 labo r analysis the engineering approach projects labor changes measured as the change in each analysis year in job-years 166 

## 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 ex ample, 20 job-years in 2020 may represent 20 full-time jobs or 40 half-time jobs in that year, or any combination of fu ll- 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 HRI 167 

## at existing coal fired EGUs;

 additional construction-related employment to manufacture and install additional new generating capacity (renewable s, 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 be ing equal, a lower heat rate is more efficient because less fuel is needed per unit of electric output.

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6A-3  lost construction-related employ ment opportunities due to redu ctions in the total amount of new generating capacity needed to be bui lt 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 change s in fuel demands arising from all the activities occurring due to complia nce with the proposed guidelines.

Some of the changes are one-time labor effect s which are associated with the building (or avoiding building) new generating ca pacity and installing HRI. This type of employment effects involves project-specific la bor that is used for 2 to 4 years to complete a specific construction and installation type of project. There are other labor effects, how ever, 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 th e lost operations and maintenance (O&M) and fuel sector labor opport unities from decisions to retire existing capacity, as well as similar lost labor opportuni ties 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 improve ments 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 “r elevant” 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 us ed in the next step.
This cost was then allocated to four ca tegories based on the estimates provided by

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6A-4 Andover Technology Partners (ATP), which were adapted from proxy projects involving
installation of combustion contro l 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 th eir associated costs would be representative of those investments EPA expects power plants to undertake for efficiency upgrades. Information on cost for these proxies were th en extrapolated to approximate the labor requirements for four broad categories of la bor – boilermakers and general construction, engineering and management support labor, labo r required to produce the equipment in upstream sectors, and labor required to supply the material s (assumed to be primarily steel) in upstream
sectors. More details about these estimates ar e provided in the Staudt (2014) report.
Based on the cost allocated in each cate gories and output per worker figures for respective industries in 2020, th e employment gains for heat rate improvement were estimated 169 

## 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 fo r 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. Estima ting 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 recei pts 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 pr oductivity. 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.

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6A-5 For these output per worker figures, a pow er sector construc tion industry (NAICS 237130) was used for general construction and bo ilermakers, Engineerin g Services (NAICS 54133) was used for the engineering and mana gement component, Machinery Manufacturing (NAICS 333) was used for the equipment sect or, and steel manufactur ing (NAICS 33121) was used for materials. Use of machinery manufact uring for equipment and steel for materials was based on an analysis of the types of materials an d equipment needed for these projects, and what
EPA determined to be the most a ppropriate industry sectors for t hose. For more details, refer to the Staudt (2014) report. 6A.2.1 Employment Changes Due to Building (o r Avoiding) New Generation Capacity
Employment changes due to new generation uni ts were based on the incremental changes in capacity (MW), capital costs ($MM), and fi xed operations and maintenance (FOM) costs ($MM) between the policy scenario a nd the base case in a given year. New capacities were aggregated by genera tion 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 th e 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 capit al 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 annual impacts on construction phase labor. Dura tion estimates were based on assumptions for

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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6A-6 construction lengths used in EPA’s IPM modeling. 171 

## Specific assumptions used for different generating technologies are show n 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 t ype were then broken down into four categories: equipment, material (which is assumed to be primar ily steel), instal lation labor, and support labor in engineering and management. Th e 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 fo r 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 we re 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 to tal direct workers per $1 million of output for the baseline year 2007 were forecasted to the years under analysis usi ng the relevant labor productivity growth rate. Table 6A-4 shows the figures for each of the five productivities:

171 

### Ibid.

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6A-7 general power plant cons truction; engineering and managemen t; material use; equipment use; and plant operators. The resulting values were multiplied by the capital costs to get the job impact.

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6A-8 Table 6A-4. Labor Productivity du e 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, a ssumed to be mostly related to the general power plant construction phase, was matched with the pow er industry specific construction sector. Engineering/management was matched to the engi neering services sector to determine their respective output per worker. For materials, EP A assumed steel to be the proxy and used the steel manufacturing sector for th is productivity. Equipment was assu med to primarily come from machinery manufacturing sector (such as turbines, engines and fans). The net labor impact for construction labor fo r a given year was adjusted to account for changes in capacity that has already taken plac e 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 impact s in any given run year does not reflect the cumulative effects of prior construction change s for the given policy scenario. The estimated amount of the change in construction-relate d labor in a single IP M 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 la bor estimates derived from the 2025 IPM run year are the average annual labor impacts in 2021 through 2025). The c onstruction labor results for 2020 represent the average labor impacts in 2017 through 2020. The plant operating employment estimate s used a simpler methodology as the one described above. The operating employment esti mates 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 ther e are obviously operating employment effects occurring in every year th roughout the entire IPM estimation period (2017- 2030), the labor analysis only estimates the singl e year labor impacts in the IPM run years: 2020, 2025 and 2030. The total direct workers for $1 m illion and labor productivity growth rate provided for plant operators in Table 6A-4 we re used to estimate the employment impact.

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6A-9 6A.2.2 Employment Changes due to Coal and Oil/Gas Retirements Employment changes due to plant retirement s 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 ba sic 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 fo ssil retirements. Thus, changes in the FOM costs for these retiring uni ts were derived by taking the product of the incremental change in capacity and the average FO M costs. These values were converted to lost employment using data from the Economic Cens us 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 loss es in upstream sectors th at 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 proj ected fuel use changes were estimated in this section. First, employment losses due to either reductions or shifts in coal demand were estimated using an approach similar to EPA’s co al employment analyses under Title IV of the Clean Air Act Amendments. Using this approach , changes in coal demand (in short tons) for

172 

### The same specific sources as cited before, however, used workers and total payroll.

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6A-10 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 -- Appal achia, Interior, and West and Wa ste 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 du e 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 natura l gas was unavailable, unlike coal labor productivities used above. Most second ary 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 comb ined 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 incremental natural gas demand for the respectiv e IPM runs to estimate the job-years for the

173 

### From EIA Annual Energy Review, Coal Mining Productivity Data. Used 2008.

174 

### Unlike the labor productivity estimates for various e quipment resources which were forecasted to 2020 using

BLS average growth rates, th is study uses the most recent historical productivity estimates fo r 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 sect ors, which would have introduced a high degree of uncertainty in forecasting productivity growth rates for future years.

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6A-11 specific year (converting the TCF of gas used projected by IPM into MMBtu using the appropriate conversion factors). In addition, the pi peline construction costs were estimated using endogenously determined gas market model parame ters 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 propor tionate to those projected for the MATS rule and were hence extrapolated from those estimates. 175 

## The job-years associated w ith the pipeline construction were included in the natural gas employment estimates.

175 

### See “Employment Estimates of Direct Labor in Respon se to the Proposed Toxics Rule in 2015”. Technical

Support Document, March 2011.

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7-1

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7-1 

## CHAPTER 7: S TATUTORY AND EXECUTIVE 

## O RDER 

## A NALYSIS



## 7.1 Executive Order 12866, Regulatory Planni ng and Review, and Executive Order 13563, Improving Regulation a nd Regulatory Review
Under Section 3(f) (1) of Executive Or der 12866 (58 FR 51735, October 4, 1993), this action is an "economically signifi cant 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 bene fits, 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 fo r this action. The EPA also pr epared an analysis of the potential costs and benefits associat ed with this action in this Regul atory Impact Analysis (RIA). Consistent with EO 12866 and EO 13563, the EP A estimated the costs and benefits for illustrative compliance approaches of implementing the proposed guidelines. This proposal sets goals to reduce CO 2 

## emissions from the electric power i ndustry. Actions taken to comply with the proposed guidelines will also reduce the emissions of directly emitted PM 2.5 

## , sulfur dioxide (SO 2 

## ) and nitrogen oxides (NO X 

## ). The benefits associated with these PM, SO 2 

## and NO X 

## reductions are referred to as co-benefits, as thes e reductions are not the primary objective of this
rule. The EPA has used the U.S. government’s soci al cost of carbon (U SG SCC) estimates (i.e., the monetary value of impacts asso ciated with a marginal change in CO 2 

## emissions in a given year), to analyze CO 2 

## climate impacts of this rulemaking. The four USG SCC estimates
are associated with different discount rates (model average at 2.5 percent di scount rate, 3 percent, and 5 percent; 95 th 

## percentile at 3 percent), and e ach 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 c limate benefits of

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7-2 approximately $17 billion with a 3 percent mode l average (2011$). The ai r pollution health cobenefits in 2020 are estimated to be $16 billion to $37 billion fo r a 3 percent discount rate, and $15 billion to $34 billion (2011$) for a 7 per cent discount rate. The annual illustrative compliance costs estimated by IPM, inclusive of demand side energy efficiency program and participant costs and MRR costs, are approxima tely $5.5 billion (2011$) in 2020. The quantified net benefits (the difference betw een monetized benefits and costs) in 2020, are estimated to be $28 billion to $49 billion using a 3 percent discou nt 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 $18 bi llion (2011$). The air pollu tion 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 $3 6 billion (2011$) for a 7 percent discount rate. The annual illustrative compliance costs estimat ed 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 approximate ly $30 billion (3 percent, model average, 2011$). The air pollution health co-ben efits in 2030 are estimated to be $25 billion to $59 billion for a 3 percent discount rate, and $23 billion to $54 bill ion (2011$) 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 differenc e between monetized benefits and costs) in 2030, are estimated to be $48 billi on to $82 billion using a 3 percen t discount rate and $46 to $77 billion assuming a 7% discount rate (model av erage, 2011$). For the Option 1 state compliance approach, net benefits in 2030 are estimated to be $49 to $84 billi on (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 fo regoing discussion, it remains clear that the benefits of this proposal are substantial, and far exceed the co sts. Additional benefit, cost and net benefit estimates for Option 1 and Option 2 are pr ovided in Chapter 8 of this report.

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7-3 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 ar e based on the recordkeeping and reporting burden associated with developing, implemen ting, and enforcing a state plan to limit CO 2 

## emissions from existing sources in the power sector. These recordkeeping and reporting requirements are specifically authorized by CA A 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 inform ation 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 assump tion that some states already have energy efficiency and renewable energy program s in place. The higher bound estimate reflects the assumption that no states have energy effici ency and renewable energy programs in place. The total annual burden for the federal governme nt (averaged over the first 3 years following promulgation of this proposed action) is estimat ed 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 di sclose or provide information to or for a federal agency. This
includes the time needed to revi ew instructions; develop, acquire , install, and utilize technology and systems for the purposes of collecting, valid ating, and verifying information, processing and maintaining information, and disclosing and providi ng information; adjust the existing ways to comply with any previously applicable instructio ns and requirements; train personnel to be able to respond to a collection of info rmation; search data sources; co mplete and review the collection of information; and transmit or ot herwise disclose the information. An agency may not conduct or sponsor, a nd a person is not required to respond to a collection of information unless it displays a currently valid OMB control number. The OMB

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7-4 control numbers for the EPA’s re gulations are listed in 40 CFR part 9 and 48 CFR chapter 15. 7.3 Regulatory Flexibility Act The Regulatory Flexibility Act (RFA) gene rally requires an agency to prepare a regulatory flexibility analysis of any rule , subject to notice and comment rulemaking requirements, under the Administrative Procedur e Act or any other statute unless the agency certifies that the rule will not have a signifi cant 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 ru le 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 spec ial district with a popul ation of less than 50,000. A small organization that is any not-for-p rofit enterprise which is independently owned and operated and is not dominant in its field.

Table 7-1. Potentially Regu lated Categories and Entities a



## Category NAICS Code Examples of Potentially Regulated
Entities

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7-5 Industry 221112 Fossil fuel electric power generating
units.

State/Local Government 221112 b 

## Fossil fuel electric power generating units owned by municipalities.

a 

### Include NAICS categories for source categories that own and operate elect ric power generating units (includes boilers and stationary combined cycle combustion turbines).

b 

### State or local government-owned and operated establishmen ts are classified according to the activity in which they are engaged.



## After considering the economi c impacts of this proposed rule on small entities, EPA certifies that this action will not have a signif icant economic impact on a substantial number of
small entities.
The proposed rule will not impose any requirements on small entities. Specifically, emission guidelines established under CAA sect ion 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 guideline s are promulgated, states establish standards on existing sources and it is those state require ments that could potentially impact small entities. Our analysis here is consistent with th e 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 w ould only arise when stat es 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 requir ed 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 b ecause 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 unprece dented amount of stakeholder outreach on

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7-6 setting emission guidelines for existing EGUs. Wh ile formulating the provisions of the proposed rule, the EPA considered the input provided over th e course of the stakeholder outreach. Section III.B. of the preamble describes the key messages fr om 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 sma ll entities while formulating the prov isions of the proposed standards. Although only new EGUs would be affected by thos e 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 poten tial impacts on small entities.
7.4 Unfunded Mandates Reform Act (UMRA)
This proposed action does not contain a fede ral mandate that may result in expenditures of $100 million or more for state, local, and tribal governments, in the aggregate, or the private sector in any one year. Specifically, the emi ssion guidelines proposed under CAA section 111(d) do not impose any direct compliance requireme nts 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 es timated 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 prov isions of the proposed standards for new EGUs. Although only new EGUs would be affected by thos e 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 performa nce 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

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7-7  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 States

On February 26, 2014, the EPA re-engaged with those governmental entities to provide a pre-proposal update on the emission guidelines fo r 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 is sue an action that has federalism implications, that imposes substantial direct complia nce 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 governme nts, or the EPA consults with state and local officials early in the process of devel oping the proposed action. The EPA has concluded that this action may ha ve federalism implications, because it may impose substantial direct compliance costs on st ate 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 an d coordinate programs for compliance with the proposed rule, as well as time to work with state legislatures as appropria te, and develop a plan
submittal.
The EPA consulted with state and local offici als early in the pro cess of developing the

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7-8 proposed action to permit them to have meaningf ul and timely input into its development. As described in the Federalism discussion in the pr eamble to the proposed st andards 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 planne d actions for new, reconstructed, modified and existing sources. The EPA invited the following 10 national organizations representing state and
local elected officials to a m eeting on April 12, 2011, in Washington DC: (1) National Governors Association; (2) National Conference of State Le gislatures, (3) Council of State Governments, (4) National League of Cities, (5) U.S. Conference of Mayors, (6) National Association of Counties, (7) International C ity/County Management Associati on, (8) National Association of Towns and Townships, (9) County Executives of America, and (10) Environmental Council of States. These 10 organizations repr esenting elected state and local o fficials have been identified by the EPA as the “Big 10” organizations appropri ate to contact for purpos e of consultation with elected officials. On February 26, 2014, the EPA re -engaged with those g overnmental entities to provide a pre-proposal update on the emissi on guidelines for existing EGUs and emission
standards for modified and reconstructed EGUs . In addition, extensive stakeholder outreach conducted by the EPA allowed state leaders, in cluding governors, environmental commissioners, energy officers, public utility commissioners, and air directors, opportunities to engage with EPA officials and provide input regarding reducing carbon pollu tion from power plants. A detailed Federalism Summary Impact Stat ement (FSIS) describing the most pressing issues raised in pre-proposal and post-proposal co mments will be forthcoming with the final rule, as required by section 6(b) of Executive Orde r 13132. In the spirit of Executive Order 13132, and consistent with EPA polic y to promote communications be tween 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 c oncern health or safety risks, such that the analysis required under
section 5-501 of the Order ha s the potential to influen ce the regulation. This action is not subject

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7-9 to EO 13045 because it does not involve decisions on environmental health or safety risks that may disproportionately affect child ren. The EPA believes that the CO 2 

## emission reductions resulting from implementation of the proposed gu idelines, as well as substantial ozone and PM 2.5 

## emission reductions as a co-benefit, w ould 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, 2 001) requires the EPA to prepare and submit a Statement of Energy Effects to the Ad ministrator of the Office of Information and Regulatory Affairs, OMB, for ac tions identified as ‘‘significa nt 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, a nd a 16 to 22 percent reduction in coal-fired electricity generation as a result of this rule. The EPA project s that electric power sector delivered natural gas prices will increase by abou t 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 Transf er and Advancement Act (NTTAA)
Section 12(d) of the NTT AA of 1995 (Public Law No. 104-113; 15 U.S.C. 272 note) directs the EPA to use Voluntary Census Sta ndards (VCS) in its regulatory and procurement
activities unless to do so would be inconsistent with appli cable law or otherwise impractical. Voluntary consensus standards are technical standa rds (e.g., materials specifications, test methods, sampling procedures, business practice s) 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 explan ations 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-appli cable VCS and to explain why such standards
should be used in this action.

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7-10 7.8 Executive Order 12898: Federal Actions to Address Environm ental Justice in Minority Populations and Low-Income Populations Executive Order 12898 (59 FR 7629, February 16, 1994) establishes federal executive policy on environmental justice. It s main provision directs federal ag encies, to the greatest extent practicable and permitted by law, to make environmental justice part of their mission by identifying and addressing, as appropriate, disp roportionately high and adverse human health or environmental effects of their programs, polic ies and activities on minority populations and lowincome populations in the U.S. Section II.A of the preamble summarizes th e 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 circumstance s. These include the poor, the elderly, the very young, those already in poor health, the disa bled, those living alone , and/or indigenous populations dependent on one or a few resources. The Administra tor placed weight on the fact that certain groups, including chil dren, 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 ma jor 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 includ e 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 resour ces such as local water and food supplies. In addition, Native American tribal communities po ssess unique vulnerabilities to climate change, particularly those on established reservations th at are restricted to reservation boundaries and therefore have limited relocation options. Tr ibal communities whose health, economic well-

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”).

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7-11 being, and cultural traditions depend upon the natural environment will likely be affected by the degradation of ecosystem goods and services asso ciated with climate ch ange. Southwest native cultures are especially vulnerable to water qua lity and availability impacts. Native Alaskan communities are likely to experience disruptiv e impacts, including shifts in the range or abundance of wild species crucial to their livelihoods and well-being. The most recent assessments continue to strength en scientific understa nding of climate chan ge risks to minority and low-income populations. This proposed rule would limit GHG emissions by establishing CO 2 

## emission guidelines for existing fossil fuel-fired E GUs. In addition to reducing CO 2 

## emissions, implementing the proposed rule would reduce other emissions from EGUs that become dispatched less frequently due to their relatively low energy efficien cy. These emission reductions will include SO 2 

## and NOx, which form ambient PM 2.5 

## and ozone in the atmosphere, and hazardous air pollutants (HAP), such as mercury and hydrochloric aci d. In the final rule revising the annual PM 2.5 

## NAAQS 177 

## , the EPA identified persons with lower soci oeconomic status as an at-risk population for experiencing adverse health effects related to PM e xposures. Persons with lower socioeconomic status have been generally f ound to have a higher prevalence of pre-existing diseases, limited access to medical treatment, a nd 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 PM 2.5 

## , ozone, and methylmercury persons with low
socioeconomic status would also benefit. Th e regulatory impact analysis (RIA) for this rulemaking, included in the docket for this rulemaking, provides additional information regarding the health and ecosystem effects a ssociated with these emission reductions. While there will be many locations with improved air quality for PM 2.5 

## , 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 pr oposed emission guidelines for existi ng fossil fuel-fired EGUs. This

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 Ce nter for Environmental Assessment – RTP Division. December. Available on the Internet at <http://cfpub.e pa.gov/ncea/cfm/recordis play.cfm?deid=216546>.

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7-12 may occur at EGUs that become dispatched more intensively than in the past because they become more energy efficient. The EPA has consid ered 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 resu lt 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 par ticular EGUs and thus to avoid any significant 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 con cerns 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 th ere are no new NAAQS vi olations and that no existing NAAQS violations are made worse. For t hose 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 requ ired by a state to make modifications for increased energy efficiency, anot her effect of the proposed CO 2 

## emission guidelines for existing
fossil fuel-fired EGUs would be increased utiliza tion of other, unmodified EGUs with relatively low GHG emissions per unit of electrical output, in particular high effici ency gas-fired EGUs.
Because such EGUs would not have been modi fied 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 alre ady occur now as demands for and sources of electrical energy evolve, but the proposed CO 2 

## 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 incr ease methylmercury concentrations in their
vicinities. Increased utilizati on generally would not cause high er peak concentrations of PM 2.5, 

## NOx, or ozone around such EGUs than is alrea dy occurring because p eak hourly or daily

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7-13 emissions generally would not change, but increase d utilization may make periods of relatively high concentrations more frequent. It should be noted that the gas-fi red sources that are likely to become dispatched more frequently than at present have very low emissions of primary particulate matter, SO 2 

## and HAP per unit of electrical output , such that local (or regional) air quality for these pollutants is likely to be affect ed very little. For natu ral gas-fired EGUS, the EPA found that regulation of HAP emissions “i s 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 and performance of coal- and NG- fired generation, they assumed SO 2 

## , PM (and Hg) emissions to be “negligible.” Their studies predict NOx emi ssions from a NGCC unit to be approximately 10 times lower than a subcritical or supercritical coal-fired boiler. Ma ny are also very well controlled for emission of NOx through the appli cation of after combustion controls such as selective catalytic redu ction, although not all gas-fired s ources are so equipped. Depending on the specificity of the state CAA section 111(d) pla n, the state may be able to predict which EGUs and communities may be in this type of situat ion and to address any concerns about localized
NO 2 

## concentrations in the design of the CAA s ection 111(d) program, or separately from the CAA section 111(d) program but before its im plementation. In any case, existing tracking systems will allow states and the EPA to be aw are of the EGUs whose utilization has increased most significantly, and thus to be able to priori tize our efforts to assess whether air quality has changed in the communities in the vicinity of su ch 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 re gional and national po llutant reductions; ho wever, there likely
would also be some locations with more times du ring the year of relative ly higher concentrations of pollutants with potential for effects on local ized 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 propos ed 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 adve rse human health or environm ental effects on minority, low income, or indigenous populations from this proposed rule. In order to provide opportunities for meaningf ul involvement early on in the rule making

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7-14 process, the EPA has hosted webinars and c onference calls on August 27, 2013 and September 9, 2013 on the proposed rule specifically for environm ental justice communities and has taken all comments and suggestions into consideration in the design of the emission guidelines.
7.10 Executive Order 13175 - Consultation an d Coordination with Indian Tribal Governments
This action does not have tribal implicati ons, as specified in Executive Order 13175 (65 FR 67249, November 9, 2000). It would not impose s ubstantial direct compliance costs on tribal governments that have affected EGUs located in their area of Indian country. Tribes are not 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 attain s treatment in a manner similar to a state (TAS) status for that purpose and is delegated authority for air quali ty planning purposes, these proposed emission guidelines would require that planning re quirements be met and emission management implementation plans be executed by the tribes. Th e EPA is aware of thre e coal-fired EGUs and one natural gas-fired EGU located in Indian countr y but is not aware of a ny 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, includ ing those located in Indian country, such as
the three coal-fired EGUs and one natural gas-fired EGU, but provi des guidance to any tribe with delegated authority to address CO 2 

## 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 environm ental 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 th e 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 existi ng sources. In addition, on April 15, 2014, prior to proposal, the EPA met with Navajo Energy Deve lopment Group officials. For this proposed action for existing EGUs, a tribe that has one or more affected EGUs located in its area of Indian

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7-15 Country 179 

## would have the opportunity, but not the obligation, to establish a CO 2 

## performance standard and a CAA section 111(d) plan for its area of Indian country.
Consultation letters were se nt to 584 tribal leaders. Th e letters provided information regarding the EPA's development of both the NSPS and emission gui delines for fossil fuel-fired
EGUs and offered consultation. No tribes have requested consultation. Tr ibes were invited to participate in the national informationa l webinar held August 27, 2013. In addition, consultation/outreach meeting was held on Septem ber 9, 2013, with tribal representatives from
some of the 584 tribes. The EPA al so met with tribal environmenta l 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 a bout the impacts of the regulati ons on EGUs and the subsequent impact on jobs and revenue for their tribes. Othe r tribes expressed concer n about the impact the regulations would have on the cost of water to th eir communities as a result of increased costs to the EGU that provide energy to transport the wate r 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 guide lines being considered by the agency (e.g., over what time period, relations hip 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 pa rticipated 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 info rm 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.

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 affect ed EGU is an NGCC facility.

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7-16

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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 Re gulatory Impact Analysis (RIA). As discussed in Chapter 1, the EPA is proposing two opti ons for state-specific rate-based CO 2 

## goals that reflect application of measures from four building bl ocks. For both Options 1 an d 2, two illustrative compliance scenarios, reflecting possible comp liance approaches with state-specific CO 2 

## goals, are analyzed in this RIA. These scenarios are a reflection of what comp liance could look like for Options 1 and 2, assuming st ates comply with the CO 2 

## 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 appr oaches analyzed in this RIA.
The EPA has used the social cost of car bon estimates presented in the 2013 Technical Support Document: Technical Upda te of the Social Cost of Carbon for Regulatory Impact Analysis Under Executive Order 12866 (2013 SCC TSD) to analyze CO 2 

## climate impacts of this rulemaking. 180 

## We refer to these estimates, which we re 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 CO 2 

## emissions in a given year). The four SCC estimates are associated with different discount rates (model av erage at 2.5 percent discount rate , 3 percent, and 5 percent; 95 th 

## percentile at 3 percent), and each increases over ti me. 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 Coun cil, 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/defau lt/files/omb/assets/inforeg/technical- update-social-cost-of-carbon-forregulator-impact-analysis.pdf.

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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, regiona l approach monetized climate benefits (in 2011$) are $17 billion in 2020, assuming a 3 per cent 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 recordkeep ing (MRR) costs, are approximately $5.5 billion (2011$) in 2020. The quan tified 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 $45 bill ion assuming a 7 percent discount rate (2011$). In comparison, net benefits for the Option 1 state compliance approa ch in 2020 are estimated to be $27 billion to $50 billion, using a 3 percent disc ount rate and $26 b illion to $46 billion assuming a 7 percent discount rate. Option 2 regional and state compliance approach be nefits, 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 complia nce approach to the proposal Option 1 in 2030, the EPA estimates this approach will yield monetized climate benefits of $30 billion (2011$) (u sing 3 percent discount rate (m odel average) as shown on Table
8-3.
In addition to CO 2 

## , implementing these proposed guidelines is expected to reduce
emissions of SO 2 

## and NO X 

## , which are precursors to formation of ambient PM 2.5 

## , as well as directly emitted fine particles. Therefore, re ducing these emissions w ould also reduce human exposure to ambient PM 2.5 

## and ozone precursors, thus the incidence of PM 2.5 

## - and ozone related health effects. These air polluti on health co-benefits in 2030 are es timated to be $254 billion to $59 billion for a 3 percent discount rate and $23 b illion to $54 billion (2 011$) for a 7 percent
discount rate. The annual illust rative compliance costs estimated using IPM, inclusive of demand side energy efficiency program and participan t costs and MRR costs, are approximately $7.3 billion (2011$) in 2030. The quantified net bene fits for the Option 1 regional in 2030 are $48 billion to $82 billion, assuming a 3 percent di scount 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

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8-3 (2011$) for the Option 1 state compliance approach.. Based upon the foregoing discussion, it remain s clear that this proposal’s combined
climate benefits and human heal th co-benefits associated with the reduction in other air pollutants substantial and far outweigh the complia nce costs for all of the regulatory options and compliance approaches. The EPA could not mo netize important categories of impacts. Unquantified impacts include those associated with changes in emissions of other pollutants that affect the climate, such as methane. In additi on, the analysis does not qua ntify co-benefits from reducing exposure to SO 2 

## , NOx, and hazardous air pollutants (e.g., mercury and hydrogen chloride), as well as ecosystem effects and visibility impairment.

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8-4 Table 8-1. Summary of Estimated Monetized Be nefits, 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 Eeffects Visibility impairment Direct exposure to SO2 and NO2 1.2 tons of Hg Ecosystem Eeffects 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 summ ary table reflect global impacts from CO 2 

### emission changes and do not account for changes in non-CO 2 

### GHG emissions. Different discount rates are applied to SCC than to the other estimates because CO 2 

### 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 PM 2.5 

### and ozone associated with emission reductions of directly emitted PM 2.5 

### , SO 2 

### and NO X 

### . The range reflects the use of concentration-response functions from different epidemiology studies. The reduction in prematur e fatalities each year accounts for over 90 percent of total monetized co-benefits from PM 2.5 

### and ozone. These models assume that all fine particles, regardless of their chemical compositi on, are equally potent in causing premat ure mortality because the scientific evidence is not yet sufficient to allow differen tiation 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 gl obal 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.

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8-5 Table 8-2. Summary of Estimated Monetized Be nefits, 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 summ ary table reflect global impacts from CO 2 

### emission changes and do not account for changes in non-CO 2 

### GHG emissions. Different discount rates are applied to SCC than to the other estimates because CO 2 

### 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 PM 2.5 

### and ozone associated with emission reductions of directly emitted PM 2.5 

### , SO 2 

### and NO X 

### . The range reflects the use of concentration-response functions from different epidemiology studies. The reduction in prematur e fatalities each year accounts for over 90 percent of total monetized co-benefits from PM 2.5 

### and ozone. These models assume that all fine particles, regardless of their chemical composition, are equally potent in causing premature mortal ity because the scientific evidence is not yet sufficient to allow differen tiation 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 gl obal 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.

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8-6 Table 8-3. Summary of Estimated Monetized Be nefits, 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 summ ary table reflect global impacts from CO 2 

### emission changes and do not account for changes in non-CO 2 

### GHG emissions. Also, different discount rates are applied to SCC than to the other estimates because CO 2 

### 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 PM 2.5 

### and ozone associated with emission reductions of directly emitted PM 2.5 

### , SO 2 

### and NO X 

### . The range reflects the use of concentration-response functions from different epidemiology studies. The reduction in prematur e fatalities each year accounts for over 90 percent of total monetized co-benefits from PM 2.5 

### and ozone. These models assume that all fine particles, regardless of their chemical compositi on, are equally potent in causing premat ure mortality because the scientific evidence is not yet sufficient to allow differen tiation 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.

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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 analys es 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 uncerta inty, both quantitativel y and qualitatively, on benefits and costs. We summarize three key el ements of our analysis of uncertainty here:

 Evaluating uncertainty in the compliance a pproaches that states will implement under Option 1 or Option 2, , which influe nces both costs and benefits.

 Assess uncertainty in the methods used to calc ulate the health co-ben efits associated with
the reduction in PM 2.5 

## and ozone and the use of a benef its-per-ton approach in estimating
these co-benefits.

 Characterizing uncertainty in the monetization of climate related benefits.

Some of these elements are evaluated using pr obabilistic 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 be nefits and costs of this rulemaking.
Uncertainty in Abatement Co sts and Compliance Approaches The calculation of the state goals is based on an eval uation of methods for reducing th e 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 w ithin the cost analysis of this rule and are included in the cost modeling in the RIA (e.g., the cost of demand-side energy efficiency programs and non-emitting generation). We have al so conducted cost analyses for alternative quantifications of the degree to which the building blocks of th e BSER determination can reduce the carbon emissions intensity of electricity ge neration, and scenarios modeled and presented in

181 

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

http://www.whitehouse.gov/omb/circu lars_a004_a-4 and OMB, 2011. Regulatory Impact Analysis: A Primer. http://www.whitehouse.gov /sites/default/files/omb/ inforeg /regpol/circular-a-4_regul atory-impact-analysis-aprimer.pdf

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8-8 this RIA illustrate the potential range of impact s of the varying specifica tions 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 reduc ing emissions intensity and will inform the basis of analyses in the final rule. However, we recogni ze 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 complia nce 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 CO 2 

## and other pollutants from fossil fuel combustion also fell (but by lesser proportions). However, from this anal ysis we see that the net benefits of the two scenarios, given consistent assumptions, do not differ notably.
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
PM 2.5 

## -related premature mortality, which accoun ts for 98 percent of the monetized PM 2.5 

## health
co-benefits.

4. We assume that all fine particles, re gardless of their chemical composition, are equally potent in causing premature mortality. This is an important assumption, because PM 2.5 

## varies considerably in composition across sources, but the scientific evidence is not yet sufficient to allow diffe rentiation of effect estimates by particle type. The PM ISA concluded that “many constituents of PM 2.5 

## 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).

5. 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

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8-9 reducing fine particles in areas wi th varied concentrations of PM 2.5 

## , including both areas that do not meet the fine particle sta ndard and those areas th at are in attainment, down to the lowest modeled concentrations.

We assume that there is a “cessation” lag between the cha nge in PM exposures and the total realization of changes in mortality effect s. Specifically, we assume that some of the incidences of premature mortality related to PM 2.5 

## exposures occur in a distributed fashion over the 20 years following exposure based on the ad vice of the SAB-HES (U.S. EPA-SAB, 2004c), which affects the valuation of mortality co-benefits at different discount rates.EPA quantitatively assessed uncertainty in the air pollution health co-benefits, including probabilistic approaches. For example, EPA provides the 95 th 

## percentile confidence interv al for avoided PM-related premature deaths and the associated economic valuation using two key epidemiology studies. Further, EPA provides the PM-related results usi ng alternate concentration-response relationship provided by an expert elicitation and alternate ozone-related results using concentration-response relationships provided by alternate epidemio logy studies. Additional quantitative analyses include sensitivity analyses for alternate income growth adjustments and cessation lags, assessments of the distribution of population e xposure in the modeling underlying the benefitper-on estimates, and a quantitative evaluation of the benefit-per-ton estimates relative to recent analyses. A qualitative descripti on of uncertainties associated w ith certain assumptions for PM, such as the linear model and equal potency acro ss 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 approa ch to quantify health co-benefits. All benefit-per-ton ha ve 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 h ealth incidence rates, or other local factors for any specific location. In a ddition, these estimates re flect the regional av erage benefit-per-ton
for each ambient PM 2.5 

## precursor emitted from EGUs, which assumes a linear atmospheric response to emission reductions. The regional be nefit-per-ton estimates, although less subject to these types of uncertainti es than national estimates , still should be interp reted with caution. Even

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8-10 though we assume that all fine particles have eq uivalent health effect s, the benefit-per-ton estimates vary between precursors depending on the location and magnitude of their impact on
PM 2.5 

## levels, which drive population exposure.
Uncertainty Associated with Estima ting the Social Cost of Carbon

The 2010 SCC TSD noted a number of limitatio ns to the SCC analysis, including the incomplete way in which the integrated a ssessment models capture catastrophic and noncatastrophic impacts, their in complete treatment of adapta tion 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 clim ate 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 thes e models understandably lags behind th e 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 t ogether they suggest that the SCC estimates are likely conservative. In part icular, the IPCC Fourth Assessme nt Report (2007) concluded that “It is very likely that [SCC estimates] undere stimate the damage costs because they cannot include many non-quantifiable impacts.”
EPA characterized significant s ources of uncertainty in the es timate of climate benefits of the emission reductions forecast by the compliance cost modeling. 182 

## The modeling underlying the development of the SCC estimates addressed un certainty in several ways. Three integrated assessment models (IAMs) were used to genera te the SCC estimates. Each IAM relied on Monte Carlo simulations specifying different possible outcomes for climate sensitivity (represented by a

182 

### As discussed in Chapter 4, EPA estimat ed the global social benefits of CO 2 

### 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 Regul atory 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 Im pact Analysis Under Executive Order 12866, Interagency 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.”

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8-11 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 S CC TSD for a full description.

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9-1 

## CHAPTER 9: B ENEFITS 

## , C OSTS 

## , AND 

## E CONOMIC 

## I MPACTS OF 

## S TANDARDS OF


## P ERFORMANCE FOR 

## R ECONSTRUCTED AND 

## M ODIFIED 

## E LECTRIC 

## U TILITY 

## G ENERATING 

## U NITS



## 9.1 Introduction The U.S. Environmental Protection Agency (EPA) is proposing emission limits for carbon dioxide (CO 2 

## ) emitted from reconstructed and modifi ed electric utility generating units (EGUs) under section 111(b) of the Clean Air Ac t (CAA). This chapter presents the proposed standards of performance for reconstructed a nd modified EGUs, as well as the expected economic impacts of the proposed standards. Ba sed on historical information that has been reported to the EPA, the EPA anticipates few co vered units will trigger the reconstruction or modification provisions in the period of anal ysis (through 2025). As a result, we do not anticipate any significant costs or benefits associ ated 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 inte ractions with the CAA 111(d) emission guidelines for existing sources. This chapter also presen ts 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 st andards for air pollutant emissions from categories of stationary sources that may reas onably 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 Enda ngerment and Cause or Contribute Findings for Greenhouse Gases Under Section 202(a) of the CAA (74 FR 66,496; Dec. 15, 2009).

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9-2 On September 20, 2013, the EPA announced its first steps under President Obama’s
Climate Action Plan to reduce CO 2 

## 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 sec tion 111(b), the EPA proposed st andards for emissions of CO 2 

## from newly constructed utility boilers and integrated gasificat ion combined cycle (IGCC) units, and for natural gas-fired stati onary combustion turbines. This action proposes standards to
address CO 2 

## emissions from reconstructed and modifi ed power plants unde r 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 r econstruction or modification. This rulemaking does not address GHG emissions from newly constructed sources or existing sources that do not undertake reconstr uction or modification. (Analysis of the impact of emission guidelines for existing sources is pr esented in the preceding chapters.) The EPA is proposing that an existing unit that becomes subject to require ments under section 111(d) will continue to be subject to those requirements even after it beco mes a modified or reconstructed source. Under this interpretation, the modified or reconstructed s ource would be subject to both the section 111(d) requirements that it had pr eviously been subject to and the modified or
reconstructed source standard under 111(b) that it became subj ect 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 complia nce with standards of performance for newly constructed sources is technologi cally and economically feasible. As described in the preamble, determinations regarding reconstructed stat us are made on a case-by-case basis, based on information submitted regarding the planned project.

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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 contro l projects) are exempt from consideration as a mo dification (40 CFR 60.2, 60.14(e)).
9.3.2 Emission Limits
This action proposes standards of performan ce for reconstructed utility boilers and IGCC units based on current best-performing gene rating 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 natura l gas combined cycle (NGCC) technology as the BSER. The proposed emissi on limits are calculated on a 12-month rolling average basis. The proposed emission limits for th ese units are shown in table 9-1. All existing sources that reconstruct after becoming subjec t to an approved CAA 111(d) state plan will remain in the plan and remain subject to regulat ory 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 CO 2 

### /MWhnet

Reconstructed Utility Boilers and IGCC Units (heat input rating <= 2,000 MMBtu/h) 2,100 lb CO 2 

### /MWhnet
Reconstructed Natural Gas-Fired Stationary Combustion Turbines (heat input rating > 850 MMBtu/h) 1,000 lb CO 2 

### /MWhgross Reconstructed Natural Gas-Fired Stationary Combustion Turbines (heat input rating <= 850 MMBtu/h) 1,100 lb CO 2 

### /MWhgross



## This action proposes that modified utility bo ilers and IGCC units must meet one of two

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9-4 alternative requirements, depending on the timing of the modification. 183 

## Sources that modify prior to becoming subject to an approved secti on 111(d) state plan would be required to meet a unit-specific numerical em ission standard that is based on the source’s best demonstrated historical performance. Specifically, a modified utility boiler or IGCC un it would be required to maintain an emission rate that is two percent le ss 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 corres ponding reconstructed source, whic hever 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, existin g 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 efficien cy improvement opportunities for the affected

source. 184 

## All existing sources that ar e subject to requirements unde r an approved section 111(d) plan would remain subject to t hose requirements after modifying. For affected modified natural gas-fired stat ionary 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 affe cted 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.

Table 9-2. Proposed Emissi on 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

183 

### Units triggering both the modification and reconstruction provisions would be subject to the reconstructed source

standards.

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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9-5 1,900 lb CO 2 

### /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 CO 2 

### /MWh-net
Modified Natural Gas-Fired Stationary Combustion Turbines (heat input rating > 850 MMBtu/h) 1,000 lb CO 2 

### /MWh-gross

Modified Natural Gas-Fired Stationary Combustion Turbines (heat input rating <= 850 MMBtu/h ) 1,100 lb CO 2 

### /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 CO 2 

### /MWh-gross

AND
111(d) requirements

Modified Natural Gas-Fired Stationary Combustion Turbines (heat input rating <= 850 MMBtu/h ) 1,100 lb CO 2 

### /MWh-gross

AND
111(d) requirements
Note: The proposed emission limits are calcula ted on a 12-month rolling average basis.



## 9.4 Impacts of the Proposed Reconstr ucted and Modified Source Standards

9.4.1 Reconstructed Sources
Historically, we are only aware of one E GU that has notified the EPA that it has
reconstructed under the reconstructi on provision of section 111(b). As a result, we anticipate that

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9-6 few EGUs will undertake reconstruction throug h 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 anticipa te any impacts on the price of electricity or energy supply. The proposed rule is not expected to raise any re source adequacy concerns, since reserve margins will not be impacted and the ru le does not impose any additional requirements on existing facilities not trigge ring the reconstruction provision. There are no macroeconomic or employment impacts expected as a result of these pr oposed standards. Due to the extremely limited data availabl e on reconstructions, it is not possible to conduct a representative illustrative analysis of wh at 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 ch ange that increases the source’s maximum achievable hourly rate of emissions, but specifica lly exempt from that definition projects that entail the installation of pollution control equipm ent or systems. The EPA expects that most of the actions EGUs are likely to take in the fo reseeable future that would be classified as “modifications” would qualify as pollution control projects. In many cases, those projects would
involve the installation of a dd-on control equipment needed to meet CAA requirements for
criteria and air toxics air pollutants. Any associated CO 2 

## 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 equipmen t changes to improve fuel efficiency to meet state requirements for implementation of the fu ture CAA section 111(d) rulemaking for existing
sources and would have the effect of increa sing a source’s maximum achievable hourly emission rate (lb CO 2 

## /hr), even while decreasing its actu al output based emission rate (lb CO 2 

## /MWh).
Because all of these actions would be treate d 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

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9-7 will result in minimal emission reductions, costs, or quantified benefits by 2025. Likewise, the Agency does not anticipate any impacts on the pr ice 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 reconstructi on provision. There are no macroeconomic or employment impacts expected as a result of these pr oposed standards. 9.4.2.1 Illustrative Analysis – Emission Reductions

In the unlikely event that a unit were to tr igger the modification provision, the proposed
standards would result in some costs and bene fits. Based on available data, we conducted an analysis of a hypothetical unit to illustrate the potential cost s and benefits of the proposed standards. The hypothetical plant presented in this section is a coal-fired boiler 185 

## and is assumed to be in compliance with all on-the-books federa l regulations. In the illustrative analysis, costs and benefits are presented for co mpliance in 2025. Section 111(b) st andards are subject to review every eight years. While we do not know when the hypothetical plant wo uld choose to undertake a modification, it is necessary to se lect a year of analysis in order to value the climate benefits and health co-benefits resulting from these pr oposed standards. We ha ve selected 2025 to approximate the end of the eight year peri od. 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 capacit y. The assumptions for capacity and efficiency were based on the average projected coal fleet in IPM in 2020. The emissi on rate specifications are (1) SO 2 

## = 0.30 lb/MMBtu (3.3% weight Illinois bituminous coal with 95% efficient scrubber), (2) NO X 

## = 0.30 lb/MMBtu (low NO X 

## burners plus overfire air and SNCR), and (3)

185 

### IGCC and NGCC units are significantly less likely to trigge r the modification provision due to the way in which

these units are constructed. Simple-cycle combustion turb ines (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 fo r 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 pract ice, 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 ha s received comments regarding more recent integration of CT units and renewables and is taking comment on the treatment of these units.

## ***E.O. 12866 Review – Draft – Do Not C ite, Quote or Release During Review***

9-8 PM = 0.024 lb/MMBtu (8 lb/MMBtu ash with 99.7% control). In th is analysis, we examine the
costs and benefits that would occu r if this unit were able to make a 4 percent, 6 percent, or 8 percent heat rate improvement as a result of meas ures implemented in order to comply with the proposed standards for modified sources.
Costs. We assume the hypothetical unit will impl ement 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 effi ciency 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 th e 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 th e 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) existi ng 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 de pending on the current a nd prior performance of
that unit. For a unit that modifi es after being covered by a sec tion 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 1 11(d) program requirements. In those cases,
there would be no additional costs to th e 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 Docume nt – GHG Abatement Measures” for information about the cost of efficiency improvements.

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9-9 Table 9-3. Annualized Costs and Fuel Saving s 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 CO 2 

## and may reduce emissions of (SO 2 

## ), nitrogen dioxide (NO x 

## ), and fine particulate matter (PM 2.5 

## ), which would lead to lower ambient concentrations of PM 2.5 

## and ozone. Table 9-4 shows the expected emission reductions from each level of efficiency improvement for the hypothetical un it. For a unit participating in a 111(d) plan, it is possible that
the CO 2 

## emission reductions could be offset by in creases 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 reduc e 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 SO 2 

## 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 ma y run more often, offsetting some of the emission decreases. Furthermore, for units that choose to co mply 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 11 1(d) program may not identify any further fuel efficiency improvements that unit can economi cally undertake after implementing the overall 111(d) program requirements. In those cases, there would be no additional emission reductions
or control costs as a re sult of this proposal.

Table 9-4. Emission Reductions from Efficiency Improvements (tons/year) 4% Efficiency Improvement 6% Efficiency Improvement 8% Efficiency Improvement
CO 2 

### 132,790 199,184 265,579
SO 2 

### 214 321 428

## ***E.O. 12866 Review – Draft – Do Not C ite, Quote or Release During Review***

9-10 NO x 

### (Total) 214 321 428
NO x 

### (Ozone Season) 89 134 178
PM 2.5 

### 17 26 34
Notes: SO 2 

### , NO X 

### , and PM 2.5 

### in short tons, CO 2 

### in metric tonnes.



## Benefits. Reducing CO 2 

## 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 CO 2 

## emissions in a given year. The EPA has applied the USG SCC estimates to the CO 2 

## reductions described above for the hypothetical unit.
The four USG SCC estimates are as follo ws: $15, $50, $74, and $153 per metric ton of CO 2 

## emissions in the year 2025 (2011$). To estimate human health co-benefit s for this hypothetical scenario, the EPA used benefit-per-ton estimates for PM 2.5 

## and ozone precursors described in detail in Chapter 4 of this RIA. Table 9-5 show s the quantified per ton co -benefits for reductions
in SO 2 

## , directly emitted PM 2.5 

## , and NO X 

## emissions in 2025.

Table 9-5. Summary of National Bene fit-per-ton Estimates for EGUs in 2025 Discount Rate
Pollutant 3% 7%
SO 2 

### $41,000 to $93,000 $37,000 to $84,000 Directly emitted PM 2.5 

### (EC+OC) $150,000 to $350,000 $140,000 to $310,000 Directly emitted PM 2.5 

### (crustal) $17,000 to $39,000 $15,000 to $35,000
NO X 

### (as PM 2.5 

### ) $6,000 to $14,000 $5,400 to $12,000
NO X 

### (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 CO 2 

## emissions by the estimates of the SCC, multiply the reduction in SO 2 

## , NO X 

## , and PM 2.5 

## emissions by the PM 2.5 

## -related BPT estimates, multiply the reductions in ozone season NO X 

## emissions by

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9-11 the ozone-related BPT estimate, and add those valu es 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 th at would occur. These benef its are subject to the same
uncertainties discussed for emission re ductions in the previous section.

Table 9-6. Estimated Global Climat e Benefits of Illustrative CO 2 

## Reductions in 2025 (millions of 2011$)*

Discount Rate and Statistic 4% Efficiency Improvement 6% Efficiency Improvement 8% Efficiency Improvement

Metric tonnes of CO 2 

### 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% (95 th 

### percentile) $20 $30 $41
* The SCC values are dollar-year and emissions-year speci fic. SCC values represent only a partial accounting of

climate impacts.

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9-12 Table 9-7. Summary of Estimated Monetized Heal th Co-benefits of Illustrative Efficiency Improvements in 2025 (thousands of 2011$)* Pollutant 3% Discount Rate 7% Discount Rate
4% Efficiency Improvement

SO 2 

### $8,900 to $20,000 $8,000 to $18,000

Directly emitted PM 2.5 

### (EC+OC) 

### $260 to $590 $240 to $530

Directly emitted PM 2.5 

### (crustal) 

### $230 to $510 $200 to $460

NOx (as PM 2.5 

### ) 

### $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

SO 2 

### $13,000 to $30,000 $12,000 to $27,000

Directly emitted PM 2.5 

### (EC+OC) 

### $400 to $900 $360 to $810

Directly emitted PM 2.5 

### (crustal) 

### $350 to $780 $310 to $710

NOx (as PM 2.5 

### ) 

### $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

SO 2 

### $18,000 to $40,000 $16,000 to $36,000

Directly emitted PM 2.5 

### (EC+OC) 

### $520 to $1,200 $470 to $1,100

Directly emitted PM 2.5 

### (crustal) 

### $450 to $1,000 $410 to $930

NOx (as PM 2.5 

### ) 

### $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 si gnificant 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 mone tized co-benefits incorpor ate the conversion from precursor emissions to

## ***E.O. 12866 Review – Draft – Do Not C ite, Quote or Release During Review***

9-13 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 seas on NOx emissions. Ozone co-benefits occur in analysis year, so they are the same for all discount rates. The h ealth co-benefits reflect the su m of the PM2.5 and ozone cobenefits and reflect the range based on adult mortality func tions (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 una vailable 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 di rectly emitted particles are di saggregated into EC+OC or crustal components using the method discussed in Appendix 4A of this RIA. 

## Table 9-8. Combined Estimated Climate Benefi ts and Health Co-Benef its 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 CO 2


### 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% (95 th 

### percentile) $20 

### $31 to $46 $30 to $44
6% Efficiency Improvement 199,184 metric tonnes CO 2



### 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% (95 th 

### percentile) $30 

### $47 to $69 $45 to $66
8% Efficiency Improvement 265,579 metric tonnes CO 2



### 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% (95 th 

### 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-ben efits do not include climate benefits or health effects from direct exposure to NO2, SO2, and HAP; ecosystem effect s; or visibility impairment. All fine particles are assumed to have equivalent health effects. The mone tized co-benefits incorpor ate the conversion from precursor emissions to

## ***E.O. 12866 Review – Draft – Do Not C ite, Quote or Release During Review***

9-14 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 seas on NOx emissions. Ozone co-benefits occur in analysis year, so they are the same for all discount rates. The h ealth co-benefits reflect the su m of the PM2.5 and ozone cobenefits and reflect the range based on adult mortality func tions (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 una vailable 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 di rectly emitted particles are di saggregated 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 illu strative analysis of a hypothetical unit implementing efficiency measur es in 2025 to comply with the proposed requirements, this proposal would re sult in a net benefit (benefits out weigh costs) for all levels of efficiency improvement and combinations of be nefits. The net benefit would be even greater when potential fuel savings are taken into acco unt. The actual costs, emission reductions, and benefits resulting from this proposal will vary ba sed 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-CO 2 

## pollutants.
9.5 Statutory and Executi ve Order Requirements
9.5.1 Executive Order 12866, Regulatory Planni ng 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 OM B recommendations have been documented in
the docket for this action. In addi tion, the EPA prepared an analysis of the potential costs and benefits associated with this action. This analysis is presented in the prec eding sections of this chapter. In the period of analysis (through 2025) th e EPA anticipates few sources will trigger either the modification or the reconstruction prov isions proposed. Because there have been a few
units that have notified the EP A of NSPS modifications in th e past, we have conducted an

## ***E.O. 12866 Review – Draft – Do Not C ite, Quote or Release During Review***

9-15 illustrative analysis of the costs and benefits fo r 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 Paperwor k Reduction Act, 44 U.S.C. 3501 et seq. Burden is defined at 5 CFR 1320.3(b). As previously stated, the EPA exp ects few modified or reconstructed EGUs in the period of analysis. Specifica lly, the EPA believes it unlikely that fossil fuel-fired electric utility steam generating units (utility boilers a nd IGCC units) or stationary combustion turbines
will take actions that would constitute modifi cations or reconstructions as defined under the EPA’s NSPS regulations. Accordingly, this propos ed 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 propo sed 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 b een assigned the EPA ICR number 2465.03. The EPA intends to codify the standards of performance in the same way for both this proposed action and the January 2014 propos al for newly constructed sources and is proposing the same recordkeeping and reporting re quirements 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 w ould impose minimal inform ation collection burden on affected sources, beyond what those sources w ould 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 Paperwor k 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

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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9-16 in this action and certain reporti ng costs, which are mandatory for all owners/operators subject to CAA section 111 national emission standards, th ere would be no new information collection costs, as the information required by this proposed rule is already collect ed and reported by other regulatory programs. The recordke eping and reporting requirement s 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 whic h 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 pr ovisions that we are proposing, if an EGU were to modify or reconstruct during the th ree-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
CO 2 

## 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 additi on, after modifications are made that enable a facility to measure net energy output, each E GU’s Data Acquisition System (DAS) would need 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 thre e months. The reporting burden for such a unit (averaged over the first three years after the effe ctive date of the standa rds) 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 respon ses 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, a nd 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, whic h 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

## ***E.O. 12866 Review – Draft – Do Not C ite, Quote or Release During Review***

9-17 addresses section in the preamble for where to submit comments to the EPA. Send comments to OMB at the Office of Information and Regulator y Affairs, Office of Management and Budget,
725 17 th 

## Street, NW, Washington, DC 20503, Attention: Desk Officer for the EPA. Since OMB is required to make a decisi on concerning the ICR between 30 a nd 60 days after publication in the Federal Register, a comment to OMB is best a ssured of having its full effect if OMB receives it by 30 days after publication in the Federal Regist er. The final rule will respond to any OMB or public comments on the information collection requi rements contained in this proposal. 9.5.3 Regulatory Flexibility Act The Regulatory Flexibility Act (RFA) gene rally 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 signifi cant 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 ru le on small entities, small entity is defined

as: 1. A small business that is defined by the SB A’s regulations at 13 CFR 121.201 (for the electric power generation industry, the sm all business size standard is an ultimate parent entity with le ss 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-p rofit enterprise which is independently owned and operated and is not dominant in its field. After considering the economic im pacts 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 boile rs, IGCC units, or st ationary combustion turbines in the period of analys is. An NSPS modification is define d as a physical or operational change that increases the source’s maximum ach ievable 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.

## ***E.O. 12866 Review – Draft – Do Not C ite, Quote or Release During Review***

9-18 Because there have been a limited number of units that have notified the EPA of NSPS modifications in the past, the RIA for this propose d rule includes an illustrative analysis of the costs and benefits for a representative unit.

Based on the analysis, the EPA estimates th at this proposed rule could result in CO 2 

## emission changes, quantified benefits, or co sts for a hypothetical uni t 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 The EPA e xpects few reconstructed fossil fuel-fired electric utility steam generating units (utility boilers and IGCC units) or stationary combustion turbines in the period of an alysis. 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 constr uct a comparable entirely new f acility. Due to the limited data available on reconstructions, it is not possible to conduct a represen tative 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 availabl e generating technology, we would expect this proposal to result in no significant CO 2 

## 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 unprec edented amount of stakeholder outreach. As part of that outreach, agency offi cials participated in many meetin gs with individu al utilities as well as meetings with electric utility associa tions. Specifically, the EP A Administrator, Gina McCarthy, participated in separate meetings w ith both the National Rural Electric Cooperative Association (NRECA) and the Am erican Public Power Associ ation (APPA). The meetings brought together leaders of the rural cooperatives and public pow er utilities from across the country. The Administrator discussed and exchan ged information on the unique challenges, in particular the financial structur e of NRECA and APPA member utili ties. A detailed discussion of the stakeholder outreach is included in the pr eamble to the emission guidelines for existing

## ***E.O. 12866 Review – Draft – Do Not C ite, Quote or Release During Review***

9-19 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 and1500, January 8, 2014), the EPA conducted outreach to representa tives of small entities while formulating the provisions of the proposed standards. Although onl y newly constructed EGUs would be affected by those proposed standards, the outreach rega rded planned actions for newly constructed, reconstructed, modified and existing sources. While formulating the provisions of the pr oposed 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 g overnments, in the aggregate, or the private sector in any one year. As prev iously stated, the EPA expects few modified or reconstructed fossil fuel-fired electric utility steam generating units (utili ty boilers and IGCC units) or stationary combustion turbines in the period of analysis. Accordi ngly, 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 pr ovisions of the proposed standards for newly constructed EGUs. This outreach regarded planne d actions for newly cons tructed, reconstructed, modified and existing sources. As described in the UMRA discussion in the preamble to the proposed standards of performance for GHG em issions from new EGUs (79 FR 1500 and 1501, January 8, 2014), the EPA consulted with the fo llowing 10 national organizations representing
state and local elected officials:

 National Governors Association

 National Conference of State Legislatures

 Council of State Governments

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9-20  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 States

On February 26, 2014, the EPA re-engaged with those governmental entities to provide a pre-proposal update on the emission guidelines fo r existing EGUs and emission standards for
modified and reconstructed EGUs. While formulating the provisions of these pr oposed standards, the EPA also considered the input provided over the course of extensiv e stakeholder outreach conducted by the EPA (see section II.G. of the preamb le 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 vari ous levels of government, as specified in EO 13132. This proposed action woul d not impose substantial direct compliance costs on state or local governments, nor woul d 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 an d local officials in the process of developing the proposed standards for newly constructed EGUs . This outreach regarded planned actions for newly constructed, reconstructed, modified a nd existing sources. The EPA engaged 10 national organizations representing state and local el ected officials. The UMRA discussion in the preamble to the proposed standards of performa nce for GHG emissions from newly constructed EGUs (79 FR 1500 and 1501, January 8, 2014) include s a description of the consultation. In

## ***E.O. 12866 Review – Draft – Do Not C ite, Quote or Release During Review***

9-21 addition, on February 26, 2014, the EPA re-engaged with those governmental entities to provide a pre-proposal update on the emission guideline s for existing EGUs and emission standards for
modified and reconstructed EGUs. While fo rmulating 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 secti on 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 specifi cally solicits comment on this proposed action
from state and local officials. 9.5.6 Executive Order 13175, Consultation and Coordination with Indian Tribal Governments
This action does not have tribal implicat ions, as specified in Executive Order 13175 (65 FR 67249, November 9, 2000). It would neither im pose substantial direct compliance costs on tribal governments, nor preempt Tribal law. This proposed rule would impose requirements on owners and operators of reconstr ucted and modified EGUs. The EPA is aware of three coal-fired EGUs located in Indian Country but is not aw are 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 ta ke actions constituting
modifications or reconstructi ons as defined under the EPA’s NSPS regulations. However, as previously stated, the EPA exp ects few modified or reconstr ucted EGUs in the period of analysis. Thus, Executive Order 13175 does not apply to this action. Although Executive Order 13175 does not appl y 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 standard s for newly constructed power plants, the EPA
offered consultation with tribal officials early in the proce ss 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 ne wly constructed, recons tructed, modified, and existing sources. The Consultation and Coordinati on 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) in cludes a description of that consultation.

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9-22 During development of this proposed regula tion, consultation letters were sent to 584 tribal leaders. The letters provided informati on regarding the EPA’s development of both the
NSPS for modified and reconstr ucted EGUs and emission guidelines for existing EGUs and offered consultation. None have requested consulta tion. 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 wa s held on September 9, 2013, with tribal representatives from some of th e 584 tribes. The EPA also met w ith tribal environmental staff with the National Tribal Air Association, by te leconference, on July 25, 2013, and December 19, 2013. In those teleconferences, the EPA provide d background information on the GHG emission guidelines to be developed and a summary of i ssues being explored by the Agency. Additional detail regarding this stakeholder outreach is in cluded in the executive order discussion for the emission guidelines for existing affected electric util ity generating units in Chapter 7 of this RIA. The EPA also held a series of listening sessi ons prior to proposal of GHG standards for newly constructed power plants. Trib es 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 w ith tribal environmental staff during the public comment period, to inform them of the content of this proposal, and offer further
consultation with tribal elected o fficials 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 c oncern health or safety risks, such that the analysis required under
section 5-501 of the Order ha s the potential to influen ce 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

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9-23 modified EGUs in the period of analysis a nd impacts on emissions, costs or energy supply
decisions for the affected electric uti lity industry to be minimal as a result. 9.5.9 National Technology Transfer and Advancement Act
Section 12(d) of the NTT AA of 1995 (Public Law No. 104-113; 15 U.S.C. 272 note) directs the EPA to use voluntary consensus standa rds (VCS) in their regulatory and procurement
activities unless to do so would be inconsistent with appli cable law or otherwise impractical. VCS are technical standards (e.g., materials spec ifications, 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 Clas sification 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 Bi odiesel Fuel Bl end 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 Standa rds 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 th at have already been review ed under the NTTAA. The EPA welcomes comments on this aspect of the pr oposed 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

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9-24 policy on environmental justice. It s main provision directs federal ag encies, to the greatest extent practicable and permitted by law, to make environmental justice part of their mission by identifying and addressing, as appropriate, disp roportionately high and adverse human health or environmental effects of their programs, policie s, 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 (u tility boilers and IGCC units) and stationary combustion turbines by establishing national emission standards for CO 2 

## . The EPA has determined that this proposed rule would not result in disproportio nately high and adverse
human health or environmental effects on mi nority, low-income, and indigenous populations because it does not affect the level of pr otection provided to human health or the environment. As previously stated, the EPA e xpects few modified or reconstructed fossil fuelfired electric utility steam generating units (u tility boilers and IGCC units) or stationary combustion turbines in the period of analysis.

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 Me dical 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, H ealth Effects Institute, Boston, MA. Lepeule, J.; F. Laden; D. Dock ery; 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 Perspe ctives, July, 120(7):965-70. Levy, J.I.; S.M. Chemerynski and J.A. Sarnat . 2005. “Ozone Exposure and Mortality: An Empiric Bayes Metaregression Analys is.” Epidemiology, 16(4): pp. 458-68.***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***9-1United StatesEnvironmental ProtectionAgencyOffice of Air Quality Planning and Standards[Name of Division]Research Triangle Park, NCPublication No. EPA-45X/X-XXXXX[Month, Year]