# Regulatory Impact Analysis for the Clean Power Plan Final Ruleii [This page intentionally left blank]iii EPA-452/R-15-003 August 2015



## Regulatory Impact Analysis for the Clean Power Plan Final Rule

U.S. Environmental Protection Agency Office of Air and Radiation Office of Air Quality Planning and Standards Research Triangle Park, NC 27711iv CONTACT INFORMATION

This document has been prepared by staff from the O ffice 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 Alexander Macpherson, U.S. Environmental Protection Agency, Office of Air Quality Planning and Standards, Research Triangle Park, North Caroli na 27711 (email: macpherson.alex@epa.gov).

ACKNOWLEDGEMENTS
Thank you to the many staff who worked on this document from EPA Off ices including the Office of Air Quality Planning and Standards, t he Office of Atmospheric Programs, and the Office of Policy. Contributions to this report were also made by ICF International and RTI International.v



### Table of Contents

L IST OF T ABLES

.................................................. ................................................... ..........................x

L IST OF F IGURES

.................................................. ................................................... ................... xvii

A CRONYMS

.................................................. ................................................... ...............................xx

E XECUTIVE S UMMARY

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

ES.1 Background and Context ............................

................................................... ...ES-1 ES.2 Summary of Clean Power Plan Final Rule ............ ...........................................ES-1 ES.3 Illustrative Plan Approaches Examined in RIA ...... ..........................................ES-3 ES.4 Emissions Reductions ..............................

................................................... ......ES-6 ES.5 Costs .............................................

................................................... .................ES-8 ES.6 Monetized Climate Benefits and Health Co-benefits . ....................................ES-10 ES.6.1 Estimating Global Climate Benefits ................ ...........................................ES-14 ES 6.2 Estimating Air Quality Health Co-Benefits.......... ......................................ES-16 ES 6.3 Combined Benefits Estimates ....................... ..............................................ES-19 ES.7 Net Benefits ......................................

................................................... ...........ES-21 ES.8 Economic Impacts ..................................

................................................... .....ES-24 ES.9 Employment Impacts ................................

................................................... ...ES-24 ES.10 References ........................................

................................................... ............ES-25

C HAPTER 1: I NTRODUCTION AND B ACKGROUND FOR THE C LEAN P OWER P LAN .................... 1-1

1.1 Introduction ......................................

................................................... ....................... 1-1 1.2 Legal, Scientific and Economic Basis for this Rulem aking ...................................... 1-1 1.2.1 Statutory Requirement .............................

................................................... .... 1-1 1.2.2 Health and Welfare Impacts from Climate Change .... .................................... 1-2 1.2.3 Market Failure ....................................

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

................................................... ....... 1-4 1.4 Background for the Final Emission Guidelines ......

................................................... 1-4 1.4.1 Base Case and Years of Analysis ................... ................................................ 1- 4 1.4.2 Definition of Affected Sources ....................

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

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

................................................... ..... 1-6 1.4.5 State Plans........................................

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

.................................................. 1-7 1.6 References ........................................

................................................... ....................... 1-8

C HAPTER 2: E LECTRIC P OWER S ECTOR I NDUSTRY P ROFILE

.................................................. . 2-1

2.1 Introduction ......................................

................................................... ....................... 2-1 2.2 Power Sector Overview .............................

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

................................................... ........... 2-1 2.2.2 Transmission ......................................

................................................... .......... 2-9vi

2.2.3 Distribution ......................................

................................................... .......... 2-10 2.3 Sales, Expenses and Prices ........................

................................................... ........... 2-11 2.3.1 Electricity Prices ................................

................................................... ........ 2-11 2.3.2 Prices of Fossil Fuels Used for Generating Electric ity................................. 2-17 2.3.3 Changes in Electricity Intensity of the U.S. Econom y Between 2002 to 2012 .............................................

................................................... ...................... 2-18 2.4 Deregulation and Restructuring ....................

................................................... ........ 2-19 2.5 Emissions of Greenhouse Gases from Electric Utiliti es .......................................... 2-24 2.6 Carbon Dioxide Control Technologies ...............

................................................... .. 2-27 2.6.1 Carbon Capture and Storage ........................ ................................................. 2 -29 2.6.2 Geologic and Geographic Considerations for Geologic Sequestration ........ 2-33 2.6.3 Availability of Geologic Sequestration in Deep Sali ne Formations............. 2-37 2.6.4 Availability of CO 2 Storage via Enhanced Oil Recovery (EOR) .......... ....... 2-37 2.7 State Policies on GHG and Clean Energy Regulation i n the Power Sector............. 2-39 2.8 Revenues and Expenses .............................

................................................... ........... 2-42 2.9 Natural Gas Market ................................

................................................... ............... 2-43 2.10 References ........................................

................................................... ..................... 2-47

C HAPTER 3: C OST , E MISSIONS , E CONOMIC , AND E NERGY I MPACTS ........................................ 3-1

3.1 Introduction ......................................

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

................................................... ....................... 3-1 3.3 Power Sector Modelling Framework ..................

................................................... .... 3-1 3.4 Recent Updates to EPA’s Base Case using IPM (v.5.15 ) .......................................... 3-4 3.5 State Goals in this Final Rule....................

................................................... ............. 3-5 3.6 Illustrative Plan Approaches Analyzed .............

................................................... ...... 3-7 3.7 Demand-Side Energy Efficiency .....................

................................................... ..... 3-12 3.7.1 Demand-Side Energy Efficiency Improvements (Electri city Demand Reductions) .......................................

................................................... .................... 3-12 3.7.2 Demand-Side Energy Efficiency Costs ............... ......................................... 3-15 3.8 Monitoring, Reporting, and Recordkeeping Costs .... .............................................. 3-16 3.9 Projected Power Sector Impacts ....................

................................................... ....... 3-19 3.9.1 Projected Emissions ...............................

................................................... .... 3-19 3.9.2 Projected Compliance Costs ........................ ................................................. 3 -21 3.9.3 Projected Compliance Actions for Emissions Reductio ns ........................... 3-23 3.9.4 Projected Generation Mix ..........................

................................................... 3-25 3.9.5 Projected Incremental Retirements ................. .............................................. 3-30 3.9.6 Projected Capacity Additions ...................... ................................................. 3 -31 3.9.7 Projected Coal Production and Natural Gas Use for t he Electric Power Sector ...........................................

................................................... ........................ 3-33 3.9.8 Projected Fuel Price, Market, and Infrastructure Im pacts ............................ 3-34 3.9.9 Projected Retail Electricity Prices ............... ................................................. 3 -35 3.9.10 Projected Electricity Bill Impacts ................ ................................................. 3 -40 3.11 Limitations of Analysis ...........................

................................................... .............. 3-43 3.12 Social Costs ......................................

................................................... ..................... 3-45 3.13 References ........................................

................................................... ..................... 3-48vii

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

.................................................. ................. 3A-1

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

................................................... ....... 3A-2 3A.1.1 Analytical Scope ..................................

................................................... ..... 3A-2 3A.1.2 Coal Mining Source Description .................... ............................................. 3A-3 3A.1.3 Natural Gas Systems Source Description ............ ........................................ 3A-4 3A.1.4 Illustrative Plan Approaches Examined ............. ......................................... 3A-6 3A.1.5 Activity Drivers ..................................

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

................................................... ............... 3A-7 3A.3 Uncertainties and Limitations .....................

................................................... .. 3A-8 3A.4 References ........................................

................................................... ............. 3A-9

C HAPTER 4: E STIMATED C LIMATE B ENEFITS AND H UMAN H EALTH C O - BENEFITS ................ 4-1

4.1 Introduction ......................................

................................................... ....................... 4-1 4.2 Estimated Climate Benefits from CO 2
.................................................. ..................... 4-1 4.2.1 Climate Change Impacts ............................

................................................... .. 4-2 4.2.2 Social Cost of Carbon .............................

................................................... ..... 4-3 4.3 Estimated Human Health Co-Benefits ................

................................................... .. 4-11 4.3.1 Health Impact Assessment for PM 2.5 and Ozone......................................... . 4-13 4.3.2 Economic Valuation for Health Co-benefits ......... ....................................... 4-18 4.3.3 Benefit-per-ton Estimates for PM 2.5
.................................................. ........... 4-20 4.3.4 Benefit-per-ton Estimates for Ozone ............... ............................................. 4-21 4.3.5 Estimated Health Co-Benefits Results .............. ........................................... 4-22 4.3.6 Characterization of Uncertainty in the Estimated He alth Co-benefits ......... 4-36 4.4 Combined Climate Benefits and Health Co-Benefits Es timates.............................. 4-42 4.5 Unquantified Co-benefits ..........................

................................................... ............ 4-46 4.5.1 HAP Impacts........................................

................................................... ...... 4-48 4.5.2 Additional NO 2 Health Co-Benefits ............................... .............................. 4-52 4.5.3 Additional SO 2 Health Co-Benefits ............................... ............................... 4-53 4.5.4 Additional NO 2 and SO 2 Welfare Co-Benefits .............................. ............... 4-54 4.5.5 Ozone Welfare Co-Benefits ......................... ................................................. 4 -55 4.5.6 Carbon Monoxide Co-Benefits ....................... .............................................. 4-55 4.5.7 Visibility Impairment Co-Benefits ................. .............................................. 4-56 4.6 References ........................................

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

A PPENDIX 4A: G ENERATING R EGIONAL B ENEFIT - PER -T ON E STIMATES ............................. 4A-1

4A.1 Overview of Benefit-per-Ton Estimates ............. ............................................. 4A-1 4A.2 Air Quality Modeling for the Proposed Clean Power P lan ............................. 4A-2 4A.3 Regional PM 2.5 Benefit-per-Ton Estimates for EGUs Derived from Air Quality Modeling of the Proposed Clean Power Plan .

.................................................. 4A-5 4A.4 Regional Ozone Benefit-per-Ton Estimates .......... ........................................ 4A-15 4A.5 References ........................................

................................................... ........... 4A-18

C HAPTER 5: E CONOMIC I MPACTS

– M ARKETS O UTSIDE THE U TILITY P OWER S ECTOR ........ 5-1viii

5.1 Introduction ......................................

................................................... ....................... 5-1 5.2 Methods............................................

................................................... ....................... 5-2 5.3 Summary of Secondary Market Impacts of Energy Price Changes ........................... 5-3 5.3.1 Share of Total Production Costs ...................

.................................................. 5-5 5.3.2 Ability to Substitute between Inputs to the Product ion Process ..................... 5-5 5.3.3 Availability of Substitute Goods and Services ..... .......................................... 5-5 5.4 Effect of Changes in Input Demand from Electricity Sector ..................................... 5-6 5.5 Conclusions .......................................

................................................... ...................... 5-6 5.6 References ........................................

................................................... ....................... 5-7

C HAPTER 6: E MPLOYMENT I MPACT A NALYSIS

.................................................. ....................... 6-1

6.1 Introduction ......................................

................................................... ....................... 6-1 6.2 Economic Theory and Employment ....................

................................................... ... 6-2 6.3 Current State of Knowledge Based on the Peer-Review ed Literature ....................... 6-6 6.3.1 Regulated Sector ..................................

................................................... ........ 6-7 6.3.2 Economy-Wide ......................................

................................................... ...... 6-9 6.3.3 Labor Supply Impacts ..............................

................................................... .. 6-11 6.4 Recent Employment Trends ..........................

................................................... ........ 6-11 6.4.1 Electric Power Generation .........................

................................................... 6-12 6.4.2 Fossil Fuel Extraction ............................

................................................... .... 6-13 6.4.3 Clean Energy Employment Trends .................... ........................................... 6-14 6.5 Projected Sectoral Employment Changes due to the Fi nal Emission Guidelines .... 6-18 6.5.1 Projected Changes in Employment in Electricity Gene ration and Fossil Fuel Extraction ...................................

................................................... ................... 6-19 6.5.2 Projected Changes in Employment in Demand-Side Ener gy Efficiency Activities ........................................

................................................... ....................... 6-25 6.6 Conclusion ........................................

................................................... .................... 6-34 6.7 References ........................................

................................................... ..................... 6-36

A PPENDIX 6A: E STIMATING S UPPLY S IDE E MPLOYMENT IMPACTS ...................................... 6A-1

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

................................................... ....... 6A-1 6A.2 Employment Changes due to Heat Rate Improvements .. ................................ 6A-3 6A.2.1 Employment Changes Due to Building (or Avoiding) Ne w Generation Capacity ..........................................

................................................... ..................... 6A-5 6A.2.2 Employment Changes due to Coal and Oil/Gas Retireme nts ...................... 6A-8 6A.2.3 Employment Changes due to Changes in Fossil Fuel Ex traction ................ 6A-9 6A.3 References ........................................

................................................... ........... 6A-10

C HAPTER 7: S TATUTORY AND E XECUTIVE O RDER A NALYSIS

.................................................. 7-1

7.1 Executive Order 12866: Regulatory Planning and Revi ew, and Executive Order 13563: Improving Regulation and Regulatory Review .

................................................... 7-1 7.2 Paperwork Reduction Act (PRA)......................

................................................... ...... 7-6 7.3 Regulatory Flexibility Act (RFA) ..................

................................................... ......... 7-7 7.4 Unfunded Mandates Reform Act (UMRA) ............... ................................................ 7- 8ix

7.5 Executive Order 13132: Federalism .................

................................................... ...... 7-9 7.6 Executive Order 13175: Consultation and Coordinatio n with Indian Tribal Governments .......................................

................................................... ......................... 7-14 7.7 Executive Order 13045: Protection of Children from Environmental Health Risks and Safety Risks ..................................

................................................... ......................... 7-16 7.8 Executive Order 13211: Actions Concerning Regulatio ns That Significantly Affect Energy Supply, Distribution, or Use ........

................................................... ......... 7-17 7.9 National Technology Transfer and Advancement Act (N TTAA) ........................... 7-17 7.10 Executive Order 12898: Federal Actions to Address E nvironmental Justice in Minority Populations and Low-Income Populations ...

................................................... 7-18 7.11 Congressional Review Act (CRA) ....................

................................................... .... 7-21

C HAPTER 8: C OMPARISON OF B ENEFITS AND C OSTS

.................................................. .............. 8-1

8.1 Comparison of Benefits and Costs ..................

................................................... ........ 8-1 8.2 Uncertainty Analysis ..............................

................................................... ................. 8-5 8.2.1 Uncertainty in Costs and Illustrative Plan Approach es .................................. 8-5 8.2.2 Uncertainty Associated with Estimating the Social C ost of Carbon .............. 8-6 8.2.3 Uncertainty Associated with PM 2.5 and Ozone Health Co-Benefits Assessment ........................................

................................................... ...................... 8-7 8.3 References ........................................

................................................... ....................... 8-9x



### L IST OF 

### T ABLES

Table ES-1. Emission Performance Rates (Adjusted Output-Weighte d-Average Pounds of CO 2 Per Net MWh from All Affected Fossil Fuel-Fired EG Us) ..............................ES-2

Table ES-2. Climate and Air Pollutant Emission Reductions for t he Rate-Based Illustrative Plan Approach ........................

................................................... ..................ES-6

Table ES-3. Climate and Air Pollutant Emission Reductions for t he Mass-Based Illustrative Plan Appproach .......................

................................................... .................ES-7

Table ES-4. Projected CO 2 Emission Reductions, Relative to 2005 ............ ........................ES-8

Table ES-5. Compliance Costs for the Illustrative Rate-Based an d Mass-Based Plan Approaches ........................................

................................................... .........................ES-9

Table ES-6. Quantified and Unquantified Benefits .............. ..............................................ES-12

Table ES-7. Combined Estimates of Climate Benefits and Health C o-Benefits for RateBased Approach (billions of 2011$) ................

................................................... .........ES-18

Table ES-8. Combined Estimates of Climate Benefits and Health C o-benefits for MassBased Approach (billions of 2011$) ................

................................................... .........ES-21

Table ES-9. Monetized Benefits, Compliance Costs, and Net Benefits Under the Ratebased Illustrative Plan Approach (billions of 2011$ ) ................................................. .ES-22

Table ES-10. Monetized Benefits, Compliance Costs, and Net Benefits under the Massbased Illustrative Plan Approach (billions of 2011$ ) ................................................. .ES-23

Table ES-11. Summary Table of Important Energy Market Impacts (P ercent Change from Base Case) ........................................

................................................... .........................ES-24 Table 2-1. Existing Electricity Generating Capacity by Energy Source, 2002 and 2012 .... 2-3

Table 2-2. Net Generation in 2002 and 2013 (Trillion kWh = TWh)............................... ... 2-5
Table 2-3. Coal and Natural Gas Generating Units, by Size, Age, Capacity, and Thermal Efficiency (Heat Rate) ....................

................................................... ................ 2-7

Table 2-4. Total U.S. Electric Power Industry Retail Sales in 2012 (billion kWh) ........... 2-11

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

................................................... ......... 2-25

Table 2-6. Greenhouse Gas Emissions from the Electricity Secto r (Generation, Transmission and Distribution), 2002 and 2012 (mill ion tons of CO 2 equivalent) ....... 2-26xi

Table 2-7. Fossil Fuel Emission Factors in EPA Base Case 5.14 IPM Power Sector Modeling Application ..............................

................................................... ................... 2-27

Table 2-8. Total CO 2 Storage Resource (DOE-NETL) ...................... ............................... 2-35

Table 2-9. Revenue and Expense Statistics for Major U.S. Inves tor-Owned Electric Utilities for 2002, 2008 and 2012 (nominal $million s) ................................................ . 2-42

Table 3-1. Statewide CO 2 Emission Performance Goals, Rate-based and Mass-ba sed ....... 3-6

Table 3-2. Demand-Side Energy Efficiency Plan Scenario: Net Cu mulative Demand Reductions [Contiguous U.S.] (GWh and as Percent of BAU Sales) ............................ 3-14

Table 3-3. Annualized Cost of Demand-Side Energy Efficiency Pl an Scenario (at discount rates of 3 percent and 7 percent, billions 2011$) ........................................... . 3-15

Table 3-4. Years 2020, 2025 and 2030: Summary of State and Ind ustry Annual Respondent Burden and Cost of Reporting and Recordk eeping Requirements (2011$) ..........................................

................................................... ............................. 3-18

Table 3-5. Projected CO 2 Emission Impacts, Relative to Base Case .......... ....................... 3-19

Table 3-6. Projected CO 2 Emission Impacts, Relative to 2005 ............... .......................... 3-20

Table 3-7. Projected Non-CO 2 Emission Impacts, 2020-2030 ...................... .................... 3-20

Table 3-8. Annualized Compliance Costs Including Monitoring, R eporting and Recordkeeping Costs Requirements (billions of 2011$ ) ............................................... 3 -22

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

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

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

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

Table 3-12. Total Generation Capacity by 2020-2030 (GW) ....... ....................................... 3-31

Table 3-13. Projected Capacity Additions, Gas (GW) ............ ............................................. 3-32

Table 3-14. Projected Capacity Additions, Renewable (GW) ...... ....................................... 3-33

Table 3-15. Coal Production for the Electric Power Sector, 2025 ....................................... 3-33

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

................................................... ..... 3-34

Table 3-17. Projected Average Minemouth and Delivered Coal Pric es (2011$/MMBtu) .. 3-35xii

Table 3-18. Projected Average Henry Hub (spot) and Delivered Na tural Gas Prices (2011$/MMBtu) .....................................

................................................... ..................... 3-35

Table 3-19. 2020 Projected Contiguous U.S. and Regional Retail Electricity Prices (cents/kWh) .......................................

................................................... .......................... 3-37

Table 3-20. 2025 Projected Contiguous U.S. and Regional Retail Electricity Prices (cents/kWh) .......................................

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

Table 3-21. 2030 Projected Contiguous U.S. and Regional Retail Electricity Prices (cents/kWh) .......................................

................................................... .......................... 3-39

Table 3-22. Projected Changes in Average Electricity Bills .... ............................................ 3-40

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

................................................... ................... 3A-6

Table 3A-2. Projected Coal Production Impacts ................. ................................................ 3A -7

Table 3A-3. Projected Natural Gas Production Impacts .......... ............................................ 3A-7

Table 3A-4. Potential Upstream Emissions Changes .............. ............................................ 3A-8

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

................................................... ............. 4-2

Table 4-2. Social Cost of CO 2 , 2015-2050 (in 2011$ per short ton) .............. ..................... 4-8

Table 4-3. Estimated Global Climate Benefits of CO 2 Reductions for the Final Emission Guidelines in 2020 (billions of 2011$) ...

................................................... ...... 4-9

Table 4-4. Estimated Global Climate Benefits of CO 2 Reductions for the Final Emission Guidelines in 2025 (billions of 2011$) ...

................................................... ...... 4-9

Table 4-5. Estimated Global Climate Benefits of CO 2 Reductions for the Final Emission Guidelines in 2030 (billions of 2011$) ...

................................................... ...... 4-9

Table 4-6. Human Health Effects of Ambient PM 2.5 and Ozone ....................................... 4-14

Table 4-7. Summary of Regional PM 2.5 Benefit-per-Ton Estimates Based on Air Quality Modeling from Proposed Clean Power Plan in 2020 (2011$) .......................... 4-23

Table 4-8. Summary of Regional PM 2.5 Benefit-per-Ton Estimates Based on Air Quality Modeling from Proposed Clean Power Plan in 2025 (2011$) .......................... 4-23

Table 4-9. Summary of Regional PM 2.5 Benefit-per-Ton Estimates Based on Air Quality Modeling from Proposed Clean Power Plan in 2030 (2011$) .......................... 4-24

Table 4-10. Emission Reductions of Criteria Pollutants for the Final Emission Guidelines Rate-based Illustrative Plan Approach in 2020 (thou sands of short tons) ..................... 4-24xiii

Table 4-11. Emission Reductions of Criteria Pollutants for the Final Emission Guidelines Rate-based Illustrative Plan Approach in 2025 (thou sands of short tons) ..................... 4-24

Table 4-12. Emission Reductions of Criteria Pollutants for the Final Emission Guidelines Rate-based Illustrative Plan Approach in 2030 (thou sands of short tons) ..................... 4-25

Table 4-13. Emission Reductions of Criteria Pollutants for the Final Emission Guidelines Mass-based Illustrative Plan Approach in 2020 (thou sands of short tons) .................... 4-25

Table 4-14. Emission Reductions of Criteria Pollutants for the Final Emission Guidelines Mass-based Illustrative Plan Approach in 2025 (thou sands of short tons) .................... 4-25

Table 4-15. Emission Reductions of Criteria Pollutants for the Final Emission Guidelines Mass-based Illustrative Plan Approach in 2030 (thou sands of short tons) .................... 4-25

Table 4-16. Summary of Estimated Monetized Health Co-Benefits f or the Final Emission Guidelines Rate-based Illustrative Plan Ap proach in 2020 (billions of 2011$) ..........................................

................................................... ............................ 4-26

Table 4-17. Summary of Estimated Monetized Health Co-Benefits f or the Final Emission Guidelines Rate-based Illustrative Plan Ap proach in 2025 (billions of 2011$) ..........................................

................................................... ............................. 4-26

Table 4-18. Summary of Estimated Monetized Health Co-Benefits f or the Final Emission Guidelines Rate-based Illustrative Plan Ap proach in 2030 (billions of 2011$) ..........................................

................................................... ............................. 4-27

Table 4-19. Summary of Estimated Monetized Health Co-Benefits f or the Final Emission Guidelines Mass-based Illustrative Plan Ap proach in 2020 (billions of 2011$) ..........................................

................................................... ............................. 4-27

Table 4-20. Summary of Estimated Monetized Health Co-Benefits f or the Final Emission Guidelines Mass-based Illustrative Plan Ap proach in 2025 (billions of 2011$) ..........................................

................................................... ............................ 4-28

Table 4-21. Summary of Estimated Monetized Health Co-Benefits f or the Final Emission Guidelines Mass-based Illustrative Plan Ap proach in 2030 (billions of 2011$) ..........................................

................................................... ............................ 4-28

Table 4-22. Summary of Avoided Health Incidences from PM 2.5 -Related and OzoneRelated Co-benefits for the Final Emission Guidelin es Rate-based Illustrative Plan Approach in 2020 ..................................

................................................... ...................... 4-29

Table 4-23. Summary of Avoided Health Incidences from PM 2.5 -Related and OzoneRelated Co-benefits for Final Emission Guidelines R ate-based Illustrative Plan Approach in 2025 ..................................

................................................... ...................... 4-30xiv

Table 4-24. Summary of Avoided Health Incidences from PM 2.5 -Related and OzoneRelated Co-Benefits for Final Emission Guidelines R ate-based Illustrative Plan Approach in 2030 ..................................

................................................... ...................... 4-31

Table 4-25. Summary of Avoided Health Incidences from PM 2.5 -Related and OzoneRelated Co-benefits for the Final Emission Guidelin es Mass-based Illustrative Plan Approach in 2020 ..................................

................................................... ...................... 4-32

Table 4-26. Summary of Avoided Health Incidences from PM 2.5 -Related and OzoneRelated Co-benefits for Final Emission Guidelines M ass-based Illustrative Plan Approach in 2025 ..................................

................................................... ...................... 4-33

Table 4-27. Summary of Avoided Health Incidences from PM 2.5 -Related and OzoneRelated Co-Benefits for Final Emission Guidelines M ass-based Illustrative Plan Approach in 2030 ..................................

................................................... ...................... 4-34

Table 4-28. Population Exposure in the Clean Power Plan Proposa l Option 1 State Scenario Modeling (used to generate the benefit-per -ton estimates) Above and Below Various Concentrations Benchmarks in the Unde rlying Epidemiology Studies 4-41

Table 4-29. Combined Climate Benefits and Health Co-Benefits fo r Final Emission Guidelines in 2020 (billions of 2011$) ............

................................................... ........... 4-44

Table 4-30. Combined Climate Benefits and Health Co-Benefits fo r Final Emission Guidelines in 2025 (billions of 2011$) ............

................................................... ........... 4-44

Table 4-31. Combined Climate Benefits and Health Co-Benefits fo r Final Emission Guidelines in 2030 (billions of 2011$) ............

................................................... ........... 4-45

Table 4-32. Unquantified Health and Welfare Co-benefits Categor ies ............................... 4-47

Table 4A-1. State Total Annual EGU Emissions for NO X for the 2011 Base Year, 2025 Base Case, and 2025 Clean Power Plan Proposal (Opti on 1 State) (in thousands of tons) ............................................

................................................... .......................... 4A-3

Table 4A-2. State Total Annual EGU Emissions for SO 2 for the 2011 Base Year, 2025 Base Case, and 2025 Clean Power Plan Proposal (Opti on 1 State) (in thousands of tons) ............................................

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

Table 4A-3. Summary of Regional PM 2.5 Benefit-per-Ton Estimates Based on Air Quality Modeling from Proposed Clean Power Plan in 2020 (2011$) ....................... 4A-10

Table 4A-4. Summary of Regional PM 2.5 Benefit-per-Ton Estimates Based on Air Quality Modeling from Proposed Clean Power Plan in 2025 (2011$) ....................... 4A-10

Table 4A-5. Summary of Regional PM 2.5 Benefit-per-Ton Estimates Based on Air Quality Modeling from Proposed Clean Power Plan in 2030 (2011$) ....................... 4A-11xv

Table 4A-6. Summary of Regional PM 2.5 Incidence-per-Ton Estimates Based on Air Quality Modeling from Proposed Clean Power Plan in 2020..................................... 4A-12

Table 4A-7. Summary of Regional PM 2.5 Incidence-per-Ton Estimates Based on Air Quality Modeling from Proposed Clean Power Plan in 2025..................................... 4A-13

Table 4A-8. Summary of Regional PM 2.5 Incidence-per-Ton Estimates Based on Air Quality Modeling from Proposed Clean Power Plan in 2030..................................... 4A-14

Table 4A-9. Summary of Regional Ozone Benefit-per-Ton Estimates Based on Air Quality Modeling from Proposed Clean Power Plan in 2020 (2011$) ....................... 4A-16

Table 4A-10. Summary of Regional Ozone Benefit-per-Ton Estimates Based on Air Quality Modeling from Proposed Clean Power Plan in 2025 (2011$) ....................... 4A-16

Table 4A-11. Summary of Regional Ozone Benefit-per-Ton Estimates Based on Air Quality Modeling from Proposed Clean Power Plan in 2030 (2011$) ....................... 4A-16

Table 4A-12. Summary of Regional Ozone Incidence-per-Ton Estimates Based on Air Quality Modeling from Proposed Clean Power Plan in 2020..................................... 4A-17

Table 4A-13. Summary of Regional Ozone Incidence-per-Ton Estimates Based on Air Quality Modeling from Proposed Clean Power Plan in 2025..................................... 4A-17

Table 4A-14. Summary of Regional Ozone Incidence-per-Ton Estimates Based on Air Quality Modeling from Proposed Clean Power Plan in 2030..................................... 4A-17

Table 5-1. Estimated Percentage Changes in Average Energy Pric es by Energy Type for the Final Emission Guidelines, Rate-based and M ass-based Illustrative Plan Approaches ........................................

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

Table 6-1. U. S. Green Goods and Services (GGS) Employment (an nual 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 a Changes in Labor Utilization, Rate-based Scenario (Number of Job-Years b of Employment in a Single Year) .................. .......................... 6-24

Table 6-5. Engineering-Based a Changes in Labor Utilization, Mass-Based Illustrat ive Plan Approach (Number of Job-Years of Employment in a Single Year) .................... 6-25
Table 6-6. Estimated Demand-Side Energy Efficiency Employment Impacts: Target 1 percent Growth in Energy Efficiency ...............

................................................... .......... 6-31

Table 6A-1. Labor Productivity Growth Rate due to Heat Rate Imp rovement ................... 6A-5

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

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

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

Table 6A-5. Average FOM Costs for Existing Coal and Oil and Gas Steam Capacity ($/kW, 2011$) .....................................

................................................... ....................... 6A-8

Table 6A-6. Labor Productivity due to Fossil Fuel Extraction .. .......................................... 6A-9

Table 7-1. Monetized Benefits, Compliance Costs, and Net Benefits Under the Ratebased Illustrative Plan Approach (billions of 2011$ ) a

.................................................. ... 7-4

Table 7-2. Monetized Benefits, Compliance Costs, and Net Benefits under the Massbased Illustrative Plan Approach (billions of 2011$ ) a

.................................................. ... 7-5xvii



### L IST OF 

### F IGURES

Figure 2-1. New Build and Retired Capacity (MW) by Fuel Type, 2 002-2012 ................... 2-4 Figure 2-2. Cumulative Distribution in 2010 of Coal and Natural Gas Electricity Capacity and Generation, by Age ...................

................................................... .............. 2-8 Figure 2-3. Fossil Fuel-Fired Electricity Generating Facilities , by Size ............................... 2-9
Figure 2-4. Average Retail Electricity Price by State (cents/kW h), 2011 ........................... 2-13 Figure 2-5. Nominal National Average Electricity Prices for Thr ee Major End-Use Categories ........................................

................................................... ........................... 2-14 Figure 2-6. Relative Increases in Nominal National Average Elec tricity Prices for Major End-Use Categories, With Inflation Indices ........

................................................... ....... 2-15
Figure 2-7. Real National Average Electricity Prices (2011$) fo r Three Major End-Use Categories ........................................

................................................... ........................... 2-16
Figure 2-8. Relative Change in Real National Average Electricit y Prices (2011$) for Three Major End-Use Categories ....................

................................................... ........... 2-17 Figure 2-9. Relative Real Prices of Fossil Fuels for Electricit y Generation; Change in National Average Real Price per MBtu Delivered to E GU ........................................... 2-17 Figure 2-10. Relative Growth of Electricity Generation, Populati on and Real GDP Since 2002 .............................................

................................................... .......................... 2-18 Figure 2-11. Relative Change of Real GDP, Population and Electri city Generation Intensity Since 2002 ..............................

................................................... ...................... 2-19 Figure 2-12. Status of State Electricity Industry Restructuring Activities ............................ 2-21
Figures 2-13 and 2-14. Capacity and Generation Mix by Ownership Type, 2002 & 2012 .... 2-23 Figures 2-15 and 2-16. Generation Capacity Built and Retired between 2002 and 2012 by Ownership Type ...................................

................................................... ..................... 2-24 Figure 2-17. Domestic Emissions of Greenhouse Gases from Major S ectors, 2002 and 2013 (million tons of CO 2 equivalent) ...................................... ..................................... 2-25
Figure 2-18. Marketable products from Syngas Generation ........ ......................................... 2-29 Figure 2-19. Post-Combustion CO 2 Capture for a Pulverized Coal Power Plant ........ ......... 2-30 Figure 2-20. Pre-Combustion CO 2 Capture for an IGCC Power Plant ................... .............. 2-31xviii

Figure 2-21. Geologic Sequestration in the Continental United St ates ................................. 2-34 Figure 2-22. Relative Change Nominal and Real (2011$) Prices of Natural Gas Delivered to the Power Sector ($/MMBtu) .....................

................................................... ............ 2-44 Figure 2-23. Relative Change in Real (2011$) Prices of Fossil Fu els Delivered to the Power Sector ($/MMBtu) ............................

................................................... ............... 2-45
Figure 3-1. Illustrative Regions for Demand-Side Energy Efficie ncy/Renewable Energy Procurement Used in this Analysis .................

................................................... .............. 3-9 Figure 3-2 Generation Mix (thousand GWh) ..................... ................................................ 3- 28 Figure 3-3. Nationwide Generation: Historical (1990-2014) and B ase Case Projections (2020, 2025, 2030) ................................

................................................... ...................... 3-29 Figure 3-4. Nationwide Generation: Historical (1990-2014) and R ate-Based Illustrative Plan Approach Projections (2020, 2025, 2030) ......

................................................... .... 3-29 Figure 3-5. Nationwide Generation: Historical (1990-2014) and M ass-Based Illustrative Plan Approach Projections (2020, 2025, 2030) ......

................................................... .... 3-30 Figure 3-6. Electricity Market Module Regions ................. ................................................. 3 -40 Figure 4-1. Monetized Health Co-benefits of Rate-based and Mass -based Illustrative Plan Approaches for the Final Emission Guidelines i n 2025 ....................................... 3-3 5
Figure 4-2. Breakdown of Monetized Health Co-benefits by Precur sor Pollutant at a 3% Discount Rate for Rate-based and Mass-based Illustr ative Plan Approaches for the Final Emission Guidelines in 2025 .................

................................................... ............ 3-36
Figure 4-3. Percentage of Adult Population (age 30+) by Annual Mean PM 2.5 Exposure in the Option 1 State Scenario Clean Power Plan Pro posal Modeling (used to generate the benefit-per-ton estimates) ...........

................................................... ............ 3-41 Figure 4-4. Cumulative Distribution of Adult Population (age 30 +) by Annual Mean PM 2.5 Exposure in the Option 1 State Scenario Clean Power Plan Proposal Modeling (used to generate the benefit-per-ton estimates) ..

................................................... ....... 3-42 Figure 4-5. Breakdown of Combined Monetized Climate and Health Co-benefits of Final Emission Guidelines in 2025 for Rate-based an d Mass-based Illustrative Plan Approaches and Pollutants (3% discount rate) ......

................................................... ..... 3-46
Figure 4A-1. Regional Breakdown ................................

................................................... .... 4A-7 Figure 6.1. Electric Power Industry Employment ................ ............................................... 6-1 2 Figure 6.2. Coal Production Employment ........................

................................................... 6-13xix

Figure 6.3. Oil and Gas Production Employment ................. .............................................. 6-14
Figure 6.4. Demand-Side Energy Efficiency Employment: Jobs per One Million Dollars (2011$) ..........................................

................................................... ............................. 6-32xx



### A CRONYMS

ACS American Cancer Society AEO Annual Energy Outlook AQ Air quality ASM Annual Survey of Manufactures ATSDR Agency for Toxic Substances and Disease Regi stry 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 Ca pture 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 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 Gasxxi

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 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 Syste m NaOH Sodium Hydroxide NATCARB National Carbon Sequestration Database and Geographic Information System NEEDS National Electric Energy Data System NEMS National Energy Modeling System NERC North American Electric Reliability Corporat ion 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 carbonxxii

OFA Overfire Air 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 Scienti fic Advisory Committee SAB-HES Science Advisory Board Health Effects Subc ommittee of the Advisory Council on Clean Air Compliance SAB-EEAC Science Advisory Board Environmental Econ omics Advisory Committee SBA Small Business Administration SBREFA Small Business Regulatory Enforcement Fairne ss 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 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 payES-1



### E XECUTIVE 

### S UMMARY

This Regulatory Impact Analysis (RIA) discusses pot ential benefits, costs, and economic impacts of the Final Carbon Pollution Emission Guid elines for Existing Stationary Sources: Electric Utility Generating Units (herein referred to as “final emission guidelines” or the “Clean Power Plan Final Rule”).

ES.1 Background and Context
The emission of greenhouse gases (GHGs) threatens A mericans' health and welfare by leading to long-lasting changes in our climate. Car bon dioxide (CO 2 ) is the primary greenhouse gas pollutant, accounting for roughly three-quarter s of global greenhouse gas emissions in 2010 and 82 percent of U.S. greenhouse gas emissions in 2013. Fossil fuel-fired electric generating units (EGUs) are by far the largest emitters of GHG s, primarily in the form of CO 2 , among stationary sources in the U.S.
In this action, the Environmental Protection Agency (EPA) is establishing final emission guidelines for states to follow in developing plans to reduce greenhouse gas emissions from existing fossil fuel-fired EGUs. Specifically, the EPA is establishing: 1) CO 2 emission performance rates representing the best system of e mission reduction (BSER) for two subcategories of existing fossil fuel-fired EGUs – fossil fuel-fired electric utility steam generating units and stationary combustion turbines , 2) state-specific CO 2 goals reflecting the
CO 2 emission performance rates, and 3) guidelines for the development, submittal and implementation of state plans that establish emissi on standards or other measures to implement
the CO 2 emission performance rates, which may be accomplis hed by meeting the state goals. This final rule will continue progress already unde rway in the U.S. to reduce CO 2 emissions from the utility power sector.
ES.2 Summary of Clean Power Plan Final Rule
Under CAA section 111(d), states must establish sta ndards of performance that reflect the degree of emission limitation achievable through th e application of the “best system of emission reduction” (BSER) that, taking into account the cos t of achieving such reduction and any non-air quality health and environmental impacts and energy requirements, the Administrator determines has been adequately demonstrated. The EPA has deter mined that the BSER is the combination ofES-2

emission rate improvements and limitations on overa ll emissions at affected EGUs that can be accomplished through any combination of one or more measures from the following three sets of measures or building blocks:

1. Improving heat rate at affected coal-fired steam EG Us.

2. Substituting increased generation from lower-emitti ng existing natural gas combined cycle units for reduced generation from higher-emit ting affected steam generating
units.

3. Substituting increased generation from new zero-emi tting generating capacity for reduced generation from affected fossil fuel-fired generating units.
Specifically, the EPA is establishing CO 2 emission performance rates for two subcategories of existing fossil fuel-fired EGUs, f ossil fuel-fired electric steam generating units and stationary combustion turbines. The rates are i ntended to represent CO 2 emission rates achievable by 2030 after a 2022-2029 interim period on an output-weighted-average basis collectively by all affected EGUs. The interim and final emission performance rates are presented in the following table:
Table ES-1. Emission Performance Rates (Adjusted Ou tput-Weighted-Average Pounds of

CO 2 Per Net MWh from All Affected Fossil Fuel-Fired EG Us)

Subcategory Interim Rate Final Rate Fossil Fuel-Fired Electric Steam Generating Units 1 ,534 1,305 Stationary Combustion Turbines 832 771

Also, states with one or more affected EGUs will be required to develop and implement plans that set emission standards for affected EGU. These emission standards may incorporate the subcategory-specific CO 2 emission performance rates set by the EPA or, in t he alternative, may be set at levels that ensure that the state’s a ffected EGUs, individually, in aggregate, or in combination with other measures undertaken by the s tate achieve the equivalent of the interim
and final CO 2 emission performance rates between 2022 and 2029 a nd by 2030, respectively.

EPA derived statewide rate-based CO 2 emissions performance goals as a weighted average of the uniform rate goals with weights base d on baseline generation for the two types of units (fossil steam and stationary combustion turbi ne) in the state. This blended rate reflects theES-3

collective emission rate a state may expect to achi eve when its baseline fleet of likely affected EGUs continues to operate at baseline levels while meeting its subcategory-specific emission performance rates reflecting the BSER.

The Clean Power Plan Final Rule also establishes an 8-year interim compliance period that begins in 2022 with a glide path for meeting i nterim CO 2 emission performance rates separated into three steps: 2022-2024, 2025-2027, a nd 2028-2029. This results in interim and final statewide goal values unique to each state’s historical blend of fossil steam and NGCC generation. Chapter 3 presents finalized state rate -based CO 2 emissions performance goals.
The EPA is also establishing mass-based statewide C O 2 emission performance goals for each state, which are also presented in Chapter 3. For more detail on the methodology that
translates CO 2 emission performance rates to mass-based CO 2 performance goes, please refer to the preamble of the Clean Power Plan Final Rule and the U.S. EPA’s CO 2 Emission Performance Rate and Goal Computation Technical Support Documen t for Final Rule, which is available in

the docket. 1

Given the flexibilities afforded states in complyin g with the emission guidelines, the benefits, cost and economic impacts reported in thi s RIA are not definitive estimates. Rather, the impact estimates are instead illustrative of approa ches that states may take.
ES.3 Illustrative Plan Approaches Examined in RIA
In the final emission guidelines, the EPA has trans lated the source category-specific CO 2 emission performance rates into state-level rate-ba sed and mass-based CO 2 goals in order to maximize the range of choices that states will have in developing their plans. Because of the range of choices available to states and the lack o f a priori knowledge about the specific choices states will make in response to the final goals, th is RIA presents two scenarios designed to achieve these goals, which we term the “rate-based” illustrative plan approach and the “massbased” illustrative plan approach.
In this final rule, states may use trading or other multi-unit compliance approaches and technologies or strategies that are not explicitly mentioned in any of the three building blocks as

1 U.S. EPA. 2015. Technical Support Document (TSD) th e Final Carbon Pollution Emission Guidelines for Ex isting Stationary Sources: Electric Utility Generating Uni ts. CO 2 Emission Performance Rate and Goal Computation.ES-4

part of their overall plans, as long as they achiev e the required emission reductions from affected fossil fuel-fired EGUs. In addition, the final rule provides additional options to allow individual
EGUs to use creditable out-of-state reductions to a chieve required CO 2 reductions, without the need for up-front interstate agreements.
The modelled implementation plan approaches reflect states and affected EGUs pursuing building block strategies such as heat rate improve ments, shifting generation to less CO 2 – intensive generation, and increased deployment of r enewable energy, which are more completely described in Chapter 3. However, the modelled strat egies are not limited to the technologies and
measures included in the BSER. While the final rule no longer includes demand-side energy efficiency potential as part of BSER, the rule does allow such potential to be used for compliance. These scenarios include a representatio n of demand-side energy efficiency compliance potential because energy efficiency is a highly cost-effective means for reducing
CO 2 from the power sector, and it is reasonable to ass ume that a regulatory requirement to
reduce CO 2 emissions will motivate parties to pursue all high ly cost-effective means for making emission reductions accordingly, regardless of what particular emission reduction measures were assumed in determining the level of that regulatory requirement. In the rate-based approach, energy efficiency activities are modeled as being u sed by EGUs as a low-cost method of demonstrating compliance with their rate-based emis sions standards. In the mass-based approach, energy efficiency activities are assumed to be adopted by states to lower demand, which in turn reduces the cost of achieving the mas s limitations.
Alternative compliance approaches other than those modelled are also possible, which may have different levels and distributions of emis sions and electricity generation as well as costs. While IPM finds a least cost way to achieve the state goals implemented through the ratebased or mass-based emissions constraints imposed i n the illustrative plan approaches, individual states or multi-state regional groups may develop a lternate approaches to achieve their state goals.
It is very important to note that the differences b etween the analytical results for the ratebased and mass-based illustrative plan approaches p resented in this RIA may not be indicative of likely differences between the approaches if implem ented by states and affected EGUs in response to the final guidelines. Rather, the two s ets of analyses are intended to illustrate twoES-5

contrasting, stylized implementation approaches to accomplish the emission performance rates
finalized in the Clean Power Plan Final Rule. In ot her words, if one approach performs differently than the other on a given metric during a given time period, this does not imply this will apply in all instances.
To present a complete picture of costs and benefits of the final emission guidelines, this RIA presents results for the analysis years 2020, 2 025, and 2030. While 2020 is before the first year of the interim compliance period (2022), the E PA expects states and affected EGUs to perform voluntary activities that will facilitate c ompliance with interim and final goals. These pre-compliance period activities might include inve stments in renewable energy or demand-side energy efficiency projects, for example, that produ ce emissions reductions in the compliance period. Activities might also include preparatory i nvestments in transmission capacity or monitoring, reporting, and recordkeeping systems. A s a result, there are likely to be benefits and costs in 2020, so these are reported in the illustr ative analysis of this RIA. Meanwhile, cost and benefits are estimated in this RIA for 2025, which is intended to represent a central period of the interim compliance time-frame as states and tribes are on glide paths toward fully meeting the
final CO 2 emission performance goals. Lastly, the RIA presen ts costs and benefits for 2030, when the emission performance goals are fully achie ved.ES-6

ES.4 Emissions Reductions

Table ES-2 shows the emission reductions associated with the modelled rate-based illustrative plan approach.

Table ES-2. Climate and Air Pollutant Emission Redu ctions for the Rate-Based

Illustrative Plan Approach 1 CO 2 (million short tons) SO 2 (thousand short tons) Annual NO X (thousand short tons) 2020 Rate-Based Approach Base Case 2,155 1,311 1,333 Final Guidelines 2,085 1,297 1,282 Emissions Change -69 -14 -50 2025 Rate-Based Approach Base Case 2,165 1,275 1,302 Final Guidelines 1,933 1,097 1,138 Emissions Change -232 -178 -165 2030 Rate-Based Approach Base Case 2,227 1,314 1,293 Final Guidelines 1,812 996 1,011 Emission Change -415 -318 -282 Source: Integrated Planning Model, 2015. Emissions change may not sum due to ro unding. 1 CO 2 emission reductions are used to estimate the clima te benefits of the guidelines. SO 2 , and NO X reductions are relevant for estimating air quality health co-benef its of the final guidelines. The final guidelines a re also expected to achieve reductions in directly emitted PM 2.5 , which we were not able to estimate for this RIA.
In 2020, the EPA estimates that CO 2 emissions will be reduced by 69 million short tons under the rate-based scenario compared to base case levels. In 2025, the EPA estimates that CO 2 emissions will be reduced by 232 million short tons under the rate-based approach compared to
base case levels. CO 2 emission reductions increase to 415 million short tons annually in 2030 when compared to the base case emissions. Table ES- 2 also shows emission reductions for criteria air pollutants (in short tons). 2

2 The final guidelines are also expected to achieve r eductions in directly emitted PM 2.5 , which we were not able to estimate for this RIA. However, the SO 2 and NO X reductions account for the large majority of the a nticipated health co-benefits. Based on analyses for the proposed rul e which included benefits from reductions in direct ly emitted PM 2.5 , those benefits accounted for less than 10 percent of total monetized health co-benefits.ES-7

Table ES-3 shows the emission reductions associated with the modeled mass-based illustrative plan approach.

Table ES-3. Climate and Air Pollutant Emission Redu ctions for the Mass-Based

Illustrative Plan Appproach 1 CO 2 (million short tons) SO 2 (thousand short tons) Annual NO X (thousand short tons) 2020 Mass-Based Approach Base Case 2,155 1,311 1,333 Final Guidelines 2,073 1,257 1,272 Emissions Change -82 -54 -60 2025 Mass-Based Approach Base Case 2,165 1,275 1,302 Final Guidelines 1,901 1,090 1,100 Emissions Change -264 -185 -203 2030 Mass-Based Approach Base Case 2,227 1,314 1,293 Final Guidelines 1,814 1,034 1,015 Emission Change -413 -280 -278 Source: Integrated Planning Model, 2015. Emissions change may not sum due to ro unding.
1 CO 2 emission reductions are used to estimate the clima te benefits of the guidelines. SO 2 , and NO X reductions are relevant for estimating air quality health co-benef its of the final guidelines. The final guidelines a re also expected to achieve reductions in directly emitted PM 2.5 , which we were not able to estimate for this RIA.
In 2020, the EPA estimates that CO 2 emissions will be reduced by 82 million short tons under the mass-based approach compared to base case level s. In 2025, the EPA estimates that CO 2 emissions will be reduced by 264 million short tons under the mass-based approach compared to
base case levels. CO 2 emission reductions increase to 413 million short tons annually in 2030 when compared to the base case emissions. Table ES- 3 also shows emission reductions for criteria air pollutants (in short tons).ES-8

Table ES-4 presents CO 2 emission reductions relative to 2005.
Table ES-4. Projected CO 2 Emission Reductions, Relative to 2005

CO 2 Emissions (million short tons) CO 2 Emissions: Change from 2005 (million short tons) CO 2 Emissions Reductions: Percent Change from 2005

2005 2020 2025 2030 2020 2025 2030 Base Case 2,683 -528 -518 -456 -20% -19% -17% Rate-based - -598 -750 -871 -22% -28% -32% Mass-based - -610 -782 -869 -23% -29% -32% Source: Integrated Planning Model, 2015.
In 2020, the EPA estimates that CO 2 emissions will be reduced by 598 million short ton s (22 percent) under the rate-based approach compared to 2005 levels. In 2025, the EPA estimates that
CO 2 emissions will be reduced by 750 million short ton s (28 percent) under the rate-based approach compared to 2005 levels. Under the rate-ba sed approach, CO 2 emission reductions increase to 871 million short tons (32 percent) in 2030 when compared to 2005 levels.
Under the mass-based approach in 2020, the EPA esti mates that CO 2 emissions will be reduced by 610 million short tons (23 percent) unde r the rate-based approach compared to 2005 levels. In 2025, the EPA estimates that CO 2 emissions will be reduced by 782 million short ton s (29 percent) under the mass-based approach compared to 2005 levels. Under the mass-based approach, CO 2 emission reductions increase to 869 million short tons (32 percent) in 2030 when compared to 2005 levels.

ES.5 Costs
The compliance cost estimates for this final actio n are represented in this analysis as the change in electric power generation costs between t he base case and illustrative plan approach policy cases, including the cost of demand-side ene rgy efficiency measures and costs associated with monitoring, reporting, and recordkeeping requi rements (MR&R). In the rate-based approach, energy efficiency activities are modeled as being used by EGUs as a low-cost method of demonstrating compliance with their rate-based e missions standards. In the mass-based approach, energy efficiency activities are assumed to be adopted by states to lower demand, which in turn reduces the cost of achieving the mas s limitations. The level of energy efficiency
measures is determined outside of IPM and is assume d to be the same in the two illustrative plan approaches. The compliance assumptions, and therefo re the projected “compliance costs” setES-9

forth in this analysis, are illustrative in nature and do not represent the full suite of compliance flexibilities states may ultimately pursue.
The annual incremental cost is the projected additi onal cost of complying with the final rule in the year analyzed and includes the net chan ge in the annualized cost of capital investment in new generating sources and heat rate improvement s at coal-fired steam generating units, 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. The total compliance cost estimates presented here include the costs associated with monitoring, reporting, and recordkeeping. 3 The costs for both illustrative plan approaches ar e reflected in Table ES-5 below and discussed more extensively in Chapter 3 of this RIA. All dollar estimates
are in 2011 dollars.
The EPA estimates the annual incremental compliance cost for the rate-based approach for final emission guidelines to be $2.5 billion in 2020, $1.0 billion in 2025 and $8.4 billion in 2030, including the costs associated with monitorin g, reporting, and recordkeeping. 4 The EPA estimates the annual incremental compliance cost fo r the mass-based approach for final emission guidelines to be $1.4 billion in 2020, $3.0 billion in 2025 and $5.1 billion in 2030, including the costs associated with monitoring, reporting, and re cordkeeping.
Table ES-5. Compliance Costs for the Illustrative R ate-Based and Mass-Based Plan

Approaches Incremental Cost from Base Case (billions of 2011$) Rate-based Approach Mass-based Approach 2020 $2.5 $1.4 2025 $1.0 $3.0 2030 $8.4 $5.1 Source: Integrated Planning Model, 2015, with post- processing to account for exogenous demand-side man agement energy efficiency costs and monitoring, reporting, and recordkeeping costs. See Chapter 3 of this RIA for more details.

3 These costs are estimated outside of the IPM modell ing framework as IPM only models the contiguous U.S . and does not incorporate monitoring, reporting, and rec ordkeeping requirements specific to the Clean Power Plan Final Rules.

4 The MR&R costs estimates are $67 million in 2020, $ 16 million in 2025 and $16 million in 2030 and are assumed to be the same for both rate-based and mass-based i llustrative plan approaches. Note the MR&R costs in 2020 are related to facilities setting up net energy output monitoring and upgrading data acquisition systems.ES-10

The costs reported in Table ES-5 represent the esti mated incremental electric utility generating costs changes from the base case plus th e estimates of demand-side energy efficiency program costs (which are paid by electric utilities ), demand-side energy efficiency participant costs (which are paid by electric utility consumers ), and MR&R costs. For example, in 2030, under the rate-based approach, the incremental elec tric utility generating costs decline by about $18.0 billion from the base case. MR&R requirements in 2030 are estimated at $16.0 million, and demand-side energy efficiency costs in 2030 are estimated to be $26.3 billion, split equally between program and participants using a 3 percent discount rate (see Chapter 3 of this RIA for more details on these estimates). These cost estima tes sum to the $8.4 billion shown in Table ES3 and represent the total costs of the rate-based i llustrative plan approach in 2030. The same approach applies in each year of analysis for the r ate-based and the mass-based illustrative plan approaches.
The compliance costs reported in Table ES-5 are not social costs. These costs represent the estimated expenditures incurred by EGUs and sta tes to comply with the BSER goals for the Clean Power Plan Final Rule. These compliance cost estimates are compared to estimates of
social benefits to derive net benefits of the final emission guidelines, which are presented later in this Executive Summary. For a more extensive discus sion of social costs and benefits, see Chapter 3 and Chapter 4, respectively, of this RIA.

ES.6 Monetized Climate Benefits and Health Co-benef its
Implementing the final emission guidelines is expec ted to reduce emissions of CO 2 and have ancillary emission reductions (i.e., co-benefi ts) 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 Impact Analysis under Executive Order 12 866 (May 2013, Revised July 2015)
, henceforth denoted as the current SC-CO 2 TSD. 5 Also, the range of combined benefits reflects

5 Technical Support Document: Technical Update of th e Social Cost of Carbon for Regulatory Impact Analy sis Under Executive Order 12866, Interagency Working Gr oup on Social Cost of Carbon, with participation by Council of Economic Advisers, Council on Environmental Qual ity, Department of Agriculture, Department of Comme rce, Department of Energy, Department of Transportation, Domestic Policy Council, Environmental Protection Agency, National Economic Council, Office of Management and Budget, Office of Science and Technology Policy, a ndES-11

different concentration-response functions for the air quality health co-benefits, but it does not capture the full range of uncertainty inherent in t he health co-benefits estimates. Furthermore, we were unable to quantify or monetize all of the clim ate benefits and health and environmental cobenefits associated with the final emission guideli nes, including reducing exposure to SO 2 , NO X , and hazardous air pollutants (e.g., mercury), as we ll as ecosystem effects and visibility improvement. The omission of these endpoints from t he monetized results should not imply that the impacts are small or unimportant. Table ES-6 pr ovides the list of the quantified and unquantified health and environmental benefits in t his analysis.

Department of Treasury (May 2013, Revised July 2015 ). Available at: <https://www.whitehouse.gov/sites/default/files/omb /inforeg/scc-tsd-final-july-2015.pdf> Accessed 7/11 /2015.ES-12

Table ES-6. 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 — 1  SC-CO 2 TSD

Climate impacts from ozone and black carbon (direct ly emitted PM) — — Ozone ISA, PM ISA 2 Other climate impacts (e.g., other GHGs such as met hane, aerosols, other impacts) — — IPCC 2 Improved Human Health (co-benefits) Reduced incidence of premature mortality from exposure to PM 2.5 Adult premature mortality based on cohort study est imates

and expert elicitation estimates (age >25 or age >3 0)   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 2 Emergency room visits for cardiovascular effects (a ll ages) — — PM ISA 2 Strokes and cerebrovascular disease (age 50-79) — — PM ISA 2 Other cardiovascular effects (e.g., other ages) — — PM ISA 3 Other respiratory effects (e.g., pulmonary function , nonasthma ER visits, non-bronchitis chronic diseases, other ages and populations) — — PM ISA 3
Reproductive and developmental effects (e.g., low b irth weight, pre-term births, etc) — — PM ISA 3,4 Cancer, mutagenicity, and genotoxicity effects — — PM ISA 3,4
Reduced incidence of mortality from exposure to ozone Premature mortality based on short-term study estim ates (all

ages)   Ozone ISA Premature mortality based on long-term study estima tes (age 30–99) — — Ozone ISA 2

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 2 Other respiratory effects (e.g., premature aging of lungs) — — Ozone ISA 3 Cardiovascular and nervous system effects — — Ozone ISA 3 Reproductive and developmental effects — — Ozone IS A 3,4ES-13

Table ES-6. Continued

Reduced incidence of morbidity from exposure to NO 2 Asthma hospital admissions (all ages) — — NO 2 ISA 2

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

Reduced incidence of morbidity from exposure to SO 2 Respiratory hospital admissions (age > 65) — — SO 2 ISA 2

Asthma emergency department visits (all ages) — — S O 2 ISA 2 Asthma exacerbation (asthmatics age 4–12) — — SO 2 ISA 2 Acute respiratory symptoms (age 7–14) — — SO 2 ISA 2 Premature mortality — — SO 2 ISA 2,3,4 Other respiratory effects (e.g., airway hyperrespon siveness and inflammation, lung function, other ages and populations) — — SO 2 ISA 2,3

Reduced incidence of morbidity from exposure to methylmercury Neurologic effects—IQ loss — — IRIS; NRC, 2000 2

Other neurologic effects (e.g., developmental delay s, memory, behavior) — — IRIS; NRC, 2000 3
Cardiovascular effects — — IRIS; NRC, 2000 3,4 Genotoxic, immunologic, and other toxic effects — — IRIS; NRC, 2000 3,4 Improved Environment (co-benefits) Reduced visibility impairment Visibility in Class 1 areas — — PM ISA 2

Visibility in residential areas — — PM ISA 2 Reduced effects on materials Household soiling — — PM ISA 2,3

Materials damage (e.g., corrosion, increased wear) — — PM ISA 3 Reduced PM deposition (metals and organics) Effects on Individual organisms and ecosystems — — PM ISA 3

Reduced vegetation and ecosystem effects from exposure to

ozone Visible foliar injury on vegetation — — Ozone ISA 2

Reduced vegetation growth and reproduction — — Ozon e ISA 2 Yield and quality of commercial forest products and crops — — Ozone ISA 2 Damage to urban ornamental plants — — Ozone ISA 3 Carbon sequestration in terrestrial ecosystems — — Ozone ISA 2 Recreational demand associated with forest aestheti cs — — Ozone ISA 3 Other non-use effects Ozone ISA 3 Ecosystem functions (e.g., water cycling, biogeoche mical cycles, net primary productivity, leaf-gas exchange
, community composition) — — Ozone ISA 3

Reduced effects from acid deposition Recreational fishing — — NO x SO x ISA 2

Tree mortality and decline — — NO x SO x ISA 3 Commercial fishing and forestry effects — — NO x SO x ISA 3 Recreational demand in terrestrial and aquatic ecos ystems — — NO x SO x ISA 3 Other non-use effects NO x SO x ISA 3 Ecosystem functions (e.g., biogeochemical cycles) — — NO x SO x ISA 3ES-14

Table ES-6. Continued

Reduced effects from nutrient enrichment Species composition and biodiversity in terrestrial and

estuarine ecosystems — — NO x SO x ISA 3 Coastal eutrophication — — NO x SO x ISA 3 Recreational demand in terrestrial and estuarine ec osystems — — NO x SO x ISA 3 Other non-use effects NO x SO x ISA 3 Ecosystem functions (e.g., biogeochemical cycles, f ire regulation) — — NO x SO x ISA 3 Reduced vegetation effects from exposure to SO 2 and NO x Injury to vegetation from SO 2 exposure — — NO x SO x ISA 3

Injury to vegetation from NO x exposure — — NO x SO x ISA 3

Reduced ecosystem effects from exposure to methylmercury Effects on fish, birds, and mammals (e.g., reproduc tive

effects) — — Mercury Study RTC 3 Commercial, subsistence and recreational fishing — — Mercury Study RTC 2

1 The global climate and related impacts of CO 2 emissions changes, such as sea level rise, are est imated within each integrated assessment model as part of the calculat ion of the SC-CO 2 . The resulting monetized damages, which are relevant for conducting the benefit-cost analys is, are used in this RIA to estimate the welfare ef fects of quantified changes in CO 2 emissions.
2 We assess these co-benefits qualitatively due to d ata and resource limitations for this analysis.

3 We assess these co-benefits qualitatively because we do not have sufficient confidence in available d ata or methods.

4 We assess these co-benefits qualitatively because current evidence is only suggestive of causality or there are other significant concerns over the strength of the assoc iation.

ES.6.1 Estimating Global Climate Benefits
We estimate the global social benefits of CO 2 emission reductions expected from this rulemaking using the SC-CO 2 estimates presented in the current SC-CO 2 TSD. We refer to these estimates, which were developed by the U.S. governm ent, as “SC-CO 2 estimates” for the
remainder of this document. The SC-CO 2 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 c hanges 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 a ir conditioning. It is typically used to assess the avoided damages as a result of regulatory actio ns (i.e., benefits of rulemakings that lead to an incremental reduction in cumulative global CO 2 emissions).

The SC-CO 2 estimates used in this analysis have been develope d over many years, using the best science available, and with input from the public. The EPA and other federal agenciesES-15

have considered the extensive public comments on wa ys to improve SC-CO 2 estimation received via the notice and comment period that was part of numerous rulemakings. In addition, OMB’s Office of Information and Regulatory Affairs recent ly issued a response to the public comments it sought through a separate comment period on the approach used to develop the SC-CO 2 estimates. 6
An interagency working group (IWG) that included th e EPA and other executive branch entities used three integrated assessment models (I AMs) to develop SC-CO 2 estimates and recommended four global values for use in regulator y analyses. The SC-CO 2 estimates represent global measures because of the distinctive nature o f the climate change problem. Emissions of greenhouse gases contribute to damages around the w orld, even when they are released in the United States, and the world’s economies are now hi ghly interconnected. Therefore, the SC-CO 2 estimates incorporate the worldwide damages caused by carbon dioxide emissions in order to reflect the global nature of the problem, and we ex pect other governments to consider the global consequences of their greenhouse gas emissions when setting their own domestic policies. See RIA Chapter 4 for more discussion.
The IWG first released the estimates in February 20 10 and updated them in 2013 using
new versions of each IAM. The SC-CO 2 values was estimated using three integrated assess ment models (DICE, FUND, and PAGE) 7 , which the IWG harmonized across three key inputs: the probability distribution for equilibrium climate se nsitivity; five scenarios for economic, population, and emissions growth; and three constan t discount rates. The 2010 SC-CO 2 Technical Support Document (2010 SC-CO 2 TSD) provides a complete discussion of the methodology and the current SC-CO 2 TSD 8 presents and discusses the updated estimates. The
four SC-CO 2 estimates are as follows: $12, $40, $60, and $120 per short ton of CO 2 emissions in the year 2020 (2011$), and each estimate increases over time. 9 These SC-CO 2 estimates are

6 See https://www.whitehouse.gov/sites/default/files /omb/inforeg/scc-response-to-comments-final-july-20 15.pdf

7 The full models names are as follows: Dynamic Inte grated Climate and Economy (DICE); Climate Framewor k for Uncertainty, Negotiation, and Distribution (FUND); and Policy Analysis of the Greenhouse Gas Effect (P AGE).

8 The IWG published the updated TSD in 2013, then is sued two minor corrections to it in July 2015.

9 The 2010 and 2013 TSDs present SC-CO 2 in 2007$ per metric ton. The estimates were adjust ed to (1) short tons for using conversion factor 0.90718474 and (2) 2011 $ using GDP Implicit Price Deflator, http://www.gpo.gov/fdsys/pkg/ECONI-2013-02/pdf/ECON I-2013-02-Pg3.pdf.ES-16

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 SC-CO 2 TSD noted a number of limitations to the SC-CO 2 analysis, including the incomplete way in which the IAMs capture catast rophic and non-catastrophic impacts, their incomplete treatment of adaptation and technologica l change, uncertainty in the extrapolation of damages to high temperatures, and assumptions regar ding risk aversion. Currently integrated assessment models do not assign value to all of the important physical, ecological, and economic impacts of climate change recognized in the climate change literature because of a lack of precise information on the nature of damages and be cause the science incorporated into these models understandably lags behind the most recent r esearch. In particular, the IPCC Fourth Assessment Report concluded that “It is very likely that [SC-CO 2 estimates] underestimate the damage costs because they cannot include many non-q uantifiable impacts.” Nonetheless, these
estimates and the discussion of their limitations r epresent the best available information about
the social benefits of CO 2 emission reductions to inform the benefit-cost ana lysis.
In addition, after careful evaluation of the full r ange of comments submitted to OMB’s Office of Information and Regulatory Affairs, the I WG continues to recommend the use of these
SC-CO 2 estimates in regulatory impact analysis. With the release of the response to comments, the IWG announced plans to obtain expert independen t advice from the National Academies of Sciences, Engineering, and Medicine (Academies) to ensure that the SC-CO 2 estimates continue
to reflect the best available scientific and econom ic information on climate change. 10 The Academies process will be informed by the public co mments received and focus on the technical merits and challenges of potential approaches to im proving the SC-CO 2 estimates in future updates.

ES 6.2 Estimating Air Quality Health Co-Benefits
The final emission guidelines would reduce emission s of precursor pollutants (e.g., SO 2 , NO X , and directly emitted particles), which in turn wo uld lower ambient concentrations of PM 2.5

10 See <https://www.whitehouse.gov/blog/2015/07/02/es timating-benefits-carbon-dioxide-emissions-reductio ns>.ES-17

and ozone. This co-benefits analysis quantifies the monetized benefits associated with the reduced exposure to these two pollutants. 11 Unlike the global SC-CO 2 estimates, the air quality health co-benefits are only estimated for the conti guous U.S. The estimates of monetized PM 2.5 co-benefits include avoided premature deaths (deriv ed from effect coefficients in two cohort studies [Krewski et al. 2009 and Lepeule et al. 2012] for adults and one for infants [Woodruff et al. 1997]), as well as avoided morbidity effects for t en 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 epidemio logy 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 use a “benefit-per-ton” approach to estimate the PM 2.5 and ozone co-benefits in this RIA. Benefit-per-ton approaches apply an average be nefit per ton derived from modeling of benefits of specific air quality scenarios to estim ates of emissions reductions for scenarios where no air quality modeling is available. The benefit-p er-ton approach we use in this RIA relies on estimates of human health responses to exposure to PM and ozone obtained from the peerreviewed scientific literature. These estimates are used in conjunction with population data, baseline health information, air quality data and e conomic valuation information to conduct health impact and economic benefits assessments.
Specifically, in this analysis, we multiplied the b enefit-per-ton estimates by the corresponding emission reductions that were generat ed from air quality modeling of the proposed Clean Power Plan. Similar to the co-benefi ts analysis conducted for the RIA for this rule at proposal, we generated regional benefit-per -ton estimates by aggregating the impacts in
BenMAP 12 to the region (i.e., East, West, and California) r ather than aggregating to the nation.
To calculate the co-benefits for the final emission guidelines, we then multiplied the regional

11 We did not estimate the co-benefits associated with reducing direct exposure to SO 2 and NO X . For this RIA, we did not estimate changes in emissions of directly e mitted particles. As a result, quantified PM 2.5 related benefits are underestimated by a relatively small amount. In the proposal RIA, the benefits from reductions in dire ctly emitted PM 2.5 were less than 10 percent of total monetized healt h co-benefits across all scenarios and years.

12 BenMAP is a computer program developed by the EPA t hat calculates the number and economic value of air pollution-related deaths and illnesses. The softwar e incorporates a database that includes many of the concentrationresponse relationships, population files, and healt h and economic data needed to quantify these impact s.ES-18

benefit-per-ton estimates for the EGU sector by the corresponding emission reductions. All benefit-per-ton estimates reflect the geographic di stribution of the modeled emissions, which may not exactly match the emission reductions in th is rulemaking, and thus they may not reflect the local variability in population density, meteor ology, exposure, baseline health incidence rates, or other local factors for any specific loca tion.

Our estimate of the monetized co-benefits is based on the EPA’s interpretation of the best available scientific literature (U.S. EPA, 2009) an d methods and supported by the EPA’s Science Advisory Board and the NAS (NRC, 2002). Below are k ey 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 th eir 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 differentia tion 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 ye t sufficient to allow differentiation
of those constituents or sources that are more clos ely 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 inc lude health co-benefits from reducing fine particles in areas with varied concen trations of PM 2.5 , including both areas that do not meet the National Ambient Air Qua lity Standard for fine particles and those areas that are in attainment, down to the lowest modeled concentrations.

3. We assume that there is a “cessation” lag between t he change in PM exposures and the total realization of changes in mortality effec ts. 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 exp osure based on the advice of the SAB-HES (U.S. EPA-SAB, 2004c), which affects the va luation of mortality cobenefits at different discount rates.ES-19

Every benefits analysis examining the potential eff ects of a change in environmental protection requirements is limited, to some extent, by data gaps, model capabilities (such as geographic coverage) and uncertainties in the under lying scientific and economic studies used to configure the benefit and cost models. In addition, given the flexibilities afforded states in complying with the emission guidelines, the co-bene fits estimated presented in this RIA are not definitive estimates, but are instead illustrative of approaches that states may take. Despite these uncertainties, we believe this analysis provides a reasonable indication of the expected health cobenefits of the air quality emission reductions for the final emission guidelines under a set of reasonable assumptions. This analysis does not incl ude 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 qualit y 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 est imates.

ES 6.3 Combined Benefits Estimates
The EPA has evaluated the range of potential impact s by combining all four SC-CO 2 values with health co-benefits values at the 3 perc ent and 7 percent discount rates. Different discount rates are applied to SC-CO 2 than to the health co-benefit estimates; because C O 2 emissions are long-lived and subsequent damages occ ur over many years. Moreover, several discount rates are applied to SC-CO 2 because the literature shows that the estimate of SC-CO 2 is sensitive to assumptions about discount rate and be cause no consensus exists on the appropriate rate to use in an intergenerational context. The U. S. government centered its attention on the average SC-CO 2 at a 3 percent discount rate but emphasized the im portance of considering all
four SC-CO 2 estimates. Table ES-7 (rate-based illustrative pla n approach) and Table ES-8 (mass-based illustrative plan approach) provide the combined climate benefits and health cobenefits for the Clean Power Plan Final Rule estima ted for 2020, 2025, and 2030 for each
discount rate combination. All dollar estimates are in 2011 dollars.ES-20

Table ES-7. Combined Estimates of Climate Benefits and Health Co-Benefits for RateBased Approach (billions of 2011$)*

SC-CO 2 Discount Rate and Statistic** Climate Benefits Only Climate Benefits plus Health Co-benefits

(Discount Rate Applied to Health Co-benefits) 3% 7% In 2020 69 million short tons CO 2 5% $0.80 $1.5 to $2.6 $1.4 to $2.5 3% $2.8 $3.5 to $4.6 $3.5 to $4.5 2.5% $4.1 $4.9 to $6.0 $4.8 to $5.9 3% (95 th percentile) $8.2 $8.9 to $10 $8.9 to $9.9 In 2025 232 million short tons CO 2 5% $3.1 $11 to $21 $9.9 to $19 3% $10 $18 to $28 $17 to $26 2.5% $15 $23 to $33 $22 to $31 3% (95 th percentile) $31 $38 to $49 $38 to $47 In 2030 415 million short tons CO 2 5% $6.4 $21 to $40 $19 to $37 3% $20 $34 to $54 $33 to $51 2.5% $29 $43 to $63 $42 to $60 3% (95 th percentile) $61 $75 to $95 $74 to $92 *All benefit estimates are rounded to two significa nt figures. Climate benefits are based on reduction s in CO 2 emissions. Co-benefits are based on regional benefi t-per-ton estimates. Ozone co-benefits occur in ana lysis year, so they are the same for all discount rates. The healt h co-benefits reflect the sum of the PM 2.5 and ozone co-benefits 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)). The monetized health co-benefits do not i nclude reduced health effects from reductions in directly emitted PM 2.5 , direct exposure to NO X , SO 2 , and HAP; ecosystem effects; or visibility impairment. See Chapter 4 for more infor mation about these estimates and for more informati on regarding the uncertainty in these estimates. **Unless otherwise specified, it is the model avera ge.ES-21

Table ES-8. Combined Estimates of Climate Benefits and Health Co-benefits for MassBased Approach (billions of 2011$)*

SC-CO 2 Discount Rate and Statistic** Climate Benefits Only Climate Benefits plus Health Co-benefits

(Discount Rate Applied to Health Co-benefits) 3% 7% In 2020 82 million short tons CO 2 5% $0.94 $2.9 to $5.7 $2.8 to $5.3 3% $3.3 $5.3 to $8.1 $5.1 to $7.7 2.5% $4.9 $6.9 to $9.7 $6.7 to $9.3 3% (95 th percentile) $9.7 $12 to $14 $11 to $14 In 2025 264 million short tons CO 2 5% $3.6 $11 to $21 $10 to $19 3% $12 $19 to $29 $18 to $27 2.5% $17 $24 to $35 $24 to $33 3% (95 th percentile) $35 $42 to $52 $42 to $51 In 2030 413 million short tons CO 2 5% $6.4 $18 to $34 $17 to $32 3% $20 $32 to $48 $31 to $46 2.5% $29 $41 to $57 $40 to $55 3% (95 th percentile) $60 $72 to $89 $71 to $86 *All benefit estimates are rounded to two significa nt figures. Climate benefits are based on reduction s in CO 2 emissions. Co-benefits are based on regional benefi t-per-ton estimates. Ozone co-benefits occur in ana lysis year, so they are the same for all discount rates. The healt h co-benefits reflect the sum of the PM 2.5 and ozone co-benefits 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)). The monetized health co-benefits do not i nclude reduced health effects from reductions in directly emitted PM 2.5 , direct exposure to NO X , SO 2 , and HAP; ecosystem effects; or visibility impairment. See Chapter 4 for more infor mation about these estimates and for more informati on regarding the uncertainty in these estimates. **Unless otherwise specified, it is the model avera ge.

ES.7 Net Benefits
Table ES-9 and ES-10 provide the estimates of the c limate benefits, health co-benefits, compliance costs and net benefits of the final emis sion guidelines for rate-based and mass-based approaches, respectively. There are additional impo rtant benefits that the EPA could not monetize. Due to current data and modeling limitati ons, our estimates of the benefits from reducing CO 2 emissions do not include important impacts like oc ean acidification or potential tipping points in natural or managed ecosystems. Un quantified benefits also include climate benefits from reducing emissions of non-CO 2 greenhouse gases and co-benefits from reducing exposure to SO 2 , NO X , and hazardous air pollutants (e.g., mercury), as well as ecosystem effects and visibility impairment. Upon considering these l imitations and uncertainties, it remains clear
that the benefits of this final rule are substantia l and far outweigh the costs.ES-22

Table ES-9. Monetized Benefits, Compliance Costs, a nd Net Benefits Under the Ratebased Illustrative Plan Approach (billions of 2011$ ) a Rate-Based Approach 2020 2025 2030 Climate Benefits b 5% discount rate $0.80 $3.1 $6.4 3% discount rate $2.8 $10 $20 2.5% discount rate $4.1 $15 $29 95th percentile at 3% discount rate $8.2 $31 $61
Air Quality Co-benefits Discount Rate
3% 7% 3% 7% 3% 7% Air Quality Health Co-benefits c $0.70 to $1.8 $0.64 to $1.7 $7.4 to $18 $6.7 to $16 $14 to $34 $13 to $31 Compliance Costs d $2.5 $1.0 $8.4 Net Benefits e $1.0 to $2.1 $1.0 to $2.0 $17 to $27 $16 to $25 $26 to $45 $25 to $43

Non-Monetized Benefits Non-monetized climate benefits Reductions in exposure to ambient NO 2 and SO 2 Reductions in mercury deposition Ecosystem benefits associated with reductions in em issions of NO X , SO 2 , PM, and mercury Visibility impairment

a All are rounded to two significant figures, so fig ures may not sum. b The climate benefit estimate in this summary table reflects global impacts from CO 2 emission changes and does not account for changes in non-CO 2 GHG emissions. Also, different discount rates are applied to SC-CO 2 than to the other estimates because CO 2 emissions are long-lived and subsequent damages oc cur over many years. The benefit estimates in this table are based on the average SC -CO 2 estimated for a 3 percent discount rate, however w e emphasize the importance and value of considering t he full range of SC-CO 2 values. As shown in the RIA, climate benefits are also estimated using the other three S C-CO 2 estimates (model average at 2.5 percent discount r ate, 3 percent, and 5 percent; 95 th percentile at 3 percent). The SC-CO 2 estimates are year-specific and increase over time

. c The air quality health co-benefits reflect reduced exposure to PM 2.5 and ozone associated with emission reductions of SO 2 and NO X . The co-benefits do not include the benefits of re ductions in directly emitted PM 2.5 . These additional benefits would increase overall benefits by a few percent based on the analyses conducted f or the proposed rule. The range reflects the use of concen tration-response functions from different epidemiol ogy studies. The reduction in premature fatalities each year acc ounts for over 98 percent of total monetized co-ben efits from PM 2.5 and ozone. These models assume that all fine partic les, regardless of their chemical composition, are equally potent in causing premature mortality because the s cientific evidence is not yet sufficient to allow d ifferentiation of effect estimates by particle type. Estimates in the table are presented for three analytical years wit h air quality cobenefits calculated using two discount rates. The e stimates of co-benefits are annual estimates in eac h of the analytical years, reflecting discounting of mortali ty benefits over the cessation lag between changes in PM 2.5 concentrations and changes in risks of premature de ath (see RIA Chapter 4 for more details), and disco unting of morbidity benefits due to the multiple years of cos ts associated with some illnesses. The estimates ar e not the present value of the benefits of the rule over the full compliance period. d Total costs are approximated by the illustrative co mpliance costs estimated using the Integrated Plann ing Model for the final emission guidelines and a discount rate o f approximately 5 percent. This estimate also inclu des monitoring, recordkeeping, and reporting costs and demand-side energy efficiency program and participant costs.

e The estimates of net benefits in this summary tabl e are calculated using the global SC-CO 2 at a 3 percent discount rate (model average). The RIA includes combined cli mate and health estimates based on additional disco unt rates.ES-23

Table ES-10. Monetized Benefits, Compliance Costs, and Net Benefits under the Massbased Illustrative Plan Approach (billions of 2011$ ) a Mass-Based Approach 2020 2025 2030 Climate Benefits b 5% discount rate $0.94 $3.6 $6.4 3% discount rate $3.3 $12 $20 2.5% discount rate $4.9 $17 $29 95th percentile at 3% discount rate $9.7 $35 $60
Air Quality Co-benefits Discount Rate
3% 7% 3% 7% 3% 7% Air Quality Health Co-benefits c $2.0 to $4.8 $1.8 to $4.4 $7.1 to $17 $6.5 to $16 $ 12 to $28 $11 to $26 Compliance Costs d $1.4 $3.0 $5.1 Net Benefits e $3.9 to $6.7 $3.7 to $6.3 $16 to $26 $15 to $24 $26 to $43 $25 to $40

Non-Monetized Benefits Non-monetized climate benefits Reductions in exposure to ambient NO 2 and SO 2 Reductions in mercury deposition Ecosystem benefits associated with reductions in em issions of NO X , SO 2 , PM, and mercury Visibility improvement

a All are rounded to two significant figures, so fig ures may not sum. b The climate benefit estimate in this summary table reflects global impacts from CO 2 emission changes and does not account for changes in non-CO 2 GHG emissions. Also, different discount rates are applied to SC-CO 2 than to the other estimates because CO 2 emissions are long-lived and subsequent damages oc cur over many years. The benefit estimates in this table are based on the average SC -CO 2 estimated for a 3 percent discount rate, however w e emphasize the importance and value of considering t he full range of SC-CO 2 values. As shown in the RIA, climate benefits are also estimated using the other three S C-CO 2 estimates (model average at 2.5 percent discount r ate, 3 percent, and 5 percent; 95 th percentile at 3 percent). The SC-CO 2 estimates are year-specific and increase over time

. c The air quality health co-benefits reflect reduced exposure to PM 2.5 and ozone associated with emission reductions of, SO 2 and NO X . The co-benefits do not include the benefits of re ductions in directly emitted PM 2.5 . These additional benefits would increase overall benefits by a few percent based on the analyses conducted f or the proposed rule. The range reflects the use of concen tration-response functions from different epidemiol ogy studies. The reduction in premature fatalities each year acc ounts for over 98 percent of total monetized co-ben efits from PM 2.5 and ozone. These models assume that all fine partic les, regardless of their chemical composition, are equally potent in causing premature mortality because the s cientific evidence is not yet sufficient to allow d ifferentiation of effect estimates by particle type. Estimates in the table are presented for three analytical years wit h air quality cobenefits calculated using two discount rates. The e stimates of co-benefits are annual estimates in eac h of the analytical years, reflecting discounting of mortali ty benefits over the cessation lag between changes in PM2.5 concentrations and changes in risks of premature de ath (see RIA Chapter 4 for more details), and disco unting of morbidity benefits due to the multiple years of cos ts associated with some illnesses. The estimates ar e not the present value of the benefits of the rule over the full compliance period. d Total costs are approximated by the illustrative co mpliance costs estimated using the Integrated Plann ing Model for the final emission guidelines and a discount rate o f approximately 5 percent. This estimate also inclu des monitoring, recordkeeping, and reporting costs and demand-side energy efficiency program and participant costs.

e The estimates of net benefits in this summary tabl e are calculated using the global SC-CO 2 at a 3 percent discount rate (model average). The RIA includes combined cli mate and health estimates based on additional disco unt rates.ES-24

ES.8 Economic Impacts
The final emission guidelines have important energy market implications. Table ES-11 presents a variety of important energy market impac ts for 2020, 2025, and 2030 for both the ratebased and mass-based illustrative plan approaches.
Table ES-11. Summary Table of Important Energy Mark et Impacts (Percent Change from

Base Case)
Rate-Based Mass-Based 2020 2025 2030 2020 2025 2030 Retail electricity prices 3% 1% 1% 3% 2% 0% Price of coal at minemouth -1% -5% -4% -1% -5% -3% Coal production for power sector use -5% -14% -25% -7% -17% -24% Price of natural gas delivered to power sector 5% - 8% 2% 4% -3% -2% Natural gas use for electricity generation 3% -1% - 1% 5% 0% -4%

Energy market impacts from the guidelines are discu ssed more extensively in Chapter 3 of this
RIA.
Additionally, changes in supply or demand for elect ricity, natural gas, and coal can impact markets for goods and services produced by s ectors that use these energy inputs in the production process or that supply those sectors. Ch anges in cost of production may result in changes in price and/or quantity produced by these sectors and these market changes may affect the profitability of firms and the economic welfare of their consumers. The EPA recognizes that these final emission guidelines provide flexibility , 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, for example, can r esult in changes in production and profitability for firms that supply those goods and services.
ES.9 Employment Impacts Executive Order 13563 directs federal agencies to c onsider the effect of regulations on job creation and employment. According to the Execu tive Order, “our regulatory system must protect public health, welfare, safety, and our env ironment while promoting economic growth, innovation, competitiveness, and job creation. It m ust be based on the best available science” (Executive Order 13563, 2011). Although standard be nefit-cost analyses have not typically included a separate analysis of regulation-induced employment impacts, we typically conductES-25

employment analyses. During the current economic re covery, employment impacts are of particular concern and questions may arise about th eir existence and magnitude.
Given the wide range of approaches that may be used to meet the requirements of the Clean Power Plan Final Rule, quantifying the associ ated employment impacts is difficult. The EPA’s illustrative employment analysis includes an estimate of projected employment impacts associated with these guidelines for the utility po wer sector, coal and natural gas production, and demand-side energy efficiency activities. These pro jections are derived, in part, from the detailed model of the utility power sector used for this reg ulatory 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 a net decrease of approximately 25, 000 job-years in 2025 for the final guidelines under the rate-based illustrative plan approach and approximately 26,000 job-years in 2025 under the mass-based approach. For 2030 the estimat es of the net decrease in job-years is 30,900 under the rate-based plan, and 33,700 under the mas s-based plan. The Agency is also offering an illustrative calculation of potential employment ef fects due to demand-side energy efficiency programs. Employment impacts from demand-side energ y efficiency programs in 2030 could range from approximately 52,000 to 83,000 jobs unde r the final guidelines. More detail about these analyses can be found in Chapter 6 of this RI A.

ES.10 References
Bell, M.L., A. McDermott, S.L. Zeger, J.M. Sarnet, and F. Dominici

. 2004. “Ozone and ShortTerm Mortality in 95 U.S. Urban Communities, 1987-2 000.” Journal of the American Medical Association. 292(19):2372-8.Docket ID EPA-HQ-OAR-2009-0472-11457 7, Technical Support Document: Social Cost of Carbon f or 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, Na tional Economic Council, Office of Energy and Climate Change, Office of Management and Budget, Office of Science and Technology Policy, and Department of Treasury (Febr uary 2010). Available at: <http://www.whitehouse.gov/sites/default/files/omb/ inforeg/for-agencies/Social-Cost-ofCarbon-for-RIA.pdf>.ES-26

Docket ID EPA-HQ-OAR-2013-0602, Technical Support D ocument: Technical Update of the Social Cost of Carbon for Regulatory Impact Analysi s 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, Depar tment of Transportation, Domestic Policy Council, Environmental Protection Agency, Na tional Economic Council, Office of Management and Budget, Office of Science and Techno logy Policy, and Department of Treasury (May 2013, Revised July 2015). Also availa ble at: < https://www.whitehouse.gov/sites/default/files/omb/ inforeg/scc-tsd-final-july-2015.pdf>. Accessed July 15, 2015.
Fann, N., K.R. Baker, and C.M. Fulcher. 2012. “Char acterizing 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. Krewski D., M. Jerrett, R.T. Burnett, R. Ma, E. Hug hes, 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 Eff ects Institute, Boston, MA. 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, Depar tment of Transportation, Environmental Protection Agency, National Economic Council, Office of Management and Budget, Office of Science and Technology Policy, an d Department of Treasury. Response to to Comments: Social Cost of Carbon for Regulatory I mpact Analysis Under Executive Order 12866. July 2015. Available at: <https://www.whitehouse.gov/sites/default/files/omb /inforeg/scc-response-to-commentsfinal-july-2015.pdf.> Accessed July 15, 2015. Intergovernmental Panel on Climate Change (IPCC). 2 007. 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/publicati ons_ipcc_fourth_assessment_report_synt hesis_report.htm>. Accessed June 6, 2015. 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. 120(7):965-70.ES-27

Levy, J.I., S.M. Chemerynski, and J.A. Sarnat. 2005 . “Ozone Exposure and Mortality: An Empiric Bayes Metaregression Analysis.” Epidemiology. 16(4):458-68. National Research Council (NRC). 2000. Toxicological Effects of Methylmercury: Committee o n the Toxicological Effects of Methylmercury .” Board on Environmental Studies and Toxicology. National Academies Press. Washington, D C. National Research Council (NRC). 2002. Estimating the Public Health Benefits of Proposed A ir Pollution Regulations . National Academies Press. Washington, DC. U.S. Environmental Protection Agency (U.S. EPA). 20 08a. Integrated Science Assessment for Sulfur Oxides—Health Criteria (Final Report) . National Center for Environmental Assessment – RTP Division, Research Triangle Park, NC. September. Available at: <http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?dei d=198843>. Accessed June 4, 2015. U.S. Environmental Protection Agency (U.S. EPA). 20 08b. Final Ozone NAAQS Regulatory Impact Analysis . EPA-452/R-08-003. Office of Air Quality Planning and Standards Health and Environmental Impacts Division, Air Benefit and Cost Group Research Triangle Park, NC. March. Available at: < http://www.epa.gov/ttnec as1/regdata/RIAs/6- ozoneriachapter6.pdf>. Accessed June 4, 2015. U.S. EPA. 2008c. Integrated Science Assessment for Oxides of Nitrogen: Health Criteria (Final Report). Research Triangle Park, NC: National Cente r for Environmental Assessment. July. Available at < http://cfpub.epa.gov/ncea/cfm/record isplay.cfm?deid=194645>. U.S. Environmental Protection Agency (U.S. EPA). 20 08c. Integrated Science Assessment for Oxides of Nitrogen - Health Criteria (Final Report)

. National Center for Environmental Assessment, Research Triangle Park, NC. July. Avail able at: <http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?dei d=194645>. Accessed June 4, 2015. U.S. Environmental Protection Agency (U.S. EPA). 20 09b. Integrated Science Assessment for Particulate Matter (Final Report) . EPA-600-R-08-139F. National Center for Environmen tal Assessment – RTP Division, Research Triangle Park, NC. December. Available at: <http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?dei d=216546>. Accessed June 4, 2015. U.S. Environmental Protection Agency (U.S. EPA). 20 10d. Section 3: Re

‐ analysis of the Benefits of Attaining Alternative Ozone Standards to Incorpo rate Current Methods . Available at: <http://www.epa.gov/ttnecas1/regdata/RIAs/s3-supple mental_analysis-updated_benefits11- 5.09.pdf >. Accessed June 4, 2015.ES-28

U.S. Environmental Protection Agency (U.S. EPA). 20 12a. Regulatory Impact Analysis for the Final Revisions to the National Ambient Air Quality Standards for Particulate Matter. EPA452/R-12-003. Office of Air Quality Planning and St andards, Health and Environmental Impacts Division, Research Triangle Park, NC. Decem ber. Available at: < http://www.epa.gov/ttnecas1/regdata/RIAs/finalria.p df>. Accessed June 4, 2015. U.S. Environmental Protection Agency (U.S. EPA). 20 13b. Integrated Science Assessment of Ozone and Related Photochemical Oxidants (Final Rep ort). EPA/600/R-10/076F. National Center for Environmental Assessment – RTP Division, Research Triangle Park. Available at: <http://cfpub.epa.gov/ncea/isa/recordisplay.cfm?dei d=247492#Download>. Accessed June 4, 2015. U.S. EPA. 2015. Technical Support Document (TSD) th e Final Carbon Pollution Emission Guidelines for Existing Stationary Sources: Electri c Utility Generating Units. CO 2 Emission Performance Rate and Goal Computation.
Woodruff, T.J., J. Grillo, and K.C. Schoendorf. 199 7. “The relationship between selected causes of postneonatal infant mortality and particulate ai r pollution in the United States.” Environmental Health Perspectives. 105(6): 608-612.1-1



### C HAPTER 

### 1: I NTRODUCTION AND 

### B ACKGROUND FOR THE 

### C LEAN 

### P OWER 

### P LAN

1.1 Introduction
This document presents estimates of potential benef its, costs, and economic impacts of illustrative approaches states may implement to com ply with the Final Carbon Pollution Emission Guidelines for Existing Stationary Sources : Electric Utility Generating Units (herein referred to as “final emission guidelines” or the “ Clean Power Plan Final Rule”). 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 R ulemaking
1.2.1 Statutory Requirement
Clean Air Act section 111, which Congress enacted a s part of the 1970 Clean Air Act Amendments, establishes mechanisms for controlling emissions of air pollutants from stationary sources. This provision requires the EPA to promulg ate a list of categories of stationary sources that the Administrator, in his or her judgment, fin ds “causes, or contributes significantly to, air pollution which may reasonably be anticipated to en danger public health or welfare.” 13 The EPA has listed more than 60 stationary source categorie s under this provision. 14 Once the EPA lists a source category, the EPA must, under CAA section 11 1(b)(1)(B), establish “standards of performance” for emissions of air pollutants from n ew sources in the source categories. 15 These standards are known as new source performance stand ards (NSPS), and they are national requirements that apply directly to the sources sub ject to them.

When the EPA establishes NSPS for new sources in a particular source category, the EPA is also required, under CAA section 111(d)(1), to prescribe regulations for states to submit plans regulating existing sources in that source ca tegory for any air pollutant that, in general, is not regulated under the CAA section 109 requirement s for the NAAQS or regulated under the CAA section 112 requirements for hazardous air poll utants (HAP). CAA section 111(d)’s mechanism for regulating existing sources differs f rom the one that CAA section 111(b) provides

13 CAA §111(b)(1)(A).

14 See 40 CFR 60 subparts Cb – OOOO.

15 CAA §111(b)(1)(B), 111(a)(1).1-2

for new sources because CAA section 111(d) contempl ates states submitting plans that establish “standards of performance” for the affected sources and that contain other measures to implement and enforce those standards.
“Standards of performance” are defined under CAA se ction 111(a)(1) as standards for
emissions that reflect the emission limitation achi evable from the “best system of emission reduction,” considering costs and other factors, th at “the Administrator determines has been adequately demonstrated.” CAA section 111(d)(1) gra nts states the authority, in applying a standard of performance to a particular source, to take into account the source’s remaining useful
life or other factors.
Under CAA section 111(d), a state must submit its p lan to the EPA for approval, and the EPA must approve the state plan if it is “satisfact ory.” 16 If a state does not submit a plan, or if the EPA does not approve a state’s plan, then the EPA m ust establish a plan for that state. 17 Once a state receives the EPA’s approval of its plan, the provisions in the plan become federally enforceable against the entity responsible for nonc ompliance, in the same manner as the provisions of an approved State Implementation Plan (SIP) under the Act.
1.2.2 Health and Welfare Impacts from Climate Chang e According to the National Research Council, “Emissi ons of CO 2 from the burning of fossil fuels have ushered in a new epoch where huma n activities will largely determine the
evolution of Earth’s climate. Because CO 2 in the atmosphere is long lived, it can effectivel y lock Earth and future generations into a range of impact s, some of which could become very severe. Therefore, emission reduction choices made today ma tter in determining impacts experienced not just over the next few decades, but in the comi ng centuries and millennia.” 18
In 2009, based on a large body of robust and compel ling scientific evidence, the EPA Administrator issued the Endangerment Finding under CAA section 202(a)(1). 19 In the

16 CAA section 111(d)(2)(A).

17 CAA section 111(d)(2)(A).

18 National Research Council, Climate Stabilization Ta rgets, p.3.
19 “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) (“Endange rment Finding”).1-3

Endangerment Finding, the Administrator found that the current, elevated concentrations of GHGs in the atmosphere—already at levels unpreceden ted in human history—may reasonably be anticipated to endanger public health and welfar e of current and future generations in the
United States.
Since the administrative record concerning the Enda ngerment Finding closed following the EPA’s 2010 Reconsideration Denial, the climate has continued to change, with new records being set for a number of climate indicators such a s global average surface temperatures, Arctic sea ice retreat, CO 2 concentrations, and sea level rise. Additionally, a number of major scientific assessments have been released that improve underst anding of the climate system and strengthen the case that GHGs endanger public health and welfa re both for current and future generations. These assessments are from the Intergovernmental Pa nel on Climate Change (IPCC), the U.S. Global Change Research Program (USGCRP), and the Na tional Research Council (NRC). These
and other assessments are discussed in more detail in the preamble and in Chapter 4 of this Regulatory Impact Assessment (RIA).

1.2.3 Market Failure
Many regulations are promulgated to correct market failures, which otherwise lead to a suboptimal allocation of resources within the free market. Air quality and pollution control regulations address “negative externalities” whereb y the market does not internalize the full opportunity cost of production borne by society as public goods such as air quality are unpriced.
GHG emissions impose costs on society, such as nega tive health and welfare impacts, that are not reflected in the market price of the g oods produced through the polluting process. For this regulatory action the good produced is ele ctricity. If a fossil fuel-fired electricity producer pollutes the atmosphere when it generates electricity, this cost will be borne not by the polluting firm but by society as a whole, thus impo sing a negative externality. The equilibrium market price of electricity may fail to incorporate the full opportunity cost to society of generating electricity. All else equal, given this externality, the composition of EGUs used to generate electricity in a free market will not be s ocially optimal, and the quantity of electricity generated may not be at the socially optimal level. Fossil fuel-fired EGUs may produce more electricity than would occur if they had to accoun t for the cost associated with this negative externality. Consequently, absent a regulation on e missions, the composition of the fleet of1-4

EGUs used to generate electricity may not be social ly optimal, and the marginal social cost of the last unit of electricity produced may exceed it s marginal social benefit. This regulation will regulation will work towards addressing this market failure by causing affected EGUs to begin to internalize the negative externality associated wit h CO 2 emissions.
1.3 Summary of Regulatory Analysis
In accordance with Executive Order 12866, Executive Order 13563, OMB Circular A-4, and the EPA’s “Guidelines for Preparing Economic An alyses,” the EPA prepared this RIA for this “significant regulatory action.” This action i s an economically significant regulatory action because it is expected to have an annual effect on the economy of $100 million or more or adversely affect in a material way the economy, a s ector of the economy, productivity, competition, jobs, the environment, public health o r safety, or state, local, or tribal governments
or communities. 20
This RIA addresses the potential costs, emission re ductions, and benefits of the final emission guidelines that are the focus of this acti on. Additionally, this RIA includes information about potential impacts on electricity markets, emp loyment, and markets outside the electricity

sector. In evaluating the impacts of the final guidelines, we analyzed a number of uncertainties. For example, the analysis includes an evaluation of two illustrative plan approaches that states and affected EGUs may take to accomplish state emis sion performance goals, a rate-based and a mass-based approach. The RIA also examines key unce rtainties in the estimated benefits of reducing carbon dioxide and other air pollutants. F or a further discussion of key evaluations of uncertainty in the regulatory analyses for this rul emaking, see Chapter 8 of this RIA.
1.4 Background for the Final Emission Guidelines
1.4.1 Base Case and Years of Analysis
The rule analyzed in this RIA finalizes emission gu idelines for states to limit CO 2 emissions from certain existing EGUs. The base case for this analysis, which uses the Integrated

20 The analysis in this RIA and the RIA that accompan ied the proposal together constitute the economic a ssessment required by CAA section 317. In the EPA’s judgment, the assessment is as extensive as practicable taki ng into account the EPA’s time, resources, and other duties and authorities.1-5

Planning Model (IPM), includes state rules that hav e been finalized and/or approved by a state’s legislature or environmental agencies, as well as f inal federal rules. The IPM Base Case v.5.15 includes the Cross-State Air Pollution Rule (CSAPR) , the Mercury and Air Toxics Rule (MATS), the proposed Carbon Pollution Standards for New Power Plants, the Cooling Water Intakes (316(b)) Rule, the Combustion Residuals fro m Electric Utilities (CCR), and other state and Federal regulations to the extent that they con tain measures, permits, or other air-related limitations or requirements. Additional legally bin ding and enforceable commitments for GHG
reductions considered in the base case are discusse d in the documentation for IPM. 21
Costs and benefits are presented for illustrative p lan approaches for the analysis years of 2020, 2025, and 2030. These years were selected bec ause they represent initial build up, interim, and full implementation years for the two illustrat ive approaches analyzed. Analyses of energy, economic, and employment impacts are presented for illustrative plan approaches in 2020, 2025, and 2030. All dollar estimates are presented in 201 1 dollars.
1.4.2 Definition of Affected Sources
For the emission guidelines, an affected EGU is any fossil fuel-fired electric utility steam generating unit or stationary combustion turbine th at was in operation or had commenced construction as of January 8, 2014, 22 and that meets the following criteria, which diffe r depending on the type of unit. To be an affected so urce, such a unit, if it is a steam generating unit or integrated gasification combined cycle (IGC C), must serve a generator capable of selling greater than 25 MW to a utility power distribution system and have a base load rating greater than 260 GJ/h (250 MMBtu/h) heat input of fossil fu el (either alone or in combination with any other fuel). If such a unit is a stationary combust ion turbine, the unit must meet the definition of a combined cycle or combined heat and power combust ion turbine, serve a generator capable of selling greater than 25 MW to a utility power distr ibution system, and have a base load rating of greater than 260 GJ/h (250 MMBtu/h). Certain EGUs a re exempt from inclusion in a state plan. For specifics on these criteria see section IV of t he preamble.

21 Detailed documentation for IPM v.5.15 is available at: http://www.epa.gov/powersectormodeling

22 Under Section 111(a) of the CAA, determination of affected sources is based on the date that the EPA proposes action on such sources. January 8, 2014 is the date the proposed GHG standards of performance for new fossil fuelfired EGUs were published in the Federal Register (79 FR 1430).1-6

When considering and understanding applicability, t he following definitions may be helpful. Simple cycle combustion turbine means any stationary combustion turbine which does not recover heat from the combustion turbine engine exhaust gases for purposes other than enhancing the performance of the stationary combust ion turbine itself. Combined cycle combustion turbine means any stationary combustion turbine which recovers heat from the combustion turbine engine exhaust gases to generate steam that is used to create additional electric power output in a steam turbine. Combined heat and power (CHP) combustion turbine means any stationary combustion turbine which recov ers heat from the combustion turbine engine exhaust gases to heat water or another mediu m, generate steam for useful purposes other than exclusively for additional electric generation , or directly uses the heat in the exhaust gases for a useful purpose.
1.4.3 Regulated Pollutant
The purpose of this CAA section 111(d) rule is to a ddress CO 2 emissions from fossil fuel-fired power plants in the U.S. because they ar e the largest domestic stationary source of emissions of carbon dioxide (CO 2 ), the most prevalent of the greenhouse gases (GHG) , which are air pollutants that the EPA has determined enda ngers public health and welfare through their contribution to climate change. This rule establish es for the first time federal emission guidelines for existing power plants that will lead to signifi cant reductions in CO 2 emissions.

1.4.4 Emission Guidelines
In this action, the Environmental Protection Agency (EPA) is establishing final emission guidelines for states to follow in developing plans to reduce greenhouse gas emissions from existing fossil fuel-fired EGUs. Specifically, the EPA is establishing: 1) CO 2 emission performance rates representing the best system of e mission reduction (BSER) for two subcategories of existing fossil fuel-fired EGUs – fossil fuel-fired electric utility steam generating units and stationary combustion turbines , 2) state-specific CO 2 goals reflecting the
CO 2 emission performance rates, and 3) guidelines for the development, submittal and implementation of state plans that establish emissi on standards or other measures to implement
the CO 2 emission performance rates, which may be accomplis hed by meeting the state goals1-7

1.4.5 State Plans
After the EPA establishes the emission guidelines t hat set forth the BSER, each state 23 shall then develop, adopt and submit a state plan u nder CAA section 111(d) that establishes standards of performance for the affected EGUs in i ts jurisdiction in order to implement the BSER. The final guidelines include three approaches that states may adopt for purposes of implementing the BSER, any one of which a state may use in its plan. These are: 1) establishing standards of performance that apply the subcategory specific CO 2 emission performance rates to their affected EGUs, 2) adopting a combination of s tandards and/or other measures that achieve state-specific rate-based goals that represent the weighted aggregate of the CO 2 emission performance rates applied to the affected EGUs in e ach state, and 3) adopting a program to meet
mass-based CO 2 emission goals that represent the equivalent of th e rate-based goal for each state. These alternatives, as well as the other opt ions we are finalizing, ensure that both states and affected EGUs enjoy the maximum flexibility and lat itude in meeting the requirements of the emission guidelines and that the BSER is fully impl emented by each state.
1.5 Organization of the Regulatory Impact Analysis
This report presents the EPA’s analysis of the pote ntial benefits, costs, and other economic effects of the final emission guidelines t o fulfill the requirements of an RIA. This RIA includes the following chapters:

• Chapter 2, Electric Power Sector Industry Profile

• Chapter 3, Cost, Emissions, Economic, and Energy Im pacts

• Chapter 4, Estimated Climate Benefits and Health Co -benefits

• Chapter 5, Economic Impacts – Markets Outside the E lectricity Sector

• Chapter 6, Employment Impact Analysis

• Chapter 7, Statutory and Executive Order Analyses

• Chapter 8, Comparison of Benefits and Costs

23 In this section, the term “state” encompasses the 48 contiguous states and the District of Columbia, and any Indian tribe that has been approved by the EPA pursuant to 40 CFR 49.9 as eligible to develop and implement a CAA sec tion 111(d) plan.1-8

1.6 References
40 CFR Chapter I [EPA–HQ–OAR–2009–0171; FRL–9091–8] RIN 2060–ZA14, “Endangerment and Cause or Contribute Findings for Greenhouse Gases under Section 202(a) of the Clean Air Act,” Federal Register / Vol. 74, No. 239 / Tuesday, December 15, 2009 / Rules and Regulations. 75 FR 49556. August 13, 2010. “EPA’s Denial of the Petitions to Reconsider the Endangerment and Cause or Contribute Findings for Greenhouse Gas es Under Section 202(a) of the Clean Air Act.”
Melillo, J.M., T.C. Richmond, and G.W. Yohe, Eds., 2014: Climate Change Impacts in the United States: The Third National Climate Assessmen t. U.S. Global Change Research Program. Available at <http://nca2014.globalchange. gov>. Accessed June 4, 2015.

National Research Council. Climate Stabilization Targets: Emissions, Concentra tions, and Impacts over Decades to Millennia . Washington, DC: The National Academies Press, 201 1. U.S. Environmental Protection Agency. EPA’s Power Sector Modeling Platform v.5.14. March 25, 2015 . Available online at <http://www.epa.gov/powersect ormodeling/psmodel514.html>. Accessed June 4, 2015.2-1



### C HAPTER 

### 2: E LECTRIC 

### P OWER 

### S ECTOR 

### I NDUSTRY 

### P ROFILE

2.1 Introduction
This chapter discusses important aspects of the pow er sector that relate to the Final Carbon Pollution Emission Guidelines for Existing Stationa ry Sources: Electric Utility Generating Units, including the types of power-sector sources affecte d by the regulation, and provides background on the power sector and EGUs. In addition, this cha pter provides some historical background on trends in the past decade in the power sector, as w ell as about existing EPA regulation of the power sector.

In the past decade there have been significant stru ctural changes in the both the mix of generating capacity and in the share of electricity generation supplied by different types of generation. These changes are the result of multipl e factors in the power sector, including normal replacements of older generating units with new uni ts, changes in the electricity intensity of the US economy, growth and regional changes in the US p opulation, technological improvements in electricity generation from both existing and new u nits, changes in the prices and availability of different fuels, and substantial growth in electric ity generation by renewable and unconventional methods. Many of these trends will continue to cont ribute to the evolution of the power sector. The evolving economics of the power sector, in part icular the increased natural gas supply and subsequent relatively low natural gas prices, have resulted in more gas being utilized as base load energy in addition to supplying electricity during peak load. This chapter presents data on the evolution of the power sector from 2002 through 201 2. Projections of new capacity and the impact of this rule on these new sources are discus sed in more detail in Chapter 4 of this RIA.

2.2 Power Sector Overview
The production and delivery of electricity to custo mers consists of three distinct segments: generation, transmission, and distribution.

2.2.1 Generation
Electricity generation is the first process in the delivery of electricity to consumers. There are two important aspects of electricity generation ; capacity and net generation. Generating Capacity refers to the maximum amount of production from an EGU in a typical hour, typically2-2

measured in megawatts (MW) or gigawatts (1 GW = 100 0 MW). Electricity Generation refers to the amount of electricity actually produced by EGUs , measured in kilowatt-hours (kWh) or gigawatt-hours (GWh = 1 million kWh). Net generatio n is the amount of electricity that is available to the grid from the EGU (i.e., excluding the amount of electricity generated but used within the generating station for operations). In a ddition to producing electricity for sale to the grid, generators perform other services important t o reliable electricity supply, such as providing backup generating capacity in the event of unexpect ed changes in demand or unexpected changes in the availability of other generators. Ot her important services provided by generators include facilitating the regulation of the voltage of supplied generation.

Individual EGUs are not used to generate electricit y 100 percent of the time. Individual EGUs are periodically not needed to meet the regula r daily and seasonal fluctuations of electricity demand. Furthermore, EGUs relying on re newable resources such as wind, sunlight and surface water to generate electricity are routi nely constrained by the availability of adequate wind, sunlight or water at different times of the d ay and season. Units are also unavailable during routine and unanticipated outages for maintenance. These factors result in the mix of generating capacity types available (e.g., the share of capaci ty of each type of EGU) being substantially
different than the mix of the share of total electr icity produced by each type of EGU in a given

season or year.
Most of the existing capacity generates electricity by creating heat to create high pressure steam that is released to rotate turbines which, in turn, create electricity. Natural gas combined cycle (NGCC) units have two generating components o perating from a single source of heat. The first cycle is a gas-fired turbine, which generates electricity directly from the heat of burning natural gas. The second cycle reuses the waste heat from the first cycle to generate steam, which is then used to generate electricity from a steam t urbine. Other EGUs generate electricity by using water or wind to rotate turbines, and a varie ty of other methods including direct photovoltaic generation also make up a small, but g rowing, share of the overall electricity supply. The generating capacity includes fossil-fue l-fired units, nuclear units, and hydroelectric and other renewable sources (see Table 2-1). Table 2-1 also shows the comparison between the generating capacity in 2002 and 2012.2-3

In 2012 the power sector consisted of over 19,000 g enerating units with a total capacity 24 of 1,168 GW, an increase of 188 GW (or 19 percent) from the capacity in 2002 (980 GW). The 188 GW increase consisted primarily of natural gas fired EGUs (134 GW) and wind generators (55 GW), with substantially smaller net increases a nd decreases in other types of generating
units.

Table 2-1. Existing Electricity Generating Capacity by Energy Source, 2002 and

2012 2002 2012 Change Between '02 and '12

Energy Source Generator

Nameplate Capacity (MW) % Total

Capacit y Generator Nameplate Capacity (MW) % Total

Capacit y % Increas
e Nameplate Capacity Change (MW) % of

Total Capacity Increase Coal 338,199 35% 336,341 29% -1% -1,858 -1% Natural Gas 1 352,128 36% 485,957 42% 38% 133,829 71% Nuclear 104,933 11% 107,938 9% 3% 3,005 2% Hydro 96,344 10% 99,099 8% 3% 2,755 1% Petroleum 66,219 7% 53,789 5% -19% -12,430 -7% Wind 4,531 0.5% 59,629 5.1% 1216% 55,098 29% Other Renewable 14,208 1.5% 20,986 1.8% 47.7% 6,778 3.6% Misc 3,023 0.3% 4,257 0.4% 40.8% 1,234 0.7% Total 979,585 100% 1,167,995 100% 19% 188,410 100% Note: This table presents generation capacity. Actu al net generation is presented in Table 2-2. Source: U.S. EIA. Downloaded from EIA Electricity Data Browser, Electric Power Plants Generating Capa city By energy source, by producer, by state back to 2000 ( annual data from EIA Form 860). Available online at : <http://www.eia.gov/electricity/data.cfm#gencapacit y.> Accessed 12/19/2014

1 Natural Gas information in this chapter (unless ot herwise stated) reflects data for all generating un its using natural gas as the primary fossil heat source. This include s Combined Cycle Combustion Turbine (31 percent of 2012 natural gas-fired capacity), Gas Turbine (30 percen t), Combined Cycle Steam (19 percent), Steam Turbin e (17 percent), and miscellaneous (< 1 percent).

24 As with all data presented in this section, this i ncludes generating capacity not only at EGUs primar ily operated to supply electricity to the grid, but also generating capacity at commercial and industrial facilities t hat produce both electricity used onsite as well as dispatched to th e grid. Unless otherwise indicated, capacity data p resented in this RIA is installed nameplate capacity (also known as nominal capacity), defined by EIA as “The maximum r ated output of a generator, prime mover, or other electr ic power production equipment under specific condit ions designated by the manufacturer.” Nameplate capacity is consistently reported to regulatory authorities with a common definition, where alternate measures of capa city (e.g., net summer capacity and net winter capa city) can use a variety of definitions and specified conditio ns.2-4

The 19 percent increase in generating capacity is t he net impact of newly built generating units, retirements of generating units, and a varie ty of increases and decreases to the nameplate capacity of individual existing units due to change s in operating equipment, changes in emission controls, etc. During the period 2002 to 2012, a to tal of 315,752 MW of new generating capacity was built and brought online, and 64,763 MW existin g units were retired. The net effect of the re-rating of existing units reduced the total capac ity by 62,579 MW. The overall net change in capacity was 188,410 MW, as shown in Table 2-1.

The newly built generating capacity was primarily n atural gas (226,605 MW), which was partially offset by gas retirements (29,859 MW). Wi nd capacity was the second largest type of new builds (55,583 MW), augmented by 2,807 MW of so lar. 25 The overall mix of newly built and retired capacity, along with the net effect, is shown on Figure 2-1.

Figure 2-1. New Build and Retired Capacity (MW) by Fuel Type, 2002-2012

Source: EIA Form 860 Not displayed: wind and solar retirements = 87 MW, net change in coal capacity = -56 MW

25 Partially offset by 87 MW retired older wind or so lar capacity.

-50,000 0 50,000 100,000 150,000 200,000 250,000 300,000 350,000

New Build Retirement Net ChangeChange in Capacity (MW)

Coal Nat Gas Wind & Solar Oil & Other2-5

In 2012, electric generating sources produced a net 4,058 trillion kWh to meet electricity demand, a 5 percent increase from 2002 (3,858 trill ion kWh). As presented in Table 2-2, almost 70 percent of electricity in 2012 was produced thro ugh the combustion of fossil fuels, primarily coal and natural gas, with coal accounting for the largest single share. Although the share of the total generation from fossil fuels in 2012 (67 perc ent) was only modestly smaller than the total fossil share in 2002 (71 percent), the mix of fossi l fuel generation changed substantially during that period. Coal generation declined by 18 percent and petroleum generation by 72 percent, while natural gas generation increased by 60 percen t. This reflects both the increase in natural gas capacity during that period as well as an incre ase in the utilization of new and existing gas EGUs during that period. Wind generation also grew from a very small portion of the overall total in 2002 to 4.1 percent of the 2012 total.

Table 2-2. Net Generation in 2002 and 2013 (Trillion kWh = TWh)

2002 2013 Change Between '02 and '13

Net Generation (TWh) Fuel

Source Share Net

Generation (TWh) Fuel

Source Share Net

Generation Change (TWh) % Change in

Net Generation Coal 1,933.1 50% 1,514.0 37% -419.1 -21.7% Natural Gas 702.5 18% 1,237.8 31% 535.3 76.2% Nuclear 780.1 20% 769.3 19% -10.7 -1.4% Hydro 255.6 7% 271.3 7% 15.7 6.1% Petroleum 94.6 2.5% 23.2 0.6% -71.4 -75.5% Wind 10.4 0.3% 140.8 3.5% 130.5 1260.0% Other Renewable 68.8 1.8% 77.5 1.9% 8.8 12.7% Misc 13.5 0.4% 12.4 0.3% -1.2 -8.7%
Total 3,858 100% 4,046 100% 188 5% Source: U.S. EIA Monthly Energy Review, December 20 14. Table 7.2a Electricity Net Generation: Total (A ll Sectors). Available online at: <http://www.eia.gov/ totalenergy/data/monthly/>. Accessed 12/19/2014

Coal-fired and nuclear generating units have histor ically supplied “base load” electricity, the portion of electricity loads which are continua lly present, and typically operate throughout all hours of the year. The 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 2-3). For example, coal-fired units less than 100 megawatts (MW) in size2-6

compose 37 percent of the total number of coal-fire d units, but only 6 percent of total coal-fired capacity. Gas-fired generation is better able to va ry output and 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 increased deman d for electricity (for example, when businesses operate throughout the day or when peopl e 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.

Table 2-3 also shows comparable data for the capaci ty and age distribution of natural gas units. Compared with the fleet of coal EGUs, the na tural gas fleet of EGUs is generally smaller and newer. While 55 percent of the coal EGU fleet i s over 500 MW per unit, 77 percent of the gas fleet is between 50 and 500 MW per unit. Many o f the largest gas units are gas-fired steamgenerating EGUs.2-7

Table 2-3. Coal and Natural Gas Generating Units, by Size, Age, Capacity, and

Thermal Efficiency (Heat Rate)

Unit Size Grouping (MW) No.

Units % of All

Units Avg.

Age Avg. Net

Summer Capacity (MW) Total Net

Summer Capacity (MW) % Total

Capacity Avg. Heat

Rate (Btu/kWh) COAL 0 – 24 223 18% 40.7 11.4 2,538 1% 11,733 25 – 49 108 9% 44.2 36.7 3,963 1% 11,990 50 – 99 157 12% 49.0 74.1 11,627 4% 11,883 100 - 149 128 10% 50.6 122.7 15,710 5% 10,971 150 - 249 181 14% 48.7 190.4 34,454 11% 10,620 250 - 499 205 16% 38.4 356.2 73,030 23% 10,502 500 - 749 187 15% 35.4 604.6 113,056 36% 10,231 750 - 999 57 5% 31.4 823.9 46,963 15% 9,942 1000 - 1500 11 1% 35.7 1259.1 13,850 4% 9,732 Total Coal 1257 100% 42.6 250.7 315,191 100% 11,013 NATURAL GAS 0 – 24 1992 37% 37.6 7.0 13,863 3% 13,531 25 – 49 410 8% 21.8 125.0 51,247 12% 9,690 50 – 99 962 18% 15.6 174.2 167,536 39% 8,489 100 - 149 802 15% 23.4 39.9 31,982 8% 11,765 150 - 249 167 3% 28.7 342.4 57,179 13% 9,311 250 - 499 982 18% 24.6 71.1 69,788 16% 12,083 500 - 749 37 1% 40.0 588.8 21,785 5% 11,569 750 - 1000 14 0.3% 35.9 820.9 11,492 3% 10,478 Total Gas 5366 100% 27.7 79.2 424,872 100% 11,652 Source: National Electric Energy Data System (NEEDS ) v.5.14 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 he at rate.) A lower heat rate indicates a higher leve l of fuel efficiency. Table is limited to coal-steam units in operation in 2013 or earlier, and excludes those u nits in NEEDS with planned retirements in 2014 or 2015.
In terms of the age of the generating units, 50 per cent of the total coal generating capacity has been in service for more than 38 years, while 5 0 percent of the natural gas capacity has been in service less than 15 years. Figure 2-2 presents the cumulative age distributions of the coal and gas fleets, highlighting the pronounced differences in the ages of the fleets of these two types of fossil-fuel generating capacity. Figure 2-2 also in cludes the distribution of generation.2-8

Figure 2-2. Cumulative Distribution in 2010 of Coal and Natural Gas Electricity

Capacity and Generation, by Age
Source: National Electric Energy Data System (NEEDS ) v.5.13 Not displayed: coal units (376 MW total, 1 percent of total) and gas units (62 MW, < .01 percent of to tal)) over 70 years old for clarity. Figure is limited to coal-st eam units in NEEDS v5.13 in operation in 2013 or ea rlier (excludes ~2,100 MW of coal-fired IGCC and fossil waste capac ity), and excludes those units in NEEDS with planne d retirements in 2014 or 2015.

The locations of existing fossil units in EPA’s Nat ional Electric Energy Data System (NEEDS) v.5.13 are shown in Figure 2-3.2-9

Figure 2-3. Fossil Fuel-Fired Electricity Generatin g Facilities, by Size
Source: National Electric Energy Data System (NEEDS ) v.5.13 Note: This map displays fossil capacity at faciliti es in the NEEDS v.5.13 IPM frame. NEEDS v.5.13 refl ects generating capacity expected to be on-line at the e nd of 2015. This includes planned new builds alread y under construction and planned retirements. In areas with a dense concentration of facilities, some faciliti es may be obscured.

2.2.2 Transmission

Transmission is the term used to describe the bulk transfer of electricity over a network of high voltage lines, from electric generators to sub stations where power is stepped down for local distribution. In the U.S. and Canada, there are thr ee separate interconnected networks of high voltage transmission lines, 26 each operating synchronously. Within each of these transmission

26 These three network interconnections are the Wester n Interconnection, comprising the western parts of both the US and Canada (approximately the area to the west o f the Rocky Mountains), the Eastern Interconnection
, comprising the eastern parts of both the US and Can ada (except those part of eastern Canada that are i n the Quebec Interconnection), and the Texas Interconnection (wh ich encompasses the portion of the Texas electricit y system commonly known as the Electric Reliability Council of Texas (ERCOT)). See map of all NERC interconnect ions at http://www.nerc.com/AboutNERC/keyplayers/Documents/ NERC_Interconnections_Color_072512.jpg2-10

networks, there are multiple areas where the operat ion of power plants is monitored and controlled by regional organizations to ensure that electricity generation and load are kept in balance. In some areas, the operation of the transm ission system is under the control of a single regional operator 27 ; in others, individual utilities 28 coordinate the operations of their generation, transmission, and distribution systems to balance t he system across their respective service
territories.

2.2.3 Distribution
Distribution of electricity involves networks of lo wer 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 transmi ssion and distribution system is the classic example of a natural monopoly, in part because it i s not practical to have more than one set of lines running from the electricity generating sourc es to substations or from substations to
residences and businesses.

Over the last few decades, several jurisdictions in the United States began restructuring the power industry to separate transmission and distrib ution from generation, ownership, and operation. Historically, the transmission system ha d been developed by vertically integrated utilities, establishing much of the existing transm ission infrastructure. However, as parts of the country have restructured the industry, transmissio n infrastructure has also been developed by transmission utilities, electric cooperatives, and merchant transmission companies, among others. Distribution, also historically developed by vertic ally integrated utilities, is now often managed by a number of utilities that purchase and sell ele ctricity, but do not generate it. As discussed below, electricity restructuring has focused primar ily on efforts to reorganize the industry to encourage competition in the generation segment of the industry, including ensuring open access of generation to the transmission and distribution services needed to deliver power to consumers. In many states, such efforts have also included sep arating generation assets from transmission

27 E.g., PMJ Interconnection, LLC, Western Area Power Administration (which comprises 4 sub-regions).

28 E.g., Los Angeles Department of Power and Water, F lorida Power and Light.2-11

and distribution assets to form distinct economic e ntities. Transmission and distribution remain price-regulated throughout the country based on the cost of service.

2.3 Sales, Expenses and Prices
These electric generating sources provide electrici ty for commercial, industrial and
residential ultimate customers. Each of the three m ajor ultimate categories consume roughly a quarter to a third of the total electricity produce d 29 (see Table 2-4). Some of these uses are highly variable, such as heating and air conditioning in r esidential and commercial buildings, while others are relatively constant, such as industrial processes that operate 24 hours a day. The distribution between the end use categories changed very little between 2002 and 2012.

Table 2-4. Total U.S. Electric Power Industry Retai l Sales in 2012 (billion kWh)

2002 2012

Sales/Direct Use (Billion kWh) Share of Total

End Use Sales/Direct

Use (Billion kWh) Share of Total End

Use

Sales Residential 1,265 35% 1,375 35.9%

Commercial 1,104 30% 1,327 34.6%
Industrial 990 27% 986 25.7% Transportation NA 7 0.2%
Other 106 3% NA

Total 3,465 95% 3,695 96%

Direct Use 166 5% 138 4%

Total End Use 3,632 100% 3,832 100%

Source: Table 2.2, EIA Electric Power Annual, 2013 Notes: Retail sales are not equal to net generat ion (Table 2-2) because net generation includes net exported electricity and loss of electricity that occurs thr ough transmission and distribution. Direct Use represents commercial and industrial fac ility use of onsite net electricity generation; and electricity sales or transfers to adjacent or co-lo cated facilities for which revenue information is n ot available. 2.3.1 Electricity Prices
Electricity prices vary substantially across the Un ited States, differing both between the ultimate customer categories and also by state and region of the country. Electricity prices are typically highest for residential and commercial cu stomers because of the relatively high costs of

29 Transportation (primarily urban and regional elect rical trains) is a fourth ultimate customer categor y which accounts less than one percent of electricity consu mption.2-12

distributing electricity to individual homes and co mmercial establishments. The high prices for
residential and commercial customers are the result both of the necessary extensive distribution network reaching to virtually every part of the cou ntry and every building, and also the fact that generating stations are increasingly located relati vely far from population centers (which increases transmission costs). Industrial customers generally pay the lowest average prices, reflecting both their proximity to generating stati ons and the fact that industrial customers receive electricity at higher voltages (which makes transmission more efficient and less expensive). Industrial customers frequently pay var iable prices for electricity, varying by the season and time of day, while residential and comme rcial prices historically have been less variable. Overall industrial customer prices are us ually considerable closer to the wholesale marginal cost of generating electricity than reside ntial and commercial prices.

On a state-by-state basis, all retail electricity p rices vary considerably. In 2011 the national average retail electricity price (all sectors) was 9.90 cents/KWh, with a range from 6.44 cents (Idaho) to 31.59 (Hawaii). The Northeast, Californi a and Alaska have average retail prices that can be as much as double those of other states (see Figure 2-4), and Hawaii has the most expensive retail price of electricity in the countr y.2-13

Figure 2-4. Average Retail Electricity Price by Sta te (cents/kWh), 2011
Average national overall retail electricity prices increased between 2002 and 2012 by 36.7 percent in nominal (current year $) terms. The amou nt of increase differed for the three major end use categories (residential, commercial and ind ustrial). National average residential prices increased the most (40.8 percent), and commercial p rices increased the least (27.9 percent). The nominal year prices for 2002 through 2012 are shown in Figure 2-5.2-14

Figure 2-5. Nominal National Average Electricity Pr ices for Three Major End-Use

Categories

Source: EIA AEO 2012, Table 2.4 Electricity prices for all three end-use categories increased more than overall inflation through this period, measured by either the GDP imp licit price deflator (23.5 percent) or the consumer price index (CPI-U, which increased by 27. 7 percent) 30 . Most of these electricity price increases occurred between 2002 and 2008; since 200 8 nominal electricity prices have been relatively stable while overall inflation continued to increase. The increase in nominal electricity prices for the major end use categories, as well as increases in the GDP price and CPI-U indices for comparison, are shown in Figure 2-6.

30 Source: Federal Reserve Economic Data, FRB St. Lou is. Available online at: http://research.stlouisfed .org/fred2/.

.0 2.0 4.0 6.0 8.0 10.0 12.0 14.0

2002 2004 2006 2008 2010 2012Electricity Price (Cents/kWh, nominal $)

Residential Commercial Industrial Total2-15

Figure 2-6. Relative Increases in Nominal National Average Electricity Prices for Major

End-Use Categories, With Inflation Indices

The real (inflation-adjusted) change in average nat ional electricity prices can be calculated using the GDP implicit price deflator. Figure 2-7 s hows real 31 (2011$) electricity prices for the three major customer categories from 1960 to 2012, and Figure 2-8 shows the relative change in real electricity prices relative to the prices in 1 960. As can be seen in the figures, the price for industrial customers has always been lower than for either residential or commercial customers, but the industrial price has been more volatile. Wh ile the industrial real price of electricity in 2012 was relatively unchanged from 1960, residentia l and commercial real prices are 23 percent and 28 percent lower respectively than in 1960.

31 All prices in this section are estimated as real 2 011 prices adjusted using the GDP implicit price de flator unless otherwise indicated.2-16

Figure 2-7. Real National Average Electricity Price s (2011$) for Three Major End-Use

Categories

Source: EIA Monthly Energy Review, April 2015, Tabl e 9.8

0 2 4 6 8 10 12 14 16 18

1960 1970 1980 1990 2000 2010Real Electricity Prices (2011$, ¢ /kWh) 

### Real Electricity Prices, 1960-2014 (including taxes)

Residential Commercial Industrial Total

-50% -40% -30% -20% -10% 0% 10% 20% 30% 40% 50% 60%

1960 1970 1980 1990 2000 2010Change in Real Electricity Prices 

### Relative Change in Electricity Prices,

1960-2014 (including taxes)

Residential Commercial Industrial Total2-17

Figure 2-8. Relative Change in Real National Averag e Electricity Prices (2011$) for

Three Major End-Use Categories
Source: EIA Monthly Energy Review, April 2015, Tabl e 9.8 2.3.2 Prices of Fossil Fuels Used for Generating El ectricity
Another important factor in the changes in electric ity prices are the changes in fuel prices for the three major fossil fuels used in electricit y generation; coal, natural gas and oil. Relative t o real prices in 2002, the national average real pric e (in 2011$) of coal delivered to EGUs in 2012 had increased by 54 percent, while the real price o f natural gas decreased by 22 percent. The real price of oil increased by 203 percent, but with oil declining as an EGU fuel (in 2012 oil generated only 1 percent of electricity) the doubli ng of oil prices had little overall impact in the electricity market. The combined real delivered pri ce of all fossil fuels in 2012 increased by 23 percent over 2002 prices. Figure 2-9 shows the rela tive changes in real price of all 3 fossil fuels
between 2002 and 2012.

Figure 2-9. Relative Real Prices of Fossil Fuels fo r Electricity Generation; Change in

National Average Real Price per MBtu Delivered to E GU

Source: EIA AEO 2012, Table 9.92-18

2.3.3 Changes in Electricity Intensity of the U.S. Economy Between 2002 to 2012
An important aspect of the changes in electricity g eneration (i.e., electricity demand) between 2002 and 2012 is that while total net gener ation increased by 4.9 percent over that period, the demand growth for generation has been l ow, and in fact was lower than both the population growth (9.2 percent) and real GDP growth (19.8 percent). Figure 2-10 shows the growth of electricity generation, population and re al GDP during this period.

Figure 2-10. Relative Growth of Electricity Generat ion, Population and Real GDP Since

2002
Sources: U.S. EIA Monthly Energy Review, December 2 014. Table 7.2a Electricity Net Generation: Total ( All Sectors). U.S. Census. Because demand for electricity generation grew more slowly than both the population and GDP, the relative electric intensity of the U.S. ec onomy improved (i.e., less electricity used per person and per real dollar of output) during 2002 t o 2012. On a per capita basis, real GDP per capita grew by 10.9 percent, increasing from $44,90 0 (in 2011$) per person in 2002 to $49,800/person in 2012. At the same time electricit y generation per capita decreased by 3.9 percent, declining from 13.4 MWh/person in 2002 to 12.8 MWh/person in 2012. The combined effect of these two changes improved the overall el ectricity efficiency of the U.S. market economy. Electricity generation per dollar of real GDP decreased 12.5 percent, declining from 299 MWh per $1 million of GDP to 261 MWh/$1 million GDP. These relative changes are shown in Figure 2-11. Figures 2-10 and 2-11 clearly show the effects of the 2007 – 20092-19

recession on both GDP and electricity generation, a s well as the effects of the subsequent economic recovery.

Figure 2-11. Relative Change of Real GDP, Populatio n and Electricity Generation

Intensity Since 2002
Sources: U.S. EIA Monthly Energy Review, December 2 014. Table 7.2a Electricity Net Generation: Total ( All Sectors). U.S. Census 2.4 Deregulation and Restructuring
The process of restructuring and deregulation of wh olesale and retail electric markets has changed the structure of the electric power industr y. In addition to reorganizing asset management between companies, restructuring sought a functional unbundling of the generation, transmission, distribution, and ancillary services the power sector has historically provided, with the aim of enhancing competition in the generation segment of the industry.

Beginning in the 1970s, government policy shifted a gainst traditional regulatory approaches and in favor of deregulation for many im portant industries, including transportation (notably commercial airlines), communications, and energy, which were all thought to be natural monopolies (prior to 1970) that warranted governmen tal control of pricing. However, deregulation efforts in the power sector were most active during the 1990s. Some of the primary drivers for deregulation of electric power included the desire for more efficient investment2-20

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 m onitoring utilities’ cost of service and establishing cost-based rates for various customer classes. Deregulation and market restructuring in the power sector involved the divestiture of gen eration from utilities, the formation of organized wholesale spot energy markets with econom ic mechanisms for the rationing of scarce transmission resources during periods of peak deman d, the introduction of retail choice programs, and the establishment of new forms of mar ket oversight and coordination.

The pace of restructuring in the electric power ind ustry slowed significantly in response to market volatility in California and financial turmo il 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 2-12). Eighteen oth er states that had seriously explored the possibility of deregulation in 2000 reported no leg islative or regulatory activity in 2001 (EIA, 2003) (“Not Active” in Figure 2-12). Currently, the re are 15 states plus the District of Columbia where price deregulation of generation (restructuri ng) has occurred (“Active” in Figure 2-12). Power sector restructuring is more or less at a sta ndstill; by 2010 there were no active proposals under review by the Federal Energy Regulatory Commi ssion (FERC) for actions aimed at wider restructuring, and no additional states have begun retail deregulation activity since that time.2-21

Figure 2-12. Status of State Electricity Industry R estructuring Activities
Source: EIA 2010. “Status of Electricity Restructur ing by State.” Available online at: <http://www.eia.gov/cneaf/electricity/page/restruct uring/restructure_elect.html>.
One major effect of the restructuring and deregulat ion of the power sector was a significant change in type of ownership of electricity generati ng units in the states that deregulated prices. Throughout most of the 20th century electricity was supplied by vertically integrated regulated utilities. The traditional integrated utilities gen eration, transmission and distribution in their designated areas, and prices were set by cost of se rvice regulations set by state government agencies (e.g., Public Utility Commissions). Deregu lation and restructuring resulted in unbundling of the vertical integration structure. T ransmission and distribution continued to operate as monopolies with cost of service regulati on, while generation shifted to a mix of ownership affiliates of traditional utility ownersh ip and some generation owned and operated by competitive companies known as Independent Power Pr oducers (IPP). The resulting generating sector differed by state or region, as the power se ctor adapted to the restructuring and deregulation requirements in each state.

By 2002 the major impacts of adapting to changes br ought about by deregulation and restructuring during the 1990s were largely in plac e. The resulting ownership mix of generating capacity (MW) in 2002 was 62 percent of the generat ing capacity owned by traditional utilities,2-22

35 percent owned by IPPs 32 , and 3 percent owned by commercial and industrial producers. The mix of electricity generated (MWh) was more heavily weighted towards the utilities, with a distribution in 2002 of 66 percent, 30 percent and 4 percent for utilities, IPPs and commercial/industrial, respectively.

Since 2002 IPPs have expanded faster than tradition al utilities, substantially increasing their share by 2012 of both capacity (58 percent ut ility, 39 percent IPPs, and 3 percent commercial/industrial) and generation (58 percent, 38 percent and 4 percent).

The mix of capacity and generation for each of the ownership types is shown in Figures 2- 13 (capacity) and 2-14 (generation). The capacity a nd generation data for commercial and industrial owners are not shown on these figures du e to the small magnitude of those ownership types. Figures 2-13 and 2-14 present the mixes in 2 002 and 2012. A portion of the shift of capacity and generation is due to sales and transfe rs of generation assets from traditional utilities to IPPs, rather than strictly the result of newly b uilt units.

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

Figures 2-13 and 2-14. Capacity and Generation Mix by Ownership Type, 2002 & 2012

The mix of capacity by fuel types that have been bu ilt and retired between 2002 and 2012 also varies significantly by type of ownership. Fig ure 2-15 presents the new capacity built during that period, showing that IPPs built the majority o f both new wind and solar generating capacity, as well as somewhat more natural gas capacity than the traditional utilities built. Figure 2-16 presents comparable data for the retired capacity, showing that utilities retired more coal and “other” capacity (mostly oil-fired) than IPPs retir ed, while the IPPs retired more natural gas capacity than the utilities retired. The retired ga s capacity was primary (60 percent) steam and
combustion turbines.

0 100,000 200,000 300,000 400,000 500,000 600,000 700,000

2002-

Utility 2012- Utility 2002-IPP 2012-IPPCapacity (MW) 

### Capacity Mix, 2002 & 2012

Nuclear Coal Gas Hydro Wind All Other 0 500,000 1,000,000 1,500,000 2,000,000 2,500,000 3,000,000

2002-

Utility 2012- Utility 2002-IPP 2012-IPPGeneration (GW) 

### Generation Mix, 2002 & 2012

Nuclear Coal Gas Hydro Wind All Other2-24

Figures 2-15 and 2-16. Generation Capacity Built an d Retired between 2002 and 2012 by Ownership Type

2.5 Emissions of Greenhouse Gases from Electric Uti lities
The burning of fossil fuels, which generates about 69 percent of our electricity nationwide, results in emissions of greenhouse gases. The power sector is a major contributor of CO 2 in particular, but also contributes to emissions of su lfur hexafluoride (SF 6 ), CH 4 , and N 2 O. In 2012, the electricity generation accounted for 38 percent of national CO 2 emissions. Including both generation and transmission (a source of SF6), the power sector accounted for 31 percent of total nationwide greenhouse gas emissions, measured in CO 2 equivalent. Table 2-5 and Figure 2-17
show the GHG emissions 33 from the power sector relative to other major econ omic sectors.
Table 2-6 shows the contributions of CO 2 and other GHGs from the power sector and other major emitting economic sectors.

33 CO 2 equivalent data in this section are calculated wit h the IPCC SAR (Second Assessment Report) GWP poten tial factors. 0 20,000 40,000 60,000 80,000 100,000 120,000 140,000 160,000 180,000 200,000

Utility IPPNew Capacity (MW) 

### Capacity Built 2002-2012 by Ownership Type

Coal Gas Wind & Solar Other 0 5,000 10,000 15,000 20,000 25,000 30,000 35,000

Utility IPPRetired Capacity (MW) 

### Capacity Retirements 2002-2012 by Ownership Type

Coal Gas Wind & Solar Other2-25

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

CO 2 equivalent)

2002 2013 Change Between '02 and '13

Sector/Source GHG

Emissions % Total

GHG Emissions GHG

Emissions % Total

GHG Emissions Change

in Emissions %

Change in Emissions % of

Total Change in Emissions Electric Power Industry 2,550 33% 2,289 31% -260 -1 0% 64% Transportation 2,158 28% 1,991 27% -167 -8% 41% Industry 1,564 20% 1,535 21% -29 -2% 7% Agriculture 618 8% 647 9% 29 5% -7% Commercial 402 5% 442 6% 40 10% -10% Residential 412 5% 413 6% 1 0% 0% US Territories 58 <1% 38 <1% -19 -33% 5%
Total GHG Emissions 7,762 100% 7,356 100% -406 -5% 100%
Sinks and Reductions -976 -972 4 0%
Net GHG Emissions 6,786 6,384 -402 -6%
Source: EPA, 2014 “Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012”, Table 2-12. Inclu des
CO 2 , CH 4 , N 2 O and SF 6 emissions.

Figure 2-17. Domestic Emissions of Greenhouse Gases from Major Sectors, 2002 and 2013

(million tons of CO 2 equivalent)
Source: EPA, 2015 “Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2013”, Table 2-12.
Not Shown: CO2e emissions from US Territories 0 1,000 2,000 3,000 4,000 5,000 6,000 7,000 8,000 9,000

2002 2013GHG Emissions (Million Tons CO2e)

Electric Power Industry Transportation Industry Agriculture Commercial Residential2-26

The amount of CO 2 emitted during the combustion of fossil fuels vari es according to the carbon content and heating value of the fuel used. The CO 2 emission factors used in IPM v5.14 (same as used in v5.13) are shown in Table 2-7. Coa l 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 natural ga s (EPA 2013).

Table 2-6. Greenhouse Gas Emissions from the Electr icity Sector (Generation,

Transmission and Distribution), 2002 and 2012 (mill ion tons of CO 2 equivalent)

2002 2013 Change Between '02 and '13 Gas/Fuel Type or Source GHG Emissions % of

Total GHG Emissions from Power Sector GHG

Emissions % of Total

GHG Emissions from Power Sector Change in

GHG Emissions % Change

in Emissions

CO 2 2,521 98.9% 2,262 98.8% -259 -10% Fossil Fuel Combustion 2,505 98.2% 2,248 98.2% -257 -10%
Coal 2,083 81.7% 1,736 75.8% -347 -17% Natural Gas 337 13.22% 487 21.28% 150 45% Petroleum 84.7 3.32% 24.7 1.08% -60.0 -71% Geothermal 0.4 0.02% 0.4 0.02% 0.0 0% Incineration of Waste 13.0 0.51% 11.1 0.49% -1.9 -14%

Other Process Uses of Carbonates 2.9 0.11% 2.4 0.11% -0.4 -15%

CH 4 0.4 0.02% 0.4 0.02% 0.0 0% Stationary Combustion* 0.4 0.02% 0.4 0.02% 0.0 0%

Incineration of Waste + +

N 2 O 13.7 0.54% 21.4 0.93% 7.7 56% Stationary Combustion* 13.2 0.52% 21.1 0.92% 7.8 59%

Incineration of Waste 0.4 0.02% 0.3 0.01% -0.1 -25%

SF 6 14.7 0.57% 5.6 0.25% -9.0 -62% Electrical Transmission and Distribution 14.7 0.57% 5.6 0.25% -9.0 -62%

Total GHG Emissions 2,550 2,289 -260
Source: EPA, 2015 “Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015”, Table 2-11 * Includes only stationary combustion emissions rel ated to the generation of electricity. ** SF 6 is not covered by this rule, which specifically reg ulates GHG emissions from combustion. + Does not exceed 0.05 Tg CO 2 Eq. or 0.05 percent.2-27

Table 2-7. Fossil Fuel Emission Factors in EPA Base Case 5.14 IPM Power Sector

Modeling Application Fuel Type Carbon Dioxide (lb/MMBtu) Coal Bituminous 202.8 – 209.6 Subbituminous 209.2 – 215.8 Lignite 212.6 – 219. Natural Gas 117.1 Fuel Oil Distillate 161.4 Residual 161.4 – 173.9 Biomass 195 Waste Fuels Waste Coal 204.7 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.5.13, Tab le 11-5. The emission factors used in Base Case 5.1 4 are
identical to the emission factors in IPM Base Case 5.13.

Note: CO 2 emissions presented here for biomass account for c ombustion only and do not reflect lifecycle emissions from initial photosynthesis (carbon sink) or harvesting activities and transportation (carbo n source).
2.6 Carbon Dioxide Control Technologies
In the power sector, current approaches available f or significantly reducing the CO 2 emissions of new fossil fuel combustion sources to meet a 1,400 lb CO 2 /MWh emission rate include the use of: (1) highly efficiency coal-fire d designs (e.g., modern supercritical or ultrasupercritical steam units) with up to 40 percent na tural gas co-firing, (2), integrated coal gasification combined cycle (IGCC) with < 10 percen t CCS or co-firing with up to 10 percent natural gas, (3) natural gas combined cycle (NGCC) combustion turbine/steam-turbine units, and/or (4) conventional coal-fired generation with carbon capture and storage (CCS). While CCS is not included in the BSER framework, it is an eme rging technology with both new build and retrofit commercial-scale EGUs coming into operatio n in 2014 and 2015 in the United States and
Canada. All of these units with CCS have received s ubstantial subsidies to further develop and demonstrate the feasibility of CCS at a commercial scale, and the costs of these new units with2-28

CCS are not indicative of anticipated future costs of new or retrofit CCS units. CCS is briefly discussed in this section as existing (but still em erging) technology that may become economically viable in the future.

Investment decisions for the optimal choice in a pa rticular situation of the type of new generating capacity capable of meeting the 1,400 lb CO 2 /MWh standard of performance depend in part on the intended primary use of new generati ng capacity. Daily peak electricity demands, involving operation for relatively few hours per ye ar, are often most economically met by simple-cycle combustion turbines (CT). Stationary C Ts used for power generation can be installed quickly, at relatively low capital cost. They can be remotely started and loaded quickly, and can follow rapid demand changes. Full-load effi ciencies of large current technology CTs are typically 30-33 percent but can be has high as 40 p ercent or more (high heating value basis), as compared to efficiencies of 50 percent or more for new combined-cycle units that recover and
use the exhaust heat otherwise wasted from a CT . A simple-cycle CT’s lower efficiency causes it to burn much more fuel to produce a MWh of elect ricity than a combined-cycle unit. Thus, when burning natural gas its CO 2 emission rate per MWh could be 40-60 percent highe r than a
more efficient NGCC unit.

Base load electricity demand can be met with NGCC g eneration, coal and other fossil-fired steam generation, and IGCC technology, as well as g eneration from sources that do not emit
CO 2 , such as nuclear and hydro. IGCC employs the use o f a gasifier to transform fossil fuels into synthesis gas (“syngas”) and heat. The syngas is us ed to fuel a combined cycle generator, and the heat from the syngas conversion can produce steam f or the steam turbine portion of the combined cycle generator. Electricity can be genera ted through this IGCC process somewhat more efficiently than through conventional boiler-s team generators. Additionally, with gasification, some of the syngas can be converted i nto other marketable products such as fertilizers and chemical feedstocks for Fisher-Trop sch processes to manufacture liquid hydrocarbons (e.g., fuels and lubricants), and CO 2 can be captured for use in EOR. Figure 2-18 shows the array of products (including electricity) and by-products that can be produced in a syngas process (NETL).2-29

Figure 2-18. Marketable products from Syngas Gener ation
Source: National Energy Technology Lab. Gasifipedia . Available online at: http://www.netl.doe.gov/research/coal/energy-system s/gasification/gasifipedia/co-generation
2.6.1 Carbon Capture and Storage
CCS can be achieved through either pre-combustion o r post-combustion capture of CO 2 from a gas stream associated with the fuel combuste d. Furthermore, CCS can be designed and operated for full capture of the CO 2 in the gas stream (i.e., above 90 percent) or for partial capture (below 90 percent). Post-combustion capture processes remove CO 2 from the exhaust gas of a combustion system – such as a utility boil er. It is referred to as “post-combustion capture” because the CO 2 is the product of the combustion of the primary fu el and the capture takes place after the combustion of that fuel. This process is described in more detail in the preamble. (See preamble section V.D.) This process is illustrated for a pulverized coal power plant in Figure 2-19. For post-combustion, a statio n's net generating output will be lower due to the energy needs of the capture process.2-30

Figure 2-19. Post-Combustion CO 2 Capture for a Pulverized Coal Power Plant
Source: Interagency Task Force on Carbon Capture an d Storage 2010

Pre-combustion capture is mainly applicable to IGCC facilities, where the fuel is converted into gaseous components (“syngas”) under heat and p ressure and some percentage of the carbon contained in the syngas is captured before combusti on. 34 For pre-combustion technology, a significant amount of energy is needed to gasify th e fuel(s). This process is illustrated in Figure 2-20. Application of post-combustion CCS with IGCC can be designed to use no water-gas shift, or single- or two-stage shift processes, to obtain varying percentages of CO 2 removal – from a “partial capture” percentage to 90 percent “full ca pture.” Pre-combustion CCS typically has a lesser impact on net energy output than does post-c ombustion CCS. For more detail on CCS technology, see the “Report of the Interagency Task Force on Carbon Capture and Storage” (2010). 35

34 Note that pre-combustion CCS is not considered the best system of emission reduction for this standar d. This information is provided for background purposes.

35 For more information on the cost and performance of CCS, see http://www.netl.doe.gov/energyanalyses/baseline_studies.html

.2-31

Figure 2-20. Pre-Combustion CO 2 Capture for an IGCC Power Plant

Source: Interagency Task Force on Carbon Capture an d Storage 2010 Carbon capture technology has been successfully app lied since 1930 on several smaller scale industrial facilities and more recently in a number of demonstration phase projects worldwide for power sector applications. In October 2014 the first commercial-scale coal-fired capture and storage project for electricity generat ion began operation at the Boundary Dam Power Station in Saskatchewan, Canada. The Boundary Dam Station is owned by the Province of Saskatchewan, and operated by SaskPower, a provi ncially owned corporation that is the primary electric utility in the Province. The comme rcial-scale demonstration project retrofit Unit 3 (a 130 MW, coal fired built in 1970, and rebuilt in 2013) at a total cost of approximately $1.5 billion (Canadian, or about $1.2 billion US), inclu ding a partial subsidy of $240 million (Canadian) by the Canadian federal government. The carbon capture system is a postcombustion process designed to capture 90 percent o f the CO 2 emitted by Unit #3. Retrofitting the carbon capture system reduced the capacity of t he unit to 110 MW. The majority of the captured CO 2 is used for an EOR project in southern Saskatchewa n. The portion of the CO 2 is being stored in a nearby research and monitoring ge ological storage facility, where the captured
CO 2 will be injected 3.4 kilometers underground into a sandstone formation located below the2-32

major coal field supplying lignite to Unit # 3. The remaining captured CO2 will be injected into deep saline formations.

In the United States, there are two commercial-scal e CCS facilities nearing completion:

1. the Kemper County Carbon Dioxide Capture and Storag e Project in Mississippi, and

2. The W.A. Parish Petra Nova CCA Project near Houston , Texas.
Construction began on the Kemper project in 2010, a nd the startup is currently scheduled for May, 2016. The Kemper project is constructing a new 524 MW lignite unit as well as a 58 MW natural gas unit. Mississippi Power (a division of Southern Power) is building and will operate the Kemper County project. The control syst em is designed to capture 65 percent of the
CO 2 generated by the plant, and is projected to captur e 3.5 million tons of CO 2 per year. The resulting CO 2 emission rate is expected to be about 800 pounds p er MWh produced. The current total cost estimate is $5.6 billion, a substantial increase from the original $2.4 billion estimate. 36 The construction has received a $270 million grant from the US Department of Energy, and $133
million in investment tax credits from the Internal Revenue Service. The captured CO 2 will be transported via a 60 mile pipeline and used for EOR projects in mature Mississippi oil fields. 37

The only other commercial-scale electricity power s ector CCS project currently under
construction in the United States is the W.A. Paris h Petra Nova CCS Project near Houston, Texas. The Parish Petra project is a 50/50 partners hip between NRG Energy (an integrated electricity company generating and supplying electr icity to 1.6 million customers in Texas) and the Nippon Oil and Gas Exploration Company. The Par ish project will retrofit a post-combustion CCS system on a portion of the flue gas from the ex isting 610 MW coal fired Unit # 8. The CCS system will treat a 240 MW slipstream of the flue g as, and is designed to capture 90 percent of
the CO 2 in the treated flue gas. The capacity rating of Un it # 8 will not be reduced due to the

36 The Mississippi Public Utilities Staff authorized an independent monitor to conduct a review of the p roject. The findings of the review are provided in a summary re port Available online at:: http://www.psc.state.ms.us/InsiteConnect/InSiteView .aspx?model=INSITE_CONNECT&queue=CTS_ARCHIVEQ &docid=328417

37 Carbon Capture and Sequestration Technologies Prog ram at MIT. Accessed 1/23/2015. https://sequestration.mit.edu/tools/projects/kemper .html2-33

CCS project because an 85 MW custom-built natural g as fired combustion turbine co-generation unit is being built on-site to provide both electri city and steam to the CCS unit. The total cost of the CCS project is estimated to be $1 billion (incl uding a $167 million grant from the US Department of Energy), and the project is expected to extract 1.4 – 1.6 million tons of CO 2 per year. The construction contract was awarded in July , 2014, and operation is expected to begin in early 2016. The CO 2 will be piped 85 miles to a reservoir for EOR in t he West Ranch Oil Field. 38

2.6.2 Geologic and Geographic Considerations for G eologic Sequestration
Geologic sequestration (GS) (i.e., long-term contai nment of a CO 2 stream in subsurface geologic formations) is technically feasible and av ailable throughout most of the United States. GS is feasible in different types of geologic forma tions including deep saline formations (formations with high salinity formation fluids) or in oil and gas formations, such as where injected CO 2 increases oil production efficiency through a proc ess referred to as enhanced oil recovery (EOR). CO 2 may also be used for other types of enhanced recov ery, such as for natural gas production. Reservoirs, such as unmineable coal seams, also offer the potential for geologic storage. The geographic availability of deep saline formations, EOR, and un-mineable coal seams is shown in Figure 2-21. Estimates of CO 2 storage resources by state compiled by the DOE’s National Carbon Sequestration Database and Ge ographic Information System (NATCARB) and published in DOE’s 2012a Carbon Utili zation and Storage Atlas (discussed below) are provided in Table 2-8.

38 US DOE (2010) “Recovery Act: W.A. Parish Post-Comb ustion CO 2 Capture and Sequestration Project”. http://www.netl.doe.gov/research/proj?k=FE0003311 A ccessed 1/23/20152-34

Figure 2-21. Geologic Sequestration in the Continen tal United States
Sources: EPA Greenhouse Gas Reporting Program; Depa rtment of Energy, NATCARB; Department of Transportation, National Pipeline Management System

.2-35

Table 2-8. Total CO 2 Storage Resource (DOE-NETL) 39 Million Tons*

State Low Estimate High Estimate ALABAMA 135,022 765,422
ALASKA 9,524 21,771
ARIZONA 143 1,290 ARKANSAS 6,812 70,184 CALIFORNIA 37,357 463,665 COLORADO 41,458 393,734 CONNECTICUT not assessed by DOE-NETL not assessed b y DOE-NETL
DELAWARE 44 44 DISTRICT OF COLUMBIA not assessed by DOE-NETL not a ssessed by DOE-NETL
FLORIDA 113,251 611,793
GEORGIA 160,210 175,322 HAWAII not assessed by DOE-NETL not assessed by DOE -NETL
IDAHO 44 430
ILLINOIS 11,045 128,772
INDIANA 35,296 75,189
IOWA 11 55
KANSAS 11,993 95,173 KENTUCKY 3,219 8,433 LOUISIANA 186,842 2,319,238 MAINE not assessed by DOE-NETL not assessed by DOE- NETL MARYLAND 2,050 2,127 MASSACHUSETTS not assessed by DOE-NETL not assessed by DOE-NETL

(Continued on next page)

39 The United States 2012 Carbon Utilization and Stor age Atlas, Fourth Edition, U.S Department of Energy , Office of Fossil Energy, National Energy Technology Labora tory (NETL).2-36

Table 2-8. Total CO 2 Storage Resource, continued

Million Tons*

State Low Estimate High Estimate MICHIGAN 20,999 52,040 MINNESOTA not assessed by DOE-NETL not assessed by DOE-NETL MISSISSIPPI 159,846 1,306,270
MISSOURI 11 187 MONTANA 93,233 1,006,100 NEBRASKA 26,202 124,826 NEVADA not assessed by DOE-NETL not assessed by DOE-NETL NEW HAMPSHIRE not assessed by DOE-NETL not asses sed by DOE-NETL
NEW JERSEY - - NEW MEXICO 47,135 395,828 NEW YORK 5,115 5,115 NORTH CAROLINA 1,477 20,271 NORTH DAKOTA 73,954 162,569 Offshore Federal Only 539,956 7,098,976
OHIO 14,837 14,837 OKLAHOMA 62,777 269,570
OREGON 7,507 103,286 PENNSYLVANIA 24,361 24,361 RHODE ISLAND not assessed by DOE-NETL not assess ed by DOE-NETL SOUTH CAROLINA 33,180 37,677 SOUTH DAKOTA 9,656 26,489 TENNESSEE 474 4,255
TEXAS 489,205 4,772,925
UTAH 28,076 265,558 VERMONT not assessed by DOE-NETL not assessed by DOE-NETL
VIRGINIA 485 3,208 WASHINGTON 40,367 547,550 WEST VIRGINIA 18,353 18,353
WISCONSIN 0 0 WYOMING 80,127 754,917
U.S. Total 2,531,653 22,147,811 * States with a “zero” value represent estimates of minimal CO 2 storage resource. States that have not yet been assessed by the RCSPs have been identified.2-37

2.6.3 Availability of Geologic Sequestration in De ep Saline Formations
DOE and the United States Geological Survey (USGS) have independently conducted preliminary analyses of the availability and potent ial CO 2 sequestration capacity of deep saline
formations in the United States. DOE estimates are compiled by the DOE’s National Carbon Sequestration Database and Geographic Information S ystem (NATCARB) using volumetric models and published in a Carbon Utilization and St orage Atlas. 40 DOE estimates that areas of the United States with appropriate geology have a s equestration potential of at least 2,244 billion
tons of CO 2 in deep saline formations. According to DOE and at least 39 states have geologic characteristics that are amenable to deep saline GS in either onshore or offshore locations. In 2013, the USGS completed its evaluation of the tech nically accessible GS resources for CO 2 in U.S. onshore areas and state waters using probabili stic assessment. 41 The USGS estimates a mean of 3,307 billion tons of subsurface CO 2 sequestration potential, including saline and oil and gas reservoirs, across the basins studied in the Un ited States. As shown in Figure 2-21, there are 39 states for which onshore and offshore deep salin e formation storage capacity has been
identified. 42

2.6.4 Availability of CO 2 Storage via Enhanced Oil Recovery (EOR)
Although the regulatory impact analysis for this ru le relies on GS in deep saline formations, the EPA also recognizes the potential f or securely sequestering CO 2 via EOR. EOR has been successfully used at numerous production f ields throughout the United States to increase oil recovery. The oil industry in the Unit ed States has over 40 years of experience with EOR. An oil industry study in 2014 identified more than 125 EOR projects in 98 fields in the
United States. 43 More than half of the projects evaluated in the st udy have been in operation for

40 The United States 2012 Carbon Utilization and Stor age Atlas, Fourth Edition, U.S. Department of Energ y, Office of Fossil Energy, National Energy Technology Labora tory (NETL).

41 U.S. Geological Survey Geologic Carbon Dioxide Sto rage Resources Assessment Team, 2013, National assessment of geologic carbon dioxide storage resou rces—Results: U.S. Geological Survey Circular 1386, p. 41, http://pubs.usgs.gov/circ/1386/.

42 Alaska is not shown in the figure; it has deep sal ine formation storage capacity, geology amenable to EOR operations, and potential GS capacity in unmineable coal.

43 Koottungal, Leena, 2014, 2014 Worldwide EOR Survey , Oil & Gas Journal, Volume 112, Issue 4, April 7, 2014 (corrected tables appear in Volume 112, Issue 5, Ma y 5, 2014).2-38

more than 10 years, and many have been in operation for more than 30 years. This experience provides a strong foundation for demonstrating succ essful CO 2 injection and monitoring technologies, which are needed for safe and secure GS that can be used for deployment of CCS across geographically diverse areas.

Currently, 12 states have active EOR operations and most have developed an extensive
CO 2 infrastructure, including pipelines, to support th e continued operation and growth of EOR. An additional 18 states are within 100 kilometers ( 62 miles) of current EOR operations (see Figure 2-21). 44 The vast majority of EOR is conducted in oil reser voirs in the Permian Basin, which extends through southwest Texas and southeast New Mexico. States where EOR is utilized include Alabama, Colorado, Louisiana, Mich igan, Mississippi, New Mexico, Oklahoma, Texas, Utah, and Wyoming.

At the project level, the volume of CO 2 already injected for EOR and the duration of operations are of similar magnitude to the duration and volume of CO 2 expected to be captured
from fossil fuel-fired EGUs. The volume of CO 2 used in EOR operations can be large (e.g., 55
million tons of CO 2 were stored in the SACROC unit in the Permian Basi n over 35 years), and operations at a single oil field may last for decad es, injecting into multiple parts of the field. 45 According to data reported to the EPA’s Greenhouse Gas Reporting Program (GHGRP), approximately 66 million tons of CO 2 were supplied to EOR in the United States in 2013. 46 Approximately 70 percent of this total CO 2 supplied was produced from natural (geologic) CO 2 sources, and approximately 30 percent was captured from anthropogenic sources. 47

A DOE-sponsored study has analyzed the geographic a vailability of applying EOR in 11 major oil producing regions of the United States an d found that there is an opportunity to

44 The distance of 100 kilometers reflects the assump tions in the DOE-NETL cost estimates.

45 Han, Weon S., McPherson, B J., Lichtner, P C., and Wang, F P. “Evaluation of CO 2 trapping mechanisms at the SACROC northern platform, Permian basin, Texas, sit e of 35 years of CO 2 injection.” American Journal of Science 310. (2010): 282-324.

46 Greenhouse Gas Reporting Program, data reported as of August 18, 2013.

47 Greenhouse Gas Reporting Program, data reported as of August 18, 2013.2-39

significantly increase the application of EOR to ar eas outside of current operations. 48 DOEsponsored geologic and engineering analyses show th at expanding EOR operations into areas additional to the capacity already identified and a pplying new methods and techniques over the next 20 years could utilize 20 billion tons of anth ropogenic CO 2 and increase total oil production by 67 billion barrels. The availability of anthropo genic CO 2 in areas outside of current sources could drive new EOR projects by making more CO 2 locally available.

2.7 State Policies on GHG and Clean Energy Regulati on in the Power Sector
Several states have also established emission perfo rmance standards or other measures to limit emissions of GHGs from new EGUs that are comp arable to or more stringent than this rulemaking.

In 2003, then-Governor George Pataki sent a letter to his counterparts in the Northeast and Mid-Atlantic inviting them to participate in the de velopment of a regional cap-and-trade program addressing power plant CO 2 emissions. This program, known as the Regional Gree nhouse Gas Initiative (RGGI), began in 2009 and sets a regiona l CO 2 cap for participating states. The currently participating states include: Connecticut , Delaware, Maine, Maryland, Massachusetts, New Hampshire, New York, Rhode Island, and Vermont. The cap covers CO 2 emissions from all fossil-fired EGUs greater than 25 MW in participati ng states, and limits total emissions to 91 million short tons in 2014. The 2014 emissions cap is a 51 percent reduction below the initial cap
in 2009 to 2011 of 188 million tons. This emissions budget is reduced 2.5 percent annually from
2015 to 2020. RGGI CO 2 allowances are sold in a quarterly auction. RGGI c onducted their 27 th quarterly allowance auction in March, 2015 the mark et clearing price was $5.41 per ton of CO 2 for current allowances, which was a record high pri ce (the February ’15 price of $5.21 was the previous record). A total of allowances for 15.3 mi llion tons were sold in the March ’15 auction,
well below the record of 38.7 million tons sold in June ’13 for $3.21.

48 “Improving Domestic Energy Security and Lowering C O 2 Emissions with “Next Generation” CO 2 -Enhanced Oil Recovery”, Advanced Resources International, Inc. ( ARI), 2011. Available online at: http://www.netl.doe.gov/research/energy-analysis/pu blications/details?pub=df02ffba-6b4b-4721-a7b4- 04a505a19185.2-40

In September 2006, California Governor Schwarzenegg er signed into law Senate Bill 1368. The law limits long-term investments in basel oad generation by the state's utilities to power plants that meet an emissions performance sta ndard jointly established by the California Energy Commission and the California Public Utiliti es Commission. The Energy Commission has designed regulations that establish a standard for new and existing baseload generation owned by, or under long-term contract to publicly o wned utilities, of 1,100 lb CO 2 /MWh-net.

In 2006 Governor Schwarzenegger also signed into la w Assembly Bill 32, the Global Warming Solutions Act of 2006. This act includes a multi-sector GHG cap-and-trade program which covers approximately 85 percent of the state GHG emissions. EGUs are included in phase I of the program, which began in 2013. Phase II beg ins in 2020 and includes upstream sources. The cap is based on a 2 percent reduction from tota l 2012 expected emissions, and declines 2 percent annually through 2014, then 3 percent each year until 2020. The AB32 cap and trade program began functioning in 2011, and functioning market is now operating on the NYMEX futures commodity market. The final 2014 market pri ce for 2014 carbon allowances was $13.65/ton of carbon. On April 17, 2015 the 2015 al lowance futures price was $13.94/ton, and the spot price was $13.73/ton.

In May 2007, Washington Governor Gregoire signed Su bstitute Senate Bill 6001,
“Baseload Electric Generation Performance” which es tablished statewide GHG emissions reduction goals, and imposed an emission standard t hat applies to any baseload electric generation that commenced operation after June 1, 2 008 and is located in Washington, whether or not that generation serves load located within t he state. Baseload generation facilities must initially comply with an emission limit of 1,100 lb CO 2 /MWh-net. In 2013 the State of Washington revised 49 the emission limit to 970 lb CO 2 /MWh-net based on a survey of available NGCC generation units commercially available in the United States.

In 1997 Oregon required a new baseload gas fired po wer plants to meet a CO 2 emission standard that was 17 percent below the most efficie nt NGCC unit operating in the United States.

49 Washington Department of Commerce, 2013. “Greenhou se Gas Emission Performance Standard for Baseload Electric Generation”. Available online at: http://w ww.commerce.wa.gov/Documents/Concise-Expl-Stmt-WSR- 13- 06-074.pdf.2-41

In 2000 Oregon established that the effective 17 pe rcent below most efficient was 675 lb
CO 2 /MWh-net. In July 2009, Oregon Governor Kulongoski signed Senate Bill 101, which mandated that facilities generating baseload electr icity, whether gas- or coal-fired, must have emissions equal to or less than 1,100 lb CO 2 /MWh-net regardless of fuel type, and prohibited utilities from entering into long-term purchase agr eements for baseload electricity with out-ofstate facilities that do not meet that standard. Na tural gas- and petroleum distillate-fired facilitie s 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 Cuomo signed the Power NY Act of 2011. Implementing regulations established CO2 emission s tandards for new and modified electric generators greater than 25 MW. The standards vary b ased on the type of facility: base load
facilities must meet a CO 2 standard of 925 lb/MWh-net or 120 lb/MMBtu, and pe aking facilities
must meet a CO 2 standard of 1,450 lbs/MWh-net or 160 lbs/MMBtu.

Several other states have enacted CO 2 regulations affecting EGUs that do not set emissio n limits, but set other regulatory requirements limit ing CO 2 emissions from EGUs. For example, Montana enacted a law in 2007 requiring the Public Service Commission to limit approvals of new equity interests in or leases of a facility use d to generate coal-based electricity to facilities that capture and sequester at least half of their C O 2 emissions. Minnesota enacted the Next Generation Energy Act in 2007 requiring increases i n power sector greenhouse gas emissions from any new large coal energy facilities built in Minnesota or the import of electricity from such a facility located out of state to be offset b y equivalent emission reductions. New Mexico enacted legislation in 2007 authorizing tax credits and cost recovery incentives for qualifying coal-fired facilities. To qualify, plants must capt ure and store emissions so that they emit less than 1,100 lbs CO 2 /MWh, among other requirements.

Additionally, most states have implemented Renewabl e Portfolio Standards (RPS), or Renewable Electricity Standards (RES). These progra ms are designed to increase the renewable share of a state’s total electricity generation. Cu rrently 29 states, the District of Columbia, and Guam have enforceable RPS or other mandatory renewa ble capacity policies, and eight states,2-42

Puerto Rico, and Guam have voluntary goals. 50 These programs vary widely in structure, enforcement, and scope.

2.8 Revenues and Expenses
Due to lower retail electricity sales, total utilit y operating revenues declined in 2012 to $271 billion from a peak of almost $300 billion in 2008. Despite revenues not returning to 2008 levels in 2012, operating expenses were appreciably lower and as a result, net income also rose in comparison to 2008 (see Table 2-9). Recent econo mic events have put downward pressure on electricity demand, thus dampening electricity pric es and consumption (utility revenues), but have also reduced the price and cost of fossil fuel s and other expenses. In 2012 electricity generation was 1.28 percent below the generation in 2011, and has declined in 4 of the past 5

years.

Table 2-9 shows that investor-owned utilities (IOUs ) earned income of about 13.0 percent compared to total revenues in 2012. The 2012 return on revenue was the third highest year for the period 2002 to 2012 (average: 11.9 percent rang e: 10.6 percent to 13.32 percent).

Table 2-9. Revenue and Expense Statistics for Major U.S. Investor-Owned Electric Utilities

for 2002, 2008 and 2012 (nominal $millions)

2002 2008 2012 Utility Operating Revenues 219,609 298,962 270,912 Electric Utility 200,360 266,124 249,166 Other Utility 19,250 32,838 21,745 Utility Operating Expenses 189,062 267,263 235,694 Electric Utility 171,604 236,572 220,722 Operation 116,660 175,887 152,379
Production 90,715 140,974 111,714
Cost of Fuel 24,149 47,337 38,998
Purchased Power 58,810 84,724 54,570
Other 7,776 8,937 18,146
Transmission 3,560 6,950 7,183
Distribution 3,117 3,997 4,181
Customer Accounts 4,168 5,286 5,086
Customer Service 1,820 3,567 5,640

50 EIA 2012a2-43 Sales 264 225 221
Admin. and General 13,018 14,718 18,353

Maintenance 10,861 14,192 15,489 Depreciation 16,199 19,049 23,677
Taxes and Other 26,716 26,202 29,177 Other Utility 17,457 30,692 14,972 Net Utility Operating Income 30,548 31,699 35,218 Source: Table 8.3, EIA Electric Power Annual, 2012 Note: This data does not include information for pu blic utilities, nor for Independent Power Producers (IPPs).

2.9 Natural Gas Market
The natural gas market in the United States has his torically experienced significant price volatility from year to year, between seasons withi n a year, can undergo major price swings during short-lived weather events (such as cold sna ps leading to short-run spikes in heating demand), and has seen a dramatic shift since 2008 d ue to increased production from shale formations . Over the last decade, the annual avera ge nominal price of gas delivered to the power sector peaked in 2008 at $9.02/MMBtu and has since fallen dramatically to a low of $3.42/MMBtu in 2012. During that time, the daily pr ice 51 of natural gas reached as high as $18.48/MMBtu and as low as $2.03. Adjusting for in flation using the GDP implicit price deflator, in $2011 the annual average price of natu ral gas delivered to the power sector peaked at $9.38/MMBtu in 2008 and has fallen dramatically to a low of $3.36 in 2012. The annual natural gas prices in both nominal and real (2011$) terms a re in Figure 2-22. A comparison of the trends in the real price of natural gas with the real pric es of delivered coal and oil are shown in Figure 2-23. Figure 2-23 shows that while the real price o f coal and oil increased from 2002 to 2012 (+54 percent and +203 percent respectively), the re al price of natural gas declined by 22 percent in the same period. Most of the decline in real nat ural gas prices occurred between 2008 (the peak price year) and 2012, during which real gas pr ices declined by 64 percent while coal and oil

51 Henry Hub daily prices. Henry Hub is a major gas d istribution hub in Louisiana; Henry Hub prices are generally seen as the primary metric for national gas prices for all end uses. The price of natural gas delivere d to electricity generation differs substantially in different regio ns of the country, and can be higher or lower than the Henry Hub national benchmark price.2-44

prices both increased by 9 percent. The sharp decli ne in natural gas prices from 2008 to 2012 was primarily caused by the rapid increase in natur al gas production from shale formations.

Figure 2-22. Relative Change Nominal and Real (2011 $) Prices of Natural Gas Delivered

to the Power Sector ($/MMBtu)

Source: http://www.eia.gov/totalenergy/data/monthly /#prices. Downloaded 2/15/2015. $0.00 $1.00 $2.00 $3.00 $4.00 $5.00 $6.00 $7.00 $8.00 $9.00 $10.00

2002 2004 2006 2008 2010 2012Natural Gas Prices ($/MMBtu)

Nominal Price Real Price2-45

Figure 2-23. Relative Change in Real (2011$) Prices of Fossil Fuels Delivered to the Power

Sector ($/MMBtu)

Source: http://www.eia.gov/totalenergy/data/monthly /#prices. Downloaded 2/15/2015.

Current and projected natural gas prices are consid erably lower than the prices observed over the past decade, largely due to advances in hy draulic fracturing and horizontal drilling techniques that have opened up new shale gas resour ces and substantially increased the supply of economically recoverable natural gas. According to AEO 2012 (EIA 2012):

Shale gas refers to natural gas that is trapped wit hin 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 prev iously uneconomical to produce. The production of natural gas from shale formations has rejuvenated the natural gas industry in the United States.
The U.S. Energy Information Administration's Annual Energy Outlook 2014 estimates that the United States possessed 2,266 trillion cubic fe et (Tcf) of technically recoverable dry natural gas resources as of January 1, 2012. Proven reserve s make up 15 percent of the technically -50% 0% 50% 100% 150% 200% 250%

2002 2004 2006 2008 2010 2012

Coal Oil Gas Average2-46

recoverable total estimate, with the remaining 85 p ercent from unproven reserves. Natural gas from proven and unproven shale resources accounts f or 611 Tcf of this resource estimate.

Many shale formations, especially the Marcellus 52 , are so large that only small portions of the entire formations have been intensively product ion-tested. Furthermore, estimates from the Marcellus and other emerging fields with few wells already drilled are likely to shift significantly over time as new geological and production informat ion becomes available. Consequently, there is some uncertainty in the estimate of technically recoverable resources, and it is regularly updated as more information is gained through drill ing and production.

At the 2012 rate of U.S. consumption (about 25.6 Tc f per year), 2,266 Tcf of natural gas is enough to supply nearly 90 years of use. The AEO 20 14 estimate of the shale gas resource base is modestly higher than the AEO 2012 estimate (2,21 4 Tcf) of shale gas production, driven by lower drilling costs and continued drilling in shal e plays with high concentrations of natural gas liquids and crude oil, which have a higher value in energy equivalent terms than dry natural

gas. 53

EIA’s projections of natural gas conditions did not change substantially in AEO 2014 from either the AEO 2012 or 2013, and EIA continues to f orecast abundant reserves consistent with the above findings. Recent historical data reported to EIA is also consistent with these trends, with 2014 being the highest year on record 54 for domestic natural gas production. 55

52 The Marcellus formation, underlying most of Pennsy lvania and West Virginia, along with portions of Ne w York and Ohio, in 2014 produced 36% of the U.S. total na tural gas extracted from shale formations.

53 For more information, see: http://www.eia.gov/fore casts/archive/aeo11/IF_all.cfm#prospectshale; http://www.eia.gov/energy_in_brief/about_shale_gas. cfm

54 The total dry gas production in 2012 from the lowe r 48 states, including both onshore and offshore pr oduction, was 23.97 Tcf, a 1.5% increase from 2013 and a 7.9% total increase from 2011

55 http://www.eia.gov/oiaf/aeo/tablebrowser/#release= AEO2014&subject=8-AEO2014&table=72- AEO2014®ion=0-0&cases=ref2014-d102413a2-47

2.10 References

Advanced Resources International. 2011. Improving Domestic Energy Security and Lowering CO 2 Emissions with “Next Generation” CO 2 -Enhanced Oil Recovery (CO 2 -EOR) . Availableonline at: http://www.netl.doe.gov/research/energyanalysis/publications/details?pub=df02ffba-6b4b-472 1-a7b4-04a505a19185. Accessed June 9, 2015. Clean Energy States Alliance (CESA). The State of State Renewable Portfolio Standards . June 2013. Available online at http://www.cesa.org/asset s/2013-Files/RPS/State-of-State-RPSsReport-Final-June-2013.pdf
Han, Weon S., McPherson, B J., Lichtner, P C., and Wang, F P. 2010. Evaluation of CO 2 trapping mechanisms at the SACROC northern platform , Permian basin, Texas, site of 35 years of CO 2 injection

. American Journal of Science 310: 282-324. Independent Monitor’s Prudency Evaluation Report fo r the Kemper County IGCC Project. April 15, 2014. Available online at: http://www.psc.state.ms.us/InsiteConnect/InSiteView .aspx?model=INSITE_CONNECT&que ue=CTS_ARCHIVEQ&docid=328417. Interagency Task Force on Carbon Capture and Storag e. Report of the Interagency Task Force on Carbon Capture and Storage . August 2010. Available online at: http://www.epa.gov/climatechange/downloads/CCS-Task -Force-Report-2010.pdf. Accessed June 9, 2015. Intergovernmental Panel on Climate Change. 2001. Climate Change 2001: The Scientific Basis

. Available online at:http://www.grida.no/publications/other/ipcc_tar/ ?src=/climate/ipcc_tar/wg1/218.htm. Accessed June 9, 2015. International Energy Agency (IEA). 2013. Tracking Clean Energy Progress 2013. Input to the Clean Energy Ministerial. Available online at: http ://www.iea.org/etp/tracking/. Accessed June 9, 2015. Koottungal, Leena. 2014. 2014 Worldwide EOR Survey , Oil & Gas Journal, Volume 112, Issue 4, April 7, 2014 (corrected tables appear in Volume 112, Issue 5, May 5, 2014). National Energy Technology Laboratory (NETL). 2008. Reducing CO 2 Emissions by Improving the Efficiency of Existing Coal-fired Power Plant F leet . Available online at http://www.netl.doe.gov/energy-analyses/pubs/CFPP%2 0Efficiency-FINAL.pdf. Accessed June 9, 2015.2-48

National Energy Technology Laboratory (NETL). 2012. The United States 2012 Carbon Utilization and Storage Atlas, Fourth Edition . Available online at: http://www.netl.doe.gov/technologies/carbon_seq/ref shelf/atlasIV/. Accessed June 9, 2015. National Energy Technology Laboratory (NETL). 2013. Energy Analyses: Cost and Performance Baselines for Fossil Energy Plants. Available online at: http://www.netl.doe.gov/energy-analyses/baseline_st udies.html. Accessed June 9, 2015. Pacific Northwest National Laboratory (PNNL). 2009. An Assessment of the Commercial Availability of Carbon Dioxide Capture and Storage Technologies as of June 2009. Available online at: http://www.pnl.gov/science/pdf/PNNL-1852 0_Status_of_CCS_062009.pdf. Accessed June 9, 2015. U.S. Energy Information Administration (U.S. EIA). 2000. Carbon Dioxide Emissions from the Generation of Electric Power in the United States. Available online at: ftp://ftp.eia.doe.gov/environment/co2emiss00.pdf. A ccessed June 9, 2015. U.S. Energy Information Administration (U.S. EIA). 2003. Electric Power Annual 2003. Available online at: http://www.eia.gov/electricity /annual/archive/03482003.pdf. Accessed June 9, 2015. U.S. Energy Information Administration (U.S. EIA). 2009. Electric Power Annual 2009

. Available online at: http://www.eia.gov/electricity /annual/archive/03482009.pdf. Accessed June 9, 2015. U.S. Energy Information Administration (U.S. EIA). 2013. Electric Power Annual 2013. Available online at: http://www.eia.gov/electricity /annual/. Accessed June 9, 2015. U.S. Energy Information Administration (U.S. EIA). 2010a. “Status of Electricity Restructuring by State.” Available online at: http://www.eia.gov/cneaf/electricity/page/restructu ring/restructure_elect.html. Accessed June 9, 2015. U.S. Energy Information Administration (U.S. EIA). 2010b. AEO 2010 Retrospective Review

. Available online at: http://www.eia.gov/forecasts/a eo/retrospective/. Accessed June 9, 2015. U.S. Energy Information Administration (U.S. EIA). 2010c. Annual Energy Outlook 2010

. Available online at: http://www.eia.gov/oiaf/archiv e/aeo10/index.html. Accessed June 9, 2015. U.S. Energy Information Administration (U.S. EIA). 2010d. Annual Energy Review 2010

. Available online at: http://www.eia.gov/totalenergy /data/annual/pdf/aer.pdf. Accessed June 9, 2015.2-49

U.S. Energy Information Administration (U.S. EIA). 2011. Annual Energy Outlook 2011

. Available online at: http://www.eia.gov/forecasts/a rchive/aeo11/. Accessed June 9, 2015. U.S. Energy Information Administration (U.S. EIA). 2012. Annual Energy Outlook 2012 (Early Release) . Available online at: http://www.eia.gov/forecasts /aeo/. Accessed June 9, 2015. U.S. Energy Information Administration (U.S. EIA). Today in Energy: Most states have Renewable Portfolio Standards. 2012a. Available online at: http://www.eia.gov/todayinenergy/detail.cfm?id=4850 . Accessed June 9, 2015. U.S. Energy Information Administration (U.S. EIA). 2013. Annual Energy Outlook 2013

. Available online at: http://www.eia.gov/forecasts/a eo/. Accessed June 9, 2015. U.S. Energy Information Administration (U.S. EIA). 2015. Monthly Energy Review, April 2015

. Available online at: http://www.eia.gov/totalenergy /data/monthly/. Accessed June 9, 2015. U.S. Environmental Protection Agency (U.S. EPA). 20 13. Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2011 . Available online at: http://www.epa.gov/climatechange/Downloads/ghgemiss ions/US-GHG-Inventory-2013- Main-Text.pdf. Accessed June 9, 2015. U.S. Geological Survey (USGS) Carbon Dioxide Storag e Resources Assessment Team. 2013. National assessment of geologic carbon dioxide stor age resources – Results: U.S. Geological Survey Circular 1386 . Available online at: http://pubs.usgs.gov/circ/13 86/. Accessed June 9, 20153-1



### C HAPTER 

### 3: C OST 

### , E MISSIONS 

### , E CONOMIC 

### , AND 

### E NERGY 

### I MPACTS

3.1 Introduction
This chapter reports the compliance cost, emissions , economic, and energy impact analysis performed for the Clean Power Plan Final R ule. EPA used the Integrated Planning Model (IPM), developed by ICF International, to con duct most of the analysis discussed in this Chapter. 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 contiguous United States for the entire power system. The IPM electricity demand projections are based on projections from the Energy Information Administrat ion (EIA), adjusted for demand-side energy efficiency measures that can be reasonably anticipa ted to occur under the Clean Power Plan.

3.2 Overview
This chapter of the RIA presents illustrative analy ses of the final rule by making assumptions about the possible approaches that Stat es might pursue as they develop their state plans. Over the last decade, EPA has conducted exte nsive analyses of regulatory actions affecting the power sector. These efforts support t he 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.
3.3 Power Sector Modelling Framework
The Integrated Planning Model (IPM), developed by I CF Consulting, is a state-of-the-art, peer-reviewed, dynamic linear programming model tha t 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 fo r the entire electric power system. EPA used IPM to project likely future electricity market con ditions with and without the Clean Power Plan Final Rule. Additional demand side energy efficienc y measures that may be adopted in response to the regulation, and the resulting changes to fut ure demand projections, are also accounted for in the analyses. The level of demand side energy ef ficiency-driven reductions in electricity demand, and their associated costs, are reported in section 3.7.
IPM is a multi-regional, dynamic, deterministic lin ear programming model of the contiguous U.S. electric power sector. It provides forecasts of least cost capacity expansion,3-2

electricity dispatch, and emission control strategi es while meeting energy demand and environmental, transmission, dispatch, and reliabil ity constraints. EPA has used IPM for over two decades to better understand power sector behav ior under future business-as-usual
conditions and to evaluate the economic and emissio n impacts of prospective environmental policies. The model is designed to reflect electric ity markets as accurately as possible. EPA uses the best available information from utilities, indu stry experts, gas and coal market experts, financial institutions, and government statistics a s the basis for the detailed power sector modeling in IPM. The model documentation provides a dditional information on the assumptions discussed here as well as all other model assumptio ns and inputs. 56
The model incorporates a detailed representation o f the fossil-fuel supply system that is used to forecast equilibrium fuel prices. The model 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 and non-power sector gas dem and and corresponding gas production and price levels. 57 This module consists of 118 supply, demand, and st orage nodes and 15 liquefied natural gas re-gasification facility locations that are tied together by a series of linkages (i.e., pipelines) that represent the North American natura l gas transmission and distribution network.
IPM also endogenously models the partial equilibriu m of coal supply and EGU coal demand levels throughout the contiguous U.S., takin g into account assumed non-power sector demand and imports/exports. IPM reflects 36 coal su pply regions, 14 coal grades, and the coal transport network, which consists of over four thou sand linkages representing rail, barge, and truck and conveyer linkages. The coal supply curves in IPM were developed during a thorough bottom-up, mine-by-mine approach that depicts the c oal choices and associated supply costs that power plants would face if selecting that coal over the modeling time horizon. The IPM

56 Detailed information and documentation of EPA’s Ba se Case using IPM (v5.15), including all the underl ying assumptions, data sources, and architecture paramet ers can be found on EPA’s website at: http://www.epa.gov/powersectormodeling

57 See Chapter 10 of EPA’s Base Case using IPM (v5.15 4) documentation, available at: http://www.epa.gov/powersectormodeling3-3

documentation outlines the methods and data used to quantify the economically recoverable coal reserves, characterize their cost, and build the 36 coal regions’ supply curves. 58
The costs presented in this RIA include both the IP M-projected annualized estimates of private compliance costs as well as the estimated c osts incurred by utilities and ratepayers to achieve demand-side energy efficiency improvements. The IPM-projected annualized estimates of private compliance costs provided in this analys is are meant to show the increase in production (generating) costs to the power sector i n response to the final rule.
To estimate these annualized costs, EPA uses a conv entional and widely accepted approach that applies a capital recovery factor (CR F) 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 i nsurance coverage required, local property taxes, and the life of capital. 59 It is important to note that there is no single CR F factor applied in the model; rather, the CRF varies across technologi es in the model in order to better simulate power sector decisionmaking.

While the CRF is used to annualize costs within IPM , a discount rate is used to estimate the net present value of the intertemporal flow of the annualized capital and operating costs. The optimization model then identifies power sector inv estment decisions that minimize the net present value of all costs over the full planning h orizon while satisfying a wide range of demand, capacity, reliability, emissions, and other constra ints. As explained in Chapter 8 of the IPM documentation, the discount rate is derived as a we ighted average cost of capital that is a function of capital structure, post-tax cost of deb t, and post-tax cost of equity. While the detailed formulation of this rate is presented in the IPM do cumentation, the rate estimated and used in the current analysis is 4.77 percent. It is important t o note that this discount rate is selected for the purposes of best simulating power sector behavior, and not for the purposes of discounting social
costs or benefits.

58 See Chapter 9 of EPA’s Base Case using IPM (v5.15) documentation, available at: http://www.epa.gov/powersectormodeling

59 See Chapter 8 of EPA’s Base Case using IPM (v5.15) documentation, available at: http://www.epa.gov/powersectormodeling.3-4

EPA has used IPM extensively over the past two deca des to analyze options for reducing power sector emissions. Previously, the model has b een 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. Recently IPM has also been used to estimate the air pollution reductions and power sector impacts of wa ter and waste regulations affecting EGUs, including Cooling Water Intakes (316(b)) Rule, Disp osal of Coal Combustion Residuals from Electric Utilities (CCR) and Steam Electric Effluen t Limitation Guidelines (ELG).
The model and EPA's input assumptions undergo perio dic formal peer review. The rulemaking process also provides opportunity for ex pert review and comment by a variety of stakeholders, including owners and operators of cap acity in the electricity 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 provi des a highly-detailed review of key input assumptions, model representation, and modeling res ults. IPM has received extensive review by energy and environmental modeling experts in a vari ety of contexts. For example, in the late 1990s, the Science Advisory Board reviewed IPM as p art of the CAA Amendments Section 812 prospective studies that are periodically conducted . The model has also undergone considerable interagency scrutiny when it was used to conduct ov er a dozen legislative analyses (performed at Congressional request) over the past decade. The Ag ency has also used the model in a number of comparative modeling exercises sponsored by Stanfor d 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 Assessmen t Group), other Federal and state agencies, environmental groups, and industry.
3.4 Recent Updates to EPA’s Base Case using IPM (v. 5.15)
The “Base Case” for this analysis is a business-as- usual scenario that would be expected under market and regulatory conditions in the absen ce of this rule. As such, the IPM base case represents the baseline for this RIA. EPA frequentl y updates the IPM base case to reflect the latest available electricity demand forecasts as we ll as expected costs and availability of new and existing generating resources, fuels, emissions con trol technologies, and regulatory requirements.3-5

EPA’s IPM modeling platform used to analyze this fi nal rule (v.5.15) incorporates updates to the version of the model used to analyze the impacts of the proposed rule (v.5.13). These updates are primarily routine calibrations wi th the Energy Information Agency's (EIA) Annual Energy Outlook (AEO), including updating the electric demand forecast consistent with the AEO 2015 and an update to natural gas supply. A dditional updates, based on the most up-todate information and/or public comments received by the EPA, include unit-level specifications (e.g., pollution control configurations), planned p ower plant construction and closures, and updated cost and performance for onshore wind and u tility-scale solar technologies. This IPM modeling platform incorporates federal and most sta te laws and regulations whose provisions were either in effect or enacted and clearly deline ated in March 2015. This update also includes two non-air federal rules affecting EGUs: Cooling W ater Intakes (316(b)) Rule and Combustion Residuals from Electric Utilities (CCR). Additional ly, all new capacity projected by the model is compliant with Clean Air Act 111(b) standards, incl uding the final standards of performance for
GHG emissions from new sources. For a detailed acco unt of all updates made to the v.5.15 modeling platform, see the Incremental Documentatio n for EPA Base Case v.5.15 Using IPM. 60
EPA 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 units in EPA modeling applications of IPM . NEEDS includes detailed information on each individual EGU, including geographic, operatin g, air emissions, and other data on every generating units in the contiguous U.S. 61

3.5 State Goals in this Final Rule
In this final rule, the EPA is establishing CO 2 emission performance rates for two categories of existing fossil fuel-fired EGUs, foss il fuel-fired electric utility steam generating units and stationary combustion turbines. The EPA h as translated the source category-specific
CO 2 emission performance rates into state-level rate-b ased and mass-based CO 2 goals in order to expand the range of choices that states have in dev eloping their plans. Due to the range of choices available to states, and the lack of a priori knowledge about the specific choices states

60 Available at: http://www.epa.gov/powersectormodeli ng/

61 The NEEDS database can be found on the EPA’s websi te for the Base Case using IPM (v5.15), <http://www.epa.gov/powersectormodeling >.3-6

will make in response to the final goals, this RIA presents two scenarios designed to achieve these goals, which we term the “rate-based” illustr ative plan approach and the “mass-based” illustrative plan approach. Table 3-1 presents the rate-based and mass-based state goals.

Table 3-1. Statewide CO 2 Emission Performance Goals, Rate-based and Mass-ba sed Rate-Based(Adjusted Output-WeightedAverage Pounds of CO 2 Per Net MWh From All Affected Fossil Fuel-Fired EGUs) Mass-Based(Adjusted Output-WeightedAverage Short Tons of CO 2 From All Affected Fossil Fuel-Fired

EGUs) State Interim Goal Final Goal Interim Goal Final Go al Alabama 1,157 1,018 62,210,288 56,880,474 Arkansas 1,304 1,130 33,683,258 30,322,632 Arizona 1,173 1,031 33,061,997 30,170,750 California 907 828 51,027,075 48,410,120 Colorado 1,362 1,174 33,387,883 29,900,397 Connecticut 852 786 7,237,865 6,941,523 Delaware 1,023 916 5,062,869 4,711,825 Florida 1,026 919 112,984,729 105,094,704 Lands of the Fort Mojave Tribe 832 771 611,103 588,519 Georgia 1,198 1,049 50,926,084 46,346,846 Iowa 1,505 1,283 28,254,411 25,018,136 Idaho 832 771 1,550,142 1,492,856 Illinois 1,456 1,245 74,800,876 66,477,157 Indiana 1,451 1,242 85,617,065 76,113,835 Kansas 1,519 1,293 24,859,333 21,990,826 Kentucky 1,509 1,286 71,312,802 63,126,121 Louisiana 1,293 1,121 39,310,314 35,427,023 Massachusetts 902 824 12,747,677 12,104,747 Maryland 1,510 1,287 16,209,396 14,347,628 Maine 842 779 2,158,184 2,073,942 Michigan 1,355 1,169 53,057,150 47,544,064 Minnesota 1,414 1,213 25,433,592 22,678,368 Missouri 1,490 1,272 62,569,433 55,462,884 Mississippi 1,061 945 27,338,313 25,304,337 Montana 1,534 1,305 12,791,330 11,303,107 Lands of the Navajo Nation 1,534 1,305 24,557,793 21,700,587 North Carolina 1,311 1,136 56,986,025 51,266,234 North Dakota 1,534 1,305 23,632,821 20,883,232 Nebraska 1,522 1,296 20,661,516 18,272,739 New Hampshire 947 858 4,243,492 3,997,579 New Jersey 885 812 17,426,381 16,599,745 New Mexico 1,325 1,146 13,815,561 12,412,6023-7 Rate-Based(Adjusted Output-WeightedAverage Pounds of CO 2 Per Net MWh From All Affected Fossil Fuel-Fired EGUs) Mass-Based(Adjusted Output-WeightedAverage Short Tons of CO 2 From All Affected Fossil Fuel-Fired

EGUs) State Interim Goal Final Goal Interim Goal Final Go al Nevada 942 855 14,344,092 13,523,584 New York 1,025 918 33,595,329 31,257,429 Ohio 1,383 1,190 82,526,513 73,769,806 Oklahoma 1,223 1,068 44,610,332 40,488,199 Oregon 964 871 8,643,164 8,118,654 Pennsylvania 1,258 1,095 99,330,827 89,822,308 Rhode Island 832 771 3,657,385 3,522,225 South Carolina 1,338 1,156 28,969,623 25,998,968 South Dakota 1,352 1,167 3,948,950 3,539,481 Tennessee 1,411 1,211 31,784,860 28,348,396 Texas 1,188 1,042 208,090,841 189,588,842 Lands of the Uintah and Ouray Reservation 1,534 1,305 2,561,445 2,263,431 Utah 1,368 1,179 26,566,380 23,778,193 Virginia 1,047 934 29,580,072 27,433,111 Washington 1,111 983 11,679,707 10,739,172 Wisconsin 1,364 1,176 31,258,356 27,986,988 West Virginia 1,534 1,305 58,083,089 51,325,342 Wyoming 1,526 1,299 35,780,052 31,634,412

3.6 Illustrative Plan Approaches Analyzed
To estimate the costs, benefits, and economic and e nergy market impacts of implementing the CPP guidelines, the EPA modeled tw o illustrative plan approaches, each at the state level, based on a rate-based approach and a m ass-based approach. The rate-based plan approach requires affected sources in each state to achieve a single average emissions rate in each period as represented by the statewide goals. The mass-based plan approach requires affected sources in each state to limit their aggre gate emissions not to exceed the mass goal for that state. The two plan types in these illustrativ e analyses represent two types of plans that are
available to the states.
In each of these scenarios, affected EGUs include:

• Existing fossil steam boilers with nameplate capaci ty greater than 25 MW

• Existing NGCC units with nameplate capacity greater than 25 MW3-8

In the rate-based scenario, generation (or avoided generation) from these additional sources represented in the model is counted toward meeting state goals:

• All renewable capacity (hydro, solar PV, wind, geot hermal) that comes online
after 2012

• Under-construction nuclear 62

• Demand-side energy efficiency in addition to levels implicit in base case electricity demand.
In the rate-based illustrative plan approach analyz ed in this RIA, the affected EGUs within each state are required to achieve an averag e emissions rate that is less than or equal to the state goals for each state. In order meet the goal for each state, the affected sources in this scenario have the ability to do one or both of the following:
1) generate in amounts within that state such that the average emissions rate is achieved,
and/or
2) include in the average emissions rate calculation n ew renewable generation or demand-side energy efficiency located outside of th e state but within each of the illustrative Interconnection-based regions shown in Figure 3-1 below. 63

62 Includes three nuclear facilities at which constru ction has already commenced: Watts Barr (TN), Vogtl e (GA), and Summer (SC)

63 In this illustrative scenario, energy efficiency/r enewable energy procurement is limited to within on e of the three illustrative regions. Since the interconnections do not always follow state borders, certain states th at fall into more than one region were grouped in regions where there was a majority of geographic territory (area) or g eneration. Depending on the elements of their respective state ’s plan, sources in states that have adopted certai n rate-based plans may be able to procure energy efficiency/rene wable energy from states outside of these illustrat ive regions. See the preamble for discussion.3-9

Figure 3-1. Illustrative Regions for Demand-Side En ergy Efficiency/Renewable Energy

Procurement Used in this Analysis
This rate-based implementation plan approach enable s some sources to emit at emission rates higher than their applicable state goal, as l ong as there is either corresponding generation coming from affected sources in that state that emi t at a lower rate and/or generation (or avoided generation) from energy efficiency/renewable energy (which is procured from within the illustrative regions, including within the source’s state). In this illustrative analysis affected EGUs may not procure emission reductions from (e.g. , by averaging their emissions with) affected EGUs located in other states (which may al so have different emission performance standards) in order to demonstrate compliance. Furt hermore in this rate-based scenario, specific generation (or avoided generation) from energy effi ciency/renewable energy procurement may only be used once for compliance toward a state goa l; in other words, while emitting sources in all states may avail themselves of qualifying energ y efficiency/renewable energy across the illustrative region, no particular energy efficienc y/renewable energy MWh can be claimed by more than one emitter as part of reaching a state g oal.
Each illustrative plan approach assumes identical l evels of demand-side energy efficiency megawatt-hour (MWh) demand reductions and associate d costs, which are specified exogenously and consistent with the energy efficien cy plan scenario performance levels3-10

described in section 3.7. Details of the implementa tion of the demand reduction are reported in the following section.
The mass-based scenario presented in this chapter i ncludes a 5 percent set-aside of allowances that would be allocated to recognize dep loyment of new renewable capacity, which is represented by lowering the capital cost of new ren ewable capacity in a compliance period by the
estimated value of the allowances in the set-aside in that period. The value of the set-aside is estimated in each model run year (i.e., simulated y ear in IPM) as the total allowances in the setasides of each state in the contiguous U.S. multipl ied by the projected average allowance price over the contiguous U.S. for that year. This total value is then assumed to apply evenly to all new renewable capacity.
Each of the two illustrative plan approaches assume s that sources within each state comply with the applicable state goals without exch anging a compliance instrument (ERC or allowance) with sources in any other state. However , in the rate-based scenario, sources are allowed to procure renewable energy or demand-side energy efficiency beyond their own state in order to adjust their effective emission rate, whic h is consistent with the conditions for rate-based implementation in any state that are described in s ection VIII of the preamble. 64 For example,
while the final rule enables states to achieve thei r mass goals with the flexibility of interstate trading, this RIA presents analysis is an illustrat ive plan approach that assumes that each state achieves its goal independently. Cooperation betwee n the states that allows for trading across states would provide EGUs with additional low cost abatement opportunities and would therefore lower the overall cost of compliance acro ss the affected states. While the illustrative plan approaches assume particular plan types that m ay limit compliance options available to affected EGUs, the equilibrium effects on generatio n, emissions, etc., in a particular state that are forecast in these analyses depend on the behavior o f generators in neighboring states in response to the regulation.
The full array of estimates for the benefits, costs , and economic impacts of this action are presented for both the illustrative rate-based and mass-based plan approaches. These illustrative plan approaches are designed to reflect, to the ext ent possible, the scope and nature of the CPP

64 In this modeling scenario, sources were only able to procure such RE and EE within the same interconn ectionbased region, while the rule does not impose a regi onal limitation to such claims in rate-based compli ance.3-11

guidelines. However, there is considerable uncertai nty with regard to the regulatory form and precise measures that states will adopt to meet the requirements, since there are considerable flexibilities afforded to the states in developing state plans. Nonetheless, the analysis of the benefits, costs, and relevant impacts of the rule a ttempts to encapsulate some of those flexibilities in order to inform states and stakeholders of the p otential overall impacts of the CPP.
It is also important to note that the analysis does not specify any particular CO 2 reduction measure to occur, with the exception of the level o f demand-side energy efficiency assumed to be adopted in response to the CPP. In other words, aside from investments in energy efficiency, the analysis allows the power system the flexibilit y to respond to average emissions rate or mass
constraints on affected sources in the illustrative scenarios to achieve the goals in the most costeffective manner determined by IPM, as specified be low. Additionally, there are other zeroemitting alternatives to replacing fossil generatio n beyond the renewable generation technologies that are part of building block 3 and the energy ef ficiency measures that were analyzed in these scenarios. For instance, while costs would be diffe rent, the impact of distributed zero-emitting generation such as residential and commercial solar would displace fossil generation in the same way that demand side energy efficiency would.
While IPM produces a cost-minimizing solution to ac hieve the state goals imposed in the illustrative scenarios, there may be yet lower-cost approaches that the states may adopt to achieve their state goals inasmuch as states and so urces take advantage of emission reduction opportunities in practice, and flexibilities afford ed under the final rule, that are not represented i n this analysis and would yield different cost and em issions outcomes.
As previously noted, the power sector modeling and analysis presented in this chapter is intended to be illustrative in nature, and reflects the EPA’s best assessment of likely impacts of the CPP under a range of approaches that states may adopt. The modeling is designed to reflect the rule’s requirements, including the timing, appl icability to sources, and flexibilities across the power system as accurately as possible to represent the nature and scope of the CPP. The analysis is a reasonable expectation of the increme ntal effects of the rule, and is consistent with past EPA analyses of power sector regulatory requir ements. The EPA has separately analyzed and considered the cost of implementing the emissio n reduction measures in BSER, which do3-12

not rely on energy efficiency measures. For this an alysis, see section V.A.4.d. of the preamble to
this final rule.
For the CPP, the analysis and projections for the y ear 2025 reflect the impacts across the power system of complying with the interim goals, a nd the analysis and projections for 2030 reflect the impacts of complying with the final goa ls. In addition to the 2025 and 2030 projections, modeling results and projections are a lso shown for 2020. There is no regulatory requirement reflected in the 2020 run-year in IPM, consistent with the final rule. These years reflect the basic run-year structure in IPM, as con figured by EPA.
Although the analysis of the CPP does not include e stimates of the costs and benefits of the CPP across each year of the rule in a year-by-y ear manner, the EPA has reflected the structure of the rule, including the interim and th e final state goals of the CPP, in a manner that is consistent with the regulatory requirements. This i s also consistent with past practice, including analysis of the Clean Air Interstate Rule, the Cros s State Air Pollution Rule, the NO X SIP Call, the Acid Rain Program, National Ambient Air Quality Standards, and state rules. These past regulatory and legislative efforts included modelin g and analysis in a similar manner, where select analytic years reflected projections of poli cy impacts for rules that include multi-year compliance periods.
3.7 Demand-Side Energy Efficiency
3.7.1 Demand-Side Energy Efficiency Improvements (E lectricity Demand Reductions) 65
While the final rule no longer includes demand-side energy efficiency potential as part of BSER, the rule does allow such potential to be used for compliance. These scenarios include a representation of demand-side energy efficiency com pliance potential because energy efficiency is a highly cost-effective means for reducing CO 2 from the power sector, and it is reasonable to assume that a regulatory requirement to reduce CO 2 emissions will motivate parties to pursue all highly cost-effective means for making emission red uctions accordingly, regardless of what particular emission reduction measures were assumed in determining the level of that regulatory requirement. The EPA has included in our illustrati ve plan scenarios (both rate- and mass-based)

65 For a more detailed discussion of the demand-side energy efficiency demand reductions and their assoc iated costs, refer to U.S. EPA. 2015. Technical Support Document (TSD) the Final Carbon Pollution Emission Guidelin es for Existing Stationary Sources: Electric Utility Gener ating Units. Demand-Side Energy Efficiency.3-13

a level of demand reduction that could be achieved, and the associated costs incurred, through implementation of demand-side energy efficiency mea sures. This “demand-side energy efficiency plan scenario” represents a level of per formance that has already been demonstrated or is required by policies (e.g., energy efficiency re source standards) of leading energy efficiency implementing states, and is consistent with a demon strated or required annual pace of performance improvement over time. The resulting le vels of demand reduction are consistent
with recent studies of achievable demand reduction potential conducted throughout the U.S. For these reasons, the demand-side energy efficiency pl an scenario represents a reasonable assumption about the level of demand-side energy ef ficiency investments that may be encouraged in response to the final CPP.
For the illustrative demand-side energy efficiency plan scenario, electricity demand reductions for each state for each year are develop ed by ramping up from a historical basis 66 to a target annual incremental demand reduction rate of 1.0 percent of electricity demand over a period of years starting in 2020, and maintaining t hat rate throughout the modeling horizon. 67 Nineteen leading states either have achieved, or ha ve established requirements that will lead them to achieve, this rate of incremental electrici ty demand reduction on an annual basis. Based on historic performance and existing state requirem ents, for each state the pace of improvement
from the state’s historical incremental demand redu ction rate is set at 0.2 percent per year, beginning in 2020, until the target rate of 1.0 per cent is achieved. States already at or above the 1.0 percent target rate are assumed to achieve a 1. 0 percent rate beginning in 2020 and sustain
that rate thereafter. 68 The incremental demand reduction rate for each sta te, for each year, is used to derive cumulative annual electricity demand redu ctions based upon information about the average life of energy efficiency measures and the distribution of measure lives across energy

66 The historical basis of the percentage of reduced electricity consumption differs for each state and is drawn from the data reported in Energy Information Administrat ion (EIA) Form 861, 2013, available at http://www.eia.gov/electricity/data/eia861/.

67 The incremental demand reduction percentage is app lied to the previous year’s electricity demand for the state.

68 This assumption may result in underestimating elec tricity demand reductions in these states in the il lustrative plan scenarios.3-14

efficiency programs. 69 The cumulative annual electricity demand reduction derived using this methodology is used to adjust base case electricity demand levels in the illustrative plan approach modeling.
To reflect the implementation of the illustrative e nergy efficiency plan scenario in modeling, the IPM base case electricity demand was adjusted exogenously to reflect the estimated future-year demand reductions calculated as described above. State-level demand reductions were scaled up to account for transmissi on losses and applied to base case generation demand in each model year to derive adjusted demand for each state, reflecting the energy efficiency plan scenario energy reductions. The dem and adjustments were applied proportionally across all segments (peak and non-peak) of the load duration curve. 70 To reflect the adjusted state-level demand within IPM model regions that cr oss state borders, energy reductions from a bisected state were distributed between the applica ble IPM model regions using a distribution approach based on reported sales in 2013 as a proxy for the distribution of energy efficiency investment opportunities.

Table 3-2 summarizes the results of the illustrativ e demand-side energy efficiency plan
scenario at the national level.
Table 3-2. Demand-Side Energy Efficiency Plan Scena rio: Net Cumulative Demand

Reductions [Contiguous U.S.] (GWh and as Percent of BAU Sales) 2020 2025 2030 Net Cumulative Demand Reduction (GWh) 23,150 194,12 6 327,092 Net Cumulative Demand Reduction as Percent of BAU S ales 0.59% 4.81% 7.83% Source: U.S. EPA. 2015. Technical Support Document (TSD) for the Final Carbon Pollution Emission Guide lines for Existing Stationary Sources: Electric Utility G enerating Units. Demand-Side Energy Efficiency.

69 The average life of demand-side energy efficiency measures used is 10.2 years. This average is repres ented using a four-tier distribution of measure lives ranging f rom 6.5 to 21.2 years. This approach is based on 20 15 analysis by Lawrence Berkeley National Laboratory and is discus sed in detail in section 8.2.6 of the Demand-Side E nergy Efficiency TSD.

70 Details and reasoning for this assumption are incl uded in U.S. EPA. 2015. Technical Support Document (TSD) for the Final Carbon Pollution Emission Guidelines for Existing Stationary Sources: Electric Utility G enerating Units. Demand-Side Energy Efficiency.3-15

3.7.2 Demand-Side Energy Efficiency Costs 71
Total costs of achieving the demand-side energy eff iciency plan scenario for each year were calculated exogenous to the power sector model ing. The power system cost impacts resulting from the illustrative plan approach analy ses were captured within IPM and include the effects of reduced demand levels driven by the ener gy efficiency scenario discussed above. The integration of the exogenously calculated demand-si de energy efficiency scenario costs with the power system cost impacts of the illustrative plan approaches are discussed in section 3.9.2. In addition to the demand reduction results, the deman d-side energy efficiency costs were based upon an estimate of the total first-year cost of sa ved energy (i.e., reduced demand), the average life of the demand-side energy efficiency measures, the distribution of those measure lives, and cost factors as greater levels of demand reductions are achieved. The total first-year cost of saved energy accounts for both the costs of the demand-si de energy efficiency programs, known as the program costs, and the additional cost to electrici ty consumers participating in the program (e.g., purchasing a more energy efficient technology), kno wn as the participant costs.
To calculate total annualized demand-side energy ef ficiency costs, first-year costs for each year for each state were levelized (at 3 perce nt and 7 percent discount rates) over the
estimated distribution of measure lives and the res ults summed for each year for each state. For example, the 2025 estimate of annualized energy efficiency cost includes levelized value of firstyear costs for energy efficiency investments made i n 2020 through 2025. The annualized costs rise in each analysis year as additional first-year costs are incurred. The annualized cost results
are summarized below in Table 3-3. The total leveli zed cost of saved energy was calculated based upon the same inputs and using a 3 percent di scount rate resulted in national average values of 9.2 cents per kWh in 2020, 8.6 cents per kWh in 2025, and 8.1 cents per kWh in 2030.
Table 3-3. Annualized Cost of Demand-Side Energy Ef ficiency Plan Scenario (at discount rates of 3 percent and 7 percent, billions 2011$)
Discount Rate 2020 2025 2030

at 3 percent 2.1 16.7 26.3 at 7 percent 2.6 20.6 32.5 Source: U.S. EPA. 2015. Technical Support Document (TSD) the Final Carbon Pollution Emission Guideline s for Existing Stationary Sources: Electric Utility Gener ating Units. Demand-Side Energy Efficiency.

71 For a more detailed discussion of the demand-side energy efficiency cost analysis, refer to the Deman d-Side Energy Efficiency TSD.3-16

The funding for demand-side energy efficiency progr ams (to cover program costs) is typically collected through a standard per kWh surc harge to the ratepayer; the regional retail price impacts analyzed from this RIA’s illustrative plan approaches assumes the recovery of these program costs through the following procedure

. 72 For each state, the first-year energy efficiency program costs are calculated for each ye ar. These costs were distributed between the applicable IPM regions using an approach based on r eported sales in 2012 as a proxy for the distribution of energy efficiency investment opport unities. These regionalized energy efficiency program costs were then incorporated into the regio nal retail price calculation as discussed in
section 3.9.9. 73 The U.S. EPA’s Demand-Side Energy Efficiency Techn ical Support Document (U.S. EPA 2015) provides complete details on the ca lculations of annualized costs and first-year costs as well as comprehensive results (by state, b y year) for the illustrative demand-side energy efficiency plan scenario.
3.8 Monitoring, Reporting, and Recordkeeping Costs
EPA projected monitoring, reporting and recordkeepi ng 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 sta te’s reduction goal. To calculate the national costs, EPA estimated that 47 states and 1,028 facil ities would be affected.
The EPA estimated that the majority of the cost to EGUs would be in calculating net energy output, which is needed whether the state pl an utilizes a rate-based or a mass-based limit. Since the majority of EGUs do have some ener gy usage meters or other equipment available to them, EPA believes a new system for ca lculating net energy output is not needed. Under the final guidelines, states are required to use monitoring and reporting requirements for
their affected EGUs to ensure that the sources are meeting the appropriate CO 2 emission performance rates or emission goals.

72 The full retail price analysis method is discussed in section 3.7.9 of this chapter.

73 The effect on equilibrium supply and demand of ele ctricity due to changing retail rates to fund energ y efficiency programs is not captured in the IPM modeling.3-17

The EPA has made it a priority to streamline report ing and monitoring requirements. In this rule, the EPA is making implementation as effi cient as possible for both the states and the affected EGUs by allowing state plans to utilize th e current monitoring and recordkeeping requirements and pathways that have already been we ll established in other EPA rulemakings. For example, under the Acid Rain Program’s continuo us emissions monitoring, 40 CFR Part 75, the EPA has established requirements for the majori ty of the EGUs that would be affected by a 111(d) state plan to monitor CO 2 emissions and report that data using the Emissions Collection and Monitoring Plan System (ECMPS). Additionally si nce the CO 2 hourly data is already reported to the EPA’s ECMPS there is no additional burden associated with the reporting of that data. Since the ECMPS pathway is already in place, the EPA will allow for states to utilize the ECMPS system to facilitate the data reporting of th e additional net energy output data required under the emission guidelines. However, because the Acid Rain Program does not require net energy output to be reported, there is some additio nal burden (Shown in Table 3-4) in updating an affected EGUs monitoring system to be able to re port the associated net energy output of an
affected EGU.
The EPA estimates that it would take three working months for a technician to retrofit any existing energy meters to meet the requirements set in the state plan. Additionally EPA
believes that 50 hours will be needed for each EGU operator to read the rule and understand how the facility will comply with the rule, based on an average reading rate of 100 words per minute and a projected rule word count of 300,000 words. 74 Also, after all modifications are made at a facility to measure net energy output, each EGU’s D ata Acquisition System (DAS) would need to be upgraded to supply the rate-based emissions v alue 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 syste m are one-time costs incurred in 2020. Recordkeeping and reporting costs substantially dec rease for the period 2021-2030. The projected costs for 2020, 2025, and 2030 are summar ized below.

74 According to one source, the average person can pr oofread at about 200 words per minute on paper and 180 words per minute on a monitor. (Source: Ziefle, M. 1988. “Effects of Display Resolution on Visual Performanc e.” Human Factors 40(4):554-68). Due to the highly technical nature of the rule requirements in subpart UUUU, a more conservative estimate of 100 words per minute was u sed to determine the burden estimate for reading an d understanding rule requirements.3-18

In calculating the cost for states to comply, EPA e stimates that each state will rely on the equivalent of two full time staff to oversee progra m implementation, assess progress, develop possible contingency measures, perform state plan r evisions and host the subsequent public meetings if revisions are indeed needed, download d ata from the ECMPS for their annual reporting and develop their annual EPA report. The burden estimate was based on an analysis of similar tasks performed under the Regional Haze Pro gram, whereby states were required to develop their list of eligible sources, draft imple mentation plans, revise initial drafts, identify baseline controls, identify data gaps, identify ini tial strategies, conduct various reviews, and manage their programs. A total estimate of 78,000 h ours of labor performed by seven states over a three-year period resulted in 3,714 hours per yea r, per entity. Due to the nature of this final rule whereby we believe the air office and the energy of fice will both be involved in performing the above-mentioned tasks, we rounded up to the equival ent of two full time staff, which totaled 4,160 hours per year. 75 Table 3-4 shows estimates of the annual state and industry respondent burden and costs of reporting and recordkeeping for 2020, 2025 and 2030.
Table 3-4. Years 2020, 2025 and 2030: Summary of St ate and Industry Annual Respondent

Burden and Cost of Reporting and Recordkeeping Requ irements (2011$)

Nationwide Totals Total Annual

Labor Burden (Hours) Total

Annual Labor Costs Total

Annualized Capital Costs Total

Annual O&M Costs Total

Annualized Costs Total Annual

Respondent Costs

State Year 2020 195,520 13,838,429 0 34,545 34,545 13,872 ,974 Year 2025 208,320 14,744,381 0 23,500 23,500 14,767 ,881 Year 2030 208,320 14,744,381 0 23,500 23,500 14,767 ,881 Industry Year 2020 581,848 49,959,446 0 1,532,000 1,532,500 51,491,446 Year 2025 0 0 0 0 0 0 Year 2030 0 0 0 0 0 0 Total Year 2020 777,368 63,797,875 0 1,566,545 1,566,545 65,364,420 Year 2025 208,320 14,744,381 0 23,500 23,500 14,767 ,881 Year 2030 208,320 14,744,381 0 23,500 23,500 14,767 ,881

75 Renewal of the ICR for the Regional Haze Rule, Sec tion 6(a) Tables 1 through 4 based on 7 states’ bur den. EPAHQ-OAR-2003-0162-0001.3-19

3.9 Projected Power Sector Impacts
The following sections present projected impacts fr om the two illustrative scenarios described above. The tables present impacts from 20 20 (prior to the initial compliance year), 2025 (representative of the interim compliance peri od), and 2030 (representative of the final compliance period). The narrative focuses on result s during the initial and final compliance periods.
3.9.1 Projected Emissions
Under the rate-based approach, EPA projects annual CO 2 reductions of 3 percent below the base case in 2020, 11 percent below the base ca se in 2025, and 19 percent below base case projections in 2030 (reaching 28 percent to 32 perc ent below 2005 emissions 76 in 2025 and 2030, respectively). For the mass-based approach, EPA pro jects annual CO 2 reductions of 4 percent below the base case in 2020, 12 percent below the b ase case in 2025 and 19 percent below base case projections in 2030 (reaching 29 percent to 32 percent below 2005 emissions 77 in 2025 and 2030, respectively). 78
Table 3-5. Projected CO 2 Emission Impacts, Relative to Base Case

CO 2 Emissions (million short tons) CO 2 Emissions: Change from Base Case (million short tons) CO 2 Emissions: Percent Change from Base Case
2020 2025 2030 2020 2025 2030 2020 2025 2030 Base Case 2,155 2,165 2,227 Rate-based 2,085 1,933 1,812 -69 -232 -415 -3% -11% -19% Mass-based 2,073 1,901 1,814 -81 -265 -413 -4% -12% -19% Source: Integrated Planning Model run by EPA, 2015

76 For purposes of these calculations, EPA has used h istorical CO 2 emissions from eGRID for 2005, which reports EGU emissions as 2,683 million short tons in the co ntiguous U.S.

77 For purposes of these calculations, EPA has used h istorical CO 2 emissions from eGRID for 2005, which reports EGU emissions as 2,683 million short tons in the co ntiguous U.S.

78 EPA also analyzed a mass-based scenario without an y set-asides using IPM, which produced a 2030 emiss ion reduction estimate of 31 percent, relative to 2005 levels (approximately a 1 percent erosion of emissi on reductions due to leakage to new sources of emissions, relativ e to both the mass-based scenario that includes the RE set-aside, and the rate-based scenario. This equates to approx imately 24 million short tons of CO 2 .). The scenario can be found in the docket for the final rule, and is called “Ma ss-based without set-aside.”3-20

Table 3-6. Projected CO 2 Emission Impacts, Relative to 2005

CO 2 Emissions (million short tons) CO 2 Emissions: Change from 2005 (million short tons) CO 2 Emissions: Percent Change from 2005

2005 2020 2025 2030 2020 2025 2030 Base Case 2,683 -528 -518 -456 -20% -19% -17% Rate-based - -598 -750 -871 -22% -28% -32% Mass-based - -610 -782 -869 -23% -29% -32% Source: Integrated Planning Model run by EPA, 2015

Under the rate-based illustrative plan approach, EP A projects a 14 percent reduction of
SO 2 , 13 percent reduction of NO X , and a 11 percent reduction of mercury in 2025, an d a 24 percent reduction of SO 2 , 22 percent reduction of NO X , and a 17 percent reduction of mercury in 2030. Under the mass-based illustrative plan approa ch, EPA projects a 15 percent reduction of
SO 2 , 16 percent reduction of NO X , and a 12 percent reduction of mercury in 2025, an d a 24 percent reduction of SO 2 , 22 percent reduction of NO X , and a 16 percent reduction of mercury in 2030. The projected non-CO 2 reductions are summarized below in Table 3-7.
Table 3-7. Projected Non-CO 2 Emission Impacts, 2020-2030

Base Case Rate-based Mass-based Rate-based Mass-ba sed 2020 SO 2 (thousand short tons) 1,311 1,297 1,257 -1.0% -4.1% NO X (thousand short tons) 1,333 1,282 1,272 -3.8% -4.5% Hg (short tons) 6.6 6.4 6.4 -2.8% -3.3% 2025 SO 2 (thousand short tons) 1,275 1,097 1,090 -14.0% -14. 5% NO X (thousand short tons) 1,302 1,138 1,100 -12.6% -15. 6% Hg (short tons) 6.6 5.9 5.8 -10.8% -12.2% 2030 SO 2 (thousand short tons) 1,314 996 1,034 -24.2% -21.3% NO X (thousand short tons) 1,293 1,011 1,015 -21.8% -21. 5% Hg (short tons) 6.8 5.6 5.8 -17.2% -15.6% Source: Integrated Planning Model run by EPA, 2015. For this RIA, we did not estimate changes in emiss ions of directly emitted particles (PM 2.5 ). While the EPA has not quantified the climate impact s of non-CO 2 emissions changes or
CO 2 emissions changes outside the electricity sector f or the final emissions guidelines, the Agency has analyzed the potential changes in upstre am methane emissions from the natural gas and coal production sectors that may result from th e illustrative approaches examined in this3-21

RIA. The EPA assessed whether the net change in ups tream methane emissions from natural gas and coal production is likely to be positive or neg ative. The EPA also assessed the potential magnitude 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 drill ing, mining, processing, and transportation in the natur al gas and coal production sectors. This analysis found that the net upstream methane emissi ons from natural gas systems and coal mines
and CO 2 emissions from flaring of methane will likely decr ease under the final emissions guidelines. Furthermore, the changes in upstream me thane emissions are small relative to the changes in direct CO 2 emissions from power plants. The projections inclu de voluntary and regulatory activities to reduce emissions from coal mining and natural gas and oil systems, including the 2012 Oil and Natural Gas NSPS. In add ition, the EPA plans to issue a proposed
rule later this summer that would build on its 2012 Oil and Gas NSPS. When these standards are finalized and implemented, they would further reduc e projected emissions from natural gas and oil systems. The technical details supporting this analysis can be found in the Appendix to this chapter.
3.9.2 Projected Compliance Costs
The power industry’s “compliance costs” are represe nted in this analysis as the change in electric power generation costs between the base ca se and illustrative CPP scenarios, including the cost of demand-side energy efficiency programs and measures and monitoring, reporting, and recordkeeping (MR&R) costs. The system costs reflec t the least cost power system outcome in which the sector employs all the flexibilities assu med in the modeling, as discussed above, and pursues the most cost-effective emission reduction opportunities in order to meet the rate- and mass-based goals, as represented in the illustrativ e plan scenarios. In simple terms, these costs are an estimate of the increased power industry exp enditures required to meet demand projections while complying with state goals, inclu ding the total demand-side energy efficiency

costs. 79 The compliance costs for the final emissions guide lines for EGUs in the contiguous U.S.

79 The compliance costs also capture the effect of ch anges in equilibrium fuel prices on the expenditure s of the electricity sector to serve demand.3-22

states is forecast using IPM. The cost of demand-si de energy efficiency programs assumed in the IPM analysis are reported in section 3.7.2.
EPA projects that the annual compliance cost of the rate-based illustrative plan scenario are $2.4 billion in 2020, $1.1 billion in 2025, and $8.5 billion in 2030 (Table 3-8). The annual compliance cost of the mass-based illustrative plan approach are estimated to be $1.4 billion in 2020, $3.0 billion in 2025, and $5.1 billion in 203 0. The different patterns of incremental cost in
each of these scenarios over 2020-2030 are consiste nt with the differences in the projected pattern of gas use and price in these scenarios. co nsistent with the differences in the projected pattern of gas use and price in these scenarios. Th e annual compliance cost is the projected additional cost of complying with the rule in the y ear analyzed and reflects the net difference in the sum of the annualized cost of capital investmen t in new generating sources and heat rate improvements at coal steam facilities, 80 the change in the ongoing costs of operating pollu tion controls, the change in expenditures on various fue ls (inclusive of changes in the price of these fuels), demand-side energy efficiency measures, and other actions associated with compliance. Relative to the base case, we expect a decrease in the total cost to generate sufficient supply for demand, which, together with the costs of demand-si de energy efficiency measures, we project will result in net cost estimates of $8.4 billion i n 2030 for the rate-based scenario and $5.1 billion
for the mass-based scenario.
Table 3-8. Annualized Compliance Costs Including Mo nitoring, Reporting and

Recordkeeping Costs Requirements (billions of 2011$ ) 2020 2025 2030 Rate-based $2.5 $1.0 $8.4 Mass-based $1.4 $3.0 $5.1 Source: Integrated Planning Model run by EPA, 2015, with post-processing to account for exogenous dema nd-side energy efficiency costs and monitoring, reporting, and recordkeeping costs. In order to contextualize EPA’s projection of the a dditional costs in 2030 across the two illustrative plan approaches evaluated in this RIA, it is useful to compare these incremental cost estimates to total projected power sector expenditu res. The power sector is expected in the base case to expend over $201 billion in 2030 to generat e, transmit, and distribute electricity to enduse consumers. In 2014, according to EIA, the power sector generated $389 billion in revenue

80 See Chapter 2 of the GHG Mitigation Measures TSD a nd EPA’s Base Case using IPM (v5.15) documentation, available at: http://www.epa.gov/powersectormodelin g3-23

from retail sales of electricity. For context, the projected costs of compliance with the final rule amount to a 4 percent increase in the cost of meeti ng electricity demand, while securing public
health and welfare benefits that are several times greater (as described in Chapters 4 and 8).
The following example uses projected results for th e year 2030 to illustrate how different components of estimated expenditures are combined t o form the full compliance costs presented in Table 3-8. In Table 3-9, we present the IPM mode ling results for the two illustrative plan scenarios in 2030 (as well as 2020 and 2025). The r esults show that annualized expenditures required to supply enough electricity to meet deman d decline by $18 billion (rate) and $21 billion (mass) from the base case in 2030. This inc remental decline is a net outcome of two simultaneous effects that move in opposite directio ns. First, imposing the CO 2 constraints represented by each illustrative plan scenario on e lectric generators would, other things equal, result in an incremental increase in expenditures t o supply any given level of electricity. However, once electricity demand is reduced to refl ect demand-side energy efficiency improvements, there is a substantial reduction in t he expenditures needed to supply a correspondingly lower amount of electricity demand.
Table 3-9. Total Power Sector Generating Costs (IPM ) (billions 2011$)
2020 2025 2030 Base Case $166.5 $178.3 $201.3 Rate-based $166.8 $162.6 $183.3 Mass-based $165.7 $164.6 $180.1 Source: Integrated Planning Model run by EPA, 2015 In order to reflect the full compliance cost attrib utable to the CPP scenarios, it is necessary to include the annualized expenditures ne eded to secure the demand-side energy efficiency improvements. As described in section 3. 7.2, EPA has estimated these energy efficiency-related expenditures to be $26.3 billion in 2030 (using a 3 percent discount rate). The energy efficiency-related expenditures include cost s incurred by parties administering energy efficiency programs and costs incurred by participa nts in those programs. As a result, this analysis finds the cost of the rate-based and mass- based illustrative plan approaches in 2030 to be $8.4 billion and $5.1 billion, respectively.
3.9.3 Projected Compliance Actions for Emissions Re ductions
Heat Rate Improvements (HRI): EPA analysis assumes that the existing coal steam electric generating fleet has, on average, the ability to im prove operating efficiency (i.e., reduce the3-24

average net heat rate, or the Btu of fuel energy ne eded to produce one kWh of net electricity output). All else held constant, an HRI allows the EGU to generate the same amount of electricity using less fuel. The decrease in requir ed fossil fuel results in a lower output-based
CO 2 emissions rate (lbs/MWh), as well as a lower varia ble cost of electricity generation. In the modeling conducted for these illustrative plan appr oaches, coal boilers have the choice to improve heat rates by 4.3 percent in the eastern il lustrative compliance region, 2.1 percent in the western illustrative compliance region, and 2.3 per cent in Texas, all at a capital cost of $100 per
kW. 81 The option for heat rate improvement is only made available in the illustrative plan approaches during the compliance period, in respons e to the final rule.
The majority of existing coal boilers are projected to adopt the aforementioned heat rate improvements. Of the 183 GW of coal projected to op erate in 2030, EPA projects that 99 GW of existing coal steam capacity (greater than 25 MW) w ill improve operating efficiency (i.e., reduce the average net heat rate) under the rate-based app roach by 2030. Under the mass-based approach, EPA projects that 88 GW of the 174 GW of coal projected to operate in 2030 will improve operating efficiency by 2030.
Generation Shifting: 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 capa city is, on average, projected to operate at a lower capacity factor for both illustrative plan ap proaches. Under the illustrative rate-based plan approach, the average 2030 capacity factor is 69 pe rcent, and under the mass-based approach, the average capacity factor for existing coal steam is 75 percent. Existing natural gas combined cycle units, which are less carbon-intensive than coal st eam capacity on an output basis, operate at noticeably higher capacity factor under both illust rative plan approaches, on average. The utilization of existing natural gas combined cycle capacity is lower than the BSER level of 75 percent 82 on an annual average basis in these illustrative p lan approaches, reflecting the fact that,

81 The option for heat rate improvement is only made available in the illustrative plan scenarios, and i s not available in the base case. For an explanation of the regiona l differences in average ability to improve heat ra tes, see GHG Mitigation Measures TSD.

82 See preamble section V.D.3-25

in practice, the most cost-effective CO 2 reduction strategies to meet each state’s goal may not require that each building block be achieved in ent irety. See Table 3-10.
Table 3-10. Projected Capacity Factor of Existing C oal Steam and Natural Gas

Combined Cycle Capacity

Existing Coal Steam Existing Natural Gas Combined C ycle 2020 2025 2030 2020 2025 2030 Base Case 77% 76% 79% 54% 56% 51% Rate-based 78% 75% 69% 56% 60% 61% Mass-based 78% 75% 75% 56% 58% 54% Source: Integrated Planning Model run by EPA, 2015 Demand-Side Energy Efficiency: Another approach for reducing emissions from affected EGUs
is to consider reductions in demand attributable to demand-side energy efficiency measures as discussed in section 3.7. In the illustrative plan approaches presented in this RIA, each state is credited for total demand-side energy efficiency im plemented in, or procured by, that state, consistent in aggregate with the state-by-state dem and reductions that are represented by the demand-side energy efficiency scenario discussed in section 3.7.1.
Deployment of Cleaner Generating Technologies: Anot her key opportunity to reduce emissions from existing sources is to build more lower- or ze ro-emitting generating resources, in particular renewable energy. These sources of electricity, inc luding wind and solar, can displace higher emitting existing sources, may be procured for comp liance with the state goals in the rate-based illustrative scenario, and are further incentivize d as a generation option in the mass-based illustrative scenario as they are not subject to th e mass-based constraint and may receive the renewable set-aside. Increased deployment results i n CO 2 reductions in both rate-based and mass-based approaches. See sections below discussin g projected impacts on generation mix and capacity.
3.9.4 Projected Generation Mix
Table 3-11 and Figure 3-2 show the generation mix i n the base case and under the two illustrative plan approaches. In both scenarios, to tal generation declines relative to the base case
as a result of the reduction in total demand attrib utable to the demand-side energy efficiency applied in the illustrative scenarios, by 5 percent in 2025 and 8 percent in 2030.3-26

Under the rate-based scenario, coal-fired generatio n is projected to decline 12 percent in 2025, and natural-gas-fired generation from existin g combined cycle capacity is projected to increase 5 percent relative to the base case. The c oal-fired fleet in 2030 generates 23 percent less than in the base case, while natural-gas-fired gene ration from existing combined cycles increases 18 percent relative to the base case. Gas-fired gen eration from new combined cycle capacity decreases in 2025 and 2030, consistent with the dec rease in new capacity (see section 3.9.6). Relative to the base case, generation from non-hydr o renewables decreases 1 percent in 2025 and increases 9 percent in 2030.
Similarly, under the mass-based scenario, coal-fire d generation is projected to decline 15 percent in 2025, and natural-gas-fired generation f rom existing combined cycle capacity is projected to increase 2 percent relative to the bas e case. The coal-fired fleet in 2030 generates 22 percent less than in the base case, while natural-g as-fired generation from existing combined cycles increases 5 percent relative to the base cas e. Gas-fired generation from new combined cycle capacity decreases 8 percent and 36 percent r elative to the base case in 2025 and 2030, respectively. Relative to the base case, generation from non-hydro renewables decreases 3 percent in 2025 and increases 8 percent in 2030.
The results presented in these illustrative complia nce scenarios suggest that existing nuclear generation could be slightly more competiti ve under a mass-based implementation than under a rate-based implementation, because the form er tends to create more wholesale price support for those generators. These scenarios do no t include potential approaches that states can take to incentivize zero-carbon baseload power.3-27

Table 3-11. Generation Mix (thousand GWh)
Base Case Rate-based Mass-based Rate-based Mass-b ased 2020 Coal 1,462 1,391 1,374 -5% -6% NG Combined Cycle (existing) 1,111 1,126 1,132 1% 2 % NG Combined Cycle (new) 33 53 69 61% 111% Combustion Turbine 15 20 17 39% 14% Oil/Gas Steam 51 51 50 0% -1% Non-Hydro Renewables 393 399 385 2% -2% Hydro 310 311 310 0% 0% Nuclear 798 792 804 -1% 1% Other 18 18 18 0% 0% Total 4,190 4,160 4,159 -1% -1% 2025 Coal 1,428 1,256 1,217 -12% -15% NG Combined Cycle (existing) 1,152 1,206 1,179 5% 2 % NG Combined Cycle (new) 113 53 104 -53% -8% Combustion Turbine 23 30 34 31% 46% Oil/Gas Steam 39 21 19 -46% -52% Non-Hydro Renewables 417 414 404 -1% -3% Hydro 340 340 340 0% 0% Nuclear 799 791 804 -1% 1% Other 17 17 18 0% 0% Total 4,328 4,128 4,118 -5% -5% 2030 Coal 1,466 1,131 1,144 -23% -22% NG Combined Cycle (existing) 1,042 1,230 1,090 18% 5% NG Combined Cycle (new) 324 100 207 -69% -36% Combustion Turbine 22 27 32 21% 46% Oil/Gas Steam 22 11 11 -52% -53% Non-Hydro Renewables 450 488 485 9% 8% Hydro 340 341 340 0% 0% Nuclear 783 777 785 -1% 0% Other 17 17 17 0% 0% Total 4,467 4,122 4,110 -8% -8% Note: “Other” mostly includes generation from MSW a nd fuel cells. Source: Integrated Planning Model ru n by EPA, 20153-28

Figure 3-2 Generation Mix (thousand GWh)
Source: Integrated Planning Model run by EPA, 2015 Under both the rate-based and mass-based approaches , the projected rate of change in coal-fired generation is consistent with recent his torical declines in coal-fired generation. Additionally, under both of these approaches, the t rends for all other types will remain consistent
with what their trends would be in the absence of t his rule. Specifically, natural-gas fired generation and renewables would be expected to incr ease without this rule, and both are expected to increase under this rule, with renewabl es increasing at a somewhat greater rate than in the absence of this rule; and nuclear, oil-fired , and other types of generation are expected to be little impacted by this rule generation mix is cons istent with recent declines in coal-fired generation and increases in gas-fired generation. S ee Figures 3-3, 3-4, and 3-5.3-29

Figure 3-3. Nationwide Generation: Historical (1990 -2014) and Base Case Projections

(2020, 2025, 2030)

Sources: Historic data (i.e., 1990-2014): U.S. Ener gy Information Administration, June 2015 Monthly En ergy Review, Table 7.2a Electricity Net Generation: Tota l (All Sectors), Available at <http://www.eia.gov/totalenergy/data/monthly/>. Pro jected data (i.e., 2020, 2025, 2030): Integrated Pl anning Model, 2015. Notes: Historic and projected data include generati on from the power, industrial, and commercial secto rs. Historic data from U.S. EIA reflects all cogenerati on, while projections from the Integrated Planning Model reflect net cogeneration.

Figure 3-4. Nationwide Generation: Historical (1990 -2014) and Rate-Based Illustrative

Plan Approach Projections (2020, 2025, 2030)

Sources: Historic data (i.e., 1990-2014): U.S. Ener gy Information Administration, June 2015 Monthly En ergy Review, Table 7.2a Electricity Net Generation: Tota l (All Sectors), Available at <http://www.eia.gov/totalenergy/data/monthly/>. Pro jected data (i.e., 2020, 2025, 2030): Integrated Pl anning Model, 2015. Notes: Historic and projected data include generati on from the power, industrial, and commercial secto rs. Historic data from U.S. EIA reflects all cogenerati on, while projections from the Integrated Planning Model reflect net cogeneration.3-30

Figure 3-5. Nationwide Generation: Historical (1990 -2014) and Mass-Based Illustrative

Plan Approach Projections (2020, 2025, 2030)

Sources: Historic data (i.e., 1990-2014): U.S. Ener gy Information Administration, June 2015 Monthly En ergy Review, Table 7.2a Electricity Net Generation: Tota l (All Sectors), Available at <http://www.eia.gov/totalenergy/data/monthly/>. Pro jected data (i.e., 2020, 2025, 2030): Integrated Pl anning Model, 2015. Notes: Historic and projected data include generati on from the power, industrial, and commercial secto rs. Historic data from U.S. EIA reflects all cogenerati on, while projections from the Integrated Planning Model reflect net cogeneration.

3.9.5 Projected Incremental Retirements
Relative to the base case, about 23 GW of additiona l coal-fired capacity is projected to be uneconomic to maintain by 2025 under the rate-base d illustrative scenario, increasing to 27 GW in 2030 (about 11-13 percent respectively of all co al-fired capacity projected to be in service in the base case). Under the mass-based scenario, abou t 29 GW of additional coal-fired capacity is projected to be uneconomic to maintain by 2025, in creasing to 38 GW by 2030 (about 14-19 percent respectively of all coal-fired capacity pro jected to be in service in the base case). Capacity changes from the base case are shown in Ta ble 3-12. 83

83 EPA examined the implications of the illustrative p lan scenarios for concerns about regional resource adequacy and the potential for concerns about reliability. T his examination can be found in U.S. EPA. 2015. Tec hnical Support Document (TSD) the Final Carbon Pollution E mission Guidelines for Existing Stationary Sources: Electric Utility Generating Units. Resource Adequac y and Reliability Analysis.

.3-31

Table 3-12. Total Generation Capacity by 2020-2030 (GW)
Base Case Rate-based Mass-based Rate-based Mass-b ased 2020 Coal 208 195 193 -6% -7% NG Combined Cycle (existing) 233 231 232 -1% 0% NG Combined Cycle (new) 4 7 9 62% 113% Combustion Turbine 141 137 137 -3% -3% Oil/Gas Steam 88 81 80 -8% -9% Non-Hydro Renewables 130 132 128 1% -2% Hydro 106 106 106 0% 0% Nuclear 100 100 101 -1% 1% Other 5 5 5 0% 0% Total 1,016 994 992 -2% -2% 2025 Coal 208 187 181 -10% -13% NG Combined Cycle (existing) 233 231 232 -1% 0% NG Combined Cycle (new) 15 7 14 -52% -9% Combustion Turbine 143 138 137 -4% -4% Oil/Gas Steam 82 71 69 -14% -16% Non-Hydro Renewables 139 137 134 -1% -3% Hydro 112 112 112 0% 0% Nuclear 100 99 101 -1% 1% Other 5 5 5 0% 0% Total 1,037 988 985 -5% -5% 2030 Coal 207 183 174 -11% -16% NG Combined Cycle (existing) 233 231 232 -1% 0% NG Combined Cycle (new) 44 14 27 -68% -38% Combustion Turbine 147 138 136 -6% -7% Oil/Gas Steam 82 70 67 -15% -18% Non-Hydro Renewables 154 174 171 13% 11% Hydro 112 112 112 0% 0% Nuclear 99 98 99 -1% 0% Other 5 5 5 0% 0% Total 1,082 1,025 1,024 -5% -5% Source: Integrated Planning Model run by EPA, 2015 3.9.6 Projected Capacity Additions
Due largely to the electricity demand reduction att ributable to the demand-side energy efficiency improvements applied in the illustrative scenarios, the EPA projects less new natural3-32

gas combined cycle capacity built under the rate-ba sed scenario than is built in the base case over the period covered by the rule. While this new NGCC capacity cannot be directly counted towards the average emissions rate used for complia nce in the rate-based approach, it can displace some generation from covered sources and t hus indirectly lower the average emissions rate from covered sources. Conversely, the EPA proj ects an overall increase in new renewable capacity. New non-hydro renewables are able to cont ribute their generation to the average emissions rate in each state or region.
Under the rate-based illustrative scenario, new nat ural gas combined cycle capacity is projected to decrease by 8 GW in 2025 and 30 GW in 2030 (52 percent and 68 percent decrease relative to the base case). New renewable capacity is projected to decrease by about 2 GW (3 percent decrease) below the base case in 2025, and increase by 20 GW (27 percent increase) by
2030.
Under the mass-based illustrative scenario, new nat ural gas combined cycle capacity is projected to decrease by 1 GW in 2025 and decrease by 17 GW in 2030 (a 9 percent and 38 percent decrease relative to the base case). New re newable capacity is projected to decrease 4 GW (7 percent) relative to the base case in 2025, a nd increase 18 GW (24 percent increase) by
2030.
Table 3-13. Projected Capacity Additions, Gas (GW)

Cumulative Capacity Additions: Gas

Combined Cycle Incremental Cumulative Capacity Additions: Gas Combined Cycle
2020 2025 2030 2020 2025 2030 Base Case 4.4 14.9 44.0 Rate-based 7.1 7.1 13.9 2.7 -7.8 -30.1 Mass-based 9.3 13.6 27.2 4.9 -1.3 -16.8 Source: Integrated Planning Model run by EPA, 20153-33

Table 3-14. Projected Capacity Additions, Renewable (GW) Cumulative Capacity Additions:

Renewables Incremental Cumulative Capacity Additions: Renewables 2020 2025 2030 2020 2025 2030 Base Case 39.1 59.1 74.1 Rate-based 40.5 57.4 94.4 1.4 -1.8 20.2 Mass-based 36.7 54.9 91.9 -2.4 -4.2 17.8 Source: Integrated Planning Model run by EPA, 2015 3.9.7 Projected Coal Production and Natural Gas Use for the Electric Power Sector Coal production is projected to decrease in 2025 an d beyond in the illustrative scenarios due to (1) improved heat rates (generating efficien cy) at existing coal units, (2) electricity demand reduction attributable to demand-side energy efficiency improvements, and (3) a shift in generation from coal to less-carbon intensive gener ation. As shown in Table 3-15, the largest decrease in coal production is projected to occur i n the western region.

Table 3-15. Coal Production for the Electric Power Sector, 2025 Coal Production (million short tons) Percent Change from Base Case Base Case Rate-based Mass-based Rate-based Mass-bas ed Appalachia 92 71 69 -23% -25%
Interior 250 242 236 -3% -6%
West 379 306 293 -19% -23%
Waste Coal 6 6 6 0% 0% Imports 1 1 1 -37% -14%
Total 729 626 606 -14% -17% Source: Integrated Planning Model run by EPA, 2015

Power sector natural gas use is projected to decrea se by about 1 percent in 2025 and 2030 under the rate-based illustrative plan scenario. In the mass-based scenario, power sector natural gas use is projected to decrease by 4.5 percent in 2030. These trends are consistent with the change in generation mix described above in Section 3.9.4.3-34

Table 3-16. Power Sector Gas Use Power Sector Gas Use (TCF) Percent Change in Power Sector Gas Use 2020 2025 2030 2020 2025 2030 Base Case 8.62 9.38 9.72 Rate-based 8.91 9.28 9.59 3.4% -1.0% -1.3% Mass-based 9.02 9.39 9.28 4.6% 0.2% -4.5% Source: Integrated Planning Model run by EPA, 2015 3.9.8 Projected Fuel Price, Market, and Infrastruct ure Impacts The impacts of the two illustrative plan scenarios on coal and natural gas prices before shipment are shown below in Table 3-17 and Table 3- 18 and are attributable to the changes in overall power sector demand for each fuel due to th e final guidelines. Coal demand decreases by 2030, resulting in a decrease in the price of coal delivered to the electric power sector. In 2030, gas demand and price decrease below the base case p rojections, due to the cumulative impact of demand-side energy efficiency improvements 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 m odeled time horizon. As such, it should be understood that the pattern of IPM natural gas pric e 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 experi enced 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). The se short-term price signals are fundamental for allowing the market to successfully align immed iate supply and demand needs. However, end-use consumers are typically shielded from exper iencing 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 pri ce arrangements take place “outside of the model” and on top of the “real-time” shorter-term p rice variation necessary to align supply and demand. Therefore, the model’s natural gas price pr ojections should not be mistaken for traditionally experienced consumer price impacts re lated to natural gas, but a reflection of expected average price changes over the period repr esented by the modeling horizon.
There are very small changes to natural gas pipelin e infrastructure needs over time, in response to the illustrative plan scenarios. These changes, compared to historical deployment of3-35

new infrastructure, are very modest. In both the ra te-based and mass-based scenarios, pipeline capacity construction through 2020 is projected to increase by less than two percent beyond base case projections. By 2030, however, the total cumul ative pipeline capacity construction built is projected to decrease compared to the base case, co nsistent with the projected decrease in total demand and natural gas use. The projected increase in pipeline capacity in the near term is largely the result of building pipeline capacity a few years earlier than projected in the base case.
Table 3-17. Projected Average Minemouth and Deliver ed Coal Prices (2011$/MMBtu)
Minemouth Delivered - Electric Power Sector 2020 2025 2030 2020 2025 2030 Base Case 1.55 1.67 1.79 2.38 2.50 2.68 Rate-based 1.54 1.58 1.73 2.34 2.35 2.46 Mass-based 1.54 1.59 1.73 2.35 2.40 2.55 Rate-based -0.8% -5.0% -3.8% -1.7% -6.2% -8.0% Mass-based -0.7% -4.7% -3.2% -1.6% -4.3% -4.6% Source: Integrated Planning Model run by EPA, 2015 Table 3-18. 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 5.20 5.12 6.01 5.25 5.17 5.98 Rate-based 5.48 4.73 6.21 5.53 4.77 6.13 Mass-based 5.40 4.97 5.92 5.45 5.00 5.86 Rate-based 5.4% -7.5% 3.3% 5.3% -7.7% 2.5% Mass-based 3.9% -3.0% -1.4% 3.8% -3.2% -2.1% Source: Integrated Planning Model run by EPA, 2015 3.9.9 Projected Retail Electricity Prices
EPA’s analysis of the illustrative rate-based plan scenario shows an increase in the national average (contiguous U.S.) retail electrici ty price of less than one percent in both 2025 and 2030, compared to the modeled base case price e stimate in those years. Under the illustrative mass-based plan scenario, EPA projects an increase in the national average (contiguous U.S.) retail electricity price of 2 percent in 2025 and 0 .01 percent in 2030.
Retail electricity prices embody generation, transm ission, distribution, taxes, and demand-side energy efficiency costs. IPM modeling p rojects changes in regional wholesale power prices and capacity payments related to impos ition of the represented CPP scenarios that3-36

are combined with EIA regional transmission and dis tribution costs to calculate changes to regional retail prices using the Retail Price Model (RPM). 84 As described in Section 3.7.2, the funding for demand-side energy efficiency (to cover program costs) is typically collected through a standard per kWh surcharge to the ratepay er and the regional retail price impacts presented here assume that these costs are recovere d by utilities in retail rates. This is an approximation, since not every utility will pass th rough the entirety of demand-side energy efficiency costs. For example, a distribution only utility may generate reductions from demandside energy efficiency, sell the associated reducti on in generation to affected EGUs (which in turn use them to demonstrate compliance), and then account for this revenue in rate determination. Furthermore, this analysis assumes t hat ratepayers in the state producing zeroemitting generation (or avoided generation) bear th e costs of such production. However, in practice, if such generation is claimed by an affec ted source in another state, part of the cost of that generation may ultimately be borne by ratepaye rs in the claiming state rather than the state in which that zero-emitting generation was located. 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 illustrative plan scenario in response t o the goals under the two scenarios. The projected changes also vary depending upon retail e lectricity market structure (e.g., cost-ofservice vs. competitive). In the mass-based approac h, treatment of allowance allocations will also have an impact on retail electricity prices. I n competitive regions, this RIA assumes that allowances are freely allocated to generators who t hen keep 100% of the freely allocated allowance value without passing this value through to ratepayers in the form of lower retail electricity prices. To the extent that implementing authorities choose to require this allowance value to be passed through to ratepayers (such as b y allocating allowances to load-serving entities who could be subject to such a requirement ), retail prices would be lower than those
shown here.

84 See documentation available at: http://www.epa.gov /powersectormodeling/3-37

Table 3-19. 2020 Projected Contiguous U.S. and Regi onal Retail Electricity Prices

(cents/kWh)

2020 Projected Retail Price (cents/kWh) Percent Cha nge from Base Case Base Case Rate-based Mass-based Rate-based Mass-bas ed ERCT 9.7 9.9 9.9 2.5% 2.1% FRCC 10.5 10.7 10.7 2.0% 1.6% MROE 9.9 10.3 10.3 4.2% 3.8% MROW 8.7 9.0 9.0 2.8% 2.3% NEWE 13.3 14.0 14.0 5.1% 5.5% NYCW 17.4 18.3 18.3 5.0% 5.3% NYLI 14.4 15.1 15.1 4.6% 5.1% NYUP 12.4 13.1 13.1 5.4% 5.3% RFCE 11.1 11.8 11.8 6.1% 6.1% RFCM 10.4 10.9 10.9 4.3% 4.3% RFCW 9.4 9.8 9.8 5.1% 4.8% SRDA 8.6 8.8 8.7 2.1% 1.7% SRGW 8.6 9.0 9.0 4.1% 4.8% SRSE 10.0 10.1 10.1 0.9% 0.5% SRCE 8.0 8.1 8.1 1.1% 0.8% SRVC 9.8 9.9 9.9 1.5% 1.2% SPNO 9.9 9.9 9.9 -0.8% -0.9% SPSO 7.9 8.1 8.1 3.2% 2.4% AZNM 10.9 11.2 11.2 2.1% 2.1% CAMX 14.3 14.8 14.7 3.3% 3.0% NWPP 6.9 7.1 7.1 3.2% 2.9% RMPA 8.7 9.0 8.9 3.1% 2.9% Contiguous U.S. 10.0 10.3 10.3 3.2% 3.0% Note: regions pictured on Figure 3-6.3-38

Table 3-20. 2025 Projected Contiguous U.S. and Regi onal Retail Electricity Prices

(cents/kWh)

2025 Projected Retail Price (cents/kWh) Percent Cha nge from Base Case Base Case Rate-based Mass-based Rate-based Mass-bas ed ERCT 10.7 11.1 10.9 3.8% 1.5% FRCC 10.2 10.2 10.3 -0.2% 1.0% MROE 9.7 10.0 10.0 2.4% 2.6% MROW 8.7 9.0 9.0 2.5% 3.1% NEWE 12.6 12.4 12.7 -1.3% 0.5% NYCW 17.0 16.9 16.9 -0.5% -0.5% NYLI 14.0 13.7 13.7 -2.2% -1.7% NYUP 11.8 11.7 11.7 -0.8% -1.3% RFCE 10.3 10.2 10.5 -0.2% 2.1% RFCM 10.4 10.4 10.6 0.5% 1.9% RFCW 9.8 9.7 10.1 -1.4% 2.4% SRDA 8.6 8.6 8.7 0.0% 1.4% SRGW 9.1 9.0 9.3 -0.9% 2.5% SRSE 9.6 9.7 9.8 1.4% 2.1% SRCE 7.8 8.0 8.0 2.6% 3.0% SRVC 9.3 9.5 9.6 1.7% 2.4% SPNO 9.8 10.0 10.2 2.9% 4.3% SPSO 8.1 8.3 8.4 2.7% 4.4% AZNM 10.7 10.9 10.9 2.2% 1.8% CAMX 13.2 13.3 13.5 0.8% 2.4% NWPP 6.8 6.9 7.0 2.1% 2.7% RMPA 8.6 8.7 8.9 2.0% 4.3% Contiguous U.S. 9.9 9.9 10.1 0.9% 2.0% Note: regions pictured on Figure 3-6.3-39

Table 3-21. 2030 Projected Contiguous U.S. and Regi onal Retail Electricity Prices

(cents/kWh)

2030 Projected Retail Price (cents/kWh) Percent Cha nge from Base Case Base Case Rate-based Mass-based Rate-based Mass-bas ed ERCT 11.6 11.4 11.3 -1.4% -2.5% FRCC 10.3 10.8 10.5 4.6% 2.3% MROE 9.7 10.3 10.3 5.9% 6.3% MROW 8.9 9.1 9.1 2.7% 2.8% NEWE 14.3 13.6 13.4 -5.4% -6.9% NYCW 19.2 18.2 18.0 -5.2% -6.4% NYLI 16.3 14.8 14.6 -9.0% -10.1% NYUP 13.6 12.7 12.5 -7.0% -8.4% RFCE 11.3 10.7 10.6 -5.6% -6.5% RFCM 10.5 10.8 10.7 3.4% 1.7% RFCW 10.4 10.5 10.5 1.2% 0.7% SRDA 9.0 9.3 9.2 3.5% 1.9% SRGW 9.7 9.6 9.7 -0.6% 0.4% SRSE 9.8 10.2 10.0 3.9% 2.1% SRCE 7.8 8.1 8.0 4.3% 3.3% SRVC 9.3 9.6 9.5 3.2% 2.0% SPNO 9.5 9.8 10.1 2.7% 5.8% SPSO 8.7 9.0 8.9 3.9% 2.0% AZNM 10.9 11.2 11.1 2.3% 2.0% CAMX 13.5 13.6 13.7 1.1% 1.4% NWPP 6.9 7.0 7.1 2.2% 2.6% RMPA 8.9 9.0 9.3 0.7% 3.5% Contiguous U.S. 10.3 10.4 10.3 0.8% 0.01% Note: regions pictured on Figure 3-6.3-40

Figure 3-6. Electricity Market Module Regions
Source: EIA (http://www.eia.gov/forecasts/aeo/pdf/n erc_map.pdf)
3.9.10 Projected Electricity Bill Impacts
The electricity price changes addressed in section 3.9.9 combine with the significant reductions in electricity demand applied in the ill ustrative approaches to affect average electricity bills. The estimated changes to average bills are s ummarized in Table 3-22, and are subject to the
same caveats described in section 3.9.9. Under the illustrative rate-based plan scenario, EPA estimates an average monthly bill increase of 2.7 p ercent in 2020 and an average bill decrease of 3.8 percent in 2025 and 7 percent in 2030. Under th e mass-based scenario, EPA estimates an average bill increase of 2.4 percent in 2020 and an average bill decrease of 2.7 percent in 2025 and 7.7 percent in 2030. These reduced electricity bills reflect the combined effects of changes in both average retail rates (driven by compliance app roaches taken to achieve the state goals) and lower electricity demand (driven by demand-side ene rgy efficiency).
Table 3-22. Projected Changes in Average Electricit y Bills
2020 2025 2030 Rate-based 2.7% -3.8% -7.0% Mass-based 2.4% -2.7% -7.7%
3.10 Adoption of a Mix of State Plan Approaches
The impact of the EGs on the marginal cost of gener ating electricity may differ for affected EGUs if a state adopts a rate-based or a m ass-based plan. Analysts have observed, in the3-41

context of the proposed EG, that the different prod uction incentives for rate and mass-based plans may encourage greater generation by the affec ted EGUs in the rate-based state. This is because the rate-based approach may yield lower mar ginal costs of electricity generation than the mass-based approach for some otherwise similar EGUs . In a rate-based program, affected EGUs may emit more if they generate more, whereas in a m ass-based approach, if an affected EGU generates more it must incur the full cost of incre asing its emissions. Some analysts have suggested that this implies that if a state with a rate-based plan shares an electricity market with another state that adopted a mass-based plan, then total CO 2 emissions may be higher than if both states adopted the same form of implementation (e.g. Burtraw et al., 2015; Bushnell et al., 2014). In each case, both states would still be abl e to demonstrate that their affected EGUs are in compliance, such that the state is achieving its st ate goal (or the uniform rates).
While these analyses identify how emissions and cos ts may be influenced by the variation in the types of plans that states adopt, they have not raised concerns about the ability of the electricity system to provide reliable and affo rdable electricity when EGUs face different regulatory incentives. The EPA believes that differ ences in state plans, along with differences in incentives from those plans, will not detrimentally affect the operation of electricity markets because EGUs in the same market are often subject t o different regulatory incentives. For example, the time-differentiated pattern of renewab le portfolio standard (RPS) adoption, their varying stringency and form, and the operation of t heir associated renewable energy credit (REC) markets, across the U.S. demonstrates how int erconnected electricity markets are able to function successfully, even with differential regul atory incentives across states. RPS are adopted at the state level and are required of load-serving entities (LSEs). In some states, LSEs and the owners of most of the fossil generation are one and the same. In other states, LSEs own no generation (either fossil or renewable), and in som e states and markets, one LSE may own generation, while another may not. Furthermore, RPS requirements for LSEs serving load in multiple states will influence the behavior of all EGUs operating the electricity market. Even with this non-uniform regulatory environment, elect ricity has been delivered affordably and reliably while at the same time, the use of renewab le energy has increased dramatically.
In the context of preexisting programs, evidence su ggests that the effect of differential regulatory structures on emissions is relatively mo dest. For example, Schennach (2000) finds that in the early years of the Title IV cap and tra de program, the increase in SO 2 emissions of3-42

Phase II units, which historically were subject to emission rate performance standards, offset the
decrease in SO 2 emissions by Phase I units in by about 5%. The EPA ’s prospective analysis of
the benefits and costs of the Cross-State Air Pollu tion Rule, which used IPM, forecast only a
small increase in SO 2 emissions from plants that were not subject to the rule (U.S.EPA 2011). The Regional Greenhouse Gas Initiative (RGGI) produ ces an annual report monitoring the trends in on CO2 emissions from electricity generation in the region and imports from outside of the region. To date, RGGI’s monitoring effort has not i dentified any significant change in CO 2 emissions or the CO 2 emission rate from non-RGGI electric generation se rving load in the RGGI region (e.g., RGGI 2014). The effect on the relativ e costs of production across similar sources affected by different regulatory approaches will, i n part, depend on the relative stringency of the different regulatory approaches, and the emission r ate of the EGUs that represent the marginal source of electricity supply in the long-run.
In practice, determining the direction and magnitud e of the effect of variation in state plan type on sector wide emissions, relative to the two illustrative plan scenarios evaluated in this RIA, would be difficult. At the outset there is a l ack of information as to what design features states might adopt in their plans and in turn what patterns of spatial and plan variation would be most appropriate to consider. Determining the chang e in sectoral costs and emissions for the situation in which subsets of states adopt differen t types of plans would require many additional assumptions regarding which states adopt which plan types and the specific features of those plans. The effect on the relative costs of generati on across states will be sensitive to these analytical choices, and therefore so will the estim ated results regarding the direction and magnitude of state plan variation on aggregate sect oral costs and emissions.
The mere existence of variation among the design of state plans would not be sufficient to conclude that there will be a notable change in emissions relative to a case with less variation. The ultimate impact of the variation will depend up on the specific plan approaches, such as the way mass-based states allocate allowances, the stat e’s goals, as well as the states’ existing generating fleets, the transmission grid, spatial v ariation in future electricity demand, and the degree of ERC and allowance trading available withi n the system, amongst other variables.
There are other features of the requirements of sta te plans in this final rulemaking that would influence the scope of emissions changes that may result from states adopting a mix of3-43

mass and rate-based plans. For example, this final rulemaking also requires that states adopting mass-based plans include a method for addressing le akage to new fossil-fired generation. These approaches are described in the preamble for this f inal rule. If states adopt programs to address leakage within their state, those programs may lead to reduced generation by EGUs in neighboring rate-based states (relative to the scen ario where those plans were not in place). For example, as shown in Burtraw et al. (2015) and Dema illy and Quirion (2006), as well as other related studies, output-based allocation to sources covered by a mass requirement would lead to reduced production by sources subject to rate-based (or no) regulation.
3.11 Limitations of Analysis
EPA’s modeling is based on expert judgment of vario us 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 polluti on 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 imp acts of regulatory actions.
The costs presented in this RIA include both the IP M-projected annualized estimates of private compliance costs as well as the estimated c osts incurred by utilities and program participants to achieve demand-side energy efficien cy improvements. The demand-side energy efficiency costs are developed based on a review of energy efficiency data and studies, and expert judgment. The EPA recognizes that significan t variation exists in these analyses reflecting data and methodological limitations. The method use d for estimating the demand-side energy efficiency costs is discussed in more detail in the Demand-Side Energy Efficiency Technical Support Document (TSD). The evaluation, measurement and verification (EM&V) of demandside energy efficiency is addressed in the section VIII, State Plans, of the preamble for the final
rule.
The base case electricity demand in IPM v.5.15 is c alibrated to reference case demand in AEO 2015. AEO 2015 demand may reflect, to some exte nt, a continuation of the impacts of state demand-side energy efficiency policies but does not explicitly represent the most significant existing state policies in this area (e.g., energy efficiency resource standards). To some degree, the implicit representation of state policies in th e EPA’s base case alters the impacts assessment,3-44

but the direction and magnitude of change is not kn own with certainty. This issue is discussed in the Demand-Side Energy Efficiency TSD.
Cost estimates for the final emission guidelines ar e based on rigorous power sector modeling using ICF’s Integrated Planning Model. 85 IPM assumes “perfect foresight” of market conditions over the time horizon modeled; to the ex tent that utilities and/or energy regulators misjudge future conditions affecting the economics of pollution control, costs may be
understated as well.
One important element of the final CPP is the flexi bility afforded to states as they develop requirements for their existing emitting so urces. Each state has discretion on how to best achieve the standards of performance and/or state g oals. As such, states can apply requirements to sources that achieve greater reductions than req uired during the interim period, and use those earlier reductions in the final period (i.e., banki ng of reductions).
In the analysis and modeling for the RIA, such flex ibilities were not explicitly modeled in the compliance scenarios. Doing so would require ad ditional assumptions about the specific opportunities states may choose to adopt in their p lans, including the form of the standard that states apply, the manner in which it is applied, an d the economic signal that such a mechanism provides to sources over time, such that sources wo uld have an incentive to make greater reductions earlier. As previously stated, the analy sis in the RIA is intended to be illustrative to inform the broad impacts of the rule across the pow er sector, and not intended to forecast the specific approaches that individual states might ch oose, and how sources might prefer to achieve the emission reductions to reflect each state plan in response to particular policy signals or requirements. Not representing banking of earlier r eductions into the final period captures this uncertainty that there is inadequate and incomplete information at this time regarding state plans in the analytic approach.
The analysis does not fully reflect the potential u nder the final rule for recognition of precompliance emission reduction measures. Under the f inal rule, states implementing a rate-based plan can recognize eligible emission reduction meas ures, including RE and demand-side energy

85 Full documentation for IPM can be found at <http:/ / http://www.epa.gov/powersectormodeling>.3-45

efficiency, implemented after 2012 for the emission reductions those measures provide during the interim and final performance periods (see prea mble Sec. VIII.K.1). In the analysis, this treatment is appropriately applied in the complianc e period to generation from renewable capacity built after 2012. However, demand-side EE is limited to recognition of impacts occurring in the compliance period that result from investments in demand-side EE that are assumed to begin after 2019 (as represented in the illustrative demand-side EE plan scenario). Additionally, under the final rule, states will hav e the opportunity to recognize certain RE and demand-side EE measures implemented after the effec tive date of the rule for the emission reductions they provide in 2020-2021 through the Cl ean Energy Incentive Program (see preamble Sec. VIII.B.2). By committing to recognize these actions in 2020-2021, states will have access to a capped pool of additional rate-based ER Cs and mass-based allowances, based on their plan type. The Clean Energy Incentive Program is not reflected in this analysis.
The illustrative mass-based implementation scenario presented in this chapter includes an RE set-aside, which is only one component of a pote ntial approach to address leakage to new sources. Please see section VIII of the preamble f or a description of how states must show that they are addressing leakage under mass-based implem entation.

3.12 Social Costs
As discussed in the EPA Guidelines for Preparing Ec onomic Analyses, social costs are the total economic burden of a regulatory action. T his burden is the sum of all opportunity costs incurred due to the regulatory action, where an opp ortunity cost is the value lost to society of any goods and services that will not be produced and co nsumed as a result of reallocating some resources towards pollution mitigation. Estimates o f social costs may be compared to the social benefits expected as a result of a regulation to as sess its net impact on society. The social costs of a regulatory action will not necessarily be equival ent to the expenditures associated with compliance. Nonetheless, here we use compliance cos ts as a proxy for social costs. This section provides a qualitative discussion of the relationsh ip between social costs and compliance cost estimates presented in this chapter.
The cost estimates for the illustrative plan scenar ios presented in this chapter are the sum of expenditures on demand-side energy efficiency an d the change in expenditures required by the electricity sector to comply with the final emissio n guidelines. These two components are3-46

estimated separately. The expenditures required to achieve the assumed demand reductions through demand-side energy efficiency programs are estimated using historical data, analysis, 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 i n demand caused by the demand-side energy efficiency measures and the increase in electricity production costs required to achieve the additional emission reductions necessary to comply with the state goals.
As described in section 3.7.1, the illustrative pla n approaches assume that, in achieving their goals, demand-side energy efficiency measures are adopted which lead to demand reductions in each year represented by the illustra tive energy efficiency plan scenario. The estimated expenditures required to achieve those de mand reductions through demand-side energy efficiency are presented in this chapter and detail ed in the Demand-Side Energy Efficiency TSD. The social cost of achieving these energy savings c omes in the form of increased expenditures on technologies and/or services that are required to l ower electricity consumption beyond the business as usual. Under the assumption of complete and well-functioning markets, the expenditures required to reduce electricity consump tion on the margin will represent society’s opportunity cost of the resources required to produ ce the energy savings.
Due to the flexibility held by states in implementi ng their compliance with the final standards these energy efficiency expenditures may be borne by end-users through direct participant expenditures or electricity rate increa ses, or by producers through reductions in their profits. While the allocation of these expenditures between consumers and producers is important for understanding the distributional impa ct of potential compliance strategies, it does not necessarily affect the opportunity cost require d for the production of the energy savings from a social perspective. However, specific design elem ents of demand-side energy efficiency
measures included to address distributional outcome s may have an effect on the economic efficiency of the programs and therefore the social cost. Another reason the expenditures associated with dem and-side energy efficiency may
differ from social costs is due to differences in t he 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 eff icient products which were associated with3-47

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 possib ility that in some cases the quality of services, outside of the energy savings, provided b y the more energy efficient products and practices are deemed more desirable by some end-use rs. For example, weatherization of buildings to reduced electricity demand associated with cooling will likely have a significant impact on natural gas use associated with heating. In either case, these real welfare impacts are not fully captured by end-use energy efficiency exp enditure 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 parad ox is the observation that end-users do not always purchase products that are more energy effic ient when the additional cost is less than the reduction in the net present value of expected elec tricity expenditures achieved by those products. 86 Such circumstances are present in the analysis pre sented in this chapter, whereby in some regions the base case and illustrative approac hes 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 pref erences, 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 Demand-S ide Energy Efficiency TSD discusses the energy paradox and provides additional hypothesis f or why consumers may not make energy efficiency investments that ostensibly seem to be i n their own interest. The TSD discussion also provides details on how the presence of additional market failures can lead to levels of energy efficiency investment that may be too low from soci ety’s perspective even if that is not the case for the end-user. In such cases there is the potent ial for properly designed energy efficiency programs to address the source of under-investment, such as principal-agent problems where there is a disconnect between those making the purc hase decision regarding energy efficient investments and energy use and those that would rec eive the benefits associated with reduced energy use through lower electricity bills.

86 An analogous situation is present when some EGUs h ave assumed to have the ability to make heat rate improvements at a capital cost that is less than th e anticipated fuel expenditure savings.3-48

The other component of compliance cost reported in this chapter is the change in resource cost (i.e., expenditures) required by the electrici ty sector to fulfill the remaining demand while making additional CO 2 emissions reductions necessary to comply with the state goals. Included in the estimate of these compliance costs, estimate d using IPM, are the cost reductions associated with the reduction in required electricity generati on due to the demand reductions from demandside energy efficiency measures and improvements in heat rate. By shifting the demand curve for electricity, demand-side energy efficiency reduces the production cost in the sector. The resource
cost estimates from IPM therefore account for the i ncreased cost of providing electricity, including changes in fuel prices associated with ch anges in their demand, while EGUs comply with their regulatory obligations (net of the reduc tion in their production costs due to lower demand resulting from demand-side energy efficiency measures).

3.13 References
Burtraw, Dallas, Karen Palmer, Sophie Pan, and Anth ony Paul. 2015. A Proximate Mirror: Greenhouse Gas Rules and Strategic Behavior under t he U.S. Clean Air Act. Resources for the Future Discussion Paper 15-02. March 2015. Bushnell, James B., Stephen P. Holland, Jonathan E. Hughes, Christopher R. Knittel. 2015. Strategic Policy Choice in State-Level Regulation: The EPA's Clean Power Plan. National Bureau of Economic Research Working Paper No. w2125 9. June, 2015. Demailly, Damien, and Philippe Quirion. 2006 "CO 2 abatement, competitiveness and leakage in the European cement industry under the EU ETS: gran dfathering versus output-based allocation." Climate Policy 6.1: 93-113. Regional Greenhouse Gas Initiative (RGGI). 2014. CO 2 Emissions from Electricity Generation and Imports in the Regional Greenhouse Gas Initiati ve: 2012 Monitoring Report. August 11, 2014. Accessed July 22, 2015: http://www.rggi.org/docs/Documents/Elec_monitoring_ report_2012_15_08_11.pdf U.S. EPA. 2010. EPA Guidelines for Preparing Econom ic Analyses Available at: <http://yosemite.epa.gov/ee/epa/eed.nsf/webpages/gu idelines.htm>. Accessed 7/11/2015. U.S. EPA. 2015. Technical Support Document (TSD) th e Final Carbon Pollution Emission Guidelines for Existing Stationary Sources: Electri c Utility Generating Units. Demand-Side Energy Efficiency.3-49

U.S. EPA. 2015. Technical Support Document (TSD) th e Final Carbon Pollution Emission Guidelines for Existing Stationary Sources: Electri c Utility Generating Units. Resource Adequacy and Reliability Analysis.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 describe the met hodology for estimating upstream methane emissions related to natural gas systems an d coal mining sectors that may result from the illustrative plan approaches examined in the Re gulatory Impact Analysis (RIA). The US Environmental Protection Agency (EPA) assessed whet her the net change in upstream methane emissions from natural gas and coal production is l ikely 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 illustrative p lan approaches 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 examine o ther potential changes in other upstream greenhouse gas emissions changes from natu ral gas systems and coal mining sectors.

The methodologies used to project upstream emission s were previously developed for the purpose of the 2014 U.S. Climate Action Report, and were subject to peer review and public review as part of the publication of that report. I n section 3A.1, the overall approach is described in brief. In section 3A.2, results are presented. S ection 3A.3 discusses uncertainties and limitations of the analysis. Finally, section 3A.4 contains a bibliography of cited resources. In the RIA for the Clean Power Plan proposal (in then sect ion 3A.3), we presented the detailed methodologies for how methane and flaring-related C O 2 projections were estimated for coal mining and natural gas systems. We rely on the same methods in this RIA, so we refer the interested reader to the proposal RIA 87 for the detailed methodological discussion. The calculations have been updated to reflect input dat a from the most recent U.S. GHG Inventory, published in April 2015.

87 Clean Power Plan proposal RIA can be found at < ht tp://www2.epa.gov/sites/production/files/2014- 06/documents/20140602ria-clean-power-plan.pdf>.3A-2

3A.1 General Approach
3A.1.1 Analytical Scope
Upstream methane and flaring-related CO 2 emissions associated with coal mining and natural gas systems were estimated for 2025 through 2030 using methodologies developed for the 2014 U.S. Climate Action Report (U.S. Departmen t of State 2014). The base year for the projections is 2013, as reported in the 2015 U.S. G HG Inventory (EPA 2015a). 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 Int egrated Planning Model (IPM), which was used in the RIA to model illustrative plan approach es. The 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 as sociated with both voluntary and regulatory programs affecting upstream methane-rela ted emissions. In the case of the voluntary programs, the rate of reductions is based on the hi storical average decrease from these programs over recent years. In the case of regulatory reduct ions, the reductions are based on the reduction rates estimated in the RIAs of relevant regulations . The projections include emissions reductions projected to result from the 2012 Oil and Natural G as New Source Performance Standards. The methodologies to estimate upstream emissions were s ubject to expert peer review and public
review in the context of the 2014 U.S. Climate Acti on Report. For more information on the review, or for the detailed methodologies used for non-CO 2 source projections in that report, including methane-related emissions from coal produ ction and natural gas systems, see “Methodologies for U.S. Greenhouse Gas Emissions Pr ojections: Non-CO 2 and Non-Energy CO 2 Sources” (EPA, 2013). Uncertainties and limitations are discussed, including a side case which incorporates additional geographic information for estimating methane from coal mining.

The term “upstream emissions” in this document refe rs to vented, fugitive and flared emissions associated with fuel production, processi ng, transmission, storage, and distribution of fuels prior to fuel combustion in electricity plant s. For this analysis, the EPA focused on upstream methane from the natural gas systems and c oal 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 in3A-3

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 methane e missions from natural gas systems and coal mining sectors within the domestic U.S., b ut did not examine emissions from potential changes in upstream emissions generated by changes in natural gas and coal production, processing, and transportation activities outside o f the US. 88 Last, the EPA did not assess potential changes in other upstream non-GHG emissio ns, such as nitrogen oxides, volatile organic compounds, and particulate matter. Table 3A -1 presents estimates of the upstream emissions discussed in this analyses for 2013, base d on the 2015 U.S. GHG Inventory.

EPA defined the boundaries of this assessment in or der to provide targeted insights into the potential net change in methane emissions from natu ral gas systems and coal production activities specifically. CO 2 emissions from flared methane are included because regulatory and voluntary programs influence the rate of methane fl aring over time and the CO 2 remaining after flaring is a methane-related GHG. Because of the mu ltiple strategies adopted in the illustrative plan approaches, 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 comple x and likely subject to data limitations and substantial uncertainties. Rather, EPA chose to lim it 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 previ ously-reviewed projection techniques, which are presented in detail below.

3A.1.2 Coal Mining Source Description
Within coal mining, this analysis covers fugitive m ethane emissions from coal mining (including pre-mining drainage) and post-mining act ivities (i.e., coal handling), including both underground and surface mining. Emissions from aban doned mines are not included. Energy-

88 While the analysis does not estimate methane emiss ions changes outside of the United States, activity factors include imports and exports of natural gas to help estimate domestic methane emissions related to trad e of natural gas, such as emissions from LNG terminals in the US or from pipelines transporting imported natural ga s within the US (or transporting natural gas within the US while en route for export).3A-4

related CO 2 emissions, such as emissions from mining equipment and vehicles transporting coal are not included. Methane, which is contained withi n coal seams and the surrounding rock strata, is released into the atmosphere when mining operati ons reduce the pressure above and/or surrounding the coal bed. The quantity of methane e mitted from these operations is a function of two primary factors: coal rank and coal depth. Coal rank is a measure of the carbon content of the coal, with higher coal ranks corresponding to h igher carbon content and generally higher methane content. Pressure increases with depth and prevents methane from migrating to the surface; as a result, underground mining operations typically emit more methane than surface mining. In addition to emissions from underground a nd surface mines, post-mining processing of
coal and abandoned mines also release methane. Post -mining emissions refer to methane retained in the coal that is released during processing, sto rage, and transport of the coal.

3A.1.3 Natural Gas Systems Source Description
Within natural gas systems, this analysis covers ve nted and fugitive methane emissions from the production, processing, transmission and s torage, and distribution segments of the natural gas system. It also includes CO 2 from flaring of natural gas. Not included are vent ed 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.
Methane and non-combustion 89 CO 2 emissions from natural gas systems are generally p rocessrelated, with normal operations, routine maintenanc e, 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 r aw gas from underground
formations. Emissions arise from the wells themselv es, gathering pipelines, and well-site gas treatment facilities (e.g., dehydrators, separators ). Major emissions source categories within the production stage include pneumatic devices, gas wel ls with liquids unloading, and gas well

89 In this document, consistent with IPCC accounting terminology, the term “combustion emissions” refers to the emissions associated with the combustion of fuel fo r useful heat and work, while “non-combustion emiss ions” refers to emissions resulting from other activities, inclu ding flaring and CO 2 removed from raw natural gas.3A-5

completions and re-completions (i.e., workovers) wi th hydraulic fracturing (EPA 2013). Flaring emissions account for the majority of the non-combu stion CO 2 emissions within the production stage. Processing: In this stage, natural gas liquids and various othe r constituents from the raw gas are removed, resulting in “pipeline-quality” gas, which is then injected into the transmission system. Fugitive methane emissions from compressors, includ ing compressor seals, are the primary emissions source from this stage. In the U.S. GHG I nventory, the majority of non-combustion
CO 2 emissions in the processing stage come from acid g as removal units, which are designed to
remove CO 2 from natural gas.
Transmission and Storage: Natural gas transmission involves high-pressure, l arge-diameter pipelines that transport gas long distances from fi eld 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. transmissi on system. Fugitive methane emissions from these compressor stations and from metering and reg ulating stations account for the majority of the emissions from this stage. Pneumatic devices an d non-combusted engine exhaust are also
sources of methane emissions from transmission faci lities. Natural gas is also injected and stored in underground formations, or liquefied and stored in above-ground tanks, during periods of lower demand (e.g., summer), and withdrawn, process ed, and distributed during periods of higher demand (e.g., winter). Compressors and dehyd rators are the primary contributors to emissions from these storage facilities. Emissions from LNG import terminals are included within the transportation and storage stage.

Distribution: Distribution pipelines take the high-pressure gas f rom the transmission system at “city gate” stations, reduce the pressure, and then distribute the gas through primarily underground mains and service lines to individual e nd users.3A-6

Table 3A-1. Base Year Upstream Methane-Related Emis sions in the U.S. GHG Inventory Emissions Source 2013 Emissions (million short tons CO 2 Eq.) Methane from Coal Mining 71.2 Underground Mining and Post-Mining 58.2 Surface Mining and Post-Mining 13.0 Methane from Natural Gas Systems 173.5 Production 51.8 Processing 25.0 Transmission and Storage 60.0 Distribution 36.7 CO 2 from flaring of natural gas 17.1 Source: 2015 U.S. GHG Inventory (EPA, 2015). A Glob al Warming Potential of 25 was used to convert meth ane emissions to CO 2 Eq. In Table 3A-1, CO 2 -equivalent methane emissions are presented using t he Fourth Assessment Report Global Warming Potential (GWP) of 25.

3A.1.4 Illustrative Plan Approaches Examined
States will ultimately determine optimal approaches to comply with the goals established in this regulatory action. The RIA depicts illustra tive plan approaches for the final emissions guidelines, reflecting a rate-based illustrative pl an or mass-based illustrative plan approach.

3A.1.5 Activity Drivers
IPM-based activity driver projections from base cas e and illustrative plan approaches underlie the estimates of upstream methane emission s. These activity drivers include domestic coal and natural gas production, imports and export s, and natural gas consumption. Table 3A-2
and Table 3A-3 summarize the IPM-based coal and nat ural gas production activity driver results
from the baseline and illustrative scenario for the final guidelines. 90

Under the final guidelines, both the rate-based and mass-based illustrative plan approaches result in reduced coal production and little change in natural gas production. We estimate that the illustrative plan approaches will result in reducti ons in coal production of 5 to 6 percent in 2020, 12 to 15 percent in 2025 and 21 to 22 percent in 20 30, relative to base case coal production.

90 Uncertainties related to activity drivers are disc ussed in the uncertainties and limitations section.3A-7

Natural gas production in the illustrative plan app roaches change by 1 percent or less in each of the years of analysis relative to production in the base case.

Table 3A-2. Projected Coal Production Impacts

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 832.4 828.7 860.1 Rate-based 791.5 725.7 674.4 -41.0 -103.0 -186.0 -5 % -12% -22% Mass-based 779.9 705.6 678.9 -52.0 -123.0 -181.0 -6 % -15% -21%
Table 3A-3. Projected Natural Gas Production Impact s
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 28.9 30.8 33.0 Rate-based 29.1 30.7 32.9 +0.2 -0.1 -0.1 +1% 0% 0% Mass-based 29.2 30.8 32.6 +0.3 +0.0 -0.4 +1% 0% -1%

3A.2 Results
The analytical results (Table 3A-4) for the final g uidelines indicates decreases in methane emissions from coal mining of 8 to 9 million short tons CO 2 Eq. in 2025, and about 14 million
short tons CO 2 Eq. in 2030. Methane from natural gas systems decr eases relative to the base case by 0 to 1 million short tons CO 2 Eq. in 2025 and 1 to 2 million short tons CO 2 Eq. in 2030. CO 2 from flaring in natural gas production does not sho w significant change relative to the base case.

Based on the actions modelled in the illustrative p lan approaches, upstream methane
emissions and CO 2 emissions are predicted to decline (see Table 3A-4 ). The final guidelines are predicted to result in a net emissions reduction of 8 to 9 million short tons CO 2 Eq. in 2025 and a
net emissions reduction of 15 to 16 million short t ons CO 2 Eq. in 2030. These net emissions changes represent the sum of changes in methane fro m coal mining, methane from natural gas systems, and CO 2 from flaring in natural gas production. The projec tions include voluntary and3A-8

regulatory activities to reduce emissions from coal mining and natural gas and oil systems, including the 2012 Oil and Natural Gas NSPS. In add ition, the EPA plans to issue a proposed
rule later this summer that would build on its 2012 Oil and Gas NSPS. When these standards are finalized and implemented, they would further reduc e projected emissions from natural gas and oil systems.

Table 3A-4. Potential Upstream Emissions Changes Emissions (million short tons CO 2 Eq.) 2020 2025 2030 Rate-based Methane from Coal Mining -3.0 -7.5 -14.0 Methane from Natural Gas Systems +1.1 -0.8 -0.6 CO 2 from NG flaring +0.2 -0.1 -0.1 Total Methane + CO 2 -1.7 -8.4 -14.8 Mass-based Methane from Coal Mining -3.8 -9.0 -13.7 Methane from Natural Gas Systems +1.5 -0.1 -2.2 CO 2 from NG flaring +0.2 +0.0 -0.3 Total Methane + CO 2 -2.2 -9.0 -16.1 Note: A Global Warming Potential of 25 was used to convert methane emissions to CO 2 Eq.
3A.3 Uncertainties and Limitations
Projections of upstream methane emissions and CO 2 emitted from flaring of methane are subject to a range of uncertainties and limitations . These uncertainties and limitations include estimating the effect of the plan approach on activ ity drivers, uncertainty in base year emissions, and uncertainties in changes in emissions factors o ver relatively long periods of time. For example, EPA’s application of IPM relies on EIA pro jections 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 de mand. 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 tha n what is presented here.

Discussion of uncertainty in historical estimates o f emissions from coal mining and natural gas systems can be found in the 2015 U.S. GHG Inven tory. 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 base line and illustrative plan approaches.3A-9

Relatively higher or lower economic growth, or chan ges in the relative prices or availability of various technologies could result in alternative es timates in the net change in upstream methane
emissions and related CO 2 emissions.

3A.4 References

IPCC. 2006. 2006 IPCC Guidelines for National Greenhouse Gas In ventories. Available at <http://www.ipcc-nggip.iges.or.jp/public/2006gl/>. Accessed June 4, 2015. 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 >. Accessed June 4, 2015. U. S. Environmental Protection Agency (U.S. EPA). 1 993. Anthropogenic Methane Emissions in the United States, Estimates for 1990: Report to Co ngress. EPA-430-R-93-003. U. S. Environmental Protection Agency (U.S. EPA). 2 011. 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/p dfs/20110728tsd.pdf>. Accessed June 4, 2015. U. S. Environmental Protection Agency (U.S. EPA). 2 012a. 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/p kg/FR-2012-08-16/pdf/2012- 16806.pdf>. Accessed June 4, 2015. U. S. Environmental Protection Agency (U.S. EPA). 2 012b. Oil and Natural Gas Sector: Standards of Performance for Crude Oil and Natural Gas Production, Transmission, and Distribution: Background Supplemental Technical Sup port Document for the Final New Source Performance Standards . Available at <http://epa.gov/airquality/oilandgas/pdfs/20120418t sd.pdf>. Accessed June 4, 2015. U. S. Environmental Protection Agency (U.S. EPA). 2 015. Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2013 . EPA-430-R-15-004. Available at <http://www.epa.gov/climatechange/ghgemissions/usin ventoryreport.html>. Accessed June 4, 2015. U. S. Environmental Protection Agency (U.S. EPA). 2 013. 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> Accessed June 4, 2015.4-1



### C HAPTER 

### 4: E STIMATED 

### C LIMATE 

### B ENEFITS AND 

### H UMAN 

### H EALTH 

### C O 

### - BENEFITS

4.1 Introduction
Implementing the Final Carbon Pollution Emission Gu idelines for Existing Stationary Sources: Electric Utility Generating Units (hereaft er referred to as the “final emission guidelines” or “Clean Power Plan Final Rule”) is ex pected to reduce emissions of carbon dioxide (CO 2 ) and have ancillary human health benefits (i.e., c o-benefits) associated with lower ambient concentrations of criteria air pollutants. This cha pter describes the methods used to estimate the monetized climate benefits and the monetized air qu ality health co-benefits associated with reducing exposure to ambient fine particulate matte r (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 p revent the EPA from monetizing the benefits from several important co-benefit categori es, including reducing direct exposure to SO 2 , NO 2 , and hazardous air pollutants (HAP), as well as ec osystem effects and visibility impairment. We qualitatively discuss these unquantified benefit s in this chapter.

This chapter provides estimates of the monetized cl imate benefits and air quality health cobenefits associated with emission reductions for th e illustrative rate-based and mass-based illustrative plan approaches across several analysi s 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 S tandards (MATS).

4.2 Estimated Climate Benefits from CO 2 The primary goal of the final emission guidelines i s to reduce emissions of CO 2 . In this section, we provide a brief overview of the 2009 En dangerment Finding and climate science assessments released since then. We also provide in formation regarding the economic valuation
of CO 2 using the Social Cost of Carbon (SC-CO 2 ), 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.4-2

Table 4-1. Climate Effects Benefits Category Specific Effect Effect Has Been Quantified Effect Has Been

Monetized More

Information Improved Environment Reduced climate effects Global climate impacts from CO 2 — 1  SCC TSD

Climate impacts from ozone and black carbon (directly emitted PM) — — Ozone ISA, PM ISA 2 Other climate impacts (e.g., other GHGs such as methane, aerosols, other impacts) — — IPCC 2

1 The global climate and related impacts of CO 2 emissions changes, such as sea level rise, are est imated within each integrated assessment model as part of the calculat ion of the SC-CO 2 . The resulting monetized damages, which are relevant for conducting the benefit-cost analys is, are used in this RIA to estimate the welfare ef fects of quantified changes in CO 2 emissions.

2 We assess these co-benefits qualitatively because we do not have sufficient confidence in available d ata or methods. 4.2.1 Climate Change Impacts
Through the implementation of CAA regulations, the EPA addresses the negative externalities caused by air pollution. In 2009, the EPA Administrator found that elevated concentrations of greenhouse gases in the atmospher e may reasonably be anticipated both to endanger public health and to endanger public welfa re. It is these adverse impacts that make it necessary for the EPA to regulate GHGs from EGU sou rces. The preamble summarizes the public health and public welfare impacts that were detailed in the 2009 Endangerment Finding. For health, these include the increased likelihood of heat waves, negative impacts on air quality, more intense hurricanes, more frequent and intense storms and heavy precipitation, and impacts on infectious and waterborne diseases. For welfare, these include reduced water supplies in some regions, increased water pollution, increased occur rences of floods and droughts, rising sea levels and damage to coastal infrastructure, increa sed peak electricity demand, changes in ecosystems, and impacts on indigenous communities.

The preamble also summarizes new scientific assessm ents and recent climatic observations. Major scientific assessments released since the 2009 Endangerment Finding have improved scientific understanding of the climate, a nd provide even more evidence that GHG emissions endanger public health and welfare for cu rrent and future generations. The National Climate Assessment (NCA3), in particular, assessed the impacts of climate change on human health in the United States, finding that, American s will be impacted by “increased extreme4-3

weather events, wildfire, decreased air quality, th reats to mental health, and illnesses transmitted by food, water, and disease-carriers such as mosqui toes and ticks.” These assessments also detail the risks to vulnerable groups such as children, th e elderly and low income households. Furthermore, the assessments present an improved un derstanding of the impacts of climate change on public welfare, higher projections of fut ure sea level rise than had been previously estimated, a better understanding of how the warmth in the next century may reach levels that would be unprecedented relative to the preceding mi llions of years of history, and new assessments of the impacts of climate change on per mafrost and ocean acidification. The impacts of GHG emissions will be realized worldwide, indepe ndent upon their location of origin, and impacts outside of the United States will produce c onsequences relevant to the United States.

4.2.2 Social Cost of Carbon
We estimate the global social benefits of CO 2 emission reductions expected from the final emission guidelines using the SC-CO 2 estimates presented in the Technical Support Document: Technical Update of the Social Cost of Carbon for R egulatory Impact Analysis Under Executive
Order 12866 (May 2013, Revised July 2015) (“current TSD”). 91 We refer to these estimates, which were developed by the U.S. government, as “SC -CO 2 estimates.” The SC-CO 2 is a metric that estimates the monetary value of impacts associ ated with marginal changes in CO 2 emissions in a given year. It includes a wide range of antici pated climate impacts, such as net changes in agricultural productivity and human health, propert y damage from increased flood risk, and changes in energy system costs, such as reduced cos ts for heating and increased costs for air conditioning. It is typically used to assess the av oided damages as a result of regulatory actions (i.e., benefits of rulemakings that lead to an incr emental reduction in cumulative global CO 2 emissions).

91 Docket ID EPA-HQ-OAR-2013-0495, Technical Support Document: Technical Update of the Social Cost of Carbon for Regulatory Impact Analysis Under Executi ve Order 12866 , Interagency Working Group on Social Cost of Carbon, with participation by Council of Economi c Advisers, Council on Environmental Quality, Depar tment of Agriculture, Department of Commerce, Department of Energy, Department of Transportation, Environmental Protection Agency, National Economic Council, Offic e of Energy and Climate Change, Office of Managemen t and Budget, Office of Science and Technology Policy, an d Department of Treasury (May 2013, Revised July 20 15). Available at: <https://www.whitehouse.gov/sites/def ault/files/omb/inforeg/scc-tsd-final-july-2015.pdf> Accessed 7/11/2015.4-4

The SC-CO 2 estimates used in this analysis were developed ove r many years, using the best science available, and with input from the pub lic. Specifically, an interagency working group (IWG) that included the EPA and other executi ve branch agencies and offices used three integrated assessment models (IAMs) to develop the SC-CO 2 estimates and recommended four global values for use in regulatory analyses. The S C-CO 2 estimates were first released in February 2010 and updated in 2013 using new version s of each IAM. As discussed further below, the IWG published two minor corrections to t he SC-CO 2 estimates in July 2015.

The SC-CO 2 estimates were developed using an ensemble of the three most widely cited integrated assessment models in the economics liter ature with the ability to estimate the SC-CO 2
. A key objective of the IWG was to draw from the ins ights of the three models while respecting the different approaches to linking GHG emissions a nd monetized damages taken by modelers in the published literature. After conducting an exten sive literature review, the interagency group selected three sets of input parameters (climate se nsitivity, socioeconomic and emissions trajectories, and discount rates) to use consistent ly in each model. All other model features were left unchanged, relying on the model developers’ be st estimates and judgments, as informed by the literature. Specifically, a common probability distribution for the equilibrium climate sensitivity parameter, which informs the strength o f climate’s response to atmospheric GHG concentrations, was used across all three models. I n addition, a common range of scenarios for the socioeconomic parameters and emissions forecast s were used in all three models. Finally, the marginal damage estimates from the three models wer e estimated using a consistent range of discount rates, 2.5, 3.0, and 5.0 percent. See the 2010 TSD for a complete discussion of the methods used to develop the estimates and the key u ncertainties, and the current TSD for the
latest estimates. 92

The SC-CO 2 estimates represent global measures because of the distinctive nature of the climate change, which is highly unusual in at least three respects. First, emissions of most GHGs contribute to damages around the world independent of the country in which they are emitted.
The SC-CO 2 must therefore incorporate the full (global) damag es caused by GHG emissions to address the global nature of the problem. Second, t he U.S. operates in a global and highly

92 See https://www.whitehouse.gov/omb/oira/social-cost-of- carbon for both TSDs.4-5

interconnected economy, such that impacts on the ot her side of the world can affect our economy. This means that the true costs of climate change to the U.S. are larger than the direct impacts that simply occur within the U.S. Third, cl imate change represents a classic public goods problem because each country’s reductions benefit e veryone else and no country can be excluded from enjoying the benefits of other countries’ redu ctions, even if it provides no reductions itself. In this situation, the only way to achieve an econo mically efficient level of emissions reductions is for countries to cooperate in providing mutually beneficial reductions beyond the level that would be justified only by their own domestic benef its. In reference to the public good nature of mitigation and its role in foreign relations, thirt een prominent academics noted that these “are compelling reasons to focus on a global SCC” in a r ecent article on the SCC (Pizer et al., 2014). In addition, as noted in OMB’s Response to Comments on the SCC, there is no bright line between domestic and global damages. Adverse impact s on other countries can have spillover effects on the United States, particularly in the a reas of national security, international trade, public health and humanitarian concerns. 93

The 2010 TSD noted a number of limitations to the S C-CO 2 analysis, including the incomplete way in which the integrated assessment m odels capture catastrophic and noncatastrophic impacts, their incomplete treatment of adaptation and technological change, uncertainty in the extrapolation of damages to high temperatures, and assumptions regarding risk aversion. Currently integrated assessment models do not assign value to all of the important physical, ecological, and economic impacts of clima te 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. 94 The limited amount of research linking climate impacts to economic damages makes the modeling
exercise even more difficult. These individual limi tations do not all work in the same direction in
terms of their influence on the SC-CO 2 estimates, though taken together they suggest that the

93 See Endangerment and Cause or Contribute Findings for Greenhouse Gases Under Section 202(a) of the Cl ean Air Act, 74 Fed. Reg. 66,496, 66,535 (Dec. 15, 2009 ) and National Research Council 2013a.

94 Climate change impacts and SCC modeling is an area of active research. For example, see: (1) Howard, Peter, “Omitted Damages: What’s Missing from the Social Co st of Carbon.” March 13, 2014, http://costofcarbon.org/files/Omitted_Damages_Whats _Missing_From_the_Social_Cost_of_Carbon.pdf ; and (2) Electric Power Research Institute, “Understanding t he Social Cost of carbon: A Technical Assessment,” October 2014, www.epri.com

.4-6

SC-CO 2 estimates are likely conservative. In particular, the IPCC Fourth Assessment Report (2007), which was the most current IPCC assessment available at the time of the IWG’s 2009- 2010 review, concluded that “It is very likely that [SC-CO 2 estimates] underestimate the damage costs because they cannot include many non-quantifi able impacts.” Since then, the peerreviewed literature has continued to support this c onclusion. For example, the IPCC Fifth Assessment report observed that SC-CO 2 estimates continue to omit various impacts that wo uld likely increase damages. The 95th percentile estima te was included in the recommended range for regulatory impact analysis to address these con cerns.
The EPA and other agencies have continued to consid er feedback on the SC-CO 2 estimates from stakeholders through a range of channels, incl uding public comments on this rulemaking
and others that use the SC-CO 2 in supporting analyses and through regular interac tions with stakeholders and research analysts implementing the SC-CO 2 methodology used by the interagency working group. The SC-CO 2 comments received on this rulemaking covered a wid e range of topics including the technical details of the modeling conducted to develop the SC-CO 2 estimates, the aggregation and presentation of the SC-CO 2 estimates, and the process by which
the SC-CO 2 estimates were derived. Many but not all commenters were supportive of the SCCO 2 and its application to this rulemaking. Commenters also provided constructive recommendations for potential opportunities to impr ove the SC-CO 2 estimates in future updates. The EPA Response to Comments document provides a su mmary and response to the SC-CO 2 comments submitted to this rulemaking.

Many of the comments EPA received were similar to t hose that OMB’s Office of Information and Regulatory Affairs received in resp onse to a separate request for public comment on the approach used to develop the estimat es. After careful evaluation of the full range of comments submitted to OMB’s Office of Info rmation and Regulatory Affairs, the IWG
continues to recommend the use of these SC-CO 2 estimates in regulatory impact analysis. With the release of the response to comments 95 , the IWG announced plans to obtain expert independent advice from the National Academies of S ciences, Engineering, and Medicine

95 See https://www.whitehouse.gov/sites/default/files/omb/ inforeg/scc-response-to-comments-final-july-2015.pd f4-7

(Academies) to ensure that the SC-CO 2 estimates continue to reflect the best available s cientific and economic information on climate change. 96 The Academies’ process will be informed by the public comments received and focus on the technical merits and challenges of potential approaches to improving the SC-CO 2 estimates in future updates.

Concurrent with OMB’s publication of the response t o comments on SC-CO 2 and announcement of the Academies process, OMB posted a revised TSD that includes two minor
technical corrections to the current estimates. One technical correction addressed an inadvertent omission of climate change damages in the last year of analysis (2300) in one model and the second addressed a minor indexing error in another model. On average the revised SC-CO 2 estimates are one dollar less than the mean SC-CO 2 estimates reported in the November 2013 revision to the May 2013 TSD. The change in the est imates associated with the 95th percentile estimates when using a 3% discount rate is slightly larger, as those estimates are heavily influenced by the results from the model that was a ffected by the indexing error.

The four SC-CO 2 estimates are as follows: $12, $40, $60, and $120 per short ton of CO 2 emissions in the year 2020 (2011$). 97 The first three values are based on the average SC -CO 2 from the three IAMs, at discount rates of 5, 3, and 2.5 percent, respectively. SC-CO 2 estimates
for several discount rates are included because the literature shows that the SC-CO 2 is quite sensitive to assumptions about the discount rate, a nd because no consensus exists on the appropriate rate to use in an intergenerational con text (where costs and benefits are incurred by different generations). The fourth value is the 95 th percentile of the SC-CO 2 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 SC-CO 2 distribution (representing less likely, but potentially catastrophic, outcomes).

96 See https://www.whitehouse.gov/blog/2015/07/02/estimati ng-benefits-carbon-dioxide-emissions-reductions

.

97 The current version of the TSD is available at: ht tps://www.whitehouse.gov/sites/default/files/omb/in foreg/scctsd-final-july-2015.pdf. The 2010 and 2013 TSDs pr esent SC-CO 2 in 2007$ per metric ton. The unrounded estimates from the current TSD were adjusted to (1) 2011$ using GDP Implicit Price Deflator (1.061374)
, http://www.bea.gov/iTable/index_nipa.cfm and (2) sh ort tons using the conversion factor of 0.90718474 metric tons in a short ton. The estimates presented in the RIA were rounded to two significant digits.4-8

Table 4-2 presents the global SC-CO 2 estimates in short tons for the years 2015 to 2050

. 98
In order to calculate the dollar value for emission reductions, the SC-CO 2 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 r ate used to estimate the SC-CO 2
. 99 The SCCO 2 increases over time because future emissions are e xpected to produce larger incremental damages as physical and economic systems become mor e stressed in response to greater climate change. Note that the interagency group estimated t he growth rate of the SC-CO 2 directly using the three integrated assessment models rather than assuming a constant annual growth rate. This helps to ensure that the estimates are internally c onsistent with other modeling assumptions. Tables 4-3 through 4-5 report the incremental clima te benefits estimated in three analysis years (2020, 2025, and 2030) for the rate-based and mass- based illustrative plan approaches.

Table 4-2. Social Cost of CO 2 , 2015-2050 (in 2011$ per short ton)*

Year Discount Rate and Statistic

5% Average 3% Average 2.5% Average 3% (95th percent ile) 2015 $11 $35 $54 $100 2020 $12 $40 $60 $120 2025 $13 $44 $65 $130 2030 $15 $48 $70 $150 2035 $17 $53 $75 $160 2040 $20 $58 $81 $180 2045 $22 $62 $86 $190 2050 $25 $66 $91 $200 * These SC-CO 2 values are stated in $/short ton and rounded to tw o significant figures. The SC-CO 2 values have been converted from $/metric ton to $/short ton usi ng the conversion factor 0.90718474 metric tons in a short ton for consistency with this rulemaking. This calculat ion does not change the underlying methodology nor does it change the meaning of the SC-CO2 estimates. For bot h metric and short tons denominated SC-CO 2 estimates, the estimates vary depending on the year of CO 2 emissions and are defined in real terms, i.e., adj usted for inflation using the GDP implicit price deflator.

98 For consistency with this rulemaking, the SC-CO 2 values have been converted from $/metric ton to $/ short ton and applied to the CO 2 reductions (short tons) to estimate climate benefi ts. Specifically, the $/metric ton estimates were multiplied by the conversion factor 0.90718474 metric tons in a short ton to yield $/short ton. This calculation does not change the underlying methodology, the mea ning of the SC-CO 2 estimates, or the final benefits estimates.

99 This analysis considered the climate impacts of on ly CO 2 emission change. As discussed below, the climate impacts of other pollutants were not calculated for the proposed guidelines. While CO 2 is the dominant GHG emitted by the sector, we recognize the representat ive facilities within these comparisons may also ha ve different emission rates for other climate forcers that will serve a minor role in determining the overall socia l cost of generation.4-9

Table 4-3. Estimated Global Climate Benefits of CO 2 Reductions for the Final Emission

Guidelines in 2020 (billions of 2011$)*
Discount rate and statistic Rate-Based Mass-Based Million short tons of CO 2 reduced 69 82

5% (average) $0.80 $0.94 3% (average) $2.8 $3.3 2.5% (average) $4.1 $4.9 3% (95 th percentile) $8.2 $9.7 * The SC-CO 2 values are dollar-year and emissions-year specific . SC-CO 2 values represent only a partial accounting of climate impacts.
Table 4-4. Estimated Global Climate Benefits of CO 2 Reductions for the Final Emission

Guidelines in 2025 (billions of 2011$)*
Discount rate and statistic Rate-Based Mass-Based Million short tons of CO 2 reduced 232 264

5% (average) $3.1 $3.6 3% (average) $10 $12 2.5% (average) $15 $17 3% (95 th percentile) $31 $35 * The SC-CO 2 values are dollar-year and emissions-year specific . SC-CO 2 values represent only a partial accounting of climate impacts.
Table 4-5. Estimated Global Climate Benefits of CO 2 Reductions for the Final Emission

Guidelines in 2030 (billions of 2011$)*
Discount rate and statistic Rate-Based Mass-Based Million short tons of CO 2 reduced 415 413

5% (average) $6.4 $6.4 3% (average) $20 $20 2.5% (average) $29 $29 3% (95 th percentile) $61 $60 * The SC-CO 2 values are dollar-year and emissions-year specific . SC-CO 2 values represent only a partial accounting of climate impacts. It is important to note that the climate benefits p resented above are associated with changes
in CO 2 emissions only. Implementing these final emission guidelines, however, will have an impact on the emissions of other pollutants that wo uld 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 polluta nts have not been calculated for the final emission guidelines. 100

100 The SC-CO 2 estimates used in this analysis are designed to as sess the climate benefits associated with changes i n CO 2 emissions only.4-10

The other emissions potentially reduced as a result of the final emission 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 e missions of these pollutants (both increases and decreases) could directly result from changes i n electricity generation, upstream fossil fuel extraction and transport, and/or downstream seconda ry 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 lea d 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 differe nt 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 th e atmosphere for hundreds or thousands of years, many of these other pollutants are short liv ed 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 impact s of these other pollutants for the final emission guidelines, the Agency has analyzed the po tential changes in upstream methane emissions from the natural gas and coal production sectors that may result from the illustrative plan approaches examined in this RIA in the appendi x to Chapter 3. The EPA assessed whether the net change in upstream methane emissions from n atural gas and coal production is likely to be positive or negative and also assessed the poten tial magnitude of changes relative to CO 2 emissions reductions anticipated at power plants. T his assessment included CO 2 emissions from the flaring of methane, but did not evaluate potent ial changes in other combustion-related CO 2 emissions, such as emissions associated with drilli ng, mining, processing, and transportation in the natural gas and coal production sectors. This a nalysis found that the net upstream CH 4 emissions from natural gas systems and coal mines a nd CO 2 emissions from flaring of methane will likely decrease under the final emission guide lines. Furthermore, the analysis suggests that4-11

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 reducing emissions of CO 2 , implementing these final emission 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. 101 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 co ncentrations of volatile organic compounds (VOCs), reducing NO X emissions would also reduce human exposure to ozon e 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 and their associated health effects, respectively. In this section, we provide an overview
of the monetized PM 2.5 and ozone-related co-benefits estimated for the fin al emission guidelines. 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). The
estimated co-benefits associated with these emissio n reductions are beyond those achieved by previous EPA rulemakings, including MATS.

There are several important considerations in asses sing the air quality-related health cobenefits for a climate-focused rulemaking. First, t hese estimated health co-benefits do not account for any climate-related air quality changes (e.g., increased ambient ozone associated with higher temperatures) but rather changes in pre cursor emissions affected by this rulemaking. Excluding climate-related air quality changes may u nderestimate ozone-related health cobenefits. It is unclear how PM 2.5 -related health co-benefits would be impacted by ex cluding

101 In the RIA for the proposed rule, we estimated the health co-benefits associated with emission reduct ions of two categories of directly emitted particles: elemental carbon plus organic carbon (EC+OC) and crustal. Cr ustal emissions are composed of compounds associated with minerals and metals from the earth’s surface, incl uding carbonates, silicates, iron, phosphates, copper, an d zinc. Often, crustal material represents particle s not classified as one of the other species (e.g., organic carbon, ele mental carbon, nitrate, sulfate, chloride, etc.). For this RIA, we did not estimate changes in emissions of directly emitt ed particles. As a result, quantified PM 2.5 related benefits are underestimated by a relatively small amount. In th e proposal RIA, the benefits from reductions in dir ectly emitted PM 2.5 were less than 10 percent of total monetized healt h co-benefits across all scenarios and years.4-12

climate-related air quality changes since the scien ce 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, 20 08a, 2008b). Third, the estimated climate benefits reported in this RIA reflect global benefits, while the estimated health co-benefits are calculated for the contiguou s U.S. only. Excluding temperature modification of air pollution risks and internation al air quality-related health benefits likely leads to underestimation of quantified health co-benefits (Anenberg et al, 2009, Jhun et al, 2014). Fourth, as noted earlier, we do not estimate the cl imate benefits associated with reductions in PM
and O 3 precursors.
Implementing the final emission guidelines may lead to reductions in ambient PM 2.5 concentrations below the National Ambient Air Quali ty Standards (NAAQS) for PM and ozone in some areas and assist other areas with attaining these NAAQS. Because the NAAQS RIAs (U.S. EPA, 2012a, 2008b, 2010d) also calculated PM and ozone benefits, there are important differences worth noting in the design and analytic al objectives of each RIA. The NAAQS RIAs illustrate the potential costs and benefits of atta ining a revised air quality standard nationwide based on an array of emission reduction strategies for different sources reflecting the application of known and unknown controls, incremental to imple mentation 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 implementing a revised NAAQS. The setting of a NAAQS does not directly res ult in costs or benefits, and as such, the EPA’s NAAQS RIAs are merely illustrative and the es timated costs and benefits are not intended to be added to the costs and benefits of other regu lations that result in specific costs of control
and emission reductions. Some of the emissions redu ctions estimated to result from implementation of the final emission guidelines may achieve some of the air quality improvements that resulted from the hypothesized at tainment strategies presented in the illustrative NAAQS RIAs. The emissions reductions f rom implementing the final emission guidelines will decrease the remaining amount of em issions reductions needed in non-attainment
areas and reduce the costs and benefits attributabl e to meeting the NAAQS.

Similar to NAAQS RIAs, the emission reduction scena rios estimated for the final emission guidelines are also illustrative. In contrast to NA AQS RIAs, all of the emission reductions for the4-13

illustrative plan approaches would occur in one wel l-characterized sector (i.e., the EGU sector). In general, the EPA is more confident in the magnit ude and location of the emission reductions for rules which require specific emission reduction s in a specific sector, for example, the recent Mercury and Air Toxics Standards. As such, emission reductions achieved under these types of promulgated rules will ultimately be reflected in t he baseline of future NAAQS analyses, which
would reduce the incremental costs and benefits ass ociated with attaining revised future NAAQS. The EPA does not re-issue illustrative RIAs outside of the rulemaking process that retroactively update the baseline to account for im plementation rules promulgated after an RIA was completed. For more information on the relation ship between illustrative analyses, such as for the NAAQS and this final emission guidelines, a nd 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 Matte r (PM ISA) (U.S. EPA, 2009b)
identified the human health effects associated with ambient PM 2.5 exposure, which include premature mortality and a variety of morbidity effe cts 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 mor tality and a variety of morbidity effects associated with acute and chronic exposures. Table 4-6 identifies the quantified and unquantified co-benefit categories captured in the EPA’s health co-benefits estimates for reduced exposure to
ambient PM 2.5 and ozone. Although the table below does not list u nquantified 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 effect s are described in detail in Chapters 5 and 6 of the PM NAAQS RIA (U.S. EPA, 2012a) and summarized l ater in this chapter. It is important to emphasize that the list of unquantified benefit cat egories is not exhaustive, nor is quantification of each effect complete.4-14

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, 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-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 nervous system effects — — Ozone ISA 2 Reproductive and developmental effects — — Ozone ISA 2,3

1 We assess these co-benefits qualitatively due to d ata 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 d ata or methods.

3 We assess these co-benefits qualitatively because c urrent evidence is only suggestive of causality or there are other significant concerns over the strength of the association.4-15

We follow a “damage-function” approach in calculati ng benefits, which estimates changes in individual health endpoints (specific effects th at can be associated with changes in air quality) and assigns values to those changes assuming indepe ndence of the values for those individual endpoints. Because the EPA rarely has the time or r esources to perform new research to measure directly, either health outcomes or their values fo r regulatory analyses, our estimates are based on the best available methods of benefits transfer, which is the science and art of adapting primary research from similar contexts to estimate benefits for the environmental quality change under analysis. In addition to transferring informa tion from other contexts to the context of this regulation, we also use a “benefit-per-ton” approac h to estimate the PM 2.5 and ozone co-benefits in this RIA. Benefit-per-ton approaches apply an av erage benefit per ton derived from modeling of benefits of specific air quality scenarios to es timates of emissions reductions for scenarios where no air quality modeling is available. Thus, t o develop estimates of benefits for this RIA, we are transferring both the underlying health and economic information from previous studies and information on air quality responses to emissio ns reductions from previous air quality modeling. This section describes the underlying bas is for the health and economic valuation estimates that inform the benefit-per-ton estimates , and the subsequent section provides an overview of the benefit-per-ton estimates, 102 which are described in detail in the appendix to t his chapter.

The benefit-per-ton approach we use in this RIA rel ies on estimates of human health responses to exposure to PM and ozone obtained from the peer-reviewed scientific literature. These estimates are used in conjunction with popula tion data, baseline health information, air quality data and economic valuation information to conduct health impact and economic benefits assessments. These assessments form the key inputs to calculating benefit-per-ton estimates. The next sections provide an overview of the health imp act assessment (HIA) methodology and additional details on several key elements.

The 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 Mappin g and

102 We have updated the benefit-per-ton estimates sinc e the proposal RIA. In this RIA, we apply benefit-p er-ton estimates that were derived from air quality modeli ng of the proposed Clean Power Plan (Option 1 State ).4-16

Analysis Program – Community Edition (BenMAP-CE) (v ersion 1.1) 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 effe cts that are directly linked to ambient PM 2.5 and ozone concentrations. There may be other indire ct health impacts associated with reducing emissions, such as occupational health exposures. E pidemiological studies generally provide estimates of the relative risks of a particular hea lth 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, 201 2a) and Ozone NAAQS RIA (U.S. EPA, 2008b, 2010d) for more information regarding the ep idemiology studies and risk coefficients applied in this analysis, and we briefly elaborate on adult premature mortality below. The size of the mortality effect estimates from epidemiological studies, the serious nature of the effect itself, and the high monetary value ascribed to reducing ri sks of premature death make mortality risk reduction the most significant health endpoint quan tified in this analysis.

4.3.1.1 Mortality Concentration-Response Functions for PM 2.5 Considering a substantial body of published scienti fic 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, includin g increased premature mortality (U.S. EPA, 2009b). The PM ISA, which was twice reviewed by the Clean Air Scientific Advisory Committee of the EPA’s Science Advisory Board (SAB-CASAC) (U.S. EPA-SAB, 2009b, 2009c), concluded that there is a c ausal 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 liter ature supports the use of a no-threshold loglinear model to portray the PM-mortality concentrat ion-response relationship while recognizing potential uncertainty about the exact shape of the concentration-response function. In addition to adult mortality discussed in more detail below, we use effect coefficients from Woodruff et al. (1997) to estimate PM-related infant mortality.
For adult PM-related mortality, we use the effect c oefficients from the most recent epidemiology studies examining two large population cohorts: the American Cancer Society4-17

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 Si x Cities cohorts produce the strongest evidence of the association between long-term PM2.5 exposure and premature mortality with support from a number of additional cohort studies. The SAB's Health Effects Subcommittee (SAB-HES) also supported using these two cohorts fo r analyses of the benefits of PM reductions (U.S. EPA-SAB, 2010a). As both the ACS and Six Citi es cohort studies have inherent strengths and weaknesses, we present PM2.5 co-benefits estima tes based on benefits-per-ton derived using
relative risk estimates from both these cohorts.

As a characterization of uncertainty regarding the adult PM 2.5 -mortality relationship, the EPA graphically presents the PM 2.5 co-benefits based on benefits-per-ton estimated usi ng C-R functions derived from EPA’s expert elicitation stu dy (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 o f the reduction in mortality among the adult U.S. population resulting from reductions in ambien t annual average PM 2.5 concentrations. In that study, twelve experts provided independent opi nions regarding the PM 2.5 -mortality concentration-response function. Because the expert s relied upon the ACS and Six Cities cohort studies to inform their concentration-response func tions, the benefits estimates based on the expert responses generally fall between benefits es timates based on these studies (see Figure 4- 1). We do not combine the expert results in order t o preserve the breadth and diversity of opinion on the expert panel. This presentation of the exper t-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 t o 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”. Althoug h it is possible that newer scientific literature could rev ise the experts’ quantitative responses if elicited again, we believe that these general conclusions ar e unlikely to change.

4.3.1.2 Mortality Concentration-Response Functions for Ozone
In 2008, the National Academies of Science (NRC, 20 08) issued a series of recommendations to the EPA regarding the quantifica tion and valuation of ozone-related shortterm mortality. Chief among these was that “...short- term exposure to ambient ozone is likely to4-18

contribute to premature deaths” and the committee r ecommended that “ozone-related mortality
be included in future estimates of the health benef its of reducing ozone exposures...” The NAS also recommended that “...the greatest emphasis be pl aced on the multicity and NMMAPS [ National Morbidity, Mortality, and Air Pollution St udy] studies without exclusion of the metaanalyses” (NRC, 2008). In view of the findings of t he National Academies panel, we estimate the co-benefits of avoiding short-term ozone mortality using the Bell et al. (2004) NMMAPS analysis, the Schwartz (2005) multi-city study, the Huang et al. (2005) multi-city study as well as effect estimates from the three meta-analyses (Bell et al. (2005), Levy et al. (2005), and Ito et al. (2005)). These studies are consistent with the stud ies used in the Ozone NAAQS RIA (U.S. EPA, 2008b, 2010d). 103 For simplicity, we report the ozone mortality esti mates in this RIA as a range reflecting application of dollar-per-ton esti mates based on Bell et al. (2004) and Levy et al. (2005) to represent the lowest and the highest co-b enefits estimates based on these six ozone mortality studies. In addition, we graphically pres ent in Figure 4-1 the estimated co-benefits based on dollar-per-ton estimates derived from all six studies mentioned above as a characterization of uncertainty regarding the ozone -mortality relationship.

4.3.2 Economic Valuation for Health Co-benefits
After quantifying the change in adverse health impa cts, we estimate the economic value of these avoided impacts. Reductions in ambient concen trations of air pollution generally lower the risk of future adverse health effects by a small am ount 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 admission s, WTP estimates are generally not available, so we use the cost of treating or mitigating the ef fect. These cost-of-illness (COI) estimates generally (although not necessarily in every case) understate the true value of reductions in risk of a health effect. They tend to reflect the direct expenditures related to treatment but not the value of avoided pain and suffering from the health effect. The unit values applied in this

103 Since the EPA received NAS advice, the Agency publ ished the Ozone ISA (U.S. EPA, 2013b) and the secon d draft Ozone Health Risk and Exposure Assessment (U. S. EPA, 2014a). Therefore, the ozone mortality stud ies applied in this analysis, while current at the time of the previous Ozone NAAQS RIAs, do not reflect t he most updated literature available. The selection of ozon e mortality studies used to estimate benefits in RI As will be revisited in the forthcoming RIA accompanying the o n-going review of the Ozone NAAQS.4-19

analysis are provided in Table 5-9 of the PM NAAQS RIA for each health endpoint (U.S. EPA, 2012a).

Avoided premature deaths account for 98 percent of monetized PM-related co-benefits and over 90 percent of monetized ozone-related co-benef its. The economics literature concerning the appropriate method for valuing reductions in premat ure mortality risk is still developing. The adoption of a value for the projected reduction in the risk of premature mortality is the subject of continuing discussion within the economics and publ ic policy analysis community. Following
the advice of the SAB’s Environmental Economics Adv isory 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 red uctions in mortality risk (U.S. EPA-SAB, 2000). The VSL approach is a summary measure for th e value of small changes in mortality risk experienced by a large number of people.

The EPA continues work to update its guidance on va luing mortality risk reductions, and the Agency consulted several times with the SAB-EEA C on this issue. Until updated guidance is available, the Agency determined that a single, pee r-reviewed estimate applied consistently, best
reflects the SAB-EEAC advice it has received. There fore, the EPA has decided to apply the VSL that was vetted and endorsed by the SAB in the Guidelines for Preparing Economic Analyses (U.S. EPA, 2014) 104 while the Agency continues its efforts to update i ts guidance on this issue. This approach calculates a mean value across VSL es timates derived from 26 labor market and contingent valuation studies published between 1974 and 1991. The mean VSL across these studies is $6.3 million (2000$). 105 We then adjust this VSL to account for the currenc y year and to account for income growth from 1990 to the analy sis year. Specifically, the VSLs applied in

104 In the updated Guidelines for Preparing Economic Analyses (U.S. EPA, 2010e), the EPA retained the VSL endorsed by the SAB with the understanding that fur ther updates to the mortality risk valuation guidan ce would be forthcoming.

105 In 1990$, this base VSL is $4.8 million.4-20

this analysis in 2011$ after adjusting for income g rowth are $9.9 million for 2020 and $10.1
million for 2025 and 2030. 106

The Agency is committed to using scientifically sou nd, appropriately reviewed evidence in valuing mortality risk reductions and has made sign ificant progress in responding to the SABEEAC’s specific recommendations. In the process, th e Agency has identified a number of important issues to be considered in updating its m ortality 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 re commendations were transmitted on July 29, 2011 (U.S. EPA-SAB, 2011). The EPA is taki ng 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 changes in PM ex posures and the total realization of changes in health effects. Although the structure of the la g is uncertain, the EPA follows the advice of the SAB-HES to assume a segmented lag structure charact erized 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 la g assumptions do not change the total number of estimated deaths but rather the timing of those deaths. Because short-term ozone-related premature mortality occurs within the analysis year, the estimated
ozone-related co-benefits are identical for all dis count rates.

4.3.3 Benefit-per-ton Estimates for PM 2.5 We used a “benefit-per-ton” approach to estimate th e PM 2.5 co-benefits in this RIA. The EPA has applied this approach in several previous R IAs (e.g., U.S. EPA, 2011b, 2011c, 2012b, 2014a). These benefit-per-ton estimates provide the total monetized human health co-benefits (the sum of premature mortality and premature morbi dity), 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 an alysis, we

106 Income growth projections are only currently avail able in BenMAP through 2024, so both the 2025 and 2 030 estimates use income growth only through 2024 and a re therefore likely underestimates.4-21

multiplied the benefit-per-ton estimates by the cor responding emission reductions that were generated from air quality modeling of the proposed Clean Power Plan.

The method used to calculate the regional benefit-p er-ton estimates is similar to the average EGU sector estimates used for the proposal (U.S. EPA, 2013a), but relies on air quality modeling of the proposed Clean Power Plan. Similar to the proposal, we generated regional benefit-per-ton estimates by aggregating the impact s in BenMAP to the region (i.e., East, West, and California) rather than aggregating to the nati on. The appendix to this chapter provides additional detail regarding these calculations.

As noted below in the characterization of uncertain ty, all benefit-per-ton estimates have inherent limitations. Specifically, all benefit-per -ton estimates reflect the geographic distribution of the modeled proposal, which may not match the em ission reductions anticipated by the final emission guidelines, and they may not reflect local variability in population density, meteorology, exposure, baseline health incidence ra tes, or other local factors for any specific location. The regional benefit-per-ton estimates, a lthough 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 heal th effects, the benefit-per-ton estimates vary between precursors depending on the location and ma gnitude 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 hu man 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 es timates for ozone based on air quality modeling for the proposed Clea n Power Plan. In contrast to the PM 2.5 estimates, the ozone estimates are not based on cha nges to annual emissions. Instead, the regional estimates (i.e., East, West, and Californi a) correspond to NO X emissions from U.S. EGUs during the ozone-season (May to September). Be cause we estimate ozone health impacts from May to September only, this approach underesti mates ozone co-benefits in areas with a longer ozone season such as southern California and Texas. These estimates assume that EGU-4-22

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 EGU s would contribute substantially to regional ozone formation. As noted above, all benefit-per-ton estimates have inherent limitations and should be interpreted with caution. We provide more detailed information regarding the generation of these estim ates in the appendix to this chapter.

4.3.5 Estimated Health Co-Benefits Results
Tables 4-7 through 4-9 provide the regional benefit -per-ton estimates for three analysis years: 2020, 2025, and 2030. Tables 4-10 through 4- 12 and 4-13 through 4-15 provide the
emission reductions estimated to occur in each anal ysis year for the rate-based and mass-based illustrative plan approaches, respectively, by regi on (i.e., East, West, and California). 107 Tables 4-16 through 4-18 and 4-19 through 4-21 summarize t he national monetized PM and ozonerelated health co-benefits estimated to occur in ea ch analysis year for the illustrative rate-based and mass-based plan approaches, respectively, by pr ecursor pollutant using discount rates of 3 percent and 7 percent. Tables 4-22 through 4-24 and 4-25 through 4-27 provide national
summaries of the reductions in estimated health inc idences associated with the illustrative ratebased and mass-based plan approaches, respectively, in each analysis year. 108 Figure 4-1 provides a visual representation of the range of es timated PM 2.5 and ozone-related co-benefits using benefit-per-ton estimates based on concentrat ion-response functions from different studies and expert opinion for the illustrative rate-based and mass-based plan approaches evaluated in 2025 as an illustrative analysis year. Figure 4-2 p rovides a breakdown of the monetized health co-benefits for the rate-based and mass-based plan approaches evaluated in 2025 as an illustrative analysis year by precursor pollutant.

107 See Chapter 3 of this RIA for more information reg arding the expected emission reductions used to cal culate the health co-benefits in this chapter. Chapter 3 also provides more information regarding the illustrativ e plan approach.

108 Incidence estimates were generated using the same “per ton” approach as used to generate the dollar b enefit per ton values. See Appendix 4-A for details.4-23

Table 4-7. Summary of Regional PM 2.5 Benefit-per-Ton Estimates Based on Air Quality

Modeling from Proposed Clean Power Plan in 2020 (20 11$)*

Pollutant Discount Rate Regional East West California

SO 2 3% $33,000 to $75,000 $6,200 to $14,000 $95,000 to $210,000 7% $30,000 to $68,000 $5,600 to $13,000 $85,000 to $190,000 Directly emitted PM 2.5 (EC+OC) 3% $140,000 to $320,000 $27,000 to $60,000 $370,000 to $830,000 7% $130,000 to $290,000 $24,000 to $54,000 $330,000 to $740,000 Directly emitted PM 2.5 (crustal) 3% $23,000 to $52,000 $11,000 to $25,000 $73,000 to $160,000 7% $21,000 to $47,000 $9,900 to $22,000 $66,000 to $150,000

NO X (as PM 2.5 ) 3% $3,100 to $7,000 $0,670 to $1,500 $22,000 to $49 ,000 7% $2,800 to $6,300 $0,610 to $1,400 $19,000 to $44 ,000 NO X (as Ozone) N/A $6,500 to $28,000 $2,000 to $8,900 $14,000 to $59,000 * The range of estimates reflects the range of epid emiology studies for avoided premature mortality fo r PM 2.5 and ozone. All estimates are rounded to two significant figures. The monetized co-benefits do not include reduced health effects from direct exposure to 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 loc ation and magnitude of their impact on PM 2.5 concentrations, which drive population exposure. T he monetized co-benefits incorporate the conversion from precursor emissions to ambient fine particles and ozone. Benefit-per-t on estimates for ozone are based on ozone season NO X emissions. Ozone co-benefits occur in analysis year , so they are the same for all discount rates. Confidence int ervals are unavailable for this analysis because ofthe 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 ce ntral estimates based on Krewski et al. (2009) and Lepeule et al. (2012).
Table 4-8. Summary of Regional PM 2.5 Benefit-per-Ton Estimates Based on Air Quality

Modeling from Proposed Clean Power Plan in 2025 (20 11$)*

Pollutant Discount Rate Regional East West California

SO 2 3% $37,000 to $83,000 $7,100 to $16,000 $110,000 to $240,000 7% $33,000 to $75,000 $6,400 to $14,000 $97,000 to $220,000 Directly emitted PM 2.5 (EC+OC) 3% $160,000 to $360,000 $30,000 to $68,000 $410,000 to $930,000 7% $140,000 to $320,000 $27,000 to $61,000 $370,000 to $830,000 Directly emitted PM 2.5 (crustal) 3% $25,000 to $58,000 $12,000 to $28,000 $82,000 to $180,000 7% $23,000 to $52,000 $11,000 to $25,000 $74,000 to $170,000

NO X (as PM 2.5 ) 3% $3,300 to $7,500 $0,750 to $1,700 $24,000 to $54 ,000 7% $3,000 to $6,800 $0,670 to $1,500 $22,000 to $49 ,000 NO X (as Ozone) N/A $7,100 to $30,000 $2,300 to $10,000 $15,000 to $66,000 * The range of estimates reflects the range of epid emiology studies for avoided premature mortality fo r PM 2.5 and ozone. All estimates are rounded to two significant figures. The monetized co-benefits do not include reduced health effects from direct exposure to 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 loc ation and magnitude of their impact on PM 2.5 concentrations, which drive population exposure. T he monetized co-benefits incorporate the conversion from precursor emissions to ambient fine particles and ozone. Benefit-per-t on estimates for ozone are based on ozone season NO X emissions. Ozone co-benefits occur in analysis year , so they are the same for all discount rates. Confidence int ervals are unavailable for this analysis because ofthe 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 ce ntral estimates based on Krewski et al. (2009) and Lepeule et al. (2012).4-24

Table 4-9. Summary of Regional PM 2.5 Benefit-per-Ton Estimates Based on Air Quality

Modeling from Proposed Clean Power Plan in 2030 (20 11$)*

Pollutant Discount

Rate Regional

East West California

SO 2 3% $40,000 to $89,000 $7,800 to $18,000 $120,000 to $270,000 7% $36,000 to $81,000 $7,100 to $16,000 $110,000 to $240,000 Directly emitted PM 2.5 (EC+OC) 3% $170,000 to $380,000 $33,000 to $75,000 $450,000 to $1,000,000 7% $150,000 to $340,000 $30,000 to $68,000 $410,000 to $920,000 Directly emitted PM 2.5 (crustal) 3% $28,000 to $62,000 $14,000 to $31,000 $90,000 to $200,000 7% $25,000 to $56,000 $13,000 to $28,000 $81,000 to $180,000

NO X (as PM 2.5 ) 3% $3,500 to $8,000 $0,820 to $1,900 $26,000 to $60 ,000 7% $3,200 to $7,200 $0,740 to $1,700 $24,000 to $54 ,000 NO X (as Ozone) N/A $7,600 to $33,000 $2,600 to $11,000 $17,000 to $73,000 * The range of estimates reflects the range of epid emiology studies for avoided premature mortality fo r PM 2.5 and ozone. All estimates are rounded to two significant figures. The monetized co-benefits do not include reduced health effects from direct exposure to 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 loc ation and magnitude of their impact on PM 2.5 concentrations, which drive population exposure. T he monetized co-benefits incorporate the conversion from precursor emissions to ambient fine particles and ozone. Benefit-per-t on estimates for ozone are based on ozone season NO X emissions. Ozone co-benefits occur in analysis year , so they are the same for all discount rates. Confidence int ervals are unavailable for this analysis because ofthe 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 ce ntral estimates based on Krewski et al. (2009) and Lepeule et al. (2012).

Table 4-10. Emission Reductions of Criteria Polluta nts for the Final Emission Guidelines

Rate-based Illustrative Plan Approach in 2020 (thou sands of short tons)* Region SO 2 All-year NOx Ozone-Season NOx East 13 50 19 West 1 1 0 California 0 0 0 National Total 14 50 19 *All emissions shown in the table are rounded, so r egional emission reductions may appear to not sum t o national total. The final emissions guidelines are also expe cted to result in reductions in directly emitted PM 2.5 , which we were not able to estimate for this RIA.

Table 4-11. Emission Reductions of Criteria Polluta nts for the Final Emission Guidelines

Rate-based Illustrative Plan Approach in 2025 (thou sands of short tons)* Region SO 2 All-year NOx Ozone-Season NOx East 171 155 67 West 7 8 3 California 1 2 0 National Total 178 165 70 *All emissions shown in the table are rounded, so r egional emission reductions may appear to not sum t o national total. The final emissions guidelines are also expe cted to result in reductions in directly emitted PM 2.5 , which we were not able to estimate for this RIA.4-25

Table 4-12. Emission Reductions of Criteria Polluta nts for the Final Emission Guidelines

Rate-based Illustrative Plan Approach in 2030 (thou sands of short tons)* Region SO 2 All-year NOx Ozone-Season NOx East 306 263 109 West 11 15 9 California 1 4 0 National Total 318 282 118 *All emissions shown in the table are rounded, so r egional emission reductions may appear to not sum t o national total. The final emissions guidelines are also expe cted to result in reductions in directly emitted PM 2.5 , which we were not able to estimate for this RIA.

Table 4-13. Emission Reductions of Criteria Polluta nts for the Final Emission Guidelines

Mass-based Illustrative Plan Approach in 2020 (thou sands of short tons)* Region SO 2 All-year NOx Ozone-Season NOx East 49 57 22 West 4 4 1 California 0 0 0 National Total 54 60 23 *All emissions shown in the table are rounded, so r egional emission reductions may appear to not sum t o national total. The final emissions guidelines are also expe cted to result in reductions in directly emitted PM 2.5 , which we were not able to estimate for this RIA.

Table 4-14. Emission Reductions of Criteria Polluta nts for the Final Emission Guidelines

Mass-based Illustrative Plan Approach in 2025 (thou sands of short tons)* Region SO 2 All-year NOx Ozone-Season NOx East 156 169 74 West 29 34 14 California 0 0 0 National Total 185 203 88 *All emissions shown in the table are rounded, so r egional emission reductions may appear to not sum t o national total. The final emissions guidelines are also expe cted to result in reductions in directly emitted PM 2.5 , which we were not able to estimate for this RIA.

Table 4-15. Emission Reductions of Criteria Polluta nts for the Final Emission Guidelines

Mass-based Illustrative Plan Approach in 2030 (thou sands of short tons)* Region SO 2 All-year NOx Ozone-Season NOx East 243 229 99 West 36 48 21 California 1 1 1 National Total 280 279 121 *All emissions shown in the table are rounded, so r egional emission reductions may appear to not sum t o national total. The final emissions guidelines are also expe cted to result in reductions in directly emitted PM 2.5 , which we were not able to estimate for this RIA.4-26

Table 4-16. Summary of Estimated Monetized Health C o-Benefits for the Final Emission

Guidelines Rate-based Illustrative Plan Approach in 2020 (billions of 2011$) * Pollutant 3% Discount Rate 7% Discount Rate SO 2 $0.44 to $0.99 $0.39 to $0.89 NOx (as PM 2.5 ) $0.14 to $0.33 $0.13 to $0.30 NOx (as Ozone) $0.12 to $0.52 $0.12 to $0.52

Total $0.70 to $1.8 $0.64 to $1.7 * All estimates are rounded to two significant figu res so numbers may not sum down columns. The estima ted monetized co-benefits do not include climate benefi ts or reduced health effects from direct exposure t o NO 2 , SO 2 , ecosystem effects, or visibility impairment. All fi ne particles are assumed to have equivalent health effects, but the benefit-per-ton estimates vary depending on the loc ation and magnitude of their impact on PM 2.5 levels, which drive population exposure. The monetized co-benefits inco rporate 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 es timates. Cobenefits for ozone are based on ozone season NOx em issions. Ozone co-benefits occur in analysis year, so they are the same for all discount rates. Confidence interva ls 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 ce ntral estimates based on Krewski et al. (2009) and Lepeule et al. (2012). For this RIA, we did not estimate changes i n emissions of directly emitted particles. As a res ult, quantified PM 2.5 related benefits are underestimated by a relativel y small amount. In the proposal RIA, the benefits f rom reductions in directly emitted PM 2.5 were less than 10 percent of total monetized healt h co-benefits across all scenarios and years.

Table 4-17. Summary of Estimated Monetized Health C o-Benefits for the Final Emission

Guidelines Rate-based Illustrative Plan Approach in 2025 (billions of 2011$) * Pollutant 3% Discount Rate 7% Discount Rate SO 2 $6.4 to $14 $5.7 to $13 NOx (as PM 2.5 ) $0.56 to $1.3 $0.50 to $1.1 NOx (as Ozone) $0.49 to $2.1 $0.49 to $2.1

Total $7.4 to $18 $6.7 to $16 * All estimates are rounded to two significant figu res so numbers may not sum down columns. The estima ted monetized co-benefits do not include climate benefi ts or reduced health effects from direct exposure t o NO 2 , SO 2 , ecosystem effects, or visibility impairment. All fi ne particles are assumed to have equivalent health effects, but the benefit-per-ton estimates vary depending on the loc ation and magnitude of their impact on PM 2.5 levels, which drive population exposure. The monetized co-benefits inco rporate 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 es timates. Cobenefits for ozone are based on ozone season NOx em issions. Ozone co-benefits occur in analysis year, so they are the same for all discount rates. Confidence interva ls 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 ce ntral estimates based on Krewski et al. (2009) and Lepeule et al. (2012). For this RIA, we did not estimate changes i n emissions of directly emitted particles. As a re sult, quantified PM 2.5 related benefits are underestimated by a relativel y small amount. In the proposal RIA, the benefits from reductions in directly emitted PM 2.5 were less than 10 percent of total monetized healt h co-benefits across all scenarios and years.4-27

Table 4-18. Summary of Estimated Monetized Health C o-Benefits for the Final Emission

Guidelines Rate-based Illustrative Plan Approach in 2030 (billions of 2011$) * Pollutant 3% Discount Rate 7% Discount Rate SO 2 $12 to $28 $11 to $25 NOx (as PM 2.5 ) $1.0 to $2.3 $0.93 to $2.1 NOx (as Ozone) $0.86 to $3.7 $0.86 to $3.7

Total $14 to $34 $13 to $31 * All estimates are rounded to two significant figu res so numbers may not sum down columns. The estima ted monetized co-benefits do not include climate benefi ts or reduced health effects from direct exposure t o NO 2 , SO 2 , ecosystem effects, or visibility impairment. All fi ne particles are assumed to have equivalent health effects, but the benefit-per-ton estimates vary depending on the loc ation and magnitude of their impact on PM 2.5 levels, which drive population exposure. The monetized co-benefits inco rporate 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 es timates. Cobenefits for ozone are based on ozone season NOx em issions. Ozone co-benefits occur in analysis year, so they are the same for all discount rates. Confidence interva ls 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 ce ntral estimates based on Krewski et al. (2009) and Lepeule et al. (2012). For this RIA, we did not estimate changes i n emissions of directly emitted particles. As a res ult, quantified PM 2.5 related benefits are underestimated by a relativel y small amount. In the proposal RIA, the benefits f rom reductions in directly emitted PM 2.5 were less than 10 percent of total monetized healt h co-benefits across all scenarios and years.

Table 4-19. Summary of Estimated Monetized Health C o-Benefits for the Final Emission

Guidelines Mass-based Illustrative Plan Approach in 2020 (billions of 2011$) * Pollutant 3% Discount Rate 7% Discount Rate SO 2 $1.7 to $3.8 $1.5 to $3.4 NOx (as PM 2.5 ) $0.17 to $0.39 $0.16 to $0.36 NOx (as Ozone) $0.14 to $0.61 $0.14 to $0.61

Total $2.0 to $4.8 $1.8 to $4.4 * All estimates are rounded to two significant figu res so numbers may not sum down columns. The estima ted monetized co-benefits do not include climate benefi ts or reduced health effects from direct exposure t o NO 2 , SO 2 , ecosystem effects, or visibility impairment. All fi ne particles are assumed to have equivalent health effects, but the benefit-per-ton estimates vary depending on the loc ation and magnitude of their impact on PM 2.5 levels, which drive population exposure. The monetized co-benefits inco rporate 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 es timates. Cobenefits for ozone are based on ozone season NOx em issions. Ozone co-benefits occur in analysis year, so they are the same for all discount rates. Confidence interva ls 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 ce ntral estimates based on Krewski et al. (2009) and Lepeule et al. (2012). For this RIA, we did not estimate changes i n emissions of directly emitted particles. As a res ult, quantified PM 2.5 related benefits are underestimated by a relativel y small amount. In the proposal RIA, the benefits f rom reductions in directly emitted PM 2.5 were less than 10 percent of total monetized healt h co-benefits across all scenarios and years.4-28

Table 4-20. Summary of Estimated Monetized Health C o-Benefits for the Final Emission

Guidelines Mass-based Illustrative Plan Approach in 2025 (billions of 2011$) * Pollutant 3% Discount Rate 7% Discount Rate SO 2 $6.0 to $13 $5.4 to $12 NOx (as PM 2.5 ) $0.58 to $1.3 $0.52 to $1.2 NOx (as Ozone) $0.56 to $2.4 $0.56 to $2.4

Total $7.1 to $17 $6.5 to $16 * All estimates are rounded to two significant figu res so numbers may not sum down columns. The estima ted monetized co-benefits do not include climate benefi ts or reduced health effects from direct exposure t o NO 2 , SO 2 , ecosystem effects, or visibility impairment. All fi ne particles are assumed to have equivalent health effects, but the benefit-per-ton estimates vary depending on the loc ation and magnitude of their impact on PM 2.5 levels, which drive population exposure. The monetized co-benefits inco rporate 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 es timates. Cobenefits for ozone are based on ozone season NOx em issions. Ozone co-benefits occur in analysis year, so they are the same for all discount rates. Confidence interva ls 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 ce ntral estimates based on Krewski et al. (2009) and Lepeule et al. (2012). For this RIA, we did not estimate changes i n emissions of directly emitted particles. As a res ult, quantified PM 2.5 related benefits are underestimated by a relativel y small amount. In the proposal RIA, the benefits f rom reductions in directly emitted PM 2.5 were less than 10 percent of total monetized healt h co-benefits across all scenarios and years.

Table 4-21. Summary of Estimated Monetized Health C o-Benefits for the Final Emission

Guidelines Mass-based Illustrative Plan Approach in 2030 (billions of 2011$) * Pollutant 3% Discount Rate 7% Discount Rate SO 2 $10 to $23 $9.0 to $20 NOx (as PM 2.5 ) $0.87 to $2.0 $0.79 to $1.8 NOx (as Ozone) $0.82 to $3.5 $0.82 to $3.5

Total $12 to $28 $11 to $26 * All estimates are rounded to two significant figu res so numbers may not sum down columns. The estima ted monetized co-benefits do not include climate benefi ts or reduced health effects from direct exposure t o NO 2 , SO 2 , ecosystem effects, or visibility impairment. All fi ne particles are assumed to have equivalent health effects, but the benefit-per-ton estimates vary depending on the loc ation and magnitude of their impact on PM 2.5 levels, which drive population exposure. The monetized co-benefits inco rporate 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 es timates. Cobenefits for ozone are based on ozone season NOx em issions. Ozone co-benefits occur in analysis year, so they are the same for all discount rates. Confidence interva ls 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 ce ntral estimates based on Krewski et al. (2009) and Lepeule et al. (2012). For this RIA, we did not estimate changes i n emissions of directly emitted particles. As a res ult, quantified PM 2.5 related benefits are underestimated by a relativel y small amount. In the proposal RIA, the benefits f rom reductions in directly emitted PM 2.5 were less than 10 percent of total monetized healt h co-benefits across all scenarios and years.4-29

Table 4-22. Summary of Avoided Health Incidences fr om PM 2.5 -Related and OzoneRelated Co-benefits for the Final Emission Guidelin es Rate-based Illustrative Plan Approach in 2020* PM 2.5 -related Health Effects Avoided Premature Mortality Krewski et al. (2009) (adult) 64 Lepeule et al. (2012) (adult) 140 Woodruff et al. (1997) (infant) 0 Avoided Morbidity Emergency department visits for asthma (all ages) 3 4 Acute bronchitis (age 8–12) 94 Lower respiratory symptoms (age 7–14) 1,200 Upper respiratory symptoms (asthmatics age 9–11) 1, 700 Minor restricted-activity days (age 18–65) 47,000 Lost work days (age 18–65) 7,900 Asthma exacerbation (age 6–18) 4,200 Hospital admissions—respiratory (all ages) 19 Hospital admissions—cardiovascular (age > 18) 23 Non-Fatal Heart Attacks (age >18) Peters et al. (2001) 73 Pooled estimate of 4 studies 8 Ozone-related Health Effects Avoided Premature Mortality Bell et al. (2004) (all ages) 11 Levy et al. (2005) (all ages) 51 Avoided Morbidity Hospital admissions—respiratory causes (ages > 65) 66 Hospital admissions—respiratory causes (ages < 2) 33 Emergency room visits for asthma (all ages) 37 Minor restricted-activity days (ages 18-65) 66,000 School absence days 23,000 * All estimates are rounded to whole numbers with t wo significant figures. Co-benefits for PM 2.5 precursors are based on regional incidence-per-ton estimates for a ll precursors. Co-benefits for ozone are based on o zone season NOx emissions. Confidence intervals are unavailable for this analysis because of the incidence-per-ton methodology. In general, the 95 th percentile confidence interval for the health impa ct function alone ranges from approximately ±30 percent for mortality incidence b ased on Krewski et al. (2009) and ±46 percent based on Lepeule et al. (2012).4-30

Table 4-23. Summary of Avoided Health Incidences fr om PM 2.5 -Related and OzoneRelated Co-benefits for Final Emission Guidelines R ate-based Illustrative Plan Approach in 2025* PM 2.5 -related Health Effects Avoided Premature Mortality Krewski et al. (2009) (adult) 740 Lepeule et al. (2012) (adult) 1,700 Woodruff et al. (1997) (infant) 2 Avoided Morbidity Emergency department visits for asthma (all ages) 3 80 Acute bronchitis (age 8–12) 1,100 Lower respiratory symptoms (age 7–14) 14,000 Upper respiratory symptoms (asthmatics age 9–11) 20 ,000 Minor restricted-activity days (age 18–65) 530,000 Lost work days (age 18–65) 89,000 Asthma exacerbation (age 6–18) 48,000 Hospital admissions—respiratory (all ages) 220 Hospital admissions—cardiovascular (age > 18) 270 Non-Fatal Heart Attacks (age >18) Peters et al. (2001) 860 Pooled estimate of 4 studies 93 Ozone-related Health Effects Avoided Premature Mortality Bell et al. (2004) (all ages) 44 Levy et al. (2005) (all ages) 200 Avoided Morbidity Hospital admissions—respiratory causes (ages > 65) 280 Hospital admissions—respiratory causes (ages < 2) 130 Emergency room visits for asthma (all ages) 140 Minor restricted-activity days (ages 18-65) 250,00 0 School absence days 87,000 * All estimates are rounded to whole numbers with t wo significant figures. Co-benefits for PM 2.5 precursors are based on regional incidence-per-ton estimates for a ll precursors. Co-benefits for ozone are based on o zone season NOx emissions. Confidence intervals are unavailable for this analysis because of the incidence-per-ton methodology. In general, the 95 th percentile confidence interval for the health impa ct function alone ranges from approximately ±30 percent for mortality incidence b ased on Krewski et al. (2009) and ±46 percent based on Lepeule et al. (2012).4-31

Table 4-24. Summary of Avoided Health Incidences fr om PM 2.5 -Related and OzoneRelated Co-Benefits for Final Emission Guidelines R ate-based Illustrative Plan Approach in 2030* PM 2.5 -related Health Effects Avoided Premature Mortality Krewski et al. (2009) (adult) 1,400 Lepeule et al. (2012) (adult) 3,200 Woodruff et al. (1997) (infant) 3 Avoided Morbidity Emergency department visits for asthma (all ages) 5 40 Acute bronchitis (age 8–12) 2,000 Lower respiratory symptoms (age 7–14) 26,000 Upper respiratory symptoms (asthmatics age 9–11) 37 ,000 Minor restricted-activity days (age 18–65) 970,000 Lost work days (age 18–65) 160,000 Asthma exacerbation (age 6–18) 90,000 Hospital admissions—respiratory (all ages) 440 Hospital admissions—cardiovascular (age > 18) 530 Non-Fatal Heart Attacks (age >18) Peters et al. (2001) 1,700 Pooled estimate of 4 studies 180 Ozone-related Health Effects Avoided Premature Mortality Bell et al. (2004) (all ages) 73 Levy et al. (2005) (all ages) 330 Avoided Morbidity Hospital admissions—respiratory causes (ages > 65) 500 Hospital admissions—respiratory causes (ages < 2) 200 Emergency room visits for asthma (all ages) 220 Minor restricted-activity days (ages 18-65) 400,00 0 School absence days 140,000 * All estimates are rounded to whole numbers with t wo significant figures. Co-benefits for PM 2.5 precursors are based on regional incidence-per-ton estimates for a ll precursors. Co-benefits for ozone are based on o zone season NOx emissions. Confidence intervals are unavailable for this analysis because of the incidence-per-ton methodology. In general, the 95 th percentile confidence interval for the health impa ct function alone ranges from approximately ±30 percent for mortality incidence b ased on Krewski et al. (2009) and ±46 percent based on Lepeule et al. (2012).4-32

Table 4-25. Summary of Avoided Health Incidences f rom PM 2.5 -Related and OzoneRelated Co-benefits for the Final Emission Guidelin es Mass-based Illustrative Plan Approach in 2020* PM 2.5 -related Health Effects Avoided Premature Mortality Krewski et al. (2009) (adult) 200 Lepeule et al. (2012) (adult) 460 Woodruff et al. (1997) (infant) 0 Avoided Morbidity Emergency department visits for asthma (all ages) 1 10 Acute bronchitis (age 8–12) 300 Lower respiratory symptoms (age 7–14) 3,800 Upper respiratory symptoms (asthmatics age 9–11) 5, 500 Minor restricted-activity days (age 18–65) 150,000 Lost work days (age 18–65) 25,000 Asthma exacerbation (age 6–18) 13,000 Hospital admissions—respiratory (all ages) 59 Hospital admissions—cardiovascular (age > 18) 73 Non-Fatal Heart Attacks (age >18) Peters et al. (2001) 230 Pooled estimate of 4 studies 25 Ozone-related Health Effects Avoided Premature Mortality Bell et al. (2004) (all ages) 13 Levy et al. (2005) (all ages) 61 Avoided Morbidity Hospital admissions—respiratory causes (ages > 65) 78 Hospital admissions—respiratory causes (ages < 2) 40 Emergency room visits for asthma (all ages) 43 Minor restricted-activity days (ages 18-65) 78,000 School absence days 27,000 * All estimates are rounded to whole numbers with t wo significant figures. Co-benefits for PM 2.5 precursors are based on regional incidence-per-ton estimates for a ll precursors. Co-benefits for ozone are based on o zone season NOx emissions. Confidence intervals are unavailable for this analysis because of the incidence-per-ton methodology. In general, the 95 th percentile confidence interval for the health impa ct function alone ranges from approximately ±30 percent for mortality incidence b ased on Krewski et al. (2009) and ±46 percent based on Lepeule et al. (2012).4-33

Table 4-26. Summary of Avoided Health Incidences fr om PM 2.5 -Related and OzoneRelated Co-benefits for Final Emission Guidelines M ass-based Illustrative Plan Approach in 2025* PM 2.5 -related Health Effects Avoided Premature Mortality Krewski et al. (2009) (adult) 700 Lepeule et al. (2012) (adult) 1,600 Woodruff et al. (1997) (infant) 2 Avoided Morbidity Emergency department visits for asthma (all ages) 3 50 Acute bronchitis (age 8–12) 1,000 Lower respiratory symptoms (age 7–14) 13,000 Upper respiratory symptoms (asthmatics age 9–11) 19 ,000 Minor restricted-activity days (age 18–65) 500,000 Lost work days (age 18–65) 84,000 Asthma exacerbation (age 6–18) 46,000 Hospital admissions—respiratory (all ages) 210 Hospital admissions—cardiovascular (age > 18) 260 Non-Fatal Heart Attacks (age >18) Peters et al. (2001) 810 Pooled estimate of 4 studies 88 Ozone-related Health Effects Avoided Premature Mortality Bell et al. (2004) (all ages) 51 Levy et al. (2005) (all ages) 230 Avoided Morbidity Hospital admissions—respiratory causes (ages > 65) 320 Hospital admissions—respiratory causes (ages < 2) 150 Emergency room visits for asthma (all ages) 160 Minor restricted-activity days (ages 18-65) 290,00 0 School absence days 100,000 * All estimates are rounded to whole numbers with t wo significant figures. Co-benefits for PM 2.5 precursors are based on regional incidence-per-ton estimates for a ll precursors. Co-benefits for ozone are based on o zone season NOx emissions. Confidence intervals are unavailable for this analysis because of the incidence-per-ton methodology. In general, the 95 th percentile confidence interval for the health impa ct function alone ranges from approximately ±30 percent for mortality incidence b ased on Krewski et al. (2009) and ±46 percent based on Lepeule et al. (2012).4-34

Table 4-27. Summary of Avoided Health Incidences fr om PM 2.5 -Related and OzoneRelated Co-Benefits for Final Emission Guidelines M ass-based Illustrative Plan Approach in 2030* PM 2.5 -related Health Effects Avoided Premature Mortality Krewski et al. (2009) (adult) 1,200 Lepeule et al. (2012) (adult) 2,600 Woodruff et al. (1997) (infant) 2 Avoided Morbidity Emergency department visits for asthma (all ages) 4 40 Acute bronchitis (age 8–12) 1,600 Lower respiratory symptoms (age 7–14) 21,000 Upper respiratory symptoms (asthmatics age 9–11) 30 ,000 Minor restricted-activity days (age 18–65) 790,000 Lost work days (age 18–65) 130,000 Asthma exacerbation (age 6–18) 74,000 Hospital admissions—respiratory (all ages) 360 Hospital admissions—cardiovascular (age > 18) 430 Non-Fatal Heart Attacks (age >18) Peters et al. (2001) 1,400 Pooled estimate of 4 studies 150 Ozone-related Health Effects Avoided Premature Mortality Bell et al. (2004) (all ages) 70 Levy et al. (2005) (all ages) 320 Avoided Morbidity Hospital admissions—respiratory causes (ages > 65) 470 Hospital admissions—respiratory causes (ages < 2) 200 Emergency room visits for asthma (all ages) 210 Minor restricted-activity days (ages 18-65) 380,00 0 School absence days 130,000 * All estimates are rounded to whole numbers with t wo significant figures. Co-benefits for PM 2.5 precursors are based on regional incidence-per-ton estimates for a ll precursors. Co-benefits for ozone are based on o zone season NOx emissions. Confidence intervals are unavailable for this analysis because of the incidence-per-ton methodology. In general, the 95 th percentile confidence interval for the health impa ct function alone ranges from approximately ±30 percent for mortality incidence b ased on Krewski et al. (2009) and ±46 percent based on Lepeule et al. (2012).4-35

Figure 4-1. Monetized Health Co-benefits of Rate-ba sed and Mass-based Illustrative

Plan Approaches for the Final Emission Guidelines i n 2025 *

*The PM 2.5 graphs show the estimated PM 2.5 co-benefits at discount rates of 3% and 7% using ef fect 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 mortali ty (Roman et al. , 2008). The results shown are not the direct results from the studies or expert elicitation; rat her, the estimates are based in part on the concent ration-response functions provided in those studies. The ozone grap hs show the estimated ozone co-benefits derived fro m 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 ye ar, so they are the same for all discount rates. Th ese estimates do not include benefits from reductions i n CO2. The monetized co-benefits do not include cli mate benefits from changes in NO 2 and SO 2 or reduced health effects from direct exposure to N O 2 , SO 2 , ecosystem effects, or visibility impairment. For this RIA, we did not est imate changes in emissions of directly emitted part icles. As a result, quantified PM 2.5 related benefits are underestimated by a relativel y small amount. In the proposal RIA, the benefits from reductions in directly emitted PM 2.5 were less than 10 percent of total monetized healt h co-benefits across all scenarios and years. **PM 2.5**  **Ozone**4-36

**Low Health Co-benefits High Health Co-benefits**

Figure 4-2. Breakdown of Monetized Health Co-benefi ts by Precursor Pollutant at a 3%

Discount Rate for Rate-based and Mass-based Illustr ative Plan Approaches for the Final Emission Guidelines in 2025*

* “Low Health Co-benefits” refers to the combined h ealth co-benefits estimated 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 . For this RIA, we did not estimate changes in emis sions of directly emitted particles. As a result, quantified PM 2.5 related benefits are underestimated by a relativel y small amount. In the proposal RIA, the benefits from reductions in direc tly emitted PM 2.5 were less than 10 percent of total monetized health co-benefits across all scenarios and years.

4.3.6 Characterization of Uncertainty in the Estima ted 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

SO2 86%

NOx (as PM2.5) 8% NOx (as Ozone) 6% 

### Rate-based

SO2 81% NOx (as PM2.5) 7%

NOx (as Ozone) 12% 

### Rate-based

SO2 84%

NOx (as PM2.5) 8%
NOx (as Ozone) 8% 

### Mass-based

SO2 78% NOx (as PM2.5) 8%

NOx (as Ozone) 14% 

### Mass-based4-37

includes many data sources as inputs, including emi ssion inventories, air quality data from models (with their associated parameters and inputs ), population data, population estimates, health effect estimates from epidemiology studies, economic data for monetizing co-benefits, and assumptions regarding the future state of the w orld (i.e., regulations, technology, and human behavior). Each of these inputs may be uncertain an d would affect the estimate of co-benefits. When the uncertainties from each stage of the analy sis are compounded, even small uncertainties can have large effects on the total quantified bene fits. In addition, the use of the benefit-per-ton approach adds additional uncertainties beyond those for analyses based directly on air quality modeling. Therefore, the estimates of co-benefits i n each analysis year should be viewed as representative of the general magnitude of co-benef its of the illustrative plan approach, rather than the actual co-benefits anticipated from implem enting the final emission guidelines.

This RIA does not include the type of detailed unce rtainty assessment found in the PM NAAQS RIA (U.S. EPA, 2012a) or the Ozone NAAQS RIA (U.S. EPA, 2008b) because we lack the necessary air quality modeling input and/or mon itoring 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 RIA can provide some info rmation regarding the uncertainty inherent in the estimated co-benefits results prese nted in this analysis. For example, sensitivity analyses conducted for the PM NAAQS RIA indicate th at alternate cessation lag assumptions could change the estimated PM 2.5 -related mortality co-benefits discounted at 3 perc ent by between 10 percent and –27 percent and that alterna tive income growth adjustments could change the PM 2.5 -related mortality co-benefits by between 33 percen t 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 unc ertainty in the benefits estimates, the PM NAAQS RIA provides an indication of the random samp ling 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 impa ct function alone ranges from approximately ±30 percent for mortality incidence based on Krewsk i et al. (2009) and ±46 percent based on Lepeule
et al. (2012).4-38

Unlike RIAs for which the EPA conducts scenario-spe cific air quality modeling, we do not have information on the specific location of the ai r quality changes associated with the final emission guidelines. As such, it is not feasible to estimate the proportion of co-benefits occurring in different locations, such as designated nonattai nment areas. Instead, we applied benefit-perton estimates, which reflect specific geographic pa tterns of emissions reductions and specific air quality and benefits modeling assumptions. For exam ple, these estimates may not reflect local variability in population density, meteorology, exp osure, baseline health incidence rates, or other local factors that might lead to an over-estimate o r under-estimate of the actual co-benefits of controlling PM and ozone precursors. Use of these b enefit-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 t hese are all based on a broad emission reduction scenario and therefore represent average benefits-p er-ton over the entire region. The benefit-perton for emission reductions in specific locations m ay be very different than the estimates presented here. To the extent that the geographic d istribution of the emissions reductions achieved by implementing the final emission guideli nes is different than the emissions in the air quality modeling of the proposal, the co-benefits m ay be underestimated or overestimated.

Our estimate of the total monetized co-benefits is based on the EPA’s interpretation of the
best available scientific literature and methods an d supported by the SAB-HES and the National Academies of Science (NRC, 2002). Below are key ass umptions 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 th eir chemical composition, are equally potent in causing premature mortality. This is an i mportant assumption, because PM 2.5 varies considerably in composition across sources, but the scientific evidence is not yet sufficient to allow differentiation of effect estim ates 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 differe ntiation 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. Thus, the estimates include health co-be nefits from reducing fine particles in areas with varied concentrations of PM 2.5 , including both areas that do not meet the fine4-39

particle standard and those areas that are in attai nment, down to the lowest modeled concentrations.

3. We assume that there is a “cessation” lag between t he 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 a dvice of the SAB-HES (U.S. EPASAB, 2004c), which affects the valuation of mortali ty 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 bulk of the o bserved PM concentrations in the epidemiological studies that are used to estimate t he benefits. Likewise, we are less confident in
the risk we estimate from simulated PM 2.5 concentrations that fall below the bulk of the obs erved
data in these studies. Concentration benchmark anal yses (e.g., lowest measured level [LML], one standard deviation below the mean of the air qualit y data in the study, etc.) allow readers to determine the portion of population exposed to annu al mean PM 2.5 levels at or above different concentrations, which provides some insight into th e 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 be en incorporated into recent RIAs and the
EPA’s Policy Assessment for Particulate Matter (U.S. EPA, 2011d). There are uncertainties inherent in identifying any particular point at whi ch our confidence in reported associations becomes appreciably less, and the scientific eviden ce provides no clear dividing line. However,
the EPA does not view these concentration benchmark s as a concentration threshold below which we would not quantify health co-benefits of a ir quality improvements. 109 Rather, the cobenefits estimates reported in this RIA are the bes t estimates because they reflect the full range of air quality concentrations associated with the e mission reduction strategies. The PM ISA concluded that the scientific evidence collectively is sufficient to conclude that the relationship between long-term PM 2.5 exposures and mortality is causal and that overall the studies support

109 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 Th reshold in the Concentration-Response Function for PM 2.5 -related Mortality (U.S. EPA, 2010b).4-40

the use of a no-threshold log-linear model to estim ate PM-related long-term mortality (U.S. EPA, 2009b).

For this analysis, policy-specific air quality data is not available, and the plan scenarios are illustrative of what states may choose to do. Howev er, we believe that it is still important to characterize the distribution of exposure to baseli ne concentrations. As a surrogate measure of mortality impacts, we provide the percentage of the population exposed at each PM 2.5 concentration in the baseline of the air quality mo deling used to calculate the benefit-per-ton estimates for this final RIA using 12 km grid cells across the contiguous U.S. It is important to note that baseline exposure is only one parameter i n the health impact function, along with baseline incidence rates population and change in a ir quality. In other words, the percentage of the population exposed to air pollution below the L ML 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 quant ify without rule-specific air quality modeling, is the shift in exposure anticipated by i mplementing the final emission guidelines. Therefore, caution is warranted when interpreting t he LML assessment in this RIA because these
results are not consistent with results from RIAs t hat had air quality modeling.

Table 4-28 provides the percentage of the populatio n exposed above and below two concentration benchmarks (i.e., LML and one standar d deviation below the mean) in the Clean Power Plan proposal modeling. Figure 4-3 shows a ba r chart of the percentage of the population exposed to various air quality levels in the propos al modeling, and Figure 4-4 shows a
cumulative distribution function of the same data. Both figures identify the LML for each of the major cohort studies.4-41

Table 4-28. Population Exposure in the Clean Power Plan Proposal Option 1 State

Scenario Modeling (used to generate the benefit-per -ton estimates) Above and Below Various Concentrations Benchmarks in the Unde rlying 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) 3% 97% 12% 88% Lepeule et al. (2012) N/A N/A 54% 46% *One standard deviation below the mean is equivalen t 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 devia tion below the mean. It is important to emphasize that although we have lower levels of confidence in levels below the LML for ea ch study, the scientific evidence does not support the existe nce of a level below which health effects from expo sure to PM 2.5 do not occur.

Figure 4-3. Percentage of Adult Population (age 30+ ) by Annual Mean PM 2.5 Exposure in

the Option 1 State Scenario Clean Power Plan Propos al Modeling (used to generate the benefit-per-ton estimates) Among the populations exposed to PM 2.5 in the baseline: 88% are exposed to PM 2.5 levels at or above the LML of the Krewski et al. (2009) study 46% 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% 30%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20**Percentage of Population Exposed**

**Baseline Annual Mean PM 2.5**  **Level (μg/m 3**  **) LML of Lepeule et al.**  **(2012) study LML of Krewski et**  **al. (2009) study**4-42

Figure 4-4. Cumulative Distribution of Adult Popula tion (age 30+) by Annual Mean

PM 2.5 Exposure in the Option 1 State Scenario Clean Power Plan Proposal Modeling (used to generate the benefit-per-ton esti mates)

4.4 Combined Climate Benefits and Health Co-Benefit s Estimates
In this analysis, we were able to monetize the esti mated benefits associated with the
decreased emissions of CO 2 and co-benefits of reduced exposure to PM 2.5 and ozone, but we
were unable to monetize the co-benefits associated with reducing exposure to mercury, carbon monoxide, SO 2 , and NO 2 , as well as ecosystem effects and visibility impai rment. In addition, there are expected to be unquantified health and we lfare impacts associated with changes in hydrogen chloride. Specifically, we estimated combi nations of climate benefits at discount rates Among the populations exposed to PM 2.5 in the baseline: 88% are exposed to PM 2.5 levels at or above the LML of the Krewski et al. (2009) study 46% 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 20**Cumulative percentage of population exposed**

**Baseline Annual Mean PM 2.5**  **Level (μg/m 3**  **) LML of Krewski et**  **al. (2009) study LML of** 

**Lepeule et al. (2012)**  **study**4-43

of 5 percent, 3 percent, 2.5 percent, and 3 percent (95 th percentile) (as recommended by the interagency working group), and health co-benefits at discount rates of 3 percent and 7 percent (as recommended by the EPA’s Guidelines for Preparing Economic Analyses [U.S. EPA, 2014]
and OMB’s Circular A-4 [OMB, 2003]).

Different discount rates are applied to SC-CO 2 than to the health co-benefit estimates
because CO 2 emissions are long-lived and subsequent damages oc cur over many years. Moreover, several rates are applied to SC-CO 2 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 SC-CO 2 interagency group centered its attention on the 3 percent discount rate but emphasized the importance of considering all four SC-CO 2 estimates. 110 The EPA has evaluated the range of potential impact s by combining all SC-CO 2 values with health co-benefits values at the 3 percent and 7 pe rcent discount rates. Combining the 3 percent
SC-CO 2 values with the 3 percent health benefit values as sumes that there is no difference in discount rates between intragenerational and interg enerational impacts.

Tables 4-29 through 4-31 provide the combined clima te and health benefits for the illustrative plan approaches evaluated for each ana lysis year: 2020, 2025, and 2030. Figure 4-5 shows the breakdown of the monetized benefits by po llutant for the illustrative plan approaches evaluated in 2025 as an illustrative analysis year using a 3 percent discount rate for both climate
and health benefits.

110 See the 2010 SCC TSD. Docket ID EPA-HQ-OAR-2009-04 72-114577 or http://www.whitehouse.gov/sites/default/files/omb/i nforeg/for-agencies/Social-Cost-of-Carbon-for-RIA.p df for details.4-44

Table 4-29. Combined Climate Benefits and Health Co -Benefits for Final Emission

Guidelines in 2020 (billions of 2011$)*

SCC Discount Rate Climate Benefits Only Climate and Health Benefits (Discount Rate Applied to Health Co-Benefits) 3% 7% Rate-based 69 million short tons CO 2 5% $0.80 $1.5 to $2.6 $1.4 to $2.5 3% $2.8 $3.5 to $4.6 $3.5 to $4.5 2.5% $4.1 $4.9 to $6.0 $4.8 to $5.9 3% (95 th percentile) $8.2 $8.9 to $10 $8.9 to $9.9 Mass-based 82 million short tons CO 2 5% $0.94 $2.9 to $5.7 $2.8 to $5.3 3% $3.3 $5.3 to $8.1 $5.1 to $7.7 2.5% $4.9 $6.9 to 9.7 $6.7 to $9.3 3% (95 th percentile) $9.6 $12 to $14 $11 to $14 *All estimates are rounded to two significant figur es. Climate benefits are based on reductions in CO 2 emissions. Co-benefits are based on regional benefit-per-ton e stimates. Co-benefits for ozone are based on ozone season NOx emissions. Ozone co-benefits occur in analysis year , so they are the same for all discount rates. The health cobenefits reflect the sum of the PM 2.5 and ozone co-benefits and reflect the range based o n 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 directly emitted PM 2.5 , direct exposure to NO 2 , SO 2 , and HAP; ecosystem effects; or visibility impairm ent.

Table 4-30. Combined Climate Benefits and Health Co -Benefits for Final Emission

Guidelines in 2025 (billions of 2011$)*

SCC Discount Rate Climate Benefits Only Climate and Health Benefits (Discount Rate Applied to Health Co-Benefits) 3% 7% Rate-based 232 million short tons CO 2 5% $3.1 $11 to $21 $9.9 to $19 3% $10 $18 to $28 $17 to $26 2.5% $15 $23 to $33 $22 to $31 3% (95 th percentile) $31 $38 to $49 $38 to $47 Mass-based 264 million short tons CO 2 5% $3.6 $11 to $21 $10 to $19 3% $12 $19 to $29 $18 to $27 2.5% $17 $24 to $34 $24 to $33 3% (95 th percentile) $35 $42 to $52 $42 to $51 *All estimates are rounded to two significant figur es. Climate benefits are based on reductions in CO 2 emissions. Co-benefits are based on regional benefit-per-ton e stimates. Co-benefits for ozone are based on ozone season NOx emissions. Ozone co-benefits occur in analysis year , so they are the same for all discount rates. The health cobenefits reflect the sum of the PM 2.5 and ozone co-benefits and reflect the range based o n 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 directly emitted PM 2.5 , direct exposure to NO 2 , SO 2 , and HAP; ecosystem effects; or visibility impairm ent.4-45

Table 4-31. Combined Climate Benefits and Health Co -Benefits for Final Emission

Guidelines in 2030 (billions of 2011$)*

SCC Discount Rate Climate Benefits Only Climate and Health Benefits (Discount Rate Applied to Health Co-Benefits) 3% 7% Rate-based 415 million short tons CO 2 5% $6.4 $21 to $40 $19 to $37 3% $20 $34 to $54 $33 to $51 2.5% $29 $43 to $63 $42 to $60 3% (95 th percentile) $61 $75 to $95 $74 to $92 Mass-based 413 million short tons CO 2 5% $6.4 $18 to $34 $17 to $32 3% $20 $32 to $48 $31 to $46 2.5% $29 $41 to $57 $40 to $55 3% (95 th percentile) $60 $72 to $89 $71 to $86 *All estimates are rounded to two significant figur es. Climate benefits are based on reductions in CO 2 emissions. Co-benefits are based on regional benefit-per-ton e stimates. Co-benefits for ozone are based on ozone season NOx emissions. Ozone co-benefits occur in analysis year , so they are the same for all discount rates. The health cobenefits reflect the sum of the PM 2.5 and ozone co-benefits and reflect the range based o n 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 directly emitted PM 2.5 , direct exposure to NO 2 , SO 2 , and HAP; ecosystem effects; or visibility impairm ent.4-46

**Low Health Co-benefits High Health Co-benefits**

Figure 4-5. Breakdown of Combined Monetized Climate and Health Co-benefits of Final

Emission Guidelines in 2025 for Rate-based and Mass -based Illustrative Plan Approaches and Pollutants (3% discount rate)*

* “Low Health Co-benefits” refers to the combined h ealth co-benefits estimated 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 . For this RIA, we did not estimate changes in emis sions of directly emitted particles. As a result, quantified PM 2.5 related benefits are underestimated by a relativel y small amount. In the proposal RIA, the benefits from reductions in direc tly emitted PM 2.5 were less than 8 percent of total monetized benefits across all scenarios and years.

4.5 Unquantified Co-benefits

The monetized co-benefits estimated in this RIA ref lect a subset of co-benefits attributable
to the health effect reductions associated with amb ient fine particles and ozone. Data, time, and

CO2 58% SO2 36%

NOx (as

PM2.5) 3% NOx (as Ozone) 3% 

### Rate-based

CO2 37% SO2

51%

NOx (as

PM2.5) 5% NOx (as Ozone) 7% 

### Rate-based

CO2 62% SO2 32%

NOx (as

PM2.5) 3% NOx (as Ozone) 3% 

### Mass-based

CO2 41% SO2

47%

NOx (as

PM2.5) 4% NOx (as Ozone) 8% 

### Mass-based4-47

resource limitations prevented the EPA from quantifying the impacts to, or monetizing the cobenefits from several important benefit categories, including co-benefits associated with exposure to several HAP (including mercury), SO 2 and NO 2 , as well as ecosystem effects, and visibility impairment due to the absence of air qua lity modeling data for these pollutants in this analysis. This does not imply that there are no co- benefits associated with changes in emissions of HAP or reductions in exposures to SO 2 and NO 2 . In this section, we provide a qualitative description of these benefits, which are listed in Table 4-32.

Table 4-32. Unquantified Health and Welfare Co-bene fits 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) — — S O 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, and other toxic effects — — IRIS; NRC, 2000 2,3 Improved Environment Reduced visibility impairment Visibility in Class 1 areas — — PM ISA 1 Visibility in residential areas — — PM ISA 14-48 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., 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 — — Ozon e 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 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 da ta and resource limitations for this RIA.
2 We assess these co-benefits qualitatively because w e do not have sufficient confidence in available da ta or methods.
3 We assess these co-benefits qualitatively because c urrent evidence is only suggestive of causality or there are other significant concerns over the strength of the association. 4.5.1 HAP Impacts
Due to methodology and resource limitations, we wer e unable to estimate the impacts associated with changes in emissions of the hazardo us air pollutants in this analysis. The EPA’s SAB-HES concluded that “the challenges for assessin g progress in health improvement as a4-49

result of reductions in emissions of hazardous air pollutants (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 d oses and the challenges of tracking health progress for diseases, such as cancer, that have lo ng latency periods” (U.S. EPA-SAB, 2008). In 2009, the EPA convened a workshop to address the in herent complexities, limitations, and uncertainties in current methods to quantify the be nefits of reducing HAP. Recommendations from this workshop included identifying research pr iorities, focusing on susceptible and vulnerable populations, and improving dose-response relationships (Gwinn et al. , 2011).

4.5.1.1 Mercury
Mercury in the environment is transformed into a mo re toxic form, methylmercury (MeHg). Because Hg is a persistent pollutant, MeHg accumulates in the food chain, especially the tissue of fish. When people consume these fish, they consume MeHg. In 2000, the NAS Study was issued which provides a thorough review o f the effects of MeHg on human health (NRC, 2000). 111 Many of the peer-reviewed articles cited in this s ection are publications originally cited in the Mercury Study. 112 In addition, the EPA has conducted literature sear ches to obtain other related and more recent publication s to complement the material summarized by
the NRC in 2000.

In its review of the literature, the NAS found neur odevelopmental effects to be the most sensitive and best documented endpoints and appropr iate for establishing a reference dose (RfD) (NRC, 2000); in particular NAS supported the use of results from neurobehavioral or neuropsychological tests. The NAS report noted that studies on animals reported sensory effects as well as effects on brain development and memory functions and supported the conclusions based on epidemiology studies. The NAS noted that t heir recommended endpoints for a RfD are associated with the ability of children to learn an d to succeed in school. They concluded the following: “The population at highest risk is the c hildren of women who consumed large amounts of fish and seafood during pregnancy. The c ommittee concludes that the risk to that

111 National Research Council (NRC). 2000 . Toxicological Effects of Methylmercury . Washington, DC: National Academies Press.

112 U.S. Environmental Protection Agency (U.S. EPA). 1 997. Mercury Study Report to Congress , EPA–HQ–OAR– 2009–0234–3054. December. Available on the Internet at <http://www.epa.gov/hg/report.htm>.4-50

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 a vailable 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 point s (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 chara cterize the effect of methylmercury exposure on blood pressure and cardiovascular function at vario us stages of life.”

Additional cardiovascular studies have been publish ed since 2000. The EPA did not develop a quantitative dose-response assessment for cardiovascular effects associated with MeHg exposures, as there is no consensus among scie ntists 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 sy stem effects. The pharmacokinetics of some of the exposure measures (such as toenail Hg l evels) are not well understood. The studies have not yet received the review and scrutiny of th e more well-established neurotoxicity data
base.

The Mercury Study noted that MeHg is not a potent m utagen but is capable of causing chromosomal damage in a number of experimental syst ems. The NAS concluded that evidence that human exposure to MeHg caused genetic damage i s inconclusive; they note that some earlier studies showing chromosomal damage in lymphocytes m ay not have controlled sufficiently for potential confounders. One study of adults living i n the Tapajós River region in Brazil (Amorim
et al. , 2000) reported a direct relationship between MeHg concentration in hair and DNA damage in lymphocytes, as well as effects on chromosomes. 113 Long-term MeHg exposures in this population were believed to occur through consumpti on of fish, suggesting that genotoxic effects

113 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 m ethyl mercury contamination in the Brazilian Amazon . An. Acad. Bras. Ciênc. 72(4): 497-507.4-51

(largely chromosomal aberrations) may result from d ietary and chronic MeHg exposures similar to and above those seen in the Faroes and Seychelle s populations.

Although exposure to some forms of Hg can result in a decrease in immune activity or an autoimmune response (ATSDR, 1999), evidence for imm unotoxic effects of MeHg is limited (NRC, 2000). 114

Based on limited human and animal data, MeHg is cla ssified as a “possible” human carcinogen by the International Agency for Research on Cancer (IARC, 1994) 115 and in IRIS (U.S. EPA, 2002). 116 The existing evidence supporting the possibility o f carcinogenic effects in humans from low-dose chronic exposures is tenuous. Multiple human epidemiological studies have found no significant association between Hg ex posure and overall cancer incidence, although a few studies have shown an association be tween Hg exposure and specific types of cancer incidence (e.g., acute leukemia and liver ca ncer) (NRC, 2000).

There is also some evidence of reproductive and ren al toxicity in humans from MeHg exposure. However, overall, human data regarding re productive, renal, and hematological toxicity from MeHg are very limited and are based o n 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 membranes of the nose, throat, and respiratory trac t. Brief exposure to 35 ppm causes throat

114 Agency for Toxic Substances and Disease Registry ( ATSDR). 1999. Toxicological Profile for Mercury. U. S. Department of Health and Human Services, Public Hea lth Service, Atlanta, GA.

115 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 tolue ne sulphonanilide. Br. J. Indust. Med. 18(Oct.):303-30 8 (as cited in NRC, 2000).

116 U.S. Environmental Protection Agency (EPA). 2002. Integrated Risk Information System (IRIS) on Methylmercury. National Center for Environmental As sessment. Office of Research and Development. Avail able at http://www.epa.gov/iris/subst/0073.htm.4-52

irritation, and levels of 50 to 100 ppm are barely tolerable for 1 hour. 117 Concentrations in typical human exposure environments are much lower than the se levels and rarely exceed the reference
concentration. 118 The greatest impact is on the upper respiratory tra ct; exposure to high concentrations can rapidly lead to swelling and spa sm of the throat and suffocation. Most seriously exposed persons have immediate onset of r apid breathing, blue coloring of the skin, and narrowing of the bronchioles. Exposure to HCl c an lead to Reactive Airways Dysfunction Syndrome (RADS), a chemically, or irritant-induced type of asthma. Children may be more vulnerable to corrosive agents than adults because of the relatively smaller diameter of their airways. Children may also be more vulnerable to ga s 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. 119

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 linked to a variety of adverse health effects associated with d irect exposure. We were unable to estimate the
health co-benefits associated with reduced NO 2 exposure in this analysis. Therefore, this analysi s only quantified and monetized the PM 2.5 and ozone co-benefits associated with the reduction s in
NO 2 emissions.

Following a comprehensive review of health evidence from epidemiologic and laboratory studies, the Integrated Science Assessment for Oxides of Nitroge n —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 . These epidemiologic and experimental studies encompass a number of endpoints including emergency department visits and hospitalizations, respiratory symptoms, airway hyperresponsiveness, a irway inflammation, and lung function. The

117 Agency for Toxic Substances and Disease Registry ( ATSDR). Medical Management Guidelines for Hydrogen Chloride. Atlanta, GA: U.S. Department of Health an d Human Services. Available at http://www.atsdr.cdc.gov/mmg/mmg.asp?id=758&tid=147 #bookmark02.

118 Table of Prioritized Chronic Dose-Response Values: http://www2.epa.gov/sites/production/files/2014- 05/documents/table1.pdf

119 U.S. Environmental Protection Agency (U.S. EPA). 1 995. “Integrated Risk Information System File of Hy drogen Chloride.” Washington, DC: Research and Development , National Center for Environmental Assessment. Thi s material is available at http://www.epa.gov/iris/su bst/0396.htm.4-53

NOx ISA also concluded that the relationship betwee n short-term NO 2 exposure and premature mortality was “suggestive but not sufficient to inf er a causal relationship,” because it is difficult 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 effect was generally smaller than that for other pollutants su ch 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 linked to a variety of adverse health effects associated with direct expos ure. We were unable to estimate the health cobenefits associated with reduced SO 2 in this analysis because we do not have air qualit y modeling data available. Therefore, this analysis o nly quantifies and monetizes the PM 2.5 cobenefits associated with the reductions in SO 2 emissions.

Following an extensive evaluation of health evidenc e 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 betwe en 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 associated with this disease. A clear concentrationresponse relationship has been demonstrated in labo ratory studies following exposures to SO 2 at concentrations between 20 and 100 ppb, both in term s of increasing severity of effect and percentage of asthmatics adversely affected. Based on our review of this information, we identified three short-term morbidity endpoints tha t the SO 2 ISA identified as a “causal relationship”: asthma exacerbation, respiratory-rel ated emergency department visits, and respiratory-related hospitalizations. The differing evidence and associated strength of the
evidence for these different effects is described i n 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 difficult to attribute the mortality risk effects to SO 2 alone. Although the SO 2 ISA stated that studies are generally consistent i n reporting a relationship between SO 2 exposure and mortality, there was a lack of robust ness of the observed associations to adjustment for other pollutants. We did not quantify these co-benefits due to data
constraints.4-54

4.5.4 Additional NO 2 and SO 2 Welfare Co-Benefits

As described in the Integrated Science Assessment for Oxides of Nitroge n and Sulfur — Ecological Criteria (NOx/SOx ISA) (U.S. EPA, 2008d), SO 2 and NOx emissions also contribute to a variety of adverse welfare effects, including those associated with acidic deposition, visibility impairment, and nutrient enrichment. Dep osition of nitrogen causes acidification, which can cause a loss of biodiversity of fishes, z ooplankton, 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 ecosyst ems. In the northeastern U.S., the surface waters affected by acidification are a source of fo od for some recreational and subsistence fishermen and for other consumers and support sever al cultural services, including aesthetic and educational services and recreational fishing. Biol ogical effects of acidification in terrestrial ecosystems are generally linked to aluminum toxicit y, which can cause reduced root growth, restricting the ability of the plant to take up wat er and nutrients. These direct effects can, in turn
, increase the sensitivity of these plants to stresse s, such as droughts, cold temperatures, insect pests, and disease leading to increased mortality o f canopy trees. Terrestrial acidification affects several important ecological services, including de clines in habitat for threatened and endangered species (cultural), declines in forest aesthetics ( cultural), declines in forest productivity (provisioning), and increases in forest soil erosio n and reductions in water retention (cultural and regulating). (U.S. EPA, 2008d)

Deposition of nitrogen is also associated with aqua tic and terrestrial nutrient enrichment. In estuarine waters, excess nutrient enrichment can le ad to eutrophication. Eutrophication of estuaries can disrupt an important source of food p roduction, particularly fish and shellfish production, and a variety of cultural ecosystem ser vices, 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 s ystems. Excessive 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 chang es, then fire frequency and intensity can also change, as nonnative grasses fuel more frequen t and more intense wildfires. (U.S. EPA, 2008d)4-55

Reductions in emissions of NO 2 and SO 2 will improve the level of visibility throughout th e United States because these gases (and the particle s of nitrate and sulfate formed from these gases) impair visibility by scattering and absorbin g light (U.S. EPA, 2009). Visibility is also referred to as visual air quality (VAQ), and it dir ectly affects people’s enjoyment of a variety of daily activities (U.S. EPA, 2009). Good visibility increases quality of life where individuals live and work, and where they travel for recreational ac tivities, including sites of unique public value, such as the Great Smoky Mountains National Park (U. S. EPA, 2009).

4.5.5 Ozone Welfare Co-Benefits
Exposure to ozone has been associated with a wide a rray of vegetation and ecosystem effects in the published literature (U.S. EPA, 2013 b). Sensitivity to ozone is highly variable across species, with over 65 plant species identifi ed as “ozone-sensitive”, many of which occur in state and national parks and forests. These effe cts include those that damage or impair the intended use of the plant or ecosystem. Such effect s can include reduced growth and/or biomass production in sensitive plant species, including fo rest trees, reduced yield and quality of crops, visible foliar injury, species composition shift, a nd changes in ecosystems and associated ecosystem services.

4.5.6 Carbon Monoxide Co-Benefits
CO in ambient air is formed primarily by the incomp lete combustion of carbon-containing fuels and photochemical reactions in the atmosphere . The amount of CO emitted from these reactions, relative to carbon dioxide (CO 2 ), is sensitive to conditions in the combustion zon e, such as fuel oxygen content, burn temperature, or m ixing 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 r ange of effects in multiple tissues and organ systems that depend on concentration and duration o f exposure. The Integrated Science Assessment for Carbon Monoxide (U.S. EPA, 2010a) concluded that short-term exposu re to CO is “likely to have a causal relationship” with card iovascular morbidity, particularly in individuals with coronary heart disease. Epidemiologic studies associate short-term CO exposure with increased risk of emergency department visits and h ospital admissions. Coronary heart disease includes those who have angina pectoris (cardiac ch est pain), as well as those who have4-56

experienced a heart attack. Other subpopulations po tentially at risk include individuals with diseases such as chronic obstructive pulmonary dise ase (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 bet ween short-term exposure to CO and respiratory morbidity and mortality. The evidence i s also suggestive of a causal relationship for birth outcomes and developmental effects following long-term exposure to CO, and for central nervous system effects linked to short- and long-te rm 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 visibility by scattering and absorbing light (U.S. EPA, 2009b). Fine particles with significant light-extin ction efficiencies include sulfates, nitrates, organic carbon, elemental carbon, and soil (Sisler, 1996). Visibility has direct significance to people’s enjoyment of daily activities and their ov erall sense of wellbeing. Good visibility increases the quality of life where individuals liv e 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 contri butor 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 esti mate visibility related benefits, and we are
also unable to determine whether the emission reduc tions associated with the final emission guidelines would be likely to have a significant im pact on visibility in urban areas or Class I

areas.

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, N o. 239, Tuesday, December 15, 2009, Rules and Regulations.4-57

Abt Associates, Inc. 2012. “BenMAP User’s Manual Ap pendices,” prepared for U.S. Research Triangle Park, NC: U. S. Environmental Protection A gency, Office of Air Quality Planning and Standards. Available at: <http://www.epa.gov/air/benmap/models/BenMAPAppendi cesOct2012.pdf>. Accessed June 6, 2015. Anenberg SC, West IJ, Fiore AM, Jaffe DA, Prather M J, Bergmann D, Cuvelier K, Dentener FJ, Duncan BN, Gauss M, Hess P, Jonson JE, Lupu A, Mack enzie IA, Marmer E, Park RJ, Sanderson MG, Schultz M, Shindell DT, Szopa S, Viva nco MG, Wild O, Zeng G. 2009. Intercontinental impacts of ozone pollution on huma n mortality . Environmental Science and Technology . 43(17): 6482-7. Bell, M.L., A. McDermott, S.L. Zeger, J.M. Sarnet, and F. Dominici

. 2004. “Ozone and ShortTerm Mortality in 95 U.S. Urban Communities, 1987-2 000.” Journal of the American Medical Association. 292(19):2372-8.
Bell, M.L., F. Dominici, and J.M. Samet. 2005. “A M eta-Analysis of Time-Series Studies of Ozone and Mortality with Comparison to the National Morbidity, Mortality, and Air Pollution Study.” Epidemiology . 16(4):436-45. Docket ID EPA-HQ-OAR-2009-0472-114577, Technical Su pport Document: Social Cost of Carbon for Regulatory Impact Analysis Under Executi ve Order 12866, Interagency Working Group on Social Cost of Carbon, with participation by the Council of Economic Advisers, Council on Environmental Quality, Department of Agr iculture, Department of Commerce, Department of Energy, Department of Transportation, Environmental Protection Agency, National Economic Council, Office of Energy and Cli mate Change, Office of Management and Budget, Office of Science and Technology Policy , and Department of Treasury (February 2010). Available at: <http://www.whitehouse.gov/sit es/default/files/omb/inforeg/foragencies/Social-Cost-of-Carbon-for-RIA.pdf>.4-58

Docket ID EPA-HQ-OAR-2013-0602, Technical Support D ocument: Technical Update of the Social Cost of Carbon for Regulatory Impact Analysi s 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, Depar tment of Transportation, Domestic Policy Council, Environmental Protection Agency, Na tional Economic Council, Office of Management and Budget, Office of Science and Techno logy Policy, and Department of Treasury (May 2013, Revised July 2015). Also availa ble at: < https://www.whitehouse.gov/sites/default/files/omb/ inforeg/scc-tsd-final-july-2015.pdf>. Accessed July 15, 2015. Gwinn, M.R., J. Craig, D.A. Axelrad, R. Cook, C. Do ckins, 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 r educing hazardous air pollutants— summary of 2009 workshop and future considerations. ” Environmental Health Perspectives. 119(1):125-30. Huang Y., F. Dominici, and M. Bell. 2005. “Bayesian Hierarchical Distributed Lag Models for Summer Ozone Exposure and Cardio-Respiratory Mortal ity.” 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/Uncertainty/pm _ee_tsd_expert_interview_summaries.p df>. Accessed June 6, 2015. Industrial Economics, Incorporated (IEc). 2009. Sec tion 812 Prospective Study of the Benefits and Costs of the Clean Air Act: Air Toxics Case Stu dy: Health Benefits of Benzene Reductions in Houston, 1990–2020. Final Report, Jul y 14, 2009. Available at: <http://www.epa.gov/air/sect812/dec09/812CAAA_Benze ne_Houston_Final_Report_July_20 09.pdf>. Accessed June 6, 2015.4-59

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, Depar tment of Transportation, Environmental Protection Agency, National Economic Council, Office of Management and Budget, Office of Science and Technology Policy, an d Department of Treasury. Response to to Comments: Social Cost of Carbon for Regulatory I mpact Analysis Under Executive Order 12866. July 2015. Available at <https://www.whitehouse.gov/sites/default/files/omb /inforeg/scc-response-to-commentsfinal-july-2015.pdf.> Accessed July 15, 2015. Intergovernmental Panel on Climate Change (IPCC). 2 007. 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/publicati ons_ipcc_fourth_assessment_report_synt hesis_report.htm>. Accessed June 6, 2015. Intergovernmental Panel on Climate Change (IPCC). 2 012: Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adap tation . A Special Report of Working Groups I and II of the Intergovernmental Panel on C limate Change. Cambridge University Press, Cambridge, UK, and New York, NY, USA. Intergovernmental Panel on Climate Change (IPCC). 2 013. Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to t he Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridg e University Press, Cambridge, United Kingdom and New York, NY, USA. Intergovernmental Panel on Climate Change (IPCC). 2 014a. Climate Change 2014: Impacts, Adaptation, and Vulnerability. Contribution of Work ing Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Ch ange. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. Intergovernmental Panel on Climate Change (IPCC). 2 014b. Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group II I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridg e University Press, Cambridge, United Kingdom and New York, NY, USA. Ito, K., S.F. De Leon, and M. Lippmann. 2005. “Asso ciations Between Ozone and Daily Mortality: Analysis and Meta-Analysis.” Epidemiology . 16(4):446-57.
Jhun I, Fann N, Zanobetti A, Hubbell B. 2014. Effec t modification of ozone-related mortality risks by temperature in 97 US cities. Environment International . 73:128-34.4-60

Karl, T., J. Melillo, and T. Peterson, Eds. 2009. Global Climate Change Impacts in the United States . Cambridge, United Kingdom: Cambridge University P ress. Krewski D., M. Jerrett, R.T. Burnett, R. Ma, E. Hug hes, Y. Shi, et al. 2009. Extended Follow-Up and Spatial Analysis of the American Cancer Society Study Linking Particulate Air Pollution and Mortality. HEI Research Report, 140, Health Effects Institute, Boston, MA. Lepeule, J., F. Laden, D. Dockery, and J. Schwartz. 2012. “Chronic Exposure to Fine Particles and Mortality: An Extended Follow-Up of the Harvard Six Cities Study from 1974 to 2009.” Environmental Health Perspectives. 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):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 Cl imate Assessment . U.S. Global Change Research Program. Available at <http://nca2014.glob alchange.gov> National Research Council (NRC). 2000. Toxicological Effects of Methylmercury: Committee o n the Toxicological Effects of Methylmercury .” Board on Environmental Studies and Toxicology. National Academies Press. Washington, D C. National Research Council (NRC). 2002. Estimating the Public Health Benefits of Proposed A ir Pollution Regulations . National Academies Press. Washington, DC. National Research Council (NRC). 2008. Estimating M ortality Risk Reduction and Economic Benefits from Controlling Ozone Air Pollution. Nati onal Academies Press. Washington, DC. National Research Council (NRC). 2010. Ocean Acidification: A National Strategy to Meet th e Challenges of a Changing Ocean . National Academies Press. Washington, DC. National Research Council (NRC). 2011a. Climate Sta bilization Targets: Emissions, Concentrations, and Impacts over Decades to Millenn ia. National Academies Press, Washington, DC. National Research Council (NRC) 2011b. National Security Implications of Climate Change fo r 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.4-61

National Research Council (NRC). 2013a. Climate and Social Stress: Implications for Securit y 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.whitehou se.gov/omb/circulars/a004/a-4.html>. Pizer, W., M. Adler, J. Aldy, D. Anthoff, M. Croppe r, K. Gillingham, M. Greenstone, B. Murray, R. Newell, R. Richels, A. Rowell, S. Waldhoff, J. W iener. 2014. “Using and improving the social cost of carbon.” Science, Vol. 346, No. 6214 , 12/05/14, pp 1189-1190. Ren, C., G.M. William, L. Morawska, K. Mengensen, a nd 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 Mortali ty in 60 Large Eastern U.S. Communities? An Assessment Using the NMMAPS Data.” Environment International

. 34:451–458. Ren, C. and S. Tong. 2006b. “Temperature Modifies t he Health Effects of Particulate Matter in Brisbane, Australia.” International Journal of Biometeorology. 51:87–96. Ren. C., G.M. Williams, and S. Tong. 2006a. “Does P articulate 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 Mo rtality 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 Associatio n between Ozone and Daily Deaths to Control for Temperature?” American Journal of Respiratory and Critical Care M edicine. 171(6): 627-31.4-62

Sisler, J.F. 1996. Spatial and Seasonal Patterns an d 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 Universit y. U.S. Environmental Protection Agency (U.S. EPA). 19 95. Regulatory Impact Analysis for the Petroleum Refinery NESHAP. Revised Draft for Promul gation . Office of Air Quality Planning and Standards, Research Triangle Park, N.C. Availab le on the Internet at < http://yosemite1.epa.gov/ee/epa/ria.nsf/vwTD/9F39F2 C26150BB21852564620063317F>. Accessed June 6, 2015. U.S. Environmental Protection Agency (U.S. EPA). 20 08a. Integrated Science Assessment for Sulfur Oxides—Health Criteria (Final Report) . National Center for Environmental Assessment – RTP Division, Research Triangle Park, NC. September. Available at: <http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?dei d=198843>. Accessed June 4, 2015. U.S. Environmental Protection Agency (U.S. EPA). 20 08b. Final Ozone NAAQS Regulatory Impact Analysis . EPA-452/R-08-003. Office of Air Quality Planning and Standards Health and Environmental Impacts Division, Air Benefit and Cost Group Research Triangle Park, NC. March. Available at: < http://www.epa.gov/ttnec as1/regdata/RIAs/6- ozoneriachapter6.pdf>. Accessed June 4, 2015. U.S. Environmental Protection Agency (U.S. EPA). 20 08c. Integrated Science Assessment for Oxides of Nitrogen - Health Criteria (Final Report)

. National Center for Environmental Assessment, Research Triangle Park, NC. July. Avail able at: <http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?dei d=194645>. Accessed June 4, 2015. U.S. Environmental Protection Agency (U.S. EPA). 20 08d. Integrated Science Assessment for Oxides of Nitrogen and Sulfur–Ecological Criteria N ational (Final Report ). National Center for Environmental Assessment – RTP Division, Resear ch Triangle Park, NC. EPA/600/R08/139. December. Available at: <http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?dei d=201485>. Accessed June 4, 2015. U.S. Environmental Protection Agency (U.S. EPA). 20 09. Integrated Science Assessment for Particulate Matter (Final Report) . EPA-600-R-08-139F. National Center for Environmen tal Assessment – RTP Division, Research Triangle Park, NC. December. Available at: <http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?dei d=216546>. Accessed June 4, 2015. U.S. Environmental Protection Agency (U.S. EPA). 20 10a. Integrated Science Assessment for Carbon Monoxide. National Center for Environmental Assessment, Resea rch Triangle Park, NC. EPA/600/R-09/019F. January. Available at: <http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?dei d=218686>. Accessed June 4, 2015.4-63

U.S. Environmental Protection Agency (U.S. EPA). 20 10b. Technical Support Document: Summary of Expert Opinions on the Existence of a Th reshold in the Concentration-Response Function for PM 2.5 -related Mortality. Research Triangle Park, NC. Jun e. Available at: <http://www.epa.gov/ttn/ecas/regdata/Benefits/thres holdstsd.pdf>. Accessed June 4, 2015. U.S. Environmental Protection Agency (U.S. EPA). 20 10c. 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-05 63-1.pdf/$file/EE-0563-1.pdf>. Accessed June 4, 2015. U.S. Environmental Protection Agency (U.S. EPA). 20 10d. Section 3: Re

‐ analysis of the Benefits of Attaining Alternative Ozone Standards to Incorpo rate Current Methods . Available at: <http://www.epa.gov/ttnecas1/regdata/RIAs/s3-supple mental_analysis-updated_benefits11- 5.09.pdf >. Accessed June 4, 2015. U.S. Environmental Protection Agency (U.S. EPA). 20 11a. The Benefits and Costs of the Clean Air Act from 1990 to 2020 . Office of Air and Radiation, Washington, DC. Marc h. Available at: <http://www.epa.gov/cleanairactbenefits/feb11/f ullreport_rev_a.pdf>. Accessed June 4, 2015. U.S. Environmental Protection Agency (U.S. EPA). 20 11b. 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/regdata/RIAs/matsriafi nal.pdf>. Accessed June 4, 2015. U.S. Environmental Protection Agency (U.S. EPA). 20 11c. Regulatory Impact Analysis: National Emission Standards for Hazardous Air Pollu tants for Industrial, Commercial, and Institutional Boilers and Process Heaters. February . Available at: <http://www.epa.gov/ttnecas1/regdata/RIAs/boilersri afinal110221_psg.pdf>. Accessed June 4, 2015. U.S. Environmental Protection Agency (U.S. EPA). 20 11d. 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 E nvironmental Impacts Division. April. Available at: <http://www.epa.gov/ttn/naaqs/standar ds/pm/data/20110419pmpafinal.pdf>. Accessed June 4, 2015.4-64

U.S. Environmental Protection Agency (U.S. EPA). 20 12a. Regulatory Impact Analysis for the Final Revisions to the National Ambient Air Quality Standards for Particulate Matter. EPA452/R-12-003. Office of Air Quality Planning and St andards, Health and Environmental Impacts Division, Research Triangle Park, NC. Decem ber. Available at: < http://www.epa.gov/ttnecas1/regdata/RIAs/finalria.p df>. Accessed June 4, 2015. U.S. Environmental Protection Agency (U.S. EPA). 20 12b. Regulatory Impact Analysis: Petroleum Refineries New Source Performance Standar ds Ja. Office of Air Quality Planning and Standards, Health and Environmental Impacts Div ision. June. Available at: <http://www.epa.gov/ttnecas1/regdata/RIAs/refinerie s_nsps_ja_final_ria.pdf>. Accessed June 4, 2015. U.S. Environmental Protection Agency (U.S. EPA). 20 13a. 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 P ark, NC. February. Available at: < http://www2.epa.gov/sites/production/files/2014- 10/documents/sourceapportionmentbpttsd.pdf >. Acces sed June 4, 2015. U.S. Environmental Protection Agency (U.S. EPA). 20 13b. Integrated Science Assessment of Ozone and Related Photochemical Oxidants (Final Rep ort). EPA/600/R-10/076F. National Center for Environmental Assessment – RTP Division, Research Triangle Park. Available at: <http://cfpub.epa.gov/ncea/isa/recordisplay.cfm?dei d=247492#Download>. Accessed June 4, 2015. U.S. Environmental Protection Agency (U.S. EPA). 20 14. Guidelines for Preparing Economic Analyses. EPA 240-R-10-001. National Center for Environmenta l Economics, Office of the Administrator. Washington, DC. Available at: <http://yosemite.epa.gov/EE\epa\eed.nsf/webpages/Gu idelines.html>. Accessed June 6, 2015. U.S. Environmental Protection Agency (U.S. EPA). 20 14a. Regulatory Impact Analysis for the Proposed Carbon Pollution Guidelines for Existing P ower Plants and Emission Standards for Modified and Reconstructed Power Plants . EPA-542/R-14-002. Office of Air Quality Planning and Standards, Research Triangle Park, NC. June. Available at <http://www.epa.gov/ttnecas1/regdata/RIAs/111dpropo salRIAfinal0602.pdf>. Accessed June 4, 2015.4-65

U.S. Environmental Protection Agency (U.S. EPA). 20 14b. Health Risk and Exposure Assessment for Ozone: Final Report . Office of Air Quality Planning and Standards, Res earch Triangle Park, NC. EPA-452/R-14-004a. Available at: <http://www.epa.gov/ttn/naaqs/standards/ozone/data/ 20140829healthrea.pdf>. Accessed June 4, 2015. U.S. Environmental Protection Agency (U.S. EPA). 20 14c. Welfare Risk and Exposure Assessment for Ozone: Final . EPA-452/R-14-005a. Office of Air Quality Planning and Standards, Research Triangle Park, NC. August. Avai lable at: <http://www.epa.gov/ttn/naaqs/standards/ozone/data/ 20141021welfarerea.pdf>. Accessed June 4, 2015. U.S. Environmental Protection Agency (U.S. EPA). 20 14d. Regulatory Impact Analysis of the Proposed Revisions to the National Ambient Air Qual ity Standards for Ground-Level Ozone. EPA-452/P-14-006. Office of Air Quality Planning an d Standards, Research Triangle Park, NC. November. Available at <http://www.epa.gov/ttne cas1/regdata/RIAs/20141125ria.pdf>. Accessed June 4, 2015. U.S. Environmental Protection Agency—Science Adviso ry Board (U.S. EPA-SAB). 2002. Workshop on the Benefits of Reductions in Exposure to Hazardous Air Pollutants: Developing Best Estimates of Dose-Response Function s An SAB Workshop Report of an EPA/SAB Workshop (Final Report). EPA-SAB-EC-WKSHP-0 2-001. January. Available at: <http://yosemite.epa.gov/sab%5CSABPRODUCT.NSF/34355 712EC011A358525719A005B F6F6/$File/ecwkshp02001%2Bappa-g.pdf>. Accessed Jun e 4, 2015. U.S. Environmental Protection Agency—Science Adviso ry Board (U.S. EPA-SAB). 2008. Characterizing Uncertainty in Particulate Matter Be nefits Using Expert Elicitation. EPACOUNCIL-08-002. July. Available at: <http://yosemite.epa.gov/sab/sabproduct.nsf/0/43B91 173651AED9E85257487004EA6CB/$F ile/EPA-COUNCIL-08-002-unsigned.pdf>. Accessed June 4, 2015. U.S. Environmental Protection Agency—Science Adviso ry Board (U.S. EPA-SAB). 2000. An SAB Report on EPA’s White Paper Valuing the Benefit s of Fatal Cancer Risk Reduction. EPA-SAB-EEAC-00-013. July. Available at: <http://yosemite.epa.gov/sab%5CSABPRODUCT.NSF/41334 524148BCCD6852571A70051 6498/$File/eeacf013.pdf>. Accessed June 4, 2015.4-66

U.S. Environmental Protection Agency—Science Adviso ry Board (U.S. EPA-SAB). 2004c. Advisory Council on Clean Air Compliance Analysis R esponse to Agency Request on Cessation Lag. EPA-COUNCIL-LTR-05-001. December. Available at: <http://yosemite.epa.gov/sab/sabproduct.nsf/0/39F44 B098DB49F3C85257170005293E0/$Fil e/council_ltr_05_001.pdf>. Accessed June 4, 2015. U.S. Environmental Protection Agency—Science Adviso ry Board (U.S. EPA-SAB). 2008. Benefits of Reducing Benzene Emissions in Houston, 1990–2020 . EPA-COUNCIL-08-001. July. Available at: <http://yosemite.epa.gov/sab/sabproduct.nsf/D4D7EC9 DAEDA8A548525748600728A83/$Fi le/EPA-COUNCIL-08-001-unsigned.pdf>. Accessed June 4, 2015. U.S. Environmental Protection Agency—Science Adviso ry Board (U.S. EPA-SAB). 2009b. Review of EPA’s Integrated Science Assessment for P articulate Matter (First External Review Draft, December 2008) . EPA-COUNCIL-09-008. May. Available at: <http://yosemite.epa.gov/sab/SABPRODUCT.NSF/81e39f4 c09954fcb85256ead006be86e/73 ACCA834AB44A10852575BD0064346B/$File/EPA-CASAC-09-0 08-unsigned.pdf>. Accessed June 4, 2015. U.S. Environmental Protection Agency—Science Adviso ry Board (U.S. EPA-SAB). 2009c. Review of Integrated Science Assessment for Particu late Matter (Second External Review Draft, July 2009) . EPA-CASAC-10-001. November. Available at: <http://yosemite.epa.gov/sab/SABPRODUCT.NSF/81e39f4 c09954fcb85256ead006be86e/15 1B1F83B023145585257678006836B9/$File/EPA-CASAC-10-0 01-unsigned.pdf>. Accessed June 4, 2015. U.S. Environmental Protection Agency—Science Adviso ry 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/9288428b 8eeea4c885257242006935a3/72D4EFA 39E48CDB28525774500738776/$File/EPA-COUNCIL-10-001- unsigned.pdf>. Accessed June 4, 2015. U.S. Environmental Protection Agency—Science Adviso ry Board (U.S. EPA-SAB). 2011. Review of Valuing Mortality Risk Reductions for Env ironmental Policy: A White Paper (December 10, 2010). EPA-SAB-11-011 July

. Available at: <http://yosemite.epa.gov/sab/sabproduct.nsf/298E1F5 0F844BC23852578DC0059A616/$File/ EPA-SAB-11-011-unsigned.pdf>. Accessed June 4, 2015

.4-67

Woodruff, T.J., J. Grillo, and K.C. Schoendorf. 199 7. “The Relationship between Selected of postneonatal infant mortality and particulate air p ollution in the United States.” Environmental Health Perspectives. 105(6): 608-612.4A-1



### A PPENDIX 

### 4A: G ENERATING 

### R EGIONAL 

### B ENEFIT 

### - PER 

### -T ON 

### E STIMATES

The purpose of this appendix is to provide addition al 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 f or generating benefit-per-ton estimates by region for the contiguous U.S. for PM 2.5 and ozone precursors emitted by the electrical generating unit (EGU) sector in the Final Carbon Po llution Emission Guidelines for Existing Stationary Sources: Electric Utility Generating Uni ts (hereafter referred to as the “final emission guidelines” or “Clean Power Plan Final Rule”).
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, 2013), the general procedure for calculating average benefit-per-ton coefficients ge nerally follows three steps. As an example, in order to calculate regional average benefit-per-ton estimates for the key precursor pollutants emitted from EGU sources, we:

1. Use air quality modeling to predict changes in ambi ent concentrations of primary PM 2.5 , nitrate, sulfate, and ozone at a 12km 2 grid resolution across the contiguous U.S. that ar e attributable to the proposed Clean Power Plan.

2. For each grid cell, estimate the health impacts, an d the economic value of these impacts, associated with the attributable ambient concentrat ions using the environmental Benefits Mapping and Analysis Program – Community Edition (B enMAP-CE v1.1). 120,121 Aggregate those impacts and economic values to the three regions of East, West, and California.

3. Divide the regional health impacts attributable to each precursor, and the regional monetary value of these impacts, by the amount of a ssociated regional precursor emissions. That is, directly emitted PM 2.5 benefits are divided by directly emitted PM 2.5 emissions, sulfate benefits are divided by SO 2 emissions, nitrate benefits are divided by NO X emissions, and ozone benefits are divided by ozone -season NO X emissions.

120 When estimating these impacts we apply effect c oefficients 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 species.
121 Previous RIAs have used earlier versions of the Be nMAP software. BenMAP-CE v1.1 provides results cons istent with earlier versions of BenMAP and is available fo r download at http://www.epa.gov/air/benmap/.4A-2

4A.2 Air Quality Modeling for the Proposed Clean Po wer Plan The EPA ran the Comprehensive Model with Extensions (CAMx) photochemical model (ENVIRON, 2014) to predict ozone and PM 2.5 concentrations for the following emissions scenarios: a 2011 base year, a 2025 base case, and the 2025 proposed Clean Power Plan (Option 1 State) scenario. Each of the CAMx model simulatio ns was performed for a nationwide modeling domain 122 using a full year of meteorological conditions for 2011. The modeling for 2011 was used as the anchor point for projecting oz one and annual PM 2.5 concentration values
for the 2025 base case and for the 2025 Clean Power Plan proposal scenario using methodologies consistent with the EPA’s air quality modeling guid ance (U.S. EPA, 2007). The air quality modeling results for the 2025 base case served as t he baseline for gauging the future year impacts on ozone and annual PM 2.5 of the Clean Power Plan proposal scenario. The 202 5 base
case reflects emissions reductions between 2011 and 2025 that are expected to result from regional and national rules including the Clean Air Interstate Rule (CAIR), the Mercury and Air Toxics Standards (MATS), mobile source rules up thr ough Tier-3, and various state emissions control programs and consent decrees. The methods f or estimating the EGU emissions for the proposal are described in Chapter 3 of the RIA for the Clean Power Plan proposal (U.S. EPA, 2014). State total annual EGU emissions for NO X and SO 2 for each of the scenarios modeled are provided in Tables 4A-1 and 4A-2, respectively. The data indicate that, overall nationwide, EGU
SO 2 and NO X emissions with proposed Option 1 (state) would be about 28% lower than the 2025
base case.

122 The modeling domain (i.e., region modeled) include s all of the lower 48 states plus adjacent portions of Canada and Mexico) at a spatial resolution of 12 km.4A-3

Table 4A-1. State Total Annual EGU Emissions for NO X for the 2011 Base Year, 2025

Base Case, and 2025 Clean Power Plan Proposal (Opti on 1 State) (in thousands of tons)

State 2011 Base Year 2025 Base Case 2025 Clean Power Plan Proposal (Option 1 State) Alabama 63 38 19 Arizona 35 17 4 Arkansas 38 43 9 California 6 33 28 Colorado 51 29 21 Connecticut 1 1 1 Delaware 4 1 1 Florida 61 52 15 Georgia 54 33 18 Idaho - 1 0 Illinois 73 38 32 Indiana 121 97 90 Iowa 40 24 24 Kansas 44 28 27 Kentucky 92 59 74 Louisiana 47 18 14 Maine 2 4 2 Maryland 19 11 11 Massachusetts 5 2 1 Michigan 75 73 51 Minnesota 32 27 13 Mississippi 26 15 3 Missouri 66 61 58 Montana 20 16 15 Nebraska 37 38 35 Nevada 7 5 3 New Hampshire 4 1 0 New Jersey 6 7 2 New Mexico 23 7 6 New York 22 11 7 North Carolina 46 35 23 North Dakota 51 51 48 Ohio 104 63 60 Oklahoma 82 52 26 Oregon 5 3 3 Pennsylvania 149 106 71 Rhode Island 0 0 1 South Carolina 25 13 8 South Dakota 11 13 8 Tennessee 27 16 13 Texas 146 144 64 Tribal Data 65 33 33 Utah 51 49 33 Vermont 0 0 04A-4

State 2011 Base Year 2025 Base Case 2025 Clean Power Plan Proposal (Option 1 State) Virginia 38 21 12 Washington 7 3 2 West Virginia 58 49 46 Wisconsin 32 19 11 Wyoming 53 50 38 National Total 2,024 1,508 1,084

Table 4A-2. State Total Annual EGU Emissions for SO 2 for the 2011 Base Year, 2025

Base Case, and 2025 Clean Power Plan Proposal (Opti on 1 State) (in thousands of tons)

State 2011 Base Year 2025 Base Case 2025 Clean Power Plan Proposal (Option 1 State) Alabama 186 79 45 Arizona 28 18 4 Arkansas 74 30 5 California 1 4 4 Colorado 45 15 10 Connecticut 1 - - Delaware 11 1 1 Florida 95 70 7 Georgia 187 37 12 Idaho - 0 0 Illinois 227 45 48 Indiana 382 126 121 Iowa 100 18 18 Kansas 39 15 15 Kentucky 246 109 119 Louisiana 93 14 11 Maine 1 1 1 Maryland 32 5 9 Massachusetts 23 1 0 Michigan 228 122 95 Minnesota 40 21 12 Mississippi 43 10 3 Missouri 205 80 76 Montana 19 18 17 Nebraska 73 25 24 Nevada 5 1 1 New Hampshire 24 0 0 New Jersey 5 7 1 New Mexico 6 4 4 New York 41 4 2 North Carolina 78 36 33 North Dakota 93 15 144A-5 State 2011 Base Year 2025 Base Case 2025 Clean Power Plan Proposal (Option 1 State) Ohio 594 105 102 Oklahoma 96 21 6 Oregon 13 1 1 Pennsylvania 338 67 47 Rhode Island 0 - - South Carolina 68 19 12 South Dakota 11 11 7 Tennessee 120 38 31 Texas 426 149 48 Tribal Data 18 19 19 Utah 22 14 10 Vermont 0 0 0 Virginia 75 8 4 Washington 1 1 1 West Virginia 103 78 47 Wisconsin 92 17 11 Wyoming 55 23 17 National Total 4,665 1,504 1,077

As indicated above, the air quality modeling was u sed to project gridded ozone and
annual PM 2.5 concentrations at the 12km 2 resolution for the 2025 base case and the Clean Po wer Plan proposal scenario modeled for this analysis. T he air quality modeling results were combined with monitored ozone and PM2.5 data to create proje cted spatial fields of annual PM 2.5 and seasonal mean (May through September) 8-hour daily maximum ozone for the 2025 base case and for the proposal scenario. These spatial fields were then used as inputs to estimate the health co-benefits of the proposed Clean Power Plan as des cribed below.

4A.3 Regional PM 2.5 Benefit-per-Ton Estimates for EGUs Derived from Air Quality Modeling of the Proposed Clean Power Plan
After estimating the 12km 2 resolution PM 2.5 benefits for each of the analysis years applied in this RIA (i.e., 2020, 2025, and 2030), w e aggregated the benefits results regionally (i.e., East, West, and California), as shown in Fig ure 4A-1. 123 While a small percentage of benefits from emissions reductions in a particular region may occur in one of the other regions, we selected each region to minimize this percentage . Thus, the benefits per ton in each region

123 This aggregation is identified as the shapefile “R eport Regions” in BenMAP’s grid definitions.4A-6

will represent well the match between where the emi ssions reductions and air quality benefits are occurring. Due to the low emissions of SO 2 , NO X , and directly emitted particles from EGUs in California and the high population density, we sepa rated out California in order not to bias the benefit-per-ton estimates for the rest of the Weste rn U.S. In order to calculate the benefit-per-ton estimates, we divided the regional benefits estimat es by the corresponding emissions, as shown in Table 4A-1. Lastly, we adjusted the benefit-per- ton estimates for a currency year of 2011$. 124
This method provides estimates of the regional aver age benefit-per-ton for a subset of the major PM 2.5 precursors emitted from EGU sources. For precursor emissions of NO X , there is generally a non-linear relationship between emissio ns and formation of PM 2.5 . This means that
each ton of NO X reduced would have a different impact on ambient P M 2.5 depending on the initial level of emissions and potentially on the l evels of emissions of other pollutants. In contrast, SO 2 is generally linear in forming PM 2.5 . For precursors like NO X which form PM 2.5 non-linearly, a marginal benefit-per-ton approach w ould better approximate the specific benefits
associated with an emissions reduction scenario for a given set of base case emissions, because it would allow the benefit-per-ton to vary depending o n the level of emissions reductions and the baseline emissions levels. However, we do not have sufficient air quality modeling data to calculate marginal benefit-per-ton estimates for th e EGU sector. Therefore, using an average benefit-per-ton estimate for NO X adds uncertainty to the co-benefits estimated in t his RIA. Because most of the estimated co-benefits for the p roposed guidelines are attributable to
reductions in SO 2 emissions, the added uncertainty is likely to be sm all.

124 Currently, BenMAP does not have an inflation ad justment to 2011$. We ran BenMAP for a currency yea r of 2010$ and calculated the benefit-per-ton estim ates in 2010$. We then adjusted the resulting benefit-per-ton estimat es to 2011$ using the Consumer Price Index.4A-7

Figure 4A-1. Regional Breakdown

In this RIA, we estimate emission reductions from E GUs using IPM. 125 IPM outputs provide endogenously projected unit level emissions of SO 2 , NO X , CO 2 , Hg, hydrogen chloride (HCl) from EGUs, but carbon monoxide, volatile orga nic compounds, ammonia and total directly emitted PM 2.5 and PM 10 emissions are post-calculated. 126 In addition, directly emitted particle emissions calculated from IPM outputs do n ot include speciation, i.e. they are only the total emissions. In order to conduct air quality mo deling, directly emitted PM 2.5 from EGUs is speciated into components during the emissions mode ling process based on emission profiles for EGUs by source classification code. Even though the se speciation profiles are not unit-specific, an emission profile based on the source classificat ion code is highly sophisticated and reflects the fuel and the unit configuration. Model-predicted co ncentrations of nitrate and sulfate include

125 See Chapter 3 of this RIA for additional informatio n regarding the Integrated Planning Model ( IPM).

126 Detailed documentation of this post-processing is available at http://www.epa.gov/powersectormodeling/docs/v513/Fl atFile_Methodology.pdf4A-8

both the directly emitted nitrate and sulfate from speciated PM 2.5 and secondarily formed nitrate
and sulfate from emissions of NO X and SO 2 , respectively.

In order to estimate the benefits associated with r educed emissions of directly emitted particles without performing air quality modeling, we must determine the fraction of total PM 2.5 emissions comprised of elemental carbon and organic carbon (EC+OC) and crustal emissions. 127 Based on the work by Fann, Baker, and Fulcher (2012 ), the national average EC+OC fraction of
emitted PM 2.5 is 10% with a range of 5% to 63% in different state s due to the different proportion of fuels. The national average is similar to the av erages for the east and west regions at 10% and 7%, respectively. Only five states had EC+OC fracti ons 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 fraction s less than 50%. In calculating the PM 2.5 cobenefits in this RIA, we estimate the emission redu ctions 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 E GUs. Because the benefit-per-ton estimates for reducing emissions of EC+OC are larger than the ben efit-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 Dakot a. 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 because of the additional unquantified uncertainties that result from the benefit transfer methods, including those related to the transfer of air quality modeling information. Instead, we refer the reader to Chapter 5 of PM NAAQS RIA (U.S. EPA, 2012a) for an indication of the combined random sam pling error in the health impact and economic valuation functions using Monte Carlo meth ods. In general, the 95 th percentile
confidence interval for the total monetized PM 2.5 benefits ranges from approximately -90% to
+180% of the central estimates based on concentrati on-response functions from Krewski et al. (2009) and Lepeule et al. (2012). The 95 th percentile confidence interval for the health impa ct

127 Crustal emissions are composed of compounds associ ated with minerals and metals from the earth’s surf ace, including carbonates, silicates, iron, phosphates, copper, and zinc. Often, crustal material represent s particles not classified as one of the other species (e.g., organ ic carbon, elemental carbon, nitrate, sulfate, chlo ride, etc.).4A-9

function alone ranges from approximately ±30% for m ortality 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 estimate s, 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-3 through 4A-5 provide the regional benef it-per-ton estimates for the EGU sector at discount rates of 3% and 7% in 2020, 2025 , and 2030 respectively. The benefit-per-ton values for 2020 and 2030 are based on applying the air quality modeling from 2025 to population
and health information from 2020 and 2030. Estimate d benefit-per-ton for these years have additional uncertainty relative to 2025 because of potential differences in atmospheric responses
to reductions in PM 2.5 precursors in those years, however, these uncertai nties are likely to be relatively small. Tables 4A-6 through 4A-8 provide the incidence per ton estimates (which follows the same general methodology as for the ben efit-per-ton calculations) for the EGU sector in 2020, 2025, and 2030 respectively, for the set o f health endpoints used to calculate the benefitper-ton estimates.4A-10Table 4A-3. Summary of Regional PM2.5Benefit-per-Ton Estimates Based on Air Quality Modeling from Proposed Clean Power Planin 2020 (2011$)*PollutantDiscountRateNationalRegionEast West CaliforniaSO23% $32,000 to $71,000 $33,000 to $75,000 $6,200 to$14,000 $95,000 to $210,0007% $28,000 to $64,000 $30,000 to $68,000 $5,600 to$13,000 $85,000 to $190,000Directly emittedPM2.5(EC+OC)3% $140,000 to $310,000 $140,000 to $320,000 $27,000 to $60,000 $370,000 to $830,0007% $120,000 to $270,000 $130,000 to $290,000 $24,000 to $54,000 $330,000 to $740,000Directly emittedPM2.5(Crustal)3% $22,000 to $49,000 $23,000 to $52,000 $11,000 to$25,000 $73,000 to $160,0007% $20,000 to $44,000 $21,000 to $47,000 $9,900 to$22,000 $66,000 to $150,000NOX(as PM2.5)3% $3,000 to $6,800 $3,100 to $7,000 $0,670 to $1,500 $22,000 to $49,0007% $2,700 to $5,600 $2,800 to $6,300 $0,610 to $1,400 $19,000 to $44,000* The range of estimates reflects the range of epidemiology studies for avoided premature mortality for PM2.5. All estimates are rounded to two significant figures. All fineparticles are assumed to have equivalent health effects, but the benefit-per-ton estimates vary depending on the location and magnitude of their impact on PM2.5levels,which drive population exposure. The monetized benefits incorporate the conversion from precursor emissions to ambient fine particles. The estimates do not includereduced health effects from direct exposure to ozone, NO2, SO2, ecosystem effects, or visibility impairment.Table 4A-4. Summary of Regional PM2.5Benefit-per-Ton Estimates Based on Air Quality Modeling from Proposed Clean Power Planin 2025 (2011$)*PollutantDiscountRateNationalRegionEast West CaliforniaSO23% $35,000 to $78,000 $37,000 to $83,000 $7,100 to$16,000 $110,000 to $240,0007% $31,000 to $70,000 $33,000 to $75,000 $6,400 to$14,000 $97,000 to $220,000Directly emittedPM2.5(EC+OC)3% $150,000 to $340,000 $160,000 to $360,000 $30,000 to $68,000 $410,000 to $930,0007% $130,000 to $290,000 $140,000 to $320,000 $27,000 to $61,000 $370,000 to $830,000Directly emittedPM2.5(Crustal)3% $24,000 to $55,000 $25,000 to $58,000 $12,000 to$28,000 $82,000 to $180,0007% $22,000 to $49,000 $23,000 to $52,000 $11,000 to$25,000 $74,000 to $170,000NOX(as PM2.5)3% $3,200 to $7,300 $3,300 to $7,500 $0,750 to $1,700 $24,000 to $54,0007% $2,900 to $6,000 $3,000 to $6,800 $0,670 to $1,500 $22,000 to $49,000* The range of estimates reflects the range of epidemiology studies for avoided premature mortality for PM2.5. All estimates are rounded to two significant figures. All fineparticles are assumed to have equivalent health effects, but the benefit-per-ton estimates vary depending on the location and magnitude of their impact on PM2.5levels,which drive population exposure. The monetized benefits incorporate the conversion from precursor emissions to ambient fine particles. The estimates do not includereduced health effects from direct exposure to ozone, NO2, SO2, ecosystem effects, or visibility impairment.4A-11Table 4A-5. Summary of Regional PM2.5Benefit-per-Ton Estimates Based on Air Quality Modeling from Proposed Clean Power Planin 2030 (2011$)*PollutantDiscountRateNationalRegionEast West CaliforniaSO23% $37,000 to $85,000 $40,000 to $89,000 $7,800 to$18,000 $120,000 to $270,0007% $34,000 to $76,000 $36,000 to $81,000 $7,100 to$16,000 $110,000 to $240,000Directly emittedPM2.5(EC+OC)3% $160,000 to $360,000 $170,000 to $380,000 $33,000 to $75,000 $450,000 to $1,000,0007% $150,000 to $320,000 $150,000 to $340,000 $30,000 to $68,000 $410,000 to $920,000Directly emittedPM2.5(Crustal)3% $26,000 to $59,000 $28,000 to $62,000 $14,000 to$31,000 $90,000 to $200,0007% $24,000 to $53,000 $25,000 to $56,000 $13,000 to$28,000 $81,000 to $180,000NOX(as PM2.5)3% $3,400 to $7,800 $3,500 to $8,000 $0,820 to $1,900 $26,000 to $60,0007% $3,100 to $6,400 $3,200 to $7,200 $0,740 to $1,700 $24,000 to $54,000* 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.4A-12Table 4A-6. Summary of Regional PM2.5Incidence-per-Ton Estimates Based on Air Quality Modeling from Proposed Clean PowerPlan in 2020*Health EndpointEastWest CaliforniaSO2NOXEC+OC Crustal SO2NOXEC+OC Crustal SO2NOXEC+OC CrustalPremature MortalityKrewskiet al.(2009) – adult 0.0037000.0003400.016000 0.0025000.0006800.0000730.0029000.0012000.0100000.0024000.040000 0.008000Lepeuleet al.(2012) – adult 0.0083000.0007700.036000 0.0057000.0015000.0001700.0066000.0027000.0230000.0054000.091000 0.018000Woodruffet al.(1997) – infants 0.0000090.0000010.000037 0.0000060.0000020.0000000.0000070.0000030.0000230.0000070.000097 0.000019MorbidityEmergency department visits for asthma 0.0019000.0001900.007800 0.0013000.0002900.0000310.0012000.0004700.0053000.0014000.022000 0.004200Acute bronchitis 0.0054000.0005100.023000 0.0037000.0013000.0002000.0052000.0021000.0190000.0050000.077000 0.015000Lower respiratory symptoms 0.0690000.0065000.300000 0.0470000.0160000.0025000.0670000.0260000.2400000.0640000.970000 0.190000Upper respiratory symptoms 0.0980000.0093000.420000 0.0680000.0230000.0036000.0950000.0380000.3400000.0920001.400000 0.270000Minor restricted-activity days 2.7000000.25000011.0000001.9000000.5800000.0780002.4000000.9200009.4000002.20000035.0000006.800000Lost work days 0.4500000.0430001.900000 0.3100000.0980000.0130000.4100000.1600001.6000000.3800006.000000 1.100000Asthma exacerbation 0.2400000.0230001.000000 0.1700000.0560000.0088000.2300000.0910000.8400000.2200003.400000 0.650000Hospital Admissions, Respiratory 0.0011000.0001000.004500 0.0007200.0001500.0000150.0006400.0002600.0025000.0005800.009400 0.001900Hospital Admissions, Cardiovascular 0.0013000.0001200.005600 0.0009100.0002000.0000190.0008200.0003300.0030000.0006800.011000 0.002200Non-fatal Heart Attacks (age>18)Peters et al (2001) 0.0041000.0003900.018000 0.0028000.0006500.0000640.0028000.0012000.0110000.0024000.041000 0.007900Pooled estimate of 4 studies 0.0004500.0000420.001900 0.0003100.0000700.0000070.0003000.0001300.0011000.0002600.004400 0.000850* All estimates are rounded to two significant figures. All fine particles are assumed to have equivalent health effects, but the incidence-per-ton estimates vary depending 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.4A-13Table 4A-7. Summary of Regional PM2.5Incidence-per-Ton Estimates Based on Air Quality Modeling from Proposed Clean PowerPlan in 2025*Health EndpointEast West CaliforniaSO2NOXEC+OC Crustal SO2NOXEC+OC Crustal SO2NOXEC+OC CrustalPremature MortalityKrewskiet al.(2009) – adult 0.0039000.0003500.017000 0.0027000.0007500.0000790.0032000.0013000.0110000.0026000.044000 0.008700Lepeuleet al.(2012) – adult 0.0089000.0008000.038000 0.0062000.0017000.0001800.0073000.0030000.0260000.0058000.099000 0.020000Woodruffet al.(1997) – infants 0.0000080.0000010.000035 0.0000060.0000020.0000000.0000070.0000030.0000220.0000070.000093 0.000018MorbidityEmergency department visits forasthma0.0020000.0002000.006300 0.0013000.0003200.0000330.0010000.0005100.0055000.0015000.018000 0.004400Acute bronchitis 0.0057000.0005200.024000 0.0039000.0013000.0002100.0056000.0022000.0200000.0053000.080000 0.015000Lower respiratory symptoms 0.0720000.0067000.310000 0.0500000.0170000.0027000.0710000.0280000.2500000.0670001.000000 0.200000Upper respiratory symptoms 0.1000000.0096000.440000 0.0710000.0240000.0038000.1000000.0400000.3600000.0960001.500000 0.280000Minor restricted-activity days 2.8000000.25000012.0000001.9000000.6100000.0830002.5000000.9700009.6000002.30000036.0000006.900000Lost work days 0.4700000.0430002.000000 0.3200000.1000000.0140000.4300000.1600001.6000000.3900006.100000 1.200000Asthma exacerbation 0.2500000.0230001.100000 0.1700000.0590000.0093000.2500000.0970000.8800000.2300003.500000 0.680000Hospital Admissions, Respiratory 0.0012000.0001100.005100 0.0008100.0001800.0000170.0007400.0003000.0028000.0006500.011000 0.002100Hospital Admissions, Cardiovascular 0.0014000.0001300.006200 0.0010000.0002200.0000220.0009300.0003800.0033000.0007500.012000 0.002400Non-fatal Heart Attacks (age>18)Peters et al (2001) 0.0046000.0004300.020000 0.0031000.0007400.0000710.0032000.0013000.0120000.0027000.046000 0.008900Pooled estimate of 4 studies 0.0004900.0000460.002100 0.0003400.0000800.0000080.0003400.0001400.0013000.0002900.004900 0.000950* All estimates are rounded to two significant figures. All fine particles are assumed to have equivalent health effects, but the incidence-per-ton estimates vary depending 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.4A-14Table 4A-8. Summary of Regional PM2.5Incidence-per-Ton Estimates Based on Air Quality Modeling from Proposed Clean PowerPlan in 2030*Health EndpointEast West CaliforniaSO2NOXEC+OC Crustal SO2NOXEC+OC Crustal SO2NOXEC+OC CrustalPremature MortalityKrewskiet al.(2009) – adult 0.0042000.0003800.018000 0.0029000.0008400.0000870.0036000.0015000.0130000.0028000.048000 0.009600Lepeuleet al.(2012) – adult 0.0096000.0008500.041000 0.0067000.0019000.0002000.0081000.0034000.0290000.0064000.110000 0.022000Woodruffet al.(1997) – infants 0.0000080.0000010.000033 0.0000050.0000020.0000000.0000070.0000030.0000210.0000060.000088 0.000017MorbidityEmergency department visits forasthma0.0016000.0001600.006600 0.0011000.0002600.0000270.0011000.0004200.0045000.0012000.019000 0.003500Acute bronchitis 0.0059000.0005400.025000 0.0041000.0014000.0002200.0059000.0023000.0210000.0054000.083000 0.016000Lower respiratory symptoms 0.0750000.0068000.320000 0.0520000.0180000.0028000.0750000.0300000.2600000.0690001.100000 0.200000Upper respiratory symptoms 0.1100000.0098000.460000 0.0740000.0260000.0040000.1100000.0420000.3700000.0990001.500000 0.290000Minor restricted-activity days 2.9000000.26000012.0000002.0000000.6500000.0880002.7000001.0000009.8000002.30000037.0000007.100000Lost work days 0.4800000.0430002.000000 0.3300000.1100000.0150000.4500000.1700001.7000000.4000006.300000 1.200000Asthma exacerbation 0.2600000.0240001.100000 0.1800000.0630000.0098000.2600000.1000000.9200000.2400003.700000 0.710000Hospital Admissions, Respiratory 0.0013000.0001200.005600 0.0009000.0002000.0000190.0008300.0003400.0032000.0007400.012000 0.002400Hospital Admissions, Cardiovascular 0.0016000.0001500.006800 0.0011000.0002500.0000240.0010000.0004200.0038000.0008500.014000 0.002700Non-fatal Heart Attacks (age>18)Peters et al (2001) 0.0050000.0004600.021000 0.0035000.0008300.0000790.0036000.0015000.0140000.0031000.052000 0.010000Pooled estimate of 4 studies 0.0005400.0000490.002300 0.0003700.0000900.0000090.0003800.0001600.0015000.0003300.005600 0.001100* All estimates are rounded to two significant figures. All fine particles are assumed to have equivalent health effects, but the incidence-per-ton estimates vary depending 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.4A-15

4A.4 Regional Ozone Benefit-per-Ton Estimates
The process for generating the regional ozone benef it-per-ton estimates is consistent with the process for PM 2.5. Ozone is not directly emitted, and is a non-linear function of NO X and VOC emissions. For the purpose of estimating benefi t-per-ton for this RIA, we assume that all of the ozone impacts from EGUs are attributable to NO X emissions. VOC emissions, which are also a precursor to ambient ozone formation, are insigni ficant from the EGU sector relative to both
NO X emissions from EGUs and the total VOC emissions in ventory. Therefore, we believe that our assumption that EGU-attributable ozone formatio n at the regional-level is due to NO X alone
is reasonable.

Similar to PM 2.5 , this method provides estimates of the regional av erage benefit-per-ton. Due to the non-linear chemistry between NO X emissions and ambient ozone, using an average benefit-per-ton estimate for NO X adds uncertainty to the ozone co-benefits estimate d 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 derived from modeling the Clean Power Pl an proposal during the ozone-season only (May to September). As shown in Table 4A-1, ozone-season NO X emissions from EGUs are slightly less than half of all-year NO X emissions. Because we estimate ozone health impacts from May to September only, th is approach underestimates ozone cobenefits in areas with longer ozone seasons such as southern California and Texas. When the underestimated benefit-per-ton estimate is multipli ed by ozone-season only NO X emission reductions, this results in an underestimate of the monetized ozone co-benefits. For illustrative purposes, Tables 4A-9 through 4A-11 provide the ozo ne benefit-per-ton estimates using both allyear NO X emissions and ozone-season only NO X for 2020, 2025, and 2030, respectively. Tables 4A-12 through 4A-14 provide the ozone season incide nce-per-ton estimates for 2020, 2025, and 2030, respectively. Similar to PM 2.5 , the ozone benefit-per-ton values for 2020 and 203 0 are based on applying the air quality modeling from 202 5 to population and health information from 2020 and 2030. Estimated benefit-per-ton for these years have additional uncertainty relative to 2025 because of potential differences in atmospheri c responses to reductions in ozone precursors4A-16

in those years. Uncertainties may be somewhat large r in the case of ozone due to high degree of dependence of ozone responses to baseline meteorolo gy and emissions levels.

Table 4A-9. Summary of Regional Ozone Benefit-per-T on Estimates Based on Air

Quality Modeling from Proposed Clean Power Plan in 2020 (2011$)* Ozone precursor Pollutant National Regional East West California Ozone season NO X $6,000 to $26,000 $6,500 to $28,000 $2,000 to $8,90 0 $14,000 to $59,000 * The range of estimates reflects the range of epid emiology studies for avoided premature mortality fo r ozone. All estimates are rounded to two significant figures. T he monetized benefits incorporate the conversion fr om NO X precursor emissions to ambient ozone.

Table 4A-10. Summary of Regional Ozone Benefit-per- Ton Estimates Based on Air

Quality Modeling from Proposed Clean Power Plan in 2025 (2011$)* Ozone precursor Pollutant National Regional East West California Ozone season NO X $6,600 to $27,000 $7,100 to $30,000 $2,300 to $10,0 00 $15,000 to $66,000 * The range of estimates reflects the range of epid emiology studies for avoided premature mortality fo r ozone. All estimates are rounded to two significant figures. T he monetized benefits incorporate the conversion fr om NO X precursor emissions to ambient ozone.

Table 4A-11. Summary of Regional Ozone Benefit-per- Ton Estimates Based on Air

Quality Modeling from Proposed Clean Power Plan in 2030 (2011$)* Ozone precursor Pollutant National Regional East West California Ozone season NO X $7,100 to $29,000 $7,600 to $33,000 $2,600 to $11,0 00 $17,000 to $73,000 * The range of estimates reflects the range of epid emiology studies for avoided premature mortality fo r ozone. All estimates are rounded to two significant figures. T he monetized benefits incorporate the conversion fr om NO X precursor emissions to ambient ozone.4A-17

Table 4A-12. Summary of Regional Ozone Incidence-per-Ton Estimates Based on Air

Quality Modeling from Proposed Clean Power Plan in 2020* Health Endpoint East West California Premature Mortality – adult Bell et al. (2004) 0.000600 0.000190 0.001300 Levy et al. (2005) 0.002800 0.000880 0.005800 Morbidity Hospital Admissions, Respiratory (ages > 65) 0.0035 00 0.000900 0.006600 Hospital Admissions, Respiratory (ages < 2) 0.00180 0 0.000780 0.003300 Emergency Room Visits, Respiratory 0.002000 0.00050 0 0.003900 Acute Respiratory Symptoms 3.500000 1.300000 8.8000 00 School Loss Days 1.200000 0.490000 3.000000 * All estimates are rounded to two significant figu res. The incidence benefit-per-ton estimates incorp orate the conversion from NO X precursor emissions to ambient ozone. These estima tes reflect ozone-season NO X emissions.

Table 4A-13. Summary of Regional Ozone Incidence-per-Ton Estimates Based on Air

Quality Modeling from Proposed Clean Power Plan in 2025* Health Endpoint East West California Premature Mortality – adult Bell et al. (2004) 0.000640 0.000210 0.001400 Levy et al. (2005) 0.002900 0.000970 0.006400 Morbidity Hospital Admissions, Respiratory (ages > 65) 0.0041 00 0.001100 0.007800 Hospital Admissions, Respiratory (ages < 2) 0.00180 0 0.000820 0.003400 Emergency Room Visits, Respiratory 0.002000 0.00054 0 0.004100 Acute Respiratory Symptoms 3.600000 1.400000 8.9000 00 School Loss Days 1.300000 0.520000 3.200000 * All estimates are rounded to two significant figu res. The incidence benefit-per-ton estimates incorp orate the conversion from NO X precursor emissions to ambient ozone. These estima tes reflect ozone-season NO X emissions.

Table 4A-14. Summary of Regional Ozone Incidence-per-Ton Estimates Based on Air

Quality Modeling from Proposed Clean Power Plan in 2030* Health Endpoint East West California Premature Mortality – adult Bell et al. (2004) 0.000640 0.000230 0.001800 Levy et al. (2005) 0.002900 0.001100 0.008200 Morbidity Hospital Admissions, Respiratory (ages > 65) 0.0044 00 0.001300 0.011000 Hospital Admissions, Respiratory (ages < 2) 0.00180 0 0.000860 0.004100 Emergency Room Visits, Respiratory 0.002000 0.00058 0 0.005000 Acute Respiratory Symptoms 3.500000 1.500000 11.000 000 School Loss Days 1.200000 0.550000 3.800000 * All estimates are rounded to two significant figu res. The incidence benefit-per-ton estimates incorp orate the conversion from NO X precursor emissions to ambient ozone. These estima tes reflect ozone-season NO X emissions.4A-18

4A.5 References
Abt Associates, Inc. 2010. “User’s Guide: Modeled A ttainment Test Software.” Available at: <http://www.epa.gov/scram001/modelingapps_mats.htm> . Accessed June 6, 2015. Abt Associates, Inc. 2012. “BenMAP User’s Manual Ap pendices,” prepared for U.S. Research Triangle Park, NC: U. S. Environmental Protection A gency, Office of Air Quality Planning and Standards. Available at: <http://www.epa.gov/air/benmap/models/BenMAPAppendi cesOct2012.pdf>. Accessed June 6, 2015. Bell, M.L., A. McDermott, S.L. Zeger, J.M. Sarnet, and F. Dominici

. 2004. “Ozone and ShortTerm Mortality in 95 U.S. Urban Communities, 1987-2 000.” Journal of the American Medical Association. 292(19):2372-8.
Bell, M.L., F. Dominici, and J.M. Samet. 2005. “A M eta-Analysis of Time-Series Studies of Ozone and Mortality with Comparison to the National Morbidity, Mortality, and Air Pollution Study.” Epidemiology . 16(4):436-45. ENVIRON International Corporation. 2014. User’s Gui de: Comprehensive Air Quality Model with Extensions, Version 6.1, Novato, CA. April. Av ailable at <http://www.camx.com>. Accessed June 6, 2015.
Fann, N., C.M. Fulcher, and B.J. Hubbell. 2009. “Th e Influence of Location, Source, and Emission Type in Estimates of the Human Health Bene fits 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. “Char acterizing 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.
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): 3580–3589. Huang Y., F. Dominici, and M. Bell. 2005. “Bayesian Hierarchical Distributed Lag Models for Summer Ozone Exposure and Cardio-Respiratory Mortal ity.” Environmetrics . 16:547-562. Ito, K., S.F. De Leon, and M. Lippmann. 2005. “Asso ciations between Ozone and Daily Mortality: Analysis and Meta-Analysis.” Epidemiology . 16(4):446-57.4A-19

Krewski D., M. Jerrett, R.T. Burnett, R. Ma, E. Hug hes, Y. Shi, et al. 2009. Extended Follow-Up and Spatial Analysis of the American Cancer Society Study Linking Particulate Air Pollution and Mortality. HEI Research Report, 140, Health Effects Institute, Boston, MA. Lepeule, J., F. Laden, D. Dockery, and J. Schwartz. 2012. “Chronic Exposure to Fine Particles and Mortality: An Extended Follow-Up of the Harvard Six Cities Study from 1974 to 2009.” Environmental Health Perspectives. 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):458-68.
Roman, H.A., K. D. Walker, T. L. Walsh, L. Conner, H. M. Richmond, B. J. Hubbell, and P. L. Kinney. 2008. “Expert Judgment Assessment of the Mo rtality 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 Associatio n between Ozone and Daily Deaths to Control for Temperature?” American Journal of Respiratory and Critical Care M edicine. 171(6): 627-31. U.S. Environmental Protection Agency (U.S. EPA). 20 07. Guidance on the Use of Models and Other Analyses for Demonstrating Attainment of Air Quality Goals for Ozone, PM 2.5 , and Regional Haze . Office of Air Quality Planning and Standards, Res earch Triangle Park, NC. Available at <http://www.epa.gov/ttn/scram/guidance /guide/final-03-pm-rh-guidance.pdf>. Accessed June 4, 2015. U.S. Environmental Protection Agency (U.S. EPA). 20 11b Regulatory Impact Analysis for the Final Mercury and Air Toxics Standards . Research Triangle Park, NC: Office of Air Quality Planning and Standards, Health and Environmental Im pacts Division. (EPA document number EPA-452/R-11-011, December). Available at <http://www.epa.gov/ttn/ecas/regdata/RIAs/matsriafi nal.pdf>. Accessed June 4, 2015. U.S. Environmental Protection Agency (U.S. EPA). 20 12a. Regulatory Impact Analysis for the Final Revisions to the National Ambient Air Quality Standards for Particulate Matter. EPA452/R-12-003. Office of Air Quality Planning and St andards, Health and Environmental Impacts Division, Research Triangle Park, NC. Decem ber. Available at: < http://www.epa.gov/ttnecas1/regdata/RIAs/finalria.p df>. Accessed June 4, 2015.4A-20

U.S. Environmental Protection Agency (U.S. EPA). 20 13. 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 P ark, NC. February. Available at: < http://www2.epa.gov/sites/production/files/2014- 10/documents/sourceapportionmentbpttsd.pdf >. Acces sed June 4, 2015. U.S. Environmental Protection Agency (U.S. EPA). 20 14. Regulatory Impact Analysis for the Proposed Carbon Pollution Guidelines for Existing P ower Plants and Emission Standards for Modified and Reconstructed Power Plants . EPA-542/R-14-002. Office of Air Quality Planning and Standards, Research Triangle Park, NC. June. Available at <http://www.epa.gov/ttnecas1/regdata/RIAs/111dpropo salRIAfinal0602.pdf>. Accessed June 4, 2015.5-1



### C HAPTER 

### 5: E CONOMIC 

### I MPACTS



### – M ARKETS 

### O UTSIDE THE 

### U TILITY 

### P OWER 

### S ECTOR

5.1 Introduction
The energy sector impacts presented in Chapter 3 of this RIA include potential changes in the prices for electricity, natural gas, and coal p otentially resulting from the Clean Power Plan Final Rule. This chapter addresses the impact of th ese potential changes on other markets and discusses some of the determinants of the magnitude of these impacts. We refer to these changes as secondary market impacts.

Under the final emission guidelines, states are not required to use any of the measures that the EPA determines constitute BSER, or use those me asures 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 guid elines, the benefits, cost and economic impacts reported in this RIA are illustrative of ac tions that states may take. The implementation approaches adopted by the states, and the strategie s adopted by affected EGUs, will ultimately drive the magnitude and timing of secondary impacts from changes in the price of electricity, and the demand for inputs by the electricity sector, on other markets that use and produce these inputs.

The flexibility afforded to states in their state p lans allows them to encourage compliance methods by affected EGUs, which include design elem ents that may mitigate or promote particular impacts based on their priorities. For e xample, states in the Regional Greenhouse Gas
Initiative use the revenues from allowance auctions to support direct bill assistance for retail consumers, fund investments in clean energy and ele ctricity demand reduction for business consumers, and support employment in the developmen t of clean and renewable energy technologies. In its recent regulations to limit gr eenhouse gas (GHG) emissions, California’s Air Resources Board designated a portion of allowances to be allocated to electric distribution companies in order to mitigate potential electricit y rate increases and their associated impacts. Other states may encourage compliance methods by af fected EGUs with particularly robust5-2

deployment of renewables, energy efficiency, or nat ural gas to promote manufacturing demand or employment in those sectors. For example, energy efficiency investments may be targeted towards reducing both electricity consumption and n atural gas or heating oil consumption, such as weatherization projects. The state plan approach and the composition of these programs will influence the effects of compliance with the final rulemaking.

To estimate the costs, benefits, and impacts of imp lementing the CPP guidelines, the EPA modeled two illustrative plan approaches: a rate-ba sed approach and a mass-based approach. Chapter 3 provides a description of the illustrativ e plan approaches analyzed. This chapter provides a quantitative assessment of the energy pr ice impacts for these illustrative approaches and a qualitative assessment of the factors that wi ll in part determine the timing and magnitude
of effects in other markets.

5.2 Methods One potential quantitative approach to evaluating t he secondary market impacts is to use a computable general equilibrium (CGE) model. CGE mod els are able to provide aggregated representations of the whole economy in equilibrium in the baseline and potentially with regulation in place. As such, CGE model may be able to capture interactions between economic sectors and provide information on changes outside of the directly regulated sector. In support of previous rulemakings, such as the 2008 Final Ozone NAAQS (U.S. EPA 2008) and the 2010 Transport Rule proposal (U.S. EPA 2010), the EPA us ed the Economic Model for Policy Analysis (EMPAX) CGE model to estimate the secondar y market effects based on the cost impacts projected by the Integrated Planning Model (IPM) for the directly regulated sector.

When considering the secondary market impacts of a regulation both the effects of the costs, the benefits of improved air quality, and th eir interaction may be relevant. Therefore, in the Second Prospective Analysis under Section 812 o f the Clean Air Act Amendments the EPA incorporated a set of health benefits arising from air quality improvement into the EMPAX CGE model when studying the economy-wide impacts of the Clean Air Act (U.S. EPA 2011). While the external Council on Clean Air Compliance Analys is (Council) review of this study stated that inclusion of benefits in an economy-wide model “rep resent[ed] a significant step forward in benefit-cost analysis” (Hammitt 2010), the EPA reco gnizes that serious technical challenges5-3

remain when attempting to evaluate the benefits and costs of potential regulatory actions using economy-wide models.

In light of these challenges, the EPA has establish ed a Science Advisory Board (SAB) panel on economy-wide modeling to consider the tech nical merits and challenges of using this analytical tool to evaluate costs, benefits, and ec onomic impacts in regulatory development. In addition, EPA is asking the panel to identify poten tial paths forward for improvements that could address the challenges posed when using economy-wid e models to evaluate the effects of regulations. The final panel membership was announced in March 2 015 and the first of multiple face-to-face meetings of the SAB panel has been sch eduled for October 2015. The EPA will use the recommendations and advice of this panel as an input into its process for improving benefitcost and economic impact analyses used to inform de cision-making at the agency.

The advice from the Science Advisory Board (SAB) pa nel formed specifically to address the subject of economy-wide modeling was not availa ble in time for this final action. Given the ongoing SAB panel on economy-wide modeling, the unc ertain nature of the ultimate energy price impacts due to the state flexibility in choos ing a plan and the compliance flexibility for affected EGUs, and the ongoing challenges of accura tely representing costs, benefits, energy efficiency improvements in economy-wide modeling, t his chapter considers the energy impacts associated with the illustrative plan approaches an alyzed and a qualitative assessment of the factors that will, in part, determine the timing an d magnitude of effects in other markets.

5.3 Summary of Secondary Market Impacts of Energy P rice Changes
Electricity, natural gas, and coal are important in puts to the production of other goods and services. Therefore, changes in the price of these commodities will shift the production costs for sectors that use electricity, natural gas, and coal in the production of other goods and services. Changes in the types and levels of inputs used by p roducers in response to electricity and fuel price changes may mitigate the production cost chan ges in these sectors. Such changes in production costs may lead to changes in the quantit ies and/or prices of the goods or services produced and changes in imports and exports.

The EPA used IPM to estimate electricity, natural g as, and coal price changes based on the illustrative plan approaches modeled for this rule. IPM is a multi-regional, dynamic,5-4

deterministic linear programming model of the U.S. electric power sector described in more detail in Chapter 3. The Retail Price Model (RPM) u ses forecast changes in wholesale prices and the cost of demand-side energy efficiency programs to forecast changes in average retail prices. The prices are average prices over consumer classes and regions weighted by the amount used. Table 5-1 shows these estimated price changes. For other results generated by IPM and the RPM, please refer to Chapter 3.

There are many factors influencing the projected na tural gas prices. IPM (and its integrated gas resource and supply module) models natural gas reserves appropriate natural gas supplies
based on a multitude of factors. Since the model si mulates perfect foresight, it anticipates future demand for natural gas and responds accordingly. In addition, IPM (and the natural gas module) are viewing a very long time horizon (through 2050) , such that the impacts in certain years may be responsive to other modeling assumptions or driv ers. 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.

Table 5-1. Estimated Percentage Changes in Average Energy Prices by Energy Type for

the Final Emission Guidelines, Rate-based and Mass- based Illustrative Plan Approaches
Rate-based 2020 2025 2030 Electricity Price Change 3.2% 0.9% 0.8% Delivered Natural Gas Price Change 5.3% -7.7% 2.5% Delivered Coal Price Change -1.7% -6.2% -8.0%

Mass-based 2020 2025 2030 Electricity Price Change 3.0% 2.0% 0.0% Delivered Natural Gas Price Change 3.8% -3.2% -2.1% Delivered Coal Price Change -1.6% -4.3% -4.6%

For years when the price of electricity, natural ga s, or coal increased, one would expect decreases in production and increases in market pri ces in sectors for which these commodities are inputs, ceteris paribus. Conversely, for 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 would lead t o smaller impacts within secondary markets. However, a number of factors in addition to the mag nitude and sign of the energy price changes,5-5

influence the magnitude of the impact on production and market prices for sectors using electricity, natural gas, or coal as inputs to prod uction. 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 attributable to those commod ities. For sectors in which the directly affected inputs are only a small portion of product ion costs, the impact will be smaller than for sectors in which these inputs make up a greater por tion of total production costs. Therefore, more energy-intensive sectors would potentially experien ce greater cost increases when electricity, natural gas, or coal prices increase, but would als o experience greater reduced costs when these input prices decrease. 128

5.3.2 Ability to Substitute between Inputs to the P roduction Process
The ease with which producers are able to substitut e other inputs for electricity, natural gas, or coal, or even amongst those commodities, in fluences the impact of price changes for these inputs. Those sectors with a greater ability to substitute across energy inputs or to other inputs will be able to, at least partially, offset the increased cost of these inputs resulting in smaller market impacts. Similarly, when prices for electricity, natural gas, or coal decrease, some sectors may choose to use more of these inputs in p lace of other more costly substitutes.

5.3.3 Availability of Substitute Goods and Services
The ability of producers in sectors experiencing ch anges 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 se ctors’ products. Substitutes may be either other domestic products or foreign imports. If close subs titutes exist, the demand for the product will in general be more elastic and the producers will b e less able to pass on the added cost through a price increase.

128 The net direct effect of this rulemaking on the pr oduction costs of a sector that is attributable to a change in the electricity price also depends on the expenditures the sector makes to reduce its demand for electrici ty under any energy efficiency program that was adopted to achie ve a state goal. That said, those expenditures may lead to other reduced expenditures for the sector, such as reduce d natural gas use from weatherization projects.5-6

Such substitution can also take place between forei gn and domestic goods within the same sector. Changes in the price of electricity, natura l gas, and coal can influence the quantities of goods imported or exported from sectors using these inputs. When the cost of domestic production increases due to more expensive inputs, imports may increase as consumers substitute towards relatively less costly foreign-produced goo ds. If imports increase because of a regulation and those imports come from countries with higher e missions per unit of production, this can result in foreign emission increases that offset so me portion of domestic decreases, an effect commonly referred to as “leakage.” Alternatively, i f those imports are less emissions-intensive than the sectors that have contracted, emissions ma y fall even further. The potential for changes in global pollutants such as carbon dioxide (CO 2 ) and other GHG emissions is noteworthy. Unlike most criteria pollutants and hazardous air p ollutants, the impacts of CO 2 emissions are not affected by the location from which those emissions originate. A more complete evaluation of the effect of this regulation on GHG emissions from other countries would account for whether those countries have, or are expected to implement, policies affecting their GHG emissions. This may include the potential that the present regulati on could change the likelihood that other countries will adopt policies affecting their GHG e missions.

5.4 Effect of Changes in Input Demand from Electri city Sector Section 5.2 focuses on the effects of changes in e nergy prices, and possible responses to those price changes, on sectors outside of the elec tricity sector. A change in demand for inputs in the electricity sector, as well as changes in deman d for energy efficiency services and products, will also influence economic activity in other sect ors of the economy. For example, there will be changes in the demand for new generation sources su ch as natural gas combined cycle units and renewables, and therefore sectors producing these t echnologies may expand. Therefore, while a sector that produces say, wind turbine blades, may face higher natural gas and electricity prices, production in that sector may ultimately increase d ue to higher demand from the electricity
sector for wind turbines.

5.5 Conclusions
Changes in the price of electricity, natural gas, a nd coal can affect markets for goods and services produced by sectors that use these energy inputs in the production process. The direction5-7

and magnitude of these impacts are influenced by a number of factors. For example, the more able producers in these sectors are to substitute a way from the use of these energy inputs, the smaller the effect of energy prices changes will be on their production cost. Changes in cost of production may lead to changes in price, quantity p roduced, and profitability of firms within secondary markets. Furthermore, the demand inputs i n the electricity sector, as well as changes in the demand for energy efficiency services and pr oducts, will also affect secondary markets. If regulation results in changes in domestic markets t hat lead to an increase in imports, increases in production in countries with more energy-intensive production may lead to changes in CO 2 emissions elsewhere. The presence and adoption of p olicies affecting GHG emissions in other countries, which may be influenced by the adoption of this final rule, may affect the change in
emissions elsewhere.

Modeling choices in IPM influence the forecast chan ges in electricity, natural gas, and coal prices in this RIA. Actual market conditions, as wi ll the plan approaches that states adopt, will ultimately influence the price changes of these ene rgy inputs and consequent effects on secondary markets.

5.6 References
Hammitt, J.K. 2010. Review of the Final Integrated Report for the Second Section 812 Prospective Study of the Benefits and Costs of the Clean Air Act. Available at <http://yosemite.epa.gov/sab/sabproduct.nsf/9288428 b8eeea4c885257242006935a3/1E6218D E3BFF682E852577FB005D46F1/$File/EPA-COUNCIL-11-001- unsigned.pdf.> Accessed June 4, 2015. U.S. Environmental Protection Agency (U.S. EPA). 20 08. Final Ozone NAAQS Regulatory Impact Analysis. Office of Air Quality Planning and Standards, Research Triangle Park, NC. Available at <http://www.epa.gov/ttn/ecas/regdata/R IAs/452_R_08_003.pdf>. Accessed June 4, 2015. U.S. Environmental Protection Agency (U.S. EPA). 20 10. Regulatory Impact Analysis for the Proposed Federal Transport Rule. Office of Air Qual ity Planning and Standards, Research Triangle Park, NC. Available at <http://www.epa.gov/ttnecas1/regdata/RIAs/proposalt rria_final.pdf>. Accessed June 4, 2015.5-8

U.S. Environmental Protection Agency (U.S. EPA). 20 11. The Benefits and Costs of the Clean Air Act from 1990 to 2020, Final Report – Rev A. Of fice of Air and Radiation, Washington, DC. Available at <http://www.epa.gov/cleanairactben efits/feb11/fullreport_rev_a.pdf>. Accessed June 4, 2015.6-1



### C HAPTER 

### 6: E MPLOYMENT 

### I MPACT 

### A NALYSIS

6.1 Introduction Executive Order 13563 directs federal agencies to c onsider regulatory impacts on job creation and employment. According to the Executive Order, “our regulatory system must protect public health, welfare, safety, and our env ironment while promoting economic growth, innovation, competitiveness, and job creation. It m ust be based on the best available science” (Executive Order 13563, 2011). Although standard be nefit-cost analyses have not typically included a separate analysis of regulation-induced employment impacts, 129 we typically conduct employment analyses for economically significant ru les. While the economy continues moving toward full-employment, employment impacts are of p articular concern and questions may arise about their existence and magnitude. This chapter d iscusses and projects potential employment impacts for the utility power, coal and natural gas production, and demand-side energy efficiency sectors which may result from the Final Carbon Poll ution Emission Guidelines for Existing Stationary Sources: Electric Utility Generating Uni ts (herein referred to as “final emission guidelines” or the “Clean Power Plan Final Rule”). 130

Section 6.2 describes the theoretical framework use d to analyze regulation-induced employment impacts, discussing how economic theory alone cannot predict whether such impacts are positive or negative. Section 6.3 prese nts an overview of the peer-reviewed literature relevant to evaluating the effect of environmental regulation on employment. Section 6.4 provides background regarding recent employment tre nds in the electricity generation, coal and natural gas extraction, renewable energy, and deman d-side energy efficiency-related sectors. Section 6.5 presents the EPA’s quantitative project ions of potential employment impacts in these sectors. These projections are based in part on a d etailed model of the electricity production sector used for this regulatory analysis. Additiona lly, this section discusses projected employment impacts due to demand-side energy effici ency activities. Section 6.6 offers several
conclusions.

129 Labor expenses do, however, contribute toward tota l costs in the EPA’s standard benefit-cost analyses

.

130 The employment analysis in this RIA is part of EPA ’s ongoing effort to “conduct continuing evaluation s of potential loss or shifts of employment which may re sult from the administration or enforcement of [the Act]” pursuant to CAA section 321(a).6-2

6.2 Economic Theory and Employment
Regulatory employment impacts are difficult to dise ntangle from other economic changes affecting employment decisions over time and across regions and industries. Labor market responses to regulation are complex. They depend on labor demand and supply elasticities and possible labor market imperfections (e.g., wage sti ckiness, 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 impa cts are composed of a mix of potential declines and gains in different areas of the econom y (e.g., the directly regulated sector, the environmental protection sector, upstream and downs tream sectors, etc.) over time. In light of these difficulties, economic theory provides a cons tructive framework for analysis.
Microeconomic theory describes how firms adjust the ir use of inputs in response to changes in economic conditions. 131 Labor is one of many inputs to production, along w ith capital, energy, and materials. In competitive markets, firm s choose inputs and outputs to maximize profit as a function of market prices and technolog ical constraints. 132,133 Berman and Bui (2001) adapt this model to analyze how environmental regul ations affect labor demand. 134 They model environmental regulation as effectively requiring c ertain factors of production, such as pollution abatement capital, at levels that firms would not o therwise choose. Berman and Bui (2001) model two components that drive changes in firm-lev el labor demand: output effects and
substitution effects. 135 Regulation affects the profit-maximizing quantity of output by changing the marginal cost of production. If a regulation ca uses marginal cost to increase, it will place upward pressure on output prices, leading to a decr ease in demand, and resulting in a decrease in production. The output effect describes how, holdin g labor intensity constant, a decrease in production causes a decrease in labor demand. As no ted by Berman and Bui, although many

131 See Layard and Walters (1978), a standard microeco nomic theory textbook, for a discussion, in Chapter 9.

132 See Hamermesh (1993), Ch. 2, for a derivation of t he firm’s labor demand function from cost-minimizat ion.

133 In this framework, labor demand is a function of q uantity of output and prices (of both outputs and i nputs).

134 Morgenstern, Pizer, and Shih (2002) develop a simi lar model.

135 The authors also discuss a third component, the im pact of regulation on factor prices, but conclude t hat this effect is unlikely to be important for large competitive f actor markets, such as labor and capital. Morgenste rn, Pizer and Shih (2002) use a very similar model, but they brea k the employment effect into three parts: 1) a dema nd effect; 2) a cost effect; and 3) a factor-shift effect.6-3

assume that regulations must increases marginal cos t, it need not be the case. A regulation could induce a firm to upgrade to less polluting and more efficient equipment that lowers the marginal cost of production. In such a case, output could in crease after firms comply with the regulation. For example, in the context of the current rule, im proving the heat rate of utility boiler increases fuel efficiency, lowering marginal production costs , and thereby potentially increasing the utility boiler’s generation. An unregulated profit-maximizi ng firm may not have chosen to install such an efficiency-improving technology if the return on investment were too low, but once the investment is required it lowers marginal productio n costs. The substitution effect describes how, holding outp ut constant, regulation affects the labor-intensity of production. Although increased e nvironmental regulation may increase use of pollution control equipment and energy to operate t hat equipment, the impact on labor demand is ambiguous. For example, equipment inspection requir ements, specialized waste handling, completing required paperwork, or pollution technol ogies that alter the production process may affect the number of workers necessary to produce a unit of output. Berman and Bui (2001) model the substitution effect as the effect of regu lation on pollution control equipment and expenditures required by the regulation and the cor responding change in the labor-intensity of production.
In summary, as output and substitution effects may be positive or negative, economic theory alone cannot predict the direction of the ne t effect of regulation on labor demand at the level of the regulated firm. Operating within the b ounds of standard economic theory, however, empirical estimation of net employment effects on r egulated firms is possible when methods and data of sufficient detail and quality are available . The extant literature, however, illustrates difficulties with empirical estimation. For example , there is a paucity of publicly-available data on plant-level employment, thus most studies must r ely on confidential plant-level employment data from the U.S. Census Bureau, typically combine d with pollution abatement expenditure data, that are too dated to be reliably informative , or other measures of the stringency of regulation. In addition, the most commonly used emp irical methods, for example, Greenstone (2002), likely overstate employment impacts because they rely on relative comparisons between more regulated and less regulated counties, which c an lead to “double counting” of impacts6-4

when production and employment shift from more regu lated towards less regulated areas. Thus these empirical methods cannot be used to estimate net employment effects. 136

. The conceptual framework described thus far focused on regulatory effects on plant-level decisions within a regulated industry. Employment i mpacts at an individual plant do not necessarily represent impacts for the sector as a w hole. The theoretical approach must be modified when applied at the industry level.
At the industry-level, labor demand is more respons ive if: (1) the price elasticity of demand for the product is high, (2) other factors o f 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. 137 For example, if all firms in an industry are faced with the same regulatory compliance costs and product demand is inelastic, t hen industry output may not change much, and output of individual firms may change slightly. 138 In this case, the output effect may be small, while the substitution effect depends on input subs titutability. 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 sma ll output effect, the total effect may be positive. As with potential effects for an individu al firm, theory cannot determine the sign or magnitude of industry-level regulatory effects on l abor 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, 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 regul ated industry, net employment impacts encompass changes in other related sectors. For exa mple, the final guidelines may increase demand for heat rate improving equipment and servic es. This increased demand may increase revenue and employment in the firms supporting this technology. At the same time, the regulated

136 See Greenstone (2002) p. 1212.

137 See Ehrenberg & Smith, p. 108.

138 This discussion draws from Berman and Bui (2001), pp. 293.6-5

industry is purchasing the equipment, and these cos ts may impact labor demand at regulated firms. Therefore, it is important to consider the n et effect of compliance actions on employment across multiple sectors or industries.
If the U.S. economy is at full employment, even a l arge-scale environmental regulation is unlikely to have a noticeable impact on aggregate n et employment. 139 Instead, labor in affected sectors would primarily be reallocated from one pro ductive use to another (e.g., from producing electricity or steel to producing high efficiency e quipment), and net national employment effects from environmental regulation would be small and tr ansitory (e.g., as workers move from one job to another). 140 Some workers may retrain or relocate in anticipati on of new requirements or require time to search for new jobs, while shortage s in some sectors or regions could bid up wages to attract workers. These adjustment costs ca n lead to local labor disruptions. Although the net change in the national workforce is expecte d to be small, localized reductions in employment may adversely impact individuals and com munities just as localized increases may have positive impacts.
If, on the other hand, the economy is operating at less than full employment, economic theory does not clearly indicate the direction or m agnitude of the net impact of environmental regulation on employment; it could cause either a s hort-run net increase or short-run net decrease (Schmalansee and Stavins, 2011). For example, the C ongressional Budget Office considered EPA’s Mercury Air Toxics Standards and regulations for industrial boilers and process heaters as potentially leading to short-run net increases in e conomic growth and employment, driven by capital investments for compliance with the regulat ions (Congressional Budget Office, 2011). An important research question is how to accommodate u nemployment as a structural feature in economic models. This feature may be important in a ssessing large-scale regulatory impacts on employment (Smith, 2012).

139 Full employment is a conceptual target for the eco nomy where everyone who wants to work and is availa ble to do so at prevailing wages is actively employed. The unemployment rate at full employment is not zero.

140 Arrow et al. 1996; see discussion on bottom of p. 8. In practic e, distributional impacts on individual workers can be important, as discussed in later paragraphs of t his section.6-6

Environmental regulation may also affect labor supp ly and productivity. In particular, pollution and other environmental risks may impact labor productivity or employees’ ability to
work. 141 While the theoretical framework for analyzing labo r supply effects is analogous to that for labor demand, it is more difficult to study emp irically. There is a small emerging literature
described in the next section that uses detailed la bor and environmental data to assess these impacts.
To summarize, economic theory provides a framework for analyzing the impacts of environmental regulation on employment. The net emp loyment effect incorporates expected employment changes (both positive and negative) in the regulated sector and other related sectors. Labor demand impacts for regulated firms, and also for the regulated industry, can be decomposed into output and substitution effects whi ch may be either negative or positive. Estimation of net employment effects for regulated sectors is possible when data of sufficient detail and quality are available. Finally, economic theory suggests that labor supply effects are also possible. In the next section, we discuss the empirical literature.
6.3 Current State of Knowledge Based on the Peer-Re viewed Literature

The labor economics literature contains an extensiv e body of peer-reviewed empirical work analyzing various aspects of labor demand, rel ying on the theoretical framework discussed in the preceding section. 142 This work focuses primarily on effects of employmen t policies such as labor taxes and minimum wages. 143 In contrast, the peer-reviewed empirical literatur e specifically estimating employment effects of envir onmental regulations is growing, but is more limited. In this section, we present an overview of the latter. As discussed in the preceding section on theory, determining the direction of emp loyment 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 additi onal inputs, including labor, and may alter the relative proportions of labor and capital used by regulated firms (and firms in other relevant industries) in their production processes.

141 E.g. Graff Zivin and Neidell (2012).

142 Again, see Hamermesh (1993) for a detailed treatme nt.

143 See Ehrenberg & Smith (2000), Chapter 4: “Employme nt Effects: Empirical Estimates” for a concise over view.6-7

Empirical studies, such as Berman and Bui (2001), s uggest that net employment impacts due to regulation were not statistically different from zero in the regulated sector. Other research, such as Greenstone (2002), suggests that more highl y regulated counties may generate fewer jobs than less regulated ones, but the methodology used likely overstates employment impacts because it relies on relative comparisons between m ore regulated and less regulated counties, which can lead to “double counting” of impacts when production and employment shift from more regulated towards less regulated areas. 144 . Moreover, environmental regulations may affect sectors that support pollution reduction earlier th an the regulated industry. Rules are usually
announced well in advance of their effective dates and then typically provide a period of time for firms to invest in technologies and process changes to meet the new requirements. When a regulation is promulgated, the initial response of firms is often to order pollution control equipment and services to enable compliance when th e regulation becomes effective. Estimates of short-term increases in demand for specialized l abor within the environmental protection sector have been prepared for several EPA regulatio ns in the past, including the Mercury and Air Toxics Standards (MATS). 145 Overall, the peer-reviewed literature does not cont ain evidence that environmental regulation has a large impact on net employment (either negative or positive) in the long run across the whole economy.
6.3.1 Regulated Sector
Several empirical studies, including Berman and Bui (2001) and Ferris, Shadbegian, and Wolverton (2014), suggest that regulation-induced n et employment impacts may be zero or slightly positive, but small in the regulated secto r. Gray et al (2014) find that pulp mills that had to comply with both the air and water regulations i n EPA’s 1998 “Cluster Rule” experienced relatively small, and not always statistically sign ificant, decreases in employment. Other research on regulated sectors suggests that employment growt h may be lower in more regulated areas (Greenstone 2002, Walker 2011, 2013). However since these latter studies compare more regulated to less regulated counties this methodolo gical approach likely overstates employment impacts to the extent that regulation causes plants to locate in one area of the country rather than

144 See Greenstone (2002) p. 1212.

145 U.S. EPA (2011b).6-8

another, which would lead to “double counting” of t he employment impacts. List et al. (2003) find some evidence that this type of geographic rel ocation may be occurring.
A small literature examines impacts of environmenta l regulations on manufacturing employment. Greenstone (2002) and Walker (2011, 201 3) study the impact of air quality regulations on manufacturing employment, estimating the effects in non-attainment areas relative to attainment areas. Kahn and Mansur (2013) study e nvironmental regulatory impacts on geographic distribution of manufacturing employment , controlling for electricity prices and labor regulation (right to work laws). Their methodology identifies employment impacts by focusing on neighboring counties with different air quality regulations. They find limited evidence that environmental regulations may cause employment to b e lower within “county-border-pairs.” This result suggests that regulation may cause an e ffective relocation of labor across a county border, but since one county’s loss is another’s ga in, such shifts cannot be transformed into an estimate of a national net effect on employment. Mo reover this result is sensitive to model specification choices.
The few studies in peer-reviewed journals evaluatin g 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 w ere 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 European Union (EU) Emissions Trading Scheme (ETS). Examining European firms from 1996-2007, Com mins 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 pane l 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 emplo yees relative to those that did not participate, but this effect is not statistically significant.
This literature suggests that the employment impact s of controlling CO 2 emissions in the European power sector were small. The degree to whi ch these studies’ results apply to the U.S. context is unclear. European policies analyzed in t hese studies effectively put a price on emissions of both existing and new sources either t hrough taxes or tradable permits with an emissions cap. An emission rate-based regulatory ap proach may not generate similar6-9

employment effects. Moreover, European firms face r elative fuel prices and market regulatory structures different from their U.S. counterparts, further complicating attempts to transfer quantitative results from the EU experience to eval uate this rule.
6.3.2 Economy-Wide
As noted above it is very difficult to estimate the net national employment impacts of environmental regulation. Given the difficulty with estimating national impacts of regulations, EPA has not generally estimated economy-wide employ ment impacts of its regulations in its benefit-cost analyses. However, in its continuing e ffort to advance the evaluation of costs, benefits, and economic impacts associated with envi ronmental regulation, EPA has formed a panel of experts as part of EPA’s Science Advisory Board (SAB) to advise EPA on the technical merits and challenges of using economy-wide economi c models to evaluate the impacts of its regulations, including the impact on net national e mployment. 146 Once EPA receives guidance from this panel it will carefully consider this inp ut and then decide if and how to proceed on economy-wide modeling of employment impacts of its regulations.
EPA received several comments regarding the potenti al net national employment impact of the proposed emission guidelines. Many of these comments referred to analyses that pre-dated the Clean Power Plan proposal, or focused on only o ne component of the proposal. 147 However, one comment was based on an “economy-wide assessmen t of the employment impacts
associated with the U.S. Environmental Protection A gency’s (EPA’s) proposed Clean Power Plan” using the Long-term Inter-industry Forecastin g Tool (LIFT) model. 148 The LIFT model, which is from the Interindustry Forecasting Project (Inforum) at the University of Maryland, has been used in the peer-reviewed academic literature 149 and has also been used to examine the

146 For further information see: http://yosemite.epa.gov/sab/sabproduct.nsf/0/07E67C F77B54734285257BB0004F87ED?OpenDocument

147 See, for example, comments EPA-HQ-OAR-2013-0602-674 3 and EPA-HQ-OAR-2013-0602-23140, within Docket ID : EPA-HQ-OAR-2013-0602.

148 See comment EPA-HQ-OAR-2013-0602-22960, within Doc ket ID : EPA-HQ-OAR-2013-0602

.

149 See, for example, Almon (1991) and Mccarthy (1991)

.6-10

economic impacts of other national policies [Meade (2009); Werling (2011)]. 150 The commenter noted that “While EPA’s analysis provides a reasona ble first approximation of the proposed rule’s employment effects, its focus on direct empl oyment impacts does not capture various indirect employment impacts that may be of interest to policymakers and the public.” [...] “These include the employment impact associated wit h changes in electricity and other energy prices (both positive and negative, depending on th e year), the productivity impacts associated with heat rate improvements at power plants, househ olds and businesses re-directing expenditures to other uses because of increased dem and-side energy efficiency, expenditures crowded out by energy efficiency expenditures, and changes in investments for air pollution
control devices.”
As mentioned previously, EPA is currently engaged i n an SAB process on economy-wide modeling. EPA will not make any determinations on whether mod eling the economy-wide impacts of its regulations – including employment i mpacts - is feasible and, if so, how and when to do this until it receives guidance from the SAB panel. While the purpose of the SAB process is not to peer review any particular economy-wide m odel, it is worth noting that the use of models such as LIFT may be addressed by one of the charge questions to the SAB: “Are there other economy-wide modeling approaches that EPA cou ld consider in conjunction with CGE models to evaluate the short run implications of an air regulation (e.g., macro-economic, disequilibrium, input/output models)? What are the advantages or disadvantages of these approaches?” 151

150 The commenter provides the following description o f the LIFT model: “LIFT is a 97-sector dynamic representation of the U.S. national economy. The mo del combines an interindustry input / output (I-O) formulation with extensive use of regression analysis to employ a ’bottom-up’ approach to macroeconomic modeling. That is, the model works like the actual economy, building m acroeconomic totals from details of industry activi ty, rather than distributing predetermined macroeconomic quant ities among industries. The commenter also describe s LIFT also captures interactions between industries acros s the economy, enabling the model to gauge how chan ges in prices, investment, or productivity in one industry cascade across the economy. In the context of the Clean Power Plan, this is an important feature for understandin g how the rule’s direct impacts for the electric po wer sector affect other industries.”

151 See p. 10, http://yosemite.epa.gov/sab/sabproduct.nsf/0/07E67C F77B54734285257BB0004F87ED/$File/Charge+Questions+2 -26-15.pdf6-11

6.3.3 Labor Supply Impacts
The empirical literature on environmental regulator y employment impacts focuses primarily on labor demand. However, there is a nasc ent literature focusing on regulation-induced effects on labor supply. 152 Although this literature is limited by empirical c hallenges, researchers have found that air quality improvements lead to re ductions in lost work days (e.g., Ostro, 1987). Limited evidence suggests worker productivity may a lso 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 lev els on productivity. They find “ozone levels well below federal air quality standards have a sig nificant impact on productivity: a 10 parts per billion (ppb) decreases in ozone concentrations inc reases worker productivity by 5.5 percent.” (Graff Zivin and Neidell, 2012, p. 3654). 153
This section (section 6.3) has outlined the challen ges associated with estimating regulatory effects on both labor demand and supply for specific sectors. These challenges make it difficult to estimate net national employment es timates that would appropriately capture the way in which costs, compliance spending, and enviro nmental benefits propagate through the

macro-economy. 6.4 Recent Employment Trends The U.S. electricity system includes employees that support electric power generation, transmission and distribution; the extraction of fo ssil fuels; renewable energy generation; and supply-side and demand-side energy efficiency. This section describes recent employment trends in the electricity system.

152 For a recent review see Graff-Zivin and Neidell (2 013).

153 The EPA is not quantifying productivity impacts of reduced pollution in this rulemaking using this st udy. In light of this recent research, however, the EPA is consid ering how best to incorporate possible productivity effects in the future.6-12

6.4.1 Electric Power Generation
In 2014, the electric power generation, transmissio n and distribution sector (NAICS 2211) employed about 390,000 workers in the U.S. 154 Installation, maintenance, and repair occupations accounted for the largest share of work ers (25 percent). 155 These categories include inspection, testing, repairing and maintaining of e lectrical equipment and/or installation and repair of cables used in electrical power and distr ibution systems. Other major occupation categories include office and administrative suppor t (18 percent), production occupations (16 percent), architecture and engineering (10 percent) , business and financial operations (7 percent) and management (7 percent). As shown in Figure 6.1, employment in the Electric Power Industry averaged about 420,000 workers 2000 to 200 5, declining to an average of about 400,000 workers for the rest of the decade, and the n declining to about 390,000 workers in 2014.

Figure 6.1. Electric Power Industry Employment

154 U.S. Bureau of Labor Statistics. “Current Employme nt Survey Seasonally Adjusted Employment for Electr ic Power Generation, Transmission, and Distribution (n ational employment).” Series ID: CES4422110001. Available at < http://www.bls.gov/data/ >. Accessed June 9, 2015

.

155 U.S. Bureau of Labor Statistics, Occupational Empl oyment Statistics, May 2014 National Industry-Speci fic Occupational Employment and Wage Estimates, Electri c Power Generation, Transmission, and Distribution (NAICS 2211). Available at: <http://www.bls.gov/oes/curren t/naics4_221100.htm>. 0 50 100 150 200 250 300 350 400 450 500 

### Power Generation and Supply Employment

(NAICS = 2211, Annual Average, 1000s of Employees)6-13

6.4.2 Fossil Fuel Extraction
6.4.2.1 Coal Mining
The coal mining sector (NAICS 2121) is primarily en gaged in coal mining and coal mine site development, excluding metal ore mining and no nmetallic mineral mining and quarrying. In 2014, BLS reported about 74,000 coal mining employe es (Figure 6.2). During the 2000 to 2014, period, coal mining employment peaked in 2011 at ab out 87,000 employees.

Figure 6.2. Coal Production Employment

6.4.2.2 Oil and Gas Extraction
In 2014, there were close to 200,000 employees in t he oil and gas extraction sector (NAICS 211). 156 This sector includes production of crude petroleum , oil from oil shale and oil sands, production of natural gas, sulfur recovery f rom 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, an d equipping wells, and other production

156 BLS, Current Employment Statistics. Seasonally adj usted employment for oil and gas extraction (nation al employment), NAICS 211. Series ID: CES1021100001. Available at < http://www.bls.gov/data/ >. Accessed June 9, 2015.6-14

activities. 157 In contrast with coal, and looking at Figure 6.3, t here has been a sharp increase in employment in this sector over the past decade.

Figure 6.3. Oil and Gas Production Employment
6.4.3 Clean Energy Employment Trends
Clean energy resources, such as energy efficiency a nd 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, t here is not a single clean energy sector in
standard national accounts classifications. Renewab le generation is not reported to the BLS separately from other electric power generation. Si milarly, manufacturers of energy efficient appliances are not reported separately from convent ional appliance manufacturers and green building design is not separate from the constructi on 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. E mployees engaged in clean energy can span many job classifications, such as experts required to design and produce a renewable or energy-

157 U.S. Bureau of Labor Statistics. 20014. Available at: <http://www.bls.gov/iag/tgs/iag211.htm Accessed Feb. 19 >. 0 50 100 150 200 250 

### Oil and Gas Extraction Employment

(NAICS = 211, Annual Average, 1000s of Employees)6-15

efficient technology, workers that supply inputs an d technicians who install service or operate equipment. As such, there are a variety of definiti ons of clean or green jobs used, some more expansive than others.
6.4.3.1 Defining Clean Energy Jobs
Two U.S. Government sources, the 2010 Department of Commerce (DOC) report, Measuring the Green Economy and the 2010 and 2011 BLS Green Goods and Services surveys have subdivided industrial classifications into “gr een” 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 “cr eated and supported in businesses that produce green products and services.” 158 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 incl usive definition of green goods and services to over 22,000 product codes in the 2007 Economic C ensus to estimate their contribution to the U.S. economy. The report found that the number of g reen 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. 159
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 environ ment or conserve natural resources. These goods and services, which are sold to customers, in clude research and development, installation, and maintenance services for renewable energy and e nergy efficiency and education and training related to green technologies and practices” but al so include recycling and natural resource

158 U.S. Department of Commerce Economics and Statisti cs Administration. 2010. “Measuring the Green Economy ,” April. Available at: <http://www.esa.doc.gov/sites/default/files/reports /documents/greeneconomyreport_0.pdf>.

159 U.S. Department of Commerce Economics and Statisti cs Administration. 2010. “Measuring the Green Econo my, April. Available at: <http://www.esa.doc.gov/sites/ default/files/reports/documents/greeneconomyreport_ 0.pdf >

.6-16

conservation, such as forestry management. 160 Based on surveys across the 325 industries it identified as potential producers of green goods an d services, BLS counts approximately 2.3 million jobs in the green economy in 2010, rising 7 .4 percent to 2.5 million in 2011, 161 compared to increases of about one percent across all occupa tions in the entire economy over the same period. 162 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) Emp loyment (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 6.4.3.2 Renewable Electricity Generation Employment Trends
The DOC report does not separate renewable energy d ata and the BLS data include only privately owned electricity generating facilities. As such, neither source isolates renewable electricity generation employment. For historical t rends in this sector, we therefore, rely on a Brookings Institution study, Muro et al. (2011). This study built a national database of “c lean economy” jobs from the bottom up, verifying each co mpany individually. 163 They include a list of categories similar but not identical to that of BLS, including agricultural and natural resources conservation, education and compliance, energy and resource efficiency, greenhouse gas reduction, environmental management and recycling, and renewable energy. This study found about 138,000 jobs in the renewable energy sector i n 2010, with an overall average annual growth rate of 3.1 percent from 2003-2010. Table 6- 2 details the national results by energy

source.

160 BLS has identified 325 detailed industries (6-digi t NAICS) as potential producers of green goods and services. Available at: <http://www.bls.gov/ggs/ggsoverview.h tm >. (Accessed on 1-14-14, last modified date: March 19, 2013.

161 U.S. Department of Labor, U.S. Bureau of Labor Sta tistics. (n.d.). 2011. “Green Goods and Services 20 10-2011.” (Retrieved on January 14, 2014). Available at :< ht tp://www.bls.gov/ggs/ggsoverview.htm >.

162 U.S. Department of Labor, U.S. Bureau of Labor Stat istics. 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.

163 <http://www.brookings.edu/~/media/Series/resources /0713_clean_economy.pdf > p. 15.6-17

Table 6-2. Renewable Electricity Generation-Related Employment Sector Jobs, 2010 2003-2010 Average Annual Growth Rate (%) Biofuels/Biomass 20,680 8.9 Geothermal 2,720 6.7 Hydropower 55,467 -3.6 Renewable Energy Services 1,981 6.3 Solar Photovoltaic 24,152 10.7 Solar Thermal 5,379 18.4 Waste-to-Energy 3,320 3.7 Wave/Ocean Power 371 20.9 Wind 24,294 14.9 Total 138,364 3.1 Source: http://www.brookings.edu/~/media/Series/resources/0 713_clean_economy.pdf, Appendix A. More recent industry data, from 2014, indicate high er employment numbers and growth in the solar sector. 164
6.4.3.3 Employment Trends in Demand-Side Energy Eff iciency Activities U.S. government data used for calculating the histo rical 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.
In addition to the “clean energy” jobs, Muro et al (2011) 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. 165 Table 6-3 details the results by energy sector.

164 The Solar Foundation, National Solar Jobs Census 2 014. < http://www.thesolarfoundation.org/national-s olarjobs-census-2014/>

165 <http://www.brookings.edu/~/media/Series/resources /0713_clean_economy.pdf > p. 15.6-18

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

Sector Jobs, 2010 2003-2010 Average Annual Growth Rate (%) Appliances 36,608 -3.1 Energy-saving Building Materials 161,896 2.5 Energy-saving Consumer Products 19,210 -2.9 Green Architecture and Construction Services 56,190 6.4 HVAC and Building Control Systems 73,600 3.3 Lighting 14,298 -1.8 Professional Energy Services 49,863 6.9 Smart Grid 15,987 8.6 Total 427,652 2.6 Source: http://www.brookings.edu/~/media/Series/resources/0 713_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.
6.5 Projected Sectoral Employment Changes due to th e Final Emission Guidelines
Affected EGUs may respond to these final CO 2 emission performance rates by placing new orders for efficiency-related or renewable ener gy equipment and services to reduce GHG emissions. Implementing the CPP Final Rule will inv olve changes in the amount of labor needed in different parts of the utility power sector. Ins talling 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 service s sectors. Specifically, the direct employment effects of supply-side initiatives include increase s in labor demand during the implementation phase for manufacturing, installing, and operating higher efficiency and renewable energy electricity generating assets, as well as making he at rate improvements at existing fossil units. Additional supply-side direct employment impacts ar e the reductions in labor demand for labor that would have been used by less efficient or high er emitting generating assets. Once implemented, increases in operating efficiency and shifting generation to existing or new NGCC units and renewable energy generation will impact t he utility power sector’s demand for fossil fuels and plans for EGU retirement and new construc tion.
In addition, EPA expects state plans may also inclu de demand-side energy efficiency policies and programs that typically change energy consumption patterns of business and residential consumers by reducing the quantity of e nergy required for a given level of production or service. Demand-side initiatives generally aim t o increase the use of cost-effective energy6-19

efficiency technologies (e.g., including more effic ient appliances and air conditioning systems, more efficient lighting devices, more efficient des ign of homes and businesses), and advance efficiency improvements in motor systems and other industrial processes. Demand-side initiatives can also directly reduce energy consump tion, such as through programs encouraging changing the thermostat during the hours a building is unoccupied or motion-detecting room light switches. Such demand-side energy efficiency initiatives directly affect employment by encouraging firms and consumers to shift to more ef ficient products and processes than would otherwise be the case. Employment in the sectors th at provide these more efficient devices and services would be expected to increase, while emplo yment in the sectors that produce less efficient devices would be expected to contract.
This generation-side employment analysis uses the c ost projections from the engineeringbased Integrated Planning Model (IPM) to project la bor demand impacts of the final emission guidelines on affected EGUs in the electricity powe r sector and the fuel production sector (coal and natural gas). These projections include effects attributable to heat rate improvements, construction of new EGUs, generation shifts, change s in fuel use, and reductions in electricity generation due to demand-side energy efficiency act ivities. To project labor requirements for demand-side energy efficiency activities, the analy sis uses a different approach that combines data on historic changes in employment and expendit ures in the energy efficiency sector with projected changes in expenditures in the sector ari sing from state implementation of the emission guidelines. We project labor impacts for the rate-b ased and mass-based illustrative plan approach.
6.5.1 Projected Changes in Employment in Electricit y 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 emission guide lines with data on labor productivity, engineering estimates of the amount and types of la bor needed to manufacture, construct, and operate different types of generating units, and pr evailing wage rates for skilled and general labor categories. This approach is different from t he economy-wide types of economic analyses discussed in section 6.2. Lacking robust peer-revie wed methods to estimate economy-wide impacts, the engineering-based analysis focuses on the supply-side direct impact on labor demand in industries closely involved with electric ity generation. The engineering approach6-20

projects labor changes measured as the change in ea ch analysis year in job-years 166 employed in the utility power sector and directly related secto rs (e.g., equipment manufacturing, fuel supply, EGU construction and generating efficiency services ). For example, this approach projects the amounts and types of labor required to implement im provements in generating efficiency. The generation efficiency improvements reduce the amoun t of fossil fuel needed. The efficiencydriven change in fuel use is included in the estima tes of the CPP’s impact on the overall changes in labor required to extract fossil fuels. Some of the quantified employment impacts in this analysis are one-time impacts, such as changes asso ciated with building new NGCC or renewable generating units. Other labor impacts wil l continue, such as changes associated with operating and maintaining generating units that wil l be retired, and labor providing the fuel supplied to newly built, retired and improved EGUs

. This analysis relies on projections and the cost an alysis from IPM, which uses industryspecific data and assumptions to estimate costs and energy impacts of the final guidelines (see Chapter 3). The EPA uses IPM to predict coal genera ting capacity that is likely to undertake improvements in heat rate efficiency (HRI). 167 IPM also predicts the guidelines’ impacts on fuel use, retirement of existing units, and construction of new ones.
The methods EPA uses to estimate the labor impacts are based on the analytical methods used in many previous EPA regulatory analyses. The most relevant prior analysis was the Regulatory Impact Analysis for the Mercury and Air Toxics Standards (MATS). While the methods used in this analysis to estimate the recur ring 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 we used in MATS (with updated data where available), the methods used to estimate the labor associated with installing new c apacity and implementing heat rate improvements were developed for the purpose of the Clean Power Plan RIA.

166 Job-years are not individual jobs, but rather the amount of work performed by the equivalent of one f ull-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 HRI could include a range of activities in the pow er plant to lower the heat rate required to generat e a net electrical output. Assuming all other things being equal, a lower heat rate is more efficient because more electricity is generated from each ton of coal.6-21

The bottom-up engineering-based labor analysis in t he MATS RIA primarily was concerned with the labor needs of retrofitting poll ution control equipment. A central feature of the supply-side labor analysis for this RIA, howeve r, involves the quantity and timing of the labor needs of building new renewable and NGCC unit s and retiring coal units. The estimated response of the utility power sector involves chang es in the amount and timing of retirements of existing coal and oil/gas units, as well as changes in the amount and timing of building new NGCC units and renewable generating capacity. In ad dition to the changes in retirements and construction of new units, there are also estimated changes in the utilization of existing generating units, and changes in the gas and coal s upply sectors.
For example, as presented in Chapter 3, the IPM ana lysis of the rate-based illustrative plan approach scenario finds that in 2025 (part way through the 2022-2029 interim plan performance period) less total generating capacity is needed than in the base case. The estimated reduction in capacity by 2025 with the rate-based s cenario is 49.4 GW less than the estimated base case capacity (a 4.8 percent net capacity redu ction). This 49.4 GW net reduction includes more retirements of coal units (an additional 22.9 GW of coal-fired capacity retired) and oil/gas steam units (an additional 9.3 GW of oil/gas retire ment) compared to the base case, as well as a reduction in the amount of new natural gas units ne eded to be built by 2025 (a decrease of 10.9 GW in new capacity from the amount forecast in the base case) and non-hydro renewables (1.7 GW less renewable capacity built). Fossil fuel util ization will also be impacted. The rate-based
scenario finds that in 2025 less coal will be used (102.9 million tons less, a 14.1 percent decrease from the base case) and less gas will also be used (0.1 TCF less, a 1.0 percent decrease).
An important aspect of the labor analysis is that b uilding new units, and all the associated construction-related labor, occurs before the new u nits become operational. While the financial costs of building the new units are amortized and r ecouped over the book life of the new equipment, the labor involved with manufacturing eq uipment 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 168 that new NGCC units take 3 years to build, and bot h natural gas combustion turbines and wind-powered re newables take 2 years.

168 Table 4.13, IPM 5.13 Documentation.6-22

Avoiding some of the need for new capacity due to b oth demand and supply efficiency improvements results in both a significant cost sav ings to consumers and the power sector, as
well as reduced emissions of both CO 2 and non-CO 2 pollutants from fossil fuel-fired generation. The avoided new capacity, however, also has signifi cant labor impacts. A portion of the labor that would have been used to build the new capacity in the base case will not be employed in the power generation sector with the implementation of the GHG guidelines, though it likely will be employed in construction elsewhere. Similarly, less labor involved with operating and providing
fuel for new units will be needed with the emission s guidelines than in the base case.
A critical component of the overall labor impacts o f 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 6.5.2. The demand-side energy efficiency
activities are increases in labor needs and estimat ed in units of jobs, while the supply-side employment impacts are estimated as job-years. The IPM labor expenditure projections are distributed across different labor categories (e.g. , general construction labor, boilermakers and engineering) using data from engineering analyses o f labor’s overall share of total expenditures, and apportionment of total labor cost to various la bor categories. Hourly labor expenditures (including wages, fringe benefits, and employer-pai d costs including taxes, insurance and administrative costs) for each category are used to estimate the labor quantity (measured in fulltime job-years) consistent with the compliance scen ario projections. Projected labor impacts arising from changes in fuel demand are primarily d erived from labor productivity data for coal mining (tons mined per employee hour) and natural g as extraction (MMBtu produced/job-year). Tables 6.4 and 6.5 present projected changes relati ve to the baseline of four labor categories:

1. manufacturing, engineering and construction for bui lding, designing and implementing heat rate improvements;
2. manufacturing and construction for new generating c apacity;
3. operating and maintenance for existing generating c apacity; 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 annu al impact occurring in each year of three6-23

different intervals. The three intervals are from 2 018 through 2020 (a three year interval), during which there are modest labor impacts from the early changes in the power utility sectors operations, from 2021 through 2025 (five years), an d 2026 through 2030 (5 years). The construction-related labor analysis are based on th e IPM estimates of the net change in capital investment that occurs during each multi-year inter val to fund building new units completed during that interval. The new build labor analysis uses the net change in capital investment to estimate the amount and type of labor needed during the interval to build the new capacity. The analysis assumes that the new build labor within ea ch interval is evenly distributed throughout
the interval. Tables 6-4 and 6-5 reflect this assum ption by presenting the average labor utilization per year during each of the three intervals.
The HRI-related labor impacts are estimated based o n the assumed capital cost of $100/kw (see section 3.9.3). The labor estimates fo r operating and maintaining generating units annually are based on IPMs estimates of Fixed Opera ting and Maintenance (FOM) and Variable Operating and Maintenance (VOM) costs. IPM estimate s FOM and VOM for each year individually, so the net changes in O&M-related lab or estimates in Tables 6-4 and 6-5 are single year estimates for 2020, 2025 and 2030. These singl e year O&M labor estimates are not merely the average annual averages labor needs throughout each multi-year interval. There are O&M labor changes occurring in the all years throughout the entire period 2020-2030, but the labor impacts in each labor category change each year. Th e fuel-related labor estimates are also singleyear estimates, and not multi-year averages. The la bor analysis of the impacts on the fuel
extraction industries uses IPM’s estimates of the n et changes in the amount of coal and natural gas in 2020, 2025 and 2030, which are inherently es timates 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 2020-2030, and the labor impact changes every year.
It should be noted that the supply-side labor impac t estimates in Tables 6-4 and 6-5 reflect the supply-side changes that will potential ly occur with each illustrative plan scenario. These labor impacts include not only the direct sup ply-side impacts of the illustrative implementation scenarios of the CPP, but also the c hanges in total generation activity that result from the demand-side energy efficiency activities e xpected to be an important component of state compliance strategies. The additional labor i mpact estimates from demand-side energy efficiency activities are presented below in sectio n 6.5.2.6-24

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.

Table 6-4. Engineering-Based a Changes in Labor Utilization, Rate-based Scenario

(Number of Job-Years b of Employment in a Single Year) Construction-related (One-time) Changes* 2018-2020 2021-2025 2026-2030 Heat Rate Improvement: Total 0 15,400 2,200 Boilermakers and General Construction 0 11,000 1,60 0 Engineering and Management 0 2,800 400 Equipment-related 0 1,200 200 Material-related 0 400 0 New Capacity Construction: Total 500 -15,600 400 Renewables 700 -5,000 23,300 Natural Gas -200 -10,600 -22,900

Recurring Changes** 2020 2025 2030 Operation and Maintenance: Total -9,100 -17,000 -19 ,600 Changes in Renewables 600 -100 1,100 Changes in Gas*** 300 -1,100 -3,700 Changes in Coal*** -8,000 -13,300 -14,700 Retired Oil and Gas -2,000 -2,500 -2,300 Fuel Extraction: Total 100 -7,800 -13,900 Coal -1,300 -7,300 -13,300 Natural Gas 1,400 -500 -600 Supply-Side Employment Impacts - Quantified -8,500 -25,000 -30,900

a Job-year estimates are derived from IPM investment and O&M cost estimates, as well as IPM fuel use es timates (tons coals or MMBtu gas).

b All job-year estimates on this are full-time equiva lent (FTE) jobs. Job estimates in the demand-side e nergy efficiency section (below) include both full-time a nd part-time jobs. *Construction-related job-year changes are one-time impacts, occurring during each year of the multi-y ear period during which construction and HRI installation acti vities occur. Construction-related figures in table are the average annual job-years in each year between the years in the range. Negative construction job-year estimates occur when additional generating capacity must be built in the base case, but is avoided in the final rule. **Recurring Changes are job-years associated with a nnual recurring jobs including operating and mainte nance activities and fuel extraction jobs. Newly built ge nerating capacity creates a recurring stream of pos itive job-years, while retiring generating capacity, as well as avoi ded new built capacity, create a stream of negative job-years. ***O&M job-year changes include changes from new, r etired and avoided capacity, and also changes arisi ng from changes in utilization (i.e., capacity factor chang es) of existing EGU capacity that continue to opera te but generate a different amount of MWh/year.6-25

Table 6-5. Engineering-Based a Changes in Labor Utilization, Mass-Based Illustrat ive Plan

Approach 

## ( Number of Job-Years of Employment in a Single Year) Construction-related (One-time) Changes* 2018-2020 2021-2025 2026-2030 Heat Rate Improvement: Total 0 14,900 800 Boilermakers and General Construction 0 10,700 600 Engineering and Management 0 2,700 100 Equipment-related 0 1,200 100 Material-related 0 300 0 New Capacity Construction: Total -1,700 -11,100 4,7 00 Renewables -1,400 -3,500 21,300 Natural Gas -300 -7,600 -16,600

Recurring Changes** 2020 2025 2030 Operation and Maintenance: Total -11,700 -21,200 -2 5,000 Changes in Renewables -900 -1,000 700 Changes in Gas*** 500 -400 -2,300 Changes in Coal*** -9,100 -17,000 -20,800 Retired Oil and Gas -2,200 -2,800 -2,600 Fuel Extraction: Total 200 -8,600 -14,300 Coal -1,800 -8,700 -12,200 Natural Gas 2,000 100 -2,100 Supply-Side Employment Impacts - Quantified -13,100 -26,000 -33,700

a Job-year estimates are derived from IPM investment and O&M cost estimates, as well as IPM fuel use es timates (tons coals or MMBtu gas).

b All job-year estimates on this are full-time equiva lent (FTE) jobs. Job estimates in the demand-side e nergy efficiency section (below) include both full-time a nd part-time jobs. *Construction-related job-year changes are one-time impacts, occurring during each year of the multi-y ear period during which construction and HRI installation acti vities occur. Construction-related figures in table are the average annual job-years in each year between the years in the range. Negative construction job-year estimates occur when additional generating capacity must be built in the base case, but is avoided in the final rule. **Recurring Changes are job-years associated with a nnual recurring jobs including operating and mainte nance activities and fuel extraction jobs. Newly built ge nerating capacity creates a recurring stream of pos itive job-years, while retiring generating capacity, as well as avoi ded new built capacity, create a stream of negative job-years. ***O&M job-year changes include changes from new, r etired and avoided capacity, and also changes arisi ng from changes in utilization (i.e., capacity factor chang es) of existing EGU capacity that continue to opera te but generate a different amount of MWh/year.

6.5.2 Projected Changes in Employment in Demand-Sid e Energy Efficiency Activities
As described in Chapter 3, EPA anticipates that thi s rule may stimulate investment in clean energy technologies and services, resulting i n considerable increases in energy efficiency.6-26

Many of these investments may support demand-side e nergy efficiency activities such as: reducing energy required for a given activity by en couraging more efficient technologies (e.g., ENERGY STAR appliances), implementing energy improv ements for existing systems (e.g., weatherization of older homes), or encouraging chan ges in behavior (e.g., reducing air conditioning during periods of high electricity dem and).We expect these increases in energy efficiency, specifically, to support a significant number of jobs in related industries. For more
information on EPA’s illustrative investment levels in demand-side energy efficiency activities, assumed to be adopted in response to the CPP, pleas e see Section 3.7 “Demand-Side Energy Efficiency” in Chapter 3 of this RIA.
In this section, we project employment impacts in demand-side energy efficiency activities arising from these guidelines using illu strative calculations. The approach uses information from power sector modeling and projecte d impacts on energy efficiency investments analyzed (see Chapter 3), and U.S. government data on employment and expenditures in energy efficiency. 169 This approach is limited by the fact that we do not know which options states will choose for demand-side energy efficiency activities and by uncertainties associated with methods. These illustrative employment projections are gross; thus they do not include impacts in other sectors of any shift in resources from oth er sectors to implement the demand-side energy efficiency activities. Nor does this analysis attem pt to quantify the positive employment impacts in other sectors arising from changes in consumer e xpenditures on electricity due to reduced electricity bills. In other words, these projection s are not attempts at estimating net national job creation. Also, this approach attempts to calculate the number of employees (full-time and parttime) rather than full-time supply-side job-years e stimated in section 6.5.1.
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 e nergy savings achieved by these programs (e.g., Allcott (2011a, 2011b), Arimura et al. (2012)). Results suggest that demand-side energy

169 Investments in demand-side energy efficiency reduce energy required for a given activity by encouragin g more efficient technologies (e.g., ENERGY STAR appliance s), implementing energy improvements for existing systems (e.g., weatherization of older homes), or e ncouraging changes in behavior (e.g., reducing air conditioning during periods of high electricity dem and).6-27

efficiency programs reduce energy use and generate small increases in consumer welfare. These policy impacts are due to low investment in energy efficiency as described in “energy paradox” literature (Gillingham, Newell, and Palmer (2009), Gillingham and Palmer (2014)). 170
Two recent articles discuss employment effects of d emand-side energy efficiency programs. Aldy (2013) describes clean energy invest ments 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 th e deployment of low-carbon technologies” (p. 137), with nearly $20 billion for energy effici ency investments. The Council of Economic Advisors (CEA) (2011) estimated higher economic act ivity and employment than would have otherwise occurred without the American Recovery an d Reinvestment Act. Using CEA’s methods to quantify job creation for the Recovery A ct, Aldy uses the share of stimulus funds for clean energy investments to estimate job-years supp orted by the Recovery Act. The largest sources of job creation in clean energy are those t hat received the largest shares of stimulus funds: renewable energy, energy efficiency, and tra nsit. 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 h istorically large fiscal stimulus specifically targeting job creation during a period of exception ally high unemployment versus environmental regulations taking effect several years from now.
Yi (2013) analyzes clean energy policies and employ ment for U.S. metropolitan areas in 2006, prior to the Recovery Act, to evaluate impact s on clean energy job growth. Implementing an additional state clean energy policy tool (renew able energy policies, GHG emissions policies, and energy efficiency polices such as energy effici ency resource standards, appliance or equipment energy efficiency standards, tax incentiv es, and public building energy efficiency standards) is associated with 1 percent more clean energy employment within that MSA. These estimates are not transferable to this rulemaking s ince states are likely to change intensity as well as number of clean energy programs.

170 For more information on this efficiency paradox se e Chapter 3 and the Technical Support Document (TSD ) for the Final Carbon Pollution Emission Guidelines for Existing Stationary Sources: Electric Utility Gener ating Units. Demand-Side Energy Efficiency6-28

Lacking a peer-reviewed methodology, we use the fol lowing 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 and divide energy efficiency employment by expenditures on energy efficiency activities to cal culate an estimate of jobs per million dollars. We then multiply this fraction by projected expendi ture in energy efficiency activities undertaken in response to these final guidelines.
Data used for calculating employment in energy effi ciency sectors comes from the “energy efficiency” industry category of the BLS Green Goods and Services survey. 171 Using BLS Green Goods and Services (GGS) information on 1 32 energy efficiency industries, as identified by BLS, 172 we adjusted the list to remove ten industries expe cted to not be directly affected by the rule, e.g. transportation. 173 Next we used this detailed list of 122 industries to extract 2011 BLS data on green employment. 174 Employment data at the most-detailed industry level (6-digit NAICS) is available only for a porti on of these 122 industries. Therefore we use both the most-detailed industry level (6-digit NAIC S) and also a more aggregate level (4-digit NAICS) to estimate a range of energy efficiency emp loyment with the 2011 BLS Green Goods
and Services data. 175

171 For more details on this survey, see section 6.5.3. 1.

172 See detailed listing available here: http://www.bl s.gov/ggs/naics_2012.xlsx. Category 2 is “Energy Ef ficiency”. More information is available here: http://www.bls. gov/ggs/ggsfaq.htm#3.

173 The ten industries we removed from the list were: NAICS 483114 Coastal and Great Lakes passenger tran sport, 483212 Inland water passenger transportation, 48511 1 Mixed mode transit systems, 485112 Commuter rail systems, 485113 Bus and other motor vehicle transit systems, 485119 Other urban transit systems, 485210 Interur ban and rural bus transportation, 485410 School and employe e bus transportation, 485999 All other ground passe nger transportation, and 926120 Transportation program a dministration. It is possible that certain energy e fficiency services and products produced by the remaining sec tors may also be applied in activities that may not be creditable for compliance with rate-based plans in, or may oth erwise be directly incentivized by, this final rule . However, there is no reason to categorically exclude the remaining sectors from this analysis.

174 BLS Green Goods and Services data is available her e: http://download.bls.gov/pub/time.series/gg/gg.data. 1.AllData. A BLS technical note indicates that the scope of the GGS survey changed between 2010 and 2011 (http://ww w.bls.gov/news.release/ggqcew.tn.htm). Some industr ies and establishments that were not previously include d in the 2010 survey were included in 2011. Rather than using the change from 2010 to 2011, here we use only the 2011 data.

175 At the 6-digit NAICS level, 27 of the 122 energy e fficiency industries listed have employment data av ailable. At the 4-digit NAICS level, 46 of the 122 energy effic iency industries listed have employment data availa ble.6-29

BLS does not collect data on energy efficiency expe nditures directly, however. Instead,
BLS collects data on the share of revenues associat ed with green goods and services, at the
establishment level. 176 We multiply data on total revenues by NAICS by the share of green revenues reported by BLS to obtain a measure of gre en revenues by industry. The only U.S. Government data source containing this revenue info rmation for all NAICS sectors is the U.S. Economic Census. This Census is conducted at 5-year intervals (the latest available year is 2012), however, making it unsuitable for directly p airing with 2011 data from BLS. Instead, we use U.S. Census Bureau data on total value of shipm ents by industry, for 2011, from the Annual Survey of Manufacturers. 177 The disadvantage of this data source is that the m anufacturing sector makes up only 50 percent of the 132 NAICS codes bel onging 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 pro jects that the same number of jobs per dollar are supported in construction and service sectors as in manufacturing. Also, the Annual Survey of Manufacturers contains data for some, but not all, detailed industry codes, e.g. 4-digit and 6-digit NAICS. We pair our BLS GGS data by industry, either by 4-digit or 6-digit NAICS, with data from the Annual Survey of Manufacturers. At the mor e detailed, 6-digit level, 17 industries have data available for both employment and total value of shipments. 178 At the less detailed, 4-digit level, 15 industries have data available for both e mployment and total value of shipments. 179 Using this approach we obtain estimates of 2.07 dem and-side energy efficiency jobs per million 2011 dollars of expenditure, using the less-detaile d industry level (4-digit NAICS), and 3.29 demand-side energy efficiency jobs per million 2011 dollars of expenditure, using the moredetailed industry level (6-digit NAICS).

176 More information is available here: http://www.bls .gov/ggs/ggsfaq.htm#5.

177 Census data on total value of shipments, by indust ry, for 2011 is available here: http://factfinder.census.gov/faces/tableservices/js f/pages/productview.xhtml?pid=ASM_2011_31GS101&prod Type =table.

178 The 17 industries are: NAICS 321219, 321991, 321992 , 327993, 327999, 332913, 332996, 333414, 333415, 334513, 334514, 334515, 335110, 335222, 335311, 335 312, and 335999.

179 The 15 industries are: NAICS 314100, 321100, 324100 , 326100, 327100, 327300, 327400, 332100, 333300, 334200, 334300, 334400, 335200, 336300, and 337900.6-30

Having calculated estimates of jobs per million dol lars of energy efficiency expenditure, we use EPA’s illustrative energy efficiency investm ent levels of the first-year costs expected for states to attain a target of 1 percent growth in de mand-side efficiency improvements (see Chapter 3.7 of this RIA for more information). If some stat es were to target rates of energy efficiency savings greater than one percent, they may see incr eased energy efficiency employment impacts, relative to the one percent growth assumed in this analysis. The first year cost of saved energy (i.e., reduced electricity demand) accounts for bot h the costs to the utilities that are funding the demand-side energy efficiency programs (known as th e program costs), and the additional cost to the end-user purchasing a more energy efficient technol ogy (known as the participant costs). 180 Total costs were divided evenly, 50 percent each, between program costs and participant costs. First-year costs are not annualized; they are the projected ex penditures on demand-side energy efficiency activities in that year. As shown in the Demand-Sid e Energy Efficiency Technical Support
Document 181 , first-year costs for achieving a 1 percent growth target 182 in energy efficiency activities are projected to be $18.1 billion (2011 $) in 2020. Multiplying this dollar expenditure by the jobs per dollar estimates results in projected employmen t impacts for demand-side energy efficiency activities ranging from 37,570 to 59,700 jobs in 20 20 depending on the jobs per million dollars estimate used: low or high. Employment impacts for demand-side energy efficiency activities range from 52,590 to 83,590 jobs in 2025, and from 52,440 to 83,360 jobs in 2030. These
estimates are shown in Table 6-6 below.

180 See Section 3.6.2, “Demand-Side Energy Efficiency Total Costs”, in this RIA, for more information.

181 U.S. EPA. 2015. Technical Support Document (TSD) t he Final Carbon Pollution Emission Guidelines for Existing Stationary Sources: Electric Utility Gener ating Units. Demand-Side Energy Efficiency.

182 The illustrative demand-side energy efficiency pla n scenario reflects each state ramping up to the 1% incremental savings target from their 2013 level, beginning in 2020. Thus, most states are below 1% in 2020. All s tates have achieved the 1% target no later than 2025 and the p lan scenario has each state remain at that level th rough 2030.6-31

Table 6-6. Estimated Demand-Side Energy Efficiency Employment Impacts: Target 1

percent Growth in Energy Efficiency

Source Factor Employment impact (jobs)*

2020 2025 2030 EPA low estimate, using BLS and Census data, and power sector modeling projections 2.07 37,570 52,590 52,440 EPA high estimate, using BLS and Census data, and power sector modeling projections 3.29 59,700 83,590 83,360
*Since these figures represent number of employees (full- or part-time) they should not be added to th e full-time equivalent job-years reported in Table 6-4 and Tabl e 6-5. Energy efficiency costs are from 1 percent g rowth target projections for the continental U.S.. First-year en ergy efficiency costs are the same for rate-based a nd mass-based scenarios. See Chapter 3 of this RIA and Demand-Sid e Energy Efficiency TSD for more information.

Although this approach has the advantage of using a range of estimates, derived from U.S. government data, on energy efficiency employme nt per million dollars in industry shipments, this approach is limited by its focus on manufacturing sectors and direction of bias (overestimation or underestimation) cannot be deter mined at this time. As stated earlier, if, rather than a one percent target, some states were to targ et rates of energy efficiency savings greater than one percent, they may see increased energy eff iciency employment impacts, relative to the one percent growth assumed in this analysis. Finall y, because states can choose to reduce emissions by means of adopting more and broader dem and-side energy efficiency programs, there is uncertainty around the mix of energy effic iency programs and their associated demandside energy efficiency employment impacts.
Our estimates of 2.07 to 3.29 demand-side energy ef ficiency jobs per million dollars of 2011 expenditure fit with other estimates in the li terature, focused on government data sources, and are on the smaller end of the range. 183 Figure 6.4 shows the range of estimates, including EPA low (2.07) and EPA high (3.29). The Department of Commerce report estimates overall employment per million dollar values, ranging from 4.65 to 4.85 (Department of Commerce 2010a). 184 The report also contains some case studies, and fo r those focused on energy efficiency, the Department of Commerce estimates 6.21 jobs per million dollars in green buildings activities

183 See Section 6.5.3 for more information on sources in the literature.

184 Calculated values using data on employment and shi pments reported in Department of Commerce (2010a), Table 2, p. 12.6-32

and 7.53 jobs per million dollars for energy effici ency appliances. 185 Lawrence Berkeley National Lab (Goldman et al. 2010) reports a wide range of estimates: from 2.5 jobs per million dollars for energy service companies (ESCOs), to 8.9 jobs p er million dollars for low income
weatherization activities. The Pacific Northwest Na tional Labs report (Anderson et al. 2014), in surveying the literature, estimates 11 jobs per mil lion dollars of initial energy efficiency
investments.

Figure 6.4. Demand-Side Energy Efficiency Employmen t: Jobs per One Million Dollars

(2011$)
There is more uncertainty involved in this approach than the standard bottom-up engineering analysis used to estimate electricity g eneration 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 st udy associated labor requirements. Demand-

185 Calculated values using data on employment and shi pments reported in Department of Commerce (2010a), Appendix 2, Table 2B, p. 4. http://www.esa.doc.gov/ sites/default/files/appendix2_0.pdf 11 8.9 7.53 6.5 6.21 6.2 4.85 4.65 3.29 2.5 2.07

0 5 10

Jobs per Million $ PNNL initial investments LBNL low income weatherization

DOC EE appliances LBNL state and local gov't EER

DOC green buildings LBNL ratepayer funded EE

DOC overall high DOC overall low EPA high LBNL ESCOs EPA low

Sources: EPA calculation, LBNL (2010), PNNL (2014), DOC (2010)



### Demand-Side EE Employment6-33

side energy efficiency improvements, in contrast, e ncompass a wide array of activities (subsidies for efficient appliances, “smart meters,” etc.). In addition, there is considerable uncertainty regarding which activities a state will choose. Thu s, 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. Finally, this approach recognizes that shifts in economic activity towards investment s in demand-side energy efficiency are accompanied by potential shifts in employment.
In addition, the EPA does not have access to bottom -up information regarding labor requirements for these activities. Use of a constan t 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 r epresent gross changes in the affected sector. As such they may over-estimate impacts to the exten t that jobs created displace workers employed elsewhere in the economy. For demand-side efficiency activities this potential overstatement may be higher than in other sectors. If s tates encourage consumers to purchase ENERGY STAR appliances, for example, currently empl oyed 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 dollar fraction used in the em ployment 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 purcha se of imported goods this projection will overstate the U.S. employment impact of those expen ditures.
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 2011 data. The labor intensity of demand-side energy efficiency will lik ely change with technological innovation in the sector. In addition, even absent technological change, labor intensity of expenditures will likely change over time as states alter their portf olio of efficiency activities (e.g., by moving to higher cost activities after exhausting low-cost ef ficiency activities).

Non-additional Activities. Here we assume that all activities financed by demand-side energy efficiency expenditures are additional to what woul d have been undertaken in the absence of6-34

these programs. For example, if utilities finance s ome actions customers would have undertaken in the absence of these programs (e.g., if a custom er receives a rebate for an energy efficient appliance that would have been purchased without th e rebate), these numbers would overestimate employment impacts of the final emissi ons guidelines.

6.6 Conclusion
This chapter presents qualitative and quantitative discussions of potential employment impacts of the final guidelines for electricity gen eration, fuel production, and demand-side energy efficiency sectors. The qualitative discussi on identifies challenges associated with estimating net employment effects and discusses ant icipated impacts related to the rule. It includes an in-depth discussion of economic theory underlying analysis of employment impacts. The employment impacts for regulated firms can be d ecomposed into output and substitution effects, both of which may be positive or negative. Consequently, economic theory alone cannot predict the direction or magnitude of a regulation’ s employment impact. It is possible to combine theory with empirical studies specific to the regul ated firms and other relevant sectors if data and methods of sufficient detail and quality are availa ble. Finally, economic theory suggests that environmental regulations may have positive impacts on labor supply and productivity as well.
We examine the peer-reviewed economics literature a nalyzing various aspects of labor demand, relying on the above theoretical framework. Determining the direction of employment effects in regulated industries is challenging beca use of the complexity of the output and substitution effects. Complying with a new or more stringent regulation may require additional inputs, including labor, and may alter the relative proportions of labor and capital used by regulated firms (and firms in other relevant indust ries) in their production processes. The
available literature illustrates some of the diffic ulties for empirical estimation: for example, there is a paucity of publicly data on plant-level employ ment, thus most studies must rely on confidential plant-level employment data from the U .S. Census Bureau, typically combined with pollution abatement expenditure data, that are too dated to be reliably informative, or other measures of the stringency of regulation. In additi on, the most commonly used empirical methods, for example, Greenstone (2002), likely ove rstate employment impacts because they rely on relative comparisons between more regulated and less regulated counties, which can lead to “double counting” of impacts when production and employment shift from more regulated6-35

towards less regulated areas. Thus these empirical methods cannot be used to estimate net employment effects. . Empirical analysis at the ind ustry level requires estimates of product demand elasticity; production factor substitutabili ty; supply elasticity of production factors; and the share of total costs contributed by wages, by i ndustry, and perhaps even by facility. Econometric studies of environmental rules converge on the finding that employment effects, whether positive or negative, have been small in re gulated sectors.
The illustrative quantitative analysis in this chap ter projects a subset of potential employment impacts in the electricity generation, f uel production, and demand-side energy efficiency sectors. States have the responsibility and flexibility to implement plans that satisfy final emissions guidelines, while affected EGUs may choose their compliance strategies from requirements imposed by these plans. 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 wi th these guidelines assuming two illustrative plan approach scenarios for the electr ic power industry, coal and natural gas production, and demand-side energy efficiency activ ities. 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 produc tivity. In the electricity, coal, and natural gas sectors, the EPA estimates that these guideline s could have an employment impact of roughly -8,500 job-years in 2020, -25,000 job-years in 2025, and -30,900 job-years in 2030 for
the rate-based scenario. For the mass-based scenari o, the EPA estimates that these guidelines could have an employment impact of roughly -13,100 job-years in 2020, -26,000 job-years in 2025, and -33,700 job-years in 2030 (see Tables 6-4 and 6-5).
Employment impacts from demand-side energy efficien cy activities are based on historic data on jobs supported per million dollars of expen diture on energy efficiency. Demand-side energy efficiency employment impacts would approxim ately range from 37,570 to 59,700 jobs in 2020, 52,590 to 83,590 jobs in 2025, and from 52,44 0 to 83,360 jobs in 2030 for both the ratebased and mass-based approaches and a 1 percent gro wth target for energy efficiency expenditures (see Table 6-6).
The IPM-generated job-year numbers for the electric ity, coal and natural gas sectors should not be added to the demand-side efficiency j ob impacts, since the former are reported in6-36

full-time equivalent job-years, whereas the latter do not distinguish between full- and part-time employment. Finally, note again that this analysis is based on two an illustrative plan approaches, and CAA section 111(d) allows each stat e to determine its state plan, by way of meeting its state-specific goal. Given the flexibil ities afforded states in implementing plans that satisfy the emission guidelines, and in the complia nce options affected EGUs may take, the impacts reported in this chapter are illustrative o f actions states may take.

6.7 References
Aldy, J.E. 2013. “A Preliminary Assessment of the A merican Recovery and Reinvestment Act’s Clean Energy Package.” Review of Environmental Economics and Policy . 7(1):136-155. Allcott, H. 2011a. “Social Norms and Energy Conserv ation.” Journal of Public Economics

. 95(9–10):1082–1095.

Allcott, H. 2011b. “ Rethinking Real-Time Electricity Pricing,” Resource and Energy Economics

. 33:820–842. Almon, C. 1991. “The Inforum Approach to Interindus try Modeling,” Economic Systems Research 3(1):1-8. Anderson, D., O. Livingston, D. Belzer, D., and M. Scott. 2014. “Assessing National Employment Impacts of Investment in Residential and Commercial Sector Energy Efficiency: Review and Example Analysis” [Technical Report]. Pa cific Northwest National Laboratory . Available at < http://www.pnnl.gov/main/publication s/external/technical_reports/PNNL23402.pdf >. Accessed June 4, 2015. Anger, N. and U. Oberndorfer. 2008. “Firm Performan ce 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 . 33(2):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. S tavins. 1996. “Benefit-Cost Analysis in Environmental, Health, and Safety Regulation: A Sta tement of Principles.” American Enterprise Institute, the Annapolis Center, and Res ources for the Future; AEI Press. Available at <http://www.hks.harvard.edu/fs/rstavins/Papers/B enefit Cost Analysis in Environmental.AEI.1996.pdf>. Accessed June 5, 2015.6-37

Berman, E. and L. T. M. Bui. 2001. “Environmental R egulation 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. “Estimatin g 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 Substitutio n. Chan, H. S., S. Li, and F. Zhang. (2013) “Firm Comp etitiveness and the European Union Emissions Trading Scheme.” Energy Policy . (63):1056-1064. Commins, N., S. Lyons, M. Schiffbauer, and R. S.J. Tol. 2011. “Climate Policy and Corporate Behaviour.” The Energy Journal . (32):51-68. Congressional Budget Office (2011), Statement of Do uglas W. Elmendorf, Director, before the Senate Budget Committee, “Policies for Increasing E conomic Growth and Employment in 2012 and 2013” (November 15)<http://www.cbo.gov/sit es/default/files/11-15- Outlook_Stimulus_Testimony.pdf>

Council of Economic Advisers. 2011. “The Economic I mpact of the American Recovery and Reinvestment Act of 2009.” Sixth quarterly report. Ehrenberg, R. G. and R. S. Smith. 2000. Modern Labor Economics: Theory and Public Policy. Addison Wesley Longman, Inc., Chapter 4. Executive Order 13563 (January 21, 2011). “Improvin g Regulation and Regulatory Review. Section 1. General Principles of Regulation.” Federal Register 76 (14): 3821-3823. Ferris, A. E., R. J. Shadbegian, A. Wolverton. 2014 . “The Effect of Environmental Regulation on Power Sector Employment: Phase I of the Title IV SO 2 Trading Program.” Journal of the Association of Environmental and Resource Economist s. 1(4): 521-553. Gillingham, K.; R. Newell and Karen Palmer. 2009. “ Energy Efficiency Economics and Policy.” Annual Review of Resource Economics . 1(1): 597-619. Gillingham, K. and K. Palmer. 2014. “Bridging the E nergy Efficiency Gap: Policy Insights from Economic Theory and Empirical Evidence.” Review of Environmental Economics and Policy. 8(1):18-38.6-38

Global Insight. 2008. U.S. Metro Economies: Current and Potential Green J obs in the U.S. Economy. U.S. Conference of Mayors. Available at: <http://www.usmayors.org/pressreleases/uploads/gree njobsreport.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 Expe ctations for Growth. Lawrence Berkeley National Laboratory, LBNL-3987E

. Graff Zivin J. and M. Neidell. 2012. “The Impact of Pollution on Worker Productivity.” American Economic Review . 102(7):3652-73.
Graff Zivin, J. and M. Neidell. 2013. “Environment, Health, and Human Capital.” Journal of Economic Literature . 51:(3):689-730. Gray, W., R. J. Shadbegian, C. Wang and M. Meral. 2 014 “Do EPA Regulations Affect Labor Demand? Evidence from the Pulp and Paper Industry”, Journal of Environmental Economics and Management. 68: 188-202. Greenstone, M. 2002. “The Impacts of Environmental Regulations on Industrial Activity: Evidence from the 1970 and 1977 Clean Air Act Amend ments 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 . 101:105–114. 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 Plant Bi rths: 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/library/Marshall/marP.html>

. Mccarthy, M.B. 1991. “Lift: Inforum's Model of the U.S. Economy,” Economic Systems Research 3(1):15-36. Meade, D.S. 2009. “An Analysis of the Economic Impa cts of the 2007 Energy Independence and Security Act”, in Energy Policy and International Competitiveness , M. Grassini and R. Bardazzi, eds. Firenze University Press, pgs. 3-28. Muro, M.; J. Rothwell; D. Saha & Partnership, w. B. 2011. Sizing the Clean Economy: A National and Regional Green Jobs Assessment. Brookings Institution, Metropolitan Policy6-39

Program. Available at <http://www.brookings.edu/~/media/series/resources/0 713_clean_economy.pdf>. Accessed June 4, 2015. Ostro, B.D. 1987. “Air Pollution and Morbidity Revi sited: 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 Co rp. Smith, K. 2012. “Reflections: In Search of Crosswal ks between Macroeconomics and Environmental Economics.” Review of Environmental Economics and Policy . 6(2):298-317. Southwest Energy Efficiency Project. 2002. “The New Mother Lode: The Potential for More Efficient Electricity Use in the Southwest.” Availa ble at: <http://www.swenergy.org/publications/nml/>. Access ed June 4, 2015. The Pew Charitable Trust. 2009. “The Clean Energy E conomy: Repowering Jobs, Businesses and Investments Across America

. ” Available at: < http://communitywealth.org/content/clean-energy-economy-repowering-jobs-businesses-and-investmentsacross-america>. Accessed June 4, 2015. The Solar Foundation in partnership with BW Researc h and GW Institute. 2014. “The National Solar Jobs Census.” Available at: <http://www.theso larfoundation.org/national-solar-jobscensus-2014/>.
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>. Accessed June 4, 2015. U.S. Department of Commerce, Economics and Statisti cs Administration. 2010a. “Measuring the Green Economy.” Available at: <http://www.esa.doc.g ov/Reports/measuring-greeneconomy>. Accessed June 4, 2015. U.S. Department of Commerce, International Trade Ad ministration. 2010b. Environmental Industries Fact Sheet, using 2008 data from Environ mental Business International, Inc., April. Available at: <http://web.ita.doc.gov/ete/eteinfo.nsf/068f3801d04 7f26e85256883006ffa54/4878b7e2fc08ac 6d85256883006c452c?OpenDocument>. Accessed June 4, 2015. U.S. Department of Labor, Bureau of Labor Statistic s. 2012a. Databases, Tables & Calculators by Subject. Available at <http://data.bls.gov/times eries/LNS14000000>. Acccessed on April 16, 2012.6-40

U.S. Department of Labor, Bureau of Labor Statistic s (2012b, April 6). “The Employment Situation—March 2012.” News Release. Available at <http://www.bls.gov/news.release/pdf/empsit.pdf>. A ccessed April 16, 2012. U.S. Department of Labor, U.S. Bureau of Labor Stat istics, (n.d.). “Employment Projections Program.” and available at: <http://www.bls.gov/emp /ep_table_102.htm>. U.S. Department of Labor, U.S. Bureau of Labor Stat istics. (n.d.). “Green Goods and Services, 2010-2011.” Available at: <http://www.bls.gov/ggs/g gsoverview.htm>. Retrieved January 14, 2014, U.S. Energy Information Agency. 2013. “Annual Energ y Outlook 2014: Early Release.” Available at: http://www.eia.gov/forecast/aeo/er/ea rly_elecgen.cfm. U.S. Environmental Protection Agency (U.S. EPA). 20 11a. The Benefits and Costs of the Clean Air Act from 1990 to 2020, Final Report – Rev A. Of fice of Air and Radiation, Washington, DC. Available at <http://www.epa.gov/cleanairactben efits/feb11/fullreport_rev_a.pdf>. Accessed June 4, 2015. U.S. Environmental Protection Agency (U.S. EPA). 20 11b. Regulatory Impact Analysis for the Mercury and Air Toxics Standards (MATS). Office of Air Quality Planning and Standards, Research Triangle Park, NC. Available at <http://www.epa.gov/ttn/ecas/regdata/RIAs/matsriafi nal.pdf>. Acessed June 4, 2015. U.S. EPA. 2015. Technical Support Document (TSD) th e Final Carbon Pollution Emission Guidelines for Existing Stationary Sources: Electri c Utility Generating Units. GHG Mitigation Measures.

U.S. International Trade Commission. “Environmental and Related Services.” Publication No. 4389, Investigation No. 332-533, March, 2013. Avail able at <http://www.usitc.gov/publications/332/pub4389.pdf> . Accessed June 4, 2015. Walker, W. R. 2011.“Environmental Regulation and La bor Reallocation." American Economic Review. 101(2): 442-47.
Walker, W. R. 2013.“ The Transitional Costs of Sect oral Reallocation: Evidence From the Clean Air Act and the Workforce." The Quarterly Journal of Economi cs 128 (4): 1787-1835.
Wei, M., S. Patadia, and D. M. Kammen. 2010. “Putti ng Renewables And Energy Efficiency to Work: How Many Jobs Can the Clean Energy Industry G enerate in the US?” Energy Policy, 38(2):919-931.6-41

Werling, J.F. 2011. “Preliminary Economic Analysis for OSHA’s Proposed Crystalline Silica Rule: Industry and Macroeconomic Impacts”, Final Re port for the Occupational Safety and Health Administration, Available at <https://www.osha.gov/silica/Employment_Analysis.pd f>. Accessed June 9, 2015. Yi, H. 2013. “Clean Energy Policies and Green Jobs : An Evaluation of Green Jobs in U.S. Metropolitan Areas.” Energy Policy . 56:644-65.6A-1



### A PPENDIX 

### 6A: E STIMATING 

### S UPPLY 

### S IDE 

### E MPLOYMENT IMPACTS

This appendix presents the methods used to estimate the supply-side employment impacts
of the Final Carbon Pollution Emission Guidelines f or Existing Stationary Sources: Electric Utility Generating Units (herein referred to as “fi nal emission guidelines” or the “Clean Power Plan Final Rule”). The focus of the employment anal ysis is limited to the direct changes in the amount of labor needed in the power, fuels and gene rating equipment sectors directly influenced by the illustrative plan approaches analyzed for th e final emission 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 ch anges to the energy markets, such as changes in electricity prices.

The methods used to estimate the supply-side employ ments are based on methods previously developed for the Mercury and Air Toxics Standards (MATS) Regulatory Impact Analysis (RIA). The methods used in this analysis t o estimate the recurring labor impacts (e.g., labor associated with operating and maintaining gen erating units, as well as labor needed to mine coal and natural gas) are the same as was used in M ATS (with updated data where available).

The labor analysis in the MATS RIA was primarily co ncerned with the labor needs of retrofitting pollution control equipment. The analy sis for the Clean Power Plan Final Rule, however, involves the quantity and timing of the la bor needs of building new renewable and natural gas, as well as making heat rate improvemen ts (HRI) at existing coal fired EGUs. These construction-related compliance activities in the C lean Power Plan Final Rule required developing additional appropriate analytical method s that were not needed for the MATS analysis. The newly developed analytical methods fo r the construction-related activities are similar in structure and overall approach to the me thods used in MATS, but required additional data and engineering information not needed in the MATS RIA.

6A.1 General Approach
The analytical approach used in this analysis is a bottom-up engineering method combining the EPA’s cost analysis of the final emis sion guidelines with data on labor6A-2

productivity, engineering estimates of the amount a nd types of labor needed to manufacture, construct, and operate different types of generatin g units, and prevailing wage rates for skilled and general labor categories. The approach involved using utility power sector projections and various energy market implications under the final emission guidelines from modeling conducted with the EPA Base Case version 5.15, using the Inte grated Planning Model (IPM) 186 , 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 engin eering approach projects labor changes measured as the change in each analysis year in job -years employed in the power generation and directly related sectors (e.g., equipment manufactu ring, fuel supply and generating efficiency services). Job-years are not individual jobs, nor a re they necessarily permanent nor full time jobs. Job-years are the amount of work performed by one f ull time equivalent (FTE) employee in one year. For example, 20 job-years in 2020 may represe nt 20 full-time jobs or 40 half-time jobs in that year, or any combination of full- and part-tim e workers such that total 20 FTEs.

The estimates of the employment impacts (both posit ive and negative) are divided into five categories:

• additional employment to make HRI 187 at existing coal fired EGUs;

• additional construction-related employment to manuf acture and install additional new generating capacity (renewables, and natural gas co mbined cycle or combustion turbine units) when needed as part of early compliance acti ons;

• lost construction-related employment opportunities due to reductions in the total amount of new generating capacity needed to be built in th e later years because of reduced overall demand for electricity because of demand-si de energy efficiency activities;

186 Results for this analysis were developed using var ious outputs from EPA’s Base Case v.5.15 using ICF’ s Integrated Planning Model (IPM). See http://www.epa .gov/powersectormodeling/ for more information.

187 Heat rate improvements could include a range of ac tivities in the power plant to lower the heat rate required to generate a net electrical output. Assuming all othe r things being equal, a lower heat rate is more eff icient because less fuel is needed per unit of electric output.6A-3

• lost operating and maintenance employment opportuni ties 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 fin al emission guidelines.
Some of the changes are one-time labor effects whic h are associated with the building (or avoiding building) new generating capacity and inst alling 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 l abor 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 th roughout the expected service life of the unit. New generating capacity also creates a need f or additional employment to provide the fuel annually to run the new capacity. There are also co ntinuing effects from the lost operations and maintenance (O&M) and fuel sector labor opportuniti es from decisions to retire existing capacity, as well as similar lost labor opportuniti es from decisions to reduce a portion of the amount of additional capacity needed in the base ca se.
6A.2 Employment Changes due to Heat Rate Improvemen ts The employment changes due to HRI were estimated ba sed on the incremental MW capacity estimated to implement such improvements b etween 2021 and 2030 as indicated by the IPM analysis presented in Chapter 3. The labor anal ysis assumes there will be no HRI-related costs jobs associated with operating or maintaining an EGU after HRI improvements are made. As described in Chapter 3 of this RIA, EPA modeled the heat rate improvements exogenously to IPM using the assumption that all “relevant” units can improve their heat rate at a capital cost of $100/kW. The labor analysis assumes that the cost o f implementing the HRI investments at a particular EGU will occur over a four year period. Hence, the labor analysis calculates the peryear cost of implementing the HRI is calculated to be $25/kW over a four year period, and these HRI cost occur in the 4 years prior to the HRI impr ovements at an individual EGU being operational.6A-4

The HRI costs were then allocated to four categorie s based on the estimates provided by Andover Technology Partners (ATP), which were adapt ed from proxy projects involving installation of combustion control retrofits, such as those installed under the Best Available Retrofit Technology (BART) submissions from coal-fi red power plants located in Wyoming and Arizona. For more details, refer to the Staudt (201 4) report. 188 The data from the BART submissions are used as proxies that are representa tive of the activities (and their associated costs) EGUs will improve use to implement the HRI.

Information on cost for these proxies were then ext rapolated to approximate the labor requirements for four broad categories of labor – b oilermakers and general construction, engineering and management support labor, labor req uired 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 pro vided in the Staudt (2014) report.

Based on the cost allocated to each labor categorie s, output per worker estimates for respective labor categories, and the assumed growth in labor productivity during the period 2021 through 2030 (with the bulk of HRI occurring betwee n 2021 and 2025), the employment gains for heat rate improvement were estimated for 2025 u sing 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 produ ctivity growth rates for the relevant sectors.

189

188 Staudt, James, Andover Technology Partners, Inc. E stimating Labor Effects of Heat Rate Improvements. Report prepared for the Clean Power Plan Proposed Rule, Ma rch 6, 2014.

189 Total value of shipments or receipts in 20012 and total employees were taken from 2012 Economic Censu s, Statistics by Industry for Mining and Manufacturing sectors. The average annual growth rate of labor p roductivity was taken from the Bureau of Labor Statistics. Aver age growth rate calculated for years 1988-2007, app lied to 2012 productivity to determine 2025 estimates of product ivity. For the construction sector, BLS productivit y growth rate data was unavailable. Because of this, and lack of reliable data on construction sector productivity g rowth, the output per worker for the construction sector was n ot forecasted to 2030, and the most recent availabl e value from 2012 was used.6A-5

Table 6A-1. Labor Productivity Growth Rate due to H eat Rate Improvement Share of the Total Capital Cost Output/Worker (2025) Labor Productivity

Growth Rate Boilermaker and Gen. Const. 40% $78,500 0% Management/Engineering 20% $156,000 0.8% Equipment 30% $542,000 2.7% Materials 10% $600,000 1.0% For these output per worker figures, a power sector construction industry (NAICS 237130) was used for general construction and boilermakers, Eng ineering Services (NAICS 54133) was used for the engineering and management component, Machi nery Manufacturing (NAICS 333) was used for the equipment sector, and steel manufactur ing (NAICS 3312) was used for materials. Use of machinery manufacturing for equipment and st eel for materials was based on an analysis of the types of materials and equipment needed for these projects, and what EPA determined to be the most appropriate industry sectors for those. For more details, refer to the Staudt (2014) report.

6A.2.1 Employment Changes Due to Building (or Avoid ing) New Generation Capacity
Employment changes due to new generation units were based on the incremental changes in capacity (MW), capital costs ($MM), and fixed op erations and maintenance (FOM) costs ($MM) between the policy scenarios and the base cas e in a given year.

New capacities were aggregated by generation type i nto the following categories:

• Combined Cycle,

• Combustion Turbine, and

• Renewables (which includes biomass, geothermal, lan dfill gas, onshore wind, and solar).
For each category, the analysis estimated the impac ts due to both the construction and operating labor requirements for corresponding capa city changes. The construction labor was estimated using information on the capital costs, w hile the operating labor was estimated using
the FOM costs.

Because IPM outputs provide annualized capital cost s ($MM), EPA first converted the annualized capital costs to changes in the total ca pital investment using the corresponding capital6A-6

charge rates. 190 These total capital investments were then converte d to annual capital investments using assumptions about the estimated duration of t he construction phase, in order to estimate the annual impacts on construction phase labor. Duratio n estimates were based on assumptions for construction lengths used in EPA’s IPM modeling. 191 Specific assumptions used for different generating technologies are shown in Table 6A-2 bel ow.
Table 6A-2. Capital Charge Rate and Duration Assump tions New Investment Technology Capital Charge Rate Construction Duration (Years) Advanced Combined Cycle 10.3% 3 Advanced Combustion Turbine 10.6% 2 Renewables Dedicated Biomass 9.5% 3 Wind (Onshore) 10.9% 3 Landfill Gas 10.9% 3 Solar 10.9% 3 Geothermal 10.9% 3
Annual capital costs for each generation type were then broken down into four categories: equipment, material (which is assumed to be primari ly steel), installation labor, and support labor in engineering and management. The percentage break downs shown in Table 6A-3 were estimated using information provided by Staudt (201 4), based primarily on published budgets for new unit assembled in a study for the National Ener gy Technology Laboratory (NETL). For more details, refer to the Staudt (2014) report. An nual 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 Genera ting 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 genera tion units were based on output ($ per worker) figures for the respective sectors. The tot al direct workers per $1 million of output for the baseline year 20012 were forecasted to the year s under analysis using the relevant labor

190 Capital charge rates obtained from EPA’s resource, EPA #450R13002: Documentation for EPA Base Case v. 5.13 using the Integrated Programming Model (IPM).

191 Ibid.6A-7

productivity growth rate. Table 6A-4 shows the figu res for each of the five productivities: general power plant construction; engineering and m anagement; material use; equipment use; and plant operators. The resulting values were mult iplied by the capital costs to get the job impact.

Table 6A-4. Labor Productivity due to New Generatin g Capacity Labor Productivity Growth Rate Workers per Million $ (20012) General Power Plant Construction 0.0% 5.0 Engineering and Management 0.8% 5.24.7 Material Use (Steel) 1.0% 1.9 Equipment Use (Machinery) 2.7% 2.1 Plant Operators 1.7% 9.9
General installation labor, assumed to be mostly re lated to the general power plant construction phase, was matched with the power indu stry specific construction sector. Engineering/management was matched to the engineeri ng services sector to determine their respective output per worker. For materials, EPA as sumed steel to be the proxy and used the steel manufacturing sector for this productivity. E quipment was assumed to primarily come from machinery manufacturing sector (such as turbines, e ngines and fans).

The net labor impact for construction labor for a g iven 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 fo r 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 derive d from the 2025 IPM run year are the average annual labor impacts in 2021 through 2025). The con struction labor results for 2020 represent the average labor impacts in 2017 through 2020.

The plant operating employment estimates used a sim pler 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 a re inherently estimates for a single year, the operating employment estimates are for a single yea r only. While there are obviously operating6A-8

employment effects occurring in every year througho ut 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 mill ion and labor productivity growth rate provided for plant operators in Table 6A-4 were use d to estimate the employment impact.

6A.2.2 Employment Changes due to Coal and Oil/Gas R etirements Employment changes due to plant retirements were ca lculated using the IPM projected changes in retirement capacities for coal and oil/g as units for the relevant year and the estimated changes in total FOM costs due to those retiring un its. Thus, the basic assumption in this analysis is that increased retirements (over the base case) will lead to reduced FOM expenditures at those plants which were assumed to lead to direct job los ses for plant workers.

In order to estimate the total FOM changes due to r etirements, EPA first estimated the average FOM costs ($/kW) for existing coal-fired an d 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 econo mic output due to fossil retirements. Thus, changes in the FOM costs for these retiring units w ere 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. 192

Table 6A-5. Average FOM Costs for Existing Coal and Oil and Gas Steam Capacity

($/kW, 2011$) 2020 2025 2030 Coal $70 $73 $74 Oil and Gas $34 $33 $33
Note that the retirement related employment losses are assumed to include losses directly affecting the utility sector, and do not include lo sses 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).

192 The same specific sources as cited before, however , used workers and total payroll.6A-9

6A.2.3 Employment Changes due to Changes in Fossil Fuel Extraction
Two types of employment impacts due to projected fu el use changes were estimated in this section. First, employment losses due to either red uctions or shifts in coal demand were estimated using an approach similar to EPA’s coal e mployment analyses under Title IV of the Clean Air Act Amendments. Using this approach, chan ges in coal demand (in short tons) for various coal supplying regions were taken from EPA’ s base case and illustrative plan scenario model runs for the final EGU GHG NSPS. These change s were converted to job-years using U.S. Energy information Administration (EIA) data on reg ional coal mining productivity (in short tons per employee hour), using 2012 labor productiv ity estimates. 193,194

Specifically, the incremental changes to coal deman d were calculated based on the coal supply regions in IPM -- Appalachia, Interior, and West and Waste Coal (which was estimated using U.S. total productivity). Worker productivity values used for estimating coal related job impacts are shown in Table 6A-6 below.

Table 6A-6. Labor Productivity due to Fossil Fuel E xtraction Labor Productivity Coal (Short tons/ employee hour) Appalachia 2.32 Interior 4.73 West 17.09 Waste 5.19 Natural Gas (MMBtu/ employee hour) 122.0 Pipeline Construction (Workers per $Million) 4.2
For natural gas demand, labor productivity per unit of natural gas was unavailable, unlike coal labor productivities used above. Most secondar y data sources (such as Census and EIA) provide estimates for the combined oil and gas extr action sector. This section thus used an adjusted labor productivity estimate for the combin ed oil and gas sector that accounts for the relative contributions of oil and natural gas in th e total sector output (in terms of the value of

193 EIA Annual Energy Review. 2012.

194 Unlike the labor productivity estimates for variou s equipment resources which were forecasted to 2020 using BLS average growth rates, the labor analysis uses t he 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 sectors, which would have introduced a high degree of uncertainty in forecasting productivity growth rates for future years.6A-10

energy output in MMBtu). This estimate of labor pro ductivity was then used with the incremental natural gas demand for the respective I PM runs to estimate the job-years for the specific year (converting the TCF of gas used proje cted by IPM into MMBtu using the appropriate conversion factors). In addition, the p ipeline construction costs were estimated using endogenously determined gas market model parameters in IPM used by EPA for the MATS rule (using assumptions for EPA’s Base Case v4.10). This analysis assumed that the need for additional pipeline would be proportionate to those projected for the MATS rule and were hence extrapolated from those estimates (U.S. EPA, 2011). The job-years associated with the pipeline construction were included in the natural gas emplo yment estimates. Worker productivity values used for estimating natural gas related job impacts are shown in Table 6A-6.

6A.3 References Staudt, James. 2014. “Estimating Labor Effects of H eat Rate Improvements. Report prepared for the Clean Power Plan Proposed Rule, March 6, 2014.” U.S. Energy Information Agency. 2012. “Annual Energ y Review 2012: Coal Mining Productivity Data.” Availableat http://www.eia.gov/ totalenergy/data/monthly/previous.cfm.. Accessed June 4, 2015 U.S. Environmental Protection Agency (U.S. EPA). 20 11. Regulatory Impact Analysis for the Mercury and Air Toxics Standards (MATS). Office of Air Quality Planning and Standards, Research Triangle Park, NC. Available at <http://www.epa.gov/ttn/ecas/regdata/RIAs/matsriafi nal.pdf>. Acessed June 4, 2015.7-1



### C HAPTER 

### 7: S TATUTORY AND 

### E XECUTIVE 

### O RDER 

### A NALYSIS

7.1 Executive Order 12866: Regulatory Planning and Review, and Executive Order 13563: Improving Regulation and Regulatory Review
This final action is an economically significant re gulatory action that was submitted to the OMB for review. Any changes made in response to OMB recommendations have been documented in the docket. The EPA prepared an analy sis of the potential costs and benefits
associated with this action.

Consistent with Executive Order 12866 and Executive Order 13563, the EPA estimated the costs and benefits for illustrative plan approa ches of implementing the guidelines. The final rule establishes: 1) state-specific carbon dioxide (CO 2 ) goals reflecting CO 2 emission performance rates for two source categories of exis ting fossil fuel-fired EGUs, fossil fuel-fired electric utility steam generating units and station ary combustion turbines, and 2) guidelines for the development, submittal and implementation of st ate plans that establish emission standards or other measures to implement the CO 2 emission performance rates. Actions taken to compl y with the guidelines will also reduce the emissions of directly-emitted PM 2.5 , SO 2 and NO X . The
benefits associated with these PM 2.5 , SO 2 and NO X reductions are referred to as co-benefits, as these reductions are not the primary objective of t his rule.

The EPA has used the social cost of carbon estimate s presented in the Technical Support Document: Technical Update of the Social Cost of Ca rbon for Regulatory Impact Analysis
Under Executive Order 12866 (May 2013, Revised July 2015) (“current TSD”) to analyze CO 2 climate impacts of this rulemaking. We refer to the se estimates, which were developed by the U.S. government, as “SC-CO 2 estimates.” The SC-CO 2 is an estimate of the monetary value of impacts associated with a marginal change in CO 2 emissions in a given year. The four SC-CO 2 estimates are associated with different discount ra tes (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 summary, the EPA provides the estimate of climate b enefits associated with the SC-CO 2 value
deemed to be central in the current TSD: the model average at 3 percent discount rate.
In the final emission guidelines, the EPA has trans lated the source category-specific CO 2 emission performance rates into equivalent state-le vel rate-based and mass-based CO 2 goals in7-2

order to maximize the range of choices that states will have in developing their plans. Because of the range of choices available to states and the la ck of a priori knowledge about the specific choices states will make in response to the final g oals, the Regulatory Impact Analysis (RIA) for this rule analyzed two implementation scenarios des igned to achieve these goals, which we term the “rate-based” illustrative plan approach and the “mass-based” illustrative plan approach.
It is very important to note that the differences b etween the analytical results for the ratebased and mass-based illustrative plan approaches p resented in the RIA may not be indicative of likely differences between the approaches if implem ented by states and affected EGUs in response to the final guidelines. Rather, the two s ets of analyses are intended to illustrate two contrasting, stylized approaches to accomplish the emission performance rates finalized in the Clean Power Plan Final Rule. In other words, if one approach performs better than the other on a given metric during a given time period, this does not imply this will apply in all instances in all time periods in all places.
The EPA estimates that, in 2020, the final guidelin es will yield monetized climate benefits (in 2011$) of approximately $2.8 billion f or the rate-based approach and $3.3 billion for the mass-based approach (3 percent model average). For the rate-based approach, the air pollution health co-benefits in 2020 are estimated to be $0.7 billion to $1.8 billion (2011$) for a 3 percent discount rate and $0.64 billion to $1.7 b illion (2011$) for a 7 percent discount rate. For the mass-based approach, the air pollution health c o-benefits in 2020 are estimated to be $2.0 billion to $4.8 billion (2011$) for a 3 percent dis count rate and $1.8 billion to $4.4 billion (2011$) for a 7 percent discount rate. The annual, illustrative compliance costs estimated by IPM and inclusive of demand-side energy efficiency prog ram and participant costs and MRR costs in 2020, are approximately $2.5 billion for the rate-b ased approach and $1.4 billion for the massbased approach (2011$). The quantified net benefits (the difference between monetized benefits and compliance costs) in 2020 are estimated to rang e from $1.0 billion to $2.1 billion (2011$) for the rate-based approach and from $3.9 billion to 6. 7 billion (2011$) for the mass-based approach, using a 3 percent discount rate (model average).
The EPA estimates that, in 2025, the final guidelin es will yield monetized climate benefits (in 2011$) of approximately $10 billion fo r the rate-based approach and $12 billion for the mass-based approach (3 percent model average). For the rate-based approach, the air7-3

pollution health co-benefits in 2025 are estimated to be $7.4 billion to $18 billion (2011$) for a 3 percent discount rate and $6.7 billion to $16 billi on (2011$) for a 7 percent discount rate. For the mass-based approach, the air pollution health co-be nefits in 2025 are estimated to be $7.1 billion to $17 billion (2011$) for a 3 percent discount rat e and $6.5 billion to $16 billion (2011$) for a 7 percent discount rate. The annual, illustrative com pliance costs estimated by IPM and inclusive of demand side energy efficiency program and partic ipant costs and MRR costs in 2025, are approximately $1.0 billion for the rate-based appro ach and $3.0 billion for the mass-based approach (2011$). The quantified net benefits (the difference between monetized benefits and compliance costs) in 2025 are estimated to range fr om $17 billion to $27 billion (2011$) for the rate-based approach and $16 billion to $26 billion (2011$) for the mass-based approach, using a 3 percent discount rate (model average).
The EPA estimates that, in 2030, the final guidelin es will yield monetized climate benefits (in 2011$) of approximately $20 billion fo r the rate-based approach and $20 billion for the mass-based approach (3 percent model average). For the rate-based approach, the air pollution health co-benefits in 2030 are estimated to be $14 billion to $34 billion (2011$) for a 3 percent discount rate and $13 billion to $31 billio n (2011$) for a 7 percent discount rate. For the mass-based approach, the air pollution health co-be nefits in 2030 are estimated to be $12 billion to $28 billion (2011$) for a 3 percent discount rat e and $11 billion to $26 billion (2011$) for a 7 percent discount rate. The annual, illustrative com pliance costs estimated by IPM and inclusive of demand side energy efficiency program and partic ipant costs and MRR costs in 2030, are approximately $8.4 billion for the rate-based appro ach and $5.1 billion for the mass-based approach (2011$). The quantified net benefits (the difference between monetized benefits and compliance costs) in 2030 are estimated to range fr om $26 billion to $45 billion (2011$) for the rate-based approach and from $26 billion to $43 bil lion (2011$) for the mass-based approach, using a 3 percent discount rate (model average).
Table 7-1 and 7-2 provide the estimates of the clim ate benefits, health co-benefits, compliance costs and net benefits of the final emis sion guidelines for rate-based and mass-based illustrative plan approaches, respectively.7-4

Table 7-1. Monetized Benefits, Compliance Costs, an d Net Benefits Under the Rate-based

Illustrative Plan Approach (billions of 2011$) a Rate-Based Approach 2020 2025 2030 Climate Benefits b 5% discount rate $0.80 $3.1 $6.4 3% discount rate $2.8 $10 $20 2.5% discount rate $4.1 $15 $29 95th percentile at 3% discount rate $8.2 $31 $61
Air Quality Co-benefits Discount Rate
3% 7% 3% 7% 3% 7% Air Quality Health Co-benefits c $0.7 to $1.8 $0.6 to $1.7 $7.4 to $18 $6.7 to $16 $ 14 to $34 $13 to $31 Compliance Costs d $2.5 $1.0 $8.4 Net Benefits e $1.0 to $2.1 $1.0 to $2.0 $17 to $27 $16 to $25 $26 to $45 $25 to $43

Non-Monetized Benefits Non-monetized climate benefits Reductions in exposure to ambient NO 2 and SO 2 Reductions in mercury deposition Ecosystem benefits associated with reductions in em issions of NO X , SO 2 , PM, and mercury Visibility impairment

a All are rounded to two significant figures, so fig ures may not sum. b The climate benefit estimate in this summary table reflects global impacts from CO 2 emission changes and does not account for changes in non-CO 2 GHG emissions. Also, different discount rates are applied to SC-CO 2 than to the other estimates because CO 2 emissions are long-lived and subsequent damages oc cur over many years. The benefit estimates in this table are based on the average SC -CO 2 estimated for a 3 percent discount rate, however w e emphasize the importance and value of considering t he full range of SC-CO 2 values. As shown in the RIA, climate benefits are also estimated using the other three S C-CO 2 estimates (model average at 2.5 percent discount r ate, 3 percent, and 5 percent; 95 th percentile at 3 percent). The SC-CO 2 estimates are year-specific and increase over time

. c The air quality 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-respo nse functions from different epidemiology studies. The reduction in pr emature fatalities each year accounts for over 98 p ercent of total monetized co-benefits from PM 2.5 and ozone. These models assume that all fine partic les, regardless of their chemical composition, are equally potent in causing premature mortality because the scientific evidenc e is not yet sufficient to allow differentiation of effect estim ates by particle type. Estimates in the table are p resented for three analytical years with air quality co-benefits calcu lated using two discount rates. The estimates of co -benefits are annual estimates in each of the analytical years, r eflecting discounting of mortality benefits over th e cessation lag between changes in PM 2.5 concentrations and changes in risks of premature d eath (see RIA Chapter 4 for more details), and discounting of morbidity benefits due to the multiple years of costs associated with som e illnesses. The estimates are not the present value of the benefits of the rule over the full compliance period. d Total costs are approximated by the illustrative co mpliance costs estimated using the Integrated Plann ing Model for the final emission guidelines and a discount rate o f approximately 5 percent. This estimate also inclu des monitoring, recordkeeping, and reporting costs and demand-side energy efficiency program and participant costs.

e The estimates of net benefits in this summary tabl e are calculated using the global SC-CO 2 at a 3 percent discount rate (model average). The RIA includes combined cli mate and health estimates based on additional disco unt rates.7-5

Table 7-2. Monetized Benefits, Compliance Costs, an d Net Benefits under the Mass-based

Illustrative Plan Approach (billions of 2011$) a Mass-Based Approach 2020 2025 2030 Climate Benefits b 5% discount rate $0.9 $3.6 $6.4 3% discount rate $3.3 $12 $20 2.5% discount rate $4.9 $17 $29 95th percentile at 3% discount rate $9.6 $35 $60
Air Quality Co-benefits Discount Rate
3% 7% 3% 7% 3% 7% Air Quality Health Co-benefits c $2.0 to $4.8 $1.8 to $4.4 $7 to $17 $7 to $16 $12 t o $28 $11 to $26 Compliance Costs d $1.4 $3.0 $5.1 Net Benefits e $3.9 to $6.7 $3.7 to $6.3 $16 to $26 $15 to $24 $26 to $43 $25 to $40

Non-Monetized Benefits Non-monetized climate benefits Reductions in exposure to ambient NO 2 and SO 2 Reductions in mercury deposition Ecosystem benefits associated with reductions in em issions of NO X , SO 2 , PM, and mercury Visibility improvement

a All are rounded to two significant figures, so fig ures may not sum. b The climate benefit estimate in this summary table reflects global impacts from CO 2 emission changes and does not account for changes in non-CO 2 GHG emissions. Also, different discount rates are applied to SC-CO 2 than to the other estimates because CO 2 emissions are long-lived and subsequent damages oc cur over many years. The benefit estimates in this table are based on the average SC -CO 2 estimated for a 3 percent discount rate, however w e emphasize the importance and value of considering t he full range of SC-CO 2 values. As shown in the RIA, climate benefits are also estimated using the other three S C-CO 2 estimates (model average at 2.5 percent discount r ate, 3 percent, and 5 percent; 95 th percentile at 3 percent). The SC-CO 2 estimates are year-specific and increase over time

. c The air quality 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-respo nse functions from different epidemiology studies. The reduction in pr emature fatalities each year accounts for over 98 p ercent of total monetized co-benefits from PM 2.5 and ozone. These models assume that all fine partic les, regardless of their chemical composition, are equally potent in causing premature mortality because the scientific evidenc e is not yet sufficient to allow differentiation of effect estim ates by particle type. Estimates in the table are p resented for three analytical years with air quality co-benefits calcu lated using two discount rates. The estimates of co -benefits are annual estimates in each of the analytical years, r eflecting discounting of mortality benefits over th e cessation lag between changes in PM 2.5 concentrations and changes in risks of premature d eath (see RIA Chapter 4 for more details), and discounting of morbidity benefits due to the multiple years of costs associated with som e illnesses. The estimates are not the present value of the benefits of the rule over the full compliance period. d Total costs are approximated by the illustrative co mpliance costs estimated using the Integrated Plann ing Model for the final emission guidelines and a discount rate o f approximately 5 percent. This estimate also inclu des monitoring, recordkeeping, and reporting costs and demand-side energy efficiency program and participant costs.

e The estimates of net benefits in this summary tabl e are calculated using the global SC-CO 2 at a 3 percent discount rate (model average). The RIA includes combined cli mate and health estimates based on additional disco unt rates.7-6

There are additional important benefits that the EP A could not monetize. Due to current data and modeling limitations, our estimates of the benefits from reducing CO 2 emissions do not include important impacts like ocean acidification or potential tipping points in natural or managed ecosystems. Unquantified benefits also incl ude climate benefits from reducing
emissions of non-CO 2 greenhouse gases (e.g., nitrous oxide and methane) and co-benefits from reducing direct exposure to SO 2 , NO X and hazardous air pollutants (e.g., mercury and hy drogen chloride), as well as from reducing ecosystem effec ts and visibility impairment. Based upon the foregoing discussion, it remains clear that the ben efits of this final action are substantial, and far exceed the costs. Additional details on benefits, c osts, and net benefits estimates are provided in
this RIA.
7.2 Paperwork Reduction Act (PRA)
The information collection requirements in this rul e have been submitted for approval to OMB under the PRA. The Information Collection Reque st (ICR) document prepared by the EPA has been assigned the EPA ICR number 2503.02. You c an find a copy of the ICR in the docket for this rule, and it is briefly summarized here. T he information collection requirements are not enforceable until OMB approves them.
This rule does not directly impose specific require ments on EGUs located in states or areas of Indian country. The rule also does not imp ose specific requirements on tribal governments that have affected EGUs located in thei r area of Indian country. For areas of Indian country, the rule establishes CO 2 emission performance goals that could be addressed through either tribal or federal plans. A tribe would have the opportunity under the Tribal Authority Rule (TAR), but not the obligation, to apply to the EPA for Treatment as State (TAS) for purposes of a CAA section 111(d) plan and, if approved by the E PA, to establish a CAA section 111(d) plan for its area of Indian country. To date, no tribe h as requested or obtained TAS eligibility for purposes of a CAA section 111(d) plan. For areas of Indian country with affected EGUs where a tribe has not applied for TAS and submitted any nee ded plan, if the EPA determines that a CAA section 111(d) plan is necessary or appropriate, th e EPA would have the responsibility to establish the plans. Because tribes are not require d to implement section 111(d) plans and because no tribe has yet sought TAS eligibility for this purpose, this action is not anticipated to7-7

impose any information collection burden on tribal governments over the 3-year period covered by this ICR.
This rule does impose specific requirements on stat e governments with affected EGUs. The information collection requirements are based o n the recordkeeping and reporting burden associated with developing, implementing, and enfor cing a plan to limit CO 2 emissions from existing sources in the utility power sector. These recordkeeping and reporting requirements are specifically authorized by CAA section 114 (42 U.S. C. 7414). All information submitted to the EPA pursuant to the recordkeeping and reporting req uirements 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 informatio n for the states (averaged over the first 3 years following promulgation) is estimated to be a range of 505,000 to 821,000 hours at a total annual labor cost of $35.8 to $58.1 million. The lo wer bound estimate reflects the assumption that some states already have energy efficiency and renewable energy programs in place. The higher bound estimate reflects the overly-conservat ive assumption that no states have energy efficiency and renewable energy programs in place.
The total annual burden for the federal government associated with the state collection of information (averaged over the first 3 years follow ing promulgation) is estimated to be 54,000 hours at a total annual labor cost of $3.00 million . 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 curr ently valid OMB control number. The OMB control numbers for the EPA’s regulations in 40 CFR are listed in 40 CFR part 9. When OMB approves this ICR, the agency will announce that ap proval in the Federal Register and publish a technical amendment to 40 CFR part 9 to display the OMB control number for the approved
information collection activities contained in this final rule.
7.3 Regulatory Flexibility Act (RFA)

The EPA certifies that this action will not have a significant economic impact on a
substantial number of small entities under the RFA. This action will not impose any requirements on small entities. Specifically, emiss ion guidelines established under CAA section7-8

111(d) do not impose any requirements on regulated entities and, thus, will not have a significant economic impact upon a substantial number of small entities. After emission guidelines are promulgated, states establish emission standards on existing sources, and it is those requirements that could potentially impact small entities.
Our analysis here is consistent with the analysis o f the analogous situation arising when the EPA establishes NAAQS, which do not impose any requirements on regulated entities. As here, any impact of a NAAQS on small entities would only arise when states take subsequent action to maintain and/or achieve the NAAQS through their state implementation plans. See American Trucking Assoc. v. EPA, 175 F.3d 1029, 104 3-45 (D.C. Cir. 1999) (NAAQS do not have significant impacts upon small entities becaus e NAAQS themselves impose no regulations upon small entities).
Nevertheless, the EPA is aware that there is substa ntial interest in the rule among small entities and, as detailed in section III.A of the p reamble to the proposed carbon pollution emission guidelines for existing EGUs (79 FR 34845- 34847; June 18, 2014) and in section II.D of the preamble to the proposed carbon pollution em ission guidelines for existing EGUs in Indian Country and U.S. Territories (79 FR 65489; N ovember 4, 2014), has conducted an unprecedented amount of stakeholder outreach. As pa rt of that outreach, agency officials participated in many meetings with individual utili ties and electric utility associations, as well as industry leaders and trade association representati ves from various industries. While formulating the provisions of the rule, the EPA considered the input provided over the course of the stakeholder outreach as well as the input provided in the many public comments.
7.4 Unfunded Mandates Reform Act (UMRA)

This action does not contain an unfunded mandate of $100 million or more as described in UMRA, 2 U.S.C. 1531–1538, and does not significa ntly or uniquely affect small governments. The emission guidelines do not impose any direct co mpliance requirements on EGUs located in states or areas of Indian country. As explained in section XII.B above, the rule also does not impose specific requirements on tribal governments that have affected EGUs located in their area of Indian country. The rule does impose specific re quirements on state governments that have affected EGUs. Specifically, states are required to develop plans to implement the guidelines under CAA section 111(d) for affected EGUs. The bur den for states to develop CAA section7-9

111(d) plans in the 3-year period following promulg ation of the rule was estimated and is listed in section XII.B above, but this burden is estimate d to be below $100 million in any one year. Thus, this rule is not subject to the requirements of section 202 or section 205 of the UMRA.
This rule is also not subject to the requirements o f section 203 of UMRA because it contains no regulatory requirements that might significantly or uniquely affect small governments. Specifically, the state governments to which rule r equirements apply are not considered small governments. In light of the interest among governmental entitie s, the EPA conducted outreach with national organizations representing state and local elected officials and tribal governmental entities while formulating the provisions of this r ule. Sections III.A and XI.F of the preamble to the proposed carbon pollution emission guidelines f or existing EGUs (79 FR 34845-34847; June 18, 2014) and sections II.D and VI.F of the preambl e to the proposed carbon pollution emission guidelines for existing EGUs in areas of Indian Cou ntry and U.S. Territories (79 FR 65489; November 4, 2014) describes the extensive stakehold er outreach the EPA has conducted on setting emission guidelines for existing EGUs. The EPA considered the input provided over the
course of the stakeholder outreach as well as the i nput provided in the many public comments when developing the provisions of these emission gu idelines.

7.5 Executive Order 13132: Federalism
The EPA has concluded that this action may have fed eralism implications, pursuant to agency policy for implementing the Order, because i t imposes substantial direct compliance costs on state or local governments, and the federa l government will not provide the funds necessary to pay those costs. As discussed in the S upporting Statement found in the docket for this rulemaking, the development of state plans wil l entail many hours of staff time to develop and coordinate programs for compliance with the rul e, as well as time to work with state legislatures as appropriate, to develop a plan subm ittal. Consistent with this determination, the EPA provides the following federalism summary impac t statement.

The EPA consulted with state and local officials ea rly in the process of developing the proposed action to permit them to have meaningful a nd timely input into its development. As described in the Federalism discussion in the pream ble to the proposed standards of performance for GHG emissions from new EGUs (79 FR 1501; Januar y 8, 2014), the EPA consulted with7-10

state and local officials in the process of develop ing the proposed standards for newly constructed EGUs. This outreach addressed planned a ctions 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, 2 011, in Washington DC: (1) National Governors Association; (2) National Conference of State Legis latures, (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 Management Association, (8) National Association of Towns and Townships, (9) County Executives of Ameri ca, and (10) Environmental Council of States. The National Association of Clean Air Agenc ies also participated. 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, as described in section III.A of the preamble to the proposed carbon pollution emission guidelines for existing EGUs (79 FR 34845- 34847; June 18, 2014), extensive stakeholder outreach conducted by the EPA allowed s tate leaders, including governors, state attorneys general, environmental commissioners, ene rgy officers, public utility commissioners, and air directors, opportunities to engage with EPA officials and provide input regarding reducing carbon pollution from power plants.
In the spirit of Executive Order 13132, and consist ent with the EPA’s policy to promote
communications between the EPA and state and local governments, the EPA specifically solicited comment on the proposed action from state and local officials. The EPA received comments from over 400 entities representing state and local governments.
Several themes emerged from state and local governm ent comments. Commenters raised concerns with the building blocks that comprise the best system of emission reduction (BSER), including the stringency of the building blocks, an d the timing of achieving interim CO 2 levels. They also identified the potential for electric sys tem reliability issues and stranded assets due to the proposed timeframe for plan submittals and CO 2 emission reductions. In addition, states commented on state plan development and implementat ion topics, including state plan approaches, early actions, trading programs, inters tate crediting for RE, and EPA guidance and
outreach.7-11

Commenters identified overarching concerns regardin g the stringency of the CO 2 goals and the timeframe for achieving reductions that enc ompassed the building blocks, the BSER, and associated timing for achievement of interim CO 2 levels. State commenters, in particular, identified changes to the stringency of the buildin g blocks, concerns with the timeframe over which reductions must be achieved, and concerns wit h the approaches and measures used for the BSER. For the final rule, in response to stakeholde r comments, the EPA has made refinements to the building blocks, the period of time over which measures are deployed, and the stringency of
emission limitations that those measures can achiev e in a practical and reasonable cost way. The
final BSER reflects those refinements.
To many commenters, the proposal’s 2020 compliance date, together with the stringency
of the interim CO 2 goal, bore significant reliability implications. I n this final rule, the agency is addressing those concerns via adjustments to the co mpliance timeframe (an 8-year interim period that begins in 2022) and to the approach for meetin g interim CO 2 emission performance rates (a glide path separated into three steps, 2022-2024, 2 025-2027, and 2028-2029), as well as a more gradual phase in of the emission reduction expectat ions. These adjustments provide more time for planning, consultation and decision making in t he formulation of state plans and in EGUs’ choices of compliance strategies. The final rule al so retains flexibilities presented in the proposal and offers additional opportunities, including oppo rtunities for trading within and between states, and other multi-state compliance approaches that wi ll further support electric system reliability. The EPA is also requiring states to consult with re levant ISOs/RTOs and/or planning/reliability authorities during plan development, and to documen t recommendations in their plans – and is providing the time for states to do so. Even with t his foundation of flexibility in place, these final guidelines further provide states with the option o f proposing amendments to approved plans in the event that unanticipated and significant reliab ility challenges arise.
Commenters provided compelling information indicati ng that it will take longer than the agency initially anticipated to adjust investments and achieve interim CO 2 reductions. Recognizing this, as well as the urgent need for ac tions to reduce GHG emissions, the EPA is requiring states to frame an initial plan by August 31, 2016, and is allowing states two additional years to submit a final plan, if justified (to be s ubmitted by August 31, 2018).7-12

States commented on state plan development and impl ementation topics that included state plan approaches, early actions being taken in to account, trading programs being allowed, interstate crediting for RE being allowed, and guid ance and outreach being provided by the EPA. For the state plan approaches, commenters expressed concerns with the proposed “portfolio approach” for state plans, including concerns with enforceability of requirements, and identified a “state commitment approach” with backstop measure s as an option for state plans. In this final rule, in response to stakeholder comments on the po rtfolio approach and alternative approaches, the EPA is finalizing a “state measures” approach t hat includes a requirement for the inclusion of backstop measures.
State commenters supported providing incentives for states and utilities to deploy CO 2 - reducing investments, such as RE and demand-side EE measures, as early as possible. The EPA recognizes the value of such early actions, and in this final rule is establishing a state-federal Clean Energy Incentive Program to reward investment in certain RE and demand-side EE projects that commence construction after the effec tive date of this rule and that generate MWh or reduce end-use energy demand during 2020 and 202 1.
Many state commenters supported the use of mass-bas ed and rate-based emission trading programs in state plans, including interstate emiss ion trading programs. The EPA also received a
number of comments from states and stakeholders abo ut the value of EPA support in developing and/or administering tracking systems to support st ate administration of rate-based and massbased emission trading programs. In this final rule , states may use trading or averaging approaches and technologies or strategies that are not explicitly mentioned in any of the three building blocks as part of their overall plans, as long as they achieve the required emission
reductions from affected fossil-fuel-fired EGUs. In addition, in response to concerns from states and power companies that the need for up-front inte rstate cooperation in developing multi-state plans could inhibit the development of interstate p rograms that could lower cost, the final rule provides additional options to allow individual EGU s to use creditable out-of-state reductions to achieve required CO 2 reductions, without the need for up-front intersta te agreements. The EPA is committed to working with states to provide support for tracking of emissions and allowances or credits, to help implement multi-state trading or a veraging approaches.7-13

In their comments, many states identified the need for the EPA to provide guidance, including guidance on RE and EE emission measuremen t and verification (EM&V), and to maintain regular contact/forums with states through out the implementation process. To provide state and local governments and other stakeholders with an understanding of the rule requirements, and to provide efficiencies where pos sible and reduce the cost and administrative burden, the EPA will continue outreach throughout t he plan development and submittal process. Outreach will include opportunities for states to p articipate in briefings, teleconferences, and meetings about the final rule. The EPA’s 10 regiona l offices will continue to be the entry point for states and tribes to ask technical and policy q uestions. The agency will host (or partner with appropriate groups to co-host) a number of webinars about various components of the final rule during the first two months after the final rule is issued. The EPA will use information from this outreach process to inform the training and other t ools that will be of most use to the states and tribes that are implementing the final rule. The EP A expects to issue guidance on specific topics, including evaluation, measurement and verification (EM&V) for RE and demand-side EE, statecommunity engagement, and resources and financial a ssistance for RE and demand-side EE. As guidance documents, tools, templates and other reso urces become available, the EPA, in consultation with the U.S. Department of Energy and other federal agencies, will continue to
make these resources available via a dedicated webs ite.
A list of the state and local government commenters has been provided to OMB and has been placed in the docket for this rulemaking. In a ddition, the detailed response to comments from these entities is contained in the EPA’s respo nse to comments document on this final rulemaking, which has also been placed in the docke t for this rulemaking.
As required by section 8(a) of Executive Order 1313 2, the EPA included a certification from its Federalism Official stating that the EPA h ad met the Executive Order’s requirements in a meaningful and timely manner when it sent the dra ft of this final action to OMB for review pursuant to Executive Order 12866. A copy of the ce rtification is included in the public version
of the official record for this final action.7-14

7.6 Executive Order 13175: Consultation and Coordin ation with Indian Tribal Governments
This action has tribal implications. However, it wi ll neither impose substantial direct compliance costs on federally recognized tribal gov ernments, nor preempt tribal law. Tribes are not required to develop or adopt CAA programs, but they may apply to the EPA for treatment in a manner similar to states (TAS) and, if approved, do so. As a result, tribes are not required to develop plans to implement the guidelines under CAA section 111(d) for affected EGUs in their areas of Indian country. To the extent that a triba l government seeks and attains TAS status for that purpose, these emission guidelines would requi re that planning requirements be met and emission management implementation plans be execute d by the tribes. The EPA notes that this rule does not directly impose specific requirements on affected EGUs, including those located in areas of Indian country, but provides guidance to a ny tribe approved by the EPA to address CO 2 emissions from EGUs subject to section 111(d) of th e CAA. The EPA also notes that none of the affected EGUs are owned or operated by tribal gover nments. As described in sections III.A and XI.F of the pre amble to the proposed carbon pollution emission guidelines for existing EGUs (79 FR 34845-34847; June 18, 2014) and sections II.D and VI.F of the preamble to the propo sed carbon pollution emission guidelines for existing EGUs in Indian Country and U.S. Territorie s (79 FR 65489; November 4, 2014), the rule was developed after extensive and vigorous out reach to tribal governments. These tribes expressed varied points of view. Some tribes raised concerns about the impacts of the regulations on EGUs located in their areas of Indian country an d the subsequent impact on jobs and revenue for their tribes. Other tribes expressed concern ab out the impact the regulations would have on the cost of water covered under treaty to their com munities as a result of increased costs to the EGU that provide energy to transport the water to t he tribes. Other tribes raised concerns about the impacts of climate change on their communities, resources, ways of life and hunting and treaty rights. The tribes were also interested in t he scope of the guidelines being considered by the agency (e.g., over what time period, relationsh ip to state and multi-state plans) and how tribes will participate in these planning activitie s.

The EPA consulted with tribal officials under the E PA Policy on Consultation and Coordination with Indian Tribes early in the proces s of developing this action to permit them to have meaningful and timely input into its developme nt. A summary of that consultation follows.7-15

Prior to issuing the supplemental proposal on Novem ber 4, 2014, the EPA consulted with
tribes as follows. The EPA held a consultation with the Ute Tribe, the Crow Nation, and the Mandan, Hidatsa, Arikara (MHA) Nation on July 18, 2 014. On August 22, 2014, the EPA held a consultation with the Fort Mojave Tribe. On Septemb er 15, 2014, the EPA held a consultation with the Navajo Nation. The Navajo Nation sent a le tter to the EPA on September 18, 2014, summarizing the information presented at the consul tation and the Navajo Nation’s position on the supplemental proposal. One issue raised by trib al officials was the potential impacts of the June 18, 2014 proposal and the supplemental proposa l on tribes with budgets that are dependent on revenue from coal mines and power plants, as wel l as employment at the mines and power plants. The tribes noted the high unemployment rate s and lack of access to basic services on their
lands. Tribal officials also asked whether the rule s will have any impact on a tribe’s ability to seek TAS. Tribal officials also expressed interest in agency actions with regard to facilitating power plant compliance with regulatory requirements . The Navajo Nation made the following recommendations in their letter of September 18, 20 14: the Navajo Nation supports a mass-based
CO 2 emission standard based on the highest historical CO 2 emissions since 1996; the Navajo Nation requests that the EPA grant the Navajo Natio n carbon credits and that the Navajo Nation retains ownership and control of such credits; buil ding block 2 is not appropriate for the Navajo Nation because there are no NGCC plants located on the Navajo Nation; building block 3 is not appropriate for the Navajo Nation because the Navaj o people already receive virtually all of their electricity from carbon-free sources (mostly hydroe lectric power) and their use of electricity is negligible compared to the generation at the power plants; building block 4 is not appropriate for the Navajo Nation because of the inadequate access to electricity, and the goal should allow for an increase in energy consumption on the Navajo Nat ion; the supplemental proposal should consider the useful life of the power plants locate d on the Navajo Nation; and the supplemental proposal should clarify that RE projects located wi thin the Navajo Nation that provide electricity outside the Navajo Nation should be counted toward meeting the relevant state’s RE goals under
the Clean Power Plan.
After issuing the supplemental proposal, the EPA he ld additional consultation with tribes. On November 18, 2014, the EPA held consultations wi th the following tribes: Fort McDowell Yavapai Nation, Fort Mojave Tribe, Hopi Tribe, Nava jo Nation, and Ak-Chin Indian Community. A consultation with the Ute Indian Tribe of the Uintah and Ouray Reservation was7-16

held on December 16, 2014 and with the Gila River I ndian Community on January 15, 2015. The Navajo Nation reiterated the concerns raised during the previous consultation. Several tribes also again indicated that they wanted to ensure they wou ld be included in the development of any tribal or federal plans for areas of Indian country . The Fort Mojave Tribe and the Navajo Nation expressed concern with using data from 2012 as the basis for the goal for their areas of Indian country; in their view, that year was not represent ative for the affected EGU. On April 28, 2015,
the EPA held an additional consultation with the Na vajo Nation. The issues raised by the Navajo Nation during the consultation included whether the EPA has the authority to set less stringent standards on a case-by-case basis, and a suggested “parity glide path” that would account and adjust for the very low electricity usage by the Na vajo Nation and promote Navajo Nation economic growth and demand. Furthermore, on July 7, 2015 the EPA conducted an additional consultation with the Navajo Nation. One of the goa ls of the consultation was for the new government of the Navajo Nation to deepen their und erstanding of the rulemaking. The questions raised by the nation had to do with goal setting an d carbon credits, the timing of the rulemaking, and the proposed federal plan. Additionally, on Jul y 14, 2015 the EPA conducted an additional consultation with the Fort Mojave Tribe. The Fort M ojave tribes expressed concerns that 2012 is not a representative year, that natural gas-fired c ombined cycle power plants should be treated differently from coal-fired power plants, and that the proposed goal for Fort Mojave was not appropriate. Additionally, they also expressed inte rest in being engaged in the federal plan process. Responses to these comments and others rec eived are available in the Response to
Comment Document that is in the docket for this rul emaking. As required by section 7(a), the
EPA’s Tribal Consultation Official has certified th at the requirements of the executive order have been met in a meaningful and timely manner. A copy of the certification is included in the
docket for this action.

7.7 Executive Order 13045: Protection of Children f rom Environmental Health Risks and Safety Risks This action is subject to Executive Order 13045 (62 FR 19885, April 23, 1997) because it is an economically significant regulatory action as defined by Executive Order 12866, and the
EPA believes that the environmental health or safet y risk addressed by this action has a disproportionate effect on children. Accordingly, t he agency has evaluated the environmental health and welfare effects of climate change on children.7-17

CO 2 is a potent greenhouse gas that contributes to cli mate change and is emitted in significant quantities by fossil fuel-fired power p lants. The EPA believes that the CO 2 emission reductions resulting from implementation of these f inal guidelines, as well as substantial ozone
and PM 2.5 emission reductions as a co-benefit, will further i mprove children’s health.

The assessment literature cited in the EPA’s 2009 E ndangerment Finding concluded that certain populations and lifestages, including child ren, the elderly, and the poor, are most
vulnerable to climate-related health effects. The a ssessment literature since 2009 strengthens these conclusions by providing more detailed findin gs regarding these groups’ vulnerabilities and the projected impacts they may experience.
These assessments describe how children’s unique ph ysiological and developmental factors contribute to making them particularly vuln erable to climate change. Impacts to children are expected from heat waves, air pollution, infect ious and waterborne illnesses, and mental health effects resulting from extreme weather event s. In addition, children are among those especially susceptible to most allergic diseases, a s well as health effects associated with heat waves, storms, and floods. Additional health concer ns may arise in low income households, especially those with children, if climate change r educes food availability and increases prices, leading to food insecurity within households.
7.8 Executive Order 13211: Actions Concerning Regul ations That Significantly Affect Energy Supply, Distribution, or Use
This action, which is a significant regulatory acti on under EO 12866, is likely to have a significant effect on the supply, distribution, or use of energy. The EPA has prepared a Statement of Energy Effects for this action as follows. We es timate a 1 to 2 percent change in retail electricity prices on average across the contiguous U.S. in 2025, and a 22 to 23 percent reduction in coal-fired electricity generation as a result of this rule. The EPA projects that utility power sector delivered natural gas prices will increase b y up to 2.5 percent in 2030. For more information on the estimated energy effects, please refer to the economic impact analysis for this proposal. The analysis is available in the RIA, whi ch is in the public docket.
7.9 National Technology Transfer and Advancement Ac t (NTTAA)
This rulemaking does not involve technical standard s.7-18

7.10 Executive Order 12898: Federal Actions to Addr ess Environmental Justice in Minority Populations and Low-Income Populations Executive Order 12898 (59 FR 7629; February 16, 199 4) establishes federal executive policy on environmental justice. Its main provision directs federal agencies, to the greatest extent practicable and permitted by law, to make environme ntal justice part of their mission by identifying and addressing, as appropriate, disprop ortionately high and adverse human health or environmental effects of their programs, policies, and activities on minority populations and lowincome populations in the U.S. The EPA defines envi ronmental justice as the fair treatment and meaningful involvement of all people regardless of race, color, national origin, or income with respect to the development, implementation, and enf orcement of environmental laws, regulations, and policies. The EPA has this goal fo r all communities and persons across this Nation. It will be achieved when everyone enjoys th e same degree of protection from environmental and health hazards and equal access t o the decision-making process to have a healthy environment in which to live, learn, and wo rk.
Leading up to this rulemaking the EPA summarized th e public health and welfare effects of GHG emissions in its 2009 Endangerment Finding. See, section VIII.A of this preamble where the EPA summarizes the public health and welf are impacts from GHG emissions that were detailed in the 2009 Endangerment Finding unde r CAA section 202(a)(1). 195 As part of the Endangerment Finding, the Administrator considered climate change risks to minority populations and low-income populations, finding tha t certain parts of the population may be especially vulnerable based on their characteristic s or circumstances. Populations that were found to be particularly vulnerable to climate chan ge risks include the poor, the elderly, the very young, those already in poor health, the disabled, those living alone, and/or indigenous populations dependent on one or a few resources. Se e sections XII.F and XII.G, above, where the
EPA discusses Consultation and Coordination with Tr ibal Governments and Protection of Children. The Administrator placed weight on the fa ct that certain groups, including children, the elderly, and the poor, are most vulnerable to clima te-related health effects.

195 “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) (“Endange rment Finding”).7-19

The record for the 2009 Endangerment Finding summar izes the strong scientific evidence in the major assessment reports by the U.S. Global Change Research Program (USGCRP), the Intergovernmental Panel on Climate Change (IPCC), a nd the National Research Council (NRC) of the National Academies that the potential impact s of climate change raise environmental justice issues. These reports concluded that poor c ommunities can be especially vulnerable to climate change impacts because they tend to have mo re limited adaptive capacities and are more dependent on climate-sensitive resources such as lo cal water and food supplies. In addition, Native American tribal communities possess unique v ulnerabilities to climate change, particularly those impacted by degradation of natur al and cultural resources within established
reservation boundaries and threats to traditional s ubsistence lifestyles. Tribal communities whose health, economic well-being, and cultural tradition s that depend upon the natural environment will likely be affected by the degradation of ecosy stem goods and services associated with climate change. The 2009 Endangerment Finding recor d also specifically noted that Southwest native cultures are especially vulnerable to water quality and availability impacts. Native Alaskan communities are already experiencing disrup tive impacts, including coastal erosion and shifts in the range or abundance of wild species cr ucial to their livelihoods and well-being.
The most recent assessments continue to strengthen scientific understanding of climate change risks to minority populations and low-income populations in the United States. 196 The new assessment literature provides more detailed fi ndings regarding these populations’ vulnerabilities and projected impacts they may expe rience. In addition, the most recent assessment reports provide new information on how s ome communities of color (more specifically, populations defined jointly by ethnic /racial characteristics and geographic location)

196 Melillo, Jerry M., Terese (T.C.) Richmond, and Gar y W. Yohe, Eds., 2014: Climate Change Impacts in the United States: The Third National Climate Assessmen t. U.S. Global Change Research Program, 841 pp.
IPCC, 2014: Climate Change 2014: Impacts, Adaptation, and Vulne rability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Asses sment Report of the Intergovernmental Panel on Clim ate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A .N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, 1132 pp.
IPCC, 2014: Climate Change 2014: Impacts, Adaptation, and Vulne rability. Part B: Regional Aspects . Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [B arros, V.R., C.B. Field, D.J. Dokken, M.D. Mastrandrea, K. J. Mach, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. M acCracken, P.R. Mastrandrea, and L.L. White (eds.)]

. Cambridge University Press, 688 pp.7-20

may be uniquely vulnerable to climate change health impacts in the United States. These reports find that certain climate change related impacts—in cluding heat waves, degraded air quality, and extreme weather events—have disproportionate effect s on low-income populations and some communities of color, raising environmental justice concerns. Existing health disparities and other inequities in these communities increase thei r vulnerability to the health effects of climate change. In addition, assessment reports also find t hat climate change poses particular threats to health, well-being, and ways of life of indigenous peoples in the United States.
As the scientific literature presented above and as the 2009 Endangerment Finding illustrates, low income populations and some commun ities of color are especially vulnerable to the health and other adverse impacts of climate cha nge. The EPA believes that communities will benefit from this final rulemaking because this act ion directly addresses the impacts of climate change by limiting GHG emissions through the establ ishment of CO 2 emission guidelines for existing affected fossil fuel-fired EGUs.
In addition to reducing CO 2 emissions, the guidelines finalized in this rulema king would
reduce other emissions from affected EGUs that redu ce generation due to higher adoption of energy efficiency and renewable energy. These emiss ion reductions will include SO 2 and NOx,
which form ambient PM 2.5 and ozone in the atmosphere, and hazardous air poll utants (HAP), such as mercury and hydrochloric acid. In the final rule revising the annual PM 2.5 NAAQS, 197 the EPA identified low-income populations as being a vu lnerable population for experiencing adverse health effects related to PM exposures. Low -income populations have been generally found to have a higher prevalence of pre-existing d iseases, limited access to medical treatment, and increased nutritional deficiencies, which can i ncrease this population’s susceptibility to PMrelated effects. 198 In areas where this rulemaking reduces exposure to PM 2.5 , ozone, and methylmercury, low-income populations will also ben efit from such emissions reductions. The RIA for this rulemaking, included in the docket for this rulemaking, provides additional

197 “National Ambient Air Quality Standards for Partic ulate Matter, Final Rule,” 78 FR 3086 (Jan. 15, 201 3).

198 U.S. Environmental Protection Agency (U.S. EPA). 20 09. Integrated Science Assessment for Particulate Matte r (Final Report) . EPA-600-R-08-139F. National Center for Environmen tal Assessment – RTP Division. December. Available on the Internet at <http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm? deid=216546 >.7-21

information regarding the health and ecosystem effe cts associated with these emission
reductions.
Additionally, as outlined in the community and envi ronmental justice considerations section IX of this preamble, the EPA has taken a nu mber of actions to help ensure that this action will not have potential disproportionately high and adverse human health or environmental
effects on overburdened communities. The EPA consul ted its May 2015, Guidance on Considering Environmental Justice During the Develo pment of Regulatory Actions , when determining what actions to take. 199 As described in the community and environmental ju stice considerations section of this preamble the EPA als o conducted a proximity analysis, which is available in the docket of this rulemaking and is d iscussed in section IX. Additionally, as outlined in sections I and IX of this preamble, the EPA has engaged with communities throughout this rulemaking and has devised a robust outreach strategy for continual engagement throughout the implementation phase of this rulemak ing.
7.11 Congressional Review Act (CRA)
This final action is subject to the CRA, and the EP A will submit a rule report to each House of the Congress and to the Comptroller Genera l of the United States. This action is a “major rule” as defined by 5 U.S.C. 804(2).

199 Guidance on Considering Environmental Justice Duri ng the Development of Regulatory Actions. http://epa.gov/environmentaljustice/resources/polic y/considering-ej-in-rulemaking-guide-final.pdf. May 2015.8-1



### C HAPTER 

### 8: C OMPARISON OF 

### B ENEFITS AND 

### C OSTS

8.1 Comparison of Benefits and Costs
The benefits, costs, and net benefits of the illust rative plan scenarios are presented in this chapter of the Regulatory Impact Analysis (RIA) for the Final Carbon Pollution Emission Guidelines for Existing Stationary Sources: Electri c Utility Generating Units. As discussed in Chapter 1, the EPA is establishing carbon dioxide ( CO 2 ) emission performance rates for two source categories of existing fossil fuel-fired EGU s, fossil fuel-fired electric utility steam generating units and stationary combustion turbines . Given the flexibilities afforded states in complying with the emission guidelines, the benefit s, cost and economic impacts reported in this RIA are not definitive estimates, but are instead i llustrative of plan approaches states may take.

The EPA has used the social cost of carbon estimate s presented in the Technical Support Document: Technical Update of the Social Cost of Ca rbon for Regulatory Impact Analysis Under Executive Order 12866 (May 2013, Revised July 2015) (“current SC-CO 2 TSD”) to analyze CO 2 climate impacts of this rulemaking. 200 We refer to these estimates, which were developed by the U.S. government, as “SC-CO 2 estimates.” The SC-CO 2 is an estimate of the monetary value of impacts associated with a margina l change in CO 2 emissions in a given year.
The four SC-CO 2 estimates are associated with different discount r ates (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 , the EPA provides the estimate of climate
benefits associated with the SC-CO 2 value deemed to be central in the current SC-CO 2 TSD (the model average at 3 percent discount rate). In addit ion to reducing CO 2 emissions, implementing these final emission guidelines is expected to redu ce emissions of SO 2 and NO X , which are precursors to formation of ambient PM 2.5 , as well as directly emitted fine particles. 201 Therefore,

200 Docket ID EPA-HQ-OAR-2013-0495, Technical Support Document: Technical Update of the Social Cost of Carbon for Regulatory Impact Analysis Under Executi ve Order 12866, Interagency Working Group on Social Cost of Carbon, with participation by Council of Economi c Advisers, Council on Environmental Quality, Depar tment of Agriculture, Department of Commerce, Department of Energy, Department of Transportation, Environmental Protection Agency, National Economic Council, Offic e of Energy and Climate Change, Office of Managemen t and Budget, Office of Science and Technology Policy, an d Department of Treasury (May 2013, Revised July 20 15). Available at: <https://www.whitehouse.gov/sites/def ault/files/omb/inforeg/scc-tsd-final-july-2015.pdf>

.

201 We did not estimate the co-benefits associated wit h reducing direct exposure to SO 2 and NO X . For this RIA, we did not estimate changes in emissions of directly e mitted particles. As a result, quantified PM 2.5 related benefits are8-2

reducing these emissions would also reduce human ex posure to ambient PM 2.5 and ozone precursors, and the associated PM 2.5 and ozone related health effects. Tables 8-1 and 8 -2 provide a summary of the climate benefits, air quality co-b enefits, and costs for the illustrative rate-based and mass-based plan scenarios.
The EPA could not monetize important categories of impacts. Due to current data and modeling limitations, our estimates of the benefits from reducing CO 2 emissions do not include important impacts like ocean acidification or poten tial tipping points in natural or managed ecosystems. Unquantified impacts also include those associated with changes in emissions of other pollutants that affect the climate, such as m ethane. In addition, the analysis does not quantify co-benefits from reducing exposure to SO 2 , NO x , and hazardous air pollutants (e.g., mercury), as well as ecosystem effects and visibili ty impairment.
Based upon the foregoing discussion, it remains cle ar that this final rule’s combined
climate benefits and human health co-benefits assoc iated with the reduction in other air pollutants substantially outweigh the costs for bot h illustrative plan scenarios.

underestimated by a relatively small amount. In th e proposal RIA, the benefits from reductions in dir ectly emitted PM 2.5 were less than 10 percent of total monetized healt h co-benefits across all scenarios and years.8-3

Table 8-1. Monetized Benefits, Costs, and Net Benef its Under the Rate-based Illustrative Plan Approach (billions of 2011$) a
Rate-Based Scenario 2020 2025 2030 Climate Benefits b 5% discount rate $0.80 $3.1 $6.4 3% discount rate $2.8 $10 $20 2.5% discount rate $4.1 $15 $29 95th percentile at 3% discount rate $8.2 $31 $61
Air Quality Co-benefits Discount Rate
3% 7% 3% 7% 3% 7% Air Quality Health Co-benefits c $0.70 to $1.8 $0.64 to $1.7 $7.4 to $18 $6.7 to $16 $14 to $34 $13 to $31 Compliance Costs d $2.5 $1.0 $8.4 Net Benefits e $1.0 to $2.1 $1.0 to $2.0 $17 to $27 $16 to $25 $26 to $45 $25 to $43

Non-Monetized Benefits Non-monetized climate benefits Reductions in exposure to ambient NO 2 and SO 2 Reductions in mercury deposition Ecosystem benefits associated with reductions in em issions of NO X , SO 2 , PM, and mercury Visibility improvement

a All are rounded to two significant figures, so fig ures may not sum. b The climate benefit estimate in this summary table reflects global impacts from CO 2 emission changes and does not account for changes in non-CO 2 GHG emissions. Also, different discount rates are applied to SC-CO 2 than to the other estimates because CO 2 emissions are long-lived and subsequent damages oc cur over many years. The benefit estimates in this table are based on the average SC -CO 2 estimated for a 3 percent discount rate. However w e emphasize the importance and value of considering t he full range of SC-CO 2 values. As shown in the RIA, climate benefits are also estimated using the other three S C-CO 2 estimates (model average at 2.5 percent discount r ate, 3 percent, and 5 percent; 95 th percentile at 3 percent). The SC-CO 2 estimates are year-specific and increase over time

. c The air quality health co-benefits reflect reduced exposure to PM 2.5 and ozone associated with emission reductions of SO 2 and NO X . The co-benefits do not include the benefits of re ductions in directly emitted PM 2.5 . These additional benefits would increase overall benefits by a few percent based on the analyses conducted f or the proposed rule. The range reflects the use of conce ntration-response functions from different epidemio logy studies. The reduction in premature fatalities each year acc ounts for over 98 percent of total monetized co-ben efits from PM 2.5 and ozone. These models assume that all fine partic les, regardless of their chemical composition, are equally potent in causing premature mortality because the s cientific evidence is not yet sufficient to allow d ifferentiation of effect estimates by particle type. d Total costs are approximated by the illustrative pl an scenario costs estimated using the Integrated Pl anning Model for the final emission guidelines and a discount ra te of approximately 5 percent. This estimate includ es monitoring, recordkeeping, and reporting costs and demand side energy efficiency program and participant costs.

e The estimates of net benefits in this summary tabl e are calculated using the global SC-CO 2 at a 3 percent discount rate (model average). The RIA includes combined cli mate and health estimates based on additional disco unt rates. f Estimates in the table are presented for three ana lytical years with air quality co-benefits calculat ed using two discount rates. The estimates of co-benefits are an nual estimates in each of the analytical years, ref lecting discounting of mortality benefits over the cessatio n lag between changes in PM 2.5 concentrations and changes in risks of premature death (see RIA Chapter 4 for mor e details), and discounting of morbidity benefits d ue to the multiple years of costs associated with some illnes ses. The estimates are not the present value of the benefits of the rule over the full compliance period.8-4

Table 8-2. Monetized Benefits, Costs, and Net Benef its Under the Mass-based Illustrative Plan Approach Scenario (billions of 20 11$) a
Mass-Based Scenario 2020 2025 2030 Climate Benefits b 5% discount rate $0.94 $3.6 $6.4 3% discount rate $3.3 $12 $20 2.5% discount rate $4.9 $17 $29 95th percentile at 3% discount rate $9.7 $35 $60
Air Quality Co-benefits Discount Rate
3% 7% 3% 7% 3% 7% Air QualityHealth Cobenefits c $2.0 to $4.8 $1.8 to $4.4 $7.1 to $17 $6.5 to $16 $ 12 to $28 $11 to $26

Costs d $1.4 $3.0 $5.1 Net Benefits e $3.9 to $6.7 $3.7 to $6.3 $16 to $26 $15 to $24 $26 to $43 $25 to $40

Non-Monetized Benefits Non-monetized climate benefits Reductions in exposure to ambient NO 2 and SO 2 Reductions in mercury deposition Ecosystem benefits associated with reductions in em issions of NO X , SO 2 , PM, and mercury Visibility improvement

a All are rounded to two significant figures, so figu res may not sum. b The climate benefit estimate in this summary table reflects global impacts from CO 2 emission changes and does not account for changes in non-CO 2 GHG emissions. Also, different discount rates are applied to SC-CO 2 than to the other estimates because CO 2 emissions are long-lived and subsequent damages oc cur over many years. The benefit estimates in this table are based on the average SC -CO 2 estimated for a 3 percent discount rate. However w e emphasize the importance and value of considering t he full range of SC-CO 2 values. As shown in the RIA, climate benefits are also estimated using the other three S C-CO 2 estimates (model average at 2.5 percent discount r ate, 3 percent, and 5 percent; 95 th percentile at 3 percent). The SC-CO 2 estimates are year-specific and increase over time

. c The air quality health co-benefits reflect reduced exposure to PM 2.5 and ozone associated with emission reductions of SO 2 and NO X . The co-benefits do not include the benefits of re ductions in directly emitted PM 2.5 . These additional benefits would increase overall benefits by a few percent based on the analyses conducted f or the proposed rule. The range reflects the use of conce ntration-response functions from different epidemio logy studies. The reduction in premature fatalities each year acc ounts for over 98 percent of total monetized co-ben efits from PM 2.5 and ozone. These models assume that all fine partic les, regardless of their chemical composition, are equally potent in causing premature mortality because the s cientific evidence is not yet sufficient to allow d ifferentiation of effect estimates by particle type. d Total costs are approximated by the illustrative pl an scenario costs estimated using the Integrated Pl anning Model for the final emission guidelines and a discount ra te of approximately 5 percent. This estimate includ es 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 SC-CO 2 at a 3 percent discount rate (model average). The RIA includes combined cli mate and health estimates based on additional disco unt rates. f Estimates in the table are presented for three ana lytical years with air quality co-benefits calculat ed using two discount rates. The estimates of co-benefits are a nnual estimates in each of the analytical years, re flecting discounting of mortality benefits over the cessatio n lag between changes in PM 2.5 concentrations and changes in risks of premature death (see RIA Chapter 4 for mor e details), and discounting of morbidity benefits d ue to the multiple years of costs associated with some illnes ses. The estimates are not the present value of th e benefits of the rule over the full compliance period.8-5

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 ann ual benefits or costs of $1 billion or more. 202 This final rulemaking surpasses that threshold for both benefits and costs. Throughout the RIA, we considered a number of sources of uncertainty, b oth quantitatively and qualitatively, on benefits and costs. We summarize three key elements of our analysis of uncertainty here:

• Evaluating uncertainty in the illustrative plan app roaches that states will implement,
which influences both costs and benefits.

• Assess uncertainty in the methods 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 approac h in estimating
these co-benefits.

• Characterizing uncertainty in monetizing climate-re lated benefits.
Some of these elements are evaluated using probabil istic techniques, whereas for others the underlying likelihoods of certain outcomes are unknown and we use scenario analysis to evaluate their potential effect on the benefits and costs of this rulemaking.

8.2.1 Uncertainty in Costs and Illustrative Plan Ap proaches
The calculation of the state goals is based on an e valuation of methods for reducing the carbon emissions intensity of electricity generatio n that may be achieved at reasonable cost. Our
best estimates of the costs of these methods of int ensity reduction are reported within the cost analysis of this rule and are included in the cost modeling in the RIA.
A source of uncertainty under this regulation is th e ultimate approach states will adopt in response to the guidelines, which will affect both the costs and benefits of this rule. For this reason we modeled two potential illustrative plan s cenarios: the rate-based illustrative plan scenario and the mass-based illustrative plan scena rio.

202 Office of Management and Budget (OMB), 2003, Circular A-4
, http://www.whitehouse.gov/omb/circulars_a004_a-4 an d OMB, 2011. Regulatory Impact Analysis: A Primer. http://www.whitehouse.gov/sites/default/files/omb/ inforeg /regpol/circular-a-4_regulatory-impact-analysis-aprimer.pdf8-6

8.2.2 Uncertainty Associated with Estimating the So cial Cost of Carbon

The 2010 SC-CO 2 TSD noted a number of limitations to the SC-CO 2 analysis, including the incomplete way in which the integrated assessme nt models (IAM) capture catastrophic and non-catastrophic impacts, their incomplete treatmen t of adaptation and technological change, uncertainty in the extrapolation of damages to high temperatures, and assumptions regarding risk
aversion. 203 Currently integrated assessment models do not assi gn value to all of the important physical, ecological, and economic impacts of clima te 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. These
individual limitations do not all work in the same direction in terms of their influence on the SCCO 2 estimates, though taken together they suggest that the SC-CO 2 estimates are likely conservative. In particular, the IPCC Fourth Assess ment Report (2007) concluded that “It is very likely that [SC-CO 2 estimates] underestimate the damage costs because they cannot include many non-quantifiable impacts” and the IPCC Fifth A ssessment report observed that SC-CO 2 estimates continue to omit various impacts that wou ld likely increase damages. The 95 th percentile estimate was included in the recommended range for regulatory impact analysis, in part, to address these concerns.
The modeling underlying the development of the SC-C O 2 estimates addressed uncertainty in several ways. An ensemble of three I AMs were used to generate the SC-CO 2 estimates to capture differences in model structure s that, in part, reflect uncertainty in the scientific literature about these relationships. Pa rametric uncertainty was explicitly addressed in each IAM, though to differing degrees, through Mont e Carlo simulations in which explicit probability distributions for key parameters were s pecified, including the equilibrium climate sensitivity, which represents the long-run responsi veness of the climate to increasing GHG concentrations. Furthermore, the analysis considere d five different socioeconomic and emissions

203 Technical Support Document: Social Cost of Carbon f or Regulatory Impact Analysis Under Executive Order 12866 , Interagency Working Group on Social Cost of Carbo n, with participation by the Council of Economic Advisers, Council on Environmental Quality, Departm ent of Agriculture, Department of Commerce, Departm ent of Energy, Department of Transportation, Environmental Protection Agency, National Economic Council, Offi ce of Energy and Climate Change, Office of Management and Budget, Office of Science and Technology Policy, a nd Department of Treasury (February 2010). Available a t: <http://www.whitehouse.gov/sites/default/files/omb/ inforeg/for-agencies/Social-Cost-of-Carbon-for-RIA. pdf>.8-7

forecasts to capture the sensitivity of the SC-CO 2 estimates to key exogenous projections used in the modeling. Finally, the results were calculated for three discount rates, which were selected, in part, to reflect uncertainty about how interest rat es may change over time and the possibility that climate damages are positively correlated with unce rtain future economic activity. This analysis produced 45 different distributions of the SC-CO 2 estimates for each emissions year. To produce a range of plausible estimates that are manageable in regulatory analysis but still reflects the uncertainty in the results four point estimates wer e recommended. The use of this range of point estimates in this rulemaking helps to reflect the u ncertainty in the SC-CO 2 estimates. Chapter 4 of this RIA provides a comprehensive discussion abo ut the methodology and application of the
SC-CO 2 ; see both the 2010 TSD and current SC-CO 2 TSD for a full description.
In addition, OMB’s Office of Information and Regula tory Affairs received comments regarding uncertainty and the SC-CO 2 estimates in response to a separate request for pu blic comment on the approach used to develop the estimat es. Commenters discussed the analyses and presentation of uncertainty in the TSD as well as t he implications of uncertainty for use of the
SC-CO 2 estimates in regulatory impact analysis. In their response, the interagency working group (IWG) acknowledged uncertainty in the SC-CO 2 estimates but disagreed with commenters that suggested the uncertainty undermines use of th e SC-CO 2 estimates in regulatory impact analysis. The IWG went on to note that the uncertai nty in the SC-CO 2 estimates is fully acknowledged and comprehensively discussed in the T SDs and supporting academic literature, and that while all regulatory impact analysis invol ves uncertainty, these analyses can provide useful information to decision makers and the publi c. See the IWG Response to Comments for the complete response. 204
8.2.3 Uncertainty Associated with PM 2.5 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 an d supported by the SAB-HES and the National Academies of Science (NRC, 2002). Below are key ass umptions underlying the estimates for
PM 2.5 -related premature mortality, which accounts for 98 percent of the monetized PM 2.5 health
co-benefits:

204 See https://www.whitehouse.gov/sites/default/files /omb/inforeg/scc-response-to-comments-final-july-20 15.pdf8-8

• We assume that all fine particles, regardless of th eir 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 differentia tion 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 ye t sufficient to allow differentiation
of those constituents or sources that are more clos ely related to specific outcomes” (U.S. EPA, 2009).

• We assume that the health impact function for fine particles is log-linear without a threshold in this analysis. Thus, the estimates inc lude health co-benefits from reducing fine particles in areas with varied concen trations 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 concentratio ns.

• We assume that there is a “cessation” lag between t he change in PM exposures and the total realization of changes in mortality effec ts. Specifically, we assume that some of the incidences of premature mortality relat ed to PM 2.5 exposures occur in a distributed fashion over the 20 years following exp osure based on the advice of the SAB-HES (U.S. EPA-SAB, 2004), which affects the val uation of mortality cobenefits at different discount rates. EPA quantitat ively assessed uncertainty in the air quality health co-benefits, including probabilistic approaches.
In addition, EPA provides the 95 th percentile confidence interval for avoided PM-rela ted premature deaths and the associated economic valuat ion using two key epidemiology studies. EPA provides the PM-related results using alternate concentration-response relationship provided by an expert elicitation and alternate ozo ne-related results using concentration-response relationships provided by alternate epidemiology st udies. In addition, we include an assessment of the distribution of population exposure in the m odeling underlying the benefit-per-ton
estimates. For further discussion and characterizat ion of those uncertainties influencing the benefit assessment, see Chapter 4 of this RIA.
As noted and described in Chapter 4 of this RIA, we use a benefit-per-ton approach to quantify health co-benefits. All benefit-per-ton es timates have inherent limitations, including that8-9

the estimates reflect the geographic distribution o f the modeled sector emissions, which may not match the emission reductions anticipated by the fi nal emission guidelines, and they may not reflect local variability in population density, me teorology, exposure, baseline health incidence rates, or other local factors for any specific loca tion. In addition, these estimates reflect the regional average benefit-per-ton for each ambient P M 2.5 precursor emitted from EGUs, which assumes a linear atmospheric response to emission r eductions. 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.

8.3 References
Docket ID EPA-HQ-OAR-2013-0602, Technical Support D ocument: Technical Update of the Social Cost of Carbon for Regulatory Impact Analysi s 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, Depar tment of Transportation, Domestic Policy Council, Environmental Protection Agency, Na tional Economic Council, Office of Management and Budget, Office of Science and Techno logy Policy, and Department of Treasury (May 2013, Revised July 2015). Also availa ble at: <https://www.whitehouse.gov/sites/default/files/omb /inforeg/scc-tsd-final-july-2015.pdf>. Accessed July 15, 2015. 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, Depar tment of Transportation, Environmental Protection Agency, National Economic Council, Office of Management and Budget, Office of Science and Technology Policy, an d Department of Treasury. Response to to Comments: Social Cost of Carbon for Regulatory I mpact Analysis Under Executive Order 12866. July 2015. Available at <https://www.whitehouse.gov/sites/default/files/omb /inforeg/scc-response-to-commentsfinal-july-2015.pdf.> Accessed July 15, 2015.8-10

Intergovernmental Panel on Climate Change (IPCC). 2 007. 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/publicati ons_ipcc_fourth_assessment_report_synt hesis_report.htm>. Accessed June 6, 2015. National Research Council (NRC). 2002. Estimating the Public Health Benefits of Proposed A ir Pollution Regulations . National Academies Press. Washington, DC. U.S. Environmental Protection Agency—Science Adviso ry Board (U.S. EPA-SAB). 2004. Advisory Council on Clean Air Compliance Analysis Response t o Agency Request on Cessation Lag. EPACOUNCIL-LTR-05-001. December. Available at <http://yosemite.epa.gov/sab/sabproduct.nsf/0/39F44 B098DB49F3C85257170005293E0/$File/co uncil_ltr_05_001.pdf>. Accessed June 4, 2015. U.S. Environmental Protection Agency (U.S. EPA). 20 09. Integrated Science Assessment for Particulate Matter (Final Report) . EPA-600-R-08-139F. National Center for Environmen tal Assessment – RTP Division, Research Triangle Park, NC. Available at <http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?dei d=216546>. Accessed June 4, 2015.United States

Environmental Protection Agency Office of Air Quality Planning and Standards Health and Environmental Impacts Division Research Triangle Park, NC Publication No. EPA-452/R-15-003 August 2015